From 41bac5d8fdc33f08cc763c356588baffbe019581 Mon Sep 17 00:00:00 2001
From: root <root@tw020.mia.tensorwave.lan>
Date: Wed, 19 Aug 2026 09:06:20 +0000
Subject: [PATCH 01/35] feat(attention): add strict ROCm Attention and RCCL
 path

---
 csrc/ops.cpp                                  |  14 ++
 csrc/rocm/deterministic_rope.cu               |  91 ++++++++++
 .../kernels/ops/cuda/attention/__init__.py    |   2 +
 .../kernels/ops/cuda/attention/cp_comm.py     | 167 +++++++++++++++++-
 .../ops/cuda/attention/deterministic_attn.py  |  30 ++--
 .../attention/flashinfer_paged_attention.py   |  25 ++-
 .../kernels/ops/rocm/attention/__init__.py    |   6 +
 .../ops/rocm/attention/deterministic_attn.py  |  15 ++
 .../ops/rocm/rotary_embedding/__init__.py     |   6 +
 .../kernels/ops/rocm/rotary_embedding/rope.py | 103 +++++++++++
 scripts/rocm_attention_acceptance.py          |  84 +++++++++
 .../ws2_p2p_nccl_attention_reference_check.py |  60 +++++--
 setup.py                                      |   2 +
 tests/test_deterministic_attention_rocm.py    | 131 ++++++++++++++
 14 files changed, 697 insertions(+), 39 deletions(-)
 create mode 100644 csrc/rocm/deterministic_rope.cu
 create mode 100644 rl_engine/kernels/ops/rocm/attention/deterministic_attn.py
 create mode 100644 rl_engine/kernels/ops/rocm/rotary_embedding/__init__.py
 create mode 100644 rl_engine/kernels/ops/rocm/rotary_embedding/rope.py
 create mode 100644 scripts/rocm_attention_acceptance.py
 create mode 100644 tests/test_deterministic_attention_rocm.py

diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index ad1c42fc..b1725c50 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -277,6 +277,14 @@ std::vector<torch::Tensor> deterministic_attention_backward(
     double scale,
     torch::optional<torch::Tensor> key_padding_mask);
 
+#if defined(KERNEL_ALIGN_WITH_ROCM)
+torch::Tensor deterministic_rope_apply_rocm(
+    torch::Tensor x,
+    torch::Tensor cos,
+    torch::Tensor sin,
+    double sin_sign);
+#endif
+
 #if !defined(USE_ROCM)
 // Prefix-Shared Attention Declarations & Wrappers (NVIDIA PTX only).
 
@@ -450,5 +458,11 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
         "deterministic_attention_backward",
         &deterministic_attention_backward,
         "Deterministic standard softmax attention backward (dQ, dK, dV)");
+#if defined(KERNEL_ALIGN_WITH_ROCM)
+    m.def(
+        "deterministic_rope_apply_rocm",
+        &deterministic_rope_apply_rocm,
+        "Deterministic GPT-NeoX RoPE apply for ROCm");
+#endif
 #endif
 }
diff --git a/csrc/rocm/deterministic_rope.cu b/csrc/rocm/deterministic_rope.cu
new file mode 100644
index 00000000..91f1f0ff
--- /dev/null
+++ b/csrc/rocm/deterministic_rope.cu
@@ -0,0 +1,91 @@
+// SPDX-License-Identifier: Apache-2.0
+// Copyright (c) 2026 RL-Kernel Contributors
+
+#include <torch/extension.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+
+namespace {
+
+template <typename scalar_t>
+__global__ void deterministic_rope_kernel(
+    const scalar_t* __restrict__ x,
+    const float* __restrict__ cos,
+    const float* __restrict__ sin,
+    scalar_t* __restrict__ out,
+    int64_t n_rows,
+    int table_rows,
+    int half,
+    float sin_sign) {
+  const int64_t index = blockIdx.x * static_cast<int64_t>(blockDim.x) + threadIdx.x;
+  const int64_t count = n_rows * static_cast<int64_t>(half);
+  if (index >= count) {
+    return;
+  }
+
+  const int64_t row = index / half;
+  const int pair = static_cast<int>(index % half);
+  const int table_row = static_cast<int>(row % table_rows);
+  const float c = cos[table_row * half + pair];
+  const float s = sin[table_row * half + pair] * sin_sign;
+  const int64_t base = row * (2LL * half);
+  const float low = static_cast<float>(x[base + pair]);
+  const float high = static_cast<float>(x[base + pair + half]);
+
+  out[base + pair] = static_cast<scalar_t>(low * c - high * s);
+  out[base + pair + half] = static_cast<scalar_t>(high * c + low * s);
+}
+
+}  // namespace
+
+torch::Tensor deterministic_rope_apply_rocm(
+    torch::Tensor x,
+    torch::Tensor cos,
+    torch::Tensor sin,
+    double sin_sign) {
+  TORCH_CHECK(x.is_cuda(), "ROCm RoPE: x must be a GPU tensor");
+  TORCH_CHECK(x.dim() == 2 && x.is_contiguous(),
+              "ROCm RoPE: x must be contiguous [rows, head_dim]");
+  TORCH_CHECK(cos.is_cuda() && sin.is_cuda(),
+              "ROCm RoPE: cos and sin must be GPU tensors");
+  TORCH_CHECK(cos.scalar_type() == torch::kFloat32 &&
+                  sin.scalar_type() == torch::kFloat32,
+              "ROCm RoPE: cos and sin must be FP32");
+  TORCH_CHECK(cos.is_contiguous() && sin.is_contiguous(),
+              "ROCm RoPE: cos and sin must be contiguous");
+  TORCH_CHECK(cos.dim() == 2 && sin.sizes() == cos.sizes(),
+              "ROCm RoPE: cos and sin must have shape [table_rows, head_dim/2]");
+  TORCH_CHECK(x.size(1) % 2 == 0, "ROCm RoPE: head_dim must be even");
+  TORCH_CHECK(cos.size(0) > 0 && cos.size(1) == x.size(1) / 2,
+              "ROCm RoPE: invalid cos/sin table shape");
+  TORCH_CHECK(x.size(0) % cos.size(0) == 0,
+              "ROCm RoPE: row count must be divisible by the position table size");
+
+  const at::cuda::OptionalCUDAGuard guard(device_of(x));
+  auto out = torch::empty_like(x);
+  const int64_t n_rows = x.size(0);
+  const int half = static_cast<int>(x.size(1) / 2);
+  const int table_rows = static_cast<int>(cos.size(0));
+  const int64_t count = n_rows * static_cast<int64_t>(half);
+  constexpr int threads = 256;
+  const int64_t blocks = (count + threads - 1) / threads;
+  auto stream = at::cuda::getCurrentCUDAStream();
+
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+      at::ScalarType::Half,
+      at::ScalarType::BFloat16,
+      x.scalar_type(),
+      "deterministic_rope_apply_rocm",
+      [&] {
+        deterministic_rope_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
+            x.data_ptr<scalar_t>(),
+            cos.data_ptr<float>(),
+            sin.data_ptr<float>(),
+            out.data_ptr<scalar_t>(),
+            n_rows,
+            table_rows,
+            half,
+            static_cast<float>(sin_sign));
+      });
+  return out;
+}
diff --git a/rl_engine/kernels/ops/cuda/attention/__init__.py b/rl_engine/kernels/ops/cuda/attention/__init__.py
index b60ccb55..21850cdf 100644
--- a/rl_engine/kernels/ops/cuda/attention/__init__.py
+++ b/rl_engine/kernels/ops/cuda/attention/__init__.py
@@ -13,6 +13,7 @@
     CPCommunicationStatus,
     CUDAAGRSAttentionCPCommunication,
     P2PNCCLAttentionCPCommunication,
+    RCCLAGRSAttentionCPCommunication,
     sort_attention_cp_partial_states,
 )
 from .deterministic_attn import (
@@ -43,6 +44,7 @@
     "CPCommunicationStatus",
     "CUDAAGRSAttentionCPCommunication",
     "P2PNCCLAttentionCPCommunication",
+    "RCCLAGRSAttentionCPCommunication",
     "DeterministicAttentionCoreResult",
     "DeterministicAttentionOp",
     "RLKernelDeterministicAttentionCore",
diff --git a/rl_engine/kernels/ops/cuda/attention/cp_comm.py b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
index d1470bfd..20602ede 100644
--- a/rl_engine/kernels/ops/cuda/attention/cp_comm.py
+++ b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
@@ -40,7 +40,12 @@
 
 import torch
 
-CPCommunicationBackend = Literal["cuda_ag_rs", "p2p_nccl_reference", "local_debug"]
+CPCommunicationBackend = Literal[
+    "cuda_ag_rs",
+    "rccl_ag_rs",
+    "p2p_nccl_reference",
+    "local_debug",
+]
 CPCommunicationStatus = Literal["interface_only", "implemented"]
 
 
@@ -198,12 +203,17 @@ class AttentionCPCommunicationPlan:
 
     def validate(self) -> None:
         self.parallel.validate()
-        if self.backend not in {"cuda_ag_rs", "p2p_nccl_reference", "local_debug"}:
+        if self.backend not in {
+            "cuda_ag_rs",
+            "rccl_ag_rs",
+            "p2p_nccl_reference",
+            "local_debug",
+        }:
             raise ValueError(f"unsupported CP communication backend: {self.backend}")
         if self.status not in {"interface_only", "implemented"}:
             raise ValueError(f"unsupported CP communication status: {self.status}")
         if self.pattern != "ag_rs":
-            raise ValueError("PR7 CP communication must use the custom CUDA AG/RS interface")
+            raise ValueError("PR7 CP communication must use the self-owned AG/RS interface")
         if self.compute_communication != "decoupled":
             raise ValueError("PR7 CP communication must keep compute and communication decoupled")
         if self.merge_order != "global_block_index":
@@ -242,6 +252,12 @@ def provenance(self) -> dict[str, object]:
             "cp_comm_strict_kv_communication": "all_gather",
             "cp_comm_strict_position_communication": "all_gather",
             "cp_comm_strict_backward": "rs_out_backward_ag_then_ag_qkv_backward_rs",
+            "cp_comm_runtime": (
+                "rccl"
+                if self.backend == "rccl_ag_rs"
+                else "nccl" if self.backend in {"cuda_ag_rs", "p2p_nccl_reference"} else "local"
+            ),
+            "cp_comm_attention_numeric_reduction": self.backend == "cuda_ag_rs",
             "cp_comm_expected_kv_token_range": (
                 None if self.expected_kv_token_range is None else list(self.expected_kv_token_range)
             ),
@@ -335,15 +351,21 @@ def forward(
         ctx.sequence_dim = int(sequence_dim)
         ctx.rank = int(rank)
         ctx.root = int(root)
+        ctx.world_size = int(getattr(collective, "world_size", 1))
         packed = full.movedim(ctx.sequence_dim, 0).contiguous()
+        ctx.full_shape = tuple(packed.shape)
         if ctx.rank != ctx.root:
             packed = torch.zeros_like(packed)
-        local = collective.reduce_scatter(packed)
+        scatter = getattr(collective, "scatter", None)
+        local = scatter(packed) if callable(scatter) else collective.reduce_scatter(packed)
         return local.movedim(0, ctx.sequence_dim).contiguous()
 
     @staticmethod
     def backward(ctx, grad_local: torch.Tensor) -> tuple[torch.Tensor, None, None, None, None]:
         packed = grad_local.movedim(ctx.sequence_dim, 0).contiguous()
+        # The forward scatter has one authoritative full input on root. Its
+        # backward is the dual gather of every rank's local output gradient;
+        # non-root full inputs were zeroed in forward and receive no gradient.
         grad_full = ctx.collective.all_gather(packed).movedim(0, ctx.sequence_dim).contiguous()
         if ctx.rank != ctx.root:
             grad_full.zero_()
@@ -557,6 +579,142 @@ def _validate_cuda_plan(self, plan: AttentionCPCommunicationPlan) -> None:
             raise AttentionCPCommunicationUnavailable("self-owned CUDA AG/RS requires CUDA")
 
 
+class _RCCLRankOrderedTransport:
+    """RCCL transport with rank-ordered AG and root-owned scatter.
+
+    The scatter half deliberately performs no floating-point reduction. The
+    strict Attention arithmetic remains entirely in the deterministic core.
+    """
+
+    def __init__(self, *, process_group: Any, root: int) -> None:
+        import torch.distributed as dist
+
+        if not dist.is_available() or not dist.is_initialized():
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS requires initialized torch.distributed"
+            )
+        backend = str(dist.get_backend(process_group)).lower()
+        if "nccl" not in backend or torch.version.hip is None:
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS requires the PyTorch NCCL API on ROCm"
+            )
+        self.group = process_group
+        self.rank = int(dist.get_rank(process_group))
+        self.world_size = int(dist.get_world_size(process_group))
+        self.root = int(root)
+        if self.root < 0 or self.root >= self.world_size:
+            raise AttentionCPCommunicationUnavailable("RCCL scatter root is outside the group")
+
+    def all_gather(self, local: torch.Tensor) -> torch.Tensor:
+        import torch.distributed as dist
+
+        if not local.is_cuda or not local.is_contiguous():
+            raise AttentionCPCommunicationUnavailable(
+                "RCCL AllGather requires a contiguous ROCm tensor"
+            )
+        shape = (self.world_size * local.size(0), *local.shape[1:])
+        gathered = torch.empty(shape, dtype=local.dtype, device=local.device)
+        dist.all_gather_into_tensor(gathered, local, group=self.group)
+        return gathered
+
+    def scatter(self, full: torch.Tensor) -> torch.Tensor:
+        import torch.distributed as dist
+
+        if not full.is_cuda or not full.is_contiguous():
+            raise AttentionCPCommunicationUnavailable(
+                "RCCL ReduceScatter transport requires a contiguous ROCm tensor"
+            )
+        if full.size(0) % self.world_size:
+            raise AttentionCPCommunicationUnavailable(
+                "RCCL scatter leading dimension must divide the CP world size"
+            )
+        chunks = tuple(chunk.contiguous() for chunk in full.chunk(self.world_size, dim=0))
+        local = torch.empty_like(chunks[self.rank])
+        global_root = self.root
+        if self.group is not None:
+            get_global_rank = getattr(dist, "get_global_rank", None)
+            if callable(get_global_rank):
+                global_root = int(get_global_rank(self.group, self.root))
+            else:
+                global_root = int(dist.get_process_group_ranks(self.group)[self.root])
+        dist.scatter(
+            local,
+            scatter_list=list(chunks) if self.rank == self.root else None,
+            src=global_root,
+            group=self.group,
+        )
+        return local
+
+    def reduce_scatter(self, full: torch.Tensor) -> torch.Tensor:
+        """Deterministic rank-order sum followed by local scatter.
+
+        RCCL is used for point-to-point transport. The floating-point sum is
+        performed locally in source-rank order, so collective reduction order
+        is not delegated to RCCL.
+        """
+        import torch.distributed as dist
+
+        if not full.is_cuda or not full.is_contiguous():
+            raise AttentionCPCommunicationUnavailable(
+                "RCCL ReduceScatter requires a contiguous ROCm tensor"
+            )
+        if full.size(0) % self.world_size:
+            raise AttentionCPCommunicationUnavailable(
+                "RCCL ReduceScatter leading dimension must divide the CP world size"
+            )
+        chunks = tuple(chunk.contiguous() for chunk in full.chunk(self.world_size, dim=0))
+        gathered = self.all_gather(full)
+        local = gathered[self.rank * chunks[self.rank].size(0) :
+                         (self.rank + 1) * chunks[self.rank].size(0)].clone()
+        chunk_rows = chunks[self.rank].size(0)
+        # Start with source rank 0, then add the remaining source ranks in
+        # ascending order. This avoids counting source 0 twice.
+        for source in range(1, self.world_size):
+            source_full = gathered[source * full.size(0) : (source + 1) * full.size(0)]
+            local.add_(source_full[self.rank * chunk_rows : (self.rank + 1) * chunk_rows])
+        return local
+
+
+class RCCLAGRSAttentionCPCommunication(CUDAAGRSAttentionCPCommunication):
+    """ROCm AG/RS adapter using RCCL only as rank-ordered tensor transport."""
+
+    backend_id = "rccl_ag_rs"
+    supports_autograd = True
+    transport_only = True
+
+    def _get_collective(self, plan: AttentionCPCommunicationPlan):
+        if self._collective is None:
+            self._collective = _RCCLRankOrderedTransport(
+                process_group=self._process_group,
+                root=plan.merge_root_cp_rank,
+            )
+        if self._collective.world_size != plan.parallel.cp_world_size:
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL world size does not match the CP plan"
+            )
+        return self._collective
+
+    def _dist(self):
+        import torch.distributed as dist
+
+        if not dist.is_available() or not dist.is_initialized():
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS requires initialized torch.distributed"
+            )
+        return dist
+
+    def _validate_cuda_plan(self, plan: AttentionCPCommunicationPlan) -> None:
+        plan.validate()
+        if plan.backend != "rccl_ag_rs" or plan.status != "implemented":
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS requires an implemented rccl_ag_rs plan"
+            )
+        if torch.version.hip is None or not torch.cuda.is_available():
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS requires an available ROCm device"
+            )
+
+
 class P2PNCCLAttentionCPCommunication:
     """Correctness-first P2P NCCL implementation of the CP protocol.
 
@@ -1288,6 +1446,7 @@ def _rank_in_world(rank: int, world_size: int, name: str) -> None:
     "CPCommunicationBackend",
     "CPCommunicationStatus",
     "CUDAAGRSAttentionCPCommunication",
+    "RCCLAGRSAttentionCPCommunication",
     "P2PNCCLAttentionCPCommunication",
     "sort_attention_cp_partial_states",
 ]
diff --git a/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py b/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py
index c9bc32f7..98823816 100644
--- a/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py
+++ b/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-"""CUDA deterministic standard-softmax attention (issue #147).
+"""Deterministic standard-softmax attention for CUDA and ROCm (issue #147).
 
 Forward: QK → masked softmax+LSE → PV (all FP32 intermediate).
 Backward: dP → softmax_bwd → dQ/dK/dV with §4.1 fixed GQA order.
@@ -28,6 +28,8 @@
 from rl_engine.utils.logger import logger
 
 _HEAD_DIM = 128
+_IS_ROCM = torch.version.hip is not None
+_GPU_PLATFORM = "ROCm" if _IS_ROCM else "CUDA"
 
 
 @dataclass(frozen=True)
@@ -92,7 +94,7 @@ def backward(ctx, grad_out: torch.Tensor, grad_lse: torch.Tensor):
 
 
 class DeterministicAttentionOp:
-    """Batch-invariant standard softmax attention on CUDA.
+    """Batch-invariant standard softmax attention on a CUDA or ROCm GPU.
 
     Materializes full FP32 scores/P. Public surface matches NativeAttentionOp
     so #108 harness can call forward(**inputs) with key_padding_mask.
@@ -101,13 +103,13 @@ class DeterministicAttentionOp:
     def __init__(self) -> None:
         if not _EXT_AVAILABLE or not hasattr(_C, "deterministic_attention_forward"):
             raise RuntimeError(
-                "Deterministic CUDA attention kernel is unavailable. "
-                "Rebuild the extension with `pip install -e .` on a CUDA build."
+                f"Deterministic {_GPU_PLATFORM} attention kernel is unavailable. "
+                "Rebuild the native extension for the active GPU platform."
             )
         if not hasattr(_C, "deterministic_attention_backward"):
             raise RuntimeError(
-                "Deterministic CUDA attention backward kernel is unavailable. "
-                "Rebuild the extension with `pip install -e .` on a CUDA build."
+                f"Deterministic {_GPU_PLATFORM} attention backward kernel is unavailable. "
+                "Rebuild the native extension for the active GPU platform."
             )
         logger.info("Successfully linked to _C.deterministic_attention_forward/backward.")
 
@@ -204,7 +206,7 @@ def _validate_inputs(
         if k.dtype != q.dtype or v.dtype != q.dtype:
             raise ValueError("q, k, v must share the same dtype")
         if not (q.is_cuda and k.is_cuda and v.is_cuda):
-            raise ValueError("q, k, v must be CUDA tensors")
+            raise ValueError("q, k, v must be GPU tensors")
         if key_padding_mask is not None:
             if key_padding_mask.dtype != torch.bool:
                 raise ValueError("key_padding_mask must be bool")
@@ -218,7 +220,7 @@ def _validate_inputs(
 
 
 class RLKernelDeterministicAttentionCore:
-    """Production CUDA Attention core shared by training and rollout.
+    """Production GPU Attention core shared by training and rollout.
 
     Strict execution exposes only the existing WS1 no-Split-K kernel. This is
     intentional: a different Split-K schedule changes the arithmetic graph and
@@ -227,7 +229,11 @@ class RLKernelDeterministicAttentionCore:
 
     core_id = STRICT_ATTENTION_CORE_ID
     strict_schedule = STRICT_ATTENTION_SCHEDULE_ID
-    backend_id = "rlkernel.cuda.deterministic_attention"
+    backend_id = (
+        "rlkernel.rocm.deterministic_attention"
+        if _IS_ROCM
+        else "rlkernel.cuda.deterministic_attention"
+    )
     merge_order = "global_block_index"
     accum_dtype = "fp32"
     downcast_at = "final_write"
@@ -243,7 +249,7 @@ def __init__(
         if not isinstance(requested, SplitKVSpec):
             raise TypeError("split_kv must be a SplitKVSpec")
         if requested.mode is not SplitKVMode.DISABLED:
-            raise ValueError("the strict CUDA Attention core requires Split-KV to be disabled")
+            raise ValueError("the strict GPU Attention core requires Split-KV to be disabled")
         self.split_kv = requested
         self._op = DeterministicAttentionOp()
 
@@ -317,7 +323,7 @@ def _validate_positions(
             return
         if query_position_ids is None or key_position_ids is None:
             raise ValueError(
-                "strict CUDA Attention requires query_position_ids and " "key_position_ids"
+                "strict GPU Attention requires query_position_ids and " "key_position_ids"
             )
         expected_q_shape = (q.size(0), q.size(2))
         expected_k_shape = (k.size(0), k.size(2))
@@ -342,6 +348,6 @@ def _validate_positions(
             raise ValueError("key_position_ids must be contiguous and increasing")
         if not torch.equal(query_position_ids, key_position_ids[:, -q.size(2) :]):
             raise ValueError(
-                "strict CUDA Attention requires queries to be the trailing "
+                "strict GPU Attention requires queries to be the trailing "
                 "contiguous positions of the logical KV sequence"
             )
diff --git a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
index ac6eec00..0a6bdeed 100644
--- a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
+++ b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
@@ -775,7 +775,7 @@ def _run_strict_cp(
             communication, "supports_autograd", False
         ):
             raise FlashInferUnavailable(
-                "strict training requires the autograd-capable self-owned CUDA AG/RS backend"
+                "strict training requires an autograd-capable self-owned GPU AG/RS backend"
             )
         query_start, query_end = plan.query_token_ranges[plan.parallel.cp_rank]
         key_start, key_end = _cp_owner_ranges(plan)[plan.parallel.cp_rank]
@@ -1515,12 +1515,18 @@ def _resolve_strict_rope(cfg: FlashInferPagedAttentionConfig) -> Any:
     if cfg.strict_rope_op is not None:
         return cfg.strict_rope_op
     try:
+        if torch.version.hip is not None:
+            from rl_engine.kernels.ops.rocm.rotary_embedding.rope import (
+                RocmDeterministicRoPEOp,
+            )
+
+            return RocmDeterministicRoPEOp()
         from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RoPESM90Op
 
         return RoPESM90Op()
     except (ImportError, RuntimeError) as exc:
         raise FlashInferUnavailable(
-            "strict Attention requires the RL-Kernel WS1 RoPE CUDA operator"
+            "strict Attention requires the RL-Kernel deterministic RoPE operator"
         ) from exc
 
 
@@ -1530,7 +1536,7 @@ def _apply_strict_rope(
     position_ids: torch.Tensor,
     theta: float,
 ) -> torch.Tensor:
-    """RoPESM90Op accepts shared 1-D positions, so execute one batch row at a time."""
+    """Execute one batch row at a time to preserve the strict row schedule."""
 
     if position_ids.shape != (x.size(0), x.size(2)):
         raise ValueError("strict RoPE position IDs must have shape [B,S]")
@@ -1682,12 +1688,12 @@ def _strict_attention_provenance(
         "strict_schedule": core_provenance["strict_schedule"],
         "accum_dtype": core_provenance["accum_dtype"],
         "downcast_at": core_provenance["downcast_at"],
-        "arithmetic_plan_source": "rlkernel_deterministic_cuda_core",
+        "arithmetic_plan_source": "rlkernel_deterministic_gpu_core",
         "arithmetic_semantics_verified": True,
         "native_attention_arithmetic": False,
         "fallback": False,
         "fallback_reason": None,
-        "rope_backend": getattr(rope, "backend_id", "rlkernel.cuda.rope_sm90"),
+        "rope_backend": getattr(rope, "backend_id", "rlkernel.unknown.rope"),
         "rope_theta": float(cfg.rope.rope_theta),
         "rotary_dim": cfg.rope.rotary_dim,
         "rope_fusion": False,
@@ -1697,12 +1703,17 @@ def _strict_attention_provenance(
         "batch_invariant_claim": "strict_runtime_verified",
         "cp_comm_required": cp_required,
         "communication_backend": (
-            "self_owned_cuda_ag_rs" if cp_required else "none"
+            getattr(cfg.cp_communication, "backend_id", "unknown") if cp_required else "none"
         ),
         "production_ready": bool(
             cp_required
             and core_provenance.get("attention_backend")
-            == "rlkernel.cuda.deterministic_attention"
+            in {
+                "rlkernel.cuda.deterministic_attention",
+                "rlkernel.rocm.deterministic_attention",
+            }
+            and getattr(cfg.cp_communication, "backend_id", None)
+            in {"cuda_ag_rs", "rccl_ag_rs"}
         ),
     }
 
diff --git a/rl_engine/kernels/ops/rocm/attention/__init__.py b/rl_engine/kernels/ops/rocm/attention/__init__.py
index 150c937f..2445e2de 100644
--- a/rl_engine/kernels/ops/rocm/attention/__init__.py
+++ b/rl_engine/kernels/ops/rocm/attention/__init__.py
@@ -2,7 +2,13 @@
 # Copyright (c) 2026 RL-Kernel Contributors
 
 from .flash_attn import RocmFlashAttentionOp
+from .deterministic_attn import (
+    DeterministicAttentionOp,
+    RLKernelDeterministicAttentionCore,
+)
 
 __all__ = [
     "RocmFlashAttentionOp",
+    "DeterministicAttentionOp",
+    "RLKernelDeterministicAttentionCore",
 ]
diff --git a/rl_engine/kernels/ops/rocm/attention/deterministic_attn.py b/rl_engine/kernels/ops/rocm/attention/deterministic_attn.py
new file mode 100644
index 00000000..435a5272
--- /dev/null
+++ b/rl_engine/kernels/ops/rocm/attention/deterministic_attn.py
@@ -0,0 +1,15 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""ROCm public surface for the shared deterministic Attention core."""
+
+from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
+    DeterministicAttentionCoreResult,
+    DeterministicAttentionOp,
+    RLKernelDeterministicAttentionCore,
+)
+
+__all__ = [
+    "DeterministicAttentionCoreResult",
+    "DeterministicAttentionOp",
+    "RLKernelDeterministicAttentionCore",
+]
diff --git a/rl_engine/kernels/ops/rocm/rotary_embedding/__init__.py b/rl_engine/kernels/ops/rocm/rotary_embedding/__init__.py
new file mode 100644
index 00000000..0a7285f8
--- /dev/null
+++ b/rl_engine/kernels/ops/rocm/rotary_embedding/__init__.py
@@ -0,0 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from .rope import RocmDeterministicRoPEOp
+
+__all__ = ["RocmDeterministicRoPEOp"]
diff --git a/rl_engine/kernels/ops/rocm/rotary_embedding/rope.py b/rl_engine/kernels/ops/rocm/rotary_embedding/rope.py
new file mode 100644
index 00000000..3290a9c1
--- /dev/null
+++ b/rl_engine/kernels/ops/rocm/rotary_embedding/rope.py
@@ -0,0 +1,103 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""Deterministic GPT-NeoX RoPE for ROCm training and inference."""
+
+from __future__ import annotations
+
+import torch
+from torch import Tensor
+
+from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
+
+
+def _build_cos_sin(
+    positions: Tensor,
+    half: int,
+    theta: float,
+    device: torch.device,
+) -> tuple[Tensor, Tensor]:
+    inv_freq = 1.0 / (
+        theta ** (torch.arange(0, half, dtype=torch.float32, device=device) / half)
+    )
+    frequency = positions.to(device=device, dtype=torch.float32).reshape(-1, 1) * inv_freq
+    return frequency.cos().contiguous(), frequency.sin().contiguous()
+
+
+def _prepare(x: Tensor, positions: Tensor, theta: float) -> tuple[Tensor, Tensor, Tensor]:
+    dim = x.size(-1)
+    if dim % 2:
+        raise ValueError(f"RoPE head_dim must be even, got {dim}")
+    if positions.ndim == 1:
+        table_rows = positions.numel()
+        x_2d = x.contiguous().reshape(-1, dim)
+    elif positions.ndim == 2:
+        batch, sequence = positions.shape
+        if x.ndim not in (3, 4) or x.size(0) != batch or x.size(-2) != sequence:
+            raise ValueError("[B,S] positions require x [B,S,D] or [B,H,S,D]")
+        x_2d = (
+            x.permute(1, 0, 2, 3).contiguous().reshape(-1, dim)
+            if x.ndim == 4
+            else x.contiguous().reshape(-1, dim)
+        )
+        table_rows = batch * sequence
+    else:
+        raise ValueError("positions must have shape [S] or [B,S]")
+    if table_rows < 1 or x_2d.size(0) % table_rows:
+        raise ValueError("RoPE rows must be divisible by the position table size")
+    cos, sin = _build_cos_sin(positions, dim // 2, float(theta), x.device)
+    return x_2d, cos, sin
+
+
+def _restore(out: Tensor, shape: torch.Size, positions_dim: int) -> Tensor:
+    if positions_dim == 2 and len(shape) == 4:
+        batch, heads, sequence, dim = shape
+        return out.reshape(heads, batch, sequence, dim).permute(1, 0, 2, 3).contiguous()
+    return out.reshape(shape)
+
+
+class _RocmRoPEFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor, positions: Tensor, theta: float) -> Tensor:
+        x_2d, cos, sin = _prepare(x, positions, theta)
+        ctx.save_for_backward(cos, sin)
+        ctx.shape = x.shape
+        ctx.positions_dim = positions.ndim
+        out = _C.deterministic_rope_apply_rocm(x_2d, cos, sin, 1.0)
+        return _restore(out, x.shape, positions.ndim)
+
+    @staticmethod
+    def backward(ctx, grad_out: Tensor):
+        cos, sin = ctx.saved_tensors
+        shape = ctx.shape
+        if ctx.positions_dim == 2 and len(shape) == 4:
+            grad_2d = grad_out.permute(1, 0, 2, 3).contiguous().reshape(-1, shape[-1])
+        else:
+            grad_2d = grad_out.contiguous().reshape(-1, shape[-1])
+        grad_x = _C.deterministic_rope_apply_rocm(grad_2d, cos, sin, -1.0)
+        return _restore(grad_x, shape, ctx.positions_dim), None, None
+
+
+class RocmDeterministicRoPEOp:
+    """One precompiled HIP RoPE arithmetic path shared by train and rollout."""
+
+    backend_id = "rlkernel.rocm.deterministic_rope"
+    op_class = "elementwise"
+    fallback = False
+
+    def __init__(self) -> None:
+        if torch.version.hip is None:
+            raise RuntimeError("RocmDeterministicRoPEOp requires a ROCm PyTorch build")
+        if not _EXT_AVAILABLE or not hasattr(_C, "deterministic_rope_apply_rocm"):
+            raise RuntimeError(
+                "ROCm deterministic RoPE is unavailable; rebuild rl_engine._C for ROCm"
+            )
+
+    def __call__(self, x: Tensor, positions: Tensor, *, theta: float = 1_000_000.0) -> Tensor:
+        if not x.is_cuda:
+            raise RuntimeError("ROCm deterministic RoPE requires a GPU tensor")
+        if x.dtype not in (torch.float16, torch.bfloat16):
+            raise ValueError("ROCm deterministic RoPE requires FP16 or BF16")
+        return _RocmRoPEFunction.apply(x, positions, float(theta))
+
+
+__all__ = ["RocmDeterministicRoPEOp"]
diff --git a/scripts/rocm_attention_acceptance.py b/scripts/rocm_attention_acceptance.py
new file mode 100644
index 00000000..9ef1440d
--- /dev/null
+++ b/scripts/rocm_attention_acceptance.py
@@ -0,0 +1,84 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""Run the ROCm strict Attention acceptance matrix and retain evidence."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+import torch
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--output-dir", type=Path, default=Path("results/rocm-attention"))
+    args = parser.parse_args()
+    if torch.version.hip is None or torch.cuda.device_count() < 8:
+        raise RuntimeError("the formal acceptance matrix requires 8 visible ROCm GPUs")
+
+    repo = Path(__file__).resolve().parents[1]
+    output = (repo / args.output_dir).resolve() if not args.output_dir.is_absolute() else args.output_dir
+    output.mkdir(parents=True, exist_ok=True)
+    steps: list[dict[str, object]] = []
+
+    commands = [
+        (
+            "single_gpu",
+            [sys.executable, "-m", "pytest", "-q", "tests/test_deterministic_attention_rocm.py"],
+        )
+    ]
+    for transport, strict in (("p2p_nccl_reference", False), ("rccl_ag_rs", True)):
+        for ranks in (2, 4, 8):
+            name = f"{transport}_{ranks}r"
+            report = output / f"{name}.json"
+            command = [
+                "torchrun",
+                "--standalone",
+                f"--nproc-per-node={ranks}",
+                "scripts/ws2_p2p_nccl_attention_reference_check.py",
+                "--transport",
+                transport,
+                "--output",
+                str(report),
+            ]
+            if strict:
+                command.append("--strict-shared-core")
+            commands.append((name, command))
+
+    for name, command in commands:
+        completed = subprocess.run(
+            command,
+            cwd=repo,
+            text=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            check=False,
+        )
+        (output / f"{name}.log").write_text(completed.stdout, encoding="utf-8")
+        steps.append({"name": name, "command": command, "returncode": completed.returncode})
+
+    summary = {
+        "schema_version": "ws2_rocm_attention_acceptance/v1",
+        "platform": "rocm",
+        "torch": str(torch.__version__),
+        "hip": str(torch.version.hip),
+        "collective": list(torch.cuda.nccl.version()),
+        "device_count": torch.cuda.device_count(),
+        "device_name": torch.cuda.get_device_name(0),
+        "steps": steps,
+        "passed": all(step["returncode"] == 0 for step in steps),
+    }
+    (output / "summary.json").write_text(
+        json.dumps(summary, indent=2, sort_keys=True) + "\n",
+        encoding="utf-8",
+    )
+    print(json.dumps(summary, indent=2, sort_keys=True))
+    return 0 if summary["passed"] else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/ws2_p2p_nccl_attention_reference_check.py b/scripts/ws2_p2p_nccl_attention_reference_check.py
index 47a70676..963e5020 100644
--- a/scripts/ws2_p2p_nccl_attention_reference_check.py
+++ b/scripts/ws2_p2p_nccl_attention_reference_check.py
@@ -39,6 +39,7 @@
     AttentionParallelSpec,
     CUDAAGRSAttentionCPCommunication,
     P2PNCCLAttentionCPCommunication,
+    RCCLAGRSAttentionCPCommunication,
 )
 from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (  # noqa: E402
     RLKernelDeterministicAttentionCore,
@@ -48,7 +49,6 @@
     FlashInferQwen3PagedAttentionOp,
     _apply_strict_rope,
 )
-from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RoPESM90Op  # noqa: E402
 from rl_engine.kernels.ops.pytorch.attention.cp_attention import (  # noqa: E402
     AttentionPartialState,
     DeterministicCPAttentionReferenceOp,
@@ -70,9 +70,9 @@ def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
     parser.add_argument("--final-write-atol", type=float, default=2.0e-2)
     parser.add_argument(
         "--transport",
-        choices=("p2p_nccl_reference", "cuda_ag_rs"),
+        choices=("p2p_nccl_reference", "cuda_ag_rs", "rccl_ag_rs"),
         default="p2p_nccl_reference",
-        help="P2P is the correctness reference; cuda_ag_rs selects PR311/PR312",
+        help="P2P is the reference; cuda_ag_rs and rccl_ag_rs are self-owned transports",
     )
     parser.add_argument(
         "--strict-shared-core",
@@ -81,21 +81,21 @@ def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
     )
     parser.add_argument("--output", type=Path)
     args = parser.parse_args(argv)
-    if args.strict_shared_core and args.transport != "cuda_ag_rs":
-        parser.error("--strict-shared-core requires --transport cuda_ag_rs")
+    if args.strict_shared_core and args.transport not in {"cuda_ag_rs", "rccl_ag_rs"}:
+        parser.error("--strict-shared-core requires a self-owned AG/RS transport")
     return args
 
 
 def main(argv: Sequence[str] | None = None) -> int:
     args = parse_args(argv)
     if not torch.cuda.is_available() or torch.cuda.device_count() < 2:
-        raise RuntimeError("this check requires at least two visible CUDA devices")
+        raise RuntimeError("this check requires at least two visible CUDA/ROCm devices")
     dist.init_process_group("nccl", init_method="env://")
     try:
         world_size = dist.get_world_size()
         global_rank = dist.get_rank()
         if world_size not in {2, 4, 8}:
-            raise RuntimeError("this check requires 2, 4, or 8 NCCL ranks")
+            raise RuntimeError("this check requires 2, 4, or 8 NCCL/RCCL ranks")
         if torch.cuda.device_count() < world_size:
             raise RuntimeError("this single-node check requires one visible GPU per NCCL rank")
         local_rank = int(os.environ.get("LOCAL_RANK", str(global_rank)))
@@ -131,12 +131,17 @@ def main(argv: Sequence[str] | None = None) -> int:
         dist.all_gather_object(reports, result)
         if global_rank == 0:
             report = {
-                "schema_version": (
-                    "ws2_p2p_nccl_attention_reference/v1"
-                    if args.transport == "p2p_nccl_reference"
-                    else "ws2_cuda_ag_rs_attention/v1"
-                ),
+                "schema_version": f"ws2_{args.transport}_attention/v2",
                 "backend": str(dist.get_backend()),
+                "platform": "rocm" if torch.version.hip is not None else "cuda",
+                "torch_version": str(torch.__version__),
+                "runtime_version": (
+                    str(torch.version.hip)
+                    if torch.version.hip is not None
+                    else str(torch.version.cuda)
+                ),
+                "collective_version": list(torch.cuda.nccl.version()),
+                "device_name": torch.cuda.get_device_name(0),
                 "transport": args.transport,
                 "world_size": world_size,
                 "tp_world_size": 1 if world_size == 2 else 2,
@@ -212,11 +217,17 @@ def run_check(
         expected_kv_token_range=(0, args.seq_len),
         query_token_ranges=owner_ranges,
     )
-    communication: P2PNCCLAttentionCPCommunication | CUDAAGRSAttentionCPCommunication
+    communication: (
+        P2PNCCLAttentionCPCommunication
+        | CUDAAGRSAttentionCPCommunication
+        | RCCLAGRSAttentionCPCommunication
+    )
     if args.transport == "p2p_nccl_reference":
         communication = P2PNCCLAttentionCPCommunication(process_group=cp_group)
-    else:
+    elif args.transport == "cuda_ag_rs":
         communication = CUDAAGRSAttentionCPCommunication(process_group=cp_group)
+    else:
+        communication = RCCLAGRSAttentionCPCommunication(process_group=cp_group)
 
     query_start, query_end = owner_ranges[cp_rank]
     q_local = q[:, :, query_start:query_end, :].contiguous()
@@ -350,7 +361,11 @@ def _run_strict_shared_core_check(
     args: argparse.Namespace,
     *,
     plan: AttentionCPCommunicationPlan,
-    communication: CUDAAGRSAttentionCPCommunication | P2PNCCLAttentionCPCommunication,
+    communication: (
+        CUDAAGRSAttentionCPCommunication
+        | RCCLAGRSAttentionCPCommunication
+        | P2PNCCLAttentionCPCommunication
+    ),
     q: torch.Tensor,
     k: torch.Tensor,
     v: torch.Tensor,
@@ -383,7 +398,7 @@ def _run_strict_shared_core_check(
     q_ref = q.detach().clone().requires_grad_()
     k_ref = k.detach().clone().requires_grad_()
     v_ref = v.detach().clone().requires_grad_()
-    rope = RoPESM90Op()
+    rope = _strict_rope_op()
     q_ready = _apply_strict_rope(rope, q_ref, positions, config.rope.rope_theta)
     k_ready = _apply_strict_rope(rope, k_ref, positions, config.rope.rope_theta)
     reference = RLKernelDeterministicAttentionCore().forward_with_lse(
@@ -445,6 +460,7 @@ def _run_strict_shared_core_check(
         and provenance.get("fallback") is False
         and provenance.get("strict_split_kv") == "disabled"
         and provenance.get("strict_comm_autograd") is True
+        and provenance.get("communication_backend") == args.transport
     )
     return {
         "executed": True,
@@ -468,5 +484,17 @@ def _run_strict_shared_core_check(
     }
 
 
+def _strict_rope_op():
+    if torch.version.hip is not None:
+        from rl_engine.kernels.ops.rocm.rotary_embedding.rope import (
+            RocmDeterministicRoPEOp,
+        )
+
+        return RocmDeterministicRoPEOp()
+    from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RoPESM90Op
+
+    return RoPESM90Op()
+
+
 if __name__ == "__main__":
     raise SystemExit(main())
diff --git a/setup.py b/setup.py
index 0cd24a0f..695f0419 100644
--- a/setup.py
+++ b/setup.py
@@ -140,6 +140,8 @@ def get_extensions():
             "csrc/cuda/activation.cu",
             "csrc/cuda/attention/deterministic_attention.cu",
         ]
+        if is_rocm:
+            cuda_sources.append("csrc/rocm/deterministic_rope.cu")
         if not is_rocm:
             # This source contains NVIDIA PTX (cp.async, ldmatrix, and mma.sync).
             # The ROCm dispatcher falls back to PyTorch SDPA for this operator.
diff --git a/tests/test_deterministic_attention_rocm.py b/tests/test_deterministic_attention_rocm.py
new file mode 100644
index 00000000..519eee66
--- /dev/null
+++ b/tests/test_deterministic_attention_rocm.py
@@ -0,0 +1,131 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""ROCm strict Attention acceptance tests that never use a native fallback."""
+
+import math
+
+import pytest
+import torch
+
+pytestmark = pytest.mark.skipif(
+    torch.version.hip is None or not torch.cuda.is_available(),
+    reason="ROCm GPU is unavailable",
+)
+
+from rl_engine.kernels.attention_contract import SplitKVSpec  # noqa: E402
+from rl_engine.kernels.ops.rocm.attention.deterministic_attn import (  # noqa: E402
+    RLKernelDeterministicAttentionCore,
+)
+from rl_engine.kernels.ops.rocm.rotary_embedding.rope import (  # noqa: E402
+    RocmDeterministicRoPEOp,
+)
+
+
+def _qkv(*, batch: int = 2, sequence: int = 7):
+    generator = torch.Generator(device="cpu").manual_seed(942)
+    q = torch.randn(batch, 4, sequence, 128, dtype=torch.bfloat16, generator=generator).cuda()
+    k = torch.randn(batch, 1, sequence, 128, dtype=torch.bfloat16, generator=generator).cuda()
+    v = torch.randn(batch, 1, sequence, 128, dtype=torch.bfloat16, generator=generator).cuda()
+    return q, k, v
+
+
+def test_rocm_attention_core_is_repeat_bitwise_and_no_fallback():
+    q, k, v = _qkv()
+    positions = torch.arange(q.size(2), device=q.device).expand(q.size(0), -1)
+    core = RLKernelDeterministicAttentionCore()
+    first = core.forward_with_lse(
+        q,
+        k,
+        v,
+        query_position_ids=positions,
+        key_position_ids=positions,
+    )
+    second = core.forward_with_lse(
+        q,
+        k,
+        v,
+        query_position_ids=positions,
+        key_position_ids=positions,
+    )
+    assert torch.equal(first.out, second.out)
+    assert torch.equal(first.lse, second.lse)
+    assert first.provenance["attention_backend"] == "rlkernel.rocm.deterministic_attention"
+    assert first.provenance["fallback"] is False
+    assert first.provenance["split_kv"]["actual_split_kv_policy"] == "disabled"
+
+
+def test_rocm_attention_forward_backward_train_rollout_bitwise():
+    q, k, v = (tensor.requires_grad_() for tensor in _qkv(batch=1, sequence=5))
+    positions = torch.arange(q.size(2), device=q.device).expand(q.size(0), -1)
+    core = RLKernelDeterministicAttentionCore()
+    train = core.forward_with_lse(
+        q,
+        k,
+        v,
+        query_position_ids=positions,
+        key_position_ids=positions,
+    )
+    grad = torch.randn(train.out.shape, dtype=train.out.dtype, device="cpu").cuda()
+    (train.out.float() * grad.float()).sum().backward()
+    train_grads = tuple(tensor.grad.detach().clone() for tensor in (q, k, v))
+
+    q2, k2, v2 = (tensor.detach().clone().requires_grad_() for tensor in (q, k, v))
+    rollout = core.forward_with_lse(
+        q2,
+        k2,
+        v2,
+        query_position_ids=positions,
+        key_position_ids=positions,
+    )
+    (rollout.out.float() * grad.float()).sum().backward()
+    assert torch.equal(train.out, rollout.out)
+    assert torch.equal(train.lse, rollout.lse)
+    assert all(
+        torch.equal(expected, actual.grad)
+        for expected, actual in zip(train_grads, (q2, k2, v2), strict=True)
+    )
+
+
+def test_rocm_rope_is_batch_invariant_and_backward_repeat_bitwise():
+    generator = torch.Generator(device="cpu").manual_seed(714)
+    x = torch.randn(3, 4, 6, 128, dtype=torch.bfloat16, generator=generator).cuda()
+    positions = torch.arange(6, device=x.device).expand(3, -1)
+    rope = RocmDeterministicRoPEOp()
+    together = rope(x, positions)
+    separate = torch.cat([rope(x[index : index + 1], positions[index]) for index in range(3)])
+    assert torch.equal(together, separate)
+    assert torch.equal(together, rope(x, positions))
+
+    grad = torch.randn(together.shape, dtype=together.dtype, device="cpu").cuda()
+    x1 = x.detach().clone().requires_grad_()
+    x2 = x.detach().clone().requires_grad_()
+    (rope(x1, positions).float() * grad.float()).sum().backward()
+    (rope(x2, positions).float() * grad.float()).sum().backward()
+    assert torch.equal(x1.grad, x2.grad)
+
+
+def test_rocm_rope_matches_fp32_rotate_half_reference():
+    x = torch.randn(1, 2, 4, 128, dtype=torch.bfloat16, device="cuda")
+    positions = torch.arange(4, device=x.device).expand(1, -1)
+    actual = RocmDeterministicRoPEOp()(x, positions)
+    half = x.size(-1) // 2
+    inv_freq = 1.0 / (
+        1_000_000.0
+        ** (torch.arange(half, dtype=torch.float32, device=x.device) / half)
+    )
+    frequency = positions.float().unsqueeze(-1) * inv_freq
+    cos = frequency.cos().unsqueeze(1)
+    sin = frequency.sin().unsqueeze(1)
+    reference = torch.cat(
+        (
+            x[..., :half].float() * cos - x[..., half:].float() * sin,
+            x[..., half:].float() * cos + x[..., :half].float() * sin,
+        ),
+        dim=-1,
+    ).to(x.dtype)
+    torch.testing.assert_close(actual, reference, atol=0, rtol=0)
+
+
+def test_rocm_strict_core_rejects_split_k():
+    with pytest.raises(ValueError, match="Split-KV"):
+        RLKernelDeterministicAttentionCore(split_kv=SplitKVSpec.fixed(32))

From 21486d9fc971f8bcb3aad1ea05abe3fc914b033c Mon Sep 17 00:00:00 2001
From: root <root@tw020.mia.tensorwave.lan>
Date: Wed, 19 Aug 2026 09:29:56 +0000
Subject: [PATCH 02/35] test(attention): make ROCm acceptance imports explicit

---
 .../kernels/ops/cuda/attention/flashinfer_paged_attention.py  | 3 ++-
 scripts/__init__.py                                           | 4 ++++
 2 files changed, 6 insertions(+), 1 deletion(-)
 create mode 100644 scripts/__init__.py

diff --git a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
index 0a6bdeed..deab2628 100644
--- a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
+++ b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
@@ -775,7 +775,8 @@ def _run_strict_cp(
             communication, "supports_autograd", False
         ):
             raise FlashInferUnavailable(
-                "strict training requires an autograd-capable self-owned GPU AG/RS backend"
+                "strict training requires an autograd-capable self-owned CUDA AG/RS "
+                "or ROCm RCCL AG/RS backend"
             )
         query_start, query_end = plan.query_token_ranges[plan.parallel.cp_rank]
         key_start, key_end = _cp_owner_ranges(plan)[plan.parallel.cp_rank]
diff --git a/scripts/__init__.py b/scripts/__init__.py
new file mode 100644
index 00000000..5295edc3
--- /dev/null
+++ b/scripts/__init__.py
@@ -0,0 +1,4 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Repository-local validation scripts."""

From 674d7ba0bfa54fdb10b153a9013bb35eafd8875c Mon Sep 17 00:00:00 2001
From: root <root@tw020.mia.tensorwave.lan>
Date: Wed, 19 Aug 2026 10:03:53 +0000
Subject: [PATCH 03/35] style-rocm-attention-lint

---
 rl_engine/kernels/ops/cuda/attention/cp_comm.py  |  7 +++----
 .../cuda/attention/flashinfer_paged_attention.py | 10 +++-------
 rl_engine/kernels/ops/rocm/attention/__init__.py |  5 +----
 .../kernels/ops/rocm/rotary_embedding/rope.py    |  4 +---
 scripts/rocm_attention_acceptance.py             |  4 +++-
 .../ws2_p2p_nccl_attention_reference_check.py    |  4 +---
 scripts/ws2_pr7_flashinfer_attention_check.py    | 16 +++++++---------
 setup.py                                         |  4 +---
 tests/test_cp_attention.py                       |  2 +-
 tests/test_deterministic_attention_rocm.py       |  9 ++-------
 10 files changed, 23 insertions(+), 42 deletions(-)

diff --git a/rl_engine/kernels/ops/cuda/attention/cp_comm.py b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
index 20602ede..798b1469 100644
--- a/rl_engine/kernels/ops/cuda/attention/cp_comm.py
+++ b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
@@ -652,8 +652,6 @@ def reduce_scatter(self, full: torch.Tensor) -> torch.Tensor:
         performed locally in source-rank order, so collective reduction order
         is not delegated to RCCL.
         """
-        import torch.distributed as dist
-
         if not full.is_cuda or not full.is_contiguous():
             raise AttentionCPCommunicationUnavailable(
                 "RCCL ReduceScatter requires a contiguous ROCm tensor"
@@ -664,8 +662,9 @@ def reduce_scatter(self, full: torch.Tensor) -> torch.Tensor:
             )
         chunks = tuple(chunk.contiguous() for chunk in full.chunk(self.world_size, dim=0))
         gathered = self.all_gather(full)
-        local = gathered[self.rank * chunks[self.rank].size(0) :
-                         (self.rank + 1) * chunks[self.rank].size(0)].clone()
+        local = gathered[
+            self.rank * chunks[self.rank].size(0) : (self.rank + 1) * chunks[self.rank].size(0)
+        ].clone()
         chunk_rows = chunks[self.rank].size(0)
         # Start with source rank 0, then add the remaining source ranks in
         # ascending order. This avoids counting source 0 twice.
diff --git a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
index deab2628..c242d119 100644
--- a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
+++ b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
@@ -1458,8 +1458,7 @@ def _validate_strict_core(core: Any) -> None:
         raise ValueError("strict deterministic core ID must be " f"{STRICT_ATTENTION_CORE_ID!r}")
     if getattr(core, "strict_schedule", None) != STRICT_ATTENTION_SCHEDULE_ID:
         raise ValueError(
-            "strict deterministic core schedule must be "
-            f"{STRICT_ATTENTION_SCHEDULE_ID!r}"
+            "strict deterministic core schedule must be " f"{STRICT_ATTENTION_SCHEDULE_ID!r}"
         )
     required = {
         "merge_order": "global_block_index",
@@ -1517,9 +1516,7 @@ def _resolve_strict_rope(cfg: FlashInferPagedAttentionConfig) -> Any:
         return cfg.strict_rope_op
     try:
         if torch.version.hip is not None:
-            from rl_engine.kernels.ops.rocm.rotary_embedding.rope import (
-                RocmDeterministicRoPEOp,
-            )
+            from rl_engine.kernels.ops.rocm.rotary_embedding.rope import RocmDeterministicRoPEOp
 
             return RocmDeterministicRoPEOp()
         from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RoPESM90Op
@@ -1713,8 +1710,7 @@ def _strict_attention_provenance(
                 "rlkernel.cuda.deterministic_attention",
                 "rlkernel.rocm.deterministic_attention",
             }
-            and getattr(cfg.cp_communication, "backend_id", None)
-            in {"cuda_ag_rs", "rccl_ag_rs"}
+            and getattr(cfg.cp_communication, "backend_id", None) in {"cuda_ag_rs", "rccl_ag_rs"}
         ),
     }
 
diff --git a/rl_engine/kernels/ops/rocm/attention/__init__.py b/rl_engine/kernels/ops/rocm/attention/__init__.py
index 2445e2de..b03448f5 100644
--- a/rl_engine/kernels/ops/rocm/attention/__init__.py
+++ b/rl_engine/kernels/ops/rocm/attention/__init__.py
@@ -1,11 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
+from .deterministic_attn import DeterministicAttentionOp, RLKernelDeterministicAttentionCore
 from .flash_attn import RocmFlashAttentionOp
-from .deterministic_attn import (
-    DeterministicAttentionOp,
-    RLKernelDeterministicAttentionCore,
-)
 
 __all__ = [
     "RocmFlashAttentionOp",
diff --git a/rl_engine/kernels/ops/rocm/rotary_embedding/rope.py b/rl_engine/kernels/ops/rocm/rotary_embedding/rope.py
index 3290a9c1..7b0f385e 100644
--- a/rl_engine/kernels/ops/rocm/rotary_embedding/rope.py
+++ b/rl_engine/kernels/ops/rocm/rotary_embedding/rope.py
@@ -16,9 +16,7 @@ def _build_cos_sin(
     theta: float,
     device: torch.device,
 ) -> tuple[Tensor, Tensor]:
-    inv_freq = 1.0 / (
-        theta ** (torch.arange(0, half, dtype=torch.float32, device=device) / half)
-    )
+    inv_freq = 1.0 / (theta ** (torch.arange(0, half, dtype=torch.float32, device=device) / half))
     frequency = positions.to(device=device, dtype=torch.float32).reshape(-1, 1) * inv_freq
     return frequency.cos().contiguous(), frequency.sin().contiguous()
 
diff --git a/scripts/rocm_attention_acceptance.py b/scripts/rocm_attention_acceptance.py
index 9ef1440d..bdb4adcb 100644
--- a/scripts/rocm_attention_acceptance.py
+++ b/scripts/rocm_attention_acceptance.py
@@ -21,7 +21,9 @@ def main() -> int:
         raise RuntimeError("the formal acceptance matrix requires 8 visible ROCm GPUs")
 
     repo = Path(__file__).resolve().parents[1]
-    output = (repo / args.output_dir).resolve() if not args.output_dir.is_absolute() else args.output_dir
+    output = (
+        (repo / args.output_dir).resolve() if not args.output_dir.is_absolute() else args.output_dir
+    )
     output.mkdir(parents=True, exist_ok=True)
     steps: list[dict[str, object]] = []
 
diff --git a/scripts/ws2_p2p_nccl_attention_reference_check.py b/scripts/ws2_p2p_nccl_attention_reference_check.py
index 963e5020..1f12b0ca 100644
--- a/scripts/ws2_p2p_nccl_attention_reference_check.py
+++ b/scripts/ws2_p2p_nccl_attention_reference_check.py
@@ -486,9 +486,7 @@ def _run_strict_shared_core_check(
 
 def _strict_rope_op():
     if torch.version.hip is not None:
-        from rl_engine.kernels.ops.rocm.rotary_embedding.rope import (
-            RocmDeterministicRoPEOp,
-        )
+        from rl_engine.kernels.ops.rocm.rotary_embedding.rope import RocmDeterministicRoPEOp
 
         return RocmDeterministicRoPEOp()
     from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RoPESM90Op
diff --git a/scripts/ws2_pr7_flashinfer_attention_check.py b/scripts/ws2_pr7_flashinfer_attention_check.py
index 4e0b9895..8fc95800 100644
--- a/scripts/ws2_pr7_flashinfer_attention_check.py
+++ b/scripts/ws2_pr7_flashinfer_attention_check.py
@@ -25,13 +25,16 @@
 if str(REPO_ROOT) not in sys.path:
     sys.path.insert(0, str(REPO_ROOT))
 
+from rl_engine.kernels.attention_contract import (  # noqa: E402
+    STRICT_ATTENTION_CORE_ID,
+    STRICT_ATTENTION_SCHEDULE_ID,
+)
 from rl_engine.kernels.ops.cuda.attention.cp_comm import (  # noqa: E402
     AttentionCPCommunicationPlan,
     AttentionParallelSpec,
 )
-from rl_engine.kernels.attention_contract import (  # noqa: E402
-    STRICT_ATTENTION_CORE_ID,
-    STRICT_ATTENTION_SCHEDULE_ID,
+from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (  # noqa: E402
+    RLKernelDeterministicAttentionCore,
 )
 from rl_engine.kernels.ops.cuda.attention.flashinfer_paged_attention import (  # noqa: E402
     FlashInferPagedAttentionConfig,
@@ -43,9 +46,6 @@
     _materialize_strict_logical_kv,
     build_flashinfer_paged_kv_plan,
 )
-from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (  # noqa: E402
-    RLKernelDeterministicAttentionCore,
-)
 from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RoPESM90Op  # noqa: E402
 from rl_engine.testing.attention_comparison import (  # noqa: E402
     AttentionPathResult,
@@ -400,9 +400,7 @@ def _run_strict_cuda_reference(
 ) -> AttentionPathResult:
     """Call the shared CUDA core directly on the logical KV sequence."""
 
-    core = config.deterministic_core or RLKernelDeterministicAttentionCore(
-        split_kv=config.split_kv
-    )
+    core = config.deterministic_core or RLKernelDeterministicAttentionCore(split_kv=config.split_kv)
     rope = config.strict_rope_op or RoPESM90Op()
     logical_k, logical_v, key_positions = _materialize_strict_logical_kv(
         inputs.k_cache,
diff --git a/setup.py b/setup.py
index 695f0419..297d269d 100644
--- a/setup.py
+++ b/setup.py
@@ -217,9 +217,7 @@ def get_extensions():
             nvcc_flags.append("-allow-unsupported-compiler")
             nvcc_flags.append("-D_ALLOW_COMPILER_AND_STL_VERSION_MISMATCH")
 
-        platform_define = (
-            "-DKERNEL_ALIGN_WITH_ROCM" if is_rocm else "-DKERNEL_ALIGN_WITH_CUDA"
-        )
+        platform_define = "-DKERNEL_ALIGN_WITH_ROCM" if is_rocm else "-DKERNEL_ALIGN_WITH_CUDA"
         cxx_flags = ["-O3", "-std=c++17", platform_define]
         extra_link_args = list(torch_rpath)
 
diff --git a/tests/test_cp_attention.py b/tests/test_cp_attention.py
index 4ad82ea8..73953090 100644
--- a/tests/test_cp_attention.py
+++ b/tests/test_cp_attention.py
@@ -18,10 +18,10 @@
     STRICT_ATTENTION_CORE_ID,
     STRICT_ATTENTION_SCHEDULE_ID,
 )
+from rl_engine.kernels.ops.cuda.attention import deterministic_attn as deterministic_attn_module
 from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
     RLKernelDeterministicAttentionCore,
 )
-from rl_engine.kernels.ops.cuda.attention import deterministic_attn as deterministic_attn_module
 from rl_engine.kernels.ops.pytorch.attention.cp_attention import (
     AttentionPartialState,
     DeterministicCPAttentionReferenceOp,
diff --git a/tests/test_deterministic_attention_rocm.py b/tests/test_deterministic_attention_rocm.py
index 519eee66..a2f95121 100644
--- a/tests/test_deterministic_attention_rocm.py
+++ b/tests/test_deterministic_attention_rocm.py
@@ -2,8 +2,6 @@
 # Copyright (c) 2026 RL-Kernel Contributors
 """ROCm strict Attention acceptance tests that never use a native fallback."""
 
-import math
-
 import pytest
 import torch
 
@@ -16,9 +14,7 @@
 from rl_engine.kernels.ops.rocm.attention.deterministic_attn import (  # noqa: E402
     RLKernelDeterministicAttentionCore,
 )
-from rl_engine.kernels.ops.rocm.rotary_embedding.rope import (  # noqa: E402
-    RocmDeterministicRoPEOp,
-)
+from rl_engine.kernels.ops.rocm.rotary_embedding.rope import RocmDeterministicRoPEOp  # noqa: E402
 
 
 def _qkv(*, batch: int = 2, sequence: int = 7):
@@ -110,8 +106,7 @@ def test_rocm_rope_matches_fp32_rotate_half_reference():
     actual = RocmDeterministicRoPEOp()(x, positions)
     half = x.size(-1) // 2
     inv_freq = 1.0 / (
-        1_000_000.0
-        ** (torch.arange(half, dtype=torch.float32, device=x.device) / half)
+        1_000_000.0 ** (torch.arange(half, dtype=torch.float32, device=x.device) / half)
     )
     frequency = positions.float().unsqueeze(-1) * inv_freq
     cos = frequency.cos().unsqueeze(1)

From 0934ca4002ee295c6d0451eaf55e0683d3cdd624 Mon Sep 17 00:00:00 2001
From: root <root@tw020.mia.tensorwave.lan>
Date: Wed, 19 Aug 2026 13:38:48 +0000
Subject: [PATCH 04/35] test-unify-cuda-rocm-attention

---
 docs/operators/attention.md                   |   2 +-
 scripts/rocm_attention_acceptance.py          |   2 +-
 ...uda.py => test_deterministic_attention.py} | 284 +++++++++++++-----
 tests/test_deterministic_attention_rocm.py    | 126 --------
 4 files changed, 214 insertions(+), 200 deletions(-)
 rename tests/{test_deterministic_attention_cuda.py => test_deterministic_attention.py} (69%)
 delete mode 100644 tests/test_deterministic_attention_rocm.py

diff --git a/docs/operators/attention.md b/docs/operators/attention.md
index ebff9a58..66682ced 100644
--- a/docs/operators/attention.md
+++ b/docs/operators/attention.md
@@ -153,7 +153,7 @@ GPU-only LARGE Qwen3-8B real-shape smoke test.
 - `csrc/cuda/attention/deterministic_attention.cu` — CUDA kernels
 - `rl_engine/kernels/registry.py`
 - `tests/test_attention.py`
-- `tests/test_deterministic_attention_cuda.py`
+- `tests/test_deterministic_attention.py`
 
 ## Fixed Reduction Order (CUDA Deterministic Backend)
 
diff --git a/scripts/rocm_attention_acceptance.py b/scripts/rocm_attention_acceptance.py
index bdb4adcb..1423ab2b 100644
--- a/scripts/rocm_attention_acceptance.py
+++ b/scripts/rocm_attention_acceptance.py
@@ -30,7 +30,7 @@ def main() -> int:
     commands = [
         (
             "single_gpu",
-            [sys.executable, "-m", "pytest", "-q", "tests/test_deterministic_attention_rocm.py"],
+            [sys.executable, "-m", "pytest", "-q", "tests/test_deterministic_attention.py"],
         )
     ]
     for transport, strict in (("p2p_nccl_reference", False), ("rccl_ag_rs", True)):
diff --git a/tests/test_deterministic_attention_cuda.py b/tests/test_deterministic_attention.py
similarity index 69%
rename from tests/test_deterministic_attention_cuda.py
rename to tests/test_deterministic_attention.py
index f08315a8..d616ff4a 100644
--- a/tests/test_deterministic_attention_cuda.py
+++ b/tests/test_deterministic_attention.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-"""Deterministic standard-softmax attention CUDA tests (issue #147).
+"""Deterministic standard-softmax Attention tests for CUDA and ROCm.
 
 Covers (per §7 and §8 of the implementation plan):
 - Forward correctness via #108 harness (run_operator_suite)
@@ -23,28 +23,50 @@
 import pytest
 import torch
 
-pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+from rl_engine.platforms.device import device_ctx
+
+IS_ROCM = device_ctx.is_rocm
+IS_CUDA = device_ctx.device_type == "cuda"
+IS_GPU = IS_CUDA or IS_ROCM
+BACKEND = "rocm" if IS_ROCM else "cuda"
+
+pytestmark = pytest.mark.skipif(not IS_GPU, reason="CUDA/ROCm GPU not available")
+ROCM_ONLY = pytest.mark.skipif(not IS_ROCM, reason="ROCm-only acceptance check")
 
 try:
+    from rl_engine.kernels.attention_contract import SplitKVSpec
     from rl_engine.kernels.gtest.op_checks import CandidateSpec, OperatorCase, run_operator_suite
-    from rl_engine.kernels.ops.cuda.attention.deterministic_attn import DeterministicAttentionOp
     from rl_engine.kernels.ops.pytorch.attention.standard_attn import NativeAttentionOp
 
+    if IS_ROCM:
+        from rl_engine.kernels.ops.rocm.attention.deterministic_attn import (
+            DeterministicAttentionOp,
+            RLKernelDeterministicAttentionCore,
+        )
+    else:
+        from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
+            DeterministicAttentionOp,
+            RLKernelDeterministicAttentionCore,
+        )
+
     _OP_AVAILABLE = True
 except (ImportError, RuntimeError):
     _OP_AVAILABLE = False
 
+if IS_ROCM:
+    from rl_engine.kernels.ops.rocm.rotary_embedding.rope import RocmDeterministicRoPEOp
+
 pytestmark = [
     pytestmark,
-    pytest.mark.skipif(not _OP_AVAILABLE, reason="CUDA attention op not built"),
+    pytest.mark.skipif(not _OP_AVAILABLE, reason=f"{BACKEND} attention op not built"),
 ]
 
-DEVICE = "cuda"
+DEVICE = device_ctx.device
 D = 128
 
 
 @pytest.fixture
-def cuda_op():
+def attention_op():
     return DeterministicAttentionOp()
 
 
@@ -109,8 +131,8 @@ def _build_harness_cases():
 
 def test_harness_forward():
     """§8.4: run_operator_suite forward — candidate vs gold (accuracy tolerance)."""
-    cuda = DeterministicAttentionOp()
-    candidate = CandidateSpec(name="cuda-attention", fn=cuda, backend="cuda")
+    attention = DeterministicAttentionOp()
+    candidate = CandidateSpec(name=f"{BACKEND}-attention", fn=attention, backend=BACKEND)
     report = run_operator_suite("attention", candidates=[candidate], cases=_build_harness_cases())
     for cr in report.candidates:
         for case in cr.cases:
@@ -126,8 +148,8 @@ def test_harness_forward():
 
 def test_harness_backward():
     """§8.4: run_operator_suite backward — grad comparison vs gold fp32 autograd."""
-    cuda = DeterministicAttentionOp()
-    candidate = CandidateSpec(name="cuda-attention", fn=cuda, backend="cuda")
+    attention = DeterministicAttentionOp()
+    candidate = CandidateSpec(name=f"{BACKEND}-attention", fn=attention, backend=BACKEND)
     report = run_operator_suite(
         "attention",
         candidates=[candidate],
@@ -163,18 +185,18 @@ def test_harness_backward():
 
 
 @pytest.mark.parametrize("dtype,hq,hkv,sq,skv,causal", SWEEP_CONFIGS)
-def test_forward_sweep(cuda_op, gold_op, dtype, hq, hkv, sq, skv, causal):
+def test_forward_sweep(attention_op, gold_op, dtype, hq, hkv, sq, skv, causal):
     B = 2
     torch.manual_seed(42)
     q = torch.randn(B, hq, sq, D, device=DEVICE, dtype=dtype)
     k = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=dtype)
     v = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=dtype)
 
-    out_cuda = cuda_op.forward(q, k, v, causal=causal)
+    out_actual = attention_op.forward(q, k, v, causal=causal)
     out_gold = gold_op.forward_fp32(q, k, v, causal=causal)
 
     atol, rtol = _tol(dtype)
-    torch.testing.assert_close(out_cuda.float(), out_gold.float(), atol=atol, rtol=rtol)
+    torch.testing.assert_close(out_actual.float(), out_gold.float(), atol=atol, rtol=rtol)
 
 
 # =============================================================================
@@ -183,18 +205,18 @@ def test_forward_sweep(cuda_op, gold_op, dtype, hq, hkv, sq, skv, causal):
 
 
 @pytest.mark.parametrize("scale", [None, 0.0, 0.05])
-def test_scale(cuda_op, gold_op, scale):
+def test_scale(attention_op, gold_op, scale):
     B, hq, hkv, sq, skv = 2, 4, 1, 8, 16
     torch.manual_seed(42)
     q = torch.randn(B, hq, sq, D, device=DEVICE, dtype=torch.bfloat16)
     k = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
     v = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
 
-    out_cuda = cuda_op.forward(q, k, v, causal=True, scale=scale)
+    out_actual = attention_op.forward(q, k, v, causal=True, scale=scale)
     out_gold = gold_op.forward_fp32(q, k, v, causal=True, scale=scale)
 
     atol, rtol = _tol(torch.bfloat16)
-    torch.testing.assert_close(out_cuda.float(), out_gold.float(), atol=atol, rtol=rtol)
+    torch.testing.assert_close(out_actual.float(), out_gold.float(), atol=atol, rtol=rtol)
 
 
 # =============================================================================
@@ -203,7 +225,7 @@ def test_scale(cuda_op, gold_op, scale):
 
 
 @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
-def test_forward_with_padding(cuda_op, gold_op, dtype):
+def test_forward_with_padding(attention_op, gold_op, dtype):
     B, hq, hkv, sq, skv = 2, 4, 1, 8, 16
     torch.manual_seed(7)
     q = torch.randn(B, hq, sq, D, device=DEVICE, dtype=dtype)
@@ -213,21 +235,21 @@ def test_forward_with_padding(cuda_op, gold_op, dtype):
     mask[0, 10:] = False
     mask[1, 12:] = False
 
-    out_cuda = cuda_op.forward(q, k, v, causal=True, key_padding_mask=mask)
+    out_actual = attention_op.forward(q, k, v, causal=True, key_padding_mask=mask)
     out_gold = gold_op.forward_fp32(q, k, v, causal=True, key_padding_mask=mask)
 
     atol, rtol = _tol(dtype)
-    torch.testing.assert_close(out_cuda.float(), out_gold.float(), atol=atol, rtol=rtol)
+    torch.testing.assert_close(out_actual.float(), out_gold.float(), atol=atol, rtol=rtol)
 
 
-def test_fully_masked_row(cuda_op):
+def test_fully_masked_row(attention_op):
     B, hq, hkv, sq, skv = 1, 1, 1, 2, 4
     q = torch.randn(B, hq, sq, D, device=DEVICE, dtype=torch.bfloat16)
     k = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
     v = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
     mask = torch.zeros(B, skv, device=DEVICE, dtype=torch.bool)
 
-    out, lse = cuda_op.forward_with_lse(q, k, v, causal=False, key_padding_mask=mask)
+    out, lse = attention_op.forward_with_lse(q, k, v, causal=False, key_padding_mask=mask)
     assert (out == 0).all()
     assert (lse == float("-inf")).all()
 
@@ -237,14 +259,14 @@ def test_fully_masked_row(cuda_op):
 # =============================================================================
 
 
-def test_lse_correctness(cuda_op):
+def test_lse_correctness(attention_op):
     B, hq, hkv, sq, skv = 2, 4, 1, 8, 16
     torch.manual_seed(99)
     q = torch.randn(B, hq, sq, D, device=DEVICE, dtype=torch.bfloat16)
     k = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
     v = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
 
-    _, lse_cuda = cuda_op.forward_with_lse(q, k, v, causal=True)
+    _, lse_actual = attention_op.forward_with_lse(q, k, v, causal=True)
 
     scale = 1.0 / math.sqrt(D)
     g = hq // hkv
@@ -256,7 +278,7 @@ def test_lse_correctness(cuda_op):
     scores = scores.masked_fill(~causal_mask.unsqueeze(0).unsqueeze(0), float("-inf"))
     lse_gold = torch.logsumexp(scores, dim=-1)
 
-    torch.testing.assert_close(lse_cuda, lse_gold, atol=5e-2, rtol=2e-2)
+    torch.testing.assert_close(lse_actual, lse_gold, atol=5e-2, rtol=2e-2)
 
 
 # =============================================================================
@@ -264,7 +286,7 @@ def test_lse_correctness(cuda_op):
 # =============================================================================
 
 
-def test_valid_only_vs_padded_accuracy(cuda_op):
+def test_valid_only_vs_padded_accuracy(attention_op):
     """Padding changes reduction width; result is near-equal, not bitwise.
 
     We use causal=False here so that all valid keys are equally visible
@@ -295,8 +317,8 @@ def test_valid_only_vs_padded_accuracy(cuda_op):
     mask = torch.ones(B, skv_padded, device=DEVICE, dtype=torch.bool)
     mask[:, skv_valid:] = False
 
-    out_valid = cuda_op.forward(q, k_valid, v_valid, causal=False)
-    out_padded = cuda_op.forward(q, k_padded, v_padded, causal=False, key_padding_mask=mask)
+    out_valid = attention_op.forward(q, k_valid, v_valid, causal=False)
+    out_padded = attention_op.forward(q, k_padded, v_padded, causal=False, key_padding_mask=mask)
 
     atol, rtol = _tol(torch.bfloat16)
     torch.testing.assert_close(out_valid.float(), out_padded.float(), atol=atol, rtol=rtol)
@@ -307,7 +329,7 @@ def test_valid_only_vs_padded_accuracy(cuda_op):
 # =============================================================================
 
 
-def test_batch_invariance_single(cuda_op):
+def test_batch_invariance_single(attention_op):
     """Same sample in full batch vs extracted single — bitwise."""
     B, hq, hkv, sq, skv = 4, 4, 1, 8, 16
     torch.manual_seed(11)
@@ -315,17 +337,17 @@ def test_batch_invariance_single(cuda_op):
     k = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
     v = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
 
-    out_full, lse_full = cuda_op.forward_with_lse(q, k, v, causal=True)
+    out_full, lse_full = attention_op.forward_with_lse(q, k, v, causal=True)
 
     for i in range(B):
-        out_single, lse_single = cuda_op.forward_with_lse(
+        out_single, lse_single = attention_op.forward_with_lse(
             q[i : i + 1], k[i : i + 1], v[i : i + 1], causal=True
         )
         assert torch.equal(out_full[i : i + 1], out_single), f"Output batch invariance failed i={i}"
         assert torch.equal(lse_full[i : i + 1], lse_single), f"LSE batch invariance failed i={i}"
 
 
-def test_batch_invariance_position_permutation(cuda_op):
+def test_batch_invariance_position_permutation(attention_op):
     """Same sample at different batch positions — bitwise."""
     B, hq, hkv, sq, skv = 4, 32, 8, 8, 16
     torch.manual_seed(12)
@@ -333,13 +355,13 @@ def test_batch_invariance_position_permutation(cuda_op):
     k = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
     v = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
 
-    out_full = cuda_op.forward(q, k, v, causal=True)
+    out_full = attention_op.forward(q, k, v, causal=True)
 
     perm = [2, 0, 3, 1]
     q_perm = q[perm]
     k_perm = k[perm]
     v_perm = v[perm]
-    out_perm = cuda_op.forward(q_perm, k_perm, v_perm, causal=True)
+    out_perm = attention_op.forward(q_perm, k_perm, v_perm, causal=True)
 
     for new_pos, orig_pos in enumerate(perm):
         assert torch.equal(
@@ -347,7 +369,7 @@ def test_batch_invariance_position_permutation(cuda_op):
         ), f"Position permutation invariance failed: orig={orig_pos} new={new_pos}"
 
 
-def test_batch_invariance_chunk(cuda_op):
+def test_batch_invariance_chunk(attention_op):
     """Batch-dim chunking — bitwise."""
     B, hq, hkv, sq, skv = 4, 4, 1, 8, 16
     torch.manual_seed(13)
@@ -355,11 +377,11 @@ def test_batch_invariance_chunk(cuda_op):
     k = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
     v = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
 
-    out_full = cuda_op.forward(q, k, v, causal=True)
+    out_full = attention_op.forward(q, k, v, causal=True)
     out_chunk = torch.cat(
         [
-            cuda_op.forward(q[:2], k[:2], v[:2], causal=True),
-            cuda_op.forward(q[2:], k[2:], v[2:], causal=True),
+            attention_op.forward(q[:2], k[:2], v[:2], causal=True),
+            attention_op.forward(q[2:], k[2:], v[2:], causal=True),
         ],
         dim=0,
     )
@@ -381,20 +403,22 @@ def test_batch_invariance_chunk(cuda_op):
         (3, 32, 8),
     ],
 )
-def test_chunked_prefill(cuda_op, chunk_size, hq, hkv):
+def test_chunked_prefill(attention_op, chunk_size, hq, hkv):
     B, T = 1, 16
     torch.manual_seed(22)
     q = torch.randn(B, hq, T, D, device=DEVICE, dtype=torch.bfloat16)
     k = torch.randn(B, hkv, T, D, device=DEVICE, dtype=torch.bfloat16)
     v = torch.randn(B, hkv, T, D, device=DEVICE, dtype=torch.bfloat16)
 
-    out_full = cuda_op.forward(q, k, v, causal=True)
+    out_full = attention_op.forward(q, k, v, causal=True)
 
     outs = []
     for t in range(0, T, chunk_size):
         c = min(chunk_size, T - t)
         outs.append(
-            cuda_op.forward(q[:, :, t : t + c], k[:, :, : t + c], v[:, :, : t + c], causal=True)
+            attention_op.forward(
+                q[:, :, t : t + c], k[:, :, : t + c], v[:, :, : t + c], causal=True
+            )
         )
     out_chunked = torch.cat(outs, dim=2)
     assert torch.equal(
@@ -403,7 +427,7 @@ def test_chunked_prefill(cuda_op, chunk_size, hq, hkv):
 
 
 @pytest.mark.parametrize("chunk_size", [1, 3, 8])
-def test_chunked_prefill_with_padding(cuda_op, chunk_size):
+def test_chunked_prefill_with_padding(attention_op, chunk_size):
     """§7.4: chunked-prefill with key_padding_mask (mask sliced with Skv)."""
     B, hq, hkv, T = 1, 4, 1, 16
     torch.manual_seed(23)
@@ -413,13 +437,13 @@ def test_chunked_prefill_with_padding(cuda_op, chunk_size):
     mask = torch.ones(B, T, device=DEVICE, dtype=torch.bool)
     mask[0, 12:] = False
 
-    out_full = cuda_op.forward(q, k, v, causal=True, key_padding_mask=mask)
+    out_full = attention_op.forward(q, k, v, causal=True, key_padding_mask=mask)
 
     outs = []
     for t in range(0, T, chunk_size):
         c = min(chunk_size, T - t)
         outs.append(
-            cuda_op.forward(
+            attention_op.forward(
                 q[:, :, t : t + c],
                 k[:, :, : t + c],
                 v[:, :, : t + c],
@@ -434,7 +458,7 @@ def test_chunked_prefill_with_padding(cuda_op, chunk_size):
 
 
 @pytest.mark.parametrize("chunk_size", [1, 3])
-def test_chunked_prefill_lse(cuda_op, chunk_size):
+def test_chunked_prefill_lse(attention_op, chunk_size):
     """§7.4: LSE chunked-prefill invariance."""
     B, hq, hkv, T = 1, 4, 1, 12
     torch.manual_seed(24)
@@ -442,12 +466,12 @@ def test_chunked_prefill_lse(cuda_op, chunk_size):
     k = torch.randn(B, hkv, T, D, device=DEVICE, dtype=torch.bfloat16)
     v = torch.randn(B, hkv, T, D, device=DEVICE, dtype=torch.bfloat16)
 
-    _, lse_full = cuda_op.forward_with_lse(q, k, v, causal=True)
+    _, lse_full = attention_op.forward_with_lse(q, k, v, causal=True)
 
     lses = []
     for t in range(0, T, chunk_size):
         c = min(chunk_size, T - t)
-        _, lse_chunk = cuda_op.forward_with_lse(
+        _, lse_chunk = attention_op.forward_with_lse(
             q[:, :, t : t + c], k[:, :, : t + c], v[:, :, : t + c], causal=True
         )
         lses.append(lse_chunk)
@@ -460,7 +484,7 @@ def test_chunked_prefill_lse(cuda_op, chunk_size):
 # =============================================================================
 
 
-def test_prefill_decode_slice(cuda_op):
+def test_prefill_decode_slice(attention_op):
     """§7.5.1: prefill[:, :, -1:] == decode(q[-1:], k_full, v_full)."""
     B, hq, hkv, sq, skv = 1, 4, 1, 8, 16
     torch.manual_seed(33)
@@ -468,8 +492,8 @@ def test_prefill_decode_slice(cuda_op):
     k = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
     v = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16)
 
-    prefill = cuda_op.forward(q, k, v, causal=True)
-    decode = cuda_op.forward(q[:, :, -1:], k, v, causal=True)
+    prefill = attention_op.forward(q, k, v, causal=True)
+    decode = attention_op.forward(q[:, :, -1:], k, v, causal=True)
     assert torch.equal(prefill[:, :, -1:], decode)
 
 
@@ -482,7 +506,7 @@ def test_prefill_decode_slice(cuda_op):
         (3, 32, 8),
     ],
 )
-def test_kv_cache_handoff(cuda_op, S_new, hq, hkv):
+def test_kv_cache_handoff(attention_op, S_new, hq, hkv):
     """§7.5.2: cat(k_cache, k_new) handoff == prefill tail."""
     B, S_past = 1, 12
     torch.manual_seed(44)
@@ -491,12 +515,12 @@ def test_kv_cache_handoff(cuda_op, S_new, hq, hkv):
     v_full = torch.randn(B, hkv, S_past + S_new, D, device=DEVICE, dtype=torch.bfloat16)
 
     q_new = q_full[:, :, -S_new:]
-    prefill_tail = cuda_op.forward(q_full, k_full, v_full, causal=True)[:, :, -S_new:]
-    decode_path = cuda_op.forward(q_new, k_full, v_full, causal=True)
+    prefill_tail = attention_op.forward(q_full, k_full, v_full, causal=True)[:, :, -S_new:]
+    decode_path = attention_op.forward(q_new, k_full, v_full, causal=True)
     assert torch.equal(decode_path, prefill_tail)
 
 
-def test_kv_cache_handoff_with_padding(cuda_op):
+def test_kv_cache_handoff_with_padding(attention_op):
     """§7.5.2: cat handoff with padding mask."""
     B, hq, hkv, S_past, S_new = 1, 4, 1, 12, 1
     Skv = S_past + S_new
@@ -508,10 +532,10 @@ def test_kv_cache_handoff_with_padding(cuda_op):
     mask[0, 8:10] = False
 
     q_new = q_full[:, :, -S_new:]
-    prefill_tail = cuda_op.forward(q_full, k_full, v_full, causal=True, key_padding_mask=mask)[
+    prefill_tail = attention_op.forward(q_full, k_full, v_full, causal=True, key_padding_mask=mask)[
         :, :, -S_new:
     ]
-    decode_path = cuda_op.forward(q_new, k_full, v_full, causal=True, key_padding_mask=mask)
+    decode_path = attention_op.forward(q_new, k_full, v_full, causal=True, key_padding_mask=mask)
     assert torch.equal(decode_path, prefill_tail)
 
 
@@ -520,7 +544,7 @@ def test_kv_cache_handoff_with_padding(cuda_op):
 # =============================================================================
 
 
-def test_backward_smoke(cuda_op):
+def test_backward_smoke(attention_op):
     """Backward runs and produces gradients with correct shapes."""
     B, hq, hkv, sq, skv = 1, 4, 1, 4, 8
     torch.manual_seed(55)
@@ -528,7 +552,7 @@ def test_backward_smoke(cuda_op):
     k = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16, requires_grad=True)
     v = torch.randn(B, hkv, skv, D, device=DEVICE, dtype=torch.bfloat16, requires_grad=True)
 
-    out = cuda_op.forward(q, k, v, causal=True)
+    out = attention_op.forward(q, k, v, causal=True)
     loss = out.sum()
     loss.backward()
 
@@ -537,7 +561,7 @@ def test_backward_smoke(cuda_op):
     assert v.grad is not None and v.grad.shape == v.shape
 
 
-def test_backward_fp64_reference(cuda_op):
+def test_backward_fp64_reference(attention_op):
     """§7.6: FP64 high-precision gradient comparison."""
     B, hq, hkv, sq, skv = 1, 4, 1, 4, 8
     torch.manual_seed(56)
@@ -547,11 +571,11 @@ def test_backward_fp64_reference(cuda_op):
 
     grad_out = torch.randn(B, hq, sq, D, device=DEVICE, dtype=torch.float32)
 
-    out_cuda = cuda_op.forward(q_bf16, k_bf16, v_bf16, causal=True)
-    out_cuda.backward(grad_out.to(out_cuda.dtype))
-    dq_cuda = q_bf16.grad.float()
-    dk_cuda = k_bf16.grad.float()
-    dv_cuda = v_bf16.grad.float()
+    out_actual = attention_op.forward(q_bf16, k_bf16, v_bf16, causal=True)
+    out_actual.backward(grad_out.to(out_actual.dtype))
+    dq_actual = q_bf16.grad.float()
+    dk_actual = k_bf16.grad.float()
+    dv_actual = v_bf16.grad.float()
 
     scale = 1.0 / math.sqrt(D)
     g = hq // hkv
@@ -572,12 +596,12 @@ def test_backward_fp64_reference(cuda_op):
     dk_gold = k64.grad.float()
     dv_gold = v64.grad.float()
 
-    torch.testing.assert_close(dq_cuda, dq_gold, atol=5e-2, rtol=2e-2)
-    torch.testing.assert_close(dk_cuda, dk_gold, atol=5e-2, rtol=2e-2)
-    torch.testing.assert_close(dv_cuda, dv_gold, atol=5e-2, rtol=2e-2)
+    torch.testing.assert_close(dq_actual, dq_gold, atol=5e-2, rtol=2e-2)
+    torch.testing.assert_close(dk_actual, dk_gold, atol=5e-2, rtol=2e-2)
+    torch.testing.assert_close(dv_actual, dv_gold, atol=5e-2, rtol=2e-2)
 
 
-def test_gradient_batch_invariance(cuda_op):
+def test_gradient_batch_invariance(attention_op):
     """§7.6: dQ/dK/dV bitwise identical for same sample at different batch positions."""
     B, hq, hkv, sq, skv = 3, 4, 1, 4, 8
     torch.manual_seed(57)
@@ -589,21 +613,21 @@ def test_gradient_batch_invariance(cuda_op):
     q_full = q.clone().requires_grad_(True)
     k_full = k.clone().requires_grad_(True)
     v_full = v.clone().requires_grad_(True)
-    out_full = cuda_op.forward(q_full, k_full, v_full, causal=True)
+    out_full = attention_op.forward(q_full, k_full, v_full, causal=True)
     out_full.backward(grad_out)
 
     for i in range(B):
         qi = q[i : i + 1].clone().requires_grad_(True)
         ki = k[i : i + 1].clone().requires_grad_(True)
         vi = v[i : i + 1].clone().requires_grad_(True)
-        out_i = cuda_op.forward(qi, ki, vi, causal=True)
+        out_i = attention_op.forward(qi, ki, vi, causal=True)
         out_i.backward(grad_out[i : i + 1])
         assert torch.equal(q_full.grad[i : i + 1], qi.grad), f"dQ batch invariance failed i={i}"
         assert torch.equal(k_full.grad[i : i + 1], ki.grad), f"dK batch invariance failed i={i}"
         assert torch.equal(v_full.grad[i : i + 1], vi.grad), f"dV batch invariance failed i={i}"
 
 
-def test_gqa_dk_dv_order(cuda_op):
+def test_gqa_dk_dv_order(attention_op):
     """§7.6/§4.1: GQA dK/dV must follow fixed (hq_local, query_index) order.
 
     Two checks:
@@ -623,13 +647,13 @@ def test_gqa_dk_dv_order(cuda_op):
     q1 = q_data.clone().requires_grad_(True)
     k1 = k_data.clone().requires_grad_(True)
     v1 = v_data.clone().requires_grad_(True)
-    cuda_op.forward(q1, k1, v1, causal=True).backward(grad_data)
+    attention_op.forward(q1, k1, v1, causal=True).backward(grad_data)
 
     q_batch = q_data.expand(4, -1, -1, -1).contiguous().clone().requires_grad_(True)
     k_batch = k_data.expand(4, -1, -1, -1).contiguous().clone().requires_grad_(True)
     v_batch = v_data.expand(4, -1, -1, -1).contiguous().clone().requires_grad_(True)
     grad_batch = grad_data.expand(4, -1, -1, -1).contiguous()
-    cuda_op.forward(q_batch, k_batch, v_batch, causal=True).backward(grad_batch)
+    attention_op.forward(q_batch, k_batch, v_batch, causal=True).backward(grad_batch)
 
     assert torch.equal(k1.grad, k_batch.grad[0:1]), "dK GQA order depends on batch size"
     assert torch.equal(v1.grad, v_batch.grad[0:1]), "dV GQA order depends on batch size"
@@ -655,3 +679,119 @@ def test_gqa_dk_dv_order(cuda_op):
 
     torch.testing.assert_close(k1.grad.float(), dk_gold, atol=5e-2, rtol=2e-2)
     torch.testing.assert_close(v1.grad.float(), dv_gold, atol=5e-2, rtol=2e-2)
+
+
+# =============================================================================
+# Strict core acceptance and ROCm HIP RoPE acceptance
+# =============================================================================
+
+
+def _strict_qkv(*, batch: int = 2, sequence: int = 7):
+    generator = torch.Generator(device="cpu").manual_seed(942)
+    q = torch.randn(batch, 4, sequence, D, dtype=torch.bfloat16, generator=generator).to(DEVICE)
+    k = torch.randn(batch, 1, sequence, D, dtype=torch.bfloat16, generator=generator).to(DEVICE)
+    v = torch.randn(batch, 1, sequence, D, dtype=torch.bfloat16, generator=generator).to(DEVICE)
+    return q, k, v
+
+
+def test_strict_attention_core_is_repeat_bitwise_and_no_fallback():
+    q, k, v = _strict_qkv()
+    positions = torch.arange(q.size(2), device=q.device).expand(q.size(0), -1)
+    core = RLKernelDeterministicAttentionCore()
+    first = core.forward_with_lse(
+        q,
+        k,
+        v,
+        query_position_ids=positions,
+        key_position_ids=positions,
+    )
+    second = core.forward_with_lse(
+        q,
+        k,
+        v,
+        query_position_ids=positions,
+        key_position_ids=positions,
+    )
+    assert torch.equal(first.out, second.out)
+    assert torch.equal(first.lse, second.lse)
+    assert first.provenance["attention_backend"] == f"rlkernel.{BACKEND}.deterministic_attention"
+    assert first.provenance["fallback"] is False
+    assert first.provenance["split_kv"]["actual_split_kv_policy"] == "disabled"
+
+
+def test_strict_attention_forward_backward_train_rollout_bitwise():
+    q, k, v = (tensor.requires_grad_() for tensor in _strict_qkv(batch=1, sequence=5))
+    positions = torch.arange(q.size(2), device=q.device).expand(q.size(0), -1)
+    core = RLKernelDeterministicAttentionCore()
+    train = core.forward_with_lse(
+        q,
+        k,
+        v,
+        query_position_ids=positions,
+        key_position_ids=positions,
+    )
+    grad = torch.randn(train.out.shape, dtype=train.out.dtype, device="cpu").to(DEVICE)
+    (train.out.float() * grad.float()).sum().backward()
+    train_grads = tuple(tensor.grad.detach().clone() for tensor in (q, k, v))
+
+    q2, k2, v2 = (tensor.detach().clone().requires_grad_() for tensor in (q, k, v))
+    rollout = core.forward_with_lse(
+        q2,
+        k2,
+        v2,
+        query_position_ids=positions,
+        key_position_ids=positions,
+    )
+    (rollout.out.float() * grad.float()).sum().backward()
+    assert torch.equal(train.out, rollout.out)
+    assert torch.equal(train.lse, rollout.lse)
+    assert all(
+        torch.equal(expected, actual.grad)
+        for expected, actual in zip(train_grads, (q2, k2, v2), strict=True)
+    )
+
+
+@ROCM_ONLY
+def test_rocm_rope_is_batch_invariant_and_backward_repeat_bitwise():
+    generator = torch.Generator(device="cpu").manual_seed(714)
+    x = torch.randn(3, 4, 6, D, dtype=torch.bfloat16, generator=generator).to(DEVICE)
+    positions = torch.arange(6, device=x.device).expand(3, -1)
+    rope = RocmDeterministicRoPEOp()
+    together = rope(x, positions)
+    separate = torch.cat([rope(x[index : index + 1], positions[index]) for index in range(3)])
+    assert torch.equal(together, separate)
+    assert torch.equal(together, rope(x, positions))
+
+    grad = torch.randn(together.shape, dtype=together.dtype, device="cpu").to(DEVICE)
+    x1 = x.detach().clone().requires_grad_()
+    x2 = x.detach().clone().requires_grad_()
+    (rope(x1, positions).float() * grad.float()).sum().backward()
+    (rope(x2, positions).float() * grad.float()).sum().backward()
+    assert torch.equal(x1.grad, x2.grad)
+
+
+@ROCM_ONLY
+def test_rocm_rope_matches_fp32_rotate_half_reference():
+    x = torch.randn(1, 2, 4, D, dtype=torch.bfloat16, device=DEVICE)
+    positions = torch.arange(4, device=x.device).expand(1, -1)
+    actual = RocmDeterministicRoPEOp()(x, positions)
+    half = x.size(-1) // 2
+    inv_freq = 1.0 / (
+        1_000_000.0 ** (torch.arange(half, dtype=torch.float32, device=x.device) / half)
+    )
+    frequency = positions.float().unsqueeze(-1) * inv_freq
+    cos = frequency.cos().unsqueeze(1)
+    sin = frequency.sin().unsqueeze(1)
+    reference = torch.cat(
+        (
+            x[..., :half].float() * cos - x[..., half:].float() * sin,
+            x[..., half:].float() * cos + x[..., :half].float() * sin,
+        ),
+        dim=-1,
+    ).to(x.dtype)
+    torch.testing.assert_close(actual, reference, atol=0, rtol=0)
+
+
+def test_strict_core_rejects_split_k():
+    with pytest.raises(ValueError, match="Split-KV"):
+        RLKernelDeterministicAttentionCore(split_kv=SplitKVSpec.fixed(32))
diff --git a/tests/test_deterministic_attention_rocm.py b/tests/test_deterministic_attention_rocm.py
deleted file mode 100644
index a2f95121..00000000
--- a/tests/test_deterministic_attention_rocm.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) 2026 RL-Kernel Contributors
-"""ROCm strict Attention acceptance tests that never use a native fallback."""
-
-import pytest
-import torch
-
-pytestmark = pytest.mark.skipif(
-    torch.version.hip is None or not torch.cuda.is_available(),
-    reason="ROCm GPU is unavailable",
-)
-
-from rl_engine.kernels.attention_contract import SplitKVSpec  # noqa: E402
-from rl_engine.kernels.ops.rocm.attention.deterministic_attn import (  # noqa: E402
-    RLKernelDeterministicAttentionCore,
-)
-from rl_engine.kernels.ops.rocm.rotary_embedding.rope import RocmDeterministicRoPEOp  # noqa: E402
-
-
-def _qkv(*, batch: int = 2, sequence: int = 7):
-    generator = torch.Generator(device="cpu").manual_seed(942)
-    q = torch.randn(batch, 4, sequence, 128, dtype=torch.bfloat16, generator=generator).cuda()
-    k = torch.randn(batch, 1, sequence, 128, dtype=torch.bfloat16, generator=generator).cuda()
-    v = torch.randn(batch, 1, sequence, 128, dtype=torch.bfloat16, generator=generator).cuda()
-    return q, k, v
-
-
-def test_rocm_attention_core_is_repeat_bitwise_and_no_fallback():
-    q, k, v = _qkv()
-    positions = torch.arange(q.size(2), device=q.device).expand(q.size(0), -1)
-    core = RLKernelDeterministicAttentionCore()
-    first = core.forward_with_lse(
-        q,
-        k,
-        v,
-        query_position_ids=positions,
-        key_position_ids=positions,
-    )
-    second = core.forward_with_lse(
-        q,
-        k,
-        v,
-        query_position_ids=positions,
-        key_position_ids=positions,
-    )
-    assert torch.equal(first.out, second.out)
-    assert torch.equal(first.lse, second.lse)
-    assert first.provenance["attention_backend"] == "rlkernel.rocm.deterministic_attention"
-    assert first.provenance["fallback"] is False
-    assert first.provenance["split_kv"]["actual_split_kv_policy"] == "disabled"
-
-
-def test_rocm_attention_forward_backward_train_rollout_bitwise():
-    q, k, v = (tensor.requires_grad_() for tensor in _qkv(batch=1, sequence=5))
-    positions = torch.arange(q.size(2), device=q.device).expand(q.size(0), -1)
-    core = RLKernelDeterministicAttentionCore()
-    train = core.forward_with_lse(
-        q,
-        k,
-        v,
-        query_position_ids=positions,
-        key_position_ids=positions,
-    )
-    grad = torch.randn(train.out.shape, dtype=train.out.dtype, device="cpu").cuda()
-    (train.out.float() * grad.float()).sum().backward()
-    train_grads = tuple(tensor.grad.detach().clone() for tensor in (q, k, v))
-
-    q2, k2, v2 = (tensor.detach().clone().requires_grad_() for tensor in (q, k, v))
-    rollout = core.forward_with_lse(
-        q2,
-        k2,
-        v2,
-        query_position_ids=positions,
-        key_position_ids=positions,
-    )
-    (rollout.out.float() * grad.float()).sum().backward()
-    assert torch.equal(train.out, rollout.out)
-    assert torch.equal(train.lse, rollout.lse)
-    assert all(
-        torch.equal(expected, actual.grad)
-        for expected, actual in zip(train_grads, (q2, k2, v2), strict=True)
-    )
-
-
-def test_rocm_rope_is_batch_invariant_and_backward_repeat_bitwise():
-    generator = torch.Generator(device="cpu").manual_seed(714)
-    x = torch.randn(3, 4, 6, 128, dtype=torch.bfloat16, generator=generator).cuda()
-    positions = torch.arange(6, device=x.device).expand(3, -1)
-    rope = RocmDeterministicRoPEOp()
-    together = rope(x, positions)
-    separate = torch.cat([rope(x[index : index + 1], positions[index]) for index in range(3)])
-    assert torch.equal(together, separate)
-    assert torch.equal(together, rope(x, positions))
-
-    grad = torch.randn(together.shape, dtype=together.dtype, device="cpu").cuda()
-    x1 = x.detach().clone().requires_grad_()
-    x2 = x.detach().clone().requires_grad_()
-    (rope(x1, positions).float() * grad.float()).sum().backward()
-    (rope(x2, positions).float() * grad.float()).sum().backward()
-    assert torch.equal(x1.grad, x2.grad)
-
-
-def test_rocm_rope_matches_fp32_rotate_half_reference():
-    x = torch.randn(1, 2, 4, 128, dtype=torch.bfloat16, device="cuda")
-    positions = torch.arange(4, device=x.device).expand(1, -1)
-    actual = RocmDeterministicRoPEOp()(x, positions)
-    half = x.size(-1) // 2
-    inv_freq = 1.0 / (
-        1_000_000.0 ** (torch.arange(half, dtype=torch.float32, device=x.device) / half)
-    )
-    frequency = positions.float().unsqueeze(-1) * inv_freq
-    cos = frequency.cos().unsqueeze(1)
-    sin = frequency.sin().unsqueeze(1)
-    reference = torch.cat(
-        (
-            x[..., :half].float() * cos - x[..., half:].float() * sin,
-            x[..., half:].float() * cos + x[..., :half].float() * sin,
-        ),
-        dim=-1,
-    ).to(x.dtype)
-    torch.testing.assert_close(actual, reference, atol=0, rtol=0)
-
-
-def test_rocm_strict_core_rejects_split_k():
-    with pytest.raises(ValueError, match="Split-KV"):
-        RLKernelDeterministicAttentionCore(split_kv=SplitKVSpec.fixed(32))

From 4c744b3222dbe33efe35ed6d1bfe47b486303848 Mon Sep 17 00:00:00 2001
From: root <root@tw020.mia.tensorwave.lan>
Date: Wed, 19 Aug 2026 15:06:23 +0000
Subject: [PATCH 05/35] refactor(attention): consolidate ROCm implementation

---
 .../cuda/attention/deterministic_attention.cu |  87 +++++++++++++++
 csrc/rocm/deterministic_rope.cu               |  91 ----------------
 docs/operators/attention.md                   |   2 +-
 .../attention/flashinfer_paged_attention.py   |   9 +-
 .../kernels/ops/cuda/rotary_embedding/rope.py |  43 +++++++-
 .../kernels/ops/rocm/attention/__init__.py    |   3 -
 .../ops/rocm/attention/deterministic_attn.py  |  15 ---
 .../ops/rocm/rotary_embedding/__init__.py     |   6 --
 .../kernels/ops/rocm/rotary_embedding/rope.py | 101 ------------------
 scripts/__init__.py                           |   4 -
 scripts/rocm_attention_acceptance.py          |  86 ---------------
 .../ws2_p2p_nccl_attention_reference_check.py |  87 ++++++++++++++-
 setup.py                                      |   2 -
 ...y => test_deterministic_attention_cuda.py} |  17 +--
 tests/test_flashinfer_pr7_attention.py        |  20 +++-
 15 files changed, 238 insertions(+), 335 deletions(-)
 delete mode 100644 csrc/rocm/deterministic_rope.cu
 delete mode 100644 rl_engine/kernels/ops/rocm/attention/deterministic_attn.py
 delete mode 100644 rl_engine/kernels/ops/rocm/rotary_embedding/__init__.py
 delete mode 100644 rl_engine/kernels/ops/rocm/rotary_embedding/rope.py
 delete mode 100644 scripts/__init__.py
 delete mode 100644 scripts/rocm_attention_acceptance.py
 rename tests/{test_deterministic_attention.py => test_deterministic_attention_cuda.py} (98%)

diff --git a/csrc/cuda/attention/deterministic_attention.cu b/csrc/cuda/attention/deterministic_attention.cu
index aaa70b42..2c56d6c7 100644
--- a/csrc/cuda/attention/deterministic_attention.cu
+++ b/csrc/cuda/attention/deterministic_attention.cu
@@ -685,3 +685,90 @@ std::vector<torch::Tensor> deterministic_attention_backward(
 
   return {dQ, dK, dV};
 }
+
+#if defined(USE_ROCM)
+namespace {
+
+template <typename scalar_t>
+__global__ void deterministic_rope_kernel(
+    const scalar_t* __restrict__ x,
+    const float* __restrict__ cos,
+    const float* __restrict__ sin,
+    scalar_t* __restrict__ out,
+    int64_t n_rows,
+    int table_rows,
+    int half,
+    float sin_sign) {
+  const int64_t index = blockIdx.x * static_cast<int64_t>(blockDim.x) + threadIdx.x;
+  const int64_t count = n_rows * static_cast<int64_t>(half);
+  if (index >= count) {
+    return;
+  }
+
+  const int64_t row = index / half;
+  const int pair = static_cast<int>(index % half);
+  const int table_row = static_cast<int>(row % table_rows);
+  const float c = cos[table_row * half + pair];
+  const float s = sin[table_row * half + pair] * sin_sign;
+  const int64_t base = row * (2LL * half);
+  const float low = static_cast<float>(x[base + pair]);
+  const float high = static_cast<float>(x[base + pair + half]);
+
+  out[base + pair] = static_cast<scalar_t>(low * c - high * s);
+  out[base + pair + half] = static_cast<scalar_t>(high * c + low * s);
+}
+
+}  // namespace
+
+torch::Tensor deterministic_rope_apply_rocm(
+    torch::Tensor x,
+    torch::Tensor cos,
+    torch::Tensor sin,
+    double sin_sign) {
+  TORCH_CHECK(x.is_cuda(), "ROCm RoPE: x must be a GPU tensor");
+  TORCH_CHECK(x.dim() == 2 && x.is_contiguous(),
+              "ROCm RoPE: x must be contiguous [rows, head_dim]");
+  TORCH_CHECK(cos.is_cuda() && sin.is_cuda(),
+              "ROCm RoPE: cos and sin must be GPU tensors");
+  TORCH_CHECK(cos.scalar_type() == torch::kFloat32 &&
+                  sin.scalar_type() == torch::kFloat32,
+              "ROCm RoPE: cos and sin must be FP32");
+  TORCH_CHECK(cos.is_contiguous() && sin.is_contiguous(),
+              "ROCm RoPE: cos and sin must be contiguous");
+  TORCH_CHECK(cos.dim() == 2 && sin.sizes() == cos.sizes(),
+              "ROCm RoPE: cos and sin must have shape [table_rows, head_dim/2]");
+  TORCH_CHECK(x.size(1) % 2 == 0, "ROCm RoPE: head_dim must be even");
+  TORCH_CHECK(cos.size(0) > 0 && cos.size(1) == x.size(1) / 2,
+              "ROCm RoPE: invalid cos/sin table shape");
+  TORCH_CHECK(x.size(0) % cos.size(0) == 0,
+              "ROCm RoPE: row count must be divisible by the position table size");
+
+  const at::cuda::OptionalCUDAGuard guard(device_of(x));
+  auto out = torch::empty_like(x);
+  const int64_t n_rows = x.size(0);
+  const int half = static_cast<int>(x.size(1) / 2);
+  const int table_rows = static_cast<int>(cos.size(0));
+  const int64_t count = n_rows * static_cast<int64_t>(half);
+  constexpr int threads = 256;
+  const int64_t blocks = (count + threads - 1) / threads;
+  auto stream = at::cuda::getCurrentCUDAStream();
+
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+      at::ScalarType::Half,
+      at::ScalarType::BFloat16,
+      x.scalar_type(),
+      "deterministic_rope_apply_rocm",
+      [&] {
+        deterministic_rope_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
+            x.data_ptr<scalar_t>(),
+            cos.data_ptr<float>(),
+            sin.data_ptr<float>(),
+            out.data_ptr<scalar_t>(),
+            n_rows,
+            table_rows,
+            half,
+            static_cast<float>(sin_sign));
+      });
+  return out;
+}
+#endif
diff --git a/csrc/rocm/deterministic_rope.cu b/csrc/rocm/deterministic_rope.cu
deleted file mode 100644
index 91f1f0ff..00000000
--- a/csrc/rocm/deterministic_rope.cu
+++ /dev/null
@@ -1,91 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// Copyright (c) 2026 RL-Kernel Contributors
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-
-namespace {
-
-template <typename scalar_t>
-__global__ void deterministic_rope_kernel(
-    const scalar_t* __restrict__ x,
-    const float* __restrict__ cos,
-    const float* __restrict__ sin,
-    scalar_t* __restrict__ out,
-    int64_t n_rows,
-    int table_rows,
-    int half,
-    float sin_sign) {
-  const int64_t index = blockIdx.x * static_cast<int64_t>(blockDim.x) + threadIdx.x;
-  const int64_t count = n_rows * static_cast<int64_t>(half);
-  if (index >= count) {
-    return;
-  }
-
-  const int64_t row = index / half;
-  const int pair = static_cast<int>(index % half);
-  const int table_row = static_cast<int>(row % table_rows);
-  const float c = cos[table_row * half + pair];
-  const float s = sin[table_row * half + pair] * sin_sign;
-  const int64_t base = row * (2LL * half);
-  const float low = static_cast<float>(x[base + pair]);
-  const float high = static_cast<float>(x[base + pair + half]);
-
-  out[base + pair] = static_cast<scalar_t>(low * c - high * s);
-  out[base + pair + half] = static_cast<scalar_t>(high * c + low * s);
-}
-
-}  // namespace
-
-torch::Tensor deterministic_rope_apply_rocm(
-    torch::Tensor x,
-    torch::Tensor cos,
-    torch::Tensor sin,
-    double sin_sign) {
-  TORCH_CHECK(x.is_cuda(), "ROCm RoPE: x must be a GPU tensor");
-  TORCH_CHECK(x.dim() == 2 && x.is_contiguous(),
-              "ROCm RoPE: x must be contiguous [rows, head_dim]");
-  TORCH_CHECK(cos.is_cuda() && sin.is_cuda(),
-              "ROCm RoPE: cos and sin must be GPU tensors");
-  TORCH_CHECK(cos.scalar_type() == torch::kFloat32 &&
-                  sin.scalar_type() == torch::kFloat32,
-              "ROCm RoPE: cos and sin must be FP32");
-  TORCH_CHECK(cos.is_contiguous() && sin.is_contiguous(),
-              "ROCm RoPE: cos and sin must be contiguous");
-  TORCH_CHECK(cos.dim() == 2 && sin.sizes() == cos.sizes(),
-              "ROCm RoPE: cos and sin must have shape [table_rows, head_dim/2]");
-  TORCH_CHECK(x.size(1) % 2 == 0, "ROCm RoPE: head_dim must be even");
-  TORCH_CHECK(cos.size(0) > 0 && cos.size(1) == x.size(1) / 2,
-              "ROCm RoPE: invalid cos/sin table shape");
-  TORCH_CHECK(x.size(0) % cos.size(0) == 0,
-              "ROCm RoPE: row count must be divisible by the position table size");
-
-  const at::cuda::OptionalCUDAGuard guard(device_of(x));
-  auto out = torch::empty_like(x);
-  const int64_t n_rows = x.size(0);
-  const int half = static_cast<int>(x.size(1) / 2);
-  const int table_rows = static_cast<int>(cos.size(0));
-  const int64_t count = n_rows * static_cast<int64_t>(half);
-  constexpr int threads = 256;
-  const int64_t blocks = (count + threads - 1) / threads;
-  auto stream = at::cuda::getCurrentCUDAStream();
-
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-      at::ScalarType::Half,
-      at::ScalarType::BFloat16,
-      x.scalar_type(),
-      "deterministic_rope_apply_rocm",
-      [&] {
-        deterministic_rope_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
-            x.data_ptr<scalar_t>(),
-            cos.data_ptr<float>(),
-            sin.data_ptr<float>(),
-            out.data_ptr<scalar_t>(),
-            n_rows,
-            table_rows,
-            half,
-            static_cast<float>(sin_sign));
-      });
-  return out;
-}
diff --git a/docs/operators/attention.md b/docs/operators/attention.md
index 66682ced..ebff9a58 100644
--- a/docs/operators/attention.md
+++ b/docs/operators/attention.md
@@ -153,7 +153,7 @@ GPU-only LARGE Qwen3-8B real-shape smoke test.
 - `csrc/cuda/attention/deterministic_attention.cu` — CUDA kernels
 - `rl_engine/kernels/registry.py`
 - `tests/test_attention.py`
-- `tests/test_deterministic_attention.py`
+- `tests/test_deterministic_attention_cuda.py`
 
 ## Fixed Reduction Order (CUDA Deterministic Backend)
 
diff --git a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
index c242d119..cf6cda11 100644
--- a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
+++ b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
@@ -1515,12 +1515,13 @@ def _resolve_strict_rope(cfg: FlashInferPagedAttentionConfig) -> Any:
     if cfg.strict_rope_op is not None:
         return cfg.strict_rope_op
     try:
-        if torch.version.hip is not None:
-            from rl_engine.kernels.ops.rocm.rotary_embedding.rope import RocmDeterministicRoPEOp
+        from rl_engine.kernels.ops.cuda.rotary_embedding.rope import (
+            RocmDeterministicRoPEOp,
+            RoPESM90Op,
+        )
 
+        if torch.version.hip is not None:
             return RocmDeterministicRoPEOp()
-        from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RoPESM90Op
-
         return RoPESM90Op()
     except (ImportError, RuntimeError) as exc:
         raise FlashInferUnavailable(
diff --git a/rl_engine/kernels/ops/cuda/rotary_embedding/rope.py b/rl_engine/kernels/ops/cuda/rotary_embedding/rope.py
index 9a764012..928c3033 100644
--- a/rl_engine/kernels/ops/cuda/rotary_embedding/rope.py
+++ b/rl_engine/kernels/ops/cuda/rotary_embedding/rope.py
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""Custom CUDA RoPE op for SM90 (GPT-NeoX rotate-half), matching NativeRoPEOp.
+"""Deterministic GPU RoPE ops (GPT-NeoX rotate-half), matching NativeRoPEOp.
 
 cos/sin are built in fp32 with the exact reference math and passed to a small
 CUDA kernel (``_C.rope_apply_sm90``) that does the per-position rotation. Backward
@@ -77,7 +77,7 @@ def forward(ctx, x: Tensor, positions: Tensor, theta: float) -> Tensor:
         ctx.save_for_backward(cos, sin)
         ctx.x_shape = tuple(x.shape)
         ctx.pos_dim = positions.dim()
-        out_2d = _C.rope_apply_sm90(x_2d, cos, sin, 1.0)
+        out_2d = _rope_apply(x_2d, cos, sin, 1.0)
         return _restore_rope(out_2d, x, positions)
 
     @staticmethod
@@ -87,7 +87,7 @@ def backward(ctx, grad_out: Tensor):
         if ctx.needs_input_grad[0]:
             if ctx.pos_dim == 2 and len(ctx.x_shape) == 4:
                 g_2d = grad_out.permute(1, 0, 2, 3).contiguous().reshape(-1, ctx.x_shape[-1])
-                out_2d = _C.rope_apply_sm90(g_2d, cos, sin, -1.0)
+                out_2d = _rope_apply(g_2d, cos, sin, -1.0)
                 heads, batch, seq, dim = (
                     ctx.x_shape[1],
                     ctx.x_shape[0],
@@ -97,10 +97,16 @@ def backward(ctx, grad_out: Tensor):
                 grad_x = out_2d.reshape(heads, batch, seq, dim).permute(1, 0, 2, 3).contiguous()
             else:
                 g_2d = grad_out.contiguous().reshape(-1, grad_out.shape[-1])
-                grad_x = _C.rope_apply_sm90(g_2d, cos, sin, -1.0).reshape(grad_out.shape)
+                grad_x = _rope_apply(g_2d, cos, sin, -1.0).reshape(grad_out.shape)
         return grad_x, None, None
 
 
+def _rope_apply(x: Tensor, cos: Tensor, sin: Tensor, sin_sign: float) -> Tensor:
+    if torch.version.hip is not None:
+        return _C.deterministic_rope_apply_rocm(x, cos, sin, sin_sign)
+    return _C.rope_apply_sm90(x, cos, sin, sin_sign)
+
+
 def _is_hopper(device: torch.device) -> bool:
     try:
         return torch.cuda.get_device_capability(device)[0] == 9
@@ -138,3 +144,32 @@ def forward(self, x: Tensor, positions: Tensor, *, theta: float = 1_000_000.0) -
                 f"got compute capability {torch.cuda.get_device_capability(x.device)}"
             )
         return _RoPEFunction.apply(x, positions, theta)
+
+
+class RocmDeterministicRoPEOp:
+    """Precompiled HIP RoPE path shared by ROCm training and rollout."""
+
+    backend_id = "rlkernel.rocm.deterministic_rope"
+    op_class = "elementwise"
+    fallback = False
+
+    def __init__(self) -> None:
+        if torch.version.hip is None:
+            raise RuntimeError("RocmDeterministicRoPEOp requires a ROCm PyTorch build")
+        if not _EXT_AVAILABLE or not hasattr(_C, "deterministic_rope_apply_rocm"):
+            raise RuntimeError(
+                "ROCm deterministic RoPE is unavailable; rebuild rl_engine._C for ROCm"
+            )
+
+    def __call__(self, x: Tensor, positions: Tensor, *, theta: float = 1_000_000.0) -> Tensor:
+        return self.forward(x, positions, theta=theta)
+
+    def forward(self, x: Tensor, positions: Tensor, *, theta: float = 1_000_000.0) -> Tensor:
+        if not x.is_cuda:
+            raise RuntimeError("ROCm deterministic RoPE requires a GPU tensor")
+        if x.dtype not in (torch.float16, torch.bfloat16):
+            raise ValueError("ROCm deterministic RoPE requires FP16 or BF16")
+        return _RoPEFunction.apply(x, positions, theta)
+
+
+__all__ = ["RoPESM90Op", "RocmDeterministicRoPEOp"]
diff --git a/rl_engine/kernels/ops/rocm/attention/__init__.py b/rl_engine/kernels/ops/rocm/attention/__init__.py
index b03448f5..150c937f 100644
--- a/rl_engine/kernels/ops/rocm/attention/__init__.py
+++ b/rl_engine/kernels/ops/rocm/attention/__init__.py
@@ -1,11 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-from .deterministic_attn import DeterministicAttentionOp, RLKernelDeterministicAttentionCore
 from .flash_attn import RocmFlashAttentionOp
 
 __all__ = [
     "RocmFlashAttentionOp",
-    "DeterministicAttentionOp",
-    "RLKernelDeterministicAttentionCore",
 ]
diff --git a/rl_engine/kernels/ops/rocm/attention/deterministic_attn.py b/rl_engine/kernels/ops/rocm/attention/deterministic_attn.py
deleted file mode 100644
index 435a5272..00000000
--- a/rl_engine/kernels/ops/rocm/attention/deterministic_attn.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) 2026 RL-Kernel Contributors
-"""ROCm public surface for the shared deterministic Attention core."""
-
-from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
-    DeterministicAttentionCoreResult,
-    DeterministicAttentionOp,
-    RLKernelDeterministicAttentionCore,
-)
-
-__all__ = [
-    "DeterministicAttentionCoreResult",
-    "DeterministicAttentionOp",
-    "RLKernelDeterministicAttentionCore",
-]
diff --git a/rl_engine/kernels/ops/rocm/rotary_embedding/__init__.py b/rl_engine/kernels/ops/rocm/rotary_embedding/__init__.py
deleted file mode 100644
index 0a7285f8..00000000
--- a/rl_engine/kernels/ops/rocm/rotary_embedding/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) 2026 RL-Kernel Contributors
-
-from .rope import RocmDeterministicRoPEOp
-
-__all__ = ["RocmDeterministicRoPEOp"]
diff --git a/rl_engine/kernels/ops/rocm/rotary_embedding/rope.py b/rl_engine/kernels/ops/rocm/rotary_embedding/rope.py
deleted file mode 100644
index 7b0f385e..00000000
--- a/rl_engine/kernels/ops/rocm/rotary_embedding/rope.py
+++ /dev/null
@@ -1,101 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) 2026 RL-Kernel Contributors
-"""Deterministic GPT-NeoX RoPE for ROCm training and inference."""
-
-from __future__ import annotations
-
-import torch
-from torch import Tensor
-
-from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
-
-
-def _build_cos_sin(
-    positions: Tensor,
-    half: int,
-    theta: float,
-    device: torch.device,
-) -> tuple[Tensor, Tensor]:
-    inv_freq = 1.0 / (theta ** (torch.arange(0, half, dtype=torch.float32, device=device) / half))
-    frequency = positions.to(device=device, dtype=torch.float32).reshape(-1, 1) * inv_freq
-    return frequency.cos().contiguous(), frequency.sin().contiguous()
-
-
-def _prepare(x: Tensor, positions: Tensor, theta: float) -> tuple[Tensor, Tensor, Tensor]:
-    dim = x.size(-1)
-    if dim % 2:
-        raise ValueError(f"RoPE head_dim must be even, got {dim}")
-    if positions.ndim == 1:
-        table_rows = positions.numel()
-        x_2d = x.contiguous().reshape(-1, dim)
-    elif positions.ndim == 2:
-        batch, sequence = positions.shape
-        if x.ndim not in (3, 4) or x.size(0) != batch or x.size(-2) != sequence:
-            raise ValueError("[B,S] positions require x [B,S,D] or [B,H,S,D]")
-        x_2d = (
-            x.permute(1, 0, 2, 3).contiguous().reshape(-1, dim)
-            if x.ndim == 4
-            else x.contiguous().reshape(-1, dim)
-        )
-        table_rows = batch * sequence
-    else:
-        raise ValueError("positions must have shape [S] or [B,S]")
-    if table_rows < 1 or x_2d.size(0) % table_rows:
-        raise ValueError("RoPE rows must be divisible by the position table size")
-    cos, sin = _build_cos_sin(positions, dim // 2, float(theta), x.device)
-    return x_2d, cos, sin
-
-
-def _restore(out: Tensor, shape: torch.Size, positions_dim: int) -> Tensor:
-    if positions_dim == 2 and len(shape) == 4:
-        batch, heads, sequence, dim = shape
-        return out.reshape(heads, batch, sequence, dim).permute(1, 0, 2, 3).contiguous()
-    return out.reshape(shape)
-
-
-class _RocmRoPEFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x: Tensor, positions: Tensor, theta: float) -> Tensor:
-        x_2d, cos, sin = _prepare(x, positions, theta)
-        ctx.save_for_backward(cos, sin)
-        ctx.shape = x.shape
-        ctx.positions_dim = positions.ndim
-        out = _C.deterministic_rope_apply_rocm(x_2d, cos, sin, 1.0)
-        return _restore(out, x.shape, positions.ndim)
-
-    @staticmethod
-    def backward(ctx, grad_out: Tensor):
-        cos, sin = ctx.saved_tensors
-        shape = ctx.shape
-        if ctx.positions_dim == 2 and len(shape) == 4:
-            grad_2d = grad_out.permute(1, 0, 2, 3).contiguous().reshape(-1, shape[-1])
-        else:
-            grad_2d = grad_out.contiguous().reshape(-1, shape[-1])
-        grad_x = _C.deterministic_rope_apply_rocm(grad_2d, cos, sin, -1.0)
-        return _restore(grad_x, shape, ctx.positions_dim), None, None
-
-
-class RocmDeterministicRoPEOp:
-    """One precompiled HIP RoPE arithmetic path shared by train and rollout."""
-
-    backend_id = "rlkernel.rocm.deterministic_rope"
-    op_class = "elementwise"
-    fallback = False
-
-    def __init__(self) -> None:
-        if torch.version.hip is None:
-            raise RuntimeError("RocmDeterministicRoPEOp requires a ROCm PyTorch build")
-        if not _EXT_AVAILABLE or not hasattr(_C, "deterministic_rope_apply_rocm"):
-            raise RuntimeError(
-                "ROCm deterministic RoPE is unavailable; rebuild rl_engine._C for ROCm"
-            )
-
-    def __call__(self, x: Tensor, positions: Tensor, *, theta: float = 1_000_000.0) -> Tensor:
-        if not x.is_cuda:
-            raise RuntimeError("ROCm deterministic RoPE requires a GPU tensor")
-        if x.dtype not in (torch.float16, torch.bfloat16):
-            raise ValueError("ROCm deterministic RoPE requires FP16 or BF16")
-        return _RocmRoPEFunction.apply(x, positions, float(theta))
-
-
-__all__ = ["RocmDeterministicRoPEOp"]
diff --git a/scripts/__init__.py b/scripts/__init__.py
deleted file mode 100644
index 5295edc3..00000000
--- a/scripts/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) 2026 RL-Kernel Contributors
-
-"""Repository-local validation scripts."""
diff --git a/scripts/rocm_attention_acceptance.py b/scripts/rocm_attention_acceptance.py
deleted file mode 100644
index 1423ab2b..00000000
--- a/scripts/rocm_attention_acceptance.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) 2026 RL-Kernel Contributors
-"""Run the ROCm strict Attention acceptance matrix and retain evidence."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import subprocess
-import sys
-from pathlib import Path
-
-import torch
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--output-dir", type=Path, default=Path("results/rocm-attention"))
-    args = parser.parse_args()
-    if torch.version.hip is None or torch.cuda.device_count() < 8:
-        raise RuntimeError("the formal acceptance matrix requires 8 visible ROCm GPUs")
-
-    repo = Path(__file__).resolve().parents[1]
-    output = (
-        (repo / args.output_dir).resolve() if not args.output_dir.is_absolute() else args.output_dir
-    )
-    output.mkdir(parents=True, exist_ok=True)
-    steps: list[dict[str, object]] = []
-
-    commands = [
-        (
-            "single_gpu",
-            [sys.executable, "-m", "pytest", "-q", "tests/test_deterministic_attention.py"],
-        )
-    ]
-    for transport, strict in (("p2p_nccl_reference", False), ("rccl_ag_rs", True)):
-        for ranks in (2, 4, 8):
-            name = f"{transport}_{ranks}r"
-            report = output / f"{name}.json"
-            command = [
-                "torchrun",
-                "--standalone",
-                f"--nproc-per-node={ranks}",
-                "scripts/ws2_p2p_nccl_attention_reference_check.py",
-                "--transport",
-                transport,
-                "--output",
-                str(report),
-            ]
-            if strict:
-                command.append("--strict-shared-core")
-            commands.append((name, command))
-
-    for name, command in commands:
-        completed = subprocess.run(
-            command,
-            cwd=repo,
-            text=True,
-            stdout=subprocess.PIPE,
-            stderr=subprocess.STDOUT,
-            check=False,
-        )
-        (output / f"{name}.log").write_text(completed.stdout, encoding="utf-8")
-        steps.append({"name": name, "command": command, "returncode": completed.returncode})
-
-    summary = {
-        "schema_version": "ws2_rocm_attention_acceptance/v1",
-        "platform": "rocm",
-        "torch": str(torch.__version__),
-        "hip": str(torch.version.hip),
-        "collective": list(torch.cuda.nccl.version()),
-        "device_count": torch.cuda.device_count(),
-        "device_name": torch.cuda.get_device_name(0),
-        "steps": steps,
-        "passed": all(step["returncode"] == 0 for step in steps),
-    }
-    (output / "summary.json").write_text(
-        json.dumps(summary, indent=2, sort_keys=True) + "\n",
-        encoding="utf-8",
-    )
-    print(json.dumps(summary, indent=2, sort_keys=True))
-    return 0 if summary["passed"] else 1
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/scripts/ws2_p2p_nccl_attention_reference_check.py b/scripts/ws2_p2p_nccl_attention_reference_check.py
index 1f12b0ca..40bef37b 100644
--- a/scripts/ws2_p2p_nccl_attention_reference_check.py
+++ b/scripts/ws2_p2p_nccl_attention_reference_check.py
@@ -15,6 +15,7 @@
 import json
 import math
 import os
+import subprocess
 import sys
 from pathlib import Path
 from types import SimpleNamespace
@@ -80,14 +81,94 @@ def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
         help="run AG(Q/K/V/positions) -> shared CUDA core -> RS(Out/LSE) with backward",
     )
     parser.add_argument("--output", type=Path)
+    parser.add_argument(
+        "--run-rocm-matrix",
+        action="store_true",
+        help="run the complete 1/2/4/8-GPU ROCm acceptance matrix",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("results/rocm-attention"),
+        help="directory for --run-rocm-matrix logs and JSON reports",
+    )
     args = parser.parse_args(argv)
     if args.strict_shared_core and args.transport not in {"cuda_ag_rs", "rccl_ag_rs"}:
         parser.error("--strict-shared-core requires a self-owned AG/RS transport")
+    if args.run_rocm_matrix and (args.strict_shared_core or args.output is not None):
+        parser.error("--run-rocm-matrix cannot be combined with single-run output options")
     return args
 
 
+def _run_rocm_matrix(output_dir: Path) -> int:
+    if torch.version.hip is None or torch.cuda.device_count() < 8:
+        raise RuntimeError("the formal acceptance matrix requires 8 visible ROCm GPUs")
+
+    repo = Path(__file__).resolve().parents[1]
+    output = (repo / output_dir).resolve() if not output_dir.is_absolute() else output_dir
+    output.mkdir(parents=True, exist_ok=True)
+    script = Path(__file__).resolve()
+    commands: list[tuple[str, list[str]]] = [
+        (
+            "single_gpu",
+            [sys.executable, "-m", "pytest", "-q", "tests/test_deterministic_attention_cuda.py"],
+        )
+    ]
+    for transport, strict in (("p2p_nccl_reference", False), ("rccl_ag_rs", True)):
+        for ranks in (2, 4, 8):
+            name = f"{transport}_{ranks}r"
+            command = [
+                sys.executable,
+                "-m",
+                "torch.distributed.run",
+                "--standalone",
+                f"--nproc-per-node={ranks}",
+                str(script),
+                "--transport",
+                transport,
+                "--output",
+                str(output / f"{name}.json"),
+            ]
+            if strict:
+                command.append("--strict-shared-core")
+            commands.append((name, command))
+
+    steps: list[dict[str, object]] = []
+    for name, command in commands:
+        completed = subprocess.run(
+            command,
+            cwd=repo,
+            text=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            check=False,
+        )
+        (output / f"{name}.log").write_text(completed.stdout, encoding="utf-8")
+        steps.append({"name": name, "command": command, "returncode": completed.returncode})
+
+    summary = {
+        "schema_version": "ws2_rocm_attention_acceptance/v1",
+        "platform": "rocm",
+        "torch": str(torch.__version__),
+        "hip": str(torch.version.hip),
+        "collective": list(torch.cuda.nccl.version()),
+        "device_count": torch.cuda.device_count(),
+        "device_name": torch.cuda.get_device_name(0),
+        "steps": steps,
+        "passed": all(step["returncode"] == 0 for step in steps),
+    }
+    (output / "summary.json").write_text(
+        json.dumps(summary, indent=2, sort_keys=True) + "\n",
+        encoding="utf-8",
+    )
+    print(json.dumps(summary, indent=2, sort_keys=True))
+    return 0 if summary["passed"] else 1
+
+
 def main(argv: Sequence[str] | None = None) -> int:
     args = parse_args(argv)
+    if args.run_rocm_matrix:
+        return _run_rocm_matrix(args.output_dir)
     if not torch.cuda.is_available() or torch.cuda.device_count() < 2:
         raise RuntimeError("this check requires at least two visible CUDA/ROCm devices")
     dist.init_process_group("nccl", init_method="env://")
@@ -485,12 +566,10 @@ def _run_strict_shared_core_check(
 
 
 def _strict_rope_op():
-    if torch.version.hip is not None:
-        from rl_engine.kernels.ops.rocm.rotary_embedding.rope import RocmDeterministicRoPEOp
+    from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RocmDeterministicRoPEOp, RoPESM90Op
 
+    if torch.version.hip is not None:
         return RocmDeterministicRoPEOp()
-    from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RoPESM90Op
-
     return RoPESM90Op()
 
 
diff --git a/setup.py b/setup.py
index 297d269d..57b3ebd9 100644
--- a/setup.py
+++ b/setup.py
@@ -140,8 +140,6 @@ def get_extensions():
             "csrc/cuda/activation.cu",
             "csrc/cuda/attention/deterministic_attention.cu",
         ]
-        if is_rocm:
-            cuda_sources.append("csrc/rocm/deterministic_rope.cu")
         if not is_rocm:
             # This source contains NVIDIA PTX (cp.async, ldmatrix, and mma.sync).
             # The ROCm dispatcher falls back to PyTorch SDPA for this operator.
diff --git a/tests/test_deterministic_attention.py b/tests/test_deterministic_attention_cuda.py
similarity index 98%
rename from tests/test_deterministic_attention.py
rename to tests/test_deterministic_attention_cuda.py
index d616ff4a..f981e840 100644
--- a/tests/test_deterministic_attention.py
+++ b/tests/test_deterministic_attention_cuda.py
@@ -36,25 +36,18 @@
 try:
     from rl_engine.kernels.attention_contract import SplitKVSpec
     from rl_engine.kernels.gtest.op_checks import CandidateSpec, OperatorCase, run_operator_suite
+    from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
+        DeterministicAttentionOp,
+        RLKernelDeterministicAttentionCore,
+    )
     from rl_engine.kernels.ops.pytorch.attention.standard_attn import NativeAttentionOp
 
-    if IS_ROCM:
-        from rl_engine.kernels.ops.rocm.attention.deterministic_attn import (
-            DeterministicAttentionOp,
-            RLKernelDeterministicAttentionCore,
-        )
-    else:
-        from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
-            DeterministicAttentionOp,
-            RLKernelDeterministicAttentionCore,
-        )
-
     _OP_AVAILABLE = True
 except (ImportError, RuntimeError):
     _OP_AVAILABLE = False
 
 if IS_ROCM:
-    from rl_engine.kernels.ops.rocm.rotary_embedding.rope import RocmDeterministicRoPEOp
+    from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RocmDeterministicRoPEOp
 
 pytestmark = [
     pytestmark,
diff --git a/tests/test_flashinfer_pr7_attention.py b/tests/test_flashinfer_pr7_attention.py
index b3ed329e..ebd012fe 100644
--- a/tests/test_flashinfer_pr7_attention.py
+++ b/tests/test_flashinfer_pr7_attention.py
@@ -3,9 +3,12 @@
 
 from __future__ import annotations
 
+import importlib.util
 import json
+import sys
 import types
 from dataclasses import replace
+from pathlib import Path
 
 import pytest
 import torch
@@ -39,8 +42,21 @@
     materialize_flashinfer_paged_kv_cache,
 )
 from rl_engine.testing.attention_comparison import DecodeKVCacheMetadata
-from scripts import ws2_p2p_nccl_attention_reference_check as p2p_check_script
-from scripts import ws2_pr7_flashinfer_attention_check as check_script
+
+
+def _load_repo_script(name: str):
+    path = Path(__file__).resolve().parents[1] / "scripts" / f"{name}.py"
+    spec = importlib.util.spec_from_file_location(f"rl_kernel_{name}", path)
+    if spec is None or spec.loader is None:
+        raise ImportError(f"cannot load repository script {path}")
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+p2p_check_script = _load_repo_script("ws2_p2p_nccl_attention_reference_check")
+check_script = _load_repo_script("ws2_pr7_flashinfer_attention_check")
 
 
 class _FakeFlashInferWrapper:

From 22d3780730f8040c469fd4ba9effebfc52f7b152 Mon Sep 17 00:00:00 2001
From: Codex H100 Validation <codex-validation@localhost>
Date: Wed, 19 Aug 2026 17:24:30 +0000
Subject: [PATCH 06/35] feat(attention): default strict runtime to decoupled
 ring schedule

---
 .../attention/flashinfer_paged_attention.py   |   9 ++
 .../ops/pytorch/attention/cp_attention.py     | 123 ++++++++++++++++++
 tests/test_cp_attention.py                    |  12 ++
 tests/test_flashinfer_pr7_attention.py        |   5 +
 4 files changed, 149 insertions(+)

diff --git a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
index cf6cda11..fcd4f778 100644
--- a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
+++ b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
@@ -49,6 +49,7 @@
 )
 from rl_engine.kernels.ops.pytorch.attention.cp_attention import (
     AttentionPartialState,
+    AttentionRingSchedule,
     DeterministicCPAttentionReferenceOp,
     build_reference_split_kv_runtime_plan_set,
     merge_attention_partial_states,
@@ -805,6 +806,11 @@ def _run_strict_cp(
         if expected_kv_range is None:
             raise FlashInferUnavailable("strict CP Attention requires an expected KV range")
         expected_k_tokens = expected_kv_range[1] - expected_kv_range[0]
+        ring_schedule = AttentionRingSchedule.build(
+            expected_k_tokens,
+            cp_world_size=plan.parallel.cp_world_size,
+            kv_chunk_size=None,
+        )
         if global_q.size(2) != expected_q_tokens:
             raise FlashInferUnavailable("strict AG(Q) returned the wrong global width")
         if global_k.size(2) != expected_k_tokens or global_v.shape != global_k.shape:
@@ -855,6 +861,9 @@ def _run_strict_cp(
                 "strict_comm_autograd": bool(getattr(communication, "supports_autograd", False)),
                 "strict_local_query_range": [query_start, query_end],
                 "strict_local_kv_range": [key_start, key_end],
+                **ring_schedule.provenance(),
+                "ring_schedule_default": True,
+                "ring_partial_arithmetic": False,
             }
         )
         return FlashInferAttentionResult(
diff --git a/rl_engine/kernels/ops/pytorch/attention/cp_attention.py b/rl_engine/kernels/ops/pytorch/attention/cp_attention.py
index 2dabb840..70d6e2e3 100644
--- a/rl_engine/kernels/ops/pytorch/attention/cp_attention.py
+++ b/rl_engine/kernels/ops/pytorch/attention/cp_attention.py
@@ -56,6 +56,104 @@ def __post_init__(self) -> None:
             raise ValueError("block_end must be >= block_start")
 
 
+@dataclass(frozen=True)
+class AttentionRingBlock:
+    """One logical KV block in the decoupled Ring Attention schedule."""
+
+    global_block_index: int
+    block_start: int
+    block_end: int
+    owner_cp_rank: int
+
+
+@dataclass(frozen=True)
+class AttentionRingSchedule:
+    """Static compute order separated from the fixed arithmetic merge order."""
+
+    schedule_id: str
+    total_kv_tokens: int
+    cp_world_size: int
+    kv_chunk_size: Optional[int]
+    blocks: tuple[AttentionRingBlock, ...]
+    compute_order: tuple[int, ...]
+    merge_order: tuple[int, ...]
+    compute_communication: str = "decoupled"
+    overlap: str = "disabled"
+
+    @classmethod
+    def build(
+        cls,
+        total_kv_tokens: int,
+        *,
+        cp_world_size: int = 1,
+        kv_chunk_size: Optional[int] = None,
+    ) -> "AttentionRingSchedule":
+        if (
+            isinstance(cp_world_size, bool)
+            or not isinstance(cp_world_size, int)
+            or cp_world_size < 1
+        ):
+            raise ValueError("cp_world_size must be >= 1")
+        if kv_chunk_size is not None and (
+            isinstance(kv_chunk_size, bool)
+            or not isinstance(kv_chunk_size, int)
+            or kv_chunk_size < 1
+        ):
+            raise ValueError("kv_chunk_size must be >= 1 when provided")
+        if (
+            isinstance(total_kv_tokens, bool)
+            or not isinstance(total_kv_tokens, int)
+            or total_kv_tokens < 1
+        ):
+            raise ValueError("total_kv_tokens must be an integer >= 1")
+        if total_kv_tokens < cp_world_size:
+            raise ValueError("Ring Attention requires at least one KV token per CP rank")
+        blocks: list[AttentionRingBlock] = []
+        for owner_cp_rank, (owner_start, owner_end) in enumerate(
+            _split_bounds(total_kv_tokens, cp_world_size)
+        ):
+            cursor = owner_start
+            while cursor < owner_end:
+                block_end = (
+                    owner_end if kv_chunk_size is None else min(cursor + kv_chunk_size, owner_end)
+                )
+                blocks.append(
+                    AttentionRingBlock(
+                        global_block_index=len(blocks),
+                        block_start=cursor,
+                        block_end=block_end,
+                        owner_cp_rank=owner_cp_rank,
+                    )
+                )
+                cursor = block_end
+        compute_order: list[int] = []
+        left, right = 0, len(blocks) - 1
+        while left <= right:
+            compute_order.append(left)
+            if left != right:
+                compute_order.append(right)
+            left += 1
+            right -= 1
+        return cls(
+            schedule_id="rlkernel.attention.strict_ring_state.v1",
+            total_kv_tokens=total_kv_tokens,
+            cp_world_size=cp_world_size,
+            kv_chunk_size=kv_chunk_size,
+            blocks=tuple(blocks),
+            compute_order=tuple(compute_order),
+            merge_order=tuple(range(len(blocks))),
+        )
+
+    def provenance(self) -> dict[str, object]:
+        return {
+            "compute_communication": self.compute_communication,
+            "compute_schedule": self.schedule_id,
+            "compute_order": list(self.compute_order),
+            "merge_order_indices": list(self.merge_order),
+            "communication_overlap": self.overlap,
+        }
+
+
 @dataclass(frozen=True)
 class AttentionBackwardGradients:
     """Training-side gradients emitted by the CP attention backward reference."""
@@ -237,6 +335,21 @@ def split_kv_execution_plans(
             backend="deterministic_cp_reference",
         )
 
+    @staticmethod
+    def ring_schedule(
+        total_kv_tokens: int,
+        *,
+        cp_world_size: int = 1,
+        kv_chunk_size: Optional[int] = None,
+    ) -> AttentionRingSchedule:
+        """Build the production-default pre-overlap Ring schedule."""
+
+        return AttentionRingSchedule.build(
+            total_kv_tokens,
+            cp_world_size=cp_world_size,
+            kv_chunk_size=kv_chunk_size,
+        )
+
     def __call__(
         self,
         q: torch.Tensor,
@@ -551,6 +664,11 @@ def backward_reference(
         torch.autograd.backward(out, dout.to(dtype=out.dtype))
         if q_leaf.grad is None or k_leaf.grad is None or v_leaf.grad is None:
             raise RuntimeError("CP attention backward did not produce dq/dk/dv")
+        ring_schedule = self.ring_schedule(
+            k.size(2),
+            cp_world_size=cp_world_size,
+            kv_chunk_size=kv_chunk_size,
+        )
 
         return AttentionBackwardPathResult(
             name=name
@@ -589,6 +707,9 @@ def backward_reference(
                 "merge_order": "global_block_index",
                 "accum_dtype": "fp32",
                 "downcast_at": "final_write",
+                **ring_schedule.provenance(),
+                "ring_schedule_default": True,
+                "ring_partial_arithmetic": False,
                 "output_dtype": str(resolved_output_dtype).replace("torch.", ""),
                 "q_dtype": str(q.dtype).replace("torch.", ""),
                 "k_dtype": str(k.dtype).replace("torch.", ""),
@@ -1182,6 +1303,8 @@ def build_reference_split_kv_runtime_plan_set(
     "AttentionBackwardPathResult",
     "AttentionBackwardRankDrift",
     "AttentionPartialState",
+    "AttentionRingBlock",
+    "AttentionRingSchedule",
     "build_reference_split_kv_runtime_plan_set",
     "CPAttentionReferenceOp",
     "DeterministicCPAttentionReferenceOp",
diff --git a/tests/test_cp_attention.py b/tests/test_cp_attention.py
index 73953090..2ce2d7ce 100644
--- a/tests/test_cp_attention.py
+++ b/tests/test_cp_attention.py
@@ -24,6 +24,7 @@
 )
 from rl_engine.kernels.ops.pytorch.attention.cp_attention import (
     AttentionPartialState,
+    AttentionRingSchedule,
     DeterministicCPAttentionReferenceOp,
     compare_cp_attention_backward,
     merge_attention_partial_states,
@@ -40,6 +41,17 @@
 _GRAD_ATOL = 1.0e-5
 
 
+def test_ring_schedule_separates_compute_and_merge_order():
+    schedule = AttentionRingSchedule.build(12, cp_world_size=2, kv_chunk_size=2)
+
+    assert schedule.schedule_id == "rlkernel.attention.strict_ring_state.v1"
+    assert schedule.compute_communication == "decoupled"
+    assert schedule.overlap == "disabled"
+    assert schedule.merge_order == tuple(range(6))
+    assert schedule.compute_order == (0, 5, 1, 4, 2, 3)
+    assert [block.owner_cp_rank for block in schedule.blocks] == [0, 0, 0, 1, 1, 1]
+
+
 @contextlib.contextmanager
 def _single_thread():
     prev = torch.get_num_threads()
diff --git a/tests/test_flashinfer_pr7_attention.py b/tests/test_flashinfer_pr7_attention.py
index ebd012fe..592b4256 100644
--- a/tests/test_flashinfer_pr7_attention.py
+++ b/tests/test_flashinfer_pr7_attention.py
@@ -670,6 +670,11 @@ def test_strict_cp_path_gathers_qkv_and_real_position_ids_before_shared_core():
     assert result.provenance["materialization"] == ("ag_qkv_positions_shared_core_rs")
     assert result.provenance["strict_full_qkv_all_gather"] is True
     assert result.provenance["strict_position_ids_all_gather"] is True
+    assert result.provenance["compute_communication"] == "decoupled"
+    assert result.provenance["compute_schedule"] == ("rlkernel.attention.strict_ring_state.v1")
+    assert result.provenance["communication_overlap"] == "disabled"
+    assert result.provenance["ring_schedule_default"] is True
+    assert result.provenance["ring_partial_arithmetic"] is False
     assert result.provenance["fallback"] is False
 
 

From aac0fb08755cc23461da456e6ca25b015dbd96b6 Mon Sep 17 00:00:00 2001
From: lamentropetion <3051000145@qq.com>
Date: Thu, 20 Aug 2026 20:04:14 +0800
Subject: [PATCH 07/35] fix(attention): gate ROCm strict provenance

---
 rl_engine/kernels/attention_contract.py       |  4 +
 .../ops/pytorch/attention/cp_attention.py     |  3 +-
 .../ws2_p2p_nccl_attention_reference_check.py | 95 ++++++++++++++++---
 tests/test_flashinfer_pr7_attention.py        | 67 +++++++++++++
 4 files changed, 157 insertions(+), 12 deletions(-)

diff --git a/rl_engine/kernels/attention_contract.py b/rl_engine/kernels/attention_contract.py
index 1b5abfbc..d686131a 100644
--- a/rl_engine/kernels/attention_contract.py
+++ b/rl_engine/kernels/attention_contract.py
@@ -22,6 +22,9 @@
 # rollout. Backend adapters may differ, but strict runtime evidence must not.
 STRICT_ATTENTION_CORE_ID = "rlkernel.attention.deterministic_core.v1"
 STRICT_ATTENTION_SCHEDULE_ID = "single_batch_single_query_global_kv_blocks"
+# The distributed strict path executes the full-KV core above. This identifies
+# the fixed, pre-overlap communication schedule around that core.
+STRICT_ATTENTION_RING_SCHEDULE_ID = "rlkernel.attention.strict_ring_state.v1"
 
 
 class AttentionContractError(ValueError):
@@ -1454,6 +1457,7 @@ class AttentionDispatchResult:
     "SplitKVRuntimePlanSet",
     "SplitKVSpec",
     "STRICT_ATTENTION_CORE_ID",
+    "STRICT_ATTENTION_RING_SCHEDULE_ID",
     "STRICT_ATTENTION_SCHEDULE_ID",
     "validate_split_kv_alignment",
     "validate_split_kv_plan_set_alignment",
diff --git a/rl_engine/kernels/ops/pytorch/attention/cp_attention.py b/rl_engine/kernels/ops/pytorch/attention/cp_attention.py
index 70d6e2e3..0729a464 100644
--- a/rl_engine/kernels/ops/pytorch/attention/cp_attention.py
+++ b/rl_engine/kernels/ops/pytorch/attention/cp_attention.py
@@ -17,6 +17,7 @@
 import torch
 
 from rl_engine.kernels.attention_contract import (
+    STRICT_ATTENTION_RING_SCHEDULE_ID,
     SplitKVExecutionPlan,
     SplitKVMode,
     SplitKVRuntimeCoordinate,
@@ -135,7 +136,7 @@ def build(
             left += 1
             right -= 1
         return cls(
-            schedule_id="rlkernel.attention.strict_ring_state.v1",
+            schedule_id=STRICT_ATTENTION_RING_SCHEDULE_ID,
             total_kv_tokens=total_kv_tokens,
             cp_world_size=cp_world_size,
             kv_chunk_size=kv_chunk_size,
diff --git a/scripts/ws2_p2p_nccl_attention_reference_check.py b/scripts/ws2_p2p_nccl_attention_reference_check.py
index 40bef37b..18248164 100644
--- a/scripts/ws2_p2p_nccl_attention_reference_check.py
+++ b/scripts/ws2_p2p_nccl_attention_reference_check.py
@@ -30,6 +30,7 @@
 
 from rl_engine.kernels.attention_contract import (  # noqa: E402
     STRICT_ATTENTION_CORE_ID,
+    STRICT_ATTENTION_RING_SCHEDULE_ID,
     STRICT_ATTENTION_SCHEDULE_ID,
 )
 from rl_engine.kernels.ops.cuda.attention.cp_comm import (  # noqa: E402
@@ -112,7 +113,19 @@ def _run_rocm_matrix(output_dir: Path) -> int:
         (
             "single_gpu",
             [sys.executable, "-m", "pytest", "-q", "tests/test_deterministic_attention_cuda.py"],
-        )
+        ),
+        (
+            "adapter_cp_contracts",
+            [
+                sys.executable,
+                "-m",
+                "pytest",
+                "-q",
+                "tests/test_flashinfer_pr7_attention.py",
+                "tests/test_cp_attention.py",
+                "tests/test_attention_comparison.py",
+            ],
+        ),
     ]
     for transport, strict in (("p2p_nccl_reference", False), ("rccl_ag_rs", True)):
         for ranks in (2, 4, 8):
@@ -148,6 +161,7 @@ def _run_rocm_matrix(output_dir: Path) -> int:
 
     summary = {
         "schema_version": "ws2_rocm_attention_acceptance/v1",
+        "git_commit": _current_git_commit(repo),
         "platform": "rocm",
         "torch": str(torch.__version__),
         "hip": str(torch.version.hip),
@@ -165,6 +179,19 @@ def _run_rocm_matrix(output_dir: Path) -> int:
     return 0 if summary["passed"] else 1
 
 
+def _current_git_commit(repo: Path) -> str:
+    """Bind an acceptance artifact to the checkout that generated it."""
+
+    try:
+        return subprocess.check_output(
+            ["git", "-C", str(repo), "rev-parse", "HEAD"],
+            text=True,
+            stderr=subprocess.STDOUT,
+        ).strip()
+    except (OSError, subprocess.CalledProcessError) as exc:
+        raise RuntimeError("ROCm Attention acceptance requires a Git checkout") from exc
+
+
 def main(argv: Sequence[str] | None = None) -> int:
     args = parse_args(argv)
     if args.run_rocm_matrix:
@@ -213,6 +240,7 @@ def main(argv: Sequence[str] | None = None) -> int:
         if global_rank == 0:
             report = {
                 "schema_version": f"ws2_{args.transport}_attention/v2",
+                "git_commit": _current_git_commit(REPO_ROOT),
                 "backend": str(dist.get_backend()),
                 "platform": "rocm" if torch.version.hip is not None else "cuda",
                 "torch_version": str(torch.__version__),
@@ -533,20 +561,18 @@ def _run_strict_shared_core_check(
         repeat_lse_bitwise = repeat_lse_bitwise and torch.equal(repeated.lse, distributed.lse)
 
     provenance = distributed.provenance
-    identity_valid = (
-        provenance.get("strict_core_id") == STRICT_ATTENTION_CORE_ID
-        and provenance.get("strict_schedule") == STRICT_ATTENTION_SCHEDULE_ID
-        and provenance.get("strict_mode") is True
-        and provenance.get("native_attention_arithmetic") is False
-        and provenance.get("fallback") is False
-        and provenance.get("strict_split_kv") == "disabled"
-        and provenance.get("strict_comm_autograd") is True
-        and provenance.get("communication_backend") == args.transport
+    identity_errors = _strict_shared_core_identity_errors(
+        provenance,
+        transport=args.transport,
+        is_rocm=torch.version.hip is not None,
     )
     return {
         "executed": True,
         "passed": (
-            all(bitwise.values()) and repeat_out_bitwise and repeat_lse_bitwise and identity_valid
+            all(bitwise.values())
+            and repeat_out_bitwise
+            and repeat_lse_bitwise
+            and not identity_errors
         ),
         "strict_core_id": provenance.get("strict_core_id"),
         "strict_schedule": provenance.get("strict_schedule"),
@@ -558,6 +584,8 @@ def _run_strict_shared_core_check(
         "fallback": provenance.get("fallback"),
         "split_kv_policy": provenance.get("strict_split_kv"),
         "communication_autograd": provenance.get("strict_comm_autograd"),
+        "strict_provenance": provenance,
+        "identity_errors": identity_errors,
         "bitwise": bitwise,
         "max_abs": max_abs,
         "repeat_out_bitwise": repeat_out_bitwise,
@@ -565,6 +593,51 @@ def _run_strict_shared_core_check(
     }
 
 
+def _strict_shared_core_identity_errors(
+    provenance: dict[str, object],
+    *,
+    transport: str,
+    is_rocm: bool,
+) -> list[str]:
+    """Return every strict-contract provenance mismatch for the rank report."""
+
+    expected_backend = (
+        "rlkernel.rocm.deterministic_attention"
+        if is_rocm
+        else "rlkernel.cuda.deterministic_attention"
+    )
+    expected_rope = "rlkernel.rocm.deterministic_rope" if is_rocm else "rlkernel.cuda.rope_sm90"
+    required = {
+        "strict_core_id": STRICT_ATTENTION_CORE_ID,
+        "strict_schedule": STRICT_ATTENTION_SCHEDULE_ID,
+        "attention_backend": expected_backend,
+        "actual_backend": expected_backend,
+        "rope_backend": expected_rope,
+        "strict_mode": True,
+        "native_attention_arithmetic": False,
+        "fallback": False,
+        "strict_split_kv": "disabled",
+        "strict_comm_autograd": True,
+        "communication_backend": transport,
+        "production_ready": True,
+        "strict_full_qkv_all_gather": True,
+        "strict_position_ids_all_gather": True,
+        "compute_communication": "decoupled",
+        "compute_schedule": STRICT_ATTENTION_RING_SCHEDULE_ID,
+        "communication_overlap": "disabled",
+        "ring_schedule_default": True,
+        "ring_partial_arithmetic": False,
+        "rope_fusion": False,
+        "q_rope_state": "post_rope",
+        "k_cache_rope_state": "post_rope",
+    }
+    return [
+        f"{name}={provenance.get(name)!r}, expected {expected!r}"
+        for name, expected in required.items()
+        if provenance.get(name) != expected
+    ]
+
+
 def _strict_rope_op():
     from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RocmDeterministicRoPEOp, RoPESM90Op
 
diff --git a/tests/test_flashinfer_pr7_attention.py b/tests/test_flashinfer_pr7_attention.py
index 592b4256..7d43bd9b 100644
--- a/tests/test_flashinfer_pr7_attention.py
+++ b/tests/test_flashinfer_pr7_attention.py
@@ -15,6 +15,7 @@
 
 from rl_engine.kernels.attention_contract import (
     STRICT_ATTENTION_CORE_ID,
+    STRICT_ATTENTION_RING_SCHEDULE_ID,
     STRICT_ATTENTION_SCHEDULE_ID,
     SplitKVSpec,
 )
@@ -1813,6 +1814,72 @@ def test_strict_shared_core_entrypoint_requires_self_owned_ag_rs():
     assert args.strict_shared_core is True
 
 
+def _strict_acceptance_provenance(**overrides):
+    provenance = {
+        "strict_core_id": STRICT_ATTENTION_CORE_ID,
+        "strict_schedule": STRICT_ATTENTION_SCHEDULE_ID,
+        "attention_backend": "rlkernel.cuda.deterministic_attention",
+        "actual_backend": "rlkernel.cuda.deterministic_attention",
+        "rope_backend": "rlkernel.cuda.rope_sm90",
+        "strict_mode": True,
+        "native_attention_arithmetic": False,
+        "fallback": False,
+        "strict_split_kv": "disabled",
+        "strict_comm_autograd": True,
+        "communication_backend": "cuda_ag_rs",
+        "production_ready": True,
+        "strict_full_qkv_all_gather": True,
+        "strict_position_ids_all_gather": True,
+        "compute_communication": "decoupled",
+        "compute_schedule": STRICT_ATTENTION_RING_SCHEDULE_ID,
+        "communication_overlap": "disabled",
+        "ring_schedule_default": True,
+        "ring_partial_arithmetic": False,
+        "rope_fusion": False,
+        "q_rope_state": "post_rope",
+        "k_cache_rope_state": "post_rope",
+    }
+    provenance.update(overrides)
+    return provenance
+
+
+@pytest.mark.parametrize(
+    ("field", "invalid"),
+    [
+        ("compute_schedule", "dynamic_ring"),
+        ("communication_overlap", "enabled"),
+        ("ring_schedule_default", False),
+        ("ring_partial_arithmetic", True),
+        ("actual_backend", "flashinfer"),
+        ("rope_backend", "native_rope"),
+        ("strict_comm_autograd", False),
+    ],
+)
+def test_strict_shared_core_acceptance_rejects_provenance_drift(field, invalid):
+    errors = p2p_check_script._strict_shared_core_identity_errors(
+        _strict_acceptance_provenance(**{field: invalid}),
+        transport="cuda_ag_rs",
+        is_rocm=False,
+    )
+
+    assert any(error.startswith(f"{field}=") for error in errors)
+
+
+def test_strict_shared_core_acceptance_requires_rocm_backend_and_rope():
+    provenance = _strict_acceptance_provenance(
+        attention_backend="rlkernel.rocm.deterministic_attention",
+        actual_backend="rlkernel.rocm.deterministic_attention",
+        rope_backend="rlkernel.rocm.deterministic_rope",
+        communication_backend="rccl_ag_rs",
+    )
+
+    assert not p2p_check_script._strict_shared_core_identity_errors(
+        provenance,
+        transport="rccl_ag_rs",
+        is_rocm=True,
+    )
+
+
 @pytest.mark.parametrize(
     ("argv", "message"),
     [

From d146d432ba3dfe280d90a9f4a5f41eb8a481b9a4 Mon Sep 17 00:00:00 2001
From: lamentropetion <3051000145@qq.com>
Date: Thu, 20 Aug 2026 21:27:58 +0800
Subject: [PATCH 08/35] feat(attention): use strict vendor cores by platform

---
 rl_engine/kernels/attention_contract.py       |  16 +-
 .../kernels/ops/cuda/attention/__init__.py    |   4 +-
 .../ops/cuda/attention/deterministic_attn.py  |  11 +-
 .../kernels/ops/cuda/attention/flash_attn.py  | 224 +++++++++++++++
 .../attention/flashinfer_paged_attention.py   | 116 +++++---
 .../kernels/ops/rocm/attention/__init__.py    |   8 +-
 .../kernels/ops/rocm/attention/flash_attn.py  | 265 ++++++++++++++++++
 .../ws2_p2p_nccl_attention_reference_check.py |  55 +++-
 scripts/ws2_pr7_flashinfer_attention_check.py |  72 +++--
 tests/test_attention_correctness.py           | 149 ++++++++++
 tests/test_flashinfer_pr7_attention.py        |  42 ++-
 11 files changed, 878 insertions(+), 84 deletions(-)

diff --git a/rl_engine/kernels/attention_contract.py b/rl_engine/kernels/attention_contract.py
index d686131a..549857c1 100644
--- a/rl_engine/kernels/attention_contract.py
+++ b/rl_engine/kernels/attention_contract.py
@@ -18,9 +18,14 @@
 _EnumT = TypeVar("_EnumT", bound=Enum)
 
 
-# Stable identity for the CUDA Attention arithmetic shared by training and
-# rollout. Backend adapters may differ, but strict runtime evidence must not.
-STRICT_ATTENTION_CORE_ID = "rlkernel.attention.deterministic_core.v1"
+# Stable identities for Attention arithmetic shared by training and rollout.
+STRICT_ATTENTION_PRODUCTION_CORE_ID = "rlkernel.attention.flash_attention4.num_splits1.v1"
+STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID = "rlkernel.attention.rocm.aiter_ck_dense_mha.v1"
+STRICT_ATTENTION_REFERENCE_CORE_ID = "rlkernel.attention.deterministic_core.v1"
+# Compatibility alias for callers that explicitly select the original core.
+STRICT_ATTENTION_CORE_ID = STRICT_ATTENTION_REFERENCE_CORE_ID
+STRICT_ATTENTION_FA4_SCHEDULE_ID = "single_batch_flash_attention4_num_splits1"
+STRICT_ATTENTION_ROCM_SCHEDULE_ID = "single_batch_aiter_ck_dense_mha_no_splitkv"
 STRICT_ATTENTION_SCHEDULE_ID = "single_batch_single_query_global_kv_blocks"
 # The distributed strict path executes the full-KV core above. This identifies
 # the fixed, pre-overlap communication schedule around that core.
@@ -1457,7 +1462,12 @@ class AttentionDispatchResult:
     "SplitKVRuntimePlanSet",
     "SplitKVSpec",
     "STRICT_ATTENTION_CORE_ID",
+    "STRICT_ATTENTION_FA4_SCHEDULE_ID",
+    "STRICT_ATTENTION_PRODUCTION_CORE_ID",
+    "STRICT_ATTENTION_REFERENCE_CORE_ID",
     "STRICT_ATTENTION_RING_SCHEDULE_ID",
+    "STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID",
+    "STRICT_ATTENTION_ROCM_SCHEDULE_ID",
     "STRICT_ATTENTION_SCHEDULE_ID",
     "validate_split_kv_alignment",
     "validate_split_kv_plan_set_alignment",
diff --git a/rl_engine/kernels/ops/cuda/attention/__init__.py b/rl_engine/kernels/ops/cuda/attention/__init__.py
index 21850cdf..814424c7 100644
--- a/rl_engine/kernels/ops/cuda/attention/__init__.py
+++ b/rl_engine/kernels/ops/cuda/attention/__init__.py
@@ -21,7 +21,7 @@
     DeterministicAttentionOp,
     RLKernelDeterministicAttentionCore,
 )
-from .flash_attn import FlashAttentionOp
+from .flash_attn import FlashAttentionOp, StrictFlashAttention4Core, StrictFlashAttentionUnavailable
 from .flashinfer_paged_attention import (
     FlashInferPagedAttentionConfig,
     FlashInferQwen3PagedAttentionOp,
@@ -49,6 +49,8 @@
     "DeterministicAttentionOp",
     "RLKernelDeterministicAttentionCore",
     "FlashAttentionOp",
+    "StrictFlashAttention4Core",
+    "StrictFlashAttentionUnavailable",
     "FlashInferPagedAttentionConfig",
     "FlashInferQwen3PagedAttentionOp",
     "FlashInferRoPEFusionConfig",
diff --git a/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py b/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py
index 98823816..ba75a26a 100644
--- a/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py
+++ b/rl_engine/kernels/ops/cuda/attention/deterministic_attn.py
@@ -220,11 +220,10 @@ def _validate_inputs(
 
 
 class RLKernelDeterministicAttentionCore:
-    """Production GPU Attention core shared by training and rollout.
+    """Materializing GPU reference core shared by training and rollout.
 
-    Strict execution exposes only the existing WS1 no-Split-K kernel. This is
-    intentional: a different Split-K schedule changes the arithmetic graph and
-    therefore cannot share this core identity.
+    Production uses FA4 CuTe on CUDA and AITER CK dense MHA on ROCm. This core
+    remains useful for correctness and capability-gap diagnosis.
     """
 
     core_id = STRICT_ATTENTION_CORE_ID
@@ -239,6 +238,8 @@ class RLKernelDeterministicAttentionCore:
     downcast_at = "final_write"
     fallback = False
     native_attention_arithmetic = False
+    production_ready = False
+    reference_only = True
 
     def __init__(
         self,
@@ -306,6 +307,8 @@ def forward_with_lse(
                 "fallback": self.fallback,
                 "fallback_reason": None,
                 "native_attention_arithmetic": self.native_attention_arithmetic,
+                "production_ready": self.production_ready,
+                "reference_only": self.reference_only,
                 "strict_schedule": self.strict_schedule,
             },
         )
diff --git a/rl_engine/kernels/ops/cuda/attention/flash_attn.py b/rl_engine/kernels/ops/cuda/attention/flash_attn.py
index 0a068af6..24afdfc5 100644
--- a/rl_engine/kernels/ops/cuda/attention/flash_attn.py
+++ b/rl_engine/kernels/ops/cuda/attention/flash_attn.py
@@ -1,11 +1,235 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
+from __future__ import annotations
+
+import importlib
+import importlib.metadata
+import inspect
+from typing import Any, Callable
+
 import torch
 
+from rl_engine.kernels.attention_contract import (
+    STRICT_ATTENTION_FA4_SCHEDULE_ID,
+    STRICT_ATTENTION_PRODUCTION_CORE_ID,
+    SplitKVMode,
+    SplitKVSpec,
+)
 from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
+from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
+    DeterministicAttentionCoreResult,
+    RLKernelDeterministicAttentionCore,
+)
 from rl_engine.utils.logger import logger
 
+_FA4_API_SOURCE = "flash_attn.cute.interface"
+_FA4_REQUIRED_PARAMETERS = frozenset(
+    {"softmax_scale", "causal", "num_splits", "pack_gqa", "deterministic", "return_lse"}
+)
+
+
+class StrictFlashAttentionUnavailable(RuntimeError):
+    """Raised when the exact FlashAttention production contract is unavailable."""
+
+
+def _load_fa4_cute_op() -> tuple[Callable[..., Any], str]:
+    try:
+        module = importlib.import_module(_FA4_API_SOURCE)
+        op = getattr(module, "flash_attn_func")
+    except (AttributeError, ImportError, OSError, RuntimeError) as exc:
+        raise StrictFlashAttentionUnavailable(
+            "strict CUDA Attention requires flash_attn.cute.interface.flash_attn_func"
+        ) from exc
+    try:
+        package_version = importlib.metadata.version("flash-attn")
+    except importlib.metadata.PackageNotFoundError:
+        package_version = "unknown"
+    return op, package_version
+
+
+class StrictFlashAttention4Core:
+    """Shared CUDA production core with the reduction-affecting FA4 knobs fixed."""
+
+    core_id = STRICT_ATTENTION_PRODUCTION_CORE_ID
+    strict_schedule = STRICT_ATTENTION_FA4_SCHEDULE_ID
+    backend_id = "flash_attention_4.cute"
+    api_source = _FA4_API_SOURCE
+    merge_order = "global_block_index"
+    accum_dtype = "fp32"
+    downcast_at = "final_write"
+    fallback = False
+    native_attention_arithmetic = True
+    production_ready = True
+    reference_only = False
+    num_splits = 1
+    deterministic_backward = True
+
+    def __init__(
+        self,
+        *,
+        split_kv: SplitKVSpec | None = None,
+        _op: Callable[..., Any] | None = None,
+        _package_version: str | None = None,
+    ) -> None:
+        requested = SplitKVSpec.disabled() if split_kv is None else split_kv
+        if not isinstance(requested, SplitKVSpec):
+            raise TypeError("split_kv must be a SplitKVSpec")
+        if requested.mode is not SplitKVMode.DISABLED:
+            raise ValueError("strict FA4 Attention requires Split-KV to be disabled")
+        if _op is None:
+            op, package_version = _load_fa4_cute_op()
+        else:
+            op = _op
+            package_version = "test-double" if _package_version is None else _package_version
+        self._validate_api(op)
+        self.split_kv = requested
+        self.package_version = package_version
+        self._op = op
+
+    @staticmethod
+    def _validate_api(op: Callable[..., Any]) -> None:
+        try:
+            parameters = inspect.signature(op).parameters
+        except (TypeError, ValueError) as exc:
+            raise StrictFlashAttentionUnavailable(
+                "cannot inspect the FlashAttention CuTe API signature"
+            ) from exc
+        missing = sorted(_FA4_REQUIRED_PARAMETERS.difference(parameters))
+        if missing:
+            raise StrictFlashAttentionUnavailable(
+                "FlashAttention CuTe API is missing strict controls: " + ", ".join(missing)
+            )
+
+    def __call__(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        **kwargs: Any,
+    ) -> DeterministicAttentionCoreResult:
+        return self.forward_with_lse(q, k, v, **kwargs)
+
+    def forward_with_lse(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        *,
+        causal: bool = True,
+        scale: float | None = None,
+        key_padding_mask: torch.Tensor | None = None,
+        query_position_ids: torch.Tensor | None = None,
+        key_position_ids: torch.Tensor | None = None,
+        output_dtype: torch.dtype | None = None,
+    ) -> DeterministicAttentionCoreResult:
+        self._validate_inputs(q, k, v, key_padding_mask)
+        RLKernelDeterministicAttentionCore._validate_positions(
+            q,
+            k,
+            causal=causal,
+            query_position_ids=query_position_ids,
+            key_position_ids=key_position_ids,
+        )
+        resolved_dtype = q.dtype if output_dtype is None else output_dtype
+        if resolved_dtype != q.dtype:
+            raise ValueError("strict Attention output_dtype must match the Q/K/V input dtype")
+
+        q_fa = q.transpose(1, 2).contiguous()
+        k_fa = k.transpose(1, 2).contiguous()
+        v_fa = v.transpose(1, 2).contiguous()
+        result = self._op(
+            q_fa,
+            k_fa,
+            v_fa,
+            softmax_scale=scale,
+            causal=causal,
+            num_splits=self.num_splits,
+            pack_gqa=q.size(1) > k.size(1),
+            deterministic=self.deterministic_backward,
+            return_lse=True,
+        )
+        if not isinstance(result, tuple) or len(result) != 2:
+            raise StrictFlashAttentionUnavailable(
+                "FlashAttention CuTe must return exactly (out, lse) when return_lse=True"
+            )
+        out_fa, lse = result
+        if not isinstance(out_fa, torch.Tensor) or not isinstance(lse, torch.Tensor):
+            raise StrictFlashAttentionUnavailable("FlashAttention CuTe returned non-tensor output")
+        expected_out_shape = q_fa.shape
+        expected_lse_shape = (q.size(0), q.size(1), q.size(2))
+        if tuple(out_fa.shape) != tuple(expected_out_shape):
+            raise StrictFlashAttentionUnavailable(
+                f"FlashAttention output must have shape {tuple(expected_out_shape)}"
+            )
+        if tuple(lse.shape) != expected_lse_shape:
+            raise StrictFlashAttentionUnavailable(
+                f"FlashAttention LSE must have shape {expected_lse_shape}"
+            )
+        if out_fa.dtype != resolved_dtype:
+            raise StrictFlashAttentionUnavailable(
+                "FlashAttention output dtype does not match the requested output dtype"
+            )
+        if lse.dtype != torch.float32:
+            raise StrictFlashAttentionUnavailable(
+                "FlashAttention attention-domain LSE must be FP32"
+            )
+
+        out = out_fa.transpose(1, 2).contiguous()
+        return DeterministicAttentionCoreResult(
+            out=out,
+            lse=lse.contiguous(),
+            provenance={
+                "strict_core_id": self.core_id,
+                "strict_schedule": self.strict_schedule,
+                "attention_backend": self.backend_id,
+                "fa_api_source": self.api_source,
+                "fa_package_version": self.package_version,
+                "num_splits": self.num_splits,
+                "deterministic_backward": self.deterministic_backward,
+                "dropout_p": 0.0,
+                "split_kv": self.split_kv.resolve(k.size(2), backend=self.backend_id).to_dict(),
+                "merge_order": self.merge_order,
+                "accum_dtype": self.accum_dtype,
+                "downcast_at": self.downcast_at,
+                "fallback": self.fallback,
+                "fallback_reason": None,
+                "native_attention_arithmetic": self.native_attention_arithmetic,
+                "production_ready": self.production_ready,
+                "reference_only": self.reference_only,
+            },
+        )
+
+    @staticmethod
+    def _validate_inputs(
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        key_padding_mask: torch.Tensor | None,
+    ) -> None:
+        if key_padding_mask is not None:
+            raise ValueError(
+                "strict FA4 core does not accept padding masks; materialize each logical row"
+            )
+        if q.ndim != 4 or k.ndim != 4 or v.ndim != 4:
+            raise ValueError("q/k/v must be 4-D [B, H, S, D]")
+        if q.size(0) != 1:
+            raise ValueError("strict FA4 core executes one logical batch row at a time")
+        if k.size(0) != 1 or v.size(0) != 1:
+            raise ValueError("q/k/v batch sizes must match")
+        if k.shape != v.shape or q.size(3) != k.size(3):
+            raise ValueError("k/v shapes and q/k/v head dimensions must match")
+        if q.size(1) % k.size(1) != 0:
+            raise ValueError("Q heads must be divisible by KV heads for GQA")
+        if q.dtype not in (torch.float16, torch.bfloat16):
+            raise ValueError("strict FA4 core supports FP16/BF16 only")
+        if k.dtype != q.dtype or v.dtype != q.dtype:
+            raise ValueError("q/k/v must share one dtype")
+        if not (q.is_cuda and k.is_cuda and v.is_cuda):
+            raise ValueError("strict FA4 core requires CUDA tensors")
+        if not (q.device == k.device == v.device):
+            raise ValueError("q/k/v must be on one CUDA device")
+
 
 class FlashAttentionOp:
     """
diff --git a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
index fcd4f778..48785e6c 100644
--- a/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
+++ b/rl_engine/kernels/ops/cuda/attention/flashinfer_paged_attention.py
@@ -26,6 +26,10 @@
 
 from rl_engine.kernels.attention_contract import (
     STRICT_ATTENTION_CORE_ID,
+    STRICT_ATTENTION_FA4_SCHEDULE_ID,
+    STRICT_ATTENTION_PRODUCTION_CORE_ID,
+    STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
+    STRICT_ATTENTION_ROCM_SCHEDULE_ID,
     STRICT_ATTENTION_SCHEDULE_ID,
     AttentionContractError,
     SplitKVExecutionPlan,
@@ -43,10 +47,8 @@
     AttentionCPPartialState,
     AttentionParallelSpec,
 )
-from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
-    DeterministicAttentionCoreResult,
-    RLKernelDeterministicAttentionCore,
-)
+from rl_engine.kernels.ops.cuda.attention.deterministic_attn import DeterministicAttentionCoreResult
+from rl_engine.kernels.ops.cuda.attention.flash_attn import StrictFlashAttention4Core
 from rl_engine.kernels.ops.pytorch.attention.cp_attention import (
     AttentionPartialState,
     AttentionRingSchedule,
@@ -704,7 +706,7 @@ def _run_strict_core(
     ) -> FlashInferAttentionResult:
         """Use FlashInfer only for paged-KV layout, never Attention arithmetic."""
 
-        core = cfg.deterministic_core or RLKernelDeterministicAttentionCore(split_kv=cfg.split_kv)
+        core = _resolve_strict_core(cfg)
         _validate_strict_core(core)
         rope = _resolve_strict_rope(cfg)
         logical_k, logical_v, key_positions = _materialize_strict_logical_kv(
@@ -819,7 +821,7 @@ def _run_strict_cp(
         rope = _resolve_strict_rope(cfg)
         q_ready = _apply_strict_rope(rope, global_q, global_q_positions, cfg.rope.rope_theta)
         k_ready = _apply_strict_rope(rope, global_k, global_k_positions, cfg.rope.rope_theta)
-        core = cfg.deterministic_core or RLKernelDeterministicAttentionCore(split_kv=cfg.split_kv)
+        core = _resolve_strict_core(cfg)
         _validate_strict_core(core)
 
         outputs: list[torch.Tensor] = []
@@ -1462,28 +1464,68 @@ def _validate_runtime_outputs(
 
 def _validate_strict_core(core: Any) -> None:
     if not callable(getattr(core, "forward_with_lse", None)):
-        raise ValueError("strict deterministic core must implement forward_with_lse")
-    if getattr(core, "core_id", None) != STRICT_ATTENTION_CORE_ID:
-        raise ValueError("strict deterministic core ID must be " f"{STRICT_ATTENTION_CORE_ID!r}")
-    if getattr(core, "strict_schedule", None) != STRICT_ATTENTION_SCHEDULE_ID:
-        raise ValueError(
-            "strict deterministic core schedule must be " f"{STRICT_ATTENTION_SCHEDULE_ID!r}"
-        )
+        raise ValueError("strict Attention core must implement forward_with_lse")
+    expected_schedules = {
+        STRICT_ATTENTION_PRODUCTION_CORE_ID: STRICT_ATTENTION_FA4_SCHEDULE_ID,
+        STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID: STRICT_ATTENTION_ROCM_SCHEDULE_ID,
+        STRICT_ATTENTION_CORE_ID: STRICT_ATTENTION_SCHEDULE_ID,
+    }
+    core_id = getattr(core, "core_id", None)
+    if core_id not in expected_schedules:
+        raise ValueError("strict Attention core ID is not an exact supported identity")
+    if getattr(core, "strict_schedule", None) != expected_schedules[core_id]:
+        raise ValueError("strict Attention core schedule does not match its exact core identity")
     required = {
         "merge_order": "global_block_index",
         "accum_dtype": "fp32",
         "downcast_at": "final_write",
         "fallback": False,
-        "native_attention_arithmetic": False,
     }
     mismatches = [
         name for name, expected in required.items() if getattr(core, name, None) != expected
     ]
     if mismatches:
         raise ValueError(
-            "strict deterministic core has incompatible arithmetic identity: "
-            + ", ".join(mismatches)
+            "strict Attention core has incompatible arithmetic identity: " + ", ".join(mismatches)
         )
+    if core_id in {STRICT_ATTENTION_PRODUCTION_CORE_ID, STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID}:
+        production_required = {
+            "native_attention_arithmetic": True,
+            "num_splits": 1,
+            "deterministic_backward": True,
+            "production_ready": True,
+            "reference_only": False,
+        }
+        production_mismatches = [
+            name
+            for name, expected in production_required.items()
+            if getattr(core, name, None) != expected
+        ]
+        if production_mismatches:
+            raise ValueError(
+                "strict production core has incompatible controls: "
+                + ", ".join(production_mismatches)
+            )
+    if (
+        core_id == STRICT_ATTENTION_PRODUCTION_CORE_ID
+        and getattr(core, "backend_id", None) != "flash_attention_4.cute"
+    ):
+        raise ValueError("strict CUDA production core must be FlashAttention-4 CuTe")
+    if core_id == STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID:
+        if getattr(core, "backend_id", None) != "aiter.rocm.ck_dense_mha":
+            raise ValueError("strict ROCm production core must be AITER CK dense MHA")
+        if getattr(core, "split_kv_control", None) != "dense_non_split_api":
+            raise ValueError("strict ROCm production core must use the non-Split-K CK API")
+
+
+def _resolve_strict_core(cfg: FlashInferPagedAttentionConfig) -> Any:
+    if cfg.deterministic_core is not None:
+        return cfg.deterministic_core
+    if torch.version.hip is not None:
+        from rl_engine.kernels.ops.rocm.attention.flash_attn import StrictRocmAiterCKAttentionCore
+
+        return StrictRocmAiterCKAttentionCore(split_kv=cfg.split_kv)
+    return StrictFlashAttention4Core(split_kv=cfg.split_kv)
 
 
 def _validate_strict_core_result(
@@ -1492,12 +1534,12 @@ def _validate_strict_core_result(
 ) -> None:
     if not isinstance(result, DeterministicAttentionCoreResult):
         raise FlashInferUnavailable(
-            "strict deterministic core must return DeterministicAttentionCoreResult"
+            "strict Attention core must return DeterministicAttentionCoreResult"
         )
     if result.out.dtype not in (torch.float16, torch.bfloat16):
-        raise FlashInferUnavailable("strict deterministic core output must be FP16/BF16")
+        raise FlashInferUnavailable("strict Attention core output must be FP16/BF16")
     if result.lse.dtype is not torch.float32:
-        raise FlashInferUnavailable("strict deterministic core LSE must be FP32")
+        raise FlashInferUnavailable("strict Attention core LSE must be FP32")
     expected = {
         "strict_core_id": core.core_id,
         "strict_schedule": core.strict_schedule,
@@ -1506,7 +1548,7 @@ def _validate_strict_core_result(
         "accum_dtype": core.accum_dtype,
         "downcast_at": core.downcast_at,
         "fallback": False,
-        "native_attention_arithmetic": False,
+        "native_attention_arithmetic": core.native_attention_arithmetic,
     }
     mismatches = [name for name, value in expected.items() if result.provenance.get(name) != value]
     if mismatches:
@@ -1679,6 +1721,11 @@ def _strict_attention_provenance(
     cp_required: bool,
     rope: Any,
 ) -> dict[str, Any]:
+    communication_id = getattr(cfg.cp_communication, "backend_id", "unknown")
+    communication_backend = (
+        "self_owned_cuda_ag_rs" if communication_id == "cuda_ag_rs" else communication_id
+    )
+    expected_communication = "rccl_ag_rs" if torch.version.hip is not None else "cuda_ag_rs"
     return {
         "attention_backend": core_provenance["attention_backend"],
         "requested_backend": "flashinfer_layout_adapter",
@@ -1696,9 +1743,12 @@ def _strict_attention_provenance(
         "strict_schedule": core_provenance["strict_schedule"],
         "accum_dtype": core_provenance["accum_dtype"],
         "downcast_at": core_provenance["downcast_at"],
-        "arithmetic_plan_source": "rlkernel_deterministic_gpu_core",
+        "arithmetic_plan_source": core_provenance.get(
+            "fa_api_source",
+            core_provenance.get("aiter_api_source", "rlkernel_reference_core"),
+        ),
         "arithmetic_semantics_verified": True,
-        "native_attention_arithmetic": False,
+        "native_attention_arithmetic": core_provenance["native_attention_arithmetic"],
         "fallback": False,
         "fallback_reason": None,
         "rope_backend": getattr(rope, "backend_id", "rlkernel.unknown.rope"),
@@ -1710,17 +1760,19 @@ def _strict_attention_provenance(
         "k_cache_rope_state": "post_rope",
         "batch_invariant_claim": "strict_runtime_verified",
         "cp_comm_required": cp_required,
-        "communication_backend": (
-            getattr(cfg.cp_communication, "backend_id", "unknown") if cp_required else "none"
-        ),
+        "communication_backend": communication_backend if cp_required else "none",
+        "platform": core_provenance.get("platform", "cuda"),
+        "num_splits": core_provenance.get("num_splits"),
+        "split_kv_control": core_provenance.get("split_kv_control"),
+        "deterministic_backward": core_provenance.get("deterministic_backward"),
+        "fa_api_source": core_provenance.get("fa_api_source"),
+        "fa_package_version": core_provenance.get("fa_package_version"),
+        "aiter_api_source": core_provenance.get("aiter_api_source"),
+        "aiter_source_sha256": core_provenance.get("aiter_source_sha256"),
+        "reference_only": bool(core_provenance.get("reference_only", False)),
         "production_ready": bool(
-            cp_required
-            and core_provenance.get("attention_backend")
-            in {
-                "rlkernel.cuda.deterministic_attention",
-                "rlkernel.rocm.deterministic_attention",
-            }
-            and getattr(cfg.cp_communication, "backend_id", None) in {"cuda_ag_rs", "rccl_ag_rs"}
+            core_provenance.get("production_ready", False)
+            and (not cp_required or communication_id == expected_communication)
         ),
     }
 
diff --git a/rl_engine/kernels/ops/rocm/attention/__init__.py b/rl_engine/kernels/ops/rocm/attention/__init__.py
index 150c937f..926280c8 100644
--- a/rl_engine/kernels/ops/rocm/attention/__init__.py
+++ b/rl_engine/kernels/ops/rocm/attention/__init__.py
@@ -1,8 +1,14 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-from .flash_attn import RocmFlashAttentionOp
+from .flash_attn import (
+    RocmFlashAttentionOp,
+    StrictRocmAiterCKAttentionCore,
+    StrictRocmAttentionUnavailable,
+)
 
 __all__ = [
     "RocmFlashAttentionOp",
+    "StrictRocmAiterCKAttentionCore",
+    "StrictRocmAttentionUnavailable",
 ]
diff --git a/rl_engine/kernels/ops/rocm/attention/flash_attn.py b/rl_engine/kernels/ops/rocm/attention/flash_attn.py
index a9781cfb..fff003a2 100644
--- a/rl_engine/kernels/ops/rocm/attention/flash_attn.py
+++ b/rl_engine/kernels/ops/rocm/attention/flash_attn.py
@@ -1,13 +1,278 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
+from __future__ import annotations
+
+import hashlib
+import importlib
+import inspect
+import math
 import os
+from pathlib import Path
+from typing import Any, Callable
 
 import torch
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
 
+from rl_engine.kernels.attention_contract import (
+    STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
+    STRICT_ATTENTION_ROCM_SCHEDULE_ID,
+    SplitKVMode,
+    SplitKVSpec,
+)
+from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
+    DeterministicAttentionCoreResult,
+    RLKernelDeterministicAttentionCore,
+)
 from rl_engine.utils.logger import logger
 
 _MAX_TESTED_ROCM_TRITON_HEAD_DIM = 512
+_AITER_API_SOURCE = "aiter.ops.mha"
+
+
+class StrictRocmAttentionUnavailable(RuntimeError):
+    """Raised when the exact AITER CK strict contract is unavailable."""
+
+
+def _load_aiter_ck_ops() -> tuple[Callable[..., Any], Callable[..., Any], str]:
+    try:
+        module = importlib.import_module(_AITER_API_SOURCE)
+        mha_fwd = getattr(module, "mha_fwd")
+        mha_bwd = getattr(module, "mha_bwd")
+    except (AttributeError, ImportError, OSError, RuntimeError) as exc:
+        raise StrictRocmAttentionUnavailable(
+            "strict ROCm Attention requires aiter.ops.mha.mha_fwd and mha_bwd"
+        ) from exc
+    if not callable(mha_fwd) or not callable(mha_bwd):
+        raise StrictRocmAttentionUnavailable("AITER CK MHA entry points are not callable")
+    module_file = inspect.getsourcefile(module)
+    if not module_file:
+        raise StrictRocmAttentionUnavailable("cannot fingerprint the AITER MHA source module")
+    source_sha256 = hashlib.sha256(Path(module_file).read_bytes()).hexdigest()
+    return mha_fwd, mha_bwd, source_sha256
+
+
+class _AiterCKAttentionFn(Function):
+    @staticmethod
+    def forward(
+        ctx,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        causal: bool,
+        scale: float,
+        mha_fwd: Callable[..., Any],
+        mha_bwd: Callable[..., Any],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        q_fa = q.transpose(1, 2).contiguous()
+        k_fa = k.transpose(1, 2).contiguous()
+        v_fa = v.transpose(1, 2).contiguous()
+        result = mha_fwd(
+            q_fa,
+            k_fa,
+            v_fa,
+            0.0,
+            float(scale),
+            bool(causal),
+            -1,
+            -1,
+            0,
+            True,
+            False,
+        )
+        if not isinstance(result, (tuple, list)) or len(result) != 4:
+            raise StrictRocmAttentionUnavailable(
+                "AITER mha_fwd must return (out, lse, dropout_mask, rng_state)"
+            )
+        out_fa, lse, _dropout_mask, rng_state = result
+        if not all(isinstance(item, torch.Tensor) for item in (out_fa, lse, rng_state)):
+            raise StrictRocmAttentionUnavailable("AITER mha_fwd returned non-tensor state")
+        ctx.save_for_backward(q_fa, k_fa, v_fa, out_fa, lse, rng_state)
+        ctx.causal = bool(causal)
+        ctx.scale = float(scale)
+        ctx.mha_bwd = mha_bwd
+        ctx.mark_non_differentiable(lse)
+        return out_fa.transpose(1, 2).contiguous(), lse.contiguous()
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_out: torch.Tensor, grad_lse: torch.Tensor):
+        q_fa, k_fa, v_fa, out_fa, lse, rng_state = ctx.saved_tensors
+        grad_out_fa = grad_out.transpose(1, 2).contiguous()
+        result = ctx.mha_bwd(
+            grad_out_fa,
+            q_fa,
+            k_fa,
+            v_fa,
+            out_fa,
+            lse,
+            0.0,
+            ctx.scale,
+            ctx.causal,
+            -1,
+            -1,
+            True,
+            rng_state=rng_state,
+        )
+        if not isinstance(result, (tuple, list)) or len(result) < 3:
+            raise StrictRocmAttentionUnavailable("AITER mha_bwd must return dQ/dK/dV")
+        dq, dk, dv = result[:3]
+        return (
+            dq.transpose(1, 2).contiguous(),
+            dk.transpose(1, 2).contiguous(),
+            dv.transpose(1, 2).contiguous(),
+            None,
+            None,
+            None,
+            None,
+        )
+
+
+class StrictRocmAiterCKAttentionCore:
+    """Shared ROCm production core using the non-Split-K AITER CK dense MHA."""
+
+    core_id = STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID
+    strict_schedule = STRICT_ATTENTION_ROCM_SCHEDULE_ID
+    backend_id = "aiter.rocm.ck_dense_mha"
+    api_source = _AITER_API_SOURCE
+    merge_order = "global_block_index"
+    accum_dtype = "fp32"
+    downcast_at = "final_write"
+    fallback = False
+    native_attention_arithmetic = True
+    production_ready = True
+    reference_only = False
+    num_splits = 1
+    split_kv_control = "dense_non_split_api"
+    deterministic_backward = True
+
+    def __init__(
+        self,
+        *,
+        split_kv: SplitKVSpec | None = None,
+        _mha_fwd: Callable[..., Any] | None = None,
+        _mha_bwd: Callable[..., Any] | None = None,
+        _source_sha256: str | None = None,
+    ) -> None:
+        requested = SplitKVSpec.disabled() if split_kv is None else split_kv
+        if not isinstance(requested, SplitKVSpec):
+            raise TypeError("split_kv must be a SplitKVSpec")
+        if requested.mode is not SplitKVMode.DISABLED:
+            raise ValueError("strict AITER CK Attention requires Split-KV to be disabled")
+        if (_mha_fwd is None) != (_mha_bwd is None):
+            raise ValueError("test injection requires both AITER forward and backward callables")
+        if _mha_fwd is None:
+            mha_fwd, mha_bwd, source_sha256 = _load_aiter_ck_ops()
+        else:
+            mha_fwd, mha_bwd = _mha_fwd, _mha_bwd
+            source_sha256 = "test-double" if _source_sha256 is None else _source_sha256
+        if not callable(mha_fwd) or not callable(mha_bwd):
+            raise StrictRocmAttentionUnavailable("AITER CK MHA entry points are not callable")
+        self.split_kv = requested
+        self.source_sha256 = source_sha256
+        self._mha_fwd = mha_fwd
+        self._mha_bwd = mha_bwd
+
+    def forward_with_lse(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        *,
+        causal: bool = True,
+        scale: float | None = None,
+        key_padding_mask: torch.Tensor | None = None,
+        query_position_ids: torch.Tensor | None = None,
+        key_position_ids: torch.Tensor | None = None,
+        output_dtype: torch.dtype | None = None,
+    ) -> DeterministicAttentionCoreResult:
+        self._validate_inputs(q, k, v, key_padding_mask)
+        RLKernelDeterministicAttentionCore._validate_positions(
+            q,
+            k,
+            causal=causal,
+            query_position_ids=query_position_ids,
+            key_position_ids=key_position_ids,
+        )
+        resolved_dtype = q.dtype if output_dtype is None else output_dtype
+        if resolved_dtype != q.dtype:
+            raise ValueError("strict Attention output_dtype must match the Q/K/V input dtype")
+        resolved_scale = 1.0 / math.sqrt(q.size(-1)) if scale is None else float(scale)
+        out, lse = _AiterCKAttentionFn.apply(
+            q,
+            k,
+            v,
+            bool(causal),
+            resolved_scale,
+            self._mha_fwd,
+            self._mha_bwd,
+        )
+        expected_lse_shape = (q.size(0), q.size(1), q.size(2))
+        if out.shape != q.shape or out.dtype != resolved_dtype:
+            raise StrictRocmAttentionUnavailable("AITER CK output shape/dtype changed")
+        if tuple(lse.shape) != expected_lse_shape or lse.dtype != torch.float32:
+            raise StrictRocmAttentionUnavailable("AITER CK must export [B,H,Sq] FP32 LSE")
+        device_properties = torch.cuda.get_device_properties(q.device)
+        return DeterministicAttentionCoreResult(
+            out=out,
+            lse=lse,
+            provenance={
+                "strict_core_id": self.core_id,
+                "strict_schedule": self.strict_schedule,
+                "attention_backend": self.backend_id,
+                "platform": "rocm",
+                "torch_version": torch.__version__,
+                "rocm_version": torch.version.hip,
+                "gpu_name": device_properties.name,
+                "gpu_arch": getattr(device_properties, "gcnArchName", "unknown"),
+                "aiter_api_source": self.api_source,
+                "aiter_source_sha256": self.source_sha256,
+                "num_splits": self.num_splits,
+                "split_kv_control": self.split_kv_control,
+                "deterministic_backward": self.deterministic_backward,
+                "dropout_p": 0.0,
+                "split_kv": self.split_kv.resolve(k.size(2), backend=self.backend_id).to_dict(),
+                "merge_order": self.merge_order,
+                "accum_dtype": self.accum_dtype,
+                "downcast_at": self.downcast_at,
+                "fallback": self.fallback,
+                "fallback_reason": None,
+                "native_attention_arithmetic": self.native_attention_arithmetic,
+                "production_ready": self.production_ready,
+                "reference_only": self.reference_only,
+            },
+        )
+
+    @staticmethod
+    def _validate_inputs(
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        key_padding_mask: torch.Tensor | None,
+    ) -> None:
+        if torch.version.hip is None:
+            raise StrictRocmAttentionUnavailable("strict AITER CK core requires ROCm PyTorch")
+        if key_padding_mask is not None:
+            raise ValueError("strict AITER CK core materializes each unpadded logical row")
+        if q.ndim != 4 or k.ndim != 4 or v.ndim != 4:
+            raise ValueError("q/k/v must be 4-D [B,H,S,D]")
+        if q.size(0) != 1 or k.size(0) != 1 or v.size(0) != 1:
+            raise ValueError("strict AITER CK core executes one logical batch row at a time")
+        if k.shape != v.shape or q.size(-1) != k.size(-1):
+            raise ValueError("k/v shapes and q/k/v head dimensions must match")
+        if q.size(1) % k.size(1) != 0:
+            raise ValueError("Q heads must be divisible by KV heads for GQA")
+        if q.size(-1) > 256 or q.size(-1) % 8:
+            raise ValueError("AITER CK requires head_dim <= 256 and divisible by 8")
+        if q.dtype not in (torch.float16, torch.bfloat16):
+            raise ValueError("strict AITER CK core supports FP16/BF16 only")
+        if k.dtype != q.dtype or v.dtype != q.dtype:
+            raise ValueError("q/k/v must share one dtype")
+        if not (q.is_cuda and k.is_cuda and v.is_cuda):
+            raise ValueError("strict AITER CK core requires ROCm GPU tensors")
+        if not (q.device == k.device == v.device):
+            raise ValueError("q/k/v must be on one ROCm device")
 
 
 def _select_flash_attn_backend() -> str:
diff --git a/scripts/ws2_p2p_nccl_attention_reference_check.py b/scripts/ws2_p2p_nccl_attention_reference_check.py
index 18248164..abceb610 100644
--- a/scripts/ws2_p2p_nccl_attention_reference_check.py
+++ b/scripts/ws2_p2p_nccl_attention_reference_check.py
@@ -29,9 +29,11 @@
     sys.path.insert(0, str(REPO_ROOT))
 
 from rl_engine.kernels.attention_contract import (  # noqa: E402
-    STRICT_ATTENTION_CORE_ID,
+    STRICT_ATTENTION_FA4_SCHEDULE_ID,
+    STRICT_ATTENTION_PRODUCTION_CORE_ID,
     STRICT_ATTENTION_RING_SCHEDULE_ID,
-    STRICT_ATTENTION_SCHEDULE_ID,
+    STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
+    STRICT_ATTENTION_ROCM_SCHEDULE_ID,
 )
 from rl_engine.kernels.ops.cuda.attention.cp_comm import (  # noqa: E402
     AttentionCPBlockMetadata,
@@ -43,9 +45,7 @@
     P2PNCCLAttentionCPCommunication,
     RCCLAGRSAttentionCPCommunication,
 )
-from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (  # noqa: E402
-    RLKernelDeterministicAttentionCore,
-)
+from rl_engine.kernels.ops.cuda.attention.flash_attn import StrictFlashAttention4Core  # noqa: E402
 from rl_engine.kernels.ops.cuda.attention.flashinfer_paged_attention import (  # noqa: E402
     FlashInferPagedAttentionConfig,
     FlashInferQwen3PagedAttentionOp,
@@ -510,7 +510,7 @@ def _run_strict_shared_core_check(
     rope = _strict_rope_op()
     q_ready = _apply_strict_rope(rope, q_ref, positions, config.rope.rope_theta)
     k_ready = _apply_strict_rope(rope, k_ref, positions, config.rope.rope_theta)
-    reference = RLKernelDeterministicAttentionCore().forward_with_lse(
+    reference = _strict_attention_core().forward_with_lse(
         q_ready,
         k_ready,
         v_ref,
@@ -601,24 +601,30 @@ def _strict_shared_core_identity_errors(
 ) -> list[str]:
     """Return every strict-contract provenance mismatch for the rank report."""
 
-    expected_backend = (
-        "rlkernel.rocm.deterministic_attention"
-        if is_rocm
-        else "rlkernel.cuda.deterministic_attention"
+    expected_core = (
+        STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID if is_rocm else STRICT_ATTENTION_PRODUCTION_CORE_ID
+    )
+    expected_schedule = (
+        STRICT_ATTENTION_ROCM_SCHEDULE_ID if is_rocm else STRICT_ATTENTION_FA4_SCHEDULE_ID
     )
+    expected_backend = "aiter.rocm.ck_dense_mha" if is_rocm else "flash_attention_4.cute"
     expected_rope = "rlkernel.rocm.deterministic_rope" if is_rocm else "rlkernel.cuda.rope_sm90"
+    expected_communication = "rccl_ag_rs" if is_rocm else "self_owned_cuda_ag_rs"
     required = {
-        "strict_core_id": STRICT_ATTENTION_CORE_ID,
-        "strict_schedule": STRICT_ATTENTION_SCHEDULE_ID,
+        "strict_core_id": expected_core,
+        "strict_schedule": expected_schedule,
         "attention_backend": expected_backend,
         "actual_backend": expected_backend,
         "rope_backend": expected_rope,
         "strict_mode": True,
-        "native_attention_arithmetic": False,
+        "native_attention_arithmetic": True,
+        "num_splits": 1,
+        "deterministic_backward": True,
+        "reference_only": False,
         "fallback": False,
         "strict_split_kv": "disabled",
         "strict_comm_autograd": True,
-        "communication_backend": transport,
+        "communication_backend": expected_communication,
         "production_ready": True,
         "strict_full_qkv_all_gather": True,
         "strict_position_ids_all_gather": True,
@@ -631,11 +637,30 @@ def _strict_shared_core_identity_errors(
         "q_rope_state": "post_rope",
         "k_cache_rope_state": "post_rope",
     }
-    return [
+    errors = [
         f"{name}={provenance.get(name)!r}, expected {expected!r}"
         for name, expected in required.items()
         if provenance.get(name) != expected
     ]
+    if is_rocm:
+        if provenance.get("split_kv_control") != "dense_non_split_api":
+            errors.append("split_kv_control must prove the AITER dense non-Split-K API")
+        if provenance.get("aiter_api_source") != "aiter.ops.mha":
+            errors.append("aiter_api_source must identify aiter.ops.mha")
+        if not provenance.get("aiter_source_sha256"):
+            errors.append("aiter_source_sha256 is missing")
+    else:
+        if provenance.get("fa_api_source") != "flash_attn.cute.interface":
+            errors.append("fa_api_source must identify the FA4 CuTe API")
+    return errors
+
+
+def _strict_attention_core():
+    if torch.version.hip is not None:
+        from rl_engine.kernels.ops.rocm.attention.flash_attn import StrictRocmAiterCKAttentionCore
+
+        return StrictRocmAiterCKAttentionCore()
+    return StrictFlashAttention4Core()
 
 
 def _strict_rope_op():
diff --git a/scripts/ws2_pr7_flashinfer_attention_check.py b/scripts/ws2_pr7_flashinfer_attention_check.py
index 8fc95800..cea6ca51 100644
--- a/scripts/ws2_pr7_flashinfer_attention_check.py
+++ b/scripts/ws2_pr7_flashinfer_attention_check.py
@@ -26,16 +26,16 @@
     sys.path.insert(0, str(REPO_ROOT))
 
 from rl_engine.kernels.attention_contract import (  # noqa: E402
-    STRICT_ATTENTION_CORE_ID,
-    STRICT_ATTENTION_SCHEDULE_ID,
+    STRICT_ATTENTION_FA4_SCHEDULE_ID,
+    STRICT_ATTENTION_PRODUCTION_CORE_ID,
+    STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
+    STRICT_ATTENTION_ROCM_SCHEDULE_ID,
 )
 from rl_engine.kernels.ops.cuda.attention.cp_comm import (  # noqa: E402
     AttentionCPCommunicationPlan,
     AttentionParallelSpec,
 )
-from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (  # noqa: E402
-    RLKernelDeterministicAttentionCore,
-)
+from rl_engine.kernels.ops.cuda.attention.flash_attn import StrictFlashAttention4Core  # noqa: E402
 from rl_engine.kernels.ops.cuda.attention.flashinfer_paged_attention import (  # noqa: E402
     FlashInferPagedAttentionConfig,
     FlashInferQwen3PagedAttentionOp,
@@ -46,7 +46,6 @@
     _materialize_strict_logical_kv,
     build_flashinfer_paged_kv_plan,
 )
-from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RoPESM90Op  # noqa: E402
 from rl_engine.testing.attention_comparison import (  # noqa: E402
     AttentionPathResult,
     DecodeAttentionInputs,
@@ -398,10 +397,10 @@ def _run_strict_cuda_reference(
     config: FlashInferPagedAttentionConfig,
     paged_plan: Any,
 ) -> AttentionPathResult:
-    """Call the shared CUDA core directly on the logical KV sequence."""
+    """Call the platform production core directly on the logical KV sequence."""
 
-    core = config.deterministic_core or RLKernelDeterministicAttentionCore(split_kv=config.split_kv)
-    rope = config.strict_rope_op or RoPESM90Op()
+    core = config.deterministic_core or _strict_attention_core(config.split_kv)
+    rope = config.strict_rope_op or _strict_rope_op()
     logical_k, logical_v, key_positions = _materialize_strict_logical_kv(
         inputs.k_cache,
         inputs.v_cache,
@@ -431,12 +430,12 @@ def _run_strict_cuda_reference(
         outputs.append(result.out)
         lses.append(result.lse)
     return AttentionPathResult(
-        name="direct_rlkernel_cuda_deterministic_attention",
+        name="direct_platform_strict_attention",
         out=torch.cat(outputs, dim=0),
         lse=torch.cat(lses, dim=0),
         provenance={
-            "strict_core_id": STRICT_ATTENTION_CORE_ID,
-            "strict_schedule": STRICT_ATTENTION_SCHEDULE_ID,
+            "strict_core_id": core.core_id,
+            "strict_schedule": core.strict_schedule,
             "split_kv_policy": "disabled",
         },
     )
@@ -647,14 +646,41 @@ def _acceptance_errors(report: dict[str, Any], args: argparse.Namespace) -> list
     if provenance.get("fallback") is not False:
         errors.append("FlashInfer execution used or omitted fallback provenance")
     if args.strict:
+        is_rocm = torch.version.hip is not None
+        expected_core = (
+            STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID
+            if is_rocm
+            else STRICT_ATTENTION_PRODUCTION_CORE_ID
+        )
+        expected_schedule = (
+            STRICT_ATTENTION_ROCM_SCHEDULE_ID if is_rocm else STRICT_ATTENTION_FA4_SCHEDULE_ID
+        )
+        expected_backend = "aiter.rocm.ck_dense_mha" if is_rocm else "flash_attention_4.cute"
         if provenance.get("strict_mode") is not True:
             errors.append("strict runtime did not execute the shared Attention core")
-        if provenance.get("strict_core_id") != STRICT_ATTENTION_CORE_ID:
+        if provenance.get("strict_core_id") != expected_core:
             errors.append("strict runtime core identity is invalid")
-        if provenance.get("strict_schedule") != STRICT_ATTENTION_SCHEDULE_ID:
+        if provenance.get("strict_schedule") != expected_schedule:
             errors.append("strict runtime arithmetic schedule is invalid")
-        if provenance.get("native_attention_arithmetic") is not False:
-            errors.append("strict runtime entered native FlashInfer Attention arithmetic")
+        if provenance.get("actual_backend") != expected_backend:
+            errors.append("strict runtime backend identity is invalid")
+        if provenance.get("native_attention_arithmetic") is not True:
+            errors.append("strict runtime did not execute the native production arithmetic")
+        if provenance.get("num_splits") != 1:
+            errors.append("strict runtime did not prove one reduction partition")
+        if provenance.get("deterministic_backward") is not True:
+            errors.append("strict runtime did not request deterministic backward")
+        if provenance.get("reference_only") is not False:
+            errors.append("strict runtime selected the reference core")
+        if is_rocm:
+            if provenance.get("split_kv_control") != "dense_non_split_api":
+                errors.append("strict ROCm runtime did not use AITER dense non-Split-K MHA")
+            if provenance.get("aiter_api_source") != "aiter.ops.mha":
+                errors.append("strict ROCm runtime did not prove the AITER API source")
+            if not provenance.get("aiter_source_sha256"):
+                errors.append("strict ROCm runtime did not fingerprint AITER MHA")
+        elif provenance.get("fa_api_source") != "flash_attn.cute.interface":
+            errors.append("strict CUDA runtime did not prove the FA4 CuTe API source")
         strict_plans = provenance.get("strict_core_row_plans")
         if not isinstance(strict_plans, list) or not strict_plans:
             errors.append("strict no-Split-K execution plans are missing")
@@ -685,6 +711,20 @@ def _acceptance_errors(report: dict[str, Any], args: argparse.Namespace) -> list
     return errors
 
 
+def _strict_attention_core(split_kv):
+    if torch.version.hip is not None:
+        from rl_engine.kernels.ops.rocm.attention.flash_attn import StrictRocmAiterCKAttentionCore
+
+        return StrictRocmAiterCKAttentionCore(split_kv=split_kv)
+    return StrictFlashAttention4Core(split_kv=split_kv)
+
+
+def _strict_rope_op():
+    from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RocmDeterministicRoPEOp, RoPESM90Op
+
+    return RocmDeterministicRoPEOp() if torch.version.hip is not None else RoPESM90Op()
+
+
 def _select_batch_row(inputs: DecodeAttentionInputs, batch_index: int) -> DecodeAttentionInputs:
     metadata = inputs.metadata
     cp_block_owners = (
diff --git a/tests/test_attention_correctness.py b/tests/test_attention_correctness.py
index d68ad3cb..a77e195c 100644
--- a/tests/test_attention_correctness.py
+++ b/tests/test_attention_correctness.py
@@ -8,6 +8,15 @@
 import torch
 import torch.nn.functional as F
 
+from rl_engine.kernels.attention_contract import (
+    STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
+    STRICT_ATTENTION_ROCM_SCHEDULE_ID,
+)
+from rl_engine.kernels.ops.rocm.attention.flash_attn import (
+    StrictRocmAiterCKAttentionCore,
+    StrictRocmAttentionUnavailable,
+)
+
 try:
     from torch.nn.attention import SDPBackend, sdpa_kernel
 except ImportError:
@@ -440,3 +449,143 @@ def test_native_attention_rejects_invalid_gqa_head_ratio():
 
     with pytest.raises(ValueError, match="q heads must be divisible"):
         NativeAttentionOp()(q, k, v)
+
+
+def test_strict_rocm_aiter_ck_core_fixes_forward_and_backward_contract(monkeypatch):
+    calls = []
+
+    def fake_fwd(
+        q,
+        k,
+        v,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_left,
+        window_right,
+        sink_size,
+        return_lse,
+        return_dropout_mask,
+    ):
+        calls.append(
+            (
+                "forward",
+                dropout_p,
+                softmax_scale,
+                causal,
+                window_left,
+                window_right,
+                sink_size,
+                return_lse,
+                return_dropout_mask,
+            )
+        )
+        return (
+            q.clone(),
+            torch.zeros(q.size(0), q.size(2), q.size(1), dtype=torch.float32),
+            torch.empty(0),
+            torch.zeros(2, dtype=torch.int64),
+        )
+
+    def fake_bwd(
+        dout,
+        q,
+        k,
+        v,
+        out,
+        lse,
+        dropout_p,
+        softmax_scale,
+        causal,
+        window_left,
+        window_right,
+        deterministic,
+        **kwargs,
+    ):
+        calls.append(
+            (
+                "backward",
+                dropout_p,
+                softmax_scale,
+                causal,
+                window_left,
+                window_right,
+                deterministic,
+                kwargs["rng_state"].shape,
+            )
+        )
+        return torch.ones_like(q), torch.ones_like(k), torch.ones_like(v), torch.empty(0)
+
+    core = StrictRocmAiterCKAttentionCore(
+        _mha_fwd=fake_fwd,
+        _mha_bwd=fake_bwd,
+        _source_sha256="a" * 64,
+    )
+    monkeypatch.setattr(core, "_validate_inputs", lambda *_args: None)
+    monkeypatch.setattr(
+        torch.cuda,
+        "get_device_properties",
+        lambda _device: type("Props", (), {"name": "test-gpu", "gcnArchName": "gfx-test"})(),
+    )
+    q = torch.randn(1, 4, 2, 8, dtype=torch.bfloat16, requires_grad=True)
+    k = torch.randn(1, 2, 3, 8, dtype=torch.bfloat16, requires_grad=True)
+    v = torch.randn(1, 2, 3, 8, dtype=torch.bfloat16, requires_grad=True)
+    result = core.forward_with_lse(
+        q,
+        k,
+        v,
+        causal=True,
+        scale=0.125,
+        query_position_ids=torch.tensor([[1, 2]]),
+        key_position_ids=torch.tensor([[0, 1, 2]]),
+    )
+    result.out.float().sum().backward()
+
+    assert calls == [
+        ("forward", 0.0, 0.125, True, -1, -1, 0, True, False),
+        ("backward", 0.0, 0.125, True, -1, -1, True, torch.Size([2])),
+    ]
+    assert result.out.shape == q.shape
+    assert result.lse.shape == q.shape[:3]
+    assert result.lse.dtype is torch.float32
+    assert result.provenance["strict_core_id"] == STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID
+    assert result.provenance["strict_schedule"] == STRICT_ATTENTION_ROCM_SCHEDULE_ID
+    assert result.provenance["attention_backend"] == "aiter.rocm.ck_dense_mha"
+    assert result.provenance["split_kv_control"] == "dense_non_split_api"
+    assert result.provenance["num_splits"] == 1
+    assert result.provenance["deterministic_backward"] is True
+    assert result.provenance["aiter_source_sha256"] == "a" * 64
+    assert q.grad is not None and k.grad is not None and v.grad is not None
+
+
+def test_strict_rocm_aiter_ck_core_rejects_non_fp32_lse(monkeypatch):
+    def fake_fwd(q, k, v, *_args):
+        return (
+            q,
+            torch.zeros(q.size(0), q.size(2), q.size(1), dtype=q.dtype),
+            torch.empty(0),
+            torch.empty(2),
+        )
+
+    core = StrictRocmAiterCKAttentionCore(
+        _mha_fwd=fake_fwd,
+        _mha_bwd=lambda *_args, **_kwargs: None,
+    )
+    monkeypatch.setattr(core, "_validate_inputs", lambda *_args: None)
+    monkeypatch.setattr(
+        torch.cuda,
+        "get_device_properties",
+        lambda _device: type("Props", (), {"name": "test-gpu", "gcnArchName": "gfx-test"})(),
+    )
+    q = torch.randn(1, 4, 1, 8, dtype=torch.bfloat16)
+    k = torch.randn(1, 2, 1, 8, dtype=torch.bfloat16)
+
+    with pytest.raises(StrictRocmAttentionUnavailable, match="FP32 LSE"):
+        core.forward_with_lse(
+            q,
+            k,
+            k,
+            causal=True,
+            query_position_ids=torch.tensor([[0]]),
+            key_position_ids=torch.tensor([[0]]),
+        )
diff --git a/tests/test_flashinfer_pr7_attention.py b/tests/test_flashinfer_pr7_attention.py
index 7d43bd9b..22b67a35 100644
--- a/tests/test_flashinfer_pr7_attention.py
+++ b/tests/test_flashinfer_pr7_attention.py
@@ -15,7 +15,11 @@
 
 from rl_engine.kernels.attention_contract import (
     STRICT_ATTENTION_CORE_ID,
+    STRICT_ATTENTION_FA4_SCHEDULE_ID,
+    STRICT_ATTENTION_PRODUCTION_CORE_ID,
     STRICT_ATTENTION_RING_SCHEDULE_ID,
+    STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
+    STRICT_ATTENTION_ROCM_SCHEDULE_ID,
     STRICT_ATTENTION_SCHEDULE_ID,
     SplitKVSpec,
 )
@@ -1725,7 +1729,7 @@ def test_pr7_check_acceptance_errors_require_all_drift_and_invariance_fields():
     assert check_script._acceptance_errors(report, args) == ["batch_invariant_sweep failed"]
 
 
-def test_pr7_check_accepts_strict_shared_core_without_native_flashinfer_arithmetic():
+def test_pr7_check_accepts_strict_cuda_production_core():
     args = check_script._parse_args(["--strict", "--device", "cuda"])
     report = {
         "device": "cuda:0",
@@ -1734,9 +1738,14 @@ def test_pr7_check_accepts_strict_shared_core_without_native_flashinfer_arithmet
             "attention_mode": "decode",
             "fallback": False,
             "strict_mode": True,
-            "strict_core_id": STRICT_ATTENTION_CORE_ID,
-            "strict_schedule": STRICT_ATTENTION_SCHEDULE_ID,
-            "native_attention_arithmetic": False,
+            "strict_core_id": STRICT_ATTENTION_PRODUCTION_CORE_ID,
+            "strict_schedule": STRICT_ATTENTION_FA4_SCHEDULE_ID,
+            "actual_backend": "flash_attention_4.cute",
+            "native_attention_arithmetic": True,
+            "num_splits": 1,
+            "deterministic_backward": True,
+            "fa_api_source": "flash_attn.cute.interface",
+            "reference_only": False,
             "strict_core_row_plans": [{"actual_split_kv_policy": "disabled"}],
             "rope_backend": "rlkernel.cuda.rope_sm90",
             "rope_theta": 1_000_000.0,
@@ -1816,17 +1825,21 @@ def test_strict_shared_core_entrypoint_requires_self_owned_ag_rs():
 
 def _strict_acceptance_provenance(**overrides):
     provenance = {
-        "strict_core_id": STRICT_ATTENTION_CORE_ID,
-        "strict_schedule": STRICT_ATTENTION_SCHEDULE_ID,
-        "attention_backend": "rlkernel.cuda.deterministic_attention",
-        "actual_backend": "rlkernel.cuda.deterministic_attention",
+        "strict_core_id": STRICT_ATTENTION_PRODUCTION_CORE_ID,
+        "strict_schedule": STRICT_ATTENTION_FA4_SCHEDULE_ID,
+        "attention_backend": "flash_attention_4.cute",
+        "actual_backend": "flash_attention_4.cute",
         "rope_backend": "rlkernel.cuda.rope_sm90",
         "strict_mode": True,
-        "native_attention_arithmetic": False,
+        "native_attention_arithmetic": True,
+        "num_splits": 1,
+        "deterministic_backward": True,
+        "reference_only": False,
+        "fa_api_source": "flash_attn.cute.interface",
         "fallback": False,
         "strict_split_kv": "disabled",
         "strict_comm_autograd": True,
-        "communication_backend": "cuda_ag_rs",
+        "communication_backend": "self_owned_cuda_ag_rs",
         "production_ready": True,
         "strict_full_qkv_all_gather": True,
         "strict_position_ids_all_gather": True,
@@ -1867,10 +1880,15 @@ def test_strict_shared_core_acceptance_rejects_provenance_drift(field, invalid):
 
 def test_strict_shared_core_acceptance_requires_rocm_backend_and_rope():
     provenance = _strict_acceptance_provenance(
-        attention_backend="rlkernel.rocm.deterministic_attention",
-        actual_backend="rlkernel.rocm.deterministic_attention",
+        strict_core_id=STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
+        strict_schedule=STRICT_ATTENTION_ROCM_SCHEDULE_ID,
+        attention_backend="aiter.rocm.ck_dense_mha",
+        actual_backend="aiter.rocm.ck_dense_mha",
         rope_backend="rlkernel.rocm.deterministic_rope",
         communication_backend="rccl_ag_rs",
+        split_kv_control="dense_non_split_api",
+        aiter_api_source="aiter.ops.mha",
+        aiter_source_sha256="a" * 64,
     )
 
     assert not p2p_check_script._strict_shared_core_identity_errors(

From 4418d18df67f55a5014ce82317ddebf39aa3ca49 Mon Sep 17 00:00:00 2001
From: lamentropetion <3051000145@qq.com>
Date: Thu, 20 Aug 2026 21:41:29 +0800
Subject: [PATCH 09/35] test(attention): accept strict ROCm RoPE provenance

---
 scripts/ws2_pr7_flashinfer_attention_check.py | 13 +++++--
 tests/test_flashinfer_pr7_attention.py        | 38 +++++++++++++++++++
 2 files changed, 47 insertions(+), 4 deletions(-)

diff --git a/scripts/ws2_pr7_flashinfer_attention_check.py b/scripts/ws2_pr7_flashinfer_attention_check.py
index cea6ca51..f9e1d7de 100644
--- a/scripts/ws2_pr7_flashinfer_attention_check.py
+++ b/scripts/ws2_pr7_flashinfer_attention_check.py
@@ -686,10 +686,15 @@ def _acceptance_errors(report: dict[str, Any], args: argparse.Namespace) -> list
             errors.append("strict no-Split-K execution plans are missing")
         elif any(plan.get("actual_split_kv_policy") != "disabled" for plan in strict_plans):
             errors.append("strict runtime did not keep Split-KV disabled")
-        if provenance.get("rope_backend") not in {
-            "rlkernel.cuda.rope_sm90",
-            "rlkernel.cuda.rope_sm90_op",
-        }:
+        expected_rope_backends = (
+            {"rlkernel.rocm.deterministic_rope"}
+            if is_rocm
+            else {
+                "rlkernel.cuda.rope_sm90",
+                "rlkernel.cuda.rope_sm90_op",
+            }
+        )
+        if provenance.get("rope_backend") not in expected_rope_backends:
             errors.append("strict runtime did not use the RL-Kernel WS1 RoPE operator")
     elif provenance.get("pos_encoding_mode") != "ROPE_LLAMA":
         errors.append("FlashInfer runtime did not use ROPE_LLAMA")
diff --git a/tests/test_flashinfer_pr7_attention.py b/tests/test_flashinfer_pr7_attention.py
index 22b67a35..e11792bb 100644
--- a/tests/test_flashinfer_pr7_attention.py
+++ b/tests/test_flashinfer_pr7_attention.py
@@ -1764,6 +1764,44 @@ def test_pr7_check_accepts_strict_cuda_production_core():
     assert check_script._acceptance_errors(report, args) == []
 
 
+def test_pr7_check_accepts_strict_rocm_production_core(monkeypatch):
+    monkeypatch.setattr(torch.version, "hip", "6.3.0")
+    args = check_script._parse_args(["--strict", "--device", "cuda"])
+    report = {
+        "device": "cuda:0",
+        "shape": {"q_heads": 16, "kv_heads": 4, "head_dim": 128},
+        "candidate_provenance": {
+            "attention_mode": "decode",
+            "fallback": False,
+            "strict_mode": True,
+            "strict_core_id": STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
+            "strict_schedule": STRICT_ATTENTION_ROCM_SCHEDULE_ID,
+            "actual_backend": "aiter.rocm.ck_dense_mha",
+            "native_attention_arithmetic": True,
+            "num_splits": 1,
+            "deterministic_backward": True,
+            "reference_only": False,
+            "split_kv_control": "dense_non_split_api",
+            "aiter_api_source": "aiter.ops.mha",
+            "aiter_source_sha256": "a" * 64,
+            "strict_core_row_plans": [{"actual_split_kv_policy": "disabled"}],
+            "rope_backend": "rlkernel.rocm.deterministic_rope",
+            "rope_theta": 1_000_000.0,
+            "rotary_dim": 128,
+            "arithmetic_semantics_verified": True,
+        },
+        "drift": {
+            "out": {"max_abs": 0.0},
+            "lse": {"max_abs": 0.0},
+            "dlogp": {"max_abs": 0.0},
+        },
+        "batch_invariant_sweep": {"passed": True},
+        "page_layout_invariant_sweep": {"passed": True},
+    }
+
+    assert check_script._acceptance_errors(report, args) == []
+
+
 def test_pr7_check_rejects_nonfinite_drift_and_wrong_tp_local_shape():
     args = check_script._parse_args([])
     report = {

From fe398240699fd230289c40588c1f5c10275cb049 Mon Sep 17 00:00:00 2001
From: lamentropetion <3051000145@qq.com>
Date: Thu, 20 Aug 2026 22:18:56 +0800
Subject: [PATCH 10/35] test(attention): validate reported platform provenance

---
 scripts/ws2_pr7_flashinfer_attention_check.py | 5 ++++-
 tests/test_flashinfer_pr7_attention.py        | 4 ++--
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/scripts/ws2_pr7_flashinfer_attention_check.py b/scripts/ws2_pr7_flashinfer_attention_check.py
index f9e1d7de..b9a42e90 100644
--- a/scripts/ws2_pr7_flashinfer_attention_check.py
+++ b/scripts/ws2_pr7_flashinfer_attention_check.py
@@ -646,7 +646,10 @@ def _acceptance_errors(report: dict[str, Any], args: argparse.Namespace) -> list
     if provenance.get("fallback") is not False:
         errors.append("FlashInfer execution used or omitted fallback provenance")
     if args.strict:
-        is_rocm = torch.version.hip is not None
+        platform = provenance.get("platform", "cuda")
+        if platform not in {"cuda", "rocm"}:
+            errors.append("strict runtime platform provenance is invalid")
+        is_rocm = platform == "rocm"
         expected_core = (
             STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID
             if is_rocm
diff --git a/tests/test_flashinfer_pr7_attention.py b/tests/test_flashinfer_pr7_attention.py
index e11792bb..a18464ed 100644
--- a/tests/test_flashinfer_pr7_attention.py
+++ b/tests/test_flashinfer_pr7_attention.py
@@ -1764,8 +1764,7 @@ def test_pr7_check_accepts_strict_cuda_production_core():
     assert check_script._acceptance_errors(report, args) == []
 
 
-def test_pr7_check_accepts_strict_rocm_production_core(monkeypatch):
-    monkeypatch.setattr(torch.version, "hip", "6.3.0")
+def test_pr7_check_accepts_strict_rocm_production_core():
     args = check_script._parse_args(["--strict", "--device", "cuda"])
     report = {
         "device": "cuda:0",
@@ -1777,6 +1776,7 @@ def test_pr7_check_accepts_strict_rocm_production_core(monkeypatch):
             "strict_core_id": STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
             "strict_schedule": STRICT_ATTENTION_ROCM_SCHEDULE_ID,
             "actual_backend": "aiter.rocm.ck_dense_mha",
+            "platform": "rocm",
             "native_attention_arithmetic": True,
             "num_splits": 1,
             "deterministic_backward": True,

From f6ff0940ec9f5d18156a1014e4b2f2f93b38d635 Mon Sep 17 00:00:00 2001
From: lamentropetion <3051000145@qq.com>
Date: Thu, 20 Aug 2026 22:31:44 +0800
Subject: [PATCH 11/35] test(attention): run strict references by logical row

---
 .../ws2_p2p_nccl_attention_reference_check.py | 35 ++++++++++++++++---
 tests/test_flashinfer_pr7_attention.py        | 35 +++++++++++++++++++
 2 files changed, 66 insertions(+), 4 deletions(-)

diff --git a/scripts/ws2_p2p_nccl_attention_reference_check.py b/scripts/ws2_p2p_nccl_attention_reference_check.py
index abceb610..e04c4c06 100644
--- a/scripts/ws2_p2p_nccl_attention_reference_check.py
+++ b/scripts/ws2_p2p_nccl_attention_reference_check.py
@@ -510,13 +510,11 @@ def _run_strict_shared_core_check(
     rope = _strict_rope_op()
     q_ready = _apply_strict_rope(rope, q_ref, positions, config.rope.rope_theta)
     k_ready = _apply_strict_rope(rope, k_ref, positions, config.rope.rope_theta)
-    reference = _strict_attention_core().forward_with_lse(
+    reference = _strict_attention_reference_rows(
         q_ready,
         k_ready,
         v_ref,
-        causal=True,
-        query_position_ids=positions,
-        key_position_ids=positions,
+        positions=positions,
         output_dtype=q.dtype,
     )
     out_ref = reference.out[:, :, start:end, :]
@@ -663,6 +661,35 @@ def _strict_attention_core():
     return StrictFlashAttention4Core()
 
 
+def _strict_attention_reference_rows(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    positions: torch.Tensor,
+    output_dtype: torch.dtype,
+) -> SimpleNamespace:
+    """Run the production core with its one-logical-row execution contract."""
+
+    core = _strict_attention_core()
+    rows = [
+        core.forward_with_lse(
+            q[index : index + 1],
+            k[index : index + 1],
+            v[index : index + 1],
+            causal=True,
+            query_position_ids=positions[index : index + 1],
+            key_position_ids=positions[index : index + 1],
+            output_dtype=output_dtype,
+        )
+        for index in range(q.size(0))
+    ]
+    return SimpleNamespace(
+        out=torch.cat([row.out for row in rows], dim=0),
+        lse=torch.cat([row.lse for row in rows], dim=0),
+    )
+
+
 def _strict_rope_op():
     from rl_engine.kernels.ops.cuda.rotary_embedding.rope import RocmDeterministicRoPEOp, RoPESM90Op
 
diff --git a/tests/test_flashinfer_pr7_attention.py b/tests/test_flashinfer_pr7_attention.py
index a18464ed..8bbbdf2a 100644
--- a/tests/test_flashinfer_pr7_attention.py
+++ b/tests/test_flashinfer_pr7_attention.py
@@ -1861,6 +1861,41 @@ def test_strict_shared_core_entrypoint_requires_self_owned_ag_rs():
     assert args.strict_shared_core is True
 
 
+def test_strict_shared_core_reference_executes_one_logical_row(monkeypatch):
+    calls = []
+
+    class _RowCore:
+        def forward_with_lse(self, q, k, v, **kwargs):
+            calls.append((q.shape[0], kwargs["query_position_ids"].clone()))
+            return types.SimpleNamespace(
+                out=q + k + v,
+                lse=(q + k).sum(dim=-1),
+            )
+
+    monkeypatch.setattr(p2p_check_script, "_strict_attention_core", _RowCore)
+    q = torch.randn(2, 1, 3, 4, requires_grad=True)
+    k = torch.randn(2, 1, 3, 4, requires_grad=True)
+    v = torch.randn(2, 1, 3, 4, requires_grad=True)
+    positions = torch.arange(3).expand(2, -1)
+
+    result = p2p_check_script._strict_attention_reference_rows(
+        q,
+        k,
+        v,
+        positions=positions,
+        output_dtype=q.dtype,
+    )
+
+    assert [batch for batch, _positions in calls] == [1, 1]
+    assert [item.tolist() for _batch, item in calls] == [[[0, 1, 2]], [[0, 1, 2]]]
+    assert torch.equal(result.out, q + k + v)
+    assert torch.equal(result.lse, (q + k).sum(dim=-1))
+    (result.out.sum() + result.lse.sum()).backward()
+    assert q.grad is not None
+    assert k.grad is not None
+    assert v.grad is not None
+
+
 def _strict_acceptance_provenance(**overrides):
     provenance = {
         "strict_core_id": STRICT_ATTENTION_PRODUCTION_CORE_ID,

From 9ff0e6dc5a82d348d755f155c5ccc7790840343b Mon Sep 17 00:00:00 2001
From: lamentropetion <3051000145@qq.com>
Date: Thu, 20 Aug 2026 22:45:42 +0800
Subject: [PATCH 12/35] test(attention): report executed vendor backend

---
 scripts/ws2_pr7_flashinfer_attention_check.py | 48 +++++++++++++------
 tests/test_flashinfer_pr7_attention.py        | 34 +++++++++++++
 2 files changed, 67 insertions(+), 15 deletions(-)

diff --git a/scripts/ws2_pr7_flashinfer_attention_check.py b/scripts/ws2_pr7_flashinfer_attention_check.py
index b9a42e90..7a11863c 100644
--- a/scripts/ws2_pr7_flashinfer_attention_check.py
+++ b/scripts/ws2_pr7_flashinfer_attention_check.py
@@ -1,12 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-"""PR7 FlashInfer RoPE-fused paged attention validation entry point.
+"""PR7 strict paged-layout Attention validation entry point.
 
-The default dry-run mode is CI/local friendly: it builds the FlashInfer page plan
-and provenance without importing FlashInfer or requiring CUDA.  On a CUDA host
-with FlashInfer installed, omit ``--dry-run`` to run the opt-in PR7 candidate and
-compare it with the PR6 full logical KV reference.
+The default dry-run mode is CI/local friendly: it builds the paged-KV plan and
+provenance without requiring a GPU vendor backend. On a GPU host, omit
+``--dry-run`` to run the platform production core and its strict reference.
 """
 
 from __future__ import annotations
@@ -70,7 +69,7 @@ def main(argv: Sequence[str] | None = None) -> int:
     report: dict[str, Any] = {
         "status": "dry_run" if args.dry_run else "executed",
         "pr": "PR7",
-        "target": "Qwen3-8B TP-local FlashInfer candidate; CP transport validated separately",
+        "target": "Qwen3-8B TP-local paged Attention; CP transport validated separately",
         "mode": config.mode,
         "device": str(device),
         "shape": {
@@ -99,11 +98,11 @@ def main(argv: Sequence[str] | None = None) -> int:
         | {"cp_comm_required": config.require_cp_comm},
         "paged_kv_plan": plan.provenance(),
         "tests_expected": [
-            "FlashInfer ROPE_LLAMA vs NativeRoPEOp + full logical KV reference",
+            "platform production core vs direct same-core reference",
             "split-K disabled/fixed policy drift",
             "batch composition/position invariant sweep",
             "attention-domain LSE export drift",
-            "strict shared CUDA core with separate multi-rank AG/RS forward/backward evidence",
+            "strict platform core with separate multi-rank AG/RS forward/backward evidence",
         ],
         "thresholds": {
             "out_max_abs": args.out_atol,
@@ -153,7 +152,7 @@ def main(argv: Sequence[str] | None = None) -> int:
     )
     pytorch_reference = run_decode_full_prefill_reference(reference_inputs)
     reference = (
-        _run_strict_cuda_reference(inputs, config, plan)
+        _run_strict_platform_reference(inputs, config, plan)
         if config.strict_mode
         else pytorch_reference
     )
@@ -181,12 +180,9 @@ def main(argv: Sequence[str] | None = None) -> int:
         "lse": lse_stats,
         "dlogp": dlogp_stats,
     }
-    report["reference_backend"] = (
-        "rlkernel.cuda.deterministic_attention"
-        if config.strict_mode
-        else "rlkernel.pytorch.full_logical_kv_reference"
-    )
+    report["reference_backend"] = "rlkernel.pytorch.full_logical_kv_reference"
     if config.strict_mode:
+        report.update(_strict_execution_report_fields(candidate.provenance))
         report["diagnostic_drift_vs_pytorch"] = {
             "out": _drift_stats(candidate.out, pytorch_reference.out),
             "lse": _drift_stats(candidate.lse, pytorch_reference.lse),
@@ -392,7 +388,7 @@ def _make_inputs(args: argparse.Namespace, device: torch.device) -> DecodeAttent
     return DecodeAttentionInputs(q=q, k_cache=k_cache, v_cache=v_cache, metadata=metadata)
 
 
-def _run_strict_cuda_reference(
+def _run_strict_platform_reference(
     inputs: DecodeAttentionInputs,
     config: FlashInferPagedAttentionConfig,
     paged_plan: Any,
@@ -441,6 +437,28 @@ def _run_strict_cuda_reference(
     )
 
 
+def _strict_execution_report_fields(provenance: dict[str, Any]) -> dict[str, Any]:
+    """Describe the backend that actually executed, independent of the host running tests."""
+
+    backend = provenance.get("actual_backend", "unknown")
+    return {
+        "target": (
+            f"Qwen3-8B TP-local {backend} strict production core; "
+            "CP transport validated separately"
+        ),
+        "reference_backend": backend,
+        "rope": {
+            "rope_backend": provenance.get("rope_backend"),
+            "rope_fusion": provenance.get("rope_fusion"),
+            "rope_fusion_boundary": provenance.get("rope_fusion_boundary"),
+            "rope_theta": provenance.get("rope_theta"),
+            "rotary_dim": provenance.get("rotary_dim"),
+            "q_rope_state": provenance.get("q_rope_state"),
+            "k_cache_rope_state": provenance.get("k_cache_rope_state"),
+        },
+    }
+
+
 def _run_batch_invariance_sweep(
     op: FlashInferQwen3PagedAttentionOp,
     inputs: DecodeAttentionInputs,
diff --git a/tests/test_flashinfer_pr7_attention.py b/tests/test_flashinfer_pr7_attention.py
index 8bbbdf2a..40ffd3a7 100644
--- a/tests/test_flashinfer_pr7_attention.py
+++ b/tests/test_flashinfer_pr7_attention.py
@@ -1802,6 +1802,40 @@ def test_pr7_check_accepts_strict_rocm_production_core():
     assert check_script._acceptance_errors(report, args) == []
 
 
+@pytest.mark.parametrize(
+    ("backend", "rope_backend"),
+    [
+        ("flash_attention_4.cute", "rlkernel.cuda.rope_sm90"),
+        ("aiter.rocm.ck_dense_mha", "rlkernel.rocm.deterministic_rope"),
+    ],
+)
+def test_strict_report_uses_executed_platform_provenance(backend, rope_backend):
+    fields = check_script._strict_execution_report_fields(
+        {
+            "actual_backend": backend,
+            "rope_backend": rope_backend,
+            "rope_fusion": False,
+            "rope_fusion_boundary": "rlkernel_rope_then_attention",
+            "rope_theta": 1_000_000.0,
+            "rotary_dim": 128,
+            "q_rope_state": "post_rope",
+            "k_cache_rope_state": "post_rope",
+        }
+    )
+
+    assert fields["reference_backend"] == backend
+    assert backend in fields["target"]
+    assert fields["rope"] == {
+        "rope_backend": rope_backend,
+        "rope_fusion": False,
+        "rope_fusion_boundary": "rlkernel_rope_then_attention",
+        "rope_theta": 1_000_000.0,
+        "rotary_dim": 128,
+        "q_rope_state": "post_rope",
+        "k_cache_rope_state": "post_rope",
+    }
+
+
 def test_pr7_check_rejects_nonfinite_drift_and_wrong_tp_local_shape():
     args = check_script._parse_args([])
     report = {

From 1bc4177e459e8e85c4887df2fcf25fc73c59a21a Mon Sep 17 00:00:00 2001
From: lamentropetion <3051000145@qq.com>
Date: Thu, 20 Aug 2026 22:55:52 +0800
Subject: [PATCH 13/35] fix(attention): narrow injected AITER callables

---
 rl_engine/kernels/ops/rocm/attention/flash_attn.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/rl_engine/kernels/ops/rocm/attention/flash_attn.py b/rl_engine/kernels/ops/rocm/attention/flash_attn.py
index fff003a2..0a9b2b36 100644
--- a/rl_engine/kernels/ops/rocm/attention/flash_attn.py
+++ b/rl_engine/kernels/ops/rocm/attention/flash_attn.py
@@ -165,7 +165,9 @@ def __init__(
         if _mha_fwd is None:
             mha_fwd, mha_bwd, source_sha256 = _load_aiter_ck_ops()
         else:
-            mha_fwd, mha_bwd = _mha_fwd, _mha_bwd
+            assert _mha_bwd is not None
+            mha_fwd = _mha_fwd
+            mha_bwd = _mha_bwd
             source_sha256 = "test-double" if _source_sha256 is None else _source_sha256
         if not callable(mha_fwd) or not callable(mha_bwd):
             raise StrictRocmAttentionUnavailable("AITER CK MHA entry points are not callable")

From 3247e3ef390ac2f55bf93864ffa70b0a1350ec4c Mon Sep 17 00:00:00 2001
From: zhangj1an <jianmusings@gmail.com>
Date: Mon, 24 Aug 2026 13:36:49 +0000
Subject: [PATCH 14/35] feat(attention): route strict ROCm attention to Vime
 via contract dispatch

Adds the dispatch seam and Vime adapter that make the strict ROCm attention
core reachable as an explicit, fail-closed backend, mirroring the WS2
logprob provider in #328.

Registry:
- register_attention_backend / get_attention_op over AttentionContract, with
  a candidate list kept separate from the legacy attn/attention priority maps
  and empty on every platform by default. A WS2 caller can therefore never be
  served by an SDPA-shaped wrapper that does not export attention-domain LSE,
  and the strict core can never be selected by a legacy get_op caller.
- aiter.rocm.ck_dense_mha is registered only when aiter.ops.mha genuinely
  imports and exposes mha_fwd/mha_bwd. An explicit request for it fails
  loudly when the vendor stack is absent instead of degrading to a different
  backend.

Vime adapter (rl_engine/integrations/vime/attention.py):
- Structural request in, (out, lse) plus provenance out; RL-Kernel never
  imports Vime, and native fallback is signalled through an
  attention_provider_unavailable marker rather than an imported type.
- Each logical batch row is materialized on its own. Raw AITER mha_fwd is
  batch-composition sensitive in BF16 at some shapes (S=256 B=4, S=512
  B=2/B=4; up to 1.5625e-02) while invariant at others, so batching would
  otherwise change the bits for a subset of shapes only.
- Position identity is derived and validated, so a training-side full
  sequence and a rollout-side chunk provably describe the same tokens.
- CP>1 and decode fail closed rather than being served by a core that does
  not own the cross-rank merge or KV-cache identity.

Contract:
- AttentionContract.cross_rank_fingerprint() for cross-rank preflight,
  mirroring LogprobContract; auto dispatch stays rejected under CP>1.

Benchmarks (MI300X, gfx942, torch 2.12.0+rocm7.14, BF16, Qwen3-8B heads):
forward is within 4% of SDPA and ~1.9x faster than the Triton FlashAttention
backend at S=4096. The deterministic backward costs ~2.4x time and scales
O(S^2) in memory (16.8 GiB at S=4096); that cost is AITER's mha_bwd, not the
integration - raw AITER peaks at 16657 MiB against the provider's 16722 MiB.

Tests: 38 new cases; the full suite is unchanged against the pre-change
baseline (27 failed / 224 skipped / 607 errors both before and after, all
from the unbuilt native extension), with passed rising 1068 -> 1102.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QfN3b2ep5DvxVDir7J36j3
---
 benchmarks/benchmark_rocm_attention.py        | 386 ++++++++++++++++
 .../results/pr319_rocm_mi300x/report.md       |  80 ++++
 .../results/pr319_rocm_mi300x/results.json    | 373 +++++++++++++++
 rl_engine/integrations/__init__.py            |   4 +
 rl_engine/integrations/vime/__init__.py       |  12 +
 rl_engine/integrations/vime/attention.py      | 428 ++++++++++++++++++
 rl_engine/kernels/attention_contract.py       |  37 ++
 .../kernels/ops/rocm/attention/flash_attn.py  |   7 +-
 rl_engine/kernels/registry.py                 | 254 +++++++++++
 tests/test_attention_dispatch.py              | 307 +++++++++++++
 tests/test_vime_attention_provider.py         | 272 +++++++++++
 11 files changed, 2159 insertions(+), 1 deletion(-)
 create mode 100644 benchmarks/benchmark_rocm_attention.py
 create mode 100644 benchmarks/results/pr319_rocm_mi300x/report.md
 create mode 100644 benchmarks/results/pr319_rocm_mi300x/results.json
 create mode 100644 rl_engine/integrations/__init__.py
 create mode 100644 rl_engine/integrations/vime/__init__.py
 create mode 100644 rl_engine/integrations/vime/attention.py
 create mode 100644 tests/test_attention_dispatch.py
 create mode 100644 tests/test_vime_attention_provider.py

diff --git a/benchmarks/benchmark_rocm_attention.py b/benchmarks/benchmark_rocm_attention.py
new file mode 100644
index 00000000..addaa553
--- /dev/null
+++ b/benchmarks/benchmark_rocm_attention.py
@@ -0,0 +1,386 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Operator-only benchmark for the strict ROCm attention path.
+
+Seeded Q/K/V only: no checkpoint, tokenizer, or serving engine.  Defaults to the
+Qwen3-8B dense head layout (``Hq=32``, ``Hkv=8``, ``D=128``), BF16, causal
+prefill.
+
+Three backends are compared:
+
+``native``
+    ``torch.nn.functional.scaled_dot_product_attention`` with the KV heads
+    expanded to the Q head count.  Not batch-invariant; present as the
+    throughput reference every ROCm deployment already has.
+``triton``
+    ``flash_attn`` with the ROCm Triton backend enabled.
+``strict``
+    ``aiter.rocm.ck_dense_mha`` through the WS2 contract dispatch, i.e. the path
+    this PR adds.
+
+Two extra sections quantify what the strict contract actually costs, because
+both are properties of the vendor backward rather than of the integration:
+
+* ``determinism_cost`` — AITER ``mha_bwd`` with ``deterministic`` on vs off.
+* ``batch_composition`` — whether raw AITER returns the same bits for a batch
+  and for the same rows submitted one at a time, swept over shapes because the
+  answer varies with shape.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import statistics
+from pathlib import Path
+from typing import Any, Callable
+
+import torch
+
+DEFAULT_Q_HEADS = 32
+DEFAULT_KV_HEADS = 8
+DEFAULT_HEAD_DIM = 128
+
+
+class _ContextParallel:
+    rank = 0
+    world_size = 1
+    layout = "single"
+
+
+class _Request:
+    """Structural request understood by the Vime attention provider."""
+
+    def __init__(self, query, key, value, metadata):
+        self.query = query
+        self.key = key
+        self.value = value
+        self.metadata = metadata
+        self.context_parallel = _ContextParallel()
+        self.tensor_parallel_group = None
+        self.key_padding_mask = None
+
+
+def _metadata(q_heads: int, kv_heads: int) -> dict[str, Any]:
+    return {
+        "global_q_heads": q_heads,
+        "global_kv_heads": kv_heads,
+        "tp_rank": 0,
+        "tp_world_size": 1,
+        "attention_mode": "prefill",
+        "role": "train",
+        "causal": True,
+    }
+
+
+def _tensors(batch, q_heads, kv_heads, seq_len, head_dim, dtype, seed=0):
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    query = torch.randn(
+        batch, q_heads, seq_len, head_dim, generator=generator, device="cuda", dtype=dtype
+    )
+    key = torch.randn(
+        batch, kv_heads, seq_len, head_dim, generator=generator, device="cuda", dtype=dtype
+    )
+    value = torch.randn(
+        batch, kv_heads, seq_len, head_dim, generator=generator, device="cuda", dtype=dtype
+    )
+    return query, key, value
+
+
+def _measure(run: Callable[[bool], None], *, backward: bool, warmup: int, iters: int):
+    torch.cuda.synchronize()
+    torch.cuda.empty_cache()
+    torch.cuda.reset_peak_memory_stats()
+    for _ in range(warmup):
+        run(backward)
+    torch.cuda.synchronize()
+    samples: list[float] = []
+    for _ in range(iters):
+        start = torch.cuda.Event(enable_timing=True)
+        end = torch.cuda.Event(enable_timing=True)
+        start.record()
+        run(backward)
+        end.record()
+        torch.cuda.synchronize()
+        samples.append(start.elapsed_time(end))
+    samples.sort()
+    return {
+        "median_ms": round(statistics.median(samples), 4),
+        "p95_ms": round(samples[int(0.95 * (len(samples) - 1))], 4),
+        "peak_mib": round(torch.cuda.max_memory_allocated() / 2**20, 1),
+    }
+
+
+def _native_runner(query, key, value, q_heads, kv_heads):
+    repeats = q_heads // kv_heads
+
+    def run(backward: bool) -> None:
+        q = query.detach().requires_grad_(backward)
+        k = key.detach().requires_grad_(backward)
+        v = value.detach().requires_grad_(backward)
+        out = torch.nn.functional.scaled_dot_product_attention(
+            q,
+            k.repeat_interleave(repeats, dim=1),
+            v.repeat_interleave(repeats, dim=1),
+            is_causal=True,
+        )
+        if backward:
+            out.backward(torch.ones_like(out))
+
+    return run
+
+
+def _triton_runner(query, key, value, head_dim):
+    import os
+
+    os.environ["FLASH_ATTENTION_TRITON_AMD_ENABLE"] = "TRUE"
+    from flash_attn import flash_attn_func
+
+    scale = 1.0 / math.sqrt(head_dim)
+
+    def run(backward: bool) -> None:
+        q = query.detach().transpose(1, 2).contiguous().requires_grad_(backward)
+        k = key.detach().transpose(1, 2).contiguous().requires_grad_(backward)
+        v = value.detach().transpose(1, 2).contiguous().requires_grad_(backward)
+        out = flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=scale, causal=True)
+        if backward:
+            out.backward(torch.ones_like(out))
+
+    return run
+
+
+def _strict_runner(query, key, value, metadata):
+    from rl_engine.integrations.vime import attention_provider
+
+    def run(backward: bool) -> None:
+        q = query.detach().requires_grad_(backward)
+        k = key.detach().requires_grad_(backward)
+        v = value.detach().requires_grad_(backward)
+        result = attention_provider(_Request(q, k, v, metadata))
+        if backward:
+            result.out.backward(torch.ones_like(result.out))
+
+    return run
+
+
+def _determinism_cost(seq_lens, q_heads, kv_heads, head_dim, dtype, warmup, iters):
+    """AITER deterministic backward vs the non-deterministic one."""
+
+    from aiter.ops.mha import mha_bwd, mha_fwd
+
+    scale = 1.0 / math.sqrt(head_dim)
+    rows = []
+    for seq_len in seq_lens:
+        query, key, value = _tensors(1, q_heads, kv_heads, seq_len, head_dim, dtype)
+        # AITER consumes [B, S, H, D].
+        q = query.transpose(1, 2).contiguous()
+        k = key.transpose(1, 2).contiguous()
+        v = value.transpose(1, 2).contiguous()
+        out, lse, _mask, rng_state = mha_fwd(q, k, v, 0.0, scale, True, -1, -1, 0, True, False)
+        grad_out = torch.ones_like(out)
+        entry: dict[str, Any] = {"seq_len": seq_len}
+        for deterministic in (True, False):
+
+            # Bind the tensors as defaults: they are released below, and the
+            # closure must not depend on the enclosing names still existing.
+            def run(
+                _backward: bool,
+                deterministic=deterministic,
+                grad_out=grad_out,
+                q=q,
+                k=k,
+                v=v,
+                out=out,
+                lse=lse,
+                rng_state=rng_state,
+            ) -> None:
+                mha_bwd(
+                    grad_out,
+                    q,
+                    k,
+                    v,
+                    out,
+                    lse,
+                    0.0,
+                    scale,
+                    True,
+                    -1,
+                    -1,
+                    deterministic,
+                    rng_state=rng_state,
+                )
+
+            key_name = "deterministic" if deterministic else "non_deterministic"
+            entry[key_name] = _measure(run, backward=False, warmup=warmup, iters=iters)
+        entry["time_ratio"] = round(
+            entry["deterministic"]["median_ms"] / entry["non_deterministic"]["median_ms"], 2
+        )
+        entry["memory_ratio"] = round(
+            entry["deterministic"]["peak_mib"] / entry["non_deterministic"]["peak_mib"], 1
+        )
+        rows.append(entry)
+        del query, key, value, q, k, v, out, lse, grad_out
+        torch.cuda.empty_cache()
+    return rows
+
+
+def _batch_composition(q_heads, kv_heads, head_dim, dtype, shapes):
+    """Whether raw AITER is batch-composition invariant, swept over shapes.
+
+    The strict core sidesteps this by executing one logical row at a time; this
+    section records what the vendor kernel does without that constraint.  The
+    sweep matters: invariance holds for most shapes and breaks for a few, so a
+    single-shape probe would report whichever answer it happened to land on.
+    """
+
+    from aiter.ops.mha import mha_fwd
+
+    scale = 1.0 / math.sqrt(head_dim)
+
+    def forward(query, key, value):
+        out, lse, _mask, _rng = mha_fwd(
+            query.transpose(1, 2).contiguous(),
+            key.transpose(1, 2).contiguous(),
+            value.transpose(1, 2).contiguous(),
+            0.0,
+            scale,
+            True,
+            -1,
+            -1,
+            0,
+            True,
+            False,
+        )
+        return out.transpose(1, 2).contiguous(), lse
+
+    rows = []
+    for batch, seq_len in shapes:
+        if batch < 2:
+            continue
+        query, key, value = _tensors(batch, q_heads, kv_heads, seq_len, head_dim, dtype, seed=11)
+        batched_out, batched_lse = forward(query, key, value)
+        worst_out = worst_lse = 0.0
+        for row in range(batch):
+            row_out, row_lse = forward(
+                query[row : row + 1], key[row : row + 1], value[row : row + 1]
+            )
+            worst_out = max(worst_out, (batched_out[row : row + 1] - row_out).abs().max().item())
+            worst_lse = max(worst_lse, (batched_lse[row : row + 1] - row_lse).abs().max().item())
+        rows.append(
+            {
+                "batch": batch,
+                "seq_len": seq_len,
+                "raw_aiter_out_max_abs": worst_out,
+                "raw_aiter_lse_max_abs": worst_lse,
+                "raw_aiter_is_batch_invariant": worst_out == 0.0 and worst_lse == 0.0,
+            }
+        )
+        print(
+            f"batch-composition B={batch} S={seq_len:5d} "
+            f"out {worst_out:.6e} lse {worst_lse:.6e} "
+            f"{'invariant' if rows[-1]['raw_aiter_is_batch_invariant'] else 'NOT INVARIANT'}",
+            flush=True,
+        )
+        del query, key, value, batched_out, batched_lse
+        torch.cuda.empty_cache()
+    return rows
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--output", type=Path, default=Path("attention_results.json"))
+    parser.add_argument("--q-heads", type=int, default=DEFAULT_Q_HEADS)
+    parser.add_argument("--kv-heads", type=int, default=DEFAULT_KV_HEADS)
+    parser.add_argument("--head-dim", type=int, default=DEFAULT_HEAD_DIM)
+    parser.add_argument("--warmup", type=int, default=10)
+    parser.add_argument("--iters", type=int, default=50)
+    parser.add_argument(
+        "--shapes",
+        default="1x1024,1x2048,1x4096,2x2048,4x2048",
+        help="comma-separated BATCHxSEQ pairs",
+    )
+    args = parser.parse_args()
+
+    if not torch.cuda.is_available():
+        raise SystemExit("this benchmark requires a ROCm (or CUDA) device")
+
+    dtype = torch.bfloat16
+    metadata = _metadata(args.q_heads, args.kv_heads)
+    shapes = [tuple(int(part) for part in pair.split("x")) for pair in args.shapes.split(",")]
+
+    rows = []
+    for batch, seq_len in shapes:
+        query, key, value = _tensors(
+            batch, args.q_heads, args.kv_heads, seq_len, args.head_dim, dtype
+        )
+        factories = {
+            "native": lambda q=query, k=key, v=value: _native_runner(
+                q, k, v, args.q_heads, args.kv_heads
+            ),
+            "triton": lambda q=query, k=key, v=value: _triton_runner(q, k, v, args.head_dim),
+            "strict": lambda q=query, k=key, v=value: _strict_runner(q, k, v, metadata),
+        }
+        for name, factory in factories.items():
+            try:
+                runner = factory()
+                forward = _measure(runner, backward=False, warmup=args.warmup, iters=args.iters)
+                combined = _measure(runner, backward=True, warmup=args.warmup, iters=args.iters)
+            except Exception as exc:  # noqa: BLE001 - report, do not abort the sweep
+                print(f"B={batch} S={seq_len} {name}: FAILED {type(exc).__name__}: {exc}")
+                continue
+            rows.append(
+                {
+                    "batch": batch,
+                    "seq_len": seq_len,
+                    "backend": name,
+                    "forward": forward,
+                    "forward_backward": combined,
+                }
+            )
+            print(
+                f"B={batch} S={seq_len:5d} {name:8s} "
+                f"fwd {forward['median_ms']:8.4f} p95 {forward['p95_ms']:8.4f} "
+                f"peak {forward['peak_mib']:9.1f} | "
+                f"fwd+bwd {combined['median_ms']:8.4f} peak {combined['peak_mib']:9.1f}",
+                flush=True,
+            )
+        del query, key, value
+        torch.cuda.empty_cache()
+
+    seq_lens = sorted({seq for _batch, seq in shapes})
+    determinism = _determinism_cost(
+        seq_lens, args.q_heads, args.kv_heads, args.head_dim, dtype, args.warmup, args.iters
+    )
+    composition_shapes = [
+        (batch, seq_len) for batch in (2, 4) for seq_len in sorted({128, 256, 512, *seq_lens})
+    ]
+    composition = _batch_composition(
+        args.q_heads, args.kv_heads, args.head_dim, dtype, composition_shapes
+    )
+
+    properties = torch.cuda.get_device_properties(0)
+    payload = {
+        "environment": {
+            "gpu": properties.name,
+            "arch": getattr(properties, "gcnArchName", "unknown"),
+            "device_count": torch.cuda.device_count(),
+            "torch": torch.__version__,
+            "hip": torch.version.hip,
+            "dtype": "bf16",
+            "q_heads": args.q_heads,
+            "kv_heads": args.kv_heads,
+            "head_dim": args.head_dim,
+        },
+        "latency": rows,
+        "determinism_cost": determinism,
+        "batch_composition": composition,
+    }
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    args.output.write_text(json.dumps(payload, indent=2))
+    print("wrote", args.output)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/results/pr319_rocm_mi300x/report.md b/benchmarks/results/pr319_rocm_mi300x/report.md
new file mode 100644
index 00000000..e5cbe016
--- /dev/null
+++ b/benchmarks/results/pr319_rocm_mi300x/report.md
@@ -0,0 +1,80 @@
+# PR #319 — strict ROCm attention, MI300X operator benchmark
+
+Operator-only: seeded Q/K/V, no checkpoint, tokenizer, or serving engine.
+
+| Field | Value |
+|---|---|
+| `gpu` | AMD Instinct MI300X |
+| `arch` | gfx942:sramecc+:xnack- |
+| `device_count` | 8 |
+| `torch` | 2.12.0+rocm7.14.0a20260608 |
+| `hip` | 7.14.60850 |
+| `dtype` | bf16 |
+| `q_heads` | 32 |
+| `kv_heads` | 8 |
+| `head_dim` | 128 |
+
+Backends: `native` = PyTorch SDPA with KV heads expanded; `triton` = flash-attn ROCm
+Triton backend; `strict` = `aiter.rocm.ck_dense_mha` through WS2 contract dispatch.
+
+## Latency and peak memory (BF16, causal prefill)
+
+| B | S | Backend | Fwd median (ms) | Fwd p95 (ms) | Fwd peak MiB | Fwd+bwd median (ms) | Fwd+bwd peak MiB |
+|---:|---:|---|---:|---:|---:|---:|---:|
+| 1 | 1024 | native | 0.1438 | 0.1668 | 36.1 | 0.4741 | 84.3 |
+| 1 | 1024 | triton | 0.3069 | 0.3300 | 32.1 | 1.7754 | 52.2 |
+| 1 | 1024 | strict | 0.4045 | 0.4261 | 40.1 | 1.0480 | 1108.5 |
+| 1 | 2048 | native | 0.2963 | 0.3018 | 72.3 | 1.0880 | 168.8 |
+| 1 | 2048 | triton | 0.6404 | 0.6551 | 64.2 | 2.2759 | 104.5 |
+| 1 | 2048 | strict | 0.4327 | 0.4475 | 80.3 | 2.0983 | 4265.0 |
+| 1 | 4096 | native | 0.7014 | 0.7219 | 144.5 | 3.2456 | 337.5 |
+| 1 | 4096 | triton | 1.4337 | 1.4886 | 128.5 | 4.6235 | 209.0 |
+| 1 | 4096 | strict | 0.7415 | 0.7529 | 160.5 | 5.8933 | 16722.0 |
+| 2 | 2048 | native | 0.4437 | 0.4696 | 144.5 | 1.9135 | 337.5 |
+| 2 | 2048 | triton | 0.9821 | 0.9917 | 128.5 | 2.6656 | 209.0 |
+| 2 | 2048 | strict | 0.7912 | 0.8101 | 120.5 | 3.9666 | 4369.3 |
+| 4 | 2048 | native | 0.8211 | 0.8666 | 289.0 | 3.5754 | 675.0 |
+| 4 | 2048 | triton | 1.7571 | 1.7957 | 257.0 | 5.1310 | 418.0 |
+| 4 | 2048 | strict | 1.3856 | 1.4119 | 226.1 | 7.7189 | 4610.3 |
+
+## Cost of the deterministic backward
+
+Raw AITER `mha_bwd`, B=1, toggling only the `deterministic` flag. Peak values include the
+already-resident forward tensors, so read the absolute det=on column for scaling.
+
+| S | det=on median (ms) | det=off median (ms) | Time | det=on peak MiB | det=off peak MiB |
+|---:|---:|---:|---:|---:|---:|
+| 1024 | 0.4455 | 0.1917 | 2.32x | 1188.3 | 180.3 |
+| 2048 | 1.3399 | 0.5687 | 2.36x | 4328.5 | 264.5 |
+| 4096 | 5.0313 | 2.0607 | 2.44x | 16753.0 | 433.0 |
+
+Deterministic backward peak memory scales as O(S^2): each doubling of S quadruples it.
+
+## Batch-composition invariance of raw AITER
+
+Batch vs the same rows submitted one at a time. Shape-dependent, which is why the strict
+core materializes one logical row per call.
+
+| B | S | out max abs | lse max abs | Invariant |
+|---:|---:|---:|---:|---|
+| 2 | 128 | 0.000000e+00 | 0.000000e+00 | yes |
+| 2 | 256 | 0.000000e+00 | 0.000000e+00 | yes |
+| 2 | 512 | 1.562500e-02 | 9.536743e-07 | **no** |
+| 2 | 1024 | 0.000000e+00 | 0.000000e+00 | yes |
+| 2 | 2048 | 0.000000e+00 | 0.000000e+00 | yes |
+| 2 | 4096 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4 | 128 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4 | 256 | 7.812500e-03 | 9.536743e-07 | **no** |
+| 4 | 512 | 1.562500e-02 | 9.536743e-07 | **no** |
+| 4 | 1024 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4 | 2048 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4 | 4096 | 0.000000e+00 | 0.000000e+00 | yes |
+
+Through the provider, every shape above is bitwise identical (max abs `0`).
+
+## Reproduce
+
+```bash
+python benchmarks/benchmark_rocm_attention.py \
+  --output benchmarks/results/pr319_rocm_mi300x/results.json
+```
diff --git a/benchmarks/results/pr319_rocm_mi300x/results.json b/benchmarks/results/pr319_rocm_mi300x/results.json
new file mode 100644
index 00000000..7aec1877
--- /dev/null
+++ b/benchmarks/results/pr319_rocm_mi300x/results.json
@@ -0,0 +1,373 @@
+{
+  "environment": {
+    "gpu": "AMD Instinct MI300X",
+    "arch": "gfx942:sramecc+:xnack-",
+    "device_count": 8,
+    "torch": "2.12.0+rocm7.14.0a20260608",
+    "hip": "7.14.60850",
+    "dtype": "bf16",
+    "q_heads": 32,
+    "kv_heads": 8,
+    "head_dim": 128
+  },
+  "latency": [
+    {
+      "batch": 1,
+      "seq_len": 1024,
+      "backend": "native",
+      "forward": {
+        "median_ms": 0.1438,
+        "p95_ms": 0.1668,
+        "peak_mib": 36.1
+      },
+      "forward_backward": {
+        "median_ms": 0.4741,
+        "p95_ms": 0.4915,
+        "peak_mib": 84.3
+      }
+    },
+    {
+      "batch": 1,
+      "seq_len": 1024,
+      "backend": "triton",
+      "forward": {
+        "median_ms": 0.3069,
+        "p95_ms": 0.33,
+        "peak_mib": 32.1
+      },
+      "forward_backward": {
+        "median_ms": 1.7754,
+        "p95_ms": 1.864,
+        "peak_mib": 52.2
+      }
+    },
+    {
+      "batch": 1,
+      "seq_len": 1024,
+      "backend": "strict",
+      "forward": {
+        "median_ms": 0.4045,
+        "p95_ms": 0.4261,
+        "peak_mib": 40.1
+      },
+      "forward_backward": {
+        "median_ms": 1.048,
+        "p95_ms": 1.0706,
+        "peak_mib": 1108.5
+      }
+    },
+    {
+      "batch": 1,
+      "seq_len": 2048,
+      "backend": "native",
+      "forward": {
+        "median_ms": 0.2963,
+        "p95_ms": 0.3018,
+        "peak_mib": 72.3
+      },
+      "forward_backward": {
+        "median_ms": 1.088,
+        "p95_ms": 1.0986,
+        "peak_mib": 168.8
+      }
+    },
+    {
+      "batch": 1,
+      "seq_len": 2048,
+      "backend": "triton",
+      "forward": {
+        "median_ms": 0.6404,
+        "p95_ms": 0.6551,
+        "peak_mib": 64.2
+      },
+      "forward_backward": {
+        "median_ms": 2.2759,
+        "p95_ms": 2.2853,
+        "peak_mib": 104.5
+      }
+    },
+    {
+      "batch": 1,
+      "seq_len": 2048,
+      "backend": "strict",
+      "forward": {
+        "median_ms": 0.4327,
+        "p95_ms": 0.4475,
+        "peak_mib": 80.3
+      },
+      "forward_backward": {
+        "median_ms": 2.0983,
+        "p95_ms": 2.2269,
+        "peak_mib": 4265.0
+      }
+    },
+    {
+      "batch": 1,
+      "seq_len": 4096,
+      "backend": "native",
+      "forward": {
+        "median_ms": 0.7014,
+        "p95_ms": 0.7219,
+        "peak_mib": 144.5
+      },
+      "forward_backward": {
+        "median_ms": 3.2456,
+        "p95_ms": 3.2945,
+        "peak_mib": 337.5
+      }
+    },
+    {
+      "batch": 1,
+      "seq_len": 4096,
+      "backend": "triton",
+      "forward": {
+        "median_ms": 1.4337,
+        "p95_ms": 1.4886,
+        "peak_mib": 128.5
+      },
+      "forward_backward": {
+        "median_ms": 4.6235,
+        "p95_ms": 4.672,
+        "peak_mib": 209.0
+      }
+    },
+    {
+      "batch": 1,
+      "seq_len": 4096,
+      "backend": "strict",
+      "forward": {
+        "median_ms": 0.7415,
+        "p95_ms": 0.7529,
+        "peak_mib": 160.5
+      },
+      "forward_backward": {
+        "median_ms": 5.8933,
+        "p95_ms": 5.9776,
+        "peak_mib": 16722.0
+      }
+    },
+    {
+      "batch": 2,
+      "seq_len": 2048,
+      "backend": "native",
+      "forward": {
+        "median_ms": 0.4437,
+        "p95_ms": 0.4696,
+        "peak_mib": 144.5
+      },
+      "forward_backward": {
+        "median_ms": 1.9135,
+        "p95_ms": 1.9372,
+        "peak_mib": 337.5
+      }
+    },
+    {
+      "batch": 2,
+      "seq_len": 2048,
+      "backend": "triton",
+      "forward": {
+        "median_ms": 0.9821,
+        "p95_ms": 0.9917,
+        "peak_mib": 128.5
+      },
+      "forward_backward": {
+        "median_ms": 2.6656,
+        "p95_ms": 2.6806,
+        "peak_mib": 209.0
+      }
+    },
+    {
+      "batch": 2,
+      "seq_len": 2048,
+      "backend": "strict",
+      "forward": {
+        "median_ms": 0.7912,
+        "p95_ms": 0.8101,
+        "peak_mib": 120.5
+      },
+      "forward_backward": {
+        "median_ms": 3.9666,
+        "p95_ms": 4.0203,
+        "peak_mib": 4369.3
+      }
+    },
+    {
+      "batch": 4,
+      "seq_len": 2048,
+      "backend": "native",
+      "forward": {
+        "median_ms": 0.8211,
+        "p95_ms": 0.8666,
+        "peak_mib": 289.0
+      },
+      "forward_backward": {
+        "median_ms": 3.5754,
+        "p95_ms": 3.6514,
+        "peak_mib": 675.0
+      }
+    },
+    {
+      "batch": 4,
+      "seq_len": 2048,
+      "backend": "triton",
+      "forward": {
+        "median_ms": 1.7571,
+        "p95_ms": 1.7957,
+        "peak_mib": 257.0
+      },
+      "forward_backward": {
+        "median_ms": 5.131,
+        "p95_ms": 5.1587,
+        "peak_mib": 418.0
+      }
+    },
+    {
+      "batch": 4,
+      "seq_len": 2048,
+      "backend": "strict",
+      "forward": {
+        "median_ms": 1.3856,
+        "p95_ms": 1.4119,
+        "peak_mib": 226.1
+      },
+      "forward_backward": {
+        "median_ms": 7.7189,
+        "p95_ms": 7.8018,
+        "peak_mib": 4610.3
+      }
+    }
+  ],
+  "determinism_cost": [
+    {
+      "seq_len": 1024,
+      "deterministic": {
+        "median_ms": 0.4455,
+        "p95_ms": 0.4746,
+        "peak_mib": 1188.3
+      },
+      "non_deterministic": {
+        "median_ms": 0.1917,
+        "p95_ms": 0.1971,
+        "peak_mib": 180.3
+      },
+      "time_ratio": 2.32,
+      "memory_ratio": 6.6
+    },
+    {
+      "seq_len": 2048,
+      "deterministic": {
+        "median_ms": 1.3399,
+        "p95_ms": 1.3742,
+        "peak_mib": 4328.5
+      },
+      "non_deterministic": {
+        "median_ms": 0.5687,
+        "p95_ms": 0.5762,
+        "peak_mib": 264.5
+      },
+      "time_ratio": 2.36,
+      "memory_ratio": 16.4
+    },
+    {
+      "seq_len": 4096,
+      "deterministic": {
+        "median_ms": 5.0313,
+        "p95_ms": 5.1068,
+        "peak_mib": 16753.0
+      },
+      "non_deterministic": {
+        "median_ms": 2.0607,
+        "p95_ms": 2.089,
+        "peak_mib": 433.0
+      },
+      "time_ratio": 2.44,
+      "memory_ratio": 38.7
+    }
+  ],
+  "batch_composition": [
+    {
+      "batch": 2,
+      "seq_len": 128,
+      "raw_aiter_out_max_abs": 0.0,
+      "raw_aiter_lse_max_abs": 0.0,
+      "raw_aiter_is_batch_invariant": true
+    },
+    {
+      "batch": 2,
+      "seq_len": 256,
+      "raw_aiter_out_max_abs": 0.0,
+      "raw_aiter_lse_max_abs": 0.0,
+      "raw_aiter_is_batch_invariant": true
+    },
+    {
+      "batch": 2,
+      "seq_len": 512,
+      "raw_aiter_out_max_abs": 0.015625,
+      "raw_aiter_lse_max_abs": 9.5367431640625e-07,
+      "raw_aiter_is_batch_invariant": false
+    },
+    {
+      "batch": 2,
+      "seq_len": 1024,
+      "raw_aiter_out_max_abs": 0.0,
+      "raw_aiter_lse_max_abs": 0.0,
+      "raw_aiter_is_batch_invariant": true
+    },
+    {
+      "batch": 2,
+      "seq_len": 2048,
+      "raw_aiter_out_max_abs": 0.0,
+      "raw_aiter_lse_max_abs": 0.0,
+      "raw_aiter_is_batch_invariant": true
+    },
+    {
+      "batch": 2,
+      "seq_len": 4096,
+      "raw_aiter_out_max_abs": 0.0,
+      "raw_aiter_lse_max_abs": 0.0,
+      "raw_aiter_is_batch_invariant": true
+    },
+    {
+      "batch": 4,
+      "seq_len": 128,
+      "raw_aiter_out_max_abs": 0.0,
+      "raw_aiter_lse_max_abs": 0.0,
+      "raw_aiter_is_batch_invariant": true
+    },
+    {
+      "batch": 4,
+      "seq_len": 256,
+      "raw_aiter_out_max_abs": 0.0078125,
+      "raw_aiter_lse_max_abs": 9.5367431640625e-07,
+      "raw_aiter_is_batch_invariant": false
+    },
+    {
+      "batch": 4,
+      "seq_len": 512,
+      "raw_aiter_out_max_abs": 0.015625,
+      "raw_aiter_lse_max_abs": 9.5367431640625e-07,
+      "raw_aiter_is_batch_invariant": false
+    },
+    {
+      "batch": 4,
+      "seq_len": 1024,
+      "raw_aiter_out_max_abs": 0.0,
+      "raw_aiter_lse_max_abs": 0.0,
+      "raw_aiter_is_batch_invariant": true
+    },
+    {
+      "batch": 4,
+      "seq_len": 2048,
+      "raw_aiter_out_max_abs": 0.0,
+      "raw_aiter_lse_max_abs": 0.0,
+      "raw_aiter_is_batch_invariant": true
+    },
+    {
+      "batch": 4,
+      "seq_len": 4096,
+      "raw_aiter_out_max_abs": 0.0,
+      "raw_aiter_lse_max_abs": 0.0,
+      "raw_aiter_is_batch_invariant": true
+    }
+  ]
+}
\ No newline at end of file
diff --git a/rl_engine/integrations/__init__.py b/rl_engine/integrations/__init__.py
new file mode 100644
index 00000000..ec3e322c
--- /dev/null
+++ b/rl_engine/integrations/__init__.py
@@ -0,0 +1,4 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Optional framework adapters owned by RL-Kernel."""
diff --git a/rl_engine/integrations/vime/__init__.py b/rl_engine/integrations/vime/__init__.py
new file mode 100644
index 00000000..7e4aa140
--- /dev/null
+++ b/rl_engine/integrations/vime/__init__.py
@@ -0,0 +1,12 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Vime adapter entry points without a Vime runtime dependency."""
+
+from .attention import AttentionProviderResult, AttentionProviderUnavailable, attention_provider
+
+__all__ = [
+    "AttentionProviderResult",
+    "AttentionProviderUnavailable",
+    "attention_provider",
+]
diff --git a/rl_engine/integrations/vime/attention.py b/rl_engine/integrations/vime/attention.py
new file mode 100644
index 00000000..c848b2d7
--- /dev/null
+++ b/rl_engine/integrations/vime/attention.py
@@ -0,0 +1,428 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Runtime strict-attention provider for Vime's Megatron backend.
+
+The adapter intentionally accepts and returns structural objects: RL-Kernel
+never imports Vime.  Vime remains responsible for materializing post-RoPE Q/K/V
+for its locally owned rows; this provider owns only the attention core
+arithmetic and the ``(out, lse)`` export.
+
+Two boundaries are deliberate and are enforced rather than documented:
+
+* The core executes one logical batch row at a time.  Batching is a layout
+  choice, so a request of ``B`` rows must produce the same bits as ``B``
+  single-row requests; the provider materializes rows independently instead of
+  trusting a vendor kernel to be batch-composition invariant.
+* CP is not a merge axis here.  The strict ROCm core owns single-rank attention
+  arithmetic only; the cross-rank ``(out, lse)`` merge lives in the CP transport
+  path.  CP rank and layout are recorded as row-ownership provenance and a
+  request that asks this provider to perform the CP merge fails closed.
+"""
+
+from __future__ import annotations
+
+import math
+from collections.abc import Mapping
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+
+from rl_engine.kernels.attention_contract import (
+    AttentionContract,
+    AttentionDType,
+    AttentionMode,
+    AttentionRole,
+    ReductionSpec,
+    ShardingSpec,
+    SplitKVSpec,
+)
+from rl_engine.kernels.ops.rocm.attention.flash_attn import BACKEND_ID
+from rl_engine.kernels.registry import kernel_registry
+
+
+class AttentionProviderUnavailable(RuntimeError):
+    """Request Vime's native attention fallback in ``auto`` mode.
+
+    Vime recognizes the marker instead of importing this class, which keeps the
+    dependency direction from Vime to RL-Kernel at runtime only.
+    """
+
+    attention_provider_unavailable = True
+
+
+@dataclass(frozen=True)
+class AttentionProviderResult:
+    """Structural result understood by the Vime attention boundary."""
+
+    out: torch.Tensor
+    lse: torch.Tensor
+    backend_id: str
+    contract_id: str
+    provenance: Mapping[str, Any]
+
+
+_DTYPE_TO_CONTRACT = {
+    torch.bfloat16: AttentionDType.BF16,
+    torch.float16: AttentionDType.FP16,
+}
+
+# Decode is deliberately absent: it requires KV-cache identity metadata that
+# this core does not materialize.  A decode request fails closed here rather
+# than being silently served by the dense prefill core over a cache the
+# provider never validated.
+_MODE_BY_NAME = {
+    "prefill": AttentionMode.PREFILL,
+    "chunked_prefill": AttentionMode.CHUNKED_PREFILL,
+}
+
+_ROLE_BY_NAME = {
+    "train": AttentionRole.TRAIN,
+    "infer": AttentionRole.INFER,
+}
+
+
+def _as_positive_int(value: Any, name: str) -> int:
+    if isinstance(value, bool) or not isinstance(value, int) or value <= 0:
+        raise AttentionProviderUnavailable(f"{name} must be a positive integer; got {value!r}")
+    return value
+
+
+def _metadata(request: Any) -> Mapping[str, Any]:
+    value = getattr(request, "metadata", None)
+    if not isinstance(value, Mapping):
+        raise AttentionProviderUnavailable("request.metadata must provide attention metadata")
+    return value
+
+
+def _request_tensor(request: Any, name: str) -> torch.Tensor:
+    value = getattr(request, name, None)
+    if not isinstance(value, torch.Tensor):
+        raise AttentionProviderUnavailable(f"request.{name} must be a torch.Tensor")
+    return value
+
+
+def _tp_coordinates(tp_group: Any) -> tuple[int, int]:
+    if tp_group is not None and hasattr(tp_group, "rank") and hasattr(tp_group, "size"):
+        return int(tp_group.rank()), int(tp_group.size())
+
+    import torch.distributed as dist
+
+    if dist.is_available() and dist.is_initialized():
+        return dist.get_rank(group=tp_group), dist.get_world_size(group=tp_group)
+    return 0, 1
+
+
+def _reject_unsupported_materializations(request: Any, metadata: Mapping[str, Any]) -> None:
+    """Fail closed on every knob that would change the numerical definition."""
+
+    if getattr(request, "key_padding_mask", None) is not None:
+        raise AttentionProviderUnavailable(
+            "strict ROCm attention materializes each unpadded logical row; "
+            "pass unpadded per-row Q/K/V instead of a key padding mask"
+        )
+    dropout_p = metadata.get("dropout_p", 0.0)
+    if dropout_p:
+        raise AttentionProviderUnavailable(
+            f"strict attention requires dropout_p=0.0; got {dropout_p!r}"
+        )
+    for unsupported in (
+        "alibi_slopes",
+        "attention_bias",
+        "logit_soft_cap",
+        "sliding_window",
+        "sink_tokens",
+    ):
+        if metadata.get(unsupported) is not None:
+            raise AttentionProviderUnavailable(
+                f"strict ROCm attention does not materialize {unsupported}"
+            )
+    window = metadata.get("window_size")
+    if window is not None and tuple(window) != (-1, -1):
+        raise AttentionProviderUnavailable(
+            f"strict ROCm attention requires a full causal/full window; got {window!r}"
+        )
+
+
+def _contract_for_request(
+    request: Any,
+) -> tuple[AttentionContract, float, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Validate the request and derive the explicit WS2 attention contract."""
+
+    metadata = _metadata(request)
+    _reject_unsupported_materializations(request, metadata)
+
+    query = _request_tensor(request, "query")
+    key = _request_tensor(request, "key")
+    value = _request_tensor(request, "value")
+    if query.ndim != 4 or key.ndim != 4 or value.ndim != 4:
+        raise AttentionProviderUnavailable("query/key/value must be 4-D [B, H, S, D] tensors")
+    if key.shape != value.shape:
+        raise AttentionProviderUnavailable("key and value must share one shape")
+    if query.shape[0] != key.shape[0]:
+        raise AttentionProviderUnavailable("query and key must share the logical batch size")
+    if query.shape[-1] != key.shape[-1]:
+        raise AttentionProviderUnavailable("query and key must share head_dim")
+    if query.dtype not in _DTYPE_TO_CONTRACT:
+        raise AttentionProviderUnavailable(
+            f"strict ROCm attention supports BF16/FP16 only; got {query.dtype}"
+        )
+    if key.dtype != query.dtype or value.dtype != query.dtype:
+        raise AttentionProviderUnavailable("query/key/value must share one dtype")
+    if not (query.device == key.device == value.device):
+        raise AttentionProviderUnavailable("query/key/value must share one device")
+
+    batch_size, local_q_heads, query_len, head_dim = query.shape
+    local_kv_heads = key.shape[1]
+    kv_len = key.shape[2]
+    if local_q_heads % local_kv_heads:
+        raise AttentionProviderUnavailable(
+            f"local Q heads={local_q_heads} must be divisible by local KV heads={local_kv_heads}"
+        )
+
+    cp = getattr(request, "context_parallel", None)
+    cp_world_size = _as_positive_int(getattr(cp, "world_size", None), "context_parallel.world_size")
+    cp_rank = getattr(cp, "rank", None)
+    if (
+        isinstance(cp_rank, bool)
+        or not isinstance(cp_rank, int)
+        or not 0 <= cp_rank < cp_world_size
+    ):
+        raise AttentionProviderUnavailable(
+            f"context_parallel.rank={cp_rank!r} is invalid for CP={cp_world_size}"
+        )
+    cp_layout = getattr(cp, "layout", None)
+    if cp_layout not in ({"single"} if cp_world_size == 1 else {"zigzag", "allgather"}):
+        raise AttentionProviderUnavailable(
+            "context_parallel layout does not describe local CP token ownership"
+        )
+    if cp_world_size > 1:
+        # The strict ROCm core is single-rank attention arithmetic.  Performing
+        # the cross-rank (out, lse) merge here would silently introduce a second
+        # merge implementation with its own order; the CP transport path owns it.
+        raise AttentionProviderUnavailable(
+            f"CP={cp_world_size} requires the strict CP transport path; this provider owns "
+            "single-rank attention arithmetic and will not perform the cross-rank "
+            "(out, lse) merge"
+        )
+
+    tp_rank, tp_world_size = _tp_coordinates(getattr(request, "tensor_parallel_group", None))
+    declared_tp_rank = metadata.get("tp_rank")
+    declared_tp_world_size = metadata.get("tp_world_size")
+    if declared_tp_rank is not None and declared_tp_rank != tp_rank:
+        raise AttentionProviderUnavailable(
+            f"metadata tp_rank={declared_tp_rank} disagrees with TP group rank={tp_rank}"
+        )
+    if declared_tp_world_size is not None and declared_tp_world_size != tp_world_size:
+        raise AttentionProviderUnavailable(
+            f"metadata tp_world_size={declared_tp_world_size} disagrees with "
+            f"TP group size={tp_world_size}"
+        )
+
+    global_q_heads = _as_positive_int(metadata.get("global_q_heads"), "global_q_heads")
+    global_kv_heads = _as_positive_int(metadata.get("global_kv_heads"), "global_kv_heads")
+    if local_q_heads * tp_world_size != global_q_heads:
+        raise AttentionProviderUnavailable(
+            "local Q heads and TP group do not cover global_q_heads exactly: "
+            f"{local_q_heads} * {tp_world_size} != {global_q_heads}"
+        )
+    if local_kv_heads * tp_world_size != global_kv_heads:
+        raise AttentionProviderUnavailable(
+            "local KV heads and TP group do not cover global_kv_heads exactly: "
+            f"{local_kv_heads} * {tp_world_size} != {global_kv_heads}"
+        )
+
+    mode_name = str(metadata.get("attention_mode", "prefill"))
+    if mode_name == "decode":
+        raise AttentionProviderUnavailable(
+            "decode requires KV-cache identity metadata (cache_position, block table, "
+            "prefix-cache key) that the strict dense core does not materialize; use the "
+            "paged decode path"
+        )
+    if mode_name not in _MODE_BY_NAME:
+        raise AttentionProviderUnavailable(f"unsupported attention_mode={mode_name!r}")
+    mode = _MODE_BY_NAME[mode_name]
+    role_name = str(metadata.get("role", "train"))
+    if role_name not in _ROLE_BY_NAME:
+        raise AttentionProviderUnavailable(f"unsupported role={role_name!r}")
+    role = _ROLE_BY_NAME[role_name]
+
+    causal = metadata.get("causal", True)
+    if not isinstance(causal, bool):
+        raise AttentionProviderUnavailable(f"causal must be a bool; got {causal!r}")
+    if mode is AttentionMode.PREFILL and query_len != kv_len:
+        raise AttentionProviderUnavailable(
+            "prefill requires the query and KV lengths to describe one logical sequence; "
+            f"got Sq={query_len} and Skv={kv_len}"
+        )
+
+    if query_len > kv_len:
+        raise AttentionProviderUnavailable(
+            f"causal attention requires Sq <= Skv; got Sq={query_len} and Skv={kv_len}"
+        )
+
+    scale = metadata.get("softmax_scale")
+    resolved_scale = 1.0 / math.sqrt(head_dim) if scale is None else float(scale)
+
+    # CP=1: this rank owns the whole logical sequence as one block.
+    sharding = ShardingSpec(
+        tp_rank=tp_rank,
+        tp_world_size=tp_world_size,
+        cp_rank=cp_rank,
+        cp_world_size=cp_world_size,
+        global_q_heads=global_q_heads,
+        global_kv_heads=global_kv_heads,
+        local_q_head_start=tp_rank * local_q_heads,
+        local_q_heads=local_q_heads,
+        local_kv_head_start=tp_rank * local_kv_heads,
+        local_kv_heads=local_kv_heads,
+        global_sequence_length=kv_len,
+        local_sequence_length=kv_len,
+        global_block_indices=(0,),
+        global_block_token_starts=(0,),
+        local_block_offsets=(0, kv_len),
+    )
+    # Causal alignment: the query block is the tail of the logical sequence, so
+    # every batch entry carries the same offset between Q row 0 and KV token 0.
+    causal_offsets = (kv_len - query_len,) * batch_size if causal else None
+
+    contract = AttentionContract(
+        role=role,
+        mode=mode,
+        dtype=_DTYPE_TO_CONTRACT[query.dtype],
+        batch_size=batch_size,
+        query_sequence_length=query_len if mode is not AttentionMode.PREFILL else kv_len,
+        head_dim=head_dim,
+        causal=causal,
+        causal_offsets=causal_offsets,
+        sharding=sharding,
+        reduction=ReductionSpec(),
+        split_kv=SplitKVSpec.disabled(),
+        kv_cache=None,
+        rope=None,
+        export_lse=True,
+    )
+    key_position_ids = _key_position_ids(metadata, query.device, batch_size, kv_len)
+    return contract, resolved_scale, query, key, value, key_position_ids
+
+
+def _key_position_ids(
+    metadata: Mapping[str, Any],
+    device: torch.device,
+    batch_size: int,
+    kv_len: int,
+) -> torch.Tensor:
+    """Resolve the global KV token positions for this request.
+
+    Position identity is part of the contract, not an implementation detail: it
+    is what makes a training-side full-sequence call and a rollout-side chunk
+    provably describe the same logical tokens.  Vime may declare it; otherwise
+    the canonical contiguous ``[0, kv_len)`` block is used.
+    """
+
+    declared = metadata.get("key_position_ids")
+    if declared is None:
+        return (
+            torch.arange(kv_len, device=device, dtype=torch.int64)
+            .unsqueeze(0)
+            .expand(batch_size, kv_len)
+            .contiguous()
+        )
+    positions = declared if isinstance(declared, torch.Tensor) else torch.as_tensor(declared)
+    positions = positions.to(device=device, dtype=torch.int64)
+    if positions.ndim == 1:
+        positions = positions.unsqueeze(0).expand(batch_size, -1)
+    if tuple(positions.shape) != (batch_size, kv_len):
+        raise AttentionProviderUnavailable(
+            f"key_position_ids must have shape {(batch_size, kv_len)}; "
+            f"got {tuple(positions.shape)}"
+        )
+    if kv_len > 1 and bool((positions[:, 1:] - positions[:, :-1] != 1).any()):
+        raise AttentionProviderUnavailable(
+            "key_position_ids must describe one contiguous increasing token block"
+        )
+    return positions.contiguous()
+
+
+def attention_provider(request: Any) -> AttentionProviderResult:
+    """Compute Vime attention on the explicit WS2 strict ROCm contract.
+
+    Materializes each logical batch row independently so batch composition
+    cannot change the bits, then returns the stacked ``(out, lse)`` together
+    with the dispatch provenance Vime records alongside the result.
+    """
+
+    contract, scale, query, key, value, key_positions = _contract_for_request(request)
+
+    dispatch = kernel_registry.get_attention_op(contract, requested_backend=BACKEND_ID)
+    if dispatch.provenance["actual_backend"] != BACKEND_ID or dispatch.provenance["fallback"]:
+        raise RuntimeError("explicit strict attention dispatch changed during materialization")
+
+    query_len = query.shape[2]
+    outs: list[torch.Tensor] = []
+    lses: list[torch.Tensor] = []
+    core_provenance: dict[str, Any] | None = None
+    for row in range(query.shape[0]):
+        row_key_positions = key_positions[row : row + 1]
+        result = dispatch.op.forward_with_lse(
+            query[row : row + 1],
+            key[row : row + 1],
+            value[row : row + 1],
+            causal=contract.causal,
+            scale=scale,
+            key_padding_mask=None,
+            query_position_ids=row_key_positions[:, -query_len:] if contract.causal else None,
+            key_position_ids=row_key_positions if contract.causal else None,
+            output_dtype=query.dtype,
+        )
+        outs.append(result.out)
+        lses.append(result.lse)
+        if core_provenance is None:
+            core_provenance = dict(result.provenance)
+
+    out = torch.cat(outs, dim=0)
+    lse = torch.cat(lses, dim=0)
+    assert core_provenance is not None  # batch_size is validated positive
+
+    provenance = dict(dispatch.provenance)
+    provenance["core"] = core_provenance
+    provenance["request"] = {
+        "query_shape": list(query.shape),
+        "key_shape": list(key.shape),
+        "dtype": str(query.dtype).replace("torch.", ""),
+        "causal": contract.causal,
+        "softmax_scale": scale,
+        "tp_rank": contract.sharding.tp_rank,
+        "tp_world_size": contract.sharding.tp_world_size,
+        "cp_rank": contract.sharding.cp_rank,
+        "cp_world_size": contract.sharding.cp_world_size,
+    }
+    provenance["execution"] = {
+        "role": "vime_attention",
+        "strict_backend": True,
+        "batch_rows_materialized_independently": True,
+        "attention_mode": contract.mode.value,
+    }
+    provenance["cp_row_ownership"] = {
+        "cp_rank": contract.sharding.cp_rank,
+        "cp_world_size": contract.sharding.cp_world_size,
+        "layout": getattr(request.context_parallel, "layout"),
+        "local_token_rows": contract.sharding.local_sequence_length,
+        "cp_is_merge_axis": False,
+    }
+    provenance["lse_domain"] = "attention"
+    return AttentionProviderResult(
+        out=out,
+        lse=lse,
+        backend_id=dispatch.capability.backend_id,
+        contract_id=contract.cross_rank_fingerprint(),
+        provenance=provenance,
+    )
+
+
+__all__ = [
+    "AttentionProviderResult",
+    "AttentionProviderUnavailable",
+    "attention_provider",
+]
diff --git a/rl_engine/kernels/attention_contract.py b/rl_engine/kernels/attention_contract.py
index 549857c1..96acdeba 100644
--- a/rl_engine/kernels/attention_contract.py
+++ b/rl_engine/kernels/attention_contract.py
@@ -11,6 +11,8 @@
 
 from __future__ import annotations
 
+import hashlib
+import json
 from dataclasses import dataclass, field
 from enum import Enum
 from typing import Any, Iterable, TypeVar
@@ -1289,6 +1291,41 @@ def to_dict(self) -> dict[str, Any]:
             "rope": rope,
         }
 
+    def cross_rank_fingerprint(self) -> str:
+        """Rank-independent identity for preflight agreement across ranks.
+
+        Excludes ``tp_rank``/``cp_rank`` and the local head/sequence bounds they
+        derive, so every rank of one logical attention invocation computes the
+        same value.  All-gathering this fingerprint together with the resolved
+        backend id and aborting on mismatch is the documented preflight for
+        distributed dispatch; ``requested_backend="auto"`` is not
+        distributed-safe without it.
+        """
+
+        payload = self.to_dict()
+        payload["sharding"] = {
+            key: value
+            for key, value in payload["sharding"].items()
+            if key
+            not in {
+                "tp_rank",
+                "cp_rank",
+                "local_q_head_start",
+                "local_q_heads",
+                "local_kv_head_start",
+                "local_kv_heads",
+                "local_sequence_length",
+                "global_block_indices",
+                "global_block_token_starts",
+                "local_block_offsets",
+            }
+        }
+        # Note: Any future extensions to this payload MUST maintain strict JSON
+        # serialization determinism across environments to prevent cross-rank
+        # hashing mismatches.
+        encoded = json.dumps(payload, sort_keys=True, separators=(",", ":"))
+        return hashlib.sha256(encoded.encode("utf-8")).hexdigest()
+
 
 @dataclass(frozen=True)
 class AttentionBackendCapability:
diff --git a/rl_engine/kernels/ops/rocm/attention/flash_attn.py b/rl_engine/kernels/ops/rocm/attention/flash_attn.py
index 0a9b2b36..1839bebe 100644
--- a/rl_engine/kernels/ops/rocm/attention/flash_attn.py
+++ b/rl_engine/kernels/ops/rocm/attention/flash_attn.py
@@ -30,6 +30,11 @@
 _MAX_TESTED_ROCM_TRITON_HEAD_DIM = 512
 _AITER_API_SOURCE = "aiter.ops.mha"
 
+# Stable dispatch identity for the strict ROCm attention core.  Kept at module
+# scope so contract-aware dispatch and the Vime adapter name one constant
+# instead of duplicating the string.
+BACKEND_ID = "aiter.rocm.ck_dense_mha"
+
 
 class StrictRocmAttentionUnavailable(RuntimeError):
     """Raised when the exact AITER CK strict contract is unavailable."""
@@ -134,7 +139,7 @@ class StrictRocmAiterCKAttentionCore:
 
     core_id = STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID
     strict_schedule = STRICT_ATTENTION_ROCM_SCHEDULE_ID
-    backend_id = "aiter.rocm.ck_dense_mha"
+    backend_id = BACKEND_ID
     api_source = _AITER_API_SOURCE
     merge_order = "global_block_index"
     accum_dtype = "fp32"
diff --git a/rl_engine/kernels/registry.py b/rl_engine/kernels/registry.py
index 24dd5f2e..613398c8 100644
--- a/rl_engine/kernels/registry.py
+++ b/rl_engine/kernels/registry.py
@@ -8,9 +8,22 @@
 
 import torch
 
+from rl_engine.kernels.attention_contract import (
+    AttentionBackendCapability,
+    AttentionContract,
+    AttentionContractError,
+    AttentionDispatchResult,
+    AttentionDType,
+    AttentionMode,
+    AttentionRole,
+)
 from rl_engine.platforms.device import device_ctx
 from rl_engine.utils.logger import logger
 
+# Dispatch policy keywords accepted by ``get_attention_op`` in place of an
+# exact backend id. They mirror ``AttentionBackendCapability.implementation_kind``.
+ATTENTION_IMPLEMENTATION_KINDS = frozenset({"production", "reference", "deterministic"})
+
 
 class _KernelEnumMeta(EnumMeta):
     """Metaclass to provide enhanced error messaging for backend lookups."""
@@ -41,6 +54,13 @@ class OpBackend(Enum, metaclass=_KernelEnumMeta):
     ROCM_AITER = "rl_engine.kernels.ops.rocm.aiter.AiterOp"
     ROCM_CK = "rl_engine.kernels.ops.rocm.composable_kernel.CKOp"
     ROCM_FLASH_ATTN = "rl_engine.kernels.ops.rocm.attention.flash_attn.RocmFlashAttentionOp"
+    # WS2 strict ROCm attention core (AITER/CK dense MHA, Split-KV disabled).
+    # Registered for contract-aware dispatch only, never in the legacy
+    # ``get_op`` priority lists: it is not a drop-in for the SDPA-shaped
+    # ``attn`` wrappers and must not be reachable as a silent fallback.
+    ROCM_STRICT_ATTENTION = (
+        "rl_engine.kernels.ops.rocm.attention.flash_attn.StrictRocmAiterCKAttentionCore"
+    )
 
     # GRPO loss (group reward normalization + clipped surrogate + KL)
     TRITON_GRPO_LOSS = "rl_engine.kernels.ops.triton.loss.grpo_loss.TritonGRPOLossOp"
@@ -167,6 +187,28 @@ def resolve_logp_op_type(
     return op_type
 
 
+def _rocm_strict_attention_available() -> bool:
+    """Return whether the strict ROCm AITER/CK attention core can actually load.
+
+    The probe mirrors the ROCm logprob backend gate: it must be true only when
+    this process can really execute the strict arithmetic, so an unavailable
+    vendor stack results in the backend never being registered rather than in a
+    registered backend that fails at materialization time.
+    """
+
+    if torch.version.hip is None:
+        return False
+    try:
+        from rl_engine.kernels.ops.rocm.attention.flash_attn import _load_aiter_ck_ops
+    except ImportError:
+        return False
+    try:
+        _load_aiter_ck_ops()
+    except Exception:  # StrictRocmAttentionUnavailable and vendor import errors
+        return False
+    return True
+
+
 class KernelRegistry:
     """
     Central dispatcher for high-performance kernels.
@@ -313,6 +355,218 @@ def __init__(self):
         self._adjust_priority_for_hardware()
         self._adjust_priority_from_env()
 
+        # WS2 attention dispatch owns its own candidate list and starts empty on
+        # every platform. The legacy ``attn`` / ``attention`` priority lists stay
+        # untouched, so a strict WS2 caller can never be served by an SDPA-shaped
+        # wrapper that does not export attention-domain LSE, and the strict core
+        # can never be picked up by a legacy caller as a silent substitution.
+        self._attention_candidates: Dict[str, list] = {
+            platform: [] for platform in self._priority_map
+        }
+        self._attention_capabilities: Dict[str, Dict[OpBackend, AttentionBackendCapability]] = {
+            platform: {} for platform in self._priority_map
+        }
+
+        # Strict ROCm production core: AITER/CK dense MHA with Split-KV disabled.
+        # Registered only when the vendor entry points genuinely load, so an
+        # explicit request on a machine without AITER fails loudly in
+        # ``get_attention_op`` instead of resolving to different arithmetic.
+        rocm_strict_attention_capability = AttentionBackendCapability(
+            backend_id="aiter.rocm.ck_dense_mha",
+            roles=frozenset({AttentionRole.TRAIN, AttentionRole.INFER}),
+            modes=frozenset({AttentionMode.PREFILL, AttentionMode.CHUNKED_PREFILL}),
+            dtypes=frozenset({AttentionDType.BF16, AttentionDType.FP16}),
+            cp_world_sizes=(1,),
+            tp_world_sizes=None,
+            exports_attention_lse=True,
+            deterministic_cp_merge=False,
+            supports_packed_varlen=False,
+            supports_kv_cache=False,
+            supports_rope_metadata=True,
+            supports_fused_rope_attention=False,
+            supports_split_kv_disabled=True,
+            supports_split_kv_fixed=False,
+            supports_split_kv_auto=False,
+            reports_actual_split_kv_plan=True,
+            implementation_kind="production",
+        )
+        if _rocm_strict_attention_available() and "rocm" in self._priority_map:
+            self.register_attention_backend(
+                OpBackend.ROCM_STRICT_ATTENTION,
+                rocm_strict_attention_capability,
+                platform="rocm",
+                prepend=True,
+            )
+
+    def register_attention_backend(
+        self,
+        backend: OpBackend,
+        capability: AttentionBackendCapability,
+        *,
+        platform: Optional[str] = None,
+        prepend: bool = False,
+    ) -> None:
+        """Register (or replace) a backend for WS2 contract-aware attention dispatch.
+
+        This is the supported seam for making an attention backend selectable by
+        ``get_attention_op`` without touching the legacy ``get_op`` priority
+        lists. Registering the same backend again replaces its capability
+        without duplicating the candidate entry.
+        """
+
+        if not isinstance(backend, OpBackend):
+            raise AttentionContractError("backend must be an OpBackend")
+        if not isinstance(capability, AttentionBackendCapability):
+            raise AttentionContractError("capability must be an AttentionBackendCapability")
+        resolved_platform = platform if platform is not None else self._platform()
+        if resolved_platform not in self._priority_map:
+            raise AttentionContractError(
+                f"unsupported platform {resolved_platform!r}; expected one of "
+                f"{sorted(self._priority_map)}"
+            )
+        candidates = self._attention_candidates.setdefault(resolved_platform, [])
+        self._attention_capabilities.setdefault(resolved_platform, {})[backend] = capability
+        if backend not in candidates:
+            if prepend:
+                candidates.insert(0, backend)
+            else:
+                candidates.append(backend)
+
+    def get_attention_op(
+        self,
+        contract: AttentionContract,
+        *,
+        requested_backend: str = "auto",
+    ) -> AttentionDispatchResult:
+        """Resolve only a backend that explicitly supports the WS2 attention contract.
+
+        Deliberately separate from the legacy ``get_op``: existing callers keep
+        their current behavior, while WS2 callers cannot silently fall back to a
+        backend with different reduction or Split-KV semantics.
+
+        ``requested_backend`` is either a case-insensitive policy keyword
+        (``auto`` | ``production`` | ``reference``) or an exact, case-sensitive
+        stable backend id. Strictness comes from the contract's capability
+        checks rather than from this policy string. With ``cp_world_size > 1``,
+        ``auto`` is rejected: per-rank auto resolution can diverge across ranks,
+        so distributed callers must name a policy or backend id.
+        """
+
+        if not isinstance(contract, AttentionContract):
+            raise AttentionContractError("contract must be an AttentionContract")
+        if not isinstance(requested_backend, str) or not requested_backend.strip():
+            raise AttentionContractError("requested_backend must be a non-empty string")
+        requested_backend = requested_backend.strip()
+        if requested_backend.lower() == "deterministic":
+            raise AttentionContractError(
+                'requested_backend="deterministic" is not a dispatch policy; request '
+                "determinism through ReductionSpec/SplitKVSpec and match it against the "
+                "backend capability flags instead"
+            )
+        if requested_backend.lower() == "auto" and contract.sharding.cp_world_size > 1:
+            raise AttentionContractError(
+                "Unsafe dispatch: requested_backend='auto' is not permitted when "
+                "cp_world_size > 1 without explicit cross-rank preflighting."
+            )
+
+        platform = self._platform()
+        candidates = self._attention_candidates.get(platform, [])
+        rejected: list[str] = []
+        # provenance["fallback"] reports only capability/load rejections of
+        # otherwise-eligible candidates; skips caused purely by the caller's own
+        # requested_backend policy filter are not fallbacks.
+        capability_rejections = 0
+
+        platform_capabilities = self._attention_capabilities.get(platform, {})
+        for backend in candidates:
+            capability = platform_capabilities.get(backend)
+            if capability is None:
+                rejected.append(f"{backend.name}: no AttentionBackendCapability declared")
+                capability_rejections += 1
+                continue
+            policy_mismatch = self._attention_policy_mismatch(requested_backend, capability)
+            if policy_mismatch is not None:
+                rejected.append(f"{backend.name}: {policy_mismatch}")
+                continue
+            capability_incompat = list(capability.incompatibilities(contract))
+            if capability_incompat:
+                rejected.append(f"{backend.name}: " + "; ".join(capability_incompat))
+                capability_rejections += 1
+                continue
+
+            op = self._get_or_create_backend(backend)
+            if op is None:
+                rejected.append(f"{backend.name}: backend could not be loaded or instantiated")
+                capability_rejections += 1
+                continue
+
+            provenance = {
+                "requested_backend": requested_backend,
+                "actual_backend": capability.backend_id,
+                "backend_enum": backend.name,
+                "platform": platform,
+                "fallback": capability_rejections > 0,
+                "fallback_reason": None,
+                "prior_rejections": list(rejected),
+                "contract": contract.to_dict(),
+                "capability": capability.to_dict(),
+            }
+            return AttentionDispatchResult(op=op, capability=capability, provenance=provenance)
+
+        details = " | ".join(rejected) if rejected else "no candidates registered"
+        requested = contract.to_dict()
+        raise RuntimeError(
+            "No attention backend supports the requested WS2 contract on "
+            f"{platform}: role={requested['role']}, mode={requested['mode']}, "
+            f"dtype={requested['dtype']}, TP={contract.sharding.tp_world_size}, "
+            f"CP={contract.sharding.cp_world_size}, "
+            f"split_kv={contract.split_kv.mode.value}. Rejections: {details}"
+        )
+
+    @staticmethod
+    def _attention_policy_mismatch(
+        requested_backend: str,
+        capability: AttentionBackendCapability,
+    ) -> str | None:
+        policy = requested_backend.lower()
+        if policy == "auto":
+            return None
+        if policy in ATTENTION_IMPLEMENTATION_KINDS:
+            if capability.implementation_kind == policy:
+                return None
+            return (
+                f"implementation_kind={capability.implementation_kind} does not satisfy "
+                f"requested_backend={policy}"
+            )
+        if capability.backend_id == requested_backend:
+            return None
+        return (
+            f"backend_id={capability.backend_id} does not match "
+            f"requested_backend={requested_backend}"
+        )
+
+    def _platform(self) -> str:
+        return self._platform_for_device(None)
+
+    def _get_or_create_backend(self, backend: OpBackend) -> Any | None:
+        if backend.name in self._instance_cache:
+            return self._instance_cache[backend.name]
+        if backend.name in self._failed_backends:
+            return None
+
+        op_class = self._load_backend(backend)
+        if op_class is None:
+            self._failed_backends.add(backend.name)
+            return None
+        try:
+            op = op_class()
+        except Exception as exc:
+            logger.error(f"Failed to instantiate {backend.name}: {exc}")
+            self._failed_backends.add(backend.name)
+            return None
+        self._instance_cache[backend.name] = op
+        return op
+
     def _adjust_priority_from_env(self):
         rocm_attn_backend = os.getenv("RL_KERNEL_ROCM_ATTN_BACKEND", "").strip().lower()
         if rocm_attn_backend in {"flash_attn", "flash-attn", "flash_attention"}:
diff --git a/tests/test_attention_dispatch.py b/tests/test_attention_dispatch.py
new file mode 100644
index 00000000..4c174557
--- /dev/null
+++ b/tests/test_attention_dispatch.py
@@ -0,0 +1,307 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Contract-aware attention dispatch: registration, policy, and fail-closed.
+
+These cases use a fresh ``KernelRegistry`` so they never mutate the process
+singleton, and they assert the property that motivates a separate dispatch
+entry point: a WS2 attention caller must never be served by a backend that
+declares different reduction, Split-KV, or LSE-export semantics.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from rl_engine.kernels.attention_contract import (
+    AttentionBackendCapability,
+    AttentionContract,
+    AttentionContractError,
+    AttentionDType,
+    AttentionMode,
+    AttentionRole,
+    ReductionSpec,
+    ShardingSpec,
+    SplitKVSpec,
+)
+from rl_engine.kernels.registry import KernelRegistry, OpBackend, _rocm_strict_attention_available
+
+
+def _contract(*, cp_world_size: int = 1, seq_len: int = 128) -> AttentionContract:
+    sharding = ShardingSpec(
+        tp_rank=0,
+        tp_world_size=1,
+        cp_rank=0,
+        cp_world_size=cp_world_size,
+        global_q_heads=32,
+        global_kv_heads=8,
+        local_q_head_start=0,
+        local_q_heads=32,
+        local_kv_head_start=0,
+        local_kv_heads=8,
+        global_sequence_length=seq_len,
+        local_sequence_length=seq_len,
+        global_block_indices=(0,),
+        global_block_token_starts=(0,),
+        local_block_offsets=(0, seq_len),
+    )
+    return AttentionContract(
+        role=AttentionRole.TRAIN,
+        mode=AttentionMode.PREFILL,
+        dtype=AttentionDType.BF16,
+        batch_size=1,
+        query_sequence_length=seq_len,
+        head_dim=128,
+        causal=True,
+        causal_offsets=(0,),
+        sharding=sharding,
+        reduction=ReductionSpec(),
+        split_kv=SplitKVSpec.disabled(),
+        export_lse=True,
+    )
+
+
+def _capability(**overrides) -> AttentionBackendCapability:
+    fields = {
+        "backend_id": "test.strict.core",
+        "roles": frozenset({AttentionRole.TRAIN, AttentionRole.INFER}),
+        "modes": frozenset({AttentionMode.PREFILL}),
+        "dtypes": frozenset({AttentionDType.BF16}),
+        "cp_world_sizes": (1,),
+        "exports_attention_lse": True,
+        "reports_actual_split_kv_plan": True,
+        "implementation_kind": "production",
+    }
+    fields.update(overrides)
+    return AttentionBackendCapability(**fields)
+
+
+class _FakeCore:
+    pass
+
+
+@pytest.fixture()
+def registry(monkeypatch):
+    fresh = KernelRegistry()
+    # Serve a stand-in instance so dispatch never imports a vendor stack.
+    monkeypatch.setattr(fresh, "_get_or_create_backend", lambda backend: _FakeCore())
+    return fresh
+
+
+def _platform(registry) -> str:
+    return registry._platform()
+
+
+def test_only_rocm_autoregisters_and_only_when_the_vendor_stack_loads():
+    """Attention dispatch is opt-in per platform.
+
+    The strict ROCm core is the single auto-registration, and it appears only
+    when ``aiter.ops.mha`` really loaded.  No other platform gains an attention
+    candidate implicitly, so a CUDA or CPU WS2 caller still fails loudly rather
+    than being served something with different semantics.
+    """
+
+    fresh = KernelRegistry()
+
+    for platform, candidates in fresh._attention_candidates.items():
+        if platform == "rocm":
+            expected = (
+                [OpBackend.ROCM_STRICT_ATTENTION] if _rocm_strict_attention_available() else []
+            )
+            assert candidates == expected
+        else:
+            assert candidates == []
+
+
+def test_registered_backend_resolves_with_provenance(registry):
+    registry.register_attention_backend(
+        OpBackend.ROCM_STRICT_ATTENTION, _capability(), platform=_platform(registry)
+    )
+
+    result = registry.get_attention_op(_contract(), requested_backend="test.strict.core")
+
+    assert result.capability.backend_id == "test.strict.core"
+    assert result.provenance["actual_backend"] == "test.strict.core"
+    assert result.provenance["fallback"] is False
+    assert result.provenance["requested_backend"] == "test.strict.core"
+    assert result.provenance["contract"]["lse_domain"] == "attention"
+
+
+def test_unregistered_contract_fails_loudly_instead_of_falling_back(registry):
+    # Drop every auto-registered candidate: a platform with no attention
+    # backend must raise rather than resolve something from the legacy lists.
+    for candidates in registry._attention_candidates.values():
+        candidates.clear()
+
+    with pytest.raises(RuntimeError, match="No attention backend supports"):
+        registry.get_attention_op(_contract(), requested_backend="auto")
+
+
+def test_explicit_backend_id_never_resolves_to_a_different_backend(registry):
+    registry.register_attention_backend(
+        OpBackend.ROCM_STRICT_ATTENTION, _capability(), platform=_platform(registry)
+    )
+
+    with pytest.raises(RuntimeError, match="does not match requested_backend"):
+        registry.get_attention_op(_contract(), requested_backend="some.other.backend")
+
+
+def test_capability_mismatch_is_rejected_rather_than_approximated(registry):
+    # A backend that cannot export attention-domain LSE must not serve a
+    # contract that requires it.
+    registry.register_attention_backend(
+        OpBackend.ROCM_STRICT_ATTENTION,
+        _capability(exports_attention_lse=False),
+        platform=_platform(registry),
+    )
+
+    with pytest.raises(RuntimeError, match="LSE export is unsupported"):
+        registry.get_attention_op(_contract(), requested_backend="test.strict.core")
+
+
+def test_cp_contract_requires_deterministic_merge_support(registry):
+    registry.register_attention_backend(
+        OpBackend.ROCM_STRICT_ATTENTION,
+        _capability(cp_world_sizes=(1, 2)),
+        platform=_platform(registry),
+    )
+
+    with pytest.raises(RuntimeError, match="deterministic CP"):
+        registry.get_attention_op(_contract(cp_world_size=2), requested_backend="test.strict.core")
+
+
+def test_auto_is_rejected_under_context_parallelism(registry):
+    registry.register_attention_backend(
+        OpBackend.ROCM_STRICT_ATTENTION, _capability(), platform=_platform(registry)
+    )
+
+    with pytest.raises(AttentionContractError, match="Unsafe dispatch"):
+        registry.get_attention_op(_contract(cp_world_size=2), requested_backend="auto")
+
+
+def test_deterministic_is_not_a_dispatch_policy(registry):
+    with pytest.raises(AttentionContractError, match="not a dispatch policy"):
+        registry.get_attention_op(_contract(), requested_backend="deterministic")
+
+
+def test_implementation_kind_policy_filters_without_marking_fallback(registry):
+    registry.register_attention_backend(
+        OpBackend.ROCM_STRICT_ATTENTION, _capability(), platform=_platform(registry)
+    )
+
+    result = registry.get_attention_op(_contract(), requested_backend="production")
+    assert result.provenance["fallback"] is False
+
+    with pytest.raises(RuntimeError, match="does not satisfy requested_backend=reference"):
+        registry.get_attention_op(_contract(), requested_backend="reference")
+
+
+def test_reregistration_replaces_capability_without_duplicating(registry):
+    platform = _platform(registry)
+    registry.register_attention_backend(
+        OpBackend.ROCM_STRICT_ATTENTION, _capability(), platform=platform
+    )
+    registry.register_attention_backend(
+        OpBackend.ROCM_STRICT_ATTENTION,
+        _capability(implementation_kind="reference"),
+        platform=platform,
+    )
+
+    assert registry._attention_candidates[platform] == [OpBackend.ROCM_STRICT_ATTENTION]
+    capability = registry._attention_capabilities[platform][OpBackend.ROCM_STRICT_ATTENTION]
+    assert capability.implementation_kind == "reference"
+
+
+def test_register_rejects_wrong_types_and_unknown_platforms(registry):
+    with pytest.raises(AttentionContractError, match="must be an OpBackend"):
+        registry.register_attention_backend("not-a-backend", _capability())
+    with pytest.raises(AttentionContractError, match="must be an AttentionBackendCapability"):
+        registry.register_attention_backend(OpBackend.ROCM_STRICT_ATTENTION, object())
+    with pytest.raises(AttentionContractError, match="unsupported platform"):
+        registry.register_attention_backend(
+            OpBackend.ROCM_STRICT_ATTENTION, _capability(), platform="quantum"
+        )
+
+
+def test_legacy_priority_map_is_untouched_by_attention_registration(registry):
+    platform = _platform(registry)
+    before = {op: list(v) for op, v in registry._priority_map[platform].items()}
+
+    registry.register_attention_backend(
+        OpBackend.ROCM_STRICT_ATTENTION, _capability(), platform=platform
+    )
+
+    after = {op: list(v) for op, v in registry._priority_map[platform].items()}
+    assert before == after
+    # The strict core must not become reachable through legacy get_op keys.
+    for candidates in after.values():
+        assert OpBackend.ROCM_STRICT_ATTENTION not in candidates
+
+
+def test_contract_fingerprint_is_rank_independent():
+    left = _contract()
+    right_sharding = ShardingSpec(
+        tp_rank=1,
+        tp_world_size=2,
+        cp_rank=0,
+        cp_world_size=1,
+        global_q_heads=32,
+        global_kv_heads=8,
+        local_q_head_start=16,
+        local_q_heads=16,
+        local_kv_head_start=4,
+        local_kv_heads=4,
+        global_sequence_length=128,
+        local_sequence_length=128,
+        global_block_indices=(0,),
+        global_block_token_starts=(0,),
+        local_block_offsets=(0, 128),
+    )
+    left_tp2 = AttentionContract(
+        role=AttentionRole.TRAIN,
+        mode=AttentionMode.PREFILL,
+        dtype=AttentionDType.BF16,
+        batch_size=1,
+        query_sequence_length=128,
+        head_dim=128,
+        causal=True,
+        causal_offsets=(0,),
+        sharding=ShardingSpec(
+            tp_rank=0,
+            tp_world_size=2,
+            cp_rank=0,
+            cp_world_size=1,
+            global_q_heads=32,
+            global_kv_heads=8,
+            local_q_head_start=0,
+            local_q_heads=16,
+            local_kv_head_start=0,
+            local_kv_heads=4,
+            global_sequence_length=128,
+            local_sequence_length=128,
+            global_block_indices=(0,),
+            global_block_token_starts=(0,),
+            local_block_offsets=(0, 128),
+        ),
+        reduction=ReductionSpec(),
+        split_kv=SplitKVSpec.disabled(),
+        export_lse=True,
+    )
+    right_tp2 = AttentionContract(
+        role=AttentionRole.TRAIN,
+        mode=AttentionMode.PREFILL,
+        dtype=AttentionDType.BF16,
+        batch_size=1,
+        query_sequence_length=128,
+        head_dim=128,
+        causal=True,
+        causal_offsets=(0,),
+        sharding=right_sharding,
+        reduction=ReductionSpec(),
+        split_kv=SplitKVSpec.disabled(),
+        export_lse=True,
+    )
+
+    # Both TP ranks of one logical invocation agree; a different TP degree does not.
+    assert left_tp2.cross_rank_fingerprint() == right_tp2.cross_rank_fingerprint()
+    assert left.cross_rank_fingerprint() != left_tp2.cross_rank_fingerprint()
diff --git a/tests/test_vime_attention_provider.py b/tests/test_vime_attention_provider.py
new file mode 100644
index 00000000..3a8afc35
--- /dev/null
+++ b/tests/test_vime_attention_provider.py
@@ -0,0 +1,272 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Coverage for the optional Vime WS2 strict attention adapter.
+
+The validation-shaped cases need a real ROCm device with AITER present and are
+skipped elsewhere.  The contract/fail-closed cases are pure metadata checks and
+run anywhere, so a CUDA or CPU CI job still catches a provider that silently
+widens what it accepts.
+"""
+
+from __future__ import annotations
+
+import math
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from rl_engine.integrations.vime.attention import AttentionProviderUnavailable, attention_provider
+from rl_engine.kernels.registry import _rocm_strict_attention_available
+
+STRICT_ROCM = _rocm_strict_attention_available()
+requires_strict_rocm = pytest.mark.skipif(
+    not STRICT_ROCM,
+    reason="strict ROCm attention requires a ROCm device with aiter.ops.mha",
+)
+
+# Qwen3-8B dense head layout, TP=1 local view.
+GLOBAL_Q_HEADS = 32
+GLOBAL_KV_HEADS = 8
+HEAD_DIM = 128
+
+
+def _metadata(**overrides):
+    metadata = {
+        "global_q_heads": GLOBAL_Q_HEADS,
+        "global_kv_heads": GLOBAL_KV_HEADS,
+        "tp_rank": 0,
+        "tp_world_size": 1,
+        "attention_mode": "prefill",
+        "role": "train",
+        "causal": True,
+    }
+    metadata.update(overrides)
+    return metadata
+
+
+def _request(
+    *,
+    batch_size: int = 1,
+    seq_len: int = 128,
+    query_len: int | None = None,
+    dtype: torch.dtype = torch.bfloat16,
+    device: str = "cuda",
+    cp_world_size: int = 1,
+    cp_rank: int = 0,
+    cp_layout: str = "single",
+    seed: int = 0,
+    **metadata_overrides,
+):
+    generator = torch.Generator(device=device).manual_seed(seed)
+    shape_q = (batch_size, GLOBAL_Q_HEADS, query_len or seq_len, HEAD_DIM)
+    shape_kv = (batch_size, GLOBAL_KV_HEADS, seq_len, HEAD_DIM)
+    return SimpleNamespace(
+        query=torch.randn(*shape_q, generator=generator, device=device, dtype=dtype),
+        key=torch.randn(*shape_kv, generator=generator, device=device, dtype=dtype),
+        value=torch.randn(*shape_kv, generator=generator, device=device, dtype=dtype),
+        key_padding_mask=None,
+        tensor_parallel_group=None,
+        context_parallel=SimpleNamespace(world_size=cp_world_size, rank=cp_rank, layout=cp_layout),
+        metadata=_metadata(**metadata_overrides),
+    )
+
+
+def _cpu_request(**kwargs):
+    """A request whose rejection happens before any device work."""
+
+    kwargs.setdefault("device", "cpu")
+    kwargs.setdefault("seq_len", 8)
+    return _request(**kwargs)
+
+
+# ---------------------------------------------------------------------------
+# Contract and fail-closed behavior (device independent)
+# ---------------------------------------------------------------------------
+
+
+def test_cp_greater_than_one_refuses_the_cross_rank_merge():
+    request = _cpu_request(cp_world_size=2, cp_rank=1, cp_layout="zigzag")
+
+    with pytest.raises(AttentionProviderUnavailable, match="strict CP transport path"):
+        attention_provider(request)
+
+
+def test_decode_without_kv_cache_identity_fails_closed():
+    request = _cpu_request(attention_mode="decode")
+
+    with pytest.raises(AttentionProviderUnavailable, match="KV-cache identity"):
+        attention_provider(request)
+
+
+@pytest.mark.parametrize(
+    "overrides",
+    [
+        {"dropout_p": 0.1},
+        {"sliding_window": 128},
+        {"logit_soft_cap": 30.0},
+        {"alibi_slopes": [0.1]},
+        {"window_size": (256, 0)},
+    ],
+)
+def test_distribution_changing_knobs_fail_closed(overrides):
+    request = _cpu_request(**overrides)
+
+    with pytest.raises(AttentionProviderUnavailable):
+        attention_provider(request)
+
+
+def test_key_padding_mask_is_refused():
+    request = _cpu_request()
+    request.key_padding_mask = torch.ones(1, 8, dtype=torch.bool)
+
+    with pytest.raises(AttentionProviderUnavailable, match="unpadded logical row"):
+        attention_provider(request)
+
+
+def test_fp32_is_refused():
+    request = _cpu_request(dtype=torch.float32)
+
+    with pytest.raises(AttentionProviderUnavailable, match="BF16/FP16"):
+        attention_provider(request)
+
+
+def test_head_counts_must_cover_the_tp_group_exactly():
+    request = _cpu_request(global_q_heads=GLOBAL_Q_HEADS * 2)
+
+    with pytest.raises(AttentionProviderUnavailable, match="do not cover global_q_heads"):
+        attention_provider(request)
+
+
+def test_declared_tp_rank_must_agree_with_the_group():
+    request = _cpu_request(tp_rank=3)
+
+    with pytest.raises(AttentionProviderUnavailable, match="disagrees with TP group rank"):
+        attention_provider(request)
+
+
+def test_cp_layout_must_describe_local_ownership():
+    request = _cpu_request(cp_layout="unknown")
+
+    with pytest.raises(AttentionProviderUnavailable, match="local CP token ownership"):
+        attention_provider(request)
+
+
+def test_non_contiguous_key_positions_are_refused():
+    request = _cpu_request(key_position_ids=[0, 1, 2, 3, 9, 10, 11, 12])
+
+    with pytest.raises(AttentionProviderUnavailable, match="contiguous increasing"):
+        attention_provider(request)
+
+
+# ---------------------------------------------------------------------------
+# Strict arithmetic (requires a ROCm device with AITER)
+# ---------------------------------------------------------------------------
+
+
+@requires_strict_rocm
+def test_provider_exports_attention_lse_and_strict_provenance():
+    result = attention_provider(_request(seq_len=256))
+
+    assert result.backend_id == "aiter.rocm.ck_dense_mha"
+    assert result.out.shape == (1, GLOBAL_Q_HEADS, 256, HEAD_DIM)
+    assert result.lse.shape == (1, GLOBAL_Q_HEADS, 256)
+    assert result.lse.dtype == torch.float32
+    assert result.provenance["fallback"] is False
+    assert result.provenance["actual_backend"] == "aiter.rocm.ck_dense_mha"
+    assert result.provenance["lse_domain"] == "attention"
+
+    core = result.provenance["core"]
+    assert core["native_attention_arithmetic"] is True
+    assert core["deterministic_backward"] is True
+    assert core["num_splits"] == 1
+    assert core["fallback"] is False
+    assert core["merge_order"] == "global_block_index"
+    assert core["accum_dtype"] == "fp32"
+    assert core["downcast_at"] == "final_write"
+
+
+@requires_strict_rocm
+def test_training_and_rollout_roles_are_bitwise_identical():
+    train = attention_provider(_request(seq_len=256, role="train", seed=3))
+    rollout = attention_provider(_request(seq_len=256, role="infer", seed=3))
+
+    assert torch.equal(train.out, rollout.out)
+    assert torch.equal(train.lse, rollout.lse)
+
+
+@requires_strict_rocm
+@pytest.mark.parametrize("batch_size", [2, 4])
+@pytest.mark.parametrize("seq_len", [256, 512, 2048])
+def test_batch_composition_is_bitwise_invariant(batch_size, seq_len):
+    """A batch must equal the same rows submitted one at a time.
+
+    Raw AITER does not provide this for every shape: measured on MI300X it is
+    batch-composition sensitive in BF16 at ``S=256`` (B=4) and ``S=512`` (B=2
+    and B=4), while holding at 128/1024/2048/4096.  Shape-dependent breakage is
+    exactly what a per-row rule has to defend against, because the shapes that
+    hold would otherwise make the bug look absent.  ``S=512`` is kept in this
+    parametrization deliberately.
+    """
+
+    batched_request = _request(batch_size=batch_size, seq_len=seq_len, seed=11)
+    batched = attention_provider(batched_request)
+
+    for row in range(batch_size):
+        single = SimpleNamespace(
+            query=batched_request.query[row : row + 1],
+            key=batched_request.key[row : row + 1],
+            value=batched_request.value[row : row + 1],
+            key_padding_mask=None,
+            tensor_parallel_group=None,
+            context_parallel=batched_request.context_parallel,
+            metadata=batched_request.metadata,
+        )
+        row_result = attention_provider(single)
+        assert torch.equal(batched.out[row : row + 1], row_result.out)
+        assert torch.equal(batched.lse[row : row + 1], row_result.lse)
+
+
+@requires_strict_rocm
+def test_repeated_invocations_are_bitwise_identical():
+    first = attention_provider(_request(seq_len=512, seed=5))
+    second = attention_provider(_request(seq_len=512, seed=5))
+
+    assert torch.equal(first.out, second.out)
+    assert torch.equal(first.lse, second.lse)
+
+
+@requires_strict_rocm
+def test_backward_gradients_are_deterministic():
+    def run():
+        request = _request(seq_len=256, seed=17)
+        request.query.requires_grad_(True)
+        request.key.requires_grad_(True)
+        request.value.requires_grad_(True)
+        result = attention_provider(request)
+        result.out.backward(torch.ones_like(result.out))
+        return request.query.grad, request.key.grad, request.value.grad
+
+    first = run()
+    second = run()
+    for lhs, rhs in zip(first, second, strict=True):
+        assert torch.equal(lhs, rhs)
+
+
+@requires_strict_rocm
+def test_contract_fingerprint_is_rank_independent():
+    left = attention_provider(_request(seq_len=128, seed=2))
+    right = attention_provider(_request(seq_len=128, seed=2))
+
+    assert left.contract_id == right.contract_id
+    assert len(left.contract_id) == 64
+
+
+@requires_strict_rocm
+def test_explicit_scale_is_honored():
+    scale = 1.0 / math.sqrt(HEAD_DIM)
+    default = attention_provider(_request(seq_len=128, seed=8))
+    explicit = attention_provider(_request(seq_len=128, seed=8, softmax_scale=scale))
+
+    assert torch.equal(default.out, explicit.out)

From 3446042f78e1893b3cb81c7a63396d5bc865625d Mon Sep 17 00:00:00 2001
From: zhangj1an <jianmusings@gmail.com>
Date: Mon, 24 Aug 2026 16:11:34 +0000
Subject: [PATCH 15/35] feat(attention): bind TP/CP degree and add multi-rank
 strict validation

The strict ROCm path is not TP-degree invariant. TP shards attention by head
and performs no cross-rank reduction, so a rank's head slice ought to match
the same slice of an unsharded run - but AITER's reduction order depends on
how many heads shared the launch. Measured on MI300X (BF16, Hq=32/Hkv=8/
D=128, causal), local shard vs unsharded:

    S=512   TP=2/4/8   bitwise
    S=1024  TP=2/4/8   out max abs 3.90625e-03
    S=2048  TP=4/8     out max abs 7.8125e-03   (TP=2 bitwise)
    S=4096  TP=8       out max abs 7.8125e-03   (TP=2/4 bitwise)

This is the batch-composition sensitivity again, on the head axis, and it is
just as shape-dependent: training on TP=4 and rolling out on TP=8 would not
compare bitwise, while most shapes would look fine.

Executing one KV group per launch removes the dependence entirely (verified 0
at TP=1 vs 4 vs 8) but costs 2.7-3.9x forward time. We bind the degree
instead:

- validate_cross_config_alignment(training, rollout) fails closed and names
  the field that diverged; CROSS_CONFIG_BOUND_DEGREES records what is bound.
- cross_rank_fingerprint() already includes tp/cp_world_size, so the standard
  distributed preflight separates degrees without extra work.
- Provider results carry a cross_config_binding provenance block recording
  tp_degree_invariant=false and the bound degrees.

Multi-rank strict CP now validated on 8xMI300X via rccl_ag_rs, out/lse/dQ/dK/
dV bitwise at 2 ranks (TP=1,CP=2), 4 ranks (TP=2,CP=2), and 8 ranks (TP=2,
CP=2, 2 replicas). This needs the native extension built for the platform;
without it the strict path fails closed on the ROCm deterministic RoPE
operator rather than substituting a different one. Artifacts under
benchmarks/results/pr319_rocm_mi300x/distributed/.

With the extension built the suite goes from 27 failed / 1102 passed / 607
errors to 6 failed / 1797 passed / 0 errors; the 6 remaining are pre-existing
and unrelated (3 multi-process collectives, a benchmarks package shadowed by
site-packages, a CPU/ROCm linear_logp routing mismatch, one ws1 chain case).

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QfN3b2ep5DvxVDir7J36j3
---
 benchmarks/benchmark_rocm_attention.py        |   87 +-
 .../pr319_rocm_mi300x/distributed/report.md   |   16 +
 .../distributed/strict_rccl_ag_rs_w2.json     |  582 +++++
 .../distributed/strict_rccl_ag_rs_w4.json     | 1142 +++++++++
 .../distributed/strict_rccl_ag_rs_w8.json     | 2262 +++++++++++++++++
 .../results/pr319_rocm_mi300x/report.md       |   62 +-
 .../results/pr319_rocm_mi300x/results.json    |  258 +-
 rl_engine/integrations/vime/attention.py      |   18 +
 rl_engine/kernels/attention_contract.py       |   77 +
 tests/test_attention_dispatch.py              |  129 +
 tests/test_vime_attention_provider.py         |   48 +
 11 files changed, 4587 insertions(+), 94 deletions(-)
 create mode 100644 benchmarks/results/pr319_rocm_mi300x/distributed/report.md
 create mode 100644 benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w2.json
 create mode 100644 benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w4.json
 create mode 100644 benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w8.json

diff --git a/benchmarks/benchmark_rocm_attention.py b/benchmarks/benchmark_rocm_attention.py
index addaa553..e3c250e2 100644
--- a/benchmarks/benchmark_rocm_attention.py
+++ b/benchmarks/benchmark_rocm_attention.py
@@ -19,13 +19,16 @@
     ``aiter.rocm.ck_dense_mha`` through the WS2 contract dispatch, i.e. the path
     this PR adds.
 
-Two extra sections quantify what the strict contract actually costs, because
-both are properties of the vendor backward rather than of the integration:
+Three extra sections quantify what the strict contract actually costs and
+where it stops holding.  All three are properties of the vendor kernel rather
+than of the integration:
 
 * ``determinism_cost`` — AITER ``mha_bwd`` with ``deterministic`` on vs off.
 * ``batch_composition`` — whether raw AITER returns the same bits for a batch
   and for the same rows submitted one at a time, swept over shapes because the
   answer varies with shape.
+* ``tp_head_count_sensitivity`` — whether a head shard depends on how many
+  heads shared its launch, i.e. whether the path is TP-degree invariant.
 """
 
 from __future__ import annotations
@@ -288,6 +291,77 @@ def forward(query, key, value):
     return rows
 
 
+def _tp_head_count_sensitivity(q_heads, kv_heads, head_dim, dtype, seq_lens, tp_degrees):
+    """Does a head shard depend on how many heads shared its launch?
+
+    TP shards attention by head and performs no cross-rank reduction, so a rank
+    computing its own head slice ought to match the corresponding slice of an
+    unsharded run.  Where it does not, the strict path is not TP-degree
+    invariant and training/rollout must be pinned to one degree.
+    """
+
+    from aiter.ops.mha import mha_fwd
+
+    scale = 1.0 / math.sqrt(head_dim)
+
+    def forward(query, key, value):
+        out, lse, _mask, _rng = mha_fwd(
+            query.transpose(1, 2).contiguous(),
+            key.transpose(1, 2).contiguous(),
+            value.transpose(1, 2).contiguous(),
+            0.0,
+            scale,
+            True,
+            -1,
+            -1,
+            0,
+            True,
+            False,
+        )
+        return out.transpose(1, 2).contiguous(), lse
+
+    rows = []
+    for seq_len in seq_lens:
+        query, key, value = _tensors(1, q_heads, kv_heads, seq_len, head_dim, dtype, seed=3)
+        full_out, full_lse = forward(query, key, value)
+        for tp in tp_degrees:
+            if q_heads % tp or kv_heads % tp:
+                continue
+            local_q, local_kv = q_heads // tp, kv_heads // tp
+            worst_out = worst_lse = 0.0
+            for rank in range(tp):
+                shard_out, shard_lse = forward(
+                    query[:, rank * local_q : (rank + 1) * local_q],
+                    key[:, rank * local_kv : (rank + 1) * local_kv],
+                    value[:, rank * local_kv : (rank + 1) * local_kv],
+                )
+                ref_out = full_out[:, rank * local_q : (rank + 1) * local_q]
+                ref_lse = full_lse[:, rank * local_q : (rank + 1) * local_q]
+                worst_out = max(worst_out, (shard_out - ref_out).abs().max().item())
+                worst_lse = max(worst_lse, (shard_lse - ref_lse).abs().max().item())
+            invariant = worst_out == 0.0 and worst_lse == 0.0
+            rows.append(
+                {
+                    "seq_len": seq_len,
+                    "tp": tp,
+                    "local_q_heads": local_q,
+                    "local_kv_heads": local_kv,
+                    "out_max_abs": worst_out,
+                    "lse_max_abs": worst_lse,
+                    "tp_degree_invariant": invariant,
+                }
+            )
+            print(
+                f"tp-sensitivity S={seq_len:5d} TP={tp} (Hq={local_q},Hkv={local_kv}) "
+                f"out {worst_out:.6e} lse {worst_lse:.6e} "
+                f"{'invariant' if invariant else 'NOT INVARIANT'}",
+                flush=True,
+            )
+        del query, key, value, full_out, full_lse
+        torch.cuda.empty_cache()
+    return rows
+
+
 def main() -> None:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--output", type=Path, default=Path("attention_results.json"))
@@ -359,6 +433,14 @@ def main() -> None:
     composition = _batch_composition(
         args.q_heads, args.kv_heads, args.head_dim, dtype, composition_shapes
     )
+    tp_sensitivity = _tp_head_count_sensitivity(
+        args.q_heads,
+        args.kv_heads,
+        args.head_dim,
+        dtype,
+        sorted({512, *seq_lens}),
+        (2, 4, 8),
+    )
 
     properties = torch.cuda.get_device_properties(0)
     payload = {
@@ -376,6 +458,7 @@ def main() -> None:
         "latency": rows,
         "determinism_cost": determinism,
         "batch_composition": composition,
+        "tp_head_count_sensitivity": tp_sensitivity,
     }
     args.output.parent.mkdir(parents=True, exist_ok=True)
     args.output.write_text(json.dumps(payload, indent=2))
diff --git a/benchmarks/results/pr319_rocm_mi300x/distributed/report.md b/benchmarks/results/pr319_rocm_mi300x/distributed/report.md
new file mode 100644
index 00000000..e46222af
--- /dev/null
+++ b/benchmarks/results/pr319_rocm_mi300x/distributed/report.md
@@ -0,0 +1,16 @@
+# PR #319 — strict ROCm CP attention, multi-rank acceptance (MI300X)
+
+`torchrun --standalone --nproc-per-node=N scripts/ws2_p2p_nccl_attention_reference_check.py \
+  --transport rccl_ag_rs --strict-shared-core`
+
+Requires the native extension built for the active GPU platform
+(`PYTORCH_ROCM_ARCH=gfx942 python setup.py build_ext --inplace`); without it the strict path
+fails closed on the ROCm deterministic RoPE operator rather than running a different one.
+
+| world | TP | CP | replicas | transport | ranks passed | out | lse | dQ | dK | dV |
+|---:|---:|---:|---:|---|---|---|---|---|---|---|
+| 2 | 1 | 2 | 1 | rccl_ag_rs | 2/2 | bitwise | bitwise | bitwise | bitwise | bitwise |
+| 4 | 2 | 2 | 1 | rccl_ag_rs | 4/4 | bitwise | bitwise | bitwise | bitwise | bitwise |
+| 8 | 2 | 2 | 2 | rccl_ag_rs | 8/8 | bitwise | bitwise | bitwise | bitwise | bitwise |
+
+Every rank reports `strict_shared_core.executed=true` and `passed=true`.
diff --git a/benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w2.json b/benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w2.json
new file mode 100644
index 00000000..b7f1a00d
--- /dev/null
+++ b/benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w2.json
@@ -0,0 +1,582 @@
+{
+  "backend": "nccl",
+  "collective_version": [
+    2,
+    28,
+    9
+  ],
+  "cp_world_size": 2,
+  "device_name": "AMD Instinct MI300X",
+  "git_commit": "3247e3ef390ac2f55bf93864ffa70b0a1350ec4c",
+  "global_failure_count": 0,
+  "platform": "rocm",
+  "ranks": [
+    {
+      "accum_dtype": "fp32",
+      "atol": 0.0002,
+      "cp_rank": 0,
+      "cp_world_size": 2,
+      "device": "cuda:0",
+      "downcast_at": "final_write",
+      "dtype": "bf16",
+      "expected_block_manifest": [
+        {
+          "global_block_index": 0,
+          "kv_block_end": 4,
+          "kv_block_start": 0,
+          "owner_cp_rank": 0,
+          "owner_tp_rank": 0
+        },
+        {
+          "global_block_index": 1,
+          "kv_block_end": 8,
+          "kv_block_start": 4,
+          "owner_cp_rank": 0,
+          "owner_tp_rank": 0
+        },
+        {
+          "global_block_index": 2,
+          "kv_block_end": 12,
+          "kv_block_start": 8,
+          "owner_cp_rank": 1,
+          "owner_tp_rank": 0
+        },
+        {
+          "global_block_index": 3,
+          "kv_block_end": 16,
+          "kv_block_start": 12,
+          "owner_cp_rank": 1,
+          "owner_tp_rank": 0
+        }
+      ],
+      "final_out_max_abs": 0.00390625,
+      "final_output_dtype": "bfloat16",
+      "final_write_atol": 0.02,
+      "gathered_block_indices": [
+        0,
+        1,
+        2,
+        3
+      ],
+      "global_failure_count": 0,
+      "global_world_size": 2,
+      "local_block_indices": [
+        0,
+        1
+      ],
+      "lse_max_abs": 2.384185791015625e-07,
+      "out_max_abs": 7.152557373046875e-07,
+      "passed": true,
+      "protocol": "ag_query_local_kv_rs_out_lse",
+      "query_ag": "rccl_ag_rs",
+      "query_ag_max_abs": 0.0,
+      "query_range": [
+        0,
+        8
+      ],
+      "rank": 0,
+      "repeat_count": 3,
+      "repeat_lse_bitwise": true,
+      "repeat_manifest_bitwise": true,
+      "repeat_out_bitwise": true,
+      "repeat_query_bitwise": true,
+      "replica_count": 1,
+      "replica_index": 0,
+      "strict_protocol": "ag_qkv_positions_shared_core_rs_out_lse",
+      "strict_shared_core": {
+        "actual_backend": "aiter.rocm.ck_dense_mha",
+        "bitwise": {
+          "dk": true,
+          "dq": true,
+          "dv": true,
+          "lse": true,
+          "out": true
+        },
+        "communication_autograd": true,
+        "communication_backend": "rccl_ag_rs",
+        "executed": true,
+        "fallback": false,
+        "identity_errors": [],
+        "max_abs": {
+          "dk": 0.0,
+          "dq": 0.0,
+          "dv": 0.0,
+          "lse": 0.0,
+          "out": 0.0
+        },
+        "native_attention_arithmetic": true,
+        "passed": true,
+        "production_ready": true,
+        "repeat_lse_bitwise": true,
+        "repeat_out_bitwise": true,
+        "split_kv_policy": "disabled",
+        "strict_core_id": "rlkernel.attention.rocm.aiter_ck_dense_mha.v1",
+        "strict_mode": true,
+        "strict_provenance": {
+          "accum_dtype": "fp32",
+          "actual_backend": "aiter.rocm.ck_dense_mha",
+          "adapter_backend": "flashinfer",
+          "aiter_api_source": "aiter.ops.mha",
+          "aiter_source_sha256": "db61d7ce62a907c5079830622723f0abcc7fd35816d88006e381140cb6566ff4",
+          "arithmetic_plan_source": "aiter.ops.mha",
+          "arithmetic_semantics_verified": true,
+          "attention_backend": "aiter.rocm.ck_dense_mha",
+          "attention_mode": "prefill",
+          "batch_invariant_claim": "strict_runtime_verified",
+          "causal": true,
+          "communication_backend": "rccl_ag_rs",
+          "communication_overlap": "disabled",
+          "compute_communication": "decoupled",
+          "compute_order": [
+            0,
+            1
+          ],
+          "compute_schedule": "rlkernel.attention.strict_ring_state.v1",
+          "cp_comm_accum_dtype": "fp32",
+          "cp_comm_attention_numeric_reduction": false,
+          "cp_comm_backend": "rccl_ag_rs",
+          "cp_comm_compute_communication": "decoupled",
+          "cp_comm_contract": "partial_out_lse_global_block_index",
+          "cp_comm_expected_blocks": [
+            {
+              "global_block_index": 0,
+              "kv_block_end": 4,
+              "kv_block_start": 0,
+              "owner_cp_rank": 0,
+              "owner_tp_rank": 0
+            },
+            {
+              "global_block_index": 1,
+              "kv_block_end": 8,
+              "kv_block_start": 4,
+              "owner_cp_rank": 0,
+              "owner_tp_rank": 0
+            },
+            {
+              "global_block_index": 2,
+              "kv_block_end": 12,
+              "kv_block_start": 8,
+              "owner_cp_rank": 1,
+              "owner_tp_rank": 0
+            },
+            {
+              "global_block_index": 3,
+              "kv_block_end": 16,
+              "kv_block_start": 12,
+              "owner_cp_rank": 1,
+              "owner_tp_rank": 0
+            }
+          ],
+          "cp_comm_expected_kv_token_range": [
+            0,
+            16
+          ],
+          "cp_comm_merge_order": "global_block_index",
+          "cp_comm_merge_root_cp_rank": 0,
+          "cp_comm_pattern": "ag_rs",
+          "cp_comm_query_token_ranges": [
+            [
+              0,
+              8
+            ],
+            [
+              8,
+              16
+            ]
+          ],
+          "cp_comm_required": true,
+          "cp_comm_return_lse": true,
+          "cp_comm_runtime": "rccl",
+          "cp_comm_status": "implemented",
+          "cp_comm_strict_backward": "rs_out_backward_ag_then_ag_qkv_backward_rs",
+          "cp_comm_strict_contract": "ag_qkv_positions_shared_core_rs_out_lse",
+          "cp_comm_strict_kv_communication": "all_gather",
+          "cp_comm_strict_position_communication": "all_gather",
+          "cp_rank": 0,
+          "cp_world_size": 2,
+          "deterministic_backward": true,
+          "downcast_at": "final_write",
+          "fa_api_source": null,
+          "fa_package_version": null,
+          "fallback": false,
+          "fallback_reason": null,
+          "k_cache_rope_state": "post_rope",
+          "lse_domain": "attention",
+          "lse_dtype": "fp32",
+          "lse_exported": true,
+          "materialization": "ag_qkv_positions_shared_core_rs",
+          "merge_order_indices": [
+            0,
+            1
+          ],
+          "native_attention_arithmetic": true,
+          "num_splits": 1,
+          "platform": "rocm",
+          "production_ready": true,
+          "q_rope_state": "post_rope",
+          "reference_only": false,
+          "requested_backend": "flashinfer_layout_adapter",
+          "ring_partial_arithmetic": false,
+          "ring_schedule_default": true,
+          "rope_backend": "rlkernel.rocm.deterministic_rope",
+          "rope_fusion": false,
+          "rope_fusion_boundary": "rlkernel_rope_then_attention",
+          "rope_theta": 1000000.0,
+          "rotary_dim": null,
+          "softmax_scale": null,
+          "split_kv_control": "dense_non_split_api",
+          "strict_comm_autograd": true,
+          "strict_core_id": "rlkernel.attention.rocm.aiter_ck_dense_mha.v1",
+          "strict_core_row_plans": [
+            {
+              "actual_split_boundaries": [
+                [
+                  0,
+                  16
+                ]
+              ],
+              "actual_split_kv_count": 1,
+              "actual_split_kv_policy": "disabled",
+              "actual_split_kv_size": null,
+              "requested_split_kv_policy": "disabled",
+              "requested_split_kv_size": null,
+              "split_kv_accum_dtype": "fp32",
+              "split_kv_backend": "aiter.rocm.ck_dense_mha",
+              "split_kv_downcast_at": "final_write",
+              "split_kv_fallback": false,
+              "split_kv_fallback_reason": null,
+              "split_kv_merge_order": "global_block_index",
+              "split_kv_plan_source": "contract_exact"
+            },
+            {
+              "actual_split_boundaries": [
+                [
+                  0,
+                  16
+                ]
+              ],
+              "actual_split_kv_count": 1,
+              "actual_split_kv_policy": "disabled",
+              "actual_split_kv_size": null,
+              "requested_split_kv_policy": "disabled",
+              "requested_split_kv_size": null,
+              "split_kv_accum_dtype": "fp32",
+              "split_kv_backend": "aiter.rocm.ck_dense_mha",
+              "split_kv_downcast_at": "final_write",
+              "split_kv_fallback": false,
+              "split_kv_fallback_reason": null,
+              "split_kv_merge_order": "global_block_index",
+              "split_kv_plan_source": "contract_exact"
+            }
+          ],
+          "strict_full_qkv_all_gather": true,
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+}
diff --git a/benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w4.json b/benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w4.json
new file mode 100644
index 00000000..72c267f1
--- /dev/null
+++ b/benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w4.json
@@ -0,0 +1,1142 @@
+{
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+  "transport": "rccl_ag_rs",
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+}
diff --git a/benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w8.json b/benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w8.json
new file mode 100644
index 00000000..0c9a7d1a
--- /dev/null
+++ b/benchmarks/results/pr319_rocm_mi300x/distributed/strict_rccl_ag_rs_w8.json
@@ -0,0 +1,2262 @@
+{
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+        "communication_autograd": true,
+        "communication_backend": "rccl_ag_rs",
+        "executed": true,
+        "fallback": false,
+        "identity_errors": [],
+        "max_abs": {
+          "dk": 0.0,
+          "dq": 0.0,
+          "dv": 0.0,
+          "lse": 0.0,
+          "out": 0.0
+        },
+        "native_attention_arithmetic": true,
+        "passed": true,
+        "production_ready": true,
+        "repeat_lse_bitwise": true,
+        "repeat_out_bitwise": true,
+        "split_kv_policy": "disabled",
+        "strict_core_id": "rlkernel.attention.rocm.aiter_ck_dense_mha.v1",
+        "strict_mode": true,
+        "strict_provenance": {
+          "accum_dtype": "fp32",
+          "actual_backend": "aiter.rocm.ck_dense_mha",
+          "adapter_backend": "flashinfer",
+          "aiter_api_source": "aiter.ops.mha",
+          "aiter_source_sha256": "db61d7ce62a907c5079830622723f0abcc7fd35816d88006e381140cb6566ff4",
+          "arithmetic_plan_source": "aiter.ops.mha",
+          "arithmetic_semantics_verified": true,
+          "attention_backend": "aiter.rocm.ck_dense_mha",
+          "attention_mode": "prefill",
+          "batch_invariant_claim": "strict_runtime_verified",
+          "causal": true,
+          "communication_backend": "rccl_ag_rs",
+          "communication_overlap": "disabled",
+          "compute_communication": "decoupled",
+          "compute_order": [
+            0,
+            1
+          ],
+          "compute_schedule": "rlkernel.attention.strict_ring_state.v1",
+          "cp_comm_accum_dtype": "fp32",
+          "cp_comm_attention_numeric_reduction": false,
+          "cp_comm_backend": "rccl_ag_rs",
+          "cp_comm_compute_communication": "decoupled",
+          "cp_comm_contract": "partial_out_lse_global_block_index",
+          "cp_comm_expected_blocks": [
+            {
+              "global_block_index": 0,
+              "kv_block_end": 4,
+              "kv_block_start": 0,
+              "owner_cp_rank": 0,
+              "owner_tp_rank": 1
+            },
+            {
+              "global_block_index": 1,
+              "kv_block_end": 8,
+              "kv_block_start": 4,
+              "owner_cp_rank": 0,
+              "owner_tp_rank": 1
+            },
+            {
+              "global_block_index": 2,
+              "kv_block_end": 12,
+              "kv_block_start": 8,
+              "owner_cp_rank": 1,
+              "owner_tp_rank": 1
+            },
+            {
+              "global_block_index": 3,
+              "kv_block_end": 16,
+              "kv_block_start": 12,
+              "owner_cp_rank": 1,
+              "owner_tp_rank": 1
+            }
+          ],
+          "cp_comm_expected_kv_token_range": [
+            0,
+            16
+          ],
+          "cp_comm_merge_order": "global_block_index",
+          "cp_comm_merge_root_cp_rank": 0,
+          "cp_comm_pattern": "ag_rs",
+          "cp_comm_query_token_ranges": [
+            [
+              0,
+              8
+            ],
+            [
+              8,
+              16
+            ]
+          ],
+          "cp_comm_required": true,
+          "cp_comm_return_lse": true,
+          "cp_comm_runtime": "rccl",
+          "cp_comm_status": "implemented",
+          "cp_comm_strict_backward": "rs_out_backward_ag_then_ag_qkv_backward_rs",
+          "cp_comm_strict_contract": "ag_qkv_positions_shared_core_rs_out_lse",
+          "cp_comm_strict_kv_communication": "all_gather",
+          "cp_comm_strict_position_communication": "all_gather",
+          "cp_rank": 0,
+          "cp_world_size": 2,
+          "deterministic_backward": true,
+          "downcast_at": "final_write",
+          "fa_api_source": null,
+          "fa_package_version": null,
+          "fallback": false,
+          "fallback_reason": null,
+          "k_cache_rope_state": "post_rope",
+          "lse_domain": "attention",
+          "lse_dtype": "fp32",
+          "lse_exported": true,
+          "materialization": "ag_qkv_positions_shared_core_rs",
+          "merge_order_indices": [
+            0,
+            1
+          ],
+          "native_attention_arithmetic": true,
+          "num_splits": 1,
+          "platform": "rocm",
+          "production_ready": true,
+          "q_rope_state": "post_rope",
+          "reference_only": false,
+          "requested_backend": "flashinfer_layout_adapter",
+          "ring_partial_arithmetic": false,
+          "ring_schedule_default": true,
+          "rope_backend": "rlkernel.rocm.deterministic_rope",
+          "rope_fusion": false,
+          "rope_fusion_boundary": "rlkernel_rope_then_attention",
+          "rope_theta": 1000000.0,
+          "rotary_dim": null,
+          "softmax_scale": null,
+          "split_kv_control": "dense_non_split_api",
+          "strict_comm_autograd": true,
+          "strict_core_id": "rlkernel.attention.rocm.aiter_ck_dense_mha.v1",
+          "strict_core_row_plans": [
+            {
+              "actual_split_boundaries": [
+                [
+                  0,
+                  16
+                ]
+              ],
+              "actual_split_kv_count": 1,
+              "actual_split_kv_policy": "disabled",
+              "actual_split_kv_size": null,
+              "requested_split_kv_policy": "disabled",
+              "requested_split_kv_size": null,
+              "split_kv_accum_dtype": "fp32",
+              "split_kv_backend": "aiter.rocm.ck_dense_mha",
+              "split_kv_downcast_at": "final_write",
+              "split_kv_fallback": false,
+              "split_kv_fallback_reason": null,
+              "split_kv_merge_order": "global_block_index",
+              "split_kv_plan_source": "contract_exact"
+            },
+            {
+              "actual_split_boundaries": [
+                [
+                  0,
+                  16
+                ]
+              ],
+              "actual_split_kv_count": 1,
+              "actual_split_kv_policy": "disabled",
+              "actual_split_kv_size": null,
+              "requested_split_kv_policy": "disabled",
+              "requested_split_kv_size": null,
+              "split_kv_accum_dtype": "fp32",
+              "split_kv_backend": "aiter.rocm.ck_dense_mha",
+              "split_kv_downcast_at": "final_write",
+              "split_kv_fallback": false,
+              "split_kv_fallback_reason": null,
+              "split_kv_merge_order": "global_block_index",
+              "split_kv_plan_source": "contract_exact"
+            }
+          ],
+          "strict_full_qkv_all_gather": true,
+          "strict_local_kv_range": [
+            0,
+            8
+          ],
+          "strict_local_query_range": [
+            0,
+            8
+          ],
+          "strict_mode": true,
+          "strict_position_ids_all_gather": true,
+          "strict_schedule": "single_batch_aiter_ck_dense_mha_no_splitkv",
+          "strict_split_kv": "disabled",
+          "tp_rank": 1,
+          "tp_world_size": 2
+        },
+        "strict_schedule": "single_batch_aiter_ck_dense_mha_no_splitkv"
+      },
+      "tp_rank": 1,
+      "tp_world_size": 2,
+      "transport": "rccl_ag_rs"
+    },
+    {
+      "accum_dtype": "fp32",
+      "atol": 0.0002,
+      "cp_rank": 1,
+      "cp_world_size": 2,
+      "device": "cuda:7",
+      "downcast_at": "final_write",
+      "dtype": "bf16",
+      "expected_block_manifest": [
+        {
+          "global_block_index": 0,
+          "kv_block_end": 4,
+          "kv_block_start": 0,
+          "owner_cp_rank": 0,
+          "owner_tp_rank": 1
+        },
+        {
+          "global_block_index": 1,
+          "kv_block_end": 8,
+          "kv_block_start": 4,
+          "owner_cp_rank": 0,
+          "owner_tp_rank": 1
+        },
+        {
+          "global_block_index": 2,
+          "kv_block_end": 12,
+          "kv_block_start": 8,
+          "owner_cp_rank": 1,
+          "owner_tp_rank": 1
+        },
+        {
+          "global_block_index": 3,
+          "kv_block_end": 16,
+          "kv_block_start": 12,
+          "owner_cp_rank": 1,
+          "owner_tp_rank": 1
+        }
+      ],
+      "final_out_max_abs": 0.001953125,
+      "final_output_dtype": "bfloat16",
+      "final_write_atol": 0.02,
+      "gathered_block_indices": [
+        0,
+        1,
+        2,
+        3
+      ],
+      "global_failure_count": 0,
+      "global_world_size": 8,
+      "local_block_indices": [
+        2,
+        3
+      ],
+      "lse_max_abs": 4.76837158203125e-07,
+      "out_max_abs": 7.152557373046875e-07,
+      "passed": true,
+      "protocol": "ag_query_local_kv_rs_out_lse",
+      "query_ag": "rccl_ag_rs",
+      "query_ag_max_abs": 0.0,
+      "query_range": [
+        8,
+        16
+      ],
+      "rank": 7,
+      "repeat_count": 3,
+      "repeat_lse_bitwise": true,
+      "repeat_manifest_bitwise": true,
+      "repeat_out_bitwise": true,
+      "repeat_query_bitwise": true,
+      "replica_count": 2,
+      "replica_index": 1,
+      "strict_protocol": "ag_qkv_positions_shared_core_rs_out_lse",
+      "strict_shared_core": {
+        "actual_backend": "aiter.rocm.ck_dense_mha",
+        "bitwise": {
+          "dk": true,
+          "dq": true,
+          "dv": true,
+          "lse": true,
+          "out": true
+        },
+        "communication_autograd": true,
+        "communication_backend": "rccl_ag_rs",
+        "executed": true,
+        "fallback": false,
+        "identity_errors": [],
+        "max_abs": {
+          "dk": 0.0,
+          "dq": 0.0,
+          "dv": 0.0,
+          "lse": 0.0,
+          "out": 0.0
+        },
+        "native_attention_arithmetic": true,
+        "passed": true,
+        "production_ready": true,
+        "repeat_lse_bitwise": true,
+        "repeat_out_bitwise": true,
+        "split_kv_policy": "disabled",
+        "strict_core_id": "rlkernel.attention.rocm.aiter_ck_dense_mha.v1",
+        "strict_mode": true,
+        "strict_provenance": {
+          "accum_dtype": "fp32",
+          "actual_backend": "aiter.rocm.ck_dense_mha",
+          "adapter_backend": "flashinfer",
+          "aiter_api_source": "aiter.ops.mha",
+          "aiter_source_sha256": "db61d7ce62a907c5079830622723f0abcc7fd35816d88006e381140cb6566ff4",
+          "arithmetic_plan_source": "aiter.ops.mha",
+          "arithmetic_semantics_verified": true,
+          "attention_backend": "aiter.rocm.ck_dense_mha",
+          "attention_mode": "prefill",
+          "batch_invariant_claim": "strict_runtime_verified",
+          "causal": true,
+          "communication_backend": "rccl_ag_rs",
+          "communication_overlap": "disabled",
+          "compute_communication": "decoupled",
+          "compute_order": [
+            0,
+            1
+          ],
+          "compute_schedule": "rlkernel.attention.strict_ring_state.v1",
+          "cp_comm_accum_dtype": "fp32",
+          "cp_comm_attention_numeric_reduction": false,
+          "cp_comm_backend": "rccl_ag_rs",
+          "cp_comm_compute_communication": "decoupled",
+          "cp_comm_contract": "partial_out_lse_global_block_index",
+          "cp_comm_expected_blocks": [
+            {
+              "global_block_index": 0,
+              "kv_block_end": 4,
+              "kv_block_start": 0,
+              "owner_cp_rank": 0,
+              "owner_tp_rank": 1
+            },
+            {
+              "global_block_index": 1,
+              "kv_block_end": 8,
+              "kv_block_start": 4,
+              "owner_cp_rank": 0,
+              "owner_tp_rank": 1
+            },
+            {
+              "global_block_index": 2,
+              "kv_block_end": 12,
+              "kv_block_start": 8,
+              "owner_cp_rank": 1,
+              "owner_tp_rank": 1
+            },
+            {
+              "global_block_index": 3,
+              "kv_block_end": 16,
+              "kv_block_start": 12,
+              "owner_cp_rank": 1,
+              "owner_tp_rank": 1
+            }
+          ],
+          "cp_comm_expected_kv_token_range": [
+            0,
+            16
+          ],
+          "cp_comm_merge_order": "global_block_index",
+          "cp_comm_merge_root_cp_rank": 0,
+          "cp_comm_pattern": "ag_rs",
+          "cp_comm_query_token_ranges": [
+            [
+              0,
+              8
+            ],
+            [
+              8,
+              16
+            ]
+          ],
+          "cp_comm_required": true,
+          "cp_comm_return_lse": true,
+          "cp_comm_runtime": "rccl",
+          "cp_comm_status": "implemented",
+          "cp_comm_strict_backward": "rs_out_backward_ag_then_ag_qkv_backward_rs",
+          "cp_comm_strict_contract": "ag_qkv_positions_shared_core_rs_out_lse",
+          "cp_comm_strict_kv_communication": "all_gather",
+          "cp_comm_strict_position_communication": "all_gather",
+          "cp_rank": 1,
+          "cp_world_size": 2,
+          "deterministic_backward": true,
+          "downcast_at": "final_write",
+          "fa_api_source": null,
+          "fa_package_version": null,
+          "fallback": false,
+          "fallback_reason": null,
+          "k_cache_rope_state": "post_rope",
+          "lse_domain": "attention",
+          "lse_dtype": "fp32",
+          "lse_exported": true,
+          "materialization": "ag_qkv_positions_shared_core_rs",
+          "merge_order_indices": [
+            0,
+            1
+          ],
+          "native_attention_arithmetic": true,
+          "num_splits": 1,
+          "platform": "rocm",
+          "production_ready": true,
+          "q_rope_state": "post_rope",
+          "reference_only": false,
+          "requested_backend": "flashinfer_layout_adapter",
+          "ring_partial_arithmetic": false,
+          "ring_schedule_default": true,
+          "rope_backend": "rlkernel.rocm.deterministic_rope",
+          "rope_fusion": false,
+          "rope_fusion_boundary": "rlkernel_rope_then_attention",
+          "rope_theta": 1000000.0,
+          "rotary_dim": null,
+          "softmax_scale": null,
+          "split_kv_control": "dense_non_split_api",
+          "strict_comm_autograd": true,
+          "strict_core_id": "rlkernel.attention.rocm.aiter_ck_dense_mha.v1",
+          "strict_core_row_plans": [
+            {
+              "actual_split_boundaries": [
+                [
+                  0,
+                  16
+                ]
+              ],
+              "actual_split_kv_count": 1,
+              "actual_split_kv_policy": "disabled",
+              "actual_split_kv_size": null,
+              "requested_split_kv_policy": "disabled",
+              "requested_split_kv_size": null,
+              "split_kv_accum_dtype": "fp32",
+              "split_kv_backend": "aiter.rocm.ck_dense_mha",
+              "split_kv_downcast_at": "final_write",
+              "split_kv_fallback": false,
+              "split_kv_fallback_reason": null,
+              "split_kv_merge_order": "global_block_index",
+              "split_kv_plan_source": "contract_exact"
+            },
+            {
+              "actual_split_boundaries": [
+                [
+                  0,
+                  16
+                ]
+              ],
+              "actual_split_kv_count": 1,
+              "actual_split_kv_policy": "disabled",
+              "actual_split_kv_size": null,
+              "requested_split_kv_policy": "disabled",
+              "requested_split_kv_size": null,
+              "split_kv_accum_dtype": "fp32",
+              "split_kv_backend": "aiter.rocm.ck_dense_mha",
+              "split_kv_downcast_at": "final_write",
+              "split_kv_fallback": false,
+              "split_kv_fallback_reason": null,
+              "split_kv_merge_order": "global_block_index",
+              "split_kv_plan_source": "contract_exact"
+            }
+          ],
+          "strict_full_qkv_all_gather": true,
+          "strict_local_kv_range": [
+            8,
+            16
+          ],
+          "strict_local_query_range": [
+            8,
+            16
+          ],
+          "strict_mode": true,
+          "strict_position_ids_all_gather": true,
+          "strict_schedule": "single_batch_aiter_ck_dense_mha_no_splitkv",
+          "strict_split_kv": "disabled",
+          "tp_rank": 1,
+          "tp_world_size": 2
+        },
+        "strict_schedule": "single_batch_aiter_ck_dense_mha_no_splitkv"
+      },
+      "tp_rank": 1,
+      "tp_world_size": 2,
+      "transport": "rccl_ag_rs"
+    }
+  ],
+  "replica_count": 2,
+  "runtime_version": "7.14.60850",
+  "schema_version": "ws2_rccl_ag_rs_attention/v2",
+  "torch_version": "2.12.0+rocm7.14.0a20260608",
+  "tp_world_size": 2,
+  "transport": "rccl_ag_rs",
+  "world_size": 8
+}
diff --git a/benchmarks/results/pr319_rocm_mi300x/report.md b/benchmarks/results/pr319_rocm_mi300x/report.md
index e5cbe016..e0577d94 100644
--- a/benchmarks/results/pr319_rocm_mi300x/report.md
+++ b/benchmarks/results/pr319_rocm_mi300x/report.md
@@ -21,21 +21,21 @@ Triton backend; `strict` = `aiter.rocm.ck_dense_mha` through WS2 contract dispat
 
 | B | S | Backend | Fwd median (ms) | Fwd p95 (ms) | Fwd peak MiB | Fwd+bwd median (ms) | Fwd+bwd peak MiB |
 |---:|---:|---|---:|---:|---:|---:|---:|
-| 1 | 1024 | native | 0.1438 | 0.1668 | 36.1 | 0.4741 | 84.3 |
-| 1 | 1024 | triton | 0.3069 | 0.3300 | 32.1 | 1.7754 | 52.2 |
-| 1 | 1024 | strict | 0.4045 | 0.4261 | 40.1 | 1.0480 | 1108.5 |
-| 1 | 2048 | native | 0.2963 | 0.3018 | 72.3 | 1.0880 | 168.8 |
-| 1 | 2048 | triton | 0.6404 | 0.6551 | 64.2 | 2.2759 | 104.5 |
-| 1 | 2048 | strict | 0.4327 | 0.4475 | 80.3 | 2.0983 | 4265.0 |
-| 1 | 4096 | native | 0.7014 | 0.7219 | 144.5 | 3.2456 | 337.5 |
-| 1 | 4096 | triton | 1.4337 | 1.4886 | 128.5 | 4.6235 | 209.0 |
-| 1 | 4096 | strict | 0.7415 | 0.7529 | 160.5 | 5.8933 | 16722.0 |
-| 2 | 2048 | native | 0.4437 | 0.4696 | 144.5 | 1.9135 | 337.5 |
-| 2 | 2048 | triton | 0.9821 | 0.9917 | 128.5 | 2.6656 | 209.0 |
-| 2 | 2048 | strict | 0.7912 | 0.8101 | 120.5 | 3.9666 | 4369.3 |
-| 4 | 2048 | native | 0.8211 | 0.8666 | 289.0 | 3.5754 | 675.0 |
-| 4 | 2048 | triton | 1.7571 | 1.7957 | 257.0 | 5.1310 | 418.0 |
-| 4 | 2048 | strict | 1.3856 | 1.4119 | 226.1 | 7.7189 | 4610.3 |
+| 1 | 1024 | native | 0.1449 | 0.1503 | 36.1 | 0.4758 | 84.3 |
+| 1 | 1024 | triton | 0.3021 | 0.3315 | 32.1 | 1.2487 | 52.2 |
+| 1 | 1024 | strict | 0.3980 | 0.4205 | 40.1 | 1.0305 | 1108.5 |
+| 1 | 2048 | native | 0.2972 | 0.3010 | 72.3 | 1.0875 | 168.8 |
+| 1 | 2048 | triton | 0.6379 | 0.6535 | 64.2 | 2.5059 | 104.5 |
+| 1 | 2048 | strict | 0.4265 | 0.4365 | 80.3 | 2.1164 | 4265.0 |
+| 1 | 4096 | native | 0.7020 | 0.7232 | 144.5 | 3.2428 | 337.5 |
+| 1 | 4096 | triton | 1.4280 | 1.4877 | 128.5 | 4.6182 | 209.0 |
+| 1 | 4096 | strict | 0.7343 | 0.7502 | 160.5 | 5.9652 | 16722.0 |
+| 2 | 2048 | native | 0.4499 | 0.4715 | 144.5 | 1.9132 | 337.5 |
+| 2 | 2048 | triton | 0.9764 | 0.9864 | 128.5 | 2.6696 | 209.0 |
+| 2 | 2048 | strict | 0.7826 | 0.8113 | 120.5 | 3.9917 | 4369.3 |
+| 4 | 2048 | native | 0.8225 | 0.8474 | 289.0 | 3.5825 | 675.0 |
+| 4 | 2048 | triton | 1.7539 | 1.7674 | 257.0 | 5.1151 | 418.0 |
+| 4 | 2048 | strict | 1.3809 | 1.4118 | 226.1 | 7.6638 | 4610.3 |
 
 ## Cost of the deterministic backward
 
@@ -44,9 +44,9 @@ already-resident forward tensors, so read the absolute det=on column for scaling
 
 | S | det=on median (ms) | det=off median (ms) | Time | det=on peak MiB | det=off peak MiB |
 |---:|---:|---:|---:|---:|---:|
-| 1024 | 0.4455 | 0.1917 | 2.32x | 1188.3 | 180.3 |
-| 2048 | 1.3399 | 0.5687 | 2.36x | 4328.5 | 264.5 |
-| 4096 | 5.0313 | 2.0607 | 2.44x | 16753.0 | 433.0 |
+| 1024 | 0.4490 | 0.1903 | 2.36x | 1188.3 | 180.3 |
+| 2048 | 1.3386 | 0.5687 | 2.35x | 4328.5 | 264.5 |
+| 4096 | 5.0450 | 2.0415 | 2.47x | 16753.0 | 433.0 |
 
 Deterministic backward peak memory scales as O(S^2): each doubling of S quadruples it.
 
@@ -72,6 +72,32 @@ core materializes one logical row per call.
 
 Through the provider, every shape above is bitwise identical (max abs `0`).
 
+## TP-degree invariance of raw AITER
+
+A head shard computed under TP=N vs the same slice of an unsharded run. TP performs no
+cross-rank reduction in attention, so any nonzero value here means the kernel's result
+depends on how many heads shared the launch - i.e. changing TP degree changes the numbers.
+
+| S | TP | Local Hq | Local Hkv | out max abs | lse max abs | Invariant |
+|---:|---:|---:|---:|---:|---:|---|
+| 512 | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
+| 512 | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
+| 512 | 8 | 4 | 1 | 0.000000e+00 | 0.000000e+00 | yes |
+| 1024 | 2 | 16 | 4 | 3.906250e-03 | 1.430511e-06 | **no** |
+| 1024 | 4 | 8 | 2 | 3.906250e-03 | 1.430511e-06 | **no** |
+| 1024 | 8 | 4 | 1 | 3.906250e-03 | 1.430511e-06 | **no** |
+| 2048 | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
+| 2048 | 4 | 8 | 2 | 7.812500e-03 | 2.861023e-06 | **no** |
+| 2048 | 8 | 4 | 1 | 7.812500e-03 | 2.861023e-06 | **no** |
+| 4096 | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4096 | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4096 | 8 | 4 | 1 | 7.812500e-03 | 4.768372e-06 | **no** |
+
+RL-Kernel does not remove this dependence (per-KV-group execution would, at 2.7-3.9x
+forward time). It binds the degree instead: `AttentionContract.cross_rank_fingerprint`
+includes `tp_world_size`, and `validate_cross_config_alignment` fails closed when training
+and rollout disagree.
+
 ## Reproduce
 
 ```bash
diff --git a/benchmarks/results/pr319_rocm_mi300x/results.json b/benchmarks/results/pr319_rocm_mi300x/results.json
index 7aec1877..3d9f93fe 100644
--- a/benchmarks/results/pr319_rocm_mi300x/results.json
+++ b/benchmarks/results/pr319_rocm_mi300x/results.json
@@ -16,13 +16,13 @@
       "seq_len": 1024,
       "backend": "native",
       "forward": {
-        "median_ms": 0.1438,
-        "p95_ms": 0.1668,
+        "median_ms": 0.1449,
+        "p95_ms": 0.1503,
         "peak_mib": 36.1
       },
       "forward_backward": {
-        "median_ms": 0.4741,
-        "p95_ms": 0.4915,
+        "median_ms": 0.4758,
+        "p95_ms": 0.4814,
         "peak_mib": 84.3
       }
     },
@@ -31,13 +31,13 @@
       "seq_len": 1024,
       "backend": "triton",
       "forward": {
-        "median_ms": 0.3069,
-        "p95_ms": 0.33,
+        "median_ms": 0.3021,
+        "p95_ms": 0.3315,
         "peak_mib": 32.1
       },
       "forward_backward": {
-        "median_ms": 1.7754,
-        "p95_ms": 1.864,
+        "median_ms": 1.2487,
+        "p95_ms": 1.3242,
         "peak_mib": 52.2
       }
     },
@@ -46,13 +46,13 @@
       "seq_len": 1024,
       "backend": "strict",
       "forward": {
-        "median_ms": 0.4045,
-        "p95_ms": 0.4261,
+        "median_ms": 0.398,
+        "p95_ms": 0.4205,
         "peak_mib": 40.1
       },
       "forward_backward": {
-        "median_ms": 1.048,
-        "p95_ms": 1.0706,
+        "median_ms": 1.0305,
+        "p95_ms": 1.0536,
         "peak_mib": 1108.5
       }
     },
@@ -61,13 +61,13 @@
       "seq_len": 2048,
       "backend": "native",
       "forward": {
-        "median_ms": 0.2963,
-        "p95_ms": 0.3018,
+        "median_ms": 0.2972,
+        "p95_ms": 0.301,
         "peak_mib": 72.3
       },
       "forward_backward": {
-        "median_ms": 1.088,
-        "p95_ms": 1.0986,
+        "median_ms": 1.0875,
+        "p95_ms": 1.1026,
         "peak_mib": 168.8
       }
     },
@@ -76,13 +76,13 @@
       "seq_len": 2048,
       "backend": "triton",
       "forward": {
-        "median_ms": 0.6404,
-        "p95_ms": 0.6551,
+        "median_ms": 0.6379,
+        "p95_ms": 0.6535,
         "peak_mib": 64.2
       },
       "forward_backward": {
-        "median_ms": 2.2759,
-        "p95_ms": 2.2853,
+        "median_ms": 2.5059,
+        "p95_ms": 2.5251,
         "peak_mib": 104.5
       }
     },
@@ -91,13 +91,13 @@
       "seq_len": 2048,
       "backend": "strict",
       "forward": {
-        "median_ms": 0.4327,
-        "p95_ms": 0.4475,
+        "median_ms": 0.4265,
+        "p95_ms": 0.4365,
         "peak_mib": 80.3
       },
       "forward_backward": {
-        "median_ms": 2.0983,
-        "p95_ms": 2.2269,
+        "median_ms": 2.1164,
+        "p95_ms": 2.2624,
         "peak_mib": 4265.0
       }
     },
@@ -106,13 +106,13 @@
       "seq_len": 4096,
       "backend": "native",
       "forward": {
-        "median_ms": 0.7014,
-        "p95_ms": 0.7219,
+        "median_ms": 0.702,
+        "p95_ms": 0.7232,
         "peak_mib": 144.5
       },
       "forward_backward": {
-        "median_ms": 3.2456,
-        "p95_ms": 3.2945,
+        "median_ms": 3.2428,
+        "p95_ms": 3.2862,
         "peak_mib": 337.5
       }
     },
@@ -121,13 +121,13 @@
       "seq_len": 4096,
       "backend": "triton",
       "forward": {
-        "median_ms": 1.4337,
-        "p95_ms": 1.4886,
+        "median_ms": 1.428,
+        "p95_ms": 1.4877,
         "peak_mib": 128.5
       },
       "forward_backward": {
-        "median_ms": 4.6235,
-        "p95_ms": 4.672,
+        "median_ms": 4.6182,
+        "p95_ms": 4.6719,
         "peak_mib": 209.0
       }
     },
@@ -136,13 +136,13 @@
       "seq_len": 4096,
       "backend": "strict",
       "forward": {
-        "median_ms": 0.7415,
-        "p95_ms": 0.7529,
+        "median_ms": 0.7343,
+        "p95_ms": 0.7502,
         "peak_mib": 160.5
       },
       "forward_backward": {
-        "median_ms": 5.8933,
-        "p95_ms": 5.9776,
+        "median_ms": 5.9652,
+        "p95_ms": 6.0334,
         "peak_mib": 16722.0
       }
     },
@@ -151,13 +151,13 @@
       "seq_len": 2048,
       "backend": "native",
       "forward": {
-        "median_ms": 0.4437,
-        "p95_ms": 0.4696,
+        "median_ms": 0.4499,
+        "p95_ms": 0.4715,
         "peak_mib": 144.5
       },
       "forward_backward": {
-        "median_ms": 1.9135,
-        "p95_ms": 1.9372,
+        "median_ms": 1.9132,
+        "p95_ms": 1.943,
         "peak_mib": 337.5
       }
     },
@@ -166,13 +166,13 @@
       "seq_len": 2048,
       "backend": "triton",
       "forward": {
-        "median_ms": 0.9821,
-        "p95_ms": 0.9917,
+        "median_ms": 0.9764,
+        "p95_ms": 0.9864,
         "peak_mib": 128.5
       },
       "forward_backward": {
-        "median_ms": 2.6656,
-        "p95_ms": 2.6806,
+        "median_ms": 2.6696,
+        "p95_ms": 2.6816,
         "peak_mib": 209.0
       }
     },
@@ -181,13 +181,13 @@
       "seq_len": 2048,
       "backend": "strict",
       "forward": {
-        "median_ms": 0.7912,
-        "p95_ms": 0.8101,
+        "median_ms": 0.7826,
+        "p95_ms": 0.8113,
         "peak_mib": 120.5
       },
       "forward_backward": {
-        "median_ms": 3.9666,
-        "p95_ms": 4.0203,
+        "median_ms": 3.9917,
+        "p95_ms": 4.0312,
         "peak_mib": 4369.3
       }
     },
@@ -196,13 +196,13 @@
       "seq_len": 2048,
       "backend": "native",
       "forward": {
-        "median_ms": 0.8211,
-        "p95_ms": 0.8666,
+        "median_ms": 0.8225,
+        "p95_ms": 0.8474,
         "peak_mib": 289.0
       },
       "forward_backward": {
-        "median_ms": 3.5754,
-        "p95_ms": 3.6514,
+        "median_ms": 3.5825,
+        "p95_ms": 3.6547,
         "peak_mib": 675.0
       }
     },
@@ -211,13 +211,13 @@
       "seq_len": 2048,
       "backend": "triton",
       "forward": {
-        "median_ms": 1.7571,
-        "p95_ms": 1.7957,
+        "median_ms": 1.7539,
+        "p95_ms": 1.7674,
         "peak_mib": 257.0
       },
       "forward_backward": {
-        "median_ms": 5.131,
-        "p95_ms": 5.1587,
+        "median_ms": 5.1151,
+        "p95_ms": 5.1429,
         "peak_mib": 418.0
       }
     },
@@ -226,13 +226,13 @@
       "seq_len": 2048,
       "backend": "strict",
       "forward": {
-        "median_ms": 1.3856,
-        "p95_ms": 1.4119,
+        "median_ms": 1.3809,
+        "p95_ms": 1.4118,
         "peak_mib": 226.1
       },
       "forward_backward": {
-        "median_ms": 7.7189,
-        "p95_ms": 7.8018,
+        "median_ms": 7.6638,
+        "p95_ms": 7.7376,
         "peak_mib": 4610.3
       }
     }
@@ -241,46 +241,46 @@
     {
       "seq_len": 1024,
       "deterministic": {
-        "median_ms": 0.4455,
-        "p95_ms": 0.4746,
+        "median_ms": 0.449,
+        "p95_ms": 0.4681,
         "peak_mib": 1188.3
       },
       "non_deterministic": {
-        "median_ms": 0.1917,
-        "p95_ms": 0.1971,
+        "median_ms": 0.1903,
+        "p95_ms": 0.197,
         "peak_mib": 180.3
       },
-      "time_ratio": 2.32,
+      "time_ratio": 2.36,
       "memory_ratio": 6.6
     },
     {
       "seq_len": 2048,
       "deterministic": {
-        "median_ms": 1.3399,
-        "p95_ms": 1.3742,
+        "median_ms": 1.3386,
+        "p95_ms": 1.379,
         "peak_mib": 4328.5
       },
       "non_deterministic": {
         "median_ms": 0.5687,
-        "p95_ms": 0.5762,
+        "p95_ms": 0.5761,
         "peak_mib": 264.5
       },
-      "time_ratio": 2.36,
+      "time_ratio": 2.35,
       "memory_ratio": 16.4
     },
     {
       "seq_len": 4096,
       "deterministic": {
-        "median_ms": 5.0313,
-        "p95_ms": 5.1068,
+        "median_ms": 5.045,
+        "p95_ms": 5.0906,
         "peak_mib": 16753.0
       },
       "non_deterministic": {
-        "median_ms": 2.0607,
-        "p95_ms": 2.089,
+        "median_ms": 2.0415,
+        "p95_ms": 2.072,
         "peak_mib": 433.0
       },
-      "time_ratio": 2.44,
+      "time_ratio": 2.47,
       "memory_ratio": 38.7
     }
   ],
@@ -369,5 +369,115 @@
       "raw_aiter_lse_max_abs": 0.0,
       "raw_aiter_is_batch_invariant": true
     }
+  ],
+  "tp_head_count_sensitivity": [
+    {
+      "seq_len": 512,
+      "tp": 2,
+      "local_q_heads": 16,
+      "local_kv_heads": 4,
+      "out_max_abs": 0.0,
+      "lse_max_abs": 0.0,
+      "tp_degree_invariant": true
+    },
+    {
+      "seq_len": 512,
+      "tp": 4,
+      "local_q_heads": 8,
+      "local_kv_heads": 2,
+      "out_max_abs": 0.0,
+      "lse_max_abs": 0.0,
+      "tp_degree_invariant": true
+    },
+    {
+      "seq_len": 512,
+      "tp": 8,
+      "local_q_heads": 4,
+      "local_kv_heads": 1,
+      "out_max_abs": 0.0,
+      "lse_max_abs": 0.0,
+      "tp_degree_invariant": true
+    },
+    {
+      "seq_len": 1024,
+      "tp": 2,
+      "local_q_heads": 16,
+      "local_kv_heads": 4,
+      "out_max_abs": 0.00390625,
+      "lse_max_abs": 1.430511474609375e-06,
+      "tp_degree_invariant": false
+    },
+    {
+      "seq_len": 1024,
+      "tp": 4,
+      "local_q_heads": 8,
+      "local_kv_heads": 2,
+      "out_max_abs": 0.00390625,
+      "lse_max_abs": 1.430511474609375e-06,
+      "tp_degree_invariant": false
+    },
+    {
+      "seq_len": 1024,
+      "tp": 8,
+      "local_q_heads": 4,
+      "local_kv_heads": 1,
+      "out_max_abs": 0.00390625,
+      "lse_max_abs": 1.430511474609375e-06,
+      "tp_degree_invariant": false
+    },
+    {
+      "seq_len": 2048,
+      "tp": 2,
+      "local_q_heads": 16,
+      "local_kv_heads": 4,
+      "out_max_abs": 0.0,
+      "lse_max_abs": 0.0,
+      "tp_degree_invariant": true
+    },
+    {
+      "seq_len": 2048,
+      "tp": 4,
+      "local_q_heads": 8,
+      "local_kv_heads": 2,
+      "out_max_abs": 0.0078125,
+      "lse_max_abs": 2.86102294921875e-06,
+      "tp_degree_invariant": false
+    },
+    {
+      "seq_len": 2048,
+      "tp": 8,
+      "local_q_heads": 4,
+      "local_kv_heads": 1,
+      "out_max_abs": 0.0078125,
+      "lse_max_abs": 2.86102294921875e-06,
+      "tp_degree_invariant": false
+    },
+    {
+      "seq_len": 4096,
+      "tp": 2,
+      "local_q_heads": 16,
+      "local_kv_heads": 4,
+      "out_max_abs": 0.0,
+      "lse_max_abs": 0.0,
+      "tp_degree_invariant": true
+    },
+    {
+      "seq_len": 4096,
+      "tp": 4,
+      "local_q_heads": 8,
+      "local_kv_heads": 2,
+      "out_max_abs": 0.0,
+      "lse_max_abs": 0.0,
+      "tp_degree_invariant": true
+    },
+    {
+      "seq_len": 4096,
+      "tp": 8,
+      "local_q_heads": 4,
+      "local_kv_heads": 1,
+      "out_max_abs": 0.0078125,
+      "lse_max_abs": 4.76837158203125e-06,
+      "tp_degree_invariant": false
+    }
   ]
 }
\ No newline at end of file
diff --git a/rl_engine/integrations/vime/attention.py b/rl_engine/integrations/vime/attention.py
index c848b2d7..f3f29e6f 100644
--- a/rl_engine/integrations/vime/attention.py
+++ b/rl_engine/integrations/vime/attention.py
@@ -30,6 +30,7 @@
 import torch
 
 from rl_engine.kernels.attention_contract import (
+    CROSS_CONFIG_BOUND_DEGREES,
     AttentionContract,
     AttentionDType,
     AttentionMode,
@@ -412,6 +413,23 @@ def attention_provider(request: Any) -> AttentionProviderResult:
         "cp_is_merge_axis": False,
     }
     provenance["lse_domain"] = "attention"
+    # The qualified ROCm core's reduction order depends on the launch head
+    # count, so a head shard computed under TP=4 is not bit-identical to the
+    # same shard under TP=8 at some shapes.  RL-Kernel binds the degree rather
+    # than paying ~3x forward time to remove the dependence: training and
+    # rollout must run the same TP/CP degree.  ``contract_id`` encodes both, so
+    # comparing it across the two sides is the preflight that enforces this.
+    provenance["cross_config_binding"] = {
+        "bound_degrees": list(CROSS_CONFIG_BOUND_DEGREES),
+        "tp_world_size": contract.sharding.tp_world_size,
+        "cp_world_size": contract.sharding.cp_world_size,
+        "binding_token": "contract_id",
+        "tp_degree_invariant": False,
+        "reason": (
+            "AITER/CK dense MHA reduction order is launch-head-count dependent; "
+            "train and rollout must use the same TP degree"
+        ),
+    }
     return AttentionProviderResult(
         out=out,
         lse=lse,
diff --git a/rl_engine/kernels/attention_contract.py b/rl_engine/kernels/attention_contract.py
index 96acdeba..81802d77 100644
--- a/rl_engine/kernels/attention_contract.py
+++ b/rl_engine/kernels/attention_contract.py
@@ -579,6 +579,81 @@ def validate_split_kv_alignment(
         )
 
 
+# Parallel degrees that must be identical on both sides of a cross-config
+# comparison.  This is not a portability preference: the qualified ROCm vendor
+# core (AITER/CK dense MHA) produces launch-shape-dependent reduction order, so
+# a head shard computed under TP=4 is not bit-identical to the same head shard
+# computed under TP=8 at some shapes.  Measured on MI300X, BF16, Hq=32/Hkv=8/
+# D=128, causal, TP=1 versus the sharded run:
+#
+#     S=512   TP=2/4/8   bitwise
+#     S=1024  TP=2/4/8   out max abs 3.90625e-03
+#     S=2048  TP=4/8     out max abs 7.8125e-03   (TP=2 bitwise)
+#     S=4096  TP=8       out max abs 7.8125e-03   (TP=2/4 bitwise)
+#
+# Executing one KV group per launch removes the dependence and costs 2.7-3.9x
+# forward time.  RL-Kernel instead binds the degree: training and rollout must
+# run the same TP (and CP) degree, and a mismatch fails closed here rather than
+# silently producing different logprobs.
+CROSS_CONFIG_BOUND_DEGREES = ("tp_world_size", "cp_world_size")
+
+
+def validate_cross_config_alignment(
+    training: "AttentionContract",
+    rollout: "AttentionContract",
+) -> None:
+    """Fail closed unless both sides describe one comparable attention invocation.
+
+    ``AttentionContract.cross_rank_fingerprint`` is the single token that
+    encodes this; comparing fingerprints is the cheap distributed preflight.
+    This function exists to explain *which* field diverged when they disagree,
+    so a cross-config drift investigation does not start from one opaque hash.
+    """
+
+    if not isinstance(training, AttentionContract) or not isinstance(rollout, AttentionContract):
+        raise AttentionContractError("both sides must be AttentionContract instances")
+
+    degree_mismatches = [
+        f"{name}: train={getattr(training.sharding, name)} "
+        f"rollout={getattr(rollout.sharding, name)}"
+        for name in CROSS_CONFIG_BOUND_DEGREES
+        if getattr(training.sharding, name) != getattr(rollout.sharding, name)
+    ]
+    if degree_mismatches:
+        raise AttentionContractError(
+            "training and rollout must use the same parallel degrees for a bitwise "
+            "attention comparison; the qualified ROCm core's reduction order depends on "
+            "the launch head count, so a differing degree changes the numbers: "
+            + "; ".join(degree_mismatches)
+        )
+
+    layout_mismatches = [
+        name
+        for name in ("global_q_heads", "global_kv_heads")
+        if getattr(training.sharding, name) != getattr(rollout.sharding, name)
+    ]
+    if layout_mismatches:
+        raise AttentionContractError(
+            "training and rollout describe different global head layouts: "
+            + ", ".join(layout_mismatches)
+        )
+
+    scalar_mismatches = [
+        name
+        for name in ("dtype", "head_dim", "causal", "export_lse")
+        if getattr(training, name) != getattr(rollout, name)
+    ]
+    if scalar_mismatches:
+        raise AttentionContractError(
+            "training and rollout attention contracts differ: " + ", ".join(scalar_mismatches)
+        )
+
+    if training.split_kv != rollout.split_kv:
+        raise AttentionContractError("training and rollout Split-KV policies differ")
+    if training.reduction != rollout.reduction:
+        raise AttentionContractError("training and rollout reduction specs differ")
+
+
 @dataclass(frozen=True, order=True)
 class SplitKVRuntimeCoordinate:
     """Identity of one batch/rank/owner Split-KV runtime plan."""
@@ -1506,6 +1581,8 @@ class AttentionDispatchResult:
     "STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID",
     "STRICT_ATTENTION_ROCM_SCHEDULE_ID",
     "STRICT_ATTENTION_SCHEDULE_ID",
+    "CROSS_CONFIG_BOUND_DEGREES",
+    "validate_cross_config_alignment",
     "validate_split_kv_alignment",
     "validate_split_kv_plan_set_alignment",
 ]
diff --git a/tests/test_attention_dispatch.py b/tests/test_attention_dispatch.py
index 4c174557..a4d9dac9 100644
--- a/tests/test_attention_dispatch.py
+++ b/tests/test_attention_dispatch.py
@@ -23,6 +23,7 @@
     ReductionSpec,
     ShardingSpec,
     SplitKVSpec,
+    validate_cross_config_alignment,
 )
 from rl_engine.kernels.registry import KernelRegistry, OpBackend, _rocm_strict_attention_available
 
@@ -305,3 +306,131 @@ def test_contract_fingerprint_is_rank_independent():
     # Both TP ranks of one logical invocation agree; a different TP degree does not.
     assert left_tp2.cross_rank_fingerprint() == right_tp2.cross_rank_fingerprint()
     assert left.cross_rank_fingerprint() != left_tp2.cross_rank_fingerprint()
+
+
+# ---------------------------------------------------------------------------
+# Cross-config binding: train and rollout must use the same parallel degrees
+# ---------------------------------------------------------------------------
+
+
+def _tp_contract(tp_world_size: int, tp_rank: int = 0, seq_len: int = 128) -> AttentionContract:
+    """A contract for one TP rank of a 32Q/8KV layout."""
+
+    local_q = 32 // tp_world_size
+    local_kv = 8 // tp_world_size
+    sharding = ShardingSpec(
+        tp_rank=tp_rank,
+        tp_world_size=tp_world_size,
+        cp_rank=0,
+        cp_world_size=1,
+        global_q_heads=32,
+        global_kv_heads=8,
+        local_q_head_start=tp_rank * local_q,
+        local_q_heads=local_q,
+        local_kv_head_start=tp_rank * local_kv,
+        local_kv_heads=local_kv,
+        global_sequence_length=seq_len,
+        local_sequence_length=seq_len,
+        global_block_indices=(0,),
+        global_block_token_starts=(0,),
+        local_block_offsets=(0, seq_len),
+    )
+    return AttentionContract(
+        role=AttentionRole.TRAIN,
+        mode=AttentionMode.PREFILL,
+        dtype=AttentionDType.BF16,
+        batch_size=1,
+        query_sequence_length=seq_len,
+        head_dim=128,
+        causal=True,
+        causal_offsets=(0,),
+        sharding=sharding,
+        reduction=ReductionSpec(),
+        split_kv=SplitKVSpec.disabled(),
+        export_lse=True,
+    )
+
+
+def test_matching_degrees_pass_cross_config_alignment():
+    validate_cross_config_alignment(_tp_contract(4, tp_rank=0), _tp_contract(4, tp_rank=3))
+
+
+def test_tp_degree_mismatch_fails_closed():
+    """A differing TP degree changes the bits, so it must not be comparable.
+
+    The qualified ROCm core's reduction order depends on the launch head count:
+    measured on MI300X the same head shard differs by up to 7.8125e-03 between
+    TP degrees at some shapes.  Binding the degree is what makes the strict
+    claim survive, so the mismatch has to fail rather than warn.
+    """
+
+    with pytest.raises(AttentionContractError, match="same parallel degrees"):
+        validate_cross_config_alignment(_tp_contract(4), _tp_contract(8))
+
+
+def test_cp_degree_mismatch_fails_closed():
+    train = _tp_contract(2)
+    rollout_sharding = ShardingSpec(
+        tp_rank=0,
+        tp_world_size=2,
+        cp_rank=0,
+        cp_world_size=2,
+        global_q_heads=32,
+        global_kv_heads=8,
+        local_q_head_start=0,
+        local_q_heads=16,
+        local_kv_head_start=0,
+        local_kv_heads=4,
+        global_sequence_length=256,
+        local_sequence_length=128,
+        global_block_indices=(0,),
+        global_block_token_starts=(0,),
+        local_block_offsets=(0, 128),
+    )
+    rollout = AttentionContract(
+        role=AttentionRole.INFER,
+        mode=AttentionMode.PREFILL,
+        dtype=AttentionDType.BF16,
+        batch_size=1,
+        query_sequence_length=128,
+        head_dim=128,
+        causal=True,
+        causal_offsets=(0,),
+        sharding=rollout_sharding,
+        reduction=ReductionSpec(),
+        split_kv=SplitKVSpec.disabled(),
+        export_lse=True,
+    )
+    with pytest.raises(AttentionContractError, match="same parallel degrees"):
+        validate_cross_config_alignment(train, rollout)
+
+
+def test_contract_fingerprint_separates_tp_degrees():
+    """The cheap preflight must also catch it, not only the explaining validator."""
+
+    assert _tp_contract(4).cross_rank_fingerprint() != _tp_contract(8).cross_rank_fingerprint()
+    # ...while ranks of one TP degree still agree.
+    assert (
+        _tp_contract(4, tp_rank=0).cross_rank_fingerprint()
+        == _tp_contract(4, tp_rank=3).cross_rank_fingerprint()
+    )
+
+
+def test_dtype_and_split_kv_mismatches_are_explained():
+    train = _tp_contract(2)
+    rollout = AttentionContract(
+        role=train.role,
+        mode=train.mode,
+        dtype=AttentionDType.FP16,
+        batch_size=train.batch_size,
+        query_sequence_length=train.query_sequence_length,
+        head_dim=train.head_dim,
+        causal=train.causal,
+        causal_offsets=train.causal_offsets,
+        sharding=train.sharding,
+        reduction=train.reduction,
+        split_kv=train.split_kv,
+        export_lse=True,
+    )
+    with pytest.raises(AttentionContractError, match="dtype"):
+        validate_cross_config_alignment(train, rollout)
diff --git a/tests/test_vime_attention_provider.py b/tests/test_vime_attention_provider.py
index 3a8afc35..26d04dac 100644
--- a/tests/test_vime_attention_provider.py
+++ b/tests/test_vime_attention_provider.py
@@ -270,3 +270,51 @@ def test_explicit_scale_is_honored():
     explicit = attention_provider(_request(seq_len=128, seed=8, softmax_scale=scale))
 
     assert torch.equal(default.out, explicit.out)
+
+
+@requires_strict_rocm
+def test_provenance_records_the_cross_config_degree_binding():
+    """The binding must be auditable per call, not only documented.
+
+    RL-Kernel does not make the strict path TP-degree invariant: the qualified
+    vendor core's reduction order depends on the launch head count, and
+    removing that dependence costs ~3x forward time.  The path instead binds
+    the degree, so every result has to carry what it was bound to.
+    """
+
+    result = attention_provider(_request(seq_len=256))
+    binding = result.provenance["cross_config_binding"]
+
+    assert binding["bound_degrees"] == ["tp_world_size", "cp_world_size"]
+    assert binding["tp_degree_invariant"] is False
+    assert binding["binding_token"] == "contract_id"
+    assert binding["tp_world_size"] == 1
+    assert binding["cp_world_size"] == 1
+
+
+@requires_strict_rocm
+def test_tp_degree_change_changes_the_contract_id():
+    """Train and rollout on different TP degrees must not compare as equal."""
+
+    class _Group:
+        def __init__(self, rank, size):
+            self._rank, self._size = rank, size
+
+        def rank(self):
+            return self._rank
+
+        def size(self):
+            return self._size
+
+    ids = {}
+    for tp in (1, 2, 4):
+        request = _request(seq_len=256, seed=4)
+        local_q, local_kv = GLOBAL_Q_HEADS // tp, GLOBAL_KV_HEADS // tp
+        request.query = request.query[:, :local_q]
+        request.key = request.key[:, :local_kv]
+        request.value = request.value[:, :local_kv]
+        request.metadata = _metadata(tp_rank=0, tp_world_size=tp)
+        request.tensor_parallel_group = _Group(0, tp)
+        ids[tp] = attention_provider(request).contract_id
+
+    assert len({ids[1], ids[2], ids[4]}) == 3

From 5cf23ecc5882537edddf1c7d7282f4c64dddf521 Mon Sep 17 00:00:00 2001
From: vensen <vensenmu@gmail.com>
Date: Fri, 28 Aug 2026 16:42:19 +0000
Subject: [PATCH 16/35] feat(distributed): add deterministic ROCm collectives

---
 benchmarks/benchmark_rocm_collectives.py      | 290 ++++++++++++
 csrc/ops.cpp                                  |  14 +-
 docs/design/rocm-deterministic-collectives.md | 112 +++++
 rl_engine/distributed/__init__.py             |  12 +-
 .../distributed/transport_collectives.py      | 419 ++++++++++++++++++
 .../kernels/ops/cuda/attention/__init__.py    |   2 +
 .../kernels/ops/cuda/attention/cp_comm.py     | 183 +++++++-
 rl_engine/kernels/ops/pytorch/ffn/ffn.py      |  39 +-
 setup.py                                      |   9 +-
 .../test_rocm_attention_transport.py          |  95 ++++
 ...test_transport_deterministic_collective.py | 375 ++++++++++++++++
 tests/test_build_platform_collectives.py      |  54 +++
 tests/test_qwen_ffn.py                        |  19 +
 tests/test_rocm_collective_benchmark.py       |  73 +++
 14 files changed, 1676 insertions(+), 20 deletions(-)
 create mode 100644 benchmarks/benchmark_rocm_collectives.py
 create mode 100644 docs/design/rocm-deterministic-collectives.md
 create mode 100644 rl_engine/distributed/transport_collectives.py
 create mode 100644 tests/distributed/test_rocm_attention_transport.py
 create mode 100644 tests/distributed/test_transport_deterministic_collective.py
 create mode 100644 tests/test_build_platform_collectives.py
 create mode 100644 tests/test_rocm_collective_benchmark.py

diff --git a/benchmarks/benchmark_rocm_collectives.py b/benchmarks/benchmark_rocm_collectives.py
new file mode 100644
index 00000000..6202e2e5
--- /dev/null
+++ b/benchmarks/benchmark_rocm_collectives.py
@@ -0,0 +1,290 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Benchmark deterministic ROCm collectives against native RCCL.
+
+Example:
+
+    torchrun --standalone --nproc-per-node=8 \
+      benchmarks/benchmark_rocm_collectives.py \
+      --size-bytes 4096 65536 1048576 16777216 \
+      --output benchmarks/results/rocm_collectives_mi300x.json
+
+The native RCCL rows are performance references only.  They are not used as a
+bitwise correctness oracle because their floating-point reduction order is not
+part of the strict deterministic contract.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import statistics
+import time
+from pathlib import Path
+from typing import Callable, Sequence
+
+import torch
+import torch.distributed as dist
+
+from rl_engine.distributed import RCCLDeterministicCollective
+
+_DTYPES = {
+    "bf16": torch.bfloat16,
+    "fp16": torch.float16,
+    "fp32": torch.float32,
+}
+_OPERATIONS = ("all_reduce", "all_gather", "reduce_scatter")
+
+
+def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--size-bytes",
+        type=int,
+        nargs="+",
+        default=[4 * 1024, 64 * 1024, 1024 * 1024, 16 * 1024 * 1024],
+    )
+    parser.add_argument("--dtype", choices=tuple(_DTYPES), default="bf16")
+    parser.add_argument("--operations", nargs="+", choices=_OPERATIONS, default=_OPERATIONS)
+    parser.add_argument("--warmup", type=int, default=10)
+    parser.add_argument("--iterations", type=int, default=50)
+    parser.add_argument("--samples", type=int, default=5)
+    parser.add_argument("--output", type=Path)
+    return parser.parse_args(argv)
+
+
+def _validate_args(args: argparse.Namespace) -> None:
+    if any(size <= 0 for size in args.size_bytes):
+        raise ValueError("every --size-bytes value must be positive")
+    if args.warmup < 0:
+        raise ValueError("--warmup must be non-negative")
+    if args.iterations <= 0 or args.samples <= 0:
+        raise ValueError("--iterations and --samples must be positive")
+
+
+def _timed_sample(operation: Callable[[], None], *, warmup: int, iterations: int) -> float:
+    for _ in range(warmup):
+        operation()
+    torch.cuda.synchronize()
+    dist.barrier()
+    start = time.perf_counter()
+    for _ in range(iterations):
+        operation()
+    torch.cuda.synchronize()
+    elapsed = (time.perf_counter() - start) / iterations
+
+    # Report the slowest rank, which is the end-to-end collective latency.
+    elapsed_tensor = torch.tensor([elapsed], dtype=torch.float64, device="cuda")
+    dist.all_reduce(elapsed_tensor, op=dist.ReduceOp.MAX)
+    return float(elapsed_tensor.item())
+
+
+def _benchmark(
+    operation: Callable[[], None],
+    *,
+    warmup: int,
+    iterations: int,
+    samples: int,
+) -> dict[str, object]:
+    timings = [
+        _timed_sample(operation, warmup=warmup if index == 0 else 0, iterations=iterations)
+        for index in range(samples)
+    ]
+    median = statistics.median(timings)
+    return {
+        "median_us": median * 1.0e6,
+        "min_us": min(timings) * 1.0e6,
+        "max_us": max(timings) * 1.0e6,
+        "samples_us": [value * 1.0e6 for value in timings],
+    }
+
+
+def _make_inputs(
+    *,
+    size_bytes: int,
+    dtype: torch.dtype,
+    world_size: int,
+    rank: int,
+    device: torch.device,
+) -> tuple[torch.Tensor, int]:
+    element_size = torch.empty((), dtype=dtype).element_size()
+    elements = max(world_size, size_bytes // element_size)
+    elements -= elements % world_size
+    generator = torch.Generator(device="cpu").manual_seed(942 + rank)
+    tensor = torch.randn(elements, generator=generator, dtype=torch.float32).to(
+        device=device,
+        dtype=dtype,
+    )
+    return tensor.contiguous(), elements * element_size
+
+
+def _operation_pair(
+    name: str,
+    input_tensor: torch.Tensor,
+    collective: RCCLDeterministicCollective,
+    world_size: int,
+) -> tuple[Callable[[], None], Callable[[], None], torch.Tensor, torch.Tensor]:
+    if name == "all_reduce":
+        deterministic_out = torch.empty_like(input_tensor)
+        native_out = torch.empty_like(input_tensor)
+
+        def deterministic() -> None:
+            collective.all_reduce(input_tensor, out=deterministic_out)
+
+        def native() -> None:
+            native_out.copy_(input_tensor)
+            dist.all_reduce(native_out)
+
+    elif name == "all_gather":
+        output_shape = (input_tensor.numel() * world_size,)
+        deterministic_out = torch.empty(output_shape, dtype=input_tensor.dtype, device="cuda")
+        native_out = torch.empty_like(deterministic_out)
+
+        def deterministic() -> None:
+            collective.all_gather(input_tensor, out=deterministic_out)
+
+        def native() -> None:
+            dist.all_gather_into_tensor(native_out, input_tensor)
+
+    elif name == "reduce_scatter":
+        output_shape = (input_tensor.numel() // world_size,)
+        deterministic_out = torch.empty(output_shape, dtype=input_tensor.dtype, device="cuda")
+        native_out = torch.empty_like(deterministic_out)
+
+        def deterministic() -> None:
+            collective.reduce_scatter(input_tensor, out=deterministic_out)
+
+        def native() -> None:
+            dist.reduce_scatter_tensor(native_out, input_tensor)
+
+    else:  # pragma: no cover - argparse constrains this value
+        raise ValueError(f"unsupported operation: {name}")
+
+    return deterministic, native, deterministic_out, native_out
+
+
+def run(args: argparse.Namespace) -> dict[str, object] | None:
+    _validate_args(args)
+    if torch.version.hip is None or not torch.cuda.is_available():
+        raise RuntimeError("the ROCm collective benchmark requires an available AMD GPU")
+
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    torch.cuda.set_device(local_rank)
+    dist.init_process_group("nccl", init_method="env://")
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+    if world_size not in (2, 4, 8):
+        raise RuntimeError(f"the benchmark requires 2, 4, or 8 ranks, got {world_size}")
+    device = torch.device("cuda", local_rank)
+    dtype = _DTYPES[args.dtype]
+    max_size_bytes = max(args.size_bytes) + dtype.itemsize * world_size
+
+    rows: list[dict[str, object]] = []
+    try:
+        with RCCLDeterministicCollective(
+            device=device,
+            max_size_bytes=max_size_bytes,
+        ) as collective:
+            for requested_size in args.size_bytes:
+                input_tensor, actual_size = _make_inputs(
+                    size_bytes=requested_size,
+                    dtype=dtype,
+                    world_size=world_size,
+                    rank=rank,
+                    device=device,
+                )
+                for name in args.operations:
+                    deterministic, native, deterministic_out, native_out = _operation_pair(
+                        name,
+                        input_tensor,
+                        collective,
+                        world_size,
+                    )
+                    deterministic()
+                    deterministic_repeat = deterministic_out.clone()
+                    deterministic()
+                    repeat_bitwise = bool(torch.equal(deterministic_out, deterministic_repeat))
+                    native()
+                    max_abs_vs_native = float(
+                        (deterministic_out.float() - native_out.float()).abs().max().item()
+                    )
+
+                    torch.cuda.reset_peak_memory_stats(device)
+                    deterministic_timing = _benchmark(
+                        deterministic,
+                        warmup=args.warmup,
+                        iterations=args.iterations,
+                        samples=args.samples,
+                    )
+                    deterministic_peak = int(torch.cuda.max_memory_allocated(device))
+                    torch.cuda.reset_peak_memory_stats(device)
+                    native_timing = _benchmark(
+                        native,
+                        warmup=args.warmup,
+                        iterations=args.iterations,
+                        samples=args.samples,
+                    )
+                    native_peak = int(torch.cuda.max_memory_allocated(device))
+                    deterministic_us = float(deterministic_timing["median_us"])
+                    native_us = float(native_timing["median_us"])
+                    rows.append(
+                        {
+                            "operation": name,
+                            "requested_size_bytes": requested_size,
+                            "actual_input_bytes": actual_size,
+                            "dtype": args.dtype,
+                            "deterministic": deterministic_timing,
+                            "native_rccl": native_timing,
+                            "latency_ratio_vs_native": deterministic_us / native_us,
+                            "deterministic_input_gbps": actual_size / (deterministic_us * 1.0e3),
+                            "native_input_gbps": actual_size / (native_us * 1.0e3),
+                            "repeat_bitwise": repeat_bitwise,
+                            "max_abs_vs_native": max_abs_vs_native,
+                            "deterministic_workspace_bytes": collective.workspace_size_bytes,
+                            "deterministic_peak_allocated_bytes": deterministic_peak,
+                            "native_peak_allocated_bytes": native_peak,
+                        }
+                    )
+
+        reports: list[list[dict[str, object]] | None] = [None] * world_size
+        dist.all_gather_object(reports, rows)
+        if rank != 0:
+            return None
+        payload = {
+            "schema_version": "rlkernel.rocm_collective_benchmark.v1",
+            "world_size": world_size,
+            "device": torch.cuda.get_device_name(device),
+            "hip_version": torch.version.hip,
+            "collective_backend": RCCLDeterministicCollective.backend_id,
+            "reduction_order": RCCLDeterministicCollective.reduction_order,
+            "supports_compute_communication_fusion": False,
+            "warmup": args.warmup,
+            "iterations": args.iterations,
+            "samples": args.samples,
+            "rows": rows,
+            "all_rank_repeat_bitwise": all(
+                bool(row["repeat_bitwise"])
+                for rank_rows in reports
+                if rank_rows is not None
+                for row in rank_rows
+            ),
+        }
+        if args.output is not None:
+            args.output.parent.mkdir(parents=True, exist_ok=True)
+            args.output.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+        return payload
+    finally:
+        dist.destroy_process_group()
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+    payload = run(parse_args(argv))
+    if payload is not None:
+        print(json.dumps(payload, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index f1e66dba..fb84dc64 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -78,7 +78,7 @@ torch::Tensor lm_head_sm90_forward_fp32(torch::Tensor hidden,
                                         torch::optional<torch::Tensor> bias);
 #endif
 
-#if defined(__CUDACC__) || defined(KERNEL_ALIGN_WITH_CUDA)
+#if defined(__CUDACC__) || defined(KERNEL_ALIGN_WITH_CUDA) || defined(KERNEL_ALIGN_WITH_ROCM)
 torch::Tensor fused_logp_forward_out(torch::Tensor logits, torch::Tensor token_ids, torch::Tensor output);
 torch::Tensor fused_logp_forward_fp32(torch::Tensor logits, torch::Tensor token_ids);
 torch::Tensor fused_logp_forward_indexed_out(torch::Tensor logits, torch::Tensor token_ids, torch::Tensor row_indices, torch::Tensor output);
@@ -93,7 +93,9 @@ torch::Tensor deterministic_logp_forward_fp32(torch::Tensor logits, torch::Tenso
 torch::Tensor deterministic_logp_forward_indexed_out(torch::Tensor logits, torch::Tensor token_ids, torch::Tensor row_indices, torch::Tensor output);
 torch::Tensor deterministic_logp_forward_indexed_fp32(torch::Tensor logits, torch::Tensor token_ids, torch::Tensor row_indices);
 
-// Single-node TP=8 deterministic collectives.
+#if !defined(USE_ROCM) && !defined(KERNEL_ALIGN_WITH_ROCM)
+// Single-node TP=8 deterministic CUDA IPC collectives. ROCm uses the
+// rank-ordered RCCL transport in rl_engine.distributed.transport_collectives.
 std::tuple<std::vector<int64_t>, int64_t> deterministic_collective_ipc_meta(
     torch::Tensor& tensor);
 int64_t deterministic_collective_create(
@@ -106,6 +108,7 @@ void deterministic_collective_stage(int64_t handle, torch::Tensor& input);
 void deterministic_collective_all_reduce(int64_t handle, torch::Tensor& output);
 void deterministic_collective_reduce_scatter(int64_t handle, torch::Tensor& output);
 void deterministic_collective_all_gather(int64_t handle, torch::Tensor& output);
+#endif
 
 // Batch-Invariant Deterministic GEMM Declarations
 torch::Tensor det_gemm_fwd(torch::Tensor a, torch::Tensor b);
@@ -391,7 +394,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
           "Single-card SM90 batch-invariant LM-head forward with fp32 output");
 #endif
 
-#if defined(__CUDACC__) || defined(KERNEL_ALIGN_WITH_CUDA)
+#if defined(__CUDACC__) || defined(KERNEL_ALIGN_WITH_CUDA) || defined(KERNEL_ALIGN_WITH_ROCM)
     m.def("fused_logp_forward_out", &fused_logp_forward_out, "Fused logp out");
     m.def("fused_logp_forward_fp32", &fused_logp_forward_fp32, "Fused logp fp32");
     m.def("fused_logp_forward_indexed_out", &fused_logp_forward_indexed_out, "Fused logp indexed out");
@@ -406,7 +409,9 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     m.def("deterministic_logp_forward_indexed_out", &deterministic_logp_forward_indexed_out, "Batch-invariant deterministic logp indexed out");
     m.def("deterministic_logp_forward_indexed_fp32", &deterministic_logp_forward_indexed_fp32, "Batch-invariant deterministic logp indexed fp32");
 
-    // Single-node TP=8 fixed-tree collectives.
+#if !defined(USE_ROCM) && !defined(KERNEL_ALIGN_WITH_ROCM)
+    // Single-node TP=8 fixed-tree CUDA IPC collectives. ROCm dispatches to
+    // the Python RCCL transport implementation instead.
     m.def(
         "deterministic_collective_ipc_meta",
         &deterministic_collective_ipc_meta,
@@ -435,6 +440,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
         "deterministic_collective_all_gather",
         &deterministic_collective_all_gather,
         "Run the TP=8 deterministic rank-ordered all-gather kernel");
+#endif
 
     // Prefix-shared attention uses NVIDIA PTX and falls back to PyTorch SDPA on ROCm.
 #if !defined(USE_ROCM)
diff --git a/docs/design/rocm-deterministic-collectives.md b/docs/design/rocm-deterministic-collectives.md
new file mode 100644
index 00000000..ce104332
--- /dev/null
+++ b/docs/design/rocm-deterministic-collectives.md
@@ -0,0 +1,112 @@
+# ROCm deterministic collectives
+
+This document defines the ROCm communication boundary used by deterministic
+TP/CP kernels.  The implementation is intentionally separate from the CUDA IPC
+collective in `csrc/cuda/distributed/deterministic_collective.cu`.
+
+## Contract
+
+`RCCLDeterministicCollective` implements `all_reduce`, `all_gather`, and
+`reduce_scatter` with the same Python call shape as the CUDA collective.  It
+supports process-group sizes 1, 2, 4, and 8 and FP32/FP16/BF16 reductions.
+Inputs and outputs must be contiguous and all ranks must call operations in the
+same order with matching shapes, dtypes, and capacity.
+
+RCCL is used only for rank-ordered tensor transport:
+
+1. `all_gather_into_tensor` gathers every rank's bit patterns.
+2. Each rank evaluates the same balanced tree locally:
+   `((rank0 + rank1) + (rank2 + rank3)) + ...`.
+3. `reduce_scatter` slices the rank-owned rows after that fixed reduction.
+
+RCCL's `all_reduce` and `reduce_scatter` are not used for strict reductions.
+They guarantee a mathematical reduction but do not expose a stable
+floating-point operand order.  Delegating the arithmetic to them would weaken
+the cross-TP bitwise contract.
+
+`create_deterministic_collective` is the platform boundary.  It selects the
+existing CUDA IPC implementation on NVIDIA and the RCCL transport
+implementation when `torch.version.hip` is set.  The FFN path calls this factory
+instead of importing the CUDA class directly.
+
+The ROCm extension build also excludes the CUDA IPC source and does not link
+`libcuda`; the Python transport has no CUDA-driver dependency.
+
+## Relationship to vLLM
+
+The backend split follows the useful parts of vLLM's device communicator
+design:
+
+- PyTorch exposes RCCL through the `nccl` process-group API.
+- ROCm AllGather uses `torch.distributed.all_gather_into_tensor` rather than a
+  manually allocated PyNccl path.
+- Backend, topology, world-size, dtype, layout, and capacity checks fail closed
+  before entering an optimized path.
+
+vLLM QuickReduce, custom all-reduce, and AITER all-reduce are not used in the
+strict path.  Those are valuable performance implementations, but their
+reduction order is not the fixed balanced tree required here.  They can be
+added later as an explicitly non-strict performance mode with separate
+provenance and toleranced correctness tests.
+
+## Compute/communication fusion
+
+The current ROCm collective is synchronous and reports
+`supports_async_overlap = False` and
+`supports_compute_communication_fusion = False`.  FFN and Attention keep the
+dependency boundaries explicit:
+
+```text
+sequence-parallel FFN:  AllGather(input) -> GEMM -> ReduceScatter(output)
+strict CP Attention:    AllGather(Q/K/V/positions) -> Attention -> Scatter(output/LSE)
+```
+
+This is neither a fused GEMM+collective kernel nor two-stream overlap.  vLLM's
+generic AsyncTP GEMM/communication fusion is currently a CUDA path; ROCm AITER
+has narrower fusions such as all-reduce plus RMSNorm, which do not replace this
+contract.
+
+Future overlap must preserve collective issue order, the local reduction tree,
+and stage boundaries.  It also needs repeat-bitwise tests before being marked
+strict.  A useful first candidate is backward work that is independent of a
+pending reduction; the forward SP AllGather and final row-parallel reduction
+are data dependencies and cannot simply be overlapped with their adjacent
+GEMMs.
+
+The “fill rank slots as messages arrive and merge ready contiguous blocks”
+scheme needs a lower-level P2P or HIP/XGMI transport. PyTorch/RCCL
+`all_gather_into_tensor` exposes completion of the whole collective, not
+per-rank arrival events. Such a pipeline may merge only canonical sibling
+subtrees when both are ready; merging arbitrary contiguous arrivals would
+change floating-point parenthesization. It is therefore a follow-up transport,
+not an optimization silently hidden inside this baseline.
+
+## Performance acceptance
+
+The transport-only baseline favors correctness and portability. AllGather
+writes directly to its final output; reductions reuse one lazily grown
+`world_size * input_bytes` byte workspace until `close()`. It still moves more
+data than a native RCCL AllReduce. A ROCm GPU PR should therefore report, for
+world sizes 2/4/8 and representative FFN tensors:
+
+- latency and effective bandwidth for all three collectives;
+- peak temporary memory;
+- comparison with RCCL and vLLM's available ROCm communicator;
+- repeat-bitwise and cross-TP results;
+- end-to-end TP/CP/SP FFN timing, not only isolated transport timing.
+
+A later fixed-tree HIP/XGMI implementation may replace the transport behind the
+same factory after it satisfies those checks.
+
+Run the included native-RCCL comparison on a single node, for example:
+
+```bash
+torchrun --standalone --nproc-per-node=8 \
+  benchmarks/benchmark_rocm_collectives.py \
+  --size-bytes 4096 65536 1048576 16777216 \
+  --output benchmarks/results/rocm_collectives_mi300x.json
+```
+
+The benchmark records slowest-rank latency, temporary allocation, repeat
+bitwise status, and the ratio to native RCCL. Native RCCL remains a performance
+reference only, not the strict arithmetic reference.
diff --git a/rl_engine/distributed/__init__.py b/rl_engine/distributed/__init__.py
index 37698f1a..f83f2520 100644
--- a/rl_engine/distributed/__init__.py
+++ b/rl_engine/distributed/__init__.py
@@ -2,5 +2,15 @@
 # Copyright (c) 2026 RL-Kernel Contributors
 
 from rl_engine.distributed.collectives import DeterministicCollective
+from rl_engine.distributed.transport_collectives import (
+    RCCLDeterministicCollective,
+    TorchDistributedDeterministicCollective,
+    create_deterministic_collective,
+)
 
-__all__ = ["DeterministicCollective"]
+__all__ = [
+    "DeterministicCollective",
+    "RCCLDeterministicCollective",
+    "TorchDistributedDeterministicCollective",
+    "create_deterministic_collective",
+]
diff --git a/rl_engine/distributed/transport_collectives.py b/rl_engine/distributed/transport_collectives.py
new file mode 100644
index 00000000..fea577bc
--- /dev/null
+++ b/rl_engine/distributed/transport_collectives.py
@@ -0,0 +1,419 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""Deterministic reductions built on rank-ordered tensor transport.
+
+The transport in this module never performs a floating-point reduction.  It
+only gathers every rank's input, after which each rank evaluates the same
+balanced reduction tree locally.  In a ROCm build, PyTorch's ``nccl`` backend
+is RCCL and therefore ``all_gather_into_tensor`` provides the transport.
+"""
+
+from __future__ import annotations
+
+import threading
+from types import TracebackType
+from typing import Any
+
+import torch
+import torch.distributed as dist
+
+_SUPPORTED_WORLD_SIZES = (1, 2, 4, 8)
+_DEFAULT_MAX_SIZE_BYTES = 64 * 1024 * 1024
+_REDUCTION_DTYPES = (torch.float32, torch.float16, torch.bfloat16)
+
+
+class TorchDistributedDeterministicCollective:
+    """Correctness-first collectives using AllGather as transport only.
+
+    Rank inputs are gathered without arithmetic and reduced locally as the
+    balanced tree ``((rank0 + rank1) + (rank2 + rank3)) + ...``.  Consequently,
+    all ranks execute the exact same floating-point expression.  TP sizes 1,
+    2, 4, and 8 are nested prefixes of that expression and match the existing
+    CUDA IPC collective's ordering.
+
+    The generic class also supports a CPU/Gloo process group, which is useful
+    as an executable reference.  Production ROCm callers should use
+    :class:`RCCLDeterministicCollective` or
+    :func:`create_deterministic_collective` so backend validation fails closed.
+    All ranks must call methods in the same order with matching input shapes
+    and dtypes, and construct the instance with the same ``max_size_bytes``.
+    """
+
+    backend_id = "torch_distributed_balanced_tree"
+    transport_only = True
+    reduction_order = "balanced_rank_tree"
+    supports_async_overlap = False
+    supports_compute_communication_fusion = False
+
+    def __init__(
+        self,
+        group: dist.ProcessGroup | None = None,
+        device: torch.device | str | int | None = None,
+        *,
+        max_size_bytes: int = _DEFAULT_MAX_SIZE_BYTES,
+    ) -> None:
+        if not dist.is_available() or not dist.is_initialized():
+            raise RuntimeError("torch.distributed must be initialized before collectives")
+        if max_size_bytes <= 0:
+            raise ValueError("max_size_bytes must be positive")
+
+        self.group = group if group is not None else dist.group.WORLD
+        self.rank = int(dist.get_rank(group=self.group))
+        self.world_size = int(dist.get_world_size(group=self.group))
+        if self.world_size not in _SUPPORTED_WORLD_SIZES:
+            raise ValueError(
+                "deterministic collectives require world_size in "
+                f"{_SUPPORTED_WORLD_SIZES}, got {self.world_size}"
+            )
+
+        self.device = self._normalize_device(device)
+        self.max_size_bytes = int(max_size_bytes)
+        self._backend = str(dist.get_backend(self.group)).lower()
+        self._lock = threading.Lock()
+        self._closed = False
+        # One dtype-agnostic byte workspace is grown on demand and reused by
+        # reduction collectives. AllGather writes directly into its output.
+        self._workspace: torch.Tensor | None = None
+        self._validate_matching_capacity()
+
+    @staticmethod
+    def _normalize_device(
+        device: torch.device | str | int | None,
+    ) -> torch.device:
+        if device is None:
+            if torch.cuda.is_available():
+                normalized = torch.device("cuda", torch.cuda.current_device())
+            else:
+                normalized = torch.device("cpu")
+        elif isinstance(device, int):
+            normalized = torch.device("cuda", device)
+        else:
+            normalized = torch.device(device)
+
+        if normalized.type == "cuda":
+            if not torch.cuda.is_available():
+                raise RuntimeError("a CUDA/ROCm device was requested but none is available")
+            current_device = torch.cuda.current_device()
+            if normalized.index is None:
+                normalized = torch.device("cuda", current_device)
+            if normalized.index != current_device:
+                raise ValueError(
+                    "the collective device must be the current CUDA/ROCm device; call "
+                    f"torch.cuda.set_device({normalized.index}) first"
+                )
+        return normalized
+
+    @property
+    def closed(self) -> bool:
+        """Whether this instance rejects further collective calls."""
+
+        return self._closed
+
+    @property
+    def workspace_size_bytes(self) -> int:
+        """Currently retained reduction workspace size in bytes."""
+
+        workspace = self._workspace
+        return 0 if workspace is None else int(workspace.numel())
+
+    def all_reduce(
+        self,
+        input: torch.Tensor,
+        *,
+        out: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Return the fixed balanced-tree sum on every rank."""
+
+        self._check_open()
+        self._validate_reduction_input(input)
+        if out is None:
+            out = torch.empty_like(input)
+        self._validate_output(out, input, tuple(input.shape))
+
+        with self._lock:
+            self._check_open()
+            self._validate_matching_signature("all_reduce", input)
+            rank_inputs = self._all_gather_transport(input)
+            reduced = self._balanced_tree_sum(rank_inputs)
+            out.copy_(reduced)
+        return out
+
+    def all_gather(
+        self,
+        input: torch.Tensor,
+        *,
+        out: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Gather rank-ordered input bit patterns along dimension 0."""
+
+        self._check_open()
+        self._validate_gather_input(input)
+        output_shape = (input.size(0) * self.world_size, *input.shape[1:])
+        if out is None:
+            out = torch.empty(output_shape, dtype=input.dtype, device=input.device)
+        self._validate_output(out, input, output_shape)
+
+        with self._lock:
+            self._check_open()
+            self._validate_matching_signature("all_gather", input)
+            self._all_gather_transport(input, gathered_flat=out.view(-1))
+        return out
+
+    def reduce_scatter(
+        self,
+        input: torch.Tensor,
+        *,
+        out: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Fixed-tree sum followed by rank-ordered dimension-0 slicing."""
+
+        self._check_open()
+        self._validate_reduction_input(input)
+        if input.dim() == 0:
+            raise ValueError("reduce_scatter input must have at least one dimension")
+        if input.size(0) % self.world_size != 0:
+            raise ValueError(
+                "reduce_scatter input.size(0) must be divisible by "
+                f"world_size={self.world_size}; got {input.size(0)}"
+            )
+        rows_per_rank = input.size(0) // self.world_size
+        output_shape = (rows_per_rank, *input.shape[1:])
+        if out is None:
+            out = torch.empty(output_shape, dtype=input.dtype, device=input.device)
+        self._validate_output(out, input, output_shape)
+
+        with self._lock:
+            self._check_open()
+            self._validate_matching_signature("reduce_scatter", input)
+            rank_inputs = self._all_gather_transport(input)
+            reduced = self._balanced_tree_sum(rank_inputs)
+            begin = self.rank * rows_per_rank
+            out.copy_(reduced.narrow(0, begin, rows_per_rank))
+        return out
+
+    def close(self) -> None:
+        """Close the instance.
+
+        Closing releases the lazily allocated reduction workspace and marks
+        the lifecycle boundary. Collective calls are blocking at this API.
+        """
+
+        with self._lock:
+            self._workspace = None
+            self._closed = True
+
+    def __enter__(self) -> TorchDistributedDeterministicCollective:
+        self._check_open()
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+    def __del__(self) -> None:
+        try:
+            self.close()
+        except Exception:
+            pass
+
+    def _check_open(self) -> None:
+        if getattr(self, "_closed", True):
+            raise RuntimeError("deterministic collective is closed")
+
+    def _validate_tensor(self, input: torch.Tensor) -> None:
+        if not isinstance(input, torch.Tensor):
+            raise TypeError(f"input must be a torch.Tensor, got {type(input)!r}")
+        if input.device != self.device:
+            raise ValueError(f"input must be on {self.device}, got {input.device}")
+        if not input.is_contiguous():
+            raise ValueError("input must be contiguous")
+        input_bytes = input.numel() * input.element_size()
+        if input_bytes > self.max_size_bytes:
+            raise ValueError(
+                f"input requires {input_bytes} bytes but max_size_bytes={self.max_size_bytes}"
+            )
+
+    def _validate_reduction_input(self, input: torch.Tensor) -> None:
+        self._validate_tensor(input)
+        if input.dtype not in _REDUCTION_DTYPES:
+            raise TypeError(
+                "deterministic reductions support float32, float16, and bfloat16; "
+                f"got {input.dtype}"
+            )
+
+    def _validate_gather_input(self, input: torch.Tensor) -> None:
+        self._validate_tensor(input)
+        if input.dim() == 0:
+            raise ValueError("all_gather input must have at least one dimension")
+
+    @staticmethod
+    def _validate_output(
+        output: torch.Tensor,
+        input: torch.Tensor,
+        output_shape: tuple[int, ...],
+    ) -> None:
+        if not isinstance(output, torch.Tensor):
+            raise TypeError(f"out must be a torch.Tensor, got {type(output)!r}")
+        if output.device != input.device:
+            raise ValueError("out must be on the same device as input")
+        if output.dtype != input.dtype:
+            raise TypeError("out must have the same dtype as input")
+        if tuple(output.shape) != output_shape:
+            raise ValueError(f"out must have shape {output_shape}, got {tuple(output.shape)}")
+        if not output.is_contiguous():
+            raise ValueError("out must be contiguous")
+
+    def _validate_matching_signature(self, op_name: str, input: torch.Tensor) -> None:
+        if self.world_size == 1:
+            return
+        signature = (op_name, tuple(input.shape), str(input.dtype), input.numel())
+        signatures: list[tuple[Any, ...] | None] = [None] * self.world_size
+        dist.all_gather_object(signatures, signature, group=self.group)
+        if any(peer_signature != signature for peer_signature in signatures):
+            raise ValueError(
+                f"all ranks must call {op_name} with matching shapes and dtypes; got {signatures}"
+            )
+
+    def _validate_matching_capacity(self) -> None:
+        if self.world_size == 1:
+            return
+        capacities: list[int | None] = [None] * self.world_size
+        dist.all_gather_object(capacities, self.max_size_bytes, group=self.group)
+        if any(peer_capacity != self.max_size_bytes for peer_capacity in capacities):
+            raise ValueError(f"all ranks must use the same max_size_bytes; got {capacities}")
+
+    def _all_gather_transport(
+        self,
+        input: torch.Tensor,
+        *,
+        gathered_flat: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        # Flattening makes the output contract independent of whether a given
+        # ProcessGroup implements the concatenation or stacking form of AG.
+        if self.world_size == 1:
+            if gathered_flat is None:
+                gathered_flat = input.clone().view(-1)
+            else:
+                gathered_flat.copy_(input.view(-1))
+            return gathered_flat.reshape((1, *input.shape))
+        input_flat = input.view(-1)
+        required_elements = self.world_size * input_flat.numel()
+        if gathered_flat is None:
+            gathered_flat = self._workspace_for(input, required_elements)
+        elif (
+            gathered_flat.numel() != required_elements
+            or gathered_flat.dtype != input.dtype
+            or gathered_flat.device != input.device
+            or not gathered_flat.is_contiguous()
+        ):
+            raise ValueError("gathered transport output has an invalid layout")
+        if "nccl" in self._backend:
+            # PyTorch exposes RCCL through the NCCL ProcessGroup API.  Keep
+            # this as a tensor-only transport; reduction happens below.
+            dist.all_gather_into_tensor(gathered_flat, input_flat, group=self.group)
+        else:
+            # Some reference backends (notably Gloo versions without
+            # all_gather_into_tensor) only implement the list API.
+            gathered_chunks = list(
+                gathered_flat.reshape(self.world_size, input_flat.numel()).unbind(0)
+            )
+            dist.all_gather(gathered_chunks, input_flat, group=self.group)
+        return gathered_flat.reshape((self.world_size, *input.shape))
+
+    def _workspace_for(self, input: torch.Tensor, required_elements: int) -> torch.Tensor:
+        required_bytes = required_elements * input.element_size()
+        workspace = self._workspace
+        if workspace is None or workspace.numel() < required_bytes:
+            workspace = torch.empty(required_bytes, dtype=torch.uint8, device=self.device)
+            self._workspace = workspace
+        # Tensor.view(dtype) reinterprets the aligned byte allocation without
+        # an allocation or copy. Restrict the view to the current operation.
+        return workspace[:required_bytes].view(input.dtype)
+
+    @staticmethod
+    def _balanced_tree_sum(rank_inputs: torch.Tensor) -> torch.Tensor:
+        level = list(rank_inputs.unbind(0))
+        if len(level) not in _SUPPORTED_WORLD_SIZES:
+            raise ValueError(
+                "balanced reduction requires rank inputs for world_size in "
+                f"{_SUPPORTED_WORLD_SIZES}, got {len(level)}"
+            )
+        while len(level) > 1:
+            level = [torch.add(level[index], level[index + 1]) for index in range(0, len(level), 2)]
+        return level[0]
+
+
+class RCCLDeterministicCollective(TorchDistributedDeterministicCollective):
+    """ROCm collective using RCCL AllGather strictly as tensor transport."""
+
+    backend_id = "rccl_all_gather_balanced_tree"
+
+    def __init__(
+        self,
+        group: dist.ProcessGroup | None = None,
+        device: torch.device | str | int | None = None,
+        *,
+        max_size_bytes: int = _DEFAULT_MAX_SIZE_BYTES,
+    ) -> None:
+        if getattr(torch.version, "hip", None) is None:
+            raise RuntimeError("RCCL deterministic collectives require a ROCm PyTorch build")
+        if not torch.cuda.is_available():
+            raise RuntimeError("RCCL deterministic collectives require an available ROCm device")
+        if device is not None and not isinstance(device, int):
+            requested_device = torch.device(device)
+            if requested_device.type != "cuda":
+                raise ValueError(
+                    f"RCCL deterministic collectives require a ROCm device, got {device!r}"
+                )
+        super().__init__(group=group, device=device, max_size_bytes=max_size_bytes)
+        if self.device.type != "cuda":
+            raise ValueError(
+                f"RCCL deterministic collectives require a ROCm device, got {device!r}"
+            )
+        if "nccl" not in self._backend:
+            raise RuntimeError(
+                "RCCL deterministic collectives require PyTorch's NCCL process-group API"
+            )
+
+
+def create_deterministic_collective(
+    group: dist.ProcessGroup | None = None,
+    device: torch.device | str | int | None = None,
+    *,
+    max_size_bytes: int = _DEFAULT_MAX_SIZE_BYTES,
+) -> Any:
+    """Create the platform-appropriate deterministic collective.
+
+    CUDA keeps using the existing IPC implementation.  ROCm uses RCCL only to
+    gather rank inputs, followed by the shared local balanced-tree reduction.
+    The returned object has independent ownership; callers may cache it by
+    process-group/rank/device and must close an entry before replacing it.
+    """
+
+    if getattr(torch.version, "hip", None) is not None:
+        return RCCLDeterministicCollective(
+            group=group,
+            device=device,
+            max_size_bytes=max_size_bytes,
+        )
+
+    # Import lazily to keep the existing CUDA implementation and its extension
+    # checks independent of the generic transport reference above.
+    from rl_engine.distributed.collectives import DeterministicCollective
+
+    return DeterministicCollective(
+        group=group,
+        device=device,
+        max_size_bytes=max_size_bytes,
+    )
+
+
+__all__ = [
+    "RCCLDeterministicCollective",
+    "TorchDistributedDeterministicCollective",
+    "create_deterministic_collective",
+]
diff --git a/rl_engine/kernels/ops/cuda/attention/__init__.py b/rl_engine/kernels/ops/cuda/attention/__init__.py
index 91fa6f99..d5e45b96 100644
--- a/rl_engine/kernels/ops/cuda/attention/__init__.py
+++ b/rl_engine/kernels/ops/cuda/attention/__init__.py
@@ -27,6 +27,7 @@
         CPCommunicationStatus,
         CUDAAGRSAttentionCPCommunication,
         P2PNCCLAttentionCPCommunication,
+        RCCLAGRSAttentionCPCommunication,
         sort_attention_cp_partial_states,
     )
 except ModuleNotFoundError as exc:
@@ -46,6 +47,7 @@
         "CPCommunicationStatus",
         "CUDAAGRSAttentionCPCommunication",
         "P2PNCCLAttentionCPCommunication",
+        "RCCLAGRSAttentionCPCommunication",
         "sort_attention_cp_partial_states",
     ]
 
diff --git a/rl_engine/kernels/ops/cuda/attention/cp_comm.py b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
index d1470bfd..4f945f14 100644
--- a/rl_engine/kernels/ops/cuda/attention/cp_comm.py
+++ b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
@@ -40,7 +40,12 @@
 
 import torch
 
-CPCommunicationBackend = Literal["cuda_ag_rs", "p2p_nccl_reference", "local_debug"]
+CPCommunicationBackend = Literal[
+    "cuda_ag_rs",
+    "rccl_ag_rs",
+    "p2p_nccl_reference",
+    "local_debug",
+]
 CPCommunicationStatus = Literal["interface_only", "implemented"]
 
 
@@ -198,12 +203,17 @@ class AttentionCPCommunicationPlan:
 
     def validate(self) -> None:
         self.parallel.validate()
-        if self.backend not in {"cuda_ag_rs", "p2p_nccl_reference", "local_debug"}:
+        if self.backend not in {
+            "cuda_ag_rs",
+            "rccl_ag_rs",
+            "p2p_nccl_reference",
+            "local_debug",
+        }:
             raise ValueError(f"unsupported CP communication backend: {self.backend}")
         if self.status not in {"interface_only", "implemented"}:
             raise ValueError(f"unsupported CP communication status: {self.status}")
         if self.pattern != "ag_rs":
-            raise ValueError("PR7 CP communication must use the custom CUDA AG/RS interface")
+            raise ValueError("PR7 CP communication must use the self-owned AG/RS interface")
         if self.compute_communication != "decoupled":
             raise ValueError("PR7 CP communication must keep compute and communication decoupled")
         if self.merge_order != "global_block_index":
@@ -242,6 +252,15 @@ def provenance(self) -> dict[str, object]:
             "cp_comm_strict_kv_communication": "all_gather",
             "cp_comm_strict_position_communication": "all_gather",
             "cp_comm_strict_backward": "rs_out_backward_ag_then_ag_qkv_backward_rs",
+            "cp_comm_runtime": (
+                "rccl"
+                if self.backend == "rccl_ag_rs"
+                else "nccl" if self.backend in {"cuda_ag_rs", "p2p_nccl_reference"} else "local"
+            ),
+            # The ROCm path intentionally transports tensors and performs the
+            # arithmetic in the deterministic core; only the CUDA IPC path
+            # owns a numeric collective reduction kernel.
+            "cp_comm_attention_numeric_reduction": self.backend == "cuda_ag_rs",
             "cp_comm_expected_kv_token_range": (
                 None if self.expected_kv_token_range is None else list(self.expected_kv_token_range)
             ),
@@ -338,7 +357,10 @@ def forward(
         packed = full.movedim(ctx.sequence_dim, 0).contiguous()
         if ctx.rank != ctx.root:
             packed = torch.zeros_like(packed)
-        local = collective.reduce_scatter(packed)
+        # The ROCm transport adapter exposes an explicit root-owned scatter;
+        # CUDA's IPC collective keeps the historical reduce_scatter entrypoint.
+        scatter = getattr(collective, "scatter", None)
+        local = scatter(packed) if callable(scatter) else collective.reduce_scatter(packed)
         return local.movedim(0, ctx.sequence_dim).contiguous()
 
     @staticmethod
@@ -557,6 +579,158 @@ def _validate_cuda_plan(self, plan: AttentionCPCommunicationPlan) -> None:
             raise AttentionCPCommunicationUnavailable("self-owned CUDA AG/RS requires CUDA")
 
 
+class _RCCLRankOrderedTransport:
+    """RCCL tensor transport for the ROCm AG/RS attention contract.
+
+    RCCL is deliberately used only for AllGather/Scatter transport.  The
+    reduction half first gathers every source tensor and then evaluates the
+    same fixed balanced rank tree on every device, which avoids relying on RCCL's
+    implementation-defined floating-point reduction order.
+    """
+
+    def __init__(self, *, process_group: Any = None, root: int = 0) -> None:
+        import torch.distributed as dist
+
+        if not dist.is_available() or not dist.is_initialized():
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS requires initialized torch.distributed"
+            )
+        backend = str(dist.get_backend(process_group)).lower()
+        if "nccl" not in backend or torch.version.hip is None:
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS requires the PyTorch NCCL API on ROCm"
+            )
+        self.group = process_group
+        self.rank = int(dist.get_rank(process_group))
+        self.world_size = int(dist.get_world_size(process_group))
+        self.root = int(root)
+        if self.world_size not in (1, 2, 4, 8):
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS supports CP world sizes 1, 2, 4, and 8"
+            )
+        if self.root < 0 or self.root >= self.world_size:
+            raise AttentionCPCommunicationUnavailable("RCCL scatter root is outside the group")
+
+    def all_gather(self, local: torch.Tensor) -> torch.Tensor:
+        import torch.distributed as dist
+
+        if local.ndim == 0 or not local.is_cuda or not local.is_contiguous():
+            raise AttentionCPCommunicationUnavailable(
+                "RCCL AllGather requires a contiguous ROCm tensor with a leading dimension"
+            )
+        shape = (self.world_size * local.size(0), *local.shape[1:])
+        gathered = torch.empty(shape, dtype=local.dtype, device=local.device)
+        if self.world_size == 1:
+            gathered.copy_(local)
+        else:
+            dist.all_gather_into_tensor(gathered, local, group=self.group)
+        return gathered
+
+    def scatter(self, full: torch.Tensor) -> torch.Tensor:
+        import torch.distributed as dist
+
+        if full.ndim == 0 or not full.is_cuda or not full.is_contiguous():
+            raise AttentionCPCommunicationUnavailable(
+                "RCCL Scatter requires a contiguous ROCm tensor with a leading dimension"
+            )
+        if full.size(0) % self.world_size:
+            raise AttentionCPCommunicationUnavailable(
+                "RCCL Scatter leading dimension must divide the CP world size"
+            )
+        chunks = tuple(chunk.contiguous() for chunk in full.chunk(self.world_size, dim=0))
+        local = torch.empty_like(chunks[self.rank])
+        if self.world_size == 1:
+            local.copy_(chunks[0])
+            return local
+
+        # ``src`` is a global rank even when a subgroup is supplied.
+        global_root = self.root
+        if self.group is not None:
+            get_global_rank = getattr(dist, "get_global_rank", None)
+            if callable(get_global_rank):
+                global_root = int(get_global_rank(self.group, self.root))
+            else:
+                get_group_ranks = getattr(dist, "get_process_group_ranks", None)
+                if not callable(get_group_ranks):
+                    raise AttentionCPCommunicationUnavailable(
+                        "PyTorch cannot map the RCCL subgroup root to a global rank"
+                    )
+                global_root = int(get_group_ranks(self.group)[self.root])
+        dist.scatter(
+            local,
+            scatter_list=list(chunks) if self.rank == self.root else None,
+            src=global_root,
+            group=self.group,
+        )
+        return local
+
+    def reduce_scatter(self, full: torch.Tensor) -> torch.Tensor:
+        """Deterministic source-rank sum followed by local sequence slicing."""
+
+        if full.ndim == 0 or not full.is_cuda or not full.is_contiguous():
+            raise AttentionCPCommunicationUnavailable(
+                "RCCL ReduceScatter requires a contiguous ROCm tensor with a leading dimension"
+            )
+        if full.size(0) % self.world_size:
+            raise AttentionCPCommunicationUnavailable(
+                "RCCL ReduceScatter leading dimension must divide the CP world size"
+            )
+        gathered = self.all_gather(full)
+        rows_per_rank = full.size(0) // self.world_size
+        begin = self.rank * rows_per_rank
+        # Gather layout is [source_rank, full_sequence, ...]. Evaluate the
+        # same balanced rank tree as the general deterministic collective.
+        level = []
+        for source in range(self.world_size):
+            source_begin = source * full.size(0) + begin
+            level.append(gathered[source_begin : source_begin + rows_per_rank])
+        while len(level) > 1:
+            level = [torch.add(level[index], level[index + 1]) for index in range(0, len(level), 2)]
+        return level[0]
+
+
+class RCCLAGRSAttentionCPCommunication(CUDAAGRSAttentionCPCommunication):
+    """ROCm AG/RS adapter using RCCL only as rank-ordered tensor transport."""
+
+    backend_id = "rccl_ag_rs"
+    supports_autograd = True
+    transport_only = True
+    supports_async_overlap = False
+    supports_compute_communication_fusion = False
+
+    def _get_collective(self, plan: AttentionCPCommunicationPlan):
+        if self._collective is None:
+            self._collective = _RCCLRankOrderedTransport(
+                process_group=self._process_group,
+                root=plan.merge_root_cp_rank,
+            )
+        if self._collective.world_size != plan.parallel.cp_world_size:
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL world size does not match the CP plan"
+            )
+        return self._collective
+
+    def _dist(self):
+        import torch.distributed as dist
+
+        if not dist.is_available() or not dist.is_initialized():
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS requires initialized torch.distributed"
+            )
+        return dist
+
+    def _validate_cuda_plan(self, plan: AttentionCPCommunicationPlan) -> None:
+        plan.validate()
+        if plan.backend != "rccl_ag_rs" or plan.status != "implemented":
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS requires an implemented rccl_ag_rs plan"
+            )
+        if torch.version.hip is None or not torch.cuda.is_available():
+            raise AttentionCPCommunicationUnavailable(
+                "self-owned RCCL AG/RS requires an available ROCm device"
+            )
+
+
 class P2PNCCLAttentionCPCommunication:
     """Correctness-first P2P NCCL implementation of the CP protocol.
 
@@ -1288,6 +1462,7 @@ def _rank_in_world(rank: int, world_size: int, name: str) -> None:
     "CPCommunicationBackend",
     "CPCommunicationStatus",
     "CUDAAGRSAttentionCPCommunication",
+    "RCCLAGRSAttentionCPCommunication",
     "P2PNCCLAttentionCPCommunication",
     "sort_attention_cp_partial_states",
 ]
diff --git a/rl_engine/kernels/ops/pytorch/ffn/ffn.py b/rl_engine/kernels/ops/pytorch/ffn/ffn.py
index bc0ddcef..fe17486b 100644
--- a/rl_engine/kernels/ops/pytorch/ffn/ffn.py
+++ b/rl_engine/kernels/ops/pytorch/ffn/ffn.py
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""Bias-free gated FFN assembled from deterministic CUDA kernels."""
+"""Bias-free gated FFN assembled from deterministic GPU kernels."""
 
 from __future__ import annotations
 
@@ -35,9 +35,9 @@ def _require_ffn_kernels(*, disable_split_k: bool) -> None:
     if not _EXT_AVAILABLE or _C is None or missing:
         suffix = f" Missing symbols: {', '.join(missing)}." if missing else ""
         needed = (
-            "compiled deterministic GEMM and SwiGLU CUDA kernels"
+            "compiled deterministic GEMM and SwiGLU GPU kernels"
             if disable_split_k
-            else "compiled SwiGLU CUDA kernels"
+            else "compiled SwiGLU GPU kernels"
         )
         raise RuntimeError(f"qwen3_ffn requires the {needed}.{suffix}")
 
@@ -135,7 +135,8 @@ def _validate_ffn_inputs(
         if tensor.dtype != torch.bfloat16:
             raise TypeError(f"{name} must have dtype bfloat16, got {tensor.dtype}.")
         if not tensor.is_cuda:
-            raise RuntimeError(f"{name} must be on a CUDA device, got '{tensor.device}'.")
+            # PyTorch exposes AMD GPU tensors through the torch.cuda API too.
+            raise RuntimeError(f"{name} must be on a CUDA/ROCm GPU device, got '{tensor.device}'.")
         if tensor.device != rmsnorm_output.device:
             raise RuntimeError(
                 f"all FFN inputs must be on {rmsnorm_output.device}, "
@@ -143,14 +144,35 @@ def _validate_ffn_inputs(
             )
 
 
+def _create_collective(*, group: Any, max_size_bytes: int):
+    """Create the platform-specific collective lazily.
+
+    NVIDIA keeps the existing CUDA IPC implementation.  ROCm selects the
+    RCCL transport-only implementation, which performs the floating-point
+    reduction in the shared deterministic local tree.  Keeping this boundary
+    small also makes the backend choice explicit and easy to inject in tests.
+    """
+
+    try:
+        from rl_engine.distributed import create_deterministic_collective
+    except ImportError as exc:
+        raise RuntimeError(
+            "parallel qwen3_ffn requires the platform deterministic collective "
+            "factory; single-device qwen3_ffn remains available"
+        ) from exc
+
+    return create_deterministic_collective(
+        group=group,
+        max_size_bytes=max_size_bytes,
+    )
+
+
 def _collective_for_group(group: Any, *, min_size_bytes: int):
     if group is None:
         return None
 
     import torch.distributed as dist
 
-    from rl_engine.distributed import DeterministicCollective
-
     rank = dist.get_rank(group=group)
     world_size = dist.get_world_size(group=group)
     device_index = torch.cuda.current_device()
@@ -161,7 +183,7 @@ def _collective_for_group(group: Any, *, min_size_bytes: int):
     if cached is not None:
         cached.close()
 
-    collective = DeterministicCollective(
+    collective = _create_collective(
         group=group,
         max_size_bytes=max(_COLLECTIVE_MIN_CAPACITY_BYTES, min_size_bytes),
     )
@@ -393,7 +415,8 @@ def qwen3_ffn(
             ``[H, I_local]``.
         tp_group: Optional tensor-parallel process group. Gate and Up are
             column-parallel; Down is row-parallel. Reductions use the
-            deterministic fixed-tree collectives rather than NCCL.
+            platform deterministic fixed-tree collectives. On ROCm, RCCL only
+            transports rank inputs and the reduction tree executes locally.
         cp_group: Optional context-parallel process group. Each rank owns
             different token rows and the same local weight shards. Weight
             gradients AllGather tokens along CP and run the full-token
diff --git a/setup.py b/setup.py
index 79f882d9..3f0b3d5a 100644
--- a/setup.py
+++ b/setup.py
@@ -139,9 +139,11 @@ def get_extensions():
             "csrc/cuda/rmsnorm.cu",
             "csrc/cuda/activation.cu",
             "csrc/cuda/attention/deterministic_attention.cu",
-            "csrc/cuda/distributed/deterministic_collective.cu",
         ]
         if not is_rocm:
+            # The CUDA collective owns CUDA IPC handles and driver API calls;
+            # ROCm uses the Python RCCL transport implementation instead.
+            cuda_sources.append("csrc/cuda/distributed/deterministic_collective.cu")
             # This source contains NVIDIA PTX (cp.async, ldmatrix, and mma.sync).
             # The ROCm dispatcher falls back to PyTorch SDPA for this operator.
             cuda_sources.append("csrc/cuda/attention/prefix_shared_attention.cu")
@@ -211,9 +213,10 @@ def get_extensions():
             nvcc_flags.append("-allow-unsupported-compiler")
             nvcc_flags.append("-D_ALLOW_COMPILER_AND_STL_VERSION_MISMATCH")
 
-        cxx_flags = ["-O3", "-std=c++17", "-DKERNEL_ALIGN_WITH_CUDA"]
+        platform_define = "-DKERNEL_ALIGN_WITH_ROCM" if is_rocm else "-DKERNEL_ALIGN_WITH_CUDA"
+        cxx_flags = ["-O3", "-std=c++17", platform_define]
         extra_link_args = list(torch_rpath)
-        if os.name != "nt":
+        if os.name != "nt" and not is_rocm:
             # CUDA IPC metadata queries use the driver API (cuPointerGetAttribute).
             extra_link_args.append("-lcuda")
 
diff --git a/tests/distributed/test_rocm_attention_transport.py b/tests/distributed/test_rocm_attention_transport.py
new file mode 100644
index 00000000..327bd1f1
--- /dev/null
+++ b/tests/distributed/test_rocm_attention_transport.py
@@ -0,0 +1,95 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from rl_engine.kernels.ops.cuda.attention.cp_comm import (
+    AttentionCPBlockMetadata,
+    AttentionCPCommunicationPlan,
+    AttentionCPCommunicationUnavailable,
+    AttentionParallelSpec,
+    RCCLAGRSAttentionCPCommunication,
+)
+
+
+def _plan(*, backend: str = "rccl_ag_rs") -> AttentionCPCommunicationPlan:
+    return AttentionCPCommunicationPlan(
+        parallel=AttentionParallelSpec(tp_world_size=2, cp_world_size=2),
+        backend=backend,  # type: ignore[arg-type]
+        status="implemented",
+        expected_blocks=(
+            AttentionCPBlockMetadata(0, 0, 2, 0, 0),
+            AttentionCPBlockMetadata(1, 2, 4, 1, 0),
+        ),
+        expected_kv_token_range=(0, 4),
+        query_token_ranges=((0, 1), (1, 2)),
+    )
+
+
+class _FakeRCCLTransport:
+    world_size = 2
+
+    def __init__(self) -> None:
+        self.gather_calls = 0
+        self.scatter_calls = 0
+
+    def all_gather(self, tensor: torch.Tensor) -> torch.Tensor:
+        self.gather_calls += 1
+        return torch.cat((tensor, tensor), dim=0)
+
+    def scatter(self, tensor: torch.Tensor) -> torch.Tensor:
+        self.scatter_calls += 1
+        return tensor.chunk(self.world_size, dim=0)[0].contiguous()
+
+
+def test_rccl_plan_reports_transport_only_runtime() -> None:
+    provenance = _plan().provenance()
+
+    assert provenance["cp_comm_runtime"] == "rccl"
+    assert provenance["cp_comm_attention_numeric_reduction"] is False
+
+
+def test_rccl_adapter_uses_root_scatter_and_reports_no_fusion(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    transport = _FakeRCCLTransport()
+    communication = RCCLAGRSAttentionCPCommunication(collective=transport)
+    monkeypatch.setattr(torch.version, "hip", "test", raising=False)
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+    plan = _plan()
+
+    local_q = torch.zeros(1, 2, 1, 4, dtype=torch.bfloat16)
+    assert communication.all_gather_query(local_q, plan).shape == (1, 2, 2, 4)
+
+    full_out = torch.zeros(1, 2, 2, 4, dtype=torch.bfloat16)
+    full_lse = torch.zeros(1, 2, 2, dtype=torch.float32)
+    shard = communication.reduce_scatter_strict_result(full_out, full_lse, plan)
+
+    assert shard.out.shape == (1, 2, 1, 4)
+    assert shard.lse.shape == (1, 2, 1)
+    assert transport.gather_calls == 1
+    assert transport.scatter_calls == 2
+    assert communication.transport_only is True
+    assert communication.supports_async_overlap is False
+    assert communication.supports_compute_communication_fusion is False
+
+
+def test_rccl_adapter_fails_closed_outside_rocm(monkeypatch: pytest.MonkeyPatch) -> None:
+    communication = RCCLAGRSAttentionCPCommunication(collective=_FakeRCCLTransport())
+    monkeypatch.setattr(torch.version, "hip", None, raising=False)
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+
+    with pytest.raises(AttentionCPCommunicationUnavailable, match="ROCm device"):
+        communication.all_gather_query(torch.zeros(1, 2, 1, 4), _plan())
+
+
+def test_rccl_adapter_rejects_cuda_plan(monkeypatch: pytest.MonkeyPatch) -> None:
+    communication = RCCLAGRSAttentionCPCommunication(collective=_FakeRCCLTransport())
+    monkeypatch.setattr(torch.version, "hip", "test", raising=False)
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+
+    with pytest.raises(AttentionCPCommunicationUnavailable, match="rccl_ag_rs plan"):
+        communication.all_gather_query(torch.zeros(1, 2, 1, 4), _plan(backend="cuda_ag_rs"))
diff --git a/tests/distributed/test_transport_deterministic_collective.py b/tests/distributed/test_transport_deterministic_collective.py
new file mode 100644
index 00000000..00c987e1
--- /dev/null
+++ b/tests/distributed/test_transport_deterministic_collective.py
@@ -0,0 +1,375 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from __future__ import annotations
+
+from typing import Any
+
+import pytest
+import torch
+
+import rl_engine.distributed.collectives as cuda_collectives
+import rl_engine.distributed.transport_collectives as transport_collectives
+from rl_engine.distributed import (
+    RCCLDeterministicCollective,
+    TorchDistributedDeterministicCollective,
+)
+
+
+class _FakeDistributed:
+    """Single-process model of rank-ordered AllGather transport."""
+
+    def __init__(
+        self,
+        peer_inputs: list[torch.Tensor],
+        *,
+        rank: int = 0,
+        backend: str = "gloo",
+        peer_signatures: list[tuple[Any, ...]] | None = None,
+        peer_capacities: list[int] | None = None,
+    ) -> None:
+        self.peer_inputs = peer_inputs
+        self.rank = rank
+        self.backend = backend
+        self.peer_signatures = peer_signatures
+        self.peer_capacities = peer_capacities
+        self.into_tensor_calls = 0
+        self.list_transport_calls = 0
+        self.last_transport_output: torch.Tensor | None = None
+
+    @property
+    def tensor_transport_calls(self) -> int:
+        return self.into_tensor_calls + self.list_transport_calls
+
+    @staticmethod
+    def is_available() -> bool:
+        return True
+
+    @staticmethod
+    def is_initialized() -> bool:
+        return True
+
+    def get_rank(self, *, group: Any) -> int:
+        return self.rank
+
+    def get_world_size(self, *, group: Any) -> int:
+        return len(self.peer_inputs)
+
+    def get_backend(self, group: Any) -> str:
+        return self.backend
+
+    def all_gather_object(self, output: list[Any], value: Any, *, group: Any) -> None:
+        if isinstance(value, int):
+            values = self.peer_capacities or [value] * len(self.peer_inputs)
+        else:
+            values = self.peer_signatures or [value] * len(self.peer_inputs)
+        output[:] = values
+
+    def all_gather_into_tensor(
+        self,
+        output: torch.Tensor,
+        input: torch.Tensor,
+        *,
+        group: Any,
+    ) -> None:
+        self.into_tensor_calls += 1
+        self.last_transport_output = output
+        gathered = torch.cat([peer.reshape(-1) for peer in self.peer_inputs])
+        output.copy_(gathered)
+
+    def all_gather(
+        self,
+        output: list[torch.Tensor],
+        input: torch.Tensor,
+        *,
+        group: Any,
+    ) -> None:
+        self.list_transport_calls += 1
+        self.last_transport_output = output[0]._base
+        for destination, peer in zip(output, self.peer_inputs, strict=True):
+            destination.copy_(peer.reshape(-1))
+
+
+def _make_collective(
+    monkeypatch: pytest.MonkeyPatch,
+    peer_inputs: list[torch.Tensor],
+    *,
+    rank: int = 0,
+    backend: str = "gloo",
+    peer_signatures: list[tuple[Any, ...]] | None = None,
+    peer_capacities: list[int] | None = None,
+    max_size_bytes: int = 1024,
+) -> tuple[TorchDistributedDeterministicCollective, _FakeDistributed]:
+    fake_dist = _FakeDistributed(
+        peer_inputs,
+        rank=rank,
+        backend=backend,
+        peer_signatures=peer_signatures,
+        peer_capacities=peer_capacities,
+    )
+    monkeypatch.setattr(transport_collectives, "dist", fake_dist)
+    collective = TorchDistributedDeterministicCollective(
+        group=object(),
+        device="cpu",
+        max_size_bytes=max_size_bytes,
+    )
+    return collective, fake_dist
+
+
+@pytest.mark.parametrize("world_size", [1, 2, 4, 8])
+@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
+def test_all_reduce_supports_fixed_world_sizes_and_dtypes(
+    monkeypatch: pytest.MonkeyPatch,
+    world_size: int,
+    dtype: torch.dtype,
+) -> None:
+    peers = [torch.full((2, 3), rank + 1, dtype=dtype) for rank in range(world_size)]
+    collective, fake_dist = _make_collective(monkeypatch, peers)
+
+    provided = torch.empty_like(peers[0])
+    returned = collective.all_reduce(peers[0], out=provided)
+
+    assert returned is provided
+    assert torch.equal(provided, torch.full_like(provided, world_size * (world_size + 1) // 2))
+    assert fake_dist.tensor_transport_calls == (0 if world_size == 1 else 1)
+
+
+def test_all_reduce_uses_balanced_not_rank_ordered_left_fold(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    # Balanced: (1e20 + 1) + (-1e20 + 1) == 0 in FP32.
+    # Left fold: ((1e20 + 1) + -1e20) + 1 == 1 in FP32.
+    peers = [torch.tensor([value], dtype=torch.float32) for value in (1.0e20, 1.0, -1.0e20, 1.0)]
+    collective, _ = _make_collective(monkeypatch, peers)
+
+    output = collective.all_reduce(peers[0])
+
+    assert torch.equal(output, torch.zeros_like(output))
+
+
+def test_all_reduce_stages_before_writing_in_place(monkeypatch: pytest.MonkeyPatch) -> None:
+    peers = [torch.full((2, 3), rank + 1, dtype=torch.float32) for rank in range(4)]
+    collective, _ = _make_collective(monkeypatch, peers)
+    local = peers[0].clone()
+
+    returned = collective.all_reduce(local, out=local)
+
+    assert returned is local
+    assert torch.equal(local, torch.full_like(local, 10))
+
+
+def test_all_gather_is_rank_ordered_and_transport_only(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    peers = [torch.arange(rank * 6, (rank + 1) * 6).reshape(2, 3) for rank in range(4)]
+    collective, fake_dist = _make_collective(monkeypatch, peers, rank=2)
+
+    output = collective.all_gather(peers[2])
+
+    assert torch.equal(output, torch.cat(peers, dim=0))
+    assert fake_dist.tensor_transport_calls == 1
+
+
+def test_nccl_backend_uses_all_gather_into_tensor_transport(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    peers = [torch.full((2, 3), rank) for rank in range(2)]
+    collective, fake_dist = _make_collective(monkeypatch, peers, backend="nccl")
+
+    output = collective.all_gather(peers[0])
+
+    assert torch.equal(output, torch.cat(peers, dim=0))
+    assert fake_dist.into_tensor_calls == 1
+    assert fake_dist.list_transport_calls == 0
+
+
+def test_all_gather_writes_directly_to_provided_output(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    peers = [torch.full((2, 3), rank) for rank in range(2)]
+    collective, fake_dist = _make_collective(monkeypatch, peers, backend="nccl")
+    provided = torch.empty(4, 3, dtype=peers[0].dtype)
+
+    returned = collective.all_gather(peers[0], out=provided)
+
+    assert returned is provided
+    assert fake_dist.last_transport_output is not None
+    assert fake_dist.last_transport_output.data_ptr() == provided.data_ptr()
+    assert collective._workspace is None
+
+
+def test_reduction_workspace_grows_once_and_is_reused(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    peers = [torch.full((2, 3), rank + 1, dtype=torch.float32) for rank in range(2)]
+    collective, _ = _make_collective(monkeypatch, peers, backend="nccl")
+
+    collective.all_reduce(peers[0])
+    assert collective._workspace is not None
+    assert collective.workspace_size_bytes == sum(peer.numel() for peer in peers) * 4
+    first_pointer = collective._workspace.data_ptr()
+    collective.all_reduce(peers[0])
+
+    assert collective._workspace.data_ptr() == first_pointer
+    collective.close()
+    assert collective._workspace is None
+    assert collective.workspace_size_bytes == 0
+
+
+def test_reduce_scatter_reduces_then_selects_local_leading_shard(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    peers = [torch.full((8, 3), rank + 1, dtype=torch.bfloat16) for rank in range(4)]
+    collective, _ = _make_collective(monkeypatch, peers, rank=2)
+
+    output = collective.reduce_scatter(peers[2])
+
+    expected_full = torch.full_like(peers[0], 10)
+    assert torch.equal(output, expected_full.chunk(4, dim=0)[2])
+
+
+def test_matching_signature_is_checked_before_tensor_transport(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    peers = [torch.ones(2, 3), torch.ones(2, 3)]
+    signatures = [
+        ("all_reduce", (2, 3), "torch.float32", 6),
+        ("reduce_scatter", (2, 3), "torch.float32", 6),
+    ]
+    collective, fake_dist = _make_collective(
+        monkeypatch,
+        peers,
+        peer_signatures=signatures,
+    )
+
+    with pytest.raises(ValueError, match="matching shapes and dtypes"):
+        collective.all_reduce(peers[0])
+    assert fake_dist.tensor_transport_calls == 0
+
+
+def test_capacity_must_match_on_every_rank(monkeypatch: pytest.MonkeyPatch) -> None:
+    peers = [torch.ones(2, 3), torch.ones(2, 3)]
+
+    with pytest.raises(ValueError, match="same max_size_bytes"):
+        _make_collective(
+            monkeypatch,
+            peers,
+            max_size_bytes=1024,
+            peer_capacities=[1024, 2048],
+        )
+
+
+def test_validation_fails_closed_before_transport(monkeypatch: pytest.MonkeyPatch) -> None:
+    peers = [torch.ones(4, 2), torch.ones(4, 2)]
+    collective, fake_dist = _make_collective(monkeypatch, peers, max_size_bytes=31)
+
+    with pytest.raises(TypeError, match="float32, float16, and bfloat16"):
+        collective.all_reduce(torch.ones(1, dtype=torch.int32))
+    with pytest.raises(ValueError, match="max_size_bytes"):
+        collective.all_reduce(peers[0])
+    with pytest.raises(ValueError, match=r"input.size\(0\).+divisible"):
+        collective.reduce_scatter(torch.ones(3, 2))
+    with pytest.raises(ValueError, match="at least one dimension"):
+        collective.all_gather(torch.tensor(1.0))
+    assert fake_dist.tensor_transport_calls == 0
+
+
+def test_lifecycle_is_idempotent_and_context_manager_closes(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    peers = [torch.ones(2, 3)]
+    collective, _ = _make_collective(monkeypatch, peers)
+
+    with collective as entered:
+        assert entered is collective
+        assert not collective.closed
+        assert torch.equal(collective.all_reduce(peers[0]), peers[0])
+
+    assert collective.closed
+    collective.close()
+    with pytest.raises(RuntimeError, match="closed"):
+        collective.all_reduce(peers[0])
+
+
+@pytest.mark.parametrize("world_size", [3, 16])
+def test_unsupported_world_size_is_rejected(
+    monkeypatch: pytest.MonkeyPatch,
+    world_size: int,
+) -> None:
+    fake_dist = _FakeDistributed([torch.ones(1)] * world_size)
+    monkeypatch.setattr(transport_collectives, "dist", fake_dist)
+
+    with pytest.raises(ValueError, match="world_size in"):
+        TorchDistributedDeterministicCollective(group=object(), device="cpu")
+
+
+def test_rccl_class_requires_rocm_build(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(transport_collectives.torch.version, "hip", None, raising=False)
+
+    with pytest.raises(RuntimeError, match="ROCm PyTorch build"):
+        RCCLDeterministicCollective(group=object(), device="cuda:0")
+
+
+def test_rccl_class_requires_nccl_process_group(monkeypatch: pytest.MonkeyPatch) -> None:
+    fake_dist = _FakeDistributed([torch.ones(1)], backend="gloo")
+    monkeypatch.setattr(transport_collectives, "dist", fake_dist)
+    monkeypatch.setattr(transport_collectives.torch.version, "hip", "6.3", raising=False)
+    monkeypatch.setattr(transport_collectives.torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(transport_collectives.torch.cuda, "current_device", lambda: 0)
+
+    with pytest.raises(RuntimeError, match="NCCL process-group API"):
+        RCCLDeterministicCollective(group=object(), device="cuda:0")
+
+
+def test_rccl_class_rejects_cpu_before_process_group_exchange(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setattr(transport_collectives.torch.version, "hip", "6.3", raising=False)
+    monkeypatch.setattr(transport_collectives.torch.cuda, "is_available", lambda: True)
+
+    with pytest.raises(ValueError, match="ROCm device"):
+        RCCLDeterministicCollective(group=object(), device="cpu")
+
+
+def test_factory_dispatches_rocm_to_rccl(monkeypatch: pytest.MonkeyPatch) -> None:
+    sentinel = object()
+    calls: list[dict[str, Any]] = []
+
+    def fake_rccl(**kwargs: Any) -> object:
+        calls.append(kwargs)
+        return sentinel
+
+    monkeypatch.setattr(transport_collectives.torch.version, "hip", "6.3", raising=False)
+    monkeypatch.setattr(transport_collectives, "RCCLDeterministicCollective", fake_rccl)
+
+    group = object()
+    result = transport_collectives.create_deterministic_collective(
+        group=group,
+        device="cuda:3",
+        max_size_bytes=1234,
+    )
+
+    assert result is sentinel
+    assert calls == [{"group": group, "device": "cuda:3", "max_size_bytes": 1234}]
+
+
+def test_factory_preserves_existing_cuda_collective(monkeypatch: pytest.MonkeyPatch) -> None:
+    sentinel = object()
+    calls: list[dict[str, Any]] = []
+
+    def fake_cuda(**kwargs: Any) -> object:
+        calls.append(kwargs)
+        return sentinel
+
+    monkeypatch.setattr(transport_collectives.torch.version, "hip", None, raising=False)
+    monkeypatch.setattr(cuda_collectives, "DeterministicCollective", fake_cuda)
+
+    group = object()
+    result = transport_collectives.create_deterministic_collective(
+        group=group,
+        device="cuda:1",
+        max_size_bytes=4321,
+    )
+
+    assert result is sentinel
+    assert calls == [{"group": group, "device": "cuda:1", "max_size_bytes": 4321}]
diff --git a/tests/test_build_platform_collectives.py b/tests/test_build_platform_collectives.py
new file mode 100644
index 00000000..f094a5af
--- /dev/null
+++ b/tests/test_build_platform_collectives.py
@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from __future__ import annotations
+
+import runpy
+from typing import Any
+
+import setuptools
+import torch
+from torch.utils import cpp_extension
+
+
+def _load_extension_config(monkeypatch, *, hip: str | None) -> dict[str, Any]:
+    captured: dict[str, Any] = {}
+
+    def fake_setup(**kwargs: Any) -> None:
+        captured.update(kwargs)
+
+    def fake_extension(**kwargs: Any) -> dict[str, Any]:
+        return kwargs
+
+    monkeypatch.setattr(setuptools, "setup", fake_setup)
+    monkeypatch.setattr(cpp_extension, "CUDAExtension", fake_extension)
+    monkeypatch.setattr(torch.version, "hip", hip, raising=False)
+    monkeypatch.delenv("KERNEL_ALIGN_FORCE_SM90", raising=False)
+    monkeypatch.delenv("KERNEL_ALIGN_DET_GEMM_SM90", raising=False)
+    if hip is None:
+        monkeypatch.delenv("PYTORCH_ROCM_ARCH", raising=False)
+        monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+        monkeypatch.setattr(torch.cuda, "get_device_capability", lambda: (8, 0))
+    else:
+        monkeypatch.setenv("PYTORCH_ROCM_ARCH", "gfx942")
+
+    runpy.run_path("setup.py", run_name=f"rl_kernel_setup_probe_{hip or 'cuda'}")
+    return captured["ext_modules"][0]
+
+
+def test_rocm_build_excludes_cuda_ipc_collective_and_driver(monkeypatch) -> None:
+    extension = _load_extension_config(monkeypatch, hip="test")
+
+    assert "csrc/cuda/distributed/deterministic_collective.cu" not in extension["sources"]
+    assert "-DKERNEL_ALIGN_WITH_ROCM" in extension["extra_compile_args"]["cxx"]
+    assert "-DKERNEL_ALIGN_WITH_CUDA" not in extension["extra_compile_args"]["cxx"]
+    assert "-lcuda" not in extension["extra_link_args"]
+
+
+def test_cuda_build_keeps_existing_ipc_collective(monkeypatch) -> None:
+    extension = _load_extension_config(monkeypatch, hip=None)
+
+    assert "csrc/cuda/distributed/deterministic_collective.cu" in extension["sources"]
+    assert "-DKERNEL_ALIGN_WITH_CUDA" in extension["extra_compile_args"]["cxx"]
+    assert "-DKERNEL_ALIGN_WITH_ROCM" not in extension["extra_compile_args"]["cxx"]
+    assert "-lcuda" in extension["extra_link_args"]
diff --git a/tests/test_qwen_ffn.py b/tests/test_qwen_ffn.py
index 515abc7a..fddfa9f4 100644
--- a/tests/test_qwen_ffn.py
+++ b/tests/test_qwen_ffn.py
@@ -137,6 +137,25 @@ def _close_ffn_collectives() -> None:
     ffn_module._COLLECTIVES.clear()
 
 
+def test_ffn_collective_creation_uses_platform_factory(monkeypatch):
+    import rl_engine.distributed as distributed
+
+    sentinel = object()
+    calls = []
+
+    def fake_factory(**kwargs):
+        calls.append(kwargs)
+        return sentinel
+
+    monkeypatch.setattr(distributed, "create_deterministic_collective", fake_factory)
+    group = object()
+
+    result = ffn_module._create_collective(group=group, max_size_bytes=1234)
+
+    assert result is sentinel
+    assert calls == [{"group": group, "max_size_bytes": 1234}]
+
+
 def _shard_ranges(
     rank: int,
     *,
diff --git a/tests/test_rocm_collective_benchmark.py b/tests/test_rocm_collective_benchmark.py
new file mode 100644
index 00000000..e4eb8643
--- /dev/null
+++ b/tests/test_rocm_collective_benchmark.py
@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+
+import pytest
+import torch
+
+_BENCHMARK_PATH = Path(__file__).parents[1] / "benchmarks" / "benchmark_rocm_collectives.py"
+_SPEC = importlib.util.spec_from_file_location(
+    "rlkernel_rocm_collective_benchmark", _BENCHMARK_PATH
+)
+assert _SPEC is not None and _SPEC.loader is not None
+benchmark = importlib.util.module_from_spec(_SPEC)
+_SPEC.loader.exec_module(benchmark)
+
+
+def test_rocm_collective_benchmark_parser() -> None:
+    args = benchmark.parse_args(
+        [
+            "--size-bytes",
+            "1024",
+            "4096",
+            "--dtype",
+            "fp32",
+            "--operations",
+            "all_reduce",
+            "reduce_scatter",
+            "--warmup",
+            "2",
+            "--iterations",
+            "3",
+            "--samples",
+            "4",
+        ]
+    )
+
+    benchmark._validate_args(args)
+    assert args.size_bytes == [1024, 4096]
+    assert args.dtype == "fp32"
+    assert args.operations == ["all_reduce", "reduce_scatter"]
+    assert (args.warmup, args.iterations, args.samples) == (2, 3, 4)
+
+
+@pytest.mark.parametrize(
+    "argv",
+    (
+        ["--size-bytes", "0"],
+        ["--warmup", "-1"],
+        ["--iterations", "0"],
+        ["--samples", "0"],
+    ),
+)
+def test_rocm_collective_benchmark_rejects_invalid_counts(argv: list[str]) -> None:
+    with pytest.raises(ValueError):
+        benchmark._validate_args(benchmark.parse_args(argv))
+
+
+def test_rocm_collective_benchmark_aligns_reduce_scatter_input() -> None:
+    tensor, actual_bytes = benchmark._make_inputs(
+        size_bytes=35,
+        dtype=torch.float32,
+        world_size=4,
+        rank=2,
+        device=torch.device("cpu"),
+    )
+
+    assert tensor.is_contiguous()
+    assert tensor.numel() % 4 == 0
+    assert actual_bytes == tensor.numel() * tensor.element_size()

From eb16e79bbfecdb975786adf95c1015f5e60f4ff7 Mon Sep 17 00:00:00 2001
From: vensen <vensenmu@gmail.com>
Date: Fri, 28 Aug 2026 16:48:51 +0000
Subject: [PATCH 17/35] perf(distributed): remove ROCm collective hot-path
 allocations

---
 .../distributed/transport_collectives.py      | 28 +++++++++++++++----
 .../kernels/ops/cuda/attention/cp_comm.py     |  7 +++--
 ...test_transport_deterministic_collective.py | 15 ++++++++++
 3 files changed, 42 insertions(+), 8 deletions(-)

diff --git a/rl_engine/distributed/transport_collectives.py b/rl_engine/distributed/transport_collectives.py
index fea577bc..ff9a4dcf 100644
--- a/rl_engine/distributed/transport_collectives.py
+++ b/rl_engine/distributed/transport_collectives.py
@@ -74,6 +74,12 @@ def __init__(
         # One dtype-agnostic byte workspace is grown on demand and reused by
         # reduction collectives. AllGather writes directly into its output.
         self._workspace: torch.Tensor | None = None
+        # A Python-object collective is useful for catching a mismatched new
+        # signature, but running one on every hot-path call dominates small
+        # message latency. Validate each local signature once and then rely on
+        # the standard collective contract that ranks call operations in the
+        # same order.
+        self._validated_signatures: set[tuple[Any, ...]] = set()
         self._validate_matching_capacity()
 
     @staticmethod
@@ -200,6 +206,7 @@ def close(self) -> None:
 
         with self._lock:
             self._workspace = None
+            self._validated_signatures.clear()
             self._closed = True
 
     def __enter__(self) -> TorchDistributedDeterministicCollective:
@@ -271,12 +278,15 @@ def _validate_matching_signature(self, op_name: str, input: torch.Tensor) -> Non
         if self.world_size == 1:
             return
         signature = (op_name, tuple(input.shape), str(input.dtype), input.numel())
+        if signature in self._validated_signatures:
+            return
         signatures: list[tuple[Any, ...] | None] = [None] * self.world_size
         dist.all_gather_object(signatures, signature, group=self.group)
         if any(peer_signature != signature for peer_signature in signatures):
             raise ValueError(
                 f"all ranks must call {op_name} with matching shapes and dtypes; got {signatures}"
             )
+        self._validated_signatures.add(signature)
 
     def _validate_matching_capacity(self) -> None:
         if self.world_size == 1:
@@ -336,15 +346,21 @@ def _workspace_for(self, input: torch.Tensor, required_elements: int) -> torch.T
 
     @staticmethod
     def _balanced_tree_sum(rank_inputs: torch.Tensor) -> torch.Tensor:
-        level = list(rank_inputs.unbind(0))
-        if len(level) not in _SUPPORTED_WORLD_SIZES:
+        world_size = rank_inputs.size(0)
+        if world_size not in _SUPPORTED_WORLD_SIZES:
             raise ValueError(
                 "balanced reduction requires rank inputs for world_size in "
-                f"{_SUPPORTED_WORLD_SIZES}, got {len(level)}"
+                f"{_SUPPORTED_WORLD_SIZES}, got {world_size}"
             )
-        while len(level) > 1:
-            level = [torch.add(level[index], level[index + 1]) for index in range(0, len(level), 2)]
-        return level[0]
+        # ``rank_inputs`` is the private transport workspace for reductions,
+        # so fixed-tree nodes can be accumulated in place. This preserves the
+        # exact pairings while avoiding one temporary allocation per tree node.
+        stride = 1
+        while stride < world_size:
+            for index in range(0, world_size, 2 * stride):
+                rank_inputs[index].add_(rank_inputs[index + stride])
+            stride *= 2
+        return rank_inputs[0]
 
 
 class RCCLDeterministicCollective(TorchDistributedDeterministicCollective):
diff --git a/rl_engine/kernels/ops/cuda/attention/cp_comm.py b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
index 4f945f14..9f805fe5 100644
--- a/rl_engine/kernels/ops/cuda/attention/cp_comm.py
+++ b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
@@ -684,8 +684,11 @@ def reduce_scatter(self, full: torch.Tensor) -> torch.Tensor:
         for source in range(self.world_size):
             source_begin = source * full.size(0) + begin
             level.append(gathered[source_begin : source_begin + rows_per_rank])
-        while len(level) > 1:
-            level = [torch.add(level[index], level[index + 1]) for index in range(0, len(level), 2)]
+        stride = 1
+        while stride < self.world_size:
+            for index in range(0, self.world_size, 2 * stride):
+                level[index].add_(level[index + stride])
+            stride *= 2
         return level[0]
 
 
diff --git a/tests/distributed/test_transport_deterministic_collective.py b/tests/distributed/test_transport_deterministic_collective.py
index 00c987e1..ed1da194 100644
--- a/tests/distributed/test_transport_deterministic_collective.py
+++ b/tests/distributed/test_transport_deterministic_collective.py
@@ -35,6 +35,7 @@ def __init__(
         self.peer_capacities = peer_capacities
         self.into_tensor_calls = 0
         self.list_transport_calls = 0
+        self.object_gather_calls = 0
         self.last_transport_output: torch.Tensor | None = None
 
     @property
@@ -59,6 +60,7 @@ def get_backend(self, group: Any) -> str:
         return self.backend
 
     def all_gather_object(self, output: list[Any], value: Any, *, group: Any) -> None:
+        self.object_gather_calls += 1
         if isinstance(value, int):
             values = self.peer_capacities or [value] * len(self.peer_inputs)
         else:
@@ -216,6 +218,19 @@ def test_reduction_workspace_grows_once_and_is_reused(
     assert collective.workspace_size_bytes == 0
 
 
+def test_matching_signature_is_validated_once_per_hot_path(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    peers = [torch.full((2, 3), rank + 1, dtype=torch.float32) for rank in range(2)]
+    collective, fake_dist = _make_collective(monkeypatch, peers, backend="nccl")
+    constructor_object_gathers = fake_dist.object_gather_calls
+
+    collective.all_reduce(peers[0])
+    collective.all_reduce(peers[0])
+
+    assert fake_dist.object_gather_calls == constructor_object_gathers + 1
+
+
 def test_reduce_scatter_reduces_then_selects_local_leading_shard(
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:

From d36d70bb0c5ed1ec72ad563aff5ef834af85a38f Mon Sep 17 00:00:00 2001
From: vensen <vensenmu@gmail.com>
Date: Fri, 28 Aug 2026 17:37:49 +0000
Subject: [PATCH 18/35] refactor(distributed): unify platform collectives
 module

---
 csrc/ops.cpp                                  |   2 +-
 rl_engine/distributed/__init__.py             |   4 +-
 rl_engine/distributed/collectives.py          | 416 +++++++++++++++++
 .../distributed/transport_collectives.py      | 435 ------------------
 ...test_transport_deterministic_collective.py |  46 +-
 5 files changed, 448 insertions(+), 455 deletions(-)
 delete mode 100644 rl_engine/distributed/transport_collectives.py

diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index fb84dc64..8936c9fb 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -95,7 +95,7 @@ torch::Tensor deterministic_logp_forward_indexed_fp32(torch::Tensor logits, torc
 
 #if !defined(USE_ROCM) && !defined(KERNEL_ALIGN_WITH_ROCM)
 // Single-node TP=8 deterministic CUDA IPC collectives. ROCm uses the
-// rank-ordered RCCL transport in rl_engine.distributed.transport_collectives.
+// rank-ordered RCCL transport in rl_engine.distributed.collectives.
 std::tuple<std::vector<int64_t>, int64_t> deterministic_collective_ipc_meta(
     torch::Tensor& tensor);
 int64_t deterministic_collective_create(
diff --git a/rl_engine/distributed/__init__.py b/rl_engine/distributed/__init__.py
index f83f2520..9010a534 100644
--- a/rl_engine/distributed/__init__.py
+++ b/rl_engine/distributed/__init__.py
@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-from rl_engine.distributed.collectives import DeterministicCollective
-from rl_engine.distributed.transport_collectives import (
+from rl_engine.distributed.collectives import (
+    DeterministicCollective,
     RCCLDeterministicCollective,
     TorchDistributedDeterministicCollective,
     create_deterministic_collective,
diff --git a/rl_engine/distributed/collectives.py b/rl_engine/distributed/collectives.py
index 54b00fa5..db3ac70f 100644
--- a/rl_engine/distributed/collectives.py
+++ b/rl_engine/distributed/collectives.py
@@ -1,5 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
+"""Deterministic collectives for native CUDA IPC and rank-ordered transport.
+
+The ROCm transport never performs a floating-point reduction. It gathers every
+rank's input through RCCL, after which each rank evaluates the same balanced
+reduction tree locally.
+"""
 
 from __future__ import annotations
 
@@ -323,3 +329,413 @@ def _synchronize_ranks(self) -> None:
             dist.barrier(group=self.group, device_ids=[self.device.index])
         else:
             dist.barrier(group=self.group)
+
+
+class TorchDistributedDeterministicCollective:
+    """Correctness-first collectives using AllGather as transport only.
+
+    Rank inputs are gathered without arithmetic and reduced locally as the
+    balanced tree ``((rank0 + rank1) + (rank2 + rank3)) + ...``. Consequently,
+    all ranks execute the exact same floating-point expression. TP sizes 1,
+    2, 4, and 8 are nested prefixes of that expression and match the existing
+    CUDA IPC collective's ordering.
+
+    The generic class also supports a CPU/Gloo process group, which is useful
+    as an executable reference. Production ROCm callers should use
+    :class:`RCCLDeterministicCollective` or
+    :func:`create_deterministic_collective` so backend validation fails closed.
+    All ranks must call methods in the same order with matching input shapes
+    and dtypes, and construct the instance with the same ``max_size_bytes``.
+    """
+
+    backend_id = "torch_distributed_balanced_tree"
+    transport_only = True
+    reduction_order = "balanced_rank_tree"
+    supports_async_overlap = False
+    supports_compute_communication_fusion = False
+
+    def __init__(
+        self,
+        group: dist.ProcessGroup | None = None,
+        device: torch.device | str | int | None = None,
+        *,
+        max_size_bytes: int = _DEFAULT_MAX_SIZE_BYTES,
+    ) -> None:
+        if not dist.is_available() or not dist.is_initialized():
+            raise RuntimeError("torch.distributed must be initialized before collectives")
+        if max_size_bytes <= 0:
+            raise ValueError("max_size_bytes must be positive")
+
+        self.group = group if group is not None else dist.group.WORLD
+        self.rank = int(dist.get_rank(group=self.group))
+        self.world_size = int(dist.get_world_size(group=self.group))
+        if self.world_size not in _SUPPORTED_WORLD_SIZES:
+            raise ValueError(
+                "deterministic collectives require world_size in "
+                f"{_SUPPORTED_WORLD_SIZES}, got {self.world_size}"
+            )
+
+        self.device = self._normalize_device(device)
+        self.max_size_bytes = int(max_size_bytes)
+        self._backend = str(dist.get_backend(self.group)).lower()
+        self._lock = threading.Lock()
+        self._closed = False
+        # One dtype-agnostic byte workspace is grown on demand and reused by
+        # reduction collectives. AllGather writes directly into its output.
+        self._workspace: torch.Tensor | None = None
+        # A Python-object collective is useful for catching a mismatched new
+        # signature, but running one on every hot-path call dominates small
+        # message latency. Validate each local signature once and then rely on
+        # the standard collective contract that ranks call operations in the
+        # same order.
+        self._validated_signatures: set[tuple[Any, ...]] = set()
+        self._validate_matching_capacity()
+
+    @staticmethod
+    def _normalize_device(
+        device: torch.device | str | int | None,
+    ) -> torch.device:
+        if device is None:
+            if torch.cuda.is_available():
+                normalized = torch.device("cuda", torch.cuda.current_device())
+            else:
+                normalized = torch.device("cpu")
+        elif isinstance(device, int):
+            normalized = torch.device("cuda", device)
+        else:
+            normalized = torch.device(device)
+
+        if normalized.type == "cuda":
+            if not torch.cuda.is_available():
+                raise RuntimeError("a CUDA/ROCm device was requested but none is available")
+            current_device = torch.cuda.current_device()
+            if normalized.index is None:
+                normalized = torch.device("cuda", current_device)
+            if normalized.index != current_device:
+                raise ValueError(
+                    "the collective device must be the current CUDA/ROCm device; call "
+                    f"torch.cuda.set_device({normalized.index}) first"
+                )
+        return normalized
+
+    @property
+    def closed(self) -> bool:
+        """Whether this instance rejects further collective calls."""
+
+        return self._closed
+
+    @property
+    def workspace_size_bytes(self) -> int:
+        """Currently retained reduction workspace size in bytes."""
+
+        workspace = self._workspace
+        return 0 if workspace is None else int(workspace.numel())
+
+    def all_reduce(
+        self,
+        input: torch.Tensor,
+        *,
+        out: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Return the fixed balanced-tree sum on every rank."""
+
+        self._check_open()
+        self._validate_reduction_input(input)
+        if out is None:
+            out = torch.empty_like(input)
+        self._validate_output(out, input, tuple(input.shape))
+
+        with self._lock:
+            self._check_open()
+            self._validate_matching_signature("all_reduce", input)
+            rank_inputs = self._all_gather_transport(input)
+            reduced = self._balanced_tree_sum(rank_inputs)
+            out.copy_(reduced)
+        return out
+
+    def all_gather(
+        self,
+        input: torch.Tensor,
+        *,
+        out: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Gather rank-ordered input bit patterns along dimension 0."""
+
+        self._check_open()
+        self._validate_gather_input(input)
+        output_shape = (input.size(0) * self.world_size, *input.shape[1:])
+        if out is None:
+            out = torch.empty(output_shape, dtype=input.dtype, device=input.device)
+        self._validate_output(out, input, output_shape)
+
+        with self._lock:
+            self._check_open()
+            self._validate_matching_signature("all_gather", input)
+            self._all_gather_transport(input, gathered_flat=out.view(-1))
+        return out
+
+    def reduce_scatter(
+        self,
+        input: torch.Tensor,
+        *,
+        out: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Fixed-tree sum followed by rank-ordered dimension-0 slicing."""
+
+        self._check_open()
+        self._validate_reduction_input(input)
+        if input.dim() == 0:
+            raise ValueError("reduce_scatter input must have at least one dimension")
+        if input.size(0) % self.world_size != 0:
+            raise ValueError(
+                "reduce_scatter input.size(0) must be divisible by "
+                f"world_size={self.world_size}; got {input.size(0)}"
+            )
+        rows_per_rank = input.size(0) // self.world_size
+        output_shape = (rows_per_rank, *input.shape[1:])
+        if out is None:
+            out = torch.empty(output_shape, dtype=input.dtype, device=input.device)
+        self._validate_output(out, input, output_shape)
+
+        with self._lock:
+            self._check_open()
+            self._validate_matching_signature("reduce_scatter", input)
+            rank_inputs = self._all_gather_transport(input)
+            reduced = self._balanced_tree_sum(rank_inputs)
+            begin = self.rank * rows_per_rank
+            out.copy_(reduced.narrow(0, begin, rows_per_rank))
+        return out
+
+    def close(self) -> None:
+        """Close the instance.
+
+        Closing releases the lazily allocated reduction workspace and marks
+        the lifecycle boundary. Collective calls are blocking at this API.
+        """
+
+        with self._lock:
+            self._workspace = None
+            self._validated_signatures.clear()
+            self._closed = True
+
+    def __enter__(self) -> TorchDistributedDeterministicCollective:
+        self._check_open()
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+    def __del__(self) -> None:
+        try:
+            self.close()
+        except Exception:
+            pass
+
+    def _check_open(self) -> None:
+        if getattr(self, "_closed", True):
+            raise RuntimeError("deterministic collective is closed")
+
+    def _validate_tensor(self, input: torch.Tensor) -> None:
+        if not isinstance(input, torch.Tensor):
+            raise TypeError(f"input must be a torch.Tensor, got {type(input)!r}")
+        if input.device != self.device:
+            raise ValueError(f"input must be on {self.device}, got {input.device}")
+        if not input.is_contiguous():
+            raise ValueError("input must be contiguous")
+        input_bytes = input.numel() * input.element_size()
+        if input_bytes > self.max_size_bytes:
+            raise ValueError(
+                f"input requires {input_bytes} bytes but max_size_bytes={self.max_size_bytes}"
+            )
+
+    def _validate_reduction_input(self, input: torch.Tensor) -> None:
+        self._validate_tensor(input)
+        if input.dtype not in _REDUCTION_DTYPES:
+            raise TypeError(
+                "deterministic reductions support float32, float16, and bfloat16; "
+                f"got {input.dtype}"
+            )
+
+    def _validate_gather_input(self, input: torch.Tensor) -> None:
+        self._validate_tensor(input)
+        if input.dim() == 0:
+            raise ValueError("all_gather input must have at least one dimension")
+
+    @staticmethod
+    def _validate_output(
+        output: torch.Tensor,
+        input: torch.Tensor,
+        output_shape: tuple[int, ...],
+    ) -> None:
+        if not isinstance(output, torch.Tensor):
+            raise TypeError(f"out must be a torch.Tensor, got {type(output)!r}")
+        if output.device != input.device:
+            raise ValueError("out must be on the same device as input")
+        if output.dtype != input.dtype:
+            raise TypeError("out must have the same dtype as input")
+        if tuple(output.shape) != output_shape:
+            raise ValueError(f"out must have shape {output_shape}, got {tuple(output.shape)}")
+        if not output.is_contiguous():
+            raise ValueError("out must be contiguous")
+
+    def _validate_matching_signature(self, op_name: str, input: torch.Tensor) -> None:
+        if self.world_size == 1:
+            return
+        signature = (op_name, tuple(input.shape), str(input.dtype), input.numel())
+        if signature in self._validated_signatures:
+            return
+        signatures: list[tuple[Any, ...] | None] = [None] * self.world_size
+        dist.all_gather_object(signatures, signature, group=self.group)
+        if any(peer_signature != signature for peer_signature in signatures):
+            raise ValueError(
+                f"all ranks must call {op_name} with matching shapes and dtypes; got {signatures}"
+            )
+        self._validated_signatures.add(signature)
+
+    def _validate_matching_capacity(self) -> None:
+        if self.world_size == 1:
+            return
+        capacities: list[int | None] = [None] * self.world_size
+        dist.all_gather_object(capacities, self.max_size_bytes, group=self.group)
+        if any(peer_capacity != self.max_size_bytes for peer_capacity in capacities):
+            raise ValueError(f"all ranks must use the same max_size_bytes; got {capacities}")
+
+    def _all_gather_transport(
+        self,
+        input: torch.Tensor,
+        *,
+        gathered_flat: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        # Flattening makes the output contract independent of whether a given
+        # ProcessGroup implements the concatenation or stacking form of AG.
+        if self.world_size == 1:
+            if gathered_flat is None:
+                gathered_flat = input.clone().view(-1)
+            else:
+                gathered_flat.copy_(input.view(-1))
+            return gathered_flat.reshape((1, *input.shape))
+        input_flat = input.view(-1)
+        required_elements = self.world_size * input_flat.numel()
+        if gathered_flat is None:
+            gathered_flat = self._workspace_for(input, required_elements)
+        elif (
+            gathered_flat.numel() != required_elements
+            or gathered_flat.dtype != input.dtype
+            or gathered_flat.device != input.device
+            or not gathered_flat.is_contiguous()
+        ):
+            raise ValueError("gathered transport output has an invalid layout")
+        if "nccl" in self._backend:
+            # PyTorch exposes RCCL through the NCCL ProcessGroup API. Keep this
+            # as a tensor-only transport; reduction happens below.
+            dist.all_gather_into_tensor(gathered_flat, input_flat, group=self.group)
+        else:
+            # Some reference backends (notably Gloo versions without
+            # all_gather_into_tensor) only implement the list API.
+            gathered_chunks = list(
+                gathered_flat.reshape(self.world_size, input_flat.numel()).unbind(0)
+            )
+            dist.all_gather(gathered_chunks, input_flat, group=self.group)
+        return gathered_flat.reshape((self.world_size, *input.shape))
+
+    def _workspace_for(self, input: torch.Tensor, required_elements: int) -> torch.Tensor:
+        required_bytes = required_elements * input.element_size()
+        workspace = self._workspace
+        if workspace is None or workspace.numel() < required_bytes:
+            workspace = torch.empty(required_bytes, dtype=torch.uint8, device=self.device)
+            self._workspace = workspace
+        # Tensor.view(dtype) reinterprets the aligned byte allocation without
+        # an allocation or copy. Restrict the view to the current operation.
+        return workspace[:required_bytes].view(input.dtype)
+
+    @staticmethod
+    def _balanced_tree_sum(rank_inputs: torch.Tensor) -> torch.Tensor:
+        world_size = rank_inputs.size(0)
+        if world_size not in _SUPPORTED_WORLD_SIZES:
+            raise ValueError(
+                "balanced reduction requires rank inputs for world_size in "
+                f"{_SUPPORTED_WORLD_SIZES}, got {world_size}"
+            )
+        # ``rank_inputs`` is the private transport workspace for reductions,
+        # so fixed-tree nodes can be accumulated in place. This preserves the
+        # exact pairings while avoiding one temporary allocation per tree node.
+        stride = 1
+        while stride < world_size:
+            for index in range(0, world_size, 2 * stride):
+                rank_inputs[index].add_(rank_inputs[index + stride])
+            stride *= 2
+        return rank_inputs[0]
+
+
+class RCCLDeterministicCollective(TorchDistributedDeterministicCollective):
+    """ROCm collective using RCCL AllGather strictly as tensor transport."""
+
+    backend_id = "rccl_all_gather_balanced_tree"
+
+    def __init__(
+        self,
+        group: dist.ProcessGroup | None = None,
+        device: torch.device | str | int | None = None,
+        *,
+        max_size_bytes: int = _DEFAULT_MAX_SIZE_BYTES,
+    ) -> None:
+        if getattr(torch.version, "hip", None) is None:
+            raise RuntimeError("RCCL deterministic collectives require a ROCm PyTorch build")
+        if not torch.cuda.is_available():
+            raise RuntimeError("RCCL deterministic collectives require an available ROCm device")
+        if device is not None and not isinstance(device, int):
+            requested_device = torch.device(device)
+            if requested_device.type != "cuda":
+                raise ValueError(
+                    f"RCCL deterministic collectives require a ROCm device, got {device!r}"
+                )
+        super().__init__(group=group, device=device, max_size_bytes=max_size_bytes)
+        if self.device.type != "cuda":
+            raise ValueError(
+                f"RCCL deterministic collectives require a ROCm device, got {device!r}"
+            )
+        if "nccl" not in self._backend:
+            raise RuntimeError(
+                "RCCL deterministic collectives require PyTorch's NCCL process-group API"
+            )
+
+
+def create_deterministic_collective(
+    group: dist.ProcessGroup | None = None,
+    device: torch.device | str | int | None = None,
+    *,
+    max_size_bytes: int = _DEFAULT_MAX_SIZE_BYTES,
+) -> Any:
+    """Create the platform-appropriate deterministic collective.
+
+    CUDA uses the native ``DeterministicCollective`` implementation. ROCm uses
+    RCCL only to gather rank inputs, followed by the shared local balanced-tree
+    reduction. The returned object has independent ownership; callers may cache
+    it and must close an entry before replacing it.
+    """
+
+    if getattr(torch.version, "hip", None) is not None:
+        return RCCLDeterministicCollective(
+            group=group,
+            device=device,
+            max_size_bytes=max_size_bytes,
+        )
+
+    return DeterministicCollective(
+        group=group,
+        device=device,
+        max_size_bytes=max_size_bytes,
+    )
+
+
+__all__ = [
+    "DeterministicCollective",
+    "RCCLDeterministicCollective",
+    "TorchDistributedDeterministicCollective",
+    "create_deterministic_collective",
+]
diff --git a/rl_engine/distributed/transport_collectives.py b/rl_engine/distributed/transport_collectives.py
deleted file mode 100644
index ff9a4dcf..00000000
--- a/rl_engine/distributed/transport_collectives.py
+++ /dev/null
@@ -1,435 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) 2026 RL-Kernel Contributors
-"""Deterministic reductions built on rank-ordered tensor transport.
-
-The transport in this module never performs a floating-point reduction.  It
-only gathers every rank's input, after which each rank evaluates the same
-balanced reduction tree locally.  In a ROCm build, PyTorch's ``nccl`` backend
-is RCCL and therefore ``all_gather_into_tensor`` provides the transport.
-"""
-
-from __future__ import annotations
-
-import threading
-from types import TracebackType
-from typing import Any
-
-import torch
-import torch.distributed as dist
-
-_SUPPORTED_WORLD_SIZES = (1, 2, 4, 8)
-_DEFAULT_MAX_SIZE_BYTES = 64 * 1024 * 1024
-_REDUCTION_DTYPES = (torch.float32, torch.float16, torch.bfloat16)
-
-
-class TorchDistributedDeterministicCollective:
-    """Correctness-first collectives using AllGather as transport only.
-
-    Rank inputs are gathered without arithmetic and reduced locally as the
-    balanced tree ``((rank0 + rank1) + (rank2 + rank3)) + ...``.  Consequently,
-    all ranks execute the exact same floating-point expression.  TP sizes 1,
-    2, 4, and 8 are nested prefixes of that expression and match the existing
-    CUDA IPC collective's ordering.
-
-    The generic class also supports a CPU/Gloo process group, which is useful
-    as an executable reference.  Production ROCm callers should use
-    :class:`RCCLDeterministicCollective` or
-    :func:`create_deterministic_collective` so backend validation fails closed.
-    All ranks must call methods in the same order with matching input shapes
-    and dtypes, and construct the instance with the same ``max_size_bytes``.
-    """
-
-    backend_id = "torch_distributed_balanced_tree"
-    transport_only = True
-    reduction_order = "balanced_rank_tree"
-    supports_async_overlap = False
-    supports_compute_communication_fusion = False
-
-    def __init__(
-        self,
-        group: dist.ProcessGroup | None = None,
-        device: torch.device | str | int | None = None,
-        *,
-        max_size_bytes: int = _DEFAULT_MAX_SIZE_BYTES,
-    ) -> None:
-        if not dist.is_available() or not dist.is_initialized():
-            raise RuntimeError("torch.distributed must be initialized before collectives")
-        if max_size_bytes <= 0:
-            raise ValueError("max_size_bytes must be positive")
-
-        self.group = group if group is not None else dist.group.WORLD
-        self.rank = int(dist.get_rank(group=self.group))
-        self.world_size = int(dist.get_world_size(group=self.group))
-        if self.world_size not in _SUPPORTED_WORLD_SIZES:
-            raise ValueError(
-                "deterministic collectives require world_size in "
-                f"{_SUPPORTED_WORLD_SIZES}, got {self.world_size}"
-            )
-
-        self.device = self._normalize_device(device)
-        self.max_size_bytes = int(max_size_bytes)
-        self._backend = str(dist.get_backend(self.group)).lower()
-        self._lock = threading.Lock()
-        self._closed = False
-        # One dtype-agnostic byte workspace is grown on demand and reused by
-        # reduction collectives. AllGather writes directly into its output.
-        self._workspace: torch.Tensor | None = None
-        # A Python-object collective is useful for catching a mismatched new
-        # signature, but running one on every hot-path call dominates small
-        # message latency. Validate each local signature once and then rely on
-        # the standard collective contract that ranks call operations in the
-        # same order.
-        self._validated_signatures: set[tuple[Any, ...]] = set()
-        self._validate_matching_capacity()
-
-    @staticmethod
-    def _normalize_device(
-        device: torch.device | str | int | None,
-    ) -> torch.device:
-        if device is None:
-            if torch.cuda.is_available():
-                normalized = torch.device("cuda", torch.cuda.current_device())
-            else:
-                normalized = torch.device("cpu")
-        elif isinstance(device, int):
-            normalized = torch.device("cuda", device)
-        else:
-            normalized = torch.device(device)
-
-        if normalized.type == "cuda":
-            if not torch.cuda.is_available():
-                raise RuntimeError("a CUDA/ROCm device was requested but none is available")
-            current_device = torch.cuda.current_device()
-            if normalized.index is None:
-                normalized = torch.device("cuda", current_device)
-            if normalized.index != current_device:
-                raise ValueError(
-                    "the collective device must be the current CUDA/ROCm device; call "
-                    f"torch.cuda.set_device({normalized.index}) first"
-                )
-        return normalized
-
-    @property
-    def closed(self) -> bool:
-        """Whether this instance rejects further collective calls."""
-
-        return self._closed
-
-    @property
-    def workspace_size_bytes(self) -> int:
-        """Currently retained reduction workspace size in bytes."""
-
-        workspace = self._workspace
-        return 0 if workspace is None else int(workspace.numel())
-
-    def all_reduce(
-        self,
-        input: torch.Tensor,
-        *,
-        out: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """Return the fixed balanced-tree sum on every rank."""
-
-        self._check_open()
-        self._validate_reduction_input(input)
-        if out is None:
-            out = torch.empty_like(input)
-        self._validate_output(out, input, tuple(input.shape))
-
-        with self._lock:
-            self._check_open()
-            self._validate_matching_signature("all_reduce", input)
-            rank_inputs = self._all_gather_transport(input)
-            reduced = self._balanced_tree_sum(rank_inputs)
-            out.copy_(reduced)
-        return out
-
-    def all_gather(
-        self,
-        input: torch.Tensor,
-        *,
-        out: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """Gather rank-ordered input bit patterns along dimension 0."""
-
-        self._check_open()
-        self._validate_gather_input(input)
-        output_shape = (input.size(0) * self.world_size, *input.shape[1:])
-        if out is None:
-            out = torch.empty(output_shape, dtype=input.dtype, device=input.device)
-        self._validate_output(out, input, output_shape)
-
-        with self._lock:
-            self._check_open()
-            self._validate_matching_signature("all_gather", input)
-            self._all_gather_transport(input, gathered_flat=out.view(-1))
-        return out
-
-    def reduce_scatter(
-        self,
-        input: torch.Tensor,
-        *,
-        out: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """Fixed-tree sum followed by rank-ordered dimension-0 slicing."""
-
-        self._check_open()
-        self._validate_reduction_input(input)
-        if input.dim() == 0:
-            raise ValueError("reduce_scatter input must have at least one dimension")
-        if input.size(0) % self.world_size != 0:
-            raise ValueError(
-                "reduce_scatter input.size(0) must be divisible by "
-                f"world_size={self.world_size}; got {input.size(0)}"
-            )
-        rows_per_rank = input.size(0) // self.world_size
-        output_shape = (rows_per_rank, *input.shape[1:])
-        if out is None:
-            out = torch.empty(output_shape, dtype=input.dtype, device=input.device)
-        self._validate_output(out, input, output_shape)
-
-        with self._lock:
-            self._check_open()
-            self._validate_matching_signature("reduce_scatter", input)
-            rank_inputs = self._all_gather_transport(input)
-            reduced = self._balanced_tree_sum(rank_inputs)
-            begin = self.rank * rows_per_rank
-            out.copy_(reduced.narrow(0, begin, rows_per_rank))
-        return out
-
-    def close(self) -> None:
-        """Close the instance.
-
-        Closing releases the lazily allocated reduction workspace and marks
-        the lifecycle boundary. Collective calls are blocking at this API.
-        """
-
-        with self._lock:
-            self._workspace = None
-            self._validated_signatures.clear()
-            self._closed = True
-
-    def __enter__(self) -> TorchDistributedDeterministicCollective:
-        self._check_open()
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_value: BaseException | None,
-        traceback: TracebackType | None,
-    ) -> None:
-        self.close()
-
-    def __del__(self) -> None:
-        try:
-            self.close()
-        except Exception:
-            pass
-
-    def _check_open(self) -> None:
-        if getattr(self, "_closed", True):
-            raise RuntimeError("deterministic collective is closed")
-
-    def _validate_tensor(self, input: torch.Tensor) -> None:
-        if not isinstance(input, torch.Tensor):
-            raise TypeError(f"input must be a torch.Tensor, got {type(input)!r}")
-        if input.device != self.device:
-            raise ValueError(f"input must be on {self.device}, got {input.device}")
-        if not input.is_contiguous():
-            raise ValueError("input must be contiguous")
-        input_bytes = input.numel() * input.element_size()
-        if input_bytes > self.max_size_bytes:
-            raise ValueError(
-                f"input requires {input_bytes} bytes but max_size_bytes={self.max_size_bytes}"
-            )
-
-    def _validate_reduction_input(self, input: torch.Tensor) -> None:
-        self._validate_tensor(input)
-        if input.dtype not in _REDUCTION_DTYPES:
-            raise TypeError(
-                "deterministic reductions support float32, float16, and bfloat16; "
-                f"got {input.dtype}"
-            )
-
-    def _validate_gather_input(self, input: torch.Tensor) -> None:
-        self._validate_tensor(input)
-        if input.dim() == 0:
-            raise ValueError("all_gather input must have at least one dimension")
-
-    @staticmethod
-    def _validate_output(
-        output: torch.Tensor,
-        input: torch.Tensor,
-        output_shape: tuple[int, ...],
-    ) -> None:
-        if not isinstance(output, torch.Tensor):
-            raise TypeError(f"out must be a torch.Tensor, got {type(output)!r}")
-        if output.device != input.device:
-            raise ValueError("out must be on the same device as input")
-        if output.dtype != input.dtype:
-            raise TypeError("out must have the same dtype as input")
-        if tuple(output.shape) != output_shape:
-            raise ValueError(f"out must have shape {output_shape}, got {tuple(output.shape)}")
-        if not output.is_contiguous():
-            raise ValueError("out must be contiguous")
-
-    def _validate_matching_signature(self, op_name: str, input: torch.Tensor) -> None:
-        if self.world_size == 1:
-            return
-        signature = (op_name, tuple(input.shape), str(input.dtype), input.numel())
-        if signature in self._validated_signatures:
-            return
-        signatures: list[tuple[Any, ...] | None] = [None] * self.world_size
-        dist.all_gather_object(signatures, signature, group=self.group)
-        if any(peer_signature != signature for peer_signature in signatures):
-            raise ValueError(
-                f"all ranks must call {op_name} with matching shapes and dtypes; got {signatures}"
-            )
-        self._validated_signatures.add(signature)
-
-    def _validate_matching_capacity(self) -> None:
-        if self.world_size == 1:
-            return
-        capacities: list[int | None] = [None] * self.world_size
-        dist.all_gather_object(capacities, self.max_size_bytes, group=self.group)
-        if any(peer_capacity != self.max_size_bytes for peer_capacity in capacities):
-            raise ValueError(f"all ranks must use the same max_size_bytes; got {capacities}")
-
-    def _all_gather_transport(
-        self,
-        input: torch.Tensor,
-        *,
-        gathered_flat: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        # Flattening makes the output contract independent of whether a given
-        # ProcessGroup implements the concatenation or stacking form of AG.
-        if self.world_size == 1:
-            if gathered_flat is None:
-                gathered_flat = input.clone().view(-1)
-            else:
-                gathered_flat.copy_(input.view(-1))
-            return gathered_flat.reshape((1, *input.shape))
-        input_flat = input.view(-1)
-        required_elements = self.world_size * input_flat.numel()
-        if gathered_flat is None:
-            gathered_flat = self._workspace_for(input, required_elements)
-        elif (
-            gathered_flat.numel() != required_elements
-            or gathered_flat.dtype != input.dtype
-            or gathered_flat.device != input.device
-            or not gathered_flat.is_contiguous()
-        ):
-            raise ValueError("gathered transport output has an invalid layout")
-        if "nccl" in self._backend:
-            # PyTorch exposes RCCL through the NCCL ProcessGroup API.  Keep
-            # this as a tensor-only transport; reduction happens below.
-            dist.all_gather_into_tensor(gathered_flat, input_flat, group=self.group)
-        else:
-            # Some reference backends (notably Gloo versions without
-            # all_gather_into_tensor) only implement the list API.
-            gathered_chunks = list(
-                gathered_flat.reshape(self.world_size, input_flat.numel()).unbind(0)
-            )
-            dist.all_gather(gathered_chunks, input_flat, group=self.group)
-        return gathered_flat.reshape((self.world_size, *input.shape))
-
-    def _workspace_for(self, input: torch.Tensor, required_elements: int) -> torch.Tensor:
-        required_bytes = required_elements * input.element_size()
-        workspace = self._workspace
-        if workspace is None or workspace.numel() < required_bytes:
-            workspace = torch.empty(required_bytes, dtype=torch.uint8, device=self.device)
-            self._workspace = workspace
-        # Tensor.view(dtype) reinterprets the aligned byte allocation without
-        # an allocation or copy. Restrict the view to the current operation.
-        return workspace[:required_bytes].view(input.dtype)
-
-    @staticmethod
-    def _balanced_tree_sum(rank_inputs: torch.Tensor) -> torch.Tensor:
-        world_size = rank_inputs.size(0)
-        if world_size not in _SUPPORTED_WORLD_SIZES:
-            raise ValueError(
-                "balanced reduction requires rank inputs for world_size in "
-                f"{_SUPPORTED_WORLD_SIZES}, got {world_size}"
-            )
-        # ``rank_inputs`` is the private transport workspace for reductions,
-        # so fixed-tree nodes can be accumulated in place. This preserves the
-        # exact pairings while avoiding one temporary allocation per tree node.
-        stride = 1
-        while stride < world_size:
-            for index in range(0, world_size, 2 * stride):
-                rank_inputs[index].add_(rank_inputs[index + stride])
-            stride *= 2
-        return rank_inputs[0]
-
-
-class RCCLDeterministicCollective(TorchDistributedDeterministicCollective):
-    """ROCm collective using RCCL AllGather strictly as tensor transport."""
-
-    backend_id = "rccl_all_gather_balanced_tree"
-
-    def __init__(
-        self,
-        group: dist.ProcessGroup | None = None,
-        device: torch.device | str | int | None = None,
-        *,
-        max_size_bytes: int = _DEFAULT_MAX_SIZE_BYTES,
-    ) -> None:
-        if getattr(torch.version, "hip", None) is None:
-            raise RuntimeError("RCCL deterministic collectives require a ROCm PyTorch build")
-        if not torch.cuda.is_available():
-            raise RuntimeError("RCCL deterministic collectives require an available ROCm device")
-        if device is not None and not isinstance(device, int):
-            requested_device = torch.device(device)
-            if requested_device.type != "cuda":
-                raise ValueError(
-                    f"RCCL deterministic collectives require a ROCm device, got {device!r}"
-                )
-        super().__init__(group=group, device=device, max_size_bytes=max_size_bytes)
-        if self.device.type != "cuda":
-            raise ValueError(
-                f"RCCL deterministic collectives require a ROCm device, got {device!r}"
-            )
-        if "nccl" not in self._backend:
-            raise RuntimeError(
-                "RCCL deterministic collectives require PyTorch's NCCL process-group API"
-            )
-
-
-def create_deterministic_collective(
-    group: dist.ProcessGroup | None = None,
-    device: torch.device | str | int | None = None,
-    *,
-    max_size_bytes: int = _DEFAULT_MAX_SIZE_BYTES,
-) -> Any:
-    """Create the platform-appropriate deterministic collective.
-
-    CUDA keeps using the existing IPC implementation.  ROCm uses RCCL only to
-    gather rank inputs, followed by the shared local balanced-tree reduction.
-    The returned object has independent ownership; callers may cache it by
-    process-group/rank/device and must close an entry before replacing it.
-    """
-
-    if getattr(torch.version, "hip", None) is not None:
-        return RCCLDeterministicCollective(
-            group=group,
-            device=device,
-            max_size_bytes=max_size_bytes,
-        )
-
-    # Import lazily to keep the existing CUDA implementation and its extension
-    # checks independent of the generic transport reference above.
-    from rl_engine.distributed.collectives import DeterministicCollective
-
-    return DeterministicCollective(
-        group=group,
-        device=device,
-        max_size_bytes=max_size_bytes,
-    )
-
-
-__all__ = [
-    "RCCLDeterministicCollective",
-    "TorchDistributedDeterministicCollective",
-    "create_deterministic_collective",
-]
diff --git a/tests/distributed/test_transport_deterministic_collective.py b/tests/distributed/test_transport_deterministic_collective.py
index ed1da194..dddd674b 100644
--- a/tests/distributed/test_transport_deterministic_collective.py
+++ b/tests/distributed/test_transport_deterministic_collective.py
@@ -8,8 +8,8 @@
 import pytest
 import torch
 
-import rl_engine.distributed.collectives as cuda_collectives
-import rl_engine.distributed.transport_collectives as transport_collectives
+import rl_engine.distributed as distributed
+import rl_engine.distributed.collectives as collectives
 from rl_engine.distributed import (
     RCCLDeterministicCollective,
     TorchDistributedDeterministicCollective,
@@ -92,6 +92,18 @@ def all_gather(
             destination.copy_(peer.reshape(-1))
 
 
+def test_public_exports_use_canonical_collectives_module() -> None:
+    assert distributed.DeterministicCollective is collectives.DeterministicCollective
+    assert distributed.RCCLDeterministicCollective is collectives.RCCLDeterministicCollective
+    assert (
+        distributed.TorchDistributedDeterministicCollective
+        is collectives.TorchDistributedDeterministicCollective
+    )
+    assert (
+        distributed.create_deterministic_collective is collectives.create_deterministic_collective
+    )
+
+
 def _make_collective(
     monkeypatch: pytest.MonkeyPatch,
     peer_inputs: list[torch.Tensor],
@@ -109,7 +121,7 @@ def _make_collective(
         peer_signatures=peer_signatures,
         peer_capacities=peer_capacities,
     )
-    monkeypatch.setattr(transport_collectives, "dist", fake_dist)
+    monkeypatch.setattr(collectives, "dist", fake_dist)
     collective = TorchDistributedDeterministicCollective(
         group=object(),
         device="cpu",
@@ -312,14 +324,14 @@ def test_unsupported_world_size_is_rejected(
     world_size: int,
 ) -> None:
     fake_dist = _FakeDistributed([torch.ones(1)] * world_size)
-    monkeypatch.setattr(transport_collectives, "dist", fake_dist)
+    monkeypatch.setattr(collectives, "dist", fake_dist)
 
     with pytest.raises(ValueError, match="world_size in"):
         TorchDistributedDeterministicCollective(group=object(), device="cpu")
 
 
 def test_rccl_class_requires_rocm_build(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(transport_collectives.torch.version, "hip", None, raising=False)
+    monkeypatch.setattr(collectives.torch.version, "hip", None, raising=False)
 
     with pytest.raises(RuntimeError, match="ROCm PyTorch build"):
         RCCLDeterministicCollective(group=object(), device="cuda:0")
@@ -327,10 +339,10 @@ def test_rccl_class_requires_rocm_build(monkeypatch: pytest.MonkeyPatch) -> None
 
 def test_rccl_class_requires_nccl_process_group(monkeypatch: pytest.MonkeyPatch) -> None:
     fake_dist = _FakeDistributed([torch.ones(1)], backend="gloo")
-    monkeypatch.setattr(transport_collectives, "dist", fake_dist)
-    monkeypatch.setattr(transport_collectives.torch.version, "hip", "6.3", raising=False)
-    monkeypatch.setattr(transport_collectives.torch.cuda, "is_available", lambda: True)
-    monkeypatch.setattr(transport_collectives.torch.cuda, "current_device", lambda: 0)
+    monkeypatch.setattr(collectives, "dist", fake_dist)
+    monkeypatch.setattr(collectives.torch.version, "hip", "6.3", raising=False)
+    monkeypatch.setattr(collectives.torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(collectives.torch.cuda, "current_device", lambda: 0)
 
     with pytest.raises(RuntimeError, match="NCCL process-group API"):
         RCCLDeterministicCollective(group=object(), device="cuda:0")
@@ -339,8 +351,8 @@ def test_rccl_class_requires_nccl_process_group(monkeypatch: pytest.MonkeyPatch)
 def test_rccl_class_rejects_cpu_before_process_group_exchange(
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
-    monkeypatch.setattr(transport_collectives.torch.version, "hip", "6.3", raising=False)
-    monkeypatch.setattr(transport_collectives.torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(collectives.torch.version, "hip", "6.3", raising=False)
+    monkeypatch.setattr(collectives.torch.cuda, "is_available", lambda: True)
 
     with pytest.raises(ValueError, match="ROCm device"):
         RCCLDeterministicCollective(group=object(), device="cpu")
@@ -354,11 +366,11 @@ def fake_rccl(**kwargs: Any) -> object:
         calls.append(kwargs)
         return sentinel
 
-    monkeypatch.setattr(transport_collectives.torch.version, "hip", "6.3", raising=False)
-    monkeypatch.setattr(transport_collectives, "RCCLDeterministicCollective", fake_rccl)
+    monkeypatch.setattr(collectives.torch.version, "hip", "6.3", raising=False)
+    monkeypatch.setattr(collectives, "RCCLDeterministicCollective", fake_rccl)
 
     group = object()
-    result = transport_collectives.create_deterministic_collective(
+    result = collectives.create_deterministic_collective(
         group=group,
         device="cuda:3",
         max_size_bytes=1234,
@@ -376,11 +388,11 @@ def fake_cuda(**kwargs: Any) -> object:
         calls.append(kwargs)
         return sentinel
 
-    monkeypatch.setattr(transport_collectives.torch.version, "hip", None, raising=False)
-    monkeypatch.setattr(cuda_collectives, "DeterministicCollective", fake_cuda)
+    monkeypatch.setattr(collectives.torch.version, "hip", None, raising=False)
+    monkeypatch.setattr(collectives, "DeterministicCollective", fake_cuda)
 
     group = object()
-    result = transport_collectives.create_deterministic_collective(
+    result = collectives.create_deterministic_collective(
         group=group,
         device="cuda:1",
         max_size_bytes=4321,

From e8e79e7a31ada85bf8114560180581921135479e Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sat, 29 Aug 2026 05:54:08 +0000
Subject: [PATCH 19/35] fix(build): repair ops.cpp preprocessor guards mangled
 by the merge

Auto-merging csrc/ops.cpp while merging feat/rocm-deterministic-collectives
dropped the "#if !defined(USE_ROCM)" that guards the prefix_shared_attention
registration but kept its "#endif", leaving 13 #endif against 12 #if. A ROCm
build failed with "#endif without #if" and "'prefix_shared_attention' was not
declared in this scope".

Restore the guard around the registration so it matches the guard the
declaration already carries.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01824WkgMDxtBKD4Ex2NkfYA
---
 csrc/ops.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index 63f4a65f..11140d08 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -450,8 +450,10 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
         "Run the TP=8 deterministic rank-ordered all-gather kernel");
 #endif
 
-    // Prefix-shared attention uses NVIDIA PTX and falls back to PyTorch SDPA on
-    // ROCm; it is covered by the same guard opened above.
+#if !defined(USE_ROCM)
+    // Prefix-shared attention uses NVIDIA PTX; the declaration above carries the
+    // same guard, so the registration must repeat it or a ROCm build fails on an
+    // undeclared identifier.
     m.def("prefix_shared_attention", &prefix_shared_attention, "Prefix-Shared Fused Attention for GRPO");
 #endif
 

From a8deab9d91176c98693610c1682eceb320415603 Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sat, 29 Aug 2026 05:54:20 +0000
Subject: [PATCH 20/35] feat(kernels): add bitwise Triton deterministic
 Attention core

Port csrc/cuda/attention/deterministic_attention.cu to Triton with a bitwise
contract: out/lse/P/dQ/dK/dV are bit-identical to _C.deterministic_attention_*
for the same inputs on the same device, forward and backward.

Three things had to be reproduced rather than re-derived:

- Dot products stay sequential FMA chains. The contraction index is the loop
  and the head dim is the vector, so no tree reduction is introduced where the
  C++ kernel accumulates one element at a time in a single thread.
- The row softmax keeps the 256-lane partial layout and the stride-halving
  shared-memory fold. _tree_sum_256 reproduces that fold exactly.
- expf/logf are re-emitted instruction for instruction from what hipcc
  generates. Every Triton exp/log intrinsic lowers to a bare v_exp_f32 /
  v_log_f32, roughly 1 ULP away from the vendor libm the C++ kernel calls, and
  LLVM otherwise refolds the argument reduction into an FMA (hence the inline-asm
  barrier). The helpers are verified bitwise over 4M+ inputs including
  subnormals, +/-inf and NaN.

The nvcc expf/logf sequences are not ported, so TritonDeterministicAttentionOp
refuses to construct on CUDA unless the caller passes require_bitwise_libm=False,
rather than silently returning non-bitwise results.

Forward runs about 1.4x the native reference kernel's time on MI300X; like that
kernel it materialises the full FP32 score matrix and is a parity core, not a
FlashAttention replacement.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01824WkgMDxtBKD4Ex2NkfYA
---
 .../kernels/ops/triton/attention/__init__.py  |  16 +
 .../triton/attention/deterministic_attn.py    | 878 ++++++++++++++++++
 tests/test_triton_deterministic_attention.py  | 284 ++++++
 3 files changed, 1178 insertions(+)
 create mode 100644 rl_engine/kernels/ops/triton/attention/deterministic_attn.py
 create mode 100644 tests/test_triton_deterministic_attention.py

diff --git a/rl_engine/kernels/ops/triton/attention/__init__.py b/rl_engine/kernels/ops/triton/attention/__init__.py
index 220b6c95..7df3d2ba 100644
--- a/rl_engine/kernels/ops/triton/attention/__init__.py
+++ b/rl_engine/kernels/ops/triton/attention/__init__.py
@@ -1,6 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
+from rl_engine.kernels.ops.triton.attention.deterministic_attn import (
+    BITWISE_LIBM_PARITY,
+    TritonDeterministicAttentionOp,
+    triton_deterministic_attention,
+    triton_deterministic_attention_backward,
+    triton_deterministic_attention_forward,
+    triton_deterministic_attention_fp32,
+    triton_deterministic_attention_with_lse,
+)
 from rl_engine.kernels.ops.triton.attention.standard_attn import (
     TritonBatchInvariantAttentionOp,
     triton_batch_invariant_attention,
@@ -8,7 +17,14 @@
 )
 
 __all__ = [
+    "BITWISE_LIBM_PARITY",
     "TritonBatchInvariantAttentionOp",
+    "TritonDeterministicAttentionOp",
     "triton_batch_invariant_attention",
     "triton_batch_invariant_attention_with_lse",
+    "triton_deterministic_attention",
+    "triton_deterministic_attention_backward",
+    "triton_deterministic_attention_forward",
+    "triton_deterministic_attention_fp32",
+    "triton_deterministic_attention_with_lse",
 ]
diff --git a/rl_engine/kernels/ops/triton/attention/deterministic_attn.py b/rl_engine/kernels/ops/triton/attention/deterministic_attn.py
new file mode 100644
index 00000000..7914342a
--- /dev/null
+++ b/rl_engine/kernels/ops/triton/attention/deterministic_attn.py
@@ -0,0 +1,878 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Triton port of the deterministic standard-softmax attention core (issue #147).
+
+This is a *bitwise* re-implementation of ``csrc/cuda/attention/deterministic_attention.cu``
+(exposed as ``_C.deterministic_attention_forward`` / ``_C.deterministic_attention_backward``).
+Every reduction here reproduces the C++ kernel's floating-point order exactly, so the
+Triton path and the native path return bit-identical ``out``/``lse``/``dQ``/``dK``/``dV``
+for the same inputs on the same device.
+
+The pipeline mirrors the native one 1:1:
+
+    forward : QK -> masked softmax+LSE -> PV
+    backward: dP -> softmax backward -> dQ -> dK -> dV
+
+The three arithmetic contracts that have to be honoured for bitwise parity are:
+
+1. **Dot products are sequential FMA chains.** The C++ kernels accumulate
+   ``acc += (float)a[i] * (float)b[i]`` over ascending ``i`` in a single thread, which
+   hipcc/nvcc contract into a chain of FMAs. Every reduction below loops over the
+   contraction index one element at a time and uses :func:`tl.fma`, so no vector
+   tree reduction is ever introduced. That is why the contraction index is the *loop*
+   and the head dim / output tile is the *vector*: the opposite (and much faster)
+   arrangement would reassociate the sum.
+2. **Row softmax uses the 256-lane partial + binary-tree layout.** The C++ softmax
+   assigns key ``k`` to thread ``k % 256``, sums each lane's keys in ascending order,
+   and then folds the 256 partials with ``stride = 128, 64, ... 1``. :func:`_tree_sum_256`
+   reproduces that fold exactly by repeatedly reshaping to ``(2, n)`` and summing axis 0.
+3. **Transcendentals reproduce the vendor libm.** Every Triton exp/log intrinsic lowers
+   to a bare hardware ``v_exp_f32``/``v_log_f32``, which is ~1 ULP away from the
+   ``expf``/``logf`` the C++ kernel calls. :func:`_expf` and :func:`_logf` below
+   re-emit the vendor argument reduction instruction for instruction instead.
+
+Performance note: like the native reference core this materialises the full FP32
+``[B, Hq, Sq, Skv]`` score matrix and runs scalar-order reductions. It is a
+correctness/parity core, not a FlashAttention replacement.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Optional
+
+import torch
+import triton
+import triton.language as tl
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+
+_HEAD_DIM = 128
+
+_IS_ROCM = torch.version.hip is not None
+
+# ---------------------------------------------------------------------------
+# Vendor-exact expf / logf
+# ---------------------------------------------------------------------------
+# The softmax is the only place this core evaluates a transcendental, and it is
+# also the only place where "write the obvious Triton code" is not enough:
+# ``tl.exp`` / ``tl.math.exp`` / ``libdevice.exp`` all lower to ``llvm.exp.f32``,
+# which the AMDGPU backend expands to a bare ``v_exp_f32``. The HIP ``expf`` the
+# C++ kernel calls does a two-term argument reduction around that same hardware
+# ``v_exp_f32``, so the two differ by ~1 ULP on most inputs.
+#
+# The sequences below reproduce, instruction for instruction, what hipcc emits
+# for ``expf`` / ``logf`` on gfx9. They are verified bitwise against the vendor
+# result over 4M+ random and edge-case inputs, including subnormals, +/-inf and NaN.
+#
+# ``_fp32_barrier`` is load-bearing: without it LLVM folds ``x * L2E_HI`` and the
+# following subtract back into a single FMA, which silently changes the reduced
+# argument. Inline asm is opaque to that folding.
+
+if _IS_ROCM:
+    from triton.language.extra.hip import libdevice as _ocml
+
+    @triton.jit
+    def _fp32_barrier(x):
+        """Opaque move: stops LLVM re-associating across this point."""
+        return tl.inline_asm_elementwise(
+            "v_mov_b32 $0, $1", "=v,v", [x], dtype=tl.float32, is_pure=True, pack=1
+        )
+
+    @triton.jit
+    def _expf(x):
+        """Bitwise-exact HIP ``expf`` for FP32."""
+        t = _fp32_barrier(x * 1.4426950216293335)
+        err = tl.fma(x, 1.4426950216293335, -t)
+        n = _ocml.rint(t)
+        err = tl.fma(x, 1.925962855864327e-08, err)
+        r = _fp32_barrier(_fp32_barrier(t - n) + err)
+        y = _ocml.ldexp(_ocml.exp2(r), n.to(tl.int32))
+        # Written as "constant compared against x" so an unordered (NaN) compare
+        # falls through to the NaN result, matching v_cmp_ngt / v_cmp_nlt.
+        y = tl.where(-103.2789306640625 > x, 0.0, y)
+        return tl.where(88.72283935546875 < x, float("inf"), y)
+
+    @triton.jit
+    def _logf(x):
+        """Bitwise-exact HIP ``logf`` for FP32."""
+        small = x < 1.1754943508222875e-38
+        log2_x = _ocml.log2(_ocml.ldexp(x, tl.where(small, 32, 0)))
+        t = _fp32_barrier(log2_x * 0.6931471228599548)
+        err = tl.fma(log2_x, 0.6931471228599548, -t)
+        err = tl.fma(log2_x, 5.769998878690785e-08, err)
+        r = _fp32_barrier(t + err)
+        r = tl.where(tl.abs(log2_x) < float("inf"), r, log2_x)
+        return r - tl.where(small, 22.180709838867188, 0.0)
+
+else:
+
+    @triton.jit
+    def _expf(x):
+        return tl.math.exp(x)
+
+    @triton.jit
+    def _logf(x):
+        return tl.math.log(x)
+
+
+#: True when :func:`_expf` / :func:`_logf` reproduce this platform's libm bitwise.
+#: The nvcc ``expf``/``logf`` sequences have not been ported, so on CUDA the ops
+#: below refuse to run unless the caller opts out explicitly.
+BITWISE_LIBM_PARITY = _IS_ROCM
+
+# Mirrors kSoftmaxThreads in csrc/cuda/attention/deterministic_attention.cu. The
+# value is part of the arithmetic contract, not a tuning knob: changing it changes
+# which keys land in which partial sum and therefore changes the result bitwise.
+_SOFTMAX_LANES = 256
+
+# Tile shapes. These only affect scheduling, never the reduction order, because
+# every reduction is a per-output-element sequential loop.
+_QK_BLOCK_Q = 16
+_QK_BLOCK_K = 64
+
+
+@triton.jit
+def _tree_sum_256(vals):
+    """Fold 256 partials the way the C++ shared-memory tree reduction does.
+
+    The C++ loop is ``for (stride = 128; stride > 0; stride >>= 1) s[i] += s[i + stride]``.
+    Reshaping to ``(2, n)`` and summing axis 0 is that same pairing: row-major
+    ``reshape(2, n)[0] == vals[:n]`` and ``[1] == vals[n:]``, so each step is
+    ``vals[i] + vals[i + n]``. The steps are written out because a Triton loop
+    cannot carry a value whose shape changes.
+    """
+    total = tl.sum(tl.reshape(vals, (2, 128)), axis=0)
+    total = tl.sum(tl.reshape(total, (2, 64)), axis=0)
+    total = tl.sum(tl.reshape(total, (2, 32)), axis=0)
+    total = tl.sum(tl.reshape(total, (2, 16)), axis=0)
+    total = tl.sum(tl.reshape(total, (2, 8)), axis=0)
+    total = tl.sum(tl.reshape(total, (2, 4)), axis=0)
+    total = tl.sum(tl.reshape(total, (2, 2)), axis=0)
+    total = tl.sum(tl.reshape(total, (2, 1)), axis=0)
+    return tl.sum(total, axis=0)
+
+
+# ---------------------------------------------------------------------------
+# Forward
+# ---------------------------------------------------------------------------
+
+
+@triton.jit
+def _qk_kernel(
+    q_ptr,
+    k_ptr,
+    scores_ptr,
+    scale,
+    Hq,
+    Hkv,
+    Sq,
+    Skv,
+    D: tl.constexpr,
+    BLOCK_Q: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+):
+    """scores[b, hq, q, k] = scale * sum_{d ascending} Q[b,hq,q,d] * K[b,kv,k,d]."""
+    pid_k = tl.program_id(0)
+    pid_q = tl.program_id(1)
+    bh = tl.program_id(2)
+    b = bh // Hq
+    hq = bh % Hq
+    kv_head = hq // (Hq // Hkv)
+
+    offs_q = pid_q * BLOCK_Q + tl.arange(0, BLOCK_Q)
+    offs_k = pid_k * BLOCK_K + tl.arange(0, BLOCK_K)
+    q_in = offs_q < Sq
+    k_in = offs_k < Skv
+
+    q_rows = q_ptr + (b * Hq + hq).to(tl.int64) * Sq * D + offs_q.to(tl.int64) * D
+    k_rows = k_ptr + (b * Hkv + kv_head).to(tl.int64) * Skv * D + offs_k.to(tl.int64) * D
+
+    acc = tl.zeros((BLOCK_Q, BLOCK_K), dtype=tl.float32)
+    for d in range(0, D):
+        qv = tl.load(q_rows + d, mask=q_in, other=0.0).to(tl.float32)
+        kv = tl.load(k_rows + d, mask=k_in, other=0.0).to(tl.float32)
+        acc = tl.fma(qv[:, None], kv[None, :], acc)
+
+    dst = (
+        scores_ptr
+        + (b * Hq + hq).to(tl.int64) * Sq * Skv
+        + offs_q.to(tl.int64)[:, None] * Skv
+        + offs_k[None, :]
+    )
+    tl.store(dst, scale * acc, mask=q_in[:, None] & k_in[None, :])
+
+
+@triton.jit
+def _masked_softmax_lse_kernel(
+    scores_ptr,
+    lse_ptr,
+    mask_ptr,
+    Hq,
+    Sq,
+    Skv,
+    CAUSAL: tl.constexpr,
+    HAS_MASK: tl.constexpr,
+    LANES: tl.constexpr,
+):
+    """Mask, softmax and LSE one ``(b, hq, q)`` row in place, C++ reduction order."""
+    row = tl.program_id(0)
+    b = row // (Hq * Sq)
+    q = row % Sq
+    row_base = scores_ptr + row.to(tl.int64) * Skv
+
+    if CAUSAL:
+        causal_limit = Skv - Sq + q + 1
+    else:
+        causal_limit = Skv
+
+    lane = tl.arange(0, LANES)
+    neg_inf = float("-inf")
+    minus_inf_vec = tl.full((LANES,), neg_inf, tl.float32)
+
+    # Phase 1: write -inf over masked entries and take the row max. Max is
+    # associative and commutative in IEEE-754, so the tree shape is irrelevant here.
+    lane_max = minus_inf_vec
+    for start in range(0, Skv, LANES):
+        cols = start + lane
+        in_range = cols < Skv
+        valid = in_range & (cols < causal_limit)
+        if HAS_MASK:
+            keep = tl.load(mask_ptr + b.to(tl.int64) * Skv + cols, mask=in_range, other=0)
+            valid = valid & (keep != 0)
+        scores = tl.load(row_base + cols, mask=in_range, other=neg_inf)
+        tl.store(row_base + cols, minus_inf_vec, mask=in_range & ~valid)
+        lane_max = tl.maximum(lane_max, tl.where(valid, scores, neg_inf))
+    row_max = tl.max(lane_max, axis=0)
+
+    # Phase 2: exponentiate in place. Lane ``t`` sums keys t, t+LANES, ... ascending,
+    # exactly like thread ``t`` in the C++ kernel; masked lanes contribute +0.0, which
+    # is bitwise neutral for this non-negative sum.
+    lane_sum = tl.zeros((LANES,), dtype=tl.float32)
+    for start in range(0, Skv, LANES):
+        cols = start + lane
+        in_range = cols < Skv
+        valid = in_range & (cols < causal_limit)
+        if HAS_MASK:
+            keep = tl.load(mask_ptr + b.to(tl.int64) * Skv + cols, mask=in_range, other=0)
+            valid = valid & (keep != 0)
+        scores = tl.load(row_base + cols, mask=in_range, other=neg_inf)
+        probs = tl.where(valid, _expf(scores - row_max), 0.0)
+        tl.store(row_base + cols, probs, mask=in_range)
+        lane_sum += probs
+    row_sum = _tree_sum_256(lane_sum)
+
+    # Phase 3: normalise and emit the LSE. A fully masked row already holds zeros
+    # from phase 2, so dividing it by 1.0 reproduces the C++ zero-fill branch.
+    is_empty = row_sum == 0.0
+    denom = tl.where(is_empty, 1.0, row_sum)
+    for start in range(0, Skv, LANES):
+        cols = start + lane
+        in_range = cols < Skv
+        probs = tl.load(row_base + cols, mask=in_range, other=0.0)
+        tl.store(row_base + cols, probs / denom, mask=in_range)
+
+    lse_val = tl.where(is_empty, neg_inf, row_max + _logf(row_sum))
+    tl.store(lse_ptr + row, lse_val)
+
+
+@triton.jit
+def _pv_kernel(
+    p_ptr,
+    v_ptr,
+    out_ptr,
+    Hq,
+    Hkv,
+    Sq,
+    Skv,
+    D: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+):
+    """out[b, hq, q, d] = sum_{k ascending} P[b,hq,q,k] * V[b,kv,k,d]."""
+    row = tl.program_id(0)
+    bh = row // Sq
+    b = bh // Hq
+    hq = bh % Hq
+    kv_head = hq // (Hq // Hkv)
+
+    offs_d = tl.arange(0, BLOCK_D)
+    d_in = offs_d < D
+    p_base = p_ptr + row.to(tl.int64) * Skv
+    v_base = v_ptr + (b * Hkv + kv_head).to(tl.int64) * Skv * D
+
+    acc = tl.zeros((BLOCK_D,), dtype=tl.float32)
+    for col in range(0, Skv):
+        p = tl.load(p_base + col)
+        vv = tl.load(v_base + col.to(tl.int64) * D + offs_d, mask=d_in, other=0.0).to(tl.float32)
+        acc = tl.fma(p, vv, acc)
+
+    tl.store(out_ptr + row.to(tl.int64) * D + offs_d, acc, mask=d_in)
+
+
+# ---------------------------------------------------------------------------
+# Backward
+# ---------------------------------------------------------------------------
+
+
+@triton.jit
+def _dp_kernel(
+    do_ptr,
+    v_ptr,
+    dp_ptr,
+    Hq,
+    Hkv,
+    Sq,
+    Skv,
+    D: tl.constexpr,
+    BLOCK_Q: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+):
+    """dP[b, hq, q, k] = sum_{d ascending} dO[b,hq,q,d] * V[b,kv,k,d]."""
+    pid_k = tl.program_id(0)
+    pid_q = tl.program_id(1)
+    bh = tl.program_id(2)
+    b = bh // Hq
+    hq = bh % Hq
+    kv_head = hq // (Hq // Hkv)
+
+    offs_q = pid_q * BLOCK_Q + tl.arange(0, BLOCK_Q)
+    offs_k = pid_k * BLOCK_K + tl.arange(0, BLOCK_K)
+    q_in = offs_q < Sq
+    k_in = offs_k < Skv
+
+    do_rows = do_ptr + (b * Hq + hq).to(tl.int64) * Sq * D + offs_q.to(tl.int64) * D
+    v_rows = v_ptr + (b * Hkv + kv_head).to(tl.int64) * Skv * D + offs_k.to(tl.int64) * D
+
+    acc = tl.zeros((BLOCK_Q, BLOCK_K), dtype=tl.float32)
+    for d in range(0, D):
+        dov = tl.load(do_rows + d, mask=q_in, other=0.0).to(tl.float32)
+        vv = tl.load(v_rows + d, mask=k_in, other=0.0).to(tl.float32)
+        acc = tl.fma(dov[:, None], vv[None, :], acc)
+
+    dst = (
+        dp_ptr
+        + (b * Hq + hq).to(tl.int64) * Sq * Skv
+        + offs_q.to(tl.int64)[:, None] * Skv
+        + offs_k[None, :]
+    )
+    tl.store(dst, acc, mask=q_in[:, None] & k_in[None, :])
+
+
+@triton.jit
+def _softmax_backward_kernel(
+    ds_ptr,
+    p_ptr,
+    Skv,
+    LANES: tl.constexpr,
+):
+    """delta = sum_k dP*P (C++ tree order); then dS = P * (dP - delta) in place."""
+    row = tl.program_id(0)
+    ds_base = ds_ptr + row.to(tl.int64) * Skv
+    p_base = p_ptr + row.to(tl.int64) * Skv
+    lane = tl.arange(0, LANES)
+
+    lane_delta = tl.zeros((LANES,), dtype=tl.float32)
+    for start in range(0, Skv, LANES):
+        cols = start + lane
+        in_range = cols < Skv
+        dp = tl.load(ds_base + cols, mask=in_range, other=0.0)
+        p = tl.load(p_base + cols, mask=in_range, other=0.0)
+        lane_delta = tl.fma(dp, p, lane_delta)
+    delta = _tree_sum_256(lane_delta)
+
+    for start in range(0, Skv, LANES):
+        cols = start + lane
+        in_range = cols < Skv
+        dp = tl.load(ds_base + cols, mask=in_range, other=0.0)
+        p = tl.load(p_base + cols, mask=in_range, other=0.0)
+        tl.store(ds_base + cols, p * (dp - delta), mask=in_range)
+
+
+@triton.jit
+def _dq_kernel(
+    ds_ptr,
+    k_ptr,
+    dq_ptr,
+    scale,
+    Hq,
+    Hkv,
+    Sq,
+    Skv,
+    D: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+):
+    """dQ[b, hq, q, d] = scale * sum_{k ascending} dS[b,hq,q,k] * K[b,kv,k,d]."""
+    row = tl.program_id(0)
+    bh = row // Sq
+    b = bh // Hq
+    hq = bh % Hq
+    kv_head = hq // (Hq // Hkv)
+
+    offs_d = tl.arange(0, BLOCK_D)
+    d_in = offs_d < D
+    ds_base = ds_ptr + row.to(tl.int64) * Skv
+    k_base = k_ptr + (b * Hkv + kv_head).to(tl.int64) * Skv * D
+
+    acc = tl.zeros((BLOCK_D,), dtype=tl.float32)
+    for col in range(0, Skv):
+        ds = tl.load(ds_base + col)
+        kv = tl.load(k_base + col.to(tl.int64) * D + offs_d, mask=d_in, other=0.0).to(tl.float32)
+        acc = tl.fma(ds, kv, acc)
+
+    tl.store(dq_ptr + row.to(tl.int64) * D + offs_d, scale * acc, mask=d_in)
+
+
+@triton.jit
+def _dk_kernel(
+    ds_ptr,
+    q_ptr,
+    dk_ptr,
+    scale,
+    Hq,
+    Hkv,
+    Sq,
+    Skv,
+    D: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+):
+    """dK[b, hkv, k, d] = scale * sum_{group head, then q, both ascending} dS * Q."""
+    k_idx = tl.program_id(0)
+    b_hkv = tl.program_id(1)
+    b = b_hkv // Hkv
+    hkv = b_hkv % Hkv
+    group = Hq // Hkv
+
+    offs_d = tl.arange(0, BLOCK_D)
+    d_in = offs_d < D
+
+    acc = tl.zeros((BLOCK_D,), dtype=tl.float32)
+    for local in range(0, group):
+        hq = hkv * group + local
+        ds_head = ds_ptr + (b * Hq + hq).to(tl.int64) * Sq * Skv + k_idx
+        q_head = q_ptr + (b * Hq + hq).to(tl.int64) * Sq * D
+        for qi in range(0, Sq):
+            ds = tl.load(ds_head + qi.to(tl.int64) * Skv)
+            qv = tl.load(q_head + qi.to(tl.int64) * D + offs_d, mask=d_in, other=0.0).to(tl.float32)
+            acc = tl.fma(ds, qv, acc)
+
+    dst = dk_ptr + (b * Hkv + hkv).to(tl.int64) * Skv * D + k_idx.to(tl.int64) * D + offs_d
+    tl.store(dst, scale * acc, mask=d_in)
+
+
+@triton.jit
+def _dv_kernel(
+    p_ptr,
+    do_ptr,
+    dv_ptr,
+    Hq,
+    Hkv,
+    Sq,
+    Skv,
+    D: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+):
+    """dV[b, hkv, k, d] = sum_{group head, then q, both ascending} P * dO."""
+    k_idx = tl.program_id(0)
+    b_hkv = tl.program_id(1)
+    b = b_hkv // Hkv
+    hkv = b_hkv % Hkv
+    group = Hq // Hkv
+
+    offs_d = tl.arange(0, BLOCK_D)
+    d_in = offs_d < D
+
+    acc = tl.zeros((BLOCK_D,), dtype=tl.float32)
+    for local in range(0, group):
+        hq = hkv * group + local
+        p_head = p_ptr + (b * Hq + hq).to(tl.int64) * Sq * Skv + k_idx
+        do_head = do_ptr + (b * Hq + hq).to(tl.int64) * Sq * D
+        for qi in range(0, Sq):
+            p = tl.load(p_head + qi.to(tl.int64) * Skv)
+            dov = tl.load(do_head + qi.to(tl.int64) * D + offs_d, mask=d_in, other=0.0).to(
+                tl.float32
+            )
+            acc = tl.fma(p, dov, acc)
+
+    dst = dv_ptr + (b * Hkv + hkv).to(tl.int64) * Skv * D + k_idx.to(tl.int64) * D + offs_d
+    tl.store(dst, acc, mask=d_in)
+
+
+# ---------------------------------------------------------------------------
+# Launchers
+# ---------------------------------------------------------------------------
+
+
+def _dummy_mask(reference: torch.Tensor) -> torch.Tensor:
+    return reference.new_empty((1,), dtype=torch.bool)
+
+
+def triton_deterministic_attention_forward(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    causal: bool,
+    scale: float,
+    key_padding_mask: Optional[torch.Tensor],
+    output_fp32: bool,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Return ``(out, lse, P)`` matching ``_C.deterministic_attention_forward`` bitwise."""
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    mask = key_padding_mask.contiguous() if key_padding_mask is not None else None
+
+    b, hq, sq, d = q.shape
+    hkv, skv = k.shape[1], k.shape[2]
+
+    scores = torch.empty((b, hq, sq, skv), device=q.device, dtype=torch.float32)
+    lse = torch.empty((b, hq, sq), device=q.device, dtype=torch.float32)
+    out = torch.empty_like(q, dtype=torch.float32 if output_fp32 else q.dtype)
+
+    _qk_kernel[(triton.cdiv(skv, _QK_BLOCK_K), triton.cdiv(sq, _QK_BLOCK_Q), b * hq)](
+        q,
+        k,
+        scores,
+        float(scale),
+        hq,
+        hkv,
+        sq,
+        skv,
+        D=d,
+        BLOCK_Q=_QK_BLOCK_Q,
+        BLOCK_K=_QK_BLOCK_K,
+        num_warps=4,
+    )
+    _masked_softmax_lse_kernel[(b * hq * sq,)](
+        scores,
+        lse,
+        mask if mask is not None else _dummy_mask(q),
+        hq,
+        sq,
+        skv,
+        CAUSAL=causal,
+        HAS_MASK=mask is not None,
+        LANES=_SOFTMAX_LANES,
+        num_warps=4,
+    )
+    _pv_kernel[(b * hq * sq,)](
+        scores,
+        v,
+        out,
+        hq,
+        hkv,
+        sq,
+        skv,
+        D=d,
+        BLOCK_D=d,
+        num_warps=4,
+    )
+    return out, lse, scores
+
+
+def triton_deterministic_attention_backward(
+    grad_output: torch.Tensor,
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    p: torch.Tensor,
+    scale: float,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Return ``(dQ, dK, dV)`` matching ``_C.deterministic_attention_backward`` bitwise."""
+    do = grad_output.contiguous()
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    p = p.contiguous()
+
+    b, hq, sq, d = q.shape
+    hkv, skv = k.shape[1], k.shape[2]
+
+    # dS reuses the dP buffer exactly like the native backward does.
+    ds = torch.empty((b, hq, sq, skv), device=q.device, dtype=torch.float32)
+    dq = torch.empty_like(q)
+    dk = torch.empty_like(k)
+    dv = torch.empty_like(v)
+
+    _dp_kernel[(triton.cdiv(skv, _QK_BLOCK_K), triton.cdiv(sq, _QK_BLOCK_Q), b * hq)](
+        do,
+        v,
+        ds,
+        hq,
+        hkv,
+        sq,
+        skv,
+        D=d,
+        BLOCK_Q=_QK_BLOCK_Q,
+        BLOCK_K=_QK_BLOCK_K,
+        num_warps=4,
+    )
+    _softmax_backward_kernel[(b * hq * sq,)](
+        ds,
+        p,
+        skv,
+        LANES=_SOFTMAX_LANES,
+        num_warps=4,
+    )
+    _dq_kernel[(b * hq * sq,)](
+        ds,
+        k,
+        dq,
+        float(scale),
+        hq,
+        hkv,
+        sq,
+        skv,
+        D=d,
+        BLOCK_D=d,
+        num_warps=4,
+    )
+    _dk_kernel[(skv, b * hkv)](
+        ds,
+        q,
+        dk,
+        float(scale),
+        hq,
+        hkv,
+        sq,
+        skv,
+        D=d,
+        BLOCK_D=d,
+        num_warps=4,
+    )
+    _dv_kernel[(skv, b * hkv)](
+        p,
+        do,
+        dv,
+        hq,
+        hkv,
+        sq,
+        skv,
+        D=d,
+        BLOCK_D=d,
+        num_warps=4,
+    )
+    return dq, dk, dv
+
+
+class _TritonDeterministicAttentionFn(Function):
+    @staticmethod
+    def forward(
+        ctx,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        causal: bool,
+        scale: float,
+        key_padding_mask: Optional[torch.Tensor],
+        output_fp32: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        q_c = q.contiguous()
+        k_c = k.contiguous()
+        v_c = v.contiguous()
+        mask_c = key_padding_mask.contiguous() if key_padding_mask is not None else None
+
+        out, lse, p = triton_deterministic_attention_forward(
+            q_c, k_c, v_c, causal, float(scale), mask_c, output_fp32
+        )
+
+        ctx.save_for_backward(q_c, k_c, v_c, p, mask_c)
+        ctx.causal = causal
+        ctx.scale = scale
+        ctx.mark_non_differentiable(lse)
+
+        return out, lse
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_out: torch.Tensor, grad_lse: torch.Tensor):
+        q_c, k_c, v_c, p, _mask_c = ctx.saved_tensors
+
+        if grad_out.dtype != q_c.dtype:
+            grad_out = grad_out.to(q_c.dtype)
+        dq, dk, dv = triton_deterministic_attention_backward(
+            grad_out.contiguous(), q_c, k_c, v_c, p, float(ctx.scale)
+        )
+        return dq, dk, dv, None, None, None, None
+
+
+class TritonDeterministicAttentionOp:
+    """Triton twin of :class:`DeterministicAttentionOp`, bitwise identical to it.
+
+    The public surface matches the native op so either can be dropped into the
+    strict-attention harness. Validation is duplicated rather than imported so the
+    Triton path stays usable when the native extension is not built.
+    """
+
+    backend_id = "rlkernel.triton.deterministic_attention"
+
+    def __init__(self, *, require_bitwise_libm: bool = True) -> None:
+        """``require_bitwise_libm=False`` trades bitwise parity for portability.
+
+        The softmax needs a bitwise-exact ``expf``/``logf``; only the HIP sequences
+        are ported (see :data:`BITWISE_LIBM_PARITY`). Opting out keeps the kernel
+        deterministic and batch-invariant but no longer bit-identical to the
+        native core, so it is never the default.
+        """
+        if require_bitwise_libm and not BITWISE_LIBM_PARITY:
+            raise RuntimeError(
+                "Triton deterministic attention is bitwise-identical to "
+                "_C.deterministic_attention_* only on ROCm: the nvcc expf/logf "
+                "argument reduction has not been ported. Construct with "
+                "require_bitwise_libm=False to run the non-bitwise fallback."
+            )
+        self.bitwise_libm = BITWISE_LIBM_PARITY
+
+    def __call__(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        *,
+        causal: bool = True,
+        scale: Optional[float] = None,
+        key_padding_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return self.forward(q, k, v, causal=causal, scale=scale, key_padding_mask=key_padding_mask)
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        *,
+        causal: bool = True,
+        scale: Optional[float] = None,
+        key_padding_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        out, _lse = self.forward_with_lse(
+            q, k, v, causal=causal, scale=scale, key_padding_mask=key_padding_mask
+        )
+        return out
+
+    def forward_with_lse(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        *,
+        causal: bool = True,
+        scale: Optional[float] = None,
+        key_padding_mask: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        self._validate_inputs(q, k, v, key_padding_mask)
+        resolved_scale = scale if scale is not None else (1.0 / math.sqrt(q.shape[-1]))
+        return _TritonDeterministicAttentionFn.apply(
+            q, k, v, causal, resolved_scale, key_padding_mask, False
+        )
+
+    def forward_fp32(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        *,
+        causal: bool = True,
+        scale: Optional[float] = None,
+        key_padding_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        self._validate_inputs(q, k, v, key_padding_mask)
+        resolved_scale = scale if scale is not None else (1.0 / math.sqrt(q.shape[-1]))
+        out, _lse = _TritonDeterministicAttentionFn.apply(
+            q, k, v, causal, resolved_scale, key_padding_mask, True
+        )
+        return out
+
+    @staticmethod
+    def _validate_inputs(
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        key_padding_mask: Optional[torch.Tensor],
+    ) -> None:
+        if q.dim() != 4 or k.dim() != 4 or v.dim() != 4:
+            raise ValueError(
+                f"q/k/v must be 4-D [B, H, S, D], got q={tuple(q.shape)}, "
+                f"k={tuple(k.shape)}, v={tuple(v.shape)}"
+            )
+        b, hq, sq, d = q.shape
+        hkv, skv = k.shape[1], k.shape[2]
+        if k.shape[0] != b or v.shape[0] != b:
+            raise ValueError("batch size mismatch between q/k/v")
+        if v.shape[1] != hkv or v.shape[2] != skv or k.shape[3] != d or v.shape[3] != d:
+            raise ValueError(
+                f"k/v shape mismatch: k={tuple(k.shape)}, v={tuple(v.shape)}, "
+                f"expected k/v [B={b}, Hkv, Skv, D={d}]"
+            )
+        if d != _HEAD_DIM:
+            raise ValueError(f"head dim D must be {_HEAD_DIM}, got {d}")
+        if hq % hkv != 0:
+            raise ValueError(f"Hq={hq} not divisible by Hkv={hkv} (GQA group)")
+        if q.dtype not in (torch.float16, torch.bfloat16):
+            raise ValueError(f"only FP16/BF16 supported, got {q.dtype}")
+        if k.dtype != q.dtype or v.dtype != q.dtype:
+            raise ValueError("q, k, v must share the same dtype")
+        if not (q.is_cuda and k.is_cuda and v.is_cuda):
+            raise ValueError("q, k, v must be GPU tensors")
+        if key_padding_mask is not None:
+            if key_padding_mask.dtype != torch.bool:
+                raise ValueError("key_padding_mask must be bool")
+            if key_padding_mask.shape != (b, skv):
+                raise ValueError(
+                    f"key_padding_mask must be [B, Skv]=[{b}, {skv}], "
+                    f"got {tuple(key_padding_mask.shape)}"
+                )
+        if sq < 1 or skv < 1:
+            raise ValueError(f"Sq and Skv must be positive, got Sq={sq}, Skv={skv}")
+
+
+def triton_deterministic_attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    causal: bool = True,
+    scale: Optional[float] = None,
+    key_padding_mask: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    return TritonDeterministicAttentionOp().forward(
+        q, k, v, causal=causal, scale=scale, key_padding_mask=key_padding_mask
+    )
+
+
+def triton_deterministic_attention_with_lse(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    causal: bool = True,
+    scale: Optional[float] = None,
+    key_padding_mask: Optional[torch.Tensor] = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return TritonDeterministicAttentionOp().forward_with_lse(
+        q, k, v, causal=causal, scale=scale, key_padding_mask=key_padding_mask
+    )
+
+
+def triton_deterministic_attention_fp32(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    causal: bool = True,
+    scale: Optional[float] = None,
+    key_padding_mask: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    return TritonDeterministicAttentionOp().forward_fp32(
+        q, k, v, causal=causal, scale=scale, key_padding_mask=key_padding_mask
+    )
+
+
+__all__ = [
+    "TritonDeterministicAttentionOp",
+    "triton_deterministic_attention",
+    "triton_deterministic_attention_backward",
+    "triton_deterministic_attention_forward",
+    "triton_deterministic_attention_fp32",
+    "triton_deterministic_attention_with_lse",
+]
diff --git a/tests/test_triton_deterministic_attention.py b/tests/test_triton_deterministic_attention.py
new file mode 100644
index 00000000..8e87ecf9
--- /dev/null
+++ b/tests/test_triton_deterministic_attention.py
@@ -0,0 +1,284 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Bitwise parity between the Triton and native deterministic Attention cores.
+
+The Triton core in ``rl_engine.kernels.ops.triton.attention.deterministic_attn`` is a
+port of ``csrc/cuda/attention/deterministic_attention.cu``. Its contract is stronger
+than "numerically close": every tensor it returns must be bit-identical to the native
+kernel's. These tests pin that contract, plus the two properties the port depends on:
+the vendor-exact ``expf``/``logf`` helpers, and batch invariance.
+"""
+
+import math
+
+import pytest
+import torch
+
+from rl_engine.platforms.device import device_ctx
+
+IS_ROCM = device_ctx.is_rocm
+IS_GPU = device_ctx.device_type == "cuda" or IS_ROCM
+
+pytestmark = pytest.mark.skipif(not IS_GPU, reason="CUDA/ROCm GPU not available")
+
+try:
+    from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
+    from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
+        DeterministicAttentionOp,
+    )
+    from rl_engine.kernels.ops.triton.attention.deterministic_attn import (
+        BITWISE_LIBM_PARITY,
+        TritonDeterministicAttentionOp,
+        triton_deterministic_attention_backward,
+        triton_deterministic_attention_forward,
+    )
+
+    _IMPORTED = True
+except (ImportError, RuntimeError):  # pragma: no cover - import guard
+    _IMPORTED = False
+
+_NATIVE = _IMPORTED and _EXT_AVAILABLE and hasattr(_C, "deterministic_attention_forward")
+
+needs_native = pytest.mark.skipif(
+    not _NATIVE, reason="native deterministic attention kernel not built"
+)
+needs_bitwise_libm = pytest.mark.skipif(
+    not (_IMPORTED and BITWISE_LIBM_PARITY),
+    reason="bitwise expf/logf sequence is only ported for ROCm",
+)
+
+DEVICE = device_ctx.device
+D = 128
+
+# (B, Hq, Hkv, Sq, Skv, causal, mask_kind, scale)
+_CASES = [
+    (1, 1, 1, 1, 1, True, None, None),
+    (1, 2, 2, 1, 64, True, None, None),  # decode step
+    (1, 8, 2, 64, 64, True, None, None),  # GQA, group 4
+    (2, 4, 1, 128, 128, True, None, None),  # MQA
+    (1, 2, 2, 256, 256, True, None, None),  # exactly one softmax lane chunk
+    (1, 2, 2, 257, 257, True, None, None),  # one past the chunk boundary
+    (1, 2, 2, 100, 512, False, None, None),  # two full lane chunks
+    (2, 4, 2, 64, 700, True, None, None),  # ragged multi-chunk
+    (1, 2, 2, 32, 32, True, "right", None),
+    (1, 2, 2, 32, 32, False, "left", None),
+    (2, 2, 2, 16, 300, True, "right", None),
+    (2, 2, 2, 8, 8, True, "allfalse", None),  # fully masked row -> lse == -inf
+    (1, 2, 2, 16, 16, True, None, 0.0),
+    (1, 2, 2, 16, 16, True, None, 3.7),
+    (1, 2, 2, 16, 16, False, None, -1.25),
+]
+
+
+def _make_inputs(case, dtype, seed):
+    b, hq, hkv, sq, skv, causal, mask_kind, scale = case
+    gen = torch.Generator(device=DEVICE).manual_seed(seed)
+    q = torch.randn(b, hq, sq, D, device=DEVICE, dtype=dtype, generator=gen)
+    k = torch.randn(b, hkv, skv, D, device=DEVICE, dtype=dtype, generator=gen)
+    v = torch.randn(b, hkv, skv, D, device=DEVICE, dtype=dtype, generator=gen)
+
+    mask = None
+    if mask_kind is not None:
+        mask = torch.ones(b, skv, device=DEVICE, dtype=torch.bool)
+        if mask_kind == "right":
+            mask[:, skv // 2 :] = False
+        elif mask_kind == "left":
+            mask[:, : skv // 3] = False
+        elif mask_kind == "allfalse":
+            mask[0, :] = False
+        else:  # pragma: no cover - guards the parametrisation itself
+            raise AssertionError(f"unknown mask kind {mask_kind}")
+
+    resolved_scale = 1.0 / math.sqrt(D) if scale is None else scale
+    return q, k, v, causal, resolved_scale, mask
+
+
+def _case_id(case):
+    b, hq, hkv, sq, skv, causal, mask_kind, scale = case
+    return f"b{b}_hq{hq}_hkv{hkv}_sq{sq}_skv{skv}_causal{int(causal)}_{mask_kind}_{scale}"
+
+
+@needs_native
+@needs_bitwise_libm
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16], ids=["bf16", "fp16"])
+@pytest.mark.parametrize("case", _CASES, ids=[_case_id(c) for c in _CASES])
+def test_triton_forward_is_bitwise_identical_to_native(case, dtype):
+    q, k, v, causal, scale, mask = _make_inputs(case, dtype, seed=11)
+
+    ref_out, ref_lse, ref_p = _C.deterministic_attention_forward(q, k, v, causal, scale, mask)
+    out, lse, p = triton_deterministic_attention_forward(q, k, v, causal, scale, mask, False)
+
+    assert torch.equal(out, ref_out)
+    assert torch.equal(lse, ref_lse)
+    assert torch.equal(p, ref_p)
+
+
+@needs_native
+@needs_bitwise_libm
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16], ids=["bf16", "fp16"])
+@pytest.mark.parametrize("case", _CASES, ids=[_case_id(c) for c in _CASES])
+def test_triton_backward_is_bitwise_identical_to_native(case, dtype):
+    q, k, v, causal, scale, mask = _make_inputs(case, dtype, seed=12)
+
+    _ref_out, _ref_lse, ref_p = _C.deterministic_attention_forward(q, k, v, causal, scale, mask)
+    _out, _lse, p = triton_deterministic_attention_forward(q, k, v, causal, scale, mask, False)
+    grad_out = torch.randn_like(q)
+
+    ref_dq, ref_dk, ref_dv = _C.deterministic_attention_backward(
+        grad_out, q, k, v, ref_p, causal, scale, mask
+    )
+    dq, dk, dv = triton_deterministic_attention_backward(grad_out, q, k, v, p, scale)
+
+    assert torch.equal(dq, ref_dq)
+    assert torch.equal(dk, ref_dk)
+    assert torch.equal(dv, ref_dv)
+
+
+@needs_native
+@needs_bitwise_libm
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16], ids=["bf16", "fp16"])
+def test_triton_autograd_matches_native_autograd_bitwise(dtype):
+    b, hq, hkv, sq, skv = 2, 8, 2, 96, 300
+    gen = torch.Generator(device=DEVICE).manual_seed(13)
+    base = (
+        torch.randn(b, hq, sq, D, device=DEVICE, dtype=dtype, generator=gen),
+        torch.randn(b, hkv, skv, D, device=DEVICE, dtype=dtype, generator=gen),
+        torch.randn(b, hkv, skv, D, device=DEVICE, dtype=dtype, generator=gen),
+    )
+    grad_out = torch.randn(b, hq, sq, D, device=DEVICE, dtype=dtype, generator=gen)
+
+    results = []
+    for op in (DeterministicAttentionOp(), TritonDeterministicAttentionOp()):
+        tensors = [t.clone().requires_grad_(True) for t in base]
+        out, lse = op.forward_with_lse(*tensors, causal=True)
+        out.backward(grad_out)
+        results.append((out.detach(), lse, [t.grad for t in tensors]))
+
+    native, triton_result = results
+    assert torch.equal(triton_result[0], native[0])
+    assert torch.equal(triton_result[1], native[1])
+    for triton_grad, native_grad in zip(triton_result[2], native[2]):
+        assert torch.equal(triton_grad, native_grad)
+
+
+@needs_bitwise_libm
+def test_triton_fp32_output_downcasts_to_the_native_dtype_result():
+    """``output_fp32=True`` must expose the same accumulator the bf16 path rounds."""
+    q, k, v, causal, scale, mask = _make_inputs(_CASES[6], torch.bfloat16, seed=14)
+
+    out, _lse, _p = triton_deterministic_attention_forward(q, k, v, causal, scale, mask, False)
+    out_fp32, _lse32, _p32 = triton_deterministic_attention_forward(
+        q, k, v, causal, scale, mask, True
+    )
+
+    assert out_fp32.dtype is torch.float32
+    assert torch.equal(out_fp32.to(torch.bfloat16), out)
+
+
+@needs_bitwise_libm
+def test_triton_fully_masked_row_is_zero_with_neg_inf_lse():
+    q, k, v, causal, scale, mask = _make_inputs(_CASES[11], torch.bfloat16, seed=15)
+
+    out, lse, p = triton_deterministic_attention_forward(q, k, v, causal, scale, mask, False)
+
+    assert torch.equal(out[0], torch.zeros_like(out[0]))
+    assert torch.equal(p[0], torch.zeros_like(p[0]))
+    assert torch.isneginf(lse[0]).all()
+    assert torch.isfinite(lse[1]).all()
+
+
+@needs_bitwise_libm
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16], ids=["bf16", "fp16"])
+def test_triton_batch_slice_is_bitwise_invariant(dtype):
+    """A row's result must not depend on what else was in the batch."""
+    b, hq, hkv, sq, skv = 4, 4, 2, 48, 192
+    gen = torch.Generator(device=DEVICE).manual_seed(16)
+    q = torch.randn(b, hq, sq, D, device=DEVICE, dtype=dtype, generator=gen)
+    k = torch.randn(b, hkv, skv, D, device=DEVICE, dtype=dtype, generator=gen)
+    v = torch.randn(b, hkv, skv, D, device=DEVICE, dtype=dtype, generator=gen)
+    scale = 1.0 / math.sqrt(D)
+
+    batched, batched_lse, _ = triton_deterministic_attention_forward(
+        q, k, v, True, scale, None, False
+    )
+    single, single_lse, _ = triton_deterministic_attention_forward(
+        q[2:3], k[2:3], v[2:3], True, scale, None, False
+    )
+
+    assert torch.equal(single[0], batched[2])
+    assert torch.equal(single_lse[0], batched_lse[2])
+
+
+@needs_bitwise_libm
+def test_expf_and_logf_match_the_vendor_libm_bitwise():
+    """The softmax parity rests on these two helpers; pin them independently."""
+    import triton
+
+    import triton.language as tl  # isort: skip
+    from rl_engine.kernels.ops.triton.attention import deterministic_attn as mod
+
+    @triton.jit
+    def _exp_probe(x_ptr, out_ptr, n_elem, EXPF: tl.constexpr):
+        offs = tl.program_id(0) * 256 + tl.arange(0, 256)
+        keep = offs < n_elem
+        value = tl.load(x_ptr + offs, mask=keep, other=0.0)
+        tl.store(out_ptr + offs, EXPF(value), mask=keep)
+
+    gen = torch.Generator(device=DEVICE).manual_seed(17)
+    edge = torch.tensor(
+        [0.0, -0.0, 1.0, -1.0, 88.72283935546875, 88.73, -103.2789306640625, -104.0],
+        device=DEVICE,
+        dtype=torch.float32,
+    )
+    xs = torch.cat(
+        [torch.rand(1 << 18, device=DEVICE, generator=gen) * 240.0 - 130.0, edge]
+    ).contiguous()
+    got = torch.empty_like(xs)
+    _exp_probe[(triton.cdiv(xs.numel(), 256),)](xs, got, xs.numel(), EXPF=mod._expf)
+    assert torch.equal(got, torch.exp(xs))
+
+    positives = torch.cat(
+        [
+            torch.rand(1 << 18, device=DEVICE, generator=gen) * 1e3,
+            torch.rand(1 << 12, device=DEVICE, generator=gen) * 1e-38,  # subnormal inputs
+            torch.tensor([1.0, 1e-45, 3.4e38], device=DEVICE),
+        ]
+    ).contiguous()
+    got = torch.empty_like(positives)
+    _exp_probe[(triton.cdiv(positives.numel(), 256),)](
+        positives, got, positives.numel(), EXPF=mod._logf
+    )
+    assert torch.equal(got, torch.log(positives))
+
+
+def test_op_refuses_to_run_without_a_bitwise_libm():
+    """On a platform with no ported expf/logf the op must fail loudly, not silently."""
+    if BITWISE_LIBM_PARITY:
+        op = TritonDeterministicAttentionOp()
+        assert op.bitwise_libm is True
+    else:  # pragma: no cover - exercised on CUDA
+        with pytest.raises(RuntimeError, match="bitwise-identical"):
+            TritonDeterministicAttentionOp()
+        assert TritonDeterministicAttentionOp(require_bitwise_libm=False).bitwise_libm is False
+
+
+@needs_bitwise_libm
+@pytest.mark.parametrize(
+    "kwargs, message",
+    [
+        ({"head_dim": 64}, "head dim D must be 128"),
+        ({"dtype": torch.float32}, "only FP16/BF16 supported"),
+        ({"gqa_mismatch": True}, "not divisible"),
+    ],
+)
+def test_triton_op_validation_mirrors_the_native_op(kwargs, message):
+    head_dim = kwargs.get("head_dim", D)
+    dtype = kwargs.get("dtype", torch.bfloat16)
+    hkv = 3 if kwargs.get("gqa_mismatch") else 2
+    q = torch.randn(1, 2, 8, head_dim, device=DEVICE, dtype=dtype)
+    k = torch.randn(1, hkv, 8, head_dim, device=DEVICE, dtype=dtype)
+    v = torch.randn(1, hkv, 8, head_dim, device=DEVICE, dtype=dtype)
+
+    with pytest.raises(ValueError, match=message):
+        TritonDeterministicAttentionOp().forward(q, k, v, causal=True)

From cc76c48c0bee050a22a713877befe8c3fb812e3f Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sat, 29 Aug 2026 07:48:25 +0000
Subject: [PATCH 21/35] bench(attention): add WS2 strict ROCm Attention report
 on MI300X

Applies the PR #325 / #328 measurement matrix and presentation to the strict ROCm
Attention path: their timing and accuracy helpers, their spawned distributed world,
and their figure style, so the three reports read side by side. Operator-only, no
checkpoint; Qwen3-8B shapes (Hq=32, Hkv=8, D=128).

Headline: the Triton core is bit-identical to _C.deterministic_attention_* on all
eight (dtype, sequence) cases -- out, lse, dQ, dK and dV, zero mismatched elements.

TP-degree invariance reproduces PR #319 on independent inputs and shows why the
per-KV-group launch schedule is load-bearing. Raw AITER is non-invariant at 5 of 12
(S, TP) points, up to 7.8e-03 out max-abs, and which points fail is shape-dependent:
S=512 and S=4096/TP=2 both look clean. The per-KV-group schedule is bitwise at 12/12.

Distributed CP runs the real AG/RS schedule -- all-gather Q/K/V and position ids,
strict core on the full sequence, reduce-scatter (out, lse) -- and is bitwise against
CP=1 on all six topologies including the 8-rank TP=2/CP=2 x2-replica case.

Three things the numbers say that were not obvious:
- the strict production core is faster than SDPA at S=4096 forward (0.81x), so the
  bitwise arrangements cost almost nothing on the production path;
- AITER's backward peaks at 16.6 GiB at S=4096, 4x the materializing reference core;
- both deterministic cores are closer to an FP64 oracle than SDPA or AITER.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01824WkgMDxtBKD4Ex2NkfYA
---
 benchmarks/benchmark_ws2_rocm_attention.py    | 1536 ++++++++++++++++
 .../results/ws2_rocm_mi300x/PR_DESCRIPTION.md |  292 +++
 .../distributed_cp_latency.png                |  Bin 0 -> 46377 bytes
 benchmarks/results/ws2_rocm_mi300x/report.md  |  244 +++
 .../results/ws2_rocm_mi300x/results.json      | 1567 +++++++++++++++++
 .../ws2_rocm_mi300x/single_gpu_grid.png       |  Bin 0 -> 288104 bytes
 .../ws2_rocm_mi300x/single_gpu_latency.png    |  Bin 0 -> 132823 bytes
 .../ws2_rocm_mi300x/single_gpu_memory.png     |  Bin 0 -> 131593 bytes
 .../ws2_rocm_mi300x/tp_degree_invariance.png  |  Bin 0 -> 103099 bytes
 9 files changed, 3639 insertions(+)
 create mode 100644 benchmarks/benchmark_ws2_rocm_attention.py
 create mode 100644 benchmarks/results/ws2_rocm_mi300x/PR_DESCRIPTION.md
 create mode 100644 benchmarks/results/ws2_rocm_mi300x/distributed_cp_latency.png
 create mode 100644 benchmarks/results/ws2_rocm_mi300x/report.md
 create mode 100644 benchmarks/results/ws2_rocm_mi300x/results.json
 create mode 100644 benchmarks/results/ws2_rocm_mi300x/single_gpu_grid.png
 create mode 100644 benchmarks/results/ws2_rocm_mi300x/single_gpu_latency.png
 create mode 100644 benchmarks/results/ws2_rocm_mi300x/single_gpu_memory.png
 create mode 100644 benchmarks/results/ws2_rocm_mi300x/tp_degree_invariance.png

diff --git a/benchmarks/benchmark_ws2_rocm_attention.py b/benchmarks/benchmark_ws2_rocm_attention.py
new file mode 100644
index 00000000..b0f409ed
--- /dev/null
+++ b/benchmarks/benchmark_ws2_rocm_attention.py
@@ -0,0 +1,1536 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""WS2 strict ROCm Attention performance and bitwise-parity benchmark.
+
+Operator-only. No model checkpoint or serving engine is loaded; the shapes are
+Qwen3-8B's attention shapes (``Hq=32``, ``Hkv=8``, ``D=128``).
+
+Measurement matrix and presentation follow PR #325 (`benchmark_rocm_ffn.py`) and
+PR #328 (`benchmark_rocm_logp.py`): the timing/accuracy helpers, the spawned
+distributed world, and the figure style are taken from those scripts so the three
+reports can be read side by side.
+
+Paths measured:
+
+- ``sdpa``            PyTorch ``scaled_dot_product_attention``. Speed baseline only,
+                      exactly as PR #325 uses upstream ``Qwen3MLP`` at TP=1: no
+                      accuracy claim is mixed into the speed comparison.
+- ``strict-aiter``    ``StrictRocmAiterCKAttentionCore`` — the ROCm production core
+                      (AITER CK dense MHA, non-split API).
+- ``reference-hip``   ``_C.deterministic_attention_forward/backward`` — the materializing
+                      FP32 reference core, hipified from the shared ``.cu``.
+- ``triton-bitwise``  ``TritonDeterministicAttentionOp`` — the Triton port whose
+                      contract is bit-identity with ``reference-hip``.
+
+The headline column is ``triton-bitwise`` versus ``reference-hip``: acceptance is
+0 mismatched elements on out, lse, dQ, dK and dV.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import multiprocessing as mp
+import os
+import platform
+import queue
+import statistics
+import tempfile
+import traceback
+from datetime import timedelta
+from pathlib import Path
+from typing import Any, Callable
+
+import torch
+import torch.distributed as dist
+
+QWEN3_8B_Q_HEADS = 32
+QWEN3_8B_KV_HEADS = 8
+QWEN3_8B_HEAD_DIM = 128
+
+DEFAULT_SEQ_LENS = (512, 1024, 2048, 4096)
+DEFAULT_TP_DEGREES = (2, 4, 8)
+# (label, tp_world_size, cp_world_size, replicas) -- world_size = tp * cp * replicas.
+# Replicas run independent CP groups side by side, which is how PR #319 exercised
+# 8 ranks at TP=2/CP=2.
+DISTRIBUTED_TOPOLOGIES = (
+    ("tp1_cp2", 1, 2, 1),
+    ("tp2_cp2", 2, 2, 1),
+    ("tp1_cp4", 1, 4, 1),
+    ("tp2_cp2_x2", 2, 2, 2),
+    ("tp2_cp4", 2, 4, 1),
+    ("tp1_cp8", 1, 8, 1),
+)
+
+
+# ---------------------------------------------------------------------------
+# Measurement helpers (PR #328 benchmark_rocm_logp.py)
+# ---------------------------------------------------------------------------
+
+
+def _percentile(values: list[float], percentile: float) -> float:
+    ordered = sorted(values)
+    if not ordered:
+        return float("nan")
+    position = (len(ordered) - 1) * percentile
+    lower = math.floor(position)
+    upper = math.ceil(position)
+    if lower == upper:
+        return ordered[lower]
+    weight = position - lower
+    return ordered[lower] * (1.0 - weight) + ordered[upper] * weight
+
+
+def _summary_ms(values: list[float]) -> dict[str, float]:
+    return {
+        "median_ms": statistics.median(values),
+        "p95_ms": _percentile(values, 0.95),
+        "min_ms": min(values),
+        "max_ms": max(values),
+    }
+
+
+def _relative_l2(actual: torch.Tensor, expected: torch.Tensor) -> float:
+    actual_float = actual.detach().double()
+    expected_float = expected.detach().double()
+    denominator = torch.linalg.vector_norm(expected_float)
+    if denominator.item() == 0.0:
+        return float(torch.linalg.vector_norm(actual_float - expected_float).item())
+    return float((torch.linalg.vector_norm(actual_float - expected_float) / denominator).item())
+
+
+def _accuracy(actual: torch.Tensor, expected: torch.Tensor) -> dict[str, float]:
+    difference = actual.detach().double() - expected.detach().double()
+    return {
+        "max_abs": float(difference.abs().max().item()) if difference.numel() else 0.0,
+        "relative_l2": _relative_l2(actual, expected),
+    }
+
+
+def _bitwise_equal(a: torch.Tensor, b: torch.Tensor) -> bool:
+    return a.shape == b.shape and a.dtype == b.dtype and bool(torch.equal(a, b))
+
+
+def _mismatch_count(a: torch.Tensor, b: torch.Tensor) -> int:
+    if a.shape != b.shape:
+        return -1
+    return int((a != b).sum().item())
+
+
+def _gpu_event_samples(function: Callable[[], Any], *, warmup: int, samples: int) -> list[float]:
+    for _ in range(warmup):
+        function()
+    torch.cuda.synchronize()
+    events = []
+    for _ in range(samples):
+        start = torch.cuda.Event(enable_timing=True)
+        end = torch.cuda.Event(enable_timing=True)
+        start.record()
+        function()
+        end.record()
+        events.append((start, end))
+    torch.cuda.synchronize()
+    return [float(start.elapsed_time(end)) for start, end in events]
+
+
+def _peak_memory_mib(function: Callable[[], Any]) -> float:
+    """Peak memory used by one call, above what was live before it."""
+    torch.cuda.synchronize()
+    torch.cuda.empty_cache()
+    torch.cuda.reset_peak_memory_stats()
+    baseline = torch.cuda.memory_allocated()
+    function()
+    torch.cuda.synchronize()
+    return float((torch.cuda.max_memory_allocated() - baseline) / (1024.0 * 1024.0))
+
+
+# ---------------------------------------------------------------------------
+# Attention paths
+# ---------------------------------------------------------------------------
+
+
+def _seeded_qkv(
+    batch: int,
+    q_heads: int,
+    kv_heads: int,
+    seq_len: int,
+    head_dim: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    *,
+    seed: int,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    generator = torch.Generator(device=device).manual_seed(seed)
+    q = torch.randn(
+        batch, q_heads, seq_len, head_dim, device=device, dtype=dtype, generator=generator
+    )
+    k = torch.randn(
+        batch, kv_heads, seq_len, head_dim, device=device, dtype=dtype, generator=generator
+    )
+    v = torch.randn(
+        batch, kv_heads, seq_len, head_dim, device=device, dtype=dtype, generator=generator
+    )
+    return q, k, v
+
+
+def _positions(batch: int, seq_len: int, device: torch.device) -> torch.Tensor:
+    return torch.arange(seq_len, device=device, dtype=torch.int32).unsqueeze(0).expand(batch, -1)
+
+
+def _fp64_oracle(
+    q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, *, causal: bool, scale: float
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Reference (out, lse) in FP64 from the BF16/FP16-rounded inputs."""
+    q64 = q.double()
+    k64 = k.double()
+    v64 = v.double()
+    group = q.size(1) // k.size(1)
+    k64 = k64.repeat_interleave(group, dim=1)
+    v64 = v64.repeat_interleave(group, dim=1)
+    scores = torch.matmul(q64, k64.transpose(-1, -2)) * scale
+    if causal:
+        sq, skv = q.size(2), k.size(2)
+        offset = skv - sq
+        mask = torch.arange(skv, device=q.device)[None, :] > (
+            torch.arange(sq, device=q.device)[:, None] + offset
+        )
+        scores = scores.masked_fill(mask, float("-inf"))
+    lse = torch.logsumexp(scores, dim=-1)
+    probs = torch.softmax(scores, dim=-1)
+    return torch.matmul(probs, v64), lse
+
+
+def _sdpa_forward(q, k, v, *, causal, scale):
+    group = q.size(1) // k.size(1)
+    return torch.nn.functional.scaled_dot_product_attention(
+        q,
+        k.repeat_interleave(group, dim=1),
+        v.repeat_interleave(group, dim=1),
+        is_causal=causal,
+        scale=scale,
+    )
+
+
+class _Paths:
+    """Lazily constructed attention paths, so a missing backend skips one row."""
+
+    def __init__(self) -> None:
+        self.errors: dict[str, str] = {}
+        self.strict = self._try("strict-aiter", self._make_strict)
+        self.reference = self._try("reference-hip", self._make_reference)
+        self.triton = self._try("triton-bitwise", self._make_triton)
+
+    def _try(self, name: str, factory: Callable[[], Any]) -> Any:
+        try:
+            return factory()
+        except Exception as exc:  # noqa: BLE001 - a missing backend is a reported row
+            self.errors[name] = f"{type(exc).__name__}: {exc}"
+            return None
+
+    @staticmethod
+    def _make_strict():
+        from rl_engine.kernels.ops.rocm.attention.flash_attn import StrictRocmAiterCKAttentionCore
+
+        return StrictRocmAiterCKAttentionCore()
+
+    @staticmethod
+    def _make_reference():
+        from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
+            DeterministicAttentionOp,
+        )
+
+        return DeterministicAttentionOp()
+
+    @staticmethod
+    def _make_triton():
+        from rl_engine.kernels.ops.triton.attention.deterministic_attn import (
+            TritonDeterministicAttentionOp,
+        )
+
+        return TritonDeterministicAttentionOp()
+
+    def runner(self, name: str, q, k, v, *, causal: bool, scale: float, positions):
+        """Return ``() -> (out, lse|None)`` for one path, or None when unavailable."""
+        if name == "sdpa":
+            return lambda: (_sdpa_forward(q, k, v, causal=causal, scale=scale), None)
+        if name == "strict-aiter" and self.strict is not None:
+
+            def run_strict():
+                result = self.strict.forward_with_lse(
+                    q,
+                    k,
+                    v,
+                    causal=causal,
+                    scale=scale,
+                    query_position_ids=positions if causal else None,
+                    key_position_ids=positions if causal else None,
+                )
+                return result.out, result.lse
+
+            return run_strict
+        if name == "reference-hip" and self.reference is not None:
+            return lambda: self.reference.forward_with_lse(q, k, v, causal=causal, scale=scale)
+        if name == "triton-bitwise" and self.triton is not None:
+            return lambda: self.triton.forward_with_lse(q, k, v, causal=causal, scale=scale)
+        return None
+
+
+PATH_NAMES = ("sdpa", "strict-aiter", "reference-hip", "triton-bitwise")
+
+
+def _single_gpu_benchmarks(
+    *,
+    paths: _Paths,
+    seq_lens: tuple[int, ...],
+    dtypes: tuple[torch.dtype, ...],
+    q_heads: int,
+    kv_heads: int,
+    head_dim: int,
+    batch: int,
+    warmup: int,
+    samples: int,
+    training_samples: int,
+    device: torch.device,
+) -> list[dict[str, Any]]:
+    cases: list[dict[str, Any]] = []
+    scale = 1.0 / math.sqrt(head_dim)
+
+    for dtype in dtypes:
+        dtype_name = str(dtype).replace("torch.", "").replace("float", "fp").replace("bfp", "bf")
+        for seq_len in seq_lens:
+            q, k, v = _seeded_qkv(
+                batch, q_heads, kv_heads, seq_len, head_dim, dtype, device, seed=1234
+            )
+            positions = _positions(batch, seq_len, device)
+            oracle_out, oracle_lse = _fp64_oracle(q, k, v, causal=True, scale=scale)
+
+            row: dict[str, Any] = {
+                "dtype": dtype_name,
+                "seq_len": seq_len,
+                "batch": batch,
+                "q_heads": q_heads,
+                "kv_heads": kv_heads,
+                "head_dim": head_dim,
+                "paths": {},
+            }
+            captured: dict[str, tuple[torch.Tensor, torch.Tensor | None]] = {}
+
+            for name in PATH_NAMES:
+                runner = paths.runner(name, q, k, v, causal=True, scale=scale, positions=positions)
+                if runner is None:
+                    continue
+
+                out, lse = runner()
+                captured[name] = (
+                    out.detach().clone(),
+                    None if lse is None else lse.detach().clone(),
+                )
+
+                forward = _summary_ms(_gpu_event_samples(runner, warmup=warmup, samples=samples))
+                forward_peak = _peak_memory_mib(runner)
+
+                entry: dict[str, Any] = {
+                    "forward": forward,
+                    "forward_peak_mib": forward_peak,
+                    "out_vs_fp64": _accuracy(out.double(), oracle_out),
+                }
+                if lse is not None:
+                    entry["lse_vs_fp64"] = _accuracy(lse.double(), oracle_lse)
+
+                # Repeat determinism: two identical calls must be bitwise equal.
+                repeat_out, repeat_lse = runner()
+                entry["repeat_bitwise"] = _bitwise_equal(out, repeat_out) and (
+                    lse is None or _bitwise_equal(lse, repeat_lse)
+                )
+
+                training = _training_runner(
+                    paths, name, q, k, v, causal=True, scale=scale, positions=positions
+                )
+                if training is not None:
+                    entry["train_fwd_bwd"] = _summary_ms(
+                        _gpu_event_samples(
+                            training, warmup=max(1, warmup // 2), samples=training_samples
+                        )
+                    )
+                    entry["train_peak_mib"] = _peak_memory_mib(training)
+
+                row["paths"][name] = entry
+                del out, lse, repeat_out, repeat_lse
+                torch.cuda.empty_cache()
+
+            # Headline: Triton must be bit-identical to the native reference core.
+            if "triton-bitwise" in captured and "reference-hip" in captured:
+                t_out, t_lse = captured["triton-bitwise"]
+                r_out, r_lse = captured["reference-hip"]
+                row["triton_vs_reference"] = {
+                    "out_mismatched": _mismatch_count(t_out, r_out),
+                    "lse_mismatched": _mismatch_count(t_lse, r_lse),
+                    "out_relative_l2": _relative_l2(t_out, r_out),
+                    "bitwise": _bitwise_equal(t_out, r_out) and _bitwise_equal(t_lse, r_lse),
+                }
+            # The production core is a different vendor kernel; report the gap, do
+            # not claim parity with it.
+            if "strict-aiter" in captured and "reference-hip" in captured:
+                s_out, s_lse = captured["strict-aiter"]
+                r_out, r_lse = captured["reference-hip"]
+                row["strict_vs_reference"] = {
+                    "out": _accuracy(s_out, r_out),
+                    "lse": _accuracy(s_lse, r_lse),
+                    "out_mismatched": _mismatch_count(s_out, r_out),
+                }
+
+            cases.append(row)
+            del q, k, v, oracle_out, oracle_lse, captured
+            torch.cuda.empty_cache()
+    return cases
+
+
+def _training_runner(paths, name, q, k, v, *, causal, scale, positions):
+    """Return ``() -> None`` running one forward+backward, or None."""
+    if name == "sdpa":
+
+        def train_sdpa() -> None:
+            qr = q.detach().requires_grad_(True)
+            kr = k.detach().requires_grad_(True)
+            vr = v.detach().requires_grad_(True)
+            out = _sdpa_forward(qr, kr, vr, causal=causal, scale=scale)
+            out.sum().backward()
+
+        return train_sdpa
+
+    op = {
+        "strict-aiter": paths.strict,
+        "reference-hip": paths.reference,
+        "triton-bitwise": paths.triton,
+    }.get(name)
+    if op is None:
+        return None
+
+    def train_op() -> None:
+        qr = q.detach().requires_grad_(True)
+        kr = k.detach().requires_grad_(True)
+        vr = v.detach().requires_grad_(True)
+        kwargs: dict[str, Any] = {"causal": causal, "scale": scale}
+        if name == "strict-aiter" and causal:
+            kwargs["query_position_ids"] = positions
+            kwargs["key_position_ids"] = positions
+        result = op.forward_with_lse(qr, kr, vr, **kwargs)
+        out = result.out if hasattr(result, "out") else result[0]
+        out.sum().backward()
+
+    return train_op
+
+
+def _backward_parity(
+    *,
+    paths: _Paths,
+    seq_lens: tuple[int, ...],
+    q_heads: int,
+    kv_heads: int,
+    head_dim: int,
+    batch: int,
+    device: torch.device,
+) -> list[dict[str, Any]]:
+    """dQ/dK/dV bitwise parity, Triton port versus the native reference core."""
+    if paths.reference is None or paths.triton is None:
+        return []
+    rows = []
+    scale = 1.0 / math.sqrt(head_dim)
+    for seq_len in seq_lens:
+        q, k, v = _seeded_qkv(
+            batch, q_heads, kv_heads, seq_len, head_dim, torch.bfloat16, device, seed=99
+        )
+        grad_out = torch.randn(
+            batch,
+            q_heads,
+            seq_len,
+            head_dim,
+            device=device,
+            dtype=torch.bfloat16,
+            generator=torch.Generator(device=device).manual_seed(100),
+        )
+        grads = {}
+        for name, op in (("reference-hip", paths.reference), ("triton-bitwise", paths.triton)):
+            qr = q.detach().requires_grad_(True)
+            kr = k.detach().requires_grad_(True)
+            vr = v.detach().requires_grad_(True)
+            out, _lse = op.forward_with_lse(qr, kr, vr, causal=True, scale=scale)
+            out.backward(grad_out)
+            grads[name] = (qr.grad.clone(), kr.grad.clone(), vr.grad.clone())
+        reference = grads["reference-hip"]
+        triton_grads = grads["triton-bitwise"]
+        rows.append(
+            {
+                "seq_len": seq_len,
+                "dq_mismatched": _mismatch_count(triton_grads[0], reference[0]),
+                "dk_mismatched": _mismatch_count(triton_grads[1], reference[1]),
+                "dv_mismatched": _mismatch_count(triton_grads[2], reference[2]),
+                "bitwise": all(_bitwise_equal(t, r) for t, r in zip(triton_grads, reference)),
+            }
+        )
+        del q, k, v, grad_out, grads
+        torch.cuda.empty_cache()
+    return rows
+
+
+def _batch_composition(
+    *,
+    paths: _Paths,
+    seq_lens: tuple[int, ...],
+    q_heads: int,
+    kv_heads: int,
+    head_dim: int,
+    device: torch.device,
+) -> list[dict[str, Any]]:
+    """A row computed alone must be bitwise equal to the same row inside a batch.
+
+    The strict ROCm core refuses ``B > 1`` outright (``_validate_inputs``: "executes
+    one logical batch row at a time"), so for that path the property is structural
+    rather than measured, and the row records that instead of a comparison.
+    """
+    rows = []
+    scale = 1.0 / math.sqrt(head_dim)
+    for seq_len in seq_lens:
+        q, k, v = _seeded_qkv(
+            4, q_heads, kv_heads, seq_len, head_dim, torch.bfloat16, device, seed=7
+        )
+        positions_batch = _positions(4, seq_len, device)
+        positions_one = _positions(1, seq_len, device)
+        row: dict[str, Any] = {"seq_len": seq_len, "paths": {}}
+        for name in PATH_NAMES:
+            if name == "strict-aiter":
+                if paths.strict is not None:
+                    row["paths"][name] = {
+                        "batch_gt1_rejected": True,
+                        "out_bitwise": True,
+                        "out_mismatched": 0,
+                        "note": "core executes one logical batch row per launch",
+                    }
+                continue
+            batched = paths.runner(
+                name, q, k, v, causal=True, scale=scale, positions=positions_batch
+            )
+            single = paths.runner(
+                name,
+                q[2:3].contiguous(),
+                k[2:3].contiguous(),
+                v[2:3].contiguous(),
+                causal=True,
+                scale=scale,
+                positions=positions_one,
+            )
+            if batched is None or single is None:
+                continue
+            batch_out, batch_lse = batched()
+            single_out, single_lse = single()
+            row["paths"][name] = {
+                "batch_gt1_rejected": False,
+                "out_bitwise": _bitwise_equal(single_out[0], batch_out[2].contiguous()),
+                "out_mismatched": _mismatch_count(single_out[0], batch_out[2].contiguous()),
+                "out_max_abs": _accuracy(single_out[0], batch_out[2])["max_abs"],
+                "lse_bitwise": (
+                    None
+                    if batch_lse is None
+                    else _bitwise_equal(single_lse[0], batch_lse[2].contiguous())
+                ),
+            }
+            del batch_out, batch_lse, single_out, single_lse
+            torch.cuda.empty_cache()
+        rows.append(row)
+        del q, k, v
+        torch.cuda.empty_cache()
+    return rows
+
+
+def _tp_head_sensitivity(
+    *,
+    paths: _Paths,
+    seq_lens: tuple[int, ...],
+    tp_degrees: tuple[int, ...],
+    q_heads: int,
+    kv_heads: int,
+    head_dim: int,
+    device: torch.device,
+) -> list[dict[str, Any]]:
+    """Is a head shard under TP=N bitwise equal to the same slice of an unsharded run?
+
+    TP performs no cross-rank reduction in attention, so any nonzero value here
+    means the kernel's arithmetic depends on how many heads shared the launch.
+    Measured both on the raw production core and through the per-KV-group launch
+    schedule that the Vime provider uses.
+    """
+    rows: list[dict[str, Any]] = []
+    scale = 1.0 / math.sqrt(head_dim)
+    for seq_len in seq_lens:
+        q, k, v = _seeded_qkv(
+            1, q_heads, kv_heads, seq_len, head_dim, torch.bfloat16, device, seed=21
+        )
+        positions = _positions(1, seq_len, device)
+
+        for schedule in ("raw_launch", "one_kv_group_per_launch"):
+            full = _tp_schedule_forward(paths, q, k, v, scale, positions, schedule)
+            if full is None:
+                continue
+            full_out, full_lse = full
+            for tp in tp_degrees:
+                if q_heads % tp or kv_heads % tp:
+                    continue
+                local_q = q_heads // tp
+                local_kv = kv_heads // tp
+                shard = _tp_schedule_forward(
+                    paths,
+                    q[:, :local_q],
+                    k[:, :local_kv],
+                    v[:, :local_kv],
+                    scale,
+                    positions,
+                    schedule,
+                )
+                shard_out, shard_lse = shard
+                rows.append(
+                    {
+                        "seq_len": seq_len,
+                        "schedule": schedule,
+                        "tp": tp,
+                        "local_q_heads": local_q,
+                        "local_kv_heads": local_kv,
+                        "out_max_abs": _accuracy(shard_out, full_out[:, :local_q])["max_abs"],
+                        "lse_max_abs": _accuracy(shard_lse, full_lse[:, :local_q])["max_abs"],
+                        "invariant": _bitwise_equal(shard_out, full_out[:, :local_q].contiguous())
+                        and _bitwise_equal(shard_lse, full_lse[:, :local_q].contiguous()),
+                    }
+                )
+                del shard_out, shard_lse
+                torch.cuda.empty_cache()
+            del full_out, full_lse
+            torch.cuda.empty_cache()
+        del q, k, v
+        torch.cuda.empty_cache()
+    return rows
+
+
+def _tp_schedule_forward(paths, q, k, v, scale, positions, schedule):
+    """Run the strict core either in one launch or one launch per KV group."""
+    if paths.strict is None:
+        return None
+    if schedule == "raw_launch":
+        result = paths.strict.forward_with_lse(
+            q,
+            k,
+            v,
+            causal=True,
+            scale=scale,
+            query_position_ids=positions,
+            key_position_ids=positions,
+        )
+        return result.out.contiguous(), result.lse.contiguous()
+
+    group = q.size(1) // k.size(1)
+    outs, lses = [], []
+    for kv_index in range(k.size(1)):
+        lo, hi = kv_index * group, (kv_index + 1) * group
+        result = paths.strict.forward_with_lse(
+            q[:, lo:hi],
+            k[:, kv_index : kv_index + 1],
+            v[:, kv_index : kv_index + 1],
+            causal=True,
+            scale=scale,
+            query_position_ids=positions,
+            key_position_ids=positions,
+        )
+        outs.append(result.out)
+        lses.append(result.lse)
+    return torch.cat(outs, dim=1).contiguous(), torch.cat(lses, dim=1).contiguous()
+
+
+# ---------------------------------------------------------------------------
+# Distributed CP (spawned world; harness shape from PR #325)
+# ---------------------------------------------------------------------------
+
+
+def _distributed_cp_worker(
+    rank: int,
+    world_size: int,
+    topology: tuple[str, int, int, int],
+    init_method: str,
+    result_queue: Any,
+    warmup: int,
+    samples: int,
+    seq_len: int,
+    q_heads: int,
+    kv_heads: int,
+    head_dim: int,
+) -> None:
+    try:
+        torch.cuda.set_device(rank)
+        dist.init_process_group(
+            backend="nccl",
+            init_method=init_method,
+            rank=rank,
+            world_size=world_size,
+            device_id=torch.device("cuda", rank),
+            timeout=timedelta(minutes=20),
+        )
+        payload = _distributed_cp_case(
+            rank,
+            world_size,
+            topology,
+            warmup=warmup,
+            samples=samples,
+            seq_len=seq_len,
+            q_heads=q_heads,
+            kv_heads=kv_heads,
+            head_dim=head_dim,
+        )
+        if rank == 0:
+            result_queue.put({"ok": True, "topology": topology[0], "payload": payload})
+    except Exception:
+        result_queue.put(
+            {
+                "ok": False,
+                "rank": rank,
+                "topology": topology[0],
+                "traceback": traceback.format_exc(),
+            }
+        )
+        raise
+    finally:
+        if dist.is_available() and dist.is_initialized():
+            dist.destroy_process_group()
+
+
+def _distributed_cp_case(
+    rank: int,
+    world_size: int,
+    topology: tuple[str, int, int],
+    *,
+    warmup: int,
+    samples: int,
+    seq_len: int,
+    q_heads: int,
+    kv_heads: int,
+    head_dim: int,
+) -> dict[str, Any]:
+    """One CP topology running the real AG/RS schedule over the RCCL transport.
+
+    Schedule (same one ``scripts/ws2_p2p_nccl_attention_reference_check.py`` accepts):
+    all-gather Q/K/V and the position ids over the CP group, run the strict core
+    once on the full sequence, then reduce-scatter the ``(out, lse)`` result back
+    to this rank's query range. Bitwise acceptance is against a CP=1 run of the
+    same core on the same full-sequence inputs.
+    """
+    from rl_engine.kernels.ops.cuda.attention.cp_comm import (
+        AttentionCPBlockMetadata,
+        AttentionCPCommunicationPlan,
+        AttentionParallelSpec,
+        RCCLAGRSAttentionCPCommunication,
+    )
+    from rl_engine.kernels.ops.rocm.attention.flash_attn import StrictRocmAiterCKAttentionCore
+
+    label, tp_world, cp_world, replicas = topology
+    device = torch.device("cuda", rank)
+    scale = 1.0 / math.sqrt(head_dim)
+    chunk_size = seq_len // (cp_world * 2)
+
+    # Ranks that share a TP index form one CP group. Every rank must call
+    # new_group for every group, in the same order.
+    group_index = rank // cp_world
+    tp_index = group_index % tp_world
+    replica_index = group_index // tp_world
+    cp_rank = rank % cp_world
+    cp_group = None
+    for slice_index in range(world_size // cp_world):
+        ranks = list(range(slice_index * cp_world, (slice_index + 1) * cp_world))
+        group = dist.new_group(ranks=ranks)
+        if slice_index == group_index:
+            cp_group = group
+
+    local_q_heads = q_heads // tp_world
+    local_kv_heads = kv_heads // tp_world
+
+    generator = torch.Generator(device="cpu").manual_seed(2357 + tp_index + 100 * replica_index)
+    q = torch.randn(
+        1, local_q_heads, seq_len, head_dim, generator=generator, dtype=torch.bfloat16
+    ).to(device)
+    k = torch.randn(
+        1, local_kv_heads, seq_len, head_dim, generator=generator, dtype=torch.bfloat16
+    ).to(device)
+    v = torch.randn(
+        1, local_kv_heads, seq_len, head_dim, generator=generator, dtype=torch.bfloat16
+    ).to(device)
+    positions = _positions(1, seq_len, device).to(torch.int32)
+
+    span = seq_len // cp_world
+    owner_ranges = tuple((i * span, (i + 1) * span) for i in range(cp_world))
+    blocks: list[AttentionCPBlockMetadata] = []
+    for owner, (owner_start, owner_end) in enumerate(owner_ranges):
+        for start in range(owner_start, owner_end, chunk_size):
+            blocks.append(
+                AttentionCPBlockMetadata(
+                    global_block_index=len(blocks),
+                    kv_block_start=start,
+                    kv_block_end=min(start + chunk_size, owner_end),
+                    owner_cp_rank=owner,
+                    owner_tp_rank=tp_index,
+                )
+            )
+    plan = AttentionCPCommunicationPlan(
+        parallel=AttentionParallelSpec(
+            tp_world_size=tp_world,
+            tp_rank=tp_index,
+            cp_world_size=cp_world,
+            cp_rank=cp_rank,
+        ),
+        backend="rccl_ag_rs",
+        status="implemented",
+        expected_blocks=tuple(blocks),
+        expected_kv_token_range=(0, seq_len),
+        query_token_ranges=owner_ranges,
+    )
+
+    core = StrictRocmAiterCKAttentionCore()
+    communication = RCCLAGRSAttentionCPCommunication(process_group=cp_group)
+
+    query_start, query_end = owner_ranges[cp_rank]
+    q_local = q[:, :, query_start:query_end, :].contiguous()
+    k_local = k[:, :, query_start:query_end, :].contiguous()
+    v_local = v[:, :, query_start:query_end, :].contiguous()
+    positions_local = positions[:, query_start:query_end].contiguous()
+
+    def cp_forward():
+        q_full = communication.all_gather_query(q_local, plan)
+        k_full, v_full = communication.all_gather_kv(k_local, v_local, plan)
+        query_positions, key_positions = communication.all_gather_position_ids(
+            positions_local, positions_local, plan
+        )
+        result = core.forward_with_lse(
+            q_full,
+            k_full,
+            v_full,
+            causal=True,
+            scale=scale,
+            query_position_ids=query_positions,
+            key_position_ids=key_positions,
+        )
+        return communication.reduce_scatter_strict_result(result.out, result.lse, plan)
+
+    shard = cp_forward()
+    forward_ms = _summary_ms(
+        _gpu_event_samples(lambda: cp_forward(), warmup=warmup, samples=samples)
+    )
+    peak = _peak_memory_mib(lambda: cp_forward())
+
+    # CP=1 acceptance: the same core on the same full-sequence inputs, then take
+    # this rank's query range out of it.
+    single = core.forward_with_lse(
+        q,
+        k,
+        v,
+        causal=True,
+        scale=scale,
+        query_position_ids=positions,
+        key_position_ids=positions,
+    )
+    expected_out = single.out[:, :, query_start:query_end, :].contiguous()
+    expected_lse = single.lse[:, :, query_start:query_end].contiguous()
+
+    out_bitwise = _bitwise_equal(shard.out.contiguous(), expected_out)
+    lse_bitwise = _bitwise_equal(shard.lse.contiguous(), expected_lse)
+    repeat = cp_forward()
+    repeat_bitwise = _bitwise_equal(shard.out.contiguous(), repeat.out.contiguous())
+
+    flags = torch.tensor(
+        [
+            1.0 if out_bitwise else 0.0,
+            1.0 if lse_bitwise else 0.0,
+            1.0 if repeat_bitwise else 0.0,
+        ],
+        device=device,
+    )
+    dist.all_reduce(flags, op=dist.ReduceOp.MIN)
+    mismatches = torch.tensor(
+        [
+            float(_mismatch_count(shard.out.contiguous(), expected_out)),
+            float(_mismatch_count(shard.lse.contiguous(), expected_lse)),
+        ],
+        device=device,
+    )
+    dist.all_reduce(mismatches, op=dist.ReduceOp.SUM)
+
+    return {
+        "topology": label,
+        "world_size": world_size,
+        "tp_world_size": tp_world,
+        "cp_world_size": cp_world,
+        "replicas": replicas,
+        "seq_len": seq_len,
+        "local_q_heads": local_q_heads,
+        "local_kv_heads": local_kv_heads,
+        "transport": "rccl_ag_rs",
+        "forward": forward_ms,
+        "peak_mib_per_rank": peak,
+        "out_bitwise_vs_cp1": bool(flags[0].item() == 1.0),
+        "lse_bitwise_vs_cp1": bool(flags[1].item() == 1.0),
+        "repeat_bitwise": bool(flags[2].item() == 1.0),
+        "out_mismatched_all_ranks": int(mismatches[0].item()),
+        "lse_mismatched_all_ranks": int(mismatches[1].item()),
+    }
+
+
+def _run_distributed_topology(
+    topology: tuple[str, int, int, int],
+    *,
+    warmup: int,
+    samples: int,
+    seq_len: int,
+    q_heads: int,
+    kv_heads: int,
+    head_dim: int,
+) -> dict[str, Any]:
+    _label, tp_world, cp_world, replicas = topology
+    world_size = tp_world * cp_world * replicas
+    context = mp.get_context("spawn")
+    with tempfile.TemporaryDirectory() as temporary_directory:
+        init_method = (Path(temporary_directory) / "rccl_init").as_uri()
+        result_queue = context.Queue()
+        processes = [
+            context.Process(
+                target=_distributed_cp_worker,
+                args=(
+                    rank,
+                    world_size,
+                    topology,
+                    init_method,
+                    result_queue,
+                    warmup,
+                    samples,
+                    seq_len,
+                    q_heads,
+                    kv_heads,
+                    head_dim,
+                ),
+            )
+            for rank in range(world_size)
+        ]
+        for process in processes:
+            process.start()
+        result = None
+        try:
+            result = result_queue.get(timeout=1800)
+        except queue.Empty as exc:
+            for process in processes:
+                if process.is_alive():
+                    process.terminate()
+            raise RuntimeError(f"timed out waiting for {topology[0]}") from exc
+        finally:
+            for process in processes:
+                process.join(timeout=90)
+                if process.is_alive():
+                    process.terminate()
+                    process.join(timeout=30)
+            result_queue.close()
+            result_queue.join_thread()
+    if result is None or not result["ok"]:
+        raise RuntimeError((result or {}).get("traceback", f"{topology[0]} returned no result"))
+    return result["payload"]
+
+
+# ---------------------------------------------------------------------------
+# Environment, report and figures
+# ---------------------------------------------------------------------------
+
+
+def _environment() -> dict[str, Any]:
+    properties = torch.cuda.get_device_properties(0) if torch.cuda.is_available() else None
+    try:
+        from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
+
+        symbols = sorted(s for s in dir(_C) if "attention" in s) if _EXT_AVAILABLE else []
+    except Exception:  # noqa: BLE001
+        symbols = []
+    try:
+        import triton
+
+        triton_version = triton.__version__
+    except Exception:  # noqa: BLE001
+        triton_version = "unavailable"
+    return {
+        "gpu": properties.name if properties else "n/a",
+        "architecture": getattr(properties, "gcnArchName", "unknown") if properties else "n/a",
+        "gpu_count": torch.cuda.device_count() if torch.cuda.is_available() else 0,
+        "hip": torch.version.hip,
+        "cuda": torch.version.cuda,
+        "torch": torch.__version__,
+        "triton": triton_version,
+        "python": platform.python_version(),
+        "extension_attention_symbols": symbols,
+        "native_collective": "torch.distributed ProcessGroupNCCL (RCCL on ROCm)",
+    }
+
+
+def _fmt(value: Any, spec: str = ".4f") -> str:
+    if value is None:
+        return "n/a"
+    if isinstance(value, bool):
+        return "yes" if value else "**no**"
+    if isinstance(value, (int,)) and not isinstance(value, bool):
+        return str(value)
+    try:
+        return format(float(value), spec)
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
+    environment = payload["environment"]
+    configuration = payload["configuration"]
+    lines: list[str] = []
+    add = lines.append
+
+    add("# WS2 strict ROCm Attention — bitwise parity and performance")
+    add("")
+    add("> Operator-only benchmark. No model checkpoint or serving engine was used;")
+    add("> the shapes are Qwen3-8B's attention shapes.")
+    add("")
+    add("## Environment")
+    add("")
+    add("| Item | Value |")
+    add("|---|---|")
+    for key in sorted(environment):
+        value = environment[key]
+        add(f"| {key} | {value if not isinstance(value, list) else ', '.join(value) or 'none'} |")
+    add("")
+
+    add("## Methodology")
+    add("")
+    add(
+        f"- Operator shape: `Hq={configuration['q_heads']}`, `Hkv={configuration['kv_heads']}`, "
+        f"`D={configuration['head_dim']}`, `B={configuration['batch']}`, causal; sequence sweep "
+        + ", ".join(str(s) for s in configuration["seq_lens"])
+        + "."
+    )
+    add("- Measured paths:")
+    add(
+        "  - `sdpa`: `torch.nn.functional.scaled_dot_product_attention`. **Speed baseline only** — "
+        "as in PR #325, no accuracy comparison is mixed into the speed table."
+    )
+    add(
+        "  - `strict-aiter`: `StrictRocmAiterCKAttentionCore`, the ROCm production core "
+        "(AITER CK dense MHA, non-split API, one logical batch row per launch)."
+    )
+    add(
+        "  - `reference-hip`: `_C.deterministic_attention_forward/backward`, the materializing "
+        "FP32 reference core hipified from the shared `.cu`."
+    )
+    add(
+        "  - `triton-bitwise`: `TritonDeterministicAttentionOp`, whose contract is bit-identity "
+        "with `reference-hip`."
+    )
+    add(
+        "- Timing: CUDA events, median and p95. Peak memory is the per-call increase in "
+        "`torch.cuda.max_memory_allocated` above what was live before the call."
+    )
+    add(
+        "- Accuracy is against an FP64 oracle over the same BF16/FP16-rounded inputs. "
+        "Repeat = two identical calls are bitwise equal; batch-invariant = a row computed "
+        "alone is bitwise equal to the same row inside a batch."
+    )
+    add(
+        f"- {configuration['warmup']} warmups, {configuration['samples']} measured forward "
+        f"samples, {configuration['training_samples']} measured forward+backward samples. "
+        "Raw medians, p95, min and max are in `results.json`."
+    )
+    add("")
+    add("Reproduce from the repository root:")
+    add("")
+    add("```bash")
+    add("python benchmarks/benchmark_ws2_rocm_attention.py \\")
+    add(f"  --seq-lens {','.join(str(s) for s in configuration['seq_lens'])} \\")
+    add(f"  --dtypes {','.join(configuration['dtypes'])} \\")
+    add(f"  --warmup {configuration['warmup']} --samples {configuration['samples']} \\")
+    add(f"  --training-samples {configuration['training_samples']} \\")
+    add("  --output-dir benchmarks/results/ws2_rocm_mi300x")
+    add("```")
+    add("")
+
+    if payload.get("unavailable_paths"):
+        add("### Unavailable paths")
+        add("")
+        for name, reason in payload["unavailable_paths"].items():
+            add(f"- `{name}`: {reason}")
+        add("")
+
+    # ---- headline
+    add("## Bitwise parity: Triton port vs the native reference core")
+    add("")
+    add("Acceptance is 0 mismatched elements. This is the contract the Triton core exists to hold.")
+    add("")
+    add("| dtype | S | out mismatched | lse mismatched | dQ | dK | dV | bitwise |")
+    add("|---|---:|---:|---:|---:|---:|---:|:---:|")
+    backward = {row["seq_len"]: row for row in payload.get("backward_parity", [])}
+    for case in payload["single_gpu"]["cases"]:
+        parity = case.get("triton_vs_reference")
+        if not parity:
+            continue
+        grads = backward.get(case["seq_len"], {}) if case["dtype"] == "bf16" else {}
+        add(
+            f"| {case['dtype']} | {case['seq_len']} | {parity['out_mismatched']} | "
+            f"{parity['lse_mismatched']} | {_fmt(grads.get('dq_mismatched'))} | "
+            f"{_fmt(grads.get('dk_mismatched'))} | {_fmt(grads.get('dv_mismatched'))} | "
+            f"{_fmt(parity['bitwise'])} |"
+        )
+    add("")
+    add("`dQ/dK/dV` are measured on the BF16 sweep only; `n/a` marks the FP16 rows.")
+    add("")
+
+    # ---- single GPU speed
+    for dtype in configuration["dtypes"]:
+        cases = [c for c in payload["single_gpu"]["cases"] if c["dtype"] == dtype]
+        if not cases:
+            continue
+        add(f"## Single-GPU Attention ({dtype})")
+        add("")
+        add("### Forward")
+        add("")
+        add(
+            "| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | "
+            "lse max-abs vs FP64 | Repeat |"
+        )
+        add("|---:|---|---:|---:|---:|---:|---:|---:|:---:|")
+        for case in cases:
+            baseline = case["paths"].get("sdpa", {}).get("forward", {}).get("median_ms")
+            for name in PATH_NAMES:
+                entry = case["paths"].get(name)
+                if not entry:
+                    continue
+                median = entry["forward"]["median_ms"]
+                ratio = f"{median / baseline:.2f}x" if baseline else "n/a"
+                add(
+                    f"| {case['seq_len']} | {name} | {median:.4f} | "
+                    f"{entry['forward']['p95_ms']:.4f} | {ratio} | "
+                    f"{entry['forward_peak_mib']:.1f} | "
+                    f"{entry['out_vs_fp64']['max_abs']:.3e} | "
+                    f"{_fmt(entry.get('lse_vs_fp64', {}).get('max_abs'), '.3e')} | "
+                    f"{_fmt(entry['repeat_bitwise'])} |"
+                )
+        add("")
+        add("### Forward+backward")
+        add("")
+        add("| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |")
+        add("|---:|---|---:|---:|---:|---:|")
+        for case in cases:
+            baseline = case["paths"].get("sdpa", {}).get("train_fwd_bwd", {}).get("median_ms")
+            for name in PATH_NAMES:
+                entry = case["paths"].get(name)
+                if not entry or "train_fwd_bwd" not in entry:
+                    continue
+                median = entry["train_fwd_bwd"]["median_ms"]
+                ratio = f"{median / baseline:.2f}x" if baseline else "n/a"
+                add(
+                    f"| {case['seq_len']} | {name} | {median:.4f} | "
+                    f"{entry['train_fwd_bwd']['p95_ms']:.4f} | {ratio} | "
+                    f"{entry['train_peak_mib']:.1f} |"
+                )
+        add("")
+
+    # ---- production core vs reference
+    add("## Production core versus the reference core")
+    add("")
+    add(
+        "These are two different kernels, so this is a tolerance comparison, not a parity "
+        "claim. It is here to size the gap, not to assert equality."
+    )
+    add("")
+    add("| dtype | S | out max-abs | out relative-L2 | lse max-abs |")
+    add("|---|---:|---:|---:|---:|")
+    for case in payload["single_gpu"]["cases"]:
+        gap = case.get("strict_vs_reference")
+        if not gap:
+            continue
+        add(
+            f"| {case['dtype']} | {case['seq_len']} | {gap['out']['max_abs']:.3e} | "
+            f"{gap['out']['relative_l2']:.3e} | {gap['lse']['max_abs']:.3e} |"
+        )
+    add("")
+
+    # ---- batch composition
+    add("## Batch-composition invariance")
+    add("")
+    add(
+        "A row computed alone must be bitwise equal to the same row inside a batch. "
+        "The strict ROCm core rejects `B > 1` outright, so for that path the property is "
+        "structural rather than measured."
+    )
+    add("")
+    add("| S | Path | Bitwise | Mismatched | Note |")
+    add("|---:|---|:---:|---:|---|")
+    for row in payload.get("batch_composition", []):
+        for name, entry in row["paths"].items():
+            note = entry.get("note", "measured")
+            add(
+                f"| {row['seq_len']} | {name} | {_fmt(entry['out_bitwise'])} | "
+                f"{entry['out_mismatched']} | {note} |"
+            )
+    add("")
+
+    # ---- TP degree
+    add("## TP-degree invariance of the strict ROCm core")
+    add("")
+    add(
+        "A head shard computed under TP=N versus the same slice of an unsharded run. TP performs "
+        "no cross-rank reduction in attention, so any nonzero value means the kernel's result "
+        "depends on how many heads shared the launch. `raw_launch` is one launch for all heads; "
+        "`one_kv_group_per_launch` is the schedule the Vime provider actually uses."
+    )
+    add("")
+    add("| S | Schedule | TP | Local Hq | Local Hkv | out max-abs | lse max-abs | Invariant |")
+    add("|---:|---|---:|---:|---:|---:|---:|:---:|")
+    for row in payload.get("tp_head_sensitivity", []):
+        add(
+            f"| {row['seq_len']} | {row['schedule']} | {row['tp']} | {row['local_q_heads']} | "
+            f"{row['local_kv_heads']} | {row['out_max_abs']:.6e} | {row['lse_max_abs']:.6e} | "
+            f"{_fmt(row['invariant'])} |"
+        )
+    add("")
+
+    # ---- distributed
+    distributed = payload.get("distributed") or []
+    if distributed:
+        add("## Distributed CP (RCCL AG/RS transport)")
+        add("")
+        add(
+            "Schedule: all-gather Q/K/V and the position ids over the CP group, run the strict "
+            "core once on the full sequence, reduce-scatter `(out, lse)` back to this rank's "
+            "query range. Acceptance is bitwise against a CP=1 run of the same core."
+        )
+        add("")
+        add(
+            "| Topology | World | TP | CP | Replicas | S | Median (ms) | p95 (ms) | "
+            "Peak MiB/rank | out bitwise | lse bitwise | Repeat |"
+        )
+        add("|---|---:|---:|---:|---:|---:|---:|---:|---:|:---:|:---:|:---:|")
+        for row in distributed:
+            if "error" in row:
+                add(
+                    f"| {row['topology']} | — | — | — | — | — | — | — | — | "
+                    "error | error | error |"
+                )
+                continue
+            add(
+                f"| {row['topology']} | {row['world_size']} | {row['tp_world_size']} | "
+                f"{row['cp_world_size']} | {row.get('replicas', 1)} | {row['seq_len']} | "
+                f"{row['forward']['median_ms']:.4f} | "
+                f"{row['forward']['p95_ms']:.4f} | {row['peak_mib_per_rank']:.1f} | "
+                f"{_fmt(row['out_bitwise_vs_cp1'])} | {_fmt(row['lse_bitwise_vs_cp1'])} | "
+                f"{_fmt(row['repeat_bitwise'])} |"
+            )
+        add("")
+        errors = [row for row in distributed if "error" in row]
+        for row in errors:
+            add(f"- `{row['topology']}` failed: {row['error']}")
+        if errors:
+            add("")
+
+    add("## Figures")
+    add("")
+    add(
+        "`reference-hip` and `triton-bitwise` allocate exactly the same buffers, so their "
+        "memory curves coincide and the later-drawn series hides the earlier one."
+    )
+    add("")
+    add("![Single-device latency and memory grid](single_gpu_grid.png)")
+    add("")
+    add("![Single-device latency](single_gpu_latency.png)")
+    add("")
+    add("![Single-device peak memory](single_gpu_memory.png)")
+    add("")
+    add("![TP-degree invariance](tp_degree_invariance.png)")
+    add("")
+    if distributed:
+        add("![Distributed CP latency](distributed_cp_latency.png)")
+        add("")
+
+    (output_directory / "report.md").write_text("\n".join(lines) + "\n")
+
+
+def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    plt.style.use("seaborn-v0_8-whitegrid")
+    plt.rcParams.update({"font.size": 10, "axes.titlesize": 11, "legend.fontsize": 8})
+
+    style = {
+        "sdpa": {"marker": "o", "color": "#888888", "linestyle": "--"},
+        "strict-aiter": {"marker": "s", "color": "#d62728"},
+        "reference-hip": {"marker": "^", "color": "#1f77b4"},
+        "triton-bitwise": {"marker": "D", "color": "#2ca02c"},
+    }
+
+    cases = [c for c in payload["single_gpu"]["cases"] if c["dtype"] == "bf16"]
+    if not cases:
+        return
+    seq_lens = sorted({c["seq_len"] for c in cases})
+    present = [n for n in PATH_NAMES if any(n in c["paths"] for c in cases)]
+
+    panels = (
+        ("forward", "median_ms", "Forward latency", "median ms", True),
+        ("train_fwd_bwd", "median_ms", "Forward+backward latency", "median ms", True),
+        ("forward_peak_mib", None, "Forward peak memory", "peak MiB above live", True),
+        ("train_peak_mib", None, "Forward+backward peak memory", "peak MiB above live", True),
+    )
+
+    def value(case, name, key, sub):
+        entry = case["paths"].get(name)
+        if entry is None or key not in entry:
+            return float("nan")
+        return entry[key][sub] if sub else entry[key]
+
+    def draw(axis, key, sub, title, ylabel, log_y):
+        for index, name in enumerate(present):
+            ys = [
+                value(next(c for c in cases if c["seq_len"] == s), name, key, sub) for s in seq_lens
+            ]
+            axis.plot(
+                seq_lens,
+                ys,
+                label=name,
+                linewidth=3.0 - 0.35 * index,
+                markersize=7 - 0.5 * index,
+                zorder=3 + index,
+                **style.get(name, {}),
+            )
+        axis.set_xscale("log", base=2)
+        if log_y:
+            axis.set_yscale("log")
+        axis.set_xlabel("sequence length")
+        axis.set_ylabel(ylabel)
+        axis.set_title(f"BF16: {title}")
+        axis.grid(True, which="both", alpha=0.3)
+        axis.legend()
+
+    for filename, chosen in (
+        ("single_gpu_latency.png", panels[:2]),
+        ("single_gpu_memory.png", panels[2:]),
+    ):
+        figure, axes = plt.subplots(1, 2, figsize=(12, 4.5))
+        for axis, (key, sub, title, ylabel, log_y) in zip(axes, chosen):
+            draw(axis, key, sub, title, ylabel, log_y)
+        figure.tight_layout()
+        figure.savefig(output_directory / filename, dpi=180)
+        plt.close(figure)
+
+    figure, axes = plt.subplots(2, 2, figsize=(13, 9))
+    for axis, (key, sub, title, ylabel, log_y) in zip(axes.flat, panels):
+        draw(axis, key, sub, title, ylabel, log_y)
+    figure.suptitle(
+        "Single-device strict Attention, BF16, Qwen3-8B shape "
+        f"(Hq={QWEN3_8B_Q_HEADS}, Hkv={QWEN3_8B_KV_HEADS}, D={QWEN3_8B_HEAD_DIM})",
+        fontsize=12,
+    )
+    figure.tight_layout(rect=(0, 0, 1, 0.96))
+    figure.savefig(output_directory / "single_gpu_grid.png", dpi=180)
+    plt.close(figure)
+
+    # TP head-count sensitivity: raw launch versus one KV group per launch.
+    tp_rows = payload.get("tp_head_sensitivity") or []
+    if tp_rows:
+        figure, axis = plt.subplots(figsize=(11, 5))
+        for schedule, marker in (("raw_launch", "o"), ("one_kv_group_per_launch", "s")):
+            rows = [r for r in tp_rows if r["schedule"] == schedule]
+            if not rows:
+                continue
+            labels = [f"S={r['seq_len']}\nTP={r['tp']}" for r in rows]
+            axis.plot(
+                range(len(rows)),
+                [max(r["out_max_abs"], 1e-12) for r in rows],
+                marker=marker,
+                label=schedule,
+                linewidth=2.4,
+            )
+        axis.set_xticks(range(len([r for r in tp_rows if r["schedule"] == "raw_launch"])))
+        axis.set_xticklabels(
+            [f"S={r['seq_len']}\nTP={r['tp']}" for r in tp_rows if r["schedule"] == "raw_launch"],
+            fontsize=8,
+        )
+        axis.set_yscale("log")
+        axis.set_ylabel("out max-abs vs unsharded slice (1e-12 == bitwise)")
+        axis.set_title("TP-degree invariance of the strict ROCm core, BF16")
+        axis.grid(True, which="both", alpha=0.3)
+        axis.legend()
+        figure.tight_layout()
+        figure.savefig(output_directory / "tp_degree_invariance.png", dpi=180)
+        plt.close(figure)
+
+    distributed = payload.get("distributed") or []
+    if distributed:
+        figure, axis = plt.subplots(figsize=(max(8, 1.5 * len(distributed)), 4.8))
+        labels = [f"{r['topology']}\nS={r['seq_len']}" for r in distributed]
+        axis.bar(
+            range(len(distributed)),
+            [r["forward"]["median_ms"] for r in distributed],
+            color="#2ca02c",
+        )
+        axis.set_xticks(range(len(distributed)))
+        axis.set_xticklabels(labels, fontsize=9)
+        axis.set_ylabel("median ms")
+        axis.set_title("Strict ROCm Attention + RCCL AG/RS CP transport, BF16")
+        axis.grid(True, axis="y", alpha=0.3)
+        figure.tight_layout()
+        figure.savefig(output_directory / "distributed_cp_latency.png", dpi=180)
+        plt.close(figure)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--output-dir", type=Path, default=Path("benchmarks/results/ws2_rocm_mi300x")
+    )
+    parser.add_argument("--warmup", type=int, default=5)
+    parser.add_argument("--samples", type=int, default=20)
+    parser.add_argument("--training-samples", type=int, default=10)
+    parser.add_argument("--batch", type=int, default=1)
+    parser.add_argument("--q-heads", type=int, default=QWEN3_8B_Q_HEADS)
+    parser.add_argument("--kv-heads", type=int, default=QWEN3_8B_KV_HEADS)
+    parser.add_argument("--head-dim", type=int, default=QWEN3_8B_HEAD_DIM)
+    parser.add_argument(
+        "--seq-lens",
+        type=lambda s: tuple(int(x) for x in s.split(",")),
+        default=DEFAULT_SEQ_LENS,
+    )
+    parser.add_argument("--dtypes", default="bf16,fp16")
+    parser.add_argument("--skip-distributed", action="store_true")
+    parser.add_argument("--skip-figures", action="store_true")
+    parser.add_argument(
+        "--distributed-only",
+        action="store_true",
+        help="Re-run only the distributed topologies and merge into an existing results.json.",
+    )
+    parser.add_argument(
+        "--report-only",
+        action="store_true",
+        help="Re-render report.md and the figures from an existing results.json.",
+    )
+    arguments = parser.parse_args()
+
+    if arguments.report_only:
+        payload = json.loads((arguments.output_dir / "results.json").read_text())
+        _write_report(payload, arguments.output_dir)
+        if not arguments.skip_figures:
+            _write_figures(payload, arguments.output_dir)
+        print(json.dumps({"output_dir": str(arguments.output_dir)}, indent=2))
+        return
+
+    if not torch.cuda.is_available():
+        raise SystemExit("this benchmark requires a ROCm or CUDA GPU")
+
+    device = torch.device("cuda", 0)
+    torch.cuda.set_device(device)
+    dtype_map = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
+    dtypes = tuple(dtype_map[name] for name in arguments.dtypes.split(","))
+
+    paths = _Paths()
+    payload: dict[str, Any] = {
+        "environment": _environment(),
+        "configuration": {
+            "batch": arguments.batch,
+            "q_heads": arguments.q_heads,
+            "kv_heads": arguments.kv_heads,
+            "head_dim": arguments.head_dim,
+            "seq_lens": list(arguments.seq_lens),
+            "dtypes": arguments.dtypes.split(","),
+            "warmup": arguments.warmup,
+            "samples": arguments.samples,
+            "training_samples": arguments.training_samples,
+        },
+        "unavailable_paths": paths.errors,
+    }
+
+    existing: dict[str, Any] = {}
+    if arguments.distributed_only:
+        existing = json.loads((arguments.output_dir / "results.json").read_text())
+        payload = dict(existing)
+        payload["environment"] = _environment()
+
+    if not arguments.distributed_only:
+        payload["single_gpu"] = {
+            "cases": _single_gpu_benchmarks(
+                paths=paths,
+                seq_lens=arguments.seq_lens,
+                dtypes=dtypes,
+                q_heads=arguments.q_heads,
+                kv_heads=arguments.kv_heads,
+                head_dim=arguments.head_dim,
+                batch=arguments.batch,
+                warmup=arguments.warmup,
+                samples=arguments.samples,
+                training_samples=arguments.training_samples,
+                device=device,
+            )
+        }
+        payload["backward_parity"] = _backward_parity(
+            paths=paths,
+            seq_lens=arguments.seq_lens,
+            q_heads=arguments.q_heads,
+            kv_heads=arguments.kv_heads,
+            head_dim=arguments.head_dim,
+            batch=arguments.batch,
+            device=device,
+        )
+        payload["batch_composition"] = _batch_composition(
+            paths=paths,
+            seq_lens=arguments.seq_lens,
+            q_heads=arguments.q_heads,
+            kv_heads=arguments.kv_heads,
+            head_dim=arguments.head_dim,
+            device=device,
+        )
+        payload["tp_head_sensitivity"] = _tp_head_sensitivity(
+            paths=paths,
+            seq_lens=arguments.seq_lens,
+            tp_degrees=DEFAULT_TP_DEGREES,
+            q_heads=arguments.q_heads,
+            kv_heads=arguments.kv_heads,
+            head_dim=arguments.head_dim,
+            device=device,
+        )
+
+    distributed: list[dict[str, Any]] = []
+    if not arguments.skip_distributed:
+        available = torch.cuda.device_count()
+        for topology in DISTRIBUTED_TOPOLOGIES:
+            if topology[1] * topology[2] * topology[3] > available:
+                continue
+            try:
+                distributed.append(
+                    _run_distributed_topology(
+                        topology,
+                        warmup=arguments.warmup,
+                        samples=arguments.samples,
+                        seq_len=arguments.seq_lens[-1] if arguments.seq_lens else 2048,
+                        q_heads=arguments.q_heads,
+                        kv_heads=arguments.kv_heads,
+                        head_dim=arguments.head_dim,
+                    )
+                )
+            except Exception as exc:  # noqa: BLE001 - a failed topology is a reported row
+                distributed.append(
+                    {"topology": topology[0], "error": f"{type(exc).__name__}: {exc}"}
+                )
+    payload["distributed"] = distributed
+
+    arguments.output_dir.mkdir(parents=True, exist_ok=True)
+    (arguments.output_dir / "results.json").write_text(json.dumps(payload, indent=2) + "\n")
+    _write_report(payload, arguments.output_dir)
+    if not arguments.skip_figures:
+        _write_figures(payload, arguments.output_dir)
+    print(json.dumps({"output_dir": str(arguments.output_dir)}, indent=2))
+
+
+if __name__ == "__main__":
+    os.environ.setdefault("NCCL_IB_DISABLE", "1")
+    main()
diff --git a/benchmarks/results/ws2_rocm_mi300x/PR_DESCRIPTION.md b/benchmarks/results/ws2_rocm_mi300x/PR_DESCRIPTION.md
new file mode 100644
index 00000000..b187f07b
--- /dev/null
+++ b/benchmarks/results/ws2_rocm_mi300x/PR_DESCRIPTION.md
@@ -0,0 +1,292 @@
+# WS2: bitwise-exact Attention on ROCm
+
+Brings the strict ROCm Attention path to a stated, measured bitwise standard, and adds a
+Triton core that is bit-identical to the native reference kernel so the arithmetic
+contract is testable without the vendor kernel.
+
+Operator-only. No model checkpoint or serving engine is loaded anywhere in this PR; the
+shapes are Qwen3-8B's (`Hq=32`, `Hkv=8`, `D=128`). Measured on 8×MI300X (`gfx942`),
+ROCm 7.14.60850, torch 2.12.0, Triton 3.7.0.
+
+---
+
+## 1. What "bitwise" means here, and what it does not
+
+Several different guarantees get conflated in attention work, so this PR states which one
+it claims at each boundary:
+
+| Scope | Claim | Enforced by |
+| --- | --- | --- |
+| Varying batch composition | **bitwise** | `B > 1` rejected at the core (§2.2) |
+| Varying padding | **bitwise** | `key_padding_mask` rejected (§2.3) |
+| Varying TP degree | **bitwise** | one KV group per launch (§2.4) |
+| Varying CP degree | **bitwise** | RCCL-as-transport AG/RS (§2.5) |
+| Triton core vs native reference core | **bitwise** | §2.8, measured in §3.1 |
+| CUDA production core vs ROCm production core | **not claimed** | different vendor kernels |
+| ROCm production core vs reference core | **not claimed** | different kernels; gap sized in §3.4 |
+
+The last two rows are deliberate. CUDA runs FlashAttention 4 CuTe and ROCm runs AITER CK
+dense MHA; the tile decomposition, the online-softmax rescale order, and MFMA versus MMA
+accumulation all differ. Nothing in the tree claims cross-platform bit equality and this
+PR does not add such a claim. What is shared across platforms is the *contract*, not the
+bits.
+
+## 2. The algorithmic arrangements
+
+The strict ROCm core does not reimplement attention. It removes every source of
+run-to-run and shape-to-shape arithmetic variation from the vendor kernel and records what
+was removed, so a mismatch becomes a contract violation rather than a debugging session.
+
+**2.1 Split-KV is structurally impossible, not merely switched off.**
+Split-KV partitions the KV axis and merges partial softmax states; the partition count
+depends on shape and occupancy, so the reduction order moves with it. CUDA can pass
+`num_splits=1` to FA4. AITER exposes no such knob, so the ROCm core binds to the dense,
+non-split API entry point instead and records `split_kv_control = "dense_non_split_api"`.
+`SplitKVSpec` must be `DISABLED`; a non-disabled spec raises in `__init__` rather than
+being quietly honoured.
+
+**2.2 Batch composition cannot change the bits, because `B > 1` is rejected.**
+`StrictRocmAiterCKAttentionCore._validate_inputs` refuses any input with `q.size(0) != 1`:
+*"strict AITER CK core executes one logical batch row at a time"*. This is stronger than
+testing for batch invariance — there is no batched launch whose arithmetic could differ
+from the single-row launch, because the batched launch does not exist. Callers materialise
+each logical row separately.
+
+**2.3 Padding never enters a reduction.**
+`key_padding_mask` is rejected outright: the core *"materializes each unpadded logical
+row"*. A padded and an unpadded run of the same logical row cannot differ, because the
+padded run is not expressible.
+
+**2.4 One KV group per launch, to make the result independent of TP degree.**
+This is the ROCm-specific problem. AITER/CK's reduction order depends on how many heads
+shared the launch, and TP performs no cross-rank reduction in attention — it is pure head
+sharding — so a head shard computed under TP=4 was *not* bit-identical to the same shard
+under TP=8 at some shapes. The provider therefore launches the core once per
+`(batch row, KV group)` and concatenates, so every launch sees exactly one KV group and its
+Q heads regardless of the TP degree that produced the shard. §3.2 measures both schedules
+side by side; the cost is real and is reported.
+
+**2.5 RCCL is a transport, never a reduction.**
+`_RCCLRankOrderedTransport` uses RCCL only for `all_gather` and a root-owned `scatter`. Its
+`reduce_scatter` first gathers every source shard and then evaluates a fixed balanced rank
+tree locally, so the floating-point combine order is ours and does not depend on RCCL's
+internal algorithm selection, which varies with message size and topology.
+
+**2.6 The vendor kernel is fingerprinted, not version-pinned.**
+AITER dispatches in Python, so a package version does not pin behaviour.
+`_load_aiter_ck_ops()` takes a **sha256 of the `aiter.ops.mha` source file** and exports it
+as `aiter_source_sha256` in the provenance, so a silent upstream change to the dispatch
+logic invalidates the recorded arithmetic identity.
+
+**2.7 Fail closed, everywhere.**
+A missing AITER entry point, a missing native extension, or a dispatch that resolves to a
+different backend raises. No path substitutes a different kernel to keep a run alive.
+
+**2.8 A reference core shared with CUDA, and a Triton port that matches it bitwise.**
+`csrc/cuda/attention/deterministic_attention.cu` is hipified to
+`csrc/hip/attention/deterministic_attention.hip`, so the *reference* core genuinely is the
+same algorithm on both platforms. This PR adds
+`rl_engine/kernels/ops/triton/attention/deterministic_attn.py`, a Triton port whose
+contract is bit-identity with that reference. Three things had to be reproduced rather than
+re-derived:
+
+- **Dot products stay sequential FMA chains.** The C++ kernel accumulates one element at a
+  time in a single thread, so the contraction index is the *loop* and the head dim is the
+  *vector*. The opposite, much faster arrangement would reassociate the sum.
+- **The row softmax keeps the 256-lane partial layout.** Key `k` belongs to lane `k % 256`;
+  each lane sums ascending, then a stride-halving fold combines the partials.
+  `_tree_sum_256` reproduces that fold exactly.
+- **`expf`/`logf` are re-emitted instruction for instruction.** Every Triton exp/log
+  intrinsic — `tl.exp`, `tl.math.exp`, `libdevice.exp` — lowers to a bare `v_exp_f32`, about
+  1 ULP away from the `expf` the C++ kernel calls, which alone broke parity on ~14% of
+  elements. The helpers reproduce hipcc's two-term argument reduction around that same
+  hardware instruction, with an inline-asm barrier to stop LLVM refolding the reduction into
+  an FMA. Verified bitwise over 4M+ inputs including subnormals, ±inf and NaN.
+
+The nvcc `expf`/`logf` sequences are not ported, so on CUDA the op refuses to construct
+unless the caller passes `require_bitwise_libm=False`, rather than silently returning
+non-bitwise results.
+
+---
+
+## 3. Results
+
+Full report, `results.json` and figures: `benchmarks/results/ws2_rocm_mi300x/`.
+Reproduce with `python benchmarks/benchmark_ws2_rocm_attention.py`.
+
+Paths: `sdpa` (`torch.nn.functional.scaled_dot_product_attention`, **speed baseline only** —
+as in PR #325, no accuracy comparison is mixed into the speed table), `strict-aiter` (the
+ROCm production core), `reference-hip` (`_C.deterministic_attention_*`), `triton-bitwise`
+(this PR).
+
+### 3.1 Headline: Triton port vs the native reference core
+
+Acceptance is 0 mismatched elements. This is the contract the Triton core exists to hold.
+
+| dtype | S | out | lse | dQ | dK | dV | bitwise |
+|---|---:|---:|---:|---:|---:|---:|:---:|
+| bf16 | 512 | 0 | 0 | 0 | 0 | 0 | yes |
+| bf16 | 1024 | 0 | 0 | 0 | 0 | 0 | yes |
+| bf16 | 2048 | 0 | 0 | 0 | 0 | 0 | yes |
+| bf16 | 4096 | 0 | 0 | 0 | 0 | 0 | yes |
+| fp16 | 512 | 0 | 0 | — | — | — | yes |
+| fp16 | 1024 | 0 | 0 | — | — | — | yes |
+| fp16 | 2048 | 0 | 0 | — | — | — | yes |
+| fp16 | 4096 | 0 | 0 | — | — | — | yes |
+
+`dQ/dK/dV` are measured on the BF16 sweep only.
+
+### 3.2 TP-degree invariance of the strict ROCm core
+
+A head shard computed under TP=N versus the same slice of an unsharded run. TP performs no
+cross-rank reduction in attention, so any nonzero value means the kernel's result depends on
+how many heads shared the launch.
+
+| S | TP | Local Hq | `raw_launch` out max-abs | `one_kv_group_per_launch` out max-abs |
+|---:|---:|---:|---:|---:|
+| 512 | 2 | 16 | 0.000000e+00 | 0.000000e+00 |
+| 512 | 4 | 8 | 0.000000e+00 | 0.000000e+00 |
+| 512 | 8 | 4 | 0.000000e+00 | 0.000000e+00 |
+| 1024 | 2 | 16 | **7.812500e-03** | 0.000000e+00 |
+| 1024 | 4 | 8 | **7.812500e-03** | 0.000000e+00 |
+| 1024 | 8 | 4 | **7.812500e-03** | 0.000000e+00 |
+| 2048 | 2 | 16 | 0.000000e+00 | 0.000000e+00 |
+| 2048 | 4 | 8 | **3.906250e-03** | 0.000000e+00 |
+| 2048 | 8 | 4 | **1.953125e-03** | 0.000000e+00 |
+| 4096 | 2 | 16 | 0.000000e+00 | 0.000000e+00 |
+| 4096 | 4 | 8 | 0.000000e+00 | 0.000000e+00 |
+| 4096 | 8 | 4 | **3.906250e-03** | 0.000000e+00 |
+
+Raw AITER is non-invariant at 5 of 12 points, and *which* points is shape-dependent — the
+failure is invisible at S=512 and at S=4096/TP=2, which is exactly what makes it dangerous:
+training at TP=4 and rolling out at TP=8 would compare fine on most shapes. The per-KV-group
+schedule is bitwise at **12 of 12**. This reproduces PR #319's finding on independent inputs.
+
+### 3.3 Single-GPU latency and memory (BF16)
+
+| S | Path | Fwd median (ms) | vs sdpa | Fwd+bwd (ms) | vs sdpa | Fwd peak MiB | Fwd+bwd peak MiB | out max-abs vs FP64 |
+|---:|---|---:|---:|---:|---:|---:|---:|---:|
+| 512 | sdpa | 0.0782 | 1.00x | 0.3228 | 1.00x | 12.1 | 32.2 | 8.195e-03 |
+| 512 | strict-aiter | 0.2428 | 3.11x | 0.6039 | 1.87x | 14.1 | 288.2 | 2.468e-02 |
+| 512 | reference-hip | 0.9659 | 12.36x | 2.9714 | 9.21x | 36.1 | 78.1 | 7.741e-03 |
+| 512 | triton-bitwise | 1.3816 | 17.68x | 4.8578 | 15.05x | 36.1 | 78.1 | 7.741e-03 |
+| 1024 | sdpa | 0.1319 | 1.00x | 0.4319 | 1.00x | 24.1 | 64.3 | 1.027e-02 |
+| 1024 | strict-aiter | 0.2468 | 1.87x | 0.9293 | 2.15x | 28.1 | 1088.4 | 2.609e-02 |
+| 1024 | reference-hip | 3.1389 | 23.79x | 12.2009 | 28.25x | 136.1 | 284.3 | 7.810e-03 |
+| 1024 | triton-bitwise | 4.8240 | 36.56x | 20.1771 | 46.71x | 136.1 | 284.3 | 7.810e-03 |
+| 2048 | sdpa | 0.2887 | 1.00x | 1.0735 | 1.00x | 48.3 | 128.8 | 7.994e-03 |
+| 2048 | strict-aiter | 0.2962 | 1.03x | 1.9019 | 1.77x | 56.3 | 4224.8 | 2.027e-02 |
+| 2048 | reference-hip | 12.8467 | 44.50x | 47.8741 | 44.60x | 528.2 | 1080.5 | 7.804e-03 |
+| 2048 | triton-bitwise | 19.4226 | 67.28x | 76.7414 | 71.49x | 528.3 | 1080.5 | 7.804e-03 |
+| 4096 | sdpa | 0.6936 | 1.00x | 3.2464 | 1.00x | 96.5 | 257.5 | 9.604e-03 |
+| 4096 | strict-aiter | **0.5644** | **0.81x** | 5.7304 | 1.77x | 112.5 | 16641.5 | 2.138e-02 |
+| 4096 | reference-hip | 49.3624 | 71.17x | 173.3365 | 53.39x | 2080.5 | 4209.0 | 7.808e-03 |
+| 4096 | triton-bitwise | 86.0655 | 124.08x | 304.1032 | 93.67x | 2080.5 | 4209.0 | 7.808e-03 |
+
+Three things worth reading off this table:
+
+- **The strict production core is not a tax at long sequence.** At S=4096 forward it is
+  *faster* than SDPA (0.81x), and its worst case across the sweep is 3.11x at S=512 where
+  absolute cost is 0.24 ms. The bitwise arrangements in §2 cost almost nothing in the
+  production path.
+- **AITER's backward is memory-hungry.** `strict-aiter` fwd+bwd peaks at 16.6 GiB at S=4096
+  versus 4.2 GiB for the materializing reference core — the reference core materializes an
+  FP32 `[B, Hq, Sq, Skv]` score matrix and is *still* 4x smaller. Worth knowing before
+  sizing a training run.
+- **The deterministic cores are the most accurate of the four.** Against an FP64 oracle they
+  sit at 7.8e-03 versus 9.6e-03 for SDPA and 2.1e-02 for AITER. Determinism here is not
+  bought with accuracy.
+
+FP16 is in the full report; the shape of the result is the same.
+
+### 3.4 Production core versus reference core
+
+Two different kernels, so this is a tolerance comparison, not a parity claim. It is here to
+size the gap.
+
+| S | out max-abs | out relative-L2 | lse max-abs |
+|---:|---:|---:|---:|
+| 512 | 3.125e-02 | 5.420e-03 | 9.537e-07 |
+| 1024 | 3.125e-02 | 5.505e-03 | 1.431e-06 |
+| 2048 | 1.562e-02 | 5.595e-03 | 1.907e-06 |
+| 4096 | 1.562e-02 | 5.633e-03 | 3.815e-06 |
+
+### 3.5 Batch-composition invariance
+
+Bitwise at every sequence length for every path. For `strict-aiter` the property is
+structural (§2.2) rather than measured: the batched launch does not exist.
+
+### 3.6 Distributed CP over the RCCL AG/RS transport
+
+Schedule: all-gather Q/K/V and the position ids over the CP group, run the strict core once
+on the full sequence, reduce-scatter `(out, lse)` back to this rank's query range. Acceptance
+is bitwise against a CP=1 run of the same core on the same inputs. S=4096, BF16.
+
+| Topology | World | TP | CP | Replicas | Local Hq/Hkv | Median (ms) | p95 (ms) | Peak MiB/rank | out bitwise | lse bitwise | Repeat |
+|---|---:|---:|---:|---:|---|---:|---:|---:|:---:|:---:|:---:|
+| `tp1_cp2` | 2 | 1 | 2 | 1 | 32/8 | 1.8352 | 1.8726 | 160.5 | yes | yes | yes |
+| `tp2_cp2` | 4 | 2 | 2 | 1 | 16/4 | 1.2114 | 1.3138 | 80.3 | yes | yes | yes |
+| `tp1_cp4` | 4 | 1 | 4 | 1 | 32/8 | 1.3973 | 1.4363 | 160.5 | yes | yes | yes |
+| `tp2_cp2_x2` | 8 | 2 | 2 | 2 | 16/4 | 1.2297 | 1.2948 | 80.3 | yes | yes | yes |
+| `tp2_cp4` | 8 | 2 | 4 | 1 | 16/4 | 1.2594 | 1.3294 | 80.3 | yes | yes | yes |
+| `tp1_cp8` | 8 | 1 | 8 | 1 | 32/8 | 1.4112 | 2.2029 | 160.5 | yes | yes | yes |
+
+All six topologies are bitwise against CP=1 on both `out` and `lse`, with 0 mismatched
+elements summed across every rank, and repeat-bitwise on every rank. `tp2_cp2_x2` is the
+8-rank case PR #319 used: two independent CP groups running side by side at TP=2/CP=2.
+
+---
+
+## 4. Figures
+
+![Single-device latency and memory grid](benchmarks/results/ws2_rocm_mi300x/single_gpu_grid.png)
+
+![TP-degree invariance](benchmarks/results/ws2_rocm_mi300x/tp_degree_invariance.png)
+
+![Distributed CP latency](benchmarks/results/ws2_rocm_mi300x/distributed_cp_latency.png)
+
+`reference-hip` and `triton-bitwise` allocate exactly the same buffers, so their memory
+curves coincide and the later-drawn series hides the earlier one.
+
+---
+
+## 5. Files
+
+| Path | What |
+| --- | --- |
+| `rl_engine/kernels/ops/triton/attention/deterministic_attn.py` | New. Triton core, bit-identical to `_C.deterministic_attention_*`. |
+| `rl_engine/kernels/ops/triton/attention/__init__.py` | Exports the new op and `BITWISE_LIBM_PARITY`. |
+| `tests/test_triton_deterministic_attention.py` | New. 71 parity / invariance tests. |
+| `benchmarks/benchmark_ws2_rocm_attention.py` | New. Measurement matrix and figures, reusing PR #325 / #328 helpers. |
+| `benchmarks/results/ws2_rocm_mi300x/` | Report, `results.json`, figures. |
+| `csrc/ops.cpp` | Fix: the merge from `feat/rocm-deterministic-collectives` dropped an `#if !defined(USE_ROCM)` around the `prefix_shared_attention` registration but kept its `#endif`, leaving 13 `#endif` against 12 `#if`. The ROCm build failed with `#endif without #if`. |
+
+## 6. Test plan
+
+- `pytest tests/test_triton_deterministic_attention.py` — 71 passed. 15 shape/mask/scale
+  configs × {bf16, fp16} × {forward, backward}, plus end-to-end autograd through both ops,
+  the fully-masked-row case, batch-slice invariance, and a direct pin on the `expf`/`logf`
+  helpers against the vendor libm.
+- `pytest tests/test_deterministic_attention_cuda.py` — 614 passed (the native core is
+  unaffected).
+- `python benchmarks/benchmark_ws2_rocm_attention.py` — the report above.
+
+## 7. Known limitations
+
+- **CUDA is not covered by the Triton core's bitwise claim.** The nvcc `expf`/`logf` argument
+  reductions are not ported, and no CUDA device was available to derive or verify them.
+  `TritonDeterministicAttentionOp` raises on CUDA unless the caller passes
+  `require_bitwise_libm=False`; a test pins that behaviour on both platforms.
+- **The Triton core is a parity core, not a FlashAttention replacement.** Like the native
+  reference it materialises the full FP32 `[B, Hq, Sq, Skv]` score matrix and runs
+  scalar-order reductions; §3.3 shows the cost.
+- **`_C` does not register `deterministic_attention_forward_fp32`.** The `.cu` defines it but
+  the pybind registration is missing, so the *native* `DeterministicAttentionOp.forward_fp32`
+  raises `AttributeError`. Pre-existing, not touched here; the Triton `forward_fp32` works and
+  its test validates against the op's own downcast instead of the native path.
+- **A stale comment contradicts the shipped TP policy.**
+  `rl_engine/integrations/vime/attention.py` still carries a comment saying RL-Kernel "binds
+  the degree rather than paying ~3x forward time", from before the merge that introduced the
+  per-KV-group launch loop. The provenance dict immediately below it correctly reports
+  `tp_degree_invariant: True`. §3.2 shows the code is right and the comment is wrong; flagged
+  here rather than silently rewritten.
diff --git a/benchmarks/results/ws2_rocm_mi300x/distributed_cp_latency.png b/benchmarks/results/ws2_rocm_mi300x/distributed_cp_latency.png
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diff --git a/benchmarks/results/ws2_rocm_mi300x/report.md b/benchmarks/results/ws2_rocm_mi300x/report.md
new file mode 100644
index 00000000..e211e9eb
--- /dev/null
+++ b/benchmarks/results/ws2_rocm_mi300x/report.md
@@ -0,0 +1,244 @@
+# WS2 strict ROCm Attention — bitwise parity and performance
+
+> Operator-only benchmark. No model checkpoint or serving engine was used;
+> the shapes are Qwen3-8B's attention shapes.
+
+## Environment
+
+| Item | Value |
+|---|---|
+| architecture | gfx942:sramecc+:xnack- |
+| cuda | None |
+| extension_attention_symbols | deterministic_attention_backward, deterministic_attention_forward |
+| gpu | AMD Instinct MI300X |
+| gpu_count | 8 |
+| hip | 7.14.60850 |
+| native_collective | torch.distributed ProcessGroupNCCL (RCCL on ROCm) |
+| python | 3.12.3 |
+| torch | 2.12.0+rocm7.14.0a20260608 |
+| triton | 3.7.0 |
+
+## Methodology
+
+- Operator shape: `Hq=32`, `Hkv=8`, `D=128`, `B=1`, causal; sequence sweep 512, 1024, 2048, 4096.
+- Measured paths:
+  - `sdpa`: `torch.nn.functional.scaled_dot_product_attention`. **Speed baseline only** — as in PR #325, no accuracy comparison is mixed into the speed table.
+  - `strict-aiter`: `StrictRocmAiterCKAttentionCore`, the ROCm production core (AITER CK dense MHA, non-split API, one logical batch row per launch).
+  - `reference-hip`: `_C.deterministic_attention_forward/backward`, the materializing FP32 reference core hipified from the shared `.cu`.
+  - `triton-bitwise`: `TritonDeterministicAttentionOp`, whose contract is bit-identity with `reference-hip`.
+- Timing: CUDA events, median and p95. Peak memory is the per-call increase in `torch.cuda.max_memory_allocated` above what was live before the call.
+- Accuracy is against an FP64 oracle over the same BF16/FP16-rounded inputs. Repeat = two identical calls are bitwise equal; batch-invariant = a row computed alone is bitwise equal to the same row inside a batch.
+- 5 warmups, 20 measured forward samples, 10 measured forward+backward samples. Raw medians, p95, min and max are in `results.json`.
+
+Reproduce from the repository root:
+
+```bash
+python benchmarks/benchmark_ws2_rocm_attention.py \
+  --seq-lens 512,1024,2048,4096 \
+  --dtypes bf16,fp16 \
+  --warmup 5 --samples 20 \
+  --training-samples 10 \
+  --output-dir benchmarks/results/ws2_rocm_mi300x
+```
+
+## Bitwise parity: Triton port vs the native reference core
+
+Acceptance is 0 mismatched elements. This is the contract the Triton core exists to hold.
+
+| dtype | S | out mismatched | lse mismatched | dQ | dK | dV | bitwise |
+|---|---:|---:|---:|---:|---:|---:|:---:|
+| bf16 | 512 | 0 | 0 | 0 | 0 | 0 | yes |
+| bf16 | 1024 | 0 | 0 | 0 | 0 | 0 | yes |
+| bf16 | 2048 | 0 | 0 | 0 | 0 | 0 | yes |
+| bf16 | 4096 | 0 | 0 | 0 | 0 | 0 | yes |
+| fp16 | 512 | 0 | 0 | n/a | n/a | n/a | yes |
+| fp16 | 1024 | 0 | 0 | n/a | n/a | n/a | yes |
+| fp16 | 2048 | 0 | 0 | n/a | n/a | n/a | yes |
+| fp16 | 4096 | 0 | 0 | n/a | n/a | n/a | yes |
+
+`dQ/dK/dV` are measured on the BF16 sweep only; `n/a` marks the FP16 rows.
+
+## Single-GPU Attention (bf16)
+
+### Forward
+
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
+|---:|---|---:|---:|---:|---:|---:|---:|:---:|
+| 512 | sdpa | 0.0782 | 0.0820 | 1.00x | 12.1 | 8.195e-03 | n/a | yes |
+| 512 | strict-aiter | 0.2428 | 0.2560 | 3.11x | 14.1 | 2.468e-02 | 8.359e-07 | yes |
+| 512 | reference-hip | 0.9659 | 1.0030 | 12.36x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
+| 512 | triton-bitwise | 1.3816 | 1.3923 | 17.68x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
+| 1024 | sdpa | 0.1319 | 0.1430 | 1.00x | 24.1 | 1.027e-02 | n/a | yes |
+| 1024 | strict-aiter | 0.2468 | 0.2578 | 1.87x | 28.1 | 2.609e-02 | 1.213e-06 | yes |
+| 1024 | reference-hip | 3.1389 | 3.1914 | 23.79x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
+| 1024 | triton-bitwise | 4.8240 | 4.8720 | 36.56x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
+| 2048 | sdpa | 0.2887 | 0.2975 | 1.00x | 48.3 | 7.994e-03 | n/a | yes |
+| 2048 | strict-aiter | 0.2962 | 0.3394 | 1.03x | 56.3 | 2.027e-02 | 2.288e-06 | yes |
+| 2048 | reference-hip | 12.8467 | 12.9922 | 44.50x | 528.2 | 7.804e-03 | 1.142e-06 | yes |
+| 2048 | triton-bitwise | 19.4226 | 19.5024 | 67.28x | 528.3 | 7.804e-03 | 1.142e-06 | yes |
+| 4096 | sdpa | 0.6936 | 0.7149 | 1.00x | 96.5 | 9.604e-03 | n/a | yes |
+| 4096 | strict-aiter | 0.5644 | 0.6076 | 0.81x | 112.5 | 2.138e-02 | 3.967e-06 | yes |
+| 4096 | reference-hip | 49.3624 | 49.5342 | 71.17x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
+| 4096 | triton-bitwise | 86.0655 | 86.5760 | 124.08x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
+
+### Forward+backward
+
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
+|---:|---|---:|---:|---:|---:|
+| 512 | sdpa | 0.3228 | 0.3510 | 1.00x | 32.2 |
+| 512 | strict-aiter | 0.6039 | 0.6560 | 1.87x | 288.2 |
+| 512 | reference-hip | 2.9714 | 2.9876 | 9.21x | 78.1 |
+| 512 | triton-bitwise | 4.8578 | 4.9374 | 15.05x | 78.1 |
+| 1024 | sdpa | 0.4319 | 0.5795 | 1.00x | 64.3 |
+| 1024 | strict-aiter | 0.9293 | 0.9651 | 2.15x | 1088.4 |
+| 1024 | reference-hip | 12.2009 | 12.2835 | 28.25x | 284.3 |
+| 1024 | triton-bitwise | 20.1771 | 20.3071 | 46.71x | 284.3 |
+| 2048 | sdpa | 1.0735 | 1.1114 | 1.00x | 128.8 |
+| 2048 | strict-aiter | 1.9019 | 1.9802 | 1.77x | 4224.8 |
+| 2048 | reference-hip | 47.8741 | 48.1225 | 44.60x | 1080.5 |
+| 2048 | triton-bitwise | 76.7414 | 76.9707 | 71.49x | 1080.5 |
+| 4096 | sdpa | 3.2464 | 3.3146 | 1.00x | 257.5 |
+| 4096 | strict-aiter | 5.7304 | 5.7861 | 1.77x | 16641.5 |
+| 4096 | reference-hip | 173.3365 | 173.5448 | 53.39x | 4209.0 |
+| 4096 | triton-bitwise | 304.1032 | 304.4993 | 93.67x | 4209.0 |
+
+## Single-GPU Attention (fp16)
+
+### Forward
+
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
+|---:|---|---:|---:|---:|---:|---:|---:|:---:|
+| 512 | sdpa | 0.0700 | 0.0735 | 1.00x | 12.1 | 1.042e-03 | n/a | yes |
+| 512 | strict-aiter | 0.2472 | 0.3623 | 3.53x | 14.1 | 2.046e-03 | 8.756e-07 | yes |
+| 512 | reference-hip | 0.9588 | 1.0180 | 13.70x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
+| 512 | triton-bitwise | 1.3660 | 1.3787 | 19.52x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
+| 1024 | sdpa | 0.1286 | 0.1435 | 1.00x | 24.1 | 1.053e-03 | n/a | yes |
+| 1024 | strict-aiter | 0.2423 | 0.2602 | 1.88x | 28.1 | 2.299e-03 | 1.250e-06 | yes |
+| 1024 | reference-hip | 3.0663 | 3.1218 | 23.84x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
+| 1024 | triton-bitwise | 4.7733 | 4.8062 | 37.11x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
+| 2048 | sdpa | 0.2866 | 0.2997 | 1.00x | 48.3 | 9.099e-04 | n/a | yes |
+| 2048 | strict-aiter | 0.2942 | 0.3034 | 1.03x | 56.3 | 2.053e-03 | 2.540e-06 | yes |
+| 2048 | reference-hip | 12.5348 | 12.7376 | 43.73x | 528.2 | 9.099e-04 | 1.103e-06 | yes |
+| 2048 | triton-bitwise | 19.1882 | 19.2599 | 66.94x | 528.3 | 9.099e-04 | 1.103e-06 | yes |
+| 4096 | sdpa | 1.0565 | 1.0855 | 1.00x | 96.5 | 9.905e-04 | n/a | yes |
+| 4096 | strict-aiter | 0.5813 | 0.6200 | 0.55x | 112.5 | 1.953e-03 | 3.805e-06 | yes |
+| 4096 | reference-hip | 48.3115 | 48.4132 | 45.73x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
+| 4096 | triton-bitwise | 84.9071 | 85.0736 | 80.36x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
+
+### Forward+backward
+
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
+|---:|---|---:|---:|---:|---:|
+| 512 | sdpa | 0.3177 | 0.3328 | 1.00x | 32.2 |
+| 512 | strict-aiter | 1.0677 | 1.1007 | 3.36x | 288.2 |
+| 512 | reference-hip | 3.0363 | 3.0744 | 9.56x | 78.1 |
+| 512 | triton-bitwise | 4.7821 | 4.8698 | 15.05x | 78.1 |
+| 1024 | sdpa | 0.4399 | 0.5001 | 1.00x | 64.4 |
+| 1024 | strict-aiter | 0.8350 | 0.9122 | 1.90x | 1088.4 |
+| 1024 | reference-hip | 12.0068 | 12.0713 | 27.29x | 284.3 |
+| 1024 | triton-bitwise | 19.3157 | 19.4640 | 43.91x | 284.3 |
+| 2048 | sdpa | 1.1766 | 1.2269 | 1.00x | 128.5 |
+| 2048 | strict-aiter | 1.9060 | 2.0250 | 1.62x | 4224.8 |
+| 2048 | reference-hip | 47.5117 | 47.7373 | 40.38x | 1080.5 |
+| 2048 | triton-bitwise | 75.7950 | 76.0022 | 64.42x | 1080.5 |
+| 4096 | sdpa | 4.0247 | 4.0579 | 1.00x | 257.0 |
+| 4096 | strict-aiter | 5.8475 | 5.8916 | 1.45x | 16641.5 |
+| 4096 | reference-hip | 171.5617 | 171.7337 | 42.63x | 4209.0 |
+| 4096 | triton-bitwise | 300.1215 | 301.8339 | 74.57x | 4209.0 |
+
+## Production core versus the reference core
+
+These are two different kernels, so this is a tolerance comparison, not a parity claim. It is here to size the gap, not to assert equality.
+
+| dtype | S | out max-abs | out relative-L2 | lse max-abs |
+|---|---:|---:|---:|---:|
+| bf16 | 512 | 3.125e-02 | 5.420e-03 | 9.537e-07 |
+| bf16 | 1024 | 3.125e-02 | 5.505e-03 | 1.431e-06 |
+| bf16 | 2048 | 1.562e-02 | 5.595e-03 | 1.907e-06 |
+| bf16 | 4096 | 1.562e-02 | 5.633e-03 | 3.815e-06 |
+| fp16 | 512 | 1.953e-03 | 4.323e-04 | 9.537e-07 |
+| fp16 | 1024 | 1.953e-03 | 4.466e-04 | 1.431e-06 |
+| fp16 | 2048 | 1.953e-03 | 4.503e-04 | 2.861e-06 |
+| fp16 | 4096 | 1.953e-03 | 4.580e-04 | 3.815e-06 |
+
+## Batch-composition invariance
+
+A row computed alone must be bitwise equal to the same row inside a batch. The strict ROCm core rejects `B > 1` outright, so for that path the property is structural rather than measured.
+
+| S | Path | Bitwise | Mismatched | Note |
+|---:|---|:---:|---:|---|
+| 512 | sdpa | yes | 0 | measured |
+| 512 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
+| 512 | reference-hip | yes | 0 | measured |
+| 512 | triton-bitwise | yes | 0 | measured |
+| 1024 | sdpa | yes | 0 | measured |
+| 1024 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
+| 1024 | reference-hip | yes | 0 | measured |
+| 1024 | triton-bitwise | yes | 0 | measured |
+| 2048 | sdpa | yes | 0 | measured |
+| 2048 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
+| 2048 | reference-hip | yes | 0 | measured |
+| 2048 | triton-bitwise | yes | 0 | measured |
+| 4096 | sdpa | yes | 0 | measured |
+| 4096 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
+| 4096 | reference-hip | yes | 0 | measured |
+| 4096 | triton-bitwise | yes | 0 | measured |
+
+## TP-degree invariance of the strict ROCm core
+
+A head shard computed under TP=N versus the same slice of an unsharded run. TP performs no cross-rank reduction in attention, so any nonzero value means the kernel's result depends on how many heads shared the launch. `raw_launch` is one launch for all heads; `one_kv_group_per_launch` is the schedule the Vime provider actually uses.
+
+| S | Schedule | TP | Local Hq | Local Hkv | out max-abs | lse max-abs | Invariant |
+|---:|---|---:|---:|---:|---:|---:|:---:|
+| 512 | raw_launch | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
+| 512 | raw_launch | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
+| 512 | raw_launch | 8 | 4 | 1 | 0.000000e+00 | 0.000000e+00 | yes |
+| 512 | one_kv_group_per_launch | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
+| 512 | one_kv_group_per_launch | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
+| 512 | one_kv_group_per_launch | 8 | 4 | 1 | 0.000000e+00 | 0.000000e+00 | yes |
+| 1024 | raw_launch | 2 | 16 | 4 | 7.812500e-03 | 1.907349e-06 | **no** |
+| 1024 | raw_launch | 4 | 8 | 2 | 7.812500e-03 | 1.907349e-06 | **no** |
+| 1024 | raw_launch | 8 | 4 | 1 | 7.812500e-03 | 1.907349e-06 | **no** |
+| 1024 | one_kv_group_per_launch | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
+| 1024 | one_kv_group_per_launch | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
+| 1024 | one_kv_group_per_launch | 8 | 4 | 1 | 0.000000e+00 | 0.000000e+00 | yes |
+| 2048 | raw_launch | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
+| 2048 | raw_launch | 4 | 8 | 2 | 3.906250e-03 | 2.861023e-06 | **no** |
+| 2048 | raw_launch | 8 | 4 | 1 | 1.953125e-03 | 2.861023e-06 | **no** |
+| 2048 | one_kv_group_per_launch | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
+| 2048 | one_kv_group_per_launch | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
+| 2048 | one_kv_group_per_launch | 8 | 4 | 1 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4096 | raw_launch | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4096 | raw_launch | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4096 | raw_launch | 8 | 4 | 1 | 3.906250e-03 | 4.768372e-06 | **no** |
+| 4096 | one_kv_group_per_launch | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4096 | one_kv_group_per_launch | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
+| 4096 | one_kv_group_per_launch | 8 | 4 | 1 | 0.000000e+00 | 0.000000e+00 | yes |
+
+## Distributed CP (RCCL AG/RS transport)
+
+Schedule: all-gather Q/K/V and the position ids over the CP group, run the strict core once on the full sequence, reduce-scatter `(out, lse)` back to this rank's query range. Acceptance is bitwise against a CP=1 run of the same core.
+
+| Topology | World | TP | CP | Replicas | S | Median (ms) | p95 (ms) | Peak MiB/rank | out bitwise | lse bitwise | Repeat |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|:---:|:---:|:---:|
+| tp1_cp2 | 2 | 1 | 2 | 1 | 4096 | 1.8352 | 1.8726 | 160.5 | yes | yes | yes |
+| tp2_cp2 | 4 | 2 | 2 | 1 | 4096 | 1.2114 | 1.3138 | 80.3 | yes | yes | yes |
+| tp1_cp4 | 4 | 1 | 4 | 1 | 4096 | 1.3973 | 1.4363 | 160.5 | yes | yes | yes |
+| tp2_cp2_x2 | 8 | 2 | 2 | 2 | 4096 | 1.2297 | 1.2948 | 80.3 | yes | yes | yes |
+| tp2_cp4 | 8 | 2 | 4 | 1 | 4096 | 1.2594 | 1.3294 | 80.3 | yes | yes | yes |
+| tp1_cp8 | 8 | 1 | 8 | 1 | 4096 | 1.4112 | 2.2029 | 160.5 | yes | yes | yes |
+
+## Figures
+
+`reference-hip` and `triton-bitwise` allocate exactly the same buffers, so their memory curves coincide and the later-drawn series hides the earlier one.
+
+![Single-device latency and memory grid](single_gpu_grid.png)
+
+![Single-device latency](single_gpu_latency.png)
+
+![Single-device peak memory](single_gpu_memory.png)
+
+![TP-degree invariance](tp_degree_invariance.png)
+
+![Distributed CP latency](distributed_cp_latency.png)
+
diff --git a/benchmarks/results/ws2_rocm_mi300x/results.json b/benchmarks/results/ws2_rocm_mi300x/results.json
new file mode 100644
index 00000000..86f6f55d
--- /dev/null
+++ b/benchmarks/results/ws2_rocm_mi300x/results.json
@@ -0,0 +1,1567 @@
+{
+  "environment": {
+    "gpu": "AMD Instinct MI300X",
+    "architecture": "gfx942:sramecc+:xnack-",
+    "gpu_count": 8,
+    "hip": "7.14.60850",
+    "cuda": null,
+    "torch": "2.12.0+rocm7.14.0a20260608",
+    "triton": "3.7.0",
+    "python": "3.12.3",
+    "extension_attention_symbols": [
+      "deterministic_attention_backward",
+      "deterministic_attention_forward"
+    ],
+    "native_collective": "torch.distributed ProcessGroupNCCL (RCCL on ROCm)"
+  },
+  "configuration": {
+    "batch": 1,
+    "q_heads": 32,
+    "kv_heads": 8,
+    "head_dim": 128,
+    "seq_lens": [
+      512,
+      1024,
+      2048,
+      4096
+    ],
+    "dtypes": [
+      "bf16",
+      "fp16"
+    ],
+    "warmup": 5,
+    "samples": 20,
+    "training_samples": 10
+  },
+  "unavailable_paths": {},
+  "single_gpu": {
+    "cases": [
+      {
+        "dtype": "bf16",
+        "seq_len": 512,
+        "batch": 1,
+        "q_heads": 32,
+        "kv_heads": 8,
+        "head_dim": 128,
+        "paths": {
+          "sdpa": {
+            "forward": {
+              "median_ms": 0.0781565010547638,
+              "p95_ms": 0.08199800290167333,
+              "min_ms": 0.07511100172996521,
+              "max_ms": 0.10779999941587448
+            },
+            "forward_peak_mib": 12.06396484375,
+            "out_vs_fp64": {
+              "max_abs": 0.008195295214645348,
+              "relative_l2": 0.001967118270169404
+            },
+            "repeat_bitwise": true,
+            "train_fwd_bwd": {
+              "median_ms": 0.322799488902092,
+              "p95_ms": 0.35098745077848437,
+              "min_ms": 0.30789700150489807,
+              "max_ms": 0.3568100035190582
+            },
+            "train_peak_mib": 32.189453125
+          },
+          "strict-aiter": {
+            "forward": {
+              "median_ms": 0.2428405061364174,
+              "p95_ms": 0.25597020536661147,
+              "min_ms": 0.23843400180339813,
+              "max_ms": 0.2588239908218384
+            },
+            "forward_peak_mib": 14.06298828125,
+            "out_vs_fp64": {
+              "max_abs": 0.024676734518057408,
+              "relative_l2": 0.00520034717487522
+            },
+            "lse_vs_fp64": {
+              "max_abs": 8.359452712269899e-07,
+              "relative_l2": 3.778278560802271e-08
+            },
+            "repeat_bitwise": true,
+            "train_fwd_bwd": {
+              "median_ms": 0.6038974821567535,
+              "p95_ms": 0.656002902984619,
+              "min_ms": 0.5818650126457214,
+              "max_ms": 0.6674320101737976
+            },
+            "train_peak_mib": 288.18896484375
+          },
+          "reference-hip": {
+            "forward": {
+              "median_ms": 0.9658550024032593,
+              "p95_ms": 1.0030183851718903,
+              "min_ms": 0.9305430054664612,
+              "max_ms": 1.0328550338745117
+            },
+            "forward_peak_mib": 36.0625,
+            "out_vs_fp64": {
+              "max_abs": 0.0077411426297544494,
+              "relative_l2": 0.0015903199116563222
+            },
+            "lse_vs_fp64": {
+              "max_abs": 8.111189337967062e-07,
+              "relative_l2": 4.340800016528509e-08
+            },
+            "repeat_bitwise": true,
+            "train_fwd_bwd": {
+              "median_ms": 2.971434473991394,
+              "p95_ms": 2.9875863194465637,
+              "min_ms": 2.9168529510498047,
+              "max_ms": 2.990001916885376
+            },
+            "train_peak_mib": 78.1259765625
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diff --git a/benchmarks/results/ws2_rocm_mi300x/single_gpu_grid.png b/benchmarks/results/ws2_rocm_mi300x/single_gpu_grid.png
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HcmV?d00001


From 0f8a4f99b402a898cd7b1685f4e5da4903848837 Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sat, 29 Aug 2026 07:57:45 +0000
Subject: [PATCH 22/35] bench(attention): measure the per-KV-group schedule and
 correct the ~3x estimate

The strict-aiter row measures the core called once for all heads, which is NOT the
production schedule -- the Vime provider launches it once per (batch row, KV group).
Reading that row as the production cost understated the bill badly.

Measured directly, per_kv_group vs raw_launch forward:

    S=512   1.7717 ms vs 0.2488 ms   7.12x
    S=1024  1.8047 ms vs 0.2467 ms   7.31x
    S=2048  1.7629 ms vs 0.3067 ms   5.75x
    S=4096  2.4506 ms vs 0.5965 ms   4.11x

So the real multiplier is 4.11-7.31x, not the "roughly 3x" the PR description carried,
and it is worst at short sequence where per-launch overhead dominates. The invariance is
still worth buying -- raw AITER is non-invariant at 5 of 12 (S, TP) points -- but the
bill is now stated correctly, and the methodology relabels strict-aiter so the row is not
mistaken for the production schedule again.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01824WkgMDxtBKD4Ex2NkfYA
---
 benchmarks/benchmark_ws2_rocm_attention.py    |  33 +++++-
 benchmarks/results/ws2_rocm_mi300x/report.md  |  13 ++-
 .../results/ws2_rocm_mi300x/results.json      | 100 +++++++++++++++++-
 3 files changed, 142 insertions(+), 4 deletions(-)

diff --git a/benchmarks/benchmark_ws2_rocm_attention.py b/benchmarks/benchmark_ws2_rocm_attention.py
index b0f409ed..f1dca12a 100644
--- a/benchmarks/benchmark_ws2_rocm_attention.py
+++ b/benchmarks/benchmark_ws2_rocm_attention.py
@@ -1017,8 +1017,9 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
         "as in PR #325, no accuracy comparison is mixed into the speed table."
     )
     add(
-        "  - `strict-aiter`: `StrictRocmAiterCKAttentionCore`, the ROCm production core "
-        "(AITER CK dense MHA, non-split API, one logical batch row per launch)."
+        "  - `strict-aiter`: `StrictRocmAiterCKAttentionCore` called **once for all heads**. "
+        "This is the core, not the production schedule: the Vime provider launches it once "
+        "per (batch row, KV group). See the per-KV-group schedule table for that cost."
     )
     add(
         "  - `reference-hip`: `_C.deterministic_attention_forward/backward`, the materializing "
@@ -1195,6 +1196,34 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
         )
     add("")
 
+    # ---- schedule cost
+    schedule = payload.get("tp_schedule_cost") or []
+    if schedule:
+        add("## Cost of the per-KV-group launch schedule")
+        add("")
+        add(
+            "§ TP-degree invariance is bought by launching the core once per "
+            "`(batch row, KV group)` instead of once for all heads. This table is that "
+            "bill. `raw_launch` is one launch for all heads and is **not** the production "
+            "schedule; `per_kv_group` is what the Vime provider actually runs "
+            "(`Hkv` launches per row)."
+        )
+        add("")
+        add(
+            "| S | Launches | sdpa (ms) | raw_launch (ms) | per_kv_group (ms) | "
+            "vs raw | vs sdpa |"
+        )
+        add("|---:|---:|---:|---:|---:|---:|---:|")
+        for row in schedule:
+            raw = row["raw_launch"]["median_ms"]
+            group = row["one_kv_group_per_launch"]["median_ms"]
+            sdpa = row["sdpa"]["median_ms"]
+            add(
+                f"| {row['seq_len']} | {row['launches']} | {sdpa:.4f} | {raw:.4f} | "
+                f"{group:.4f} | {group / raw:.2f}x | {group / sdpa:.2f}x |"
+            )
+        add("")
+
     # ---- distributed
     distributed = payload.get("distributed") or []
     if distributed:
diff --git a/benchmarks/results/ws2_rocm_mi300x/report.md b/benchmarks/results/ws2_rocm_mi300x/report.md
index e211e9eb..2f26369e 100644
--- a/benchmarks/results/ws2_rocm_mi300x/report.md
+++ b/benchmarks/results/ws2_rocm_mi300x/report.md
@@ -23,7 +23,7 @@
 - Operator shape: `Hq=32`, `Hkv=8`, `D=128`, `B=1`, causal; sequence sweep 512, 1024, 2048, 4096.
 - Measured paths:
   - `sdpa`: `torch.nn.functional.scaled_dot_product_attention`. **Speed baseline only** — as in PR #325, no accuracy comparison is mixed into the speed table.
-  - `strict-aiter`: `StrictRocmAiterCKAttentionCore`, the ROCm production core (AITER CK dense MHA, non-split API, one logical batch row per launch).
+  - `strict-aiter`: `StrictRocmAiterCKAttentionCore` called **once for all heads**. This is the core, not the production schedule: the Vime provider launches it once per (batch row, KV group). See the per-KV-group schedule table for that cost.
   - `reference-hip`: `_C.deterministic_attention_forward/backward`, the materializing FP32 reference core hipified from the shared `.cu`.
   - `triton-bitwise`: `TritonDeterministicAttentionOp`, whose contract is bit-identity with `reference-hip`.
 - Timing: CUDA events, median and p95. Peak memory is the per-call increase in `torch.cuda.max_memory_allocated` above what was live before the call.
@@ -215,6 +215,17 @@ A head shard computed under TP=N versus the same slice of an unsharded run. TP p
 | 4096 | one_kv_group_per_launch | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
 | 4096 | one_kv_group_per_launch | 8 | 4 | 1 | 0.000000e+00 | 0.000000e+00 | yes |
 
+## Cost of the per-KV-group launch schedule
+
+§ TP-degree invariance is bought by launching the core once per `(batch row, KV group)` instead of once for all heads. This table is that bill. `raw_launch` is one launch for all heads and is **not** the production schedule; `per_kv_group` is what the Vime provider actually runs (`Hkv` launches per row).
+
+| S | Launches | sdpa (ms) | raw_launch (ms) | per_kv_group (ms) | vs raw | vs sdpa |
+|---:|---:|---:|---:|---:|---:|---:|
+| 512 | 8 | 0.0721 | 0.2488 | 1.7717 | 7.12x | 24.56x |
+| 1024 | 8 | 0.1355 | 0.2467 | 1.8047 | 7.31x | 13.32x |
+| 2048 | 8 | 0.2848 | 0.3067 | 1.7629 | 5.75x | 6.19x |
+| 4096 | 8 | 0.7679 | 0.5965 | 2.4506 | 4.11x | 3.19x |
+
 ## Distributed CP (RCCL AG/RS transport)
 
 Schedule: all-gather Q/K/V and the position ids over the CP group, run the strict core once on the full sequence, reduce-scatter `(out, lse)` back to this rank's query range. Acceptance is bitwise against a CP=1 run of the same core.
diff --git a/benchmarks/results/ws2_rocm_mi300x/results.json b/benchmarks/results/ws2_rocm_mi300x/results.json
index 86f6f55d..443477a5 100644
--- a/benchmarks/results/ws2_rocm_mi300x/results.json
+++ b/benchmarks/results/ws2_rocm_mi300x/results.json
@@ -1563,5 +1563,103 @@
       "out_mismatched_all_ranks": 0,
       "lse_mismatched_all_ranks": 0
     }
+  ],
+  "tp_schedule_cost": [
+    {
+      "seq_len": 512,
+      "raw_launch": {
+        "median_ms": 0.2488495036959648,
+        "p95_ms": 0.2928414404392243,
+        "min_ms": 0.23362700641155243,
+        "max_ms": 0.31050100922584534
+      },
+      "raw_launch_peak_mib": 14.06298828125,
+      "one_kv_group_per_launch": {
+        "median_ms": 1.77167147397995,
+        "p95_ms": 1.8451894819736483,
+        "min_ms": 1.7313319444656372,
+        "max_ms": 1.953421950340271
+      },
+      "one_kv_group_per_launch_peak_mib": 8.125,
+      "sdpa": {
+        "median_ms": 0.07214749976992607,
+        "p95_ms": 0.10037899836897851,
+        "min_ms": 0.06942299753427505,
+        "max_ms": 0.11312799900770187
+      },
+      "launches": 8
+    },
+    {
+      "seq_len": 1024,
+      "raw_launch": {
+        "median_ms": 0.24672599881887436,
+        "p95_ms": 0.28001638650894184,
+        "min_ms": 0.24015699326992035,
+        "max_ms": 0.5281450152397156
+      },
+      "raw_launch_peak_mib": 28.12548828125,
+      "one_kv_group_per_launch": {
+        "median_ms": 1.8047409653663635,
+        "p95_ms": 2.558191692829132,
+        "min_ms": 1.7322529554367065,
+        "max_ms": 2.5664119720458984
+      },
+      "one_kv_group_per_launch_peak_mib": 16.25,
+      "sdpa": {
+        "median_ms": 0.1354615017771721,
+        "p95_ms": 0.14607899487018586,
+        "min_ms": 0.11909700185060501,
+        "max_ms": 0.1464969962835312
+      },
+      "launches": 8
+    },
+    {
+      "seq_len": 2048,
+      "raw_launch": {
+        "median_ms": 0.30673499405384064,
+        "p95_ms": 0.3156995430588722,
+        "min_ms": 0.2910729944705963,
+        "max_ms": 0.3489980101585388
+      },
+      "raw_launch_peak_mib": 56.25048828125,
+      "one_kv_group_per_launch": {
+        "median_ms": 1.7629389762878418,
+        "p95_ms": 1.8040032982826233,
+        "min_ms": 1.7420680522918701,
+        "max_ms": 1.8337639570236206
+      },
+      "one_kv_group_per_launch_peak_mib": 32.5,
+      "sdpa": {
+        "median_ms": 0.28476350009441376,
+        "p95_ms": 0.29218203872442244,
+        "min_ms": 0.279776006937027,
+        "max_ms": 0.31859299540519714
+      },
+      "launches": 8
+    },
+    {
+      "seq_len": 4096,
+      "raw_launch": {
+        "median_ms": 0.5964860022068024,
+        "p95_ms": 0.6361419618129731,
+        "min_ms": 0.5617139935493469,
+        "max_ms": 0.6870610117912292
+      },
+      "raw_launch_peak_mib": 112.50048828125,
+      "one_kv_group_per_launch": {
+        "median_ms": 2.450620412826538,
+        "p95_ms": 4.374842977523804,
+        "min_ms": 2.0698349475860596,
+        "max_ms": 4.4635910987854
+      },
+      "one_kv_group_per_launch_peak_mib": 65.0,
+      "sdpa": {
+        "median_ms": 0.7679409980773926,
+        "p95_ms": 0.8042640864849091,
+        "min_ms": 0.6916279792785645,
+        "max_ms": 0.8177750110626221
+      },
+      "launches": 8
+    }
   ]
-}
+}
\ No newline at end of file

From e24b5944b7e5c65e27e63626d597ce9c23d50958 Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sat, 29 Aug 2026 08:01:23 +0000
Subject: [PATCH 23/35] bench(attention): drop the stale pre-per-KV-group
 MI300X performance artifacts

benchmarks/results/pr319_rocm_mi300x/{report.md,results.json} hold forward and
backward timings taken before the per-KV-group launch rule landed, so they measure
a schedule the code no longer runs. The PR description already carried a "these
numbers are stale" banner over them; benchmarks/results/ws2_rocm_mi300x/ supersedes
them in full.

The distributed/ subdirectory stays: it is multi-rank bitwise acceptance evidence for
the full strict shared core including ROCm RoPE, which is a different gate from the
new benchmark and does not go stale the way timings do.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01824WkgMDxtBKD4Ex2NkfYA
---
 .../results/pr319_rocm_mi300x/report.md       | 106 ----
 .../results/pr319_rocm_mi300x/results.json    | 483 ------------------
 2 files changed, 589 deletions(-)
 delete mode 100644 benchmarks/results/pr319_rocm_mi300x/report.md
 delete mode 100644 benchmarks/results/pr319_rocm_mi300x/results.json

diff --git a/benchmarks/results/pr319_rocm_mi300x/report.md b/benchmarks/results/pr319_rocm_mi300x/report.md
deleted file mode 100644
index e0577d94..00000000
--- a/benchmarks/results/pr319_rocm_mi300x/report.md
+++ /dev/null
@@ -1,106 +0,0 @@
-# PR #319 — strict ROCm attention, MI300X operator benchmark
-
-Operator-only: seeded Q/K/V, no checkpoint, tokenizer, or serving engine.
-
-| Field | Value |
-|---|---|
-| `gpu` | AMD Instinct MI300X |
-| `arch` | gfx942:sramecc+:xnack- |
-| `device_count` | 8 |
-| `torch` | 2.12.0+rocm7.14.0a20260608 |
-| `hip` | 7.14.60850 |
-| `dtype` | bf16 |
-| `q_heads` | 32 |
-| `kv_heads` | 8 |
-| `head_dim` | 128 |
-
-Backends: `native` = PyTorch SDPA with KV heads expanded; `triton` = flash-attn ROCm
-Triton backend; `strict` = `aiter.rocm.ck_dense_mha` through WS2 contract dispatch.
-
-## Latency and peak memory (BF16, causal prefill)
-
-| B | S | Backend | Fwd median (ms) | Fwd p95 (ms) | Fwd peak MiB | Fwd+bwd median (ms) | Fwd+bwd peak MiB |
-|---:|---:|---|---:|---:|---:|---:|---:|
-| 1 | 1024 | native | 0.1449 | 0.1503 | 36.1 | 0.4758 | 84.3 |
-| 1 | 1024 | triton | 0.3021 | 0.3315 | 32.1 | 1.2487 | 52.2 |
-| 1 | 1024 | strict | 0.3980 | 0.4205 | 40.1 | 1.0305 | 1108.5 |
-| 1 | 2048 | native | 0.2972 | 0.3010 | 72.3 | 1.0875 | 168.8 |
-| 1 | 2048 | triton | 0.6379 | 0.6535 | 64.2 | 2.5059 | 104.5 |
-| 1 | 2048 | strict | 0.4265 | 0.4365 | 80.3 | 2.1164 | 4265.0 |
-| 1 | 4096 | native | 0.7020 | 0.7232 | 144.5 | 3.2428 | 337.5 |
-| 1 | 4096 | triton | 1.4280 | 1.4877 | 128.5 | 4.6182 | 209.0 |
-| 1 | 4096 | strict | 0.7343 | 0.7502 | 160.5 | 5.9652 | 16722.0 |
-| 2 | 2048 | native | 0.4499 | 0.4715 | 144.5 | 1.9132 | 337.5 |
-| 2 | 2048 | triton | 0.9764 | 0.9864 | 128.5 | 2.6696 | 209.0 |
-| 2 | 2048 | strict | 0.7826 | 0.8113 | 120.5 | 3.9917 | 4369.3 |
-| 4 | 2048 | native | 0.8225 | 0.8474 | 289.0 | 3.5825 | 675.0 |
-| 4 | 2048 | triton | 1.7539 | 1.7674 | 257.0 | 5.1151 | 418.0 |
-| 4 | 2048 | strict | 1.3809 | 1.4118 | 226.1 | 7.6638 | 4610.3 |
-
-## Cost of the deterministic backward
-
-Raw AITER `mha_bwd`, B=1, toggling only the `deterministic` flag. Peak values include the
-already-resident forward tensors, so read the absolute det=on column for scaling.
-
-| S | det=on median (ms) | det=off median (ms) | Time | det=on peak MiB | det=off peak MiB |
-|---:|---:|---:|---:|---:|---:|
-| 1024 | 0.4490 | 0.1903 | 2.36x | 1188.3 | 180.3 |
-| 2048 | 1.3386 | 0.5687 | 2.35x | 4328.5 | 264.5 |
-| 4096 | 5.0450 | 2.0415 | 2.47x | 16753.0 | 433.0 |
-
-Deterministic backward peak memory scales as O(S^2): each doubling of S quadruples it.
-
-## Batch-composition invariance of raw AITER
-
-Batch vs the same rows submitted one at a time. Shape-dependent, which is why the strict
-core materializes one logical row per call.
-
-| B | S | out max abs | lse max abs | Invariant |
-|---:|---:|---:|---:|---|
-| 2 | 128 | 0.000000e+00 | 0.000000e+00 | yes |
-| 2 | 256 | 0.000000e+00 | 0.000000e+00 | yes |
-| 2 | 512 | 1.562500e-02 | 9.536743e-07 | **no** |
-| 2 | 1024 | 0.000000e+00 | 0.000000e+00 | yes |
-| 2 | 2048 | 0.000000e+00 | 0.000000e+00 | yes |
-| 2 | 4096 | 0.000000e+00 | 0.000000e+00 | yes |
-| 4 | 128 | 0.000000e+00 | 0.000000e+00 | yes |
-| 4 | 256 | 7.812500e-03 | 9.536743e-07 | **no** |
-| 4 | 512 | 1.562500e-02 | 9.536743e-07 | **no** |
-| 4 | 1024 | 0.000000e+00 | 0.000000e+00 | yes |
-| 4 | 2048 | 0.000000e+00 | 0.000000e+00 | yes |
-| 4 | 4096 | 0.000000e+00 | 0.000000e+00 | yes |
-
-Through the provider, every shape above is bitwise identical (max abs `0`).
-
-## TP-degree invariance of raw AITER
-
-A head shard computed under TP=N vs the same slice of an unsharded run. TP performs no
-cross-rank reduction in attention, so any nonzero value here means the kernel's result
-depends on how many heads shared the launch - i.e. changing TP degree changes the numbers.
-
-| S | TP | Local Hq | Local Hkv | out max abs | lse max abs | Invariant |
-|---:|---:|---:|---:|---:|---:|---|
-| 512 | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
-| 512 | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
-| 512 | 8 | 4 | 1 | 0.000000e+00 | 0.000000e+00 | yes |
-| 1024 | 2 | 16 | 4 | 3.906250e-03 | 1.430511e-06 | **no** |
-| 1024 | 4 | 8 | 2 | 3.906250e-03 | 1.430511e-06 | **no** |
-| 1024 | 8 | 4 | 1 | 3.906250e-03 | 1.430511e-06 | **no** |
-| 2048 | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
-| 2048 | 4 | 8 | 2 | 7.812500e-03 | 2.861023e-06 | **no** |
-| 2048 | 8 | 4 | 1 | 7.812500e-03 | 2.861023e-06 | **no** |
-| 4096 | 2 | 16 | 4 | 0.000000e+00 | 0.000000e+00 | yes |
-| 4096 | 4 | 8 | 2 | 0.000000e+00 | 0.000000e+00 | yes |
-| 4096 | 8 | 4 | 1 | 7.812500e-03 | 4.768372e-06 | **no** |
-
-RL-Kernel does not remove this dependence (per-KV-group execution would, at 2.7-3.9x
-forward time). It binds the degree instead: `AttentionContract.cross_rank_fingerprint`
-includes `tp_world_size`, and `validate_cross_config_alignment` fails closed when training
-and rollout disagree.
-
-## Reproduce
-
-```bash
-python benchmarks/benchmark_rocm_attention.py \
-  --output benchmarks/results/pr319_rocm_mi300x/results.json
-```
diff --git a/benchmarks/results/pr319_rocm_mi300x/results.json b/benchmarks/results/pr319_rocm_mi300x/results.json
deleted file mode 100644
index 3d9f93fe..00000000
--- a/benchmarks/results/pr319_rocm_mi300x/results.json
+++ /dev/null
@@ -1,483 +0,0 @@
-{
-  "environment": {
-    "gpu": "AMD Instinct MI300X",
-    "arch": "gfx942:sramecc+:xnack-",
-    "device_count": 8,
-    "torch": "2.12.0+rocm7.14.0a20260608",
-    "hip": "7.14.60850",
-    "dtype": "bf16",
-    "q_heads": 32,
-    "kv_heads": 8,
-    "head_dim": 128
-  },
-  "latency": [
-    {
-      "batch": 1,
-      "seq_len": 1024,
-      "backend": "native",
-      "forward": {
-        "median_ms": 0.1449,
-        "p95_ms": 0.1503,
-        "peak_mib": 36.1
-      },
-      "forward_backward": {
-        "median_ms": 0.4758,
-        "p95_ms": 0.4814,
-        "peak_mib": 84.3
-      }
-    },
-    {
-      "batch": 1,
-      "seq_len": 1024,
-      "backend": "triton",
-      "forward": {
-        "median_ms": 0.3021,
-        "p95_ms": 0.3315,
-        "peak_mib": 32.1
-      },
-      "forward_backward": {
-        "median_ms": 1.2487,
-        "p95_ms": 1.3242,
-        "peak_mib": 52.2
-      }
-    },
-    {
-      "batch": 1,
-      "seq_len": 1024,
-      "backend": "strict",
-      "forward": {
-        "median_ms": 0.398,
-        "p95_ms": 0.4205,
-        "peak_mib": 40.1
-      },
-      "forward_backward": {
-        "median_ms": 1.0305,
-        "p95_ms": 1.0536,
-        "peak_mib": 1108.5
-      }
-    },
-    {
-      "batch": 1,
-      "seq_len": 2048,
-      "backend": "native",
-      "forward": {
-        "median_ms": 0.2972,
-        "p95_ms": 0.301,
-        "peak_mib": 72.3
-      },
-      "forward_backward": {
-        "median_ms": 1.0875,
-        "p95_ms": 1.1026,
-        "peak_mib": 168.8
-      }
-    },
-    {
-      "batch": 1,
-      "seq_len": 2048,
-      "backend": "triton",
-      "forward": {
-        "median_ms": 0.6379,
-        "p95_ms": 0.6535,
-        "peak_mib": 64.2
-      },
-      "forward_backward": {
-        "median_ms": 2.5059,
-        "p95_ms": 2.5251,
-        "peak_mib": 104.5
-      }
-    },
-    {
-      "batch": 1,
-      "seq_len": 2048,
-      "backend": "strict",
-      "forward": {
-        "median_ms": 0.4265,
-        "p95_ms": 0.4365,
-        "peak_mib": 80.3
-      },
-      "forward_backward": {
-        "median_ms": 2.1164,
-        "p95_ms": 2.2624,
-        "peak_mib": 4265.0
-      }
-    },
-    {
-      "batch": 1,
-      "seq_len": 4096,
-      "backend": "native",
-      "forward": {
-        "median_ms": 0.702,
-        "p95_ms": 0.7232,
-        "peak_mib": 144.5
-      },
-      "forward_backward": {
-        "median_ms": 3.2428,
-        "p95_ms": 3.2862,
-        "peak_mib": 337.5
-      }
-    },
-    {
-      "batch": 1,
-      "seq_len": 4096,
-      "backend": "triton",
-      "forward": {
-        "median_ms": 1.428,
-        "p95_ms": 1.4877,
-        "peak_mib": 128.5
-      },
-      "forward_backward": {
-        "median_ms": 4.6182,
-        "p95_ms": 4.6719,
-        "peak_mib": 209.0
-      }
-    },
-    {
-      "batch": 1,
-      "seq_len": 4096,
-      "backend": "strict",
-      "forward": {
-        "median_ms": 0.7343,
-        "p95_ms": 0.7502,
-        "peak_mib": 160.5
-      },
-      "forward_backward": {
-        "median_ms": 5.9652,
-        "p95_ms": 6.0334,
-        "peak_mib": 16722.0
-      }
-    },
-    {
-      "batch": 2,
-      "seq_len": 2048,
-      "backend": "native",
-      "forward": {
-        "median_ms": 0.4499,
-        "p95_ms": 0.4715,
-        "peak_mib": 144.5
-      },
-      "forward_backward": {
-        "median_ms": 1.9132,
-        "p95_ms": 1.943,
-        "peak_mib": 337.5
-      }
-    },
-    {
-      "batch": 2,
-      "seq_len": 2048,
-      "backend": "triton",
-      "forward": {
-        "median_ms": 0.9764,
-        "p95_ms": 0.9864,
-        "peak_mib": 128.5
-      },
-      "forward_backward": {
-        "median_ms": 2.6696,
-        "p95_ms": 2.6816,
-        "peak_mib": 209.0
-      }
-    },
-    {
-      "batch": 2,
-      "seq_len": 2048,
-      "backend": "strict",
-      "forward": {
-        "median_ms": 0.7826,
-        "p95_ms": 0.8113,
-        "peak_mib": 120.5
-      },
-      "forward_backward": {
-        "median_ms": 3.9917,
-        "p95_ms": 4.0312,
-        "peak_mib": 4369.3
-      }
-    },
-    {
-      "batch": 4,
-      "seq_len": 2048,
-      "backend": "native",
-      "forward": {
-        "median_ms": 0.8225,
-        "p95_ms": 0.8474,
-        "peak_mib": 289.0
-      },
-      "forward_backward": {
-        "median_ms": 3.5825,
-        "p95_ms": 3.6547,
-        "peak_mib": 675.0
-      }
-    },
-    {
-      "batch": 4,
-      "seq_len": 2048,
-      "backend": "triton",
-      "forward": {
-        "median_ms": 1.7539,
-        "p95_ms": 1.7674,
-        "peak_mib": 257.0
-      },
-      "forward_backward": {
-        "median_ms": 5.1151,
-        "p95_ms": 5.1429,
-        "peak_mib": 418.0
-      }
-    },
-    {
-      "batch": 4,
-      "seq_len": 2048,
-      "backend": "strict",
-      "forward": {
-        "median_ms": 1.3809,
-        "p95_ms": 1.4118,
-        "peak_mib": 226.1
-      },
-      "forward_backward": {
-        "median_ms": 7.6638,
-        "p95_ms": 7.7376,
-        "peak_mib": 4610.3
-      }
-    }
-  ],
-  "determinism_cost": [
-    {
-      "seq_len": 1024,
-      "deterministic": {
-        "median_ms": 0.449,
-        "p95_ms": 0.4681,
-        "peak_mib": 1188.3
-      },
-      "non_deterministic": {
-        "median_ms": 0.1903,
-        "p95_ms": 0.197,
-        "peak_mib": 180.3
-      },
-      "time_ratio": 2.36,
-      "memory_ratio": 6.6
-    },
-    {
-      "seq_len": 2048,
-      "deterministic": {
-        "median_ms": 1.3386,
-        "p95_ms": 1.379,
-        "peak_mib": 4328.5
-      },
-      "non_deterministic": {
-        "median_ms": 0.5687,
-        "p95_ms": 0.5761,
-        "peak_mib": 264.5
-      },
-      "time_ratio": 2.35,
-      "memory_ratio": 16.4
-    },
-    {
-      "seq_len": 4096,
-      "deterministic": {
-        "median_ms": 5.045,
-        "p95_ms": 5.0906,
-        "peak_mib": 16753.0
-      },
-      "non_deterministic": {
-        "median_ms": 2.0415,
-        "p95_ms": 2.072,
-        "peak_mib": 433.0
-      },
-      "time_ratio": 2.47,
-      "memory_ratio": 38.7
-    }
-  ],
-  "batch_composition": [
-    {
-      "batch": 2,
-      "seq_len": 128,
-      "raw_aiter_out_max_abs": 0.0,
-      "raw_aiter_lse_max_abs": 0.0,
-      "raw_aiter_is_batch_invariant": true
-    },
-    {
-      "batch": 2,
-      "seq_len": 256,
-      "raw_aiter_out_max_abs": 0.0,
-      "raw_aiter_lse_max_abs": 0.0,
-      "raw_aiter_is_batch_invariant": true
-    },
-    {
-      "batch": 2,
-      "seq_len": 512,
-      "raw_aiter_out_max_abs": 0.015625,
-      "raw_aiter_lse_max_abs": 9.5367431640625e-07,
-      "raw_aiter_is_batch_invariant": false
-    },
-    {
-      "batch": 2,
-      "seq_len": 1024,
-      "raw_aiter_out_max_abs": 0.0,
-      "raw_aiter_lse_max_abs": 0.0,
-      "raw_aiter_is_batch_invariant": true
-    },
-    {
-      "batch": 2,
-      "seq_len": 2048,
-      "raw_aiter_out_max_abs": 0.0,
-      "raw_aiter_lse_max_abs": 0.0,
-      "raw_aiter_is_batch_invariant": true
-    },
-    {
-      "batch": 2,
-      "seq_len": 4096,
-      "raw_aiter_out_max_abs": 0.0,
-      "raw_aiter_lse_max_abs": 0.0,
-      "raw_aiter_is_batch_invariant": true
-    },
-    {
-      "batch": 4,
-      "seq_len": 128,
-      "raw_aiter_out_max_abs": 0.0,
-      "raw_aiter_lse_max_abs": 0.0,
-      "raw_aiter_is_batch_invariant": true
-    },
-    {
-      "batch": 4,
-      "seq_len": 256,
-      "raw_aiter_out_max_abs": 0.0078125,
-      "raw_aiter_lse_max_abs": 9.5367431640625e-07,
-      "raw_aiter_is_batch_invariant": false
-    },
-    {
-      "batch": 4,
-      "seq_len": 512,
-      "raw_aiter_out_max_abs": 0.015625,
-      "raw_aiter_lse_max_abs": 9.5367431640625e-07,
-      "raw_aiter_is_batch_invariant": false
-    },
-    {
-      "batch": 4,
-      "seq_len": 1024,
-      "raw_aiter_out_max_abs": 0.0,
-      "raw_aiter_lse_max_abs": 0.0,
-      "raw_aiter_is_batch_invariant": true
-    },
-    {
-      "batch": 4,
-      "seq_len": 2048,
-      "raw_aiter_out_max_abs": 0.0,
-      "raw_aiter_lse_max_abs": 0.0,
-      "raw_aiter_is_batch_invariant": true
-    },
-    {
-      "batch": 4,
-      "seq_len": 4096,
-      "raw_aiter_out_max_abs": 0.0,
-      "raw_aiter_lse_max_abs": 0.0,
-      "raw_aiter_is_batch_invariant": true
-    }
-  ],
-  "tp_head_count_sensitivity": [
-    {
-      "seq_len": 512,
-      "tp": 2,
-      "local_q_heads": 16,
-      "local_kv_heads": 4,
-      "out_max_abs": 0.0,
-      "lse_max_abs": 0.0,
-      "tp_degree_invariant": true
-    },
-    {
-      "seq_len": 512,
-      "tp": 4,
-      "local_q_heads": 8,
-      "local_kv_heads": 2,
-      "out_max_abs": 0.0,
-      "lse_max_abs": 0.0,
-      "tp_degree_invariant": true
-    },
-    {
-      "seq_len": 512,
-      "tp": 8,
-      "local_q_heads": 4,
-      "local_kv_heads": 1,
-      "out_max_abs": 0.0,
-      "lse_max_abs": 0.0,
-      "tp_degree_invariant": true
-    },
-    {
-      "seq_len": 1024,
-      "tp": 2,
-      "local_q_heads": 16,
-      "local_kv_heads": 4,
-      "out_max_abs": 0.00390625,
-      "lse_max_abs": 1.430511474609375e-06,
-      "tp_degree_invariant": false
-    },
-    {
-      "seq_len": 1024,
-      "tp": 4,
-      "local_q_heads": 8,
-      "local_kv_heads": 2,
-      "out_max_abs": 0.00390625,
-      "lse_max_abs": 1.430511474609375e-06,
-      "tp_degree_invariant": false
-    },
-    {
-      "seq_len": 1024,
-      "tp": 8,
-      "local_q_heads": 4,
-      "local_kv_heads": 1,
-      "out_max_abs": 0.00390625,
-      "lse_max_abs": 1.430511474609375e-06,
-      "tp_degree_invariant": false
-    },
-    {
-      "seq_len": 2048,
-      "tp": 2,
-      "local_q_heads": 16,
-      "local_kv_heads": 4,
-      "out_max_abs": 0.0,
-      "lse_max_abs": 0.0,
-      "tp_degree_invariant": true
-    },
-    {
-      "seq_len": 2048,
-      "tp": 4,
-      "local_q_heads": 8,
-      "local_kv_heads": 2,
-      "out_max_abs": 0.0078125,
-      "lse_max_abs": 2.86102294921875e-06,
-      "tp_degree_invariant": false
-    },
-    {
-      "seq_len": 2048,
-      "tp": 8,
-      "local_q_heads": 4,
-      "local_kv_heads": 1,
-      "out_max_abs": 0.0078125,
-      "lse_max_abs": 2.86102294921875e-06,
-      "tp_degree_invariant": false
-    },
-    {
-      "seq_len": 4096,
-      "tp": 2,
-      "local_q_heads": 16,
-      "local_kv_heads": 4,
-      "out_max_abs": 0.0,
-      "lse_max_abs": 0.0,
-      "tp_degree_invariant": true
-    },
-    {
-      "seq_len": 4096,
-      "tp": 4,
-      "local_q_heads": 8,
-      "local_kv_heads": 2,
-      "out_max_abs": 0.0,
-      "lse_max_abs": 0.0,
-      "tp_degree_invariant": true
-    },
-    {
-      "seq_len": 4096,
-      "tp": 8,
-      "local_q_heads": 4,
-      "local_kv_heads": 1,
-      "out_max_abs": 0.0078125,
-      "lse_max_abs": 4.76837158203125e-06,
-      "tp_degree_invariant": false
-    }
-  ]
-}
\ No newline at end of file

From f4602c069c4f88053ad2fe27d96d58e864e1ae87 Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sat, 29 Aug 2026 08:32:59 +0000
Subject: [PATCH 24/35] bench(attention): support CPU and CUDA hosts, add the
 exactness heatmap

Three gaps, all found by comparing against what PR #325 actually plots and by
walking the code paths an H100 run would take.

Charts. PR #325's most distinctive figure is a mismatch heatmap (topology x tensor
category, annotated, RdYlGn), and we had no equivalent even though the data was
already collected. exactness_matrix.png adds it: Triton-vs-reference on the left,
CP-topology-vs-CP=1 on the right. Cells that were never measured (fp16 gradients)
render as "n/m" on grey rather than 0, which would read as measured-and-equal. The
distributed chart also had no baseline, so it could not show what the transport
costs; it is now grouped bars against CP=1, which puts AG/RS at 2.2-3.3x.

Host support. Timing falls back to wall clock and peak memory to an RSS high-water
delta from /proc when there is no device, and NativeAttentionOp joins as
pytorch-native -- the one non-SDPA path that also runs on the host. --device
selects, and the GPU-only sections are skipped rather than failed.

CUDA support, which the H100 run would otherwise have hit head-on. AITER does not
exist there, so strict-fa4 (StrictFlashAttention4Core) is the production core on
CUDA; TritonDeterministicAttentionOp raises on CUDA by design, so it is now built
with require_bitwise_libm=BITWISE_LIBM_PARITY and measured without claiming parity;
and the distributed case had StrictRocmAiterCKAttentionCore and the RCCL transport
hard-coded, which would have failed every CP topology on an H100 -- it now dispatches
on torch.version.hip. reference-hip is renamed reference-native, since on CUDA that
same .cu is not a HIP build.

--compare-with LABEL=PATH merges another platform's results.json into the report, so
mi300x, cpu and h100 runs land in one table with a Platform column; a missing row
means the backend cannot exist there, not that it failed.

Verified on ROCm after the refactor: all six CP topologies still bitwise against
CP=1 with transport reported as rccl_ag_rs.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01824WkgMDxtBKD4Ex2NkfYA
---
 benchmarks/benchmark_ws2_rocm_attention.py    | 627 ++++++++++++++----
 .../distributed_cp_latency.png                | Bin 46377 -> 65704 bytes
 .../ws2_rocm_mi300x/exactness_matrix.png      | Bin 0 -> 155510 bytes
 benchmarks/results/ws2_rocm_mi300x/report.md  |  20 +-
 .../results/ws2_rocm_mi300x/results.json      |  86 ++-
 5 files changed, 570 insertions(+), 163 deletions(-)
 create mode 100644 benchmarks/results/ws2_rocm_mi300x/exactness_matrix.png

diff --git a/benchmarks/benchmark_ws2_rocm_attention.py b/benchmarks/benchmark_ws2_rocm_attention.py
index f1dca12a..4078b7d8 100644
--- a/benchmarks/benchmark_ws2_rocm_attention.py
+++ b/benchmarks/benchmark_ws2_rocm_attention.py
@@ -18,18 +18,19 @@
                       accuracy claim is mixed into the speed comparison.
 - ``strict-aiter``    ``StrictRocmAiterCKAttentionCore`` — the ROCm production core
                       (AITER CK dense MHA, non-split API).
-- ``reference-hip``   ``_C.deterministic_attention_forward/backward`` — the materializing
+- ``reference-native``   ``_C.deterministic_attention_forward/backward`` — the materializing
                       FP32 reference core, hipified from the shared ``.cu``.
 - ``triton-bitwise``  ``TritonDeterministicAttentionOp`` — the Triton port whose
-                      contract is bit-identity with ``reference-hip``.
+                      contract is bit-identity with ``reference-native``.
 
-The headline column is ``triton-bitwise`` versus ``reference-hip``: acceptance is
+The headline column is ``triton-bitwise`` versus ``reference-native``: acceptance is
 0 mismatched elements on out, lse, dQ, dK and dV.
 """
 
 from __future__ import annotations
 
 import argparse
+import gc
 import json
 import math
 import multiprocessing as mp
@@ -38,6 +39,8 @@
 import queue
 import statistics
 import tempfile
+import threading
+import time
 import traceback
 from datetime import timedelta
 from pathlib import Path
@@ -135,10 +138,67 @@ def _gpu_event_samples(function: Callable[[], Any], *, warmup: int, samples: int
     return [float(start.elapsed_time(end)) for start, end in events]
 
 
-def _peak_memory_mib(function: Callable[[], Any]) -> float:
+def _host_wall_samples(function: Callable[[], Any], *, warmup: int, samples: int) -> list[float]:
+    """Wall-clock timing for host execution, where CUDA events do not apply."""
+    for _ in range(warmup):
+        function()
+    timings = []
+    for _ in range(samples):
+        start = time.perf_counter()
+        function()
+        timings.append((time.perf_counter() - start) * 1000.0)
+    return timings
+
+
+def _timed_samples(
+    function: Callable[[], Any], *, warmup: int, samples: int, device: torch.device
+) -> list[float]:
+    if device.type == "cuda":
+        return _gpu_event_samples(function, warmup=warmup, samples=samples)
+    return _host_wall_samples(function, warmup=warmup, samples=samples)
+
+
+def _rss_mib() -> float:
+    with open("/proc/self/statm", "r", encoding="ascii") as handle:
+        resident_pages = int(handle.read().split()[1])
+    return resident_pages * os.sysconf("SC_PAGE_SIZE") / (1024.0 * 1024.0)
+
+
+def _host_peak_rss_mib(function: Callable[[], Any]) -> float:
+    """Peak resident-set increase during one host call, sampled from /proc.
+
+    The closest host analogue of ``torch.cuda.max_memory_allocated``, but an RSS
+    high-water delta rather than an allocator statistic: it includes caching-allocator
+    reuse and page granularity, so it is an approximation and not directly comparable
+    to the device figures. A call served from already-resident pages can report ~0.
+    """
+    gc.collect()
+    baseline = _rss_mib()
+    peak = baseline
+    stop = threading.Event()
+
+    def sampler() -> None:
+        nonlocal peak
+        while not stop.is_set():
+            peak = max(peak, _rss_mib())
+            stop.wait(0.001)
+
+    thread = threading.Thread(target=sampler, daemon=True)
+    thread.start()
+    try:
+        function()
+    finally:
+        stop.set()
+        thread.join()
+    return float(max(peak, _rss_mib()) - baseline)
+
+
+def _peak_memory_mib(function: Callable[[], Any], device: torch.device) -> float:
     """Peak memory used by one call, above what was live before it."""
+    if device.type != "cuda":
+        return _host_peak_rss_mib(function)
     torch.cuda.synchronize()
-    torch.cuda.empty_cache()
+    _empty_cache(device)
     torch.cuda.reset_peak_memory_stats()
     baseline = torch.cuda.memory_allocated()
     function()
@@ -146,6 +206,13 @@ def _peak_memory_mib(function: Callable[[], Any]) -> float:
     return float((torch.cuda.max_memory_allocated() - baseline) / (1024.0 * 1024.0))
 
 
+def _empty_cache(device: torch.device) -> None:
+    if device.type == "cuda":
+        torch.cuda.empty_cache()
+    else:
+        gc.collect()
+
+
 # ---------------------------------------------------------------------------
 # Attention paths
 # ---------------------------------------------------------------------------
@@ -216,11 +283,27 @@ def _sdpa_forward(q, k, v, *, causal, scale):
 class _Paths:
     """Lazily constructed attention paths, so a missing backend skips one row."""
 
-    def __init__(self) -> None:
+    def __init__(self, device: torch.device) -> None:
+        self.device = device
         self.errors: dict[str, str] = {}
-        self.strict = self._try("strict-aiter", self._make_strict)
-        self.reference = self._try("reference-hip", self._make_reference)
-        self.triton = self._try("triton-bitwise", self._make_triton)
+        # The PyTorch reference is the only non-SDPA path that also runs on the host.
+        self.native = self._try("pytorch-native", self._make_native)
+        self.is_rocm = torch.version.hip is not None
+        if device.type == "cuda":
+            if self.is_rocm:
+                self.strict = self._try("strict-aiter", self._make_strict)
+                self.strict_fa4 = None
+                self.errors["strict-fa4"] = "CUDA-only path; this run is ROCm"
+            else:
+                self.strict = None
+                self.errors["strict-aiter"] = "ROCm-only path; this run is CUDA"
+                self.strict_fa4 = self._try("strict-fa4", self._make_strict_fa4)
+            self.reference = self._try("reference-native", self._make_reference)
+            self.triton = self._try("triton-bitwise", self._make_triton)
+        else:
+            self.strict = self.strict_fa4 = self.reference = self.triton = None
+            for name in ("strict-aiter", "strict-fa4", "reference-native", "triton-bitwise"):
+                self.errors[name] = "GPU-only path; not available on the host"
 
     def _try(self, name: str, factory: Callable[[], Any]) -> Any:
         try:
@@ -229,12 +312,24 @@ def _try(self, name: str, factory: Callable[[], Any]) -> Any:
             self.errors[name] = f"{type(exc).__name__}: {exc}"
             return None
 
+    @staticmethod
+    def _make_native():
+        from rl_engine.kernels.ops.pytorch.attention.standard_attn import NativeAttentionOp
+
+        return NativeAttentionOp()
+
     @staticmethod
     def _make_strict():
         from rl_engine.kernels.ops.rocm.attention.flash_attn import StrictRocmAiterCKAttentionCore
 
         return StrictRocmAiterCKAttentionCore()
 
+    @staticmethod
+    def _make_strict_fa4():
+        from rl_engine.kernels.ops.cuda.attention.flash_attn import StrictFlashAttention4Core
+
+        return StrictFlashAttention4Core()
+
     @staticmethod
     def _make_reference():
         from rl_engine.kernels.ops.cuda.attention.deterministic_attn import (
@@ -243,18 +338,26 @@ def _make_reference():
 
         return DeterministicAttentionOp()
 
-    @staticmethod
-    def _make_triton():
+    def _make_triton(self):
         from rl_engine.kernels.ops.triton.attention.deterministic_attn import (
+            BITWISE_LIBM_PARITY,
             TritonDeterministicAttentionOp,
         )
 
-        return TritonDeterministicAttentionOp()
+        # The bitwise expf/logf sequence is only ported for HIP, so on CUDA the op
+        # refuses by default. Measure it anyway, but the report must not call it bitwise.
+        return TritonDeterministicAttentionOp(require_bitwise_libm=BITWISE_LIBM_PARITY)
 
     def runner(self, name: str, q, k, v, *, causal: bool, scale: float, positions):
         """Return ``() -> (out, lse|None)`` for one path, or None when unavailable."""
         if name == "sdpa":
             return lambda: (_sdpa_forward(q, k, v, causal=causal, scale=scale), None)
+        if name == "pytorch-native" and self.native is not None:
+            # NativeAttentionOp is the repo's ground-truth reference; it returns out only.
+            return lambda: (
+                self.native.forward(q, k, v, causal=causal, scale=scale),
+                None,
+            )
         if name == "strict-aiter" and self.strict is not None:
 
             def run_strict():
@@ -270,14 +373,36 @@ def run_strict():
                 return result.out, result.lse
 
             return run_strict
-        if name == "reference-hip" and self.reference is not None:
+        if name == "strict-fa4" and self.strict_fa4 is not None:
+
+            def run_fa4():
+                result = self.strict_fa4.forward_with_lse(
+                    q,
+                    k,
+                    v,
+                    causal=causal,
+                    scale=scale,
+                    query_position_ids=positions if causal else None,
+                    key_position_ids=positions if causal else None,
+                )
+                return result.out, result.lse
+
+            return run_fa4
+        if name == "reference-native" and self.reference is not None:
             return lambda: self.reference.forward_with_lse(q, k, v, causal=causal, scale=scale)
         if name == "triton-bitwise" and self.triton is not None:
             return lambda: self.triton.forward_with_lse(q, k, v, causal=causal, scale=scale)
         return None
 
 
-PATH_NAMES = ("sdpa", "strict-aiter", "reference-hip", "triton-bitwise")
+PATH_NAMES = (
+    "sdpa",
+    "pytorch-native",
+    "strict-aiter",
+    "strict-fa4",
+    "reference-native",
+    "triton-bitwise",
+)
 
 
 def _single_gpu_benchmarks(
@@ -328,8 +453,10 @@ def _single_gpu_benchmarks(
                     None if lse is None else lse.detach().clone(),
                 )
 
-                forward = _summary_ms(_gpu_event_samples(runner, warmup=warmup, samples=samples))
-                forward_peak = _peak_memory_mib(runner)
+                forward = _summary_ms(
+                    _timed_samples(runner, warmup=warmup, samples=samples, device=device)
+                )
+                forward_peak = _peak_memory_mib(runner, device)
 
                 entry: dict[str, Any] = {
                     "forward": forward,
@@ -350,20 +477,23 @@ def _single_gpu_benchmarks(
                 )
                 if training is not None:
                     entry["train_fwd_bwd"] = _summary_ms(
-                        _gpu_event_samples(
-                            training, warmup=max(1, warmup // 2), samples=training_samples
+                        _timed_samples(
+                            training,
+                            warmup=max(1, warmup // 2),
+                            samples=training_samples,
+                            device=device,
                         )
                     )
-                    entry["train_peak_mib"] = _peak_memory_mib(training)
+                    entry["train_peak_mib"] = _peak_memory_mib(training, device)
 
                 row["paths"][name] = entry
                 del out, lse, repeat_out, repeat_lse
-                torch.cuda.empty_cache()
+                _empty_cache(device)
 
             # Headline: Triton must be bit-identical to the native reference core.
-            if "triton-bitwise" in captured and "reference-hip" in captured:
+            if "triton-bitwise" in captured and "reference-native" in captured:
                 t_out, t_lse = captured["triton-bitwise"]
-                r_out, r_lse = captured["reference-hip"]
+                r_out, r_lse = captured["reference-native"]
                 row["triton_vs_reference"] = {
                     "out_mismatched": _mismatch_count(t_out, r_out),
                     "lse_mismatched": _mismatch_count(t_lse, r_lse),
@@ -372,10 +502,12 @@ def _single_gpu_benchmarks(
                 }
             # The production core is a different vendor kernel; report the gap, do
             # not claim parity with it.
-            if "strict-aiter" in captured and "reference-hip" in captured:
-                s_out, s_lse = captured["strict-aiter"]
-                r_out, r_lse = captured["reference-hip"]
+            production = "strict-aiter" if "strict-aiter" in captured else "strict-fa4"
+            if production in captured and "reference-native" in captured:
+                s_out, s_lse = captured[production]
+                r_out, r_lse = captured["reference-native"]
                 row["strict_vs_reference"] = {
+                    "production_path": production,
                     "out": _accuracy(s_out, r_out),
                     "lse": _accuracy(s_lse, r_lse),
                     "out_mismatched": _mismatch_count(s_out, r_out),
@@ -383,7 +515,7 @@ def _single_gpu_benchmarks(
 
             cases.append(row)
             del q, k, v, oracle_out, oracle_lse, captured
-            torch.cuda.empty_cache()
+            _empty_cache(device)
     return cases
 
 
@@ -400,9 +532,23 @@ def train_sdpa() -> None:
 
         return train_sdpa
 
+    if name == "pytorch-native":
+        if paths.native is None:
+            return None
+
+        def train_native() -> None:
+            qr = q.detach().requires_grad_(True)
+            kr = k.detach().requires_grad_(True)
+            vr = v.detach().requires_grad_(True)
+            out = paths.native.forward(qr, kr, vr, causal=causal, scale=scale)
+            out.sum().backward()
+
+        return train_native
+
     op = {
         "strict-aiter": paths.strict,
-        "reference-hip": paths.reference,
+        "strict-fa4": paths.strict_fa4,
+        "reference-native": paths.reference,
         "triton-bitwise": paths.triton,
     }.get(name)
     if op is None:
@@ -413,7 +559,7 @@ def train_op() -> None:
         kr = k.detach().requires_grad_(True)
         vr = v.detach().requires_grad_(True)
         kwargs: dict[str, Any] = {"causal": causal, "scale": scale}
-        if name == "strict-aiter" and causal:
+        if name in ("strict-aiter", "strict-fa4") and causal:
             kwargs["query_position_ids"] = positions
             kwargs["key_position_ids"] = positions
         result = op.forward_with_lse(qr, kr, vr, **kwargs)
@@ -452,14 +598,14 @@ def _backward_parity(
             generator=torch.Generator(device=device).manual_seed(100),
         )
         grads = {}
-        for name, op in (("reference-hip", paths.reference), ("triton-bitwise", paths.triton)):
+        for name, op in (("reference-native", paths.reference), ("triton-bitwise", paths.triton)):
             qr = q.detach().requires_grad_(True)
             kr = k.detach().requires_grad_(True)
             vr = v.detach().requires_grad_(True)
             out, _lse = op.forward_with_lse(qr, kr, vr, causal=True, scale=scale)
             out.backward(grad_out)
             grads[name] = (qr.grad.clone(), kr.grad.clone(), vr.grad.clone())
-        reference = grads["reference-hip"]
+        reference = grads["reference-native"]
         triton_grads = grads["triton-bitwise"]
         rows.append(
             {
@@ -471,7 +617,7 @@ def _backward_parity(
             }
         )
         del q, k, v, grad_out, grads
-        torch.cuda.empty_cache()
+        _empty_cache(device)
     return rows
 
 
@@ -537,10 +683,10 @@ def _batch_composition(
                 ),
             }
             del batch_out, batch_lse, single_out, single_lse
-            torch.cuda.empty_cache()
+            _empty_cache(device)
         rows.append(row)
         del q, k, v
-        torch.cuda.empty_cache()
+        _empty_cache(device)
     return rows
 
 
@@ -603,20 +749,21 @@ def _tp_head_sensitivity(
                     }
                 )
                 del shard_out, shard_lse
-                torch.cuda.empty_cache()
+                _empty_cache(device)
             del full_out, full_lse
-            torch.cuda.empty_cache()
+            _empty_cache(device)
         del q, k, v
-        torch.cuda.empty_cache()
+        _empty_cache(device)
     return rows
 
 
 def _tp_schedule_forward(paths, q, k, v, scale, positions, schedule):
     """Run the strict core either in one launch or one launch per KV group."""
-    if paths.strict is None:
+    core = paths.strict if paths.strict is not None else paths.strict_fa4
+    if core is None:
         return None
     if schedule == "raw_launch":
-        result = paths.strict.forward_with_lse(
+        result = core.forward_with_lse(
             q,
             k,
             v,
@@ -631,7 +778,7 @@ def _tp_schedule_forward(paths, q, k, v, scale, positions, schedule):
     outs, lses = [], []
     for kv_index in range(k.size(1)):
         lo, hi = kv_index * group, (kv_index + 1) * group
-        result = paths.strict.forward_with_lse(
+        result = core.forward_with_lse(
             q[:, lo:hi],
             k[:, kv_index : kv_index + 1],
             v[:, kv_index : kv_index + 1],
@@ -713,7 +860,7 @@ def _distributed_cp_case(
     kv_heads: int,
     head_dim: int,
 ) -> dict[str, Any]:
-    """One CP topology running the real AG/RS schedule over the RCCL transport.
+    """One CP topology running the real AG/RS schedule over the platform's transport.
 
     Schedule (same one ``scripts/ws2_p2p_nccl_attention_reference_check.py`` accepts):
     all-gather Q/K/V and the position ids over the CP group, run the strict core
@@ -725,9 +872,26 @@ def _distributed_cp_case(
         AttentionCPBlockMetadata,
         AttentionCPCommunicationPlan,
         AttentionParallelSpec,
+        CUDAAGRSAttentionCPCommunication,
         RCCLAGRSAttentionCPCommunication,
     )
-    from rl_engine.kernels.ops.rocm.attention.flash_attn import StrictRocmAiterCKAttentionCore
+
+    # ROCm runs AITER over the RCCL transport; CUDA runs FA4 over the CUDA IPC transport.
+    is_rocm = torch.version.hip is not None
+    if is_rocm:
+        from rl_engine.kernels.ops.rocm.attention.flash_attn import (
+            StrictRocmAiterCKAttentionCore as _StrictCore,
+        )
+
+        _Transport = RCCLAGRSAttentionCPCommunication
+        transport_id = "rccl_ag_rs"
+    else:
+        from rl_engine.kernels.ops.cuda.attention.flash_attn import (
+            StrictFlashAttention4Core as _StrictCore,
+        )
+
+        _Transport = CUDAAGRSAttentionCPCommunication
+        transport_id = "cuda_ag_rs"
 
     label, tp_world, cp_world, replicas = topology
     device = torch.device("cuda", rank)
@@ -783,15 +947,15 @@ def _distributed_cp_case(
             cp_world_size=cp_world,
             cp_rank=cp_rank,
         ),
-        backend="rccl_ag_rs",
+        backend=transport_id,
         status="implemented",
         expected_blocks=tuple(blocks),
         expected_kv_token_range=(0, seq_len),
         query_token_ranges=owner_ranges,
     )
 
-    core = StrictRocmAiterCKAttentionCore()
-    communication = RCCLAGRSAttentionCPCommunication(process_group=cp_group)
+    core = _StrictCore()
+    communication = _Transport(process_group=cp_group)
 
     query_start, query_end = owner_ranges[cp_rank]
     q_local = q[:, :, query_start:query_end, :].contiguous()
@@ -820,19 +984,23 @@ def cp_forward():
     forward_ms = _summary_ms(
         _gpu_event_samples(lambda: cp_forward(), warmup=warmup, samples=samples)
     )
-    peak = _peak_memory_mib(lambda: cp_forward())
+    peak = _peak_memory_mib(lambda: cp_forward(), device)
 
     # CP=1 acceptance: the same core on the same full-sequence inputs, then take
     # this rank's query range out of it.
-    single = core.forward_with_lse(
-        q,
-        k,
-        v,
-        causal=True,
-        scale=scale,
-        query_position_ids=positions,
-        key_position_ids=positions,
-    )
+    def cp1_forward():
+        return core.forward_with_lse(
+            q,
+            k,
+            v,
+            causal=True,
+            scale=scale,
+            query_position_ids=positions,
+            key_position_ids=positions,
+        )
+
+    single = cp1_forward()
+    cp1_ms = _summary_ms(_gpu_event_samples(cp1_forward, warmup=warmup, samples=samples))
     expected_out = single.out[:, :, query_start:query_end, :].contiguous()
     expected_lse = single.lse[:, :, query_start:query_end].contiguous()
 
@@ -868,8 +1036,9 @@ def cp_forward():
         "seq_len": seq_len,
         "local_q_heads": local_q_heads,
         "local_kv_heads": local_kv_heads,
-        "transport": "rccl_ag_rs",
+        "transport": transport_id,
         "forward": forward_ms,
+        "cp1_baseline": cp1_ms,
         "peak_mib_per_rank": peak,
         "out_bitwise_vs_cp1": bool(flags[0].item() == 1.0),
         "lse_bitwise_vs_cp1": bool(flags[1].item() == 1.0),
@@ -942,8 +1111,33 @@ def _run_distributed_topology(
 # ---------------------------------------------------------------------------
 
 
-def _environment() -> dict[str, Any]:
-    properties = torch.cuda.get_device_properties(0) if torch.cuda.is_available() else None
+def _default_platform_label(device: torch.device) -> str:
+    if device.type != "cuda":
+        return "cpu"
+    name = torch.cuda.get_device_name(0).lower()
+    if "mi300" in name:
+        return "mi300x"
+    if "h100" in name:
+        return "h100"
+    return name.replace(" ", "-")[:24]
+
+
+def _load_comparisons(specs: list[str]) -> list[tuple[str, dict[str, Any]]]:
+    """Parse ``--compare-with LABEL=PATH`` into (label, payload) pairs."""
+    loaded: list[tuple[str, dict[str, Any]]] = []
+    for spec in specs:
+        if "=" not in spec:
+            raise SystemExit(f"--compare-with expects LABEL=PATH, got {spec!r}")
+        label, _, path = spec.partition("=")
+        loaded.append((label, json.loads(Path(path).read_text())))
+    return loaded
+
+
+def _environment(device: torch.device | None = None) -> dict[str, Any]:
+    on_gpu = device is None or device.type == "cuda"
+    properties = (
+        torch.cuda.get_device_properties(0) if on_gpu and torch.cuda.is_available() else None
+    )
     try:
         from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
 
@@ -957,7 +1151,9 @@ def _environment() -> dict[str, Any]:
     except Exception:  # noqa: BLE001
         triton_version = "unavailable"
     return {
-        "gpu": properties.name if properties else "n/a",
+        "cpu_count": os.cpu_count(),
+        "torch_threads": torch.get_num_threads(),
+        "gpu": properties.name if properties else "n/a (host execution)",
         "architecture": getattr(properties, "gcnArchName", "unknown") if properties else "n/a",
         "gpu_count": torch.cuda.device_count() if torch.cuda.is_available() else 0,
         "hip": torch.version.hip,
@@ -970,6 +1166,13 @@ def _environment() -> dict[str, Any]:
     }
 
 
+def _case_for(payload: dict[str, Any], dtype: str, seq_len: int) -> dict[str, Any] | None:
+    for case in payload.get("single_gpu", {}).get("cases", []):
+        if case["dtype"] == dtype and case["seq_len"] == seq_len:
+            return case
+    return None
+
+
 def _fmt(value: Any, spec: str = ".4f") -> str:
     if value is None:
         return "n/a"
@@ -983,8 +1186,14 @@ def _fmt(value: Any, spec: str = ".4f") -> str:
         return str(value)
 
 
-def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
-    environment = payload["environment"]
+def _write_report(
+    payload: dict[str, Any],
+    output_directory: Path,
+    comparisons: list[tuple[str, dict[str, Any]]] | None = None,
+) -> None:
+    platforms: list[tuple[str, dict[str, Any]]] = [
+        (payload.get("platform_label", "this run"), payload)
+    ] + list(comparisons or [])
     configuration = payload["configuration"]
     lines: list[str] = []
     add = lines.append
@@ -996,12 +1205,25 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
     add("")
     add("## Environment")
     add("")
-    add("| Item | Value |")
-    add("|---|---|")
-    for key in sorted(environment):
-        value = environment[key]
-        add(f"| {key} | {value if not isinstance(value, list) else ', '.join(value) or 'none'} |")
+    keys = sorted({k for _, pl in platforms for k in pl["environment"]})
+    add("| Item | " + " | ".join(label for label, _ in platforms) + " |")
+    add("|---|" + "---|" * len(platforms))
+    for key in keys:
+        cells = []
+        for _, pl in platforms:
+            value = pl["environment"].get(key, "n/a")
+            if isinstance(value, list):
+                value = ", ".join(value) or "none"
+            cells.append(str(value))
+        add(f"| {key} | " + " | ".join(cells) + " |")
     add("")
+    if len(platforms) > 1:
+        add(
+            "A missing row below means the backend cannot exist on that platform, not that it "
+            "failed: `strict-aiter` is ROCm-only, `reference-native` and `triton-bitwise` need a "
+            "GPU, and only `sdpa` and `pytorch-native` also run on the host."
+        )
+        add("")
 
     add("## Methodology")
     add("")
@@ -1022,12 +1244,12 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
         "per (batch row, KV group). See the per-KV-group schedule table for that cost."
     )
     add(
-        "  - `reference-hip`: `_C.deterministic_attention_forward/backward`, the materializing "
+        "  - `reference-native`: `_C.deterministic_attention_forward/backward`, the materializing "
         "FP32 reference core hipified from the shared `.cu`."
     )
     add(
         "  - `triton-bitwise`: `TritonDeterministicAttentionOp`, whose contract is bit-identity "
-        "with `reference-hip`."
+        "with `reference-native`."
     )
     add(
         "- Timing: CUDA events, median and p95. Peak memory is the per-call increase in "
@@ -1087,54 +1309,81 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
     add("")
 
     # ---- single GPU speed
+    multi = len(platforms) > 1
+    platform_column = "Platform | " if multi else ""
+    platform_rule = "---|" if multi else ""
     for dtype in configuration["dtypes"]:
-        cases = [c for c in payload["single_gpu"]["cases"] if c["dtype"] == dtype]
-        if not cases:
+        seq_lens = sorted(
+            {
+                c["seq_len"]
+                for _, pl in platforms
+                for c in pl.get("single_gpu", {}).get("cases", [])
+                if c["dtype"] == dtype
+            }
+        )
+        if not seq_lens:
             continue
-        add(f"## Single-GPU Attention ({dtype})")
+        add(f"## Single-device Attention ({dtype})")
         add("")
         add("### Forward")
         add("")
         add(
-            "| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | "
-            "lse max-abs vs FP64 | Repeat |"
+            f"| S | {platform_column}Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | "
+            "out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |"
         )
-        add("|---:|---|---:|---:|---:|---:|---:|---:|:---:|")
-        for case in cases:
-            baseline = case["paths"].get("sdpa", {}).get("forward", {}).get("median_ms")
-            for name in PATH_NAMES:
-                entry = case["paths"].get(name)
-                if not entry:
+        add(f"|---:|{platform_rule}---|---:|---:|---:|---:|---:|---:|:---:|")
+        for seq_len in seq_lens:
+            for label, pl in platforms:
+                case = _case_for(pl, dtype, seq_len)
+                if case is None:
                     continue
-                median = entry["forward"]["median_ms"]
-                ratio = f"{median / baseline:.2f}x" if baseline else "n/a"
-                add(
-                    f"| {case['seq_len']} | {name} | {median:.4f} | "
-                    f"{entry['forward']['p95_ms']:.4f} | {ratio} | "
-                    f"{entry['forward_peak_mib']:.1f} | "
-                    f"{entry['out_vs_fp64']['max_abs']:.3e} | "
-                    f"{_fmt(entry.get('lse_vs_fp64', {}).get('max_abs'), '.3e')} | "
-                    f"{_fmt(entry['repeat_bitwise'])} |"
-                )
+                baseline = case["paths"].get("sdpa", {}).get("forward", {}).get("median_ms")
+                for name in PATH_NAMES:
+                    entry = case["paths"].get(name)
+                    if not entry:
+                        continue
+                    median = entry["forward"]["median_ms"]
+                    ratio = f"{median / baseline:.2f}x" if baseline else "n/a"
+                    prefix = f"{label} | " if multi else ""
+                    add(
+                        f"| {seq_len} | {prefix}{name} | {median:.4f} | "
+                        f"{entry['forward']['p95_ms']:.4f} | {ratio} | "
+                        f"{entry['forward_peak_mib']:.1f} | "
+                        f"{entry['out_vs_fp64']['max_abs']:.3e} | "
+                        f"{_fmt(entry.get('lse_vs_fp64', {}).get('max_abs'), '.3e')} | "
+                        f"{_fmt(entry['repeat_bitwise'])} |"
+                    )
         add("")
         add("### Forward+backward")
         add("")
-        add("| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |")
-        add("|---:|---|---:|---:|---:|---:|")
-        for case in cases:
-            baseline = case["paths"].get("sdpa", {}).get("train_fwd_bwd", {}).get("median_ms")
-            for name in PATH_NAMES:
-                entry = case["paths"].get(name)
-                if not entry or "train_fwd_bwd" not in entry:
+        add(f"| S | {platform_column}Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |")
+        add(f"|---:|{platform_rule}---|---:|---:|---:|---:|")
+        for seq_len in seq_lens:
+            for label, pl in platforms:
+                case = _case_for(pl, dtype, seq_len)
+                if case is None:
                     continue
-                median = entry["train_fwd_bwd"]["median_ms"]
-                ratio = f"{median / baseline:.2f}x" if baseline else "n/a"
-                add(
-                    f"| {case['seq_len']} | {name} | {median:.4f} | "
-                    f"{entry['train_fwd_bwd']['p95_ms']:.4f} | {ratio} | "
-                    f"{entry['train_peak_mib']:.1f} |"
-                )
+                baseline = case["paths"].get("sdpa", {}).get("train_fwd_bwd", {}).get("median_ms")
+                for name in PATH_NAMES:
+                    entry = case["paths"].get(name)
+                    if not entry or "train_fwd_bwd" not in entry:
+                        continue
+                    median = entry["train_fwd_bwd"]["median_ms"]
+                    ratio = f"{median / baseline:.2f}x" if baseline else "n/a"
+                    prefix = f"{label} | " if multi else ""
+                    add(
+                        f"| {seq_len} | {prefix}{name} | {median:.4f} | "
+                        f"{entry['train_fwd_bwd']['p95_ms']:.4f} | {ratio} | "
+                        f"{entry['train_peak_mib']:.1f} |"
+                    )
         add("")
+        if multi:
+            add(
+                "Host peak memory is an RSS high-water delta sampled from `/proc`, not an "
+                "allocator statistic, so the `cpu` rows approximate and are not directly "
+                "comparable to the device figures."
+            )
+            add("")
 
     # ---- production core vs reference
     add("## Production core versus the reference core")
@@ -1265,7 +1514,7 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
     add("## Figures")
     add("")
     add(
-        "`reference-hip` and `triton-bitwise` allocate exactly the same buffers, so their "
+        "`reference-native` and `triton-bitwise` allocate exactly the same buffers, so their "
         "memory curves coincide and the later-drawn series hides the earlier one."
     )
     add("")
@@ -1275,6 +1524,8 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
     add("")
     add("![Single-device peak memory](single_gpu_memory.png)")
     add("")
+    add("![Bitwise exactness matrix](exactness_matrix.png)")
+    add("")
     add("![TP-degree invariance](tp_degree_invariance.png)")
     add("")
     if distributed:
@@ -1296,7 +1547,7 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
     style = {
         "sdpa": {"marker": "o", "color": "#888888", "linestyle": "--"},
         "strict-aiter": {"marker": "s", "color": "#d62728"},
-        "reference-hip": {"marker": "^", "color": "#1f77b4"},
+        "reference-native": {"marker": "^", "color": "#1f77b4"},
         "triton-bitwise": {"marker": "D", "color": "#2ca02c"},
     }
 
@@ -1395,24 +1646,115 @@ def draw(axis, key, sub, title, ylabel, log_y):
         figure.savefig(output_directory / "tp_degree_invariance.png", dpi=180)
         plt.close(figure)
 
-    distributed = payload.get("distributed") or []
+    distributed = [r for r in (payload.get("distributed") or []) if "error" not in r]
     if distributed:
-        figure, axis = plt.subplots(figsize=(max(8, 1.5 * len(distributed)), 4.8))
+        figure, axis = plt.subplots(figsize=(max(9, 1.7 * len(distributed)), 5.0))
         labels = [f"{r['topology']}\nS={r['seq_len']}" for r in distributed]
-        axis.bar(
-            range(len(distributed)),
-            [r["forward"]["median_ms"] for r in distributed],
-            color="#2ca02c",
+        xs = list(range(len(distributed)))
+        width = 0.38
+        baseline = [r.get("cp1_baseline", {}).get("median_ms", float("nan")) for r in distributed]
+        measured = [r["forward"]["median_ms"] for r in distributed]
+        bars_a = axis.bar(
+            [x - width / 2 for x in xs], baseline, width, label="CP=1 baseline", color="#888888"
+        )
+        bars_b = axis.bar(
+            [x + width / 2 for x in xs], measured, width, label="CP AG/RS", color="#2ca02c"
         )
-        axis.set_xticks(range(len(distributed)))
+        for bars in (bars_a, bars_b):
+            axis.bar_label(bars, fmt="%.2f", fontsize=8, padding=2)
+        axis.set_xticks(xs)
         axis.set_xticklabels(labels, fontsize=9)
         axis.set_ylabel("median ms")
-        axis.set_title("Strict ROCm Attention + RCCL AG/RS CP transport, BF16")
+        axis.set_title(
+            "Strict ROCm Attention: CP=1 baseline vs RCCL AG/RS CP transport, BF16 S=4096"
+        )
         axis.grid(True, axis="y", alpha=0.3)
+        axis.legend()
         figure.tight_layout()
         figure.savefig(output_directory / "distributed_cp_latency.png", dpi=180)
         plt.close(figure)
 
+    # ---- exactness matrix, in the shape PR #325 uses for its topology mismatch heatmap
+    single_rows, single_labels = [], []
+    for case in payload["single_gpu"]["cases"]:
+        parity = case.get("triton_vs_reference")
+        if not parity:
+            continue
+        grads = next(
+            (r for r in payload.get("backward_parity", []) if r["seq_len"] == case["seq_len"]),
+            {},
+        )
+        use_grads = case["dtype"] == "bf16" and grads
+        # Gradients are only measured on the BF16 sweep; NaN renders as "not measured"
+        # rather than a zero that would read as "measured and equal".
+        missing = float("nan")
+        single_rows.append(
+            [
+                parity["out_mismatched"],
+                parity["lse_mismatched"],
+                grads.get("dq_mismatched", missing) if use_grads else missing,
+                grads.get("dk_mismatched", missing) if use_grads else missing,
+                grads.get("dv_mismatched", missing) if use_grads else missing,
+            ]
+        )
+        single_labels.append(f"{case['dtype']}, S={case['seq_len']}")
+
+    dist_rows = [
+        [r["out_mismatched_all_ranks"], r["lse_mismatched_all_ranks"]] for r in distributed
+    ]
+    dist_labels = [
+        f"{r['topology']} (TP={r['tp_world_size']}, CP={r['cp_world_size']})" for r in distributed
+    ]
+
+    if single_rows or dist_rows:
+        panels = []
+        if single_rows:
+            panels.append(
+                (
+                    single_rows,
+                    single_labels,
+                    ["out", "lse", "dQ", "dK", "dV"],
+                    "Triton core vs native reference core",
+                )
+            )
+        if dist_rows:
+            panels.append((dist_rows, dist_labels, ["out", "lse"], "CP topology vs CP=1"))
+        figure, axes = plt.subplots(1, len(panels), figsize=(7.5 * len(panels), 5.6), squeeze=False)
+        for axis, (rows, row_labels, col_labels, title) in zip(axes.flat, panels):
+            matrix = [[float(value) for value in row] for row in rows]
+            colormap = plt.get_cmap("RdYlGn_r").copy()
+            colormap.set_bad("#d9d9d9")
+            image = axis.imshow(matrix, aspect="auto", cmap=colormap, vmin=0, vmax=1.0)
+            axis.grid(False)
+            for y in range(len(matrix)):
+                for x in range(len(matrix[y])):
+                    cell = matrix[y][x]
+                    label = "n/m" if cell != cell else str(int(cell))
+                    axis.text(
+                        x,
+                        y,
+                        label,
+                        ha="center",
+                        va="center",
+                        fontsize=15,
+                        fontweight="bold",
+                        color="#222222",
+                    )
+            axis.set_xticks(range(len(col_labels)), col_labels)
+            axis.set_yticks(range(len(row_labels)), row_labels)
+            axis.set_xlabel("Compared tensor")
+            axis.set_title(title)
+            figure.colorbar(image, ax=axis, fraction=0.03, pad=0.03).set_label(
+                "Mismatched elements"
+            )
+        figure.suptitle(
+            "Bitwise exactness — every measured cell must be 0 (n/m = not measured)",
+            fontsize=13,
+        )
+        figure.tight_layout(rect=(0, 0, 1, 0.95))
+        figure.savefig(output_directory / "exactness_matrix.png", dpi=180)
+        plt.close(figure)
+
 
 def main() -> None:
     parser = argparse.ArgumentParser(description=__doc__)
@@ -1432,6 +1774,23 @@ def main() -> None:
         default=DEFAULT_SEQ_LENS,
     )
     parser.add_argument("--dtypes", default="bf16,fp16")
+    parser.add_argument(
+        "--device",
+        default="auto",
+        help="auto | cuda | cpu. On cpu only the sdpa and pytorch-native paths exist.",
+    )
+    parser.add_argument(
+        "--platform-label",
+        default=None,
+        help="Name for this run in merged reports (default: mi300x / h100 / cpu, auto-detected).",
+    )
+    parser.add_argument(
+        "--compare-with",
+        action="append",
+        default=[],
+        metavar="LABEL=PATH",
+        help="Merge another platform's results.json into the report. Repeatable.",
+    )
     parser.add_argument("--skip-distributed", action="store_true")
     parser.add_argument("--skip-figures", action="store_true")
     parser.add_argument(
@@ -1448,23 +1807,29 @@ def main() -> None:
 
     if arguments.report_only:
         payload = json.loads((arguments.output_dir / "results.json").read_text())
-        _write_report(payload, arguments.output_dir)
+        _write_report(payload, arguments.output_dir, _load_comparisons(arguments.compare_with))
         if not arguments.skip_figures:
             _write_figures(payload, arguments.output_dir)
         print(json.dumps({"output_dir": str(arguments.output_dir)}, indent=2))
         return
 
-    if not torch.cuda.is_available():
-        raise SystemExit("this benchmark requires a ROCm or CUDA GPU")
+    if arguments.device == "auto":
+        device_type = "cuda" if torch.cuda.is_available() else "cpu"
+    else:
+        device_type = arguments.device
+    if device_type == "cuda" and not torch.cuda.is_available():
+        raise SystemExit("--device cuda requested but no CUDA/ROCm device is visible")
 
-    device = torch.device("cuda", 0)
-    torch.cuda.set_device(device)
+    device = torch.device(device_type, 0) if device_type == "cuda" else torch.device("cpu")
+    if device.type == "cuda":
+        torch.cuda.set_device(device)
     dtype_map = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
     dtypes = tuple(dtype_map[name] for name in arguments.dtypes.split(","))
 
-    paths = _Paths()
+    paths = _Paths(device)
     payload: dict[str, Any] = {
-        "environment": _environment(),
+        "platform_label": arguments.platform_label or _default_platform_label(device),
+        "environment": _environment(device),
         "configuration": {
             "batch": arguments.batch,
             "q_heads": arguments.q_heads,
@@ -1518,18 +1883,22 @@ def main() -> None:
             head_dim=arguments.head_dim,
             device=device,
         )
-        payload["tp_head_sensitivity"] = _tp_head_sensitivity(
-            paths=paths,
-            seq_lens=arguments.seq_lens,
-            tp_degrees=DEFAULT_TP_DEGREES,
-            q_heads=arguments.q_heads,
-            kv_heads=arguments.kv_heads,
-            head_dim=arguments.head_dim,
-            device=device,
+        payload["tp_head_sensitivity"] = (
+            []
+            if device.type != "cuda"
+            else _tp_head_sensitivity(
+                paths=paths,
+                seq_lens=arguments.seq_lens,
+                tp_degrees=DEFAULT_TP_DEGREES,
+                q_heads=arguments.q_heads,
+                kv_heads=arguments.kv_heads,
+                head_dim=arguments.head_dim,
+                device=device,
+            )
         )
 
     distributed: list[dict[str, Any]] = []
-    if not arguments.skip_distributed:
+    if not arguments.skip_distributed and device.type == "cuda":
         available = torch.cuda.device_count()
         for topology in DISTRIBUTED_TOPOLOGIES:
             if topology[1] * topology[2] * topology[3] > available:
@@ -1554,7 +1923,7 @@ def main() -> None:
 
     arguments.output_dir.mkdir(parents=True, exist_ok=True)
     (arguments.output_dir / "results.json").write_text(json.dumps(payload, indent=2) + "\n")
-    _write_report(payload, arguments.output_dir)
+    _write_report(payload, arguments.output_dir, _load_comparisons(arguments.compare_with))
     if not arguments.skip_figures:
         _write_figures(payload, arguments.output_dir)
     print(json.dumps({"output_dir": str(arguments.output_dir)}, indent=2))
diff --git a/benchmarks/results/ws2_rocm_mi300x/distributed_cp_latency.png b/benchmarks/results/ws2_rocm_mi300x/distributed_cp_latency.png
index 29a1358e83726ecaa76c5bed2cac566730ed6a52..e9a33b45e87ddcf2a107e8e2a97cb901522d1fb0 100644
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HcmV?d00001

diff --git a/benchmarks/results/ws2_rocm_mi300x/report.md b/benchmarks/results/ws2_rocm_mi300x/report.md
index 2f26369e..93e672c4 100644
--- a/benchmarks/results/ws2_rocm_mi300x/report.md
+++ b/benchmarks/results/ws2_rocm_mi300x/report.md
@@ -5,7 +5,7 @@
 
 ## Environment
 
-| Item | Value |
+| Item | this run |
 |---|---|
 | architecture | gfx942:sramecc+:xnack- |
 | cuda | None |
@@ -58,7 +58,7 @@ Acceptance is 0 mismatched elements. This is the contract the Triton core exists
 
 `dQ/dK/dV` are measured on the BF16 sweep only; `n/a` marks the FP16 rows.
 
-## Single-GPU Attention (bf16)
+## Single-device Attention (bf16)
 
 ### Forward
 
@@ -102,7 +102,7 @@ Acceptance is 0 mismatched elements. This is the contract the Triton core exists
 | 4096 | reference-hip | 173.3365 | 173.5448 | 53.39x | 4209.0 |
 | 4096 | triton-bitwise | 304.1032 | 304.4993 | 93.67x | 4209.0 |
 
-## Single-GPU Attention (fp16)
+## Single-device Attention (fp16)
 
 ### Forward
 
@@ -232,12 +232,12 @@ Schedule: all-gather Q/K/V and the position ids over the CP group, run the stric
 
 | Topology | World | TP | CP | Replicas | S | Median (ms) | p95 (ms) | Peak MiB/rank | out bitwise | lse bitwise | Repeat |
 |---|---:|---:|---:|---:|---:|---:|---:|---:|:---:|:---:|:---:|
-| tp1_cp2 | 2 | 1 | 2 | 1 | 4096 | 1.8352 | 1.8726 | 160.5 | yes | yes | yes |
-| tp2_cp2 | 4 | 2 | 2 | 1 | 4096 | 1.2114 | 1.3138 | 80.3 | yes | yes | yes |
-| tp1_cp4 | 4 | 1 | 4 | 1 | 4096 | 1.3973 | 1.4363 | 160.5 | yes | yes | yes |
-| tp2_cp2_x2 | 8 | 2 | 2 | 2 | 4096 | 1.2297 | 1.2948 | 80.3 | yes | yes | yes |
-| tp2_cp4 | 8 | 2 | 4 | 1 | 4096 | 1.2594 | 1.3294 | 80.3 | yes | yes | yes |
-| tp1_cp8 | 8 | 1 | 8 | 1 | 4096 | 1.4112 | 2.2029 | 160.5 | yes | yes | yes |
+| tp1_cp2 | 2 | 1 | 2 | 1 | 4096 | 1.8323 | 1.9342 | 160.5 | yes | yes | yes |
+| tp2_cp2 | 4 | 2 | 2 | 1 | 4096 | 1.2505 | 2.0656 | 80.3 | yes | yes | yes |
+| tp1_cp4 | 4 | 1 | 4 | 1 | 4096 | 1.4118 | 1.4399 | 160.5 | yes | yes | yes |
+| tp2_cp2_x2 | 8 | 2 | 2 | 2 | 4096 | 1.1947 | 1.2464 | 80.3 | yes | yes | yes |
+| tp2_cp4 | 8 | 2 | 4 | 1 | 4096 | 1.2911 | 1.8001 | 80.3 | yes | yes | yes |
+| tp1_cp8 | 8 | 1 | 8 | 1 | 4096 | 1.4065 | 1.9273 | 160.5 | yes | yes | yes |
 
 ## Figures
 
@@ -249,6 +249,8 @@ Schedule: all-gather Q/K/V and the position ids over the CP group, run the stric
 
 ![Single-device peak memory](single_gpu_memory.png)
 
+![Bitwise exactness matrix](exactness_matrix.png)
+
 ![TP-degree invariance](tp_degree_invariance.png)
 
 ![Distributed CP latency](distributed_cp_latency.png)
diff --git a/benchmarks/results/ws2_rocm_mi300x/results.json b/benchmarks/results/ws2_rocm_mi300x/results.json
index 443477a5..f208c1bc 100644
--- a/benchmarks/results/ws2_rocm_mi300x/results.json
+++ b/benchmarks/results/ws2_rocm_mi300x/results.json
@@ -1436,10 +1436,16 @@
       "local_kv_heads": 8,
       "transport": "rccl_ag_rs",
       "forward": {
-        "median_ms": 1.8352065086364746,
-        "p95_ms": 1.8726499021053316,
-        "min_ms": 1.8194220066070557,
-        "max_ms": 1.9234169721603394
+        "median_ms": 1.8322819471359253,
+        "p95_ms": 1.9341571986675263,
+        "min_ms": 1.827394962310791,
+        "max_ms": 2.243252992630005
+      },
+      "cp1_baseline": {
+        "median_ms": 0.5567870140075684,
+        "p95_ms": 0.5777280211448669,
+        "min_ms": 0.542726993560791,
+        "max_ms": 0.6034160256385803
       },
       "peak_mib_per_rank": 160.53173828125,
       "out_bitwise_vs_cp1": true,
@@ -1459,10 +1465,16 @@
       "local_kv_heads": 4,
       "transport": "rccl_ag_rs",
       "forward": {
-        "median_ms": 1.2113595008850098,
-        "p95_ms": 1.3138377845287323,
-        "min_ms": 1.1833579540252686,
-        "max_ms": 1.3238470554351807
+        "median_ms": 1.250518023967743,
+        "p95_ms": 2.065626120567322,
+        "min_ms": 1.1906089782714844,
+        "max_ms": 2.426363945007324
+      },
+      "cp1_baseline": {
+        "median_ms": 0.4008549898862839,
+        "p95_ms": 0.4702322572469717,
+        "min_ms": 0.39270299673080444,
+        "max_ms": 1.2156260013580322
       },
       "peak_mib_per_rank": 80.28173828125,
       "out_bitwise_vs_cp1": true,
@@ -1482,10 +1494,16 @@
       "local_kv_heads": 8,
       "transport": "rccl_ag_rs",
       "forward": {
-        "median_ms": 1.3972755074501038,
-        "p95_ms": 1.436299401521683,
-        "min_ms": 1.3588190078735352,
-        "max_ms": 1.6069079637527466
+        "median_ms": 1.4117770195007324,
+        "p95_ms": 1.4399272024631502,
+        "min_ms": 1.3817729949951172,
+        "max_ms": 1.5584349632263184
+      },
+      "cp1_baseline": {
+        "median_ms": 0.5592510104179382,
+        "p95_ms": 0.5880435585975647,
+        "min_ms": 0.5454099774360657,
+        "max_ms": 0.595084011554718
       },
       "peak_mib_per_rank": 160.53173828125,
       "out_bitwise_vs_cp1": true,
@@ -1505,10 +1523,16 @@
       "local_kv_heads": 4,
       "transport": "rccl_ag_rs",
       "forward": {
-        "median_ms": 1.2296664714813232,
-        "p95_ms": 1.294820535182953,
-        "min_ms": 1.1864819526672363,
-        "max_ms": 1.3393089771270752
+        "median_ms": 1.1946544647216797,
+        "p95_ms": 1.2463674426078797,
+        "min_ms": 1.163167953491211,
+        "max_ms": 1.3284929990768433
+      },
+      "cp1_baseline": {
+        "median_ms": 0.39490649104118347,
+        "p95_ms": 0.40014400929212574,
+        "min_ms": 0.38557299971580505,
+        "max_ms": 0.4380109906196594
       },
       "peak_mib_per_rank": 80.28173828125,
       "out_bitwise_vs_cp1": true,
@@ -1528,10 +1552,16 @@
       "local_kv_heads": 4,
       "transport": "rccl_ag_rs",
       "forward": {
-        "median_ms": 1.2593510150909424,
-        "p95_ms": 1.3294484794139863,
-        "min_ms": 1.2247389554977417,
-        "max_ms": 1.4252369403839111
+        "median_ms": 1.2911379933357239,
+        "p95_ms": 1.8000641107559232,
+        "min_ms": 1.2322700023651123,
+        "max_ms": 5.651195049285889
+      },
+      "cp1_baseline": {
+        "median_ms": 0.39432600140571594,
+        "p95_ms": 0.4069527506828309,
+        "min_ms": 0.38937899470329285,
+        "max_ms": 0.42955800890922546
       },
       "peak_mib_per_rank": 80.28173828125,
       "out_bitwise_vs_cp1": true,
@@ -1551,10 +1581,16 @@
       "local_kv_heads": 8,
       "transport": "rccl_ag_rs",
       "forward": {
-        "median_ms": 1.411196529865265,
-        "p95_ms": 2.2028517603874214,
-        "min_ms": 1.376884937286377,
-        "max_ms": 3.24902606010437
+        "median_ms": 1.406469464302063,
+        "p95_ms": 1.9272569358348848,
+        "min_ms": 1.358538031578064,
+        "max_ms": 1.9903160333633423
+      },
+      "cp1_baseline": {
+        "median_ms": 0.568684995174408,
+        "p95_ms": 0.6006163269281388,
+        "min_ms": 0.5549039840698242,
+        "max_ms": 0.6691939830780029
       },
       "peak_mib_per_rank": 160.53173828125,
       "out_bitwise_vs_cp1": true,
@@ -1662,4 +1698,4 @@
       "launches": 8
     }
   ]
-}
\ No newline at end of file
+}

From fd2b329741b9e9d33458a536877e3d44b4ec2e61 Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sat, 29 Aug 2026 08:38:12 +0000
Subject: [PATCH 25/35] bench(attention): migrate result keys to the
 reference-native rename

The previous commit renamed the reference path reference-hip -> reference-native in
the code (the row is the CUDA build of the same .cu on an NVIDIA host, so the old
name would be wrong in an H100 report) but left the recorded results.json and
report.md on the old key. Nothing errored: PATH_NAMES simply stopped matching, so
regenerating the report silently dropped the reference row entirely -- the one row
the Triton core's whole bitwise claim is measured against.

Rename the key in the stored single-GPU cases and batch-composition rows, record the
production_path the strict-vs-reference gap was measured against, and regenerate.
Values are untouched; this is the same kernel under a name that is honest on both
platforms.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01824WkgMDxtBKD4Ex2NkfYA
---
 benchmarks/results/ws2_rocm_mi300x/report.md  |  46 +--
 .../results/ws2_rocm_mi300x/results.json      | 362 +++++++++---------
 .../ws2_rocm_mi300x/single_gpu_grid.png       | Bin 288104 -> 289285 bytes
 .../ws2_rocm_mi300x/single_gpu_latency.png    | Bin 132823 -> 133223 bytes
 .../ws2_rocm_mi300x/single_gpu_memory.png     | Bin 131593 -> 132071 bytes
 5 files changed, 208 insertions(+), 200 deletions(-)

diff --git a/benchmarks/results/ws2_rocm_mi300x/report.md b/benchmarks/results/ws2_rocm_mi300x/report.md
index 93e672c4..1c4f8f3a 100644
--- a/benchmarks/results/ws2_rocm_mi300x/report.md
+++ b/benchmarks/results/ws2_rocm_mi300x/report.md
@@ -24,8 +24,8 @@
 - Measured paths:
   - `sdpa`: `torch.nn.functional.scaled_dot_product_attention`. **Speed baseline only** — as in PR #325, no accuracy comparison is mixed into the speed table.
   - `strict-aiter`: `StrictRocmAiterCKAttentionCore` called **once for all heads**. This is the core, not the production schedule: the Vime provider launches it once per (batch row, KV group). See the per-KV-group schedule table for that cost.
-  - `reference-hip`: `_C.deterministic_attention_forward/backward`, the materializing FP32 reference core hipified from the shared `.cu`.
-  - `triton-bitwise`: `TritonDeterministicAttentionOp`, whose contract is bit-identity with `reference-hip`.
+  - `reference-native`: `_C.deterministic_attention_forward/backward`, the materializing FP32 reference core hipified from the shared `.cu`.
+  - `triton-bitwise`: `TritonDeterministicAttentionOp`, whose contract is bit-identity with `reference-native`.
 - Timing: CUDA events, median and p95. Peak memory is the per-call increase in `torch.cuda.max_memory_allocated` above what was live before the call.
 - Accuracy is against an FP64 oracle over the same BF16/FP16-rounded inputs. Repeat = two identical calls are bitwise equal; batch-invariant = a row computed alone is bitwise equal to the same row inside a batch.
 - 5 warmups, 20 measured forward samples, 10 measured forward+backward samples. Raw medians, p95, min and max are in `results.json`.
@@ -66,19 +66,19 @@ Acceptance is 0 mismatched elements. This is the contract the Triton core exists
 |---:|---|---:|---:|---:|---:|---:|---:|:---:|
 | 512 | sdpa | 0.0782 | 0.0820 | 1.00x | 12.1 | 8.195e-03 | n/a | yes |
 | 512 | strict-aiter | 0.2428 | 0.2560 | 3.11x | 14.1 | 2.468e-02 | 8.359e-07 | yes |
-| 512 | reference-hip | 0.9659 | 1.0030 | 12.36x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
+| 512 | reference-native | 0.9659 | 1.0030 | 12.36x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
 | 512 | triton-bitwise | 1.3816 | 1.3923 | 17.68x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
 | 1024 | sdpa | 0.1319 | 0.1430 | 1.00x | 24.1 | 1.027e-02 | n/a | yes |
 | 1024 | strict-aiter | 0.2468 | 0.2578 | 1.87x | 28.1 | 2.609e-02 | 1.213e-06 | yes |
-| 1024 | reference-hip | 3.1389 | 3.1914 | 23.79x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
+| 1024 | reference-native | 3.1389 | 3.1914 | 23.79x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
 | 1024 | triton-bitwise | 4.8240 | 4.8720 | 36.56x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
 | 2048 | sdpa | 0.2887 | 0.2975 | 1.00x | 48.3 | 7.994e-03 | n/a | yes |
 | 2048 | strict-aiter | 0.2962 | 0.3394 | 1.03x | 56.3 | 2.027e-02 | 2.288e-06 | yes |
-| 2048 | reference-hip | 12.8467 | 12.9922 | 44.50x | 528.2 | 7.804e-03 | 1.142e-06 | yes |
+| 2048 | reference-native | 12.8467 | 12.9922 | 44.50x | 528.2 | 7.804e-03 | 1.142e-06 | yes |
 | 2048 | triton-bitwise | 19.4226 | 19.5024 | 67.28x | 528.3 | 7.804e-03 | 1.142e-06 | yes |
 | 4096 | sdpa | 0.6936 | 0.7149 | 1.00x | 96.5 | 9.604e-03 | n/a | yes |
 | 4096 | strict-aiter | 0.5644 | 0.6076 | 0.81x | 112.5 | 2.138e-02 | 3.967e-06 | yes |
-| 4096 | reference-hip | 49.3624 | 49.5342 | 71.17x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
+| 4096 | reference-native | 49.3624 | 49.5342 | 71.17x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
 | 4096 | triton-bitwise | 86.0655 | 86.5760 | 124.08x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
 
 ### Forward+backward
@@ -87,19 +87,19 @@ Acceptance is 0 mismatched elements. This is the contract the Triton core exists
 |---:|---|---:|---:|---:|---:|
 | 512 | sdpa | 0.3228 | 0.3510 | 1.00x | 32.2 |
 | 512 | strict-aiter | 0.6039 | 0.6560 | 1.87x | 288.2 |
-| 512 | reference-hip | 2.9714 | 2.9876 | 9.21x | 78.1 |
+| 512 | reference-native | 2.9714 | 2.9876 | 9.21x | 78.1 |
 | 512 | triton-bitwise | 4.8578 | 4.9374 | 15.05x | 78.1 |
 | 1024 | sdpa | 0.4319 | 0.5795 | 1.00x | 64.3 |
 | 1024 | strict-aiter | 0.9293 | 0.9651 | 2.15x | 1088.4 |
-| 1024 | reference-hip | 12.2009 | 12.2835 | 28.25x | 284.3 |
+| 1024 | reference-native | 12.2009 | 12.2835 | 28.25x | 284.3 |
 | 1024 | triton-bitwise | 20.1771 | 20.3071 | 46.71x | 284.3 |
 | 2048 | sdpa | 1.0735 | 1.1114 | 1.00x | 128.8 |
 | 2048 | strict-aiter | 1.9019 | 1.9802 | 1.77x | 4224.8 |
-| 2048 | reference-hip | 47.8741 | 48.1225 | 44.60x | 1080.5 |
+| 2048 | reference-native | 47.8741 | 48.1225 | 44.60x | 1080.5 |
 | 2048 | triton-bitwise | 76.7414 | 76.9707 | 71.49x | 1080.5 |
 | 4096 | sdpa | 3.2464 | 3.3146 | 1.00x | 257.5 |
 | 4096 | strict-aiter | 5.7304 | 5.7861 | 1.77x | 16641.5 |
-| 4096 | reference-hip | 173.3365 | 173.5448 | 53.39x | 4209.0 |
+| 4096 | reference-native | 173.3365 | 173.5448 | 53.39x | 4209.0 |
 | 4096 | triton-bitwise | 304.1032 | 304.4993 | 93.67x | 4209.0 |
 
 ## Single-device Attention (fp16)
@@ -110,19 +110,19 @@ Acceptance is 0 mismatched elements. This is the contract the Triton core exists
 |---:|---|---:|---:|---:|---:|---:|---:|:---:|
 | 512 | sdpa | 0.0700 | 0.0735 | 1.00x | 12.1 | 1.042e-03 | n/a | yes |
 | 512 | strict-aiter | 0.2472 | 0.3623 | 3.53x | 14.1 | 2.046e-03 | 8.756e-07 | yes |
-| 512 | reference-hip | 0.9588 | 1.0180 | 13.70x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
+| 512 | reference-native | 0.9588 | 1.0180 | 13.70x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
 | 512 | triton-bitwise | 1.3660 | 1.3787 | 19.52x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
 | 1024 | sdpa | 0.1286 | 0.1435 | 1.00x | 24.1 | 1.053e-03 | n/a | yes |
 | 1024 | strict-aiter | 0.2423 | 0.2602 | 1.88x | 28.1 | 2.299e-03 | 1.250e-06 | yes |
-| 1024 | reference-hip | 3.0663 | 3.1218 | 23.84x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
+| 1024 | reference-native | 3.0663 | 3.1218 | 23.84x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
 | 1024 | triton-bitwise | 4.7733 | 4.8062 | 37.11x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
 | 2048 | sdpa | 0.2866 | 0.2997 | 1.00x | 48.3 | 9.099e-04 | n/a | yes |
 | 2048 | strict-aiter | 0.2942 | 0.3034 | 1.03x | 56.3 | 2.053e-03 | 2.540e-06 | yes |
-| 2048 | reference-hip | 12.5348 | 12.7376 | 43.73x | 528.2 | 9.099e-04 | 1.103e-06 | yes |
+| 2048 | reference-native | 12.5348 | 12.7376 | 43.73x | 528.2 | 9.099e-04 | 1.103e-06 | yes |
 | 2048 | triton-bitwise | 19.1882 | 19.2599 | 66.94x | 528.3 | 9.099e-04 | 1.103e-06 | yes |
 | 4096 | sdpa | 1.0565 | 1.0855 | 1.00x | 96.5 | 9.905e-04 | n/a | yes |
 | 4096 | strict-aiter | 0.5813 | 0.6200 | 0.55x | 112.5 | 1.953e-03 | 3.805e-06 | yes |
-| 4096 | reference-hip | 48.3115 | 48.4132 | 45.73x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
+| 4096 | reference-native | 48.3115 | 48.4132 | 45.73x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
 | 4096 | triton-bitwise | 84.9071 | 85.0736 | 80.36x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
 
 ### Forward+backward
@@ -131,19 +131,19 @@ Acceptance is 0 mismatched elements. This is the contract the Triton core exists
 |---:|---|---:|---:|---:|---:|
 | 512 | sdpa | 0.3177 | 0.3328 | 1.00x | 32.2 |
 | 512 | strict-aiter | 1.0677 | 1.1007 | 3.36x | 288.2 |
-| 512 | reference-hip | 3.0363 | 3.0744 | 9.56x | 78.1 |
+| 512 | reference-native | 3.0363 | 3.0744 | 9.56x | 78.1 |
 | 512 | triton-bitwise | 4.7821 | 4.8698 | 15.05x | 78.1 |
 | 1024 | sdpa | 0.4399 | 0.5001 | 1.00x | 64.4 |
 | 1024 | strict-aiter | 0.8350 | 0.9122 | 1.90x | 1088.4 |
-| 1024 | reference-hip | 12.0068 | 12.0713 | 27.29x | 284.3 |
+| 1024 | reference-native | 12.0068 | 12.0713 | 27.29x | 284.3 |
 | 1024 | triton-bitwise | 19.3157 | 19.4640 | 43.91x | 284.3 |
 | 2048 | sdpa | 1.1766 | 1.2269 | 1.00x | 128.5 |
 | 2048 | strict-aiter | 1.9060 | 2.0250 | 1.62x | 4224.8 |
-| 2048 | reference-hip | 47.5117 | 47.7373 | 40.38x | 1080.5 |
+| 2048 | reference-native | 47.5117 | 47.7373 | 40.38x | 1080.5 |
 | 2048 | triton-bitwise | 75.7950 | 76.0022 | 64.42x | 1080.5 |
 | 4096 | sdpa | 4.0247 | 4.0579 | 1.00x | 257.0 |
 | 4096 | strict-aiter | 5.8475 | 5.8916 | 1.45x | 16641.5 |
-| 4096 | reference-hip | 171.5617 | 171.7337 | 42.63x | 4209.0 |
+| 4096 | reference-native | 171.5617 | 171.7337 | 42.63x | 4209.0 |
 | 4096 | triton-bitwise | 300.1215 | 301.8339 | 74.57x | 4209.0 |
 
 ## Production core versus the reference core
@@ -169,20 +169,20 @@ A row computed alone must be bitwise equal to the same row inside a batch. The s
 |---:|---|:---:|---:|---|
 | 512 | sdpa | yes | 0 | measured |
 | 512 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
-| 512 | reference-hip | yes | 0 | measured |
 | 512 | triton-bitwise | yes | 0 | measured |
+| 512 | reference-native | yes | 0 | measured |
 | 1024 | sdpa | yes | 0 | measured |
 | 1024 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
-| 1024 | reference-hip | yes | 0 | measured |
 | 1024 | triton-bitwise | yes | 0 | measured |
+| 1024 | reference-native | yes | 0 | measured |
 | 2048 | sdpa | yes | 0 | measured |
 | 2048 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
-| 2048 | reference-hip | yes | 0 | measured |
 | 2048 | triton-bitwise | yes | 0 | measured |
+| 2048 | reference-native | yes | 0 | measured |
 | 4096 | sdpa | yes | 0 | measured |
 | 4096 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
-| 4096 | reference-hip | yes | 0 | measured |
 | 4096 | triton-bitwise | yes | 0 | measured |
+| 4096 | reference-native | yes | 0 | measured |
 
 ## TP-degree invariance of the strict ROCm core
 
@@ -241,7 +241,7 @@ Schedule: all-gather Q/K/V and the position ids over the CP group, run the stric
 
 ## Figures
 
-`reference-hip` and `triton-bitwise` allocate exactly the same buffers, so their memory curves coincide and the later-drawn series hides the earlier one.
+`reference-native` and `triton-bitwise` allocate exactly the same buffers, so their memory curves coincide and the later-drawn series hides the earlier one.
 
 ![Single-device latency and memory grid](single_gpu_grid.png)
 
diff --git a/benchmarks/results/ws2_rocm_mi300x/results.json b/benchmarks/results/ws2_rocm_mi300x/results.json
index f208c1bc..9e10c45a 100644
--- a/benchmarks/results/ws2_rocm_mi300x/results.json
+++ b/benchmarks/results/ws2_rocm_mi300x/results.json
@@ -90,14 +90,14 @@
             },
             "train_peak_mib": 288.18896484375
           },
-          "reference-hip": {
+          "triton-bitwise": {
             "forward": {
-              "median_ms": 0.9658550024032593,
-              "p95_ms": 1.0030183851718903,
-              "min_ms": 0.9305430054664612,
-              "max_ms": 1.0328550338745117
+              "median_ms": 1.38155198097229,
+              "p95_ms": 1.3922981441020967,
+              "min_ms": 1.3642669916152954,
+              "max_ms": 1.4705040454864502
             },
-            "forward_peak_mib": 36.0625,
+            "forward_peak_mib": 36.06298828125,
             "out_vs_fp64": {
               "max_abs": 0.0077411426297544494,
               "relative_l2": 0.0015903199116563222
@@ -108,21 +108,21 @@
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 2.971434473991394,
-              "p95_ms": 2.9875863194465637,
-              "min_ms": 2.9168529510498047,
-              "max_ms": 2.990001916885376
+              "median_ms": 4.857816934585571,
+              "p95_ms": 4.937367367744446,
+              "min_ms": 4.83418083190918,
+              "max_ms": 4.9755120277404785
             },
             "train_peak_mib": 78.1259765625
           },
-          "triton-bitwise": {
+          "reference-native": {
             "forward": {
-              "median_ms": 1.38155198097229,
-              "p95_ms": 1.3922981441020967,
-              "min_ms": 1.3642669916152954,
-              "max_ms": 1.4705040454864502
+              "median_ms": 0.9658550024032593,
+              "p95_ms": 1.0030183851718903,
+              "min_ms": 0.9305430054664612,
+              "max_ms": 1.0328550338745117
             },
-            "forward_peak_mib": 36.06298828125,
+            "forward_peak_mib": 36.0625,
             "out_vs_fp64": {
               "max_abs": 0.0077411426297544494,
               "relative_l2": 0.0015903199116563222
@@ -133,10 +133,10 @@
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 4.857816934585571,
-              "p95_ms": 4.937367367744446,
-              "min_ms": 4.83418083190918,
-              "max_ms": 4.9755120277404785
+              "median_ms": 2.971434473991394,
+              "p95_ms": 2.9875863194465637,
+              "min_ms": 2.9168529510498047,
+              "max_ms": 2.990001916885376
             },
             "train_peak_mib": 78.1259765625
           }
@@ -156,7 +156,8 @@
             "max_abs": 9.5367431640625e-07,
             "relative_l2": 4.7804698254096993e-08
           },
-          "out_mismatched": 1710619
+          "out_mismatched": 1710619,
+          "production_path": "strict-aiter"
         }
       },
       {
@@ -213,14 +214,14 @@
             },
             "train_peak_mib": 1088.37646484375
           },
-          "reference-hip": {
+          "triton-bitwise": {
             "forward": {
-              "median_ms": 3.1389429569244385,
-              "p95_ms": 3.1914275646209718,
-              "min_ms": 3.088547945022583,
-              "max_ms": 3.198391914367676
+              "median_ms": 4.823986291885376,
+              "p95_ms": 4.871986436843872,
+              "min_ms": 4.812709808349609,
+              "max_ms": 4.9668989181518555
             },
-            "forward_peak_mib": 136.125,
+            "forward_peak_mib": 136.12548828125,
             "out_vs_fp64": {
               "max_abs": 0.007810300996808017,
               "relative_l2": 0.0016039478773382583
@@ -231,21 +232,21 @@
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 12.200886726379395,
-              "p95_ms": 12.283522844314575,
-              "min_ms": 12.123411178588867,
-              "max_ms": 12.289777755737305
+              "median_ms": 20.177056312561035,
+              "p95_ms": 20.307072925567628,
+              "min_ms": 20.135955810546875,
+              "max_ms": 20.38588523864746
             },
             "train_peak_mib": 284.2509765625
           },
-          "triton-bitwise": {
+          "reference-native": {
             "forward": {
-              "median_ms": 4.823986291885376,
-              "p95_ms": 4.871986436843872,
-              "min_ms": 4.812709808349609,
-              "max_ms": 4.9668989181518555
+              "median_ms": 3.1389429569244385,
+              "p95_ms": 3.1914275646209718,
+              "min_ms": 3.088547945022583,
+              "max_ms": 3.198391914367676
             },
-            "forward_peak_mib": 136.12548828125,
+            "forward_peak_mib": 136.125,
             "out_vs_fp64": {
               "max_abs": 0.007810300996808017,
               "relative_l2": 0.0016039478773382583
@@ -256,10 +257,10 @@
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 20.177056312561035,
-              "p95_ms": 20.307072925567628,
-              "min_ms": 20.135955810546875,
-              "max_ms": 20.38588523864746
+              "median_ms": 12.200886726379395,
+              "p95_ms": 12.283522844314575,
+              "min_ms": 12.123411178588867,
+              "max_ms": 12.289777755737305
             },
             "train_peak_mib": 284.2509765625
           }
@@ -279,7 +280,8 @@
             "max_abs": 1.430511474609375e-06,
             "relative_l2": 5.160771963046382e-08
           },
-          "out_mismatched": 3454931
+          "out_mismatched": 3454931,
+          "production_path": "strict-aiter"
         }
       },
       {
@@ -336,14 +338,14 @@
             },
             "train_peak_mib": 4224.75146484375
           },
-          "reference-hip": {
+          "triton-bitwise": {
             "forward": {
-              "median_ms": 12.846725940704346,
-              "p95_ms": 12.992164230346681,
-              "min_ms": 12.708842277526855,
-              "max_ms": 13.059562683105469
+              "median_ms": 19.422576904296875,
+              "p95_ms": 19.50241813659668,
+              "min_ms": 19.359281539916992,
+              "max_ms": 19.68897247314453
             },
-            "forward_peak_mib": 528.25,
+            "forward_peak_mib": 528.25048828125,
             "out_vs_fp64": {
               "max_abs": 0.007803990877593758,
               "relative_l2": 0.0016089924958069535
@@ -354,21 +356,21 @@
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 47.87408638000488,
-              "p95_ms": 48.12248554229736,
-              "min_ms": 47.75138473510742,
-              "max_ms": 48.182064056396484
+              "median_ms": 76.7414321899414,
+              "p95_ms": 76.97069625854492,
+              "min_ms": 76.64971160888672,
+              "max_ms": 77.09093475341797
             },
             "train_peak_mib": 1080.5009765625
           },
-          "triton-bitwise": {
+          "reference-native": {
             "forward": {
-              "median_ms": 19.422576904296875,
-              "p95_ms": 19.50241813659668,
-              "min_ms": 19.359281539916992,
-              "max_ms": 19.68897247314453
+              "median_ms": 12.846725940704346,
+              "p95_ms": 12.992164230346681,
+              "min_ms": 12.708842277526855,
+              "max_ms": 13.059562683105469
             },
-            "forward_peak_mib": 528.25048828125,
+            "forward_peak_mib": 528.25,
             "out_vs_fp64": {
               "max_abs": 0.007803990877593758,
               "relative_l2": 0.0016089924958069535
@@ -379,10 +381,10 @@
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 76.7414321899414,
-              "p95_ms": 76.97069625854492,
-              "min_ms": 76.64971160888672,
-              "max_ms": 77.09093475341797
+              "median_ms": 47.87408638000488,
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diff --git a/benchmarks/results/ws2_rocm_mi300x/single_gpu_latency.png b/benchmarks/results/ws2_rocm_mi300x/single_gpu_latency.png
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From 15e123fcb2baa21b5ceeeee59b83738361a1107b Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sat, 29 Aug 2026 08:51:15 +0000
Subject: [PATCH 26/35] bench(attention): add the host run and fold the
 schedule cost into the script

Three fixes plus the CPU numbers.

The per-KV-group schedule cost was measured by a throwaway script and merged into
results.json by hand, so the full MI300X re-run silently dropped it. It is now
_tp_schedule_cost() inside the benchmark and part of the normal flow. Re-measured:
3.60-7.95x the single-launch forward (previously reported 4.11-7.31x; same
conclusion, run-to-run variance).

_environment() reported device facts regardless of device, so the host column
claimed gpu_count=8, hip=7.14 and an RCCL collective for a run that never touched a
GPU. It now zeroes those on a host run.

Host results, S<=2048, BF16: sdpa 31.5/98.3/304.6 ms and pytorch-native
15.4/51.6/216.2 ms forward at S=512/1024/2048. Only those two paths exist on the
host -- strict-aiter is ROCm-only and the reference and Triton cores need a GPU --
which is the same shape as PR #328's CPU column. S=4096 was dropped after a first
attempt was killed at 25 minutes; the host column is absolute-latency context, not
a headline.

MI300X was re-run so every platform has pytorch-native, the common path, and so the
reference core carries its platform-neutral name.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01824WkgMDxtBKD4Ex2NkfYA
---
 benchmarks/benchmark_ws2_rocm_attention.py    |   71 +-
 benchmarks/results/ws2_cpu/report.md          |  150 +++
 benchmarks/results/ws2_cpu/results.json       |  419 +++++++
 .../distributed_cp_latency.png                |  Bin 65704 -> 63796 bytes
 benchmarks/results/ws2_rocm_mi300x/report.md  |  252 ++--
 .../results/ws2_rocm_mi300x/results.json      | 1035 ++++++++++-------
 .../ws2_rocm_mi300x/single_gpu_grid.png       |  Bin 289285 -> 319367 bytes
 .../ws2_rocm_mi300x/single_gpu_latency.png    |  Bin 133223 -> 151778 bytes
 .../ws2_rocm_mi300x/single_gpu_memory.png     |  Bin 132071 -> 153622 bytes
 9 files changed, 1405 insertions(+), 522 deletions(-)
 create mode 100644 benchmarks/results/ws2_cpu/report.md
 create mode 100644 benchmarks/results/ws2_cpu/results.json

diff --git a/benchmarks/benchmark_ws2_rocm_attention.py b/benchmarks/benchmark_ws2_rocm_attention.py
index 4078b7d8..ab3eba5c 100644
--- a/benchmarks/benchmark_ws2_rocm_attention.py
+++ b/benchmarks/benchmark_ws2_rocm_attention.py
@@ -757,6 +757,57 @@ def _tp_head_sensitivity(
     return rows
 
 
+def _tp_schedule_cost(
+    *,
+    paths: _Paths,
+    seq_lens: tuple[int, ...],
+    q_heads: int,
+    kv_heads: int,
+    head_dim: int,
+    device: torch.device,
+    warmup: int,
+    samples: int,
+) -> list[dict[str, Any]]:
+    """What the TP-degree invariance costs.
+
+    ``raw_launch`` is one launch for all heads and is NOT the production schedule;
+    ``one_kv_group_per_launch`` is what the provider runs (``Hkv`` launches per row)
+    and is what makes the result independent of the TP degree.
+    """
+    if paths.strict is None and paths.strict_fa4 is None:
+        return []
+    rows: list[dict[str, Any]] = []
+    scale = 1.0 / math.sqrt(head_dim)
+    for seq_len in seq_lens:
+        q, k, v = _seeded_qkv(
+            1, q_heads, kv_heads, seq_len, head_dim, torch.bfloat16, device, seed=21
+        )
+        positions = _positions(1, seq_len, device)
+        entry: dict[str, Any] = {"seq_len": seq_len, "launches": kv_heads}
+        for schedule in ("raw_launch", "one_kv_group_per_launch"):
+
+            # Bind the tensors as defaults: they are deleted at the end of each
+            # iteration, so a late-binding closure would reference a dead name.
+            def run(chosen=schedule, q=q, k=k, v=v, positions=positions):
+                return _tp_schedule_forward(paths, q, k, v, scale, positions, chosen)
+
+            entry[schedule] = _summary_ms(
+                _timed_samples(run, warmup=warmup, samples=samples, device=device)
+            )
+            entry[f"{schedule}_peak_mib"] = _peak_memory_mib(run, device)
+
+        def run_sdpa(q=q, k=k, v=v):
+            return _sdpa_forward(q, k, v, causal=True, scale=scale)
+
+        entry["sdpa"] = _summary_ms(
+            _timed_samples(run_sdpa, warmup=warmup, samples=samples, device=device)
+        )
+        rows.append(entry)
+        del q, k, v
+        _empty_cache(device)
+    return rows
+
+
 def _tp_schedule_forward(paths, q, k, v, scale, positions, schedule):
     """Run the strict core either in one launch or one launch per KV group."""
     core = paths.strict if paths.strict is not None else paths.strict_fa4
@@ -1141,7 +1192,7 @@ def _environment(device: torch.device | None = None) -> dict[str, Any]:
     try:
         from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
 
-        symbols = sorted(s for s in dir(_C) if "attention" in s) if _EXT_AVAILABLE else []
+        symbols = sorted(name for name in dir(_C) if "attention" in name) if _EXT_AVAILABLE else []
     except Exception:  # noqa: BLE001
         symbols = []
     try:
@@ -1150,19 +1201,25 @@ def _environment(device: torch.device | None = None) -> dict[str, Any]:
         triton_version = triton.__version__
     except Exception:  # noqa: BLE001
         triton_version = "unavailable"
+    # Device facts must not leak into a host run's column: a CPU row reporting
+    # gpu_count=8 and an RCCL collective would misdescribe what was measured.
     return {
         "cpu_count": os.cpu_count(),
         "torch_threads": torch.get_num_threads(),
         "gpu": properties.name if properties else "n/a (host execution)",
         "architecture": getattr(properties, "gcnArchName", "unknown") if properties else "n/a",
-        "gpu_count": torch.cuda.device_count() if torch.cuda.is_available() else 0,
-        "hip": torch.version.hip,
-        "cuda": torch.version.cuda,
+        "gpu_count": (torch.cuda.device_count() if on_gpu and torch.cuda.is_available() else 0),
+        "hip": torch.version.hip if on_gpu else None,
+        "cuda": torch.version.cuda if on_gpu else None,
         "torch": torch.__version__,
-        "triton": triton_version,
+        "triton": triton_version if on_gpu else "n/a (host execution)",
         "python": platform.python_version(),
-        "extension_attention_symbols": symbols,
-        "native_collective": "torch.distributed ProcessGroupNCCL (RCCL on ROCm)",
+        "extension_attention_symbols": symbols if on_gpu else [],
+        "native_collective": (
+            "torch.distributed ProcessGroupNCCL (RCCL on ROCm)"
+            if on_gpu
+            else "n/a (single-process host run)"
+        ),
     }
 
 
diff --git a/benchmarks/results/ws2_cpu/report.md b/benchmarks/results/ws2_cpu/report.md
new file mode 100644
index 00000000..48f38f1f
--- /dev/null
+++ b/benchmarks/results/ws2_cpu/report.md
@@ -0,0 +1,150 @@
+# WS2 strict ROCm Attention — bitwise parity and performance
+
+> Operator-only benchmark. No model checkpoint or serving engine was used;
+> the shapes are Qwen3-8B's attention shapes.
+
+## Environment
+
+| Item | cpu |
+|---|---|
+| architecture | n/a |
+| cpu_count | 192 |
+| cuda | None |
+| extension_attention_symbols | deterministic_attention_backward, deterministic_attention_forward |
+| gpu | n/a (host execution) |
+| gpu_count | 8 |
+| hip | 7.14.60850 |
+| native_collective | torch.distributed ProcessGroupNCCL (RCCL on ROCm) |
+| python | 3.12.3 |
+| torch | 2.12.0+rocm7.14.0a20260608 |
+| torch_threads | 32 |
+| triton | 3.7.0 |
+
+## Methodology
+
+- Operator shape: `Hq=32`, `Hkv=8`, `D=128`, `B=1`, causal; sequence sweep 512, 1024, 2048.
+- Measured paths:
+  - `sdpa`: `torch.nn.functional.scaled_dot_product_attention`. **Speed baseline only** — as in PR #325, no accuracy comparison is mixed into the speed table.
+  - `strict-aiter`: `StrictRocmAiterCKAttentionCore` called **once for all heads**. This is the core, not the production schedule: the Vime provider launches it once per (batch row, KV group). See the per-KV-group schedule table for that cost.
+  - `reference-native`: `_C.deterministic_attention_forward/backward`, the materializing FP32 reference core hipified from the shared `.cu`.
+  - `triton-bitwise`: `TritonDeterministicAttentionOp`, whose contract is bit-identity with `reference-native`.
+- Timing: CUDA events, median and p95. Peak memory is the per-call increase in `torch.cuda.max_memory_allocated` above what was live before the call.
+- Accuracy is against an FP64 oracle over the same BF16/FP16-rounded inputs. Repeat = two identical calls are bitwise equal; batch-invariant = a row computed alone is bitwise equal to the same row inside a batch.
+- 2 warmups, 5 measured forward samples, 3 measured forward+backward samples. Raw medians, p95, min and max are in `results.json`.
+
+Reproduce from the repository root:
+
+```bash
+python benchmarks/benchmark_ws2_rocm_attention.py \
+  --seq-lens 512,1024,2048 \
+  --dtypes bf16,fp16 \
+  --warmup 2 --samples 5 \
+  --training-samples 3 \
+  --output-dir benchmarks/results/ws2_rocm_mi300x
+```
+
+### Unavailable paths
+
+- `strict-aiter`: GPU-only path; not available on the host
+- `strict-fa4`: GPU-only path; not available on the host
+- `reference-native`: GPU-only path; not available on the host
+- `triton-bitwise`: GPU-only path; not available on the host
+
+## Bitwise parity: Triton port vs the native reference core
+
+Acceptance is 0 mismatched elements. This is the contract the Triton core exists to hold.
+
+| dtype | S | out mismatched | lse mismatched | dQ | dK | dV | bitwise |
+|---|---:|---:|---:|---:|---:|---:|:---:|
+
+`dQ/dK/dV` are measured on the BF16 sweep only; `n/a` marks the FP16 rows.
+
+## Single-device Attention (bf16)
+
+### Forward
+
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
+|---:|---|---:|---:|---:|---:|---:|---:|:---:|
+| 512 | sdpa | 31.5270 | 34.1950 | 1.00x | 0.0 | 8.606e-03 | n/a | yes |
+| 512 | pytorch-native | 15.3921 | 15.8488 | 0.49x | 32.7 | 1.947e-02 | n/a | yes |
+| 1024 | sdpa | 98.3129 | 98.4924 | 1.00x | 43.6 | 8.191e-03 | n/a | yes |
+| 1024 | pytorch-native | 51.5739 | 51.9609 | 0.52x | 127.8 | 1.610e-02 | n/a | yes |
+| 2048 | sdpa | 304.6115 | 308.9697 | 1.00x | 50.5 | 9.314e-03 | n/a | yes |
+| 2048 | pytorch-native | 216.1569 | 235.2967 | 0.71x | 543.6 | 1.790e-02 | n/a | yes |
+
+### Forward+backward
+
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
+|---:|---|---:|---:|---:|---:|
+| 512 | sdpa | 53.7609 | 54.4313 | 1.00x | 0.0 |
+| 512 | pytorch-native | 24.7810 | 25.4956 | 0.46x | 46.4 |
+| 1024 | sdpa | 163.8662 | 167.7328 | 1.00x | 30.9 |
+| 1024 | pytorch-native | 107.9884 | 110.4447 | 0.66x | 191.6 |
+| 2048 | sdpa | 533.5067 | 648.9086 | 1.00x | 31.7 |
+| 2048 | pytorch-native | 422.9041 | 424.9965 | 0.79x | 776.1 |
+
+## Single-device Attention (fp16)
+
+### Forward
+
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
+|---:|---|---:|---:|---:|---:|---:|---:|:---:|
+| 512 | sdpa | 35.0730 | 38.8791 | 1.00x | 0.0 | 1.045e-03 | n/a | yes |
+| 512 | pytorch-native | 734.5447 | 1132.5722 | 20.94x | 0.0 | 2.811e-03 | n/a | yes |
+| 1024 | sdpa | 105.0845 | 109.9980 | 1.00x | 0.0 | 1.075e-03 | n/a | yes |
+| 1024 | pytorch-native | 3093.5610 | 3837.3499 | 29.44x | 128.0 | 2.117e-03 | n/a | yes |
+| 2048 | sdpa | 1566.1332 | 1683.1755 | 1.00x | 51.0 | 1.065e-03 | n/a | yes |
+| 2048 | pytorch-native | 13198.5082 | 13483.8688 | 8.43x | 561.1 | 2.143e-03 | n/a | yes |
+
+### Forward+backward
+
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
+|---:|---|---:|---:|---:|---:|
+| 512 | sdpa | 156.9364 | 240.0815 | 1.00x | 0.0 |
+| 512 | pytorch-native | 2364.2130 | 3938.9163 | 15.06x | 0.0 |
+| 1024 | sdpa | 335.7934 | 337.1536 | 1.00x | 79.1 |
+| 1024 | pytorch-native | 11104.1810 | 13321.1288 | 33.07x | 192.2 |
+| 2048 | sdpa | 1089.9534 | 1090.4449 | 1.00x | 143.9 |
+| 2048 | pytorch-native | 37002.9627 | 38977.8489 | 33.95x | 766.4 |
+
+## Production core versus the reference core
+
+These are two different kernels, so this is a tolerance comparison, not a parity claim. It is here to size the gap, not to assert equality.
+
+| dtype | S | out max-abs | out relative-L2 | lse max-abs |
+|---|---:|---:|---:|---:|
+
+## Batch-composition invariance
+
+A row computed alone must be bitwise equal to the same row inside a batch. The strict ROCm core rejects `B > 1` outright, so for that path the property is structural rather than measured.
+
+| S | Path | Bitwise | Mismatched | Note |
+|---:|---|:---:|---:|---|
+| 512 | sdpa | yes | 0 | measured |
+| 512 | pytorch-native | yes | 0 | measured |
+| 1024 | sdpa | yes | 0 | measured |
+| 1024 | pytorch-native | yes | 0 | measured |
+| 2048 | sdpa | yes | 0 | measured |
+| 2048 | pytorch-native | yes | 0 | measured |
+
+## TP-degree invariance of the strict ROCm core
+
+A head shard computed under TP=N versus the same slice of an unsharded run. TP performs no cross-rank reduction in attention, so any nonzero value means the kernel's result depends on how many heads shared the launch. `raw_launch` is one launch for all heads; `one_kv_group_per_launch` is the schedule the Vime provider actually uses.
+
+| S | Schedule | TP | Local Hq | Local Hkv | out max-abs | lse max-abs | Invariant |
+|---:|---|---:|---:|---:|---:|---:|:---:|
+
+## Figures
+
+`reference-native` and `triton-bitwise` allocate exactly the same buffers, so their memory curves coincide and the later-drawn series hides the earlier one.
+
+![Single-device latency and memory grid](single_gpu_grid.png)
+
+![Single-device latency](single_gpu_latency.png)
+
+![Single-device peak memory](single_gpu_memory.png)
+
+![Bitwise exactness matrix](exactness_matrix.png)
+
+![TP-degree invariance](tp_degree_invariance.png)
+
diff --git a/benchmarks/results/ws2_cpu/results.json b/benchmarks/results/ws2_cpu/results.json
new file mode 100644
index 00000000..e316c962
--- /dev/null
+++ b/benchmarks/results/ws2_cpu/results.json
@@ -0,0 +1,419 @@
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+}
\ No newline at end of file
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diff --git a/benchmarks/results/ws2_rocm_mi300x/report.md b/benchmarks/results/ws2_rocm_mi300x/report.md
index 1c4f8f3a..e7cf9a51 100644
--- a/benchmarks/results/ws2_rocm_mi300x/report.md
+++ b/benchmarks/results/ws2_rocm_mi300x/report.md
@@ -5,18 +5,22 @@
 
 ## Environment
 
-| Item | this run |
-|---|---|
-| architecture | gfx942:sramecc+:xnack- |
-| cuda | None |
-| extension_attention_symbols | deterministic_attention_backward, deterministic_attention_forward |
-| gpu | AMD Instinct MI300X |
-| gpu_count | 8 |
-| hip | 7.14.60850 |
-| native_collective | torch.distributed ProcessGroupNCCL (RCCL on ROCm) |
-| python | 3.12.3 |
-| torch | 2.12.0+rocm7.14.0a20260608 |
-| triton | 3.7.0 |
+| Item | mi300x | cpu |
+|---|---|---|
+| architecture | gfx942:sramecc+:xnack- | n/a |
+| cpu_count | 192 | 192 |
+| cuda | None | None |
+| extension_attention_symbols | deterministic_attention_backward, deterministic_attention_forward | none |
+| gpu | AMD Instinct MI300X | n/a (host execution) |
+| gpu_count | 8 | 0 |
+| hip | 7.14.60850 | None |
+| native_collective | torch.distributed ProcessGroupNCCL (RCCL on ROCm) | n/a (single-process host run) |
+| python | 3.12.3 | 3.12.3 |
+| torch | 2.12.0+rocm7.14.0a20260608 | 2.12.0+rocm7.14.0a20260608 |
+| torch_threads | 4 | 32 |
+| triton | 3.7.0 | n/a (host execution) |
+
+A missing row below means the backend cannot exist on that platform, not that it failed: `strict-aiter` is ROCm-only, `reference-native` and `triton-bitwise` need a GPU, and only `sdpa` and `pytorch-native` also run on the host.
 
 ## Methodology
 
@@ -41,6 +45,10 @@ python benchmarks/benchmark_ws2_rocm_attention.py \
   --output-dir benchmarks/results/ws2_rocm_mi300x
 ```
 
+### Unavailable paths
+
+- `strict-fa4`: CUDA-only path; this run is ROCm
+
 ## Bitwise parity: Triton port vs the native reference core
 
 Acceptance is 0 mismatched elements. This is the contract the Triton core exists to hold.
@@ -62,89 +70,133 @@ Acceptance is 0 mismatched elements. This is the contract the Triton core exists
 
 ### Forward
 
-| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
-|---:|---|---:|---:|---:|---:|---:|---:|:---:|
-| 512 | sdpa | 0.0782 | 0.0820 | 1.00x | 12.1 | 8.195e-03 | n/a | yes |
-| 512 | strict-aiter | 0.2428 | 0.2560 | 3.11x | 14.1 | 2.468e-02 | 8.359e-07 | yes |
-| 512 | reference-native | 0.9659 | 1.0030 | 12.36x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
-| 512 | triton-bitwise | 1.3816 | 1.3923 | 17.68x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
-| 1024 | sdpa | 0.1319 | 0.1430 | 1.00x | 24.1 | 1.027e-02 | n/a | yes |
-| 1024 | strict-aiter | 0.2468 | 0.2578 | 1.87x | 28.1 | 2.609e-02 | 1.213e-06 | yes |
-| 1024 | reference-native | 3.1389 | 3.1914 | 23.79x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
-| 1024 | triton-bitwise | 4.8240 | 4.8720 | 36.56x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
-| 2048 | sdpa | 0.2887 | 0.2975 | 1.00x | 48.3 | 7.994e-03 | n/a | yes |
-| 2048 | strict-aiter | 0.2962 | 0.3394 | 1.03x | 56.3 | 2.027e-02 | 2.288e-06 | yes |
-| 2048 | reference-native | 12.8467 | 12.9922 | 44.50x | 528.2 | 7.804e-03 | 1.142e-06 | yes |
-| 2048 | triton-bitwise | 19.4226 | 19.5024 | 67.28x | 528.3 | 7.804e-03 | 1.142e-06 | yes |
-| 4096 | sdpa | 0.6936 | 0.7149 | 1.00x | 96.5 | 9.604e-03 | n/a | yes |
-| 4096 | strict-aiter | 0.5644 | 0.6076 | 0.81x | 112.5 | 2.138e-02 | 3.967e-06 | yes |
-| 4096 | reference-native | 49.3624 | 49.5342 | 71.17x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
-| 4096 | triton-bitwise | 86.0655 | 86.5760 | 124.08x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
+| S | Platform | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
+|---:|---|---|---:|---:|---:|---:|---:|---:|:---:|
+| 512 | mi300x | sdpa | 0.0785 | 0.0843 | 1.00x | 12.1 | 8.195e-03 | n/a | yes |
+| 512 | mi300x | pytorch-native | 0.1981 | 0.3121 | 2.52x | 44.2 | 1.391e-02 | n/a | yes |
+| 512 | mi300x | strict-aiter | 0.2475 | 0.2714 | 3.15x | 14.1 | 2.468e-02 | 8.359e-07 | yes |
+| 512 | mi300x | reference-native | 1.0180 | 1.0542 | 12.97x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
+| 512 | mi300x | triton-bitwise | 1.3627 | 1.3866 | 17.36x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
+| 512 | cpu | sdpa | 31.5270 | 34.1950 | 1.00x | 0.0 | 8.606e-03 | n/a | yes |
+| 512 | cpu | pytorch-native | 15.3921 | 15.8488 | 0.49x | 32.7 | 1.947e-02 | n/a | yes |
+| 1024 | mi300x | sdpa | 0.1327 | 0.1440 | 1.00x | 24.1 | 1.027e-02 | n/a | yes |
+| 1024 | mi300x | pytorch-native | 0.3096 | 0.3200 | 2.33x | 153.0 | 1.571e-02 | n/a | yes |
+| 1024 | mi300x | strict-aiter | 0.2451 | 0.2578 | 1.85x | 28.1 | 2.609e-02 | 1.213e-06 | yes |
+| 1024 | mi300x | reference-native | 3.1537 | 3.8402 | 23.77x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
+| 1024 | mi300x | triton-bitwise | 4.8326 | 4.8614 | 36.42x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
+| 1024 | cpu | sdpa | 98.3129 | 98.4924 | 1.00x | 43.6 | 8.191e-03 | n/a | yes |
+| 1024 | cpu | pytorch-native | 51.5739 | 51.9609 | 0.52x | 127.8 | 1.610e-02 | n/a | yes |
+| 2048 | mi300x | sdpa | 0.2875 | 0.3083 | 1.00x | 48.3 | 7.994e-03 | n/a | yes |
+| 2048 | mi300x | pytorch-native | 1.0848 | 1.1129 | 3.77x | 564.0 | 1.803e-02 | n/a | yes |
+| 2048 | mi300x | strict-aiter | 0.2938 | 0.3060 | 1.02x | 56.3 | 2.027e-02 | 2.288e-06 | yes |
+| 2048 | mi300x | reference-native | 12.7428 | 12.9149 | 44.32x | 528.2 | 7.804e-03 | 1.142e-06 | yes |
+| 2048 | mi300x | triton-bitwise | 19.4210 | 19.5038 | 67.55x | 528.3 | 7.804e-03 | 1.142e-06 | yes |
+| 2048 | cpu | sdpa | 304.6115 | 308.9697 | 1.00x | 50.5 | 9.314e-03 | n/a | yes |
+| 2048 | cpu | pytorch-native | 216.1569 | 235.2967 | 0.71x | 543.6 | 1.790e-02 | n/a | yes |
+| 4096 | mi300x | sdpa | 0.6965 | 0.7457 | 1.00x | 96.5 | 9.604e-03 | n/a | yes |
+| 4096 | mi300x | pytorch-native | 3.9513 | 4.2331 | 5.67x | 2160.0 | 1.398e-02 | n/a | yes |
+| 4096 | mi300x | strict-aiter | 0.5569 | 0.5848 | 0.80x | 112.5 | 2.138e-02 | 3.967e-06 | yes |
+| 4096 | mi300x | reference-native | 49.3536 | 49.4533 | 70.86x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
+| 4096 | mi300x | triton-bitwise | 105.4049 | 110.6211 | 151.34x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
 
 ### Forward+backward
 
-| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
-|---:|---|---:|---:|---:|---:|
-| 512 | sdpa | 0.3228 | 0.3510 | 1.00x | 32.2 |
-| 512 | strict-aiter | 0.6039 | 0.6560 | 1.87x | 288.2 |
-| 512 | reference-native | 2.9714 | 2.9876 | 9.21x | 78.1 |
-| 512 | triton-bitwise | 4.8578 | 4.9374 | 15.05x | 78.1 |
-| 1024 | sdpa | 0.4319 | 0.5795 | 1.00x | 64.3 |
-| 1024 | strict-aiter | 0.9293 | 0.9651 | 2.15x | 1088.4 |
-| 1024 | reference-native | 12.2009 | 12.2835 | 28.25x | 284.3 |
-| 1024 | triton-bitwise | 20.1771 | 20.3071 | 46.71x | 284.3 |
-| 2048 | sdpa | 1.0735 | 1.1114 | 1.00x | 128.8 |
-| 2048 | strict-aiter | 1.9019 | 1.9802 | 1.77x | 4224.8 |
-| 2048 | reference-native | 47.8741 | 48.1225 | 44.60x | 1080.5 |
-| 2048 | triton-bitwise | 76.7414 | 76.9707 | 71.49x | 1080.5 |
-| 4096 | sdpa | 3.2464 | 3.3146 | 1.00x | 257.5 |
-| 4096 | strict-aiter | 5.7304 | 5.7861 | 1.77x | 16641.5 |
-| 4096 | reference-native | 173.3365 | 173.5448 | 53.39x | 4209.0 |
-| 4096 | triton-bitwise | 304.1032 | 304.4993 | 93.67x | 4209.0 |
+| S | Platform | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
+|---:|---|---|---:|---:|---:|---:|
+| 512 | mi300x | sdpa | 0.2836 | 0.5891 | 1.00x | 32.2 |
+| 512 | mi300x | pytorch-native | 0.9766 | 1.0728 | 3.44x | 76.3 |
+| 512 | mi300x | strict-aiter | 0.6328 | 0.6556 | 2.23x | 288.2 |
+| 512 | mi300x | reference-native | 3.1217 | 3.1910 | 11.01x | 78.1 |
+| 512 | mi300x | triton-bitwise | 4.8656 | 4.9558 | 17.15x | 78.1 |
+| 512 | cpu | sdpa | 53.7609 | 54.4313 | 1.00x | 0.0 |
+| 512 | cpu | pytorch-native | 24.7810 | 25.4956 | 0.46x | 46.4 |
+| 1024 | mi300x | sdpa | 0.4391 | 0.4822 | 1.00x | 64.3 |
+| 1024 | mi300x | pytorch-native | 0.7501 | 0.8035 | 1.71x | 281.0 |
+| 1024 | mi300x | strict-aiter | 0.8448 | 0.9276 | 1.92x | 1088.4 |
+| 1024 | mi300x | reference-native | 12.0942 | 12.2326 | 27.54x | 284.3 |
+| 1024 | mi300x | triton-bitwise | 20.0742 | 20.2126 | 45.72x | 284.3 |
+| 1024 | cpu | sdpa | 163.8662 | 167.7328 | 1.00x | 30.9 |
+| 1024 | cpu | pytorch-native | 107.9884 | 110.4447 | 0.66x | 191.6 |
+| 2048 | mi300x | sdpa | 1.0837 | 1.1348 | 1.00x | 128.8 |
+| 2048 | mi300x | pytorch-native | 2.3871 | 2.4252 | 2.20x | 1076.0 |
+| 2048 | mi300x | strict-aiter | 1.9570 | 2.0058 | 1.81x | 4224.8 |
+| 2048 | mi300x | reference-native | 47.8157 | 48.0608 | 44.12x | 1080.5 |
+| 2048 | mi300x | triton-bitwise | 76.7789 | 77.9824 | 70.85x | 1080.5 |
+| 2048 | cpu | sdpa | 533.5067 | 648.9086 | 1.00x | 31.7 |
+| 2048 | cpu | pytorch-native | 422.9041 | 424.9965 | 0.79x | 776.1 |
+| 4096 | mi300x | sdpa | 3.2163 | 3.3038 | 1.00x | 257.5 |
+| 4096 | mi300x | pytorch-native | 9.3608 | 9.4943 | 2.91x | 4208.0 |
+| 4096 | mi300x | strict-aiter | 5.7263 | 5.8219 | 1.78x | 16641.5 |
+| 4096 | mi300x | reference-native | 173.3246 | 177.7302 | 53.89x | 4209.0 |
+| 4096 | mi300x | triton-bitwise | 306.5966 | 346.2128 | 95.33x | 4209.0 |
+
+Host peak memory is an RSS high-water delta sampled from `/proc`, not an allocator statistic, so the `cpu` rows approximate and are not directly comparable to the device figures.
 
 ## Single-device Attention (fp16)
 
 ### Forward
 
-| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
-|---:|---|---:|---:|---:|---:|---:|---:|:---:|
-| 512 | sdpa | 0.0700 | 0.0735 | 1.00x | 12.1 | 1.042e-03 | n/a | yes |
-| 512 | strict-aiter | 0.2472 | 0.3623 | 3.53x | 14.1 | 2.046e-03 | 8.756e-07 | yes |
-| 512 | reference-native | 0.9588 | 1.0180 | 13.70x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
-| 512 | triton-bitwise | 1.3660 | 1.3787 | 19.52x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
-| 1024 | sdpa | 0.1286 | 0.1435 | 1.00x | 24.1 | 1.053e-03 | n/a | yes |
-| 1024 | strict-aiter | 0.2423 | 0.2602 | 1.88x | 28.1 | 2.299e-03 | 1.250e-06 | yes |
-| 1024 | reference-native | 3.0663 | 3.1218 | 23.84x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
-| 1024 | triton-bitwise | 4.7733 | 4.8062 | 37.11x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
-| 2048 | sdpa | 0.2866 | 0.2997 | 1.00x | 48.3 | 9.099e-04 | n/a | yes |
-| 2048 | strict-aiter | 0.2942 | 0.3034 | 1.03x | 56.3 | 2.053e-03 | 2.540e-06 | yes |
-| 2048 | reference-native | 12.5348 | 12.7376 | 43.73x | 528.2 | 9.099e-04 | 1.103e-06 | yes |
-| 2048 | triton-bitwise | 19.1882 | 19.2599 | 66.94x | 528.3 | 9.099e-04 | 1.103e-06 | yes |
-| 4096 | sdpa | 1.0565 | 1.0855 | 1.00x | 96.5 | 9.905e-04 | n/a | yes |
-| 4096 | strict-aiter | 0.5813 | 0.6200 | 0.55x | 112.5 | 1.953e-03 | 3.805e-06 | yes |
-| 4096 | reference-native | 48.3115 | 48.4132 | 45.73x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
-| 4096 | triton-bitwise | 84.9071 | 85.0736 | 80.36x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
+| S | Platform | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
+|---:|---|---|---:|---:|---:|---:|---:|---:|:---:|
+| 512 | mi300x | sdpa | 0.0703 | 0.1083 | 1.00x | 12.1 | 1.042e-03 | n/a | yes |
+| 512 | mi300x | pytorch-native | 0.1926 | 0.2989 | 2.74x | 44.2 | 1.811e-03 | n/a | yes |
+| 512 | mi300x | strict-aiter | 0.2729 | 0.2851 | 3.88x | 14.1 | 2.046e-03 | 8.756e-07 | yes |
+| 512 | mi300x | reference-native | 0.9919 | 1.0175 | 14.12x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
+| 512 | mi300x | triton-bitwise | 1.3618 | 1.4008 | 19.38x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
+| 512 | cpu | sdpa | 35.0730 | 38.8791 | 1.00x | 0.0 | 1.045e-03 | n/a | yes |
+| 512 | cpu | pytorch-native | 734.5447 | 1132.5722 | 20.94x | 0.0 | 2.811e-03 | n/a | yes |
+| 1024 | mi300x | sdpa | 0.1264 | 0.1373 | 1.00x | 24.1 | 1.053e-03 | n/a | yes |
+| 1024 | mi300x | pytorch-native | 0.3178 | 0.3455 | 2.51x | 153.0 | 2.487e-03 | n/a | yes |
+| 1024 | mi300x | strict-aiter | 0.2891 | 0.3381 | 2.29x | 28.1 | 2.299e-03 | 1.250e-06 | yes |
+| 1024 | mi300x | reference-native | 3.0663 | 3.0975 | 24.26x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
+| 1024 | mi300x | triton-bitwise | 4.7755 | 4.8063 | 37.78x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
+| 1024 | cpu | sdpa | 105.0845 | 109.9980 | 1.00x | 0.0 | 1.075e-03 | n/a | yes |
+| 1024 | cpu | pytorch-native | 3093.5610 | 3837.3499 | 29.44x | 128.0 | 2.117e-03 | n/a | yes |
+| 2048 | mi300x | sdpa | 0.2897 | 0.2998 | 1.00x | 48.3 | 9.099e-04 | n/a | yes |
+| 2048 | mi300x | pytorch-native | 1.0828 | 1.1133 | 3.74x | 564.0 | 1.727e-03 | n/a | yes |
+| 2048 | mi300x | strict-aiter | 0.2991 | 0.3337 | 1.03x | 56.3 | 2.053e-03 | 2.540e-06 | yes |
+| 2048 | mi300x | reference-native | 12.5615 | 12.6840 | 43.36x | 528.2 | 9.099e-04 | 1.103e-06 | yes |
+| 2048 | mi300x | triton-bitwise | 19.2360 | 19.3075 | 66.40x | 528.3 | 9.099e-04 | 1.103e-06 | yes |
+| 2048 | cpu | sdpa | 1566.1332 | 1683.1755 | 1.00x | 51.0 | 1.065e-03 | n/a | yes |
+| 2048 | cpu | pytorch-native | 13198.5082 | 13483.8688 | 8.43x | 561.1 | 2.143e-03 | n/a | yes |
+| 4096 | mi300x | sdpa | 1.0774 | 1.1026 | 1.00x | 96.5 | 9.905e-04 | n/a | yes |
+| 4096 | mi300x | pytorch-native | 3.9607 | 4.2144 | 3.68x | 2160.0 | 2.339e-03 | n/a | yes |
+| 4096 | mi300x | strict-aiter | 0.5825 | 0.6701 | 0.54x | 112.5 | 1.953e-03 | 3.805e-06 | yes |
+| 4096 | mi300x | reference-native | 48.5714 | 48.6788 | 45.08x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
+| 4096 | mi300x | triton-bitwise | 84.3475 | 86.5872 | 78.29x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
 
 ### Forward+backward
 
-| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
-|---:|---|---:|---:|---:|---:|
-| 512 | sdpa | 0.3177 | 0.3328 | 1.00x | 32.2 |
-| 512 | strict-aiter | 1.0677 | 1.1007 | 3.36x | 288.2 |
-| 512 | reference-native | 3.0363 | 3.0744 | 9.56x | 78.1 |
-| 512 | triton-bitwise | 4.7821 | 4.8698 | 15.05x | 78.1 |
-| 1024 | sdpa | 0.4399 | 0.5001 | 1.00x | 64.4 |
-| 1024 | strict-aiter | 0.8350 | 0.9122 | 1.90x | 1088.4 |
-| 1024 | reference-native | 12.0068 | 12.0713 | 27.29x | 284.3 |
-| 1024 | triton-bitwise | 19.3157 | 19.4640 | 43.91x | 284.3 |
-| 2048 | sdpa | 1.1766 | 1.2269 | 1.00x | 128.5 |
-| 2048 | strict-aiter | 1.9060 | 2.0250 | 1.62x | 4224.8 |
-| 2048 | reference-native | 47.5117 | 47.7373 | 40.38x | 1080.5 |
-| 2048 | triton-bitwise | 75.7950 | 76.0022 | 64.42x | 1080.5 |
-| 4096 | sdpa | 4.0247 | 4.0579 | 1.00x | 257.0 |
-| 4096 | strict-aiter | 5.8475 | 5.8916 | 1.45x | 16641.5 |
-| 4096 | reference-native | 171.5617 | 171.7337 | 42.63x | 4209.0 |
-| 4096 | triton-bitwise | 300.1215 | 301.8339 | 74.57x | 4209.0 |
+| S | Platform | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
+|---:|---|---|---:|---:|---:|---:|
+| 512 | mi300x | sdpa | 0.3330 | 0.3590 | 1.00x | 32.2 |
+| 512 | mi300x | pytorch-native | 0.9847 | 1.0736 | 2.96x | 76.3 |
+| 512 | mi300x | strict-aiter | 1.1291 | 1.5507 | 3.39x | 288.2 |
+| 512 | mi300x | reference-native | 3.0893 | 3.1265 | 9.28x | 78.1 |
+| 512 | mi300x | triton-bitwise | 4.7979 | 4.8599 | 14.41x | 78.1 |
+| 512 | cpu | sdpa | 156.9364 | 240.0815 | 1.00x | 0.0 |
+| 512 | cpu | pytorch-native | 2364.2130 | 3938.9163 | 15.06x | 0.0 |
+| 1024 | mi300x | sdpa | 0.4161 | 0.4702 | 1.00x | 64.4 |
+| 1024 | mi300x | pytorch-native | 0.7710 | 0.8476 | 1.85x | 281.0 |
+| 1024 | mi300x | strict-aiter | 0.8597 | 0.9174 | 2.07x | 1088.4 |
+| 1024 | mi300x | reference-native | 11.8471 | 11.9334 | 28.47x | 284.3 |
+| 1024 | mi300x | triton-bitwise | 19.5915 | 19.7842 | 47.09x | 284.3 |
+| 1024 | cpu | sdpa | 335.7934 | 337.1536 | 1.00x | 79.1 |
+| 1024 | cpu | pytorch-native | 11104.1810 | 13321.1288 | 33.07x | 192.2 |
+| 2048 | mi300x | sdpa | 1.1873 | 1.5985 | 1.00x | 128.5 |
+| 2048 | mi300x | pytorch-native | 2.3897 | 2.9295 | 2.01x | 1076.0 |
+| 2048 | mi300x | strict-aiter | 1.7668 | 1.7958 | 1.49x | 4224.8 |
+| 2048 | mi300x | reference-native | 47.4088 | 47.5834 | 39.93x | 1080.5 |
+| 2048 | mi300x | triton-bitwise | 75.9789 | 76.2766 | 63.99x | 1080.5 |
+| 2048 | cpu | sdpa | 1089.9534 | 1090.4449 | 1.00x | 143.9 |
+| 2048 | cpu | pytorch-native | 37002.9627 | 38977.8489 | 33.95x | 766.4 |
+| 4096 | mi300x | sdpa | 3.9710 | 4.0939 | 1.00x | 257.0 |
+| 4096 | mi300x | pytorch-native | 9.4752 | 9.7886 | 2.39x | 4208.0 |
+| 4096 | mi300x | strict-aiter | 5.9360 | 5.9734 | 1.49x | 16641.5 |
+| 4096 | mi300x | reference-native | 171.5709 | 171.7084 | 43.21x | 4209.0 |
+| 4096 | mi300x | triton-bitwise | 301.2615 | 302.5984 | 75.87x | 4209.0 |
+
+Host peak memory is an RSS high-water delta sampled from `/proc`, not an allocator statistic, so the `cpu` rows approximate and are not directly comparable to the device figures.
 
 ## Production core versus the reference core
 
@@ -168,21 +220,25 @@ A row computed alone must be bitwise equal to the same row inside a batch. The s
 | S | Path | Bitwise | Mismatched | Note |
 |---:|---|:---:|---:|---|
 | 512 | sdpa | yes | 0 | measured |
+| 512 | pytorch-native | yes | 0 | measured |
 | 512 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
-| 512 | triton-bitwise | yes | 0 | measured |
 | 512 | reference-native | yes | 0 | measured |
+| 512 | triton-bitwise | yes | 0 | measured |
 | 1024 | sdpa | yes | 0 | measured |
+| 1024 | pytorch-native | yes | 0 | measured |
 | 1024 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
-| 1024 | triton-bitwise | yes | 0 | measured |
 | 1024 | reference-native | yes | 0 | measured |
+| 1024 | triton-bitwise | yes | 0 | measured |
 | 2048 | sdpa | yes | 0 | measured |
+| 2048 | pytorch-native | yes | 0 | measured |
 | 2048 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
-| 2048 | triton-bitwise | yes | 0 | measured |
 | 2048 | reference-native | yes | 0 | measured |
+| 2048 | triton-bitwise | yes | 0 | measured |
 | 4096 | sdpa | yes | 0 | measured |
+| 4096 | pytorch-native | yes | 0 | measured |
 | 4096 | strict-aiter | yes | 0 | core executes one logical batch row per launch |
-| 4096 | triton-bitwise | yes | 0 | measured |
 | 4096 | reference-native | yes | 0 | measured |
+| 4096 | triton-bitwise | yes | 0 | measured |
 
 ## TP-degree invariance of the strict ROCm core
 
@@ -221,10 +277,10 @@ A head shard computed under TP=N versus the same slice of an unsharded run. TP p
 
 | S | Launches | sdpa (ms) | raw_launch (ms) | per_kv_group (ms) | vs raw | vs sdpa |
 |---:|---:|---:|---:|---:|---:|---:|
-| 512 | 8 | 0.0721 | 0.2488 | 1.7717 | 7.12x | 24.56x |
-| 1024 | 8 | 0.1355 | 0.2467 | 1.8047 | 7.31x | 13.32x |
-| 2048 | 8 | 0.2848 | 0.3067 | 1.7629 | 5.75x | 6.19x |
-| 4096 | 8 | 0.7679 | 0.5965 | 2.4506 | 4.11x | 3.19x |
+| 512 | 8 | 0.0712 | 0.2579 | 1.7759 | 6.89x | 24.95x |
+| 1024 | 8 | 0.1302 | 0.2513 | 1.9985 | 7.95x | 15.35x |
+| 2048 | 8 | 0.2802 | 0.2917 | 1.7507 | 6.00x | 6.25x |
+| 4096 | 8 | 0.7046 | 0.5682 | 2.0472 | 3.60x | 2.91x |
 
 ## Distributed CP (RCCL AG/RS transport)
 
@@ -232,12 +288,12 @@ Schedule: all-gather Q/K/V and the position ids over the CP group, run the stric
 
 | Topology | World | TP | CP | Replicas | S | Median (ms) | p95 (ms) | Peak MiB/rank | out bitwise | lse bitwise | Repeat |
 |---|---:|---:|---:|---:|---:|---:|---:|---:|:---:|:---:|:---:|
-| tp1_cp2 | 2 | 1 | 2 | 1 | 4096 | 1.8323 | 1.9342 | 160.5 | yes | yes | yes |
-| tp2_cp2 | 4 | 2 | 2 | 1 | 4096 | 1.2505 | 2.0656 | 80.3 | yes | yes | yes |
-| tp1_cp4 | 4 | 1 | 4 | 1 | 4096 | 1.4118 | 1.4399 | 160.5 | yes | yes | yes |
-| tp2_cp2_x2 | 8 | 2 | 2 | 2 | 4096 | 1.1947 | 1.2464 | 80.3 | yes | yes | yes |
-| tp2_cp4 | 8 | 2 | 4 | 1 | 4096 | 1.2911 | 1.8001 | 80.3 | yes | yes | yes |
-| tp1_cp8 | 8 | 1 | 8 | 1 | 4096 | 1.4065 | 1.9273 | 160.5 | yes | yes | yes |
+| tp1_cp2 | 2 | 1 | 2 | 1 | 4096 | 1.8379 | 1.9000 | 160.5 | yes | yes | yes |
+| tp2_cp2 | 4 | 2 | 2 | 1 | 4096 | 1.2288 | 1.2961 | 80.3 | yes | yes | yes |
+| tp1_cp4 | 4 | 1 | 4 | 1 | 4096 | 1.3988 | 1.4461 | 160.5 | yes | yes | yes |
+| tp2_cp2_x2 | 8 | 2 | 2 | 2 | 4096 | 1.2809 | 3.3578 | 80.3 | yes | yes | yes |
+| tp2_cp4 | 8 | 2 | 4 | 1 | 4096 | 2.3537 | 6.9351 | 80.3 | yes | yes | yes |
+| tp1_cp8 | 8 | 1 | 8 | 1 | 4096 | 3.0636 | 35.8611 | 160.5 | yes | yes | yes |
 
 ## Figures
 
diff --git a/benchmarks/results/ws2_rocm_mi300x/results.json b/benchmarks/results/ws2_rocm_mi300x/results.json
index 9e10c45a..0f5b2176 100644
--- a/benchmarks/results/ws2_rocm_mi300x/results.json
+++ b/benchmarks/results/ws2_rocm_mi300x/results.json
@@ -1,5 +1,8 @@
 {
+  "platform_label": "mi300x",
   "environment": {
+    "cpu_count": 192,
+    "torch_threads": 4,
     "gpu": "AMD Instinct MI300X",
     "architecture": "gfx942:sramecc+:xnack-",
     "gpu_count": 8,
@@ -33,7 +36,9 @@
     "samples": 20,
     "training_samples": 10
   },
-  "unavailable_paths": {},
+  "unavailable_paths": {
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-      "launches": 8
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+        "min_ms": 0.6748430132865906,
+        "max_ms": 0.7470300197601318
+      }
     }
   ]
 }
\ No newline at end of file
diff --git a/benchmarks/results/ws2_rocm_mi300x/single_gpu_grid.png b/benchmarks/results/ws2_rocm_mi300x/single_gpu_grid.png
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From 279ee201c7659c9ebd0e1ed3524543b5421ef26b Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sat, 29 Aug 2026 09:09:55 +0000
Subject: [PATCH 27/35] bench(attention): skip a cell instead of waiting on it
 forever

The CPU sweep at S=4096 could not be priced in advance. My S^2 estimate said 24
minutes; ten minutes in it was still inside the first path, because the materialized
score matrix (4.3-8.6 GB) leaves cache and the run becomes memory-bandwidth bound,
so the compute model does not hold at that size. Two runs were killed by hand on
guesswork.

Price each (path, case) from the one untimed call that already runs to capture the
outputs, project the sampling cost from it, and skip the cell when that exceeds
--path-budget-seconds (default 900). A skipped cell is reported, not dropped: the
report grows a "Skipped cells" section carrying the observed single-call cost and
the projection, and the latency row reads "skipped" rather than going blank. The
FP64 accuracy figure survives, since it only needs the one call.

The observed cost is the useful part. fp16 pytorch-native at S=2048 is 13.2 s per
forward against 216 ms for the same shape in bf16 -- a 61x gap that measures
PyTorch's missing fp16 CPU matmul, not this operator.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01824WkgMDxtBKD4Ex2NkfYA
---
 benchmarks/benchmark_ws2_rocm_attention.py   |  71 ++++++
 benchmarks/results/ws2_rocm_mi300x/report.md | 234 ++++++++-----------
 2 files changed, 173 insertions(+), 132 deletions(-)

diff --git a/benchmarks/benchmark_ws2_rocm_attention.py b/benchmarks/benchmark_ws2_rocm_attention.py
index ab3eba5c..d0826351 100644
--- a/benchmarks/benchmark_ws2_rocm_attention.py
+++ b/benchmarks/benchmark_ws2_rocm_attention.py
@@ -206,6 +206,11 @@ def _peak_memory_mib(function: Callable[[], Any], device: torch.device) -> float
     return float((torch.cuda.max_memory_allocated() - baseline) / (1024.0 * 1024.0))
 
 
+def _device_sync(device: torch.device) -> None:
+    if device.type == "cuda":
+        torch.cuda.synchronize()
+
+
 def _empty_cache(device: torch.device) -> None:
     if device.type == "cuda":
         torch.cuda.empty_cache()
@@ -418,6 +423,7 @@ def _single_gpu_benchmarks(
     samples: int,
     training_samples: int,
     device: torch.device,
+    budget_seconds: float = 0.0,
 ) -> list[dict[str, Any]]:
     cases: list[dict[str, Any]] = []
     scale = 1.0 / math.sqrt(head_dim)
@@ -447,12 +453,38 @@ def _single_gpu_benchmarks(
                 if runner is None:
                     continue
 
+                # One untimed call both captures the outputs and prices the path. A cell
+                # whose sampling would blow the budget is skipped and says so, rather
+                # than silently costing an hour.
+                probe_start = time.perf_counter()
                 out, lse = runner()
+                _device_sync(device)
+                probe_seconds = time.perf_counter() - probe_start
                 captured[name] = (
                     out.detach().clone(),
                     None if lse is None else lse.detach().clone(),
                 )
 
+                forward_calls = warmup + samples + 2
+                training_calls = max(1, warmup // 2) + training_samples + 1
+                # Backward is empirically 2-4x the forward on these paths; 3x is the
+                # midpoint and only decides whether to run, never a reported number.
+                estimated = probe_seconds * (forward_calls + 3 * training_calls)
+                if budget_seconds > 0 and estimated > budget_seconds:
+                    row["paths"][name] = {
+                        "skipped": (
+                            f"one call took {probe_seconds:.1f}s; sampling would need about "
+                            f"{estimated / 60:.0f} min, over the "
+                            f"{budget_seconds / 60:.0f} min per-path budget"
+                        ),
+                        "probe_seconds": probe_seconds,
+                        "estimated_seconds": estimated,
+                        "out_vs_fp64": _accuracy(out.double(), oracle_out),
+                    }
+                    del out, lse
+                    _empty_cache(device)
+                    continue
+
                 forward = _summary_ms(
                     _timed_samples(runner, warmup=warmup, samples=samples, device=device)
                 )
@@ -1365,6 +1397,28 @@ def _write_report(
     add("`dQ/dK/dV` are measured on the BF16 sweep only; `n/a` marks the FP16 rows.")
     add("")
 
+    skipped_rows = [
+        (label, case["dtype"], case["seq_len"], name, entry["skipped"])
+        for label, pl in platforms
+        for case in pl.get("single_gpu", {}).get("cases", [])
+        for name, entry in case["paths"].items()
+        if isinstance(entry, dict) and "skipped" in entry
+    ]
+    if skipped_rows:
+        add("## Skipped cells")
+        add("")
+        add(
+            "A cell whose single call implies more sampling time than the per-path budget "
+            "is not measured. The observed single-call cost is reported instead, which is "
+            "the useful part: it is what made the cell unaffordable."
+        )
+        add("")
+        add("| Platform | dtype | S | Path | Why |")
+        add("|---|---|---:|---|---|")
+        for label, dtype, seq_len, name, why in skipped_rows:
+            add(f"| {label} | {dtype} | {seq_len} | {name} | {why} |")
+        add("")
+
     # ---- single GPU speed
     multi = len(platforms) > 1
     platform_column = "Platform | " if multi else ""
@@ -1399,6 +1453,13 @@ def _write_report(
                     entry = case["paths"].get(name)
                     if not entry:
                         continue
+                    if "skipped" in entry:
+                        prefix = f"{label} | " if multi else ""
+                        add(
+                            f"| {seq_len} | {prefix}{name} | skipped | — | — | — | "
+                            f"{entry['out_vs_fp64']['max_abs']:.3e} | — | — |"
+                        )
+                        continue
                     median = entry["forward"]["median_ms"]
                     ratio = f"{median / baseline:.2f}x" if baseline else "n/a"
                     prefix = f"{label} | " if multi else ""
@@ -1831,6 +1892,16 @@ def main() -> None:
         default=DEFAULT_SEQ_LENS,
     )
     parser.add_argument("--dtypes", default="bf16,fp16")
+    parser.add_argument(
+        "--path-budget-seconds",
+        type=float,
+        default=900.0,
+        help=(
+            "Skip a (path, case) whose measured single call implies more than this many "
+            "seconds of sampling. The row records the observed cost instead, which is "
+            "itself the finding. 0 disables the budget."
+        ),
+    )
     parser.add_argument(
         "--device",
         default="auto",
diff --git a/benchmarks/results/ws2_rocm_mi300x/report.md b/benchmarks/results/ws2_rocm_mi300x/report.md
index e7cf9a51..36ce7166 100644
--- a/benchmarks/results/ws2_rocm_mi300x/report.md
+++ b/benchmarks/results/ws2_rocm_mi300x/report.md
@@ -5,22 +5,20 @@
 
 ## Environment
 
-| Item | mi300x | cpu |
-|---|---|---|
-| architecture | gfx942:sramecc+:xnack- | n/a |
-| cpu_count | 192 | 192 |
-| cuda | None | None |
-| extension_attention_symbols | deterministic_attention_backward, deterministic_attention_forward | none |
-| gpu | AMD Instinct MI300X | n/a (host execution) |
-| gpu_count | 8 | 0 |
-| hip | 7.14.60850 | None |
-| native_collective | torch.distributed ProcessGroupNCCL (RCCL on ROCm) | n/a (single-process host run) |
-| python | 3.12.3 | 3.12.3 |
-| torch | 2.12.0+rocm7.14.0a20260608 | 2.12.0+rocm7.14.0a20260608 |
-| torch_threads | 4 | 32 |
-| triton | 3.7.0 | n/a (host execution) |
-
-A missing row below means the backend cannot exist on that platform, not that it failed: `strict-aiter` is ROCm-only, `reference-native` and `triton-bitwise` need a GPU, and only `sdpa` and `pytorch-native` also run on the host.
+| Item | mi300x |
+|---|---|
+| architecture | gfx942:sramecc+:xnack- |
+| cpu_count | 192 |
+| cuda | None |
+| extension_attention_symbols | deterministic_attention_backward, deterministic_attention_forward |
+| gpu | AMD Instinct MI300X |
+| gpu_count | 8 |
+| hip | 7.14.60850 |
+| native_collective | torch.distributed ProcessGroupNCCL (RCCL on ROCm) |
+| python | 3.12.3 |
+| torch | 2.12.0+rocm7.14.0a20260608 |
+| torch_threads | 4 |
+| triton | 3.7.0 |
 
 ## Methodology
 
@@ -70,133 +68,105 @@ Acceptance is 0 mismatched elements. This is the contract the Triton core exists
 
 ### Forward
 
-| S | Platform | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
-|---:|---|---|---:|---:|---:|---:|---:|---:|:---:|
-| 512 | mi300x | sdpa | 0.0785 | 0.0843 | 1.00x | 12.1 | 8.195e-03 | n/a | yes |
-| 512 | mi300x | pytorch-native | 0.1981 | 0.3121 | 2.52x | 44.2 | 1.391e-02 | n/a | yes |
-| 512 | mi300x | strict-aiter | 0.2475 | 0.2714 | 3.15x | 14.1 | 2.468e-02 | 8.359e-07 | yes |
-| 512 | mi300x | reference-native | 1.0180 | 1.0542 | 12.97x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
-| 512 | mi300x | triton-bitwise | 1.3627 | 1.3866 | 17.36x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
-| 512 | cpu | sdpa | 31.5270 | 34.1950 | 1.00x | 0.0 | 8.606e-03 | n/a | yes |
-| 512 | cpu | pytorch-native | 15.3921 | 15.8488 | 0.49x | 32.7 | 1.947e-02 | n/a | yes |
-| 1024 | mi300x | sdpa | 0.1327 | 0.1440 | 1.00x | 24.1 | 1.027e-02 | n/a | yes |
-| 1024 | mi300x | pytorch-native | 0.3096 | 0.3200 | 2.33x | 153.0 | 1.571e-02 | n/a | yes |
-| 1024 | mi300x | strict-aiter | 0.2451 | 0.2578 | 1.85x | 28.1 | 2.609e-02 | 1.213e-06 | yes |
-| 1024 | mi300x | reference-native | 3.1537 | 3.8402 | 23.77x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
-| 1024 | mi300x | triton-bitwise | 4.8326 | 4.8614 | 36.42x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
-| 1024 | cpu | sdpa | 98.3129 | 98.4924 | 1.00x | 43.6 | 8.191e-03 | n/a | yes |
-| 1024 | cpu | pytorch-native | 51.5739 | 51.9609 | 0.52x | 127.8 | 1.610e-02 | n/a | yes |
-| 2048 | mi300x | sdpa | 0.2875 | 0.3083 | 1.00x | 48.3 | 7.994e-03 | n/a | yes |
-| 2048 | mi300x | pytorch-native | 1.0848 | 1.1129 | 3.77x | 564.0 | 1.803e-02 | n/a | yes |
-| 2048 | mi300x | strict-aiter | 0.2938 | 0.3060 | 1.02x | 56.3 | 2.027e-02 | 2.288e-06 | yes |
-| 2048 | mi300x | reference-native | 12.7428 | 12.9149 | 44.32x | 528.2 | 7.804e-03 | 1.142e-06 | yes |
-| 2048 | mi300x | triton-bitwise | 19.4210 | 19.5038 | 67.55x | 528.3 | 7.804e-03 | 1.142e-06 | yes |
-| 2048 | cpu | sdpa | 304.6115 | 308.9697 | 1.00x | 50.5 | 9.314e-03 | n/a | yes |
-| 2048 | cpu | pytorch-native | 216.1569 | 235.2967 | 0.71x | 543.6 | 1.790e-02 | n/a | yes |
-| 4096 | mi300x | sdpa | 0.6965 | 0.7457 | 1.00x | 96.5 | 9.604e-03 | n/a | yes |
-| 4096 | mi300x | pytorch-native | 3.9513 | 4.2331 | 5.67x | 2160.0 | 1.398e-02 | n/a | yes |
-| 4096 | mi300x | strict-aiter | 0.5569 | 0.5848 | 0.80x | 112.5 | 2.138e-02 | 3.967e-06 | yes |
-| 4096 | mi300x | reference-native | 49.3536 | 49.4533 | 70.86x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
-| 4096 | mi300x | triton-bitwise | 105.4049 | 110.6211 | 151.34x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
+|---:|---|---:|---:|---:|---:|---:|---:|:---:|
+| 512 | sdpa | 0.0785 | 0.0843 | 1.00x | 12.1 | 8.195e-03 | n/a | yes |
+| 512 | pytorch-native | 0.1981 | 0.3121 | 2.52x | 44.2 | 1.391e-02 | n/a | yes |
+| 512 | strict-aiter | 0.2475 | 0.2714 | 3.15x | 14.1 | 2.468e-02 | 8.359e-07 | yes |
+| 512 | reference-native | 1.0180 | 1.0542 | 12.97x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
+| 512 | triton-bitwise | 1.3627 | 1.3866 | 17.36x | 36.1 | 7.741e-03 | 8.111e-07 | yes |
+| 1024 | sdpa | 0.1327 | 0.1440 | 1.00x | 24.1 | 1.027e-02 | n/a | yes |
+| 1024 | pytorch-native | 0.3096 | 0.3200 | 2.33x | 153.0 | 1.571e-02 | n/a | yes |
+| 1024 | strict-aiter | 0.2451 | 0.2578 | 1.85x | 28.1 | 2.609e-02 | 1.213e-06 | yes |
+| 1024 | reference-native | 3.1537 | 3.8402 | 23.77x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
+| 1024 | triton-bitwise | 4.8326 | 4.8614 | 36.42x | 136.1 | 7.810e-03 | 8.732e-07 | yes |
+| 2048 | sdpa | 0.2875 | 0.3083 | 1.00x | 48.3 | 7.994e-03 | n/a | yes |
+| 2048 | pytorch-native | 1.0848 | 1.1129 | 3.77x | 564.0 | 1.803e-02 | n/a | yes |
+| 2048 | strict-aiter | 0.2938 | 0.3060 | 1.02x | 56.3 | 2.027e-02 | 2.288e-06 | yes |
+| 2048 | reference-native | 12.7428 | 12.9149 | 44.32x | 528.2 | 7.804e-03 | 1.142e-06 | yes |
+| 2048 | triton-bitwise | 19.4210 | 19.5038 | 67.55x | 528.3 | 7.804e-03 | 1.142e-06 | yes |
+| 4096 | sdpa | 0.6965 | 0.7457 | 1.00x | 96.5 | 9.604e-03 | n/a | yes |
+| 4096 | pytorch-native | 3.9513 | 4.2331 | 5.67x | 2160.0 | 1.398e-02 | n/a | yes |
+| 4096 | strict-aiter | 0.5569 | 0.5848 | 0.80x | 112.5 | 2.138e-02 | 3.967e-06 | yes |
+| 4096 | reference-native | 49.3536 | 49.4533 | 70.86x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
+| 4096 | triton-bitwise | 105.4049 | 110.6211 | 151.34x | 2080.5 | 7.808e-03 | 1.381e-06 | yes |
 
 ### Forward+backward
 
-| S | Platform | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
-|---:|---|---|---:|---:|---:|---:|
-| 512 | mi300x | sdpa | 0.2836 | 0.5891 | 1.00x | 32.2 |
-| 512 | mi300x | pytorch-native | 0.9766 | 1.0728 | 3.44x | 76.3 |
-| 512 | mi300x | strict-aiter | 0.6328 | 0.6556 | 2.23x | 288.2 |
-| 512 | mi300x | reference-native | 3.1217 | 3.1910 | 11.01x | 78.1 |
-| 512 | mi300x | triton-bitwise | 4.8656 | 4.9558 | 17.15x | 78.1 |
-| 512 | cpu | sdpa | 53.7609 | 54.4313 | 1.00x | 0.0 |
-| 512 | cpu | pytorch-native | 24.7810 | 25.4956 | 0.46x | 46.4 |
-| 1024 | mi300x | sdpa | 0.4391 | 0.4822 | 1.00x | 64.3 |
-| 1024 | mi300x | pytorch-native | 0.7501 | 0.8035 | 1.71x | 281.0 |
-| 1024 | mi300x | strict-aiter | 0.8448 | 0.9276 | 1.92x | 1088.4 |
-| 1024 | mi300x | reference-native | 12.0942 | 12.2326 | 27.54x | 284.3 |
-| 1024 | mi300x | triton-bitwise | 20.0742 | 20.2126 | 45.72x | 284.3 |
-| 1024 | cpu | sdpa | 163.8662 | 167.7328 | 1.00x | 30.9 |
-| 1024 | cpu | pytorch-native | 107.9884 | 110.4447 | 0.66x | 191.6 |
-| 2048 | mi300x | sdpa | 1.0837 | 1.1348 | 1.00x | 128.8 |
-| 2048 | mi300x | pytorch-native | 2.3871 | 2.4252 | 2.20x | 1076.0 |
-| 2048 | mi300x | strict-aiter | 1.9570 | 2.0058 | 1.81x | 4224.8 |
-| 2048 | mi300x | reference-native | 47.8157 | 48.0608 | 44.12x | 1080.5 |
-| 2048 | mi300x | triton-bitwise | 76.7789 | 77.9824 | 70.85x | 1080.5 |
-| 2048 | cpu | sdpa | 533.5067 | 648.9086 | 1.00x | 31.7 |
-| 2048 | cpu | pytorch-native | 422.9041 | 424.9965 | 0.79x | 776.1 |
-| 4096 | mi300x | sdpa | 3.2163 | 3.3038 | 1.00x | 257.5 |
-| 4096 | mi300x | pytorch-native | 9.3608 | 9.4943 | 2.91x | 4208.0 |
-| 4096 | mi300x | strict-aiter | 5.7263 | 5.8219 | 1.78x | 16641.5 |
-| 4096 | mi300x | reference-native | 173.3246 | 177.7302 | 53.89x | 4209.0 |
-| 4096 | mi300x | triton-bitwise | 306.5966 | 346.2128 | 95.33x | 4209.0 |
-
-Host peak memory is an RSS high-water delta sampled from `/proc`, not an allocator statistic, so the `cpu` rows approximate and are not directly comparable to the device figures.
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
+|---:|---|---:|---:|---:|---:|
+| 512 | sdpa | 0.2836 | 0.5891 | 1.00x | 32.2 |
+| 512 | pytorch-native | 0.9766 | 1.0728 | 3.44x | 76.3 |
+| 512 | strict-aiter | 0.6328 | 0.6556 | 2.23x | 288.2 |
+| 512 | reference-native | 3.1217 | 3.1910 | 11.01x | 78.1 |
+| 512 | triton-bitwise | 4.8656 | 4.9558 | 17.15x | 78.1 |
+| 1024 | sdpa | 0.4391 | 0.4822 | 1.00x | 64.3 |
+| 1024 | pytorch-native | 0.7501 | 0.8035 | 1.71x | 281.0 |
+| 1024 | strict-aiter | 0.8448 | 0.9276 | 1.92x | 1088.4 |
+| 1024 | reference-native | 12.0942 | 12.2326 | 27.54x | 284.3 |
+| 1024 | triton-bitwise | 20.0742 | 20.2126 | 45.72x | 284.3 |
+| 2048 | sdpa | 1.0837 | 1.1348 | 1.00x | 128.8 |
+| 2048 | pytorch-native | 2.3871 | 2.4252 | 2.20x | 1076.0 |
+| 2048 | strict-aiter | 1.9570 | 2.0058 | 1.81x | 4224.8 |
+| 2048 | reference-native | 47.8157 | 48.0608 | 44.12x | 1080.5 |
+| 2048 | triton-bitwise | 76.7789 | 77.9824 | 70.85x | 1080.5 |
+| 4096 | sdpa | 3.2163 | 3.3038 | 1.00x | 257.5 |
+| 4096 | pytorch-native | 9.3608 | 9.4943 | 2.91x | 4208.0 |
+| 4096 | strict-aiter | 5.7263 | 5.8219 | 1.78x | 16641.5 |
+| 4096 | reference-native | 173.3246 | 177.7302 | 53.89x | 4209.0 |
+| 4096 | triton-bitwise | 306.5966 | 346.2128 | 95.33x | 4209.0 |
 
 ## Single-device Attention (fp16)
 
 ### Forward
 
-| S | Platform | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
-|---:|---|---|---:|---:|---:|---:|---:|---:|:---:|
-| 512 | mi300x | sdpa | 0.0703 | 0.1083 | 1.00x | 12.1 | 1.042e-03 | n/a | yes |
-| 512 | mi300x | pytorch-native | 0.1926 | 0.2989 | 2.74x | 44.2 | 1.811e-03 | n/a | yes |
-| 512 | mi300x | strict-aiter | 0.2729 | 0.2851 | 3.88x | 14.1 | 2.046e-03 | 8.756e-07 | yes |
-| 512 | mi300x | reference-native | 0.9919 | 1.0175 | 14.12x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
-| 512 | mi300x | triton-bitwise | 1.3618 | 1.4008 | 19.38x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
-| 512 | cpu | sdpa | 35.0730 | 38.8791 | 1.00x | 0.0 | 1.045e-03 | n/a | yes |
-| 512 | cpu | pytorch-native | 734.5447 | 1132.5722 | 20.94x | 0.0 | 2.811e-03 | n/a | yes |
-| 1024 | mi300x | sdpa | 0.1264 | 0.1373 | 1.00x | 24.1 | 1.053e-03 | n/a | yes |
-| 1024 | mi300x | pytorch-native | 0.3178 | 0.3455 | 2.51x | 153.0 | 2.487e-03 | n/a | yes |
-| 1024 | mi300x | strict-aiter | 0.2891 | 0.3381 | 2.29x | 28.1 | 2.299e-03 | 1.250e-06 | yes |
-| 1024 | mi300x | reference-native | 3.0663 | 3.0975 | 24.26x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
-| 1024 | mi300x | triton-bitwise | 4.7755 | 4.8063 | 37.78x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
-| 1024 | cpu | sdpa | 105.0845 | 109.9980 | 1.00x | 0.0 | 1.075e-03 | n/a | yes |
-| 1024 | cpu | pytorch-native | 3093.5610 | 3837.3499 | 29.44x | 128.0 | 2.117e-03 | n/a | yes |
-| 2048 | mi300x | sdpa | 0.2897 | 0.2998 | 1.00x | 48.3 | 9.099e-04 | n/a | yes |
-| 2048 | mi300x | pytorch-native | 1.0828 | 1.1133 | 3.74x | 564.0 | 1.727e-03 | n/a | yes |
-| 2048 | mi300x | strict-aiter | 0.2991 | 0.3337 | 1.03x | 56.3 | 2.053e-03 | 2.540e-06 | yes |
-| 2048 | mi300x | reference-native | 12.5615 | 12.6840 | 43.36x | 528.2 | 9.099e-04 | 1.103e-06 | yes |
-| 2048 | mi300x | triton-bitwise | 19.2360 | 19.3075 | 66.40x | 528.3 | 9.099e-04 | 1.103e-06 | yes |
-| 2048 | cpu | sdpa | 1566.1332 | 1683.1755 | 1.00x | 51.0 | 1.065e-03 | n/a | yes |
-| 2048 | cpu | pytorch-native | 13198.5082 | 13483.8688 | 8.43x | 561.1 | 2.143e-03 | n/a | yes |
-| 4096 | mi300x | sdpa | 1.0774 | 1.1026 | 1.00x | 96.5 | 9.905e-04 | n/a | yes |
-| 4096 | mi300x | pytorch-native | 3.9607 | 4.2144 | 3.68x | 2160.0 | 2.339e-03 | n/a | yes |
-| 4096 | mi300x | strict-aiter | 0.5825 | 0.6701 | 0.54x | 112.5 | 1.953e-03 | 3.805e-06 | yes |
-| 4096 | mi300x | reference-native | 48.5714 | 48.6788 | 45.08x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
-| 4096 | mi300x | triton-bitwise | 84.3475 | 86.5872 | 78.29x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
+|---:|---|---:|---:|---:|---:|---:|---:|:---:|
+| 512 | sdpa | 0.0703 | 0.1083 | 1.00x | 12.1 | 1.042e-03 | n/a | yes |
+| 512 | pytorch-native | 0.1926 | 0.2989 | 2.74x | 44.2 | 1.811e-03 | n/a | yes |
+| 512 | strict-aiter | 0.2729 | 0.2851 | 3.88x | 14.1 | 2.046e-03 | 8.756e-07 | yes |
+| 512 | reference-native | 0.9919 | 1.0175 | 14.12x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
+| 512 | triton-bitwise | 1.3618 | 1.4008 | 19.38x | 36.1 | 9.702e-04 | 8.340e-07 | yes |
+| 1024 | sdpa | 0.1264 | 0.1373 | 1.00x | 24.1 | 1.053e-03 | n/a | yes |
+| 1024 | pytorch-native | 0.3178 | 0.3455 | 2.51x | 153.0 | 2.487e-03 | n/a | yes |
+| 1024 | strict-aiter | 0.2891 | 0.3381 | 2.29x | 28.1 | 2.299e-03 | 1.250e-06 | yes |
+| 1024 | reference-native | 3.0663 | 3.0975 | 24.26x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
+| 1024 | triton-bitwise | 4.7755 | 4.8063 | 37.78x | 136.1 | 9.757e-04 | 1.011e-06 | yes |
+| 2048 | sdpa | 0.2897 | 0.2998 | 1.00x | 48.3 | 9.099e-04 | n/a | yes |
+| 2048 | pytorch-native | 1.0828 | 1.1133 | 3.74x | 564.0 | 1.727e-03 | n/a | yes |
+| 2048 | strict-aiter | 0.2991 | 0.3337 | 1.03x | 56.3 | 2.053e-03 | 2.540e-06 | yes |
+| 2048 | reference-native | 12.5615 | 12.6840 | 43.36x | 528.2 | 9.099e-04 | 1.103e-06 | yes |
+| 2048 | triton-bitwise | 19.2360 | 19.3075 | 66.40x | 528.3 | 9.099e-04 | 1.103e-06 | yes |
+| 4096 | sdpa | 1.0774 | 1.1026 | 1.00x | 96.5 | 9.905e-04 | n/a | yes |
+| 4096 | pytorch-native | 3.9607 | 4.2144 | 3.68x | 2160.0 | 2.339e-03 | n/a | yes |
+| 4096 | strict-aiter | 0.5825 | 0.6701 | 0.54x | 112.5 | 1.953e-03 | 3.805e-06 | yes |
+| 4096 | reference-native | 48.5714 | 48.6788 | 45.08x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
+| 4096 | triton-bitwise | 84.3475 | 86.5872 | 78.29x | 2080.5 | 9.681e-04 | 1.511e-06 | yes |
 
 ### Forward+backward
 
-| S | Platform | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
-|---:|---|---|---:|---:|---:|---:|
-| 512 | mi300x | sdpa | 0.3330 | 0.3590 | 1.00x | 32.2 |
-| 512 | mi300x | pytorch-native | 0.9847 | 1.0736 | 2.96x | 76.3 |
-| 512 | mi300x | strict-aiter | 1.1291 | 1.5507 | 3.39x | 288.2 |
-| 512 | mi300x | reference-native | 3.0893 | 3.1265 | 9.28x | 78.1 |
-| 512 | mi300x | triton-bitwise | 4.7979 | 4.8599 | 14.41x | 78.1 |
-| 512 | cpu | sdpa | 156.9364 | 240.0815 | 1.00x | 0.0 |
-| 512 | cpu | pytorch-native | 2364.2130 | 3938.9163 | 15.06x | 0.0 |
-| 1024 | mi300x | sdpa | 0.4161 | 0.4702 | 1.00x | 64.4 |
-| 1024 | mi300x | pytorch-native | 0.7710 | 0.8476 | 1.85x | 281.0 |
-| 1024 | mi300x | strict-aiter | 0.8597 | 0.9174 | 2.07x | 1088.4 |
-| 1024 | mi300x | reference-native | 11.8471 | 11.9334 | 28.47x | 284.3 |
-| 1024 | mi300x | triton-bitwise | 19.5915 | 19.7842 | 47.09x | 284.3 |
-| 1024 | cpu | sdpa | 335.7934 | 337.1536 | 1.00x | 79.1 |
-| 1024 | cpu | pytorch-native | 11104.1810 | 13321.1288 | 33.07x | 192.2 |
-| 2048 | mi300x | sdpa | 1.1873 | 1.5985 | 1.00x | 128.5 |
-| 2048 | mi300x | pytorch-native | 2.3897 | 2.9295 | 2.01x | 1076.0 |
-| 2048 | mi300x | strict-aiter | 1.7668 | 1.7958 | 1.49x | 4224.8 |
-| 2048 | mi300x | reference-native | 47.4088 | 47.5834 | 39.93x | 1080.5 |
-| 2048 | mi300x | triton-bitwise | 75.9789 | 76.2766 | 63.99x | 1080.5 |
-| 2048 | cpu | sdpa | 1089.9534 | 1090.4449 | 1.00x | 143.9 |
-| 2048 | cpu | pytorch-native | 37002.9627 | 38977.8489 | 33.95x | 766.4 |
-| 4096 | mi300x | sdpa | 3.9710 | 4.0939 | 1.00x | 257.0 |
-| 4096 | mi300x | pytorch-native | 9.4752 | 9.7886 | 2.39x | 4208.0 |
-| 4096 | mi300x | strict-aiter | 5.9360 | 5.9734 | 1.49x | 16641.5 |
-| 4096 | mi300x | reference-native | 171.5709 | 171.7084 | 43.21x | 4209.0 |
-| 4096 | mi300x | triton-bitwise | 301.2615 | 302.5984 | 75.87x | 4209.0 |
-
-Host peak memory is an RSS high-water delta sampled from `/proc`, not an allocator statistic, so the `cpu` rows approximate and are not directly comparable to the device figures.
+| S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
+|---:|---|---:|---:|---:|---:|
+| 512 | sdpa | 0.3330 | 0.3590 | 1.00x | 32.2 |
+| 512 | pytorch-native | 0.9847 | 1.0736 | 2.96x | 76.3 |
+| 512 | strict-aiter | 1.1291 | 1.5507 | 3.39x | 288.2 |
+| 512 | reference-native | 3.0893 | 3.1265 | 9.28x | 78.1 |
+| 512 | triton-bitwise | 4.7979 | 4.8599 | 14.41x | 78.1 |
+| 1024 | sdpa | 0.4161 | 0.4702 | 1.00x | 64.4 |
+| 1024 | pytorch-native | 0.7710 | 0.8476 | 1.85x | 281.0 |
+| 1024 | strict-aiter | 0.8597 | 0.9174 | 2.07x | 1088.4 |
+| 1024 | reference-native | 11.8471 | 11.9334 | 28.47x | 284.3 |
+| 1024 | triton-bitwise | 19.5915 | 19.7842 | 47.09x | 284.3 |
+| 2048 | sdpa | 1.1873 | 1.5985 | 1.00x | 128.5 |
+| 2048 | pytorch-native | 2.3897 | 2.9295 | 2.01x | 1076.0 |
+| 2048 | strict-aiter | 1.7668 | 1.7958 | 1.49x | 4224.8 |
+| 2048 | reference-native | 47.4088 | 47.5834 | 39.93x | 1080.5 |
+| 2048 | triton-bitwise | 75.9789 | 76.2766 | 63.99x | 1080.5 |
+| 4096 | sdpa | 3.9710 | 4.0939 | 1.00x | 257.0 |
+| 4096 | pytorch-native | 9.4752 | 9.7886 | 2.39x | 4208.0 |
+| 4096 | strict-aiter | 5.9360 | 5.9734 | 1.49x | 16641.5 |
+| 4096 | reference-native | 171.5709 | 171.7084 | 43.21x | 4209.0 |
+| 4096 | triton-bitwise | 301.2615 | 302.5984 | 75.87x | 4209.0 |
 
 ## Production core versus the reference core
 

From f0513d2c4769d3f757f6ff980864e39355f73c3a Mon Sep 17 00:00:00 2001
From: vensen <vensenmu@gmail.com>
Date: Sat, 29 Aug 2026 09:43:49 +0000
Subject: [PATCH 28/35] chore: trigger dco app

Signed-off-by: vensen <vensenmu@gmail.com>

From 1a2e4fce5e00149b46720c15ba3ddd206ac88ec5 Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sat, 29 Aug 2026 09:50:26 +0000
Subject: [PATCH 29/35] bench(attention): add the CPU host sweep and a hard
 per-cell time budget

CPU host numbers for the two paths that exist without a device: torch SDPA and
NativeAttentionOp. S=512/1024/2048, bf16 and fp16. Timing is wall clock and peak
memory an RSS high-water delta from /proc, so the host figures approximate and are
not directly comparable to the device columns.

The budget is now enforced twice: a pre-flight projection from the one untimed call
that already runs to capture outputs, and a wall clock inside the sampling loops,
because the projection under-estimates once the materialized score matrix leaves
cache. A cell that runs out of budget is truncated and flagged rather than dropped,
and one that cannot start is listed under "Skipped cells" with its observed
single-call cost.

Worth knowing before reading the host column: fp16 pytorch-native is 21.1 s per
forward at S=2048 against 228 ms for the same shape in bf16. That is PyTorch having
no optimized fp16 CPU matmul, not a property of this operator.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01824WkgMDxtBKD4Ex2NkfYA
---
 benchmarks/benchmark_ws2_rocm_attention.py |  61 +++-
 benchmarks/results/ws2_cpu/report.md       |  60 ++--
 benchmarks/results/ws2_cpu/results.json    | 322 ++++++++++++---------
 3 files changed, 261 insertions(+), 182 deletions(-)

diff --git a/benchmarks/benchmark_ws2_rocm_attention.py b/benchmarks/benchmark_ws2_rocm_attention.py
index d0826351..e8a0c7b2 100644
--- a/benchmarks/benchmark_ws2_rocm_attention.py
+++ b/benchmarks/benchmark_ws2_rocm_attention.py
@@ -122,12 +122,18 @@ def _mismatch_count(a: torch.Tensor, b: torch.Tensor) -> int:
     return int((a != b).sum().item())
 
 
-def _gpu_event_samples(function: Callable[[], Any], *, warmup: int, samples: int) -> list[float]:
+def _gpu_event_samples(
+    function: Callable[[], Any], *, warmup: int, samples: int, deadline: float = 0.0
+) -> list[float]:
     for _ in range(warmup):
         function()
+        if deadline and time.perf_counter() > deadline:
+            break
     torch.cuda.synchronize()
     events = []
     for _ in range(samples):
+        if deadline and events and time.perf_counter() > deadline:
+            break
         start = torch.cuda.Event(enable_timing=True)
         end = torch.cuda.Event(enable_timing=True)
         start.record()
@@ -138,12 +144,18 @@ def _gpu_event_samples(function: Callable[[], Any], *, warmup: int, samples: int
     return [float(start.elapsed_time(end)) for start, end in events]
 
 
-def _host_wall_samples(function: Callable[[], Any], *, warmup: int, samples: int) -> list[float]:
+def _host_wall_samples(
+    function: Callable[[], Any], *, warmup: int, samples: int, deadline: float = 0.0
+) -> list[float]:
     """Wall-clock timing for host execution, where CUDA events do not apply."""
     for _ in range(warmup):
         function()
+        if deadline and time.perf_counter() > deadline:
+            break
     timings = []
     for _ in range(samples):
+        if deadline and timings and time.perf_counter() > deadline:
+            break
         start = time.perf_counter()
         function()
         timings.append((time.perf_counter() - start) * 1000.0)
@@ -151,11 +163,23 @@ def _host_wall_samples(function: Callable[[], Any], *, warmup: int, samples: int
 
 
 def _timed_samples(
-    function: Callable[[], Any], *, warmup: int, samples: int, device: torch.device
+    function: Callable[[], Any],
+    *,
+    warmup: int,
+    samples: int,
+    device: torch.device,
+    deadline: float = 0.0,
 ) -> list[float]:
+    """Sample, stopping early once ``deadline`` (a perf_counter value) passes.
+
+    The pre-flight projection can under-estimate badly: at S=4096 the materialized
+    score matrix leaves cache and the compute model stops holding. So the budget is
+    also enforced here as a hard wall clock, not only as an estimate. At least one
+    sample is always taken, so a row is never empty.
+    """
     if device.type == "cuda":
-        return _gpu_event_samples(function, warmup=warmup, samples=samples)
-    return _host_wall_samples(function, warmup=warmup, samples=samples)
+        return _gpu_event_samples(function, warmup=warmup, samples=samples, deadline=deadline)
+    return _host_wall_samples(function, warmup=warmup, samples=samples, deadline=deadline)
 
 
 def _rss_mib() -> float:
@@ -485,13 +509,17 @@ def _single_gpu_benchmarks(
                     _empty_cache(device)
                     continue
 
-                forward = _summary_ms(
-                    _timed_samples(runner, warmup=warmup, samples=samples, device=device)
+                deadline = time.perf_counter() + budget_seconds if budget_seconds else 0.0
+                forward_ms = _timed_samples(
+                    runner, warmup=warmup, samples=samples, device=device, deadline=deadline
                 )
+                forward = _summary_ms(forward_ms)
                 forward_peak = _peak_memory_mib(runner, device)
 
                 entry: dict[str, Any] = {
                     "forward": forward,
+                    "forward_samples": len(forward_ms),
+                    "forward_truncated": len(forward_ms) < samples,
                     "forward_peak_mib": forward_peak,
                     "out_vs_fp64": _accuracy(out.double(), oracle_out),
                 }
@@ -508,14 +536,17 @@ def _single_gpu_benchmarks(
                     paths, name, q, k, v, causal=True, scale=scale, positions=positions
                 )
                 if training is not None:
-                    entry["train_fwd_bwd"] = _summary_ms(
-                        _timed_samples(
-                            training,
-                            warmup=max(1, warmup // 2),
-                            samples=training_samples,
-                            device=device,
-                        )
+                    train_deadline = time.perf_counter() + budget_seconds if budget_seconds else 0.0
+                    train_ms = _timed_samples(
+                        training,
+                        warmup=max(1, warmup // 2),
+                        samples=training_samples,
+                        device=device,
+                        deadline=train_deadline,
                     )
+                    entry["train_fwd_bwd"] = _summary_ms(train_ms)
+                    entry["train_samples"] = len(train_ms)
+                    entry["train_truncated"] = len(train_ms) < training_samples
                     entry["train_peak_mib"] = _peak_memory_mib(training, device)
 
                 row["paths"][name] = entry
@@ -1895,7 +1926,7 @@ def main() -> None:
     parser.add_argument(
         "--path-budget-seconds",
         type=float,
-        default=900.0,
+        default=300.0,
         help=(
             "Skip a (path, case) whose measured single call implies more than this many "
             "seconds of sampling. The row records the observed cost instead, which is "
diff --git a/benchmarks/results/ws2_cpu/report.md b/benchmarks/results/ws2_cpu/report.md
index 48f38f1f..e5c5b510 100644
--- a/benchmarks/results/ws2_cpu/report.md
+++ b/benchmarks/results/ws2_cpu/report.md
@@ -10,15 +10,15 @@
 | architecture | n/a |
 | cpu_count | 192 |
 | cuda | None |
-| extension_attention_symbols | deterministic_attention_backward, deterministic_attention_forward |
+| extension_attention_symbols | none |
 | gpu | n/a (host execution) |
-| gpu_count | 8 |
-| hip | 7.14.60850 |
-| native_collective | torch.distributed ProcessGroupNCCL (RCCL on ROCm) |
+| gpu_count | 0 |
+| hip | None |
+| native_collective | n/a (single-process host run) |
 | python | 3.12.3 |
 | torch | 2.12.0+rocm7.14.0a20260608 |
-| torch_threads | 32 |
-| triton | 3.7.0 |
+| torch_threads | 192 |
+| triton | n/a (host execution) |
 
 ## Methodology
 
@@ -65,23 +65,23 @@ Acceptance is 0 mismatched elements. This is the contract the Triton core exists
 
 | S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
 |---:|---|---:|---:|---:|---:|---:|---:|:---:|
-| 512 | sdpa | 31.5270 | 34.1950 | 1.00x | 0.0 | 8.606e-03 | n/a | yes |
-| 512 | pytorch-native | 15.3921 | 15.8488 | 0.49x | 32.7 | 1.947e-02 | n/a | yes |
-| 1024 | sdpa | 98.3129 | 98.4924 | 1.00x | 43.6 | 8.191e-03 | n/a | yes |
-| 1024 | pytorch-native | 51.5739 | 51.9609 | 0.52x | 127.8 | 1.610e-02 | n/a | yes |
-| 2048 | sdpa | 304.6115 | 308.9697 | 1.00x | 50.5 | 9.314e-03 | n/a | yes |
-| 2048 | pytorch-native | 216.1569 | 235.2967 | 0.71x | 543.6 | 1.790e-02 | n/a | yes |
+| 512 | sdpa | 38.1068 | 43.2369 | 1.00x | 0.1 | 8.606e-03 | n/a | yes |
+| 512 | pytorch-native | 7.0910 | 9.0480 | 0.19x | 0.0 | 1.947e-02 | n/a | yes |
+| 1024 | sdpa | 174.7517 | 252.6524 | 1.00x | 109.8 | 8.191e-03 | n/a | yes |
+| 1024 | pytorch-native | 58.5536 | 87.5402 | 0.34x | 130.2 | 1.610e-02 | n/a | yes |
+| 2048 | sdpa | 192.7010 | 232.4717 | 1.00x | 111.9 | 9.314e-03 | n/a | yes |
+| 2048 | pytorch-native | 227.7564 | 307.3500 | 1.18x | 548.6 | 1.790e-02 | n/a | yes |
 
 ### Forward+backward
 
 | S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
 |---:|---|---:|---:|---:|---:|
-| 512 | sdpa | 53.7609 | 54.4313 | 1.00x | 0.0 |
-| 512 | pytorch-native | 24.7810 | 25.4956 | 0.46x | 46.4 |
-| 1024 | sdpa | 163.8662 | 167.7328 | 1.00x | 30.9 |
-| 1024 | pytorch-native | 107.9884 | 110.4447 | 0.66x | 191.6 |
-| 2048 | sdpa | 533.5067 | 648.9086 | 1.00x | 31.7 |
-| 2048 | pytorch-native | 422.9041 | 424.9965 | 0.79x | 776.1 |
+| 512 | sdpa | 170.4205 | 175.4842 | 1.00x | 7.5 |
+| 512 | pytorch-native | 48.0205 | 156.2514 | 0.28x | 64.8 |
+| 1024 | sdpa | 297.3551 | 358.7597 | 1.00x | 135.3 |
+| 1024 | pytorch-native | 267.1181 | 313.6076 | 0.90x | 190.8 |
+| 2048 | sdpa | 555.5966 | 569.2160 | 1.00x | 112.9 |
+| 2048 | pytorch-native | 489.2399 | 550.9883 | 0.88x | 815.6 |
 
 ## Single-device Attention (fp16)
 
@@ -89,23 +89,23 @@ Acceptance is 0 mismatched elements. This is the contract the Triton core exists
 
 | S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB | out max-abs vs FP64 | lse max-abs vs FP64 | Repeat |
 |---:|---|---:|---:|---:|---:|---:|---:|:---:|
-| 512 | sdpa | 35.0730 | 38.8791 | 1.00x | 0.0 | 1.045e-03 | n/a | yes |
-| 512 | pytorch-native | 734.5447 | 1132.5722 | 20.94x | 0.0 | 2.811e-03 | n/a | yes |
-| 1024 | sdpa | 105.0845 | 109.9980 | 1.00x | 0.0 | 1.075e-03 | n/a | yes |
-| 1024 | pytorch-native | 3093.5610 | 3837.3499 | 29.44x | 128.0 | 2.117e-03 | n/a | yes |
-| 2048 | sdpa | 1566.1332 | 1683.1755 | 1.00x | 51.0 | 1.065e-03 | n/a | yes |
-| 2048 | pytorch-native | 13198.5082 | 13483.8688 | 8.43x | 561.1 | 2.143e-03 | n/a | yes |
+| 512 | sdpa | 31.7511 | 33.4260 | 1.00x | 0.0 | 1.045e-03 | n/a | yes |
+| 512 | pytorch-native | 697.2643 | 707.5163 | 21.96x | 0.0 | 2.811e-03 | n/a | yes |
+| 1024 | sdpa | 96.1593 | 113.6039 | 1.00x | 79.9 | 1.075e-03 | n/a | yes |
+| 1024 | pytorch-native | 2726.1935 | 2804.8509 | 28.35x | 65.7 | 2.117e-03 | n/a | yes |
+| 2048 | sdpa | 250.5798 | 286.6925 | 1.00x | 81.5 | 1.065e-03 | n/a | yes |
+| 2048 | pytorch-native | 21112.2733 | 21152.1648 | 84.25x | 515.2 | 2.143e-03 | n/a | yes |
 
 ### Forward+backward
 
 | S | Path | Median (ms) | p95 (ms) | vs sdpa | Peak MiB |
 |---:|---|---:|---:|---:|---:|
-| 512 | sdpa | 156.9364 | 240.0815 | 1.00x | 0.0 |
-| 512 | pytorch-native | 2364.2130 | 3938.9163 | 15.06x | 0.0 |
-| 1024 | sdpa | 335.7934 | 337.1536 | 1.00x | 79.1 |
-| 1024 | pytorch-native | 11104.1810 | 13321.1288 | 33.07x | 192.2 |
-| 2048 | sdpa | 1089.9534 | 1090.4449 | 1.00x | 143.9 |
-| 2048 | pytorch-native | 37002.9627 | 38977.8489 | 33.95x | 766.4 |
+| 512 | sdpa | 204.9800 | 242.0397 | 1.00x | 0.0 |
+| 512 | pytorch-native | 2154.7387 | 2248.4103 | 10.51x | 0.0 |
+| 1024 | sdpa | 386.7709 | 387.0739 | 1.00x | 79.7 |
+| 1024 | pytorch-native | 8579.2534 | 12587.9295 | 22.18x | 189.2 |
+| 2048 | sdpa | 1122.4169 | 1212.3130 | 1.00x | 79.7 |
+| 2048 | pytorch-native | 53718.0483 | 54692.0301 | 47.86x | 764.6 |
 
 ## Production core versus the reference core
 
diff --git a/benchmarks/results/ws2_cpu/results.json b/benchmarks/results/ws2_cpu/results.json
index e316c962..d6bc0783 100644
--- a/benchmarks/results/ws2_cpu/results.json
+++ b/benchmarks/results/ws2_cpu/results.json
@@ -2,7 +2,7 @@
   "platform_label": "cpu",
   "environment": {
     "cpu_count": 192,
-    "torch_threads": 32,
+    "torch_threads": 192,
     "gpu": "n/a (host execution)",
     "architecture": "n/a",
     "gpu_count": 0,
@@ -50,45 +50,53 @@
         "paths": {
           "sdpa": {
             "forward": {
-              "median_ms": 31.52696695178747,
-              "p95_ms": 34.19500198215246,
-              "min_ms": 29.54479120671749,
-              "max_ms": 34.68872793018818
-            },
-            "forward_peak_mib": 0.01953125,
+              "median_ms": 38.106778636574745,
+              "p95_ms": 43.23689788579941,
+              "min_ms": 28.195404447615147,
+              "max_ms": 44.51697692275047
+            },
+            "forward_samples": 5,
+            "forward_truncated": false,
+            "forward_peak_mib": 0.0859375,
             "out_vs_fp64": {
               "max_abs": 0.00860644332381133,
               "relative_l2": 0.001973059990724691
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 53.76091971993446,
-              "p95_ms": 54.43128254264593,
-              "min_ms": 53.622123785316944,
-              "max_ms": 54.50576730072498
-            },
-            "train_peak_mib": 0.01953125
+              "median_ms": 170.42051907628775,
+              "p95_ms": 175.48417346552014,
+              "min_ms": 132.9573979601264,
+              "max_ms": 176.0468017309904
+            },
+            "train_samples": 3,
+            "train_truncated": false,
+            "train_peak_mib": 7.4765625
           },
           "pytorch-native": {
             "forward": {
-              "median_ms": 15.392081812024117,
-              "p95_ms": 15.848848782479763,
-              "min_ms": 14.35321755707264,
-              "max_ms": 15.881960280239582
+              "median_ms": 7.091020233929157,
+              "p95_ms": 9.047956205904484,
+              "min_ms": 4.163389094173908,
+              "max_ms": 9.431971237063408
             },
-            "forward_peak_mib": 32.73828125,
+            "forward_samples": 5,
+            "forward_truncated": false,
+            "forward_peak_mib": 0.0,
             "out_vs_fp64": {
               "max_abs": 0.01947146352388973,
               "relative_l2": 0.004330172876834844
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 24.780988693237305,
-              "p95_ms": 25.495624728500843,
-              "min_ms": 19.345969893038273,
-              "max_ms": 25.575028732419014
-            },
-            "train_peak_mib": 46.40234375
+              "median_ms": 48.0204951018095,
+              "p95_ms": 156.25138729810712,
+              "min_ms": 43.106830678880215,
+              "max_ms": 168.27704198658466
+            },
+            "train_samples": 3,
+            "train_truncated": false,
+            "train_peak_mib": 64.8203125
           }
         }
       },
@@ -102,45 +110,53 @@
         "paths": {
           "sdpa": {
             "forward": {
-              "median_ms": 98.31287991255522,
-              "p95_ms": 98.49240202456713,
-              "min_ms": 98.21581654250622,
-              "max_ms": 98.5084418207407
-            },
-            "forward_peak_mib": 43.56640625,
+              "median_ms": 174.75166637450457,
+              "p95_ms": 252.65235546976325,
+              "min_ms": 164.2555631697178,
+              "max_ms": 271.48198056966066
+            },
+            "forward_samples": 5,
+            "forward_truncated": false,
+            "forward_peak_mib": 109.82421875,
             "out_vs_fp64": {
               "max_abs": 0.008190526417996224,
               "relative_l2": 0.002021095362635071
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 163.86622097343206,
-              "p95_ms": 167.7327803336084,
-              "min_ms": 160.76263692229986,
-              "max_ms": 168.16239804029465
-            },
-            "train_peak_mib": 30.93359375
+              "median_ms": 297.35514242202044,
+              "p95_ms": 358.75966083258385,
+              "min_ms": 296.0860254243016,
+              "max_ms": 365.5823851004243
+            },
+            "train_samples": 3,
+            "train_truncated": false,
+            "train_peak_mib": 135.33203125
           },
           "pytorch-native": {
             "forward": {
-              "median_ms": 51.57393869012594,
-              "p95_ms": 51.960853673517704,
-              "min_ms": 50.85334833711386,
-              "max_ms": 51.99178960174322
-            },
-            "forward_peak_mib": 127.80859375,
+              "median_ms": 58.55359323322773,
+              "p95_ms": 87.5402009114623,
+              "min_ms": 54.07743901014328,
+              "max_ms": 92.4938665702939
+            },
+            "forward_samples": 5,
+            "forward_truncated": false,
+            "forward_peak_mib": 130.1875,
             "out_vs_fp64": {
               "max_abs": 0.016100370761021,
               "relative_l2": 0.004516966595870689
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 107.98836220055819,
-              "p95_ms": 110.44468563050032,
-              "min_ms": 106.3336431980133,
-              "max_ms": 110.71761045604944
-            },
-            "train_peak_mib": 191.6171875
+              "median_ms": 267.11810380220413,
+              "p95_ms": 313.60761895775795,
+              "min_ms": 249.75966848433018,
+              "max_ms": 318.7731206417084
+            },
+            "train_samples": 3,
+            "train_truncated": false,
+            "train_peak_mib": 190.8046875
           }
         }
       },
@@ -154,45 +170,53 @@
         "paths": {
           "sdpa": {
             "forward": {
-              "median_ms": 304.6115078032017,
-              "p95_ms": 308.9697081595659,
-              "min_ms": 303.94353438168764,
-              "max_ms": 309.84257347881794
-            },
-            "forward_peak_mib": 50.52734375,
+              "median_ms": 192.7009578794241,
+              "p95_ms": 232.47170187532902,
+              "min_ms": 168.04722882807255,
+              "max_ms": 234.8661571741104
+            },
+            "forward_samples": 5,
+            "forward_truncated": false,
+            "forward_peak_mib": 111.88671875,
             "out_vs_fp64": {
               "max_abs": 0.00931387262518557,
               "relative_l2": 0.002037123315687043
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 533.5067352280021,
-              "p95_ms": 648.9086364395915,
-              "min_ms": 529.2324423789978,
-              "max_ms": 661.731069907546
-            },
-            "train_peak_mib": 31.70703125
+              "median_ms": 555.5966263636947,
+              "p95_ms": 569.2159625701606,
+              "min_ms": 521.6728867962956,
+              "max_ms": 570.7292221486568
+            },
+            "train_samples": 3,
+            "train_truncated": false,
+            "train_peak_mib": 112.91796875
           },
           "pytorch-native": {
             "forward": {
-              "median_ms": 216.15692228078842,
-              "p95_ms": 235.2967405691743,
-              "min_ms": 196.53789699077606,
-              "max_ms": 239.01280853897333
-            },
-            "forward_peak_mib": 543.61328125,
+              "median_ms": 227.75637917220592,
+              "p95_ms": 307.34998527914286,
+              "min_ms": 209.55913793295622,
+              "max_ms": 317.96263344585896
+            },
+            "forward_samples": 5,
+            "forward_truncated": false,
+            "forward_peak_mib": 548.5625,
             "out_vs_fp64": {
               "max_abs": 0.0178950704113916,
               "relative_l2": 0.004656276082238093
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 422.90409468114376,
-              "p95_ms": 424.99645575881004,
-              "min_ms": 417.9189233109355,
-              "max_ms": 425.2289403229952
-            },
-            "train_peak_mib": 776.13671875
+              "median_ms": 489.23985194414854,
+              "p95_ms": 550.9883309714496,
+              "min_ms": 478.3158637583256,
+              "max_ms": 557.8492730855942
+            },
+            "train_samples": 3,
+            "train_truncated": false,
+            "train_peak_mib": 815.6015625
           }
         }
       },
@@ -206,11 +230,13 @@
         "paths": {
           "sdpa": {
             "forward": {
-              "median_ms": 35.07298789918423,
-              "p95_ms": 38.87907024472952,
-              "min_ms": 35.05528252571821,
-              "max_ms": 39.82974961400032
+              "median_ms": 31.751069240272045,
+              "p95_ms": 33.425997383892536,
+              "min_ms": 29.143651947379112,
+              "max_ms": 33.47691521048546
             },
+            "forward_samples": 5,
+            "forward_truncated": false,
             "forward_peak_mib": 0.0,
             "out_vs_fp64": {
               "max_abs": 0.0010446820342941976,
@@ -218,20 +244,24 @@
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 156.93644620478153,
-              "p95_ms": 240.0815368629992,
-              "min_ms": 110.2369837462902,
-              "max_ms": 249.31988026946783
+              "median_ms": 204.98001947999,
+              "p95_ms": 242.03968066722155,
+              "min_ms": 192.74852704256773,
+              "max_ms": 246.15742079913616
             },
+            "train_samples": 3,
+            "train_truncated": false,
             "train_peak_mib": 0.0
           },
           "pytorch-native": {
             "forward": {
-              "median_ms": 734.5447409898043,
-              "p95_ms": 1132.5721632689238,
-              "min_ms": 692.6448438316584,
-              "max_ms": 1182.6508231461048
+              "median_ms": 697.2642932087183,
+              "p95_ms": 707.5163403525949,
+              "min_ms": 684.5130370929837,
+              "max_ms": 709.9788626655936
             },
+            "forward_samples": 5,
+            "forward_truncated": false,
             "forward_peak_mib": 0.0,
             "out_vs_fp64": {
               "max_abs": 0.002810583458336957,
@@ -239,11 +269,13 @@
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 2364.212989807129,
-              "p95_ms": 3938.916347548365,
-              "min_ms": 2019.2379858344793,
-              "max_ms": 4113.883387297392
+              "median_ms": 2154.7386962920427,
+              "p95_ms": 2248.4102914109826,
+              "min_ms": 2068.0867824703455,
+              "max_ms": 2258.818246424198
             },
+            "train_samples": 3,
+            "train_truncated": false,
             "train_peak_mib": 0.0
           }
         }
@@ -258,45 +290,53 @@
         "paths": {
           "sdpa": {
             "forward": {
-              "median_ms": 105.08450027555227,
-              "p95_ms": 109.9980441853404,
-              "min_ms": 104.55065779387951,
-              "max_ms": 110.61816569417715
-            },
-            "forward_peak_mib": 0.0,
+              "median_ms": 96.15929331630468,
+              "p95_ms": 113.60393781214952,
+              "min_ms": 95.77664453536272,
+              "max_ms": 116.9054713100195
+            },
+            "forward_samples": 5,
+            "forward_truncated": false,
+            "forward_peak_mib": 79.91796875,
             "out_vs_fp64": {
               "max_abs": 0.0010750978941995726,
               "relative_l2": 0.00025550971614830375
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 335.7933955267072,
-              "p95_ms": 337.15363508090377,
-              "min_ms": 335.052122361958,
-              "max_ms": 337.30477280914783
-            },
-            "train_peak_mib": 79.14453125
+              "median_ms": 386.77085004746914,
+              "p95_ms": 387.07391703501344,
+              "min_ms": 327.51816138625145,
+              "max_ms": 387.1075911447406
+            },
+            "train_samples": 3,
+            "train_truncated": false,
+            "train_peak_mib": 79.66015625
           },
           "pytorch-native": {
             "forward": {
-              "median_ms": 3093.56103092432,
-              "p95_ms": 3837.349909357726,
-              "min_ms": 3021.5385919436812,
-              "max_ms": 4018.383893184364
-            },
-            "forward_peak_mib": 128.04296875,
+              "median_ms": 2726.1935137212276,
+              "p95_ms": 2804.850871488452,
+              "min_ms": 2686.2719180062413,
+              "max_ms": 2823.695234954357
+            },
+            "forward_samples": 5,
+            "forward_truncated": false,
+            "forward_peak_mib": 65.7421875,
             "out_vs_fp64": {
               "max_abs": 0.002116843588507722,
               "relative_l2": 0.0005576223691406817
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 11104.180980473757,
-              "p95_ms": 13321.12881867215,
-              "min_ms": 8533.20586308837,
-              "max_ms": 13567.45635624975
-            },
-            "train_peak_mib": 192.1953125
+              "median_ms": 8579.253423959017,
+              "p95_ms": 12587.929507251827,
+              "min_ms": 8301.5665281564,
+              "max_ms": 13033.33796095103
+            },
+            "train_samples": 3,
+            "train_truncated": false,
+            "train_peak_mib": 189.20703125
           }
         }
       },
@@ -310,45 +350,53 @@
         "paths": {
           "sdpa": {
             "forward": {
-              "median_ms": 1566.133220680058,
-              "p95_ms": 1683.1755116581917,
-              "min_ms": 1212.3965499922633,
-              "max_ms": 1689.712654799223
-            },
-            "forward_peak_mib": 51.04296875,
+              "median_ms": 250.57981442660093,
+              "p95_ms": 286.6925349459052,
+              "min_ms": 194.098518230021,
+              "max_ms": 291.58885311335325
+            },
+            "forward_samples": 5,
+            "forward_truncated": false,
+            "forward_peak_mib": 81.46484375,
             "out_vs_fp64": {
               "max_abs": 0.0010650871035631226,
               "relative_l2": 0.0002589060030057917
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 1089.9534411728382,
-              "p95_ms": 1090.444867964834,
-              "min_ms": 1087.7068433910608,
-              "max_ms": 1090.4994709417224
-            },
-            "train_peak_mib": 143.85546875
+              "median_ms": 1122.4169470369816,
+              "p95_ms": 1212.3129985295236,
+              "min_ms": 1065.4740231111646,
+              "max_ms": 1222.3014486953616
+            },
+            "train_samples": 3,
+            "train_truncated": false,
+            "train_peak_mib": 79.66015625
           },
           "pytorch-native": {
             "forward": {
-              "median_ms": 13198.508179746568,
-              "p95_ms": 13483.868804574013,
-              "min_ms": 12426.476489752531,
-              "max_ms": 13548.32206852734
-            },
-            "forward_peak_mib": 561.1328125,
+              "median_ms": 21112.273322418332,
+              "p95_ms": 21152.164766564965,
+              "min_ms": 20729.920755140483,
+              "max_ms": 21160.758836194873
+            },
+            "forward_samples": 5,
+            "forward_truncated": false,
+            "forward_peak_mib": 515.18359375,
             "out_vs_fp64": {
               "max_abs": 0.0021428477134313173,
               "relative_l2": 0.0005852208464456109
             },
             "repeat_bitwise": true,
             "train_fwd_bwd": {
-              "median_ms": 37002.96266376972,
-              "p95_ms": 38977.848869841546,
-              "min_ms": 35062.47301120311,
-              "max_ms": 39197.28067051619
-            },
-            "train_peak_mib": 766.4140625
+              "median_ms": 53718.04826427251,
+              "p95_ms": 54692.0300597325,
+              "min_ms": 53648.340058512986,
+              "max_ms": 54800.25025922805
+            },
+            "train_samples": 3,
+            "train_truncated": false,
+            "train_peak_mib": 764.57421875
           }
         }
       }
@@ -416,4 +464,4 @@
   ],
   "tp_head_sensitivity": [],
   "distributed": []
-}
\ No newline at end of file
+}

From a55233fc7f53e7634259dc650904b3f268c69d9d Mon Sep 17 00:00:00 2001
From: maxiaosong1124 <maxiaosong7890@outlook.com>
Date: Sat, 29 Aug 2026 10:02:04 +0000
Subject: [PATCH 30/35] perf(distributed): optimize deterministic ROCm
 collectives

Signed-off-by: maxiaosong1124 <maxiaosong7890@outlook.com>
---
 csrc/ops.cpp                                  |   98 ++
 .../distributed/deterministic_collective.hip  | 1099 +++++++++++++++++
 docs/design/rocm-deterministic-collectives.md |   55 +-
 rl_engine/_C.pyi                              |   51 +
 rl_engine/distributed/collectives.py          |  438 ++++++-
 .../kernels/ops/cuda/attention/cp_comm.py     |   18 +-
 rl_engine/kernels/ops/pytorch/ffn/ffn.py      |   35 +-
 setup.py                                      |    4 +
 .../test_deterministic_all_gather.py          |    9 +-
 .../test_deterministic_all_reduce.py          |   23 +-
 .../test_deterministic_reduce_scatter.py      |   47 +-
 ...test_transport_deterministic_collective.py |   73 ++
 tests/test_build_platform_collectives.py      |    1 +
 13 files changed, 1894 insertions(+), 57 deletions(-)
 create mode 100644 csrc/rocm/distributed/deterministic_collective.hip

diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index 8936c9fb..9a7ca60b 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -110,6 +110,54 @@ void deterministic_collective_reduce_scatter(int64_t handle, torch::Tensor& outp
 void deterministic_collective_all_gather(int64_t handle, torch::Tensor& output);
 #endif
 
+#if defined(KERNEL_ALIGN_WITH_ROCM)
+// ROCm keeps arithmetic in a fixed balanced tree while using either RCCL or
+// HIP IPC for rank-ordered transport. These kernels expose the local and IPC
+// reduction paths without changing the CUDA implementation.
+void deterministic_collective_rocm_all_reduce(
+    torch::Tensor rank_inputs,
+    torch::Tensor output);
+void deterministic_collective_rocm_reduce_scatter(
+    torch::Tensor rank_inputs,
+    torch::Tensor output);
+torch::Tensor deterministic_collective_rocm_ipc_allocate(int64_t size_bytes);
+std::tuple<std::vector<int64_t>, int64_t>
+deterministic_collective_rocm_ipc_meta(torch::Tensor tensor);
+int64_t deterministic_collective_rocm_ipc_create(
+    torch::Tensor staging,
+    const std::vector<std::vector<int64_t>>& handles,
+    const std::vector<int64_t>& offsets,
+    int64_t rank);
+void deterministic_collective_rocm_ipc_synchronize(int64_t handle);
+void deterministic_collective_rocm_ipc_destroy(int64_t handle);
+void deterministic_collective_rocm_ipc_stage(int64_t handle, torch::Tensor input);
+void deterministic_collective_rocm_ipc_all_reduce(
+    int64_t handle,
+    torch::Tensor output);
+void deterministic_collective_rocm_ipc_all_reduce_input(
+    int64_t handle,
+    torch::Tensor input,
+    torch::Tensor output);
+void deterministic_collective_rocm_ipc_reduce_scatter(
+    int64_t handle,
+    torch::Tensor output);
+void deterministic_collective_rocm_ipc_reduce_scatter_input(
+    int64_t handle,
+    torch::Tensor input,
+    torch::Tensor output);
+void deterministic_collective_rocm_ipc_reduce_scatter_many(
+    int64_t handle,
+    const std::vector<torch::Tensor>& inputs,
+    const std::vector<torch::Tensor>& outputs);
+void deterministic_collective_rocm_ipc_all_gather(
+    int64_t handle,
+    torch::Tensor output);
+void deterministic_collective_rocm_ipc_all_gather_input(
+    int64_t handle,
+    torch::Tensor input,
+    torch::Tensor output);
+#endif
+
 // Batch-Invariant Deterministic GEMM Declarations
 torch::Tensor det_gemm_fwd(torch::Tensor a, torch::Tensor b);
 torch::Tensor det_gemm_fwd_rhs_transposed(torch::Tensor a, torch::Tensor bt);
@@ -442,6 +490,56 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
         "Run the TP=8 deterministic rank-ordered all-gather kernel");
 #endif
 
+#if defined(KERNEL_ALIGN_WITH_ROCM)
+    m.def(
+        "deterministic_collective_rocm_all_reduce",
+        &deterministic_collective_rocm_all_reduce,
+        "Run the ROCm fixed-tree all-reduce kernel");
+    m.def(
+        "deterministic_collective_rocm_reduce_scatter",
+        &deterministic_collective_rocm_reduce_scatter,
+        "Run the ROCm fixed-tree reduce-scatter kernel");
+    m.def("deterministic_collective_rocm_ipc_meta",
+          &deterministic_collective_rocm_ipc_meta,
+          "Export a ROCm allocation for IPC deterministic collectives");
+    m.def("deterministic_collective_rocm_ipc_allocate",
+          &deterministic_collective_rocm_ipc_allocate,
+          "Allocate ROCm memory that supports IPC export");
+    m.def("deterministic_collective_rocm_ipc_create",
+          &deterministic_collective_rocm_ipc_create,
+          "Create a ROCm IPC deterministic collective state");
+    m.def("deterministic_collective_rocm_ipc_destroy",
+          &deterministic_collective_rocm_ipc_destroy,
+          "Destroy a ROCm IPC deterministic collective state");
+    m.def("deterministic_collective_rocm_ipc_synchronize",
+          &deterministic_collective_rocm_ipc_synchronize,
+          "Wait until every rank finishes reading ROCm IPC staging");
+    m.def("deterministic_collective_rocm_ipc_stage",
+          &deterministic_collective_rocm_ipc_stage,
+          "Stage an input for ROCm IPC deterministic collectives");
+    m.def("deterministic_collective_rocm_ipc_all_reduce",
+          &deterministic_collective_rocm_ipc_all_reduce,
+          "Run a direct ROCm IPC fixed-tree all-reduce");
+    m.def("deterministic_collective_rocm_ipc_all_reduce_input",
+          &deterministic_collective_rocm_ipc_all_reduce_input,
+          "Stage and run a direct ROCm IPC fixed-tree all-reduce");
+    m.def("deterministic_collective_rocm_ipc_reduce_scatter",
+          &deterministic_collective_rocm_ipc_reduce_scatter,
+          "Run a direct ROCm IPC fixed-tree reduce-scatter");
+    m.def("deterministic_collective_rocm_ipc_reduce_scatter_input",
+          &deterministic_collective_rocm_ipc_reduce_scatter_input,
+          "Stage and run a direct ROCm IPC fixed-tree reduce-scatter");
+    m.def("deterministic_collective_rocm_ipc_reduce_scatter_many",
+          &deterministic_collective_rocm_ipc_reduce_scatter_many,
+          "Run multiple ROCm IPC fixed-tree reduce-scatters with one synchronization");
+    m.def("deterministic_collective_rocm_ipc_all_gather",
+          &deterministic_collective_rocm_ipc_all_gather,
+          "Run a direct ROCm IPC rank-ordered all-gather");
+    m.def("deterministic_collective_rocm_ipc_all_gather_input",
+          &deterministic_collective_rocm_ipc_all_gather_input,
+          "Stage and run a direct ROCm IPC rank-ordered all-gather");
+#endif
+
     // Prefix-shared attention uses NVIDIA PTX and falls back to PyTorch SDPA on ROCm.
 #if !defined(USE_ROCM)
     m.def("prefix_shared_attention", &prefix_shared_attention, "Prefix-Shared Fused Attention for GRPO");
diff --git a/csrc/rocm/distributed/deterministic_collective.hip b/csrc/rocm/distributed/deterministic_collective.hip
new file mode 100644
index 00000000..c8448300
--- /dev/null
+++ b/csrc/rocm/distributed/deterministic_collective.hip
@@ -0,0 +1,1099 @@
+// ROCm fixed-tree reduction kernels for the RCCL transport collective.
+//
+// RCCL is intentionally used only to transport rank-ordered tensors. The
+// kernels below perform the arithmetic locally in the exact balanced tree used
+// by the Python reference implementation. ReduceScatter receives a view that
+// contains only the destination rank's shard, so it does not reduce unrelated
+// rows.
+
+#include <ATen/hip/HIPContext.h>
+#include <c10/hip/HIPGuard.h>
+#include <c10/hip/HIPException.h>
+#include <torch/extension.h>
+#include <hip/hip_bf16.h>
+#include <hip/hip_fp16.h>
+
+#include <algorithm>
+#include <array>
+#include <cstdint>
+#include <cstring>
+#include <memory>
+#include <tuple>
+#include <vector>
+
+namespace {
+
+constexpr int kThreads = 256;
+constexpr int kMaxBlocks = 4096;
+constexpr int kMaxWorldSize = 8;
+constexpr int64_t kIPCControlBytes = 256;
+constexpr int64_t kIPCReadyOffset = 0;
+constexpr int64_t kIPCDoneOffset = 64;
+constexpr int64_t kIPCCloseOffset = 128;
+
+struct PeerPointers {
+  const void* values[kMaxWorldSize];
+};
+
+struct PeerSignals {
+  uint64_t* ready[kMaxWorldSize];
+  uint64_t* done[kMaxWorldSize];
+  uint64_t* closed[kMaxWorldSize];
+};
+
+template <typename scalar_t>
+__device__ __forceinline__ scalar_t ordered_add(scalar_t lower, scalar_t upper) {
+  // Keep every parent as a separate expression. ROCm builds do not enable
+  // fast-math, so this is the same dtype operation as torch.add_ for the
+  // supported floating-point dtypes.
+  return lower + upper;
+}
+
+template <typename scalar_t, int WorldSize>
+__device__ __forceinline__ scalar_t fixed_tree_reduce(
+    const scalar_t* values,
+    int64_t rank_stride,
+    int64_t index) {
+  static_assert(
+      WorldSize == 1 || WorldSize == 2 || WorldSize == 4 || WorldSize == 8,
+      "unsupported deterministic collective world size");
+  if constexpr (WorldSize == 1) {
+    return values[index];
+  } else {
+    const scalar_t sum01 = ordered_add(
+        values[index],
+        values[rank_stride + index]);
+    if constexpr (WorldSize == 2) {
+      return sum01;
+    } else {
+      const scalar_t sum23 = ordered_add(
+          values[2 * rank_stride + index],
+          values[3 * rank_stride + index]);
+      const scalar_t sum03 = ordered_add(sum01, sum23);
+      if constexpr (WorldSize == 4) {
+        return sum03;
+      } else {
+        const scalar_t sum45 = ordered_add(
+            values[4 * rank_stride + index],
+            values[5 * rank_stride + index]);
+        const scalar_t sum67 = ordered_add(
+            values[6 * rank_stride + index],
+            values[7 * rank_stride + index]);
+        const scalar_t sum47 = ordered_add(sum45, sum67);
+        return ordered_add(sum03, sum47);
+      }
+    }
+  }
+}
+
+template <typename scalar_t, int WorldSize>
+__global__ void fixed_tree_reduce_kernel(
+    const scalar_t* __restrict__ values,
+    scalar_t* __restrict__ output,
+    int64_t rank_stride,
+    int64_t element_count) {
+  const int64_t thread_index =
+      static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  const int64_t stride = static_cast<int64_t>(gridDim.x) * blockDim.x;
+  for (int64_t index = thread_index; index < element_count; index += stride) {
+    output[index] = fixed_tree_reduce<scalar_t, WorldSize>(values, rank_stride, index);
+  }
+}
+
+template <typename scalar_t>
+void launch_fixed_tree_reduce(
+    const scalar_t* values,
+    scalar_t* output,
+    int64_t rank_stride,
+    int64_t element_count,
+    int64_t world_size,
+    hipStream_t stream) {
+  const int blocks = static_cast<int>(std::min<int64_t>(
+      kMaxBlocks,
+      (element_count + kThreads - 1) / kThreads));
+  switch (world_size) {
+    case 1:
+      hipLaunchKernelGGL(
+          (fixed_tree_reduce_kernel<scalar_t, 1>),
+          dim3(blocks),
+          dim3(kThreads),
+          0,
+          stream,
+          values,
+          output,
+          rank_stride,
+          element_count);
+      break;
+    case 2:
+      hipLaunchKernelGGL(
+          (fixed_tree_reduce_kernel<scalar_t, 2>),
+          dim3(blocks),
+          dim3(kThreads),
+          0,
+          stream,
+          values,
+          output,
+          rank_stride,
+          element_count);
+      break;
+    case 4:
+      hipLaunchKernelGGL(
+          (fixed_tree_reduce_kernel<scalar_t, 4>),
+          dim3(blocks),
+          dim3(kThreads),
+          0,
+          stream,
+          values,
+          output,
+          rank_stride,
+          element_count);
+      break;
+    case 8:
+      hipLaunchKernelGGL(
+          (fixed_tree_reduce_kernel<scalar_t, 8>),
+          dim3(blocks),
+          dim3(kThreads),
+          0,
+          stream,
+          values,
+          output,
+          rank_stride,
+          element_count);
+      break;
+    default:
+      TORCH_CHECK(false, "unsupported deterministic collective world size ", world_size);
+  }
+  C10_CUDA_KERNEL_LAUNCH_CHECK();
+}
+
+void validate_inputs(
+    const torch::Tensor& rank_inputs,
+    const torch::Tensor& output,
+    const char* name) {
+  TORCH_CHECK(rank_inputs.is_cuda(), name, ": rank_inputs must be a CUDA/ROCm tensor");
+  TORCH_CHECK(output.is_cuda(), name, ": output must be a CUDA/ROCm tensor");
+  TORCH_CHECK(rank_inputs.scalar_type() == output.scalar_type(), name, ": dtype mismatch");
+  TORCH_CHECK(rank_inputs.dim() >= 1, name, ": rank_inputs must have a rank dimension");
+  TORCH_CHECK(rank_inputs.size(0) == 1 || rank_inputs.size(0) == 2 ||
+                  rank_inputs.size(0) == 4 || rank_inputs.size(0) == 8,
+              name, ": unsupported rank dimension ", rank_inputs.size(0));
+  TORCH_CHECK(rank_inputs.select(0, 0).is_contiguous(),
+              name, ": each rank slice must be contiguous");
+  TORCH_CHECK(output.is_contiguous(), name, ": output must be contiguous");
+  TORCH_CHECK(rank_inputs.device() == output.device(), name, ": device mismatch");
+  TORCH_CHECK(rank_inputs.numel() == output.numel() * rank_inputs.size(0),
+              name, ": rank_inputs/output element count mismatch");
+}
+
+void launch_dispatch(
+    const torch::Tensor& rank_inputs,
+    const torch::Tensor& output,
+    const char* name) {
+  validate_inputs(rank_inputs, output, name);
+  const int64_t world_size = rank_inputs.size(0);
+  const int64_t element_count = output.numel();
+  if (element_count == 0) {
+    return;
+  }
+  const int64_t rank_stride = rank_inputs.stride(0);
+  const auto stream = at::cuda::getCurrentCUDAStream();
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+      at::ScalarType::Half,
+      at::ScalarType::BFloat16,
+      rank_inputs.scalar_type(),
+      "deterministic_collective_rocm_fixed_tree",
+      [&] {
+        launch_fixed_tree_reduce<scalar_t>(
+            rank_inputs.data_ptr<scalar_t>(),
+            output.data_ptr<scalar_t>(),
+            rank_stride,
+            element_count,
+            world_size,
+            stream);
+      });
+}
+
+template <typename scalar_t, int WorldSize>
+__device__ __forceinline__ scalar_t ipc_fixed_tree_reduce(
+    const PeerPointers& peers,
+    int64_t index) {
+  const auto* rank0 = static_cast<const scalar_t*>(peers.values[0]);
+  if constexpr (WorldSize == 1) {
+    return rank0[index];
+  } else {
+    const auto* rank1 = static_cast<const scalar_t*>(peers.values[1]);
+    const scalar_t sum01 = ordered_add(rank0[index], rank1[index]);
+    if constexpr (WorldSize == 2) {
+      return sum01;
+    } else {
+      const auto* rank2 = static_cast<const scalar_t*>(peers.values[2]);
+      const auto* rank3 = static_cast<const scalar_t*>(peers.values[3]);
+      const scalar_t sum23 = ordered_add(rank2[index], rank3[index]);
+      const scalar_t sum03 = ordered_add(sum01, sum23);
+      if constexpr (WorldSize == 4) {
+        return sum03;
+      } else {
+        const auto* rank4 = static_cast<const scalar_t*>(peers.values[4]);
+        const auto* rank5 = static_cast<const scalar_t*>(peers.values[5]);
+        const auto* rank6 = static_cast<const scalar_t*>(peers.values[6]);
+        const auto* rank7 = static_cast<const scalar_t*>(peers.values[7]);
+        const scalar_t sum45 = ordered_add(rank4[index], rank5[index]);
+        const scalar_t sum67 = ordered_add(rank6[index], rank7[index]);
+        return ordered_add(sum03, ordered_add(sum45, sum67));
+      }
+    }
+  }
+}
+
+template <typename packed_t>
+__device__ __forceinline__ packed_t ordered_add_packed(
+    packed_t lower,
+    packed_t upper);
+
+template <>
+__device__ __forceinline__ __half2 ordered_add_packed(
+    __half2 lower,
+    __half2 upper) {
+  return __hadd2(lower, upper);
+}
+
+template <>
+__device__ __forceinline__ __hip_bfloat162 ordered_add_packed(
+    __hip_bfloat162 lower,
+    __hip_bfloat162 upper) {
+  return __hadd2(lower, upper);
+}
+
+template <typename packed_t, int WorldSize>
+__device__ __forceinline__ packed_t ipc_fixed_tree_reduce_packed(
+    const PeerPointers& peers,
+    int64_t index) {
+  const auto* rank0 = static_cast<const packed_t*>(peers.values[0]);
+  if constexpr (WorldSize == 1) {
+    return rank0[index];
+  } else {
+    const auto* rank1 = static_cast<const packed_t*>(peers.values[1]);
+    const packed_t sum01 = ordered_add_packed(rank0[index], rank1[index]);
+    if constexpr (WorldSize == 2) {
+      return sum01;
+    } else {
+      const auto* rank2 = static_cast<const packed_t*>(peers.values[2]);
+      const auto* rank3 = static_cast<const packed_t*>(peers.values[3]);
+      const packed_t sum23 = ordered_add_packed(rank2[index], rank3[index]);
+      const packed_t sum03 = ordered_add_packed(sum01, sum23);
+      if constexpr (WorldSize == 4) {
+        return sum03;
+      } else {
+        const auto* rank4 = static_cast<const packed_t*>(peers.values[4]);
+        const auto* rank5 = static_cast<const packed_t*>(peers.values[5]);
+        const auto* rank6 = static_cast<const packed_t*>(peers.values[6]);
+        const auto* rank7 = static_cast<const packed_t*>(peers.values[7]);
+        const packed_t sum45 = ordered_add_packed(rank4[index], rank5[index]);
+        const packed_t sum67 = ordered_add_packed(rank6[index], rank7[index]);
+        return ordered_add_packed(sum03, ordered_add_packed(sum45, sum67));
+      }
+    }
+  }
+}
+
+template <typename scalar_t, int WorldSize>
+__global__ void ipc_fixed_tree_reduce_kernel(
+    PeerPointers peers,
+    scalar_t* __restrict__ output,
+    int64_t input_offset,
+    int64_t element_count) {
+  const int64_t thread_index =
+      static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  const int64_t stride = static_cast<int64_t>(gridDim.x) * blockDim.x;
+  for (int64_t index = thread_index; index < element_count; index += stride) {
+    output[index] = ipc_fixed_tree_reduce<scalar_t, WorldSize>(
+        peers,
+        input_offset + index);
+  }
+}
+
+template <typename packed_t, int WorldSize>
+__global__ void ipc_fixed_tree_reduce_packed_kernel(
+    PeerPointers peers,
+    packed_t* __restrict__ output,
+    int64_t input_offset,
+    int64_t element_count) {
+  const int64_t thread_index =
+      static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  const int64_t stride = static_cast<int64_t>(gridDim.x) * blockDim.x;
+  for (int64_t index = thread_index; index < element_count; index += stride) {
+    output[index] = ipc_fixed_tree_reduce_packed<packed_t, WorldSize>(
+        peers,
+        input_offset + index);
+  }
+}
+
+template <typename scalar_t>
+void launch_ipc_fixed_tree_reduce(
+    const PeerPointers& peers,
+    scalar_t* output,
+    int64_t input_offset,
+    int64_t element_count,
+    int64_t world_size,
+    hipStream_t stream) {
+  const int blocks = static_cast<int>(std::min<int64_t>(
+      kMaxBlocks,
+      (element_count + kThreads - 1) / kThreads));
+  switch (world_size) {
+    case 1:
+      hipLaunchKernelGGL(
+          (ipc_fixed_tree_reduce_kernel<scalar_t, 1>),
+          dim3(blocks), dim3(kThreads), 0, stream,
+          peers, output, input_offset, element_count);
+      break;
+    case 2:
+      hipLaunchKernelGGL(
+          (ipc_fixed_tree_reduce_kernel<scalar_t, 2>),
+          dim3(blocks), dim3(kThreads), 0, stream,
+          peers, output, input_offset, element_count);
+      break;
+    case 4:
+      hipLaunchKernelGGL(
+          (ipc_fixed_tree_reduce_kernel<scalar_t, 4>),
+          dim3(blocks), dim3(kThreads), 0, stream,
+          peers, output, input_offset, element_count);
+      break;
+    case 8:
+      hipLaunchKernelGGL(
+          (ipc_fixed_tree_reduce_kernel<scalar_t, 8>),
+          dim3(blocks), dim3(kThreads), 0, stream,
+          peers, output, input_offset, element_count);
+      break;
+    default:
+      TORCH_CHECK(false, "unsupported deterministic collective world size ", world_size);
+  }
+  C10_CUDA_KERNEL_LAUNCH_CHECK();
+}
+
+template <typename packed_t>
+void launch_ipc_fixed_tree_reduce_packed(
+    const PeerPointers& peers,
+    packed_t* output,
+    int64_t input_offset,
+    int64_t element_count,
+    int64_t world_size,
+    hipStream_t stream) {
+  const int blocks = static_cast<int>(std::min<int64_t>(
+      kMaxBlocks,
+      (element_count + kThreads - 1) / kThreads));
+  switch (world_size) {
+    case 1:
+      hipLaunchKernelGGL(
+          (ipc_fixed_tree_reduce_packed_kernel<packed_t, 1>),
+          dim3(blocks), dim3(kThreads), 0, stream,
+          peers, output, input_offset, element_count);
+      break;
+    case 2:
+      hipLaunchKernelGGL(
+          (ipc_fixed_tree_reduce_packed_kernel<packed_t, 2>),
+          dim3(blocks), dim3(kThreads), 0, stream,
+          peers, output, input_offset, element_count);
+      break;
+    case 4:
+      hipLaunchKernelGGL(
+          (ipc_fixed_tree_reduce_packed_kernel<packed_t, 4>),
+          dim3(blocks), dim3(kThreads), 0, stream,
+          peers, output, input_offset, element_count);
+      break;
+    case 8:
+      hipLaunchKernelGGL(
+          (ipc_fixed_tree_reduce_packed_kernel<packed_t, 8>),
+          dim3(blocks), dim3(kThreads), 0, stream,
+          peers, output, input_offset, element_count);
+      break;
+    default:
+      TORCH_CHECK(false, "unsupported deterministic collective world size ", world_size);
+  }
+  C10_CUDA_KERNEL_LAUNCH_CHECK();
+}
+
+__global__ void ipc_wait_signal_kernel(
+    PeerSignals signals,
+    uint64_t sequence,
+    int64_t world_size,
+    bool wait_for_done) {
+  if (blockIdx.x != 0 || threadIdx.x != 0) {
+    return;
+  }
+  for (int peer = 0; peer < world_size; ++peer) {
+    uint64_t* signal = wait_for_done ? signals.done[peer] : signals.ready[peer];
+    while (__hip_atomic_load(
+               signal,
+               __ATOMIC_ACQUIRE,
+               __HIP_MEMORY_SCOPE_SYSTEM) < sequence) {
+      __builtin_amdgcn_s_sleep(1);
+    }
+  }
+}
+
+__global__ void ipc_mark_signal_kernel(uint64_t* signal, uint64_t sequence) {
+  if (blockIdx.x == 0 && threadIdx.x == 0) {
+    __hip_atomic_store(
+        signal,
+        sequence,
+        __ATOMIC_RELEASE,
+        __HIP_MEMORY_SCOPE_SYSTEM);
+  }
+}
+
+__global__ void ipc_mark_ready_and_wait_kernel(
+    PeerSignals signals,
+    int64_t rank,
+    uint64_t sequence,
+    int64_t world_size) {
+  if (blockIdx.x != 0 || threadIdx.x != 0) {
+    return;
+  }
+  __hip_atomic_store(
+      signals.ready[rank],
+      sequence,
+      __ATOMIC_RELEASE,
+      __HIP_MEMORY_SCOPE_SYSTEM);
+  for (int peer = 0; peer < world_size; ++peer) {
+    while (__hip_atomic_load(
+               signals.ready[peer],
+               __ATOMIC_ACQUIRE,
+               __HIP_MEMORY_SCOPE_SYSTEM) < sequence) {
+      __builtin_amdgcn_s_sleep(1);
+    }
+  }
+}
+
+__global__ void ipc_close_and_wait_kernel(
+    PeerSignals signals,
+    int64_t rank,
+    int64_t world_size) {
+  if (blockIdx.x != 0 || threadIdx.x != 0) {
+    return;
+  }
+  for (int peer = 0; peer < world_size; ++peer) {
+    __hip_atomic_fetch_add(
+        signals.closed[peer],
+        static_cast<uint64_t>(1),
+        __ATOMIC_ACQ_REL,
+        __HIP_MEMORY_SCOPE_SYSTEM);
+  }
+  while (__hip_atomic_load(
+             signals.closed[rank],
+             __ATOMIC_ACQUIRE,
+             __HIP_MEMORY_SCOPE_SYSTEM) < static_cast<uint64_t>(world_size)) {
+    __builtin_amdgcn_s_sleep(1);
+  }
+}
+
+__global__ void ipc_all_gather_uint4_kernel(
+    PeerPointers peers,
+    uint4* __restrict__ output,
+    int64_t vectors_per_rank,
+    int64_t world_size) {
+  const int64_t total_vectors = vectors_per_rank * world_size;
+  const int64_t thread_index =
+      static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  const int64_t stride = static_cast<int64_t>(gridDim.x) * blockDim.x;
+  for (int64_t index = thread_index; index < total_vectors; index += stride) {
+    const int peer = static_cast<int>(index / vectors_per_rank);
+    const int64_t peer_index = index - static_cast<int64_t>(peer) * vectors_per_rank;
+    output[index] = static_cast<const uint4*>(peers.values[peer])[peer_index];
+  }
+}
+
+__global__ void ipc_all_gather_bytes_kernel(
+    PeerPointers peers,
+    uint8_t* __restrict__ output,
+    int64_t bytes_per_rank,
+    int64_t world_size) {
+  const int64_t total_bytes = bytes_per_rank * world_size;
+  const int64_t thread_index =
+      static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
+  const int64_t stride = static_cast<int64_t>(gridDim.x) * blockDim.x;
+  for (int64_t index = thread_index; index < total_bytes; index += stride) {
+    const int peer = static_cast<int>(index / bytes_per_rank);
+    const int64_t peer_index = index - static_cast<int64_t>(peer) * bytes_per_rank;
+    output[index] = static_cast<const uint8_t*>(peers.values[peer])[peer_index];
+  }
+}
+
+void launch_ipc_all_gather(
+    const PeerPointers& peers,
+    void* output,
+    int64_t bytes_per_rank,
+    int64_t world_size,
+    hipStream_t stream) {
+  if (bytes_per_rank == 0) {
+    return;
+  }
+  if (bytes_per_rank % static_cast<int64_t>(sizeof(uint4)) == 0 &&
+      reinterpret_cast<uintptr_t>(output) % alignof(uint4) == 0) {
+    const int64_t vectors_per_rank = bytes_per_rank / sizeof(uint4);
+    const int64_t total_vectors = vectors_per_rank * world_size;
+    const int blocks = static_cast<int>(std::min<int64_t>(
+        kMaxBlocks,
+        (total_vectors + kThreads - 1) / kThreads));
+    hipLaunchKernelGGL(
+        ipc_all_gather_uint4_kernel,
+        dim3(blocks),
+        dim3(kThreads),
+        0,
+        stream,
+        peers,
+        static_cast<uint4*>(output),
+        vectors_per_rank,
+        world_size);
+  } else {
+    const int64_t total_bytes = bytes_per_rank * world_size;
+    const int blocks = static_cast<int>(std::min<int64_t>(
+        kMaxBlocks,
+        (total_bytes + kThreads - 1) / kThreads));
+    hipLaunchKernelGGL(
+        ipc_all_gather_bytes_kernel,
+        dim3(blocks),
+        dim3(kThreads),
+        0,
+        stream,
+        peers,
+        static_cast<uint8_t*>(output),
+        bytes_per_rank,
+        world_size);
+  }
+  C10_CUDA_KERNEL_LAUNCH_CHECK();
+}
+
+void launch_wait_signal(
+    const PeerSignals& signals,
+    uint64_t sequence,
+    int64_t world_size,
+    bool wait_for_done,
+    hipStream_t stream) {
+  hipLaunchKernelGGL(
+      ipc_wait_signal_kernel,
+      dim3(1),
+      dim3(1),
+      0,
+      stream,
+      signals,
+      sequence,
+      world_size,
+      wait_for_done);
+  C10_CUDA_KERNEL_LAUNCH_CHECK();
+}
+
+void launch_mark_signal(uint64_t* signal, uint64_t sequence, hipStream_t stream) {
+  hipLaunchKernelGGL(
+      ipc_mark_signal_kernel,
+      dim3(1),
+      dim3(1),
+      0,
+      stream,
+      signal,
+      sequence);
+  C10_CUDA_KERNEL_LAUNCH_CHECK();
+}
+
+void launch_mark_ready_and_wait(
+    const PeerSignals& signals,
+    int64_t rank,
+    uint64_t sequence,
+    int64_t world_size,
+    hipStream_t stream) {
+  hipLaunchKernelGGL(
+      ipc_mark_ready_and_wait_kernel,
+      dim3(1),
+      dim3(1),
+      0,
+      stream,
+      signals,
+      rank,
+      sequence,
+      world_size);
+  C10_CUDA_KERNEL_LAUNCH_CHECK();
+}
+
+void launch_close_and_wait(
+    const PeerSignals& signals,
+    int64_t rank,
+    int64_t world_size,
+    hipStream_t stream) {
+  hipLaunchKernelGGL(
+      ipc_close_and_wait_kernel,
+      dim3(1),
+      dim3(1),
+      0,
+      stream,
+      signals,
+      rank,
+      world_size);
+  C10_CUDA_KERNEL_LAUNCH_CHECK();
+}
+
+class ROCmIPCCollectiveState {
+ public:
+  ROCmIPCCollectiveState(
+      torch::Tensor staging,
+      const std::vector<std::vector<int64_t>>& handles,
+      const std::vector<int64_t>& offsets,
+      int64_t rank)
+      : rank_(rank),
+        world_size_(static_cast<int64_t>(handles.size())),
+        device_index_(staging.get_device()),
+        capacity_bytes_(staging.numel() * staging.element_size() - kIPCControlBytes) {
+    TORCH_CHECK(staging.is_cuda(), "ROCm IPC staging buffer must be on device");
+    TORCH_CHECK(staging.is_contiguous(), "ROCm IPC staging buffer must be contiguous");
+    TORCH_CHECK(staging.scalar_type() == torch::kUInt8,
+                "ROCm IPC staging buffer must have dtype uint8");
+    TORCH_CHECK(capacity_bytes_ > 0, "ROCm IPC staging capacity must be positive");
+    TORCH_CHECK(
+        world_size_ == 1 || world_size_ == 2 || world_size_ == 4 || world_size_ == 8,
+        "ROCm IPC deterministic collectives require world size 1, 2, 4, or 8");
+    TORCH_CHECK(offsets.size() == handles.size(), "one IPC offset is required per rank");
+    TORCH_CHECK(rank_ >= 0 && rank_ < world_size_, "invalid ROCm IPC rank");
+
+    set_peer_pointers(rank_, staging.data_ptr());
+    try {
+      for (int peer = 0; peer < world_size_; ++peer) {
+        if (peer == rank_) {
+          continue;
+        }
+        TORCH_CHECK(handles[peer].size() == sizeof(hipIpcMemHandle_t),
+                    "invalid ROCm IPC handle size for rank ", peer);
+        TORCH_CHECK(offsets[peer] >= 0, "invalid negative ROCm IPC offset");
+        hipIpcMemHandle_t handle{};
+        auto* raw_handle = reinterpret_cast<uint8_t*>(&handle);
+        for (size_t byte = 0; byte < sizeof(handle); ++byte) {
+          TORCH_CHECK(handles[peer][byte] >= 0 && handles[peer][byte] <= 255,
+                      "invalid ROCm IPC handle byte for rank ", peer);
+          raw_handle[byte] = static_cast<uint8_t>(handles[peer][byte]);
+        }
+        void* base = nullptr;
+        C10_HIP_CHECK(hipIpcOpenMemHandle(
+            &base,
+            handle,
+            hipIpcMemLazyEnablePeerAccess));
+        imported_bases_[peer] = base;
+        set_peer_pointers(
+            peer,
+            static_cast<uint8_t*>(base) + offsets[peer]);
+      }
+    } catch (...) {
+      close_imports();
+      throw;
+    }
+  }
+
+  ~ROCmIPCCollectiveState() {
+    int previous_device = -1;
+    if (hipGetDevice(&previous_device) == hipSuccess && previous_device != device_index_) {
+      if (hipSetDevice(device_index_) != hipSuccess) {
+        return;
+      }
+    }
+    close_imports();
+    if (previous_device >= 0 && previous_device != device_index_) {
+      C10_CUDA_IGNORE_ERROR(hipSetDevice(previous_device));
+    }
+  }
+
+  int device_index() const {
+    return device_index_;
+  }
+
+  void stage(torch::Tensor input, hipStream_t stream) {
+    check_tensor(input, "input");
+    const int64_t input_bytes = input.numel() * input.element_size();
+    TORCH_CHECK(input_bytes <= capacity_bytes_,
+                "input exceeds ROCm IPC staging capacity");
+    ++sequence_;
+    launch_wait_signal(
+        signals_,
+        sequence_ - 1,
+        world_size_,
+        true,
+        stream);
+    if (input_bytes > 0) {
+      C10_HIP_CHECK(hipMemcpyAsync(
+          const_cast<void*>(peers_.values[rank_]),
+          input.data_ptr(),
+          input_bytes,
+          hipMemcpyDeviceToDevice,
+          stream));
+    }
+    launch_mark_ready_and_wait(
+        signals_,
+        rank_,
+        sequence_,
+        world_size_,
+        stream);
+    staged_bytes_ = input_bytes;
+    staged_type_ = input.scalar_type();
+  }
+
+  void all_reduce(torch::Tensor output, hipStream_t stream) const {
+    check_reduction_output(output, staged_bytes_, "all_reduce");
+    launch(output, 0, output.numel(), stream);
+    launch_mark_signal(signals_.done[rank_], sequence_, stream);
+  }
+
+  void reduce_scatter(torch::Tensor output, hipStream_t stream) const {
+    check_reduction_output(
+        output,
+        staged_bytes_ / world_size_,
+        "reduce_scatter");
+    launch(
+        output,
+        rank_ * output.numel(),
+        output.numel(),
+        stream);
+    launch_mark_signal(signals_.done[rank_], sequence_, stream);
+  }
+
+  void reduce_scatter_many(
+      const std::vector<torch::Tensor>& inputs,
+      const std::vector<torch::Tensor>& outputs,
+      hipStream_t stream) {
+    TORCH_CHECK(!inputs.empty(), "reduce_scatter_many requires at least one input");
+    TORCH_CHECK(inputs.size() == outputs.size(),
+                "reduce_scatter_many input/output count mismatch");
+
+    int64_t total_bytes = 0;
+    const auto scalar_type = inputs.front().scalar_type();
+    for (size_t index = 0; index < inputs.size(); ++index) {
+      const auto& input = inputs[index];
+      const auto& output = outputs[index];
+      check_tensor(input, "reduce_scatter_many input");
+      check_tensor(output, "reduce_scatter_many output");
+      TORCH_CHECK(input.scalar_type() == scalar_type && output.scalar_type() == scalar_type,
+                  "reduce_scatter_many dtype mismatch");
+      const int64_t input_bytes = input.numel() * input.element_size();
+      TORCH_CHECK(input_bytes == output.numel() * output.element_size() * world_size_,
+                  "reduce_scatter_many output size mismatch");
+      TORCH_CHECK(input_bytes <= capacity_bytes_ - total_bytes,
+                  "reduce_scatter_many inputs exceed ROCm IPC staging capacity");
+      total_bytes += input_bytes;
+    }
+
+    ++sequence_;
+    launch_wait_signal(signals_, sequence_ - 1, world_size_, true, stream);
+    int64_t byte_offset = 0;
+    for (const auto& input : inputs) {
+      const int64_t input_bytes = input.numel() * input.element_size();
+      if (input_bytes > 0) {
+        C10_HIP_CHECK(hipMemcpyAsync(
+            static_cast<uint8_t*>(const_cast<void*>(peers_.values[rank_])) + byte_offset,
+            input.data_ptr(),
+            input_bytes,
+            hipMemcpyDeviceToDevice,
+            stream));
+      }
+      byte_offset += input_bytes;
+    }
+    launch_mark_ready_and_wait(
+        signals_,
+        rank_,
+        sequence_,
+        world_size_,
+        stream);
+
+    int64_t element_offset = 0;
+    for (size_t index = 0; index < outputs.size(); ++index) {
+      const auto& input = inputs[index];
+      const auto& output = outputs[index];
+      launch(
+          output,
+          element_offset + rank_ * output.numel(),
+          output.numel(),
+          stream);
+      element_offset += input.numel();
+    }
+    launch_mark_signal(signals_.done[rank_], sequence_, stream);
+  }
+
+  void all_gather(torch::Tensor output, hipStream_t stream) const {
+    check_tensor(output, "all_gather");
+    TORCH_CHECK(staged_type_ != at::ScalarType::Undefined, "stage must be called first");
+    TORCH_CHECK(output.scalar_type() == staged_type_, "all_gather dtype mismatch");
+    TORCH_CHECK(
+        output.numel() * output.element_size() == staged_bytes_ * world_size_,
+        "all_gather output size mismatch");
+    launch_ipc_all_gather(
+        peers_,
+        output.data_ptr(),
+        staged_bytes_,
+        world_size_,
+        stream);
+    launch_mark_signal(signals_.done[rank_], sequence_, stream);
+  }
+
+  void synchronize(hipStream_t stream) const {
+    launch_wait_signal(signals_, sequence_, world_size_, true, stream);
+    launch_close_and_wait(signals_, rank_, world_size_, stream);
+  }
+
+ private:
+  void set_peer_pointers(int peer, void* allocation_base) {
+    auto* bytes = static_cast<uint8_t*>(allocation_base);
+    signals_.ready[peer] = reinterpret_cast<uint64_t*>(bytes + kIPCReadyOffset);
+    signals_.done[peer] = reinterpret_cast<uint64_t*>(bytes + kIPCDoneOffset);
+    signals_.closed[peer] = reinterpret_cast<uint64_t*>(bytes + kIPCCloseOffset);
+    peers_.values[peer] = bytes + kIPCControlBytes;
+  }
+
+  void check_tensor(const torch::Tensor& tensor, const char* name) const {
+    TORCH_CHECK(tensor.is_cuda(), name, " must be a ROCm tensor");
+    TORCH_CHECK(tensor.is_contiguous(), name, " must be contiguous");
+    TORCH_CHECK(tensor.get_device() == device_index_, name, " device mismatch");
+  }
+
+  void check_reduction_output(
+      const torch::Tensor& output,
+      int64_t expected_bytes,
+      const char* name) const {
+    check_tensor(output, name);
+    TORCH_CHECK(staged_type_ != at::ScalarType::Undefined, "stage must be called first");
+    TORCH_CHECK(output.scalar_type() == staged_type_, name, " dtype mismatch");
+    TORCH_CHECK(output.numel() * output.element_size() == expected_bytes,
+                name, " output size mismatch");
+  }
+
+  void launch(
+      torch::Tensor output,
+      int64_t input_offset,
+      int64_t element_count,
+      hipStream_t stream) const {
+    if (element_count == 0) {
+      return;
+    }
+    if (element_count % 2 == 0 && input_offset % 2 == 0) {
+      if (output.scalar_type() == at::ScalarType::Half &&
+          reinterpret_cast<uintptr_t>(output.data_ptr()) % alignof(__half2) == 0) {
+        launch_ipc_fixed_tree_reduce_packed<__half2>(
+            peers_,
+            reinterpret_cast<__half2*>(output.data_ptr()),
+            input_offset / 2,
+            element_count / 2,
+            world_size_,
+            stream);
+        return;
+      }
+      if (output.scalar_type() == at::ScalarType::BFloat16 &&
+          reinterpret_cast<uintptr_t>(output.data_ptr()) %
+                  alignof(__hip_bfloat162) ==
+              0) {
+        launch_ipc_fixed_tree_reduce_packed<__hip_bfloat162>(
+            peers_,
+            reinterpret_cast<__hip_bfloat162*>(output.data_ptr()),
+            input_offset / 2,
+            element_count / 2,
+            world_size_,
+            stream);
+        return;
+      }
+    }
+    AT_DISPATCH_FLOATING_TYPES_AND2(
+        at::ScalarType::Half,
+        at::ScalarType::BFloat16,
+        output.scalar_type(),
+        "deterministic_collective_rocm_ipc_fixed_tree",
+        [&] {
+          launch_ipc_fixed_tree_reduce<scalar_t>(
+              peers_,
+              output.data_ptr<scalar_t>(),
+              input_offset,
+              element_count,
+              world_size_,
+              stream);
+        });
+  }
+
+  void close_imports() noexcept {
+    for (int peer = 0; peer < world_size_; ++peer) {
+      if (imported_bases_[peer] != nullptr) {
+        C10_CUDA_IGNORE_ERROR(hipIpcCloseMemHandle(imported_bases_[peer]));
+        imported_bases_[peer] = nullptr;
+      }
+    }
+  }
+
+  int64_t rank_;
+  int64_t world_size_;
+  int device_index_;
+  int64_t capacity_bytes_;
+  int64_t staged_bytes_{0};
+  at::ScalarType staged_type_{at::ScalarType::Undefined};
+  uint64_t sequence_{0};
+  PeerPointers peers_{};
+  PeerSignals signals_{};
+  std::array<void*, kMaxWorldSize> imported_bases_{};
+};
+
+ROCmIPCCollectiveState* ipc_state(int64_t handle) {
+  TORCH_CHECK(handle != 0, "ROCm IPC collective handle is closed");
+  return reinterpret_cast<ROCmIPCCollectiveState*>(handle);
+}
+
+}  // namespace
+
+void deterministic_collective_rocm_all_reduce(
+    torch::Tensor rank_inputs,
+    torch::Tensor output) {
+  launch_dispatch(rank_inputs, output, "deterministic_collective_rocm_all_reduce");
+}
+
+void deterministic_collective_rocm_reduce_scatter(
+    torch::Tensor rank_inputs,
+    torch::Tensor output) {
+  launch_dispatch(rank_inputs, output, "deterministic_collective_rocm_reduce_scatter");
+}
+
+torch::Tensor deterministic_collective_rocm_ipc_allocate(int64_t size_bytes) {
+  TORCH_CHECK(size_bytes > 0, "ROCm IPC allocation size must be positive");
+  int device_index = -1;
+  C10_HIP_CHECK(hipGetDevice(&device_index));
+  const int64_t allocation_bytes = size_bytes + kIPCControlBytes;
+  void* pointer = nullptr;
+  C10_HIP_CHECK(hipMalloc(&pointer, static_cast<size_t>(allocation_bytes)));
+  C10_HIP_CHECK(hipMemset(pointer, 0, static_cast<size_t>(kIPCControlBytes)));
+  const auto options = torch::TensorOptions()
+                           .dtype(torch::kUInt8)
+                           .device(torch::Device(torch::kCUDA, device_index));
+  return torch::from_blob(
+      pointer,
+      {allocation_bytes},
+      [device_index](void* allocation) {
+        int previous_device = -1;
+        if (hipGetDevice(&previous_device) != hipSuccess) {
+          return;
+        }
+        if (previous_device != device_index && hipSetDevice(device_index) != hipSuccess) {
+          return;
+        }
+        C10_CUDA_IGNORE_ERROR(hipFree(allocation));
+        if (previous_device != device_index) {
+          C10_CUDA_IGNORE_ERROR(hipSetDevice(previous_device));
+        }
+      },
+      options);
+}
+
+std::tuple<std::vector<int64_t>, int64_t>
+deterministic_collective_rocm_ipc_meta(torch::Tensor tensor) {
+  const c10::cuda::CUDAGuard device_guard(tensor.device());
+  TORCH_CHECK(tensor.is_cuda(), "ROCm IPC tensor must be on device");
+  TORCH_CHECK(tensor.is_contiguous(), "ROCm IPC tensor must be contiguous");
+  TORCH_CHECK(tensor.numel() > 0, "ROCm IPC tensor must be non-empty");
+
+  hipIpcMemHandle_t handle{};
+  const hipError_t export_error = hipIpcGetMemHandle(&handle, tensor.data_ptr());
+  TORCH_CHECK(
+      export_error == hipSuccess,
+      "hipIpcGetMemHandle failed: ",
+      hipGetErrorString(export_error));
+  const auto* raw_handle = reinterpret_cast<const uint8_t*>(&handle);
+  std::vector<int64_t> bytes(sizeof(handle));
+  for (size_t byte = 0; byte < sizeof(handle); ++byte) {
+    bytes[byte] = raw_handle[byte];
+  }
+  return std::make_tuple(bytes, 0);
+}
+
+int64_t deterministic_collective_rocm_ipc_create(
+    torch::Tensor staging,
+    const std::vector<std::vector<int64_t>>& handles,
+    const std::vector<int64_t>& offsets,
+    int64_t rank) {
+  const c10::cuda::CUDAGuard device_guard(staging.device());
+  auto state = std::make_unique<ROCmIPCCollectiveState>(
+      staging,
+      handles,
+      offsets,
+      rank);
+  return reinterpret_cast<int64_t>(state.release());
+}
+
+void deterministic_collective_rocm_ipc_destroy(int64_t handle) {
+  delete ipc_state(handle);
+}
+
+void deterministic_collective_rocm_ipc_synchronize(int64_t handle) {
+  auto* state = ipc_state(handle);
+  const c10::cuda::CUDAGuard device_guard(
+      torch::Device(torch::kCUDA, state->device_index()));
+  const auto stream = at::cuda::getCurrentCUDAStream();
+  state->synchronize(stream);
+}
+
+void deterministic_collective_rocm_ipc_stage(int64_t handle, torch::Tensor input) {
+  const c10::cuda::CUDAGuard device_guard(input.device());
+  const auto stream = at::cuda::getCurrentCUDAStream();
+  ipc_state(handle)->stage(input, stream);
+}
+
+void deterministic_collective_rocm_ipc_all_reduce(
+    int64_t handle,
+    torch::Tensor output) {
+  const c10::cuda::CUDAGuard device_guard(output.device());
+  const auto stream = at::cuda::getCurrentCUDAStream();
+  ipc_state(handle)->all_reduce(output, stream);
+}
+
+void deterministic_collective_rocm_ipc_all_reduce_input(
+    int64_t handle,
+    torch::Tensor input,
+    torch::Tensor output) {
+  const c10::cuda::CUDAGuard device_guard(input.device());
+  const auto stream = at::cuda::getCurrentCUDAStream();
+  auto* state = ipc_state(handle);
+  state->stage(input, stream);
+  state->all_reduce(output, stream);
+}
+
+void deterministic_collective_rocm_ipc_reduce_scatter(
+    int64_t handle,
+    torch::Tensor output) {
+  const c10::cuda::CUDAGuard device_guard(output.device());
+  const auto stream = at::cuda::getCurrentCUDAStream();
+  ipc_state(handle)->reduce_scatter(output, stream);
+}
+
+void deterministic_collective_rocm_ipc_reduce_scatter_input(
+    int64_t handle,
+    torch::Tensor input,
+    torch::Tensor output) {
+  const c10::cuda::CUDAGuard device_guard(input.device());
+  const auto stream = at::cuda::getCurrentCUDAStream();
+  auto* state = ipc_state(handle);
+  state->stage(input, stream);
+  state->reduce_scatter(output, stream);
+}
+
+void deterministic_collective_rocm_ipc_reduce_scatter_many(
+    int64_t handle,
+    const std::vector<torch::Tensor>& inputs,
+    const std::vector<torch::Tensor>& outputs) {
+  TORCH_CHECK(!inputs.empty(), "reduce_scatter_many requires at least one input");
+  const c10::cuda::CUDAGuard device_guard(inputs.front().device());
+  const auto stream = at::cuda::getCurrentCUDAStream();
+  ipc_state(handle)->reduce_scatter_many(inputs, outputs, stream);
+}
+
+void deterministic_collective_rocm_ipc_all_gather(
+    int64_t handle,
+    torch::Tensor output) {
+  const c10::cuda::CUDAGuard device_guard(output.device());
+  const auto stream = at::cuda::getCurrentCUDAStream();
+  ipc_state(handle)->all_gather(output, stream);
+}
+
+void deterministic_collective_rocm_ipc_all_gather_input(
+    int64_t handle,
+    torch::Tensor input,
+    torch::Tensor output) {
+  const c10::cuda::CUDAGuard device_guard(input.device());
+  const auto stream = at::cuda::getCurrentCUDAStream();
+  auto* state = ipc_state(handle);
+  state->stage(input, stream);
+  state->all_gather(output, stream);
+}
diff --git a/docs/design/rocm-deterministic-collectives.md b/docs/design/rocm-deterministic-collectives.md
index ce104332..c105a537 100644
--- a/docs/design/rocm-deterministic-collectives.md
+++ b/docs/design/rocm-deterministic-collectives.md
@@ -12,12 +12,42 @@ supports process-group sizes 1, 2, 4, and 8 and FP32/FP16/BF16 reductions.
 Inputs and outputs must be contiguous and all ranks must call operations in the
 same order with matching shapes, dtypes, and capacity.
 
-RCCL is used only for rank-ordered tensor transport:
-
-1. `all_gather_into_tensor` gathers every rank's bit patterns.
-2. Each rank evaluates the same balanced tree locally:
-   `((rank0 + rank1) + (rank2 + rank3)) + ...`.
-3. `reduce_scatter` slices the rank-owned rows after that fixed reduction.
+Single-node ROCm uses a dedicated `hipMalloc` staging allocation on every rank.
+The handles are exchanged once during construction and imported with HIP IPC.
+Each call copies its local input into staging, publishes a system-scope GPU
+sequence flag, and waits for every peer's matching sequence. The HIP kernel
+then reads rank-ordered peer memory and evaluates exactly
+`((rank0 + rank1) + (rank2 + rank3)) + ...`. A second sequence flag prevents a
+rank from overwriting staging until every peer has finished reading it.
+
+FP16 and BF16 use two-element vector loads and `hadd2`. This changes only the
+number of elements carried by an instruction: every scalar element retains the
+same dtype, rank order, and expression grouping. FP32 and unaligned tails use
+the scalar kernel. The executable Python tree remains the fallback for
+CPU/reference backends and extensions built without the optional HIP source.
+
+The measured MI300X routing policy is:
+
+- AllReduce up to 768 KiB uses the direct IPC fixed-tree kernel.
+- AllReduce from 768 KiB to 2.125 MiB uses the rank-major RCCL transport fallback.
+- AllReduce at 2.125 MiB and above performs IPC ReduceScatter followed by RCCL
+  AllGather of the already-reduced shards.
+- AllGather up to 256 KiB uses IPC peer copies; larger messages use RCCL.
+- ReduceScatter uses IPC for all supported sizes and reduces only the local
+  destination shard.
+
+The ready publication and peer wait share one GPU kernel. The store is a
+system-scope release and every peer load is a system-scope acquire. Done flags
+remain a separate generation barrier because they protect staging reuse. At
+close, every rank atomically acknowledges every peer allocation and waits for
+all acknowledgements before releasing its local staging memory.
+
+Sequence-parallel FFN backward has two independent ReduceScatter lanes (gate
+and up input gradients). On IPC, `reduce_scatter_many` copies the lanes into
+disjoint staging ranges under one ready/done generation and launches one fixed
+tree per output lane. It does not concatenate the inputs or mix their trees.
+The RCCL fallback retains a measured packed-payload crossover because a larger
+rank-major AllGather can be slower than two smaller calls.
 
 RCCL's `all_reduce` and `reduce_scatter` are not used for strict reductions.
 They guarantee a mathematical reduction but do not expose a stable
@@ -51,7 +81,7 @@ provenance and toleranced correctness tests.
 
 ## Compute/communication fusion
 
-The current ROCm collective is synchronous and reports
+The current ROCm collective is stream ordered and reports
 `supports_async_overlap = False` and
 `supports_compute_communication_fusion = False`.  FFN and Attention keep the
 dependency boundaries explicit:
@@ -83,10 +113,9 @@ not an optimization silently hidden inside this baseline.
 
 ## Performance acceptance
 
-The transport-only baseline favors correctness and portability. AllGather
-writes directly to its final output; reductions reuse one lazily grown
-`world_size * input_bytes` byte workspace until `close()`. It still moves more
-data than a native RCCL AllReduce. A ROCm GPU PR should therefore report, for
+The IPC path favors a fixed arithmetic tree over native-RCCL reduction speed.
+Large AllReduce avoids reducing the full tensor on every rank, but still moves
+more data than a native RCCL AllReduce. A ROCm GPU PR should therefore report, for
 world sizes 2/4/8 and representative FFN tensors:
 
 - latency and effective bandwidth for all three collectives;
@@ -95,8 +124,8 @@ world sizes 2/4/8 and representative FFN tensors:
 - repeat-bitwise and cross-TP results;
 - end-to-end TP/CP/SP FFN timing, not only isolated transport timing.
 
-A later fixed-tree HIP/XGMI implementation may replace the transport behind the
-same factory after it satisfies those checks.
+Multi-node or unsupported IPC configurations fall back behind the same factory
+after topology and symbol checks fail closed.
 
 Run the included native-RCCL comparison on a single node, for example:
 
diff --git a/rl_engine/_C.pyi b/rl_engine/_C.pyi
index 8727a7c9..d8109245 100644
--- a/rl_engine/_C.pyi
+++ b/rl_engine/_C.pyi
@@ -16,6 +16,14 @@ def deterministic_collective_stage(handle: int, input: torch.Tensor) -> None: ..
 def deterministic_collective_all_reduce(handle: int, output: torch.Tensor) -> None: ...
 def deterministic_collective_reduce_scatter(handle: int, output: torch.Tensor) -> None: ...
 def deterministic_collective_all_gather(handle: int, output: torch.Tensor) -> None: ...
+def deterministic_collective_rocm_all_reduce(
+    rank_inputs: torch.Tensor,
+    output: torch.Tensor,
+) -> None: ...
+def deterministic_collective_rocm_reduce_scatter(
+    rank_inputs: torch.Tensor,
+    output: torch.Tensor,
+) -> None: ...
 def fused_logp(logits: torch.Tensor, token_ids: torch.Tensor) -> torch.Tensor: ...
 def fused_logp_sm90(logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: ...
 def batch_invariant_logp_sm90(
@@ -215,3 +223,46 @@ def rmsnorm_backward_dw(
     rstd: torch.Tensor,
     mask: torch.Tensor,
 ) -> torch.Tensor: ...
+def deterministic_collective_rocm_ipc_allocate(size_bytes: int) -> torch.Tensor: ...
+def deterministic_collective_rocm_ipc_meta(tensor: torch.Tensor) -> tuple[list[int], int]: ...
+def deterministic_collective_rocm_ipc_create(
+    staging: torch.Tensor,
+    handles: list[list[int]],
+    offsets: list[int],
+    rank: int,
+) -> int: ...
+def deterministic_collective_rocm_ipc_synchronize(handle: int) -> None: ...
+def deterministic_collective_rocm_ipc_destroy(handle: int) -> None: ...
+def deterministic_collective_rocm_ipc_stage(handle: int, input: torch.Tensor) -> None: ...
+def deterministic_collective_rocm_ipc_all_reduce(
+    handle: int,
+    output: torch.Tensor,
+) -> None: ...
+def deterministic_collective_rocm_ipc_all_reduce_input(
+    handle: int,
+    input: torch.Tensor,
+    output: torch.Tensor,
+) -> None: ...
+def deterministic_collective_rocm_ipc_reduce_scatter(
+    handle: int,
+    output: torch.Tensor,
+) -> None: ...
+def deterministic_collective_rocm_ipc_reduce_scatter_input(
+    handle: int,
+    input: torch.Tensor,
+    output: torch.Tensor,
+) -> None: ...
+def deterministic_collective_rocm_ipc_reduce_scatter_many(
+    handle: int,
+    inputs: tuple[torch.Tensor, ...],
+    outputs: tuple[torch.Tensor, ...],
+) -> None: ...
+def deterministic_collective_rocm_ipc_all_gather(
+    handle: int,
+    output: torch.Tensor,
+) -> None: ...
+def deterministic_collective_rocm_ipc_all_gather_input(
+    handle: int,
+    input: torch.Tensor,
+    output: torch.Tensor,
+) -> None: ...
diff --git a/rl_engine/distributed/collectives.py b/rl_engine/distributed/collectives.py
index db3ac70f..b9cca962 100644
--- a/rl_engine/distributed/collectives.py
+++ b/rl_engine/distributed/collectives.py
@@ -1,10 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""Deterministic collectives for native CUDA IPC and rank-ordered transport.
+"""Deterministic collectives for CUDA IPC and ROCm rank-ordered transport.
 
-The ROCm transport never performs a floating-point reduction. It gathers every
-rank's input through RCCL, after which each rank evaluates the same balanced
-reduction tree locally.
+ROCm uses HIP IPC where it wins and RCCL otherwise. Reduction arithmetic stays
+outside RCCL and follows the same fixed balanced rank tree on every rank.
 """
 
 from __future__ import annotations
@@ -19,6 +18,14 @@
 
 _SUPPORTED_WORLD_SIZES = (1, 2, 4, 8)
 _DEFAULT_MAX_SIZE_BYTES = 64 * 1024 * 1024
+# Packing two independent lanes saves a collective launch for small tensors,
+# but doubles the message size seen by RCCL. On MI300X, separate AllGather
+# transports win once the packed payload reaches the multi-megabyte regime.
+# Keep the crossover explicit and easy to retune with new RCCL releases.
+_PACKED_REDUCE_SCATTER_MAX_BYTES = 8 * 1024 * 1024
+_ROCM_IPC_DIRECT_ALL_REDUCE_MAX_BYTES = 768 * 1024
+_ROCM_IPC_SHARDED_ALL_REDUCE_MIN_BYTES = 2176 * 1024
+_ROCM_IPC_ALL_GATHER_MAX_BYTES = 256 * 1024
 _REDUCTION_DTYPES = (torch.float32, torch.float16, torch.bfloat16)
 
 
@@ -227,6 +234,31 @@ def reduce_scatter(
             self._synchronize_ranks()
         return out
 
+    def reduce_scatter_many(
+        self,
+        inputs: tuple[torch.Tensor, ...] | list[torch.Tensor],
+        *,
+        outs: tuple[torch.Tensor, ...] | list[torch.Tensor] | None = None,
+    ) -> tuple[torch.Tensor, ...]:
+        """Compatibility fallback for CUDA IPC collectives.
+
+        The native CUDA IPC backend has no packed transport primitive yet, so
+        it preserves its established behavior by issuing the individual
+        fixed-tree calls. The ROCm transport subclass overrides this method
+        with a packed implementation.
+        """
+
+        values = tuple(inputs)
+        if not values:
+            raise ValueError("reduce_scatter_many requires at least one input")
+        if outs is not None and len(outs) != len(values):
+            raise ValueError("reduce_scatter_many outs must match the number of inputs")
+        results = tuple(
+            self.reduce_scatter(value, out=None if outs is None else outs[index])
+            for index, value in enumerate(values)
+        )
+        return results
+
     def close(self) -> None:
         """Release imported CUDA IPC mappings after the last collective call."""
 
@@ -380,6 +412,10 @@ def __init__(
         self._backend = str(dist.get_backend(self.group)).lower()
         self._lock = threading.Lock()
         self._closed = False
+        # Keep a lifecycle marker for callers that historically inspected the
+        # CUDA IPC collective's ``_handle`` while managing the cache. Concrete
+        # transports own any native resource through their own state.
+        self._handle = id(self)
         # One dtype-agnostic byte workspace is grown on demand and reused by
         # reduction collectives. AllGather writes directly into its output.
         self._workspace: torch.Tensor | None = None
@@ -444,13 +480,23 @@ def all_reduce(
         if out is None:
             out = torch.empty_like(input)
         self._validate_output(out, input, tuple(input.shape))
+        if self.world_size == 1:
+            out.copy_(input)
+            return out
 
         with self._lock:
             self._check_open()
             self._validate_matching_signature("all_reduce", input)
+            if self._direct_all_reduce(input, out):
+                return out
             rank_inputs = self._all_gather_transport(input)
-            reduced = self._balanced_tree_sum(rank_inputs)
-            out.copy_(reduced)
+            if not self._fused_reduction(
+                rank_inputs,
+                out,
+                operation="all_reduce",
+            ):
+                reduced = self._balanced_tree_sum(rank_inputs)
+                out.copy_(reduced)
         return out
 
     def all_gather(
@@ -467,10 +513,15 @@ def all_gather(
         if out is None:
             out = torch.empty(output_shape, dtype=input.dtype, device=input.device)
         self._validate_output(out, input, output_shape)
+        if self.world_size == 1:
+            out.copy_(input)
+            return out
 
         with self._lock:
             self._check_open()
             self._validate_matching_signature("all_gather", input)
+            if self._direct_all_gather(input, out):
+                return out
             self._all_gather_transport(input, gathered_flat=out.view(-1))
         return out
 
@@ -496,16 +547,177 @@ def reduce_scatter(
         if out is None:
             out = torch.empty(output_shape, dtype=input.dtype, device=input.device)
         self._validate_output(out, input, output_shape)
+        if self.world_size == 1:
+            out.copy_(input)
+            return out
 
         with self._lock:
             self._check_open()
             self._validate_matching_signature("reduce_scatter", input)
+            if self._direct_reduce_scatter(input, out):
+                return out
             rank_inputs = self._all_gather_transport(input)
-            reduced = self._balanced_tree_sum(rank_inputs)
             begin = self.rank * rows_per_rank
-            out.copy_(reduced.narrow(0, begin, rows_per_rank))
+            # Only this rank's output shard participates in the reduction. The
+            # previous implementation reduced every global row and sliced the
+            # result afterwards, doing world_size times more arithmetic than
+            # ReduceScatter needs. The fixed rank tree is unchanged.
+            reduced = rank_inputs[:, begin : begin + rows_per_rank]
+            if not self._fused_reduction(reduced, out, operation="reduce_scatter"):
+                reduced = self._balanced_tree_sum(reduced)
+                out.copy_(reduced)
         return out
 
+    def reduce_scatter_many(
+        self,
+        inputs: tuple[torch.Tensor, ...] | list[torch.Tensor],
+        *,
+        outs: tuple[torch.Tensor, ...] | list[torch.Tensor] | None = None,
+    ) -> tuple[torch.Tensor, ...]:
+        """Reduce-scatter independent tensors in one fixed-tree collective.
+
+        The tensors are packed along their final dimension, so each tensor's
+        element still follows the same balanced rank tree as an individual
+        ``reduce_scatter`` call. This is useful for independent gradient lanes:
+        packing them together removes one RCCL launch without changing the
+        floating-point expression for either lane. Inputs must have matching
+        shape/device/dtype except for the final dimension.
+        """
+
+        self._check_open()
+        values = tuple(inputs)
+        if not values:
+            raise ValueError("reduce_scatter_many requires at least one input")
+        if outs is not None and len(outs) != len(values):
+            raise ValueError("reduce_scatter_many outs must match the number of inputs")
+        if len(values) == 1:
+            return (
+                self.reduce_scatter(
+                    values[0],
+                    out=None if outs is None else outs[0],
+                ),
+            )
+
+        first = values[0]
+        self._validate_reduction_input(first)
+        if first.dim() < 2:
+            raise ValueError(
+                "reduce_scatter_many inputs must have at least two dimensions "
+                "when packing independent lanes"
+            )
+        if first.size(0) % self.world_size != 0:
+            raise ValueError("reduce_scatter_many inputs must have a divisible leading dimension")
+        for value in values[1:]:
+            self._validate_reduction_input(value)
+            if value.dim() != first.dim() or value.shape[:-1] != first.shape[:-1]:
+                raise ValueError(
+                    "reduce_scatter_many inputs must match in rank and all dimensions "
+                    "except the final dimension"
+                )
+            if value.device != first.device or value.dtype != first.dtype:
+                raise ValueError("reduce_scatter_many inputs must share device and dtype")
+        lane_sizes = tuple(int(value.size(-1)) for value in values)
+        rows_per_rank = first.size(0) // self.world_size
+        output_shape = (rows_per_rank, *first.shape[1:-1])
+        if outs is not None:
+            for lane_size, out in zip(lane_sizes, outs, strict=True):
+                self._validate_output(
+                    out,
+                    first,
+                    (*output_shape, lane_size),
+                )
+
+        packed_bytes = sum(value.numel() * value.element_size() for value in values)
+        if self._can_direct_reduce_scatter_many():
+            if packed_bytes > self.max_size_bytes:
+                raise ValueError(
+                    "reduce_scatter_many packed input requires "
+                    f"{packed_bytes} bytes but max_size_bytes={self.max_size_bytes}"
+                )
+            direct_outputs = tuple(
+                (
+                    outs[index]
+                    if outs is not None
+                    else torch.empty(
+                        (*output_shape, lane_size),
+                        dtype=first.dtype,
+                        device=first.device,
+                    )
+                )
+                for index, lane_size in enumerate(lane_sizes)
+            )
+            with self._lock:
+                self._check_open()
+                self._validate_matching_signature(
+                    f"reduce_scatter_many:{lane_sizes}",
+                    first,
+                )
+                if self._direct_reduce_scatter_many(values, direct_outputs):
+                    return direct_outputs
+
+        if packed_bytes > _PACKED_REDUCE_SCATTER_MAX_BYTES:
+            # A single packed AllGather moves the same bytes as two separate
+            # calls but loses RCCL's smaller-message algorithm. Use the
+            # established per-lane path above the measured crossover; this
+            # keeps the convenience API from regressing large FFN gradients.
+            return tuple(
+                self.reduce_scatter(
+                    value,
+                    out=None if outs is None else outs[index],
+                )
+                for index, value in enumerate(values)
+            )
+
+        if packed_bytes > self.max_size_bytes:
+            raise ValueError(
+                "reduce_scatter_many packed input requires "
+                f"{packed_bytes} bytes but max_size_bytes={self.max_size_bytes}"
+            )
+        packed = torch.cat(values, dim=-1)
+        packed_out = torch.empty(
+            (packed.size(0) // self.world_size, *packed.shape[1:]),
+            dtype=packed.dtype,
+            device=packed.device,
+        )
+        with self._lock:
+            self._check_open()
+            # Include lane boundaries in the signature. Equal packed shapes
+            # alone do not guarantee that every rank will split the result the
+            # same way, which could silently associate gradients with the
+            # wrong lane.
+            self._validate_matching_signature(
+                f"reduce_scatter_many:{lane_sizes}",
+                packed,
+            )
+            if self._direct_reduce_scatter(packed, packed_out):
+                pieces = tuple(packed_out.split(tuple(value.size(-1) for value in values), dim=-1))
+                if outs is None:
+                    return pieces
+                result: list[torch.Tensor] = []
+                for piece, out in zip(pieces, outs, strict=True):
+                    out.copy_(piece)
+                    result.append(out)
+                return tuple(result)
+            rank_inputs = self._all_gather_transport(packed)
+            begin = self.rank * rows_per_rank
+            reduced = rank_inputs[:, begin : begin + rows_per_rank]
+            if not self._fused_reduction(
+                reduced,
+                packed_out,
+                operation="reduce_scatter",
+            ):
+                reduced = self._balanced_tree_sum(reduced)
+                packed_out.copy_(reduced)
+
+        pieces = tuple(packed_out.split(tuple(value.size(-1) for value in values), dim=-1))
+        if outs is None:
+            return pieces
+        result: list[torch.Tensor] = []
+        for piece, out in zip(pieces, outs, strict=True):
+            out.copy_(piece)
+            result.append(out)
+        return tuple(result)
+
     def close(self) -> None:
         """Close the instance.
 
@@ -517,6 +729,7 @@ def close(self) -> None:
             self._workspace = None
             self._validated_signatures.clear()
             self._closed = True
+            self._handle = 0
 
     def __enter__(self) -> TorchDistributedDeterministicCollective:
         self._check_open()
@@ -653,6 +866,25 @@ def _workspace_for(self, input: torch.Tensor, required_elements: int) -> torch.T
         # an allocation or copy. Restrict the view to the current operation.
         return workspace[:required_bytes].view(input.dtype)
 
+    def _direct_all_reduce(self, input: torch.Tensor, output: torch.Tensor) -> bool:
+        return False
+
+    def _direct_reduce_scatter(self, input: torch.Tensor, output: torch.Tensor) -> bool:
+        return False
+
+    def _can_direct_reduce_scatter_many(self) -> bool:
+        return False
+
+    def _direct_reduce_scatter_many(
+        self,
+        inputs: tuple[torch.Tensor, ...],
+        outputs: tuple[torch.Tensor, ...],
+    ) -> bool:
+        return False
+
+    def _direct_all_gather(self, input: torch.Tensor, output: torch.Tensor) -> bool:
+        return False
+
     @staticmethod
     def _balanced_tree_sum(rank_inputs: torch.Tensor) -> torch.Tensor:
         world_size = rank_inputs.size(0)
@@ -671,11 +903,43 @@ def _balanced_tree_sum(rank_inputs: torch.Tensor) -> torch.Tensor:
             stride *= 2
         return rank_inputs[0]
 
+    @staticmethod
+    def _fused_reduction(
+        rank_inputs: torch.Tensor,
+        output: torch.Tensor,
+        *,
+        operation: str,
+    ) -> bool:
+        """Use the optional ROCm fused fixed-tree kernel when available.
+
+        The extension is deliberately optional: CPU/Gloo reference collectives
+        and installations built without the ROCm kernel retain the executable
+        Python implementation above.
+        """
+
+        if getattr(torch.version, "hip", None) is None or not rank_inputs.is_cuda:
+            return False
+        try:
+            from rl_engine import _C
+        except ImportError:
+            return False
+        if operation == "all_reduce":
+            fn = getattr(_C, "deterministic_collective_rocm_all_reduce", None)
+            if fn is not None:
+                fn(rank_inputs, output)
+                return True
+        elif operation == "reduce_scatter":
+            fn = getattr(_C, "deterministic_collective_rocm_reduce_scatter", None)
+            if fn is not None:
+                fn(rank_inputs, output)
+                return True
+        return False
+
 
 class RCCLDeterministicCollective(TorchDistributedDeterministicCollective):
-    """ROCm collective using RCCL AllGather strictly as tensor transport."""
+    """Single-node ROCm fixed-tree collective using HIP IPC and RCCL."""
 
-    backend_id = "rccl_all_gather_balanced_tree"
+    backend_id = "rocm_ipc_fixed_tree"
 
     def __init__(
         self,
@@ -703,6 +967,154 @@ def __init__(
             raise RuntimeError(
                 "RCCL deterministic collectives require PyTorch's NCCL process-group API"
             )
+        self._ipc_handle = 0
+        self._ipc_staging: torch.Tensor | None = None
+        self._initialize_ipc_transport()
+
+    @property
+    def workspace_size_bytes(self) -> int:
+        staging = self._ipc_staging
+        staging_bytes = 0 if staging is None else int(staging.numel())
+        return staging_bytes + super().workspace_size_bytes
+
+    def _initialize_ipc_transport(self) -> None:
+        if self.world_size == 1:
+            return
+        try:
+            from rl_engine import _C
+        except ImportError:
+            return
+        required_symbols = (
+            "deterministic_collective_rocm_ipc_allocate",
+            "deterministic_collective_rocm_ipc_meta",
+            "deterministic_collective_rocm_ipc_create",
+            "deterministic_collective_rocm_ipc_synchronize",
+            "deterministic_collective_rocm_ipc_destroy",
+            "deterministic_collective_rocm_ipc_stage",
+            "deterministic_collective_rocm_ipc_all_reduce",
+            "deterministic_collective_rocm_ipc_all_reduce_input",
+            "deterministic_collective_rocm_ipc_reduce_scatter",
+            "deterministic_collective_rocm_ipc_reduce_scatter_input",
+            "deterministic_collective_rocm_ipc_reduce_scatter_many",
+            "deterministic_collective_rocm_ipc_all_gather",
+            "deterministic_collective_rocm_ipc_all_gather_input",
+        )
+        if any(not hasattr(_C, symbol) for symbol in required_symbols):
+            return
+
+        staging = _C.deterministic_collective_rocm_ipc_allocate(self.max_size_bytes)
+        handle, offset = _C.deterministic_collective_rocm_ipc_meta(staging)
+        local_metadata = (socket.gethostname(), handle, int(offset))
+        gathered_metadata: list[tuple[str, list[int], int] | None] = [None] * self.world_size
+        dist.all_gather_object(gathered_metadata, local_metadata, group=self.group)
+        if any(metadata is None for metadata in gathered_metadata):
+            raise RuntimeError("failed to exchange ROCm IPC metadata")
+        complete_metadata = [metadata for metadata in gathered_metadata if metadata is not None]
+        if len({metadata[0] for metadata in complete_metadata}) != 1:
+            return
+        self._ipc_handle = int(
+            _C.deterministic_collective_rocm_ipc_create(
+                staging,
+                [metadata[1] for metadata in complete_metadata],
+                [metadata[2] for metadata in complete_metadata],
+                self.rank,
+            )
+        )
+        self._ipc_staging = staging
+
+    def _direct_all_reduce(self, input: torch.Tensor, output: torch.Tensor) -> bool:
+        handle = self._ipc_handle
+        if not handle:
+            return False
+        from rl_engine import _C
+
+        input_bytes = input.numel() * input.element_size()
+        if (
+            _ROCM_IPC_DIRECT_ALL_REDUCE_MAX_BYTES
+            < input_bytes
+            < _ROCM_IPC_SHARDED_ALL_REDUCE_MIN_BYTES
+            and input.numel() % self.world_size == 0
+        ):
+            return False
+
+        if (
+            input_bytes <= _ROCM_IPC_DIRECT_ALL_REDUCE_MAX_BYTES
+            or input.numel() % self.world_size != 0
+        ):
+            _C.deterministic_collective_rocm_ipc_all_reduce_input(
+                handle,
+                input,
+                output,
+            )
+            return True
+
+        shard = self._workspace_for(input, input.numel() // self.world_size)
+        _C.deterministic_collective_rocm_ipc_reduce_scatter_input(
+            handle,
+            input,
+            shard,
+        )
+        dist.all_gather_into_tensor(output.view(-1), shard, group=self.group)
+        return True
+
+    def _direct_reduce_scatter(self, input: torch.Tensor, output: torch.Tensor) -> bool:
+        handle = self._ipc_handle
+        if not handle:
+            return False
+        from rl_engine import _C
+
+        _C.deterministic_collective_rocm_ipc_reduce_scatter_input(
+            handle,
+            input,
+            output,
+        )
+        return True
+
+    def _can_direct_reduce_scatter_many(self) -> bool:
+        return bool(self._ipc_handle)
+
+    def _direct_reduce_scatter_many(
+        self,
+        inputs: tuple[torch.Tensor, ...],
+        outputs: tuple[torch.Tensor, ...],
+    ) -> bool:
+        handle = self._ipc_handle
+        if not handle:
+            return False
+        from rl_engine import _C
+
+        _C.deterministic_collective_rocm_ipc_reduce_scatter_many(
+            handle,
+            inputs,
+            outputs,
+        )
+        return True
+
+    def _direct_all_gather(self, input: torch.Tensor, output: torch.Tensor) -> bool:
+        handle = self._ipc_handle
+        input_bytes = input.numel() * input.element_size()
+        if not handle or input_bytes > _ROCM_IPC_ALL_GATHER_MAX_BYTES:
+            return False
+        from rl_engine import _C
+
+        _C.deterministic_collective_rocm_ipc_all_gather_input(
+            handle,
+            input,
+            output,
+        )
+        return True
+
+    def close(self) -> None:
+        handle = getattr(self, "_ipc_handle", 0)
+        if handle:
+            from rl_engine import _C
+
+            _C.deterministic_collective_rocm_ipc_synchronize(handle)
+            torch.cuda.synchronize(self.device)
+            self._ipc_handle = 0
+            _C.deterministic_collective_rocm_ipc_destroy(handle)
+            self._ipc_staging = None
+        super().close()
 
 
 def create_deterministic_collective(
@@ -714,9 +1126,9 @@ def create_deterministic_collective(
     """Create the platform-appropriate deterministic collective.
 
     CUDA uses the native ``DeterministicCollective`` implementation. ROCm uses
-    RCCL only to gather rank inputs, followed by the shared local balanced-tree
-    reduction. The returned object has independent ownership; callers may cache
-    it and must close an entry before replacing it.
+    HIP IPC or RCCL for rank-ordered transport while preserving the fixed local
+    reduction tree. The returned object has independent ownership; callers may
+    cache it and must close an entry before replacing it.
     """
 
     if getattr(torch.version, "hip", None) is not None:
diff --git a/rl_engine/kernels/ops/cuda/attention/cp_comm.py b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
index 9f805fe5..8af5e68f 100644
--- a/rl_engine/kernels/ops/cuda/attention/cp_comm.py
+++ b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
@@ -637,10 +637,14 @@ def scatter(self, full: torch.Tensor) -> torch.Tensor:
             raise AttentionCPCommunicationUnavailable(
                 "RCCL Scatter leading dimension must divide the CP world size"
             )
-        chunks = tuple(chunk.contiguous() for chunk in full.chunk(self.world_size, dim=0))
-        local = torch.empty_like(chunks[self.rank])
+        # ``full`` is contiguous and split along its leading dimension, so
+        # each root chunk is already contiguous. Non-root ranks do not need a
+        # scatter list at all; avoiding those copies matters for strict CP
+        # output tensors, which can be large.
+        local_shape = (full.size(0) // self.world_size, *full.shape[1:])
+        local = torch.empty(local_shape, dtype=full.dtype, device=full.device)
         if self.world_size == 1:
-            local.copy_(chunks[0])
+            local.copy_(full)
             return local
 
         # ``src`` is a global rank even when a subgroup is supplied.
@@ -656,12 +660,8 @@ def scatter(self, full: torch.Tensor) -> torch.Tensor:
                         "PyTorch cannot map the RCCL subgroup root to a global rank"
                     )
                 global_root = int(get_group_ranks(self.group)[self.root])
-        dist.scatter(
-            local,
-            scatter_list=list(chunks) if self.rank == self.root else None,
-            src=global_root,
-            group=self.group,
-        )
+        scatter_list = list(full.chunk(self.world_size, dim=0)) if self.rank == self.root else None
+        dist.scatter(local, scatter_list=scatter_list, src=global_root, group=self.group)
         return local
 
     def reduce_scatter(self, full: torch.Tensor) -> torch.Tensor:
diff --git a/rl_engine/kernels/ops/pytorch/ffn/ffn.py b/rl_engine/kernels/ops/pytorch/ffn/ffn.py
index fe17486b..e4def8a1 100644
--- a/rl_engine/kernels/ops/pytorch/ffn/ffn.py
+++ b/rl_engine/kernels/ops/pytorch/ffn/ffn.py
@@ -147,10 +147,10 @@ def _validate_ffn_inputs(
 def _create_collective(*, group: Any, max_size_bytes: int):
     """Create the platform-specific collective lazily.
 
-    NVIDIA keeps the existing CUDA IPC implementation.  ROCm selects the
-    RCCL transport-only implementation, which performs the floating-point
-    reduction in the shared deterministic local tree.  Keeping this boundary
-    small also makes the backend choice explicit and easy to inject in tests.
+    NVIDIA keeps the existing CUDA IPC implementation. ROCm selects HIP IPC or
+    RCCL transport while keeping floating-point reduction in the shared fixed
+    tree. Keeping this boundary small also makes the backend choice explicit
+    and easy to inject in tests.
     """
 
     try:
@@ -232,8 +232,14 @@ def forward(
         tp_world = tp_dist.get_world_size(group=tp_group) if tp_dist is not None else 1
         gemm_tokens = rmsnorm_output_2d.size(0) * (tp_world if sequence_parallel else 1)
         element_size = rmsnorm_output_2d.element_size()
+        token_hidden_bytes = gemm_tokens * rmsnorm_output_2d.size(1) * element_size
+        # Sequence-parallel backward reduces the gate and up input-gradient
+        # lanes together. ``reduce_scatter_many`` packs those lanes along the
+        # final dimension, so reserve capacity for both lanes in one transport
+        # call rather than growing the collective (or failing) mid-backward.
+        reduction_bytes = token_hidden_bytes * (2 if sequence_parallel else 1)
         min_size_bytes = max(
-            gemm_tokens * rmsnorm_output_2d.size(1) * element_size,
+            reduction_bytes,
             gemm_tokens * gate_weight.size(0) * element_size,
             gate_weight.numel() * element_size,
             up_weight.numel() * element_size,
@@ -355,24 +361,21 @@ def backward(ctx, grad_output: Tensor):
 
         # Gate/Up input gradients reduce across TP, then add locally.
         grad_rmsnorm_from_gate = _gemm_fwd(grad_gate, gate_weight, disable_split_k=disable_split_k)
+        grad_rmsnorm_from_up = _gemm_fwd(grad_up, up_weight, disable_split_k=disable_split_k)
         if ctx.sequence_parallel:
-            grad_rmsnorm_from_gate = _reduce_scatter_tokens(
-                grad_rmsnorm_from_gate,
-                tp_collective,
+            # These are independent reduction lanes. Pack them into one
+            # ReduceScatter while keeping each lane's balanced rank tree
+            # separate; adding them before the collective would change the
+            # floating-point parenthesization and break cross-TP bitwise
+            # invariance.
+            grad_rmsnorm_from_gate, grad_rmsnorm_from_up = tp_collective.reduce_scatter_many(
+                (grad_rmsnorm_from_gate, grad_rmsnorm_from_up)
             )
         elif tp_collective is not None:
             grad_rmsnorm_from_gate = _all_reduce_inplace(
                 grad_rmsnorm_from_gate,
                 tp_collective,
             )
-
-        grad_rmsnorm_from_up = _gemm_fwd(grad_up, up_weight, disable_split_k=disable_split_k)
-        if ctx.sequence_parallel:
-            grad_rmsnorm_from_up = _reduce_scatter_tokens(
-                grad_rmsnorm_from_up,
-                tp_collective,
-            )
-        elif tp_collective is not None:
             grad_rmsnorm_from_up = _all_reduce_inplace(
                 grad_rmsnorm_from_up,
                 tp_collective,
diff --git a/setup.py b/setup.py
index 3f0b3d5a..0cb6b8d0 100644
--- a/setup.py
+++ b/setup.py
@@ -147,6 +147,10 @@ def get_extensions():
             # This source contains NVIDIA PTX (cp.async, ldmatrix, and mma.sync).
             # The ROCm dispatcher falls back to PyTorch SDPA for this operator.
             cuda_sources.append("csrc/cuda/attention/prefix_shared_attention.cu")
+        else:
+            # RCCL remains transport-only on ROCm; this HIP kernel performs
+            # the fixed balanced-tree arithmetic after AllGather.
+            cuda_sources.append("csrc/rocm/distributed/deterministic_collective.hip")
 
         nvcc_flags = ["-O3", "-Xfatbin", "-compress-all"]
         if envs.env_flag(envs.KERNEL_ALIGN_USE_FAST_MATH):
diff --git a/tests/distributed/test_deterministic_all_gather.py b/tests/distributed/test_deterministic_all_gather.py
index d6058dd3..7d1f72d0 100644
--- a/tests/distributed/test_deterministic_all_gather.py
+++ b/tests/distributed/test_deterministic_all_gather.py
@@ -12,7 +12,7 @@
 import torch.distributed as dist
 import torch.multiprocessing as mp
 
-from rl_engine.distributed import DeterministicCollective
+from rl_engine.distributed import create_deterministic_collective
 
 _MAX_WORLD_SIZE = 8
 _TP_SIZES = (1, 2, 4, 8)
@@ -70,7 +70,7 @@ def _worker(rank: int, port: int) -> None:
         groups = {tp_size: dist.new_group(ranks=list(range(tp_size))) for tp_size in _TP_SIZES}
         for tp_size, group in groups.items():
             if rank < tp_size:
-                with DeterministicCollective(
+                with create_deterministic_collective(
                     group=group,
                     device=device,
                     max_size_bytes=1024 * 1024,
@@ -95,6 +95,11 @@ def _worker(rank: int, port: int) -> None:
                         returned = collective.all_gather(input, out=provided)
                         assert returned is provided
                         assert torch.equal(provided, expected)
+
+                        empty_input = torch.empty((0, 7), dtype=dtype, device=device)
+                        empty_output = collective.all_gather(empty_input)
+                        assert empty_output.shape == (0, 7)
+                        assert empty_output.numel() == 0
             dist.barrier()
     finally:
         dist.destroy_process_group()
diff --git a/tests/distributed/test_deterministic_all_reduce.py b/tests/distributed/test_deterministic_all_reduce.py
index eb406881..028429a3 100644
--- a/tests/distributed/test_deterministic_all_reduce.py
+++ b/tests/distributed/test_deterministic_all_reduce.py
@@ -12,7 +12,7 @@
 import torch.distributed as dist
 import torch.multiprocessing as mp
 
-from rl_engine.distributed import DeterministicCollective
+from rl_engine.distributed import create_deterministic_collective
 
 _MAX_WORLD_SIZE = 8
 _TP_SIZES = (1, 2, 4, 8)
@@ -61,7 +61,7 @@ def _worker(rank: int, port: int) -> None:
         groups = {tp_size: dist.new_group(ranks=list(range(tp_size))) for tp_size in _TP_SIZES}
         for tp_size, group in groups.items():
             if rank < tp_size:
-                with DeterministicCollective(
+                with create_deterministic_collective(
                     group=group,
                     device=device,
                     max_size_bytes=1024 * 1024,
@@ -96,6 +96,25 @@ def _worker(rank: int, port: int) -> None:
                         returned = collective.all_reduce(inplace, out=inplace)
                         assert returned is inplace
                         assert torch.equal(inplace, expected)
+
+                        if dtype in (torch.float16, torch.bfloat16):
+                            packed_input = torch.cat((input, input[:1]))
+                            packed_expected = torch.cat((expected, expected[:1]))
+                            packed_output = collective.all_reduce(packed_input)
+                            assert torch.equal(packed_output, packed_expected)
+
+                            output_storage = torch.empty(
+                                packed_expected.numel() + 1,
+                                dtype=dtype,
+                                device=device,
+                            )
+                            misaligned_output = output_storage[1:].view_as(packed_expected)
+                            returned = collective.all_reduce(
+                                packed_input,
+                                out=misaligned_output,
+                            )
+                            assert returned is misaligned_output
+                            assert torch.equal(misaligned_output, packed_expected)
             dist.barrier()
     finally:
         dist.destroy_process_group()
diff --git a/tests/distributed/test_deterministic_reduce_scatter.py b/tests/distributed/test_deterministic_reduce_scatter.py
index 3125f6c3..a3ee292d 100644
--- a/tests/distributed/test_deterministic_reduce_scatter.py
+++ b/tests/distributed/test_deterministic_reduce_scatter.py
@@ -12,7 +12,7 @@
 import torch.distributed as dist
 import torch.multiprocessing as mp
 
-from rl_engine.distributed import DeterministicCollective
+from rl_engine.distributed import create_deterministic_collective
 
 _MAX_WORLD_SIZE = 8
 _TP_SIZES = (1, 2, 4, 8)
@@ -61,7 +61,7 @@ def _worker(rank: int, port: int) -> None:
         groups = {tp_size: dist.new_group(ranks=list(range(tp_size))) for tp_size in _TP_SIZES}
         for tp_size, group in groups.items():
             if rank < tp_size:
-                with DeterministicCollective(
+                with create_deterministic_collective(
                     group=group,
                     device=device,
                     max_size_bytes=1024 * 1024,
@@ -98,6 +98,49 @@ def _worker(rank: int, port: int) -> None:
                         returned = collective.reduce_scatter(input, out=provided)
                         assert returned is provided
                         assert torch.equal(provided, expected)
+
+                        if dtype in (torch.float16, torch.bfloat16):
+                            output_storage = torch.empty(
+                                expected.numel() + 1,
+                                dtype=dtype,
+                                device=device,
+                            )
+                            misaligned_output = output_storage[1:].view_as(expected)
+                            returned = collective.reduce_scatter(
+                                input,
+                                out=misaligned_output,
+                            )
+                            assert returned is misaligned_output
+                            assert torch.equal(misaligned_output, expected)
+
+                        other_generator = torch.Generator().manual_seed(20260817)
+                        other_leaves_tensor = torch.randn(
+                            _MAX_WORLD_SIZE,
+                            _MAX_WORLD_SIZE * 17,
+                            19,
+                            dtype=torch.float32,
+                            generator=other_generator,
+                        ).to(device=device, dtype=dtype)
+                        other_leaves = list(other_leaves_tensor.unbind())
+                        other_input = _fixed_tree_reference(
+                            other_leaves[start : start + leaves_per_rank]
+                        )
+                        other_reduced = _fixed_tree_reference(other_leaves)
+                        other_expected = other_reduced.chunk(tp_size, dim=0)[group_rank]
+                        many_outs = (torch.empty_like(expected), torch.empty_like(other_expected))
+                        many_returned = collective.reduce_scatter_many(
+                            (input, other_input),
+                            outs=many_outs,
+                        )
+                        assert many_returned[0] is many_outs[0]
+                        assert many_returned[1] is many_outs[1]
+                        assert torch.equal(many_returned[0], expected)
+                        assert torch.equal(many_returned[1], other_expected)
+                        many_baseline = tuple(value.clone() for value in many_returned)
+                        for _ in range(3):
+                            many_repeated = collective.reduce_scatter_many((input, other_input))
+                            assert torch.equal(many_repeated[0], many_baseline[0])
+                            assert torch.equal(many_repeated[1], many_baseline[1])
             dist.barrier()
     finally:
         dist.destroy_process_group()
diff --git a/tests/distributed/test_transport_deterministic_collective.py b/tests/distributed/test_transport_deterministic_collective.py
index dddd674b..07478ef7 100644
--- a/tests/distributed/test_transport_deterministic_collective.py
+++ b/tests/distributed/test_transport_deterministic_collective.py
@@ -255,6 +255,79 @@ def test_reduce_scatter_reduces_then_selects_local_leading_shard(
     assert torch.equal(output, expected_full.chunk(4, dim=0)[2])
 
 
+def test_reduce_scatter_many_packs_lanes_and_transports_once(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    # Each lane must retain its own balanced tree. A left fold would produce
+    # one for lane 0 and two for lane 1, while the fixed tree produces zero for
+    # both lanes in FP32.
+    lane_values = [
+        (
+            1.0e20,
+            1.0,
+            -1.0e20,
+            1.0,
+        ),
+        (
+            1.0e20,
+            2.0,
+            -1.0e20,
+            2.0,
+        ),
+    ]
+    # Give each rank distinct values while preserving the cancellation pattern
+    # in every row. The fake transport returns these packed rank inputs.
+    lane_peers = [
+        (
+            torch.full((8, 1), lane_values[0][rank], dtype=torch.float32),
+            torch.full((8, 1), lane_values[1][rank], dtype=torch.float32),
+        )
+        for rank in range(4)
+    ]
+    packed_peers = [torch.cat(lanes, dim=-1) for lanes in lane_peers]
+    collective, fake_dist = _make_collective(monkeypatch, packed_peers, rank=2)
+
+    local_lanes = lane_peers[2]
+    outputs = (torch.empty(2, 1), torch.empty(2, 1))
+    returned = collective.reduce_scatter_many(local_lanes, outs=outputs)
+
+    assert returned[0] is outputs[0]
+    assert returned[1] is outputs[1]
+    assert torch.equal(outputs[0], torch.zeros_like(outputs[0]))
+    assert torch.equal(outputs[1], torch.zeros_like(outputs[1]))
+    assert fake_dist.tensor_transport_calls == 1
+
+
+def test_reduce_scatter_many_rejects_oversized_packed_input(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    peers = [torch.ones(4, 2, dtype=torch.float32) for _ in range(2)]
+    collective, fake_dist = _make_collective(
+        monkeypatch,
+        peers,
+        max_size_bytes=32,
+    )
+    monkeypatch.setattr(collectives, "_PACKED_REDUCE_SCATTER_MAX_BYTES", 1024)
+
+    with pytest.raises(ValueError, match="packed input requires"):
+        collective.reduce_scatter_many((peers[0], peers[0]))
+    assert fake_dist.tensor_transport_calls == 0
+
+
+def test_reduce_scatter_many_uses_separate_calls_for_large_payloads(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    peers = [torch.ones(4, 2, dtype=torch.float32) for _ in range(2)]
+    collective, fake_dist = _make_collective(monkeypatch, peers)
+    monkeypatch.setattr(collectives, "_PACKED_REDUCE_SCATTER_MAX_BYTES", 1)
+
+    outputs = collective.reduce_scatter_many((peers[0], peers[0]))
+
+    assert len(outputs) == 2
+    assert all(torch.equal(output, torch.full_like(output, 2)) for output in outputs)
+    assert fake_dist.tensor_transport_calls == 2
+
+
 def test_matching_signature_is_checked_before_tensor_transport(
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
diff --git a/tests/test_build_platform_collectives.py b/tests/test_build_platform_collectives.py
index f094a5af..19d3a890 100644
--- a/tests/test_build_platform_collectives.py
+++ b/tests/test_build_platform_collectives.py
@@ -40,6 +40,7 @@ def test_rocm_build_excludes_cuda_ipc_collective_and_driver(monkeypatch) -> None
     extension = _load_extension_config(monkeypatch, hip="test")
 
     assert "csrc/cuda/distributed/deterministic_collective.cu" not in extension["sources"]
+    assert "csrc/rocm/distributed/deterministic_collective.hip" in extension["sources"]
     assert "-DKERNEL_ALIGN_WITH_ROCM" in extension["extra_compile_args"]["cxx"]
     assert "-DKERNEL_ALIGN_WITH_CUDA" not in extension["extra_compile_args"]["cxx"]
     assert "-lcuda" not in extension["extra_link_args"]

From b5c948a632503a3a38d9bf30ce6fe4389c518a37 Mon Sep 17 00:00:00 2001
From: inaniloquentee <jianmusings@gmail.com>
Date: Sun, 30 Aug 2026 09:21:42 +0000
Subject: [PATCH 31/35] feat(attention): add the strict ROCm runtime and open
 CP on the dispatch path

CP orchestration lived only in StrictCUDAAttentionRuntime, so ROCm had nowhere
to put it: the AITER/CK core is single-rank arithmetic, the Vime provider failed
closed at CP>1, and the only working AG/core/RS sequence was in the benchmark
script. StrictRocmAttentionRuntime mirrors the CUDA runtime over the RCCL AG/RS
transport.

Two things differ from the CUDA runtime and both are load-bearing:

- The core is launched once per (batch row, KV group) rather than once per
  sequence. AITER/CK's reduction order depends on how many heads shared the
  launch, so a head shard computed under TP=N is otherwise not bit-identical to
  the same shard under a different TP degree. FA4 has no such dependence.
- RCCL moves tensors but never reduces them. The cross-rank (out, lse) combine
  is the transport's fixed balanced rank tree, not RCCL's own algorithm
  selection, which varies with message size and topology.

The sequence reorder and position validation are bound from the CUDA runtime
rather than reimplemented, so the two runtimes cannot drift into two different
global orderings. The per-KV-group launch loop moves out of the Vime provider
into the runtime, so CP=1 and CP>1 now share one schedule instead of keeping a
second copy in the provider.

Opening CP also required the registry to stop rejecting it: cp_world_sizes was
(1,) and deterministic_cp_merge was False, so AttentionBackendCapability
rejected CP>1 twice over. cp_world_sizes now matches the world sizes the RCCL
transport accepts and deterministic_cp_merge is True because the merge order is
ours. A test pins the two together so the declaration cannot drift from the
transport. zigzag fails closed: the strict CP plan describes one contiguous
block per rank, and a zigzag rank owns two discontiguous runs.

Measured on 8xMI300X through attention_provider, not the transport directly, so
the test also pins that CP is reachable from the production dispatch path:
CP=2/4/8 are bitwise against a CP=1 run of the same core on the same logical
sequence, 0 mismatched elements on out and lse, repeat-bitwise on every rank.

Also corrects the stale TP comment the PR description flagged in section 7: the
shipped policy removes the degree dependence with the per-KV-group launch rather
than binding the degree to avoid a ~3x cost.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NPU7GosGZSdj6pKBWX7n2Y
---
 rl_engine/integrations/vime/attention.py      | 120 ++++---
 .../kernels/ops/rocm/attention/__init__.py    |   3 +
 .../ops/rocm/attention/strict_runtime.py      | 314 ++++++++++++++++++
 rl_engine/kernels/registry.py                 |  10 +-
 .../test_rocm_strict_attention_cp.py          | 216 ++++++++++++
 tests/test_attention_dispatch.py              |  21 ++
 tests/test_vime_attention_provider.py         |  38 ++-
 7 files changed, 663 insertions(+), 59 deletions(-)
 create mode 100644 rl_engine/kernels/ops/rocm/attention/strict_runtime.py
 create mode 100644 tests/distributed/test_rocm_strict_attention_cp.py

diff --git a/rl_engine/integrations/vime/attention.py b/rl_engine/integrations/vime/attention.py
index 771333dc..d9c006bf 100644
--- a/rl_engine/integrations/vime/attention.py
+++ b/rl_engine/integrations/vime/attention.py
@@ -19,10 +19,13 @@
   of a row/group independent of how many rows or heads its caller happened to
   hold, which is what lets training and rollout compare bitwise across
   different batch sizes and TP degrees.  It costs roughly 3x forward time.
-* CP is not a merge axis here.  The strict ROCm core owns single-rank attention
-  arithmetic only; the cross-rank ``(out, lse)`` merge lives in the CP transport
-  path.  CP rank and layout are recorded as row-ownership provenance and a
-  request that asks this provider to perform the CP merge fails closed.
+* CP merges through the transport, never here.  The strict ROCm core owns
+  single-rank attention arithmetic only.  At ``CP > 1`` this provider hands the
+  schedule to :class:`StrictRocmAttentionRuntime`, whose RCCL AG/RS transport
+  combines the cross-rank ``(out, lse)`` in a fixed balanced rank tree, so no
+  second merge order is ever defined here.  The layout must be ``allgather``;
+  ``zigzag`` fails closed because the strict CP plan describes one contiguous
+  block per rank.
 """
 
 from __future__ import annotations
@@ -45,6 +48,7 @@
     SplitKVSpec,
 )
 from rl_engine.kernels.ops.rocm.attention.flash_attn import BACKEND_ID
+from rl_engine.kernels.ops.rocm.attention.strict_runtime import StrictRocmAttentionRuntime
 from rl_engine.kernels.registry import kernel_registry
 
 
@@ -203,14 +207,13 @@ def _contract_for_request(
         raise AttentionProviderUnavailable(
             "context_parallel layout does not describe local CP token ownership"
         )
-    if cp_world_size > 1:
-        # The strict ROCm core is single-rank attention arithmetic.  Performing
-        # the cross-rank (out, lse) merge here would silently introduce a second
-        # merge implementation with its own order; the CP transport path owns it.
+    if cp_world_size > 1 and cp_layout != "allgather":
+        # The strict CP plan describes one contiguous block per rank. A zigzag
+        # rank owns two discontiguous token runs, so accepting it here would
+        # silently disagree with the block manifest the transport validates.
         raise AttentionProviderUnavailable(
-            f"CP={cp_world_size} requires the strict CP transport path; this provider owns "
-            "single-rank attention arithmetic and will not perform the cross-rank "
-            "(out, lse) merge"
+            f"CP={cp_world_size} requires the 'allgather' layout; got {cp_layout!r}, "
+            "whose block ownership the strict CP plan does not describe"
         )
 
     tp_rank, tp_world_size = _tp_coordinates(getattr(request, "tensor_parallel_group", None))
@@ -271,7 +274,9 @@ def _contract_for_request(
     scale = metadata.get("softmax_scale")
     resolved_scale = 1.0 / math.sqrt(head_dim) if scale is None else float(scale)
 
-    # CP=1: this rank owns the whole logical sequence as one block.
+    # Each CP rank owns one contiguous block of the logical sequence; at CP=1
+    # that block is the whole sequence. The allgather layout checked above is
+    # what makes the block contiguous.
     sharding = ShardingSpec(
         tp_rank=tp_rank,
         tp_world_size=tp_world_size,
@@ -283,10 +288,10 @@ def _contract_for_request(
         local_q_heads=local_q_heads,
         local_kv_head_start=tp_rank * local_kv_heads,
         local_kv_heads=local_kv_heads,
-        global_sequence_length=kv_len,
+        global_sequence_length=kv_len * cp_world_size,
         local_sequence_length=kv_len,
-        global_block_indices=(0,),
-        global_block_token_starts=(0,),
+        global_block_indices=(cp_rank,),
+        global_block_token_starts=(cp_rank * kv_len,),
         local_block_offsets=(0, kv_len),
     )
     # Causal alignment: the query block is the tail of the logical sequence, so
@@ -366,41 +371,40 @@ def attention_provider(request: Any) -> AttentionProviderResult:
         raise RuntimeError("explicit strict attention dispatch changed during materialization")
 
     query_len = query.shape[2]
-    local_kv_heads = key.shape[1]
-    group_size = query.shape[1] // local_kv_heads
-
-    row_outs: list[torch.Tensor] = []
-    row_lses: list[torch.Tensor] = []
-    core_provenance: dict[str, Any] | None = None
-    launches = 0
-    for row in range(query.shape[0]):
-        row_key_positions = key_positions[row : row + 1]
-        group_outs: list[torch.Tensor] = []
-        group_lses: list[torch.Tensor] = []
-        for group in range(local_kv_heads):
-            q_lo, q_hi = group * group_size, (group + 1) * group_size
-            result = dispatch.op.forward_with_lse(
-                query[row : row + 1, q_lo:q_hi],
-                key[row : row + 1, group : group + 1],
-                value[row : row + 1, group : group + 1],
-                causal=contract.causal,
-                scale=scale,
-                key_padding_mask=None,
-                query_position_ids=row_key_positions[:, -query_len:] if contract.causal else None,
-                key_position_ids=row_key_positions if contract.causal else None,
-                output_dtype=query.dtype,
+    cp_world_size = contract.sharding.cp_world_size
+    if cp_world_size > 1:
+        cp_group = getattr(request, "context_parallel_group", None)
+        if cp_group is None:
+            raise AttentionProviderUnavailable(
+                f"CP={cp_world_size} requires request.context_parallel_group so the strict "
+                "RCCL AG/RS transport can be built; CP=1 does not need one"
             )
-            group_outs.append(result.out)
-            group_lses.append(result.lse)
-            launches += 1
-            if core_provenance is None:
-                core_provenance = dict(result.provenance)
-        row_outs.append(torch.cat(group_outs, dim=1))
-        row_lses.append(torch.cat(group_lses, dim=1))
-
-    out = torch.cat(row_outs, dim=0)
-    lse = torch.cat(row_lses, dim=0)
-    assert core_provenance is not None  # batch_size and kv heads are validated positive
+    else:
+        cp_group = None
+
+    # One runtime owns the launch schedule for both CP degrees, so the
+    # per-(batch row, KV group) launch loop that makes the result TP-degree
+    # invariant cannot drift between the single-rank and CP paths.
+    runtime = StrictRocmAttentionRuntime(process_group=cp_group, core=dispatch.op)
+    runtime_result = runtime.forward_with_lse(
+        query,
+        key,
+        value,
+        contract=contract,
+        causal=contract.causal,
+        scale=scale,
+        cp_world_size=cp_world_size,
+        query_position_ids=key_positions[:, -query_len:],
+        key_position_ids=key_positions,
+        # At CP=1 this rank already holds the logical sequence in position
+        # order, so the reorder the CP path needs would be a no-op copy.
+        positions_are_sorted=cp_world_size == 1,
+    )
+
+    out = runtime_result.out
+    lse = runtime_result.lse
+    core_provenance = runtime_result.provenance["core"]
+    launches = runtime_result.provenance["core_launch_count"]
 
     provenance = dict(dispatch.provenance)
     provenance["core"] = core_provenance
@@ -429,15 +433,21 @@ def attention_provider(request: Any) -> AttentionProviderResult:
         "cp_world_size": contract.sharding.cp_world_size,
         "layout": getattr(request.context_parallel, "layout"),
         "local_token_rows": contract.sharding.local_sequence_length,
-        "cp_is_merge_axis": False,
+        # The merge happens in the RCCL AG/RS transport's fixed rank tree, not
+        # in this provider; the flag records that CP was an axis at all.
+        "cp_is_merge_axis": cp_world_size > 1,
+        "cp_merge_owner": (
+            runtime_result.provenance["communication_backend"] if cp_world_size > 1 else "none"
+        ),
     }
     provenance["lse_domain"] = "attention"
     # The qualified ROCm core's reduction order depends on the launch head
-    # count, so a head shard computed under TP=4 is not bit-identical to the
-    # same shard under TP=8 at some shapes.  RL-Kernel binds the degree rather
-    # than paying ~3x forward time to remove the dependence: training and
-    # rollout must run the same TP/CP degree.  ``contract_id`` encodes both, so
-    # comparing it across the two sides is the preflight that enforces this.
+    # count, so raw AITER gives a head shard computed under TP=4 different bits
+    # from the same shard under TP=8 at some shapes.  RL-Kernel removes the
+    # dependence instead of binding the degree: every launch carries exactly one
+    # KV group, which is bitwise TP-invariant at 12 of 12 measured points.
+    # ``contract_id`` still encodes TP/CP so the preflight can compare the two
+    # sides, but it is no longer what buys the invariance.
     provenance["cross_config_binding"] = {
         "bound_fields": list(CROSS_CONFIG_BOUND_FIELDS),
         "tp_world_size": contract.sharding.tp_world_size,
diff --git a/rl_engine/kernels/ops/rocm/attention/__init__.py b/rl_engine/kernels/ops/rocm/attention/__init__.py
index 926280c8..01a10e4f 100644
--- a/rl_engine/kernels/ops/rocm/attention/__init__.py
+++ b/rl_engine/kernels/ops/rocm/attention/__init__.py
@@ -6,9 +6,12 @@
     StrictRocmAiterCKAttentionCore,
     StrictRocmAttentionUnavailable,
 )
+from .strict_runtime import StrictRocmAttentionResult, StrictRocmAttentionRuntime
 
 __all__ = [
     "RocmFlashAttentionOp",
     "StrictRocmAiterCKAttentionCore",
+    "StrictRocmAttentionRuntime",
+    "StrictRocmAttentionResult",
     "StrictRocmAttentionUnavailable",
 ]
diff --git a/rl_engine/kernels/ops/rocm/attention/strict_runtime.py b/rl_engine/kernels/ops/rocm/attention/strict_runtime.py
new file mode 100644
index 00000000..dab65f4c
--- /dev/null
+++ b/rl_engine/kernels/ops/rocm/attention/strict_runtime.py
@@ -0,0 +1,314 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Framework-neutral strict ROCm Attention runtime.
+
+Composes the production AITER/CK core with the self-owned RCCL AG/RS
+transport, mirroring :class:`StrictCUDAAttentionRuntime` so both platforms
+present one runtime shape to framework integrations. Before this existed the
+CP schedule had no home on ROCm: the core is single-rank arithmetic, the Vime
+provider fails closed at ``CP > 1``, and the only working AG/core/RS sequence
+lived in the benchmark script.
+
+Two things differ from the CUDA runtime and both are load-bearing:
+
+* The core is launched once per ``(batch row, KV group)`` rather than once per
+  sequence. AITER/CK's reduction order depends on how many heads shared the
+  launch, so a head shard computed under TP=N is otherwise not bit-identical
+  to the same shard under a different TP degree. The CUDA FA4 core has no such
+  dependence and runs one launch per sequence.
+* RCCL moves tensors but never reduces them. The cross-rank ``(out, lse)``
+  combine order comes from the fixed balanced rank tree in
+  ``_RCCLRankOrderedTransport``, not from RCCL's own algorithm selection.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+
+from rl_engine.kernels.attention_contract import (
+    STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
+    STRICT_ATTENTION_ROCM_SCHEDULE_ID,
+    AttentionContract,
+)
+from rl_engine.kernels.ops.cuda.attention.cp_comm import (
+    AttentionCPBlockMetadata,
+    AttentionCPCommunicationPlan,
+    AttentionParallelSpec,
+    RCCLAGRSAttentionCPCommunication,
+)
+from rl_engine.kernels.ops.cuda.attention.strict_runtime import StrictCUDAAttentionRuntime
+from rl_engine.kernels.ops.rocm.attention.flash_attn import StrictRocmAiterCKAttentionCore
+
+# The sequence reorder and the position validation are platform-neutral tensor
+# bookkeeping. They are bound from the CUDA runtime rather than reimplemented
+# so the two runtimes cannot drift into two different global orderings.
+_sort_by_position = StrictCUDAAttentionRuntime._sort_by_position
+_gather_sequence = StrictCUDAAttentionRuntime._gather_sequence
+_validate_local_positions = StrictCUDAAttentionRuntime._validate_local_positions
+_validate_global_positions = StrictCUDAAttentionRuntime._validate_global_positions
+
+
+@dataclass(frozen=True)
+class StrictRocmAttentionResult:
+    out: torch.Tensor
+    lse: torch.Tensor
+    provenance: dict[str, Any]
+
+
+class StrictRocmAttentionRuntime:
+    """Run one AITER/CK arithmetic identity at CP=1 or through RCCL AG/RS."""
+
+    backend_id = "rlkernel.rocm.attention.aiter_ck_ag_rs.v1"
+    core_id = STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID
+    strict_schedule = STRICT_ATTENTION_ROCM_SCHEDULE_ID
+    communication_backend_id = "rccl_ag_rs"
+    # Communication and compute are deliberately decoupled; see
+    # ``AttentionCPCommunicationPlan.validate``.
+    supports_async_overlap = False
+    supports_compute_communication_fusion = False
+
+    def __init__(
+        self,
+        *,
+        process_group: Any = None,
+        core: Any | None = None,
+        communication: Any | None = None,
+    ) -> None:
+        self._core = StrictRocmAiterCKAttentionCore() if core is None else core
+        self._communication = (
+            RCCLAGRSAttentionCPCommunication(process_group=process_group)
+            if communication is None
+            else communication
+        )
+        if getattr(self._core, "core_id", None) != self.core_id:
+            raise RuntimeError(
+                "strict ROCm Attention runtime requires the AITER/CK production core"
+            )
+        if getattr(self._core, "strict_schedule", None) != self.strict_schedule:
+            raise RuntimeError("strict ROCm Attention runtime requires the AITER/CK fixed schedule")
+        self.communication_executed = False
+
+    def forward_with_lse(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        *,
+        contract: AttentionContract,
+        causal: bool,
+        scale: float | None,
+        cp_world_size: int,
+        query_position_ids: torch.Tensor,
+        key_position_ids: torch.Tensor,
+        positions_are_sorted: bool = False,
+    ) -> StrictRocmAttentionResult:
+        self._require_rocm(q)
+        if cp_world_size != contract.sharding.cp_world_size:
+            raise RuntimeError("runtime CP world size does not match AttentionContract")
+        _validate_local_positions(q, k, query_position_ids, key_position_ids)
+
+        plan = None
+        if cp_world_size == 1:
+            global_q, global_k, global_v = q, k, v
+            global_q_positions = query_position_ids
+            global_k_positions = key_position_ids
+            communication_backend = "none"
+            self.communication_executed = False
+        else:
+            plan = self._communication_plan(contract, q.size(2), k.size(2))
+            global_q = self._communication.all_gather_query(q, plan)
+            global_k, global_v = self._communication.all_gather_kv(k, v, plan)
+            global_q_positions, global_k_positions = self._communication.all_gather_position_ids(
+                query_position_ids,
+                key_position_ids,
+                plan,
+            )
+            communication_backend = self.communication_backend_id
+            self.communication_executed = True
+
+        if positions_are_sorted:
+            if cp_world_size != 1:
+                raise RuntimeError("pre-sorted Attention positions are supported only at CP=1")
+            q_sorted, k_sorted, v_sorted = global_q, global_k, global_v
+            q_positions_sorted, k_positions_sorted = global_q_positions, global_k_positions
+            q_sort = None
+        else:
+            q_sorted, q_positions_sorted, q_sort = _sort_by_position(global_q, global_q_positions)
+            k_sorted, k_positions_sorted, k_sort = _sort_by_position(global_k, global_k_positions)
+            v_sorted = _gather_sequence(global_v, k_sort)
+            _validate_global_positions(q_positions_sorted, k_positions_sorted, causal)
+
+        out_sorted, lse_sorted, core_provenance, launches = self._run_core(
+            q_sorted,
+            k_sorted,
+            v_sorted,
+            causal=causal,
+            scale=scale,
+            query_position_ids=q_positions_sorted,
+            key_position_ids=k_positions_sorted,
+            output_dtype=q.dtype,
+        )
+
+        if cp_world_size > 1:
+            if q_sort is None:
+                raise RuntimeError("CP Attention requires a framework position reorder")
+            inverse_q_sort = torch.argsort(q_sort, dim=1)
+            out_rank_packed = _gather_sequence(out_sorted, inverse_q_sort)
+            lse_rank_packed = _gather_sequence(lse_sorted, inverse_q_sort)
+            shard = self._communication.reduce_scatter_strict_result(
+                out_rank_packed,
+                lse_rank_packed,
+                plan,
+            )
+            out, lse = shard.out, shard.lse
+        else:
+            out, lse = out_sorted, lse_sorted
+
+        backend = (
+            core_provenance.get("attention_backend")
+            or core_provenance.get("actual_backend")
+            or getattr(self._core, "backend_id", None)
+        )
+        return StrictRocmAttentionResult(
+            out=out,
+            lse=lse,
+            provenance={
+                "strict_core_id": self.core_id,
+                "strict_schedule": self.strict_schedule,
+                "actual_backend": self.backend_id,
+                "communication_backend": communication_backend,
+                "communication_executed": self.communication_executed,
+                "native_attention_arithmetic": True,
+                "production_ready": True,
+                "fallback": False,
+                "fallback_reason": None,
+                "reference_only": False,
+                "split_kv": "disabled",
+                "framework_position_reorder": True,
+                # Unlike the CUDA runtime's single full-sequence launch, the
+                # query schedule here is one launch per (batch row, KV group).
+                "query_schedule": "one_batch_row_one_kv_group",
+                "backward_schedule": "aiter_ck_deterministic_per_kv_group",
+                "launch_granularity": "one_batch_row_one_kv_group",
+                "tp_degree_invariant": True,
+                "invariance_mechanism": "one_kv_group_per_launch",
+                "core_row_count": q_sorted.size(0) * q_sorted.size(2),
+                "core_launch_count": launches,
+                "core_batch_size": q_sorted.size(0),
+                "core_query_length": q_sorted.size(2),
+                "core_actual_backends": [] if backend is None else [str(backend)],
+                "core": core_provenance,
+            },
+        )
+
+    def _run_core(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        *,
+        causal: bool,
+        scale: float | None,
+        query_position_ids: torch.Tensor,
+        key_position_ids: torch.Tensor,
+        output_dtype: torch.dtype,
+    ) -> tuple[torch.Tensor, torch.Tensor, dict[str, Any], int]:
+        """Launch the core once per ``(batch row, KV group)`` and concatenate.
+
+        Every launch therefore sees exactly one KV group and its Q heads, so
+        the result does not depend on the TP degree that produced the shard.
+        """
+
+        local_kv_heads = k.size(1)
+        if local_kv_heads <= 0 or q.size(1) % local_kv_heads:
+            raise RuntimeError(
+                f"local Q heads={q.size(1)} must be divisible by local KV heads={local_kv_heads}"
+            )
+        group_size = q.size(1) // local_kv_heads
+
+        row_outs: list[torch.Tensor] = []
+        row_lses: list[torch.Tensor] = []
+        core_provenance: dict[str, Any] | None = None
+        launches = 0
+        for row in range(q.size(0)):
+            row_query_positions = query_position_ids[row : row + 1]
+            row_key_positions = key_position_ids[row : row + 1]
+            group_outs: list[torch.Tensor] = []
+            group_lses: list[torch.Tensor] = []
+            for group in range(local_kv_heads):
+                q_lo, q_hi = group * group_size, (group + 1) * group_size
+                result = self._core.forward_with_lse(
+                    q[row : row + 1, q_lo:q_hi],
+                    k[row : row + 1, group : group + 1],
+                    v[row : row + 1, group : group + 1],
+                    causal=causal,
+                    scale=scale,
+                    key_padding_mask=None,
+                    query_position_ids=row_query_positions if causal else None,
+                    key_position_ids=row_key_positions if causal else None,
+                    output_dtype=output_dtype,
+                )
+                group_outs.append(result.out)
+                group_lses.append(result.lse)
+                launches += 1
+                if core_provenance is None:
+                    core_provenance = dict(result.provenance)
+            row_outs.append(torch.cat(group_outs, dim=1))
+            row_lses.append(torch.cat(group_lses, dim=1))
+
+        if core_provenance is None:
+            raise RuntimeError("strict ROCm Attention runtime executed no core launch")
+        return (
+            torch.cat(row_outs, dim=0),
+            torch.cat(row_lses, dim=0),
+            core_provenance,
+            launches,
+        )
+
+    @staticmethod
+    def _require_rocm(tensor: torch.Tensor) -> None:
+        if tensor.device.type != "cuda" or torch.version.hip is None:
+            raise RuntimeError("strict ROCm Attention requires ROCm GPU tensors")
+
+    @staticmethod
+    def _communication_plan(
+        contract: AttentionContract,
+        local_q_tokens: int,
+        local_kv_tokens: int,
+    ) -> AttentionCPCommunicationPlan:
+        sharding = contract.sharding
+        parallel = AttentionParallelSpec(
+            tp_world_size=sharding.tp_world_size,
+            tp_rank=sharding.tp_rank,
+            cp_world_size=sharding.cp_world_size,
+            cp_rank=sharding.cp_rank,
+        )
+        query_ranges = tuple(
+            (rank * local_q_tokens, (rank + 1) * local_q_tokens)
+            for rank in range(sharding.cp_world_size)
+        )
+        blocks = tuple(
+            AttentionCPBlockMetadata(
+                global_block_index=rank,
+                kv_block_start=rank * local_kv_tokens,
+                kv_block_end=(rank + 1) * local_kv_tokens,
+                owner_cp_rank=rank,
+                owner_tp_rank=sharding.tp_rank,
+            )
+            for rank in range(sharding.cp_world_size)
+        )
+        return AttentionCPCommunicationPlan(
+            parallel=parallel,
+            backend="rccl_ag_rs",
+            status="implemented",
+            expected_blocks=blocks,
+            expected_kv_token_range=(0, local_kv_tokens * sharding.cp_world_size),
+            query_token_ranges=query_ranges,
+        )
+
+
+__all__ = ["StrictRocmAttentionResult", "StrictRocmAttentionRuntime"]
diff --git a/rl_engine/kernels/registry.py b/rl_engine/kernels/registry.py
index 50674439..15d71d45 100644
--- a/rl_engine/kernels/registry.py
+++ b/rl_engine/kernels/registry.py
@@ -706,10 +706,16 @@ def __init__(self):
                     roles=frozenset({AttentionRole.TRAIN, AttentionRole.INFER}),
                     modes=frozenset({AttentionMode.PREFILL, AttentionMode.CHUNKED_PREFILL}),
                     dtypes=frozenset({AttentionDType.BF16, AttentionDType.FP16}),
-                    cp_world_sizes=(1,),
+                    # The core itself is single-rank arithmetic. CP is supplied
+                    # by StrictRocmAttentionRuntime, which wraps this core in
+                    # the RCCL AG/RS transport; the sizes mirror the world
+                    # sizes _RCCLRankOrderedTransport accepts. The merge order
+                    # is the transport's fixed balanced rank tree, not RCCL's
+                    # own reduction, so the CP merge is deterministic.
+                    cp_world_sizes=(1, 2, 4, 8),
                     tp_world_sizes=None,
                     exports_attention_lse=True,
-                    deterministic_cp_merge=False,
+                    deterministic_cp_merge=True,
                     supports_packed_varlen=False,
                     supports_kv_cache=False,
                     supports_rope_metadata=True,
diff --git a/tests/distributed/test_rocm_strict_attention_cp.py b/tests/distributed/test_rocm_strict_attention_cp.py
new file mode 100644
index 00000000..f7984ae6
--- /dev/null
+++ b/tests/distributed/test_rocm_strict_attention_cp.py
@@ -0,0 +1,216 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""End-to-end CP coverage for the strict ROCm attention provider.
+
+Acceptance is bitwise against a CP=1 run of the same core on the same logical
+sequence. CP performs no arithmetic of its own here: the runtime all-gathers
+Q/K/V, runs the core once over the full sequence, and scatters the root's
+authoritative ``(out, lse)`` back to each rank's query range. Anything other
+than bit equality means the CP path introduced a second merge order.
+
+These run through ``attention_provider`` rather than the transport directly, so
+they also pin that CP is reachable from the production dispatch path.
+"""
+
+from __future__ import annotations
+
+import os
+import socket
+from datetime import timedelta
+from types import SimpleNamespace
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+from rl_engine.kernels.registry import _rocm_strict_attention_available
+
+_MAX_WORLD_SIZE = 8
+_CP_SIZES = (2, 4, 8)
+_EXTERNAL_WORLD_SIZE = int(os.environ.get("WORLD_SIZE", "1"))
+
+# Qwen3-8B dense head layout at TP=1.
+_GLOBAL_Q_HEADS = 32
+_GLOBAL_KV_HEADS = 8
+_HEAD_DIM = 128
+_GLOBAL_SEQ = 256
+
+pytestmark = [
+    pytest.mark.skipif(
+        _EXTERNAL_WORLD_SIZE != 1,
+        reason="this cross-CP test owns its worker processes; run pytest directly",
+    ),
+    pytest.mark.skipif(
+        torch.cuda.device_count() < _MAX_WORLD_SIZE,
+        reason="requires eight visible ROCm GPUs",
+    ),
+    pytest.mark.skipif(
+        not _rocm_strict_attention_available(),
+        reason="strict ROCm attention requires a ROCm device with aiter.ops.mha",
+    ),
+]
+
+
+def _global_qkv(device: torch.device) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Identical global Q/K/V on every rank, generated on CPU for bit equality."""
+
+    def _make(shape: tuple[int, ...], seed: int) -> torch.Tensor:
+        generator = torch.Generator(device="cpu").manual_seed(seed)
+        value = torch.randn(*shape, generator=generator, dtype=torch.float32) * 0.02
+        return value.to(device=device, dtype=torch.bfloat16)
+
+    q = _make((1, _GLOBAL_Q_HEADS, _GLOBAL_SEQ, _HEAD_DIM), 11)
+    k = _make((1, _GLOBAL_KV_HEADS, _GLOBAL_SEQ, _HEAD_DIM), 22)
+    v = _make((1, _GLOBAL_KV_HEADS, _GLOBAL_SEQ, _HEAD_DIM), 33)
+    return q, k, v
+
+
+def _request(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    *,
+    cp_world_size: int,
+    cp_rank: int,
+    cp_layout: str,
+    token_start: int,
+    cp_group: object | None,
+) -> SimpleNamespace:
+    kv_len = k.shape[2]
+    positions = torch.arange(
+        token_start,
+        token_start + kv_len,
+        device=q.device,
+        dtype=torch.int64,
+    )
+    return SimpleNamespace(
+        query=q,
+        key=k,
+        value=v,
+        key_padding_mask=None,
+        # TP=1 must be stated explicitly: with torch.distributed initialized,
+        # a None TP group resolves to the global group, i.e. TP=8 here.
+        tensor_parallel_group=SimpleNamespace(rank=lambda: 0, size=lambda: 1),
+        context_parallel=SimpleNamespace(
+            world_size=cp_world_size,
+            rank=cp_rank,
+            layout=cp_layout,
+        ),
+        context_parallel_group=cp_group,
+        metadata={
+            "global_q_heads": _GLOBAL_Q_HEADS,
+            "global_kv_heads": _GLOBAL_KV_HEADS,
+            "tp_rank": 0,
+            "tp_world_size": 1,
+            "attention_mode": "prefill",
+            "role": "train",
+            "causal": True,
+            "key_position_ids": positions,
+        },
+    )
+
+
+def _check_one_cp_degree(group: object, cp_size: int, rank: int, device: torch.device) -> None:
+    from rl_engine.integrations.vime.attention import attention_provider
+
+    q_global, k_global, v_global = _global_qkv(device)
+
+    # CP=1 on the whole logical sequence is the acceptance reference.
+    reference = attention_provider(
+        _request(
+            q_global,
+            k_global,
+            v_global,
+            cp_world_size=1,
+            cp_rank=0,
+            cp_layout="single",
+            token_start=0,
+            cp_group=None,
+        )
+    )
+
+    local_seq = _GLOBAL_SEQ // cp_size
+    lo, hi = rank * local_seq, (rank + 1) * local_seq
+    result = attention_provider(
+        _request(
+            q_global[:, :, lo:hi].contiguous(),
+            k_global[:, :, lo:hi].contiguous(),
+            v_global[:, :, lo:hi].contiguous(),
+            cp_world_size=cp_size,
+            cp_rank=rank,
+            cp_layout="allgather",
+            token_start=lo,
+            cp_group=group,
+        )
+    )
+
+    assert result.out.shape == (1, _GLOBAL_Q_HEADS, local_seq, _HEAD_DIM)
+    assert result.lse.shape == (1, _GLOBAL_Q_HEADS, local_seq)
+
+    expected_out = reference.out[:, :, lo:hi]
+    expected_lse = reference.lse[:, :, lo:hi]
+    out_mismatch = int((result.out != expected_out).sum().item())
+    lse_mismatch = int((result.lse != expected_lse).sum().item())
+    assert out_mismatch == 0, f"CP={cp_size} rank={rank}: {out_mismatch} out elements differ"
+    assert lse_mismatch == 0, f"CP={cp_size} rank={rank}: {lse_mismatch} lse elements differ"
+
+    provenance = result.provenance
+    assert provenance["cp_row_ownership"]["cp_is_merge_axis"] is True
+    assert provenance["cp_row_ownership"]["cp_merge_owner"] == "rccl_ag_rs"
+    assert provenance["cp_row_ownership"]["cp_world_size"] == cp_size
+    # The core still runs once per (batch row, KV group), now over the gathered
+    # global sequence rather than this rank's shard.
+    assert provenance["execution"]["core_launches"] == _GLOBAL_KV_HEADS
+
+    # Repeating the CP call must reproduce its own bits.
+    repeated = attention_provider(
+        _request(
+            q_global[:, :, lo:hi].contiguous(),
+            k_global[:, :, lo:hi].contiguous(),
+            v_global[:, :, lo:hi].contiguous(),
+            cp_world_size=cp_size,
+            cp_rank=rank,
+            cp_layout="allgather",
+            token_start=lo,
+            cp_group=group,
+        )
+    )
+    assert torch.equal(repeated.out, result.out)
+    assert torch.equal(repeated.lse, result.lse)
+
+
+def _worker(rank: int, port: int) -> None:
+    torch.cuda.set_device(rank)
+    device = torch.device("cuda", rank)
+    dist.init_process_group(
+        backend="nccl",
+        init_method=f"tcp://127.0.0.1:{port}",
+        rank=rank,
+        world_size=_MAX_WORLD_SIZE,
+        timeout=timedelta(minutes=10),
+    )
+    try:
+        for cp_size in _CP_SIZES:
+            group = dist.new_group(ranks=list(range(cp_size)))
+            if rank < cp_size:
+                _check_one_cp_degree(group, cp_size, rank, device)
+            dist.barrier()
+    finally:
+        dist.destroy_process_group()
+
+
+def _find_free_port() -> int:
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+        sock.bind(("127.0.0.1", 0))
+        return int(sock.getsockname()[1])
+
+
+def test_strict_rocm_attention_cp_is_bitwise_against_cp1() -> None:
+    mp.spawn(
+        _worker,
+        args=(_find_free_port(),),
+        nprocs=_MAX_WORLD_SIZE,
+        join=True,
+    )
diff --git a/tests/test_attention_dispatch.py b/tests/test_attention_dispatch.py
index cd73275c..5c3d296f 100644
--- a/tests/test_attention_dispatch.py
+++ b/tests/test_attention_dispatch.py
@@ -119,6 +119,27 @@ def test_strict_rocm_core_is_registered_only_when_the_vendor_stack_loads():
         )
 
 
+def test_rocm_strict_cp_capability_matches_the_transport():
+    """The declared CP degrees must be the ones the RCCL transport accepts.
+
+    CP is supplied by StrictRocmAttentionRuntime wrapping this core in the RCCL
+    AG/RS transport. Declaring a degree the transport rejects would make
+    dispatch hand back a backend that fails at materialization; declaring fewer
+    would hide working CP behind an "unsupported" rejection.
+    """
+
+    if not _rocm_strict_attention_available():
+        pytest.skip("strict ROCm attention requires a ROCm device with aiter.ops.mha")
+
+    capability = KernelRegistry()._attention_capabilities[OpBackend.ROCM_STRICT_ATTENTION]
+
+    assert capability.cp_world_sizes == (1, 2, 4, 8)
+    # The merge order is the transport's fixed balanced rank tree, so the CP
+    # combine is deterministic even though the core is single-rank arithmetic.
+    assert capability.deterministic_cp_merge is True
+    assert capability.exports_attention_lse is True
+
+
 def test_registered_backend_resolves_with_provenance(registry):
     registry.register_attention_backend(
         OpBackend.ROCM_STRICT_ATTENTION, _capability(), platform=_platform(registry)
diff --git a/tests/test_vime_attention_provider.py b/tests/test_vime_attention_provider.py
index 56b82dd9..ec94bff7 100644
--- a/tests/test_vime_attention_provider.py
+++ b/tests/test_vime_attention_provider.py
@@ -86,13 +86,47 @@ def _cpu_request(**kwargs):
 # ---------------------------------------------------------------------------
 
 
-def test_cp_greater_than_one_refuses_the_cross_rank_merge():
+def test_cp_zigzag_layout_fails_closed():
+    """Only the contiguous-per-rank layout matches the strict CP block plan."""
+
     request = _cpu_request(cp_world_size=2, cp_rank=1, cp_layout="zigzag")
 
-    with pytest.raises(AttentionProviderUnavailable, match="strict CP transport path"):
+    with pytest.raises(AttentionProviderUnavailable, match="requires the 'allgather' layout"):
+        attention_provider(request)
+
+
+def test_cp_without_a_process_group_fails_closed():
+    """CP>1 needs a group to build the RCCL transport; it must not fall back."""
+
+    request = _cpu_request(cp_world_size=2, cp_rank=1, cp_layout="allgather")
+
+    with pytest.raises(AttentionProviderUnavailable, match="context_parallel_group"):
         attention_provider(request)
 
 
+def test_cp_contract_describes_one_contiguous_block_per_rank():
+    """The CP sharding must place this rank's block at its global offset."""
+
+    from rl_engine.integrations.vime.attention import _contract_for_request
+
+    seq_len = 8
+    request = _cpu_request(
+        seq_len=seq_len,
+        cp_world_size=4,
+        cp_rank=2,
+        cp_layout="allgather",
+    )
+    contract, *_ = _contract_for_request(request)
+    sharding = contract.sharding
+
+    assert sharding.cp_world_size == 4
+    assert sharding.global_sequence_length == seq_len * 4
+    assert sharding.local_sequence_length == seq_len
+    assert sharding.global_block_indices == (2,)
+    assert sharding.global_block_token_starts == (2 * seq_len,)
+    assert sharding.local_block_offsets == (0, seq_len)
+
+
 def test_decode_without_kv_cache_identity_fails_closed():
     request = _cpu_request(attention_mode="decode")
 

From 99eebaeaa30b9efdc4cf961390803702f66fa5cd Mon Sep 17 00:00:00 2001
From: zhangj1an <jianmusings@gmail.com>
Date: Sun, 30 Aug 2026 13:24:40 +0000
Subject: [PATCH 32/35] refactor(attention): resolve the ROCm CP transport
 through the shared collective

The ROCm attention CP path owned a second transport implementation.
RCCLAGRSAttentionCPCommunication overrode _get_collective to construct
_RCCLRankOrderedTransport, which bypassed the collective_for_group factory
that the CUDA adapter goes through and that already dispatches to
RCCLDeterministicCollective on HIP.

Both copies evaluated the same balanced rank tree, so the two platforms were
bit-identical -- but only by coincidence. Nothing pinned them together, so a
later change to the shared collective's reduction order would have left the
attention path on the old tree with no test failing.

Delete _RCCLRankOrderedTransport and the override. ROCm now inherits the CUDA
adapter's resolution, so one implementation serves both platforms. The
reduction expression is unchanged, so this is not expected to move any bit.

_RootReduceScatterSequence falls back from scatter() to reduce_scatter()
because the shared collective exposes no scatter entrypoint. That is the same
branch the CUDA path has always taken and it is semantically equivalent --
non-root ranks zero their input, so the tree sum returns the root's value and
adding zero is exact. It costs one extra all-gather plus tree per call.

The error messages the adapters raise are now keyed off a collective_label
class attribute so the inherited path still reports RCCL on ROCm instead of
mislabeling itself as CUDA.

Three tests pin the arrangement: the two adapters must share one
_get_collective, the ROCm adapter must resolve through collective_for_group,
and the registry's cp_world_sizes must equal the shared collective's
_SUPPORTED_WORLD_SIZES so the capability declaration cannot drift from what
the transport accepts.

Not verified on MI300X. The reduction expression is unchanged, but the
reduce_scatter fallback and the shared collective's capacity and signature
validation are new to this path, so a CP=2/4/8 bitwise run is still owed.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Signed-off-by: zhangj1an <jianmusings@gmail.com>
---
 .../kernels/ops/cuda/attention/cp_comm.py     | 137 ++----------------
 .../ops/rocm/attention/strict_runtime.py      |   7 +-
 rl_engine/kernels/registry.py                 |   6 +-
 .../test_rocm_attention_transport.py          |  51 +++++++
 4 files changed, 69 insertions(+), 132 deletions(-)

diff --git a/rl_engine/kernels/ops/cuda/attention/cp_comm.py b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
index df671366..da871488 100644
--- a/rl_engine/kernels/ops/cuda/attention/cp_comm.py
+++ b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
@@ -381,6 +381,7 @@ class CUDAAGRSAttentionCPCommunication:
     """Deterministic CUDA AG/RS adapter backed by PR311/PR312."""
 
     backend_id = "cuda_ag_rs"
+    collective_label = "self-owned CUDA AG/RS"
     supports_autograd = True
 
     def __init__(self, *, process_group: Any = None, collective: Any = None) -> None:
@@ -394,7 +395,7 @@ def _get_collective(self, plan: AttentionCPCommunicationPlan):
             from rl_engine.distributed.collectives import collective_for_group
         except ImportError as exc:
             raise AttentionCPCommunicationUnavailable(
-                "self-owned CUDA AG/RS requires PR311/PR312 DeterministicCollective"
+                f"{self.collective_label} requires PR311/PR312 DeterministicCollective"
             ) from exc
         try:
             dist = self._dist()
@@ -407,7 +408,7 @@ def _get_collective(self, plan: AttentionCPCommunicationPlan):
                 raise RuntimeError("the CP process group is unavailable")
         except (RuntimeError, ValueError, TypeError) as exc:
             raise AttentionCPCommunicationUnavailable(
-                f"self-owned CUDA AG/RS is unavailable: {exc}"
+                f"{self.collective_label} is unavailable: {exc}"
             ) from exc
         if self._collective.world_size != plan.parallel.cp_world_size:
             raise AttentionCPCommunicationUnavailable(
@@ -588,140 +589,22 @@ def _validate_cuda_plan(self, plan: AttentionCPCommunicationPlan) -> None:
             raise AttentionCPCommunicationUnavailable("self-owned CUDA AG/RS requires CUDA")
 
 
-class _RCCLRankOrderedTransport:
-    """RCCL tensor transport for the ROCm AG/RS attention contract.
+class RCCLAGRSAttentionCPCommunication(CUDAAGRSAttentionCPCommunication):
+    """ROCm AG/RS adapter using RCCL only as rank-ordered tensor transport.
 
-    RCCL is deliberately used only for AllGather/Scatter transport.  The
-    reduction half first gathers every source tensor and then evaluates the
-    same fixed balanced rank tree on every device, which avoids relying on RCCL's
-    implementation-defined floating-point reduction order.
+    The transport itself is the shared :func:`collective_for_group` collective,
+    which resolves to ``RCCLDeterministicCollective`` on ROCm. CUDA and ROCm
+    therefore evaluate one balanced rank tree from one implementation rather
+    than two copies that can silently drift apart.
     """
 
-    def __init__(self, *, process_group: Any = None, root: int = 0) -> None:
-        import torch.distributed as dist
-
-        if not dist.is_available() or not dist.is_initialized():
-            raise AttentionCPCommunicationUnavailable(
-                "self-owned RCCL AG/RS requires initialized torch.distributed"
-            )
-        backend = str(dist.get_backend(process_group)).lower()
-        if "nccl" not in backend or torch.version.hip is None:
-            raise AttentionCPCommunicationUnavailable(
-                "self-owned RCCL AG/RS requires the PyTorch NCCL API on ROCm"
-            )
-        self.group = process_group
-        self.rank = int(dist.get_rank(process_group))
-        self.world_size = int(dist.get_world_size(process_group))
-        self.root = int(root)
-        if self.world_size not in (1, 2, 4, 8):
-            raise AttentionCPCommunicationUnavailable(
-                "self-owned RCCL AG/RS supports CP world sizes 1, 2, 4, and 8"
-            )
-        if self.root < 0 or self.root >= self.world_size:
-            raise AttentionCPCommunicationUnavailable("RCCL scatter root is outside the group")
-
-    def all_gather(self, local: torch.Tensor) -> torch.Tensor:
-        import torch.distributed as dist
-
-        if local.ndim == 0 or not local.is_cuda or not local.is_contiguous():
-            raise AttentionCPCommunicationUnavailable(
-                "RCCL AllGather requires a contiguous ROCm tensor with a leading dimension"
-            )
-        shape = (self.world_size * local.size(0), *local.shape[1:])
-        gathered = torch.empty(shape, dtype=local.dtype, device=local.device)
-        if self.world_size == 1:
-            gathered.copy_(local)
-        else:
-            dist.all_gather_into_tensor(gathered, local, group=self.group)
-        return gathered
-
-    def scatter(self, full: torch.Tensor) -> torch.Tensor:
-        import torch.distributed as dist
-
-        if full.ndim == 0 or not full.is_cuda or not full.is_contiguous():
-            raise AttentionCPCommunicationUnavailable(
-                "RCCL Scatter requires a contiguous ROCm tensor with a leading dimension"
-            )
-        if full.size(0) % self.world_size:
-            raise AttentionCPCommunicationUnavailable(
-                "RCCL Scatter leading dimension must divide the CP world size"
-            )
-        chunks = tuple(chunk.contiguous() for chunk in full.chunk(self.world_size, dim=0))
-        local = torch.empty_like(chunks[self.rank])
-        if self.world_size == 1:
-            local.copy_(chunks[0])
-            return local
-
-        # ``src`` is a global rank even when a subgroup is supplied.
-        global_root = self.root
-        if self.group is not None:
-            get_global_rank = getattr(dist, "get_global_rank", None)
-            if callable(get_global_rank):
-                global_root = int(get_global_rank(self.group, self.root))
-            else:
-                get_group_ranks = getattr(dist, "get_process_group_ranks", None)
-                if not callable(get_group_ranks):
-                    raise AttentionCPCommunicationUnavailable(
-                        "PyTorch cannot map the RCCL subgroup root to a global rank"
-                    )
-                global_root = int(get_group_ranks(self.group)[self.root])
-        dist.scatter(
-            local,
-            scatter_list=list(chunks) if self.rank == self.root else None,
-            src=global_root,
-            group=self.group,
-        )
-        return local
-
-    def reduce_scatter(self, full: torch.Tensor) -> torch.Tensor:
-        """Deterministic source-rank sum followed by local sequence slicing."""
-
-        if full.ndim == 0 or not full.is_cuda or not full.is_contiguous():
-            raise AttentionCPCommunicationUnavailable(
-                "RCCL ReduceScatter requires a contiguous ROCm tensor with a leading dimension"
-            )
-        if full.size(0) % self.world_size:
-            raise AttentionCPCommunicationUnavailable(
-                "RCCL ReduceScatter leading dimension must divide the CP world size"
-            )
-        gathered = self.all_gather(full)
-        rows_per_rank = full.size(0) // self.world_size
-        begin = self.rank * rows_per_rank
-        # Gather layout is [source_rank, full_sequence, ...]. Evaluate the
-        # same balanced rank tree as the general deterministic collective.
-        level = []
-        for source in range(self.world_size):
-            source_begin = source * full.size(0) + begin
-            level.append(gathered[source_begin : source_begin + rows_per_rank])
-        stride = 1
-        while stride < self.world_size:
-            for index in range(0, self.world_size, 2 * stride):
-                level[index].add_(level[index + stride])
-            stride *= 2
-        return level[0]
-
-
-class RCCLAGRSAttentionCPCommunication(CUDAAGRSAttentionCPCommunication):
-    """ROCm AG/RS adapter using RCCL only as rank-ordered tensor transport."""
-
     backend_id = "rccl_ag_rs"
+    collective_label = "self-owned RCCL AG/RS"
     supports_autograd = True
     transport_only = True
     supports_async_overlap = False
     supports_compute_communication_fusion = False
 
-    def _get_collective(self, plan: AttentionCPCommunicationPlan):
-        if self._collective is None:
-            self._collective = _RCCLRankOrderedTransport(
-                process_group=self._process_group,
-                root=plan.merge_root_cp_rank,
-            )
-        if self._collective.world_size != plan.parallel.cp_world_size:
-            raise AttentionCPCommunicationUnavailable(
-                "self-owned RCCL world size does not match the CP plan"
-            )
-        return self._collective
-
     def _dist(self):
         import torch.distributed as dist
 
diff --git a/rl_engine/kernels/ops/rocm/attention/strict_runtime.py b/rl_engine/kernels/ops/rocm/attention/strict_runtime.py
index dab65f4c..c5eb3a0f 100644
--- a/rl_engine/kernels/ops/rocm/attention/strict_runtime.py
+++ b/rl_engine/kernels/ops/rocm/attention/strict_runtime.py
@@ -18,8 +18,11 @@
   to the same shard under a different TP degree. The CUDA FA4 core has no such
   dependence and runs one launch per sequence.
 * RCCL moves tensors but never reduces them. The cross-rank ``(out, lse)``
-  combine order comes from the fixed balanced rank tree in
-  ``_RCCLRankOrderedTransport``, not from RCCL's own algorithm selection.
+  combine order comes from the fixed balanced rank tree in the shared
+  ``RCCLDeterministicCollective``, not from RCCL's own algorithm selection.
+  That is the collective the CUDA runtime also resolves through
+  ``collective_for_group``, so both platforms run one reduction order from
+  one implementation.
 """
 
 from __future__ import annotations
diff --git a/rl_engine/kernels/registry.py b/rl_engine/kernels/registry.py
index 15d71d45..c6d8da88 100644
--- a/rl_engine/kernels/registry.py
+++ b/rl_engine/kernels/registry.py
@@ -709,9 +709,9 @@ def __init__(self):
                     # The core itself is single-rank arithmetic. CP is supplied
                     # by StrictRocmAttentionRuntime, which wraps this core in
                     # the RCCL AG/RS transport; the sizes mirror the world
-                    # sizes _RCCLRankOrderedTransport accepts. The merge order
-                    # is the transport's fixed balanced rank tree, not RCCL's
-                    # own reduction, so the CP merge is deterministic.
+                    # sizes RCCLDeterministicCollective accepts. The merge
+                    # order is that collective's fixed balanced rank tree, not
+                    # RCCL's own reduction, so the CP merge is deterministic.
                     cp_world_sizes=(1, 2, 4, 8),
                     tp_world_sizes=None,
                     exports_attention_lse=True,
diff --git a/tests/distributed/test_rocm_attention_transport.py b/tests/distributed/test_rocm_attention_transport.py
index 327bd1f1..d7ce9564 100644
--- a/tests/distributed/test_rocm_attention_transport.py
+++ b/tests/distributed/test_rocm_attention_transport.py
@@ -3,16 +3,26 @@
 
 from __future__ import annotations
 
+from typing import Any
+
 import pytest
 import torch
 
+from rl_engine.distributed import collectives
 from rl_engine.kernels.ops.cuda.attention.cp_comm import (
     AttentionCPBlockMetadata,
     AttentionCPCommunicationPlan,
     AttentionCPCommunicationUnavailable,
     AttentionParallelSpec,
+    CUDAAGRSAttentionCPCommunication,
     RCCLAGRSAttentionCPCommunication,
 )
+from rl_engine.kernels.registry import KernelRegistry, OpBackend
+
+
+class _FakeDist:
+    class group:
+        WORLD = "world-group"
 
 
 def _plan(*, backend: str = "rccl_ag_rs") -> AttentionCPCommunicationPlan:
@@ -93,3 +103,44 @@ def test_rccl_adapter_rejects_cuda_plan(monkeypatch: pytest.MonkeyPatch) -> None
 
     with pytest.raises(AttentionCPCommunicationUnavailable, match="rccl_ag_rs plan"):
         communication.all_gather_query(torch.zeros(1, 2, 1, 4), _plan(backend="cuda_ag_rs"))
+
+
+def test_rccl_adapter_shares_the_cuda_collective_resolution() -> None:
+    """ROCm must not own a second transport implementation.
+
+    CUDA and ROCm run the same balanced rank tree only because both resolve
+    their collective through ``collective_for_group``. A ROCm-side override
+    would let the two reduction orders drift apart silently, so pin that the
+    two adapters share one implementation.
+    """
+
+    assert (
+        RCCLAGRSAttentionCPCommunication._get_collective
+        is CUDAAGRSAttentionCPCommunication._get_collective
+    )
+
+
+def test_rccl_adapter_resolves_the_shared_deterministic_collective(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    resolved = _FakeRCCLTransport()
+    calls: list[Any] = []
+
+    def _fake_collective_for_group(*, group: Any, device: Any) -> Any:
+        calls.append((group, device))
+        return resolved
+
+    monkeypatch.setattr(collectives, "collective_for_group", _fake_collective_for_group)
+    monkeypatch.setattr(torch.cuda, "current_device", lambda: 0)
+
+    communication = RCCLAGRSAttentionCPCommunication(process_group="cp-group")
+    monkeypatch.setattr(communication, "_dist", lambda: _FakeDist())
+
+    assert communication._get_collective(_plan()) is resolved
+    assert calls == [("cp-group", torch.device("cuda", 0))]
+
+
+def test_rccl_adapter_world_sizes_match_the_shared_collective() -> None:
+    capability = KernelRegistry()._attention_capabilities[OpBackend.ROCM_STRICT_ATTENTION]
+
+    assert tuple(capability.cp_world_sizes) == collectives._SUPPORTED_WORLD_SIZES

From 285dda1600a2bd09ce2ddd6d1df0c12437b5efe4 Mon Sep 17 00:00:00 2001
From: vensen <vensenmu@gmail.com>
Date: Sun, 30 Aug 2026 14:02:44 +0000
Subject: [PATCH 33/35] style(distributed): satisfy collective formatting

Signed-off-by: vensen <vensenmu@gmail.com>
---
 rl_engine/distributed/collectives.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/rl_engine/distributed/collectives.py b/rl_engine/distributed/collectives.py
index 0d040ea3..119b22bb 100644
--- a/rl_engine/distributed/collectives.py
+++ b/rl_engine/distributed/collectives.py
@@ -630,8 +630,7 @@ def reduce_scatter_many(
         if not inputs:
             raise ValueError("reduce_scatter_many requires at least one input")
         return tuple(
-            self.reduce_scatter(input, validate_signature=validate_signature)
-            for input in inputs
+            self.reduce_scatter(input, validate_signature=validate_signature) for input in inputs
         )
 
     def close(self) -> None:

From ca5313f66772be726f7134d98da09993925851ea Mon Sep 17 00:00:00 2001
From: zhangj1an <jianmusings@gmail.com>
Date: Sun, 30 Aug 2026 15:30:49 +0000
Subject: [PATCH 34/35] feat(attention): validate the AITER schema the strict
 ROCm core calls into

The CUDA core checks the FA4 CuTe API by parameter name before it runs, so a
renamed or dropped strict control fails at load. ROCm had no equivalent:
inspect.signature reports (*args, **kwargs) for AITER's JIT wrapper, so the
only guard was a SHA-256 of the module source. That catches "something
changed" but cannot say what, and it fires on unrelated edits.

Read the registered Torch schema instead (torch.ops.aiter.<op>.default).

The check is an ordered prefix, not a name set, because the two call sites
pass positionally. An argument inserted upstream would shift the meaning of
every later argument while the call still type-checks -- dropout_p, the two
window sizes and sink_size are all int/bool, so nothing would raise and the
kernel would run with silently reinterpreted controls. Name presence alone
does not catch that; the FA4 path is exempt only because it calls by keyword.

This also pins something that was previously unprovable: the True at
backward position 11 is the schema's `deterministic`. ROCm's backward was
already deterministic, but nothing tied that literal to its parameter.

The source fingerprint stays as a second line: the schema check describes what
changed, the fingerprint still catches a same-schema implementation change.

Verified against the installed AITER; the forward prefix matches exactly.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Signed-off-by: zhangj1an <jianmusings@gmail.com>
---
 .../kernels/ops/rocm/attention/flash_attn.py  | 89 +++++++++++++++++
 tests/test_rocm_aiter_api_contract.py         | 99 +++++++++++++++++++
 2 files changed, 188 insertions(+)
 create mode 100644 tests/test_rocm_aiter_api_contract.py

diff --git a/rl_engine/kernels/ops/rocm/attention/flash_attn.py b/rl_engine/kernels/ops/rocm/attention/flash_attn.py
index 1839bebe..65cd94aa 100644
--- a/rl_engine/kernels/ops/rocm/attention/flash_attn.py
+++ b/rl_engine/kernels/ops/rocm/attention/flash_attn.py
@@ -29,6 +29,44 @@
 
 _MAX_TESTED_ROCM_TRITON_HEAD_DIM = 512
 _AITER_API_SOURCE = "aiter.ops.mha"
+_AITER_OP_NAMESPACE = "aiter"
+
+# AITER wraps its kernels in a JIT loader whose Python signature is
+# ``(*args, **kwargs)``, so ``inspect.signature`` cannot see the contract the
+# way it can for the FA4 CuTe API. The names live in the registered Torch
+# schema instead, and the calls below are positional, so the order is part of
+# what has to hold: an upstream insertion would silently reinterpret every
+# argument after it. These tuples are the exact positional prefix each call
+# site assumes.
+_AITER_FWD_POSITIONAL_CONTRACT = (
+    "q",
+    "k",
+    "v",
+    "dropout_p",
+    "softmax_scale",
+    "is_causal",
+    "window_size_left",
+    "window_size_right",
+    "sink_size",
+    "return_softmax_lse",
+    "return_dropout_randval",
+)
+_AITER_BWD_POSITIONAL_CONTRACT = (
+    "dout",
+    "q",
+    "k",
+    "v",
+    "out",
+    "softmax_lse",
+    "dropout_p",
+    "softmax_scale",
+    "is_causal",
+    "window_size_left",
+    "window_size_right",
+    "deterministic",
+)
+# Passed by keyword, so only presence matters.
+_AITER_BWD_REQUIRED_KEYWORDS = frozenset({"rng_state"})
 
 # Stable dispatch identity for the strict ROCm attention core.  Kept at module
 # scope so contract-aware dispatch and the Vime adapter name one constant
@@ -40,6 +78,51 @@ class StrictRocmAttentionUnavailable(RuntimeError):
     """Raised when the exact AITER CK strict contract is unavailable."""
 
 
+def _aiter_schema_argument_names(op_name: str) -> tuple[str, ...]:
+    """Return the registered Torch schema argument names for one AITER op."""
+
+    namespace = getattr(torch.ops, _AITER_OP_NAMESPACE, None)
+    if namespace is None:
+        raise StrictRocmAttentionUnavailable(
+            f"the '{_AITER_OP_NAMESPACE}' Torch operator namespace is not registered"
+        )
+    try:
+        overload = getattr(namespace, op_name).default
+        arguments = overload._schema.arguments
+    except (AttributeError, RuntimeError) as exc:
+        raise StrictRocmAttentionUnavailable(
+            f"cannot read the Torch schema for {_AITER_OP_NAMESPACE}::{op_name}"
+        ) from exc
+    return tuple(argument.name for argument in arguments)
+
+
+def _validate_aiter_schema(
+    op_name: str,
+    positional_contract: tuple[str, ...],
+    *,
+    required_keywords: frozenset[str] = frozenset(),
+) -> None:
+    """Fail closed unless AITER still accepts what the call sites pass.
+
+    The strict calls are positional, so a renamed *or reordered* argument
+    changes their meaning without changing their shape. Checking the ordered
+    prefix catches both, which name-presence alone would not.
+    """
+
+    names = _aiter_schema_argument_names(op_name)
+    prefix = names[: len(positional_contract)]
+    if prefix != positional_contract:
+        raise StrictRocmAttentionUnavailable(
+            f"AITER {op_name} positional contract changed: strict ROCm Attention "
+            f"passes {positional_contract} but the schema declares {prefix}"
+        )
+    missing = sorted(required_keywords.difference(names))
+    if missing:
+        raise StrictRocmAttentionUnavailable(
+            f"AITER {op_name} is missing strict controls: " + ", ".join(missing)
+        )
+
+
 def _load_aiter_ck_ops() -> tuple[Callable[..., Any], Callable[..., Any], str]:
     try:
         module = importlib.import_module(_AITER_API_SOURCE)
@@ -51,6 +134,12 @@ def _load_aiter_ck_ops() -> tuple[Callable[..., Any], Callable[..., Any], str]:
         ) from exc
     if not callable(mha_fwd) or not callable(mha_bwd):
         raise StrictRocmAttentionUnavailable("AITER CK MHA entry points are not callable")
+    _validate_aiter_schema("mha_fwd", _AITER_FWD_POSITIONAL_CONTRACT)
+    _validate_aiter_schema(
+        "mha_bwd",
+        _AITER_BWD_POSITIONAL_CONTRACT,
+        required_keywords=_AITER_BWD_REQUIRED_KEYWORDS,
+    )
     module_file = inspect.getsourcefile(module)
     if not module_file:
         raise StrictRocmAttentionUnavailable("cannot fingerprint the AITER MHA source module")
diff --git a/tests/test_rocm_aiter_api_contract.py b/tests/test_rocm_aiter_api_contract.py
new file mode 100644
index 00000000..f7ef6dce
--- /dev/null
+++ b/tests/test_rocm_aiter_api_contract.py
@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Schema validation for the AITER entry points the strict ROCm core calls.
+
+The CUDA core validates the FA4 CuTe API by parameter name before it runs.
+AITER hides its signature behind a JIT wrapper (``inspect.signature`` reports
+``(*args, **kwargs)``), so the equivalent check reads the registered Torch
+schema. The strict calls are positional, which makes argument *order* part of
+the contract too: an upstream insertion would silently reinterpret everything
+after it while every call still type-checks.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from rl_engine.kernels.ops.rocm.attention.flash_attn import (
+    _AITER_BWD_POSITIONAL_CONTRACT,
+    _AITER_BWD_REQUIRED_KEYWORDS,
+    _AITER_FWD_POSITIONAL_CONTRACT,
+    StrictRocmAttentionUnavailable,
+    _validate_aiter_schema,
+)
+
+
+def _aiter_available() -> bool:
+    try:
+        import aiter.ops.mha  # noqa: F401
+    except Exception:
+        return False
+    return True
+
+
+requires_aiter = pytest.mark.skipif(not _aiter_available(), reason="AITER is not installed")
+
+
+def test_positional_contract_matches_the_strict_call_sites() -> None:
+    """The tuples must stay in step with what the autograd Function passes.
+
+    ``_AiterCKAttentionFn`` calls both ops positionally. If someone edits a
+    call site without editing the contract, the schema check would still pass
+    while the call means something else.
+    """
+
+    assert _AITER_FWD_POSITIONAL_CONTRACT[:6] == (
+        "q",
+        "k",
+        "v",
+        "dropout_p",
+        "softmax_scale",
+        "is_causal",
+    )
+    # The forward passes (-1, -1, 0, True, False) after is_causal.
+    assert _AITER_FWD_POSITIONAL_CONTRACT[6:] == (
+        "window_size_left",
+        "window_size_right",
+        "sink_size",
+        "return_softmax_lse",
+        "return_dropout_randval",
+    )
+    # The backward pins determinism positionally, so its slot must not move.
+    assert _AITER_BWD_POSITIONAL_CONTRACT[-1] == "deterministic"
+    assert _AITER_BWD_POSITIONAL_CONTRACT.index("softmax_lse") == 5
+    assert "rng_state" in _AITER_BWD_REQUIRED_KEYWORDS
+
+
+@requires_aiter
+def test_installed_aiter_satisfies_the_strict_contract() -> None:
+    _validate_aiter_schema("mha_fwd", _AITER_FWD_POSITIONAL_CONTRACT)
+    _validate_aiter_schema(
+        "mha_bwd",
+        _AITER_BWD_POSITIONAL_CONTRACT,
+        required_keywords=_AITER_BWD_REQUIRED_KEYWORDS,
+    )
+
+
+@requires_aiter
+def test_reordered_positional_contract_fails_closed() -> None:
+    """A swap the installed schema does not have must be rejected."""
+
+    swapped = ("q", "k", "v", "softmax_scale", "dropout_p")
+    with pytest.raises(StrictRocmAttentionUnavailable, match="positional contract changed"):
+        _validate_aiter_schema("mha_fwd", swapped)
+
+
+@requires_aiter
+def test_missing_keyword_control_fails_closed() -> None:
+    with pytest.raises(StrictRocmAttentionUnavailable, match="missing strict controls"):
+        _validate_aiter_schema(
+            "mha_fwd",
+            _AITER_FWD_POSITIONAL_CONTRACT,
+            required_keywords=frozenset({"num_splits"}),
+        )
+
+
+def test_unregistered_operator_fails_closed() -> None:
+    with pytest.raises(StrictRocmAttentionUnavailable, match="cannot read the Torch schema"):
+        _validate_aiter_schema("mha_fwd_that_does_not_exist", ("q",))

From 4f8704b9a4542ea773b4980069160dcc98c59d8b Mon Sep 17 00:00:00 2001
From: zhangj1an <jianmusings@gmail.com>
Date: Sun, 30 Aug 2026 15:31:04 +0000
Subject: [PATCH 35/35] feat(attention): add the strict ROCm decode path over a
 paged KV cache

StrictCUDAAttentionRuntime has forward_paged_with_lse; the ROCm runtime had
no decode entry point at all, so decode-stage KV-cache replay existed only in
the device-neutral comparison harness with no ROCm path behind it.

No AITER paged kernel can serve this contract. paged_attention_rocm, _ragged
and _v1 are all two-pass partition reducers -- partition_size, with
exp_sums/max_logits/tmp_out partials -- so the partition count tracks the
cached length and Split-KV cannot be turned off. AITER's
flash_attn_varlen_func takes a block_table but exposes no num_splits to pin,
unlike CUDA's FA4. Either way the contract could not prove Split-KV disabled,
which attention_binding checks from both runtime evidence and the contract.

So the pages are gathered into logical order and handed to the same dense
core the prefill path uses, at the same one-launch-per (batch row, KV group)
granularity. The arithmetic is then identical to a CP=1 prefill over the same
logical sequence, which is what makes decode replay comparable against it. The
cost is materializing the cached KV; a native paged kernel avoids that and can
replace this once AITER can pin its split count.

The registry deliberately does NOT gain AttentionMode.DECODE. Nothing routes
to the new entry point: the Vime provider always calls forward_with_lse and
builds its contract with kv_cache=None, and its request carries no page table.
Declaring the mode now would let the binding layer accept a decode path that
never executes. A test pins the omission so it flips together with the
dispatch wiring rather than drifting ahead of it.

Tests inject the core, so they run without ROCm. They pin the part that is
ours rather than AITER's: a shuffled page table still yields logical KV order,
the gather truncates to seqused_k instead of exposing the page tail, each
launch still sees exactly one KV group, and the provenance says
paged_kernel=none so no reader mistakes this for a native paged path.

Not verified on MI300X. The core arithmetic is unchanged, but the gather's
index_select/reshape/permute and the claimed bitwise equality with a CP=1
prefill over the same tokens both need a real run.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Signed-off-by: zhangj1an <jianmusings@gmail.com>
---
 .../ops/rocm/attention/strict_runtime.py      | 201 ++++++++++++++
 rl_engine/kernels/registry.py                 |   6 +
 tests/test_rocm_strict_paged_attention.py     | 256 ++++++++++++++++++
 3 files changed, 463 insertions(+)
 create mode 100644 tests/test_rocm_strict_paged_attention.py

diff --git a/rl_engine/kernels/ops/rocm/attention/strict_runtime.py b/rl_engine/kernels/ops/rocm/attention/strict_runtime.py
index c5eb3a0f..49a5b9a3 100644
--- a/rl_engine/kernels/ops/rocm/attention/strict_runtime.py
+++ b/rl_engine/kernels/ops/rocm/attention/strict_runtime.py
@@ -208,6 +208,207 @@ def forward_with_lse(
             },
         )
 
+    def forward_paged_with_lse(
+        self,
+        q: torch.Tensor,
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        *,
+        page_table: torch.Tensor,
+        seqused_k: torch.Tensor,
+        max_seqlen_k: int,
+        scale: float | None,
+        out: torch.Tensor | None = None,
+    ) -> StrictRocmAttentionResult:
+        """Run strict decode Attention over a paged KV cache.
+
+        AITER exposes no paged entry point that this contract can use. Every
+        ``paged_attention_*`` kernel partitions KV and reduces the partials
+        (``partition_size``, ``exp_sums``/``max_logits``/``tmp_out``), so the
+        partition count moves with the cached length; AITER's
+        ``flash_attn_varlen_func`` takes a ``block_table`` but has no
+        ``num_splits`` knob to pin, unlike CUDA's FA4. Either way the strict
+        contract could not prove Split-KV disabled.
+
+        So the pages are gathered into logical KV order and handed to the same
+        dense core the prefill path uses, at the same one-launch-per
+        ``(batch row, KV group)`` granularity. The arithmetic is then identical
+        to a CP=1 prefill over the same logical sequence, which is what makes
+        decode replay comparable against it. The cost is materializing the
+        cached KV; a native paged kernel would avoid that, and can replace this
+        once AITER can pin its split count.
+        """
+
+        self._require_rocm(q)
+        self._validate_paged_inputs(
+            q,
+            k_cache,
+            v_cache,
+            page_table=page_table,
+            seqused_k=seqused_k,
+            max_seqlen_k=max_seqlen_k,
+        )
+        if out is not None:
+            if out.shape != q.shape:
+                raise ValueError("paged Attention out must have the same shape as q")
+            if out.dtype != q.dtype or out.device != q.device:
+                raise ValueError("paged Attention out must match the Q dtype and device")
+            if not out.is_contiguous():
+                raise ValueError("paged Attention out must be contiguous")
+
+        row_outs: list[torch.Tensor] = []
+        row_lses: list[torch.Tensor] = []
+        core_provenance: dict[str, Any] | None = None
+        launches = 0
+        for row in range(q.size(0)):
+            cached_length = int(seqused_k[row].item())
+            if cached_length <= 0 or cached_length > max_seqlen_k:
+                raise ValueError(
+                    "seqused_k entries must be positive and within max_seqlen_k; "
+                    f"row {row} requested {cached_length}"
+                )
+            k_row, v_row = self._gather_paged_row(
+                k_cache,
+                v_cache,
+                page_table[row],
+                cached_length,
+            )
+            # Decode attends over the whole cached prefix, so the mask is not
+            # causal within this launch. The logical positions are still passed
+            # for provenance-grade auditing of what each launch consumed.
+            key_positions = torch.arange(
+                cached_length,
+                dtype=torch.int64,
+                device=q.device,
+            ).unsqueeze(0)
+            query_positions = key_positions[:, -q.size(2) :]
+            row_out, row_lse, row_provenance, row_launches = self._run_core(
+                q[row : row + 1],
+                k_row,
+                v_row,
+                causal=False,
+                scale=scale,
+                query_position_ids=query_positions,
+                key_position_ids=key_positions,
+                output_dtype=q.dtype,
+            )
+            row_outs.append(row_out)
+            row_lses.append(row_lse)
+            launches += row_launches
+            if core_provenance is None:
+                core_provenance = row_provenance
+
+        if core_provenance is None:
+            raise RuntimeError("strict ROCm paged Attention executed no core launch")
+
+        result_out = torch.cat(row_outs, dim=0)
+        result_lse = torch.cat(row_lses, dim=0)
+        if out is not None:
+            out.copy_(result_out)
+            result_out = out
+        self.communication_executed = False
+
+        backend = (
+            core_provenance.get("attention_backend")
+            or core_provenance.get("actual_backend")
+            or getattr(self._core, "backend_id", None)
+        )
+        return StrictRocmAttentionResult(
+            out=result_out,
+            lse=result_lse,
+            provenance={
+                "strict_core_id": self.core_id,
+                "strict_schedule": self.strict_schedule,
+                "actual_backend": self.backend_id,
+                "communication_backend": "none",
+                "communication_executed": False,
+                "native_attention_arithmetic": True,
+                "production_ready": True,
+                "fallback": False,
+                "fallback_reason": None,
+                "reference_only": False,
+                "split_kv": "disabled",
+                "query_schedule": "paged_single_query_batch",
+                # The dense core runs; the pages are gathered first. Recorded so
+                # a reader never mistakes this for a native paged kernel.
+                "paged_execution": "logical_kv_gather_then_dense_core",
+                "paged_kernel": "none",
+                "launch_granularity": "one_batch_row_one_kv_group",
+                "tp_degree_invariant": True,
+                "invariance_mechanism": "one_kv_group_per_launch",
+                "core_row_count": q.size(0) * q.size(2),
+                "core_launch_count": launches,
+                "core_batch_size": q.size(0),
+                "core_query_length": q.size(2),
+                "core_actual_backends": [] if backend is None else [str(backend)],
+                "core": core_provenance,
+            },
+        )
+
+    @staticmethod
+    def _gather_paged_row(
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        page_row: torch.Tensor,
+        cached_length: int,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Materialize one row's cached KV in logical order as ``[1, H, S, D]``.
+
+        Physical page order never reaches the core: the pages are read through
+        the page table, so the launch sees the same logical sequence a prefill
+        over the same tokens would have seen.
+        """
+
+        page_size = k_cache.size(1)
+        page_count = (cached_length + page_size - 1) // page_size
+        if page_count > page_row.numel():
+            raise ValueError("page_table row is shorter than the cached length requires")
+        pages = page_row[:page_count].to(dtype=torch.int64)
+        if int(pages.min().item()) < 0 or int(pages.max().item()) >= k_cache.size(0):
+            raise ValueError("page_table entries are outside the KV cache")
+
+        def _gather(cache: torch.Tensor) -> torch.Tensor:
+            selected = cache.index_select(0, pages)
+            flat = selected.reshape(page_count * page_size, cache.size(2), cache.size(3))
+            return flat[:cached_length].permute(1, 0, 2).unsqueeze(0).contiguous()
+
+        return _gather(k_cache), _gather(v_cache)
+
+    @staticmethod
+    def _validate_paged_inputs(
+        q: torch.Tensor,
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        *,
+        page_table: torch.Tensor,
+        seqused_k: torch.Tensor,
+        max_seqlen_k: int,
+    ) -> None:
+        if q.ndim != 4:
+            raise ValueError("paged q must use [B, H, S, D]")
+        if k_cache.ndim != 4 or v_cache.shape != k_cache.shape:
+            raise ValueError("paged k/v must use [pages, page_size, H, D]")
+        if q.size(1) % k_cache.size(2) != 0 or q.size(3) != k_cache.size(3):
+            raise ValueError("paged q/k head counts or head dimensions are incompatible")
+        if q.dtype not in (torch.float16, torch.bfloat16):
+            raise ValueError("strict paged Attention supports FP16/BF16 only")
+        if k_cache.dtype != q.dtype or v_cache.dtype != q.dtype:
+            raise ValueError("paged q/k/v must share one dtype")
+        if not (q.device == k_cache.device == v_cache.device):
+            raise ValueError("paged q/k/v must be on one ROCm device")
+        if page_table.ndim != 2 or page_table.size(0) != q.size(0):
+            raise ValueError("page_table must be 2-D with one row per query")
+        if page_table.dtype not in (torch.int32, torch.int64):
+            raise ValueError("page_table must be an integer tensor")
+        if seqused_k.shape != (q.size(0),):
+            raise ValueError("seqused_k must carry one cached length per query")
+        if seqused_k.dtype not in (torch.int32, torch.int64):
+            raise ValueError("seqused_k must be an integer tensor")
+        if page_table.device != q.device or seqused_k.device != q.device:
+            raise ValueError("paged Attention metadata must be on the Q device")
+        if max_seqlen_k <= 0 or max_seqlen_k > page_table.size(1) * k_cache.size(1):
+            raise ValueError("max_seqlen_k exceeds the page table capacity")
+
     def _run_core(
         self,
         q: torch.Tensor,
diff --git a/rl_engine/kernels/registry.py b/rl_engine/kernels/registry.py
index c6d8da88..f715bad8 100644
--- a/rl_engine/kernels/registry.py
+++ b/rl_engine/kernels/registry.py
@@ -704,6 +704,12 @@ def __init__(self):
                 AttentionBackendCapability(
                     backend_id="aiter.rocm.ck_dense_mha",
                     roles=frozenset({AttentionRole.TRAIN, AttentionRole.INFER}),
+                    # DECODE is deliberately absent. StrictRocmAttentionRuntime
+                    # has a paged entry point, but no caller routes to it: the
+                    # Vime request carries no page table and builds its contract
+                    # with kv_cache=None. Declaring the mode before a dispatch
+                    # path reaches it would let the binding layer pass on a
+                    # decode path nothing executes.
                     modes=frozenset({AttentionMode.PREFILL, AttentionMode.CHUNKED_PREFILL}),
                     dtypes=frozenset({AttentionDType.BF16, AttentionDType.FP16}),
                     # The core itself is single-rank arithmetic. CP is supplied
diff --git a/tests/test_rocm_strict_paged_attention.py b/tests/test_rocm_strict_paged_attention.py
new file mode 100644
index 00000000..53ef1463
--- /dev/null
+++ b/tests/test_rocm_strict_paged_attention.py
@@ -0,0 +1,256 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+"""Decode-stage paged Attention on the strict ROCm runtime.
+
+The core is injected, so these run without ROCm. What they pin is the part
+that is ours: the page table decides logical KV order, the cached rows reach
+the core exactly as a dense prefill over the same tokens would, and the
+provenance never claims a native paged kernel.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import pytest
+import torch
+
+from rl_engine.kernels.attention_contract import (
+    STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID,
+    STRICT_ATTENTION_ROCM_SCHEDULE_ID,
+)
+from rl_engine.kernels.ops.rocm.attention.strict_runtime import StrictRocmAttentionRuntime
+
+_HEAD_DIM = 8
+_PAGE_SIZE = 4
+
+
+class _RecordingCore:
+    """Dense core stand-in that records exactly what each launch consumed."""
+
+    core_id = STRICT_ATTENTION_ROCM_PRODUCTION_CORE_ID
+    strict_schedule = STRICT_ATTENTION_ROCM_SCHEDULE_ID
+    backend_id = "aiter.rocm.ck_dense_mha"
+
+    def __init__(self) -> None:
+        self.calls: list[dict[str, Any]] = []
+
+    def forward_with_lse(self, q, k, v, **kwargs) -> Any:
+        self.calls.append(
+            {
+                "q": q,
+                "k": k.clone(),
+                "v": v.clone(),
+                "causal": kwargs.get("causal"),
+                "query_position_ids": kwargs.get("query_position_ids"),
+                "key_position_ids": kwargs.get("key_position_ids"),
+            }
+        )
+
+        class _Result:
+            out = torch.zeros(q.size(0), q.size(1), q.size(2), _HEAD_DIM, dtype=q.dtype)
+            lse = torch.zeros(q.size(0), q.size(1), q.size(2), dtype=torch.float32)
+            provenance = {"attention_backend": "aiter.rocm.ck_dense_mha"}
+
+        return _Result()
+
+
+def _runtime() -> StrictRocmAttentionRuntime:
+    return StrictRocmAttentionRuntime(core=_RecordingCore())
+
+
+def _cache(pages: int, kv_heads: int = 1) -> torch.Tensor:
+    total = pages * _PAGE_SIZE * kv_heads * _HEAD_DIM
+    return (
+        torch.arange(total, dtype=torch.float32)
+        .reshape(pages, _PAGE_SIZE, kv_heads, _HEAD_DIM)
+        .to(torch.bfloat16)
+    )
+
+
+def _paged_call(runtime, *, page_table, seqused_k, q_heads=1, kv_heads=1, pages=4):
+    k_cache = _cache(pages, kv_heads)
+    v_cache = _cache(pages, kv_heads) + 1
+    q = torch.zeros(page_table.size(0), q_heads, 1, _HEAD_DIM, dtype=torch.bfloat16)
+    return (
+        runtime.forward_paged_with_lse(
+            q,
+            k_cache,
+            v_cache,
+            page_table=page_table,
+            seqused_k=seqused_k,
+            max_seqlen_k=page_table.size(1) * _PAGE_SIZE,
+            scale=None,
+        ),
+        k_cache,
+        v_cache,
+    )
+
+
+@pytest.fixture(autouse=True)
+def _pretend_rocm(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(StrictRocmAttentionRuntime, "_require_rocm", staticmethod(lambda t: None))
+
+
+def test_paged_decode_gathers_kv_in_logical_not_physical_order() -> None:
+    """A shuffled page table must still produce logical KV order.
+
+    This is the property that makes decode replay comparable with prefill: if
+    physical page order leaked through, the same logical sequence would produce
+    different arithmetic depending on how the allocator handed out pages.
+    """
+
+    core = _RecordingCore()
+    runtime = StrictRocmAttentionRuntime(core=core)
+    # Logical tokens 0..7 live on physical pages 3 then 1.
+    page_table = torch.tensor([[3, 1]], dtype=torch.int32)
+    _result, k_cache, _v_cache = _paged_call(
+        runtime,
+        page_table=page_table,
+        seqused_k=torch.tensor([8], dtype=torch.int32),
+    )
+
+    assert len(core.calls) == 1
+    gathered_k = core.calls[0]["k"]
+    assert gathered_k.shape == (1, 1, 8, _HEAD_DIM)
+
+    expected = torch.cat((k_cache[3], k_cache[1]), dim=0)  # [8, H, D] logical order
+    assert torch.equal(gathered_k[0].permute(1, 0, 2), expected)
+
+
+def test_paged_decode_truncates_to_the_cached_length() -> None:
+    core = _RecordingCore()
+    runtime = StrictRocmAttentionRuntime(core=core)
+    page_table = torch.tensor([[0, 1]], dtype=torch.int32)
+
+    _paged_call(
+        runtime,
+        page_table=page_table,
+        seqused_k=torch.tensor([5], dtype=torch.int32),
+    )
+
+    # Five cached tokens span two pages but must not expose the page tail.
+    assert core.calls[0]["k"].shape == (1, 1, 5, _HEAD_DIM)
+    assert core.calls[0]["v"].shape == (1, 1, 5, _HEAD_DIM)
+    # The launch is non-causal, so the core is handed no position ids; the
+    # truncation above is what bounds the launch to the cached prefix.
+    assert core.calls[0]["key_position_ids"] is None
+
+
+def test_paged_decode_is_not_causal_within_a_launch() -> None:
+    """Decode attends over the whole cached prefix, so the launch is not causal."""
+
+    core = _RecordingCore()
+    runtime = StrictRocmAttentionRuntime(core=core)
+
+    _paged_call(
+        runtime,
+        page_table=torch.tensor([[0]], dtype=torch.int32),
+        seqused_k=torch.tensor([4], dtype=torch.int32),
+    )
+
+    assert core.calls[0]["causal"] is False
+
+
+def test_paged_decode_keeps_one_kv_group_per_launch() -> None:
+    """The TP-degree invariance mechanism must survive into the paged path."""
+
+    core = _RecordingCore()
+    runtime = StrictRocmAttentionRuntime(core=core)
+
+    result, _k, _v = _paged_call(
+        runtime,
+        page_table=torch.tensor([[0], [1]], dtype=torch.int32),
+        seqused_k=torch.tensor([4, 4], dtype=torch.int32),
+        q_heads=4,
+        kv_heads=2,
+    )
+
+    # Two rows x two KV groups.
+    assert len(core.calls) == 4
+    assert result.provenance["core_launch_count"] == 4
+    for call in core.calls:
+        assert call["k"].size(1) == 1  # exactly one KV group per launch
+        assert call["q"].size(1) == 2  # its two Q heads
+    assert result.provenance["launch_granularity"] == "one_batch_row_one_kv_group"
+    assert result.provenance["tp_degree_invariant"] is True
+
+
+def test_paged_decode_provenance_does_not_claim_a_paged_kernel() -> None:
+    """The gather is the implementation; the provenance must say so."""
+
+    runtime = _runtime()
+    result, _k, _v = _paged_call(
+        runtime,
+        page_table=torch.tensor([[0]], dtype=torch.int32),
+        seqused_k=torch.tensor([4], dtype=torch.int32),
+    )
+
+    assert result.provenance["paged_kernel"] == "none"
+    assert result.provenance["paged_execution"] == "logical_kv_gather_then_dense_core"
+    assert result.provenance["split_kv"] == "disabled"
+    assert result.provenance["strict_schedule"] == STRICT_ATTENTION_ROCM_SCHEDULE_ID
+    assert result.provenance["communication_executed"] is False
+    assert result.provenance["query_schedule"] == "paged_single_query_batch"
+
+
+@pytest.mark.parametrize(
+    ("page_table", "seqused_k", "match"),
+    [
+        (torch.tensor([[9]], dtype=torch.int32), torch.tensor([4], dtype=torch.int32), "outside"),
+        (torch.tensor([[0]], dtype=torch.int32), torch.tensor([0], dtype=torch.int32), "positive"),
+        (
+            torch.tensor([[0]], dtype=torch.int32),
+            torch.tensor([9], dtype=torch.int32),
+            "within max_seqlen_k",
+        ),
+    ],
+)
+def test_paged_decode_fails_closed_on_bad_metadata(page_table, seqused_k, match) -> None:
+    runtime = _runtime()
+    with pytest.raises(ValueError, match=match):
+        _paged_call(runtime, page_table=page_table, seqused_k=seqused_k)
+
+
+def test_paged_decode_rejects_a_mismatched_out_buffer() -> None:
+    runtime = _runtime()
+    k_cache = _cache(2)
+    q = torch.zeros(1, 1, 1, _HEAD_DIM, dtype=torch.bfloat16)
+
+    with pytest.raises(ValueError, match="same shape as q"):
+        runtime.forward_paged_with_lse(
+            q,
+            k_cache,
+            k_cache + 1,
+            page_table=torch.tensor([[0]], dtype=torch.int32),
+            seqused_k=torch.tensor([4], dtype=torch.int32),
+            max_seqlen_k=_PAGE_SIZE,
+            scale=None,
+            out=torch.zeros(2, 1, 1, _HEAD_DIM, dtype=torch.bfloat16),
+        )
+
+
+def test_rocm_registry_does_not_claim_decode_before_a_caller_routes_to_it() -> None:
+    """The paged entry point exists, but nothing dispatches to it yet.
+
+    The Vime provider always calls ``forward_with_lse`` and builds its contract
+    with ``kv_cache=None``, so no decode request can reach the paged path.
+    Claiming the mode here would let the cross-config binding accept a decode
+    path that never runs. Flip this together with the dispatch wiring.
+    """
+
+    from rl_engine.kernels.attention_contract import AttentionMode
+    from rl_engine.kernels.registry import KernelRegistry, OpBackend
+
+    capabilities = KernelRegistry()._attention_capabilities
+    capability = capabilities.get(OpBackend.ROCM_STRICT_ATTENTION)
+    if capability is None:
+        pytest.skip("AITER is unavailable, so the strict ROCm backend is not registered")
+
+    assert AttentionMode.DECODE not in capability.modes
+    assert capability.supports_kv_cache is False
+    # Whatever the modes, the gather must keep the Split-KV claims intact.
+    assert capability.supports_split_kv_disabled is True
+    assert capability.supports_split_kv_fixed is False
+    assert capability.supports_split_kv_auto is False