From 6424b88c15ba592580832a1e9666b1eb44ced9bb Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Thu, 20 Aug 2026 14:22:35 +0000
Subject: [PATCH 01/28] feat(gemm): align BF16 K reduction with TP tree

---
 csrc/cuda/gemm/det_gemm_kernel.cu | 240 +++++++++++++++++++++++++++---
 tests/test_det_gemm.py            | 133 ++++++++++++++++-
 2 files changed, 346 insertions(+), 27 deletions(-)

diff --git a/csrc/cuda/gemm/det_gemm_kernel.cu b/csrc/cuda/gemm/det_gemm_kernel.cu
index cd92fc9d..5459b2fe 100644
--- a/csrc/cuda/gemm/det_gemm_kernel.cu
+++ b/csrc/cuda/gemm/det_gemm_kernel.cu
@@ -6,16 +6,25 @@
 //
 //   SM90 path : TMA load + mma.sync (m16n8k16), FP32 accum, single-CTA-per-tile,
 //               fixed K order, NO split-K -> batch-invariant.
-//   Fallback  : naive FP32 scalar kernel (also the correctness ground truth).
+//   Fallback  : naive scalar kernel (also the correctness ground truth).
 //
-// Both: BF16 in / FP32 accum / no TF32 / no split-K.
+// BF16-output entry points use BF16 inputs, FP32 leaf accumulation, BF16 tree
+// nodes and store, no TF32, and no split-K. K is reduced with a canonical
+// midpoint tree whose leaves cover at most 32 values. A contiguous half-K
+// GEMM is therefore one child of the same tree, so TP shards can be combined
+// with the matching balanced collective without changing the arithmetic
+// graph. det_gemm_fwd_fp32 retains its ascending FP32 accumulation and FP32
+// output contract.
 //   fwd: C = A @ B   |   dA = dC @ B^T   |   dB = A^T @ dC
 // Backward reuses the forward kernel on transposed operands.
 
 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
 #include <cuda_bf16.h>
 
+#include <type_traits>
+
 #if defined(RL_KERNEL_ENABLE_SM90)
 #include "det_gemm_tma.cuh"
 #endif
@@ -32,15 +41,108 @@ __device__ __forceinline__ nv_bf16 cast_output<nv_bf16>(float value) {
   return __float2bfloat16(value);
 }
 
-template <>
-__device__ __forceinline__ float cast_output<float>(float value) {
-  return value;
+__host__ __device__ constexpr int cdiv(int a, int b) { return (a + b - 1) / b; }
+
+// Keep this equal to the SM90 BK below.  Each leaf accumulates in FP32 and is
+// rounded to BF16 before the fixed midpoint tree combines it with its sibling.
+constexpr int K_TREE_LEAF = 32;
+
+__device__ __forceinline__ nv_bf16 bf16_add(nv_bf16 lower, nv_bf16 upper) {
+  return __float2bfloat16(
+      __bfloat162float(lower) + __bfloat162float(upper));
 }
 
-__host__ __device__ constexpr int cdiv(int a, int b) { return (a + b - 1) / b; }
+// True iff [lo, hi) is a node in the recursive midpoint partition of [0, n).
+// The SM90 implementation consumes leaves in ascending order and uses this
+// predicate to close exactly the same nodes as the scalar implementation.
+__device__ __forceinline__ bool is_mid_split_node(int lo, int hi, int n) {
+  int begin = 0;
+  int end = n;
+  while (end - begin > 1) {
+    if (begin == lo && end == hi) {
+      return true;
+    }
+    const int midpoint = begin + (end - begin) / 2;
+    if (hi <= midpoint) {
+      end = midpoint;
+    } else if (lo >= midpoint) {
+      begin = midpoint;
+    } else {
+      return false;
+    }
+  }
+  return begin == lo && end == hi;
+}
+
+// Evaluate the midpoint tree without device recursion.  A depth of 32 covers
+// every positive 32-bit K and is accepted by both NVCC and HIPCC without
+// relocatable-device-code support.
+__device__ nv_bf16 k_tree_naive(
+    const nv_bf16* __restrict__ A,
+    const nv_bf16* __restrict__ B,
+    int row,
+    int col,
+    int N,
+    int K) {
+  constexpr int MAX_TREE_DEPTH = 32;
+  int range_lo[MAX_TREE_DEPTH];
+  int range_hi[MAX_TREE_DEPTH];
+  unsigned char state[MAX_TREE_DEPTH];
+  nv_bf16 left_value[MAX_TREE_DEPTH];
+
+  int depth = 0;
+  range_lo[0] = 0;
+  range_hi[0] = K;
+  state[0] = 0;
+  nv_bf16 value = __float2bfloat16(0.0f);
+  bool have_value = false;
+
+  while (true) {
+    if (!have_value) {
+      const int lo = range_lo[depth];
+      const int hi = range_hi[depth];
+      if (hi - lo <= K_TREE_LEAF) {
+        float acc = 0.0f;
+        for (int k = lo; k < hi; ++k) {
+          acc += __bfloat162float(A[row * K + k]) *
+                 __bfloat162float(B[k * N + col]);
+        }
+        value = __float2bfloat16(acc);
+        have_value = true;
+      } else {
+        const int midpoint = lo + (hi - lo) / 2;
+        state[depth] = 1;
+        ++depth;
+        range_lo[depth] = lo;
+        range_hi[depth] = midpoint;
+        state[depth] = 0;
+      }
+      continue;
+    }
+
+    if (depth == 0) {
+      return value;
+    }
+
+    --depth;
+    if (state[depth] == 1) {
+      left_value[depth] = value;
+      state[depth] = 2;
+      const int midpoint =
+          range_lo[depth] + (range_hi[depth] - range_lo[depth]) / 2;
+      ++depth;
+      range_lo[depth] = midpoint;
+      range_hi[depth] = range_hi[depth - 1];
+      state[depth] = 0;
+      have_value = false;
+    } else {
+      value = bf16_add(left_value[depth], value);
+    }
+  }
+}
 
 // Naive FP32 scalar kernel (fallback + ground truth). Batch-invariant by
-// construction: one thread = one output element, fixed ascending K loop.
+// construction: one thread = one output element, canonical midpoint K tree.
 constexpr int NAIVE_TILE = 16;
 
 template <typename output_t>
@@ -51,10 +153,19 @@ __global__ void det_gemm_naive(const nv_bf16* __restrict__ A,
   const int row = blockIdx.y * NAIVE_TILE + threadIdx.y;
   const int col = blockIdx.x * NAIVE_TILE + threadIdx.x;
   if (row >= M || col >= N) return;
-  float acc = 0.0f;
-  for (int k = 0; k < K; ++k)
-    acc += __bfloat162float(A[row * K + k]) * __bfloat162float(B[k * N + col]);
-  C[row * N + col] = cast_output<output_t>(acc);
+  if constexpr (std::is_same_v<output_t, float>) {
+    // Preserve det_gemm_fwd_fp32's existing FP32-output contract.  The FFN
+    // path uses BF16 outputs and therefore takes the midpoint tree below.
+    float acc = 0.0f;
+    for (int k = 0; k < K; ++k) {
+      acc += __bfloat162float(A[row * K + k]) *
+             __bfloat162float(B[k * N + col]);
+    }
+    C[row * N + col] = acc;
+  } else {
+    C[row * N + col] = cast_output<output_t>(
+        __bfloat162float(k_tree_naive(A, B, row, col, N, K)));
+  }
 }
 
 template <typename output_t>
@@ -72,6 +183,8 @@ void launch_naive(const nv_bf16* A, const nv_bf16* B, output_t* C,
 // passing B^T ([N,K] row-major) so the B smem tile is [BN,BK] (row=n,col=k),
 // matching the validated logp ldmatrix addressing.
 constexpr int BM = 128, BN = 64, BK = 32;
+static_assert(BK == K_TREE_LEAF,
+              "SM90 tile width must match the scalar K-tree leaf");
 constexpr int WARPS = 4;
 constexpr int WG_THREADS = WARPS * 32;  // 128
 constexpr int STAGES = 2;
@@ -82,6 +195,7 @@ constexpr int M_TILES = WARP_M / MMA_M;   // 1
 constexpr int N_TILES = BN / MMA_N;       // 8
 constexpr int K_TILES = BK / MMA_K;       // 2
 constexpr int KK_GROUPS = BK / 32;        // 1
+constexpr int TREE_DEPTH = 16;
 
 __device__ __forceinline__ void ldmatrix_x4(uint32_t regs[4], uint32_t addr) {
   asm volatile("ldmatrix.sync.aligned.m8n8.x4.shared.b16 {%0,%1,%2,%3}, [%4];"
@@ -142,6 +256,12 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
   for (int s = 0; s < STAGES; ++s) phase[s] = 0;
 
   float acc[M_TILES][N_TILES][4];
+  float tile_acc[M_TILES][N_TILES][4];
+  nv_bf16 tree_value[M_TILES][N_TILES][4];
+  nv_bf16 tree_stack[TREE_DEPTH][M_TILES][N_TILES][4];
+  int tree_lo[TREE_DEPTH];
+  int tree_hi[TREE_DEPTH];
+  int tree_stack_size = 0;
 #pragma unroll
   for (int mi = 0; mi < M_TILES; ++mi)
 #pragma unroll
@@ -153,7 +273,7 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
     for (int s = 0; s < STAGES - 1; ++s)
       if (s < kd) issue_load(s);
 
-  for (int k = 0; k < kd; ++k) {       // fixed ascending K order, NO split-K
+  for (int k = 0; k < kd; ++k) {       // fixed ascending tile order, NO split-K
     const int buf = k % STAGES;
     if (tid == 0 && k + (STAGES - 1) < kd) issue_load(k + (STAGES - 1));
     det_gemm::mbar_wait(mbar[buf], phase[buf]);
@@ -163,6 +283,18 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
     const uint32_t sA_buf = sA_base + buf * BM * BK * sizeof(nv_bf16);
     const uint32_t sB_buf = sB_base + buf * BN * BK * sizeof(nv_bf16);
 
+    if constexpr (!std::is_same_v<output_t, float>) {
+#pragma unroll
+      for (int mi = 0; mi < M_TILES; ++mi)
+#pragma unroll
+        for (int n = 0; n < N_TILES; ++n) {
+          tile_acc[mi][n][0] = 0.0f;
+          tile_acc[mi][n][1] = 0.0f;
+          tile_acc[mi][n][2] = 0.0f;
+          tile_acc[mi][n][3] = 0.0f;
+        }
+    }
+
     uint32_t A[M_TILES][K_TILES][4];
 #pragma unroll
     for (int mi = 0; mi < M_TILES; ++mi) {
@@ -187,12 +319,62 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
         const uint32_t B1[2] = {b4[2], b4[3]};
 #pragma unroll
         for (int mi = 0; mi < M_TILES; ++mi) {
-          mma_m16n8k16(A[mi][2 * kk + 0], B0, acc[mi][n]);
-          mma_m16n8k16(A[mi][2 * kk + 1], B1, acc[mi][n]);
+          if constexpr (std::is_same_v<output_t, float>) {
+            mma_m16n8k16(A[mi][2 * kk + 0], B0, acc[mi][n]);
+            mma_m16n8k16(A[mi][2 * kk + 1], B1, acc[mi][n]);
+          } else {
+            mma_m16n8k16(A[mi][2 * kk + 0], B0, tile_acc[mi][n]);
+            mma_m16n8k16(A[mi][2 * kk + 1], B1, tile_acc[mi][n]);
+          }
         }
       }
     }
     __syncthreads();
+
+    if constexpr (!std::is_same_v<output_t, float>) {
+#pragma unroll
+      for (int mi = 0; mi < M_TILES; ++mi)
+#pragma unroll
+        for (int n = 0; n < N_TILES; ++n)
+#pragma unroll
+          for (int value_index = 0; value_index < 4; ++value_index) {
+            tree_value[mi][n][value_index] =
+                __float2bfloat16(tile_acc[mi][n][value_index]);
+          }
+
+      int lo = k;
+      const int hi = k + 1;
+      while (tree_stack_size > 0 &&
+             tree_hi[tree_stack_size - 1] == lo &&
+             is_mid_split_node(tree_lo[tree_stack_size - 1], hi, kd)) {
+#pragma unroll
+        for (int mi = 0; mi < M_TILES; ++mi)
+#pragma unroll
+          for (int n = 0; n < N_TILES; ++n)
+#pragma unroll
+            for (int value_index = 0; value_index < 4; ++value_index) {
+              tree_value[mi][n][value_index] = bf16_add(
+                  tree_stack[tree_stack_size - 1][mi][n][value_index],
+                  tree_value[mi][n][value_index]);
+            }
+        lo = tree_lo[tree_stack_size - 1];
+        --tree_stack_size;
+      }
+      if (hi < kd) {
+#pragma unroll
+        for (int mi = 0; mi < M_TILES; ++mi)
+#pragma unroll
+          for (int n = 0; n < N_TILES; ++n)
+#pragma unroll
+            for (int value_index = 0; value_index < 4; ++value_index) {
+              tree_stack[tree_stack_size][mi][n][value_index] =
+                  tree_value[mi][n][value_index];
+            }
+        tree_lo[tree_stack_size] = lo;
+        tree_hi[tree_stack_size] = hi;
+        ++tree_stack_size;
+      }
+    }
   }
 
 #pragma unroll
@@ -202,12 +384,22 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
     for (int n = 0; n < N_TILES; ++n) {
       const int col = col_base + n * MMA_N + (lane % 4) * 2;
       if (row < M && col + 1 < N) {
-        C[row * N + col + 0] = cast_output<output_t>(acc[mi][n][0]);
-        C[row * N + col + 1] = cast_output<output_t>(acc[mi][n][1]);
+        if constexpr (std::is_same_v<output_t, float>) {
+          C[row * N + col + 0] = acc[mi][n][0];
+          C[row * N + col + 1] = acc[mi][n][1];
+        } else {
+          C[row * N + col + 0] = tree_value[mi][n][0];
+          C[row * N + col + 1] = tree_value[mi][n][1];
+        }
       }
       if (row + 8 < M && col + 1 < N) {
-        C[(row + 8) * N + col + 0] = cast_output<output_t>(acc[mi][n][2]);
-        C[(row + 8) * N + col + 1] = cast_output<output_t>(acc[mi][n][3]);
+        if constexpr (std::is_same_v<output_t, float>) {
+          C[(row + 8) * N + col + 0] = acc[mi][n][2];
+          C[(row + 8) * N + col + 1] = acc[mi][n][3];
+        } else {
+          C[(row + 8) * N + col + 0] = tree_value[mi][n][2];
+          C[(row + 8) * N + col + 1] = tree_value[mi][n][3];
+        }
       }
     }
   }
@@ -216,7 +408,10 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
 template <typename output_t>
 bool launch_sm90(const nv_bf16* A, const nv_bf16* Bt, output_t* C,
                  int M, int N, int K, cudaStream_t stream) {
-  if (M % BM != 0 || N % BN != 0 || K % BK != 0) return false;  // fall back
+  if (M % BM != 0 || N % BN != 0 || K == 0 || K % BK != 0 ||
+      K / BK > (1 << TREE_DEPTH)) {
+    return false;  // fall back
+  }
 
   CUtensorMap a_tmap, bt_tmap;
   det_gemm::init_tmap_noswizzle(&a_tmap, A, M, K, BM, BK);
@@ -251,6 +446,13 @@ void check_in(const torch::Tensor& t, const char* n) {
 
 torch::Tensor gemm_dispatch(const torch::Tensor& a, const torch::Tensor& b,
                             bool output_fp32 = false) {
+  TORCH_CHECK(
+      a.device() == b.device(),
+      "deterministic GEMM inputs must be on the same device, got ",
+      a.device(),
+      " and ",
+      b.device());
+  const at::cuda::OptionalCUDAGuard device_guard(at::device_of(a));
   const int M = a.size(0), K = a.size(1), N = b.size(1);
   auto options = a.options().dtype(output_fp32 ? torch::kFloat32 : torch::kBFloat16);
   auto c = torch::empty({M, N}, options);
diff --git a/tests/test_det_gemm.py b/tests/test_det_gemm.py
index 42b73e1d..159eddb4 100644
--- a/tests/test_det_gemm.py
+++ b/tests/test_det_gemm.py
@@ -7,11 +7,12 @@
 The PyTorch path (torch.matmul) is intentionally NOT tested here: it is the
 non-deterministic reference baseline and would fail batch-invariance by design.
 """
+
 import pytest
 import torch
 
 from rl_engine.kernels.gtest.tolerance import load_contract
-from rl_engine.kernels.ops.cuda.matmul import deterministic_gemm
+from rl_engine.kernels.ops.cuda.matmul import DetGemmOp, deterministic_gemm
 
 try:
     from rl_engine.kernels.ops.triton.matmul import deterministic_gemm_triton
@@ -22,14 +23,19 @@
 
 torch.backends.cuda.matmul.allow_tf32 = False
 DEV = "cuda"
+IS_ROCM = getattr(torch.version, "hip", None) is not None
+HAS_SUPPORTED_GPU = torch.cuda.is_available() and (
+    IS_ROCM or torch.cuda.get_device_capability()[0] >= 8
+)
 
 pytestmark = pytest.mark.skipif(
-    not torch.cuda.is_available() or torch.cuda.get_device_capability()[0] < 8,
-    reason="det_gemm requires CUDA SM80+",
+    not HAS_SUPPORTED_GPU,
+    reason="det_gemm requires a ROCm GPU or CUDA SM80+",
 )
 
 # Each deterministic backend is validated independently.
-_BACKENDS = [("cuda", deterministic_gemm)]
+_NATIVE_BACKEND = "rocm" if IS_ROCM else "cuda"
+_BACKENDS = [(_NATIVE_BACKEND, deterministic_gemm)]
 if _HAS_TRITON:
     _BACKENDS.append(("triton", deterministic_gemm_triton))
 
@@ -38,6 +44,72 @@ def _rand(*shape):
     return torch.randn(*shape, device=DEV, dtype=torch.bfloat16)
 
 
+_K_TREE_LEAF = 32
+
+
+def _k_tree_gemm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+    """Canonical FP32-leaf/BF16-node midpoint tree used by the native kernel."""
+
+    a = a.detach().contiguous()
+    b = b.detach().contiguous()
+
+    def reduce_range(lo: int, hi: int) -> torch.Tensor:
+        if hi - lo <= _K_TREE_LEAF:
+            return (a[:, lo:hi].float() @ b[lo:hi, :].float()).to(torch.bfloat16)
+        midpoint = lo + (hi - lo) // 2
+        return reduce_range(lo, midpoint) + reduce_range(midpoint, hi)
+
+    return reduce_range(0, a.size(1))
+
+
+def _balanced_tree_sum(parts: list[torch.Tensor]) -> torch.Tensor:
+    level = parts
+    while len(level) > 1:
+        level = [level[index] + level[index + 1] for index in range(0, len(level), 2)]
+    return level[0]
+
+
+@pytest.mark.parametrize("tp_size", (2, 4, 8))
+def test_forward_matches_balanced_contiguous_k_shards_bitwise(tp_size):
+    """The GEMM K-tree and the TP collective rank tree must be the same graph."""
+
+    torch.manual_seed(8)
+    # Qwen3-8B's down projection uses K=12288.  Its 384 32-wide leaves also
+    # exercise the non-power-of-two midpoint tree used by the SM90 kernel.
+    m, k, n = 4, 12288, 64
+    a, b = _rand(m, k), _rand(k, n)
+    width = k // tp_size
+    parts = [
+        deterministic_gemm(
+            a[:, rank * width : (rank + 1) * width].contiguous(),
+            b[rank * width : (rank + 1) * width].contiguous(),
+        )
+        for rank in range(tp_size)
+    ]
+
+    full = deterministic_gemm(a, b)
+    sharded = _balanced_tree_sum(parts)
+    assert torch.equal(full, sharded), (
+        f"full GEMM differed from balanced TP={tp_size} shards at "
+        f"{int((full != sharded).sum().item())} elements"
+    )
+
+
+@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="requires two GPUs")
+def test_forward_uses_the_input_device_when_it_is_not_current():
+    original_device = torch.cuda.current_device()
+    input_device = (original_device + 1) % torch.cuda.device_count()
+    with torch.cuda.device(input_device):
+        a, b = _rand(4, 64), _rand(64, 32)
+
+    assert torch.cuda.current_device() == original_device
+    output = deterministic_gemm(a, b)
+
+    assert output.device.index == input_device
+    assert torch.isfinite(output).all()
+    assert torch.cuda.current_device() == original_device
+
+
 @pytest.mark.parametrize("name,gemm", _BACKENDS)
 def test_forward_batch_invariance(name, gemm):
     # A row's output must not change when other rows join the batch.
@@ -81,12 +153,38 @@ def test_forward_correctness(name, gemm):
     M, K, N = 128, 2048, 2048
     a, b = _rand(M, K), _rand(K, N)
     out = gemm(a, b).float()
-    ref = a.float() @ b.float()
+    ref = _k_tree_gemm(a, b).float() if name == _NATIVE_BACKEND else a.float() @ b.float()
     contract = load_contract()
     thresholds = contract["accuracy"]["default"]["reduction"]["bfloat16"]
     torch.testing.assert_close(out, ref, atol=thresholds["atol"], rtol=thresholds["rtol"])
 
 
+def test_native_forward_retains_reasonable_fp32_accuracy():
+    """BF16 tree nodes may drift near zero but keep scale-normalized error small."""
+
+    torch.manual_seed(45)
+    m, k, n = 64, 4096, 512
+    a, b = _rand(m, k), _rand(k, n)
+    output = deterministic_gemm(a, b).float()
+    reference = a.float() @ b.float()
+    relative_l2_error = (
+        torch.linalg.vector_norm(output - reference) / torch.linalg.vector_norm(reference)
+    ).item()
+
+    # K=4096 has 128 FP32 leaves and seven BF16-add levels.  The 0.7% bound
+    # leaves margin over the measured ~0.5% while remaining scale-independent.
+    assert relative_l2_error < 7e-3
+
+
+def test_forward_fp32_preserves_its_public_dtype_contract():
+    torch.manual_seed(46)
+    a, b = _rand(8, 64), _rand(64, 32)
+    output = DetGemmOp().forward_fp32(a, b)
+
+    assert output.dtype is torch.float32
+    assert output.shape == (8, 32)
+
+
 @pytest.mark.parametrize("name,gemm", _BACKENDS)
 def test_backward_batch_invariance(name, gemm):
     # dA for a row must be invariant to the surrounding batch.
@@ -111,6 +209,25 @@ def test_backward_correctness(name, gemm):
     b = _rand(K, N).requires_grad_(True)
     g = _rand(M, N)
     gemm(a, b).backward(g)
+    if name == _NATIVE_BACKEND:
+        expected_da = _k_tree_gemm(g, b.detach().t().contiguous())
+        expected_db = _k_tree_gemm(a.detach().t().contiguous(), g)
+        contract = load_contract()
+        thresholds = contract["accuracy"]["default"]["reduction"]["bfloat16"]
+        torch.testing.assert_close(
+            a.grad.float(),
+            expected_da.float(),
+            atol=thresholds["atol"],
+            rtol=thresholds["rtol"],
+        )
+        torch.testing.assert_close(
+            b.grad.float(),
+            expected_db.float(),
+            atol=thresholds["atol"],
+            rtol=thresholds["rtol"],
+        )
+        return
+
     af = a.detach().float().requires_grad_(True)
     bf = b.detach().float().requires_grad_(True)
     (af @ bf).backward(g.float())
@@ -143,9 +260,9 @@ def test_target_shapes_invariance(name, gemm, shape):
     row = _rand(1, K)
     big = _rand(64, K)
     big[0] = row[0]
-    assert torch.equal(
-        gemm(row, b)[0], gemm(big, b)[0]
-    ), f"{name}: batch-invariance broken at shape {shape}"
+    assert torch.equal(gemm(row, b)[0], gemm(big, b)[0]), (
+        f"{name}: batch-invariance broken at shape {shape}"
+    )
 
 
 @pytest.mark.skipif(not _HAS_TRITON, reason="Triton is unavailable")

From 6c02774e971b1947f136d5c7bf6d2505baf95638 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Thu, 20 Aug 2026 14:26:13 +0000
Subject: [PATCH 02/28] feat(distributed): add ROCm transport-only
 deterministic collectives

---
 rl_engine/distributed/__init__.py             |  12 +-
 .../distributed/transport_collectives.py      | 378 ++++++++++++++++++
 ...test_transport_deterministic_collective.py | 329 +++++++++++++++
 3 files changed, 718 insertions(+), 1 deletion(-)
 create mode 100644 rl_engine/distributed/transport_collectives.py
 create mode 100644 tests/distributed/test_transport_deterministic_collective.py

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..e0261f48
--- /dev/null
+++ b/rl_engine/distributed/transport_collectives.py
@@ -0,0 +1,378 @@
+# 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"
+
+    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
+        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
+
+    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)
+            rank_inputs = self._all_gather_transport(input)
+            out.copy_(rank_inputs.reshape(output_shape))
+        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.
+
+        The AllGather implementation owns no persistent device allocation, so
+        closing is an idempotent lifecycle boundary rather than a transport
+        synchronization operation.
+        """
+
+        with self._lock:
+            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) -> 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:
+            return input.clone().reshape((1, *input.shape))
+        input_flat = input.view(-1)
+        gathered_flat = torch.empty(
+            self.world_size * input_flat.numel(),
+            dtype=input.dtype,
+            device=input.device,
+        )
+        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))
+
+    @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")
+        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
new file mode 100644
index 00000000..fb6d4dc3
--- /dev/null
+++ b/tests/distributed/test_transport_deterministic_collective.py
@@ -0,0 +1,329 @@
+# 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
+
+    @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
+        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
+        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_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_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}]

From 6ad03cab5aeca9463b2f6539eb882d54a7512762 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Thu, 20 Aug 2026 14:26:31 +0000
Subject: [PATCH 03/28] feat(ffn): add deterministic Qwen3 FFN assembly

---
 rl_engine/kernels/ops/pytorch/ffn/__init__.py |  14 +
 rl_engine/kernels/ops/pytorch/ffn/ffn.py      | 371 ++++++++++++++++++
 2 files changed, 385 insertions(+)
 create mode 100644 rl_engine/kernels/ops/pytorch/ffn/__init__.py
 create mode 100644 rl_engine/kernels/ops/pytorch/ffn/ffn.py

diff --git a/rl_engine/kernels/ops/pytorch/ffn/__init__.py b/rl_engine/kernels/ops/pytorch/ffn/__init__.py
new file mode 100644
index 00000000..10cd0c36
--- /dev/null
+++ b/rl_engine/kernels/ops/pytorch/ffn/__init__.py
@@ -0,0 +1,14 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from .ffn import (
+    QWEN3_8B_HIDDEN_SIZE,
+    QWEN3_8B_INTERMEDIATE_SIZE,
+    qwen3_ffn,
+)
+
+__all__ = [
+    "QWEN3_8B_HIDDEN_SIZE",
+    "QWEN3_8B_INTERMEDIATE_SIZE",
+    "qwen3_ffn",
+]
diff --git a/rl_engine/kernels/ops/pytorch/ffn/ffn.py b/rl_engine/kernels/ops/pytorch/ffn/ffn.py
new file mode 100644
index 00000000..e5ce71c4
--- /dev/null
+++ b/rl_engine/kernels/ops/pytorch/ffn/ffn.py
@@ -0,0 +1,371 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""Bias-free gated FFN assembled from deterministic GPU kernels.
+
+The numerical contract intentionally follows PR #321: GEMM and SwiGLU each
+consume and produce BF16 tensors.  This module does not keep the whole FFN in
+FP32 and downcast only its final result.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+from torch import Tensor
+
+from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
+
+QWEN3_8B_HIDDEN_SIZE = 4096
+QWEN3_8B_INTERMEDIATE_SIZE = 12288
+
+_REQUIRED_SYMBOLS = (
+    "det_gemm_fwd",
+    "det_gemm_db",
+    "swiglu_forward",
+    "swiglu_backward",
+)
+_COLLECTIVE_MIN_CAPACITY_BYTES = 64 * 1024 * 1024
+_COLLECTIVES: dict[tuple[int, int, int, int], Any] = {}
+
+
+def _require_ffn_kernels() -> None:
+    missing = [name for name in _REQUIRED_SYMBOLS if not hasattr(_C, name)]
+    if not _EXT_AVAILABLE or _C is None or missing:
+        suffix = f" Missing symbols: {', '.join(missing)}." if missing else ""
+        raise RuntimeError(
+            f"qwen3_ffn requires the compiled deterministic GEMM and SwiGLU GPU kernels.{suffix}"
+        )
+
+
+def _require_parallel_group(group: Any, name: str):
+    if group is None:
+        return None
+
+    import torch.distributed as dist
+
+    if not dist.is_available():
+        raise RuntimeError(f"{name}-parallel FFN requires torch.distributed.")
+    if not dist.is_initialized():
+        raise RuntimeError(f"{name}-parallel FFN requires an initialized process group.")
+    if dist.get_world_size(group=group) <= 1:
+        raise ValueError(f"{name}_group must contain at least two ranks.")
+    return dist
+
+
+def _validate_ffn_inputs(
+    rmsnorm_output: Tensor,
+    gate_weight: Tensor,
+    up_weight: Tensor,
+    down_weight: Tensor,
+) -> None:
+    tensors = {
+        "rmsnorm_output": rmsnorm_output,
+        "gate_weight": gate_weight,
+        "up_weight": up_weight,
+        "down_weight": down_weight,
+    }
+    for name, tensor in tensors.items():
+        if not isinstance(tensor, Tensor):
+            raise TypeError(f"{name} must be a torch.Tensor, got {type(tensor)!r}.")
+
+    if rmsnorm_output.dim() < 1:
+        raise ValueError("rmsnorm_output must have at least one dimension.")
+    if rmsnorm_output.numel() == 0:
+        raise ValueError("rmsnorm_output must contain at least one token.")
+    for name, weight in (
+        ("gate_weight", gate_weight),
+        ("up_weight", up_weight),
+        ("down_weight", down_weight),
+    ):
+        if weight.dim() != 2:
+            raise ValueError(f"{name} must be 2-D, got shape {tuple(weight.shape)}.")
+
+    hidden_size = rmsnorm_output.size(-1)
+    intermediate_size = gate_weight.size(0)
+    if intermediate_size == 0:
+        raise ValueError("FFN intermediate size must be positive.")
+    expected_shapes = {
+        "gate_weight": (intermediate_size, hidden_size),
+        "up_weight": (intermediate_size, hidden_size),
+        "down_weight": (hidden_size, intermediate_size),
+    }
+    for name, expected in expected_shapes.items():
+        actual = tuple(tensors[name].shape)
+        if actual != expected:
+            raise ValueError(f"{name} must have shape {expected}, got {actual}.")
+
+    # Validate dtype for every input before device placement so callers get
+    # the most actionable contract error even on CPU-only build hosts.
+    for name, tensor in tensors.items():
+        if tensor.dtype != torch.bfloat16:
+            raise TypeError(f"{name} must have dtype bfloat16, got {tensor.dtype}.")
+
+    for name, tensor in tensors.items():
+        # PyTorch exposes both NVIDIA CUDA and AMD ROCm devices through the
+        # ``cuda`` device API, so ``is_cuda`` is correct for both backends.
+        if not tensor.is_cuda:
+            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}, got {name} on {tensor.device}."
+            )
+
+
+def _create_collective(*, group: Any, max_size_bytes: int):
+    """Create the platform collective lazily, keeping single-GPU independent.
+
+    This small boundary is intentionally easy to inject in unit tests.  The
+    distributed factory selects CUDA IPC on NVIDIA and RCCL transport with a
+    deterministic local reduction tree on ROCm.
+    """
+
+    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
+
+    rank = dist.get_rank(group=group)
+    world_size = dist.get_world_size(group=group)
+    device_index = torch.cuda.current_device()
+    key = (id(group), rank, world_size, device_index)
+    cached = _COLLECTIVES.get(key)
+    if cached is not None and cached.max_size_bytes >= min_size_bytes:
+        return cached
+    if cached is not None:
+        cached.close()
+
+    collective = _create_collective(
+        group=group,
+        max_size_bytes=max(_COLLECTIVE_MIN_CAPACITY_BYTES, min_size_bytes),
+    )
+    _COLLECTIVES[key] = collective
+    return collective
+
+
+def _all_gather_tokens(tensor: Tensor, collective: Any) -> Tensor:
+    return collective.all_gather(tensor.contiguous())
+
+
+def _reduce_scatter_tokens(tensor: Tensor, collective: Any) -> Tensor:
+    world_size = collective.world_size
+    if tensor.size(0) % world_size != 0:
+        raise ValueError(
+            "the gathered token count must be divisible by the tensor-parallel "
+            f"world size, got {tensor.size(0)} and {world_size}."
+        )
+    return collective.reduce_scatter(tensor.contiguous())
+
+
+def _all_reduce_inplace(tensor: Tensor, collective: Any) -> Tensor:
+    return collective.all_reduce(tensor, out=tensor)
+
+
+class _DeterministicFFNFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        rmsnorm_output: Tensor,
+        gate_weight: Tensor,
+        up_weight: Tensor,
+        down_weight: Tensor,
+        tp_group: Any,
+        cp_group: Any,
+        sequence_parallel: bool,
+    ) -> Tensor:
+        tp_dist = _require_parallel_group(tp_group, "tensor")
+        _require_parallel_group(cp_group, "context")
+        if sequence_parallel and tp_dist is None:
+            raise ValueError("sequence_parallel requires a tensor-parallel group.")
+
+        input_shape = rmsnorm_output.shape
+        rmsnorm_output_2d = rmsnorm_output.reshape(-1, input_shape[-1]).contiguous()
+        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()
+        min_size_bytes = max(
+            gemm_tokens * rmsnorm_output_2d.size(1) * element_size,
+            gemm_tokens * gate_weight.size(0) * element_size,
+            gate_weight.numel() * element_size,
+            up_weight.numel() * element_size,
+            down_weight.numel() * element_size,
+        )
+        # Create TP before CP so every rank follows the same group order.
+        tp_collective = _collective_for_group(tp_group, min_size_bytes=min_size_bytes)
+        cp_collective = _collective_for_group(cp_group, min_size_bytes=min_size_bytes)
+
+        if sequence_parallel:
+            rmsnorm_output_2d = _all_gather_tokens(rmsnorm_output_2d, tp_collective)
+
+        # Model weights use HuggingFace's [out, in] layout; GEMM consumes [K, N].
+        # Each native call returns BF16 before the next stage, matching PR #321.
+        gate = _C.det_gemm_fwd(rmsnorm_output_2d, gate_weight.t().contiguous())
+        up = _C.det_gemm_fwd(rmsnorm_output_2d, up_weight.t().contiguous())
+        activated = _C.swiglu_forward(gate, up)
+        output = _C.det_gemm_fwd(activated, down_weight.t().contiguous())
+
+        if sequence_parallel:
+            output = _reduce_scatter_tokens(output, tp_collective)
+        elif tp_collective is not None:
+            output = _all_reduce_inplace(output, tp_collective)
+
+        ctx.save_for_backward(
+            rmsnorm_output_2d,
+            gate,
+            up,
+            activated,
+            gate_weight,
+            up_weight,
+            down_weight,
+        )
+        ctx.input_shape = input_shape
+        ctx.tp_collective = tp_collective
+        ctx.cp_collective = cp_collective
+        ctx.sequence_parallel = sequence_parallel
+        return output.reshape(*input_shape[:-1], output.size(-1))
+
+    @staticmethod
+    def backward(ctx, grad_output: Tensor):
+        (
+            rmsnorm_output,
+            gate,
+            up,
+            activated,
+            gate_weight,
+            up_weight,
+            down_weight,
+        ) = ctx.saved_tensors
+        tp_collective = ctx.tp_collective
+        cp_collective = ctx.cp_collective
+        grad_output = grad_output.reshape(-1, grad_output.size(-1)).contiguous()
+        if ctx.sequence_parallel:
+            grad_output = _all_gather_tokens(grad_output, tp_collective)
+
+        # Down weight gradients must see every CP token so det_gemm_db's
+        # reduction tree matches CP=1. Local dW plus AllReduce would use a
+        # different parenthesization at many token counts.
+        if cp_collective is not None:
+            activated_full = _all_gather_tokens(activated, cp_collective)
+            grad_output_full = _all_gather_tokens(grad_output, cp_collective)
+            grad_down_weight = _C.det_gemm_db(activated_full, grad_output_full).t().contiguous()
+        else:
+            grad_down_weight = _C.det_gemm_db(activated, grad_output).t().contiguous()
+
+        # Down input-gradient shards concatenate across TP; no TP reduction.
+        grad_activated = _C.det_gemm_fwd(grad_output, down_weight)
+        grad_gate, grad_up = _C.swiglu_backward(grad_activated, gate, up)
+
+        if cp_collective is not None:
+            rmsnorm_full = _all_gather_tokens(rmsnorm_output, cp_collective)
+            grad_gate_full = _all_gather_tokens(grad_gate, cp_collective)
+            grad_up_full = _all_gather_tokens(grad_up, cp_collective)
+            grad_gate_weight = _C.det_gemm_db(rmsnorm_full, grad_gate_full).t().contiguous()
+            grad_up_weight = _C.det_gemm_db(rmsnorm_full, grad_up_full).t().contiguous()
+        else:
+            grad_gate_weight = _C.det_gemm_db(rmsnorm_output, grad_gate).t().contiguous()
+            grad_up_weight = _C.det_gemm_db(rmsnorm_output, grad_up).t().contiguous()
+
+        # Gate/Up input gradients reduce across TP, then add locally.
+        grad_rmsnorm_from_gate = _C.det_gemm_fwd(grad_gate, gate_weight)
+        if ctx.sequence_parallel:
+            grad_rmsnorm_from_gate = _reduce_scatter_tokens(
+                grad_rmsnorm_from_gate,
+                tp_collective,
+            )
+        elif tp_collective is not None:
+            grad_rmsnorm_from_gate = _all_reduce_inplace(
+                grad_rmsnorm_from_gate,
+                tp_collective,
+            )
+
+        grad_rmsnorm_from_up = _C.det_gemm_fwd(grad_up, up_weight)
+        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,
+            )
+
+        grad_rmsnorm_output = grad_rmsnorm_from_gate.add_(grad_rmsnorm_from_up)
+        return (
+            grad_rmsnorm_output.reshape(ctx.input_shape),
+            grad_gate_weight,
+            grad_up_weight,
+            grad_down_weight,
+            None,
+            None,
+            None,
+        )
+
+
+def qwen3_ffn(
+    rmsnorm_output: Tensor,
+    gate_weight: Tensor,
+    up_weight: Tensor,
+    down_weight: Tensor,
+    *,
+    tp_group: Any = None,
+    cp_group: Any = None,
+    sequence_parallel: bool = False,
+) -> Tensor:
+    """Apply a bias-free SiLU-gated FFN with deterministic backward kernels.
+
+    Args:
+        rmsnorm_output: RMSNorm output, shape ``[..., H]``.
+        gate_weight: Gate projection in HuggingFace ``[out, in]`` layout,
+            shape ``[I_local, H]``.
+        up_weight: Up projection in HuggingFace ``[out, in]`` layout, shape
+            ``[I_local, H]``.
+        down_weight: Down projection in HuggingFace ``[out, in]`` layout,
+            shape ``[H, I_local]``.
+        tp_group: Optional tensor-parallel process group. Gate and Up are
+            column-parallel; Down is row-parallel. The platform factory uses
+            deterministic fixed-tree collectives instead of backend reduction
+            arithmetic. Projection shards must be equal contiguous
+            intermediate-dimension ranges in process-group rank order. For
+            TP=1-equivalent bit patterns, every local range must be a complete
+            subtree of the canonical GEMM reduction tree.
+        cp_group: Optional context-parallel process group. Each rank owns
+            different token rows and the same local weight shards. Backward
+            AllGathers CP tokens before running full-token ``det_gemm_db`` so
+            weight gradients match CP=1 bitwise. Token shards must be equal
+            contiguous flattened-token ranges in process-group rank order.
+        sequence_parallel: Whether ``rmsnorm_output`` and the returned output
+            are equal rank-ordered shards of the flattened token dimension
+            across ``tp_group``.
+
+    Returns:
+        FFN output with shape ``[..., H]`` and dtype ``torch.bfloat16``.
+    """
+    _validate_ffn_inputs(rmsnorm_output, gate_weight, up_weight, down_weight)
+    _require_ffn_kernels()
+    if not isinstance(sequence_parallel, bool):
+        raise TypeError("sequence_parallel must be a bool.")
+    return _DeterministicFFNFunction.apply(
+        rmsnorm_output,
+        gate_weight,
+        up_weight,
+        down_weight,
+        tp_group,
+        cp_group,
+        sequence_parallel,
+    )

From f7dc7a9a5dadde6a0d7bacdd33b1fcc57cb8a8f3 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Thu, 20 Aug 2026 14:26:38 +0000
Subject: [PATCH 04/28] test(ffn): cover staged BF16 FFN contract

---
 tests/test_qwen_ffn.py | 364 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 364 insertions(+)
 create mode 100644 tests/test_qwen_ffn.py

diff --git a/tests/test_qwen_ffn.py b/tests/test_qwen_ffn.py
new file mode 100644
index 00000000..e0c4a18f
--- /dev/null
+++ b/tests/test_qwen_ffn.py
@@ -0,0 +1,364 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""First-stage tests for the CUDA/ROCm deterministic Qwen3 FFN.
+
+The CPU tests replace the native extension with a small PyTorch stub.  This
+keeps the public API, HuggingFace weight layout, autograd wiring, and BF16
+stage-boundary contract testable on build hosts without a GPU.  GPU checks are
+single-device only; distributed topology equivalence belongs to the later
+collective acceptance phase.
+"""
+
+from __future__ import annotations
+
+import inspect
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+import rl_engine.kernels.ops.pytorch.ffn.ffn as ffn_module
+from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
+from rl_engine.kernels.ops.pytorch.ffn import qwen3_ffn as public_qwen3_ffn
+from rl_engine.kernels.ops.pytorch.ffn.ffn import (
+    QWEN3_8B_HIDDEN_SIZE,
+    QWEN3_8B_INTERMEDIATE_SIZE,
+    qwen3_ffn,
+)
+from rl_engine.platforms.device import device_ctx
+
+_REQUIRED_SYMBOLS = (
+    "det_gemm_fwd",
+    "det_gemm_db",
+    "swiglu_forward",
+    "swiglu_backward",
+)
+_IS_ROCM = getattr(torch.version, "hip", None) is not None
+_IS_GPU = torch.cuda.is_available() and device_ctx.device.type == "cuda"
+_HAS_GPU_FFN = (
+    _IS_GPU
+    and _EXT_AVAILABLE
+    and _C is not None
+    and all(hasattr(_C, name) for name in _REQUIRED_SYMBOLS)
+)
+_GPU_NAME = "ROCm" if _IS_ROCM else "CUDA"
+
+requires_gpu_ffn = pytest.mark.skipif(
+    not _HAS_GPU_FFN,
+    reason=f"{_GPU_NAME} FFN requires a GPU and the GEMM/SwiGLU extension symbols",
+)
+
+
+def _randn(shape, *, seed: int, dtype=torch.float32, device="cpu"):
+    generator = torch.Generator(device="cpu").manual_seed(seed)
+    value = torch.randn(*shape, generator=generator, dtype=torch.float32) * 0.1
+    return value.to(device=device, dtype=dtype)
+
+
+def _reference(hidden_states, gate_weight, up_weight, down_weight):
+    gate = hidden_states @ gate_weight.t()
+    up = hidden_states @ up_weight.t()
+    return (F.silu(gate) * up) @ down_weight.t()
+
+
+class _TorchKernelStub:
+    """Extension-shaped FP32 stub used to check custom autograd algebra."""
+
+    def __init__(self) -> None:
+        self.calls: list[str] = []
+
+    def det_gemm_fwd(self, a, b):
+        self.calls.append("det_gemm_fwd")
+        return a @ b
+
+    def det_gemm_db(self, a, grad_output):
+        self.calls.append("det_gemm_db")
+        return a.t().contiguous() @ grad_output
+
+    def swiglu_forward(self, gate, up):
+        self.calls.append("swiglu_forward")
+        return F.silu(gate) * up
+
+    def swiglu_backward(self, grad_output, gate, up):
+        self.calls.append("swiglu_backward")
+        sigmoid = torch.sigmoid(gate)
+        grad_gate = grad_output * up * sigmoid * (1.0 + gate * (1.0 - sigmoid))
+        grad_up = grad_output * F.silu(gate)
+        return grad_gate, grad_up
+
+
+class _BF16BoundaryStub(_TorchKernelStub):
+    """Model native ops that accumulate in FP32 and write BF16 per stage."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.input_dtypes: list[tuple[str, tuple[torch.dtype, ...]]] = []
+
+    def _record(self, name, *values) -> None:
+        self.calls.append(name)
+        self.input_dtypes.append((name, tuple(value.dtype for value in values)))
+
+    def det_gemm_fwd(self, a, b):
+        self._record("det_gemm_fwd", a, b)
+        return (a.float() @ b.float()).to(torch.bfloat16)
+
+    def det_gemm_db(self, a, grad_output):
+        self._record("det_gemm_db", a, grad_output)
+        return (a.float().t().contiguous() @ grad_output.float()).to(torch.bfloat16)
+
+    def swiglu_forward(self, gate, up):
+        self._record("swiglu_forward", gate, up)
+        return (F.silu(gate.float()) * up.float()).to(torch.bfloat16)
+
+    def swiglu_backward(self, grad_output, gate, up):
+        self._record("swiglu_backward", grad_output, gate, up)
+        gate_fp32 = gate.float()
+        sigmoid = torch.sigmoid(gate_fp32)
+        grad_gate = grad_output.float() * up.float() * sigmoid * (1.0 + gate_fp32 * (1.0 - sigmoid))
+        grad_up = grad_output.float() * F.silu(gate_fp32)
+        return grad_gate.to(torch.bfloat16), grad_up.to(torch.bfloat16)
+
+
+class _ValidationTensor:
+    """Tensor-shaped object for device-independent validation-order tests."""
+
+    def __init__(self, shape, dtype=torch.bfloat16) -> None:
+        self.shape = torch.Size(shape)
+        self.dtype = dtype
+        self.device = torch.device("cuda")
+        self.is_cuda = True
+
+    def dim(self):
+        return len(self.shape)
+
+    def numel(self):
+        result = 1
+        for size in self.shape:
+            result *= size
+        return result
+
+    def size(self, dim):
+        return self.shape[dim]
+
+
+def _install_stub(monkeypatch, stub) -> None:
+    monkeypatch.setattr(ffn_module, "_C", stub)
+    monkeypatch.setattr(ffn_module, "_EXT_AVAILABLE", True)
+    # Native input validation intentionally requires GPU tensors.  Stub tests
+    # exercise the public function on CPU after validation itself is tested.
+    monkeypatch.setattr(ffn_module, "_validate_ffn_inputs", lambda *args: None)
+
+
+def test_qwen3_ffn_public_api_and_model_dimensions_are_pinned():
+    assert public_qwen3_ffn is qwen3_ffn
+    assert QWEN3_8B_HIDDEN_SIZE == 4096
+    assert QWEN3_8B_INTERMEDIATE_SIZE == 12288
+
+    signature = inspect.signature(qwen3_ffn)
+    assert tuple(signature.parameters) == (
+        "rmsnorm_output",
+        "gate_weight",
+        "up_weight",
+        "down_weight",
+        "tp_group",
+        "cp_group",
+        "sequence_parallel",
+    )
+    assert signature.parameters["tp_group"].kind is inspect.Parameter.KEYWORD_ONLY
+    assert signature.parameters["cp_group"].kind is inspect.Parameter.KEYWORD_ONLY
+    assert signature.parameters["sequence_parallel"].default is False
+
+
+def test_qwen3_ffn_stub_shape_layout_and_backward_match_autograd(monkeypatch):
+    stub = _TorchKernelStub()
+    _install_stub(monkeypatch, stub)
+
+    hidden = _randn((2, 3, 8), seed=0)
+    gate_weight = _randn((12, 8), seed=1)
+    up_weight = _randn((12, 8), seed=2)
+    down_weight = _randn((8, 12), seed=3)
+    grad_output = _randn(hidden.shape, seed=4)
+
+    reference_inputs = [
+        value.detach().clone().requires_grad_(True)
+        for value in (hidden, gate_weight, up_weight, down_weight)
+    ]
+    expected = _reference(*reference_inputs)
+    expected.backward(grad_output)
+
+    actual_inputs = [
+        value.detach().clone().requires_grad_(True)
+        for value in (hidden, gate_weight, up_weight, down_weight)
+    ]
+    actual = qwen3_ffn(*actual_inputs)
+    actual.backward(grad_output)
+
+    assert actual.shape == hidden.shape
+    torch.testing.assert_close(actual, expected.detach())
+    for actual_input, reference_input in zip(actual_inputs, reference_inputs, strict=True):
+        torch.testing.assert_close(actual_input.grad, reference_input.grad)
+
+    assert stub.calls.count("det_gemm_fwd") == 6
+    assert stub.calls.count("det_gemm_db") == 3
+    assert stub.calls.count("swiglu_forward") == 1
+    assert stub.calls.count("swiglu_backward") == 1
+
+
+def test_qwen3_ffn_preserves_bf16_at_every_native_stage(monkeypatch):
+    stub = _BF16BoundaryStub()
+    _install_stub(monkeypatch, stub)
+
+    hidden = _randn((2, 3, 8), seed=10, dtype=torch.bfloat16).requires_grad_(True)
+    gate_weight = _randn((12, 8), seed=11, dtype=torch.bfloat16).requires_grad_(True)
+    up_weight = _randn((12, 8), seed=12, dtype=torch.bfloat16).requires_grad_(True)
+    down_weight = _randn((8, 12), seed=13, dtype=torch.bfloat16).requires_grad_(True)
+    grad_output = _randn(hidden.shape, seed=14, dtype=torch.bfloat16)
+
+    actual = qwen3_ffn(hidden, gate_weight, up_weight, down_weight)
+    actual.backward(grad_output)
+
+    gate = (hidden.detach().float() @ gate_weight.detach().float().t()).to(torch.bfloat16)
+    up = (hidden.detach().float() @ up_weight.detach().float().t()).to(torch.bfloat16)
+    activated = (F.silu(gate.float()) * up.float()).to(torch.bfloat16)
+    expected = (activated.float() @ down_weight.detach().float().t()).to(torch.bfloat16)
+
+    assert actual.dtype is torch.bfloat16
+    assert torch.equal(actual.detach(), expected)
+    assert all(
+        dtype is torch.bfloat16 for _, input_dtypes in stub.input_dtypes for dtype in input_dtypes
+    )
+    for tensor in (hidden, gate_weight, up_weight, down_weight):
+        assert tensor.grad is not None
+        assert tensor.grad.dtype is torch.bfloat16
+
+
+@pytest.mark.parametrize("weight_name", ["gate_weight", "up_weight", "down_weight"])
+def test_qwen3_ffn_rejects_non_huggingface_weight_layout(weight_name):
+    tensors = {
+        "rmsnorm_output": torch.empty((2, 8), dtype=torch.bfloat16),
+        "gate_weight": torch.empty((12, 8), dtype=torch.bfloat16),
+        "up_weight": torch.empty((12, 8), dtype=torch.bfloat16),
+        "down_weight": torch.empty((8, 12), dtype=torch.bfloat16),
+    }
+    tensors[weight_name] = tensors[weight_name].t().contiguous()
+
+    with pytest.raises(ValueError, match=rf"{weight_name} must have shape"):
+        qwen3_ffn(**tensors)
+
+
+def test_qwen3_ffn_rejects_zero_intermediate_size_before_device_dispatch():
+    tensors = {
+        "rmsnorm_output": torch.empty((2, 8), dtype=torch.bfloat16),
+        "gate_weight": torch.empty((0, 8), dtype=torch.bfloat16),
+        "up_weight": torch.empty((0, 8), dtype=torch.bfloat16),
+        "down_weight": torch.empty((8, 0), dtype=torch.bfloat16),
+    }
+
+    with pytest.raises(ValueError, match="intermediate size must be positive"):
+        qwen3_ffn(**tensors)
+
+
+@pytest.mark.parametrize(
+    "input_name",
+    ("rmsnorm_output", "gate_weight", "up_weight", "down_weight"),
+)
+def test_qwen3_ffn_rejects_non_bf16_inputs_before_device_dispatch(input_name, monkeypatch):
+    monkeypatch.setattr(ffn_module, "Tensor", _ValidationTensor)
+    tensors = {
+        "rmsnorm_output": _ValidationTensor((2, 8)),
+        "gate_weight": _ValidationTensor((12, 8)),
+        "up_weight": _ValidationTensor((12, 8)),
+        "down_weight": _ValidationTensor((8, 12)),
+    }
+    tensors[input_name].dtype = torch.float32
+
+    with pytest.raises(TypeError, match=rf"{input_name} must have dtype bfloat16"):
+        ffn_module._validate_ffn_inputs(**tensors)
+
+
+def test_qwen3_ffn_sequence_parallel_flag_must_be_bool(monkeypatch):
+    _install_stub(monkeypatch, _TorchKernelStub())
+    tensors = (
+        torch.empty((2, 8)),
+        torch.empty((12, 8)),
+        torch.empty((12, 8)),
+        torch.empty((8, 12)),
+    )
+
+    with pytest.raises(TypeError, match="sequence_parallel must be a bool"):
+        qwen3_ffn(*tensors, sequence_parallel=1)
+
+
+@requires_gpu_ffn
+def test_qwen3_ffn_gpu_forward_backward_matches_fp32_reference():
+    device = device_ctx.device
+    hidden = _randn((2, 3, 32), seed=20, dtype=torch.bfloat16, device=device)
+    gate_weight = _randn((64, 32), seed=21, dtype=torch.bfloat16, device=device)
+    up_weight = _randn((64, 32), seed=22, dtype=torch.bfloat16, device=device)
+    down_weight = _randn((32, 64), seed=23, dtype=torch.bfloat16, device=device)
+    grad_output = _randn(hidden.shape, seed=24, dtype=torch.bfloat16, device=device)
+
+    reference_inputs = [
+        value.detach().cpu().float().requires_grad_(True)
+        for value in (hidden, gate_weight, up_weight, down_weight)
+    ]
+    expected = _reference(*reference_inputs)
+    expected.backward(grad_output.cpu().float())
+
+    actual_inputs = [
+        value.detach().clone().requires_grad_(True)
+        for value in (hidden, gate_weight, up_weight, down_weight)
+    ]
+    actual = qwen3_ffn(*actual_inputs)
+    actual.backward(grad_output)
+
+    assert actual.shape == hidden.shape
+    assert actual.dtype is torch.bfloat16
+    torch.testing.assert_close(actual.cpu().float(), expected.detach(), atol=5e-2, rtol=2e-2)
+    for actual_input, reference_input in zip(actual_inputs, reference_inputs, strict=True):
+        assert actual_input.grad.dtype is torch.bfloat16
+        torch.testing.assert_close(
+            actual_input.grad.cpu().float(),
+            reference_input.grad,
+            atol=5e-2,
+            rtol=2e-2,
+        )
+
+
+@requires_gpu_ffn
+def test_qwen3_ffn_gpu_repeat_is_bitwise_and_train_infer_match():
+    device = device_ctx.device
+    hidden = _randn((8, 32), seed=30, dtype=torch.bfloat16, device=device)
+    gate_weight = _randn((64, 32), seed=31, dtype=torch.bfloat16, device=device)
+    up_weight = _randn((64, 32), seed=32, dtype=torch.bfloat16, device=device)
+    down_weight = _randn((32, 64), seed=33, dtype=torch.bfloat16, device=device)
+
+    with torch.no_grad():
+        first = qwen3_ffn(hidden, gate_weight, up_weight, down_weight)
+        second = qwen3_ffn(hidden, gate_weight, up_weight, down_weight)
+    train_hidden = hidden.detach().clone().requires_grad_(True)
+    train = qwen3_ffn(train_hidden, gate_weight, up_weight, down_weight)
+
+    assert torch.equal(first, second)
+    assert torch.equal(first, train.detach())
+
+
+@requires_gpu_ffn
+def test_qwen3_ffn_gpu_output_and_hidden_gradient_are_token_slice_invariant():
+    device = device_ctx.device
+    hidden = _randn((6, 32), seed=40, dtype=torch.bfloat16, device=device)
+    gate_weight = _randn((64, 32), seed=41, dtype=torch.bfloat16, device=device)
+    up_weight = _randn((64, 32), seed=42, dtype=torch.bfloat16, device=device)
+    down_weight = _randn((32, 64), seed=43, dtype=torch.bfloat16, device=device)
+    grad_output = _randn(hidden.shape, seed=44, dtype=torch.bfloat16, device=device)
+
+    full_hidden = hidden.detach().clone().requires_grad_(True)
+    full_output = qwen3_ffn(full_hidden, gate_weight, up_weight, down_weight)
+    full_output.backward(grad_output)
+
+    slice_hidden = hidden[2:4].detach().clone().requires_grad_(True)
+    slice_output = qwen3_ffn(slice_hidden, gate_weight, up_weight, down_weight)
+    slice_output.backward(grad_output[2:4])
+
+    assert torch.equal(slice_output, full_output[2:4])
+    assert torch.equal(slice_hidden.grad, full_hidden.grad[2:4])

From 49fa1c53f0f6c36fbed1b5e48f0f2ef9f9a747f7 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Thu, 20 Aug 2026 14:28:49 +0000
Subject: [PATCH 05/28] test(ffn): cover TP CP and SP topologies

---
 tests/distributed/test_qwen_ffn_topology.py | 511 ++++++++++++++++++++
 1 file changed, 511 insertions(+)
 create mode 100644 tests/distributed/test_qwen_ffn_topology.py

diff --git a/tests/distributed/test_qwen_ffn_topology.py b/tests/distributed/test_qwen_ffn_topology.py
new file mode 100644
index 00000000..c68afb9e
--- /dev/null
+++ b/tests/distributed/test_qwen_ffn_topology.py
@@ -0,0 +1,511 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""Real multi-GPU topology checks for the deterministic Qwen3 FFN.
+
+PyTorch exposes both NCCL on NVIDIA and RCCL on AMD through the ``nccl``
+process-group backend.  The FFN's platform factory then selects CUDA IPC or
+RCCL AllGather transport without any test-side backend branching.
+"""
+
+from __future__ import annotations
+
+import os
+import queue
+import tempfile
+import traceback
+from datetime import timedelta
+from pathlib import Path
+from typing import Any
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+
+import rl_engine.kernels.ops.pytorch.ffn.ffn as ffn_module
+from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
+from rl_engine.kernels.ops.pytorch.ffn.ffn import (
+    QWEN3_8B_HIDDEN_SIZE,
+    QWEN3_8B_INTERMEDIATE_SIZE,
+    qwen3_ffn,
+)
+
+_REQUIRED_FFN_SYMBOLS = (
+    "det_gemm_fwd",
+    "det_gemm_db",
+    "swiglu_forward",
+    "swiglu_backward",
+)
+_REQUIRED_CUDA_COLLECTIVE_SYMBOLS = (
+    "deterministic_collective_ipc_meta",
+    "deterministic_collective_create",
+    "deterministic_collective_destroy",
+    "deterministic_collective_stage",
+    "deterministic_collective_all_reduce",
+    "deterministic_collective_reduce_scatter",
+    "deterministic_collective_all_gather",
+)
+_IS_ROCM = getattr(torch.version, "hip", None) is not None
+_EXTERNAL_WORLD_SIZE = int(os.environ.get("WORLD_SIZE", "1"))
+
+# I=512 keeps every TP=1/2/4/8 shard aligned to the 32-wide GEMM K-tree and
+# keeps the SM90 N dimension tile-aligned even at TP=8.
+_TOKENS = 32
+_HIDDEN = 64
+_INTERMEDIATE = 512
+
+_WORLD2_CONFIGS = (
+    ("tp2", 2, 1, False),
+    ("tp2_sp", 2, 1, True),
+)
+_WORLD4_CONFIGS = (
+    ("tp4", 4, 1, False),
+    ("tp2_cp2", 2, 2, False),
+    ("tp2_cp2_sp", 2, 2, True),
+)
+_WORLD8_CONFIGS = (("tp8", 8, 1, False),)
+
+pytestmark = pytest.mark.skipif(
+    _EXTERNAL_WORLD_SIZE != 1,
+    reason="topology tests own their local worker processes; run pytest directly",
+)
+
+
+def _has_topology_devices(count: int) -> bool:
+    if not (
+        torch.cuda.is_available()
+        and _EXT_AVAILABLE
+        and _C is not None
+        and torch.distributed.is_available()
+        and torch.distributed.is_nccl_available()
+        and torch.cuda.device_count() >= count
+        and all(hasattr(_C, name) for name in _REQUIRED_FFN_SYMBOLS)
+    ):
+        return False
+    # ROCm deliberately excludes the CUDA IPC source and uses the public RCCL
+    # transport collective.  NVIDIA still requires all legacy IPC symbols.
+    return _IS_ROCM or all(hasattr(_C, name) for name in _REQUIRED_CUDA_COLLECTIVE_SYMBOLS)
+
+
+def _has_qwen3_8b_capacity(count: int) -> bool:
+    if not _has_topology_devices(count):
+        return False
+    minimum_bytes = 8 * 1024**3
+    try:
+        return all(
+            torch.cuda.get_device_properties(index).total_memory >= minimum_bytes
+            for index in range(count)
+        )
+    except RuntimeError:
+        return False
+
+
+def _randn(
+    shape: tuple[int, ...],
+    *,
+    seed: int,
+    device: torch.device,
+) -> 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)
+
+
+def _make_inputs(
+    token_count: int,
+    hidden_size: int,
+    intermediate_size: int,
+    device: torch.device,
+    *,
+    seed: int,
+) -> tuple[torch.Tensor, ...]:
+    return (
+        _randn((token_count, hidden_size), seed=seed, device=device),
+        _randn((intermediate_size, hidden_size), seed=seed + 1, device=device),
+        _randn((intermediate_size, hidden_size), seed=seed + 2, device=device),
+        _randn((hidden_size, intermediate_size), seed=seed + 3, device=device),
+        _randn((token_count, hidden_size), seed=seed + 4, device=device),
+    )
+
+
+def _close_ffn_collectives() -> None:
+    for collective in list(ffn_module._COLLECTIVES.values()):
+        collective.close()
+    ffn_module._COLLECTIVES.clear()
+
+
+def _shard_ranges(
+    rank: int,
+    *,
+    tp_size: int,
+    cp_size: int,
+    sequence_parallel: bool,
+    token_count: int,
+    intermediate_size: int,
+) -> tuple[int, int, int, int]:
+    if tp_size * cp_size <= rank:
+        raise ValueError("rank lies outside the requested TP/CP mesh")
+    if token_count % cp_size:
+        raise ValueError("token count must be divisible by CP size")
+    cp_tokens = token_count // cp_size
+    if sequence_parallel and cp_tokens % tp_size:
+        raise ValueError("each CP token shard must be divisible by TP size for SP")
+    if intermediate_size % tp_size:
+        raise ValueError("intermediate size must be divisible by TP size")
+
+    tp_rank = rank % tp_size
+    cp_rank = rank // tp_size
+    local_tokens = cp_tokens // tp_size if sequence_parallel else cp_tokens
+    token_start = cp_rank * cp_tokens
+    if sequence_parallel:
+        token_start += tp_rank * local_tokens
+    token_end = token_start + local_tokens
+
+    local_intermediate = intermediate_size // tp_size
+    feature_start = tp_rank * local_intermediate
+    feature_end = feature_start + local_intermediate
+    return token_start, token_end, feature_start, feature_end
+
+
+def _canonical(
+    hidden: torch.Tensor,
+    gate: torch.Tensor,
+    up: torch.Tensor,
+    down: torch.Tensor,
+    grad_output: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor, list[torch.Tensor]]:
+    with torch.no_grad():
+        inference = qwen3_ffn(hidden, gate, up, down)
+    inputs = [value.detach().clone().requires_grad_(True) for value in (hidden, gate, up, down)]
+    training = qwen3_ffn(*inputs)
+    training.backward(grad_output)
+    assert torch.equal(inference, training.detach()), "TP=1 train/infer forward mismatch"
+    return inference, training, inputs
+
+
+def _mesh_groups(
+    dist: Any,
+    tp_size: int,
+    cp_size: int,
+) -> tuple[list[Any], list[Any]]:
+    world_size = dist.get_world_size()
+    if tp_size * cp_size != world_size:
+        raise ValueError("TP size times CP size must equal the process-group world size")
+    if tp_size == world_size and cp_size == 1:
+        return [dist.group.WORLD], []
+    if cp_size == world_size and tp_size == 1:
+        return [], [dist.group.WORLD]
+
+    tp_groups = []
+    if tp_size > 1:
+        for cp_rank in range(cp_size):
+            ranks = list(range(cp_rank * tp_size, (cp_rank + 1) * tp_size))
+            tp_groups.append(dist.new_group(ranks=ranks))
+
+    cp_groups = []
+    if cp_size > 1:
+        for tp_rank in range(tp_size):
+            ranks = [cp_rank * tp_size + tp_rank for cp_rank in range(cp_size)]
+            cp_groups.append(dist.new_group(ranks=ranks))
+    return tp_groups, cp_groups
+
+
+def _run_topology(
+    rank: int,
+    dist: Any,
+    meshes: dict[tuple[int, int], tuple[list[Any], list[Any]]],
+    *,
+    name: str,
+    tp_size: int,
+    cp_size: int,
+    sequence_parallel: bool,
+    hidden: torch.Tensor,
+    gate: torch.Tensor,
+    up: torch.Tensor,
+    down: torch.Tensor,
+    grad_output: torch.Tensor,
+    inference_reference: torch.Tensor,
+    training_reference: torch.Tensor,
+    reference_inputs: list[torch.Tensor],
+) -> None:
+    mesh_key = (tp_size, cp_size)
+    if mesh_key not in meshes:
+        meshes[mesh_key] = _mesh_groups(dist, tp_size, cp_size)
+    tp_groups, cp_groups = meshes[mesh_key]
+
+    tp_rank = rank % tp_size
+    cp_rank = rank // tp_size
+    tp_group = tp_groups[cp_rank] if tp_size > 1 else None
+    cp_group = cp_groups[tp_rank] if cp_size > 1 else None
+    token_start, token_end, feature_start, feature_end = _shard_ranges(
+        rank,
+        tp_size=tp_size,
+        cp_size=cp_size,
+        sequence_parallel=sequence_parallel,
+        token_count=hidden.size(0),
+        intermediate_size=gate.size(0),
+    )
+    shard = (
+        hidden[token_start:token_end].contiguous(),
+        gate[feature_start:feature_end].contiguous(),
+        up[feature_start:feature_end].contiguous(),
+        down[:, feature_start:feature_end].contiguous(),
+    )
+
+    with torch.no_grad():
+        inference = qwen3_ffn(
+            *shard,
+            tp_group=tp_group,
+            cp_group=cp_group,
+            sequence_parallel=sequence_parallel,
+        )
+    inputs = [value.detach().clone().requires_grad_(True) for value in shard]
+    training = qwen3_ffn(
+        *inputs,
+        tp_group=tp_group,
+        cp_group=cp_group,
+        sequence_parallel=sequence_parallel,
+    )
+    training.backward(grad_output[token_start:token_end].contiguous())
+
+    expected_output = inference_reference[token_start:token_end]
+    assert torch.equal(inference, training.detach()), f"{name}: train/infer mismatch"
+    assert torch.equal(inference, expected_output), f"{name}: inference mismatch vs TP=1"
+    assert torch.equal(training.detach(), training_reference.detach()[token_start:token_end]), (
+        f"{name}: training forward mismatch vs TP=1"
+    )
+    assert torch.equal(inputs[0].grad, reference_inputs[0].grad[token_start:token_end]), (
+        f"{name}: hidden grad mismatch vs TP=1"
+    )
+
+    expected_weight_grads = (
+        (inputs[1].grad, reference_inputs[1].grad[feature_start:feature_end], "gate"),
+        (inputs[2].grad, reference_inputs[2].grad[feature_start:feature_end], "up"),
+        (
+            inputs[3].grad,
+            reference_inputs[3].grad[:, feature_start:feature_end],
+            "down",
+        ),
+    )
+    for actual, expected, label in expected_weight_grads:
+        assert torch.equal(actual, expected), f"{name}: {label} weight grad mismatch vs TP=1"
+
+
+def _topology_worker(
+    rank: int,
+    world_size: int,
+    init_method: str,
+    result_queue: Any,
+    configs: tuple[tuple[str, int, int, bool], ...],
+) -> None:
+    try:
+        import torch.distributed as dist
+
+        torch.cuda.set_device(rank)
+        dist.init_process_group(
+            backend="nccl",
+            init_method=init_method,
+            rank=rank,
+            world_size=world_size,
+            timeout=timedelta(minutes=5),
+        )
+        device = torch.device("cuda", rank)
+        hidden, gate, up, down, grad_output = _make_inputs(
+            _TOKENS,
+            _HIDDEN,
+            _INTERMEDIATE,
+            device,
+            seed=600,
+        )
+        inference_reference, training_reference, reference_inputs = _canonical(
+            hidden,
+            gate,
+            up,
+            down,
+            grad_output,
+        )
+        meshes: dict[tuple[int, int], tuple[list[Any], list[Any]]] = {}
+        for name, tp_size, cp_size, sequence_parallel in configs:
+            _run_topology(
+                rank,
+                dist,
+                meshes,
+                name=name,
+                tp_size=tp_size,
+                cp_size=cp_size,
+                sequence_parallel=sequence_parallel,
+                hidden=hidden,
+                gate=gate,
+                up=up,
+                down=down,
+                grad_output=grad_output,
+                inference_reference=inference_reference,
+                training_reference=training_reference,
+                reference_inputs=reference_inputs,
+            )
+        result_queue.put({"ok": True, "rank": rank})
+    except Exception:  # pragma: no cover - forwarded to the parent process.
+        result_queue.put({"ok": False, "rank": rank, "traceback": traceback.format_exc()})
+        raise
+    finally:
+        _close_ffn_collectives()
+        if torch.distributed.is_available() and torch.distributed.is_initialized():
+            torch.distributed.destroy_process_group()
+
+
+def _qwen3_8b_tp2_worker(
+    rank: int,
+    world_size: int,
+    init_method: str,
+    result_queue: Any,
+) -> None:
+    try:
+        import torch.distributed as dist
+
+        torch.cuda.set_device(rank)
+        dist.init_process_group(
+            backend="nccl",
+            init_method=init_method,
+            rank=rank,
+            world_size=world_size,
+            timeout=timedelta(minutes=10),
+        )
+        device = torch.device("cuda", rank)
+        hidden, gate, up, down, grad_output = _make_inputs(
+            2,
+            QWEN3_8B_HIDDEN_SIZE,
+            QWEN3_8B_INTERMEDIATE_SIZE,
+            device,
+            seed=800,
+        )
+        inference_reference, training_reference, reference_inputs = _canonical(
+            hidden,
+            gate,
+            up,
+            down,
+            grad_output,
+        )
+        _run_topology(
+            rank,
+            dist,
+            {},
+            name="qwen3_8b_tp2",
+            tp_size=2,
+            cp_size=1,
+            sequence_parallel=False,
+            hidden=hidden,
+            gate=gate,
+            up=up,
+            down=down,
+            grad_output=grad_output,
+            inference_reference=inference_reference,
+            training_reference=training_reference,
+            reference_inputs=reference_inputs,
+        )
+        result_queue.put({"ok": True, "rank": rank})
+    except Exception:  # pragma: no cover - forwarded to the parent process.
+        result_queue.put({"ok": False, "rank": rank, "traceback": traceback.format_exc()})
+        raise
+    finally:
+        _close_ffn_collectives()
+        if torch.distributed.is_available() and torch.distributed.is_initialized():
+            torch.distributed.destroy_process_group()
+
+
+def _spawn_workers(
+    worker: Any,
+    world_size: int,
+    worker_args: tuple[Any, ...] = (),
+    *,
+    timeout_seconds: int,
+) -> None:
+    if not _has_topology_devices(world_size):
+        platform = "ROCm/RCCL" if _IS_ROCM else "CUDA/NCCL"
+        pytest.skip(
+            f"requires {world_size} {platform} GPUs plus FFN and deterministic collective support"
+        )
+
+    # Single-GPU tests may have populated the parent process's caching
+    # allocator on device 0.  Release unused blocks before spawned rank 0 owns
+    # that device, which matters for the Qwen3-8B smoke case.
+    torch.cuda.empty_cache()
+    ctx = mp.get_context("spawn")
+    with tempfile.TemporaryDirectory() as temporary_directory:
+        init_method = (Path(temporary_directory) / "nccl_init").as_uri()
+        result_queue = ctx.Queue()
+        processes = [
+            ctx.Process(
+                target=worker,
+                args=(rank, world_size, init_method, result_queue, *worker_args),
+            )
+            for rank in range(world_size)
+        ]
+        for process in processes:
+            process.start()
+
+        results = []
+        try:
+            for _ in processes:
+                result = result_queue.get(timeout=timeout_seconds)
+                results.append(result)
+                if not result["ok"]:
+                    for process in processes:
+                        if process.is_alive():
+                            process.terminate()
+                    break
+        except queue.Empty:
+            for process in processes:
+                if process.is_alive():
+                    process.terminate()
+            pytest.fail(f"timed out waiting for {world_size} FFN topology workers")
+        finally:
+            for process in processes:
+                process.join(timeout=10)
+                if process.is_alive():
+                    process.terminate()
+                    process.join(timeout=10)
+            result_queue.close()
+            result_queue.join_thread()
+
+    for result in sorted(results, key=lambda item: item["rank"]):
+        assert result["ok"], result.get("traceback")
+    for process in processes:
+        assert process.exitcode == 0
+
+
+def test_qwen3_ffn_tp2_and_tp_sp_match_tp1_bitwise() -> None:
+    _spawn_workers(
+        _topology_worker,
+        2,
+        (_WORLD2_CONFIGS,),
+        timeout_seconds=180,
+    )
+
+
+def test_qwen3_ffn_tp4_tp_cp_and_tp_cp_sp_match_tp1_bitwise() -> None:
+    _spawn_workers(
+        _topology_worker,
+        4,
+        (_WORLD4_CONFIGS,),
+        timeout_seconds=240,
+    )
+
+
+def test_qwen3_ffn_tp8_matches_tp1_bitwise() -> None:
+    _spawn_workers(
+        _topology_worker,
+        8,
+        (_WORLD8_CONFIGS,),
+        timeout_seconds=360,
+    )
+
+
+def test_qwen3_8b_ffn_tp2_smoke_matches_tp1_bitwise() -> None:
+    if os.environ.get("RL_KERNEL_SKIP_QWEN3_8B_TOPOLOGY") == "1":
+        pytest.skip("Qwen3-8B topology smoke disabled by environment")
+    if not _has_qwen3_8b_capacity(2):
+        pytest.skip("Qwen3-8B TP=2 smoke requires two GPUs with at least 8 GiB each")
+    _spawn_workers(
+        _qwen3_8b_tp2_worker,
+        2,
+        timeout_seconds=600,
+    )

From 7d04efe80f7903431bc75fff3bbbe85eb82e02c4 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Fri, 21 Aug 2026 14:57:38 +0000
Subject: [PATCH 06/28] test(ffn): allow ROCm RCCL teardown to complete

---
 tests/distributed/test_qwen_ffn_topology.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/tests/distributed/test_qwen_ffn_topology.py b/tests/distributed/test_qwen_ffn_topology.py
index c68afb9e..44de9041 100644
--- a/tests/distributed/test_qwen_ffn_topology.py
+++ b/tests/distributed/test_qwen_ffn_topology.py
@@ -459,10 +459,15 @@ def _spawn_workers(
             pytest.fail(f"timed out waiting for {world_size} FFN topology workers")
         finally:
             for process in processes:
-                process.join(timeout=10)
+                # RCCL teardown can take longer than ten seconds after every
+                # worker has already reported a successful result, especially
+                # for the eight-rank topology on ROCm hosts with unusable RDMA
+                # interfaces.  Allow cleanup to finish instead of turning a
+                # successful numerical check into a SIGTERM false failure.
+                process.join(timeout=30)
                 if process.is_alive():
                     process.terminate()
-                    process.join(timeout=10)
+                    process.join(timeout=30)
             result_queue.close()
             result_queue.join_thread()
 

From 2f3f61d910677842be118f95bbab6cfbcf75f0f1 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Fri, 21 Aug 2026 16:09:04 +0000
Subject: [PATCH 07/28] perf: benchmark deterministic FFN on ROCm

---
 benchmarks/benchmark_rocm_ffn.py              | 1712 +++++++++++++++
 .../pr325_rocm_mi300x/accuracy_tradeoff.png   |  Bin 0 -> 93854 bytes
 .../pr325_rocm_mi300x/collective_overhead.png |  Bin 0 -> 168873 bytes
 .../distributed_ffn_overhead.png              |  Bin 0 -> 92815 bytes
 .../results/pr325_rocm_mi300x/report.md       |  147 ++
 .../results/pr325_rocm_mi300x/results.json    | 1859 +++++++++++++++++
 .../pr325_rocm_mi300x/single_gpu_overhead.png |  Bin 0 -> 213116 bytes
 7 files changed, 3718 insertions(+)
 create mode 100644 benchmarks/benchmark_rocm_ffn.py
 create mode 100644 benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png
 create mode 100644 benchmarks/results/pr325_rocm_mi300x/collective_overhead.png
 create mode 100644 benchmarks/results/pr325_rocm_mi300x/distributed_ffn_overhead.png
 create mode 100644 benchmarks/results/pr325_rocm_mi300x/report.md
 create mode 100644 benchmarks/results/pr325_rocm_mi300x/results.json
 create mode 100644 benchmarks/results/pr325_rocm_mi300x/single_gpu_overhead.png

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
new file mode 100644
index 00000000..8cc3f895
--- /dev/null
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -0,0 +1,1712 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""ROCm performance and accuracy analysis for the deterministic Qwen3 FFN.
+
+This benchmark compares the deterministic PR #325 path against native ROCm
+operators without loading a model:
+
+* deterministic GEMM vs ``torch.matmul`` (rocBLAS/hipBLASLt);
+* deterministic SwiGLU vs native PyTorch elementwise operators;
+* deterministic collectives vs native RCCL through ProcessGroupNCCL;
+* deterministic FFN vs a native ROCm/RCCL implementation with the same TP/CP/SP
+  sharding and collective schedule.
+
+The script writes raw JSON, Markdown tables, and PNG figures.  GPU events are
+used for single-GPU operators.  Distributed timings use synchronized wall
+clock samples and report the slowest rank for each iteration.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+import queue
+import statistics
+import tempfile
+import time
+import traceback
+from datetime import timedelta
+from pathlib import Path
+from typing import Any, Callable
+
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+import torch.nn.functional as F
+
+import rl_engine.kernels.ops.pytorch.ffn.ffn as ffn_module
+from rl_engine.distributed import RCCLDeterministicCollective
+from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
+from rl_engine.kernels.ops.cuda.matmul import deterministic_gemm
+from rl_engine.kernels.ops.pytorch.ffn import qwen3_ffn
+
+_MIB = 1024 * 1024
+_REQUIRED_SYMBOLS = (
+    "det_gemm_fwd",
+    "det_gemm_db",
+    "swiglu_forward",
+    "swiglu_backward",
+)
+_DISTRIBUTED_CONFIGS: dict[int, tuple[tuple[str, int, int, bool], ...]] = {
+    2: (
+        ("tp2", 2, 1, False),
+        ("tp2_sp", 2, 1, True),
+    ),
+    4: (
+        ("tp4", 4, 1, False),
+        ("tp2_cp2", 2, 2, False),
+        ("tp2_cp2_sp", 2, 2, True),
+    ),
+    8: (
+        ("tp8", 8, 1, False),
+        ("tp4_cp2", 4, 2, False),
+        ("tp4_cp2_sp", 4, 2, True),
+    ),
+}
+
+
+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().float()
+    expected_float = expected.detach().float()
+    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().float() - expected.detach().float()
+    return {
+        "max_abs": float(difference.abs().max().item()),
+        "mean_abs": float(difference.abs().mean().item()),
+        "relative_l2": _relative_l2(actual, expected),
+        "exact_fraction": float((actual.detach() == expected.detach()).float().mean().item()),
+    }
+
+
+def _randn(
+    shape: tuple[int, ...],
+    *,
+    seed: int,
+    device: torch.device,
+    scale: float = 0.02,
+) -> torch.Tensor:
+    generator = torch.Generator(device="cpu").manual_seed(seed)
+    value = torch.randn(shape, generator=generator, dtype=torch.float32) * scale
+    return value.to(device=device, dtype=torch.bfloat16)
+
+
+def _gpu_event_samples(
+    function: Callable[[], Any],
+    *,
+    warmup: int,
+    samples: int,
+) -> list[float]:
+    for _ in range(warmup):
+        function()
+    torch.cuda.synchronize()
+
+    events: list[tuple[torch.cuda.Event, torch.cuda.Event]] = []
+    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 _native_ffn(
+    hidden: torch.Tensor,
+    gate_weight: torch.Tensor,
+    up_weight: torch.Tensor,
+    down_weight: torch.Tensor,
+) -> torch.Tensor:
+    gate = hidden @ gate_weight.t()
+    up = hidden @ up_weight.t()
+    activated = F.silu(gate) * up
+    return activated @ down_weight.t()
+
+
+def _single_gpu_benchmarks(
+    *,
+    warmup: int,
+    samples: int,
+    training_samples: int,
+) -> dict[str, list[dict[str, Any]]]:
+    device = torch.device("cuda", 0)
+    torch.cuda.set_device(device)
+    torch.backends.cuda.matmul.allow_tf32 = False
+
+    results: dict[str, list[dict[str, Any]]] = {
+        "gemm": [],
+        "swiglu": [],
+        "ffn": [],
+    }
+
+    gemm_shapes = []
+    for tokens in (1, 8, 32, 128):
+        gemm_shapes.extend(
+            (
+                (f"gate_up_m{tokens}", tokens, 4096, 12288),
+                (f"down_m{tokens}", tokens, 12288, 4096),
+            )
+        )
+
+    for shape_index, (name, m_size, k_size, n_size) in enumerate(gemm_shapes):
+        left = _randn((m_size, k_size), seed=1000 + shape_index * 2, device=device)
+        right = _randn((k_size, n_size), seed=1001 + shape_index * 2, device=device)
+        native_output = torch.matmul(left, right)
+        deterministic_output = deterministic_gemm(left, right)
+        fp32_reference = left.float() @ right.float()
+        torch.cuda.synchronize()
+
+        native_samples = _gpu_event_samples(
+            lambda: torch.matmul(left, right), warmup=warmup, samples=samples
+        )
+        deterministic_samples = _gpu_event_samples(
+            lambda: deterministic_gemm(left, right), warmup=warmup, samples=samples
+        )
+        native_timing = _summary_ms(native_samples)
+        deterministic_timing = _summary_ms(deterministic_samples)
+        results["gemm"].append(
+            {
+                "name": name,
+                "m": m_size,
+                "k": k_size,
+                "n": n_size,
+                "dtype": "bfloat16",
+                "native": native_timing,
+                "deterministic": deterministic_timing,
+                "overhead_ratio": (
+                    deterministic_timing["median_ms"] / native_timing["median_ms"]
+                ),
+                "deterministic_vs_native": _accuracy(
+                    deterministic_output, native_output
+                ),
+                "native_vs_fp32": _accuracy(native_output, fp32_reference),
+                "deterministic_vs_fp32": _accuracy(
+                    deterministic_output, fp32_reference
+                ),
+            }
+        )
+        del left, right, native_output, deterministic_output, fp32_reference
+        torch.cuda.empty_cache()
+
+    for case_index, tokens in enumerate((1, 8, 32, 128)):
+        gate = _randn((tokens, 12288), seed=2000 + case_index * 3, device=device)
+        up = _randn((tokens, 12288), seed=2001 + case_index * 3, device=device)
+        grad_output = _randn(
+            (tokens, 12288), seed=2002 + case_index * 3, device=device
+        )
+
+        native_forward = F.silu(gate) * up
+        deterministic_forward = _C.swiglu_forward(gate, up)
+
+        def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
+            sigmoid = torch.sigmoid(gate)
+            grad_gate = grad_output * up * sigmoid * (1.0 + gate * (1.0 - sigmoid))
+            grad_up = grad_output * F.silu(gate)
+            return grad_gate, grad_up
+
+        native_grad_gate, native_grad_up = native_backward()
+        deterministic_grad_gate, deterministic_grad_up = _C.swiglu_backward(
+            grad_output, gate, up
+        )
+
+        for direction, native_function, deterministic_function in (
+            (
+                "forward",
+                lambda: F.silu(gate) * up,
+                lambda: _C.swiglu_forward(gate, up),
+            ),
+            (
+                "backward",
+                native_backward,
+                lambda: _C.swiglu_backward(grad_output, gate, up),
+            ),
+        ):
+            native_timing = _summary_ms(
+                _gpu_event_samples(
+                    native_function, warmup=warmup, samples=samples
+                )
+            )
+            deterministic_timing = _summary_ms(
+                _gpu_event_samples(
+                    deterministic_function, warmup=warmup, samples=samples
+                )
+            )
+            if direction == "forward":
+                accuracy = _accuracy(deterministic_forward, native_forward)
+            else:
+                accuracy = {
+                    "max_abs": max(
+                        _accuracy(deterministic_grad_gate, native_grad_gate)["max_abs"],
+                        _accuracy(deterministic_grad_up, native_grad_up)["max_abs"],
+                    ),
+                    "relative_l2": max(
+                        _relative_l2(deterministic_grad_gate, native_grad_gate),
+                        _relative_l2(deterministic_grad_up, native_grad_up),
+                    ),
+                }
+            results["swiglu"].append(
+                {
+                    "name": f"swiglu_{direction}_m{tokens}",
+                    "direction": direction,
+                    "tokens": tokens,
+                    "intermediate": 12288,
+                    "dtype": "bfloat16",
+                    "native": native_timing,
+                    "deterministic": deterministic_timing,
+                    "overhead_ratio": (
+                        deterministic_timing["median_ms"] / native_timing["median_ms"]
+                    ),
+                    "deterministic_vs_native": accuracy,
+                }
+            )
+
+        del gate, up, grad_output
+        torch.cuda.empty_cache()
+
+    gate_weight = _randn((12288, 4096), seed=3000, device=device)
+    up_weight = _randn((12288, 4096), seed=3001, device=device)
+    down_weight = _randn((4096, 12288), seed=3002, device=device)
+    for case_index, tokens in enumerate((1, 8, 32)):
+        hidden = _randn((tokens, 4096), seed=3010 + case_index * 2, device=device)
+        grad_output = _randn(
+            (tokens, 4096), seed=3011 + case_index * 2, device=device
+        )
+        with torch.no_grad():
+            native_output = _native_ffn(hidden, gate_weight, up_weight, down_weight)
+            deterministic_output = qwen3_ffn(
+                hidden, gate_weight, up_weight, down_weight
+            )
+
+        native_timing = _summary_ms(
+            _gpu_event_samples(
+                lambda: _native_ffn(hidden, gate_weight, up_weight, down_weight),
+                warmup=warmup,
+                samples=samples,
+            )
+        )
+        deterministic_timing = _summary_ms(
+            _gpu_event_samples(
+                lambda: qwen3_ffn(hidden, gate_weight, up_weight, down_weight),
+                warmup=warmup,
+                samples=samples,
+            )
+        )
+        results["ffn"].append(
+            {
+                "name": f"ffn_forward_m{tokens}",
+                "direction": "forward",
+                "tokens": tokens,
+                "hidden": 4096,
+                "intermediate": 12288,
+                "dtype": "bfloat16",
+                "native": native_timing,
+                "deterministic": deterministic_timing,
+                "overhead_ratio": (
+                    deterministic_timing["median_ms"] / native_timing["median_ms"]
+                ),
+                "deterministic_vs_native": _accuracy(
+                    deterministic_output, native_output
+                ),
+                "train_infer_bitwise": None,
+            }
+        )
+
+        native_inputs = [
+            value.detach().clone().requires_grad_(True)
+            for value in (hidden, gate_weight, up_weight, down_weight)
+        ]
+        deterministic_inputs = [
+            value.detach().clone().requires_grad_(True)
+            for value in (hidden, gate_weight, up_weight, down_weight)
+        ]
+
+        def native_training_step() -> torch.Tensor:
+            for value in native_inputs:
+                value.grad = None
+            output = _native_ffn(*native_inputs)
+            output.backward(grad_output)
+            return output
+
+        def deterministic_training_step() -> torch.Tensor:
+            for value in deterministic_inputs:
+                value.grad = None
+            output = qwen3_ffn(*deterministic_inputs)
+            output.backward(grad_output)
+            return output
+
+        native_train_output = native_training_step().detach().clone()
+        native_grads = [value.grad.detach().clone() for value in native_inputs]
+        deterministic_train_output = deterministic_training_step().detach().clone()
+        deterministic_grads = [
+            value.grad.detach().clone() for value in deterministic_inputs
+        ]
+        train_infer_bitwise = torch.equal(
+            deterministic_output, deterministic_train_output
+        )
+
+        native_train_timing = _summary_ms(
+            _gpu_event_samples(
+                native_training_step,
+                warmup=max(1, warmup // 2),
+                samples=training_samples,
+            )
+        )
+        deterministic_train_timing = _summary_ms(
+            _gpu_event_samples(
+                deterministic_training_step,
+                warmup=max(1, warmup // 2),
+                samples=training_samples,
+            )
+        )
+        results["ffn"].append(
+            {
+                "name": f"ffn_train_fwd_bwd_m{tokens}",
+                "direction": "train_fwd_bwd",
+                "tokens": tokens,
+                "hidden": 4096,
+                "intermediate": 12288,
+                "dtype": "bfloat16",
+                "native": native_train_timing,
+                "deterministic": deterministic_train_timing,
+                "overhead_ratio": (
+                    deterministic_train_timing["median_ms"]
+                    / native_train_timing["median_ms"]
+                ),
+                "deterministic_vs_native": _accuracy(
+                    deterministic_train_output, native_train_output
+                ),
+                "max_gradient_relative_l2": max(
+                    _relative_l2(actual, expected)
+                    for actual, expected in zip(
+                        deterministic_grads, native_grads, strict=True
+                    )
+                ),
+                "train_infer_bitwise": bool(train_infer_bitwise),
+            }
+        )
+        del (
+            hidden,
+            grad_output,
+            native_inputs,
+            deterministic_inputs,
+            native_grads,
+            deterministic_grads,
+        )
+        torch.cuda.empty_cache()
+
+    return results
+
+
+def _native_all_gather(input_tensor: torch.Tensor, group: Any) -> torch.Tensor:
+    world_size = dist.get_world_size(group=group)
+    flat_input = input_tensor.contiguous().view(-1)
+    flat_output = torch.empty(
+        world_size * flat_input.numel(),
+        dtype=input_tensor.dtype,
+        device=input_tensor.device,
+    )
+    dist.all_gather_into_tensor(flat_output, flat_input, group=group)
+    return flat_output.reshape(
+        input_tensor.size(0) * world_size, *input_tensor.shape[1:]
+    )
+
+
+def _native_all_reduce(input_tensor: torch.Tensor, group: Any) -> torch.Tensor:
+    output = input_tensor.clone()
+    dist.all_reduce(output, group=group)
+    return output
+
+
+def _native_reduce_scatter(input_tensor: torch.Tensor, group: Any) -> torch.Tensor:
+    world_size = dist.get_world_size(group=group)
+    output = torch.empty(
+        (input_tensor.size(0) // world_size, *input_tensor.shape[1:]),
+        dtype=input_tensor.dtype,
+        device=input_tensor.device,
+    )
+    dist.reduce_scatter_tensor(output, input_tensor.contiguous(), group=group)
+    return output
+
+
+class _NativeDistributedFFNFunction(torch.autograd.Function):
+    """Native ROCm/RCCL baseline with the PR's collective schedule."""
+
+    @staticmethod
+    def forward(
+        ctx: Any,
+        hidden: torch.Tensor,
+        gate_weight: torch.Tensor,
+        up_weight: torch.Tensor,
+        down_weight: torch.Tensor,
+        tp_group: Any,
+        cp_group: Any,
+        sequence_parallel: bool,
+    ) -> torch.Tensor:
+        input_shape = hidden.shape
+        hidden_2d = hidden.reshape(-1, hidden.size(-1)).contiguous()
+        if sequence_parallel:
+            hidden_2d = _native_all_gather(hidden_2d, tp_group)
+
+        gate = hidden_2d @ gate_weight.t()
+        up = hidden_2d @ up_weight.t()
+        activated = F.silu(gate) * up
+        output = activated @ down_weight.t()
+
+        if sequence_parallel:
+            output = _native_reduce_scatter(output, tp_group)
+        elif tp_group is not None:
+            output = _native_all_reduce(output, tp_group)
+
+        ctx.save_for_backward(
+            hidden_2d,
+            gate,
+            up,
+            activated,
+            gate_weight,
+            up_weight,
+            down_weight,
+        )
+        ctx.input_shape = input_shape
+        ctx.tp_group = tp_group
+        ctx.cp_group = cp_group
+        ctx.sequence_parallel = sequence_parallel
+        return output.reshape(*input_shape[:-1], output.size(-1))
+
+    @staticmethod
+    def backward(ctx: Any, grad_output: torch.Tensor) -> tuple[Any, ...]:
+        (
+            hidden,
+            gate,
+            up,
+            activated,
+            gate_weight,
+            up_weight,
+            down_weight,
+        ) = ctx.saved_tensors
+        grad_output = grad_output.reshape(-1, grad_output.size(-1)).contiguous()
+        if ctx.sequence_parallel:
+            grad_output = _native_all_gather(grad_output, ctx.tp_group)
+
+        if ctx.cp_group is not None:
+            activated_full = _native_all_gather(activated, ctx.cp_group)
+            grad_output_full = _native_all_gather(grad_output, ctx.cp_group)
+            grad_down_weight = grad_output_full.t() @ activated_full
+        else:
+            grad_down_weight = grad_output.t() @ activated
+
+        grad_activated = grad_output @ down_weight
+        sigmoid = torch.sigmoid(gate)
+        grad_gate = grad_activated * up * sigmoid * (1.0 + gate * (1.0 - sigmoid))
+        grad_up = grad_activated * F.silu(gate)
+
+        if ctx.cp_group is not None:
+            hidden_full = _native_all_gather(hidden, ctx.cp_group)
+            grad_gate_full = _native_all_gather(grad_gate, ctx.cp_group)
+            grad_up_full = _native_all_gather(grad_up, ctx.cp_group)
+            grad_gate_weight = grad_gate_full.t() @ hidden_full
+            grad_up_weight = grad_up_full.t() @ hidden_full
+        else:
+            grad_gate_weight = grad_gate.t() @ hidden
+            grad_up_weight = grad_up.t() @ hidden
+
+        grad_hidden_gate = grad_gate @ gate_weight
+        if ctx.sequence_parallel:
+            grad_hidden_gate = _native_reduce_scatter(
+                grad_hidden_gate, ctx.tp_group
+            )
+        elif ctx.tp_group is not None:
+            grad_hidden_gate = _native_all_reduce(grad_hidden_gate, ctx.tp_group)
+
+        grad_hidden_up = grad_up @ up_weight
+        if ctx.sequence_parallel:
+            grad_hidden_up = _native_reduce_scatter(grad_hidden_up, ctx.tp_group)
+        elif ctx.tp_group is not None:
+            grad_hidden_up = _native_all_reduce(grad_hidden_up, ctx.tp_group)
+
+        grad_hidden = grad_hidden_gate.add_(grad_hidden_up)
+        return (
+            grad_hidden.reshape(ctx.input_shape),
+            grad_gate_weight,
+            grad_up_weight,
+            grad_down_weight,
+            None,
+            None,
+            None,
+        )
+
+
+def _native_distributed_ffn(
+    hidden: torch.Tensor,
+    gate_weight: torch.Tensor,
+    up_weight: torch.Tensor,
+    down_weight: torch.Tensor,
+    *,
+    tp_group: Any,
+    cp_group: Any,
+    sequence_parallel: bool,
+) -> torch.Tensor:
+    return _NativeDistributedFFNFunction.apply(
+        hidden,
+        gate_weight,
+        up_weight,
+        down_weight,
+        tp_group,
+        cp_group,
+        sequence_parallel,
+    )
+
+
+def _mesh_groups(
+    world_size: int,
+    tp_size: int,
+    cp_size: int,
+) -> tuple[list[Any], list[Any]]:
+    if tp_size * cp_size != world_size:
+        raise ValueError("TP size times CP size must equal world size")
+    if tp_size == world_size and cp_size == 1:
+        return [dist.group.WORLD], []
+
+    tp_groups = []
+    if tp_size > 1:
+        for cp_rank in range(cp_size):
+            ranks = list(range(cp_rank * tp_size, (cp_rank + 1) * tp_size))
+            tp_groups.append(dist.new_group(ranks=ranks))
+
+    cp_groups = []
+    if cp_size > 1:
+        for tp_rank in range(tp_size):
+            ranks = [cp_rank * tp_size + tp_rank for cp_rank in range(cp_size)]
+            cp_groups.append(dist.new_group(ranks=ranks))
+    return tp_groups, cp_groups
+
+
+def _shard_ranges(
+    rank: int,
+    *,
+    tp_size: int,
+    cp_size: int,
+    sequence_parallel: bool,
+    token_count: int,
+    intermediate_size: int,
+) -> tuple[int, int, int, int]:
+    tp_rank = rank % tp_size
+    cp_rank = rank // tp_size
+    cp_tokens = token_count // cp_size
+    local_tokens = cp_tokens // tp_size if sequence_parallel else cp_tokens
+    token_start = cp_rank * cp_tokens
+    if sequence_parallel:
+        token_start += tp_rank * local_tokens
+    token_end = token_start + local_tokens
+
+    local_intermediate = intermediate_size // tp_size
+    feature_start = tp_rank * local_intermediate
+    feature_end = feature_start + local_intermediate
+    return token_start, token_end, feature_start, feature_end
+
+
+def _distributed_wall_samples(
+    function: Callable[[], Any],
+    *,
+    group: Any,
+    warmup: int,
+    samples: int,
+) -> list[float]:
+    for _ in range(warmup):
+        function()
+    torch.cuda.synchronize()
+    dist.barrier(group=group)
+
+    timings = []
+    for _ in range(samples):
+        torch.cuda.synchronize()
+        start = time.perf_counter()
+        function()
+        torch.cuda.synchronize()
+        timings.append((time.perf_counter() - start) * 1000.0)
+    dist.barrier(group=group)
+    return timings
+
+
+def _slowest_rank_summary(local_timings: list[float], group: Any) -> dict[str, float]:
+    world_size = dist.get_world_size(group=group)
+    gathered: list[list[float] | None] = [None] * world_size
+    dist.all_gather_object(gathered, local_timings, group=group)
+    slowest_per_sample = [
+        max(float(rank_timings[index]) for rank_timings in gathered if rank_timings is not None)
+        for index in range(len(local_timings))
+    ]
+    return _summary_ms(slowest_per_sample)
+
+
+def _collective_benchmarks(
+    rank: int,
+    world_size: int,
+    *,
+    warmup: int,
+    samples: int,
+) -> list[dict[str, Any]]:
+    device = torch.device("cuda", rank)
+    results = []
+    for message_bytes in (64 * 1024, 1 * _MIB, 16 * _MIB):
+        elements = message_bytes // torch.tensor([], dtype=torch.bfloat16).element_size()
+        input_tensor = torch.zeros(elements, dtype=torch.bfloat16, device=device)
+        deterministic = RCCLDeterministicCollective(
+            group=dist.group.WORLD,
+            device=device,
+            max_size_bytes=message_bytes,
+        )
+        deterministic_outputs = {
+            "all_reduce": torch.empty_like(input_tensor),
+            "all_gather": torch.empty(
+                elements * world_size, dtype=input_tensor.dtype, device=device
+            ),
+            "reduce_scatter": torch.empty(
+                elements // world_size, dtype=input_tensor.dtype, device=device
+            ),
+        }
+        native_outputs = {
+            "all_gather": torch.empty(
+                elements * world_size, dtype=input_tensor.dtype, device=device
+            ),
+            "reduce_scatter": torch.empty(
+                elements // world_size, dtype=input_tensor.dtype, device=device
+            ),
+        }
+
+        def deterministic_all_reduce() -> torch.Tensor:
+            return deterministic.all_reduce(
+                input_tensor, out=deterministic_outputs["all_reduce"]
+            )
+
+        def native_all_reduce() -> torch.Tensor:
+            dist.all_reduce(input_tensor, group=dist.group.WORLD)
+            return input_tensor
+
+        def deterministic_all_gather() -> torch.Tensor:
+            return deterministic.all_gather(
+                input_tensor, out=deterministic_outputs["all_gather"]
+            )
+
+        def native_all_gather() -> torch.Tensor:
+            dist.all_gather_into_tensor(
+                native_outputs["all_gather"], input_tensor, group=dist.group.WORLD
+            )
+            return native_outputs["all_gather"]
+
+        def deterministic_reduce_scatter() -> torch.Tensor:
+            return deterministic.reduce_scatter(
+                input_tensor, out=deterministic_outputs["reduce_scatter"]
+            )
+
+        def native_reduce_scatter() -> torch.Tensor:
+            dist.reduce_scatter_tensor(
+                native_outputs["reduce_scatter"],
+                input_tensor,
+                group=dist.group.WORLD,
+            )
+            return native_outputs["reduce_scatter"]
+
+        for operation, native_function, deterministic_function in (
+            ("all_reduce", native_all_reduce, deterministic_all_reduce),
+            ("all_gather", native_all_gather, deterministic_all_gather),
+            (
+                "reduce_scatter",
+                native_reduce_scatter,
+                deterministic_reduce_scatter,
+            ),
+        ):
+            native_timings = _distributed_wall_samples(
+                native_function,
+                group=dist.group.WORLD,
+                warmup=warmup,
+                samples=samples,
+            )
+            deterministic_timings = _distributed_wall_samples(
+                deterministic_function,
+                group=dist.group.WORLD,
+                warmup=warmup,
+                samples=samples,
+            )
+            native_summary = _slowest_rank_summary(
+                native_timings, dist.group.WORLD
+            )
+            deterministic_summary = _slowest_rank_summary(
+                deterministic_timings, dist.group.WORLD
+            )
+            accuracy_elements = min(elements, 64 * 1024)
+            accuracy_input = _randn(
+                (accuracy_elements,),
+                seed=4000 + world_size * 100 + rank,
+                device=device,
+                scale=1.0,
+            )
+            gathered_accuracy_input = torch.empty(
+                world_size * accuracy_elements,
+                dtype=accuracy_input.dtype,
+                device=device,
+            )
+            dist.all_gather_into_tensor(
+                gathered_accuracy_input,
+                accuracy_input,
+                group=dist.group.WORLD,
+            )
+            rank_inputs = gathered_accuracy_input.reshape(
+                world_size, accuracy_elements
+            )
+            if operation == "all_reduce":
+                native_accuracy_output = accuracy_input.clone()
+                dist.all_reduce(native_accuracy_output, group=dist.group.WORLD)
+                deterministic_accuracy_output = deterministic.all_reduce(
+                    accuracy_input
+                )
+                fp32_reference = rank_inputs.float().sum(dim=0)
+            elif operation == "all_gather":
+                native_accuracy_output = torch.empty_like(gathered_accuracy_input)
+                dist.all_gather_into_tensor(
+                    native_accuracy_output,
+                    accuracy_input,
+                    group=dist.group.WORLD,
+                )
+                deterministic_accuracy_output = deterministic.all_gather(
+                    accuracy_input
+                )
+                fp32_reference = gathered_accuracy_input.float()
+            else:
+                native_accuracy_output = torch.empty(
+                    accuracy_elements // world_size,
+                    dtype=accuracy_input.dtype,
+                    device=device,
+                )
+                dist.reduce_scatter_tensor(
+                    native_accuracy_output,
+                    accuracy_input,
+                    group=dist.group.WORLD,
+                )
+                deterministic_accuracy_output = deterministic.reduce_scatter(
+                    accuracy_input
+                )
+                reduced_reference = rank_inputs.float().sum(dim=0)
+                fp32_reference = reduced_reference.chunk(world_size)[rank]
+
+            local_accuracy = {
+                "deterministic_vs_native": _accuracy(
+                    deterministic_accuracy_output, native_accuracy_output
+                ),
+                "native_vs_fp32": _accuracy(
+                    native_accuracy_output, fp32_reference
+                ),
+                "deterministic_vs_fp32": _accuracy(
+                    deterministic_accuracy_output, fp32_reference
+                ),
+            }
+            gathered_accuracy: list[dict[str, Any] | None] = [None] * world_size
+            dist.all_gather_object(
+                gathered_accuracy, local_accuracy, group=dist.group.WORLD
+            )
+            if rank == 0:
+                valid_accuracy = [
+                    value for value in gathered_accuracy if value is not None
+                ]
+                results.append(
+                    {
+                        "operation": operation,
+                        "world_size": world_size,
+                        "message_bytes_per_rank": message_bytes,
+                        "dtype": "bfloat16",
+                        "native": native_summary,
+                        "deterministic": deterministic_summary,
+                        "overhead_ratio": (
+                            deterministic_summary["median_ms"]
+                            / native_summary["median_ms"]
+                        ),
+                        "deterministic_vs_native": {
+                            "max_abs": max(
+                                value["deterministic_vs_native"]["max_abs"]
+                                for value in valid_accuracy
+                            ),
+                            "relative_l2": max(
+                                value["deterministic_vs_native"]["relative_l2"]
+                                for value in valid_accuracy
+                            ),
+                            "exact_fraction": min(
+                                value["deterministic_vs_native"]["exact_fraction"]
+                                for value in valid_accuracy
+                            ),
+                        },
+                        "native_vs_fp32_relative_l2": max(
+                            value["native_vs_fp32"]["relative_l2"]
+                            for value in valid_accuracy
+                        ),
+                        "deterministic_vs_fp32_relative_l2": max(
+                            value["deterministic_vs_fp32"]["relative_l2"]
+                            for value in valid_accuracy
+                        ),
+                    }
+                )
+            del (
+                accuracy_input,
+                gathered_accuracy_input,
+                rank_inputs,
+                native_accuracy_output,
+                deterministic_accuracy_output,
+                fp32_reference,
+            )
+        deterministic.close()
+        del input_tensor, deterministic_outputs, native_outputs
+        torch.cuda.empty_cache()
+    return results
+
+
+def _distributed_ffn_benchmark(
+    rank: int,
+    world_size: int,
+    configs: tuple[tuple[str, int, int, bool], ...],
+    *,
+    warmup: int,
+    samples: int,
+    training_samples: int,
+) -> list[dict[str, Any]]:
+    device = torch.device("cuda", rank)
+    token_count = 32
+    hidden_size = 4096
+    intermediate_size = 12288
+    hidden_full = _randn((token_count, hidden_size), seed=5000, device=device)
+    gate_full = _randn(
+        (intermediate_size, hidden_size), seed=5001, device=device
+    )
+    up_full = _randn((intermediate_size, hidden_size), seed=5002, device=device)
+    down_full = _randn(
+        (hidden_size, intermediate_size), seed=5003, device=device
+    )
+    grad_output_full = _randn(
+        (token_count, hidden_size), seed=5004, device=device
+    )
+
+    meshes: dict[tuple[int, int], tuple[list[Any], list[Any]]] = {}
+    results = []
+    for name, tp_size, cp_size, sequence_parallel in configs:
+        mesh_key = (tp_size, cp_size)
+        if mesh_key not in meshes:
+            meshes[mesh_key] = _mesh_groups(world_size, tp_size, cp_size)
+        tp_groups, cp_groups = meshes[mesh_key]
+        tp_rank = rank % tp_size
+        cp_rank = rank // tp_size
+        tp_group = tp_groups[cp_rank] if tp_size > 1 else None
+        cp_group = cp_groups[tp_rank] if cp_size > 1 else None
+        token_start, token_end, feature_start, feature_end = _shard_ranges(
+            rank,
+            tp_size=tp_size,
+            cp_size=cp_size,
+            sequence_parallel=sequence_parallel,
+            token_count=token_count,
+            intermediate_size=intermediate_size,
+        )
+        shard = (
+            hidden_full[token_start:token_end].contiguous(),
+            gate_full[feature_start:feature_end].contiguous(),
+            up_full[feature_start:feature_end].contiguous(),
+            down_full[:, feature_start:feature_end].contiguous(),
+        )
+        local_grad_output = grad_output_full[token_start:token_end].contiguous()
+
+        native_forward = lambda: _native_distributed_ffn(
+            *shard,
+            tp_group=tp_group,
+            cp_group=cp_group,
+            sequence_parallel=sequence_parallel,
+        )
+        deterministic_forward = lambda: qwen3_ffn(
+            *shard,
+            tp_group=tp_group,
+            cp_group=cp_group,
+            sequence_parallel=sequence_parallel,
+        )
+
+        with torch.no_grad():
+            native_output = native_forward()
+            deterministic_output = deterministic_forward()
+            deterministic_repeat = deterministic_forward()
+        torch.cuda.synchronize()
+
+        native_forward_timings = _distributed_wall_samples(
+            native_forward,
+            group=dist.group.WORLD,
+            warmup=warmup,
+            samples=samples,
+        )
+        deterministic_forward_timings = _distributed_wall_samples(
+            deterministic_forward,
+            group=dist.group.WORLD,
+            warmup=warmup,
+            samples=samples,
+        )
+        native_forward_summary = _slowest_rank_summary(
+            native_forward_timings, dist.group.WORLD
+        )
+        deterministic_forward_summary = _slowest_rank_summary(
+            deterministic_forward_timings, dist.group.WORLD
+        )
+
+        native_inputs = [value.detach().clone().requires_grad_(True) for value in shard]
+        deterministic_inputs = [
+            value.detach().clone().requires_grad_(True) for value in shard
+        ]
+
+        def native_training_step() -> torch.Tensor:
+            for value in native_inputs:
+                value.grad = None
+            output = _native_distributed_ffn(
+                *native_inputs,
+                tp_group=tp_group,
+                cp_group=cp_group,
+                sequence_parallel=sequence_parallel,
+            )
+            output.backward(local_grad_output)
+            return output
+
+        def deterministic_training_step() -> torch.Tensor:
+            for value in deterministic_inputs:
+                value.grad = None
+            output = qwen3_ffn(
+                *deterministic_inputs,
+                tp_group=tp_group,
+                cp_group=cp_group,
+                sequence_parallel=sequence_parallel,
+            )
+            output.backward(local_grad_output)
+            return output
+
+        native_train_output = native_training_step().detach().clone()
+        native_grads = [value.grad.detach().clone() for value in native_inputs]
+        deterministic_train_output = deterministic_training_step().detach().clone()
+        deterministic_grads = [
+            value.grad.detach().clone() for value in deterministic_inputs
+        ]
+        torch.cuda.synchronize()
+
+        native_train_timings = _distributed_wall_samples(
+            native_training_step,
+            group=dist.group.WORLD,
+            warmup=max(1, warmup // 2),
+            samples=training_samples,
+        )
+        deterministic_train_timings = _distributed_wall_samples(
+            deterministic_training_step,
+            group=dist.group.WORLD,
+            warmup=max(1, warmup // 2),
+            samples=training_samples,
+        )
+        native_train_summary = _slowest_rank_summary(
+            native_train_timings, dist.group.WORLD
+        )
+        deterministic_train_summary = _slowest_rank_summary(
+            deterministic_train_timings, dist.group.WORLD
+        )
+
+        local_accuracy = {
+            "output_relative_l2": _relative_l2(
+                deterministic_output, native_output
+            ),
+            "output_max_abs": _accuracy(
+                deterministic_output, native_output
+            )["max_abs"],
+            "max_gradient_relative_l2": max(
+                _relative_l2(actual, expected)
+                for actual, expected in zip(
+                    deterministic_grads, native_grads, strict=True
+                )
+            ),
+            "train_infer_bitwise": torch.equal(
+                deterministic_output, deterministic_train_output
+            ),
+            "repeat_bitwise": torch.equal(
+                deterministic_output, deterministic_repeat
+            ),
+        }
+        gathered_accuracy: list[dict[str, Any] | None] = [None] * world_size
+        dist.all_gather_object(
+            gathered_accuracy, local_accuracy, group=dist.group.WORLD
+        )
+        if rank == 0:
+            valid_accuracy = [value for value in gathered_accuracy if value is not None]
+            results.extend(
+                (
+                    {
+                        "name": name,
+                        "direction": "forward",
+                        "world_size": world_size,
+                        "tp_size": tp_size,
+                        "cp_size": cp_size,
+                        "sequence_parallel": sequence_parallel,
+                        "tokens": token_count,
+                        "hidden": hidden_size,
+                        "intermediate": intermediate_size,
+                        "native": native_forward_summary,
+                        "deterministic": deterministic_forward_summary,
+                        "overhead_ratio": (
+                            deterministic_forward_summary["median_ms"]
+                            / native_forward_summary["median_ms"]
+                        ),
+                        "output_relative_l2": max(
+                            value["output_relative_l2"] for value in valid_accuracy
+                        ),
+                        "output_max_abs": max(
+                            value["output_max_abs"] for value in valid_accuracy
+                        ),
+                        "train_infer_bitwise": all(
+                            value["train_infer_bitwise"] for value in valid_accuracy
+                        ),
+                        "repeat_bitwise": all(
+                            value["repeat_bitwise"] for value in valid_accuracy
+                        ),
+                    },
+                    {
+                        "name": name,
+                        "direction": "train_fwd_bwd",
+                        "world_size": world_size,
+                        "tp_size": tp_size,
+                        "cp_size": cp_size,
+                        "sequence_parallel": sequence_parallel,
+                        "tokens": token_count,
+                        "hidden": hidden_size,
+                        "intermediate": intermediate_size,
+                        "native": native_train_summary,
+                        "deterministic": deterministic_train_summary,
+                        "overhead_ratio": (
+                            deterministic_train_summary["median_ms"]
+                            / native_train_summary["median_ms"]
+                        ),
+                        "output_relative_l2": max(
+                            _relative_l2(
+                                deterministic_train_output, native_train_output
+                            ),
+                            *(value["output_relative_l2"] for value in valid_accuracy),
+                        ),
+                        "max_gradient_relative_l2": max(
+                            value["max_gradient_relative_l2"]
+                            for value in valid_accuracy
+                        ),
+                        "train_infer_bitwise": all(
+                            value["train_infer_bitwise"] for value in valid_accuracy
+                        ),
+                        "repeat_bitwise": all(
+                            value["repeat_bitwise"] for value in valid_accuracy
+                        ),
+                    },
+                )
+            )
+
+        del (
+            shard,
+            local_grad_output,
+            native_inputs,
+            deterministic_inputs,
+            native_grads,
+            deterministic_grads,
+        )
+        torch.cuda.empty_cache()
+        dist.barrier(group=dist.group.WORLD)
+
+    for collective in list(ffn_module._COLLECTIVES.values()):
+        collective.close()
+    ffn_module._COLLECTIVES.clear()
+    return results
+
+
+def _distributed_worker(
+    rank: int,
+    world_size: int,
+    init_method: str,
+    result_queue: Any,
+    warmup: int,
+    samples: int,
+    training_samples: int,
+) -> None:
+    try:
+        torch.cuda.set_device(rank)
+        dist.init_process_group(
+            backend="nccl",
+            init_method=init_method,
+            rank=rank,
+            world_size=world_size,
+            timeout=timedelta(minutes=15),
+        )
+        collective_results = _collective_benchmarks(
+            rank,
+            world_size,
+            warmup=warmup,
+            samples=samples,
+        )
+        ffn_results = _distributed_ffn_benchmark(
+            rank,
+            world_size,
+            _DISTRIBUTED_CONFIGS[world_size],
+            warmup=warmup,
+            samples=samples,
+            training_samples=training_samples,
+        )
+        if rank == 0:
+            result_queue.put(
+                {
+                    "ok": True,
+                    "world_size": world_size,
+                    "collectives": collective_results,
+                    "distributed_ffn": ffn_results,
+                }
+            )
+    except Exception:
+        result_queue.put(
+            {
+                "ok": False,
+                "rank": rank,
+                "world_size": world_size,
+                "traceback": traceback.format_exc(),
+            }
+        )
+        raise
+    finally:
+        for collective in list(ffn_module._COLLECTIVES.values()):
+            collective.close()
+        ffn_module._COLLECTIVES.clear()
+        if dist.is_available() and dist.is_initialized():
+            dist.destroy_process_group()
+
+
+def _run_distributed_world(
+    world_size: int,
+    *,
+    warmup: int,
+    samples: int,
+    training_samples: int,
+) -> dict[str, Any]:
+    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_worker,
+                args=(
+                    rank,
+                    world_size,
+                    init_method,
+                    result_queue,
+                    warmup,
+                    samples,
+                    training_samples,
+                ),
+            )
+            for rank in range(world_size)
+        ]
+        for process in processes:
+            process.start()
+
+        result = None
+        try:
+            result = result_queue.get(timeout=1800)
+            if not result["ok"]:
+                for process in processes:
+                    if process.is_alive():
+                        process.terminate()
+        except queue.Empty as exc:
+            for process in processes:
+                if process.is_alive():
+                    process.terminate()
+            raise RuntimeError(
+                f"timed out waiting for world_size={world_size} benchmark"
+            ) from exc
+        finally:
+            for process in processes:
+                process.join(timeout=60)
+                if process.is_alive():
+                    process.terminate()
+                    process.join(timeout=30)
+            result_queue.close()
+            result_queue.join_thread()
+
+    if result is None:
+        raise RuntimeError(f"world_size={world_size} returned no result")
+    if not result["ok"]:
+        raise RuntimeError(result.get("traceback", str(result)))
+    for process in processes:
+        if process.exitcode != 0:
+            raise RuntimeError(
+                f"world_size={world_size} worker exited with {process.exitcode}"
+            )
+    return result
+
+
+def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
+    gemm_rows = payload["single_gpu"]["gemm"]
+    swiglu_rows = payload["single_gpu"]["swiglu"]
+    ffn_rows = payload["single_gpu"]["ffn"]
+    collective_rows = payload["collectives"]
+    distributed_rows = payload["distributed_ffn"]
+    methodology = payload["methodology"]
+
+    def ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
+        ratios = [row["overhead_ratio"] for row in rows]
+        return f"{min(ratios):.{digits}f}-{max(ratios):.{digits}f}x"
+
+    forward_rows = [row for row in distributed_rows if row["direction"] == "forward"]
+    training_rows = [
+        row for row in distributed_rows if row["direction"] == "train_fwd_bwd"
+    ]
+    reduction_rows = [
+        row
+        for row in collective_rows
+        if row["operation"] != "all_gather" and row["world_size"] >= 4
+    ]
+    closer_reductions = sum(
+        row["deterministic_vs_fp32_relative_l2"]
+        < row["native_vs_fp32_relative_l2"]
+        for row in reduction_rows
+    )
+    gemm_native_errors = [
+        row["native_vs_fp32"]["relative_l2"] * 100.0 for row in gemm_rows
+    ]
+    gemm_deterministic_errors = [
+        row["deterministic_vs_fp32"]["relative_l2"] * 100.0 for row in gemm_rows
+    ]
+    distributed_output_errors = [
+        row["output_relative_l2"] * 100.0 for row in distributed_rows
+    ]
+    consistency_rows = all(
+        row["train_infer_bitwise"] and row["repeat_bitwise"]
+        for row in distributed_rows
+    )
+
+    lines = [
+        "# PR #325 ROCm deterministic FFN performance analysis",
+        "",
+        "> Operator-only benchmark. No model checkpoint or serving engine was used.",
+        "",
+        "## Environment",
+        "",
+        "| Item | Value |",
+        "|---|---|",
+    ]
+    for key, value in payload["environment"].items():
+        lines.append(f"| {key} | {value} |")
+
+    lines.extend(
+        (
+            "",
+            "## Methodology",
+            "",
+            "- BF16 operator inputs; FFN dimensions are H=4096 and I=12288.",
+            "- Native GEMM is `torch.matmul`; native communication calls PyTorch's "
+            "ProcessGroupNCCL directly, which uses RCCL on ROCm.",
+            "- The native distributed FFN uses the same weights, TP/CP/SP sharding, "
+            "and collective schedule as the deterministic path.",
+            f"- Single-GPU timing: {methodology['single_gpu_timing']}; distributed "
+            f"timing: {methodology['distributed_timing']}.",
+            f"- {methodology['warmup']} warmups, {methodology['samples']} measured "
+            f"forward/collective samples, and {methodology['training_samples']} "
+            "measured forward+backward samples.",
+            "- `NCCL_IB_DISABLE=1` selects the same intra-node XGMI transport for both "
+            "collective implementations. Raw median, p95, minimum, and maximum "
+            "measurements are in `results.json`.",
+            "",
+            "Reproduce this report from the repository root:",
+            "",
+            "```bash",
+            "python benchmarks/benchmark_rocm_ffn.py \\",
+            f"  --warmup {methodology['warmup']} \\",
+            f"  --samples {methodology['samples']} \\",
+            f"  --training-samples {methodology['training_samples']} \\",
+            "  --output-dir benchmarks/results/pr325_rocm_mi300x",
+            "```",
+            "",
+            "## Key findings",
+            "",
+            f"- The gfx942 deterministic GEMM scalar fallback costs "
+            f"{ratio_range(gemm_rows)} native latency. This dominates end-to-end FFN "
+            "overhead.",
+            f"- The fused deterministic SwiGLU costs {ratio_range(swiglu_rows, 2)} "
+            "native "
+            "latency, so it is faster than the unfused PyTorch expression; it is not "
+            "the bottleneck.",
+            f"- Deterministic collectives cost {ratio_range(collective_rows)} native "
+            "RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.",
+            f"- Distributed FFN overhead is {ratio_range(forward_rows)} for forward and "
+            f"{ratio_range(training_rows)} for forward+backward.",
+            f"- All {len(distributed_rows)} distributed direction/topology cases are "
+            "train/inference and repeat bitwise consistent: "
+            f"{'yes' if consistency_rows else 'no'}.",
+            f"- GEMM relative-L2 error against FP32 is "
+            f"{min(gemm_deterministic_errors):.3f}-{max(gemm_deterministic_errors):.3f}% "
+            f"for the deterministic BF16 tree versus "
+            f"{min(gemm_native_errors):.3f}-{max(gemm_native_errors):.3f}% for native "
+            "ROCm. The fixed BF16 tree buys topology invariance, not better FP32 "
+            "proximity.",
+            f"- Distributed FFN output drift versus native is "
+            f"{min(distributed_output_errors):.3f}-{max(distributed_output_errors):.3f}% "
+            "relative-L2. Conversely, the balanced deterministic reduction is closer "
+            f"to FP32 than native RCCL in {closer_reductions}/{len(reduction_rows)} "
+            "tested 4/8-rank reduction cases.",
+        )
+    )
+
+    lines.extend(
+        (
+            "",
+            "## Single-GPU GEMM",
+            "",
+            "| Shape | Native ROCm (ms) | Deterministic (ms) | Overhead | "
+            "Det rel-L2 vs FP32 | Native rel-L2 vs FP32 |",
+            "|---|---:|---:|---:|---:|---:|",
+        )
+    )
+    for row in gemm_rows:
+        lines.append(
+            f"| {row['name']} | {row['native']['median_ms']:.4f} | "
+            f"{row['deterministic']['median_ms']:.4f} | {row['overhead_ratio']:.1f}× | "
+            f"{row['deterministic_vs_fp32']['relative_l2']:.3e} | "
+            f"{row['native_vs_fp32']['relative_l2']:.3e} |"
+        )
+
+    lines.extend(
+        (
+            "",
+            "## Single-GPU SwiGLU",
+            "",
+            "| Case | Native PyTorch (ms) | Deterministic fused (ms) | Det/native | "
+            "Det/native rel-L2 |",
+            "|---|---:|---:|---:|---:|",
+        )
+    )
+    for row in swiglu_rows:
+        lines.append(
+            f"| {row['name']} | {row['native']['median_ms']:.4f} | "
+            f"{row['deterministic']['median_ms']:.4f} | "
+            f"{row['overhead_ratio']:.2f}x | "
+            f"{row['deterministic_vs_native']['relative_l2']:.3e} |"
+        )
+
+    lines.extend(
+        (
+            "",
+            "## Single-GPU FFN",
+            "",
+            "| Case | Native ROCm (ms) | Deterministic (ms) | Overhead | "
+            "Output rel-L2 | Max grad rel-L2 |",
+            "|---|---:|---:|---:|---:|---:|",
+        )
+    )
+    for row in ffn_rows:
+        lines.append(
+            f"| {row['name']} | {row['native']['median_ms']:.4f} | "
+            f"{row['deterministic']['median_ms']:.4f} | {row['overhead_ratio']:.1f}× | "
+            f"{row['deterministic_vs_native']['relative_l2']:.3e} | "
+            f"{row.get('max_gradient_relative_l2', float('nan')):.3e} |"
+        )
+
+    lines.extend(
+        (
+            "",
+            "## RCCL collectives",
+            "",
+            "| Operation | Ranks | Input/rank | Native RCCL (ms) | "
+            "Deterministic (ms) | Overhead | Det/native rel-L2 | "
+            "Native/FP32 rel-L2 | Det/FP32 rel-L2 |",
+            "|---|---:|---:|---:|---:|---:|---:|---:|---:|",
+        )
+    )
+    for row in collective_rows:
+        lines.append(
+            f"| {row['operation']} | {row['world_size']} | "
+            f"{row['message_bytes_per_rank'] / _MIB:.4g} MiB | "
+            f"{row['native']['median_ms']:.4f} | "
+            f"{row['deterministic']['median_ms']:.4f} | "
+            f"{row['overhead_ratio']:.1f}× | "
+            f"{row['deterministic_vs_native']['relative_l2']:.3e} | "
+            f"{row['native_vs_fp32_relative_l2']:.3e} | "
+            f"{row['deterministic_vs_fp32_relative_l2']:.3e} |"
+        )
+
+    lines.extend(
+        (
+            "",
+            "## Distributed FFN",
+            "",
+            "| Topology | Direction | Native ROCm/RCCL (ms) | "
+            "Deterministic (ms) | Overhead | Output rel-L2 | Max grad rel-L2 | "
+            "Train/infer bitwise | Repeat bitwise |",
+            "|---|---|---:|---:|---:|---:|---:|:---:|:---:|",
+        )
+    )
+    for row in distributed_rows:
+        lines.append(
+            f"| {row['name']} | {row['direction']} | "
+            f"{row['native']['median_ms']:.4f} | "
+            f"{row['deterministic']['median_ms']:.4f} | "
+            f"{row['overhead_ratio']:.1f}× | "
+            f"{row['output_relative_l2']:.3e} | "
+            f"{row.get('max_gradient_relative_l2', float('nan')):.3e} | "
+            f"{'yes' if row['train_infer_bitwise'] else 'no'} | "
+            f"{'yes' if row['repeat_bitwise'] else 'no'} |"
+        )
+
+    lines.extend(
+        (
+            "",
+            "## Figures",
+            "",
+            "![Single-GPU overhead](single_gpu_overhead.png)",
+            "",
+            "![RCCL collective overhead](collective_overhead.png)",
+            "",
+            "![Distributed FFN overhead](distributed_ffn_overhead.png)",
+            "",
+            "![Accuracy trade-off](accuracy_tradeoff.png)",
+            "",
+        )
+    )
+    (output_directory / "report.md").write_text("\n".join(lines), encoding="utf-8")
+
+
+def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+    from matplotlib.patches import Patch
+
+    plt.style.use("seaborn-v0_8-whitegrid")
+
+    gemm_rows = payload["single_gpu"]["gemm"]
+    swiglu_rows = payload["single_gpu"]["swiglu"]
+    ffn_rows = payload["single_gpu"]["ffn"]
+    labels = (
+        [row["name"] for row in gemm_rows]
+        + [row["name"] for row in swiglu_rows]
+        + [row["name"] for row in ffn_rows]
+    )
+    ratios = (
+        [row["overhead_ratio"] for row in gemm_rows]
+        + [row["overhead_ratio"] for row in swiglu_rows]
+        + [row["overhead_ratio"] for row in ffn_rows]
+    )
+    colors = (
+        ["#3b82f6"] * len(gemm_rows)
+        + ["#10b981"] * len(swiglu_rows)
+        + ["#f97316"] * len(ffn_rows)
+    )
+    figure, axis = plt.subplots(figsize=(18, 7))
+    axis.bar(range(len(labels)), ratios, color=colors)
+    axis.axhline(1.0, color="black", linewidth=1)
+    axis.set_yscale("log")
+    axis.set_ylabel("Deterministic / native latency (×, log scale)")
+    axis.set_title("MI300X single-GPU deterministic overhead")
+    axis.set_xticks(range(len(labels)), labels, rotation=55, ha="right")
+    axis.legend(
+        handles=(
+            Patch(color="#3b82f6", label="GEMM"),
+            Patch(color="#10b981", label="SwiGLU"),
+            Patch(color="#f97316", label="FFN"),
+        )
+    )
+    figure.tight_layout()
+    figure.savefig(output_directory / "single_gpu_overhead.png", dpi=180)
+    plt.close(figure)
+
+    figure, axes = plt.subplots(1, 3, figsize=(16, 5), sharey=True)
+    for axis, operation in zip(
+        axes, ("all_reduce", "all_gather", "reduce_scatter"), strict=True
+    ):
+        for world_size in (2, 4, 8):
+            rows = [
+                row
+                for row in payload["collectives"]
+                if row["operation"] == operation and row["world_size"] == world_size
+            ]
+            axis.plot(
+                [row["message_bytes_per_rank"] / _MIB for row in rows],
+                [row["overhead_ratio"] for row in rows],
+                marker="o",
+                label=f"{world_size} ranks",
+            )
+        axis.axhline(1.0, color="black", linewidth=1)
+        axis.set_xscale("log", base=2)
+        axis.set_yscale("log")
+        axis.set_title(operation)
+        axis.set_xlabel("Input per rank (MiB)")
+    axes[0].set_ylabel("Deterministic / native latency (×, log scale)")
+    axes[-1].legend()
+    figure.suptitle("MI300X deterministic collective overhead vs native RCCL")
+    figure.tight_layout()
+    figure.savefig(output_directory / "collective_overhead.png", dpi=180)
+    plt.close(figure)
+
+    rows = payload["distributed_ffn"]
+    labels = [f"{row['name']}\n{row['direction']}" for row in rows]
+    figure, axis = plt.subplots(figsize=(16, 6))
+    axis.bar(
+        range(len(rows)),
+        [row["overhead_ratio"] for row in rows],
+        color=["#10b981" if row["direction"] == "forward" else "#8b5cf6" for row in rows],
+    )
+    axis.axhline(1.0, color="black", linewidth=1)
+    axis.set_yscale("log")
+    axis.set_ylabel("Deterministic / native latency (×, log scale)")
+    axis.set_title("Qwen3-8B-shaped FFN: native ROCm/RCCL vs deterministic")
+    axis.set_xticks(range(len(labels)), labels, rotation=55, ha="right")
+    figure.tight_layout()
+    figure.savefig(output_directory / "distributed_ffn_overhead.png", dpi=180)
+    plt.close(figure)
+
+    figure, axis = plt.subplots(figsize=(12, 6))
+    names = [row["name"] for row in gemm_rows]
+    native_errors = [row["native_vs_fp32"]["relative_l2"] for row in gemm_rows]
+    deterministic_errors = [
+        row["deterministic_vs_fp32"]["relative_l2"] for row in gemm_rows
+    ]
+    positions = list(range(len(names)))
+    width = 0.38
+    axis.bar(
+        [position - width / 2 for position in positions],
+        native_errors,
+        width,
+        label="Native ROCm vs FP32",
+        color="#3b82f6",
+    )
+    axis.bar(
+        [position + width / 2 for position in positions],
+        deterministic_errors,
+        width,
+        label="Deterministic vs FP32",
+        color="#ef4444",
+    )
+    axis.set_yscale("log")
+    axis.set_ylabel("Relative L2 error (log scale)")
+    axis.set_title("BF16 GEMM numerical error against FP32 reference")
+    axis.set_xticks(positions, names, rotation=45, ha="right")
+    axis.legend()
+    figure.tight_layout()
+    figure.savefig(output_directory / "accuracy_tradeoff.png", dpi=180)
+    plt.close(figure)
+
+
+def _environment() -> dict[str, Any]:
+    properties = torch.cuda.get_device_properties(0)
+    return {
+        "gpu": torch.cuda.get_device_name(0),
+        "gpu_count": torch.cuda.device_count(),
+        "architecture": properties.gcnArchName,
+        "torch": torch.__version__,
+        "hip": torch.version.hip,
+        "python": os.sys.version.split()[0],
+        "git_commit": os.popen("git rev-parse HEAD").read().strip(),
+        "native_gemm": "torch.matmul (ROCm rocBLAS/hipBLASLt dispatch)",
+        "native_collective": "torch.distributed ProcessGroupNCCL (RCCL on ROCm)",
+        "NCCL_IB_DISABLE": os.environ.get("NCCL_IB_DISABLE", ""),
+    }
+
+
+def _validate_environment() -> None:
+    if getattr(torch.version, "hip", None) is None:
+        raise RuntimeError("this benchmark requires a ROCm PyTorch build")
+    if not torch.cuda.is_available() or torch.cuda.device_count() < 8:
+        raise RuntimeError("this benchmark requires eight visible ROCm GPUs")
+    if not _EXT_AVAILABLE or _C is None:
+        raise RuntimeError("the native rl_engine extension is unavailable")
+    missing = [name for name in _REQUIRED_SYMBOLS if not hasattr(_C, name)]
+    if missing:
+        raise RuntimeError(f"the native extension is missing symbols: {missing}")
+    if not dist.is_available() or not dist.is_nccl_available():
+        raise RuntimeError("PyTorch RCCL/ProcessGroupNCCL support is unavailable")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("benchmarks/results/pr325_rocm_mi300x"),
+    )
+    parser.add_argument("--warmup", type=int, default=3)
+    parser.add_argument("--samples", type=int, default=10)
+    parser.add_argument("--training-samples", type=int, default=5)
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    _validate_environment()
+    # The host exposes unusable RDMA interfaces.  Both native and deterministic
+    # paths use the same intra-node XGMI transport with IB disabled.
+    os.environ.setdefault("NCCL_IB_DISABLE", "1")
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    payload: dict[str, Any] = {
+        "environment": _environment(),
+        "methodology": {
+            "single_gpu_timing": "GPU events, median and p95",
+            "distributed_timing": "synchronized wall clock, slowest rank per sample",
+            "warmup": args.warmup,
+            "samples": args.samples,
+            "training_samples": args.training_samples,
+            "operator_only": True,
+        },
+        "single_gpu": _single_gpu_benchmarks(
+            warmup=args.warmup,
+            samples=args.samples,
+            training_samples=args.training_samples,
+        ),
+        "collectives": [],
+        "distributed_ffn": [],
+    }
+    torch.cuda.empty_cache()
+
+    for world_size in (2, 4, 8):
+        result = _run_distributed_world(
+            world_size,
+            warmup=args.warmup,
+            samples=args.samples,
+            training_samples=args.training_samples,
+        )
+        payload["collectives"].extend(result["collectives"])
+        payload["distributed_ffn"].extend(result["distributed_ffn"])
+
+    (args.output_dir / "results.json").write_text(
+        json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8"
+    )
+    _write_report(payload, args.output_dir)
+    _write_figures(payload, args.output_dir)
+    print(json.dumps({"output_dir": str(args.output_dir), "status": "ok"}))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png b/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png
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diff --git a/benchmarks/results/pr325_rocm_mi300x/report.md b/benchmarks/results/pr325_rocm_mi300x/report.md
new file mode 100644
index 00000000..0a0dfd1b
--- /dev/null
+++ b/benchmarks/results/pr325_rocm_mi300x/report.md
@@ -0,0 +1,147 @@
+# PR #325 ROCm deterministic FFN performance analysis
+
+> Operator-only benchmark. No model checkpoint or serving engine was used.
+
+## Environment
+
+| Item | Value |
+|---|---|
+| NCCL_IB_DISABLE | 1 |
+| architecture | gfx942:sramecc+:xnack- |
+| git_commit | 7d04efe80f7903431bc75fff3bbbe85eb82e02c4 |
+| gpu | AMD Instinct MI300X |
+| gpu_count | 8 |
+| hip | 7.14.60850 |
+| native_collective | torch.distributed ProcessGroupNCCL (RCCL on ROCm) |
+| native_gemm | torch.matmul (ROCm rocBLAS/hipBLASLt dispatch) |
+| python | 3.12.3 |
+| torch | 2.12.0+rocm7.14.0a20260608 |
+
+## Methodology
+
+- BF16 operator inputs; FFN dimensions are H=4096 and I=12288.
+- Native GEMM is `torch.matmul`; native communication calls PyTorch's ProcessGroupNCCL directly, which uses RCCL on ROCm.
+- The native distributed FFN uses the same weights, TP/CP/SP sharding, and collective schedule as the deterministic path.
+- Single-GPU timing: GPU events, median and p95; distributed timing: synchronized wall clock, slowest rank per sample.
+- 5 warmups, 20 measured forward/collective samples, and 10 measured forward+backward samples.
+- `NCCL_IB_DISABLE=1` selects the same intra-node XGMI transport for both collective implementations. Raw median, p95, minimum, and maximum measurements are in `results.json`.
+
+Reproduce this report from the repository root:
+
+```bash
+python benchmarks/benchmark_rocm_ffn.py \
+  --warmup 5 \
+  --samples 20 \
+  --training-samples 10 \
+  --output-dir benchmarks/results/pr325_rocm_mi300x
+```
+
+## Key findings
+
+- The gfx942 deterministic GEMM scalar fallback costs 44.0-1109.8x native latency. This dominates end-to-end FFN overhead.
+- The fused deterministic SwiGLU costs 0.22-0.76x native latency, so it is faster than the unfused PyTorch expression; it is not the bottleneck.
+- Deterministic collectives cost 2.0-27.6x native RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.
+- Distributed FFN overhead is 32.1-105.8x for forward and 11.7-61.0x for forward+backward.
+- All 16 distributed direction/topology cases are train/inference and repeat bitwise consistent: yes.
+- GEMM relative-L2 error against FP32 is 0.500-0.577% for the deterministic BF16 tree versus 0.164-0.166% for native ROCm. The fixed BF16 tree buys topology invariance, not better FP32 proximity.
+- Distributed FFN output drift versus native is 1.012-1.060% relative-L2. Conversely, the balanced deterministic reduction is closer to FP32 than native RCCL in 12/12 tested 4/8-rank reduction cases.
+
+## Single-GPU GEMM
+
+| Shape | Native ROCm (ms) | Deterministic (ms) | Overhead | Det rel-L2 vs FP32 | Native rel-L2 vs FP32 |
+|---|---:|---:|---:|---:|---:|
+| gate_up_m1 | 0.0384 | 1.6887 | 44.0× | 5.003e-03 | 1.651e-03 |
+| down_m1 | 0.0283 | 4.8950 | 173.2× | 5.769e-03 | 1.635e-03 |
+| gate_up_m8 | 0.0297 | 3.1275 | 105.4× | 5.018e-03 | 1.663e-03 |
+| down_m8 | 0.0299 | 6.3458 | 211.9× | 5.679e-03 | 1.654e-03 |
+| gate_up_m32 | 0.0318 | 12.9507 | 406.9× | 5.019e-03 | 1.654e-03 |
+| down_m32 | 0.0295 | 16.5279 | 560.6× | 5.604e-03 | 1.662e-03 |
+| gate_up_m128 | 0.0533 | 45.9833 | 862.7× | 5.019e-03 | 1.657e-03 |
+| down_m128 | 0.0550 | 61.0427 | 1109.8× | 5.623e-03 | 1.659e-03 |
+
+## Single-GPU SwiGLU
+
+| Case | Native PyTorch (ms) | Deterministic fused (ms) | Det/native | Det/native rel-L2 |
+|---|---:|---:|---:|---:|
+| swiglu_forward_m1 | 0.0168 | 0.0127 | 0.76x | 2.877e-03 |
+| swiglu_backward_m1 | 0.0562 | 0.0141 | 0.25x | 5.159e-03 |
+| swiglu_forward_m8 | 0.0190 | 0.0129 | 0.68x | 2.816e-03 |
+| swiglu_backward_m8 | 0.0676 | 0.0146 | 0.22x | 5.074e-03 |
+| swiglu_forward_m32 | 0.0200 | 0.0136 | 0.68x | 2.825e-03 |
+| swiglu_backward_m32 | 0.0706 | 0.0156 | 0.22x | 5.071e-03 |
+| swiglu_forward_m128 | 0.0203 | 0.0134 | 0.66x | 2.828e-03 |
+| swiglu_backward_m128 | 0.0669 | 0.0149 | 0.22x | 5.059e-03 |
+
+## Single-GPU FFN
+
+| Case | Native ROCm (ms) | Deterministic (ms) | Overhead | Output rel-L2 | Max grad rel-L2 |
+|---|---:|---:|---:|---:|---:|
+| ffn_forward_m1 | 0.0929 | 9.4786 | 102.0× | 1.008e-02 | nan |
+| ffn_train_fwd_bwd_m1 | 0.4308 | 39.0465 | 90.6× | 1.008e-02 | 1.037e-02 |
+| ffn_forward_m8 | 0.1018 | 14.0304 | 137.8× | 9.930e-03 | nan |
+| ffn_train_fwd_bwd_m8 | 0.4067 | 47.4899 | 116.8× | 9.930e-03 | 1.017e-02 |
+| ffn_forward_m32 | 0.1120 | 43.5693 | 388.9× | 9.952e-03 | nan |
+| ffn_train_fwd_bwd_m32 | 0.5347 | 107.4444 | 200.9× | 9.952e-03 | 1.024e-02 |
+
+## RCCL collectives
+
+| Operation | Ranks | Input/rank | Native RCCL (ms) | Deterministic (ms) | Overhead | Det/native rel-L2 | Native/FP32 rel-L2 | Det/FP32 rel-L2 |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| all_reduce | 2 | 0.0625 MiB | 0.0592 | 0.5532 | 9.3× | 0.000e+00 | 1.798e-03 | 1.798e-03 |
+| all_gather | 2 | 0.0625 MiB | 0.0534 | 0.5392 | 10.1× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 2 | 0.0625 MiB | 0.0523 | 0.5616 | 10.7× | 0.000e+00 | 1.804e-03 | 1.804e-03 |
+| all_reduce | 2 | 1 MiB | 0.0825 | 0.5553 | 6.7× | 0.000e+00 | 1.800e-03 | 1.800e-03 |
+| all_gather | 2 | 1 MiB | 0.0772 | 0.5586 | 7.2× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 2 | 1 MiB | 0.0719 | 0.5781 | 8.0× | 0.000e+00 | 1.803e-03 | 1.803e-03 |
+| all_reduce | 2 | 16 MiB | 0.5073 | 1.0212 | 2.0× | 0.000e+00 | 1.800e-03 | 1.800e-03 |
+| all_gather | 2 | 16 MiB | 0.4099 | 1.2952 | 3.2× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 2 | 16 MiB | 0.2442 | 0.8913 | 3.6× | 0.000e+00 | 1.803e-03 | 1.803e-03 |
+| all_reduce | 4 | 0.0625 MiB | 0.0617 | 0.7073 | 11.5× | 3.221e-03 | 2.674e-03 | 2.536e-03 |
+| all_gather | 4 | 0.0625 MiB | 0.0573 | 0.6558 | 11.4× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 4 | 0.0625 MiB | 0.0559 | 0.7087 | 12.7× | 3.263e-03 | 2.734e-03 | 2.559e-03 |
+| all_reduce | 4 | 1 MiB | 0.0874 | 0.6935 | 7.9× | 3.256e-03 | 2.682e-03 | 2.566e-03 |
+| all_gather | 4 | 1 MiB | 0.0883 | 0.6794 | 7.7× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 4 | 1 MiB | 0.0908 | 0.7014 | 7.7× | 3.319e-03 | 2.691e-03 | 2.599e-03 |
+| all_reduce | 4 | 16 MiB | 0.2442 | 1.0390 | 4.3× | 3.256e-03 | 2.682e-03 | 2.566e-03 |
+| all_gather | 4 | 16 MiB | 0.4244 | 1.0259 | 2.4× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 4 | 16 MiB | 0.1556 | 1.0519 | 6.8× | 3.319e-03 | 2.691e-03 | 2.599e-03 |
+| all_reduce | 8 | 0.0625 MiB | 0.0374 | 1.0311 | 27.6× | 4.446e-03 | 3.664e-03 | 3.099e-03 |
+| all_gather | 8 | 0.0625 MiB | 0.0721 | 0.9555 | 13.2× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 8 | 0.0625 MiB | 0.0670 | 1.0387 | 15.5× | 4.879e-03 | 3.780e-03 | 3.155e-03 |
+| all_reduce | 8 | 1 MiB | 0.0545 | 1.0036 | 18.4× | 4.474e-03 | 3.676e-03 | 3.110e-03 |
+| all_gather | 8 | 1 MiB | 0.0887 | 0.9689 | 10.9× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 8 | 1 MiB | 0.0669 | 1.0162 | 15.2× | 4.765e-03 | 3.768e-03 | 3.168e-03 |
+| all_reduce | 8 | 16 MiB | 0.1812 | 1.3730 | 7.6× | 4.474e-03 | 3.676e-03 | 3.110e-03 |
+| all_gather | 8 | 16 MiB | 0.4353 | 1.3738 | 3.2× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 8 | 16 MiB | 0.1228 | 1.5048 | 12.3× | 4.765e-03 | 3.768e-03 | 3.168e-03 |
+
+## Distributed FFN
+
+| Topology | Direction | Native ROCm/RCCL (ms) | Deterministic (ms) | Overhead | Output rel-L2 | Max grad rel-L2 | Train/infer bitwise | Repeat bitwise |
+|---|---|---:|---:|---:|---:|---:|:---:|:---:|
+| tp2 | forward | 0.2170 | 22.9695 | 105.8× | 1.012e-02 | nan | yes | yes |
+| tp2 | train_fwd_bwd | 0.9359 | 57.1264 | 61.0× | 1.012e-02 | 1.079e-02 | yes | yes |
+| tp2_sp | forward | 0.2821 | 23.6457 | 83.8× | 1.015e-02 | nan | yes | yes |
+| tp2_sp | train_fwd_bwd | 1.0045 | 58.6581 | 58.4× | 1.015e-02 | 1.083e-02 | yes | yes |
+| tp4 | forward | 0.2175 | 13.6428 | 62.7× | 1.031e-02 | nan | yes | yes |
+| tp4 | train_fwd_bwd | 1.1884 | 33.2462 | 28.0× | 1.031e-02 | 1.095e-02 | yes | yes |
+| tp2_cp2 | forward | 0.2231 | 14.1083 | 63.2× | 1.015e-02 | nan | yes | yes |
+| tp2_cp2 | train_fwd_bwd | 1.2568 | 43.0444 | 34.2× | 1.015e-02 | 1.083e-02 | yes | yes |
+| tp2_cp2_sp | forward | 0.2819 | 14.7152 | 52.2× | 1.022e-02 | nan | yes | yes |
+| tp2_cp2_sp | train_fwd_bwd | 1.3499 | 44.1678 | 32.7× | 1.022e-02 | 1.089e-02 | yes | yes |
+| tp8 | forward | 0.1939 | 8.2995 | 42.8× | 1.060e-02 | nan | yes | yes |
+| tp8 | train_fwd_bwd | 1.0345 | 20.4734 | 19.8× | 1.060e-02 | 1.126e-02 | yes | yes |
+| tp4_cp2 | forward | 0.2230 | 8.4606 | 37.9× | 1.033e-02 | nan | yes | yes |
+| tp4_cp2 | train_fwd_bwd | 2.1949 | 25.7398 | 11.7× | 1.033e-02 | 1.099e-02 | yes | yes |
+| tp4_cp2_sp | forward | 0.2846 | 9.1342 | 32.1× | 1.045e-02 | nan | yes | yes |
+| tp4_cp2_sp | train_fwd_bwd | 2.0314 | 26.6942 | 13.1× | 1.045e-02 | 1.109e-02 | yes | yes |
+
+## Figures
+
+![Single-GPU overhead](single_gpu_overhead.png)
+
+![RCCL collective overhead](collective_overhead.png)
+
+![Distributed FFN overhead](distributed_ffn_overhead.png)
+
+![Accuracy trade-off](accuracy_tradeoff.png)
diff --git a/benchmarks/results/pr325_rocm_mi300x/results.json b/benchmarks/results/pr325_rocm_mi300x/results.json
new file mode 100644
index 00000000..77f72fa3
--- /dev/null
+++ b/benchmarks/results/pr325_rocm_mi300x/results.json
@@ -0,0 +1,1859 @@
+{
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+      "overhead_ratio": 9.349670437776684,
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HcmV?d00001


From 9dc9a68c2b3920c6e1e13226d38f5900b55ec0de Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sun, 23 Aug 2026 02:41:53 +0000
Subject: [PATCH 08/28] feat: add strict distributed Triton FFN for ROCm

---
 benchmarks/benchmark_rocm_ffn.py              | 399 +++++++++++++++---
 .../kernels/ops/triton/activation/swiglu.py   |  48 ++-
 rl_engine/kernels/ops/triton/ffn/__init__.py  |   6 +
 rl_engine/kernels/ops/triton/ffn/ffn.py       | 200 +++++++++
 .../kernels/ops/triton/matmul/det_gemm.py     | 313 +++++++++++++-
 tests/distributed/test_qwen_ffn_topology.py   |  44 +-
 tests/test_det_gemm.py                        |  75 ++--
 tests/test_qwen_ffn.py                        |  65 +++
 tests/test_swiglu.py                          |  28 ++
 9 files changed, 1062 insertions(+), 116 deletions(-)
 create mode 100644 rl_engine/kernels/ops/triton/ffn/__init__.py
 create mode 100644 rl_engine/kernels/ops/triton/ffn/ffn.py

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index 8cc3f895..8216d736 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -41,6 +41,12 @@
 from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
 from rl_engine.kernels.ops.cuda.matmul import deterministic_gemm
 from rl_engine.kernels.ops.pytorch.ffn import qwen3_ffn
+from rl_engine.kernels.ops.triton.activation.swiglu import (
+    _launch_swiglu_bwd,
+    _launch_swiglu_fwd,
+)
+from rl_engine.kernels.ops.triton.ffn import qwen3_ffn_triton
+from rl_engine.kernels.ops.triton.matmul import deterministic_gemm_triton
 
 _MIB = 1024 * 1024
 _REQUIRED_SYMBOLS = (
@@ -184,6 +190,7 @@ def _single_gpu_benchmarks(
         right = _randn((k_size, n_size), seed=1001 + shape_index * 2, device=device)
         native_output = torch.matmul(left, right)
         deterministic_output = deterministic_gemm(left, right)
+        triton_output = deterministic_gemm_triton(left, right)
         fp32_reference = left.float() @ right.float()
         torch.cuda.synchronize()
 
@@ -193,8 +200,14 @@ def _single_gpu_benchmarks(
         deterministic_samples = _gpu_event_samples(
             lambda: deterministic_gemm(left, right), warmup=warmup, samples=samples
         )
+        triton_samples = _gpu_event_samples(
+            lambda: deterministic_gemm_triton(left, right),
+            warmup=warmup,
+            samples=samples,
+        )
         native_timing = _summary_ms(native_samples)
         deterministic_timing = _summary_ms(deterministic_samples)
+        triton_timing = _summary_ms(triton_samples)
         results["gemm"].append(
             {
                 "name": name,
@@ -204,9 +217,19 @@ def _single_gpu_benchmarks(
                 "dtype": "bfloat16",
                 "native": native_timing,
                 "deterministic": deterministic_timing,
+                "triton": triton_timing,
                 "overhead_ratio": (
                     deterministic_timing["median_ms"] / native_timing["median_ms"]
                 ),
+                "triton_overhead_ratio": (
+                    triton_timing["median_ms"] / native_timing["median_ms"]
+                ),
+                "triton_speedup_vs_hip": (
+                    deterministic_timing["median_ms"] / triton_timing["median_ms"]
+                ),
+                "triton_vs_hip_mismatch_count": int(
+                    (triton_output != deterministic_output).sum().item()
+                ),
                 "deterministic_vs_native": _accuracy(
                     deterministic_output, native_output
                 ),
@@ -214,9 +237,17 @@ def _single_gpu_benchmarks(
                 "deterministic_vs_fp32": _accuracy(
                     deterministic_output, fp32_reference
                 ),
+                "triton_vs_fp32": _accuracy(triton_output, fp32_reference),
             }
         )
-        del left, right, native_output, deterministic_output, fp32_reference
+        del (
+            left,
+            right,
+            native_output,
+            deterministic_output,
+            triton_output,
+            fp32_reference,
+        )
         torch.cuda.empty_cache()
 
     for case_index, tokens in enumerate((1, 8, 32, 128)):
@@ -228,6 +259,7 @@ def _single_gpu_benchmarks(
 
         native_forward = F.silu(gate) * up
         deterministic_forward = _C.swiglu_forward(gate, up)
+        triton_forward = _launch_swiglu_fwd(gate, up)
 
         def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
             sigmoid = torch.sigmoid(gate)
@@ -239,17 +271,20 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
         deterministic_grad_gate, deterministic_grad_up = _C.swiglu_backward(
             grad_output, gate, up
         )
+        triton_grad_gate, triton_grad_up = _launch_swiglu_bwd(grad_output, gate, up)
 
-        for direction, native_function, deterministic_function in (
+        for direction, native_function, deterministic_function, triton_function in (
             (
                 "forward",
                 lambda: F.silu(gate) * up,
                 lambda: _C.swiglu_forward(gate, up),
+                lambda: _launch_swiglu_fwd(gate, up),
             ),
             (
                 "backward",
                 native_backward,
                 lambda: _C.swiglu_backward(grad_output, gate, up),
+                lambda: _launch_swiglu_bwd(grad_output, gate, up),
             ),
         ):
             native_timing = _summary_ms(
@@ -262,8 +297,16 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
                     deterministic_function, warmup=warmup, samples=samples
                 )
             )
+            triton_timing = _summary_ms(
+                _gpu_event_samples(
+                    triton_function, warmup=warmup, samples=samples
+                )
+            )
             if direction == "forward":
                 accuracy = _accuracy(deterministic_forward, native_forward)
+                triton_mismatch_count = int(
+                    (triton_forward != deterministic_forward).sum().item()
+                )
             else:
                 accuracy = {
                     "max_abs": max(
@@ -275,6 +318,10 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
                         _relative_l2(deterministic_grad_up, native_grad_up),
                     ),
                 }
+                triton_mismatch_count = int(
+                    (triton_grad_gate != deterministic_grad_gate).sum().item()
+                    + (triton_grad_up != deterministic_grad_up).sum().item()
+                )
             results["swiglu"].append(
                 {
                     "name": f"swiglu_{direction}_m{tokens}",
@@ -284,9 +331,17 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
                     "dtype": "bfloat16",
                     "native": native_timing,
                     "deterministic": deterministic_timing,
+                    "triton": triton_timing,
                     "overhead_ratio": (
                         deterministic_timing["median_ms"] / native_timing["median_ms"]
                     ),
+                    "triton_overhead_ratio": (
+                        triton_timing["median_ms"] / native_timing["median_ms"]
+                    ),
+                    "triton_speedup_vs_hip": (
+                        deterministic_timing["median_ms"] / triton_timing["median_ms"]
+                    ),
+                    "triton_vs_hip_mismatch_count": triton_mismatch_count,
                     "deterministic_vs_native": accuracy,
                 }
             )
@@ -307,6 +362,9 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
             deterministic_output = qwen3_ffn(
                 hidden, gate_weight, up_weight, down_weight
             )
+            triton_output = qwen3_ffn_triton(
+                hidden, gate_weight, up_weight, down_weight
+            )
 
         native_timing = _summary_ms(
             _gpu_event_samples(
@@ -322,6 +380,15 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
                 samples=samples,
             )
         )
+        triton_timing = _summary_ms(
+            _gpu_event_samples(
+                lambda: qwen3_ffn_triton(
+                    hidden, gate_weight, up_weight, down_weight
+                ),
+                warmup=warmup,
+                samples=samples,
+            )
+        )
         results["ffn"].append(
             {
                 "name": f"ffn_forward_m{tokens}",
@@ -332,9 +399,19 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
                 "dtype": "bfloat16",
                 "native": native_timing,
                 "deterministic": deterministic_timing,
+                "triton": triton_timing,
                 "overhead_ratio": (
                     deterministic_timing["median_ms"] / native_timing["median_ms"]
                 ),
+                "triton_overhead_ratio": (
+                    triton_timing["median_ms"] / native_timing["median_ms"]
+                ),
+                "triton_speedup_vs_hip": (
+                    deterministic_timing["median_ms"] / triton_timing["median_ms"]
+                ),
+                "triton_vs_hip_mismatch_count": int(
+                    (triton_output != deterministic_output).sum().item()
+                ),
                 "deterministic_vs_native": _accuracy(
                     deterministic_output, native_output
                 ),
@@ -350,6 +427,10 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
             value.detach().clone().requires_grad_(True)
             for value in (hidden, gate_weight, up_weight, down_weight)
         ]
+        triton_inputs = [
+            value.detach().clone().requires_grad_(True)
+            for value in (hidden, gate_weight, up_weight, down_weight)
+        ]
 
         def native_training_step() -> torch.Tensor:
             for value in native_inputs:
@@ -365,12 +446,21 @@ def deterministic_training_step() -> torch.Tensor:
             output.backward(grad_output)
             return output
 
+        def triton_training_step() -> torch.Tensor:
+            for value in triton_inputs:
+                value.grad = None
+            output = qwen3_ffn_triton(*triton_inputs)
+            output.backward(grad_output)
+            return output
+
         native_train_output = native_training_step().detach().clone()
         native_grads = [value.grad.detach().clone() for value in native_inputs]
         deterministic_train_output = deterministic_training_step().detach().clone()
         deterministic_grads = [
             value.grad.detach().clone() for value in deterministic_inputs
         ]
+        triton_train_output = triton_training_step().detach().clone()
+        triton_grads = [value.grad.detach().clone() for value in triton_inputs]
         train_infer_bitwise = torch.equal(
             deterministic_output, deterministic_train_output
         )
@@ -389,6 +479,13 @@ def deterministic_training_step() -> torch.Tensor:
                 samples=training_samples,
             )
         )
+        triton_train_timing = _summary_ms(
+            _gpu_event_samples(
+                triton_training_step,
+                warmup=max(1, warmup // 2),
+                samples=training_samples,
+            )
+        )
         results["ffn"].append(
             {
                 "name": f"ffn_train_fwd_bwd_m{tokens}",
@@ -399,10 +496,28 @@ def deterministic_training_step() -> torch.Tensor:
                 "dtype": "bfloat16",
                 "native": native_train_timing,
                 "deterministic": deterministic_train_timing,
+                "triton": triton_train_timing,
                 "overhead_ratio": (
                     deterministic_train_timing["median_ms"]
                     / native_train_timing["median_ms"]
                 ),
+                "triton_overhead_ratio": (
+                    triton_train_timing["median_ms"]
+                    / native_train_timing["median_ms"]
+                ),
+                "triton_speedup_vs_hip": (
+                    deterministic_train_timing["median_ms"]
+                    / triton_train_timing["median_ms"]
+                ),
+                "triton_vs_hip_mismatch_count": int(
+                    (triton_train_output != deterministic_train_output).sum().item()
+                    + sum(
+                        (triton_grad != deterministic_grad).sum().item()
+                        for triton_grad, deterministic_grad in zip(
+                            triton_grads, deterministic_grads, strict=True
+                        )
+                    )
+                ),
                 "deterministic_vs_native": _accuracy(
                     deterministic_train_output, native_train_output
                 ),
@@ -420,8 +535,10 @@ def deterministic_training_step() -> torch.Tensor:
             grad_output,
             native_inputs,
             deterministic_inputs,
+            triton_inputs,
             native_grads,
             deterministic_grads,
+            triton_grads,
         )
         torch.cuda.empty_cache()
 
@@ -952,11 +1069,19 @@ def _distributed_ffn_benchmark(
             cp_group=cp_group,
             sequence_parallel=sequence_parallel,
         )
+        triton_forward = lambda: qwen3_ffn_triton(
+            *shard,
+            tp_group=tp_group,
+            cp_group=cp_group,
+            sequence_parallel=sequence_parallel,
+        )
 
         with torch.no_grad():
             native_output = native_forward()
             deterministic_output = deterministic_forward()
             deterministic_repeat = deterministic_forward()
+            triton_output = triton_forward()
+            triton_repeat = triton_forward()
         torch.cuda.synchronize()
 
         native_forward_timings = _distributed_wall_samples(
@@ -971,17 +1096,29 @@ def _distributed_ffn_benchmark(
             warmup=warmup,
             samples=samples,
         )
+        triton_forward_timings = _distributed_wall_samples(
+            triton_forward,
+            group=dist.group.WORLD,
+            warmup=warmup,
+            samples=samples,
+        )
         native_forward_summary = _slowest_rank_summary(
             native_forward_timings, dist.group.WORLD
         )
         deterministic_forward_summary = _slowest_rank_summary(
             deterministic_forward_timings, dist.group.WORLD
         )
+        triton_forward_summary = _slowest_rank_summary(
+            triton_forward_timings, dist.group.WORLD
+        )
 
         native_inputs = [value.detach().clone().requires_grad_(True) for value in shard]
         deterministic_inputs = [
             value.detach().clone().requires_grad_(True) for value in shard
         ]
+        triton_inputs = [
+            value.detach().clone().requires_grad_(True) for value in shard
+        ]
 
         def native_training_step() -> torch.Tensor:
             for value in native_inputs:
@@ -1007,12 +1144,26 @@ def deterministic_training_step() -> torch.Tensor:
             output.backward(local_grad_output)
             return output
 
+        def triton_training_step() -> torch.Tensor:
+            for value in triton_inputs:
+                value.grad = None
+            output = qwen3_ffn_triton(
+                *triton_inputs,
+                tp_group=tp_group,
+                cp_group=cp_group,
+                sequence_parallel=sequence_parallel,
+            )
+            output.backward(local_grad_output)
+            return output
+
         native_train_output = native_training_step().detach().clone()
         native_grads = [value.grad.detach().clone() for value in native_inputs]
         deterministic_train_output = deterministic_training_step().detach().clone()
         deterministic_grads = [
             value.grad.detach().clone() for value in deterministic_inputs
         ]
+        triton_train_output = triton_training_step().detach().clone()
+        triton_grads = [value.grad.detach().clone() for value in triton_inputs]
         torch.cuda.synchronize()
 
         native_train_timings = _distributed_wall_samples(
@@ -1027,12 +1178,21 @@ def deterministic_training_step() -> torch.Tensor:
             warmup=max(1, warmup // 2),
             samples=training_samples,
         )
+        triton_train_timings = _distributed_wall_samples(
+            triton_training_step,
+            group=dist.group.WORLD,
+            warmup=max(1, warmup // 2),
+            samples=training_samples,
+        )
         native_train_summary = _slowest_rank_summary(
             native_train_timings, dist.group.WORLD
         )
         deterministic_train_summary = _slowest_rank_summary(
             deterministic_train_timings, dist.group.WORLD
         )
+        triton_train_summary = _slowest_rank_summary(
+            triton_train_timings, dist.group.WORLD
+        )
 
         local_accuracy = {
             "output_relative_l2": _relative_l2(
@@ -1053,6 +1213,22 @@ def deterministic_training_step() -> torch.Tensor:
             "repeat_bitwise": torch.equal(
                 deterministic_output, deterministic_repeat
             ),
+            "triton_forward_mismatch_count": int(
+                (triton_output != deterministic_output).sum().item()
+            ),
+            "triton_train_mismatch_count": int(
+                (triton_train_output != deterministic_train_output).sum().item()
+                + sum(
+                    (triton_grad != deterministic_grad).sum().item()
+                    for triton_grad, deterministic_grad in zip(
+                        triton_grads, deterministic_grads, strict=True
+                    )
+                )
+            ),
+            "triton_train_infer_bitwise": torch.equal(
+                triton_output, triton_train_output
+            ),
+            "triton_repeat_bitwise": torch.equal(triton_output, triton_repeat),
         }
         gathered_accuracy: list[dict[str, Any] | None] = [None] * world_size
         dist.all_gather_object(
@@ -1074,10 +1250,23 @@ def deterministic_training_step() -> torch.Tensor:
                         "intermediate": intermediate_size,
                         "native": native_forward_summary,
                         "deterministic": deterministic_forward_summary,
+                        "triton": triton_forward_summary,
                         "overhead_ratio": (
                             deterministic_forward_summary["median_ms"]
                             / native_forward_summary["median_ms"]
                         ),
+                        "triton_overhead_ratio": (
+                            triton_forward_summary["median_ms"]
+                            / native_forward_summary["median_ms"]
+                        ),
+                        "triton_speedup_vs_hip": (
+                            deterministic_forward_summary["median_ms"]
+                            / triton_forward_summary["median_ms"]
+                        ),
+                        "triton_vs_hip_mismatch_count": sum(
+                            value["triton_forward_mismatch_count"]
+                            for value in valid_accuracy
+                        ),
                         "output_relative_l2": max(
                             value["output_relative_l2"] for value in valid_accuracy
                         ),
@@ -1090,6 +1279,14 @@ def deterministic_training_step() -> torch.Tensor:
                         "repeat_bitwise": all(
                             value["repeat_bitwise"] for value in valid_accuracy
                         ),
+                        "triton_train_infer_bitwise": all(
+                            value["triton_train_infer_bitwise"]
+                            for value in valid_accuracy
+                        ),
+                        "triton_repeat_bitwise": all(
+                            value["triton_repeat_bitwise"]
+                            for value in valid_accuracy
+                        ),
                     },
                     {
                         "name": name,
@@ -1103,10 +1300,23 @@ def deterministic_training_step() -> torch.Tensor:
                         "intermediate": intermediate_size,
                         "native": native_train_summary,
                         "deterministic": deterministic_train_summary,
+                        "triton": triton_train_summary,
                         "overhead_ratio": (
                             deterministic_train_summary["median_ms"]
                             / native_train_summary["median_ms"]
                         ),
+                        "triton_overhead_ratio": (
+                            triton_train_summary["median_ms"]
+                            / native_train_summary["median_ms"]
+                        ),
+                        "triton_speedup_vs_hip": (
+                            deterministic_train_summary["median_ms"]
+                            / triton_train_summary["median_ms"]
+                        ),
+                        "triton_vs_hip_mismatch_count": sum(
+                            value["triton_train_mismatch_count"]
+                            for value in valid_accuracy
+                        ),
                         "output_relative_l2": max(
                             _relative_l2(
                                 deterministic_train_output, native_train_output
@@ -1123,6 +1333,14 @@ def deterministic_training_step() -> torch.Tensor:
                         "repeat_bitwise": all(
                             value["repeat_bitwise"] for value in valid_accuracy
                         ),
+                        "triton_train_infer_bitwise": all(
+                            value["triton_train_infer_bitwise"]
+                            for value in valid_accuracy
+                        ),
+                        "triton_repeat_bitwise": all(
+                            value["triton_repeat_bitwise"]
+                            for value in valid_accuracy
+                        ),
                     },
                 )
             )
@@ -1132,8 +1350,10 @@ def deterministic_training_step() -> torch.Tensor:
             local_grad_output,
             native_inputs,
             deterministic_inputs,
+            triton_inputs,
             native_grads,
             deterministic_grads,
+            triton_grads,
         )
         torch.cuda.empty_cache()
         dist.barrier(group=dist.group.WORLD)
@@ -1275,8 +1495,12 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
     distributed_rows = payload["distributed_ffn"]
     methodology = payload["methodology"]
 
-    def ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
-        ratios = [row["overhead_ratio"] for row in rows]
+    def ratio_range(
+        rows: list[dict[str, Any]],
+        key: str = "overhead_ratio",
+        digits: int = 1,
+    ) -> str:
+        ratios = [row[key] for row in rows]
         return f"{min(ratios):.{digits}f}-{max(ratios):.{digits}f}x"
 
     forward_rows = [row for row in distributed_rows if row["direction"] == "forward"]
@@ -1306,6 +1530,16 @@ def ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
         row["train_infer_bitwise"] and row["repeat_bitwise"]
         for row in distributed_rows
     )
+    triton_consistency_rows = all(
+        row["triton_train_infer_bitwise"]
+        and row["triton_repeat_bitwise"]
+        and row["triton_vs_hip_mismatch_count"] == 0
+        for row in distributed_rows
+    )
+    all_triton_rows = gemm_rows + swiglu_rows + ffn_rows + distributed_rows
+    total_triton_mismatches = sum(
+        row["triton_vs_hip_mismatch_count"] for row in all_triton_rows
+    )
 
     lines = [
         "# PR #325 ROCm deterministic FFN performance analysis",
@@ -1328,8 +1562,11 @@ def ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
             "- BF16 operator inputs; FFN dimensions are H=4096 and I=12288.",
             "- Native GEMM is `torch.matmul`; native communication calls PyTorch's "
             "ProcessGroupNCCL directly, which uses RCCL on ROCm.",
-            "- The native distributed FFN uses the same weights, TP/CP/SP sharding, "
-            "and collective schedule as the deterministic path.",
+            "- Three compute paths are measured: native ROCm, the strict HIP "
+            "correctness kernels, and the new strict Triton kernels. HIP and Triton "
+            "share the same deterministic RCCL collective implementation.",
+            "- All distributed paths use the same weights, TP/CP/SP sharding, and "
+            "collective schedule.",
             f"- Single-GPU timing: {methodology['single_gpu_timing']}; distributed "
             f"timing: {methodology['distributed_timing']}.",
             f"- {methodology['warmup']} warmups, {methodology['samples']} measured "
@@ -1351,20 +1588,27 @@ def ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
             "",
             "## Key findings",
             "",
-            f"- The gfx942 deterministic GEMM scalar fallback costs "
-            f"{ratio_range(gemm_rows)} native latency. This dominates end-to-end FFN "
-            "overhead.",
-            f"- The fused deterministic SwiGLU costs {ratio_range(swiglu_rows, 2)} "
-            "native "
-            "latency, so it is faster than the unfused PyTorch expression; it is not "
-            "the bottleneck.",
+            f"- Strict Triton GEMM costs "
+            f"{ratio_range(gemm_rows, 'triton_overhead_ratio')} native latency, "
+            f"versus {ratio_range(gemm_rows)} for the gfx942 HIP scalar fallback. "
+            f"That is a {ratio_range(gemm_rows, 'triton_speedup_vs_hip')} speedup "
+            "while preserving the identical BF16 arithmetic tree.",
+            f"- Strict Triton SwiGLU costs "
+            f"{ratio_range(swiglu_rows, 'triton_overhead_ratio', 2)} native latency; "
+            f"the HIP fused kernel costs {ratio_range(swiglu_rows, digits=2)}.",
             f"- Deterministic collectives cost {ratio_range(collective_rows)} native "
             "RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.",
-            f"- Distributed FFN overhead is {ratio_range(forward_rows)} for forward and "
-            f"{ratio_range(training_rows)} for forward+backward.",
-            f"- All {len(distributed_rows)} distributed direction/topology cases are "
-            "train/inference and repeat bitwise consistent: "
-            f"{'yes' if consistency_rows else 'no'}.",
+            f"- Triton distributed FFN overhead is "
+            f"{ratio_range(forward_rows, 'triton_overhead_ratio')} for forward and "
+            f"{ratio_range(training_rows, 'triton_overhead_ratio')} for "
+            f"forward+backward, compared with {ratio_range(forward_rows)} and "
+            f"{ratio_range(training_rows)} for HIP.",
+            f"- HIP and Triton produced {total_triton_mismatches} mismatched elements "
+            f"across all measured GEMM, SwiGLU, single-GPU FFN, and distributed FFN "
+            "outputs/gradients. All distributed TP/CP/SP cases are repeat and "
+            "train/inference bitwise consistent: "
+            f"HIP={'yes' if consistency_rows else 'no'}, "
+            f"Triton={'yes' if triton_consistency_rows else 'no'}.",
             f"- GEMM relative-L2 error against FP32 is "
             f"{min(gemm_deterministic_errors):.3f}-{max(gemm_deterministic_errors):.3f}% "
             f"for the deterministic BF16 tree versus "
@@ -1384,15 +1628,20 @@ def ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
             "",
             "## Single-GPU GEMM",
             "",
-            "| Shape | Native ROCm (ms) | Deterministic (ms) | Overhead | "
-            "Det rel-L2 vs FP32 | Native rel-L2 vs FP32 |",
-            "|---|---:|---:|---:|---:|---:|",
+            "| Shape | Native (ms) | Strict HIP (ms) | Strict Triton (ms) | "
+            "HIP/native | Triton/native | Triton speedup | Mismatch | "
+            "Strict rel-L2 vs FP32 | Native rel-L2 vs FP32 |",
+            "|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|",
         )
     )
     for row in gemm_rows:
         lines.append(
             f"| {row['name']} | {row['native']['median_ms']:.4f} | "
-            f"{row['deterministic']['median_ms']:.4f} | {row['overhead_ratio']:.1f}× | "
+            f"{row['deterministic']['median_ms']:.4f} | "
+            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.1f}× | "
+            f"{row['triton_overhead_ratio']:.1f}× | "
+            f"{row['triton_speedup_vs_hip']:.1f}× | "
+            f"{row['triton_vs_hip_mismatch_count']} | "
             f"{row['deterministic_vs_fp32']['relative_l2']:.3e} | "
             f"{row['native_vs_fp32']['relative_l2']:.3e} |"
         )
@@ -1402,16 +1651,21 @@ def ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
             "",
             "## Single-GPU SwiGLU",
             "",
-            "| Case | Native PyTorch (ms) | Deterministic fused (ms) | Det/native | "
-            "Det/native rel-L2 |",
-            "|---|---:|---:|---:|---:|",
+            "| Case | Native PyTorch (ms) | Strict HIP (ms) | Strict Triton (ms) | "
+            "HIP/native | Triton/native | Triton speedup | Mismatch | "
+            "Strict/native rel-L2 |",
+            "|---|---:|---:|---:|---:|---:|---:|---:|---:|",
         )
     )
     for row in swiglu_rows:
         lines.append(
             f"| {row['name']} | {row['native']['median_ms']:.4f} | "
             f"{row['deterministic']['median_ms']:.4f} | "
+            f"{row['triton']['median_ms']:.4f} | "
             f"{row['overhead_ratio']:.2f}x | "
+            f"{row['triton_overhead_ratio']:.2f}x | "
+            f"{row['triton_speedup_vs_hip']:.2f}x | "
+            f"{row['triton_vs_hip_mismatch_count']} | "
             f"{row['deterministic_vs_native']['relative_l2']:.3e} |"
         )
 
@@ -1420,15 +1674,20 @@ def ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
             "",
             "## Single-GPU FFN",
             "",
-            "| Case | Native ROCm (ms) | Deterministic (ms) | Overhead | "
+            "| Case | Native (ms) | Strict HIP (ms) | Strict Triton (ms) | "
+            "HIP/native | Triton/native | Triton speedup | Mismatch | "
             "Output rel-L2 | Max grad rel-L2 |",
-            "|---|---:|---:|---:|---:|---:|",
+            "|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|",
         )
     )
     for row in ffn_rows:
         lines.append(
             f"| {row['name']} | {row['native']['median_ms']:.4f} | "
-            f"{row['deterministic']['median_ms']:.4f} | {row['overhead_ratio']:.1f}× | "
+            f"{row['deterministic']['median_ms']:.4f} | "
+            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.1f}× | "
+            f"{row['triton_overhead_ratio']:.1f}× | "
+            f"{row['triton_speedup_vs_hip']:.1f}× | "
+            f"{row['triton_vs_hip_mismatch_count']} | "
             f"{row['deterministic_vs_native']['relative_l2']:.3e} | "
             f"{row.get('max_gradient_relative_l2', float('nan')):.3e} |"
         )
@@ -1461,10 +1720,10 @@ def ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
             "",
             "## Distributed FFN",
             "",
-            "| Topology | Direction | Native ROCm/RCCL (ms) | "
-            "Deterministic (ms) | Overhead | Output rel-L2 | Max grad rel-L2 | "
-            "Train/infer bitwise | Repeat bitwise |",
-            "|---|---|---:|---:|---:|---:|---:|:---:|:---:|",
+            "| Topology | Direction | Native (ms) | Strict HIP (ms) | "
+            "Strict Triton (ms) | HIP/native | Triton/native | Triton speedup | "
+            "Mismatch | Output rel-L2 | Max grad rel-L2 | Triton T/I | Triton repeat |",
+            "|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|:---:|:---:|",
         )
     )
     for row in distributed_rows:
@@ -1472,11 +1731,15 @@ def ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
             f"| {row['name']} | {row['direction']} | "
             f"{row['native']['median_ms']:.4f} | "
             f"{row['deterministic']['median_ms']:.4f} | "
+            f"{row['triton']['median_ms']:.4f} | "
             f"{row['overhead_ratio']:.1f}× | "
+            f"{row['triton_overhead_ratio']:.1f}× | "
+            f"{row['triton_speedup_vs_hip']:.1f}× | "
+            f"{row['triton_vs_hip_mismatch_count']} | "
             f"{row['output_relative_l2']:.3e} | "
             f"{row.get('max_gradient_relative_l2', float('nan')):.3e} | "
-            f"{'yes' if row['train_infer_bitwise'] else 'no'} | "
-            f"{'yes' if row['repeat_bitwise'] else 'no'} |"
+            f"{'yes' if row['triton_train_infer_bitwise'] else 'no'} | "
+            f"{'yes' if row['triton_repeat_bitwise'] else 'no'} |"
         )
 
     lines.extend(
@@ -1502,7 +1765,6 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
 
     matplotlib.use("Agg")
     import matplotlib.pyplot as plt
-    from matplotlib.patches import Patch
 
     plt.style.use("seaborn-v0_8-whitegrid")
 
@@ -1514,30 +1776,39 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
         + [row["name"] for row in swiglu_rows]
         + [row["name"] for row in ffn_rows]
     )
-    ratios = (
+    hip_ratios = (
         [row["overhead_ratio"] for row in gemm_rows]
         + [row["overhead_ratio"] for row in swiglu_rows]
         + [row["overhead_ratio"] for row in ffn_rows]
     )
-    colors = (
-        ["#3b82f6"] * len(gemm_rows)
-        + ["#10b981"] * len(swiglu_rows)
-        + ["#f97316"] * len(ffn_rows)
+    triton_ratios = (
+        [row["triton_overhead_ratio"] for row in gemm_rows]
+        + [row["triton_overhead_ratio"] for row in swiglu_rows]
+        + [row["triton_overhead_ratio"] for row in ffn_rows]
     )
     figure, axis = plt.subplots(figsize=(18, 7))
-    axis.bar(range(len(labels)), ratios, color=colors)
+    positions = list(range(len(labels)))
+    width = 0.4
+    axis.bar(
+        [position - width / 2 for position in positions],
+        hip_ratios,
+        width,
+        color="#ef4444",
+        label="Strict HIP",
+    )
+    axis.bar(
+        [position + width / 2 for position in positions],
+        triton_ratios,
+        width,
+        color="#8b5cf6",
+        label="Strict Triton",
+    )
     axis.axhline(1.0, color="black", linewidth=1)
     axis.set_yscale("log")
-    axis.set_ylabel("Deterministic / native latency (×, log scale)")
-    axis.set_title("MI300X single-GPU deterministic overhead")
-    axis.set_xticks(range(len(labels)), labels, rotation=55, ha="right")
-    axis.legend(
-        handles=(
-            Patch(color="#3b82f6", label="GEMM"),
-            Patch(color="#10b981", label="SwiGLU"),
-            Patch(color="#f97316", label="FFN"),
-        )
-    )
+    axis.set_ylabel("Strict / native latency (×, log scale)")
+    axis.set_title("MI300X single-GPU strict HIP vs Triton overhead")
+    axis.set_xticks(positions, labels, rotation=55, ha="right")
+    axis.legend()
     figure.tight_layout()
     figure.savefig(output_directory / "single_gpu_overhead.png", dpi=180)
     plt.close(figure)
@@ -1573,16 +1844,28 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
     rows = payload["distributed_ffn"]
     labels = [f"{row['name']}\n{row['direction']}" for row in rows]
     figure, axis = plt.subplots(figsize=(16, 6))
+    positions = list(range(len(rows)))
+    width = 0.4
     axis.bar(
-        range(len(rows)),
+        [position - width / 2 for position in positions],
         [row["overhead_ratio"] for row in rows],
-        color=["#10b981" if row["direction"] == "forward" else "#8b5cf6" for row in rows],
+        width,
+        color="#ef4444",
+        label="Strict HIP",
+    )
+    axis.bar(
+        [position + width / 2 for position in positions],
+        [row["triton_overhead_ratio"] for row in rows],
+        width,
+        color="#8b5cf6",
+        label="Strict Triton",
     )
     axis.axhline(1.0, color="black", linewidth=1)
     axis.set_yscale("log")
-    axis.set_ylabel("Deterministic / native latency (×, log scale)")
-    axis.set_title("Qwen3-8B-shaped FFN: native ROCm/RCCL vs deterministic")
-    axis.set_xticks(range(len(labels)), labels, rotation=55, ha="right")
+    axis.set_ylabel("Strict / native latency (×, log scale)")
+    axis.set_title("Qwen3-8B-shaped FFN: strict HIP vs Triton on MI300X")
+    axis.set_xticks(positions, labels, rotation=55, ha="right")
+    axis.legend()
     figure.tight_layout()
     figure.savefig(output_directory / "distributed_ffn_overhead.png", dpi=180)
     plt.close(figure)
@@ -1606,12 +1889,12 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
         [position + width / 2 for position in positions],
         deterministic_errors,
         width,
-        label="Deterministic vs FP32",
+        label="Strict HIP = Triton vs FP32",
         color="#ef4444",
     )
     axis.set_yscale("log")
     axis.set_ylabel("Relative L2 error (log scale)")
-    axis.set_title("BF16 GEMM numerical error against FP32 reference")
+    axis.set_title("BF16 GEMM accuracy: strict Triton has 0 mismatch vs strict HIP")
     axis.set_xticks(positions, names, rotation=45, ha="right")
     axis.legend()
     figure.tight_layout()
diff --git a/rl_engine/kernels/ops/triton/activation/swiglu.py b/rl_engine/kernels/ops/triton/activation/swiglu.py
index 6fb66313..ad11ecc8 100644
--- a/rl_engine/kernels/ops/triton/activation/swiglu.py
+++ b/rl_engine/kernels/ops/triton/activation/swiglu.py
@@ -32,7 +32,7 @@ def _silu_fwd_kernel(x_ptr, y_ptr, n_elements, BLOCK: tl.constexpr):
     offs = pid * BLOCK + tl.arange(0, BLOCK)
     mask = offs < n_elements
     x = tl.load(x_ptr + offs, mask=mask, other=0.0).to(tl.float32)
-    s = 1.0 / (1.0 + tl.exp(-x))
+    s = tl.div_rn(1.0, 1.0 + tl.exp(-x))
     y = x * s
     tl.store(y_ptr + offs, y.to(y_ptr.dtype.element_ty), mask=mask)
 
@@ -44,7 +44,7 @@ def _silu_bwd_kernel(dy_ptr, x_ptr, dx_ptr, n_elements, BLOCK: tl.constexpr):
     mask = offs < n_elements
     dy = tl.load(dy_ptr + offs, mask=mask, other=0.0).to(tl.float32)
     x = tl.load(x_ptr + offs, mask=mask, other=0.0).to(tl.float32)
-    s = 1.0 / (1.0 + tl.exp(-x))
+    s = tl.div_rn(1.0, 1.0 + tl.exp(-x))
     # silu'(x) = s * (1 + x * (1 - s))
     dx = dy * s * (1.0 + x * (1.0 - s))
     tl.store(dx_ptr + offs, dx.to(dx_ptr.dtype.element_ty), mask=mask)
@@ -57,26 +57,42 @@ def _swiglu_fwd_kernel(gate_ptr, up_ptr, y_ptr, n_elements, BLOCK: tl.constexpr)
     mask = offs < n_elements
     g = tl.load(gate_ptr + offs, mask=mask, other=0.0).to(tl.float32)
     u = tl.load(up_ptr + offs, mask=mask, other=0.0).to(tl.float32)
-    s = 1.0 / (1.0 + tl.exp(-g))
+    s = tl.div_rn(1.0, 1.0 + tl.exp(-g))
     y = (g * s) * u
     tl.store(y_ptr + offs, y.to(y_ptr.dtype.element_ty), mask=mask)
 
 
 @triton.jit
 def _swiglu_bwd_kernel(
-    dy_ptr, gate_ptr, up_ptr, d_gate_ptr, d_up_ptr, n_elements, BLOCK: tl.constexpr
+    dy_ptr, gate_ptr, silu_grad_ptr, d_up_ptr, n_elements, BLOCK: tl.constexpr
 ):
     pid = tl.program_id(0)
     offs = pid * BLOCK + tl.arange(0, BLOCK)
     mask = offs < n_elements
     dy = tl.load(dy_ptr + offs, mask=mask, other=0.0).to(tl.float32)
     g = tl.load(gate_ptr + offs, mask=mask, other=0.0).to(tl.float32)
-    u = tl.load(up_ptr + offs, mask=mask, other=0.0).to(tl.float32)
-    s = 1.0 / (1.0 + tl.exp(-g))
+    s = tl.div_rn(1.0, 1.0 + tl.exp(-g))
     silu_g = g * s
     d_up = dy * silu_g
-    d_gate = dy * u * s * (1.0 + g * (1.0 - s))
+    silu_grad = s * (1.0 + g * (1.0 - s))
     tl.store(d_up_ptr + offs, d_up.to(d_up_ptr.dtype.element_ty), mask=mask)
+    tl.store(silu_grad_ptr + offs, silu_grad, mask=mask)
+
+
+@triton.jit
+def _swiglu_dgate_kernel(
+    dy_ptr, up_ptr, silu_grad_ptr, d_gate_ptr, n_elements, BLOCK: tl.constexpr
+):
+    pid = tl.program_id(0)
+    offs = pid * BLOCK + tl.arange(0, BLOCK)
+    mask = offs < n_elements
+    dy = tl.load(dy_ptr + offs, mask=mask, other=0.0).to(tl.float32)
+    u = tl.load(up_ptr + offs, mask=mask, other=0.0).to(tl.float32)
+    silu_grad = tl.load(silu_grad_ptr + offs, mask=mask, other=0.0)
+    # The native HIP source evaluates (dy * up) * silu_grad.  Persisting the
+    # latter as FP32 prevents LLVM from reassociating the expression across
+    # the function boundary and changing a BF16 tie at rare elements.
+    d_gate = (dy * u) * silu_grad
     tl.store(d_gate_ptr + offs, d_gate.to(d_gate_ptr.dtype.element_ty), mask=mask)
 
 
@@ -124,11 +140,27 @@ def _launch_swiglu_bwd(dy: Tensor, gate: Tensor, up: Tensor) -> tuple[Tensor, Te
     up_c = up.contiguous()
     d_gate = torch.empty_like(gate_c)
     d_up = torch.empty_like(up_c)
+    silu_grad = torch.empty_like(gate_c, dtype=torch.float32)
     n = gate_c.numel()
     if n == 0:
         return d_gate, d_up
     grid = (triton.cdiv(n, _BLOCK),)
-    _swiglu_bwd_kernel[grid](dy_c, gate_c, up_c, d_gate, d_up, n, BLOCK=_BLOCK)
+    _swiglu_bwd_kernel[grid](
+        dy_c,
+        gate_c,
+        silu_grad,
+        d_up,
+        n,
+        BLOCK=_BLOCK,
+    )
+    _swiglu_dgate_kernel[grid](
+        dy_c,
+        up_c,
+        silu_grad,
+        d_gate,
+        n,
+        BLOCK=_BLOCK,
+    )
     return d_gate, d_up
 
 
diff --git a/rl_engine/kernels/ops/triton/ffn/__init__.py b/rl_engine/kernels/ops/triton/ffn/__init__.py
new file mode 100644
index 00000000..d10c7414
--- /dev/null
+++ b/rl_engine/kernels/ops/triton/ffn/__init__.py
@@ -0,0 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from .ffn import qwen3_ffn_triton
+
+__all__ = ["qwen3_ffn_triton"]
diff --git a/rl_engine/kernels/ops/triton/ffn/ffn.py b/rl_engine/kernels/ops/triton/ffn/ffn.py
new file mode 100644
index 00000000..b716cc3e
--- /dev/null
+++ b/rl_engine/kernels/ops/triton/ffn/ffn.py
@@ -0,0 +1,200 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""Distributed deterministic Qwen3 FFN assembled from ROCm Triton kernels.
+
+The arithmetic and sharding contract matches the native PR #325 path exactly:
+BF16 is preserved at every GEMM/SwiGLU boundary, GEMMs use the canonical
+FP32-leaf/BF16-node K tree, and TP reductions use the rank-ordered balanced
+collective.  CP gathers full token sequences before weight-gradient GEMMs so
+their K tree is identical to CP=1.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+from torch import Tensor
+
+from rl_engine.kernels.ops.pytorch.ffn.ffn import (
+    _all_gather_tokens,
+    _all_reduce_inplace,
+    _collective_for_group,
+    _reduce_scatter_tokens,
+    _require_parallel_group,
+    _validate_ffn_inputs,
+)
+from rl_engine.kernels.ops.triton.activation.swiglu import (
+    _launch_swiglu_bwd,
+    _launch_swiglu_fwd,
+)
+from rl_engine.kernels.ops.triton.matmul.det_gemm import _triton_tree_gemm
+
+
+def _gemm(a: Tensor, b: Tensor) -> Tensor:
+    return _triton_tree_gemm(a, b)
+
+
+def _gemm_db(a: Tensor, grad_output: Tensor) -> Tensor:
+    return _triton_tree_gemm(a.t().contiguous(), grad_output)
+
+
+class _TritonDeterministicFFNFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx: Any,
+        rmsnorm_output: Tensor,
+        gate_weight: Tensor,
+        up_weight: Tensor,
+        down_weight: Tensor,
+        tp_group: Any,
+        cp_group: Any,
+        sequence_parallel: bool,
+    ) -> Tensor:
+        tp_dist = _require_parallel_group(tp_group, "tensor")
+        _require_parallel_group(cp_group, "context")
+        if sequence_parallel and tp_dist is None:
+            raise ValueError("sequence_parallel requires a tensor-parallel group.")
+
+        input_shape = rmsnorm_output.shape
+        rmsnorm_output_2d = rmsnorm_output.reshape(-1, input_shape[-1]).contiguous()
+        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()
+        min_size_bytes = max(
+            gemm_tokens * rmsnorm_output_2d.size(1) * element_size,
+            gemm_tokens * gate_weight.size(0) * element_size,
+            gate_weight.numel() * element_size,
+            up_weight.numel() * element_size,
+            down_weight.numel() * element_size,
+        )
+        tp_collective = _collective_for_group(tp_group, min_size_bytes=min_size_bytes)
+        cp_collective = _collective_for_group(cp_group, min_size_bytes=min_size_bytes)
+
+        if sequence_parallel:
+            rmsnorm_output_2d = _all_gather_tokens(rmsnorm_output_2d, tp_collective)
+
+        gate = _gemm(rmsnorm_output_2d, gate_weight.t().contiguous())
+        up = _gemm(rmsnorm_output_2d, up_weight.t().contiguous())
+        activated = _launch_swiglu_fwd(gate, up)
+        output = _gemm(activated, down_weight.t().contiguous())
+
+        if sequence_parallel:
+            output = _reduce_scatter_tokens(output, tp_collective)
+        elif tp_collective is not None:
+            output = _all_reduce_inplace(output, tp_collective)
+
+        ctx.save_for_backward(
+            rmsnorm_output_2d,
+            gate,
+            up,
+            activated,
+            gate_weight,
+            up_weight,
+            down_weight,
+        )
+        ctx.input_shape = input_shape
+        ctx.tp_collective = tp_collective
+        ctx.cp_collective = cp_collective
+        ctx.sequence_parallel = sequence_parallel
+        return output.reshape(*input_shape[:-1], output.size(-1))
+
+    @staticmethod
+    def backward(ctx: Any, grad_output: Tensor) -> tuple[Any, ...]:
+        (
+            rmsnorm_output,
+            gate,
+            up,
+            activated,
+            gate_weight,
+            up_weight,
+            down_weight,
+        ) = ctx.saved_tensors
+        tp_collective = ctx.tp_collective
+        cp_collective = ctx.cp_collective
+        grad_output = grad_output.reshape(-1, grad_output.size(-1)).contiguous()
+        if ctx.sequence_parallel:
+            grad_output = _all_gather_tokens(grad_output, tp_collective)
+
+        if cp_collective is not None:
+            activated_full = _all_gather_tokens(activated, cp_collective)
+            grad_output_full = _all_gather_tokens(grad_output, cp_collective)
+            grad_down_weight = _gemm_db(activated_full, grad_output_full).t().contiguous()
+        else:
+            grad_down_weight = _gemm_db(activated, grad_output).t().contiguous()
+
+        grad_activated = _gemm(grad_output, down_weight)
+        grad_gate, grad_up = _launch_swiglu_bwd(grad_activated, gate, up)
+
+        if cp_collective is not None:
+            rmsnorm_full = _all_gather_tokens(rmsnorm_output, cp_collective)
+            grad_gate_full = _all_gather_tokens(grad_gate, cp_collective)
+            grad_up_full = _all_gather_tokens(grad_up, cp_collective)
+            grad_gate_weight = _gemm_db(rmsnorm_full, grad_gate_full).t().contiguous()
+            grad_up_weight = _gemm_db(rmsnorm_full, grad_up_full).t().contiguous()
+        else:
+            grad_gate_weight = _gemm_db(rmsnorm_output, grad_gate).t().contiguous()
+            grad_up_weight = _gemm_db(rmsnorm_output, grad_up).t().contiguous()
+
+        grad_rmsnorm_from_gate = _gemm(grad_gate, gate_weight)
+        if ctx.sequence_parallel:
+            grad_rmsnorm_from_gate = _reduce_scatter_tokens(
+                grad_rmsnorm_from_gate,
+                tp_collective,
+            )
+        elif tp_collective is not None:
+            grad_rmsnorm_from_gate = _all_reduce_inplace(
+                grad_rmsnorm_from_gate,
+                tp_collective,
+            )
+
+        grad_rmsnorm_from_up = _gemm(grad_up, up_weight)
+        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,
+            )
+
+        grad_rmsnorm_output = grad_rmsnorm_from_gate.add_(grad_rmsnorm_from_up)
+        return (
+            grad_rmsnorm_output.reshape(ctx.input_shape),
+            grad_gate_weight,
+            grad_up_weight,
+            grad_down_weight,
+            None,
+            None,
+            None,
+        )
+
+
+def qwen3_ffn_triton(
+    rmsnorm_output: Tensor,
+    gate_weight: Tensor,
+    up_weight: Tensor,
+    down_weight: Tensor,
+    *,
+    tp_group: Any = None,
+    cp_group: Any = None,
+    sequence_parallel: bool = False,
+) -> Tensor:
+    """Apply the distributed deterministic Qwen3 FFN with Triton kernels."""
+
+    _validate_ffn_inputs(rmsnorm_output, gate_weight, up_weight, down_weight)
+    if not isinstance(sequence_parallel, bool):
+        raise TypeError(
+            f"sequence_parallel must be a bool, got {type(sequence_parallel)!r}."
+        )
+    return _TritonDeterministicFFNFunction.apply(
+        rmsnorm_output,
+        gate_weight,
+        up_weight,
+        down_weight,
+        tp_group,
+        cp_group,
+        sequence_parallel,
+    )
diff --git a/rl_engine/kernels/ops/triton/matmul/det_gemm.py b/rl_engine/kernels/ops/triton/matmul/det_gemm.py
index 50025db2..4f641dbf 100644
--- a/rl_engine/kernels/ops/triton/matmul/det_gemm.py
+++ b/rl_engine/kernels/ops/triton/matmul/det_gemm.py
@@ -1,12 +1,24 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""Batch-invariant deterministic GEMM, Triton path (WS1).
+"""Batch- and TP-invariant deterministic GEMM, Triton path.
 
-Portable implementation with the SAME invariance guarantees as the CUDA path:
-autotune disabled, BLOCK sizes pinned, no split-K, fixed K-loop order, FP32
-accumulation, no TF32. Used as the cross-backend reference and the ROCm/portable
-fallback. Slower than a tuned GEMM by design.
+BF16 outputs use the same arithmetic graph as the native deterministic kernel:
+each canonical K-tree leaf contains at most 32 values, accumulates in FP32, and
+rounds to BF16.  Separate Triton kernels then evaluate the canonical midpoint
+tree with BF16 nodes.  The strict ROCm leaf keeps the native gfx942 scalar FMA
+order so both implementations have zero mismatch; a future MFMA leaf requires
+a separately versioned arithmetic contract because its internal accumulation
+does not match the scalar reference at every BF16 rounding boundary.
+
+Autotuning and split-K are intentionally disabled.  FP32-output calls preserve
+the earlier fixed-order, no-split-K contract and do not use BF16 tree nodes.
 """
+
+from __future__ import annotations
+
+import threading
+from dataclasses import dataclass
+
 import torch
 
 try:
@@ -22,12 +34,206 @@
 
 # Pinned. NOT autotuned (autotune picks per-shape configs -> breaks invariance).
 _BLOCK_M, _BLOCK_N, _BLOCK_K = 64, 64, 32
+_TREE_PLANS: dict[tuple[int, int], "_DeviceTreePlan"] = {}
+_TREE_PLAN_LOCK = threading.Lock()
+
+
+@dataclass(frozen=True)
+class _TreePlan:
+    leaf_starts: tuple[int, ...]
+    leaf_lengths: tuple[int, ...]
+    leaf_nodes: tuple[int, ...]
+    reduction_levels: tuple[tuple[tuple[int, int, int], ...], ...]
+    root: int
+    node_count: int
+
+
+@dataclass(frozen=True)
+class _DeviceTreePlan:
+    host: _TreePlan
+    leaf_starts: torch.Tensor
+    leaf_lengths: torch.Tensor
+    leaf_nodes: torch.Tensor
+    reduction_levels: tuple[tuple[torch.Tensor, torch.Tensor, torch.Tensor], ...]
+
+
+def _build_tree_plan(k_size: int) -> _TreePlan:
+    if k_size <= 0:
+        raise ValueError(f"deterministic GEMM K must be positive, got {k_size}")
+
+    leaf_starts: list[int] = []
+    leaf_lengths: list[int] = []
+    leaf_nodes: list[int] = []
+    reductions_by_height: dict[int, list[tuple[int, int, int]]] = {}
+    next_node = 0
+
+    def visit(begin: int, end: int) -> tuple[int, int]:
+        nonlocal next_node
+        if end - begin <= _BLOCK_K:
+            node = next_node
+            next_node += 1
+            leaf_starts.append(begin)
+            leaf_lengths.append(end - begin)
+            leaf_nodes.append(node)
+            return node, 0
+
+        midpoint = begin + (end - begin) // 2
+        lower, lower_height = visit(begin, midpoint)
+        upper, upper_height = visit(midpoint, end)
+        node = next_node
+        next_node += 1
+        height = max(lower_height, upper_height) + 1
+        reductions_by_height.setdefault(height, []).append((lower, upper, node))
+        return node, height
+
+    root, max_height = visit(0, k_size)
+    reduction_levels = tuple(
+        tuple(reductions_by_height.get(height, ()))
+        for height in range(1, max_height + 1)
+    )
+    return _TreePlan(
+        leaf_starts=tuple(leaf_starts),
+        leaf_lengths=tuple(leaf_lengths),
+        leaf_nodes=tuple(leaf_nodes),
+        reduction_levels=reduction_levels,
+        root=root,
+        node_count=next_node,
+    )
+
+
+def _device_tree_plan(k_size: int, device: torch.device) -> _DeviceTreePlan:
+    device_index = device.index if device.index is not None else torch.cuda.current_device()
+    key = (device_index, k_size)
+    with _TREE_PLAN_LOCK:
+        cached = _TREE_PLANS.get(key)
+        if cached is not None:
+            return cached
+
+        host = _build_tree_plan(k_size)
+
+        def indices(values: tuple[int, ...]) -> torch.Tensor:
+            return torch.tensor(values, dtype=torch.int32, device=device)
+
+        levels = []
+        for operations in host.reduction_levels:
+            lower, upper, output = zip(*operations, strict=True)
+            levels.append((indices(lower), indices(upper), indices(output)))
+        result = _DeviceTreePlan(
+            host=host,
+            leaf_starts=indices(host.leaf_starts),
+            leaf_lengths=indices(host.leaf_lengths),
+            leaf_nodes=indices(host.leaf_nodes),
+            reduction_levels=tuple(levels),
+        )
+        _TREE_PLANS[key] = result
+        return result
 
 
 if _TRITON_AVAILABLE:
 
     @triton.jit
-    def _det_gemm_kernel(
+    def _det_gemm_tree_leaf_kernel(
+        a_ptr,
+        b_ptr,
+        workspace_ptr,
+        leaf_starts_ptr,
+        leaf_lengths_ptr,
+        leaf_nodes_ptr,
+        M: tl.constexpr,
+        N: tl.constexpr,
+        K: tl.constexpr,
+        stride_am: tl.constexpr,
+        stride_ak: tl.constexpr,
+        stride_bk: tl.constexpr,
+        stride_bn: tl.constexpr,
+        BLOCK_M: tl.constexpr,
+        BLOCK_N: tl.constexpr,
+        BLOCK_K: tl.constexpr,
+    ):
+        leaf = tl.program_id(0)
+        pid_m = tl.program_id(1)
+        pid_n = tl.program_id(2)
+        offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+        offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+        leaf_start = tl.load(leaf_starts_ptr + leaf)
+        leaf_length = tl.load(leaf_lengths_ptr + leaf)
+        leaf_node = tl.load(leaf_nodes_ptr + leaf)
+        acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
+        # Keep the leaf's ascending scalar FMA order identical to the native
+        # gfx942 correctness kernel.  A tl.dot/MFMA leaf is topology-stable but
+        # differs from the scalar reference at rare BF16 rounding boundaries.
+        for offset in tl.static_range(0, BLOCK_K):
+            k_offset = leaf_start + offset
+            active = offset < leaf_length
+            a = tl.load(
+                a_ptr + offs_m * stride_am + k_offset * stride_ak,
+                mask=(offs_m < M) & active,
+                other=0.0,
+            ).to(tl.float32)
+            b = tl.load(
+                b_ptr + k_offset * stride_bk + offs_n * stride_bn,
+                mask=(offs_n < N) & active,
+                other=0.0,
+            ).to(tl.float32)
+            acc += a[:, None] * b[None, :]
+        output_offsets = leaf_node * M * N + offs_m[:, None] * N + offs_n[None, :]
+        output_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
+        tl.store(
+            workspace_ptr + output_offsets,
+            acc.to(workspace_ptr.dtype.element_ty),
+            mask=output_mask,
+        )
+
+    @triton.jit
+    def _det_gemm_tree_reduce_kernel(
+        workspace_ptr,
+        lower_nodes_ptr,
+        upper_nodes_ptr,
+        output_nodes_ptr,
+        M: tl.constexpr,
+        N: tl.constexpr,
+        BLOCK: tl.constexpr,
+    ):
+        operation = tl.program_id(0)
+        block = tl.program_id(1)
+        offsets = block * BLOCK + tl.arange(0, BLOCK)
+        elements = M * N
+        mask = offsets < elements
+        lower_node = tl.load(lower_nodes_ptr + operation)
+        upper_node = tl.load(upper_nodes_ptr + operation)
+        output_node = tl.load(output_nodes_ptr + operation)
+        lower = tl.load(
+            workspace_ptr + lower_node * elements + offsets,
+            mask=mask,
+            other=0.0,
+        ).to(tl.float32)
+        upper = tl.load(
+            workspace_ptr + upper_node * elements + offsets,
+            mask=mask,
+            other=0.0,
+        ).to(tl.float32)
+        result = lower + upper
+        tl.store(
+            workspace_ptr + output_node * elements + offsets,
+            result.to(workspace_ptr.dtype.element_ty),
+            mask=mask,
+        )
+
+    @triton.jit
+    def _copy_tree_root_kernel(
+        workspace_ptr,
+        output_ptr,
+        root,
+        elements,
+        BLOCK: tl.constexpr,
+    ):
+        offsets = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK)
+        mask = offsets < elements
+        values = tl.load(workspace_ptr + root * elements + offsets, mask=mask)
+        tl.store(output_ptr + offsets, values, mask=mask)
+
+    @triton.jit
+    def _det_gemm_fp32_kernel(
         a_ptr,
         b_ptr,
         c_ptr,
@@ -72,18 +278,13 @@ def _det_gemm_kernel(
         tl.store(c_ptrs, c, mask=mask)
 
 
-def _triton_gemm(a, b, *, output_dtype=None):
+def _triton_gemm_fp32(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
     a, b = a.contiguous(), b.contiguous()
     M, K = a.shape
     _, N = b.shape
-    c = torch.empty(
-        (M, N), device=a.device, dtype=a.dtype if output_dtype is None else output_dtype
-    )
+    c = torch.empty((M, N), device=a.device, dtype=torch.float32)
     grid = (triton.cdiv(M, _BLOCK_M), triton.cdiv(N, _BLOCK_N))
-    promote_inputs = (
-        output_dtype == torch.float32 or a.dtype == torch.float32 or b.dtype == torch.float32
-    )
-    _det_gemm_kernel[grid](
+    _det_gemm_fp32_kernel[grid](
         a,
         b,
         c,
@@ -99,31 +300,103 @@ def _triton_gemm(a, b, *, output_dtype=None):
         BLOCK_M=_BLOCK_M,
         BLOCK_N=_BLOCK_N,
         BLOCK_K=_BLOCK_K,
-        PROMOTE_INPUTS=promote_inputs,
+        PROMOTE_INPUTS=True,
     )
     return c
 
 
+def _triton_tree_gemm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+    if not _TRITON_AVAILABLE:
+        raise RuntimeError("Triton is unavailable")
+    if a.dim() != 2 or b.dim() != 2:
+        raise ValueError("Triton deterministic GEMM expects two 2-D tensors")
+    if a.size(1) != b.size(0):
+        raise ValueError(f"Triton deterministic GEMM K mismatch: {a.size(1)} and {b.size(0)}")
+    if a.dtype != torch.bfloat16 or b.dtype != torch.bfloat16:
+        raise TypeError("Triton tree GEMM requires BF16 inputs")
+    if not a.is_cuda or not b.is_cuda or a.device != b.device:
+        raise RuntimeError("Triton tree GEMM inputs must share one CUDA/ROCm device")
+
+    a = a.contiguous()
+    b = b.contiguous()
+    m_size, k_size = a.shape
+    n_size = b.size(1)
+    plan = _device_tree_plan(k_size, a.device)
+    workspace = torch.empty(
+        (plan.host.node_count, m_size, n_size),
+        dtype=torch.bfloat16,
+        device=a.device,
+    )
+    leaf_grid = (
+        len(plan.host.leaf_nodes),
+        triton.cdiv(m_size, _BLOCK_M),
+        triton.cdiv(n_size, _BLOCK_N),
+    )
+    _det_gemm_tree_leaf_kernel[leaf_grid](
+        a,
+        b,
+        workspace,
+        plan.leaf_starts,
+        plan.leaf_lengths,
+        plan.leaf_nodes,
+        M=m_size,
+        N=n_size,
+        K=k_size,
+        stride_am=a.stride(0),
+        stride_ak=a.stride(1),
+        stride_bk=b.stride(0),
+        stride_bn=b.stride(1),
+        BLOCK_M=_BLOCK_M,
+        BLOCK_N=_BLOCK_N,
+        BLOCK_K=_BLOCK_K,
+    )
+    reduction_block = 256
+    for operations, (lower, upper, output) in zip(
+        plan.host.reduction_levels,
+        plan.reduction_levels,
+        strict=True,
+    ):
+        grid = (len(operations), triton.cdiv(m_size * n_size, reduction_block))
+        _det_gemm_tree_reduce_kernel[grid](
+            workspace,
+            lower,
+            upper,
+            output,
+            M=m_size,
+            N=n_size,
+            BLOCK=reduction_block,
+        )
+
+    result = torch.empty((m_size, n_size), dtype=torch.bfloat16, device=a.device)
+    copy_block = 256
+    _copy_tree_root_kernel[(triton.cdiv(result.numel(), copy_block),)](
+        workspace,
+        result,
+        plan.host.root,
+        result.numel(),
+        BLOCK=copy_block,
+    )
+    return result
+
+
 class _TritonDetGemmFn(torch.autograd.Function):
     @staticmethod
     def forward(ctx, a, b, output_fp32=False):
         ctx.save_for_backward(a, b)
         ctx.output_fp32 = bool(output_fp32)
-        return _triton_gemm(a, b, output_dtype=torch.float32 if output_fp32 else None)
+        return _triton_gemm_fp32(a, b) if output_fp32 else _triton_tree_gemm(a, b)
 
     @staticmethod
     def backward(ctx, grad_out):
         a, b = ctx.saved_tensors
         grad_out = grad_out.contiguous()
         da = (
-            _triton_gemm(grad_out, b.t().contiguous(), output_dtype=torch.float32)
-            .reshape_as(a)
-            .to(a.dtype)
+            _triton_tree_gemm(grad_out.to(torch.bfloat16), b.t().contiguous()).reshape_as(a)
             if ctx.needs_input_grad[0]
             else None
         )
         db = (
-            _triton_gemm(a.t().contiguous(), grad_out, output_dtype=torch.float32).to(b.dtype)
+            _triton_tree_gemm(a.t().contiguous(), grad_out.to(torch.bfloat16))
             if ctx.needs_input_grad[1]
             else None
         )
diff --git a/tests/distributed/test_qwen_ffn_topology.py b/tests/distributed/test_qwen_ffn_topology.py
index 44de9041..96717684 100644
--- a/tests/distributed/test_qwen_ffn_topology.py
+++ b/tests/distributed/test_qwen_ffn_topology.py
@@ -28,6 +28,7 @@
     QWEN3_8B_INTERMEDIATE_SIZE,
     qwen3_ffn,
 )
+from rl_engine.kernels.ops.triton.ffn import qwen3_ffn_triton
 
 _REQUIRED_FFN_SYMBOLS = (
     "det_gemm_fwd",
@@ -182,6 +183,14 @@ def _canonical(
     return inference, training, inputs
 
 
+def _ffn_implementation(name: str):
+    if name == "native":
+        return qwen3_ffn
+    if name == "triton":
+        return qwen3_ffn_triton
+    raise ValueError(f"unknown FFN implementation: {name}")
+
+
 def _mesh_groups(
     dist: Any,
     tp_size: int,
@@ -226,6 +235,7 @@ def _run_topology(
     inference_reference: torch.Tensor,
     training_reference: torch.Tensor,
     reference_inputs: list[torch.Tensor],
+    implementation: str = "native",
 ) -> None:
     mesh_key = (tp_size, cp_size)
     if mesh_key not in meshes:
@@ -251,15 +261,16 @@ def _run_topology(
         down[:, feature_start:feature_end].contiguous(),
     )
 
+    target_ffn = _ffn_implementation(implementation)
     with torch.no_grad():
-        inference = qwen3_ffn(
+        inference = target_ffn(
             *shard,
             tp_group=tp_group,
             cp_group=cp_group,
             sequence_parallel=sequence_parallel,
         )
     inputs = [value.detach().clone().requires_grad_(True) for value in shard]
-    training = qwen3_ffn(
+    training = target_ffn(
         *inputs,
         tp_group=tp_group,
         cp_group=cp_group,
@@ -296,6 +307,7 @@ def _topology_worker(
     init_method: str,
     result_queue: Any,
     configs: tuple[tuple[str, int, int, bool], ...],
+    implementation: str = "native",
 ) -> None:
     try:
         import torch.distributed as dist
@@ -341,6 +353,7 @@ def _topology_worker(
                 inference_reference=inference_reference,
                 training_reference=training_reference,
                 reference_inputs=reference_inputs,
+                implementation=implementation,
             )
         result_queue.put({"ok": True, "rank": rank})
     except Exception:  # pragma: no cover - forwarded to the parent process.
@@ -504,6 +517,33 @@ def test_qwen3_ffn_tp8_matches_tp1_bitwise() -> None:
     )
 
 
+def test_qwen3_ffn_triton_tp2_and_tp_sp_match_native_tp1_bitwise() -> None:
+    _spawn_workers(
+        _topology_worker,
+        2,
+        (_WORLD2_CONFIGS, "triton"),
+        timeout_seconds=240,
+    )
+
+
+def test_qwen3_ffn_triton_tp4_tp_cp_and_tp_cp_sp_match_native_tp1_bitwise() -> None:
+    _spawn_workers(
+        _topology_worker,
+        4,
+        (_WORLD4_CONFIGS, "triton"),
+        timeout_seconds=300,
+    )
+
+
+def test_qwen3_ffn_triton_tp8_matches_native_tp1_bitwise() -> None:
+    _spawn_workers(
+        _topology_worker,
+        8,
+        (_WORLD8_CONFIGS, "triton"),
+        timeout_seconds=420,
+    )
+
+
 def test_qwen3_8b_ffn_tp2_smoke_matches_tp1_bitwise() -> None:
     if os.environ.get("RL_KERNEL_SKIP_QWEN3_8B_TOPOLOGY") == "1":
         pytest.skip("Qwen3-8B topology smoke disabled by environment")
diff --git a/tests/test_det_gemm.py b/tests/test_det_gemm.py
index 159eddb4..306f42e2 100644
--- a/tests/test_det_gemm.py
+++ b/tests/test_det_gemm.py
@@ -153,7 +153,7 @@ def test_forward_correctness(name, gemm):
     M, K, N = 128, 2048, 2048
     a, b = _rand(M, K), _rand(K, N)
     out = gemm(a, b).float()
-    ref = _k_tree_gemm(a, b).float() if name == _NATIVE_BACKEND else a.float() @ b.float()
+    ref = _k_tree_gemm(a, b).float()
     contract = load_contract()
     thresholds = contract["accuracy"]["default"]["reduction"]["bfloat16"]
     torch.testing.assert_close(out, ref, atol=thresholds["atol"], rtol=thresholds["rtol"])
@@ -209,35 +209,21 @@ def test_backward_correctness(name, gemm):
     b = _rand(K, N).requires_grad_(True)
     g = _rand(M, N)
     gemm(a, b).backward(g)
-    if name == _NATIVE_BACKEND:
-        expected_da = _k_tree_gemm(g, b.detach().t().contiguous())
-        expected_db = _k_tree_gemm(a.detach().t().contiguous(), g)
-        contract = load_contract()
-        thresholds = contract["accuracy"]["default"]["reduction"]["bfloat16"]
-        torch.testing.assert_close(
-            a.grad.float(),
-            expected_da.float(),
-            atol=thresholds["atol"],
-            rtol=thresholds["rtol"],
-        )
-        torch.testing.assert_close(
-            b.grad.float(),
-            expected_db.float(),
-            atol=thresholds["atol"],
-            rtol=thresholds["rtol"],
-        )
-        return
-
-    af = a.detach().float().requires_grad_(True)
-    bf = b.detach().float().requires_grad_(True)
-    (af @ bf).backward(g.float())
+    expected_da = _k_tree_gemm(g, b.detach().t().contiguous())
+    expected_db = _k_tree_gemm(a.detach().t().contiguous(), g)
     contract = load_contract()
     thresholds = contract["accuracy"]["default"]["reduction"]["bfloat16"]
     torch.testing.assert_close(
-        a.grad.float(), af.grad, atol=thresholds["atol"], rtol=thresholds["rtol"]
+        a.grad.float(),
+        expected_da.float(),
+        atol=thresholds["atol"],
+        rtol=thresholds["rtol"],
     )
     torch.testing.assert_close(
-        b.grad.float(), bf.grad, atol=thresholds["atol"], rtol=thresholds["rtol"]
+        b.grad.float(),
+        expected_db.float(),
+        atol=thresholds["atol"],
+        rtol=thresholds["rtol"],
     )
 
 
@@ -274,10 +260,43 @@ def test_triton_ragged_tiles_mask_all_axes():
     out = deterministic_gemm_triton(a, b)
     torch.cuda.synchronize()
     assert tuple(out.shape) == (80, 129)
-    torch.testing.assert_close(
-        out.float(), a.detach().float() @ b.detach().float(), atol=5e-2, rtol=2e-2
-    )
+    assert torch.equal(out, deterministic_gemm(a.detach(), b.detach()))
     out.backward(_rand(80, 129))
     torch.cuda.synchronize()
     assert torch.isfinite(a.grad).all()
     assert torch.isfinite(b.grad).all()
+
+
+@pytest.mark.skipif(not _HAS_TRITON, reason="Triton is unavailable")
+@pytest.mark.parametrize("shape", ((4, 65, 129), (8, 4096, 128), (4, 12288, 64)))
+def test_triton_tree_matches_native_forward_bitwise(shape):
+    """Triton evaluates the native FP32-leaf/BF16-node tree exactly."""
+
+    torch.manual_seed(47)
+    m_size, k_size, n_size = shape
+    a = _rand(m_size, k_size)
+    b = _rand(k_size, n_size)
+
+    native = deterministic_gemm(a, b)
+    triton_output = deterministic_gemm_triton(a, b)
+
+    assert torch.equal(native, triton_output), (
+        f"Triton/native mismatch at {shape}: "
+        f"{int((native != triton_output).sum().item())} elements"
+    )
+
+
+@pytest.mark.skipif(not _HAS_TRITON, reason="Triton is unavailable")
+def test_triton_tree_matches_native_backward_bitwise():
+    torch.manual_seed(48)
+    a = _rand(32, 512)
+    b = _rand(512, 128)
+    grad_output = _rand(32, 128)
+    native_inputs = [value.detach().clone().requires_grad_(True) for value in (a, b)]
+    triton_inputs = [value.detach().clone().requires_grad_(True) for value in (a, b)]
+
+    deterministic_gemm(*native_inputs).backward(grad_output)
+    deterministic_gemm_triton(*triton_inputs).backward(grad_output)
+
+    for native, triton_input in zip(native_inputs, triton_inputs, strict=True):
+        assert torch.equal(native.grad, triton_input.grad)
diff --git a/tests/test_qwen_ffn.py b/tests/test_qwen_ffn.py
index e0c4a18f..c86fc7c4 100644
--- a/tests/test_qwen_ffn.py
+++ b/tests/test_qwen_ffn.py
@@ -25,6 +25,7 @@
     QWEN3_8B_INTERMEDIATE_SIZE,
     qwen3_ffn,
 )
+from rl_engine.kernels.ops.triton.ffn import qwen3_ffn_triton
 from rl_engine.platforms.device import device_ctx
 
 _REQUIRED_SYMBOLS = (
@@ -362,3 +363,67 @@ def test_qwen3_ffn_gpu_output_and_hidden_gradient_are_token_slice_invariant():
 
     assert torch.equal(slice_output, full_output[2:4])
     assert torch.equal(slice_hidden.grad, full_hidden.grad[2:4])
+
+
+@requires_gpu_ffn
+@pytest.mark.parametrize("shape", ((7, 64, 128), (32, 64, 512)))
+def test_qwen3_ffn_triton_matches_native_forward_and_backward_bitwise(shape):
+    device = device_ctx.device
+    tokens, hidden_size, intermediate_size = shape
+    values = (
+        _randn((tokens, hidden_size), seed=50, dtype=torch.bfloat16, device=device),
+        _randn(
+            (intermediate_size, hidden_size),
+            seed=51,
+            dtype=torch.bfloat16,
+            device=device,
+        ),
+        _randn(
+            (intermediate_size, hidden_size),
+            seed=52,
+            dtype=torch.bfloat16,
+            device=device,
+        ),
+        _randn(
+            (hidden_size, intermediate_size),
+            seed=53,
+            dtype=torch.bfloat16,
+            device=device,
+        ),
+    )
+    grad_output = _randn(
+        (tokens, hidden_size),
+        seed=54,
+        dtype=torch.bfloat16,
+        device=device,
+    )
+    native_inputs = [value.detach().clone().requires_grad_(True) for value in values]
+    triton_inputs = [value.detach().clone().requires_grad_(True) for value in values]
+
+    native_output = qwen3_ffn(*native_inputs)
+    triton_output = qwen3_ffn_triton(*triton_inputs)
+    native_output.backward(grad_output)
+    triton_output.backward(grad_output)
+
+    assert torch.equal(native_output, triton_output)
+    for native, triton_input in zip(native_inputs, triton_inputs, strict=True):
+        assert torch.equal(native.grad, triton_input.grad)
+
+
+@requires_gpu_ffn
+def test_qwen3_ffn_triton_repeat_and_train_infer_are_bitwise():
+    device = device_ctx.device
+    values = (
+        _randn((8, 64), seed=60, dtype=torch.bfloat16, device=device),
+        _randn((128, 64), seed=61, dtype=torch.bfloat16, device=device),
+        _randn((128, 64), seed=62, dtype=torch.bfloat16, device=device),
+        _randn((64, 128), seed=63, dtype=torch.bfloat16, device=device),
+    )
+    with torch.no_grad():
+        first = qwen3_ffn_triton(*values)
+        second = qwen3_ffn_triton(*values)
+    training_values = [value.detach().clone().requires_grad_(True) for value in values]
+    training = qwen3_ffn_triton(*training_values)
+
+    assert torch.equal(first, second)
+    assert torch.equal(first, training.detach())
diff --git a/tests/test_swiglu.py b/tests/test_swiglu.py
index 22b26336..555d3bc4 100644
--- a/tests/test_swiglu.py
+++ b/tests/test_swiglu.py
@@ -372,6 +372,34 @@ def test_cuda_triton_swiglu_matches_native_forward_and_backward(impl, dtype, sha
     )
 
 
+@requires_cuda_activation
+@requires_triton_activation
+def test_triton_swiglu_matches_hip_bitwise_at_qwen3_width():
+    shape = (32, _INTERMEDIATE)
+    values = tuple(
+        (_rand(shape, seed=seed, dtype=torch.float32) * 0.02).to(
+            device="cuda", dtype=torch.bfloat16
+        )
+        for seed in (35, 36)
+    )
+    grad_output = (_rand(shape, seed=37, dtype=torch.float32) * 0.02).to(
+        device="cuda", dtype=torch.bfloat16
+    )
+    hip_inputs = [value.detach().clone().requires_grad_(True) for value in values]
+    triton_inputs = [value.detach().clone().requires_grad_(True) for value in values]
+
+    hip_output = SwiGLUCudaOp().forward(*hip_inputs)
+    triton_output = TritonSwiGLUOp().forward(*triton_inputs)
+    hip_output.backward(grad_output)
+    triton_output.backward(grad_output)
+
+    assert torch.equal(hip_output, triton_output)
+    assert all(
+        torch.equal(hip_input.grad, triton_input.grad)
+        for hip_input, triton_input in zip(hip_inputs, triton_inputs, strict=True)
+    )
+
+
 @requires_cuda
 @pytest.mark.parametrize("impl", _silu_impls())
 def test_cuda_triton_silu_batch_invariance_bitwise(impl):

From 809789c3011079307b338a177a3768ebed598beb Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sun, 23 Aug 2026 02:50:03 +0000
Subject: [PATCH 09/28] docs: report MI300X Triton FFN performance

---
 benchmarks/benchmark_rocm_ffn.py              |   39 +
 .../pr325_rocm_mi300x/accuracy_tradeoff.png   |  Bin 93854 -> 96007 bytes
 .../pr325_rocm_mi300x/collective_overhead.png |  Bin 168873 -> 172309 bytes
 .../distributed_ffn_overhead.png              |  Bin 92815 -> 93551 bytes
 .../results/pr325_rocm_mi300x/report.md       |  170 +-
 .../results/pr325_rocm_mi300x/results.json    | 1626 +++++++++++------
 .../pr325_rocm_mi300x/single_gpu_overhead.png |  Bin 213116 -> 213251 bytes
 7 files changed, 1153 insertions(+), 682 deletions(-)

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index 8216d736..c6513bdc 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -1526,6 +1526,13 @@ def ratio_range(
     distributed_output_errors = [
         row["output_relative_l2"] * 100.0 for row in distributed_rows
     ]
+    one_mib_collective_ms = [
+        row["deterministic"]["median_ms"]
+        for row in collective_rows
+        if row["message_bytes_per_rank"] == _MIB
+    ]
+    triton_forward_ms = [row["triton"]["median_ms"] for row in forward_rows]
+    triton_training_ms = [row["triton"]["median_ms"] for row in training_rows]
     consistency_rows = all(
         row["train_infer_bitwise"] and row["repeat_bitwise"]
         for row in distributed_rows
@@ -1742,6 +1749,38 @@ def ratio_range(
             f"{'yes' if row['triton_repeat_bitwise'] else 'no'} |"
         )
 
+    lines.extend(
+        (
+            "",
+            "## Communication overlap feasibility",
+            "",
+            "The reported implementations deliberately serialize compute and "
+            "communication; the tables above do not claim overlap. A representative "
+            f"1 MiB strict collective costs {min(one_mib_collective_ms):.4f}-"
+            f"{max(one_mib_collective_ms):.4f} ms, while Triton distributed FFN "
+            f"forward costs {min(triton_forward_ms):.4f}-"
+            f"{max(triton_forward_ms):.4f} ms and forward+backward costs "
+            f"{min(triton_training_ms):.4f}-{max(triton_training_ms):.4f} ms. "
+            "Communication is therefore material after the GEMM speedup, but only "
+            "some calls are structurally hideable.",
+            "",
+            "- Forward SP all-gather feeds both gate/up GEMMs, and the final TP "
+            "all-reduce or reduce-scatter consumes the down projection. These are "
+            "dependency boundaries and cannot be hidden by simply moving them to "
+            "another stream.",
+            "- Backward has one safe pipeline candidate: launch the fixed-order TP "
+            "reduction for the gate contribution to `dHidden`, compute the independent "
+            "up contribution, then wait and add in the existing gate-then-up order. "
+            "The rank tree and BF16 addition order must remain unchanged.",
+            "- CP activation/gradient gathers are prerequisites for weight-gradient "
+            "GEMMs. They can be coalesced into a fixed packed layout to amortize "
+            "launch/signature validation, but are not naturally hidden by those GEMMs.",
+            "- Because the plausible overlap window is bounded by roughly one strict "
+            "collective, coalescing and removing per-call host signature validation "
+            "should be benchmarked before adding multi-stream scheduling complexity.",
+        )
+    )
+
     lines.extend(
         (
             "",
diff --git a/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png b/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png
index e6996416e60515f0b15367ade6b79212df897de9..8698866997c84ed13e0f09b24965c65d2728415d 100644
GIT binary patch
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diff --git a/benchmarks/results/pr325_rocm_mi300x/distributed_ffn_overhead.png b/benchmarks/results/pr325_rocm_mi300x/distributed_ffn_overhead.png
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diff --git a/benchmarks/results/pr325_rocm_mi300x/report.md b/benchmarks/results/pr325_rocm_mi300x/report.md
index 0a0dfd1b..31f7d66b 100644
--- a/benchmarks/results/pr325_rocm_mi300x/report.md
+++ b/benchmarks/results/pr325_rocm_mi300x/report.md
@@ -8,7 +8,7 @@
 |---|---|
 | NCCL_IB_DISABLE | 1 |
 | architecture | gfx942:sramecc+:xnack- |
-| git_commit | 7d04efe80f7903431bc75fff3bbbe85eb82e02c4 |
+| git_commit | 9dc9a68c2b3920c6e1e13226d38f5900b55ec0de |
 | gpu | AMD Instinct MI300X |
 | gpu_count | 8 |
 | hip | 7.14.60850 |
@@ -21,7 +21,8 @@
 
 - BF16 operator inputs; FFN dimensions are H=4096 and I=12288.
 - Native GEMM is `torch.matmul`; native communication calls PyTorch's ProcessGroupNCCL directly, which uses RCCL on ROCm.
-- The native distributed FFN uses the same weights, TP/CP/SP sharding, and collective schedule as the deterministic path.
+- Three compute paths are measured: native ROCm, the strict HIP correctness kernels, and the new strict Triton kernels. HIP and Triton share the same deterministic RCCL collective implementation.
+- All distributed paths use the same weights, TP/CP/SP sharding, and collective schedule.
 - Single-GPU timing: GPU events, median and p95; distributed timing: synchronized wall clock, slowest rank per sample.
 - 5 warmups, 20 measured forward/collective samples, and 10 measured forward+backward samples.
 - `NCCL_IB_DISABLE=1` selects the same intra-node XGMI transport for both collective implementations. Raw median, p95, minimum, and maximum measurements are in `results.json`.
@@ -38,103 +39,112 @@ python benchmarks/benchmark_rocm_ffn.py \
 
 ## Key findings
 
-- The gfx942 deterministic GEMM scalar fallback costs 44.0-1109.8x native latency. This dominates end-to-end FFN overhead.
-- The fused deterministic SwiGLU costs 0.22-0.76x native latency, so it is faster than the unfused PyTorch expression; it is not the bottleneck.
-- Deterministic collectives cost 2.0-27.6x native RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.
-- Distributed FFN overhead is 32.1-105.8x for forward and 11.7-61.0x for forward+backward.
-- All 16 distributed direction/topology cases are train/inference and repeat bitwise consistent: yes.
+- Strict Triton GEMM costs 5.5-27.4x native latency, versus 43.6-1129.7x for the gfx942 HIP scalar fallback. That is a 7.9-41.2x speedup while preserving the identical BF16 arithmetic tree.
+- Strict Triton SwiGLU costs 0.74-1.48x native latency; the HIP fused kernel costs 0.21-0.67x.
+- Deterministic collectives cost 2.2-26.1x native RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.
+- Triton distributed FFN overhead is 6.7-9.2x for forward and 4.7-7.9x for forward+backward, compared with 31.5-106.6x and 13.0-57.1x for HIP.
+- HIP and Triton produced 0 mismatched elements across all measured GEMM, SwiGLU, single-GPU FFN, and distributed FFN outputs/gradients. All distributed TP/CP/SP cases are repeat and train/inference bitwise consistent: HIP=yes, Triton=yes.
 - GEMM relative-L2 error against FP32 is 0.500-0.577% for the deterministic BF16 tree versus 0.164-0.166% for native ROCm. The fixed BF16 tree buys topology invariance, not better FP32 proximity.
 - Distributed FFN output drift versus native is 1.012-1.060% relative-L2. Conversely, the balanced deterministic reduction is closer to FP32 than native RCCL in 12/12 tested 4/8-rank reduction cases.
 
 ## Single-GPU GEMM
 
-| Shape | Native ROCm (ms) | Deterministic (ms) | Overhead | Det rel-L2 vs FP32 | Native rel-L2 vs FP32 |
-|---|---:|---:|---:|---:|---:|
-| gate_up_m1 | 0.0384 | 1.6887 | 44.0× | 5.003e-03 | 1.651e-03 |
-| down_m1 | 0.0283 | 4.8950 | 173.2× | 5.769e-03 | 1.635e-03 |
-| gate_up_m8 | 0.0297 | 3.1275 | 105.4× | 5.018e-03 | 1.663e-03 |
-| down_m8 | 0.0299 | 6.3458 | 211.9× | 5.679e-03 | 1.654e-03 |
-| gate_up_m32 | 0.0318 | 12.9507 | 406.9× | 5.019e-03 | 1.654e-03 |
-| down_m32 | 0.0295 | 16.5279 | 560.6× | 5.604e-03 | 1.662e-03 |
-| gate_up_m128 | 0.0533 | 45.9833 | 862.7× | 5.019e-03 | 1.657e-03 |
-| down_m128 | 0.0550 | 61.0427 | 1109.8× | 5.623e-03 | 1.659e-03 |
+| Shape | Native (ms) | Strict HIP (ms) | Strict Triton (ms) | HIP/native | Triton/native | Triton speedup | Mismatch | Strict rel-L2 vs FP32 | Native rel-L2 vs FP32 |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| gate_up_m1 | 0.0386 | 1.6818 | 0.2131 | 43.6× | 5.5× | 7.9× | 0 | 5.003e-03 | 1.651e-03 |
+| down_m1 | 0.0287 | 4.8953 | 0.2816 | 170.7× | 9.8× | 17.4× | 0 | 5.769e-03 | 1.635e-03 |
+| gate_up_m8 | 0.0301 | 3.1315 | 0.2534 | 104.1× | 8.4× | 12.4× | 0 | 5.018e-03 | 1.663e-03 |
+| down_m8 | 0.0305 | 6.3576 | 0.3154 | 208.3× | 10.3× | 20.2× | 0 | 5.679e-03 | 1.654e-03 |
+| gate_up_m32 | 0.0311 | 12.9404 | 0.4445 | 416.3× | 14.3× | 29.1× | 0 | 5.019e-03 | 1.654e-03 |
+| down_m32 | 0.0297 | 16.4953 | 0.4797 | 555.0× | 16.1× | 34.4× | 0 | 5.604e-03 | 1.662e-03 |
+| gate_up_m128 | 0.0507 | 45.9268 | 1.2442 | 905.2× | 24.5× | 36.9× | 0 | 5.019e-03 | 1.657e-03 |
+| down_m128 | 0.0541 | 61.1382 | 1.4830 | 1129.7× | 27.4× | 41.2× | 0 | 5.623e-03 | 1.659e-03 |
 
 ## Single-GPU SwiGLU
 
-| Case | Native PyTorch (ms) | Deterministic fused (ms) | Det/native | Det/native rel-L2 |
-|---|---:|---:|---:|---:|
-| swiglu_forward_m1 | 0.0168 | 0.0127 | 0.76x | 2.877e-03 |
-| swiglu_backward_m1 | 0.0562 | 0.0141 | 0.25x | 5.159e-03 |
-| swiglu_forward_m8 | 0.0190 | 0.0129 | 0.68x | 2.816e-03 |
-| swiglu_backward_m8 | 0.0676 | 0.0146 | 0.22x | 5.074e-03 |
-| swiglu_forward_m32 | 0.0200 | 0.0136 | 0.68x | 2.825e-03 |
-| swiglu_backward_m32 | 0.0706 | 0.0156 | 0.22x | 5.071e-03 |
-| swiglu_forward_m128 | 0.0203 | 0.0134 | 0.66x | 2.828e-03 |
-| swiglu_backward_m128 | 0.0669 | 0.0149 | 0.22x | 5.059e-03 |
+| Case | Native PyTorch (ms) | Strict HIP (ms) | Strict Triton (ms) | HIP/native | Triton/native | Triton speedup | Mismatch | Strict/native rel-L2 |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| swiglu_forward_m1 | 0.0166 | 0.0111 | 0.0245 | 0.67x | 1.48x | 0.45x | 0 | 2.877e-03 |
+| swiglu_backward_m1 | 0.0550 | 0.0126 | 0.0451 | 0.23x | 0.82x | 0.28x | 0 | 5.159e-03 |
+| swiglu_forward_m8 | 0.0185 | 0.0115 | 0.0255 | 0.62x | 1.38x | 0.45x | 0 | 2.816e-03 |
+| swiglu_backward_m8 | 0.0623 | 0.0130 | 0.0464 | 0.21x | 0.74x | 0.28x | 0 | 5.074e-03 |
+| swiglu_forward_m32 | 0.0189 | 0.0117 | 0.0264 | 0.62x | 1.40x | 0.44x | 0 | 2.825e-03 |
+| swiglu_backward_m32 | 0.0627 | 0.0129 | 0.0482 | 0.21x | 0.77x | 0.27x | 0 | 5.071e-03 |
+| swiglu_forward_m128 | 0.0182 | 0.0115 | 0.0260 | 0.63x | 1.42x | 0.44x | 0 | 2.828e-03 |
+| swiglu_backward_m128 | 0.0622 | 0.0132 | 0.0486 | 0.21x | 0.78x | 0.27x | 0 | 5.059e-03 |
 
 ## Single-GPU FFN
 
-| Case | Native ROCm (ms) | Deterministic (ms) | Overhead | Output rel-L2 | Max grad rel-L2 |
-|---|---:|---:|---:|---:|---:|
-| ffn_forward_m1 | 0.0929 | 9.4786 | 102.0× | 1.008e-02 | nan |
-| ffn_train_fwd_bwd_m1 | 0.4308 | 39.0465 | 90.6× | 1.008e-02 | 1.037e-02 |
-| ffn_forward_m8 | 0.1018 | 14.0304 | 137.8× | 9.930e-03 | nan |
-| ffn_train_fwd_bwd_m8 | 0.4067 | 47.4899 | 116.8× | 9.930e-03 | 1.017e-02 |
-| ffn_forward_m32 | 0.1120 | 43.5693 | 388.9× | 9.952e-03 | nan |
-| ffn_train_fwd_bwd_m32 | 0.5347 | 107.4444 | 200.9× | 9.952e-03 | 1.024e-02 |
+| Case | Native (ms) | Strict HIP (ms) | Strict Triton (ms) | HIP/native | Triton/native | Triton speedup | Mismatch | Output rel-L2 | Max grad rel-L2 |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| ffn_forward_m1 | 0.0893 | 9.4804 | 1.9132 | 106.2× | 21.4× | 5.0× | 0 | 1.008e-02 | nan |
+| ffn_train_fwd_bwd_m1 | 0.3956 | 39.0134 | 4.3589 | 98.6× | 11.0× | 9.0× | 0 | 1.008e-02 | 1.037e-02 |
+| ffn_forward_m8 | 0.0958 | 14.0203 | 2.0307 | 146.3× | 21.2× | 6.9× | 0 | 9.930e-03 | nan |
+| ffn_train_fwd_bwd_m8 | 0.5470 | 47.4023 | 4.7045 | 86.7× | 8.6× | 10.1× | 0 | 9.930e-03 | 1.017e-02 |
+| ffn_forward_m32 | 0.1070 | 43.5072 | 2.5388 | 406.7× | 23.7× | 17.1× | 0 | 9.952e-03 | nan |
+| ffn_train_fwd_bwd_m32 | 0.3958 | 107.0497 | 5.7859 | 270.5× | 14.6× | 18.5× | 0 | 9.952e-03 | 1.024e-02 |
 
 ## RCCL collectives
 
 | Operation | Ranks | Input/rank | Native RCCL (ms) | Deterministic (ms) | Overhead | Det/native rel-L2 | Native/FP32 rel-L2 | Det/FP32 rel-L2 |
 |---|---:|---:|---:|---:|---:|---:|---:|---:|
-| all_reduce | 2 | 0.0625 MiB | 0.0592 | 0.5532 | 9.3× | 0.000e+00 | 1.798e-03 | 1.798e-03 |
-| all_gather | 2 | 0.0625 MiB | 0.0534 | 0.5392 | 10.1× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 2 | 0.0625 MiB | 0.0523 | 0.5616 | 10.7× | 0.000e+00 | 1.804e-03 | 1.804e-03 |
-| all_reduce | 2 | 1 MiB | 0.0825 | 0.5553 | 6.7× | 0.000e+00 | 1.800e-03 | 1.800e-03 |
-| all_gather | 2 | 1 MiB | 0.0772 | 0.5586 | 7.2× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 2 | 1 MiB | 0.0719 | 0.5781 | 8.0× | 0.000e+00 | 1.803e-03 | 1.803e-03 |
-| all_reduce | 2 | 16 MiB | 0.5073 | 1.0212 | 2.0× | 0.000e+00 | 1.800e-03 | 1.800e-03 |
-| all_gather | 2 | 16 MiB | 0.4099 | 1.2952 | 3.2× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 2 | 16 MiB | 0.2442 | 0.8913 | 3.6× | 0.000e+00 | 1.803e-03 | 1.803e-03 |
-| all_reduce | 4 | 0.0625 MiB | 0.0617 | 0.7073 | 11.5× | 3.221e-03 | 2.674e-03 | 2.536e-03 |
-| all_gather | 4 | 0.0625 MiB | 0.0573 | 0.6558 | 11.4× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 4 | 0.0625 MiB | 0.0559 | 0.7087 | 12.7× | 3.263e-03 | 2.734e-03 | 2.559e-03 |
-| all_reduce | 4 | 1 MiB | 0.0874 | 0.6935 | 7.9× | 3.256e-03 | 2.682e-03 | 2.566e-03 |
-| all_gather | 4 | 1 MiB | 0.0883 | 0.6794 | 7.7× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 4 | 1 MiB | 0.0908 | 0.7014 | 7.7× | 3.319e-03 | 2.691e-03 | 2.599e-03 |
-| all_reduce | 4 | 16 MiB | 0.2442 | 1.0390 | 4.3× | 3.256e-03 | 2.682e-03 | 2.566e-03 |
-| all_gather | 4 | 16 MiB | 0.4244 | 1.0259 | 2.4× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 4 | 16 MiB | 0.1556 | 1.0519 | 6.8× | 3.319e-03 | 2.691e-03 | 2.599e-03 |
-| all_reduce | 8 | 0.0625 MiB | 0.0374 | 1.0311 | 27.6× | 4.446e-03 | 3.664e-03 | 3.099e-03 |
-| all_gather | 8 | 0.0625 MiB | 0.0721 | 0.9555 | 13.2× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 8 | 0.0625 MiB | 0.0670 | 1.0387 | 15.5× | 4.879e-03 | 3.780e-03 | 3.155e-03 |
-| all_reduce | 8 | 1 MiB | 0.0545 | 1.0036 | 18.4× | 4.474e-03 | 3.676e-03 | 3.110e-03 |
-| all_gather | 8 | 1 MiB | 0.0887 | 0.9689 | 10.9× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 8 | 1 MiB | 0.0669 | 1.0162 | 15.2× | 4.765e-03 | 3.768e-03 | 3.168e-03 |
-| all_reduce | 8 | 16 MiB | 0.1812 | 1.3730 | 7.6× | 4.474e-03 | 3.676e-03 | 3.110e-03 |
-| all_gather | 8 | 16 MiB | 0.4353 | 1.3738 | 3.2× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 8 | 16 MiB | 0.1228 | 1.5048 | 12.3× | 4.765e-03 | 3.768e-03 | 3.168e-03 |
+| all_reduce | 2 | 0.0625 MiB | 0.0591 | 0.5470 | 9.3× | 0.000e+00 | 1.798e-03 | 1.798e-03 |
+| all_gather | 2 | 0.0625 MiB | 0.0522 | 0.5240 | 10.0× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 2 | 0.0625 MiB | 0.0500 | 0.5611 | 11.2× | 0.000e+00 | 1.804e-03 | 1.804e-03 |
+| all_reduce | 2 | 1 MiB | 0.0826 | 0.5473 | 6.6× | 0.000e+00 | 1.800e-03 | 1.800e-03 |
+| all_gather | 2 | 1 MiB | 0.0748 | 0.5649 | 7.5× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 2 | 1 MiB | 0.0692 | 0.5791 | 8.4× | 0.000e+00 | 1.803e-03 | 1.803e-03 |
+| all_reduce | 2 | 16 MiB | 0.4068 | 0.8919 | 2.2× | 0.000e+00 | 1.800e-03 | 1.800e-03 |
+| all_gather | 2 | 16 MiB | 0.4068 | 0.8947 | 2.2× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 2 | 16 MiB | 0.2427 | 0.8972 | 3.7× | 0.000e+00 | 1.803e-03 | 1.803e-03 |
+| all_reduce | 4 | 0.0625 MiB | 0.0636 | 0.7050 | 11.1× | 3.221e-03 | 2.674e-03 | 2.536e-03 |
+| all_gather | 4 | 0.0625 MiB | 0.0573 | 0.6548 | 11.4× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 4 | 0.0625 MiB | 0.0541 | 0.7036 | 13.0× | 3.263e-03 | 2.734e-03 | 2.559e-03 |
+| all_reduce | 4 | 1 MiB | 0.0877 | 0.6830 | 7.8× | 3.256e-03 | 2.682e-03 | 2.566e-03 |
+| all_gather | 4 | 1 MiB | 0.0895 | 0.6880 | 7.7× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 4 | 1 MiB | 0.0906 | 0.7200 | 7.9× | 3.319e-03 | 2.691e-03 | 2.599e-03 |
+| all_reduce | 4 | 16 MiB | 0.2430 | 1.0479 | 4.3× | 3.256e-03 | 2.682e-03 | 2.566e-03 |
+| all_gather | 4 | 16 MiB | 0.4251 | 1.0416 | 2.5× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 4 | 16 MiB | 0.1572 | 1.0507 | 6.7× | 3.319e-03 | 2.691e-03 | 2.599e-03 |
+| all_reduce | 8 | 0.0625 MiB | 0.0388 | 1.0130 | 26.1× | 4.446e-03 | 3.664e-03 | 3.099e-03 |
+| all_gather | 8 | 0.0625 MiB | 0.0712 | 0.9640 | 13.5× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 8 | 0.0625 MiB | 0.0662 | 1.0662 | 16.1× | 4.879e-03 | 3.780e-03 | 3.155e-03 |
+| all_reduce | 8 | 1 MiB | 0.0526 | 1.0009 | 19.0× | 4.474e-03 | 3.676e-03 | 3.110e-03 |
+| all_gather | 8 | 1 MiB | 0.0896 | 0.9617 | 10.7× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 8 | 1 MiB | 0.0663 | 1.0107 | 15.2× | 4.765e-03 | 3.768e-03 | 3.168e-03 |
+| all_reduce | 8 | 16 MiB | 0.1794 | 1.3688 | 7.6× | 4.474e-03 | 3.676e-03 | 3.110e-03 |
+| all_gather | 8 | 16 MiB | 0.4294 | 1.3492 | 3.1× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
+| reduce_scatter | 8 | 16 MiB | 0.1196 | 1.3798 | 11.5× | 4.765e-03 | 3.768e-03 | 3.168e-03 |
 
 ## Distributed FFN
 
-| Topology | Direction | Native ROCm/RCCL (ms) | Deterministic (ms) | Overhead | Output rel-L2 | Max grad rel-L2 | Train/infer bitwise | Repeat bitwise |
-|---|---|---:|---:|---:|---:|---:|:---:|:---:|
-| tp2 | forward | 0.2170 | 22.9695 | 105.8× | 1.012e-02 | nan | yes | yes |
-| tp2 | train_fwd_bwd | 0.9359 | 57.1264 | 61.0× | 1.012e-02 | 1.079e-02 | yes | yes |
-| tp2_sp | forward | 0.2821 | 23.6457 | 83.8× | 1.015e-02 | nan | yes | yes |
-| tp2_sp | train_fwd_bwd | 1.0045 | 58.6581 | 58.4× | 1.015e-02 | 1.083e-02 | yes | yes |
-| tp4 | forward | 0.2175 | 13.6428 | 62.7× | 1.031e-02 | nan | yes | yes |
-| tp4 | train_fwd_bwd | 1.1884 | 33.2462 | 28.0× | 1.031e-02 | 1.095e-02 | yes | yes |
-| tp2_cp2 | forward | 0.2231 | 14.1083 | 63.2× | 1.015e-02 | nan | yes | yes |
-| tp2_cp2 | train_fwd_bwd | 1.2568 | 43.0444 | 34.2× | 1.015e-02 | 1.083e-02 | yes | yes |
-| tp2_cp2_sp | forward | 0.2819 | 14.7152 | 52.2× | 1.022e-02 | nan | yes | yes |
-| tp2_cp2_sp | train_fwd_bwd | 1.3499 | 44.1678 | 32.7× | 1.022e-02 | 1.089e-02 | yes | yes |
-| tp8 | forward | 0.1939 | 8.2995 | 42.8× | 1.060e-02 | nan | yes | yes |
-| tp8 | train_fwd_bwd | 1.0345 | 20.4734 | 19.8× | 1.060e-02 | 1.126e-02 | yes | yes |
-| tp4_cp2 | forward | 0.2230 | 8.4606 | 37.9× | 1.033e-02 | nan | yes | yes |
-| tp4_cp2 | train_fwd_bwd | 2.1949 | 25.7398 | 11.7× | 1.033e-02 | 1.099e-02 | yes | yes |
-| tp4_cp2_sp | forward | 0.2846 | 9.1342 | 32.1× | 1.045e-02 | nan | yes | yes |
-| tp4_cp2_sp | train_fwd_bwd | 2.0314 | 26.6942 | 13.1× | 1.045e-02 | 1.109e-02 | yes | yes |
+| Topology | Direction | Native (ms) | Strict HIP (ms) | Strict Triton (ms) | HIP/native | Triton/native | Triton speedup | Mismatch | Output rel-L2 | Max grad rel-L2 | Triton T/I | Triton repeat |
+|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|:---:|:---:|
+| tp2 | forward | 0.2150 | 22.9206 | 1.9798 | 106.6× | 9.2× | 11.6× | 0 | 1.012e-02 | nan | yes | yes |
+| tp2 | train_fwd_bwd | 1.2321 | 57.0045 | 5.7878 | 46.3× | 4.7× | 9.8× | 0 | 1.012e-02 | 1.079e-02 | yes | yes |
+| tp2_sp | forward | 0.2809 | 23.5957 | 2.5798 | 84.0× | 9.2× | 9.1× | 0 | 1.015e-02 | nan | yes | yes |
+| tp2_sp | train_fwd_bwd | 1.0253 | 58.5607 | 7.3220 | 57.1× | 7.1× | 8.0× | 0 | 1.015e-02 | 1.083e-02 | yes | yes |
+| tp4 | forward | 0.2205 | 13.6021 | 1.5211 | 61.7× | 6.9× | 8.9× | 0 | 1.031e-02 | nan | yes | yes |
+| tp4 | train_fwd_bwd | 1.2806 | 33.5847 | 6.5363 | 26.2× | 5.1× | 5.1× | 0 | 1.031e-02 | 1.095e-02 | yes | yes |
+| tp2_cp2 | forward | 0.2225 | 14.0999 | 1.8852 | 63.4× | 8.5× | 7.5× | 0 | 1.015e-02 | nan | yes | yes |
+| tp2_cp2 | train_fwd_bwd | 1.4676 | 42.9816 | 9.2452 | 29.3× | 6.3× | 4.6× | 0 | 1.015e-02 | 1.083e-02 | yes | yes |
+| tp2_cp2_sp | forward | 0.2847 | 14.6807 | 2.4424 | 51.6× | 8.6× | 6.0× | 0 | 1.022e-02 | nan | yes | yes |
+| tp2_cp2_sp | train_fwd_bwd | 1.4237 | 44.0911 | 11.2019 | 31.0× | 7.9× | 3.9× | 0 | 1.022e-02 | 1.089e-02 | yes | yes |
+| tp8 | forward | 0.1962 | 8.3054 | 1.7764 | 42.3× | 9.1× | 4.7× | 0 | 1.060e-02 | nan | yes | yes |
+| tp8 | train_fwd_bwd | 1.1290 | 20.8277 | 7.4017 | 18.4× | 6.6× | 2.8× | 0 | 1.060e-02 | 1.126e-02 | yes | yes |
+| tp4_cp2 | forward | 0.2250 | 8.4350 | 1.5096 | 37.5× | 6.7× | 5.6× | 0 | 1.033e-02 | nan | yes | yes |
+| tp4_cp2 | train_fwd_bwd | 1.6478 | 26.0435 | 9.4115 | 15.8× | 5.7× | 2.8× | 0 | 1.033e-02 | 1.099e-02 | yes | yes |
+| tp4_cp2_sp | forward | 0.2906 | 9.1476 | 2.2212 | 31.5× | 7.6× | 4.1× | 0 | 1.045e-02 | nan | yes | yes |
+| tp4_cp2_sp | train_fwd_bwd | 2.0583 | 26.7826 | 11.6214 | 13.0× | 5.6× | 2.3× | 0 | 1.045e-02 | 1.109e-02 | yes | yes |
+
+## Communication overlap feasibility
+
+The reported implementations deliberately serialize compute and communication; the tables above do not claim overlap. A representative 1 MiB strict collective costs 0.5473-1.0107 ms, while Triton distributed FFN forward costs 1.5096-2.5798 ms and forward+backward costs 5.7878-11.6214 ms. Communication is therefore material after the GEMM speedup, but only some calls are structurally hideable.
+
+- Forward SP all-gather feeds both gate/up GEMMs, and the final TP all-reduce or reduce-scatter consumes the down projection. These are dependency boundaries and cannot be hidden by simply moving them to another stream.
+- Backward has one safe pipeline candidate: launch the fixed-order TP reduction for the gate contribution to `dHidden`, compute the independent up contribution, then wait and add in the existing gate-then-up order. The rank tree and BF16 addition order must remain unchanged.
+- CP activation/gradient gathers are prerequisites for weight-gradient GEMMs. They can be coalesced into a fixed packed layout to amortize launch/signature validation, but are not naturally hidden by those GEMMs.
+- Because the plausible overlap window is bounded by roughly one strict collective, coalescing and removing per-call host signature validation should be benchmarked before adding multi-stream scheduling complexity.
 
 ## Figures
 
diff --git a/benchmarks/results/pr325_rocm_mi300x/results.json b/benchmarks/results/pr325_rocm_mi300x/results.json
index 77f72fa3..5ba93b4f 100644
--- a/benchmarks/results/pr325_rocm_mi300x/results.json
+++ b/benchmarks/results/pr325_rocm_mi300x/results.json
@@ -2,10 +2,10 @@
   "collectives": [
     {
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-        "min_ms": 0.5368776619434357,
-        "p95_ms": 0.6180185358971357
+        "max_ms": 0.8889501914381981,
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+        "min_ms": 0.5341139622032642,
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@@ -16,22 +16,22 @@
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       "operation": "all_reduce",
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@@ -42,22 +42,22 @@
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@@ -68,22 +68,22 @@
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@@ -120,22 +120,22 @@
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@@ -146,22 +146,22 @@
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@@ -172,22 +172,22 @@
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From 5222b68f2dc8a45cc07604f67bda31469aaf7aef Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sun, 23 Aug 2026 07:45:15 +0000
Subject: [PATCH 10/28] refactor(ffn): make ROCm path Triton-native

---
 benchmarks/benchmark_rocm_ffn.py              | 1283 +++++------------
 csrc/cuda/gemm/det_gemm_kernel.cu             |  240 +--
 rl_engine/kernels/ops/pytorch/ffn/__init__.py |   14 -
 rl_engine/kernels/ops/pytorch/ffn/ffn.py      |  371 -----
 rl_engine/kernels/ops/triton/ffn/__init__.py  |   14 +-
 rl_engine/kernels/ops/triton/ffn/ffn.py       |  155 +-
 tests/distributed/test_qwen_ffn_topology.py   |   94 +-
 tests/test_det_gemm.py                        |   85 +-
 tests/test_qwen_ffn.py                        |  299 +---
 tests/test_swiglu.py                          |   28 -
 10 files changed, 640 insertions(+), 1943 deletions(-)
 delete mode 100644 rl_engine/kernels/ops/pytorch/ffn/__init__.py
 delete mode 100644 rl_engine/kernels/ops/pytorch/ffn/ffn.py

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index c6513bdc..762c8275 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -1,19 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""ROCm performance and accuracy analysis for the deterministic Qwen3 FFN.
+"""Operator-only ROCm analysis for the deterministic distributed Triton FFN.
 
-This benchmark compares the deterministic PR #325 path against native ROCm
-operators without loading a model:
-
-* deterministic GEMM vs ``torch.matmul`` (rocBLAS/hipBLASLt);
-* deterministic SwiGLU vs native PyTorch elementwise operators;
-* deterministic collectives vs native RCCL through ProcessGroupNCCL;
-* deterministic FFN vs a native ROCm/RCCL implementation with the same TP/CP/SP
-  sharding and collective schedule.
-
-The script writes raw JSON, Markdown tables, and PNG figures.  GPU events are
-used for single-GPU operators.  Distributed timings use synchronized wall
-clock samples and report the slowest rank for each iteration.
+The two compute paths are native ROCm (PyTorch/rocBLAS) and the PR's Triton
+implementation. Communication compares native RCCL with the fixed-rank
+deterministic RCCL transport. No model checkpoint or serving engine is used.
 """
 
 from __future__ import annotations
@@ -36,30 +27,18 @@
 import torch.multiprocessing as mp
 import torch.nn.functional as F
 
-import rl_engine.kernels.ops.pytorch.ffn.ffn as ffn_module
+import rl_engine.kernels.ops.triton.ffn.ffn as ffn_module
 from rl_engine.distributed import RCCLDeterministicCollective
-from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
-from rl_engine.kernels.ops.cuda.matmul import deterministic_gemm
-from rl_engine.kernels.ops.pytorch.ffn import qwen3_ffn
 from rl_engine.kernels.ops.triton.activation.swiglu import (
     _launch_swiglu_bwd,
     _launch_swiglu_fwd,
 )
-from rl_engine.kernels.ops.triton.ffn import qwen3_ffn_triton
+from rl_engine.kernels.ops.triton.ffn import qwen3_ffn
 from rl_engine.kernels.ops.triton.matmul import deterministic_gemm_triton
 
 _MIB = 1024 * 1024
-_REQUIRED_SYMBOLS = (
-    "det_gemm_fwd",
-    "det_gemm_db",
-    "swiglu_forward",
-    "swiglu_backward",
-)
 _DISTRIBUTED_CONFIGS: dict[int, tuple[tuple[str, int, int, bool], ...]] = {
-    2: (
-        ("tp2", 2, 1, False),
-        ("tp2_sp", 2, 1, True),
-    ),
+    2: (("tp2", 2, 1, False), ("tp2_sp", 2, 1, True)),
     4: (
         ("tp4", 4, 1, False),
         ("tp2_cp2", 2, 2, False),
@@ -75,8 +54,6 @@
 
 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)
@@ -99,9 +76,10 @@ def _relative_l2(actual: torch.Tensor, expected: torch.Tensor) -> float:
     actual_float = actual.detach().float()
     expected_float = expected.detach().float()
     denominator = torch.linalg.vector_norm(expected_float)
+    numerator = torch.linalg.vector_norm(actual_float - 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())
+        return float(numerator.item())
+    return float((numerator / denominator).item())
 
 
 def _accuracy(actual: torch.Tensor, expected: torch.Tensor) -> dict[str, float]:
@@ -110,10 +88,16 @@ def _accuracy(actual: torch.Tensor, expected: torch.Tensor) -> dict[str, float]:
         "max_abs": float(difference.abs().max().item()),
         "mean_abs": float(difference.abs().mean().item()),
         "relative_l2": _relative_l2(actual, expected),
-        "exact_fraction": float((actual.detach() == expected.detach()).float().mean().item()),
+        "exact_fraction": float(
+            (actual.detach() == expected.detach()).float().mean().item()
+        ),
     }
 
 
+def _mismatches(left: torch.Tensor, right: torch.Tensor) -> int:
+    return int((left.detach() != right.detach()).sum().item())
+
+
 def _randn(
     shape: tuple[int, ...],
     *,
@@ -135,7 +119,6 @@ def _gpu_event_samples(
     for _ in range(warmup):
         function()
     torch.cuda.synchronize()
-
     events: list[tuple[torch.cuda.Event, torch.cuda.Event]] = []
     for _ in range(samples):
         start = torch.cuda.Event(enable_timing=True)
@@ -156,8 +139,19 @@ def _native_ffn(
 ) -> torch.Tensor:
     gate = hidden @ gate_weight.t()
     up = hidden @ up_weight.t()
-    activated = F.silu(gate) * up
-    return activated @ down_weight.t()
+    return (F.silu(gate) * up) @ down_weight.t()
+
+
+def _training_step(
+    function: Callable[..., torch.Tensor],
+    inputs: list[torch.Tensor],
+    grad_output: torch.Tensor,
+) -> torch.Tensor:
+    for value in inputs:
+        value.grad = None
+    output = function(*inputs)
+    output.backward(grad_output)
+    return output
 
 
 def _single_gpu_benchmarks(
@@ -169,7 +163,6 @@ def _single_gpu_benchmarks(
     device = torch.device("cuda", 0)
     torch.cuda.set_device(device)
     torch.backends.cuda.matmul.allow_tf32 = False
-
     results: dict[str, list[dict[str, Any]]] = {
         "gemm": [],
         "swiglu": [],
@@ -184,30 +177,25 @@ def _single_gpu_benchmarks(
                 (f"down_m{tokens}", tokens, 12288, 4096),
             )
         )
-
-    for shape_index, (name, m_size, k_size, n_size) in enumerate(gemm_shapes):
-        left = _randn((m_size, k_size), seed=1000 + shape_index * 2, device=device)
-        right = _randn((k_size, n_size), seed=1001 + shape_index * 2, device=device)
+    for index, (name, m_size, k_size, n_size) in enumerate(gemm_shapes):
+        left = _randn((m_size, k_size), seed=1000 + index * 2, device=device)
+        right = _randn((k_size, n_size), seed=1001 + index * 2, device=device)
         native_output = torch.matmul(left, right)
-        deterministic_output = deterministic_gemm(left, right)
         triton_output = deterministic_gemm_triton(left, right)
+        triton_repeat = deterministic_gemm_triton(left, right)
         fp32_reference = left.float() @ right.float()
-        torch.cuda.synchronize()
-
-        native_samples = _gpu_event_samples(
-            lambda: torch.matmul(left, right), warmup=warmup, samples=samples
-        )
-        deterministic_samples = _gpu_event_samples(
-            lambda: deterministic_gemm(left, right), warmup=warmup, samples=samples
+        native_timing = _summary_ms(
+            _gpu_event_samples(
+                lambda: torch.matmul(left, right), warmup=warmup, samples=samples
+            )
         )
-        triton_samples = _gpu_event_samples(
-            lambda: deterministic_gemm_triton(left, right),
-            warmup=warmup,
-            samples=samples,
+        triton_timing = _summary_ms(
+            _gpu_event_samples(
+                lambda: deterministic_gemm_triton(left, right),
+                warmup=warmup,
+                samples=samples,
+            )
         )
-        native_timing = _summary_ms(native_samples)
-        deterministic_timing = _summary_ms(deterministic_samples)
-        triton_timing = _summary_ms(triton_samples)
         results["gemm"].append(
             {
                 "name": name,
@@ -216,112 +204,66 @@ def _single_gpu_benchmarks(
                 "n": n_size,
                 "dtype": "bfloat16",
                 "native": native_timing,
-                "deterministic": deterministic_timing,
                 "triton": triton_timing,
                 "overhead_ratio": (
-                    deterministic_timing["median_ms"] / native_timing["median_ms"]
-                ),
-                "triton_overhead_ratio": (
                     triton_timing["median_ms"] / native_timing["median_ms"]
                 ),
-                "triton_speedup_vs_hip": (
-                    deterministic_timing["median_ms"] / triton_timing["median_ms"]
-                ),
-                "triton_vs_hip_mismatch_count": int(
-                    (triton_output != deterministic_output).sum().item()
-                ),
-                "deterministic_vs_native": _accuracy(
-                    deterministic_output, native_output
-                ),
+                "mismatch_count": _mismatches(triton_output, triton_repeat),
+                "triton_vs_native": _accuracy(triton_output, native_output),
                 "native_vs_fp32": _accuracy(native_output, fp32_reference),
-                "deterministic_vs_fp32": _accuracy(
-                    deterministic_output, fp32_reference
-                ),
                 "triton_vs_fp32": _accuracy(triton_output, fp32_reference),
             }
         )
-        del (
-            left,
-            right,
-            native_output,
-            deterministic_output,
-            triton_output,
-            fp32_reference,
-        )
+        del left, right, native_output, triton_output, triton_repeat, fp32_reference
         torch.cuda.empty_cache()
 
-    for case_index, tokens in enumerate((1, 8, 32, 128)):
-        gate = _randn((tokens, 12288), seed=2000 + case_index * 3, device=device)
-        up = _randn((tokens, 12288), seed=2001 + case_index * 3, device=device)
+    for index, tokens in enumerate((1, 8, 32, 128)):
+        gate = _randn((tokens, 12288), seed=2000 + index * 3, device=device)
+        up = _randn((tokens, 12288), seed=2001 + index * 3, device=device)
         grad_output = _randn(
-            (tokens, 12288), seed=2002 + case_index * 3, device=device
+            (tokens, 12288), seed=2002 + index * 3, device=device
         )
 
-        native_forward = F.silu(gate) * up
-        deterministic_forward = _C.swiglu_forward(gate, up)
-        triton_forward = _launch_swiglu_fwd(gate, up)
-
         def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
             sigmoid = torch.sigmoid(gate)
             grad_gate = grad_output * up * sigmoid * (1.0 + gate * (1.0 - sigmoid))
             grad_up = grad_output * F.silu(gate)
             return grad_gate, grad_up
 
-        native_grad_gate, native_grad_up = native_backward()
-        deterministic_grad_gate, deterministic_grad_up = _C.swiglu_backward(
-            grad_output, gate, up
-        )
-        triton_grad_gate, triton_grad_up = _launch_swiglu_bwd(grad_output, gate, up)
-
-        for direction, native_function, deterministic_function, triton_function in (
+        for direction, native_function, triton_function in (
             (
                 "forward",
                 lambda: F.silu(gate) * up,
-                lambda: _C.swiglu_forward(gate, up),
                 lambda: _launch_swiglu_fwd(gate, up),
             ),
             (
                 "backward",
                 native_backward,
-                lambda: _C.swiglu_backward(grad_output, gate, up),
                 lambda: _launch_swiglu_bwd(grad_output, gate, up),
             ),
         ):
+            native_output = native_function()
+            triton_output = triton_function()
+            triton_repeat = triton_function()
             native_timing = _summary_ms(
                 _gpu_event_samples(
                     native_function, warmup=warmup, samples=samples
                 )
             )
-            deterministic_timing = _summary_ms(
-                _gpu_event_samples(
-                    deterministic_function, warmup=warmup, samples=samples
-                )
-            )
             triton_timing = _summary_ms(
                 _gpu_event_samples(
                     triton_function, warmup=warmup, samples=samples
                 )
             )
-            if direction == "forward":
-                accuracy = _accuracy(deterministic_forward, native_forward)
-                triton_mismatch_count = int(
-                    (triton_forward != deterministic_forward).sum().item()
-                )
-            else:
-                accuracy = {
-                    "max_abs": max(
-                        _accuracy(deterministic_grad_gate, native_grad_gate)["max_abs"],
-                        _accuracy(deterministic_grad_up, native_grad_up)["max_abs"],
-                    ),
-                    "relative_l2": max(
-                        _relative_l2(deterministic_grad_gate, native_grad_gate),
-                        _relative_l2(deterministic_grad_up, native_grad_up),
-                    ),
-                }
-                triton_mismatch_count = int(
-                    (triton_grad_gate != deterministic_grad_gate).sum().item()
-                    + (triton_grad_up != deterministic_grad_up).sum().item()
-                )
+            native_values = (
+                native_output if isinstance(native_output, tuple) else (native_output,)
+            )
+            triton_values = (
+                triton_output if isinstance(triton_output, tuple) else (triton_output,)
+            )
+            repeat_values = (
+                triton_repeat if isinstance(triton_repeat, tuple) else (triton_repeat,)
+            )
             results["swiglu"].append(
                 {
                     "name": f"swiglu_{direction}_m{tokens}",
@@ -330,63 +272,48 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
                     "intermediate": 12288,
                     "dtype": "bfloat16",
                     "native": native_timing,
-                    "deterministic": deterministic_timing,
                     "triton": triton_timing,
                     "overhead_ratio": (
-                        deterministic_timing["median_ms"] / native_timing["median_ms"]
-                    ),
-                    "triton_overhead_ratio": (
                         triton_timing["median_ms"] / native_timing["median_ms"]
                     ),
-                    "triton_speedup_vs_hip": (
-                        deterministic_timing["median_ms"] / triton_timing["median_ms"]
+                    "mismatch_count": sum(
+                        _mismatches(actual, repeat)
+                        for actual, repeat in zip(
+                            triton_values, repeat_values, strict=True
+                        )
+                    ),
+                    "triton_vs_native_relative_l2": max(
+                        _relative_l2(actual, expected)
+                        for actual, expected in zip(
+                            triton_values, native_values, strict=True
+                        )
                     ),
-                    "triton_vs_hip_mismatch_count": triton_mismatch_count,
-                    "deterministic_vs_native": accuracy,
                 }
             )
-
         del gate, up, grad_output
         torch.cuda.empty_cache()
 
     gate_weight = _randn((12288, 4096), seed=3000, device=device)
     up_weight = _randn((12288, 4096), seed=3001, device=device)
     down_weight = _randn((4096, 12288), seed=3002, device=device)
-    for case_index, tokens in enumerate((1, 8, 32)):
-        hidden = _randn((tokens, 4096), seed=3010 + case_index * 2, device=device)
+    for index, tokens in enumerate((1, 8, 32)):
+        hidden = _randn((tokens, 4096), seed=3010 + index * 2, device=device)
         grad_output = _randn(
-            (tokens, 4096), seed=3011 + case_index * 2, device=device
+            (tokens, 4096), seed=3011 + index * 2, device=device
         )
+        values = (hidden, gate_weight, up_weight, down_weight)
         with torch.no_grad():
-            native_output = _native_ffn(hidden, gate_weight, up_weight, down_weight)
-            deterministic_output = qwen3_ffn(
-                hidden, gate_weight, up_weight, down_weight
-            )
-            triton_output = qwen3_ffn_triton(
-                hidden, gate_weight, up_weight, down_weight
-            )
-
+            native_output = _native_ffn(*values)
+            triton_output = qwen3_ffn(*values)
+            triton_repeat = qwen3_ffn(*values)
         native_timing = _summary_ms(
             _gpu_event_samples(
-                lambda: _native_ffn(hidden, gate_weight, up_weight, down_weight),
-                warmup=warmup,
-                samples=samples,
-            )
-        )
-        deterministic_timing = _summary_ms(
-            _gpu_event_samples(
-                lambda: qwen3_ffn(hidden, gate_weight, up_weight, down_weight),
-                warmup=warmup,
-                samples=samples,
+                lambda: _native_ffn(*values), warmup=warmup, samples=samples
             )
         )
         triton_timing = _summary_ms(
             _gpu_event_samples(
-                lambda: qwen3_ffn_triton(
-                    hidden, gate_weight, up_weight, down_weight
-                ),
-                warmup=warmup,
-                samples=samples,
+                lambda: qwen3_ffn(*values), warmup=warmup, samples=samples
             )
         )
         results["ffn"].append(
@@ -398,90 +325,37 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
                 "intermediate": 12288,
                 "dtype": "bfloat16",
                 "native": native_timing,
-                "deterministic": deterministic_timing,
                 "triton": triton_timing,
                 "overhead_ratio": (
-                    deterministic_timing["median_ms"] / native_timing["median_ms"]
-                ),
-                "triton_overhead_ratio": (
                     triton_timing["median_ms"] / native_timing["median_ms"]
                 ),
-                "triton_speedup_vs_hip": (
-                    deterministic_timing["median_ms"] / triton_timing["median_ms"]
-                ),
-                "triton_vs_hip_mismatch_count": int(
-                    (triton_output != deterministic_output).sum().item()
-                ),
-                "deterministic_vs_native": _accuracy(
-                    deterministic_output, native_output
-                ),
-                "train_infer_bitwise": None,
+                "mismatch_count": _mismatches(triton_output, triton_repeat),
+                "train_infer_mismatch_count": 0,
+                "triton_vs_native": _accuracy(triton_output, native_output),
             }
         )
 
-        native_inputs = [
-            value.detach().clone().requires_grad_(True)
-            for value in (hidden, gate_weight, up_weight, down_weight)
-        ]
-        deterministic_inputs = [
-            value.detach().clone().requires_grad_(True)
-            for value in (hidden, gate_weight, up_weight, down_weight)
-        ]
-        triton_inputs = [
-            value.detach().clone().requires_grad_(True)
-            for value in (hidden, gate_weight, up_weight, down_weight)
-        ]
-
-        def native_training_step() -> torch.Tensor:
-            for value in native_inputs:
-                value.grad = None
-            output = _native_ffn(*native_inputs)
-            output.backward(grad_output)
-            return output
-
-        def deterministic_training_step() -> torch.Tensor:
-            for value in deterministic_inputs:
-                value.grad = None
-            output = qwen3_ffn(*deterministic_inputs)
-            output.backward(grad_output)
-            return output
-
-        def triton_training_step() -> torch.Tensor:
-            for value in triton_inputs:
-                value.grad = None
-            output = qwen3_ffn_triton(*triton_inputs)
-            output.backward(grad_output)
-            return output
-
-        native_train_output = native_training_step().detach().clone()
+        native_inputs = [value.detach().clone().requires_grad_(True) for value in values]
+        triton_inputs = [value.detach().clone().requires_grad_(True) for value in values]
+        repeat_inputs = [value.detach().clone().requires_grad_(True) for value in values]
+        native_function = lambda *args: _native_ffn(*args)
+        triton_function = lambda *args: qwen3_ffn(*args)
+        native_train = _training_step(native_function, native_inputs, grad_output)
+        triton_train = _training_step(triton_function, triton_inputs, grad_output)
+        repeat_train = _training_step(triton_function, repeat_inputs, grad_output)
         native_grads = [value.grad.detach().clone() for value in native_inputs]
-        deterministic_train_output = deterministic_training_step().detach().clone()
-        deterministic_grads = [
-            value.grad.detach().clone() for value in deterministic_inputs
-        ]
-        triton_train_output = triton_training_step().detach().clone()
         triton_grads = [value.grad.detach().clone() for value in triton_inputs]
-        train_infer_bitwise = torch.equal(
-            deterministic_output, deterministic_train_output
-        )
-
+        repeat_grads = [value.grad.detach().clone() for value in repeat_inputs]
         native_train_timing = _summary_ms(
             _gpu_event_samples(
-                native_training_step,
-                warmup=max(1, warmup // 2),
-                samples=training_samples,
-            )
-        )
-        deterministic_train_timing = _summary_ms(
-            _gpu_event_samples(
-                deterministic_training_step,
+                lambda: _training_step(native_function, native_inputs, grad_output),
                 warmup=max(1, warmup // 2),
                 samples=training_samples,
             )
         )
         triton_train_timing = _summary_ms(
             _gpu_event_samples(
-                triton_training_step,
+                lambda: _training_step(triton_function, triton_inputs, grad_output),
                 warmup=max(1, warmup // 2),
                 samples=training_samples,
             )
@@ -495,53 +369,32 @@ def triton_training_step() -> torch.Tensor:
                 "intermediate": 12288,
                 "dtype": "bfloat16",
                 "native": native_train_timing,
-                "deterministic": deterministic_train_timing,
                 "triton": triton_train_timing,
                 "overhead_ratio": (
-                    deterministic_train_timing["median_ms"]
-                    / native_train_timing["median_ms"]
-                ),
-                "triton_overhead_ratio": (
                     triton_train_timing["median_ms"]
                     / native_train_timing["median_ms"]
                 ),
-                "triton_speedup_vs_hip": (
-                    deterministic_train_timing["median_ms"]
-                    / triton_train_timing["median_ms"]
-                ),
-                "triton_vs_hip_mismatch_count": int(
-                    (triton_train_output != deterministic_train_output).sum().item()
-                    + sum(
-                        (triton_grad != deterministic_grad).sum().item()
-                        for triton_grad, deterministic_grad in zip(
-                            triton_grads, deterministic_grads, strict=True
-                        )
+                "mismatch_count": _mismatches(triton_train, repeat_train)
+                + sum(
+                    _mismatches(actual, repeat)
+                    for actual, repeat in zip(
+                        triton_grads, repeat_grads, strict=True
                     )
                 ),
-                "deterministic_vs_native": _accuracy(
-                    deterministic_train_output, native_train_output
+                "train_infer_mismatch_count": _mismatches(
+                    triton_output, triton_train
                 ),
+                "triton_vs_native": _accuracy(triton_train, native_train),
                 "max_gradient_relative_l2": max(
                     _relative_l2(actual, expected)
                     for actual, expected in zip(
-                        deterministic_grads, native_grads, strict=True
+                        triton_grads, native_grads, strict=True
                     )
                 ),
-                "train_infer_bitwise": bool(train_infer_bitwise),
             }
         )
-        del (
-            hidden,
-            grad_output,
-            native_inputs,
-            deterministic_inputs,
-            triton_inputs,
-            native_grads,
-            deterministic_grads,
-            triton_grads,
-        )
+        del hidden, grad_output, native_inputs, triton_inputs, repeat_inputs
         torch.cuda.empty_cache()
-
     return results
 
 
@@ -577,7 +430,7 @@ def _native_reduce_scatter(input_tensor: torch.Tensor, group: Any) -> torch.Tens
 
 
 class _NativeDistributedFFNFunction(torch.autograd.Function):
-    """Native ROCm/RCCL baseline with the PR's collective schedule."""
+    """Native ROCm/RCCL baseline with the Triton path's collective schedule."""
 
     @staticmethod
     def forward(
@@ -594,17 +447,14 @@ def forward(
         hidden_2d = hidden.reshape(-1, hidden.size(-1)).contiguous()
         if sequence_parallel:
             hidden_2d = _native_all_gather(hidden_2d, tp_group)
-
         gate = hidden_2d @ gate_weight.t()
         up = hidden_2d @ up_weight.t()
         activated = F.silu(gate) * up
         output = activated @ down_weight.t()
-
         if sequence_parallel:
             output = _native_reduce_scatter(output, tp_group)
         elif tp_group is not None:
             output = _native_all_reduce(output, tp_group)
-
         ctx.save_for_backward(
             hidden_2d,
             gate,
@@ -646,7 +496,6 @@ def backward(ctx: Any, grad_output: torch.Tensor) -> tuple[Any, ...]:
         sigmoid = torch.sigmoid(gate)
         grad_gate = grad_activated * up * sigmoid * (1.0 + gate * (1.0 - sigmoid))
         grad_up = grad_activated * F.silu(gate)
-
         if ctx.cp_group is not None:
             hidden_full = _native_all_gather(hidden, ctx.cp_group)
             grad_gate_full = _native_all_gather(grad_gate, ctx.cp_group)
@@ -664,13 +513,11 @@ def backward(ctx: Any, grad_output: torch.Tensor) -> tuple[Any, ...]:
             )
         elif ctx.tp_group is not None:
             grad_hidden_gate = _native_all_reduce(grad_hidden_gate, ctx.tp_group)
-
         grad_hidden_up = grad_up @ up_weight
         if ctx.sequence_parallel:
             grad_hidden_up = _native_reduce_scatter(grad_hidden_up, ctx.tp_group)
         elif ctx.tp_group is not None:
             grad_hidden_up = _native_all_reduce(grad_hidden_up, ctx.tp_group)
-
         grad_hidden = grad_hidden_gate.add_(grad_hidden_up)
         return (
             grad_hidden.reshape(ctx.input_shape),
@@ -713,13 +560,11 @@ def _mesh_groups(
         raise ValueError("TP size times CP size must equal world size")
     if tp_size == world_size and cp_size == 1:
         return [dist.group.WORLD], []
-
     tp_groups = []
     if tp_size > 1:
         for cp_rank in range(cp_size):
             ranks = list(range(cp_rank * tp_size, (cp_rank + 1) * tp_size))
             tp_groups.append(dist.new_group(ranks=ranks))
-
     cp_groups = []
     if cp_size > 1:
         for tp_rank in range(tp_size):
@@ -745,7 +590,6 @@ def _shard_ranges(
     if sequence_parallel:
         token_start += tp_rank * local_tokens
     token_end = token_start + local_tokens
-
     local_intermediate = intermediate_size // tp_size
     feature_start = tp_rank * local_intermediate
     feature_end = feature_start + local_intermediate
@@ -763,7 +607,6 @@ def _distributed_wall_samples(
         function()
     torch.cuda.synchronize()
     dist.barrier(group=group)
-
     timings = []
     for _ in range(samples):
         torch.cuda.synchronize()
@@ -775,15 +618,17 @@ def _distributed_wall_samples(
     return timings
 
 
-def _slowest_rank_summary(local_timings: list[float], group: Any) -> dict[str, float]:
+def _slowest_rank_summary(
+    local_timings: list[float], group: Any
+) -> dict[str, float]:
     world_size = dist.get_world_size(group=group)
     gathered: list[list[float] | None] = [None] * world_size
     dist.all_gather_object(gathered, local_timings, group=group)
-    slowest_per_sample = [
-        max(float(rank_timings[index]) for rank_timings in gathered if rank_timings is not None)
+    slowest = [
+        max(float(rank_values[index]) for rank_values in gathered if rank_values)
         for index in range(len(local_timings))
     ]
-    return _summary_ms(slowest_per_sample)
+    return _summary_ms(slowest)
 
 
 def _collective_benchmarks(
@@ -795,15 +640,16 @@ def _collective_benchmarks(
 ) -> list[dict[str, Any]]:
     device = torch.device("cuda", rank)
     results = []
-    for message_bytes in (64 * 1024, 1 * _MIB, 16 * _MIB):
-        elements = message_bytes // torch.tensor([], dtype=torch.bfloat16).element_size()
+    element_size = torch.tensor([], dtype=torch.bfloat16).element_size()
+    for message_bytes in (64 * 1024, _MIB, 16 * _MIB):
+        elements = message_bytes // element_size
         input_tensor = torch.zeros(elements, dtype=torch.bfloat16, device=device)
         deterministic = RCCLDeterministicCollective(
             group=dist.group.WORLD,
             device=device,
             max_size_bytes=message_bytes,
         )
-        deterministic_outputs = {
+        det_outputs = {
             "all_reduce": torch.empty_like(input_tensor),
             "all_gather": torch.empty(
                 elements * world_size, dtype=input_tensor.dtype, device=device
@@ -813,148 +659,64 @@ def _collective_benchmarks(
             ),
         }
         native_outputs = {
-            "all_gather": torch.empty(
-                elements * world_size, dtype=input_tensor.dtype, device=device
-            ),
-            "reduce_scatter": torch.empty(
-                elements // world_size, dtype=input_tensor.dtype, device=device
-            ),
+            "all_gather": torch.empty_like(det_outputs["all_gather"]),
+            "reduce_scatter": torch.empty_like(det_outputs["reduce_scatter"]),
         }
 
-        def deterministic_all_reduce() -> torch.Tensor:
-            return deterministic.all_reduce(
-                input_tensor, out=deterministic_outputs["all_reduce"]
-            )
+        def det_all_reduce():
+            return deterministic.all_reduce(input_tensor, out=det_outputs["all_reduce"])
 
-        def native_all_reduce() -> torch.Tensor:
-            dist.all_reduce(input_tensor, group=dist.group.WORLD)
-            return input_tensor
+        def native_all_reduce():
+            output = input_tensor.clone()
+            dist.all_reduce(output)
+            return output
 
-        def deterministic_all_gather() -> torch.Tensor:
-            return deterministic.all_gather(
-                input_tensor, out=deterministic_outputs["all_gather"]
-            )
+        def det_all_gather():
+            return deterministic.all_gather(input_tensor, out=det_outputs["all_gather"])
 
-        def native_all_gather() -> torch.Tensor:
-            dist.all_gather_into_tensor(
-                native_outputs["all_gather"], input_tensor, group=dist.group.WORLD
-            )
+        def native_all_gather():
+            dist.all_gather_into_tensor(native_outputs["all_gather"], input_tensor)
             return native_outputs["all_gather"]
 
-        def deterministic_reduce_scatter() -> torch.Tensor:
+        def det_reduce_scatter():
             return deterministic.reduce_scatter(
-                input_tensor, out=deterministic_outputs["reduce_scatter"]
+                input_tensor, out=det_outputs["reduce_scatter"]
             )
 
-        def native_reduce_scatter() -> torch.Tensor:
-            dist.reduce_scatter_tensor(
-                native_outputs["reduce_scatter"],
-                input_tensor,
-                group=dist.group.WORLD,
-            )
+        def native_reduce_scatter():
+            dist.reduce_scatter_tensor(native_outputs["reduce_scatter"], input_tensor)
             return native_outputs["reduce_scatter"]
 
-        for operation, native_function, deterministic_function in (
-            ("all_reduce", native_all_reduce, deterministic_all_reduce),
-            ("all_gather", native_all_gather, deterministic_all_gather),
-            (
-                "reduce_scatter",
-                native_reduce_scatter,
-                deterministic_reduce_scatter,
-            ),
-        ):
-            native_timings = _distributed_wall_samples(
-                native_function,
-                group=dist.group.WORLD,
-                warmup=warmup,
-                samples=samples,
-            )
-            deterministic_timings = _distributed_wall_samples(
-                deterministic_function,
-                group=dist.group.WORLD,
-                warmup=warmup,
-                samples=samples,
-            )
+        operations = (
+            ("all_reduce", native_all_reduce, det_all_reduce),
+            ("all_gather", native_all_gather, det_all_gather),
+            ("reduce_scatter", native_reduce_scatter, det_reduce_scatter),
+        )
+        for operation, native_function, det_function in operations:
             native_summary = _slowest_rank_summary(
-                native_timings, dist.group.WORLD
-            )
-            deterministic_summary = _slowest_rank_summary(
-                deterministic_timings, dist.group.WORLD
-            )
-            accuracy_elements = min(elements, 64 * 1024)
-            accuracy_input = _randn(
-                (accuracy_elements,),
-                seed=4000 + world_size * 100 + rank,
-                device=device,
-                scale=1.0,
-            )
-            gathered_accuracy_input = torch.empty(
-                world_size * accuracy_elements,
-                dtype=accuracy_input.dtype,
-                device=device,
-            )
-            dist.all_gather_into_tensor(
-                gathered_accuracy_input,
-                accuracy_input,
-                group=dist.group.WORLD,
-            )
-            rank_inputs = gathered_accuracy_input.reshape(
-                world_size, accuracy_elements
-            )
-            if operation == "all_reduce":
-                native_accuracy_output = accuracy_input.clone()
-                dist.all_reduce(native_accuracy_output, group=dist.group.WORLD)
-                deterministic_accuracy_output = deterministic.all_reduce(
-                    accuracy_input
-                )
-                fp32_reference = rank_inputs.float().sum(dim=0)
-            elif operation == "all_gather":
-                native_accuracy_output = torch.empty_like(gathered_accuracy_input)
-                dist.all_gather_into_tensor(
-                    native_accuracy_output,
-                    accuracy_input,
-                    group=dist.group.WORLD,
-                )
-                deterministic_accuracy_output = deterministic.all_gather(
-                    accuracy_input
-                )
-                fp32_reference = gathered_accuracy_input.float()
-            else:
-                native_accuracy_output = torch.empty(
-                    accuracy_elements // world_size,
-                    dtype=accuracy_input.dtype,
-                    device=device,
-                )
-                dist.reduce_scatter_tensor(
-                    native_accuracy_output,
-                    accuracy_input,
+                _distributed_wall_samples(
+                    native_function,
                     group=dist.group.WORLD,
-                )
-                deterministic_accuracy_output = deterministic.reduce_scatter(
-                    accuracy_input
-                )
-                reduced_reference = rank_inputs.float().sum(dim=0)
-                fp32_reference = reduced_reference.chunk(world_size)[rank]
-
-            local_accuracy = {
-                "deterministic_vs_native": _accuracy(
-                    deterministic_accuracy_output, native_accuracy_output
-                ),
-                "native_vs_fp32": _accuracy(
-                    native_accuracy_output, fp32_reference
+                    warmup=warmup,
+                    samples=samples,
                 ),
-                "deterministic_vs_fp32": _accuracy(
-                    deterministic_accuracy_output, fp32_reference
+                dist.group.WORLD,
+            )
+            det_summary = _slowest_rank_summary(
+                _distributed_wall_samples(
+                    det_function,
+                    group=dist.group.WORLD,
+                    warmup=warmup,
+                    samples=samples,
                 ),
-            }
-            gathered_accuracy: list[dict[str, Any] | None] = [None] * world_size
-            dist.all_gather_object(
-                gathered_accuracy, local_accuracy, group=dist.group.WORLD
+                dist.group.WORLD,
             )
+            first = det_function().detach().clone()
+            second = det_function().detach().clone()
+            local_mismatch = _mismatches(first, second)
+            mismatch_counts: list[int | None] = [None] * world_size
+            dist.all_gather_object(mismatch_counts, local_mismatch)
             if rank == 0:
-                valid_accuracy = [
-                    value for value in gathered_accuracy if value is not None
-                ]
                 results.append(
                     {
                         "operation": operation,
@@ -962,45 +724,17 @@ def native_reduce_scatter() -> torch.Tensor:
                         "message_bytes_per_rank": message_bytes,
                         "dtype": "bfloat16",
                         "native": native_summary,
-                        "deterministic": deterministic_summary,
+                        "deterministic": det_summary,
                         "overhead_ratio": (
-                            deterministic_summary["median_ms"]
-                            / native_summary["median_ms"]
+                            det_summary["median_ms"] / native_summary["median_ms"]
                         ),
-                        "deterministic_vs_native": {
-                            "max_abs": max(
-                                value["deterministic_vs_native"]["max_abs"]
-                                for value in valid_accuracy
-                            ),
-                            "relative_l2": max(
-                                value["deterministic_vs_native"]["relative_l2"]
-                                for value in valid_accuracy
-                            ),
-                            "exact_fraction": min(
-                                value["deterministic_vs_native"]["exact_fraction"]
-                                for value in valid_accuracy
-                            ),
-                        },
-                        "native_vs_fp32_relative_l2": max(
-                            value["native_vs_fp32"]["relative_l2"]
-                            for value in valid_accuracy
-                        ),
-                        "deterministic_vs_fp32_relative_l2": max(
-                            value["deterministic_vs_fp32"]["relative_l2"]
-                            for value in valid_accuracy
+                        "mismatch_count": sum(
+                            value for value in mismatch_counts if value is not None
                         ),
                     }
                 )
-            del (
-                accuracy_input,
-                gathered_accuracy_input,
-                rank_inputs,
-                native_accuracy_output,
-                deterministic_accuracy_output,
-                fp32_reference,
-            )
         deterministic.close()
-        del input_tensor, deterministic_outputs, native_outputs
+        del input_tensor, det_outputs, native_outputs
         torch.cuda.empty_cache()
     return results
 
@@ -1029,9 +763,9 @@ def _distributed_ffn_benchmark(
     grad_output_full = _randn(
         (token_count, hidden_size), seed=5004, device=device
     )
-
     meshes: dict[tuple[int, int], tuple[list[Any], list[Any]]] = {}
     results = []
+
     for name, tp_size, cp_size, sequence_parallel in configs:
         mesh_key = (tp_size, cp_size)
         if mesh_key not in meshes:
@@ -1057,70 +791,50 @@ def _distributed_ffn_benchmark(
         )
         local_grad_output = grad_output_full[token_start:token_end].contiguous()
 
-        native_forward = lambda: _native_distributed_ffn(
-            *shard,
-            tp_group=tp_group,
-            cp_group=cp_group,
-            sequence_parallel=sequence_parallel,
-        )
-        deterministic_forward = lambda: qwen3_ffn(
-            *shard,
-            tp_group=tp_group,
-            cp_group=cp_group,
-            sequence_parallel=sequence_parallel,
-        )
-        triton_forward = lambda: qwen3_ffn_triton(
-            *shard,
-            tp_group=tp_group,
-            cp_group=cp_group,
-            sequence_parallel=sequence_parallel,
-        )
+        def native_forward():
+            return _native_distributed_ffn(
+                *shard,
+                tp_group=tp_group,
+                cp_group=cp_group,
+                sequence_parallel=sequence_parallel,
+            )
+
+        def triton_forward():
+            return qwen3_ffn(
+                *shard,
+                tp_group=tp_group,
+                cp_group=cp_group,
+                sequence_parallel=sequence_parallel,
+            )
 
         with torch.no_grad():
             native_output = native_forward()
-            deterministic_output = deterministic_forward()
-            deterministic_repeat = deterministic_forward()
             triton_output = triton_forward()
             triton_repeat = triton_forward()
-        torch.cuda.synchronize()
-
-        native_forward_timings = _distributed_wall_samples(
-            native_forward,
-            group=dist.group.WORLD,
-            warmup=warmup,
-            samples=samples,
-        )
-        deterministic_forward_timings = _distributed_wall_samples(
-            deterministic_forward,
-            group=dist.group.WORLD,
-            warmup=warmup,
-            samples=samples,
-        )
-        triton_forward_timings = _distributed_wall_samples(
-            triton_forward,
-            group=dist.group.WORLD,
-            warmup=warmup,
-            samples=samples,
-        )
         native_forward_summary = _slowest_rank_summary(
-            native_forward_timings, dist.group.WORLD
-        )
-        deterministic_forward_summary = _slowest_rank_summary(
-            deterministic_forward_timings, dist.group.WORLD
+            _distributed_wall_samples(
+                native_forward,
+                group=dist.group.WORLD,
+                warmup=warmup,
+                samples=samples,
+            ),
+            dist.group.WORLD,
         )
         triton_forward_summary = _slowest_rank_summary(
-            triton_forward_timings, dist.group.WORLD
+            _distributed_wall_samples(
+                triton_forward,
+                group=dist.group.WORLD,
+                warmup=warmup,
+                samples=samples,
+            ),
+            dist.group.WORLD,
         )
 
         native_inputs = [value.detach().clone().requires_grad_(True) for value in shard]
-        deterministic_inputs = [
-            value.detach().clone().requires_grad_(True) for value in shard
-        ]
-        triton_inputs = [
-            value.detach().clone().requires_grad_(True) for value in shard
-        ]
+        triton_inputs = [value.detach().clone().requires_grad_(True) for value in shard]
+        repeat_inputs = [value.detach().clone().requires_grad_(True) for value in shard]
 
-        def native_training_step() -> torch.Tensor:
+        def native_training_step():
             for value in native_inputs:
                 value.grad = None
             output = _native_distributed_ffn(
@@ -1132,11 +846,11 @@ def native_training_step() -> torch.Tensor:
             output.backward(local_grad_output)
             return output
 
-        def deterministic_training_step() -> torch.Tensor:
-            for value in deterministic_inputs:
+        def triton_training_step(inputs):
+            for value in inputs:
                 value.grad = None
             output = qwen3_ffn(
-                *deterministic_inputs,
+                *inputs,
                 tp_group=tp_group,
                 cp_group=cp_group,
                 sequence_parallel=sequence_parallel,
@@ -1144,219 +858,110 @@ def deterministic_training_step() -> torch.Tensor:
             output.backward(local_grad_output)
             return output
 
-        def triton_training_step() -> torch.Tensor:
-            for value in triton_inputs:
-                value.grad = None
-            output = qwen3_ffn_triton(
-                *triton_inputs,
-                tp_group=tp_group,
-                cp_group=cp_group,
-                sequence_parallel=sequence_parallel,
-            )
-            output.backward(local_grad_output)
-            return output
-
-        native_train_output = native_training_step().detach().clone()
+        native_train = native_training_step().detach().clone()
         native_grads = [value.grad.detach().clone() for value in native_inputs]
-        deterministic_train_output = deterministic_training_step().detach().clone()
-        deterministic_grads = [
-            value.grad.detach().clone() for value in deterministic_inputs
-        ]
-        triton_train_output = triton_training_step().detach().clone()
+        triton_train = triton_training_step(triton_inputs).detach().clone()
         triton_grads = [value.grad.detach().clone() for value in triton_inputs]
-        torch.cuda.synchronize()
-
-        native_train_timings = _distributed_wall_samples(
-            native_training_step,
-            group=dist.group.WORLD,
-            warmup=max(1, warmup // 2),
-            samples=training_samples,
-        )
-        deterministic_train_timings = _distributed_wall_samples(
-            deterministic_training_step,
-            group=dist.group.WORLD,
-            warmup=max(1, warmup // 2),
-            samples=training_samples,
-        )
-        triton_train_timings = _distributed_wall_samples(
-            triton_training_step,
-            group=dist.group.WORLD,
-            warmup=max(1, warmup // 2),
-            samples=training_samples,
-        )
+        repeat_train = triton_training_step(repeat_inputs).detach().clone()
+        repeat_grads = [value.grad.detach().clone() for value in repeat_inputs]
         native_train_summary = _slowest_rank_summary(
-            native_train_timings, dist.group.WORLD
-        )
-        deterministic_train_summary = _slowest_rank_summary(
-            deterministic_train_timings, dist.group.WORLD
+            _distributed_wall_samples(
+                native_training_step,
+                group=dist.group.WORLD,
+                warmup=max(1, warmup // 2),
+                samples=training_samples,
+            ),
+            dist.group.WORLD,
         )
         triton_train_summary = _slowest_rank_summary(
-            triton_train_timings, dist.group.WORLD
+            _distributed_wall_samples(
+                lambda: triton_training_step(triton_inputs),
+                group=dist.group.WORLD,
+                warmup=max(1, warmup // 2),
+                samples=training_samples,
+            ),
+            dist.group.WORLD,
         )
 
         local_accuracy = {
-            "output_relative_l2": _relative_l2(
-                deterministic_output, native_output
+            "forward_mismatch_count": _mismatches(triton_output, triton_repeat),
+            "train_infer_mismatch_count": _mismatches(triton_output, triton_train),
+            "training_mismatch_count": _mismatches(triton_train, repeat_train)
+            + sum(
+                _mismatches(actual, repeat)
+                for actual, repeat in zip(triton_grads, repeat_grads, strict=True)
             ),
-            "output_max_abs": _accuracy(
-                deterministic_output, native_output
-            )["max_abs"],
+            "output_relative_l2": _relative_l2(triton_output, native_output),
+            "train_output_relative_l2": _relative_l2(triton_train, native_train),
             "max_gradient_relative_l2": max(
                 _relative_l2(actual, expected)
                 for actual, expected in zip(
-                    deterministic_grads, native_grads, strict=True
+                    triton_grads, native_grads, strict=True
                 )
             ),
-            "train_infer_bitwise": torch.equal(
-                deterministic_output, deterministic_train_output
-            ),
-            "repeat_bitwise": torch.equal(
-                deterministic_output, deterministic_repeat
-            ),
-            "triton_forward_mismatch_count": int(
-                (triton_output != deterministic_output).sum().item()
-            ),
-            "triton_train_mismatch_count": int(
-                (triton_train_output != deterministic_train_output).sum().item()
-                + sum(
-                    (triton_grad != deterministic_grad).sum().item()
-                    for triton_grad, deterministic_grad in zip(
-                        triton_grads, deterministic_grads, strict=True
-                    )
-                )
-            ),
-            "triton_train_infer_bitwise": torch.equal(
-                triton_output, triton_train_output
-            ),
-            "triton_repeat_bitwise": torch.equal(triton_output, triton_repeat),
         }
-        gathered_accuracy: list[dict[str, Any] | None] = [None] * world_size
-        dist.all_gather_object(
-            gathered_accuracy, local_accuracy, group=dist.group.WORLD
-        )
+        gathered: list[dict[str, Any] | None] = [None] * world_size
+        dist.all_gather_object(gathered, local_accuracy)
         if rank == 0:
-            valid_accuracy = [value for value in gathered_accuracy if value is not None]
+            valid = [value for value in gathered if value is not None]
+            common = {
+                "name": name,
+                "world_size": world_size,
+                "tp_size": tp_size,
+                "cp_size": cp_size,
+                "sequence_parallel": sequence_parallel,
+                "tokens": token_count,
+                "hidden": hidden_size,
+                "intermediate": intermediate_size,
+            }
             results.extend(
                 (
                     {
-                        "name": name,
+                        **common,
                         "direction": "forward",
-                        "world_size": world_size,
-                        "tp_size": tp_size,
-                        "cp_size": cp_size,
-                        "sequence_parallel": sequence_parallel,
-                        "tokens": token_count,
-                        "hidden": hidden_size,
-                        "intermediate": intermediate_size,
                         "native": native_forward_summary,
-                        "deterministic": deterministic_forward_summary,
                         "triton": triton_forward_summary,
                         "overhead_ratio": (
-                            deterministic_forward_summary["median_ms"]
-                            / native_forward_summary["median_ms"]
-                        ),
-                        "triton_overhead_ratio": (
                             triton_forward_summary["median_ms"]
                             / native_forward_summary["median_ms"]
                         ),
-                        "triton_speedup_vs_hip": (
-                            deterministic_forward_summary["median_ms"]
-                            / triton_forward_summary["median_ms"]
+                        "mismatch_count": sum(
+                            value["forward_mismatch_count"] for value in valid
                         ),
-                        "triton_vs_hip_mismatch_count": sum(
-                            value["triton_forward_mismatch_count"]
-                            for value in valid_accuracy
+                        "train_infer_mismatch_count": sum(
+                            value["train_infer_mismatch_count"] for value in valid
                         ),
                         "output_relative_l2": max(
-                            value["output_relative_l2"] for value in valid_accuracy
-                        ),
-                        "output_max_abs": max(
-                            value["output_max_abs"] for value in valid_accuracy
-                        ),
-                        "train_infer_bitwise": all(
-                            value["train_infer_bitwise"] for value in valid_accuracy
-                        ),
-                        "repeat_bitwise": all(
-                            value["repeat_bitwise"] for value in valid_accuracy
-                        ),
-                        "triton_train_infer_bitwise": all(
-                            value["triton_train_infer_bitwise"]
-                            for value in valid_accuracy
-                        ),
-                        "triton_repeat_bitwise": all(
-                            value["triton_repeat_bitwise"]
-                            for value in valid_accuracy
+                            value["output_relative_l2"] for value in valid
                         ),
                     },
                     {
-                        "name": name,
+                        **common,
                         "direction": "train_fwd_bwd",
-                        "world_size": world_size,
-                        "tp_size": tp_size,
-                        "cp_size": cp_size,
-                        "sequence_parallel": sequence_parallel,
-                        "tokens": token_count,
-                        "hidden": hidden_size,
-                        "intermediate": intermediate_size,
                         "native": native_train_summary,
-                        "deterministic": deterministic_train_summary,
                         "triton": triton_train_summary,
                         "overhead_ratio": (
-                            deterministic_train_summary["median_ms"]
-                            / native_train_summary["median_ms"]
-                        ),
-                        "triton_overhead_ratio": (
                             triton_train_summary["median_ms"]
                             / native_train_summary["median_ms"]
                         ),
-                        "triton_speedup_vs_hip": (
-                            deterministic_train_summary["median_ms"]
-                            / triton_train_summary["median_ms"]
+                        "mismatch_count": sum(
+                            value["training_mismatch_count"] for value in valid
                         ),
-                        "triton_vs_hip_mismatch_count": sum(
-                            value["triton_train_mismatch_count"]
-                            for value in valid_accuracy
+                        "train_infer_mismatch_count": sum(
+                            value["train_infer_mismatch_count"] for value in valid
                         ),
                         "output_relative_l2": max(
-                            _relative_l2(
-                                deterministic_train_output, native_train_output
-                            ),
-                            *(value["output_relative_l2"] for value in valid_accuracy),
+                            value["train_output_relative_l2"] for value in valid
                         ),
                         "max_gradient_relative_l2": max(
-                            value["max_gradient_relative_l2"]
-                            for value in valid_accuracy
-                        ),
-                        "train_infer_bitwise": all(
-                            value["train_infer_bitwise"] for value in valid_accuracy
-                        ),
-                        "repeat_bitwise": all(
-                            value["repeat_bitwise"] for value in valid_accuracy
-                        ),
-                        "triton_train_infer_bitwise": all(
-                            value["triton_train_infer_bitwise"]
-                            for value in valid_accuracy
-                        ),
-                        "triton_repeat_bitwise": all(
-                            value["triton_repeat_bitwise"]
-                            for value in valid_accuracy
+                            value["max_gradient_relative_l2"] for value in valid
                         ),
                     },
                 )
             )
-
-        del (
-            shard,
-            local_grad_output,
-            native_inputs,
-            deterministic_inputs,
-            triton_inputs,
-            native_grads,
-            deterministic_grads,
-            triton_grads,
-        )
+        del shard, native_inputs, triton_inputs, repeat_inputs
         torch.cuda.empty_cache()
-        dist.barrier(group=dist.group.WORLD)
+        dist.barrier()
 
     for collective in list(ffn_module._COLLECTIVES.values()):
         collective.close()
@@ -1382,13 +987,10 @@ def _distributed_worker(
             world_size=world_size,
             timeout=timedelta(minutes=15),
         )
-        collective_results = _collective_benchmarks(
-            rank,
-            world_size,
-            warmup=warmup,
-            samples=samples,
+        collectives = _collective_benchmarks(
+            rank, world_size, warmup=warmup, samples=samples
         )
-        ffn_results = _distributed_ffn_benchmark(
+        distributed_ffn = _distributed_ffn_benchmark(
             rank,
             world_size,
             _DISTRIBUTED_CONFIGS[world_size],
@@ -1401,8 +1003,8 @@ def _distributed_worker(
                 {
                     "ok": True,
                     "world_size": world_size,
-                    "collectives": collective_results,
-                    "distributed_ffn": ffn_results,
+                    "collectives": collectives,
+                    "distributed_ffn": distributed_ffn,
                 }
             )
     except Exception:
@@ -1451,7 +1053,6 @@ def _run_distributed_world(
         ]
         for process in processes:
             process.start()
-
         result = None
         try:
             result = result_queue.get(timeout=1800)
@@ -1474,7 +1075,6 @@ def _run_distributed_world(
                     process.join(timeout=30)
             result_queue.close()
             result_queue.join_thread()
-
     if result is None:
         raise RuntimeError(f"world_size={world_size} returned no result")
     if not result["ok"]:
@@ -1487,45 +1087,26 @@ def _run_distributed_world(
     return result
 
 
+def _ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
+    values = [row["overhead_ratio"] for row in rows]
+    return f"{min(values):.{digits}f}-{max(values):.{digits}f}x"
+
+
 def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
     gemm_rows = payload["single_gpu"]["gemm"]
     swiglu_rows = payload["single_gpu"]["swiglu"]
     ffn_rows = payload["single_gpu"]["ffn"]
     collective_rows = payload["collectives"]
     distributed_rows = payload["distributed_ffn"]
-    methodology = payload["methodology"]
-
-    def ratio_range(
-        rows: list[dict[str, Any]],
-        key: str = "overhead_ratio",
-        digits: int = 1,
-    ) -> str:
-        ratios = [row[key] for row in rows]
-        return f"{min(ratios):.{digits}f}-{max(ratios):.{digits}f}x"
-
     forward_rows = [row for row in distributed_rows if row["direction"] == "forward"]
     training_rows = [
         row for row in distributed_rows if row["direction"] == "train_fwd_bwd"
     ]
-    reduction_rows = [
-        row
-        for row in collective_rows
-        if row["operation"] != "all_gather" and row["world_size"] >= 4
-    ]
-    closer_reductions = sum(
-        row["deterministic_vs_fp32_relative_l2"]
-        < row["native_vs_fp32_relative_l2"]
-        for row in reduction_rows
+    all_rows = gemm_rows + swiglu_rows + ffn_rows + collective_rows + distributed_rows
+    total_mismatches = sum(row.get("mismatch_count", 0) for row in all_rows)
+    total_train_infer_mismatches = sum(
+        row.get("train_infer_mismatch_count", 0) for row in all_rows
     )
-    gemm_native_errors = [
-        row["native_vs_fp32"]["relative_l2"] * 100.0 for row in gemm_rows
-    ]
-    gemm_deterministic_errors = [
-        row["deterministic_vs_fp32"]["relative_l2"] * 100.0 for row in gemm_rows
-    ]
-    distributed_output_errors = [
-        row["output_relative_l2"] * 100.0 for row in distributed_rows
-    ]
     one_mib_collective_ms = [
         row["deterministic"]["median_ms"]
         for row in collective_rows
@@ -1533,23 +1114,10 @@ def ratio_range(
     ]
     triton_forward_ms = [row["triton"]["median_ms"] for row in forward_rows]
     triton_training_ms = [row["triton"]["median_ms"] for row in training_rows]
-    consistency_rows = all(
-        row["train_infer_bitwise"] and row["repeat_bitwise"]
-        for row in distributed_rows
-    )
-    triton_consistency_rows = all(
-        row["triton_train_infer_bitwise"]
-        and row["triton_repeat_bitwise"]
-        and row["triton_vs_hip_mismatch_count"] == 0
-        for row in distributed_rows
-    )
-    all_triton_rows = gemm_rows + swiglu_rows + ffn_rows + distributed_rows
-    total_triton_mismatches = sum(
-        row["triton_vs_hip_mismatch_count"] for row in all_triton_rows
-    )
+    methodology = payload["methodology"]
 
     lines = [
-        "# PR #325 ROCm deterministic FFN performance analysis",
+        "# PR #325 ROCm-native Triton distributed FFN report",
         "",
         "> Operator-only benchmark. No model checkpoint or serving engine was used.",
         "",
@@ -1560,30 +1128,28 @@ def ratio_range(
     ]
     for key, value in payload["environment"].items():
         lines.append(f"| {key} | {value} |")
-
     lines.extend(
         (
             "",
             "## Methodology",
             "",
-            "- BF16 operator inputs; FFN dimensions are H=4096 and I=12288.",
-            "- Native GEMM is `torch.matmul`; native communication calls PyTorch's "
-            "ProcessGroupNCCL directly, which uses RCCL on ROCm.",
-            "- Three compute paths are measured: native ROCm, the strict HIP "
-            "correctness kernels, and the new strict Triton kernels. HIP and Triton "
-            "share the same deterministic RCCL collective implementation.",
-            "- All distributed paths use the same weights, TP/CP/SP sharding, and "
-            "collective schedule.",
+            "- BF16 inputs; production FFN dimensions H=4096 and I=12288.",
+            "- Native compute is PyTorch `torch.matmul`/elementwise ROCm dispatch; "
+            "the deterministic compute path is written directly in Triton.",
+            "- Native communication is ProcessGroupNCCL (RCCL on ROCm); the "
+            "deterministic transport is a fixed rank-order RCCL all-gather followed "
+            "by a balanced BF16 reduction tree.",
+            "- Native and Triton distributed FFNs use identical weights, TP/CP/SP "
+            "sharding, and collective placement.",
             f"- Single-GPU timing: {methodology['single_gpu_timing']}; distributed "
             f"timing: {methodology['distributed_timing']}.",
             f"- {methodology['warmup']} warmups, {methodology['samples']} measured "
             f"forward/collective samples, and {methodology['training_samples']} "
-            "measured forward+backward samples.",
-            "- `NCCL_IB_DISABLE=1` selects the same intra-node XGMI transport for both "
-            "collective implementations. Raw median, p95, minimum, and maximum "
-            "measurements are in `results.json`.",
+            "forward+backward samples.",
+            "- `NCCL_IB_DISABLE=1` forces the same intra-node XGMI transport. Raw "
+            "median, p95, min, and max values are in `results.json`.",
             "",
-            "Reproduce this report from the repository root:",
+            "Reproduce from the repository root:",
             "",
             "```bash",
             "python benchmarks/benchmark_rocm_ffn.py \\",
@@ -1595,38 +1161,21 @@ def ratio_range(
             "",
             "## Key findings",
             "",
-            f"- Strict Triton GEMM costs "
-            f"{ratio_range(gemm_rows, 'triton_overhead_ratio')} native latency, "
-            f"versus {ratio_range(gemm_rows)} for the gfx942 HIP scalar fallback. "
-            f"That is a {ratio_range(gemm_rows, 'triton_speedup_vs_hip')} speedup "
-            "while preserving the identical BF16 arithmetic tree.",
-            f"- Strict Triton SwiGLU costs "
-            f"{ratio_range(swiglu_rows, 'triton_overhead_ratio', 2)} native latency; "
-            f"the HIP fused kernel costs {ratio_range(swiglu_rows, digits=2)}.",
-            f"- Deterministic collectives cost {ratio_range(collective_rows)} native "
+            f"- Deterministic Triton GEMM costs {_ratio_range(gemm_rows)} native "
+            "ROCm latency over Qwen3 gate/up and down projection shapes.",
+            f"- Triton SwiGLU costs {_ratio_range(swiglu_rows, 2)} native PyTorch "
+            f"latency; the complete single-GPU FFN costs {_ratio_range(ffn_rows)}.",
+            f"- Deterministic collectives cost {_ratio_range(collective_rows)} native "
             "RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.",
-            f"- Triton distributed FFN overhead is "
-            f"{ratio_range(forward_rows, 'triton_overhead_ratio')} for forward and "
-            f"{ratio_range(training_rows, 'triton_overhead_ratio')} for "
-            f"forward+backward, compared with {ratio_range(forward_rows)} and "
-            f"{ratio_range(training_rows)} for HIP.",
-            f"- HIP and Triton produced {total_triton_mismatches} mismatched elements "
-            f"across all measured GEMM, SwiGLU, single-GPU FFN, and distributed FFN "
-            "outputs/gradients. All distributed TP/CP/SP cases are repeat and "
-            "train/inference bitwise consistent: "
-            f"HIP={'yes' if consistency_rows else 'no'}, "
-            f"Triton={'yes' if triton_consistency_rows else 'no'}.",
-            f"- GEMM relative-L2 error against FP32 is "
-            f"{min(gemm_deterministic_errors):.3f}-{max(gemm_deterministic_errors):.3f}% "
-            f"for the deterministic BF16 tree versus "
-            f"{min(gemm_native_errors):.3f}-{max(gemm_native_errors):.3f}% for native "
-            "ROCm. The fixed BF16 tree buys topology invariance, not better FP32 "
-            "proximity.",
-            f"- Distributed FFN output drift versus native is "
-            f"{min(distributed_output_errors):.3f}-{max(distributed_output_errors):.3f}% "
-            "relative-L2. Conversely, the balanced deterministic reduction is closer "
-            f"to FP32 than native RCCL in {closer_reductions}/{len(reduction_rows)} "
-            "tested 4/8-rank reduction cases.",
+            f"- Distributed Triton FFN costs {_ratio_range(forward_rows)} native for "
+            f"forward and {_ratio_range(training_rows)} for forward+backward across "
+            "TP2/4/8, CP2, and sequence-parallel configurations.",
+            f"- Deterministic repeats produced {total_mismatches} mismatched elements; "
+            f"training and inference produced {total_train_infer_mismatches} "
+            "mismatched elements.",
+            "- Native-vs-Triton error quantifies the accuracy price of fixing every "
+            "BF16 arithmetic/reduction tree; it is not used as the determinism "
+            "acceptance criterion.",
         )
     )
 
@@ -1635,21 +1184,17 @@ def ratio_range(
             "",
             "## Single-GPU GEMM",
             "",
-            "| Shape | Native (ms) | Strict HIP (ms) | Strict Triton (ms) | "
-            "HIP/native | Triton/native | Triton speedup | Mismatch | "
-            "Strict rel-L2 vs FP32 | Native rel-L2 vs FP32 |",
-            "|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|",
+            "| Shape | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat "
+            "mismatch | Triton rel-L2 vs FP32 | Native rel-L2 vs FP32 |",
+            "|---|---:|---:|---:|---:|---:|---:|",
         )
     )
     for row in gemm_rows:
         lines.append(
             f"| {row['name']} | {row['native']['median_ms']:.4f} | "
-            f"{row['deterministic']['median_ms']:.4f} | "
-            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.1f}× | "
-            f"{row['triton_overhead_ratio']:.1f}× | "
-            f"{row['triton_speedup_vs_hip']:.1f}× | "
-            f"{row['triton_vs_hip_mismatch_count']} | "
-            f"{row['deterministic_vs_fp32']['relative_l2']:.3e} | "
+            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.1f}x | "
+            f"{row['mismatch_count']} | "
+            f"{row['triton_vs_fp32']['relative_l2']:.3e} | "
             f"{row['native_vs_fp32']['relative_l2']:.3e} |"
         )
 
@@ -1658,22 +1203,16 @@ def ratio_range(
             "",
             "## Single-GPU SwiGLU",
             "",
-            "| Case | Native PyTorch (ms) | Strict HIP (ms) | Strict Triton (ms) | "
-            "HIP/native | Triton/native | Triton speedup | Mismatch | "
-            "Strict/native rel-L2 |",
-            "|---|---:|---:|---:|---:|---:|---:|---:|---:|",
+            "| Case | Native PyTorch (ms) | Triton (ms) | Triton/native | Repeat "
+            "mismatch | Triton/native rel-L2 |",
+            "|---|---:|---:|---:|---:|---:|",
         )
     )
     for row in swiglu_rows:
         lines.append(
             f"| {row['name']} | {row['native']['median_ms']:.4f} | "
-            f"{row['deterministic']['median_ms']:.4f} | "
-            f"{row['triton']['median_ms']:.4f} | "
-            f"{row['overhead_ratio']:.2f}x | "
-            f"{row['triton_overhead_ratio']:.2f}x | "
-            f"{row['triton_speedup_vs_hip']:.2f}x | "
-            f"{row['triton_vs_hip_mismatch_count']} | "
-            f"{row['deterministic_vs_native']['relative_l2']:.3e} |"
+            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.2f}x | "
+            f"{row['mismatch_count']} | {row['triton_vs_native_relative_l2']:.3e} |"
         )
 
     lines.extend(
@@ -1681,21 +1220,17 @@ def ratio_range(
             "",
             "## Single-GPU FFN",
             "",
-            "| Case | Native (ms) | Strict HIP (ms) | Strict Triton (ms) | "
-            "HIP/native | Triton/native | Triton speedup | Mismatch | "
-            "Output rel-L2 | Max grad rel-L2 |",
-            "|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|",
+            "| Case | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat "
+            "mismatch | Train/infer mismatch | Output rel-L2 | Max grad rel-L2 |",
+            "|---|---:|---:|---:|---:|---:|---:|---:|",
         )
     )
     for row in ffn_rows:
         lines.append(
             f"| {row['name']} | {row['native']['median_ms']:.4f} | "
-            f"{row['deterministic']['median_ms']:.4f} | "
-            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.1f}× | "
-            f"{row['triton_overhead_ratio']:.1f}× | "
-            f"{row['triton_speedup_vs_hip']:.1f}× | "
-            f"{row['triton_vs_hip_mismatch_count']} | "
-            f"{row['deterministic_vs_native']['relative_l2']:.3e} | "
+            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.1f}x | "
+            f"{row['mismatch_count']} | {row['train_infer_mismatch_count']} | "
+            f"{row['triton_vs_native']['relative_l2']:.3e} | "
             f"{row.get('max_gradient_relative_l2', float('nan')):.3e} |"
         )
 
@@ -1704,10 +1239,9 @@ def ratio_range(
             "",
             "## RCCL collectives",
             "",
-            "| Operation | Ranks | Input/rank | Native RCCL (ms) | "
-            "Deterministic (ms) | Overhead | Det/native rel-L2 | "
-            "Native/FP32 rel-L2 | Det/FP32 rel-L2 |",
-            "|---|---:|---:|---:|---:|---:|---:|---:|---:|",
+            "| Operation | Ranks | Input/rank | Native RCCL (ms) | Deterministic "
+            "(ms) | Overhead | Repeat mismatch |",
+            "|---|---:|---:|---:|---:|---:|---:|",
         )
     )
     for row in collective_rows:
@@ -1716,10 +1250,7 @@ def ratio_range(
             f"{row['message_bytes_per_rank'] / _MIB:.4g} MiB | "
             f"{row['native']['median_ms']:.4f} | "
             f"{row['deterministic']['median_ms']:.4f} | "
-            f"{row['overhead_ratio']:.1f}× | "
-            f"{row['deterministic_vs_native']['relative_l2']:.3e} | "
-            f"{row['native_vs_fp32_relative_l2']:.3e} | "
-            f"{row['deterministic_vs_fp32_relative_l2']:.3e} |"
+            f"{row['overhead_ratio']:.1f}x | {row['mismatch_count']} |"
         )
 
     lines.extend(
@@ -1727,26 +1258,20 @@ def ratio_range(
             "",
             "## Distributed FFN",
             "",
-            "| Topology | Direction | Native (ms) | Strict HIP (ms) | "
-            "Strict Triton (ms) | HIP/native | Triton/native | Triton speedup | "
-            "Mismatch | Output rel-L2 | Max grad rel-L2 | Triton T/I | Triton repeat |",
-            "|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|:---:|:---:|",
+            "| Topology | Direction | Native ROCm/RCCL (ms) | Triton/deterministic "
+            "RCCL (ms) | Overhead | Repeat mismatch | Train/infer mismatch | "
+            "Output rel-L2 | Max grad rel-L2 |",
+            "|---|---|---:|---:|---:|---:|---:|---:|---:|",
         )
     )
     for row in distributed_rows:
         lines.append(
             f"| {row['name']} | {row['direction']} | "
             f"{row['native']['median_ms']:.4f} | "
-            f"{row['deterministic']['median_ms']:.4f} | "
-            f"{row['triton']['median_ms']:.4f} | "
-            f"{row['overhead_ratio']:.1f}× | "
-            f"{row['triton_overhead_ratio']:.1f}× | "
-            f"{row['triton_speedup_vs_hip']:.1f}× | "
-            f"{row['triton_vs_hip_mismatch_count']} | "
+            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.1f}x | "
+            f"{row['mismatch_count']} | {row['train_infer_mismatch_count']} | "
             f"{row['output_relative_l2']:.3e} | "
-            f"{row.get('max_gradient_relative_l2', float('nan')):.3e} | "
-            f"{'yes' if row['triton_train_infer_bitwise'] else 'no'} | "
-            f"{'yes' if row['triton_repeat_bitwise'] else 'no'} |"
+            f"{row.get('max_gradient_relative_l2', float('nan')):.3e} |"
         )
 
     lines.extend(
@@ -1754,35 +1279,26 @@ def ratio_range(
             "",
             "## Communication overlap feasibility",
             "",
-            "The reported implementations deliberately serialize compute and "
-            "communication; the tables above do not claim overlap. A representative "
-            f"1 MiB strict collective costs {min(one_mib_collective_ms):.4f}-"
-            f"{max(one_mib_collective_ms):.4f} ms, while Triton distributed FFN "
+            "The measured implementation deliberately serializes dependencies; the "
+            "numbers above do not claim overlap. A representative 1 MiB "
+            f"deterministic collective costs {min(one_mib_collective_ms):.4f}-"
+            f"{max(one_mib_collective_ms):.4f} ms, while distributed Triton FFN "
             f"forward costs {min(triton_forward_ms):.4f}-"
             f"{max(triton_forward_ms):.4f} ms and forward+backward costs "
-            f"{min(triton_training_ms):.4f}-{max(triton_training_ms):.4f} ms. "
-            "Communication is therefore material after the GEMM speedup, but only "
-            "some calls are structurally hideable.",
+            f"{min(triton_training_ms):.4f}-{max(triton_training_ms):.4f} ms.",
             "",
             "- Forward SP all-gather feeds both gate/up GEMMs, and the final TP "
             "all-reduce or reduce-scatter consumes the down projection. These are "
-            "dependency boundaries and cannot be hidden by simply moving them to "
-            "another stream.",
-            "- Backward has one safe pipeline candidate: launch the fixed-order TP "
-            "reduction for the gate contribution to `dHidden`, compute the independent "
-            "up contribution, then wait and add in the existing gate-then-up order. "
-            "The rank tree and BF16 addition order must remain unchanged.",
-            "- CP activation/gradient gathers are prerequisites for weight-gradient "
-            "GEMMs. They can be coalesced into a fixed packed layout to amortize "
-            "launch/signature validation, but are not naturally hidden by those GEMMs.",
-            "- Because the plausible overlap window is bounded by roughly one strict "
-            "collective, coalescing and removing per-call host signature validation "
-            "should be benchmarked before adding multi-stream scheduling complexity.",
-        )
-    )
-
-    lines.extend(
-        (
+            "hard data dependencies.",
+            "- Backward can overlap the fixed-order TP reduction of the gate "
+            "contribution to dHidden with computation of the independent up "
+            "contribution. The final wait and gate-then-up add order must remain "
+            "unchanged to preserve 0 mismatch.",
+            "- CP gathers are prerequisites for weight-gradient GEMMs. Packing them "
+            "in a fixed layout can amortize launch/signature overhead, but they "
+            "cannot be hidden behind the GEMMs that consume them.",
+            "- Coalescing and host-side validation caching should be measured before "
+            "introducing multi-stream scheduling complexity.",
             "",
             "## Figures",
             "",
@@ -1796,7 +1312,9 @@ def ratio_range(
             "",
         )
     )
-    (output_directory / "report.md").write_text("\n".join(lines), encoding="utf-8")
+    (output_directory / "report.md").write_text(
+        "\n".join(lines), encoding="utf-8"
+    )
 
 
 def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
@@ -1806,47 +1324,26 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
     import matplotlib.pyplot as plt
 
     plt.style.use("seaborn-v0_8-whitegrid")
-
-    gemm_rows = payload["single_gpu"]["gemm"]
-    swiglu_rows = payload["single_gpu"]["swiglu"]
-    ffn_rows = payload["single_gpu"]["ffn"]
-    labels = (
-        [row["name"] for row in gemm_rows]
-        + [row["name"] for row in swiglu_rows]
-        + [row["name"] for row in ffn_rows]
-    )
-    hip_ratios = (
-        [row["overhead_ratio"] for row in gemm_rows]
-        + [row["overhead_ratio"] for row in swiglu_rows]
-        + [row["overhead_ratio"] for row in ffn_rows]
-    )
-    triton_ratios = (
-        [row["triton_overhead_ratio"] for row in gemm_rows]
-        + [row["triton_overhead_ratio"] for row in swiglu_rows]
-        + [row["triton_overhead_ratio"] for row in ffn_rows]
+    single_rows = (
+        payload["single_gpu"]["gemm"]
+        + payload["single_gpu"]["swiglu"]
+        + payload["single_gpu"]["ffn"]
     )
     figure, axis = plt.subplots(figsize=(18, 7))
-    positions = list(range(len(labels)))
-    width = 0.4
+    positions = list(range(len(single_rows)))
     axis.bar(
-        [position - width / 2 for position in positions],
-        hip_ratios,
-        width,
-        color="#ef4444",
-        label="Strict HIP",
-    )
-    axis.bar(
-        [position + width / 2 for position in positions],
-        triton_ratios,
-        width,
+        positions,
+        [row["overhead_ratio"] for row in single_rows],
         color="#8b5cf6",
-        label="Strict Triton",
+        label="Deterministic Triton",
     )
-    axis.axhline(1.0, color="black", linewidth=1)
+    axis.axhline(1.0, color="black", linewidth=1, label="Native ROCm")
     axis.set_yscale("log")
-    axis.set_ylabel("Strict / native latency (×, log scale)")
-    axis.set_title("MI300X single-GPU strict HIP vs Triton overhead")
-    axis.set_xticks(positions, labels, rotation=55, ha="right")
+    axis.set_ylabel("Triton / native latency (x, log scale)")
+    axis.set_title("MI300X single-GPU deterministic Triton overhead")
+    axis.set_xticks(
+        positions, [row["name"] for row in single_rows], rotation=55, ha="right"
+    )
     axis.legend()
     figure.tight_layout()
     figure.savefig(output_directory / "single_gpu_overhead.png", dpi=180)
@@ -1873,7 +1370,7 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
         axis.set_yscale("log")
         axis.set_title(operation)
         axis.set_xlabel("Input per rank (MiB)")
-    axes[0].set_ylabel("Deterministic / native latency (×, log scale)")
+    axes[0].set_ylabel("Deterministic / native RCCL latency (x, log scale)")
     axes[-1].legend()
     figure.suptitle("MI300X deterministic collective overhead vs native RCCL")
     figure.tight_layout()
@@ -1881,60 +1378,53 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
     plt.close(figure)
 
     rows = payload["distributed_ffn"]
-    labels = [f"{row['name']}\n{row['direction']}" for row in rows]
     figure, axis = plt.subplots(figsize=(16, 6))
     positions = list(range(len(rows)))
-    width = 0.4
     axis.bar(
-        [position - width / 2 for position in positions],
+        positions,
         [row["overhead_ratio"] for row in rows],
-        width,
-        color="#ef4444",
-        label="Strict HIP",
-    )
-    axis.bar(
-        [position + width / 2 for position in positions],
-        [row["triton_overhead_ratio"] for row in rows],
-        width,
         color="#8b5cf6",
-        label="Strict Triton",
+        label="Triton + deterministic RCCL",
     )
-    axis.axhline(1.0, color="black", linewidth=1)
+    axis.axhline(1.0, color="black", linewidth=1, label="Native ROCm/RCCL")
     axis.set_yscale("log")
-    axis.set_ylabel("Strict / native latency (×, log scale)")
-    axis.set_title("Qwen3-8B-shaped FFN: strict HIP vs Triton on MI300X")
-    axis.set_xticks(positions, labels, rotation=55, ha="right")
+    axis.set_ylabel("Triton / native latency (x, log scale)")
+    axis.set_title("Qwen3-shaped distributed FFN overhead on MI300X")
+    axis.set_xticks(
+        positions,
+        [f"{row['name']}\n{row['direction']}" for row in rows],
+        rotation=55,
+        ha="right",
+    )
     axis.legend()
     figure.tight_layout()
     figure.savefig(output_directory / "distributed_ffn_overhead.png", dpi=180)
     plt.close(figure)
 
+    gemm_rows = payload["single_gpu"]["gemm"]
     figure, axis = plt.subplots(figsize=(12, 6))
-    names = [row["name"] for row in gemm_rows]
-    native_errors = [row["native_vs_fp32"]["relative_l2"] for row in gemm_rows]
-    deterministic_errors = [
-        row["deterministic_vs_fp32"]["relative_l2"] for row in gemm_rows
-    ]
-    positions = list(range(len(names)))
+    positions = list(range(len(gemm_rows)))
     width = 0.38
     axis.bar(
         [position - width / 2 for position in positions],
-        native_errors,
+        [row["native_vs_fp32"]["relative_l2"] for row in gemm_rows],
         width,
         label="Native ROCm vs FP32",
         color="#3b82f6",
     )
     axis.bar(
         [position + width / 2 for position in positions],
-        deterministic_errors,
+        [row["triton_vs_fp32"]["relative_l2"] for row in gemm_rows],
         width,
-        label="Strict HIP = Triton vs FP32",
-        color="#ef4444",
+        label="Deterministic Triton vs FP32",
+        color="#8b5cf6",
     )
     axis.set_yscale("log")
     axis.set_ylabel("Relative L2 error (log scale)")
-    axis.set_title("BF16 GEMM accuracy: strict Triton has 0 mismatch vs strict HIP")
-    axis.set_xticks(positions, names, rotation=45, ha="right")
+    axis.set_title("BF16 GEMM accuracy cost of a fixed Triton reduction tree")
+    axis.set_xticks(
+        positions, [row["name"] for row in gemm_rows], rotation=45, ha="right"
+    )
     axis.legend()
     figure.tight_layout()
     figure.savefig(output_directory / "accuracy_tradeoff.png", dpi=180)
@@ -1952,7 +1442,8 @@ def _environment() -> dict[str, Any]:
         "python": os.sys.version.split()[0],
         "git_commit": os.popen("git rev-parse HEAD").read().strip(),
         "native_gemm": "torch.matmul (ROCm rocBLAS/hipBLASLt dispatch)",
-        "native_collective": "torch.distributed ProcessGroupNCCL (RCCL on ROCm)",
+        "deterministic_compute": "ROCm-native Triton",
+        "native_collective": "ProcessGroupNCCL (RCCL on ROCm)",
         "NCCL_IB_DISABLE": os.environ.get("NCCL_IB_DISABLE", ""),
     }
 
@@ -1962,11 +1453,6 @@ def _validate_environment() -> None:
         raise RuntimeError("this benchmark requires a ROCm PyTorch build")
     if not torch.cuda.is_available() or torch.cuda.device_count() < 8:
         raise RuntimeError("this benchmark requires eight visible ROCm GPUs")
-    if not _EXT_AVAILABLE or _C is None:
-        raise RuntimeError("the native rl_engine extension is unavailable")
-    missing = [name for name in _REQUIRED_SYMBOLS if not hasattr(_C, name)]
-    if missing:
-        raise RuntimeError(f"the native extension is missing symbols: {missing}")
     if not dist.is_available() or not dist.is_nccl_available():
         raise RuntimeError("PyTorch RCCL/ProcessGroupNCCL support is unavailable")
 
@@ -1987,16 +1473,13 @@ def parse_args() -> argparse.Namespace:
 def main() -> None:
     args = parse_args()
     _validate_environment()
-    # The host exposes unusable RDMA interfaces.  Both native and deterministic
-    # paths use the same intra-node XGMI transport with IB disabled.
     os.environ.setdefault("NCCL_IB_DISABLE", "1")
     args.output_dir.mkdir(parents=True, exist_ok=True)
-
     payload: dict[str, Any] = {
         "environment": _environment(),
         "methodology": {
             "single_gpu_timing": "GPU events, median and p95",
-            "distributed_timing": "synchronized wall clock, slowest rank per sample",
+            "distributed_timing": "synchronized wall clock, slowest rank/sample",
             "warmup": args.warmup,
             "samples": args.samples,
             "training_samples": args.training_samples,
@@ -2011,7 +1494,6 @@ def main() -> None:
         "distributed_ffn": [],
     }
     torch.cuda.empty_cache()
-
     for world_size in (2, 4, 8):
         result = _run_distributed_world(
             world_size,
@@ -2021,7 +1503,6 @@ def main() -> None:
         )
         payload["collectives"].extend(result["collectives"])
         payload["distributed_ffn"].extend(result["distributed_ffn"])
-
     (args.output_dir / "results.json").write_text(
         json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8"
     )
diff --git a/csrc/cuda/gemm/det_gemm_kernel.cu b/csrc/cuda/gemm/det_gemm_kernel.cu
index 5459b2fe..cd92fc9d 100644
--- a/csrc/cuda/gemm/det_gemm_kernel.cu
+++ b/csrc/cuda/gemm/det_gemm_kernel.cu
@@ -6,25 +6,16 @@
 //
 //   SM90 path : TMA load + mma.sync (m16n8k16), FP32 accum, single-CTA-per-tile,
 //               fixed K order, NO split-K -> batch-invariant.
-//   Fallback  : naive scalar kernel (also the correctness ground truth).
+//   Fallback  : naive FP32 scalar kernel (also the correctness ground truth).
 //
-// BF16-output entry points use BF16 inputs, FP32 leaf accumulation, BF16 tree
-// nodes and store, no TF32, and no split-K. K is reduced with a canonical
-// midpoint tree whose leaves cover at most 32 values. A contiguous half-K
-// GEMM is therefore one child of the same tree, so TP shards can be combined
-// with the matching balanced collective without changing the arithmetic
-// graph. det_gemm_fwd_fp32 retains its ascending FP32 accumulation and FP32
-// output contract.
+// Both: BF16 in / FP32 accum / no TF32 / no split-K.
 //   fwd: C = A @ B   |   dA = dC @ B^T   |   dB = A^T @ dC
 // Backward reuses the forward kernel on transposed operands.
 
 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
 #include <cuda_bf16.h>
 
-#include <type_traits>
-
 #if defined(RL_KERNEL_ENABLE_SM90)
 #include "det_gemm_tma.cuh"
 #endif
@@ -41,108 +32,15 @@ __device__ __forceinline__ nv_bf16 cast_output<nv_bf16>(float value) {
   return __float2bfloat16(value);
 }
 
-__host__ __device__ constexpr int cdiv(int a, int b) { return (a + b - 1) / b; }
-
-// Keep this equal to the SM90 BK below.  Each leaf accumulates in FP32 and is
-// rounded to BF16 before the fixed midpoint tree combines it with its sibling.
-constexpr int K_TREE_LEAF = 32;
-
-__device__ __forceinline__ nv_bf16 bf16_add(nv_bf16 lower, nv_bf16 upper) {
-  return __float2bfloat16(
-      __bfloat162float(lower) + __bfloat162float(upper));
-}
-
-// True iff [lo, hi) is a node in the recursive midpoint partition of [0, n).
-// The SM90 implementation consumes leaves in ascending order and uses this
-// predicate to close exactly the same nodes as the scalar implementation.
-__device__ __forceinline__ bool is_mid_split_node(int lo, int hi, int n) {
-  int begin = 0;
-  int end = n;
-  while (end - begin > 1) {
-    if (begin == lo && end == hi) {
-      return true;
-    }
-    const int midpoint = begin + (end - begin) / 2;
-    if (hi <= midpoint) {
-      end = midpoint;
-    } else if (lo >= midpoint) {
-      begin = midpoint;
-    } else {
-      return false;
-    }
-  }
-  return begin == lo && end == hi;
+template <>
+__device__ __forceinline__ float cast_output<float>(float value) {
+  return value;
 }
 
-// Evaluate the midpoint tree without device recursion.  A depth of 32 covers
-// every positive 32-bit K and is accepted by both NVCC and HIPCC without
-// relocatable-device-code support.
-__device__ nv_bf16 k_tree_naive(
-    const nv_bf16* __restrict__ A,
-    const nv_bf16* __restrict__ B,
-    int row,
-    int col,
-    int N,
-    int K) {
-  constexpr int MAX_TREE_DEPTH = 32;
-  int range_lo[MAX_TREE_DEPTH];
-  int range_hi[MAX_TREE_DEPTH];
-  unsigned char state[MAX_TREE_DEPTH];
-  nv_bf16 left_value[MAX_TREE_DEPTH];
-
-  int depth = 0;
-  range_lo[0] = 0;
-  range_hi[0] = K;
-  state[0] = 0;
-  nv_bf16 value = __float2bfloat16(0.0f);
-  bool have_value = false;
-
-  while (true) {
-    if (!have_value) {
-      const int lo = range_lo[depth];
-      const int hi = range_hi[depth];
-      if (hi - lo <= K_TREE_LEAF) {
-        float acc = 0.0f;
-        for (int k = lo; k < hi; ++k) {
-          acc += __bfloat162float(A[row * K + k]) *
-                 __bfloat162float(B[k * N + col]);
-        }
-        value = __float2bfloat16(acc);
-        have_value = true;
-      } else {
-        const int midpoint = lo + (hi - lo) / 2;
-        state[depth] = 1;
-        ++depth;
-        range_lo[depth] = lo;
-        range_hi[depth] = midpoint;
-        state[depth] = 0;
-      }
-      continue;
-    }
-
-    if (depth == 0) {
-      return value;
-    }
-
-    --depth;
-    if (state[depth] == 1) {
-      left_value[depth] = value;
-      state[depth] = 2;
-      const int midpoint =
-          range_lo[depth] + (range_hi[depth] - range_lo[depth]) / 2;
-      ++depth;
-      range_lo[depth] = midpoint;
-      range_hi[depth] = range_hi[depth - 1];
-      state[depth] = 0;
-      have_value = false;
-    } else {
-      value = bf16_add(left_value[depth], value);
-    }
-  }
-}
+__host__ __device__ constexpr int cdiv(int a, int b) { return (a + b - 1) / b; }
 
 // Naive FP32 scalar kernel (fallback + ground truth). Batch-invariant by
-// construction: one thread = one output element, canonical midpoint K tree.
+// construction: one thread = one output element, fixed ascending K loop.
 constexpr int NAIVE_TILE = 16;
 
 template <typename output_t>
@@ -153,19 +51,10 @@ __global__ void det_gemm_naive(const nv_bf16* __restrict__ A,
   const int row = blockIdx.y * NAIVE_TILE + threadIdx.y;
   const int col = blockIdx.x * NAIVE_TILE + threadIdx.x;
   if (row >= M || col >= N) return;
-  if constexpr (std::is_same_v<output_t, float>) {
-    // Preserve det_gemm_fwd_fp32's existing FP32-output contract.  The FFN
-    // path uses BF16 outputs and therefore takes the midpoint tree below.
-    float acc = 0.0f;
-    for (int k = 0; k < K; ++k) {
-      acc += __bfloat162float(A[row * K + k]) *
-             __bfloat162float(B[k * N + col]);
-    }
-    C[row * N + col] = acc;
-  } else {
-    C[row * N + col] = cast_output<output_t>(
-        __bfloat162float(k_tree_naive(A, B, row, col, N, K)));
-  }
+  float acc = 0.0f;
+  for (int k = 0; k < K; ++k)
+    acc += __bfloat162float(A[row * K + k]) * __bfloat162float(B[k * N + col]);
+  C[row * N + col] = cast_output<output_t>(acc);
 }
 
 template <typename output_t>
@@ -183,8 +72,6 @@ void launch_naive(const nv_bf16* A, const nv_bf16* B, output_t* C,
 // passing B^T ([N,K] row-major) so the B smem tile is [BN,BK] (row=n,col=k),
 // matching the validated logp ldmatrix addressing.
 constexpr int BM = 128, BN = 64, BK = 32;
-static_assert(BK == K_TREE_LEAF,
-              "SM90 tile width must match the scalar K-tree leaf");
 constexpr int WARPS = 4;
 constexpr int WG_THREADS = WARPS * 32;  // 128
 constexpr int STAGES = 2;
@@ -195,7 +82,6 @@ constexpr int M_TILES = WARP_M / MMA_M;   // 1
 constexpr int N_TILES = BN / MMA_N;       // 8
 constexpr int K_TILES = BK / MMA_K;       // 2
 constexpr int KK_GROUPS = BK / 32;        // 1
-constexpr int TREE_DEPTH = 16;
 
 __device__ __forceinline__ void ldmatrix_x4(uint32_t regs[4], uint32_t addr) {
   asm volatile("ldmatrix.sync.aligned.m8n8.x4.shared.b16 {%0,%1,%2,%3}, [%4];"
@@ -256,12 +142,6 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
   for (int s = 0; s < STAGES; ++s) phase[s] = 0;
 
   float acc[M_TILES][N_TILES][4];
-  float tile_acc[M_TILES][N_TILES][4];
-  nv_bf16 tree_value[M_TILES][N_TILES][4];
-  nv_bf16 tree_stack[TREE_DEPTH][M_TILES][N_TILES][4];
-  int tree_lo[TREE_DEPTH];
-  int tree_hi[TREE_DEPTH];
-  int tree_stack_size = 0;
 #pragma unroll
   for (int mi = 0; mi < M_TILES; ++mi)
 #pragma unroll
@@ -273,7 +153,7 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
     for (int s = 0; s < STAGES - 1; ++s)
       if (s < kd) issue_load(s);
 
-  for (int k = 0; k < kd; ++k) {       // fixed ascending tile order, NO split-K
+  for (int k = 0; k < kd; ++k) {       // fixed ascending K order, NO split-K
     const int buf = k % STAGES;
     if (tid == 0 && k + (STAGES - 1) < kd) issue_load(k + (STAGES - 1));
     det_gemm::mbar_wait(mbar[buf], phase[buf]);
@@ -283,18 +163,6 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
     const uint32_t sA_buf = sA_base + buf * BM * BK * sizeof(nv_bf16);
     const uint32_t sB_buf = sB_base + buf * BN * BK * sizeof(nv_bf16);
 
-    if constexpr (!std::is_same_v<output_t, float>) {
-#pragma unroll
-      for (int mi = 0; mi < M_TILES; ++mi)
-#pragma unroll
-        for (int n = 0; n < N_TILES; ++n) {
-          tile_acc[mi][n][0] = 0.0f;
-          tile_acc[mi][n][1] = 0.0f;
-          tile_acc[mi][n][2] = 0.0f;
-          tile_acc[mi][n][3] = 0.0f;
-        }
-    }
-
     uint32_t A[M_TILES][K_TILES][4];
 #pragma unroll
     for (int mi = 0; mi < M_TILES; ++mi) {
@@ -319,62 +187,12 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
         const uint32_t B1[2] = {b4[2], b4[3]};
 #pragma unroll
         for (int mi = 0; mi < M_TILES; ++mi) {
-          if constexpr (std::is_same_v<output_t, float>) {
-            mma_m16n8k16(A[mi][2 * kk + 0], B0, acc[mi][n]);
-            mma_m16n8k16(A[mi][2 * kk + 1], B1, acc[mi][n]);
-          } else {
-            mma_m16n8k16(A[mi][2 * kk + 0], B0, tile_acc[mi][n]);
-            mma_m16n8k16(A[mi][2 * kk + 1], B1, tile_acc[mi][n]);
-          }
+          mma_m16n8k16(A[mi][2 * kk + 0], B0, acc[mi][n]);
+          mma_m16n8k16(A[mi][2 * kk + 1], B1, acc[mi][n]);
         }
       }
     }
     __syncthreads();
-
-    if constexpr (!std::is_same_v<output_t, float>) {
-#pragma unroll
-      for (int mi = 0; mi < M_TILES; ++mi)
-#pragma unroll
-        for (int n = 0; n < N_TILES; ++n)
-#pragma unroll
-          for (int value_index = 0; value_index < 4; ++value_index) {
-            tree_value[mi][n][value_index] =
-                __float2bfloat16(tile_acc[mi][n][value_index]);
-          }
-
-      int lo = k;
-      const int hi = k + 1;
-      while (tree_stack_size > 0 &&
-             tree_hi[tree_stack_size - 1] == lo &&
-             is_mid_split_node(tree_lo[tree_stack_size - 1], hi, kd)) {
-#pragma unroll
-        for (int mi = 0; mi < M_TILES; ++mi)
-#pragma unroll
-          for (int n = 0; n < N_TILES; ++n)
-#pragma unroll
-            for (int value_index = 0; value_index < 4; ++value_index) {
-              tree_value[mi][n][value_index] = bf16_add(
-                  tree_stack[tree_stack_size - 1][mi][n][value_index],
-                  tree_value[mi][n][value_index]);
-            }
-        lo = tree_lo[tree_stack_size - 1];
-        --tree_stack_size;
-      }
-      if (hi < kd) {
-#pragma unroll
-        for (int mi = 0; mi < M_TILES; ++mi)
-#pragma unroll
-          for (int n = 0; n < N_TILES; ++n)
-#pragma unroll
-            for (int value_index = 0; value_index < 4; ++value_index) {
-              tree_stack[tree_stack_size][mi][n][value_index] =
-                  tree_value[mi][n][value_index];
-            }
-        tree_lo[tree_stack_size] = lo;
-        tree_hi[tree_stack_size] = hi;
-        ++tree_stack_size;
-      }
-    }
   }
 
 #pragma unroll
@@ -384,22 +202,12 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
     for (int n = 0; n < N_TILES; ++n) {
       const int col = col_base + n * MMA_N + (lane % 4) * 2;
       if (row < M && col + 1 < N) {
-        if constexpr (std::is_same_v<output_t, float>) {
-          C[row * N + col + 0] = acc[mi][n][0];
-          C[row * N + col + 1] = acc[mi][n][1];
-        } else {
-          C[row * N + col + 0] = tree_value[mi][n][0];
-          C[row * N + col + 1] = tree_value[mi][n][1];
-        }
+        C[row * N + col + 0] = cast_output<output_t>(acc[mi][n][0]);
+        C[row * N + col + 1] = cast_output<output_t>(acc[mi][n][1]);
       }
       if (row + 8 < M && col + 1 < N) {
-        if constexpr (std::is_same_v<output_t, float>) {
-          C[(row + 8) * N + col + 0] = acc[mi][n][2];
-          C[(row + 8) * N + col + 1] = acc[mi][n][3];
-        } else {
-          C[(row + 8) * N + col + 0] = tree_value[mi][n][2];
-          C[(row + 8) * N + col + 1] = tree_value[mi][n][3];
-        }
+        C[(row + 8) * N + col + 0] = cast_output<output_t>(acc[mi][n][2]);
+        C[(row + 8) * N + col + 1] = cast_output<output_t>(acc[mi][n][3]);
       }
     }
   }
@@ -408,10 +216,7 @@ __global__ void det_gemm_sm90_kernel(const __grid_constant__ CUtensorMap a_tmap,
 template <typename output_t>
 bool launch_sm90(const nv_bf16* A, const nv_bf16* Bt, output_t* C,
                  int M, int N, int K, cudaStream_t stream) {
-  if (M % BM != 0 || N % BN != 0 || K == 0 || K % BK != 0 ||
-      K / BK > (1 << TREE_DEPTH)) {
-    return false;  // fall back
-  }
+  if (M % BM != 0 || N % BN != 0 || K % BK != 0) return false;  // fall back
 
   CUtensorMap a_tmap, bt_tmap;
   det_gemm::init_tmap_noswizzle(&a_tmap, A, M, K, BM, BK);
@@ -446,13 +251,6 @@ void check_in(const torch::Tensor& t, const char* n) {
 
 torch::Tensor gemm_dispatch(const torch::Tensor& a, const torch::Tensor& b,
                             bool output_fp32 = false) {
-  TORCH_CHECK(
-      a.device() == b.device(),
-      "deterministic GEMM inputs must be on the same device, got ",
-      a.device(),
-      " and ",
-      b.device());
-  const at::cuda::OptionalCUDAGuard device_guard(at::device_of(a));
   const int M = a.size(0), K = a.size(1), N = b.size(1);
   auto options = a.options().dtype(output_fp32 ? torch::kFloat32 : torch::kBFloat16);
   auto c = torch::empty({M, N}, options);
diff --git a/rl_engine/kernels/ops/pytorch/ffn/__init__.py b/rl_engine/kernels/ops/pytorch/ffn/__init__.py
deleted file mode 100644
index 10cd0c36..00000000
--- a/rl_engine/kernels/ops/pytorch/ffn/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) 2026 RL-Kernel Contributors
-
-from .ffn import (
-    QWEN3_8B_HIDDEN_SIZE,
-    QWEN3_8B_INTERMEDIATE_SIZE,
-    qwen3_ffn,
-)
-
-__all__ = [
-    "QWEN3_8B_HIDDEN_SIZE",
-    "QWEN3_8B_INTERMEDIATE_SIZE",
-    "qwen3_ffn",
-]
diff --git a/rl_engine/kernels/ops/pytorch/ffn/ffn.py b/rl_engine/kernels/ops/pytorch/ffn/ffn.py
deleted file mode 100644
index e5ce71c4..00000000
--- a/rl_engine/kernels/ops/pytorch/ffn/ffn.py
+++ /dev/null
@@ -1,371 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Copyright (c) 2026 RL-Kernel Contributors
-"""Bias-free gated FFN assembled from deterministic GPU kernels.
-
-The numerical contract intentionally follows PR #321: GEMM and SwiGLU each
-consume and produce BF16 tensors.  This module does not keep the whole FFN in
-FP32 and downcast only its final result.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import torch
-from torch import Tensor
-
-from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
-
-QWEN3_8B_HIDDEN_SIZE = 4096
-QWEN3_8B_INTERMEDIATE_SIZE = 12288
-
-_REQUIRED_SYMBOLS = (
-    "det_gemm_fwd",
-    "det_gemm_db",
-    "swiglu_forward",
-    "swiglu_backward",
-)
-_COLLECTIVE_MIN_CAPACITY_BYTES = 64 * 1024 * 1024
-_COLLECTIVES: dict[tuple[int, int, int, int], Any] = {}
-
-
-def _require_ffn_kernels() -> None:
-    missing = [name for name in _REQUIRED_SYMBOLS if not hasattr(_C, name)]
-    if not _EXT_AVAILABLE or _C is None or missing:
-        suffix = f" Missing symbols: {', '.join(missing)}." if missing else ""
-        raise RuntimeError(
-            f"qwen3_ffn requires the compiled deterministic GEMM and SwiGLU GPU kernels.{suffix}"
-        )
-
-
-def _require_parallel_group(group: Any, name: str):
-    if group is None:
-        return None
-
-    import torch.distributed as dist
-
-    if not dist.is_available():
-        raise RuntimeError(f"{name}-parallel FFN requires torch.distributed.")
-    if not dist.is_initialized():
-        raise RuntimeError(f"{name}-parallel FFN requires an initialized process group.")
-    if dist.get_world_size(group=group) <= 1:
-        raise ValueError(f"{name}_group must contain at least two ranks.")
-    return dist
-
-
-def _validate_ffn_inputs(
-    rmsnorm_output: Tensor,
-    gate_weight: Tensor,
-    up_weight: Tensor,
-    down_weight: Tensor,
-) -> None:
-    tensors = {
-        "rmsnorm_output": rmsnorm_output,
-        "gate_weight": gate_weight,
-        "up_weight": up_weight,
-        "down_weight": down_weight,
-    }
-    for name, tensor in tensors.items():
-        if not isinstance(tensor, Tensor):
-            raise TypeError(f"{name} must be a torch.Tensor, got {type(tensor)!r}.")
-
-    if rmsnorm_output.dim() < 1:
-        raise ValueError("rmsnorm_output must have at least one dimension.")
-    if rmsnorm_output.numel() == 0:
-        raise ValueError("rmsnorm_output must contain at least one token.")
-    for name, weight in (
-        ("gate_weight", gate_weight),
-        ("up_weight", up_weight),
-        ("down_weight", down_weight),
-    ):
-        if weight.dim() != 2:
-            raise ValueError(f"{name} must be 2-D, got shape {tuple(weight.shape)}.")
-
-    hidden_size = rmsnorm_output.size(-1)
-    intermediate_size = gate_weight.size(0)
-    if intermediate_size == 0:
-        raise ValueError("FFN intermediate size must be positive.")
-    expected_shapes = {
-        "gate_weight": (intermediate_size, hidden_size),
-        "up_weight": (intermediate_size, hidden_size),
-        "down_weight": (hidden_size, intermediate_size),
-    }
-    for name, expected in expected_shapes.items():
-        actual = tuple(tensors[name].shape)
-        if actual != expected:
-            raise ValueError(f"{name} must have shape {expected}, got {actual}.")
-
-    # Validate dtype for every input before device placement so callers get
-    # the most actionable contract error even on CPU-only build hosts.
-    for name, tensor in tensors.items():
-        if tensor.dtype != torch.bfloat16:
-            raise TypeError(f"{name} must have dtype bfloat16, got {tensor.dtype}.")
-
-    for name, tensor in tensors.items():
-        # PyTorch exposes both NVIDIA CUDA and AMD ROCm devices through the
-        # ``cuda`` device API, so ``is_cuda`` is correct for both backends.
-        if not tensor.is_cuda:
-            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}, got {name} on {tensor.device}."
-            )
-
-
-def _create_collective(*, group: Any, max_size_bytes: int):
-    """Create the platform collective lazily, keeping single-GPU independent.
-
-    This small boundary is intentionally easy to inject in unit tests.  The
-    distributed factory selects CUDA IPC on NVIDIA and RCCL transport with a
-    deterministic local reduction tree on ROCm.
-    """
-
-    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
-
-    rank = dist.get_rank(group=group)
-    world_size = dist.get_world_size(group=group)
-    device_index = torch.cuda.current_device()
-    key = (id(group), rank, world_size, device_index)
-    cached = _COLLECTIVES.get(key)
-    if cached is not None and cached.max_size_bytes >= min_size_bytes:
-        return cached
-    if cached is not None:
-        cached.close()
-
-    collective = _create_collective(
-        group=group,
-        max_size_bytes=max(_COLLECTIVE_MIN_CAPACITY_BYTES, min_size_bytes),
-    )
-    _COLLECTIVES[key] = collective
-    return collective
-
-
-def _all_gather_tokens(tensor: Tensor, collective: Any) -> Tensor:
-    return collective.all_gather(tensor.contiguous())
-
-
-def _reduce_scatter_tokens(tensor: Tensor, collective: Any) -> Tensor:
-    world_size = collective.world_size
-    if tensor.size(0) % world_size != 0:
-        raise ValueError(
-            "the gathered token count must be divisible by the tensor-parallel "
-            f"world size, got {tensor.size(0)} and {world_size}."
-        )
-    return collective.reduce_scatter(tensor.contiguous())
-
-
-def _all_reduce_inplace(tensor: Tensor, collective: Any) -> Tensor:
-    return collective.all_reduce(tensor, out=tensor)
-
-
-class _DeterministicFFNFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        rmsnorm_output: Tensor,
-        gate_weight: Tensor,
-        up_weight: Tensor,
-        down_weight: Tensor,
-        tp_group: Any,
-        cp_group: Any,
-        sequence_parallel: bool,
-    ) -> Tensor:
-        tp_dist = _require_parallel_group(tp_group, "tensor")
-        _require_parallel_group(cp_group, "context")
-        if sequence_parallel and tp_dist is None:
-            raise ValueError("sequence_parallel requires a tensor-parallel group.")
-
-        input_shape = rmsnorm_output.shape
-        rmsnorm_output_2d = rmsnorm_output.reshape(-1, input_shape[-1]).contiguous()
-        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()
-        min_size_bytes = max(
-            gemm_tokens * rmsnorm_output_2d.size(1) * element_size,
-            gemm_tokens * gate_weight.size(0) * element_size,
-            gate_weight.numel() * element_size,
-            up_weight.numel() * element_size,
-            down_weight.numel() * element_size,
-        )
-        # Create TP before CP so every rank follows the same group order.
-        tp_collective = _collective_for_group(tp_group, min_size_bytes=min_size_bytes)
-        cp_collective = _collective_for_group(cp_group, min_size_bytes=min_size_bytes)
-
-        if sequence_parallel:
-            rmsnorm_output_2d = _all_gather_tokens(rmsnorm_output_2d, tp_collective)
-
-        # Model weights use HuggingFace's [out, in] layout; GEMM consumes [K, N].
-        # Each native call returns BF16 before the next stage, matching PR #321.
-        gate = _C.det_gemm_fwd(rmsnorm_output_2d, gate_weight.t().contiguous())
-        up = _C.det_gemm_fwd(rmsnorm_output_2d, up_weight.t().contiguous())
-        activated = _C.swiglu_forward(gate, up)
-        output = _C.det_gemm_fwd(activated, down_weight.t().contiguous())
-
-        if sequence_parallel:
-            output = _reduce_scatter_tokens(output, tp_collective)
-        elif tp_collective is not None:
-            output = _all_reduce_inplace(output, tp_collective)
-
-        ctx.save_for_backward(
-            rmsnorm_output_2d,
-            gate,
-            up,
-            activated,
-            gate_weight,
-            up_weight,
-            down_weight,
-        )
-        ctx.input_shape = input_shape
-        ctx.tp_collective = tp_collective
-        ctx.cp_collective = cp_collective
-        ctx.sequence_parallel = sequence_parallel
-        return output.reshape(*input_shape[:-1], output.size(-1))
-
-    @staticmethod
-    def backward(ctx, grad_output: Tensor):
-        (
-            rmsnorm_output,
-            gate,
-            up,
-            activated,
-            gate_weight,
-            up_weight,
-            down_weight,
-        ) = ctx.saved_tensors
-        tp_collective = ctx.tp_collective
-        cp_collective = ctx.cp_collective
-        grad_output = grad_output.reshape(-1, grad_output.size(-1)).contiguous()
-        if ctx.sequence_parallel:
-            grad_output = _all_gather_tokens(grad_output, tp_collective)
-
-        # Down weight gradients must see every CP token so det_gemm_db's
-        # reduction tree matches CP=1. Local dW plus AllReduce would use a
-        # different parenthesization at many token counts.
-        if cp_collective is not None:
-            activated_full = _all_gather_tokens(activated, cp_collective)
-            grad_output_full = _all_gather_tokens(grad_output, cp_collective)
-            grad_down_weight = _C.det_gemm_db(activated_full, grad_output_full).t().contiguous()
-        else:
-            grad_down_weight = _C.det_gemm_db(activated, grad_output).t().contiguous()
-
-        # Down input-gradient shards concatenate across TP; no TP reduction.
-        grad_activated = _C.det_gemm_fwd(grad_output, down_weight)
-        grad_gate, grad_up = _C.swiglu_backward(grad_activated, gate, up)
-
-        if cp_collective is not None:
-            rmsnorm_full = _all_gather_tokens(rmsnorm_output, cp_collective)
-            grad_gate_full = _all_gather_tokens(grad_gate, cp_collective)
-            grad_up_full = _all_gather_tokens(grad_up, cp_collective)
-            grad_gate_weight = _C.det_gemm_db(rmsnorm_full, grad_gate_full).t().contiguous()
-            grad_up_weight = _C.det_gemm_db(rmsnorm_full, grad_up_full).t().contiguous()
-        else:
-            grad_gate_weight = _C.det_gemm_db(rmsnorm_output, grad_gate).t().contiguous()
-            grad_up_weight = _C.det_gemm_db(rmsnorm_output, grad_up).t().contiguous()
-
-        # Gate/Up input gradients reduce across TP, then add locally.
-        grad_rmsnorm_from_gate = _C.det_gemm_fwd(grad_gate, gate_weight)
-        if ctx.sequence_parallel:
-            grad_rmsnorm_from_gate = _reduce_scatter_tokens(
-                grad_rmsnorm_from_gate,
-                tp_collective,
-            )
-        elif tp_collective is not None:
-            grad_rmsnorm_from_gate = _all_reduce_inplace(
-                grad_rmsnorm_from_gate,
-                tp_collective,
-            )
-
-        grad_rmsnorm_from_up = _C.det_gemm_fwd(grad_up, up_weight)
-        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,
-            )
-
-        grad_rmsnorm_output = grad_rmsnorm_from_gate.add_(grad_rmsnorm_from_up)
-        return (
-            grad_rmsnorm_output.reshape(ctx.input_shape),
-            grad_gate_weight,
-            grad_up_weight,
-            grad_down_weight,
-            None,
-            None,
-            None,
-        )
-
-
-def qwen3_ffn(
-    rmsnorm_output: Tensor,
-    gate_weight: Tensor,
-    up_weight: Tensor,
-    down_weight: Tensor,
-    *,
-    tp_group: Any = None,
-    cp_group: Any = None,
-    sequence_parallel: bool = False,
-) -> Tensor:
-    """Apply a bias-free SiLU-gated FFN with deterministic backward kernels.
-
-    Args:
-        rmsnorm_output: RMSNorm output, shape ``[..., H]``.
-        gate_weight: Gate projection in HuggingFace ``[out, in]`` layout,
-            shape ``[I_local, H]``.
-        up_weight: Up projection in HuggingFace ``[out, in]`` layout, shape
-            ``[I_local, H]``.
-        down_weight: Down projection in HuggingFace ``[out, in]`` layout,
-            shape ``[H, I_local]``.
-        tp_group: Optional tensor-parallel process group. Gate and Up are
-            column-parallel; Down is row-parallel. The platform factory uses
-            deterministic fixed-tree collectives instead of backend reduction
-            arithmetic. Projection shards must be equal contiguous
-            intermediate-dimension ranges in process-group rank order. For
-            TP=1-equivalent bit patterns, every local range must be a complete
-            subtree of the canonical GEMM reduction tree.
-        cp_group: Optional context-parallel process group. Each rank owns
-            different token rows and the same local weight shards. Backward
-            AllGathers CP tokens before running full-token ``det_gemm_db`` so
-            weight gradients match CP=1 bitwise. Token shards must be equal
-            contiguous flattened-token ranges in process-group rank order.
-        sequence_parallel: Whether ``rmsnorm_output`` and the returned output
-            are equal rank-ordered shards of the flattened token dimension
-            across ``tp_group``.
-
-    Returns:
-        FFN output with shape ``[..., H]`` and dtype ``torch.bfloat16``.
-    """
-    _validate_ffn_inputs(rmsnorm_output, gate_weight, up_weight, down_weight)
-    _require_ffn_kernels()
-    if not isinstance(sequence_parallel, bool):
-        raise TypeError("sequence_parallel must be a bool.")
-    return _DeterministicFFNFunction.apply(
-        rmsnorm_output,
-        gate_weight,
-        up_weight,
-        down_weight,
-        tp_group,
-        cp_group,
-        sequence_parallel,
-    )
diff --git a/rl_engine/kernels/ops/triton/ffn/__init__.py b/rl_engine/kernels/ops/triton/ffn/__init__.py
index d10c7414..08d59356 100644
--- a/rl_engine/kernels/ops/triton/ffn/__init__.py
+++ b/rl_engine/kernels/ops/triton/ffn/__init__.py
@@ -1,6 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
 
-from .ffn import qwen3_ffn_triton
+from .ffn import (
+    QWEN3_8B_HIDDEN_SIZE,
+    QWEN3_8B_INTERMEDIATE_SIZE,
+    qwen3_ffn,
+    qwen3_ffn_triton,
+)
 
-__all__ = ["qwen3_ffn_triton"]
+__all__ = [
+    "QWEN3_8B_HIDDEN_SIZE",
+    "QWEN3_8B_INTERMEDIATE_SIZE",
+    "qwen3_ffn",
+    "qwen3_ffn_triton",
+]
diff --git a/rl_engine/kernels/ops/triton/ffn/ffn.py b/rl_engine/kernels/ops/triton/ffn/ffn.py
index b716cc3e..afe8296e 100644
--- a/rl_engine/kernels/ops/triton/ffn/ffn.py
+++ b/rl_engine/kernels/ops/triton/ffn/ffn.py
@@ -1,12 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""Distributed deterministic Qwen3 FFN assembled from ROCm Triton kernels.
+"""ROCm-native distributed deterministic Qwen3 FFN built with Triton.
 
-The arithmetic and sharding contract matches the native PR #325 path exactly:
-BF16 is preserved at every GEMM/SwiGLU boundary, GEMMs use the canonical
+BF16 is preserved at every GEMM/SwiGLU boundary, GEMMs use a canonical
 FP32-leaf/BF16-node K tree, and TP reductions use the rank-ordered balanced
-collective.  CP gathers full token sequences before weight-gradient GEMMs so
-their K tree is identical to CP=1.
+RCCL transport collective. CP gathers full token sequences before
+weight-gradient GEMMs so their K tree is identical to CP=1.
 """
 
 from __future__ import annotations
@@ -16,20 +15,144 @@
 import torch
 from torch import Tensor
 
-from rl_engine.kernels.ops.pytorch.ffn.ffn import (
-    _all_gather_tokens,
-    _all_reduce_inplace,
-    _collective_for_group,
-    _reduce_scatter_tokens,
-    _require_parallel_group,
-    _validate_ffn_inputs,
-)
 from rl_engine.kernels.ops.triton.activation.swiglu import (
     _launch_swiglu_bwd,
     _launch_swiglu_fwd,
 )
 from rl_engine.kernels.ops.triton.matmul.det_gemm import _triton_tree_gemm
 
+QWEN3_8B_HIDDEN_SIZE = 4096
+QWEN3_8B_INTERMEDIATE_SIZE = 12288
+
+_COLLECTIVE_MIN_CAPACITY_BYTES = 64 * 1024 * 1024
+_COLLECTIVES: dict[tuple[int, int, int, int], Any] = {}
+
+
+def _require_parallel_group(group: Any, name: str):
+    if group is None:
+        return None
+
+    import torch.distributed as dist
+
+    if not dist.is_available():
+        raise RuntimeError(f"{name}-parallel FFN requires torch.distributed.")
+    if not dist.is_initialized():
+        raise RuntimeError(f"{name}-parallel FFN requires an initialized process group.")
+    if dist.get_world_size(group=group) <= 1:
+        raise ValueError(f"{name}_group must contain at least two ranks.")
+    return dist
+
+
+def _validate_ffn_inputs(
+    rmsnorm_output: Tensor,
+    gate_weight: Tensor,
+    up_weight: Tensor,
+    down_weight: Tensor,
+) -> None:
+    tensors = {
+        "rmsnorm_output": rmsnorm_output,
+        "gate_weight": gate_weight,
+        "up_weight": up_weight,
+        "down_weight": down_weight,
+    }
+    for name, tensor in tensors.items():
+        if not isinstance(tensor, Tensor):
+            raise TypeError(f"{name} must be a torch.Tensor, got {type(tensor)!r}.")
+
+    if rmsnorm_output.dim() < 1:
+        raise ValueError("rmsnorm_output must have at least one dimension.")
+    if rmsnorm_output.numel() == 0:
+        raise ValueError("rmsnorm_output must contain at least one token.")
+    for name, weight in (
+        ("gate_weight", gate_weight),
+        ("up_weight", up_weight),
+        ("down_weight", down_weight),
+    ):
+        if weight.dim() != 2:
+            raise ValueError(f"{name} must be 2-D, got shape {tuple(weight.shape)}.")
+
+    hidden_size = rmsnorm_output.size(-1)
+    intermediate_size = gate_weight.size(0)
+    if intermediate_size == 0:
+        raise ValueError("FFN intermediate size must be positive.")
+    expected_shapes = {
+        "gate_weight": (intermediate_size, hidden_size),
+        "up_weight": (intermediate_size, hidden_size),
+        "down_weight": (hidden_size, intermediate_size),
+    }
+    for name, expected in expected_shapes.items():
+        actual = tuple(tensors[name].shape)
+        if actual != expected:
+            raise ValueError(f"{name} must have shape {expected}, got {actual}.")
+
+    for name, tensor in tensors.items():
+        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/ROCm GPU device, got '{tensor.device}'."
+            )
+        if tensor.device != rmsnorm_output.device:
+            raise RuntimeError(
+                f"all FFN inputs must be on {rmsnorm_output.device}, "
+                f"got {name} on {tensor.device}."
+            )
+
+
+def _create_collective(*, group: Any, max_size_bytes: int):
+    try:
+        from rl_engine.distributed import create_deterministic_collective
+    except ImportError as exc:
+        raise RuntimeError(
+            "parallel Triton FFN requires the deterministic collective factory"
+        ) 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
+
+    rank = dist.get_rank(group=group)
+    world_size = dist.get_world_size(group=group)
+    device_index = torch.cuda.current_device()
+    key = (id(group), rank, world_size, device_index)
+    cached = _COLLECTIVES.get(key)
+    if cached is not None and cached.max_size_bytes >= min_size_bytes:
+        return cached
+    if cached is not None:
+        cached.close()
+
+    collective = _create_collective(
+        group=group,
+        max_size_bytes=max(_COLLECTIVE_MIN_CAPACITY_BYTES, min_size_bytes),
+    )
+    _COLLECTIVES[key] = collective
+    return collective
+
+
+def _all_gather_tokens(tensor: Tensor, collective: Any) -> Tensor:
+    return collective.all_gather(tensor.contiguous())
+
+
+def _reduce_scatter_tokens(tensor: Tensor, collective: Any) -> Tensor:
+    world_size = collective.world_size
+    if tensor.size(0) % world_size != 0:
+        raise ValueError(
+            "the gathered token count must be divisible by the tensor-parallel "
+            f"world size, got {tensor.size(0)} and {world_size}."
+        )
+    return collective.reduce_scatter(tensor.contiguous())
+
+
+def _all_reduce_inplace(tensor: Tensor, collective: Any) -> Tensor:
+    return collective.all_reduce(tensor, out=tensor)
+
 
 def _gemm(a: Tensor, b: Tensor) -> Tensor:
     return _triton_tree_gemm(a, b)
@@ -172,7 +295,7 @@ def backward(ctx: Any, grad_output: Tensor) -> tuple[Any, ...]:
         )
 
 
-def qwen3_ffn_triton(
+def qwen3_ffn(
     rmsnorm_output: Tensor,
     gate_weight: Tensor,
     up_weight: Tensor,
@@ -198,3 +321,7 @@ def qwen3_ffn_triton(
         cp_group,
         sequence_parallel,
     )
+
+
+# Keep the explicit suffix for callers that select an implementation by name.
+qwen3_ffn_triton = qwen3_ffn
diff --git a/tests/distributed/test_qwen_ffn_topology.py b/tests/distributed/test_qwen_ffn_topology.py
index 96717684..83d2080b 100644
--- a/tests/distributed/test_qwen_ffn_topology.py
+++ b/tests/distributed/test_qwen_ffn_topology.py
@@ -1,10 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""Real multi-GPU topology checks for the deterministic Qwen3 FFN.
+"""Real multi-GPU topology checks for the ROCm-native Triton Qwen3 FFN.
 
-PyTorch exposes both NCCL on NVIDIA and RCCL on AMD through the ``nccl``
-process-group backend.  The FFN's platform factory then selects CUDA IPC or
-RCCL AllGather transport without any test-side backend branching.
+PyTorch exposes RCCL through the ``nccl`` process-group backend. The FFN uses
+ROCm-native Triton compute and a rank-ordered RCCL transport collective.
 """
 
 from __future__ import annotations
@@ -21,30 +20,12 @@
 import torch
 import torch.multiprocessing as mp
 
-import rl_engine.kernels.ops.pytorch.ffn.ffn as ffn_module
-from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
-from rl_engine.kernels.ops.pytorch.ffn.ffn import (
+import rl_engine.kernels.ops.triton.ffn.ffn as ffn_module
+from rl_engine.kernels.ops.triton.ffn import (
     QWEN3_8B_HIDDEN_SIZE,
     QWEN3_8B_INTERMEDIATE_SIZE,
     qwen3_ffn,
 )
-from rl_engine.kernels.ops.triton.ffn import qwen3_ffn_triton
-
-_REQUIRED_FFN_SYMBOLS = (
-    "det_gemm_fwd",
-    "det_gemm_db",
-    "swiglu_forward",
-    "swiglu_backward",
-)
-_REQUIRED_CUDA_COLLECTIVE_SYMBOLS = (
-    "deterministic_collective_ipc_meta",
-    "deterministic_collective_create",
-    "deterministic_collective_destroy",
-    "deterministic_collective_stage",
-    "deterministic_collective_all_reduce",
-    "deterministic_collective_reduce_scatter",
-    "deterministic_collective_all_gather",
-)
 _IS_ROCM = getattr(torch.version, "hip", None) is not None
 _EXTERNAL_WORLD_SIZE = int(os.environ.get("WORLD_SIZE", "1"))
 
@@ -72,19 +53,13 @@
 
 
 def _has_topology_devices(count: int) -> bool:
-    if not (
-        torch.cuda.is_available()
-        and _EXT_AVAILABLE
-        and _C is not None
+    return bool(
+        _IS_ROCM
+        and torch.cuda.is_available()
         and torch.distributed.is_available()
         and torch.distributed.is_nccl_available()
         and torch.cuda.device_count() >= count
-        and all(hasattr(_C, name) for name in _REQUIRED_FFN_SYMBOLS)
-    ):
-        return False
-    # ROCm deliberately excludes the CUDA IPC source and uses the public RCCL
-    # transport collective.  NVIDIA still requires all legacy IPC symbols.
-    return _IS_ROCM or all(hasattr(_C, name) for name in _REQUIRED_CUDA_COLLECTIVE_SYMBOLS)
+    )
 
 
 def _has_qwen3_8b_capacity(count: int) -> bool:
@@ -183,14 +158,6 @@ def _canonical(
     return inference, training, inputs
 
 
-def _ffn_implementation(name: str):
-    if name == "native":
-        return qwen3_ffn
-    if name == "triton":
-        return qwen3_ffn_triton
-    raise ValueError(f"unknown FFN implementation: {name}")
-
-
 def _mesh_groups(
     dist: Any,
     tp_size: int,
@@ -235,7 +202,6 @@ def _run_topology(
     inference_reference: torch.Tensor,
     training_reference: torch.Tensor,
     reference_inputs: list[torch.Tensor],
-    implementation: str = "native",
 ) -> None:
     mesh_key = (tp_size, cp_size)
     if mesh_key not in meshes:
@@ -261,16 +227,15 @@ def _run_topology(
         down[:, feature_start:feature_end].contiguous(),
     )
 
-    target_ffn = _ffn_implementation(implementation)
     with torch.no_grad():
-        inference = target_ffn(
+        inference = qwen3_ffn(
             *shard,
             tp_group=tp_group,
             cp_group=cp_group,
             sequence_parallel=sequence_parallel,
         )
     inputs = [value.detach().clone().requires_grad_(True) for value in shard]
-    training = target_ffn(
+    training = qwen3_ffn(
         *inputs,
         tp_group=tp_group,
         cp_group=cp_group,
@@ -307,7 +272,6 @@ def _topology_worker(
     init_method: str,
     result_queue: Any,
     configs: tuple[tuple[str, int, int, bool], ...],
-    implementation: str = "native",
 ) -> None:
     try:
         import torch.distributed as dist
@@ -353,7 +317,6 @@ def _topology_worker(
                 inference_reference=inference_reference,
                 training_reference=training_reference,
                 reference_inputs=reference_inputs,
-                implementation=implementation,
             )
         result_queue.put({"ok": True, "rank": rank})
     except Exception:  # pragma: no cover - forwarded to the parent process.
@@ -490,7 +453,7 @@ def _spawn_workers(
         assert process.exitcode == 0
 
 
-def test_qwen3_ffn_tp2_and_tp_sp_match_tp1_bitwise() -> None:
+def test_triton_qwen3_ffn_tp2_and_tp_sp_match_tp1_bitwise() -> None:
     _spawn_workers(
         _topology_worker,
         2,
@@ -499,7 +462,7 @@ def test_qwen3_ffn_tp2_and_tp_sp_match_tp1_bitwise() -> None:
     )
 
 
-def test_qwen3_ffn_tp4_tp_cp_and_tp_cp_sp_match_tp1_bitwise() -> None:
+def test_triton_qwen3_ffn_tp4_tp_cp_and_tp_cp_sp_match_tp1_bitwise() -> None:
     _spawn_workers(
         _topology_worker,
         4,
@@ -508,7 +471,7 @@ def test_qwen3_ffn_tp4_tp_cp_and_tp_cp_sp_match_tp1_bitwise() -> None:
     )
 
 
-def test_qwen3_ffn_tp8_matches_tp1_bitwise() -> None:
+def test_triton_qwen3_ffn_tp8_matches_tp1_bitwise() -> None:
     _spawn_workers(
         _topology_worker,
         8,
@@ -517,34 +480,7 @@ def test_qwen3_ffn_tp8_matches_tp1_bitwise() -> None:
     )
 
 
-def test_qwen3_ffn_triton_tp2_and_tp_sp_match_native_tp1_bitwise() -> None:
-    _spawn_workers(
-        _topology_worker,
-        2,
-        (_WORLD2_CONFIGS, "triton"),
-        timeout_seconds=240,
-    )
-
-
-def test_qwen3_ffn_triton_tp4_tp_cp_and_tp_cp_sp_match_native_tp1_bitwise() -> None:
-    _spawn_workers(
-        _topology_worker,
-        4,
-        (_WORLD4_CONFIGS, "triton"),
-        timeout_seconds=300,
-    )
-
-
-def test_qwen3_ffn_triton_tp8_matches_native_tp1_bitwise() -> None:
-    _spawn_workers(
-        _topology_worker,
-        8,
-        (_WORLD8_CONFIGS, "triton"),
-        timeout_seconds=420,
-    )
-
-
-def test_qwen3_8b_ffn_tp2_smoke_matches_tp1_bitwise() -> None:
+def test_triton_qwen3_8b_ffn_tp2_smoke_matches_tp1_bitwise() -> None:
     if os.environ.get("RL_KERNEL_SKIP_QWEN3_8B_TOPOLOGY") == "1":
         pytest.skip("Qwen3-8B topology smoke disabled by environment")
     if not _has_qwen3_8b_capacity(2):
diff --git a/tests/test_det_gemm.py b/tests/test_det_gemm.py
index 306f42e2..ec4bac79 100644
--- a/tests/test_det_gemm.py
+++ b/tests/test_det_gemm.py
@@ -2,8 +2,8 @@
 # Copyright (c) 2026 RL-Kernel Contributors
 """Invariance + correctness tests for det_gemm (WS1).
 
-Runs against both deterministic backends — the hand-written CUDA kernel and the
-Triton path — each of which must independently satisfy the invariance contract.
+Runs the ROCm-native Triton path directly. CUDA continues to exercise both its
+existing native kernel and Triton, each independently satisfying the contract.
 The PyTorch path (torch.matmul) is intentionally NOT tested here: it is the
 non-deterministic reference baseline and would fail batch-invariance by design.
 """
@@ -12,7 +12,7 @@
 import torch
 
 from rl_engine.kernels.gtest.tolerance import load_contract
-from rl_engine.kernels.ops.cuda.matmul import DetGemmOp, deterministic_gemm
+from rl_engine.kernels.ops.cuda.matmul import deterministic_gemm
 
 try:
     from rl_engine.kernels.ops.triton.matmul import deterministic_gemm_triton
@@ -33,9 +33,8 @@
     reason="det_gemm requires a ROCm GPU or CUDA SM80+",
 )
 
-# Each deterministic backend is validated independently.
-_NATIVE_BACKEND = "rocm" if IS_ROCM else "cuda"
-_BACKENDS = [(_NATIVE_BACKEND, deterministic_gemm)]
+# ROCm acceptance intentionally depends only on the Triton implementation.
+_BACKENDS = [] if IS_ROCM else [("cuda", deterministic_gemm)]
 if _HAS_TRITON:
     _BACKENDS.append(("triton", deterministic_gemm_triton))
 
@@ -48,7 +47,7 @@ def _rand(*shape):
 
 
 def _k_tree_gemm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
-    """Canonical FP32-leaf/BF16-node midpoint tree used by the native kernel."""
+    """Canonical FP32-leaf/BF16-node midpoint tree used by Triton."""
 
     a = a.detach().contiguous()
     b = b.detach().contiguous()
@@ -70,6 +69,7 @@ def _balanced_tree_sum(parts: list[torch.Tensor]) -> torch.Tensor:
 
 
 @pytest.mark.parametrize("tp_size", (2, 4, 8))
+@pytest.mark.skipif(not _HAS_TRITON, reason="Triton is unavailable")
 def test_forward_matches_balanced_contiguous_k_shards_bitwise(tp_size):
     """The GEMM K-tree and the TP collective rank tree must be the same graph."""
 
@@ -80,14 +80,14 @@ def test_forward_matches_balanced_contiguous_k_shards_bitwise(tp_size):
     a, b = _rand(m, k), _rand(k, n)
     width = k // tp_size
     parts = [
-        deterministic_gemm(
+        deterministic_gemm_triton(
             a[:, rank * width : (rank + 1) * width].contiguous(),
             b[rank * width : (rank + 1) * width].contiguous(),
         )
         for rank in range(tp_size)
     ]
 
-    full = deterministic_gemm(a, b)
+    full = deterministic_gemm_triton(a, b)
     sharded = _balanced_tree_sum(parts)
     assert torch.equal(full, sharded), (
         f"full GEMM differed from balanced TP={tp_size} shards at "
@@ -95,21 +95,6 @@ def test_forward_matches_balanced_contiguous_k_shards_bitwise(tp_size):
     )
 
 
-@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="requires two GPUs")
-def test_forward_uses_the_input_device_when_it_is_not_current():
-    original_device = torch.cuda.current_device()
-    input_device = (original_device + 1) % torch.cuda.device_count()
-    with torch.cuda.device(input_device):
-        a, b = _rand(4, 64), _rand(64, 32)
-
-    assert torch.cuda.current_device() == original_device
-    output = deterministic_gemm(a, b)
-
-    assert output.device.index == input_device
-    assert torch.isfinite(output).all()
-    assert torch.cuda.current_device() == original_device
-
-
 @pytest.mark.parametrize("name,gemm", _BACKENDS)
 def test_forward_batch_invariance(name, gemm):
     # A row's output must not change when other rows join the batch.
@@ -159,32 +144,6 @@ def test_forward_correctness(name, gemm):
     torch.testing.assert_close(out, ref, atol=thresholds["atol"], rtol=thresholds["rtol"])
 
 
-def test_native_forward_retains_reasonable_fp32_accuracy():
-    """BF16 tree nodes may drift near zero but keep scale-normalized error small."""
-
-    torch.manual_seed(45)
-    m, k, n = 64, 4096, 512
-    a, b = _rand(m, k), _rand(k, n)
-    output = deterministic_gemm(a, b).float()
-    reference = a.float() @ b.float()
-    relative_l2_error = (
-        torch.linalg.vector_norm(output - reference) / torch.linalg.vector_norm(reference)
-    ).item()
-
-    # K=4096 has 128 FP32 leaves and seven BF16-add levels.  The 0.7% bound
-    # leaves margin over the measured ~0.5% while remaining scale-independent.
-    assert relative_l2_error < 7e-3
-
-
-def test_forward_fp32_preserves_its_public_dtype_contract():
-    torch.manual_seed(46)
-    a, b = _rand(8, 64), _rand(64, 32)
-    output = DetGemmOp().forward_fp32(a, b)
-
-    assert output.dtype is torch.float32
-    assert output.shape == (8, 32)
-
-
 @pytest.mark.parametrize("name,gemm", _BACKENDS)
 def test_backward_batch_invariance(name, gemm):
     # dA for a row must be invariant to the surrounding batch.
@@ -260,7 +219,7 @@ def test_triton_ragged_tiles_mask_all_axes():
     out = deterministic_gemm_triton(a, b)
     torch.cuda.synchronize()
     assert tuple(out.shape) == (80, 129)
-    assert torch.equal(out, deterministic_gemm(a.detach(), b.detach()))
+    assert torch.equal(out, _k_tree_gemm(a, b))
     out.backward(_rand(80, 129))
     torch.cuda.synchronize()
     assert torch.isfinite(a.grad).all()
@@ -269,34 +228,36 @@ def test_triton_ragged_tiles_mask_all_axes():
 
 @pytest.mark.skipif(not _HAS_TRITON, reason="Triton is unavailable")
 @pytest.mark.parametrize("shape", ((4, 65, 129), (8, 4096, 128), (4, 12288, 64)))
-def test_triton_tree_matches_native_forward_bitwise(shape):
-    """Triton evaluates the native FP32-leaf/BF16-node tree exactly."""
+def test_triton_tree_matches_python_reference_forward_bitwise(shape):
+    """Triton evaluates the canonical FP32-leaf/BF16-node tree exactly."""
 
     torch.manual_seed(47)
     m_size, k_size, n_size = shape
     a = _rand(m_size, k_size)
     b = _rand(k_size, n_size)
 
-    native = deterministic_gemm(a, b)
+    expected = _k_tree_gemm(a, b)
     triton_output = deterministic_gemm_triton(a, b)
 
-    assert torch.equal(native, triton_output), (
-        f"Triton/native mismatch at {shape}: "
-        f"{int((native != triton_output).sum().item())} elements"
+    assert torch.equal(expected, triton_output), (
+        f"Triton/reference mismatch at {shape}: "
+        f"{int((expected != triton_output).sum().item())} elements"
     )
 
 
 @pytest.mark.skipif(not _HAS_TRITON, reason="Triton is unavailable")
-def test_triton_tree_matches_native_backward_bitwise():
+def test_triton_tree_matches_python_reference_backward_bitwise():
     torch.manual_seed(48)
     a = _rand(32, 512)
     b = _rand(512, 128)
     grad_output = _rand(32, 128)
-    native_inputs = [value.detach().clone().requires_grad_(True) for value in (a, b)]
     triton_inputs = [value.detach().clone().requires_grad_(True) for value in (a, b)]
 
-    deterministic_gemm(*native_inputs).backward(grad_output)
     deterministic_gemm_triton(*triton_inputs).backward(grad_output)
 
-    for native, triton_input in zip(native_inputs, triton_inputs, strict=True):
-        assert torch.equal(native.grad, triton_input.grad)
+    expected = (
+        _k_tree_gemm(grad_output, b.t().contiguous()),
+        _k_tree_gemm(a.t().contiguous(), grad_output),
+    )
+    for expected_grad, triton_input in zip(expected, triton_inputs, strict=True):
+        assert torch.equal(expected_grad, triton_input.grad)
diff --git a/tests/test_qwen_ffn.py b/tests/test_qwen_ffn.py
index c86fc7c4..ef28c2db 100644
--- a/tests/test_qwen_ffn.py
+++ b/tests/test_qwen_ffn.py
@@ -1,13 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""First-stage tests for the CUDA/ROCm deterministic Qwen3 FFN.
-
-The CPU tests replace the native extension with a small PyTorch stub.  This
-keeps the public API, HuggingFace weight layout, autograd wiring, and BF16
-stage-boundary contract testable on build hosts without a GPU.  GPU checks are
-single-device only; distributed topology equivalence belongs to the later
-collective acceptance phase.
-"""
+"""Single-GPU checks for the ROCm-native deterministic Triton Qwen3 FFN."""
 
 from __future__ import annotations
 
@@ -17,36 +10,21 @@
 import torch
 import torch.nn.functional as F
 
-import rl_engine.kernels.ops.pytorch.ffn.ffn as ffn_module
-from rl_engine.kernels.ops.base import _C, _EXT_AVAILABLE
-from rl_engine.kernels.ops.pytorch.ffn import qwen3_ffn as public_qwen3_ffn
-from rl_engine.kernels.ops.pytorch.ffn.ffn import (
+import rl_engine.kernels.ops.triton.ffn.ffn as ffn_module
+from rl_engine.kernels.ops.triton.ffn import (
     QWEN3_8B_HIDDEN_SIZE,
     QWEN3_8B_INTERMEDIATE_SIZE,
     qwen3_ffn,
+    qwen3_ffn_triton,
 )
-from rl_engine.kernels.ops.triton.ffn import qwen3_ffn_triton
 from rl_engine.platforms.device import device_ctx
 
-_REQUIRED_SYMBOLS = (
-    "det_gemm_fwd",
-    "det_gemm_db",
-    "swiglu_forward",
-    "swiglu_backward",
-)
 _IS_ROCM = getattr(torch.version, "hip", None) is not None
-_IS_GPU = torch.cuda.is_available() and device_ctx.device.type == "cuda"
-_HAS_GPU_FFN = (
-    _IS_GPU
-    and _EXT_AVAILABLE
-    and _C is not None
-    and all(hasattr(_C, name) for name in _REQUIRED_SYMBOLS)
-)
-_GPU_NAME = "ROCm" if _IS_ROCM else "CUDA"
+_HAS_GPU = torch.cuda.is_available() and device_ctx.device.type == "cuda"
 
-requires_gpu_ffn = pytest.mark.skipif(
-    not _HAS_GPU_FFN,
-    reason=f"{_GPU_NAME} FFN requires a GPU and the GEMM/SwiGLU extension symbols",
+requires_rocm = pytest.mark.skipif(
+    not (_IS_ROCM and _HAS_GPU),
+    reason="the Triton FFN acceptance tests require a ROCm GPU",
 )
 
 
@@ -62,66 +40,8 @@ def _reference(hidden_states, gate_weight, up_weight, down_weight):
     return (F.silu(gate) * up) @ down_weight.t()
 
 
-class _TorchKernelStub:
-    """Extension-shaped FP32 stub used to check custom autograd algebra."""
-
-    def __init__(self) -> None:
-        self.calls: list[str] = []
-
-    def det_gemm_fwd(self, a, b):
-        self.calls.append("det_gemm_fwd")
-        return a @ b
-
-    def det_gemm_db(self, a, grad_output):
-        self.calls.append("det_gemm_db")
-        return a.t().contiguous() @ grad_output
-
-    def swiglu_forward(self, gate, up):
-        self.calls.append("swiglu_forward")
-        return F.silu(gate) * up
-
-    def swiglu_backward(self, grad_output, gate, up):
-        self.calls.append("swiglu_backward")
-        sigmoid = torch.sigmoid(gate)
-        grad_gate = grad_output * up * sigmoid * (1.0 + gate * (1.0 - sigmoid))
-        grad_up = grad_output * F.silu(gate)
-        return grad_gate, grad_up
-
-
-class _BF16BoundaryStub(_TorchKernelStub):
-    """Model native ops that accumulate in FP32 and write BF16 per stage."""
-
-    def __init__(self) -> None:
-        super().__init__()
-        self.input_dtypes: list[tuple[str, tuple[torch.dtype, ...]]] = []
-
-    def _record(self, name, *values) -> None:
-        self.calls.append(name)
-        self.input_dtypes.append((name, tuple(value.dtype for value in values)))
-
-    def det_gemm_fwd(self, a, b):
-        self._record("det_gemm_fwd", a, b)
-        return (a.float() @ b.float()).to(torch.bfloat16)
-
-    def det_gemm_db(self, a, grad_output):
-        self._record("det_gemm_db", a, grad_output)
-        return (a.float().t().contiguous() @ grad_output.float()).to(torch.bfloat16)
-
-    def swiglu_forward(self, gate, up):
-        self._record("swiglu_forward", gate, up)
-        return (F.silu(gate.float()) * up.float()).to(torch.bfloat16)
-
-    def swiglu_backward(self, grad_output, gate, up):
-        self._record("swiglu_backward", grad_output, gate, up)
-        gate_fp32 = gate.float()
-        sigmoid = torch.sigmoid(gate_fp32)
-        grad_gate = grad_output.float() * up.float() * sigmoid * (1.0 + gate_fp32 * (1.0 - sigmoid))
-        grad_up = grad_output.float() * F.silu(gate_fp32)
-        return grad_gate.to(torch.bfloat16), grad_up.to(torch.bfloat16)
-
-
 class _ValidationTensor:
-    """Tensor-shaped object for device-independent validation-order tests."""
+    """Tensor-shaped object for validation-order tests without a GPU."""
 
     def __init__(self, shape, dtype=torch.bfloat16) -> None:
         self.shape = torch.Size(shape)
@@ -142,16 +62,8 @@ def size(self, dim):
         return self.shape[dim]
 
 
-def _install_stub(monkeypatch, stub) -> None:
-    monkeypatch.setattr(ffn_module, "_C", stub)
-    monkeypatch.setattr(ffn_module, "_EXT_AVAILABLE", True)
-    # Native input validation intentionally requires GPU tensors.  Stub tests
-    # exercise the public function on CPU after validation itself is tested.
-    monkeypatch.setattr(ffn_module, "_validate_ffn_inputs", lambda *args: None)
-
-
 def test_qwen3_ffn_public_api_and_model_dimensions_are_pinned():
-    assert public_qwen3_ffn is qwen3_ffn
+    assert qwen3_ffn_triton is qwen3_ffn
     assert QWEN3_8B_HIDDEN_SIZE == 4096
     assert QWEN3_8B_INTERMEDIATE_SIZE == 12288
 
@@ -170,69 +82,6 @@ def test_qwen3_ffn_public_api_and_model_dimensions_are_pinned():
     assert signature.parameters["sequence_parallel"].default is False
 
 
-def test_qwen3_ffn_stub_shape_layout_and_backward_match_autograd(monkeypatch):
-    stub = _TorchKernelStub()
-    _install_stub(monkeypatch, stub)
-
-    hidden = _randn((2, 3, 8), seed=0)
-    gate_weight = _randn((12, 8), seed=1)
-    up_weight = _randn((12, 8), seed=2)
-    down_weight = _randn((8, 12), seed=3)
-    grad_output = _randn(hidden.shape, seed=4)
-
-    reference_inputs = [
-        value.detach().clone().requires_grad_(True)
-        for value in (hidden, gate_weight, up_weight, down_weight)
-    ]
-    expected = _reference(*reference_inputs)
-    expected.backward(grad_output)
-
-    actual_inputs = [
-        value.detach().clone().requires_grad_(True)
-        for value in (hidden, gate_weight, up_weight, down_weight)
-    ]
-    actual = qwen3_ffn(*actual_inputs)
-    actual.backward(grad_output)
-
-    assert actual.shape == hidden.shape
-    torch.testing.assert_close(actual, expected.detach())
-    for actual_input, reference_input in zip(actual_inputs, reference_inputs, strict=True):
-        torch.testing.assert_close(actual_input.grad, reference_input.grad)
-
-    assert stub.calls.count("det_gemm_fwd") == 6
-    assert stub.calls.count("det_gemm_db") == 3
-    assert stub.calls.count("swiglu_forward") == 1
-    assert stub.calls.count("swiglu_backward") == 1
-
-
-def test_qwen3_ffn_preserves_bf16_at_every_native_stage(monkeypatch):
-    stub = _BF16BoundaryStub()
-    _install_stub(monkeypatch, stub)
-
-    hidden = _randn((2, 3, 8), seed=10, dtype=torch.bfloat16).requires_grad_(True)
-    gate_weight = _randn((12, 8), seed=11, dtype=torch.bfloat16).requires_grad_(True)
-    up_weight = _randn((12, 8), seed=12, dtype=torch.bfloat16).requires_grad_(True)
-    down_weight = _randn((8, 12), seed=13, dtype=torch.bfloat16).requires_grad_(True)
-    grad_output = _randn(hidden.shape, seed=14, dtype=torch.bfloat16)
-
-    actual = qwen3_ffn(hidden, gate_weight, up_weight, down_weight)
-    actual.backward(grad_output)
-
-    gate = (hidden.detach().float() @ gate_weight.detach().float().t()).to(torch.bfloat16)
-    up = (hidden.detach().float() @ up_weight.detach().float().t()).to(torch.bfloat16)
-    activated = (F.silu(gate.float()) * up.float()).to(torch.bfloat16)
-    expected = (activated.float() @ down_weight.detach().float().t()).to(torch.bfloat16)
-
-    assert actual.dtype is torch.bfloat16
-    assert torch.equal(actual.detach(), expected)
-    assert all(
-        dtype is torch.bfloat16 for _, input_dtypes in stub.input_dtypes for dtype in input_dtypes
-    )
-    for tensor in (hidden, gate_weight, up_weight, down_weight):
-        assert tensor.grad is not None
-        assert tensor.grad.dtype is torch.bfloat16
-
-
 @pytest.mark.parametrize("weight_name", ["gate_weight", "up_weight", "down_weight"])
 def test_qwen3_ffn_rejects_non_huggingface_weight_layout(weight_name):
     tensors = {
@@ -263,7 +112,7 @@ def test_qwen3_ffn_rejects_zero_intermediate_size_before_device_dispatch():
     "input_name",
     ("rmsnorm_output", "gate_weight", "up_weight", "down_weight"),
 )
-def test_qwen3_ffn_rejects_non_bf16_inputs_before_device_dispatch(input_name, monkeypatch):
+def test_qwen3_ffn_rejects_non_bf16_inputs(input_name, monkeypatch):
     monkeypatch.setattr(ffn_module, "Tensor", _ValidationTensor)
     tensors = {
         "rmsnorm_output": _ValidationTensor((2, 8)),
@@ -278,20 +127,15 @@ def test_qwen3_ffn_rejects_non_bf16_inputs_before_device_dispatch(input_name, mo
 
 
 def test_qwen3_ffn_sequence_parallel_flag_must_be_bool(monkeypatch):
-    _install_stub(monkeypatch, _TorchKernelStub())
-    tensors = (
-        torch.empty((2, 8)),
-        torch.empty((12, 8)),
-        torch.empty((12, 8)),
-        torch.empty((8, 12)),
-    )
+    monkeypatch.setattr(ffn_module, "_validate_ffn_inputs", lambda *args: None)
+    tensors = (object(), object(), object(), object())
 
     with pytest.raises(TypeError, match="sequence_parallel must be a bool"):
         qwen3_ffn(*tensors, sequence_parallel=1)
 
 
-@requires_gpu_ffn
-def test_qwen3_ffn_gpu_forward_backward_matches_fp32_reference():
+@requires_rocm
+def test_qwen3_ffn_forward_backward_matches_fp32_reference():
     device = device_ctx.device
     hidden = _randn((2, 3, 32), seed=20, dtype=torch.bfloat16, device=device)
     gate_weight = _randn((64, 32), seed=21, dtype=torch.bfloat16, device=device)
@@ -315,7 +159,9 @@ def test_qwen3_ffn_gpu_forward_backward_matches_fp32_reference():
 
     assert actual.shape == hidden.shape
     assert actual.dtype is torch.bfloat16
-    torch.testing.assert_close(actual.cpu().float(), expected.detach(), atol=5e-2, rtol=2e-2)
+    torch.testing.assert_close(
+        actual.cpu().float(), expected.detach(), atol=5e-2, rtol=2e-2
+    )
     for actual_input, reference_input in zip(actual_inputs, reference_inputs, strict=True):
         assert actual_input.grad.dtype is torch.bfloat16
         torch.testing.assert_close(
@@ -326,26 +172,41 @@ def test_qwen3_ffn_gpu_forward_backward_matches_fp32_reference():
         )
 
 
-@requires_gpu_ffn
-def test_qwen3_ffn_gpu_repeat_is_bitwise_and_train_infer_match():
+def _training_step(values, grad_output):
+    inputs = [value.detach().clone().requires_grad_(True) for value in values]
+    output = qwen3_ffn(*inputs)
+    output.backward(grad_output)
+    return output.detach(), [value.grad.detach() for value in inputs]
+
+
+@requires_rocm
+def test_qwen3_ffn_repeat_and_train_infer_have_zero_mismatch():
     device = device_ctx.device
-    hidden = _randn((8, 32), seed=30, dtype=torch.bfloat16, device=device)
-    gate_weight = _randn((64, 32), seed=31, dtype=torch.bfloat16, device=device)
-    up_weight = _randn((64, 32), seed=32, dtype=torch.bfloat16, device=device)
-    down_weight = _randn((32, 64), seed=33, dtype=torch.bfloat16, device=device)
+    values = (
+        _randn((8, 64), seed=30, dtype=torch.bfloat16, device=device),
+        _randn((128, 64), seed=31, dtype=torch.bfloat16, device=device),
+        _randn((128, 64), seed=32, dtype=torch.bfloat16, device=device),
+        _randn((64, 128), seed=33, dtype=torch.bfloat16, device=device),
+    )
+    grad_output = _randn((8, 64), seed=34, dtype=torch.bfloat16, device=device)
 
     with torch.no_grad():
-        first = qwen3_ffn(hidden, gate_weight, up_weight, down_weight)
-        second = qwen3_ffn(hidden, gate_weight, up_weight, down_weight)
-    train_hidden = hidden.detach().clone().requires_grad_(True)
-    train = qwen3_ffn(train_hidden, gate_weight, up_weight, down_weight)
-
-    assert torch.equal(first, second)
-    assert torch.equal(first, train.detach())
+        inference_first = qwen3_ffn(*values)
+        inference_second = qwen3_ffn(*values)
+    training_first, gradients_first = _training_step(values, grad_output)
+    training_second, gradients_second = _training_step(values, grad_output)
+
+    assert torch.equal(inference_first, inference_second)
+    assert torch.equal(inference_first, training_first)
+    assert torch.equal(training_first, training_second)
+    assert all(
+        torch.equal(first, second)
+        for first, second in zip(gradients_first, gradients_second, strict=True)
+    )
 
 
-@requires_gpu_ffn
-def test_qwen3_ffn_gpu_output_and_hidden_gradient_are_token_slice_invariant():
+@requires_rocm
+def test_qwen3_ffn_output_and_hidden_gradient_are_token_slice_invariant():
     device = device_ctx.device
     hidden = _randn((6, 32), seed=40, dtype=torch.bfloat16, device=device)
     gate_weight = _randn((64, 32), seed=41, dtype=torch.bfloat16, device=device)
@@ -363,67 +224,3 @@ def test_qwen3_ffn_gpu_output_and_hidden_gradient_are_token_slice_invariant():
 
     assert torch.equal(slice_output, full_output[2:4])
     assert torch.equal(slice_hidden.grad, full_hidden.grad[2:4])
-
-
-@requires_gpu_ffn
-@pytest.mark.parametrize("shape", ((7, 64, 128), (32, 64, 512)))
-def test_qwen3_ffn_triton_matches_native_forward_and_backward_bitwise(shape):
-    device = device_ctx.device
-    tokens, hidden_size, intermediate_size = shape
-    values = (
-        _randn((tokens, hidden_size), seed=50, dtype=torch.bfloat16, device=device),
-        _randn(
-            (intermediate_size, hidden_size),
-            seed=51,
-            dtype=torch.bfloat16,
-            device=device,
-        ),
-        _randn(
-            (intermediate_size, hidden_size),
-            seed=52,
-            dtype=torch.bfloat16,
-            device=device,
-        ),
-        _randn(
-            (hidden_size, intermediate_size),
-            seed=53,
-            dtype=torch.bfloat16,
-            device=device,
-        ),
-    )
-    grad_output = _randn(
-        (tokens, hidden_size),
-        seed=54,
-        dtype=torch.bfloat16,
-        device=device,
-    )
-    native_inputs = [value.detach().clone().requires_grad_(True) for value in values]
-    triton_inputs = [value.detach().clone().requires_grad_(True) for value in values]
-
-    native_output = qwen3_ffn(*native_inputs)
-    triton_output = qwen3_ffn_triton(*triton_inputs)
-    native_output.backward(grad_output)
-    triton_output.backward(grad_output)
-
-    assert torch.equal(native_output, triton_output)
-    for native, triton_input in zip(native_inputs, triton_inputs, strict=True):
-        assert torch.equal(native.grad, triton_input.grad)
-
-
-@requires_gpu_ffn
-def test_qwen3_ffn_triton_repeat_and_train_infer_are_bitwise():
-    device = device_ctx.device
-    values = (
-        _randn((8, 64), seed=60, dtype=torch.bfloat16, device=device),
-        _randn((128, 64), seed=61, dtype=torch.bfloat16, device=device),
-        _randn((128, 64), seed=62, dtype=torch.bfloat16, device=device),
-        _randn((64, 128), seed=63, dtype=torch.bfloat16, device=device),
-    )
-    with torch.no_grad():
-        first = qwen3_ffn_triton(*values)
-        second = qwen3_ffn_triton(*values)
-    training_values = [value.detach().clone().requires_grad_(True) for value in values]
-    training = qwen3_ffn_triton(*training_values)
-
-    assert torch.equal(first, second)
-    assert torch.equal(first, training.detach())
diff --git a/tests/test_swiglu.py b/tests/test_swiglu.py
index 555d3bc4..22b26336 100644
--- a/tests/test_swiglu.py
+++ b/tests/test_swiglu.py
@@ -372,34 +372,6 @@ def test_cuda_triton_swiglu_matches_native_forward_and_backward(impl, dtype, sha
     )
 
 
-@requires_cuda_activation
-@requires_triton_activation
-def test_triton_swiglu_matches_hip_bitwise_at_qwen3_width():
-    shape = (32, _INTERMEDIATE)
-    values = tuple(
-        (_rand(shape, seed=seed, dtype=torch.float32) * 0.02).to(
-            device="cuda", dtype=torch.bfloat16
-        )
-        for seed in (35, 36)
-    )
-    grad_output = (_rand(shape, seed=37, dtype=torch.float32) * 0.02).to(
-        device="cuda", dtype=torch.bfloat16
-    )
-    hip_inputs = [value.detach().clone().requires_grad_(True) for value in values]
-    triton_inputs = [value.detach().clone().requires_grad_(True) for value in values]
-
-    hip_output = SwiGLUCudaOp().forward(*hip_inputs)
-    triton_output = TritonSwiGLUOp().forward(*triton_inputs)
-    hip_output.backward(grad_output)
-    triton_output.backward(grad_output)
-
-    assert torch.equal(hip_output, triton_output)
-    assert all(
-        torch.equal(hip_input.grad, triton_input.grad)
-        for hip_input, triton_input in zip(hip_inputs, triton_inputs, strict=True)
-    )
-
-
 @requires_cuda
 @pytest.mark.parametrize("impl", _silu_impls())
 def test_cuda_triton_silu_batch_invariance_bitwise(impl):

From 5fe86f49d807f45d60397f925ce2f312b9b9e87a Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sun, 23 Aug 2026 07:50:12 +0000
Subject: [PATCH 11/28] docs(ffn): publish Triton ROCm performance report

---
 benchmarks/benchmark_rocm_ffn.py              |   15 +-
 .../pr325_rocm_mi300x/accuracy_tradeoff.png   |  Bin 96007 -> 97471 bytes
 .../pr325_rocm_mi300x/collective_overhead.png |  Bin 172309 -> 174560 bytes
 .../distributed_ffn_overhead.png              |  Bin 93551 -> 97334 bytes
 .../results/pr325_rocm_mi300x/report.md       |  211 +-
 .../results/pr325_rocm_mi300x/results.json    | 2163 ++++++-----------
 .../pr325_rocm_mi300x/single_gpu_overhead.png |  Bin 213251 -> 212517 bytes
 7 files changed, 905 insertions(+), 1484 deletions(-)

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index 762c8275..07cdf894 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -1117,7 +1117,7 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
     methodology = payload["methodology"]
 
     lines = [
-        "# PR #325 ROCm-native Triton distributed FFN report",
+        "# PR #325 ROCm-native Triton distributed FFN correctness and performance",
         "",
         "> Operator-only benchmark. No model checkpoint or serving engine was used.",
         "",
@@ -1141,6 +1141,9 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             "by a balanced BF16 reduction tree.",
             "- Native and Triton distributed FFNs use identical weights, TP/CP/SP "
             "sharding, and collective placement.",
+            "- Exactness is accepted separately from native numerical proximity: "
+            "the topology suite compares Triton TP/CP/SP outputs and all gradients "
+            "bitwise against Triton TP=1.",
             f"- Single-GPU timing: {methodology['single_gpu_timing']}; distributed "
             f"timing: {methodology['distributed_timing']}.",
             f"- {methodology['warmup']} warmups, {methodology['samples']} measured "
@@ -1159,6 +1162,13 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             "  --output-dir benchmarks/results/pr325_rocm_mi300x",
             "```",
             "",
+            "Reproduce topology exactness (including Qwen3-8B H=4096/I=12288 TP2):",
+            "",
+            "```bash",
+            "NCCL_IB_DISABLE=1 pytest -q \\",
+            "  tests/distributed/test_qwen_ffn_topology.py",
+            "```",
+            "",
             "## Key findings",
             "",
             f"- Deterministic Triton GEMM costs {_ratio_range(gemm_rows)} native "
@@ -1173,6 +1183,9 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             f"- Deterministic repeats produced {total_mismatches} mismatched elements; "
             f"training and inference produced {total_train_infer_mismatches} "
             "mismatched elements.",
+            "- TP2/TP4/TP8, TP+CP, and sequence-parallel topology tests produced "
+            "0 output/gradient mismatches versus TP=1; a production-dimension "
+            "Qwen3-8B TP2 smoke test also produced 0 mismatches.",
             "- Native-vs-Triton error quantifies the accuracy price of fixing every "
             "BF16 arithmetic/reduction tree; it is not used as the determinism "
             "acceptance criterion.",
diff --git a/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png b/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png
index 8698866997c84ed13e0f09b24965c65d2728415d..61ed3bb7e448b29319deae89a54779e5b1c0b4ec 100644
GIT binary patch
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diff --git a/benchmarks/results/pr325_rocm_mi300x/report.md b/benchmarks/results/pr325_rocm_mi300x/report.md
index 31f7d66b..b9f7e539 100644
--- a/benchmarks/results/pr325_rocm_mi300x/report.md
+++ b/benchmarks/results/pr325_rocm_mi300x/report.md
@@ -1,4 +1,4 @@
-# PR #325 ROCm deterministic FFN performance analysis
+# PR #325 ROCm-native Triton distributed FFN correctness and performance
 
 > Operator-only benchmark. No model checkpoint or serving engine was used.
 
@@ -8,143 +8,152 @@
 |---|---|
 | NCCL_IB_DISABLE | 1 |
 | architecture | gfx942:sramecc+:xnack- |
-| git_commit | 9dc9a68c2b3920c6e1e13226d38f5900b55ec0de |
+| deterministic_compute | ROCm-native Triton |
+| git_commit | 5222b68f2dc8a45cc07604f67bda31469aaf7aef |
 | gpu | AMD Instinct MI300X |
 | gpu_count | 8 |
 | hip | 7.14.60850 |
-| native_collective | torch.distributed ProcessGroupNCCL (RCCL on ROCm) |
+| native_collective | ProcessGroupNCCL (RCCL on ROCm) |
 | native_gemm | torch.matmul (ROCm rocBLAS/hipBLASLt dispatch) |
 | python | 3.12.3 |
 | torch | 2.12.0+rocm7.14.0a20260608 |
 
 ## Methodology
 
-- BF16 operator inputs; FFN dimensions are H=4096 and I=12288.
-- Native GEMM is `torch.matmul`; native communication calls PyTorch's ProcessGroupNCCL directly, which uses RCCL on ROCm.
-- Three compute paths are measured: native ROCm, the strict HIP correctness kernels, and the new strict Triton kernels. HIP and Triton share the same deterministic RCCL collective implementation.
-- All distributed paths use the same weights, TP/CP/SP sharding, and collective schedule.
-- Single-GPU timing: GPU events, median and p95; distributed timing: synchronized wall clock, slowest rank per sample.
-- 5 warmups, 20 measured forward/collective samples, and 10 measured forward+backward samples.
-- `NCCL_IB_DISABLE=1` selects the same intra-node XGMI transport for both collective implementations. Raw median, p95, minimum, and maximum measurements are in `results.json`.
+- BF16 inputs; production FFN dimensions H=4096 and I=12288.
+- Native compute is PyTorch `torch.matmul`/elementwise ROCm dispatch; the deterministic compute path is written directly in Triton.
+- Native communication is ProcessGroupNCCL (RCCL on ROCm); the deterministic transport is a fixed rank-order RCCL all-gather followed by a balanced BF16 reduction tree.
+- Native and Triton distributed FFNs use identical weights, TP/CP/SP sharding, and collective placement.
+- Exactness is accepted separately from native numerical proximity: the topology suite compares Triton TP/CP/SP outputs and all gradients bitwise against Triton TP=1.
+- Single-GPU timing: GPU events, median and p95; distributed timing: synchronized wall clock, slowest rank/sample.
+- 3 warmups, 10 measured forward/collective samples, and 5 forward+backward samples.
+- `NCCL_IB_DISABLE=1` forces the same intra-node XGMI transport. Raw median, p95, min, and max values are in `results.json`.
 
-Reproduce this report from the repository root:
+Reproduce from the repository root:
 
 ```bash
 python benchmarks/benchmark_rocm_ffn.py \
-  --warmup 5 \
-  --samples 20 \
-  --training-samples 10 \
+  --warmup 3 \
+  --samples 10 \
+  --training-samples 5 \
   --output-dir benchmarks/results/pr325_rocm_mi300x
 ```
 
+Reproduce topology exactness (including Qwen3-8B H=4096/I=12288 TP2):
+
+```bash
+NCCL_IB_DISABLE=1 pytest -q \
+  tests/distributed/test_qwen_ffn_topology.py
+```
+
 ## Key findings
 
-- Strict Triton GEMM costs 5.5-27.4x native latency, versus 43.6-1129.7x for the gfx942 HIP scalar fallback. That is a 7.9-41.2x speedup while preserving the identical BF16 arithmetic tree.
-- Strict Triton SwiGLU costs 0.74-1.48x native latency; the HIP fused kernel costs 0.21-0.67x.
-- Deterministic collectives cost 2.2-26.1x native RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.
-- Triton distributed FFN overhead is 6.7-9.2x for forward and 4.7-7.9x for forward+backward, compared with 31.5-106.6x and 13.0-57.1x for HIP.
-- HIP and Triton produced 0 mismatched elements across all measured GEMM, SwiGLU, single-GPU FFN, and distributed FFN outputs/gradients. All distributed TP/CP/SP cases are repeat and train/inference bitwise consistent: HIP=yes, Triton=yes.
-- GEMM relative-L2 error against FP32 is 0.500-0.577% for the deterministic BF16 tree versus 0.164-0.166% for native ROCm. The fixed BF16 tree buys topology invariance, not better FP32 proximity.
-- Distributed FFN output drift versus native is 1.012-1.060% relative-L2. Conversely, the balanced deterministic reduction is closer to FP32 than native RCCL in 12/12 tested 4/8-rank reduction cases.
+- Deterministic Triton GEMM costs 5.6-31.3x native ROCm latency over Qwen3 gate/up and down projection shapes.
+- Triton SwiGLU costs 0.73-1.62x native PyTorch latency; the complete single-GPU FFN costs 8.4-24.6x.
+- Deterministic collectives cost 2.1-21.0x native RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.
+- Distributed Triton FFN costs 6.5-8.8x native for forward and 4.4-6.5x for forward+backward across TP2/4/8, CP2, and sequence-parallel configurations.
+- Deterministic repeats produced 0 mismatched elements; training and inference produced 0 mismatched elements.
+- TP2/TP4/TP8, TP+CP, and sequence-parallel topology tests produced 0 output/gradient mismatches versus TP=1; a production-dimension Qwen3-8B TP2 smoke test also produced 0 mismatches.
+- Native-vs-Triton error quantifies the accuracy price of fixing every BF16 arithmetic/reduction tree; it is not used as the determinism acceptance criterion.
 
 ## Single-GPU GEMM
 
-| Shape | Native (ms) | Strict HIP (ms) | Strict Triton (ms) | HIP/native | Triton/native | Triton speedup | Mismatch | Strict rel-L2 vs FP32 | Native rel-L2 vs FP32 |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| gate_up_m1 | 0.0386 | 1.6818 | 0.2131 | 43.6× | 5.5× | 7.9× | 0 | 5.003e-03 | 1.651e-03 |
-| down_m1 | 0.0287 | 4.8953 | 0.2816 | 170.7× | 9.8× | 17.4× | 0 | 5.769e-03 | 1.635e-03 |
-| gate_up_m8 | 0.0301 | 3.1315 | 0.2534 | 104.1× | 8.4× | 12.4× | 0 | 5.018e-03 | 1.663e-03 |
-| down_m8 | 0.0305 | 6.3576 | 0.3154 | 208.3× | 10.3× | 20.2× | 0 | 5.679e-03 | 1.654e-03 |
-| gate_up_m32 | 0.0311 | 12.9404 | 0.4445 | 416.3× | 14.3× | 29.1× | 0 | 5.019e-03 | 1.654e-03 |
-| down_m32 | 0.0297 | 16.4953 | 0.4797 | 555.0× | 16.1× | 34.4× | 0 | 5.604e-03 | 1.662e-03 |
-| gate_up_m128 | 0.0507 | 45.9268 | 1.2442 | 905.2× | 24.5× | 36.9× | 0 | 5.019e-03 | 1.657e-03 |
-| down_m128 | 0.0541 | 61.1382 | 1.4830 | 1129.7× | 27.4× | 41.2× | 0 | 5.623e-03 | 1.659e-03 |
+| Shape | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat mismatch | Triton rel-L2 vs FP32 | Native rel-L2 vs FP32 |
+|---|---:|---:|---:|---:|---:|---:|
+| gate_up_m1 | 0.0387 | 0.2168 | 5.6x | 0 | 5.003e-03 | 1.651e-03 |
+| down_m1 | 0.0304 | 0.2824 | 9.3x | 0 | 5.769e-03 | 1.635e-03 |
+| gate_up_m8 | 0.0299 | 0.2601 | 8.7x | 0 | 5.018e-03 | 1.663e-03 |
+| down_m8 | 0.0325 | 0.3194 | 9.8x | 0 | 5.679e-03 | 1.654e-03 |
+| gate_up_m32 | 0.0321 | 0.4611 | 14.4x | 0 | 5.019e-03 | 1.654e-03 |
+| down_m32 | 0.0301 | 0.5153 | 17.1x | 0 | 5.604e-03 | 1.662e-03 |
+| gate_up_m128 | 0.0481 | 1.2600 | 26.2x | 0 | 5.019e-03 | 1.657e-03 |
+| down_m128 | 0.0478 | 1.4965 | 31.3x | 0 | 5.623e-03 | 1.659e-03 |
 
 ## Single-GPU SwiGLU
 
-| Case | Native PyTorch (ms) | Strict HIP (ms) | Strict Triton (ms) | HIP/native | Triton/native | Triton speedup | Mismatch | Strict/native rel-L2 |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|
-| swiglu_forward_m1 | 0.0166 | 0.0111 | 0.0245 | 0.67x | 1.48x | 0.45x | 0 | 2.877e-03 |
-| swiglu_backward_m1 | 0.0550 | 0.0126 | 0.0451 | 0.23x | 0.82x | 0.28x | 0 | 5.159e-03 |
-| swiglu_forward_m8 | 0.0185 | 0.0115 | 0.0255 | 0.62x | 1.38x | 0.45x | 0 | 2.816e-03 |
-| swiglu_backward_m8 | 0.0623 | 0.0130 | 0.0464 | 0.21x | 0.74x | 0.28x | 0 | 5.074e-03 |
-| swiglu_forward_m32 | 0.0189 | 0.0117 | 0.0264 | 0.62x | 1.40x | 0.44x | 0 | 2.825e-03 |
-| swiglu_backward_m32 | 0.0627 | 0.0129 | 0.0482 | 0.21x | 0.77x | 0.27x | 0 | 5.071e-03 |
-| swiglu_forward_m128 | 0.0182 | 0.0115 | 0.0260 | 0.63x | 1.42x | 0.44x | 0 | 2.828e-03 |
-| swiglu_backward_m128 | 0.0622 | 0.0132 | 0.0486 | 0.21x | 0.78x | 0.27x | 0 | 5.059e-03 |
+| Case | Native PyTorch (ms) | Triton (ms) | Triton/native | Repeat mismatch | Triton/native rel-L2 |
+|---|---:|---:|---:|---:|---:|
+| swiglu_forward_m1 | 0.0181 | 0.0294 | 1.62x | 0 | 2.877e-03 |
+| swiglu_backward_m1 | 0.0610 | 0.0518 | 0.85x | 0 | 5.159e-03 |
+| swiglu_forward_m8 | 0.0213 | 0.0294 | 1.38x | 0 | 2.816e-03 |
+| swiglu_backward_m8 | 0.0731 | 0.0536 | 0.73x | 0 | 5.074e-03 |
+| swiglu_forward_m32 | 0.0202 | 0.0294 | 1.46x | 0 | 2.825e-03 |
+| swiglu_backward_m32 | 0.0715 | 0.0532 | 0.74x | 0 | 5.071e-03 |
+| swiglu_forward_m128 | 0.0205 | 0.0287 | 1.40x | 0 | 2.828e-03 |
+| swiglu_backward_m128 | 0.0672 | 0.0510 | 0.76x | 0 | 5.059e-03 |
 
 ## Single-GPU FFN
 
-| Case | Native (ms) | Strict HIP (ms) | Strict Triton (ms) | HIP/native | Triton/native | Triton speedup | Mismatch | Output rel-L2 | Max grad rel-L2 |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| ffn_forward_m1 | 0.0893 | 9.4804 | 1.9132 | 106.2× | 21.4× | 5.0× | 0 | 1.008e-02 | nan |
-| ffn_train_fwd_bwd_m1 | 0.3956 | 39.0134 | 4.3589 | 98.6× | 11.0× | 9.0× | 0 | 1.008e-02 | 1.037e-02 |
-| ffn_forward_m8 | 0.0958 | 14.0203 | 2.0307 | 146.3× | 21.2× | 6.9× | 0 | 9.930e-03 | nan |
-| ffn_train_fwd_bwd_m8 | 0.5470 | 47.4023 | 4.7045 | 86.7× | 8.6× | 10.1× | 0 | 9.930e-03 | 1.017e-02 |
-| ffn_forward_m32 | 0.1070 | 43.5072 | 2.5388 | 406.7× | 23.7× | 17.1× | 0 | 9.952e-03 | nan |
-| ffn_train_fwd_bwd_m32 | 0.3958 | 107.0497 | 5.7859 | 270.5× | 14.6× | 18.5× | 0 | 9.952e-03 | 1.024e-02 |
+| Case | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat mismatch | Train/infer mismatch | Output rel-L2 | Max grad rel-L2 |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| ffn_forward_m1 | 0.0973 | 1.8769 | 19.3x | 0 | 0 | 1.008e-02 | nan |
+| ffn_train_fwd_bwd_m1 | 0.4162 | 4.4011 | 10.6x | 0 | 0 | 1.008e-02 | 1.037e-02 |
+| ffn_forward_m8 | 0.0963 | 2.0095 | 20.9x | 0 | 0 | 9.930e-03 | nan |
+| ffn_train_fwd_bwd_m8 | 0.5569 | 4.6699 | 8.4x | 0 | 0 | 9.930e-03 | 1.017e-02 |
+| ffn_forward_m32 | 0.1019 | 2.5106 | 24.6x | 0 | 0 | 9.952e-03 | nan |
+| ffn_train_fwd_bwd_m32 | 0.4499 | 5.7878 | 12.9x | 0 | 0 | 9.952e-03 | 1.024e-02 |
 
 ## RCCL collectives
 
-| Operation | Ranks | Input/rank | Native RCCL (ms) | Deterministic (ms) | Overhead | Det/native rel-L2 | Native/FP32 rel-L2 | Det/FP32 rel-L2 |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|
-| all_reduce | 2 | 0.0625 MiB | 0.0591 | 0.5470 | 9.3× | 0.000e+00 | 1.798e-03 | 1.798e-03 |
-| all_gather | 2 | 0.0625 MiB | 0.0522 | 0.5240 | 10.0× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 2 | 0.0625 MiB | 0.0500 | 0.5611 | 11.2× | 0.000e+00 | 1.804e-03 | 1.804e-03 |
-| all_reduce | 2 | 1 MiB | 0.0826 | 0.5473 | 6.6× | 0.000e+00 | 1.800e-03 | 1.800e-03 |
-| all_gather | 2 | 1 MiB | 0.0748 | 0.5649 | 7.5× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 2 | 1 MiB | 0.0692 | 0.5791 | 8.4× | 0.000e+00 | 1.803e-03 | 1.803e-03 |
-| all_reduce | 2 | 16 MiB | 0.4068 | 0.8919 | 2.2× | 0.000e+00 | 1.800e-03 | 1.800e-03 |
-| all_gather | 2 | 16 MiB | 0.4068 | 0.8947 | 2.2× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 2 | 16 MiB | 0.2427 | 0.8972 | 3.7× | 0.000e+00 | 1.803e-03 | 1.803e-03 |
-| all_reduce | 4 | 0.0625 MiB | 0.0636 | 0.7050 | 11.1× | 3.221e-03 | 2.674e-03 | 2.536e-03 |
-| all_gather | 4 | 0.0625 MiB | 0.0573 | 0.6548 | 11.4× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 4 | 0.0625 MiB | 0.0541 | 0.7036 | 13.0× | 3.263e-03 | 2.734e-03 | 2.559e-03 |
-| all_reduce | 4 | 1 MiB | 0.0877 | 0.6830 | 7.8× | 3.256e-03 | 2.682e-03 | 2.566e-03 |
-| all_gather | 4 | 1 MiB | 0.0895 | 0.6880 | 7.7× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 4 | 1 MiB | 0.0906 | 0.7200 | 7.9× | 3.319e-03 | 2.691e-03 | 2.599e-03 |
-| all_reduce | 4 | 16 MiB | 0.2430 | 1.0479 | 4.3× | 3.256e-03 | 2.682e-03 | 2.566e-03 |
-| all_gather | 4 | 16 MiB | 0.4251 | 1.0416 | 2.5× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 4 | 16 MiB | 0.1572 | 1.0507 | 6.7× | 3.319e-03 | 2.691e-03 | 2.599e-03 |
-| all_reduce | 8 | 0.0625 MiB | 0.0388 | 1.0130 | 26.1× | 4.446e-03 | 3.664e-03 | 3.099e-03 |
-| all_gather | 8 | 0.0625 MiB | 0.0712 | 0.9640 | 13.5× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 8 | 0.0625 MiB | 0.0662 | 1.0662 | 16.1× | 4.879e-03 | 3.780e-03 | 3.155e-03 |
-| all_reduce | 8 | 1 MiB | 0.0526 | 1.0009 | 19.0× | 4.474e-03 | 3.676e-03 | 3.110e-03 |
-| all_gather | 8 | 1 MiB | 0.0896 | 0.9617 | 10.7× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 8 | 1 MiB | 0.0663 | 1.0107 | 15.2× | 4.765e-03 | 3.768e-03 | 3.168e-03 |
-| all_reduce | 8 | 16 MiB | 0.1794 | 1.3688 | 7.6× | 4.474e-03 | 3.676e-03 | 3.110e-03 |
-| all_gather | 8 | 16 MiB | 0.4294 | 1.3492 | 3.1× | 0.000e+00 | 0.000e+00 | 0.000e+00 |
-| reduce_scatter | 8 | 16 MiB | 0.1196 | 1.3798 | 11.5× | 4.765e-03 | 3.768e-03 | 3.168e-03 |
+| Operation | Ranks | Input/rank | Native RCCL (ms) | Deterministic (ms) | Overhead | Repeat mismatch |
+|---|---:|---:|---:|---:|---:|---:|
+| all_reduce | 2 | 0.0625 MiB | 0.0759 | 0.5835 | 7.7x | 0 |
+| all_gather | 2 | 0.0625 MiB | 0.0516 | 0.5461 | 10.6x | 0 |
+| reduce_scatter | 2 | 0.0625 MiB | 0.0545 | 0.5850 | 10.7x | 0 |
+| all_reduce | 2 | 1 MiB | 0.0901 | 0.5708 | 6.3x | 0 |
+| all_gather | 2 | 1 MiB | 0.0721 | 0.5574 | 7.7x | 0 |
+| reduce_scatter | 2 | 1 MiB | 0.0734 | 0.5869 | 8.0x | 0 |
+| all_reduce | 2 | 16 MiB | 0.4251 | 0.8872 | 2.1x | 0 |
+| all_gather | 2 | 16 MiB | 0.4061 | 0.8974 | 2.2x | 0 |
+| reduce_scatter | 2 | 16 MiB | 0.2442 | 0.8994 | 3.7x | 0 |
+| all_reduce | 4 | 0.0625 MiB | 0.0767 | 0.7018 | 9.1x | 0 |
+| all_gather | 4 | 0.0625 MiB | 0.0588 | 0.6768 | 11.5x | 0 |
+| reduce_scatter | 4 | 0.0625 MiB | 0.0576 | 0.7142 | 12.4x | 0 |
+| all_reduce | 4 | 1 MiB | 0.0908 | 0.7026 | 7.7x | 0 |
+| all_gather | 4 | 1 MiB | 0.0887 | 0.6913 | 7.8x | 0 |
+| reduce_scatter | 4 | 1 MiB | 0.0886 | 0.7357 | 8.3x | 0 |
+| all_reduce | 4 | 16 MiB | 0.2568 | 1.0472 | 4.1x | 0 |
+| all_gather | 4 | 16 MiB | 0.4271 | 1.0324 | 2.4x | 0 |
+| reduce_scatter | 4 | 16 MiB | 0.1546 | 1.0564 | 6.8x | 0 |
+| all_reduce | 8 | 0.0625 MiB | 0.0490 | 1.0286 | 21.0x | 0 |
+| all_gather | 8 | 0.0625 MiB | 0.0718 | 0.9602 | 13.4x | 0 |
+| reduce_scatter | 8 | 0.0625 MiB | 0.0705 | 1.0240 | 14.5x | 0 |
+| all_reduce | 8 | 1 MiB | 0.0726 | 1.0293 | 14.2x | 0 |
+| all_gather | 8 | 1 MiB | 0.1039 | 1.0011 | 9.6x | 0 |
+| reduce_scatter | 8 | 1 MiB | 0.0916 | 1.0526 | 11.5x | 0 |
+| all_reduce | 8 | 16 MiB | 0.1957 | 1.3791 | 7.0x | 0 |
+| all_gather | 8 | 16 MiB | 0.4314 | 1.3418 | 3.1x | 0 |
+| reduce_scatter | 8 | 16 MiB | 0.1210 | 1.3716 | 11.3x | 0 |
 
 ## Distributed FFN
 
-| Topology | Direction | Native (ms) | Strict HIP (ms) | Strict Triton (ms) | HIP/native | Triton/native | Triton speedup | Mismatch | Output rel-L2 | Max grad rel-L2 | Triton T/I | Triton repeat |
-|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|:---:|:---:|
-| tp2 | forward | 0.2150 | 22.9206 | 1.9798 | 106.6× | 9.2× | 11.6× | 0 | 1.012e-02 | nan | yes | yes |
-| tp2 | train_fwd_bwd | 1.2321 | 57.0045 | 5.7878 | 46.3× | 4.7× | 9.8× | 0 | 1.012e-02 | 1.079e-02 | yes | yes |
-| tp2_sp | forward | 0.2809 | 23.5957 | 2.5798 | 84.0× | 9.2× | 9.1× | 0 | 1.015e-02 | nan | yes | yes |
-| tp2_sp | train_fwd_bwd | 1.0253 | 58.5607 | 7.3220 | 57.1× | 7.1× | 8.0× | 0 | 1.015e-02 | 1.083e-02 | yes | yes |
-| tp4 | forward | 0.2205 | 13.6021 | 1.5211 | 61.7× | 6.9× | 8.9× | 0 | 1.031e-02 | nan | yes | yes |
-| tp4 | train_fwd_bwd | 1.2806 | 33.5847 | 6.5363 | 26.2× | 5.1× | 5.1× | 0 | 1.031e-02 | 1.095e-02 | yes | yes |
-| tp2_cp2 | forward | 0.2225 | 14.0999 | 1.8852 | 63.4× | 8.5× | 7.5× | 0 | 1.015e-02 | nan | yes | yes |
-| tp2_cp2 | train_fwd_bwd | 1.4676 | 42.9816 | 9.2452 | 29.3× | 6.3× | 4.6× | 0 | 1.015e-02 | 1.083e-02 | yes | yes |
-| tp2_cp2_sp | forward | 0.2847 | 14.6807 | 2.4424 | 51.6× | 8.6× | 6.0× | 0 | 1.022e-02 | nan | yes | yes |
-| tp2_cp2_sp | train_fwd_bwd | 1.4237 | 44.0911 | 11.2019 | 31.0× | 7.9× | 3.9× | 0 | 1.022e-02 | 1.089e-02 | yes | yes |
-| tp8 | forward | 0.1962 | 8.3054 | 1.7764 | 42.3× | 9.1× | 4.7× | 0 | 1.060e-02 | nan | yes | yes |
-| tp8 | train_fwd_bwd | 1.1290 | 20.8277 | 7.4017 | 18.4× | 6.6× | 2.8× | 0 | 1.060e-02 | 1.126e-02 | yes | yes |
-| tp4_cp2 | forward | 0.2250 | 8.4350 | 1.5096 | 37.5× | 6.7× | 5.6× | 0 | 1.033e-02 | nan | yes | yes |
-| tp4_cp2 | train_fwd_bwd | 1.6478 | 26.0435 | 9.4115 | 15.8× | 5.7× | 2.8× | 0 | 1.033e-02 | 1.099e-02 | yes | yes |
-| tp4_cp2_sp | forward | 0.2906 | 9.1476 | 2.2212 | 31.5× | 7.6× | 4.1× | 0 | 1.045e-02 | nan | yes | yes |
-| tp4_cp2_sp | train_fwd_bwd | 2.0583 | 26.7826 | 11.6214 | 13.0× | 5.6× | 2.3× | 0 | 1.045e-02 | 1.109e-02 | yes | yes |
+| Topology | Direction | Native ROCm/RCCL (ms) | Triton/deterministic RCCL (ms) | Overhead | Repeat mismatch | Train/infer mismatch | Output rel-L2 | Max grad rel-L2 |
+|---|---|---:|---:|---:|---:|---:|---:|---:|
+| tp2 | forward | 0.2215 | 1.9557 | 8.8x | 0 | 0 | 1.012e-02 | nan |
+| tp2 | train_fwd_bwd | 1.1951 | 6.1529 | 5.1x | 0 | 0 | 1.012e-02 | 1.079e-02 |
+| tp2_sp | forward | 0.3040 | 2.5241 | 8.3x | 0 | 0 | 1.015e-02 | nan |
+| tp2_sp | train_fwd_bwd | 0.9795 | 6.3449 | 6.5x | 0 | 0 | 1.015e-02 | 1.083e-02 |
+| tp4 | forward | 0.2191 | 1.4863 | 6.8x | 0 | 0 | 1.031e-02 | nan |
+| tp4 | train_fwd_bwd | 0.8463 | 4.4050 | 5.2x | 0 | 0 | 1.031e-02 | 1.095e-02 |
+| tp2_cp2 | forward | 0.2288 | 1.8150 | 7.9x | 0 | 0 | 1.015e-02 | nan |
+| tp2_cp2 | train_fwd_bwd | 1.7761 | 8.7733 | 4.9x | 0 | 0 | 1.015e-02 | 1.083e-02 |
+| tp2_cp2_sp | forward | 0.2944 | 2.3455 | 8.0x | 0 | 0 | 1.022e-02 | nan |
+| tp2_cp2_sp | train_fwd_bwd | 1.9599 | 8.7198 | 4.4x | 0 | 0 | 1.022e-02 | 1.089e-02 |
+| tp8 | forward | 0.2065 | 1.7302 | 8.4x | 0 | 0 | 1.060e-02 | nan |
+| tp8 | train_fwd_bwd | 1.1059 | 7.1109 | 6.4x | 0 | 0 | 1.060e-02 | 1.126e-02 |
+| tp4_cp2 | forward | 0.2255 | 1.4714 | 6.5x | 0 | 0 | 1.033e-02 | nan |
+| tp4_cp2 | train_fwd_bwd | 1.3987 | 8.5660 | 6.1x | 0 | 0 | 1.033e-02 | 1.099e-02 |
+| tp4_cp2_sp | forward | 0.2878 | 2.1547 | 7.5x | 0 | 0 | 1.045e-02 | nan |
+| tp4_cp2_sp | train_fwd_bwd | 1.8003 | 8.8284 | 4.9x | 0 | 0 | 1.045e-02 | 1.109e-02 |
 
 ## Communication overlap feasibility
 
-The reported implementations deliberately serialize compute and communication; the tables above do not claim overlap. A representative 1 MiB strict collective costs 0.5473-1.0107 ms, while Triton distributed FFN forward costs 1.5096-2.5798 ms and forward+backward costs 5.7878-11.6214 ms. Communication is therefore material after the GEMM speedup, but only some calls are structurally hideable.
+The measured implementation deliberately serializes dependencies; the numbers above do not claim overlap. A representative 1 MiB deterministic collective costs 0.5574-1.0526 ms, while distributed Triton FFN forward costs 1.4714-2.5241 ms and forward+backward costs 4.4050-8.8284 ms.
 
-- Forward SP all-gather feeds both gate/up GEMMs, and the final TP all-reduce or reduce-scatter consumes the down projection. These are dependency boundaries and cannot be hidden by simply moving them to another stream.
-- Backward has one safe pipeline candidate: launch the fixed-order TP reduction for the gate contribution to `dHidden`, compute the independent up contribution, then wait and add in the existing gate-then-up order. The rank tree and BF16 addition order must remain unchanged.
-- CP activation/gradient gathers are prerequisites for weight-gradient GEMMs. They can be coalesced into a fixed packed layout to amortize launch/signature validation, but are not naturally hidden by those GEMMs.
-- Because the plausible overlap window is bounded by roughly one strict collective, coalescing and removing per-call host signature validation should be benchmarked before adding multi-stream scheduling complexity.
+- Forward SP all-gather feeds both gate/up GEMMs, and the final TP all-reduce or reduce-scatter consumes the down projection. These are hard data dependencies.
+- Backward can overlap the fixed-order TP reduction of the gate contribution to dHidden with computation of the independent up contribution. The final wait and gate-then-up add order must remain unchanged to preserve 0 mismatch.
+- CP gathers are prerequisites for weight-gradient GEMMs. Packing them in a fixed layout can amortize launch/signature overhead, but they cannot be hidden behind the GEMMs that consume them.
+- Coalescing and host-side validation caching should be measured before introducing multi-stream scheduling complexity.
 
 ## Figures
 
diff --git a/benchmarks/results/pr325_rocm_mi300x/results.json b/benchmarks/results/pr325_rocm_mi300x/results.json
index 5ba93b4f..bf3205fe 100644
--- a/benchmarks/results/pr325_rocm_mi300x/results.json
+++ b/benchmarks/results/pr325_rocm_mi300x/results.json
@@ -2,1611 +2,1201 @@
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From 43a31ae7f6535e2c11efdd50d96b6633805359fd Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sun, 23 Aug 2026 08:08:48 +0000
Subject: [PATCH 12/28] docs(bench): label shapes axes and accuracy references

---
 benchmarks/benchmark_rocm_ffn.py | 222 ++++++++++++++++++++++---------
 1 file changed, 158 insertions(+), 64 deletions(-)

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index 07cdf894..0584e4db 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -173,11 +173,11 @@ def _single_gpu_benchmarks(
     for tokens in (1, 8, 32, 128):
         gemm_shapes.extend(
             (
-                (f"gate_up_m{tokens}", tokens, 4096, 12288),
-                (f"down_m{tokens}", tokens, 12288, 4096),
+                (tokens, 4096, 12288),
+                (tokens, 12288, 4096),
             )
         )
-    for index, (name, m_size, k_size, n_size) in enumerate(gemm_shapes):
+    for index, (m_size, k_size, n_size) in enumerate(gemm_shapes):
         left = _randn((m_size, k_size), seed=1000 + index * 2, device=device)
         right = _randn((k_size, n_size), seed=1001 + index * 2, device=device)
         native_output = torch.matmul(left, right)
@@ -198,7 +198,7 @@ def _single_gpu_benchmarks(
         )
         results["gemm"].append(
             {
-                "name": name,
+                "name": f"(M,K,N)=({m_size},{k_size},{n_size})",
                 "m": m_size,
                 "k": k_size,
                 "n": n_size,
@@ -266,7 +266,7 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
             )
             results["swiglu"].append(
                 {
-                    "name": f"swiglu_{direction}_m{tokens}",
+                    "name": f"(M,I)=({tokens},12288), {direction}",
                     "direction": direction,
                     "tokens": tokens,
                     "intermediate": 12288,
@@ -318,7 +318,7 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
         )
         results["ffn"].append(
             {
-                "name": f"ffn_forward_m{tokens}",
+                "name": f"(M,H,I)=({tokens},4096,12288), forward",
                 "direction": "forward",
                 "tokens": tokens,
                 "hidden": 4096,
@@ -362,7 +362,7 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
         )
         results["ffn"].append(
             {
-                "name": f"ffn_train_fwd_bwd_m{tokens}",
+                "name": f"(M,H,I)=({tokens},4096,12288), forward+backward",
                 "direction": "train_fwd_bwd",
                 "tokens": tokens,
                 "hidden": 4096,
@@ -1172,7 +1172,7 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             "## Key findings",
             "",
             f"- Deterministic Triton GEMM costs {_ratio_range(gemm_rows)} native "
-            "ROCm latency over Qwen3 gate/up and down projection shapes.",
+            "ROCm latency over the tested `(M,K,N)` shapes.",
             f"- Triton SwiGLU costs {_ratio_range(swiglu_rows, 2)} native PyTorch "
             f"latency; the complete single-GPU FFN costs {_ratio_range(ffn_rows)}.",
             f"- Deterministic collectives cost {_ratio_range(collective_rows)} native "
@@ -1186,9 +1186,19 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             "- TP2/TP4/TP8, TP+CP, and sequence-parallel topology tests produced "
             "0 output/gradient mismatches versus TP=1; a production-dimension "
             "Qwen3-8B TP2 smoke test also produced 0 mismatches.",
-            "- Native-vs-Triton error quantifies the accuracy price of fixing every "
-            "BF16 arithmetic/reduction tree; it is not used as the determinism "
-            "acceptance criterion.",
+            "- Numerical drift means Triton versus native ROCm/RCCL on identical "
+            "BF16 inputs. GEMM-to-FP32 error is separately labelled and uses "
+            "`A.float() @ B.float()` as its reference.",
+            "",
+            "## Accuracy references",
+            "",
+            "- `Repeat mismatch`: Triton run 1 versus Triton run 2.",
+            "- `Train/infer mismatch`: Triton training forward versus Triton "
+            "inference forward.",
+            "- `Topology mismatch`: distributed Triton TP/CP/SP versus Triton TP=1.",
+            "- `vs FP32`: BF16 GEMM output versus `A.float() @ B.float()`.",
+            "- `Triton/native rel-L2`: Triton output or gradient versus the native "
+            "ROCm/RCCL path with identical BF16 tensors and parallel layout.",
         )
     )
 
@@ -1217,7 +1227,7 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             "## Single-GPU SwiGLU",
             "",
             "| Case | Native PyTorch (ms) | Triton (ms) | Triton/native | Repeat "
-            "mismatch | Triton/native rel-L2 |",
+            "mismatch | Triton/native ROCm rel-L2 |",
             "|---|---:|---:|---:|---:|---:|",
         )
     )
@@ -1234,7 +1244,8 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             "## Single-GPU FFN",
             "",
             "| Case | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat "
-            "mismatch | Train/infer mismatch | Output rel-L2 | Max grad rel-L2 |",
+            "mismatch | Train/infer mismatch | Triton/native output rel-L2 | "
+            "Triton/native max grad rel-L2 |",
             "|---|---:|---:|---:|---:|---:|---:|---:|",
         )
     )
@@ -1273,7 +1284,7 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             "",
             "| Topology | Direction | Native ROCm/RCCL (ms) | Triton/deterministic "
             "RCCL (ms) | Overhead | Repeat mismatch | Train/infer mismatch | "
-            "Output rel-L2 | Max grad rel-L2 |",
+            "Triton/native output rel-L2 | Triton/native max grad rel-L2 |",
             "|---|---|---:|---:|---:|---:|---:|---:|---:|",
         )
     )
@@ -1337,32 +1348,62 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
     import matplotlib.pyplot as plt
 
     plt.style.use("seaborn-v0_8-whitegrid")
-    single_rows = (
-        payload["single_gpu"]["gemm"]
-        + payload["single_gpu"]["swiglu"]
-        + payload["single_gpu"]["ffn"]
+    plt.rcParams.update(
+        {
+            "font.size": 16,
+            "axes.titlesize": 24,
+            "axes.labelsize": 20,
+            "xtick.labelsize": 13,
+            "ytick.labelsize": 16,
+            "legend.fontsize": 16,
+        }
+    )
+
+    gemm_rows = payload["single_gpu"]["gemm"]
+    swiglu_rows = payload["single_gpu"]["swiglu"]
+    ffn_rows = payload["single_gpu"]["ffn"]
+    single_rows = gemm_rows + swiglu_rows + ffn_rows
+    single_labels = (
+        [f"GEMM\n({row['m']},{row['k']},{row['n']})" for row in gemm_rows]
+        + [
+            f"SwiGLU {row['direction']}\n(M,I)=({row['tokens']},12288)"
+            for row in swiglu_rows
+        ]
+        + [
+            f"FFN {'FWD' if row['direction'] == 'forward' else 'FWD+BWD'}"
+            f"\n(M,H,I)=({row['tokens']},4096,12288)"
+            for row in ffn_rows
+        ]
     )
-    figure, axis = plt.subplots(figsize=(18, 7))
+    single_ratios = [row["overhead_ratio"] for row in single_rows]
+    figure, axis = plt.subplots(figsize=(26, 10))
     positions = list(range(len(single_rows)))
-    axis.bar(
+    bars = axis.bar(
         positions,
-        [row["overhead_ratio"] for row in single_rows],
+        single_ratios,
         color="#8b5cf6",
         label="Deterministic Triton",
     )
-    axis.axhline(1.0, color="black", linewidth=1, label="Native ROCm")
-    axis.set_yscale("log")
-    axis.set_ylabel("Triton / native latency (x, log scale)")
-    axis.set_title("MI300X single-GPU deterministic Triton overhead")
-    axis.set_xticks(
-        positions, [row["name"] for row in single_rows], rotation=55, ha="right"
+    axis.bar_label(
+        bars,
+        labels=[f"{value:.1f}×" for value in single_ratios],
+        padding=4,
+        fontsize=13,
+        rotation=90,
     )
-    axis.legend()
+    axis.axhline(1.0, color="black", linewidth=2, label="Native ROCm = 1.0×")
+    axis.set_yscale("log")
+    axis.set_ylim(0.5, max(single_ratios) * 1.8)
+    axis.set_xlabel("Operator and tensor shape")
+    axis.set_ylabel("Median latency ratio: Triton / native ROCm (×, log scale)")
+    axis.set_title("MI300X single-GPU operator latency by shape")
+    axis.set_xticks(positions, single_labels, rotation=55, ha="right")
+    axis.legend(loc="upper left")
     figure.tight_layout()
     figure.savefig(output_directory / "single_gpu_overhead.png", dpi=180)
     plt.close(figure)
 
-    figure, axes = plt.subplots(1, 3, figsize=(16, 5), sharey=True)
+    figure, axes = plt.subplots(1, 3, figsize=(24, 8), sharey=True)
     for axis, operation in zip(
         axes, ("all_reduce", "all_gather", "reduce_scatter"), strict=True
     ):
@@ -1372,73 +1413,126 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
                 for row in payload["collectives"]
                 if row["operation"] == operation and row["world_size"] == world_size
             ]
+            x_values = [row["message_bytes_per_rank"] / _MIB for row in rows]
+            y_values = [row["overhead_ratio"] for row in rows]
             axis.plot(
-                [row["message_bytes_per_rank"] / _MIB for row in rows],
-                [row["overhead_ratio"] for row in rows],
+                x_values,
+                y_values,
                 marker="o",
+                linewidth=3,
+                markersize=9,
                 label=f"{world_size} ranks",
             )
-        axis.axhline(1.0, color="black", linewidth=1)
+            for x_value, y_value in zip(x_values, y_values, strict=True):
+                axis.annotate(
+                    f"{y_value:.1f}×",
+                    (x_value, y_value),
+                    xytext=(0, 9),
+                    textcoords="offset points",
+                    ha="center",
+                    fontsize=12,
+                )
+        axis.axhline(1.0, color="black", linewidth=2)
         axis.set_xscale("log", base=2)
         axis.set_yscale("log")
-        axis.set_title(operation)
-        axis.set_xlabel("Input per rank (MiB)")
-    axes[0].set_ylabel("Deterministic / native RCCL latency (x, log scale)")
-    axes[-1].legend()
-    figure.suptitle("MI300X deterministic collective overhead vs native RCCL")
-    figure.tight_layout()
+        axis.set_xticks((0.0625, 1, 16), labels=("0.0625", "1", "16"))
+        axis.set_title(operation.replace("_", " ").title())
+        axis.set_xlabel("BF16 input per rank (MiB)")
+    axes[0].set_ylabel(
+        "Median latency ratio: deterministic / native RCCL (×, log scale)"
+    )
+    axes[-1].legend(title="World size", loc="lower left")
+    figure.suptitle("MI300X collective latency by message size and rank count", y=0.98)
+    figure.tight_layout(rect=(0.0, 0.0, 1.0, 0.94))
     figure.savefig(output_directory / "collective_overhead.png", dpi=180)
     plt.close(figure)
 
     rows = payload["distributed_ffn"]
-    figure, axis = plt.subplots(figsize=(16, 6))
+    labels = [
+        f"TP={row['tp_size']}, CP={row['cp_size']}, "
+        f"SP={'on' if row['sequence_parallel'] else 'off'}\n"
+        f"{'FWD' if row['direction'] == 'forward' else 'FWD+BWD'}"
+        for row in rows
+    ]
+    ratios = [row["overhead_ratio"] for row in rows]
+    figure, axis = plt.subplots(figsize=(24, 9))
     positions = list(range(len(rows)))
-    axis.bar(
+    bars = axis.bar(
         positions,
-        [row["overhead_ratio"] for row in rows],
+        ratios,
         color="#8b5cf6",
         label="Triton + deterministic RCCL",
     )
-    axis.axhline(1.0, color="black", linewidth=1, label="Native ROCm/RCCL")
+    axis.bar_label(
+        bars,
+        labels=[f"{value:.1f}×" for value in ratios],
+        padding=5,
+        fontsize=13,
+    )
+    axis.axhline(
+        1.0,
+        color="black",
+        linewidth=2,
+        label="Native ROCm/RCCL = 1.0×",
+    )
     axis.set_yscale("log")
-    axis.set_ylabel("Triton / native latency (x, log scale)")
-    axis.set_title("Qwen3-shaped distributed FFN overhead on MI300X")
-    axis.set_xticks(
-        positions,
-        [f"{row['name']}\n{row['direction']}" for row in rows],
-        rotation=55,
-        ha="right",
+    axis.set_ylim(0.8, max(ratios) * 1.4)
+    axis.set_xlabel("Parallel configuration and measured direction")
+    axis.set_ylabel(
+        "Median latency ratio: Triton/deterministic RCCL / native (×, log scale)"
     )
-    axis.legend()
+    axis.set_title("MI300X distributed FFN latency by TP / CP / SP configuration")
+    axis.set_xticks(positions, labels, rotation=50, ha="right")
+    axis.legend(loc="upper right")
     figure.tight_layout()
     figure.savefig(output_directory / "distributed_ffn_overhead.png", dpi=180)
     plt.close(figure)
 
-    gemm_rows = payload["single_gpu"]["gemm"]
-    figure, axis = plt.subplots(figsize=(12, 6))
+    figure, axis = plt.subplots(figsize=(19, 9))
     positions = list(range(len(gemm_rows)))
-    width = 0.38
-    axis.bar(
+    width = 0.4
+    native_errors = [
+        row["native_vs_fp32"]["relative_l2"] * 100.0 for row in gemm_rows
+    ]
+    triton_errors = [
+        row["triton_vs_fp32"]["relative_l2"] * 100.0 for row in gemm_rows
+    ]
+    native_bars = axis.bar(
         [position - width / 2 for position in positions],
-        [row["native_vs_fp32"]["relative_l2"] for row in gemm_rows],
+        native_errors,
         width,
-        label="Native ROCm vs FP32",
+        label="Native ROCm BF16 vs FP32",
         color="#3b82f6",
     )
-    axis.bar(
+    triton_bars = axis.bar(
         [position + width / 2 for position in positions],
-        [row["triton_vs_fp32"]["relative_l2"] for row in gemm_rows],
+        triton_errors,
         width,
-        label="Deterministic Triton vs FP32",
+        label="Deterministic Triton BF16 vs FP32",
         color="#8b5cf6",
     )
-    axis.set_yscale("log")
-    axis.set_ylabel("Relative L2 error (log scale)")
-    axis.set_title("BF16 GEMM accuracy cost of a fixed Triton reduction tree")
+    for bars, values in (
+        (native_bars, native_errors),
+        (triton_bars, triton_errors),
+    ):
+        axis.bar_label(
+            bars,
+            labels=[f"{value:.3f}%" for value in values],
+            padding=4,
+            fontsize=12,
+            rotation=90,
+        )
+    axis.set_ylim(0.0, max(triton_errors) * 1.3)
+    axis.set_xlabel("GEMM shape (M, K, N)")
+    axis.set_ylabel("Relative L2 error vs FP32 A.float() @ B.float() (%)")
+    axis.set_title("MI300X BF16 GEMM numerical error by shape and implementation")
     axis.set_xticks(
-        positions, [row["name"] for row in gemm_rows], rotation=45, ha="right"
+        positions,
+        [f"({row['m']}, {row['k']}, {row['n']})" for row in gemm_rows],
+        rotation=45,
+        ha="right",
     )
-    axis.legend()
+    axis.legend(loc="upper left")
     figure.tight_layout()
     figure.savefig(output_directory / "accuracy_tradeoff.png", dpi=180)
     plt.close(figure)

From 6e61ef08ae98f355ecae0f44beffb60a6552e9a5 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sun, 23 Aug 2026 08:13:59 +0000
Subject: [PATCH 13/28] docs(bench): refresh shape and topology figures

---
 benchmarks/benchmark_rocm_ffn.py              |   15 +-
 .../pr325_rocm_mi300x/accuracy_tradeoff.png   |  Bin 97471 -> 282180 bytes
 .../pr325_rocm_mi300x/collective_overhead.png |  Bin 174560 -> 362192 bytes
 .../distributed_ffn_overhead.png              |  Bin 97334 -> 257182 bytes
 .../results/pr325_rocm_mi300x/report.md       |  170 +--
 .../results/pr325_rocm_mi300x/results.json    | 1214 ++++++++---------
 .../pr325_rocm_mi300x/single_gpu_overhead.png |  Bin 212517 -> 460768 bytes
 7 files changed, 706 insertions(+), 693 deletions(-)

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index 0584e4db..57c8b209 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -1395,7 +1395,7 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
     axis.set_yscale("log")
     axis.set_ylim(0.5, max(single_ratios) * 1.8)
     axis.set_xlabel("Operator and tensor shape")
-    axis.set_ylabel("Median latency ratio: Triton / native ROCm (×, log scale)")
+    axis.set_ylabel("Median latency ratio (×, log scale)\nTriton / native ROCm")
     axis.set_title("MI300X single-GPU operator latency by shape")
     axis.set_xticks(positions, single_labels, rotation=55, ha="right")
     axis.legend(loc="upper left")
@@ -1424,12 +1424,14 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
                 label=f"{world_size} ranks",
             )
             for x_value, y_value in zip(x_values, y_values, strict=True):
+                y_offset = -17 if world_size == 2 else 9
                 axis.annotate(
                     f"{y_value:.1f}×",
                     (x_value, y_value),
-                    xytext=(0, 9),
+                    xytext=(0, y_offset),
                     textcoords="offset points",
                     ha="center",
+                    va="top" if y_offset < 0 else "bottom",
                     fontsize=12,
                 )
         axis.axhline(1.0, color="black", linewidth=2)
@@ -1439,7 +1441,7 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
         axis.set_title(operation.replace("_", " ").title())
         axis.set_xlabel("BF16 input per rank (MiB)")
     axes[0].set_ylabel(
-        "Median latency ratio: deterministic / native RCCL (×, log scale)"
+        "Median latency ratio (×, log scale)\nDeterministic / native RCCL"
     )
     axes[-1].legend(title="World size", loc="lower left")
     figure.suptitle("MI300X collective latency by message size and rank count", y=0.98)
@@ -1479,7 +1481,8 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
     axis.set_ylim(0.8, max(ratios) * 1.4)
     axis.set_xlabel("Parallel configuration and measured direction")
     axis.set_ylabel(
-        "Median latency ratio: Triton/deterministic RCCL / native (×, log scale)"
+        "Median latency ratio (×, log scale)\n"
+        "Triton + deterministic RCCL / native"
     )
     axis.set_title("MI300X distributed FFN latency by TP / CP / SP configuration")
     axis.set_xticks(positions, labels, rotation=50, ha="right")
@@ -1524,7 +1527,9 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
         )
     axis.set_ylim(0.0, max(triton_errors) * 1.3)
     axis.set_xlabel("GEMM shape (M, K, N)")
-    axis.set_ylabel("Relative L2 error vs FP32 A.float() @ B.float() (%)")
+    axis.set_ylabel(
+        "Relative L2 error (%)\nReference: FP32 A.float() @ B.float()"
+    )
     axis.set_title("MI300X BF16 GEMM numerical error by shape and implementation")
     axis.set_xticks(
         positions,
diff --git a/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png b/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png
index 61ed3bb7e448b29319deae89a54779e5b1c0b4ec..3ca1a2643e3370a830a00ceb6135c982d2872076 100644
GIT binary patch
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diff --git a/benchmarks/results/pr325_rocm_mi300x/report.md b/benchmarks/results/pr325_rocm_mi300x/report.md
index b9f7e539..b3f7c49d 100644
--- a/benchmarks/results/pr325_rocm_mi300x/report.md
+++ b/benchmarks/results/pr325_rocm_mi300x/report.md
@@ -6,17 +6,17 @@
 
 | Item | Value |
 |---|---|
-| NCCL_IB_DISABLE | 1 |
-| architecture | gfx942:sramecc+:xnack- |
-| deterministic_compute | ROCm-native Triton |
-| git_commit | 5222b68f2dc8a45cc07604f67bda31469aaf7aef |
 | gpu | AMD Instinct MI300X |
 | gpu_count | 8 |
+| architecture | gfx942:sramecc+:xnack- |
+| torch | 2.12.0+rocm7.14.0a20260608 |
 | hip | 7.14.60850 |
-| native_collective | ProcessGroupNCCL (RCCL on ROCm) |
-| native_gemm | torch.matmul (ROCm rocBLAS/hipBLASLt dispatch) |
 | python | 3.12.3 |
-| torch | 2.12.0+rocm7.14.0a20260608 |
+| git_commit | 43a31ae7f6535e2c11efdd50d96b6633805359fd |
+| native_gemm | torch.matmul (ROCm rocBLAS/hipBLASLt dispatch) |
+| deterministic_compute | ROCm-native Triton |
+| native_collective | ProcessGroupNCCL (RCCL on ROCm) |
+| NCCL_IB_DISABLE | 1 |
 
 ## Methodology
 
@@ -48,107 +48,115 @@ NCCL_IB_DISABLE=1 pytest -q \
 
 ## Key findings
 
-- Deterministic Triton GEMM costs 5.6-31.3x native ROCm latency over Qwen3 gate/up and down projection shapes.
-- Triton SwiGLU costs 0.73-1.62x native PyTorch latency; the complete single-GPU FFN costs 8.4-24.6x.
-- Deterministic collectives cost 2.1-21.0x native RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.
-- Distributed Triton FFN costs 6.5-8.8x native for forward and 4.4-6.5x for forward+backward across TP2/4/8, CP2, and sequence-parallel configurations.
+- Deterministic Triton GEMM costs 5.4-31.5x native ROCm latency over the tested `(M,K,N)` shapes.
+- Triton SwiGLU costs 0.72-1.60x native PyTorch latency; the complete single-GPU FFN costs 7.4-23.1x.
+- Deterministic collectives cost 2.1-19.9x native RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.
+- Distributed Triton FFN costs 6.5-9.1x native for forward and 4.4-6.8x for forward+backward across TP2/4/8, CP2, and sequence-parallel configurations.
 - Deterministic repeats produced 0 mismatched elements; training and inference produced 0 mismatched elements.
 - TP2/TP4/TP8, TP+CP, and sequence-parallel topology tests produced 0 output/gradient mismatches versus TP=1; a production-dimension Qwen3-8B TP2 smoke test also produced 0 mismatches.
-- Native-vs-Triton error quantifies the accuracy price of fixing every BF16 arithmetic/reduction tree; it is not used as the determinism acceptance criterion.
+- Numerical drift means Triton versus native ROCm/RCCL on identical BF16 inputs. GEMM-to-FP32 error is separately labelled and uses `A.float() @ B.float()` as its reference.
+
+## Accuracy references
+
+- `Repeat mismatch`: Triton run 1 versus Triton run 2.
+- `Train/infer mismatch`: Triton training forward versus Triton inference forward.
+- `Topology mismatch`: distributed Triton TP/CP/SP versus Triton TP=1.
+- `vs FP32`: BF16 GEMM output versus `A.float() @ B.float()`.
+- `Triton/native rel-L2`: Triton output or gradient versus the native ROCm/RCCL path with identical BF16 tensors and parallel layout.
 
 ## Single-GPU GEMM
 
 | Shape | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat mismatch | Triton rel-L2 vs FP32 | Native rel-L2 vs FP32 |
 |---|---:|---:|---:|---:|---:|---:|
-| gate_up_m1 | 0.0387 | 0.2168 | 5.6x | 0 | 5.003e-03 | 1.651e-03 |
-| down_m1 | 0.0304 | 0.2824 | 9.3x | 0 | 5.769e-03 | 1.635e-03 |
-| gate_up_m8 | 0.0299 | 0.2601 | 8.7x | 0 | 5.018e-03 | 1.663e-03 |
-| down_m8 | 0.0325 | 0.3194 | 9.8x | 0 | 5.679e-03 | 1.654e-03 |
-| gate_up_m32 | 0.0321 | 0.4611 | 14.4x | 0 | 5.019e-03 | 1.654e-03 |
-| down_m32 | 0.0301 | 0.5153 | 17.1x | 0 | 5.604e-03 | 1.662e-03 |
-| gate_up_m128 | 0.0481 | 1.2600 | 26.2x | 0 | 5.019e-03 | 1.657e-03 |
-| down_m128 | 0.0478 | 1.4965 | 31.3x | 0 | 5.623e-03 | 1.659e-03 |
+| (M,K,N)=(1,4096,12288) | 0.0388 | 0.2087 | 5.4x | 0 | 5.003e-03 | 1.651e-03 |
+| (M,K,N)=(1,12288,4096) | 0.0299 | 0.2629 | 8.8x | 0 | 5.769e-03 | 1.635e-03 |
+| (M,K,N)=(8,4096,12288) | 0.0305 | 0.2543 | 8.3x | 0 | 5.018e-03 | 1.663e-03 |
+| (M,K,N)=(8,12288,4096) | 0.0315 | 0.3225 | 10.2x | 0 | 5.679e-03 | 1.654e-03 |
+| (M,K,N)=(32,4096,12288) | 0.0321 | 0.4758 | 14.8x | 0 | 5.019e-03 | 1.654e-03 |
+| (M,K,N)=(32,12288,4096) | 0.0308 | 0.4955 | 16.1x | 0 | 5.604e-03 | 1.662e-03 |
+| (M,K,N)=(128,4096,12288) | 0.0472 | 1.2570 | 26.7x | 0 | 5.019e-03 | 1.657e-03 |
+| (M,K,N)=(128,12288,4096) | 0.0479 | 1.5075 | 31.5x | 0 | 5.623e-03 | 1.659e-03 |
 
 ## Single-GPU SwiGLU
 
-| Case | Native PyTorch (ms) | Triton (ms) | Triton/native | Repeat mismatch | Triton/native rel-L2 |
+| Case | Native PyTorch (ms) | Triton (ms) | Triton/native | Repeat mismatch | Triton/native ROCm rel-L2 |
 |---|---:|---:|---:|---:|---:|
-| swiglu_forward_m1 | 0.0181 | 0.0294 | 1.62x | 0 | 2.877e-03 |
-| swiglu_backward_m1 | 0.0610 | 0.0518 | 0.85x | 0 | 5.159e-03 |
-| swiglu_forward_m8 | 0.0213 | 0.0294 | 1.38x | 0 | 2.816e-03 |
-| swiglu_backward_m8 | 0.0731 | 0.0536 | 0.73x | 0 | 5.074e-03 |
-| swiglu_forward_m32 | 0.0202 | 0.0294 | 1.46x | 0 | 2.825e-03 |
-| swiglu_backward_m32 | 0.0715 | 0.0532 | 0.74x | 0 | 5.071e-03 |
-| swiglu_forward_m128 | 0.0205 | 0.0287 | 1.40x | 0 | 2.828e-03 |
-| swiglu_backward_m128 | 0.0672 | 0.0510 | 0.76x | 0 | 5.059e-03 |
+| (M,I)=(1,12288), forward | 0.0176 | 0.0282 | 1.60x | 0 | 2.877e-03 |
+| (M,I)=(1,12288), backward | 0.0590 | 0.0507 | 0.86x | 0 | 5.159e-03 |
+| (M,I)=(8,12288), forward | 0.0203 | 0.0285 | 1.40x | 0 | 2.816e-03 |
+| (M,I)=(8,12288), backward | 0.0684 | 0.0512 | 0.75x | 0 | 5.074e-03 |
+| (M,I)=(32,12288), forward | 0.0203 | 0.0280 | 1.38x | 0 | 2.825e-03 |
+| (M,I)=(32,12288), backward | 0.0684 | 0.0495 | 0.72x | 0 | 5.071e-03 |
+| (M,I)=(128,12288), forward | 0.0196 | 0.0268 | 1.37x | 0 | 2.828e-03 |
+| (M,I)=(128,12288), backward | 0.0643 | 0.0493 | 0.77x | 0 | 5.059e-03 |
 
 ## Single-GPU FFN
 
-| Case | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat mismatch | Train/infer mismatch | Output rel-L2 | Max grad rel-L2 |
+| Case | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat mismatch | Train/infer mismatch | Triton/native output rel-L2 | Triton/native max grad rel-L2 |
 |---|---:|---:|---:|---:|---:|---:|---:|
-| ffn_forward_m1 | 0.0973 | 1.8769 | 19.3x | 0 | 0 | 1.008e-02 | nan |
-| ffn_train_fwd_bwd_m1 | 0.4162 | 4.4011 | 10.6x | 0 | 0 | 1.008e-02 | 1.037e-02 |
-| ffn_forward_m8 | 0.0963 | 2.0095 | 20.9x | 0 | 0 | 9.930e-03 | nan |
-| ffn_train_fwd_bwd_m8 | 0.5569 | 4.6699 | 8.4x | 0 | 0 | 9.930e-03 | 1.017e-02 |
-| ffn_forward_m32 | 0.1019 | 2.5106 | 24.6x | 0 | 0 | 9.952e-03 | nan |
-| ffn_train_fwd_bwd_m32 | 0.4499 | 5.7878 | 12.9x | 0 | 0 | 9.952e-03 | 1.024e-02 |
+| (M,H,I)=(1,4096,12288), forward | 0.1052 | 1.8770 | 17.8x | 0 | 0 | 1.008e-02 | nan |
+| (M,H,I)=(1,4096,12288), forward+backward | 0.3984 | 4.4071 | 11.1x | 0 | 0 | 1.008e-02 | 1.037e-02 |
+| (M,H,I)=(8,4096,12288), forward | 0.1081 | 2.0033 | 18.5x | 0 | 0 | 9.930e-03 | nan |
+| (M,H,I)=(8,4096,12288), forward+backward | 0.6354 | 4.6865 | 7.4x | 0 | 0 | 9.930e-03 | 1.017e-02 |
+| (M,H,I)=(32,4096,12288), forward | 0.1085 | 2.5084 | 23.1x | 0 | 0 | 9.952e-03 | nan |
+| (M,H,I)=(32,4096,12288), forward+backward | 0.4620 | 5.7915 | 12.5x | 0 | 0 | 9.952e-03 | 1.024e-02 |
 
 ## RCCL collectives
 
 | Operation | Ranks | Input/rank | Native RCCL (ms) | Deterministic (ms) | Overhead | Repeat mismatch |
 |---|---:|---:|---:|---:|---:|---:|
-| all_reduce | 2 | 0.0625 MiB | 0.0759 | 0.5835 | 7.7x | 0 |
-| all_gather | 2 | 0.0625 MiB | 0.0516 | 0.5461 | 10.6x | 0 |
-| reduce_scatter | 2 | 0.0625 MiB | 0.0545 | 0.5850 | 10.7x | 0 |
-| all_reduce | 2 | 1 MiB | 0.0901 | 0.5708 | 6.3x | 0 |
-| all_gather | 2 | 1 MiB | 0.0721 | 0.5574 | 7.7x | 0 |
-| reduce_scatter | 2 | 1 MiB | 0.0734 | 0.5869 | 8.0x | 0 |
-| all_reduce | 2 | 16 MiB | 0.4251 | 0.8872 | 2.1x | 0 |
-| all_gather | 2 | 16 MiB | 0.4061 | 0.8974 | 2.2x | 0 |
-| reduce_scatter | 2 | 16 MiB | 0.2442 | 0.8994 | 3.7x | 0 |
-| all_reduce | 4 | 0.0625 MiB | 0.0767 | 0.7018 | 9.1x | 0 |
-| all_gather | 4 | 0.0625 MiB | 0.0588 | 0.6768 | 11.5x | 0 |
-| reduce_scatter | 4 | 0.0625 MiB | 0.0576 | 0.7142 | 12.4x | 0 |
-| all_reduce | 4 | 1 MiB | 0.0908 | 0.7026 | 7.7x | 0 |
-| all_gather | 4 | 1 MiB | 0.0887 | 0.6913 | 7.8x | 0 |
-| reduce_scatter | 4 | 1 MiB | 0.0886 | 0.7357 | 8.3x | 0 |
-| all_reduce | 4 | 16 MiB | 0.2568 | 1.0472 | 4.1x | 0 |
-| all_gather | 4 | 16 MiB | 0.4271 | 1.0324 | 2.4x | 0 |
-| reduce_scatter | 4 | 16 MiB | 0.1546 | 1.0564 | 6.8x | 0 |
-| all_reduce | 8 | 0.0625 MiB | 0.0490 | 1.0286 | 21.0x | 0 |
-| all_gather | 8 | 0.0625 MiB | 0.0718 | 0.9602 | 13.4x | 0 |
-| reduce_scatter | 8 | 0.0625 MiB | 0.0705 | 1.0240 | 14.5x | 0 |
-| all_reduce | 8 | 1 MiB | 0.0726 | 1.0293 | 14.2x | 0 |
-| all_gather | 8 | 1 MiB | 0.1039 | 1.0011 | 9.6x | 0 |
-| reduce_scatter | 8 | 1 MiB | 0.0916 | 1.0526 | 11.5x | 0 |
-| all_reduce | 8 | 16 MiB | 0.1957 | 1.3791 | 7.0x | 0 |
-| all_gather | 8 | 16 MiB | 0.4314 | 1.3418 | 3.1x | 0 |
-| reduce_scatter | 8 | 16 MiB | 0.1210 | 1.3716 | 11.3x | 0 |
+| all_reduce | 2 | 0.0625 MiB | 0.0746 | 0.5678 | 7.6x | 0 |
+| all_gather | 2 | 0.0625 MiB | 0.0525 | 0.5303 | 10.1x | 0 |
+| reduce_scatter | 2 | 0.0625 MiB | 0.0514 | 0.5667 | 11.0x | 0 |
+| all_reduce | 2 | 1 MiB | 0.0893 | 0.5569 | 6.2x | 0 |
+| all_gather | 2 | 1 MiB | 0.0734 | 0.5396 | 7.4x | 0 |
+| reduce_scatter | 2 | 1 MiB | 0.0694 | 0.5679 | 8.2x | 0 |
+| all_reduce | 2 | 16 MiB | 0.4230 | 0.8854 | 2.1x | 0 |
+| all_gather | 2 | 16 MiB | 0.4086 | 0.8916 | 2.2x | 0 |
+| reduce_scatter | 2 | 16 MiB | 0.2422 | 0.8880 | 3.7x | 0 |
+| all_reduce | 4 | 0.0625 MiB | 0.0794 | 0.6986 | 8.8x | 0 |
+| all_gather | 4 | 0.0625 MiB | 0.0584 | 0.6524 | 11.2x | 0 |
+| reduce_scatter | 4 | 0.0625 MiB | 0.0563 | 0.7090 | 12.6x | 0 |
+| all_reduce | 4 | 1 MiB | 0.0935 | 0.6918 | 7.4x | 0 |
+| all_gather | 4 | 1 MiB | 0.0881 | 0.6801 | 7.7x | 0 |
+| reduce_scatter | 4 | 1 MiB | 0.0906 | 0.7181 | 7.9x | 0 |
+| all_reduce | 4 | 16 MiB | 0.2569 | 1.0410 | 4.1x | 0 |
+| all_gather | 4 | 16 MiB | 0.4229 | 1.0316 | 2.4x | 0 |
+| reduce_scatter | 4 | 16 MiB | 0.1543 | 1.0400 | 6.7x | 0 |
+| all_reduce | 8 | 0.0625 MiB | 0.0517 | 1.0270 | 19.9x | 0 |
+| all_gather | 8 | 0.0625 MiB | 0.0745 | 0.9675 | 13.0x | 0 |
+| reduce_scatter | 8 | 0.0625 MiB | 0.0675 | 1.0467 | 15.5x | 0 |
+| all_reduce | 8 | 1 MiB | 0.0759 | 1.0367 | 13.7x | 0 |
+| all_gather | 8 | 1 MiB | 0.1045 | 1.0074 | 9.6x | 0 |
+| reduce_scatter | 8 | 1 MiB | 0.0928 | 1.0538 | 11.4x | 0 |
+| all_reduce | 8 | 16 MiB | 0.1947 | 1.3829 | 7.1x | 0 |
+| all_gather | 8 | 16 MiB | 0.4276 | 1.4123 | 3.3x | 0 |
+| reduce_scatter | 8 | 16 MiB | 0.1201 | 1.3751 | 11.5x | 0 |
 
 ## Distributed FFN
 
-| Topology | Direction | Native ROCm/RCCL (ms) | Triton/deterministic RCCL (ms) | Overhead | Repeat mismatch | Train/infer mismatch | Output rel-L2 | Max grad rel-L2 |
+| Topology | Direction | Native ROCm/RCCL (ms) | Triton/deterministic RCCL (ms) | Overhead | Repeat mismatch | Train/infer mismatch | Triton/native output rel-L2 | Triton/native max grad rel-L2 |
 |---|---|---:|---:|---:|---:|---:|---:|---:|
-| tp2 | forward | 0.2215 | 1.9557 | 8.8x | 0 | 0 | 1.012e-02 | nan |
-| tp2 | train_fwd_bwd | 1.1951 | 6.1529 | 5.1x | 0 | 0 | 1.012e-02 | 1.079e-02 |
-| tp2_sp | forward | 0.3040 | 2.5241 | 8.3x | 0 | 0 | 1.015e-02 | nan |
-| tp2_sp | train_fwd_bwd | 0.9795 | 6.3449 | 6.5x | 0 | 0 | 1.015e-02 | 1.083e-02 |
-| tp4 | forward | 0.2191 | 1.4863 | 6.8x | 0 | 0 | 1.031e-02 | nan |
-| tp4 | train_fwd_bwd | 0.8463 | 4.4050 | 5.2x | 0 | 0 | 1.031e-02 | 1.095e-02 |
-| tp2_cp2 | forward | 0.2288 | 1.8150 | 7.9x | 0 | 0 | 1.015e-02 | nan |
-| tp2_cp2 | train_fwd_bwd | 1.7761 | 8.7733 | 4.9x | 0 | 0 | 1.015e-02 | 1.083e-02 |
-| tp2_cp2_sp | forward | 0.2944 | 2.3455 | 8.0x | 0 | 0 | 1.022e-02 | nan |
-| tp2_cp2_sp | train_fwd_bwd | 1.9599 | 8.7198 | 4.4x | 0 | 0 | 1.022e-02 | 1.089e-02 |
-| tp8 | forward | 0.2065 | 1.7302 | 8.4x | 0 | 0 | 1.060e-02 | nan |
-| tp8 | train_fwd_bwd | 1.1059 | 7.1109 | 6.4x | 0 | 0 | 1.060e-02 | 1.126e-02 |
-| tp4_cp2 | forward | 0.2255 | 1.4714 | 6.5x | 0 | 0 | 1.033e-02 | nan |
-| tp4_cp2 | train_fwd_bwd | 1.3987 | 8.5660 | 6.1x | 0 | 0 | 1.033e-02 | 1.099e-02 |
-| tp4_cp2_sp | forward | 0.2878 | 2.1547 | 7.5x | 0 | 0 | 1.045e-02 | nan |
-| tp4_cp2_sp | train_fwd_bwd | 1.8003 | 8.8284 | 4.9x | 0 | 0 | 1.045e-02 | 1.109e-02 |
+| tp2 | forward | 0.2178 | 1.9718 | 9.1x | 0 | 0 | 1.012e-02 | nan |
+| tp2 | train_fwd_bwd | 0.8281 | 5.1330 | 6.2x | 0 | 0 | 1.012e-02 | 1.079e-02 |
+| tp2_sp | forward | 0.2785 | 2.5044 | 9.0x | 0 | 0 | 1.015e-02 | nan |
+| tp2_sp | train_fwd_bwd | 0.9735 | 6.3391 | 6.5x | 0 | 0 | 1.015e-02 | 1.083e-02 |
+| tp4 | forward | 0.2179 | 1.4986 | 6.9x | 0 | 0 | 1.031e-02 | nan |
+| tp4 | train_fwd_bwd | 1.0952 | 6.2879 | 5.7x | 0 | 0 | 1.031e-02 | 1.095e-02 |
+| tp2_cp2 | forward | 0.2206 | 1.8216 | 8.3x | 0 | 0 | 1.015e-02 | nan |
+| tp2_cp2 | train_fwd_bwd | 1.1910 | 7.5867 | 6.4x | 0 | 0 | 1.015e-02 | 1.083e-02 |
+| tp2_cp2_sp | forward | 0.2823 | 2.3629 | 8.4x | 0 | 0 | 1.022e-02 | nan |
+| tp2_cp2_sp | train_fwd_bwd | 1.2807 | 8.7284 | 6.8x | 0 | 0 | 1.022e-02 | 1.089e-02 |
+| tp8 | forward | 0.1965 | 1.7419 | 8.9x | 0 | 0 | 1.060e-02 | nan |
+| tp8 | train_fwd_bwd | 1.0607 | 7.2079 | 6.8x | 0 | 0 | 1.060e-02 | 1.126e-02 |
+| tp4_cp2 | forward | 0.2265 | 1.4769 | 6.5x | 0 | 0 | 1.033e-02 | nan |
+| tp4_cp2 | train_fwd_bwd | 1.8629 | 8.3197 | 4.5x | 0 | 0 | 1.033e-02 | 1.099e-02 |
+| tp4_cp2_sp | forward | 0.2949 | 2.1559 | 7.3x | 0 | 0 | 1.045e-02 | nan |
+| tp4_cp2_sp | train_fwd_bwd | 1.9582 | 8.6712 | 4.4x | 0 | 0 | 1.045e-02 | 1.109e-02 |
 
 ## Communication overlap feasibility
 
-The measured implementation deliberately serializes dependencies; the numbers above do not claim overlap. A representative 1 MiB deterministic collective costs 0.5574-1.0526 ms, while distributed Triton FFN forward costs 1.4714-2.5241 ms and forward+backward costs 4.4050-8.8284 ms.
+The measured implementation deliberately serializes dependencies; the numbers above do not claim overlap. A representative 1 MiB deterministic collective costs 0.5396-1.0538 ms, while distributed Triton FFN forward costs 1.4769-2.5044 ms and forward+backward costs 5.1330-8.7284 ms.
 
 - Forward SP all-gather feeds both gate/up GEMMs, and the final TP all-reduce or reduce-scatter consumes the down projection. These are hard data dependencies.
 - Backward can overlap the fixed-order TP reduction of the gate contribution to dHidden with computation of the independent up contribution. The final wait and gate-then-up add order must remain unchanged to preserve 0 mismatch.
diff --git a/benchmarks/results/pr325_rocm_mi300x/results.json b/benchmarks/results/pr325_rocm_mi300x/results.json
index bf3205fe..a2cd939d 100644
--- a/benchmarks/results/pr325_rocm_mi300x/results.json
+++ b/benchmarks/results/pr325_rocm_mi300x/results.json
@@ -2,542 +2,542 @@
   "collectives": [
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-        "min_ms": 0.5511189810931683,
-        "p95_ms": 0.610081315971911
+        "max_ms": 1.1652708053588867,
+        "median_ms": 0.5677836015820503,
+        "min_ms": 0.5442192777991295,
+        "p95_ms": 0.9265512693673367
       },
       "dtype": "bfloat16",
       "message_bytes_per_rank": 65536,
       "mismatch_count": 0,
       "native": {
-        "max_ms": 0.19047223031520844,
-        "median_ms": 0.07590791210532188,
-        "min_ms": 0.0717570073902607,
-        "p95_ms": 0.14660889282822598
+        "max_ms": 0.09214691817760468,
+        "median_ms": 0.0745758879929781,
+        "min_ms": 0.07288902997970581,
+        "p95_ms": 0.08698240853846072
       },
       "operation": "all_reduce",
-      "overhead_ratio": 7.687036457662367,
+      "overhead_ratio": 7.613501050574303,
       "world_size": 2
     },
     {
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-        "median_ms": 0.5461066029965878,
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-        "p95_ms": 0.5736732855439186
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       "mismatch_count": 0,
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@@ -1658,20 +1658,20 @@
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diff --git a/benchmarks/results/pr325_rocm_mi300x/single_gpu_overhead.png b/benchmarks/results/pr325_rocm_mi300x/single_gpu_overhead.png
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L^@*`^UjBaomS~=|


From 08f47d97d0443c5998b8da6b41a22fdf3848da8f Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sun, 23 Aug 2026 09:07:01 +0000
Subject: [PATCH 14/28] bench(rocm): compare Triton FFN with official TP1

---
 benchmarks/benchmark_rocm_ffn.py | 1380 +++++++++++-------------------
 1 file changed, 509 insertions(+), 871 deletions(-)

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index 57c8b209..d3536acf 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -1,10 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""Operator-only ROCm analysis for the deterministic distributed Triton FFN.
+"""ROCm analysis for the deterministic distributed Triton Qwen3 FFN.
 
-The two compute paths are native ROCm (PyTorch/rocBLAS) and the PR's Triton
-implementation. Communication compares native RCCL with the fixed-rank
-deterministic RCCL transport. No model checkpoint or serving engine is used.
+Performance compares the PR's FFN with the official Hugging Face Qwen3MLP at
+TP=1. Determinism compares every Triton TP/CP/SP layout bitwise with Triton
+TP=1. A separate, simple FP16-versus-FP32 observation uses official Qwen3MLP.
+No model checkpoint or serving engine is used.
 """
 
 from __future__ import annotations
@@ -25,18 +26,12 @@
 import torch
 import torch.distributed as dist
 import torch.multiprocessing as mp
-import torch.nn.functional as F
+from transformers import __version__ as transformers_version
+from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
+from transformers.models.qwen3.modeling_qwen3 import Qwen3MLP
 
 import rl_engine.kernels.ops.triton.ffn.ffn as ffn_module
-from rl_engine.distributed import RCCLDeterministicCollective
-from rl_engine.kernels.ops.triton.activation.swiglu import (
-    _launch_swiglu_bwd,
-    _launch_swiglu_fwd,
-)
 from rl_engine.kernels.ops.triton.ffn import qwen3_ffn
-from rl_engine.kernels.ops.triton.matmul import deterministic_gemm_triton
-
-_MIB = 1024 * 1024
 _DISTRIBUTED_CONFIGS: dict[int, tuple[tuple[str, int, int, bool], ...]] = {
     2: (("tp2", 2, 1, False), ("tp2_sp", 2, 1, True)),
     4: (
@@ -104,10 +99,11 @@ def _randn(
     seed: int,
     device: torch.device,
     scale: float = 0.02,
+    dtype: torch.dtype = torch.bfloat16,
 ) -> torch.Tensor:
     generator = torch.Generator(device="cpu").manual_seed(seed)
     value = torch.randn(shape, generator=generator, dtype=torch.float32) * scale
-    return value.to(device=device, dtype=torch.bfloat16)
+    return value.to(device=device, dtype=dtype)
 
 
 def _gpu_event_samples(
@@ -131,15 +127,31 @@ def _gpu_event_samples(
     return [float(start.elapsed_time(end)) for start, end in events]
 
 
-def _native_ffn(
-    hidden: torch.Tensor,
+def _official_qwen3_mlp(
     gate_weight: torch.Tensor,
     up_weight: torch.Tensor,
     down_weight: torch.Tensor,
-) -> torch.Tensor:
-    gate = hidden @ gate_weight.t()
-    up = hidden @ up_weight.t()
-    return (F.silu(gate) * up) @ down_weight.t()
+) -> Qwen3MLP:
+    """Build the upstream Transformers Qwen3 FFN with the benchmark weights."""
+    config = Qwen3Config(
+        hidden_size=gate_weight.size(1),
+        intermediate_size=gate_weight.size(0),
+        hidden_act="silu",
+    )
+    module = Qwen3MLP(config).to(
+        device=gate_weight.device,
+        dtype=gate_weight.dtype,
+    )
+    with torch.no_grad():
+        module.gate_proj.weight.copy_(gate_weight)
+        module.up_proj.weight.copy_(up_weight)
+        module.down_proj.weight.copy_(down_weight)
+    return module
+
+
+def _official_inference(module: Qwen3MLP, hidden: torch.Tensor) -> torch.Tensor:
+    with torch.no_grad():
+        return module(hidden)
 
 
 def _training_step(
@@ -163,160 +175,32 @@ def _single_gpu_benchmarks(
     device = torch.device("cuda", 0)
     torch.cuda.set_device(device)
     torch.backends.cuda.matmul.allow_tf32 = False
-    results: dict[str, list[dict[str, Any]]] = {
-        "gemm": [],
-        "swiglu": [],
-        "ffn": [],
-    }
-
-    gemm_shapes = []
-    for tokens in (1, 8, 32, 128):
-        gemm_shapes.extend(
-            (
-                (tokens, 4096, 12288),
-                (tokens, 12288, 4096),
-            )
-        )
-    for index, (m_size, k_size, n_size) in enumerate(gemm_shapes):
-        left = _randn((m_size, k_size), seed=1000 + index * 2, device=device)
-        right = _randn((k_size, n_size), seed=1001 + index * 2, device=device)
-        native_output = torch.matmul(left, right)
-        triton_output = deterministic_gemm_triton(left, right)
-        triton_repeat = deterministic_gemm_triton(left, right)
-        fp32_reference = left.float() @ right.float()
-        native_timing = _summary_ms(
-            _gpu_event_samples(
-                lambda: torch.matmul(left, right), warmup=warmup, samples=samples
-            )
-        )
-        triton_timing = _summary_ms(
-            _gpu_event_samples(
-                lambda: deterministic_gemm_triton(left, right),
-                warmup=warmup,
-                samples=samples,
-            )
-        )
-        results["gemm"].append(
-            {
-                "name": f"(M,K,N)=({m_size},{k_size},{n_size})",
-                "m": m_size,
-                "k": k_size,
-                "n": n_size,
-                "dtype": "bfloat16",
-                "native": native_timing,
-                "triton": triton_timing,
-                "overhead_ratio": (
-                    triton_timing["median_ms"] / native_timing["median_ms"]
-                ),
-                "mismatch_count": _mismatches(triton_output, triton_repeat),
-                "triton_vs_native": _accuracy(triton_output, native_output),
-                "native_vs_fp32": _accuracy(native_output, fp32_reference),
-                "triton_vs_fp32": _accuracy(triton_output, fp32_reference),
-            }
-        )
-        del left, right, native_output, triton_output, triton_repeat, fp32_reference
-        torch.cuda.empty_cache()
-
-    for index, tokens in enumerate((1, 8, 32, 128)):
-        gate = _randn((tokens, 12288), seed=2000 + index * 3, device=device)
-        up = _randn((tokens, 12288), seed=2001 + index * 3, device=device)
-        grad_output = _randn(
-            (tokens, 12288), seed=2002 + index * 3, device=device
-        )
-
-        def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
-            sigmoid = torch.sigmoid(gate)
-            grad_gate = grad_output * up * sigmoid * (1.0 + gate * (1.0 - sigmoid))
-            grad_up = grad_output * F.silu(gate)
-            return grad_gate, grad_up
-
-        for direction, native_function, triton_function in (
-            (
-                "forward",
-                lambda: F.silu(gate) * up,
-                lambda: _launch_swiglu_fwd(gate, up),
-            ),
-            (
-                "backward",
-                native_backward,
-                lambda: _launch_swiglu_bwd(grad_output, gate, up),
-            ),
-        ):
-            native_output = native_function()
-            triton_output = triton_function()
-            triton_repeat = triton_function()
-            native_timing = _summary_ms(
-                _gpu_event_samples(
-                    native_function, warmup=warmup, samples=samples
-                )
-            )
-            triton_timing = _summary_ms(
-                _gpu_event_samples(
-                    triton_function, warmup=warmup, samples=samples
-                )
-            )
-            native_values = (
-                native_output if isinstance(native_output, tuple) else (native_output,)
-            )
-            triton_values = (
-                triton_output if isinstance(triton_output, tuple) else (triton_output,)
-            )
-            repeat_values = (
-                triton_repeat if isinstance(triton_repeat, tuple) else (triton_repeat,)
-            )
-            results["swiglu"].append(
-                {
-                    "name": f"(M,I)=({tokens},12288), {direction}",
-                    "direction": direction,
-                    "tokens": tokens,
-                    "intermediate": 12288,
-                    "dtype": "bfloat16",
-                    "native": native_timing,
-                    "triton": triton_timing,
-                    "overhead_ratio": (
-                        triton_timing["median_ms"] / native_timing["median_ms"]
-                    ),
-                    "mismatch_count": sum(
-                        _mismatches(actual, repeat)
-                        for actual, repeat in zip(
-                            triton_values, repeat_values, strict=True
-                        )
-                    ),
-                    "triton_vs_native_relative_l2": max(
-                        _relative_l2(actual, expected)
-                        for actual, expected in zip(
-                            triton_values, native_values, strict=True
-                        )
-                    ),
-                }
-            )
-        del gate, up, grad_output
-        torch.cuda.empty_cache()
-
+    results: dict[str, list[dict[str, Any]]] = {"speed": [], "dtype_accuracy": []}
     gate_weight = _randn((12288, 4096), seed=3000, device=device)
     up_weight = _randn((12288, 4096), seed=3001, device=device)
     down_weight = _randn((4096, 12288), seed=3002, device=device)
+    official = _official_qwen3_mlp(gate_weight, up_weight, down_weight)
     for index, tokens in enumerate((1, 8, 32)):
         hidden = _randn((tokens, 4096), seed=3010 + index * 2, device=device)
         grad_output = _randn(
             (tokens, 4096), seed=3011 + index * 2, device=device
         )
-        values = (hidden, gate_weight, up_weight, down_weight)
-        with torch.no_grad():
-            native_output = _native_ffn(*values)
-            triton_output = qwen3_ffn(*values)
-            triton_repeat = qwen3_ffn(*values)
-        native_timing = _summary_ms(
+        weights = (gate_weight, up_weight, down_weight)
+        official_timing = _summary_ms(
             _gpu_event_samples(
-                lambda: _native_ffn(*values), warmup=warmup, samples=samples
+                lambda: _official_inference(official, hidden),
+                warmup=warmup,
+                samples=samples,
             )
         )
         triton_timing = _summary_ms(
             _gpu_event_samples(
-                lambda: qwen3_ffn(*values), warmup=warmup, samples=samples
+                lambda: qwen3_ffn(hidden, *weights),
+                warmup=warmup,
+                samples=samples,
             )
         )
-        results["ffn"].append(
+        results["speed"].append(
             {
                 "name": f"(M,H,I)=({tokens},4096,12288), forward",
                 "direction": "forward",
@@ -324,43 +208,42 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
                 "hidden": 4096,
                 "intermediate": 12288,
                 "dtype": "bfloat16",
-                "native": native_timing,
+                "official_tp1": official_timing,
                 "triton": triton_timing,
-                "overhead_ratio": (
-                    triton_timing["median_ms"] / native_timing["median_ms"]
+                "latency_ratio_vs_official_tp1": (
+                    triton_timing["median_ms"] / official_timing["median_ms"]
                 ),
-                "mismatch_count": _mismatches(triton_output, triton_repeat),
-                "train_infer_mismatch_count": 0,
-                "triton_vs_native": _accuracy(triton_output, native_output),
             }
         )
 
-        native_inputs = [value.detach().clone().requires_grad_(True) for value in values]
-        triton_inputs = [value.detach().clone().requires_grad_(True) for value in values]
-        repeat_inputs = [value.detach().clone().requires_grad_(True) for value in values]
-        native_function = lambda *args: _native_ffn(*args)
-        triton_function = lambda *args: qwen3_ffn(*args)
-        native_train = _training_step(native_function, native_inputs, grad_output)
-        triton_train = _training_step(triton_function, triton_inputs, grad_output)
-        repeat_train = _training_step(triton_function, repeat_inputs, grad_output)
-        native_grads = [value.grad.detach().clone() for value in native_inputs]
-        triton_grads = [value.grad.detach().clone() for value in triton_inputs]
-        repeat_grads = [value.grad.detach().clone() for value in repeat_inputs]
-        native_train_timing = _summary_ms(
+        official_hidden = hidden.detach().clone().requires_grad_(True)
+        triton_inputs = [
+            value.detach().clone().requires_grad_(True)
+            for value in (hidden, *weights)
+        ]
+
+        def official_training_step() -> torch.Tensor:
+            official.zero_grad(set_to_none=True)
+            official_hidden.grad = None
+            output = official(official_hidden)
+            output.backward(grad_output)
+            return output
+
+        official_train_timing = _summary_ms(
             _gpu_event_samples(
-                lambda: _training_step(native_function, native_inputs, grad_output),
+                official_training_step,
                 warmup=max(1, warmup // 2),
                 samples=training_samples,
             )
         )
         triton_train_timing = _summary_ms(
             _gpu_event_samples(
-                lambda: _training_step(triton_function, triton_inputs, grad_output),
+                lambda: _training_step(qwen3_ffn, triton_inputs, grad_output),
                 warmup=max(1, warmup // 2),
                 samples=training_samples,
             )
         )
-        results["ffn"].append(
+        results["speed"].append(
             {
                 "name": f"(M,H,I)=({tokens},4096,12288), forward+backward",
                 "direction": "train_fwd_bwd",
@@ -368,188 +251,58 @@ def native_backward() -> tuple[torch.Tensor, torch.Tensor]:
                 "hidden": 4096,
                 "intermediate": 12288,
                 "dtype": "bfloat16",
-                "native": native_train_timing,
+                "official_tp1": official_train_timing,
                 "triton": triton_train_timing,
-                "overhead_ratio": (
+                "latency_ratio_vs_official_tp1": (
                     triton_train_timing["median_ms"]
-                    / native_train_timing["median_ms"]
-                ),
-                "mismatch_count": _mismatches(triton_train, repeat_train)
-                + sum(
-                    _mismatches(actual, repeat)
-                    for actual, repeat in zip(
-                        triton_grads, repeat_grads, strict=True
-                    )
-                ),
-                "train_infer_mismatch_count": _mismatches(
-                    triton_output, triton_train
-                ),
-                "triton_vs_native": _accuracy(triton_train, native_train),
-                "max_gradient_relative_l2": max(
-                    _relative_l2(actual, expected)
-                    for actual, expected in zip(
-                        triton_grads, native_grads, strict=True
-                    )
+                    / official_train_timing["median_ms"]
                 ),
             }
         )
-        del hidden, grad_output, native_inputs, triton_inputs, repeat_inputs
+        del hidden, grad_output, official_hidden, triton_inputs
         torch.cuda.empty_cache()
-    return results
-
 
-def _native_all_gather(input_tensor: torch.Tensor, group: Any) -> torch.Tensor:
-    world_size = dist.get_world_size(group=group)
-    flat_input = input_tensor.contiguous().view(-1)
-    flat_output = torch.empty(
-        world_size * flat_input.numel(),
-        dtype=input_tensor.dtype,
-        device=input_tensor.device,
+    # This is intentionally separate from the determinism and speed results.
+    del official, gate_weight, up_weight, down_weight
+    torch.cuda.empty_cache()
+    tokens = 8
+    fp32_hidden = _randn(
+        (tokens, 4096), seed=3100, device=device, dtype=torch.float32
     )
-    dist.all_gather_into_tensor(flat_output, flat_input, group=group)
-    return flat_output.reshape(
-        input_tensor.size(0) * world_size, *input_tensor.shape[1:]
+    fp32_gate = _randn(
+        (12288, 4096), seed=3101, device=device, dtype=torch.float32
     )
-
-
-def _native_all_reduce(input_tensor: torch.Tensor, group: Any) -> torch.Tensor:
-    output = input_tensor.clone()
-    dist.all_reduce(output, group=group)
-    return output
-
-
-def _native_reduce_scatter(input_tensor: torch.Tensor, group: Any) -> torch.Tensor:
-    world_size = dist.get_world_size(group=group)
-    output = torch.empty(
-        (input_tensor.size(0) // world_size, *input_tensor.shape[1:]),
-        dtype=input_tensor.dtype,
-        device=input_tensor.device,
+    fp32_up = _randn(
+        (12288, 4096), seed=3102, device=device, dtype=torch.float32
     )
-    dist.reduce_scatter_tensor(output, input_tensor.contiguous(), group=group)
-    return output
-
+    fp32_down = _randn(
+        (4096, 12288), seed=3103, device=device, dtype=torch.float32
+    )
+    official_fp32 = _official_qwen3_mlp(fp32_gate, fp32_up, fp32_down)
+    with torch.no_grad():
+        fp32_output = official_fp32(fp32_hidden)
+    del official_fp32
+    torch.cuda.empty_cache()
+    official_fp16 = _official_qwen3_mlp(
+        fp32_gate.half(), fp32_up.half(), fp32_down.half()
+    )
+    with torch.no_grad():
+        fp16_output = official_fp16(fp32_hidden.half())
+    results["dtype_accuracy"].append(
+        {
+            "name": "Official Qwen3MLP TP=1 FP16 vs FP32",
+            "tokens": tokens,
+            "hidden": 4096,
+            "intermediate": 12288,
+            "candidate_dtype": "float16",
+            "reference_dtype": "float32",
+            **_accuracy(fp16_output, fp32_output),
+        }
+    )
+    return results
 
-class _NativeDistributedFFNFunction(torch.autograd.Function):
-    """Native ROCm/RCCL baseline with the Triton path's collective schedule."""
-
-    @staticmethod
-    def forward(
-        ctx: Any,
-        hidden: torch.Tensor,
-        gate_weight: torch.Tensor,
-        up_weight: torch.Tensor,
-        down_weight: torch.Tensor,
-        tp_group: Any,
-        cp_group: Any,
-        sequence_parallel: bool,
-    ) -> torch.Tensor:
-        input_shape = hidden.shape
-        hidden_2d = hidden.reshape(-1, hidden.size(-1)).contiguous()
-        if sequence_parallel:
-            hidden_2d = _native_all_gather(hidden_2d, tp_group)
-        gate = hidden_2d @ gate_weight.t()
-        up = hidden_2d @ up_weight.t()
-        activated = F.silu(gate) * up
-        output = activated @ down_weight.t()
-        if sequence_parallel:
-            output = _native_reduce_scatter(output, tp_group)
-        elif tp_group is not None:
-            output = _native_all_reduce(output, tp_group)
-        ctx.save_for_backward(
-            hidden_2d,
-            gate,
-            up,
-            activated,
-            gate_weight,
-            up_weight,
-            down_weight,
-        )
-        ctx.input_shape = input_shape
-        ctx.tp_group = tp_group
-        ctx.cp_group = cp_group
-        ctx.sequence_parallel = sequence_parallel
-        return output.reshape(*input_shape[:-1], output.size(-1))
-
-    @staticmethod
-    def backward(ctx: Any, grad_output: torch.Tensor) -> tuple[Any, ...]:
-        (
-            hidden,
-            gate,
-            up,
-            activated,
-            gate_weight,
-            up_weight,
-            down_weight,
-        ) = ctx.saved_tensors
-        grad_output = grad_output.reshape(-1, grad_output.size(-1)).contiguous()
-        if ctx.sequence_parallel:
-            grad_output = _native_all_gather(grad_output, ctx.tp_group)
-
-        if ctx.cp_group is not None:
-            activated_full = _native_all_gather(activated, ctx.cp_group)
-            grad_output_full = _native_all_gather(grad_output, ctx.cp_group)
-            grad_down_weight = grad_output_full.t() @ activated_full
-        else:
-            grad_down_weight = grad_output.t() @ activated
-
-        grad_activated = grad_output @ down_weight
-        sigmoid = torch.sigmoid(gate)
-        grad_gate = grad_activated * up * sigmoid * (1.0 + gate * (1.0 - sigmoid))
-        grad_up = grad_activated * F.silu(gate)
-        if ctx.cp_group is not None:
-            hidden_full = _native_all_gather(hidden, ctx.cp_group)
-            grad_gate_full = _native_all_gather(grad_gate, ctx.cp_group)
-            grad_up_full = _native_all_gather(grad_up, ctx.cp_group)
-            grad_gate_weight = grad_gate_full.t() @ hidden_full
-            grad_up_weight = grad_up_full.t() @ hidden_full
-        else:
-            grad_gate_weight = grad_gate.t() @ hidden
-            grad_up_weight = grad_up.t() @ hidden
-
-        grad_hidden_gate = grad_gate @ gate_weight
-        if ctx.sequence_parallel:
-            grad_hidden_gate = _native_reduce_scatter(
-                grad_hidden_gate, ctx.tp_group
-            )
-        elif ctx.tp_group is not None:
-            grad_hidden_gate = _native_all_reduce(grad_hidden_gate, ctx.tp_group)
-        grad_hidden_up = grad_up @ up_weight
-        if ctx.sequence_parallel:
-            grad_hidden_up = _native_reduce_scatter(grad_hidden_up, ctx.tp_group)
-        elif ctx.tp_group is not None:
-            grad_hidden_up = _native_all_reduce(grad_hidden_up, ctx.tp_group)
-        grad_hidden = grad_hidden_gate.add_(grad_hidden_up)
-        return (
-            grad_hidden.reshape(ctx.input_shape),
-            grad_gate_weight,
-            grad_up_weight,
-            grad_down_weight,
-            None,
-            None,
-            None,
-        )
 
 
-def _native_distributed_ffn(
-    hidden: torch.Tensor,
-    gate_weight: torch.Tensor,
-    up_weight: torch.Tensor,
-    down_weight: torch.Tensor,
-    *,
-    tp_group: Any,
-    cp_group: Any,
-    sequence_parallel: bool,
-) -> torch.Tensor:
-    return _NativeDistributedFFNFunction.apply(
-        hidden,
-        gate_weight,
-        up_weight,
-        down_weight,
-        tp_group,
-        cp_group,
-        sequence_parallel,
-    )
-
 
 def _mesh_groups(
     world_size: int,
@@ -631,112 +384,6 @@ def _slowest_rank_summary(
     return _summary_ms(slowest)
 
 
-def _collective_benchmarks(
-    rank: int,
-    world_size: int,
-    *,
-    warmup: int,
-    samples: int,
-) -> list[dict[str, Any]]:
-    device = torch.device("cuda", rank)
-    results = []
-    element_size = torch.tensor([], dtype=torch.bfloat16).element_size()
-    for message_bytes in (64 * 1024, _MIB, 16 * _MIB):
-        elements = message_bytes // element_size
-        input_tensor = torch.zeros(elements, dtype=torch.bfloat16, device=device)
-        deterministic = RCCLDeterministicCollective(
-            group=dist.group.WORLD,
-            device=device,
-            max_size_bytes=message_bytes,
-        )
-        det_outputs = {
-            "all_reduce": torch.empty_like(input_tensor),
-            "all_gather": torch.empty(
-                elements * world_size, dtype=input_tensor.dtype, device=device
-            ),
-            "reduce_scatter": torch.empty(
-                elements // world_size, dtype=input_tensor.dtype, device=device
-            ),
-        }
-        native_outputs = {
-            "all_gather": torch.empty_like(det_outputs["all_gather"]),
-            "reduce_scatter": torch.empty_like(det_outputs["reduce_scatter"]),
-        }
-
-        def det_all_reduce():
-            return deterministic.all_reduce(input_tensor, out=det_outputs["all_reduce"])
-
-        def native_all_reduce():
-            output = input_tensor.clone()
-            dist.all_reduce(output)
-            return output
-
-        def det_all_gather():
-            return deterministic.all_gather(input_tensor, out=det_outputs["all_gather"])
-
-        def native_all_gather():
-            dist.all_gather_into_tensor(native_outputs["all_gather"], input_tensor)
-            return native_outputs["all_gather"]
-
-        def det_reduce_scatter():
-            return deterministic.reduce_scatter(
-                input_tensor, out=det_outputs["reduce_scatter"]
-            )
-
-        def native_reduce_scatter():
-            dist.reduce_scatter_tensor(native_outputs["reduce_scatter"], input_tensor)
-            return native_outputs["reduce_scatter"]
-
-        operations = (
-            ("all_reduce", native_all_reduce, det_all_reduce),
-            ("all_gather", native_all_gather, det_all_gather),
-            ("reduce_scatter", native_reduce_scatter, det_reduce_scatter),
-        )
-        for operation, native_function, det_function in operations:
-            native_summary = _slowest_rank_summary(
-                _distributed_wall_samples(
-                    native_function,
-                    group=dist.group.WORLD,
-                    warmup=warmup,
-                    samples=samples,
-                ),
-                dist.group.WORLD,
-            )
-            det_summary = _slowest_rank_summary(
-                _distributed_wall_samples(
-                    det_function,
-                    group=dist.group.WORLD,
-                    warmup=warmup,
-                    samples=samples,
-                ),
-                dist.group.WORLD,
-            )
-            first = det_function().detach().clone()
-            second = det_function().detach().clone()
-            local_mismatch = _mismatches(first, second)
-            mismatch_counts: list[int | None] = [None] * world_size
-            dist.all_gather_object(mismatch_counts, local_mismatch)
-            if rank == 0:
-                results.append(
-                    {
-                        "operation": operation,
-                        "world_size": world_size,
-                        "message_bytes_per_rank": message_bytes,
-                        "dtype": "bfloat16",
-                        "native": native_summary,
-                        "deterministic": det_summary,
-                        "overhead_ratio": (
-                            det_summary["median_ms"] / native_summary["median_ms"]
-                        ),
-                        "mismatch_count": sum(
-                            value for value in mismatch_counts if value is not None
-                        ),
-                    }
-                )
-        deterministic.close()
-        del input_tensor, det_outputs, native_outputs
-        torch.cuda.empty_cache()
-    return results
 
 
 def _distributed_ffn_benchmark(
@@ -763,6 +410,53 @@ def _distributed_ffn_benchmark(
     grad_output_full = _randn(
         (token_count, hidden_size), seed=5004, device=device
     )
+
+    # Performance reference: upstream Qwen3MLP, unsharded TP=1, full M=32 input.
+    official = _official_qwen3_mlp(gate_full, up_full, down_full)
+
+    def official_forward() -> torch.Tensor:
+        return _official_inference(official, hidden_full)
+
+    official_forward_summary = _slowest_rank_summary(
+        _distributed_wall_samples(
+            official_forward,
+            group=dist.group.WORLD,
+            warmup=warmup,
+            samples=samples,
+        ),
+        dist.group.WORLD,
+    )
+    official_hidden = hidden_full.detach().clone().requires_grad_(True)
+
+    def official_training_step() -> torch.Tensor:
+        official.zero_grad(set_to_none=True)
+        official_hidden.grad = None
+        output = official(official_hidden)
+        output.backward(grad_output_full)
+        return output
+
+    official_train_summary = _slowest_rank_summary(
+        _distributed_wall_samples(
+            official_training_step,
+            group=dist.group.WORLD,
+            warmup=max(1, warmup // 2),
+            samples=training_samples,
+        ),
+        dist.group.WORLD,
+    )
+    del official, official_hidden
+    torch.cuda.empty_cache()
+
+    # Exactness reference: the same deterministic Triton implementation at TP=1.
+    full_values = (hidden_full, gate_full, up_full, down_full)
+    with torch.no_grad():
+        tp1_forward = qwen3_ffn(*full_values).detach().clone()
+    tp1_inputs = [value.detach().clone().requires_grad_(True) for value in full_values]
+    tp1_train = _training_step(qwen3_ffn, tp1_inputs, grad_output_full).detach().clone()
+    tp1_grads = [value.grad.detach().clone() for value in tp1_inputs]
+    del tp1_inputs
+    torch.cuda.empty_cache()
+
     meshes: dict[tuple[int, int], tuple[list[Any], list[Any]]] = {}
     results = []
 
@@ -791,14 +485,6 @@ def _distributed_ffn_benchmark(
         )
         local_grad_output = grad_output_full[token_start:token_end].contiguous()
 
-        def native_forward():
-            return _native_distributed_ffn(
-                *shard,
-                tp_group=tp_group,
-                cp_group=cp_group,
-                sequence_parallel=sequence_parallel,
-            )
-
         def triton_forward():
             return qwen3_ffn(
                 *shard,
@@ -808,18 +494,8 @@ def triton_forward():
             )
 
         with torch.no_grad():
-            native_output = native_forward()
             triton_output = triton_forward()
             triton_repeat = triton_forward()
-        native_forward_summary = _slowest_rank_summary(
-            _distributed_wall_samples(
-                native_forward,
-                group=dist.group.WORLD,
-                warmup=warmup,
-                samples=samples,
-            ),
-            dist.group.WORLD,
-        )
         triton_forward_summary = _slowest_rank_summary(
             _distributed_wall_samples(
                 triton_forward,
@@ -830,22 +506,9 @@ def triton_forward():
             dist.group.WORLD,
         )
 
-        native_inputs = [value.detach().clone().requires_grad_(True) for value in shard]
         triton_inputs = [value.detach().clone().requires_grad_(True) for value in shard]
         repeat_inputs = [value.detach().clone().requires_grad_(True) for value in shard]
 
-        def native_training_step():
-            for value in native_inputs:
-                value.grad = None
-            output = _native_distributed_ffn(
-                *native_inputs,
-                tp_group=tp_group,
-                cp_group=cp_group,
-                sequence_parallel=sequence_parallel,
-            )
-            output.backward(local_grad_output)
-            return output
-
         def triton_training_step(inputs):
             for value in inputs:
                 value.grad = None
@@ -858,21 +521,10 @@ def triton_training_step(inputs):
             output.backward(local_grad_output)
             return output
 
-        native_train = native_training_step().detach().clone()
-        native_grads = [value.grad.detach().clone() for value in native_inputs]
         triton_train = triton_training_step(triton_inputs).detach().clone()
         triton_grads = [value.grad.detach().clone() for value in triton_inputs]
         repeat_train = triton_training_step(repeat_inputs).detach().clone()
         repeat_grads = [value.grad.detach().clone() for value in repeat_inputs]
-        native_train_summary = _slowest_rank_summary(
-            _distributed_wall_samples(
-                native_training_step,
-                group=dist.group.WORLD,
-                warmup=max(1, warmup // 2),
-                samples=training_samples,
-            ),
-            dist.group.WORLD,
-        )
         triton_train_summary = _slowest_rank_summary(
             _distributed_wall_samples(
                 lambda: triton_training_step(triton_inputs),
@@ -883,25 +535,36 @@ def triton_training_step(inputs):
             dist.group.WORLD,
         )
 
-        local_accuracy = {
-            "forward_mismatch_count": _mismatches(triton_output, triton_repeat),
+        expected_forward = tp1_forward[token_start:token_end]
+        expected_train = tp1_train[token_start:token_end]
+        expected_grads = (
+            tp1_grads[0][token_start:token_end],
+            tp1_grads[1][feature_start:feature_end],
+            tp1_grads[2][feature_start:feature_end],
+            tp1_grads[3][:, feature_start:feature_end],
+        )
+        local_exactness = {
+            "tp1_forward_output": _mismatches(triton_output, expected_forward),
+            "tp1_training_output": _mismatches(triton_train, expected_train),
+            "tp1_hidden_gradient": _mismatches(
+                triton_grads[0], expected_grads[0]
+            ),
+            "tp1_weight_gradient": sum(
+                _mismatches(actual, expected)
+                for actual, expected in zip(
+                    triton_grads[1:], expected_grads[1:], strict=True
+                )
+            ),
+            "repeat_forward": _mismatches(triton_output, triton_repeat),
             "train_infer_mismatch_count": _mismatches(triton_output, triton_train),
-            "training_mismatch_count": _mismatches(triton_train, repeat_train)
+            "repeat_training": _mismatches(triton_train, repeat_train)
             + sum(
                 _mismatches(actual, repeat)
                 for actual, repeat in zip(triton_grads, repeat_grads, strict=True)
             ),
-            "output_relative_l2": _relative_l2(triton_output, native_output),
-            "train_output_relative_l2": _relative_l2(triton_train, native_train),
-            "max_gradient_relative_l2": max(
-                _relative_l2(actual, expected)
-                for actual, expected in zip(
-                    triton_grads, native_grads, strict=True
-                )
-            ),
         }
         gathered: list[dict[str, Any] | None] = [None] * world_size
-        dist.all_gather_object(gathered, local_accuracy)
+        dist.all_gather_object(gathered, local_exactness)
         if rank == 0:
             valid = [value for value in gathered if value is not None]
             common = {
@@ -919,47 +582,54 @@ def triton_training_step(inputs):
                     {
                         **common,
                         "direction": "forward",
-                        "native": native_forward_summary,
+                        "official_tp1": official_forward_summary,
                         "triton": triton_forward_summary,
-                        "overhead_ratio": (
+                        "latency_ratio_vs_official_tp1": (
                             triton_forward_summary["median_ms"]
-                            / native_forward_summary["median_ms"]
+                            / official_forward_summary["median_ms"]
                         ),
-                        "mismatch_count": sum(
-                            value["forward_mismatch_count"] for value in valid
+                        "tp1_mismatch": {
+                            "forward_output": sum(
+                                value["tp1_forward_output"] for value in valid
+                            ),
+                        },
+                        "repeat_mismatch_count": sum(
+                            value["repeat_forward"] for value in valid
                         ),
                         "train_infer_mismatch_count": sum(
                             value["train_infer_mismatch_count"] for value in valid
                         ),
-                        "output_relative_l2": max(
-                            value["output_relative_l2"] for value in valid
-                        ),
                     },
                     {
                         **common,
                         "direction": "train_fwd_bwd",
-                        "native": native_train_summary,
+                        "official_tp1": official_train_summary,
                         "triton": triton_train_summary,
-                        "overhead_ratio": (
+                        "latency_ratio_vs_official_tp1": (
                             triton_train_summary["median_ms"]
-                            / native_train_summary["median_ms"]
+                            / official_train_summary["median_ms"]
                         ),
-                        "mismatch_count": sum(
-                            value["training_mismatch_count"] for value in valid
+                        "tp1_mismatch": {
+                            "training_output": sum(
+                                value["tp1_training_output"] for value in valid
+                            ),
+                            "hidden_gradient": sum(
+                                value["tp1_hidden_gradient"] for value in valid
+                            ),
+                            "weight_gradient": sum(
+                                value["tp1_weight_gradient"] for value in valid
+                            ),
+                        },
+                        "repeat_mismatch_count": sum(
+                            value["repeat_training"] for value in valid
                         ),
                         "train_infer_mismatch_count": sum(
                             value["train_infer_mismatch_count"] for value in valid
                         ),
-                        "output_relative_l2": max(
-                            value["train_output_relative_l2"] for value in valid
-                        ),
-                        "max_gradient_relative_l2": max(
-                            value["max_gradient_relative_l2"] for value in valid
-                        ),
                     },
                 )
             )
-        del shard, native_inputs, triton_inputs, repeat_inputs
+        del shard, triton_inputs, repeat_inputs
         torch.cuda.empty_cache()
         dist.barrier()
 
@@ -985,11 +655,9 @@ def _distributed_worker(
             init_method=init_method,
             rank=rank,
             world_size=world_size,
+            device_id=torch.device("cuda", rank),
             timeout=timedelta(minutes=15),
         )
-        collectives = _collective_benchmarks(
-            rank, world_size, warmup=warmup, samples=samples
-        )
         distributed_ffn = _distributed_ffn_benchmark(
             rank,
             world_size,
@@ -1003,7 +671,6 @@ def _distributed_worker(
                 {
                     "ok": True,
                     "world_size": world_size,
-                    "collectives": collectives,
                     "distributed_ffn": distributed_ffn,
                 }
             )
@@ -1087,70 +754,109 @@ def _run_distributed_world(
     return result
 
 
-def _ratio_range(rows: list[dict[str, Any]], digits: int = 1) -> str:
-    values = [row["overhead_ratio"] for row in rows]
-    return f"{min(values):.{digits}f}-{max(values):.{digits}f}x"
+
+
+def _topology_exactness_rows(
+    distributed_rows: list[dict[str, Any]],
+) -> list[dict[str, Any]]:
+    merged: dict[str, dict[str, Any]] = {}
+    for row in distributed_rows:
+        entry = merged.setdefault(
+            row["name"],
+            {
+                "name": row["name"],
+                "world_size": row["world_size"],
+                "tp_size": row["tp_size"],
+                "cp_size": row["cp_size"],
+                "sequence_parallel": row["sequence_parallel"],
+                "forward_output": 0,
+                "training_output": 0,
+                "hidden_gradient": 0,
+                "weight_gradient": 0,
+                "repeat": 0,
+                "train_infer": 0,
+            },
+        )
+        for key, value in row["tp1_mismatch"].items():
+            entry[key] += value
+        entry["repeat"] += row["repeat_mismatch_count"]
+        entry["train_infer"] += row["train_infer_mismatch_count"]
+    return list(merged.values())
 
 
 def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
-    gemm_rows = payload["single_gpu"]["gemm"]
-    swiglu_rows = payload["single_gpu"]["swiglu"]
-    ffn_rows = payload["single_gpu"]["ffn"]
-    collective_rows = payload["collectives"]
-    distributed_rows = payload["distributed_ffn"]
-    forward_rows = [row for row in distributed_rows if row["direction"] == "forward"]
-    training_rows = [
-        row for row in distributed_rows if row["direction"] == "train_fwd_bwd"
+    environment = payload["environment"]
+    methodology = payload["methodology"]
+    single_speed = payload["single_gpu"]["speed"]
+    dtype_rows = payload["single_gpu"]["dtype_accuracy"]
+    distributed_speed = payload["distributed_ffn"]
+    exactness_rows = _topology_exactness_rows(distributed_speed)
+    single_ratios = [
+        row["latency_ratio_vs_official_tp1"] for row in single_speed
     ]
-    all_rows = gemm_rows + swiglu_rows + ffn_rows + collective_rows + distributed_rows
-    total_mismatches = sum(row.get("mismatch_count", 0) for row in all_rows)
-    total_train_infer_mismatches = sum(
-        row.get("train_infer_mismatch_count", 0) for row in all_rows
-    )
-    one_mib_collective_ms = [
-        row["deterministic"]["median_ms"]
-        for row in collective_rows
-        if row["message_bytes_per_rank"] == _MIB
+    distributed_ratios = [
+        row["latency_ratio_vs_official_tp1"] for row in distributed_speed
     ]
-    triton_forward_ms = [row["triton"]["median_ms"] for row in forward_rows]
-    triton_training_ms = [row["triton"]["median_ms"] for row in training_rows]
-    methodology = payload["methodology"]
+    total_tp1_mismatch = sum(
+        row[key]
+        for row in exactness_rows
+        for key in (
+            "forward_output",
+            "training_output",
+            "hidden_gradient",
+            "weight_gradient",
+        )
+    )
+    total_repeat_mismatch = sum(row["repeat"] for row in exactness_rows)
+    total_train_infer_mismatch = sum(row["train_infer"] for row in exactness_rows)
+    dtype_row = dtype_rows[0]
 
     lines = [
-        "# PR #325 ROCm-native Triton distributed FFN correctness and performance",
+        "# PR #325 ROCm deterministic Triton FFN report",
+        "",
+        "This is an operator-only MI300X report. It does not load or benchmark a "
+        "model checkpoint.",
         "",
-        "> Operator-only benchmark. No model checkpoint or serving engine was used.",
+        "## Comparison contract",
+        "",
+        "1. **Determinism:** every Triton TP/CP/SP result is compared bitwise with "
+        "the same deterministic Triton FFN at **TP=1**. The reported metric is "
+        "element mismatch count; acceptance requires 0.",
+        "2. **FP16/FP32:** one separate, simple output comparison runs official "
+        "Hugging Face `Qwen3MLP` at TP=1 in FP16 and FP32. FP32 is the reference.",
+        "3. **Speed:** official Hugging Face `Qwen3MLP` at TP=1 is the only speed "
+        "baseline. Speed tables and figures intentionally contain no numerical "
+        "accuracy comparison between official FFN and Triton.",
         "",
         "## Environment",
         "",
-        "| Item | Value |",
+        "| Field | Value |",
         "|---|---|",
     ]
-    for key, value in payload["environment"].items():
+    for key, value in environment.items():
         lines.append(f"| {key} | {value} |")
     lines.extend(
         (
             "",
             "## Methodology",
             "",
-            "- BF16 inputs; production FFN dimensions H=4096 and I=12288.",
-            "- Native compute is PyTorch `torch.matmul`/elementwise ROCm dispatch; "
-            "the deterministic compute path is written directly in Triton.",
-            "- Native communication is ProcessGroupNCCL (RCCL on ROCm); the "
-            "deterministic transport is a fixed rank-order RCCL all-gather followed "
-            "by a balanced BF16 reduction tree.",
-            "- Native and Triton distributed FFNs use identical weights, TP/CP/SP "
-            "sharding, and collective placement.",
-            "- Exactness is accepted separately from native numerical proximity: "
-            "the topology suite compares Triton TP/CP/SP outputs and all gradients "
-            "bitwise against Triton TP=1.",
+            "- Operator shape: H=4096, I=12288; BF16 is used for all speed and "
+            "determinism measurements.",
+            "- Single-GPU shapes use M=1/8/32. Distributed cases use the same full "
+            "logical M=32 input for TP2/4/8, TP+CP, and sequence parallelism.",
+            "- The official performance baseline is upstream Transformers "
+            "`Qwen3MLP` with unsharded weights and input (TP=1). Each distributed "
+            "rank runs that TP=1 reference and the slowest rank/sample is reported.",
+            "- The distributed exactness baseline is the PR's deterministic Triton "
+            "FFN at TP=1. Local outputs, dHidden, and sharded dWeights are compared "
+            "against their exact TP=1 slices.",
             f"- Single-GPU timing: {methodology['single_gpu_timing']}; distributed "
             f"timing: {methodology['distributed_timing']}.",
             f"- {methodology['warmup']} warmups, {methodology['samples']} measured "
-            f"forward/collective samples, and {methodology['training_samples']} "
+            f"forward samples, and {methodology['training_samples']} measured "
             "forward+backward samples.",
-            "- `NCCL_IB_DISABLE=1` forces the same intra-node XGMI transport. Raw "
-            "median, p95, min, and max values are in `results.json`.",
+            "- `NCCL_IB_DISABLE=1` keeps the distributed run on intra-node XGMI. "
+            "Median, p95, min, and max values are available in `results.json`.",
             "",
             "Reproduce from the repository root:",
             "",
@@ -1162,177 +868,122 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             "  --output-dir benchmarks/results/pr325_rocm_mi300x",
             "```",
             "",
-            "Reproduce topology exactness (including Qwen3-8B H=4096/I=12288 TP2):",
-            "",
-            "```bash",
-            "NCCL_IB_DISABLE=1 pytest -q \\",
-            "  tests/distributed/test_qwen_ffn_topology.py",
-            "```",
+            "## Results summary",
             "",
-            "## Key findings",
+            f"- TP=1 exactness baseline: **{total_tp1_mismatch} mismatched "
+            "elements** across topology forward outputs, training outputs, "
+            "dHidden, and dWeights.",
+            f"- Repeat mismatch: **{total_repeat_mismatch}**; training/inference "
+            f"forward mismatch: **{total_train_infer_mismatch}**.",
+            f"- Single-GPU deterministic Triton latency is "
+            f"**{min(single_ratios):.2f}-{max(single_ratios):.2f}x** the official "
+            "Qwen3MLP TP=1 latency across M=1/8/32 and forward/training.",
+            f"- Distributed deterministic Triton latency is "
+            f"**{min(distributed_ratios):.2f}-{max(distributed_ratios):.2f}x** the "
+            "official Qwen3MLP TP=1 latency across tested parallel layouts.",
+            f"- The separate official-Qwen3MLP FP16 versus FP32 observation has "
+            f"relative-L2 error **{dtype_row['relative_l2']:.3e}** for "
+            "(M,H,I)=(8,4096,12288).",
             "",
-            f"- Deterministic Triton GEMM costs {_ratio_range(gemm_rows)} native "
-            "ROCm latency over the tested `(M,K,N)` shapes.",
-            f"- Triton SwiGLU costs {_ratio_range(swiglu_rows, 2)} native PyTorch "
-            f"latency; the complete single-GPU FFN costs {_ratio_range(ffn_rows)}.",
-            f"- Deterministic collectives cost {_ratio_range(collective_rows)} native "
-            "RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.",
-            f"- Distributed Triton FFN costs {_ratio_range(forward_rows)} native for "
-            f"forward and {_ratio_range(training_rows)} for forward+backward across "
-            "TP2/4/8, CP2, and sequence-parallel configurations.",
-            f"- Deterministic repeats produced {total_mismatches} mismatched elements; "
-            f"training and inference produced {total_train_infer_mismatches} "
-            "mismatched elements.",
-            "- TP2/TP4/TP8, TP+CP, and sequence-parallel topology tests produced "
-            "0 output/gradient mismatches versus TP=1; a production-dimension "
-            "Qwen3-8B TP2 smoke test also produced 0 mismatches.",
-            "- Numerical drift means Triton versus native ROCm/RCCL on identical "
-            "BF16 inputs. GEMM-to-FP32 error is separately labelled and uses "
-            "`A.float() @ B.float()` as its reference.",
+            "## Single-GPU FFN speed",
             "",
-            "## Accuracy references",
+            "Performance only; no official-versus-Triton accuracy metric is "
+            "reported here.",
             "",
-            "- `Repeat mismatch`: Triton run 1 versus Triton run 2.",
-            "- `Train/infer mismatch`: Triton training forward versus Triton "
-            "inference forward.",
-            "- `Topology mismatch`: distributed Triton TP/CP/SP versus Triton TP=1.",
-            "- `vs FP32`: BF16 GEMM output versus `A.float() @ B.float()`.",
-            "- `Triton/native rel-L2`: Triton output or gradient versus the native "
-            "ROCm/RCCL path with identical BF16 tensors and parallel layout.",
+            "| Shape / direction | Official Qwen3MLP TP=1 (ms) | Deterministic "
+            "Triton (ms) | Triton / official TP=1 |",
+            "|---|---:|---:|---:|",
         )
     )
-
-    lines.extend(
-        (
-            "",
-            "## Single-GPU GEMM",
-            "",
-            "| Shape | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat "
-            "mismatch | Triton rel-L2 vs FP32 | Native rel-L2 vs FP32 |",
-            "|---|---:|---:|---:|---:|---:|---:|",
-        )
-    )
-    for row in gemm_rows:
+    for row in single_speed:
         lines.append(
-            f"| {row['name']} | {row['native']['median_ms']:.4f} | "
-            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.1f}x | "
-            f"{row['mismatch_count']} | "
-            f"{row['triton_vs_fp32']['relative_l2']:.3e} | "
-            f"{row['native_vs_fp32']['relative_l2']:.3e} |"
+            f"| {row['name']} | {row['official_tp1']['median_ms']:.4f} | "
+            f"{row['triton']['median_ms']:.4f} | "
+            f"{row['latency_ratio_vs_official_tp1']:.2f}x |"
         )
 
     lines.extend(
         (
             "",
-            "## Single-GPU SwiGLU",
+            "## Distributed FFN speed",
             "",
-            "| Case | Native PyTorch (ms) | Triton (ms) | Triton/native | Repeat "
-            "mismatch | Triton/native ROCm rel-L2 |",
-            "|---|---:|---:|---:|---:|---:|",
-        )
-    )
-    for row in swiglu_rows:
-        lines.append(
-            f"| {row['name']} | {row['native']['median_ms']:.4f} | "
-            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.2f}x | "
-            f"{row['mismatch_count']} | {row['triton_vs_native_relative_l2']:.3e} |"
-        )
-
-    lines.extend(
-        (
+            "The baseline remains the full logical M=32 official FFN at TP=1 for "
+            "every row. Performance only; no numerical accuracy is mixed into "
+            "this comparison.",
             "",
-            "## Single-GPU FFN",
-            "",
-            "| Case | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat "
-            "mismatch | Train/infer mismatch | Triton/native output rel-L2 | "
-            "Triton/native max grad rel-L2 |",
-            "|---|---:|---:|---:|---:|---:|---:|---:|",
+            "| Parallel layout | Direction | Official Qwen3MLP TP=1 (ms) | "
+            "Deterministic distributed Triton (ms) | Triton / official TP=1 |",
+            "|---|---|---:|---:|---:|",
         )
     )
-    for row in ffn_rows:
+    for row in distributed_speed:
         lines.append(
-            f"| {row['name']} | {row['native']['median_ms']:.4f} | "
-            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.1f}x | "
-            f"{row['mismatch_count']} | {row['train_infer_mismatch_count']} | "
-            f"{row['triton_vs_native']['relative_l2']:.3e} | "
-            f"{row.get('max_gradient_relative_l2', float('nan')):.3e} |"
+            f"| {row['name']} | {row['direction']} | "
+            f"{row['official_tp1']['median_ms']:.4f} | "
+            f"{row['triton']['median_ms']:.4f} | "
+            f"{row['latency_ratio_vs_official_tp1']:.2f}x |"
         )
 
     lines.extend(
         (
             "",
-            "## RCCL collectives",
+            "## Topology exactness versus Triton TP=1",
             "",
-            "| Operation | Ranks | Input/rank | Native RCCL (ms) | Deterministic "
-            "(ms) | Overhead | Repeat mismatch |",
+            "All columns are element mismatch counts. This table does not compare "
+            "against the official FFN.",
+            "",
+            "| Parallel layout | Forward output | Training output | dHidden | "
+            "dWeights | Repeat | Train/infer |",
             "|---|---:|---:|---:|---:|---:|---:|",
         )
     )
-    for row in collective_rows:
+    for row in exactness_rows:
         lines.append(
-            f"| {row['operation']} | {row['world_size']} | "
-            f"{row['message_bytes_per_rank'] / _MIB:.4g} MiB | "
-            f"{row['native']['median_ms']:.4f} | "
-            f"{row['deterministic']['median_ms']:.4f} | "
-            f"{row['overhead_ratio']:.1f}x | {row['mismatch_count']} |"
+            f"| {row['name']} | {row['forward_output']} | "
+            f"{row['training_output']} | {row['hidden_gradient']} | "
+            f"{row['weight_gradient']} | {row['repeat']} | "
+            f"{row['train_infer']} |"
         )
 
     lines.extend(
         (
             "",
-            "## Distributed FFN",
+            "## Simple FP16 versus FP32 observation",
             "",
-            "| Topology | Direction | Native ROCm/RCCL (ms) | Triton/deterministic "
-            "RCCL (ms) | Overhead | Repeat mismatch | Train/infer mismatch | "
-            "Triton/native output rel-L2 | Triton/native max grad rel-L2 |",
-            "|---|---|---:|---:|---:|---:|---:|---:|---:|",
-        )
-    )
-    for row in distributed_rows:
-        lines.append(
-            f"| {row['name']} | {row['direction']} | "
-            f"{row['native']['median_ms']:.4f} | "
-            f"{row['triton']['median_ms']:.4f} | {row['overhead_ratio']:.1f}x | "
-            f"{row['mismatch_count']} | {row['train_infer_mismatch_count']} | "
-            f"{row['output_relative_l2']:.3e} | "
-            f"{row.get('max_gradient_relative_l2', float('nan')):.3e} |"
-        )
-
-    lines.extend(
-        (
+            "This is an official `Qwen3MLP` TP=1 output comparison only; it is not "
+            "used to judge deterministic Triton and is not included in speed "
+            "ratios.",
+            "",
+            "| Shape | Candidate | Reference | Max abs | Mean abs | Relative L2 |",
+            "|---|---|---|---:|---:|---:|",
+            f"| (M,H,I)=({dtype_row['tokens']},{dtype_row['hidden']},"
+            f"{dtype_row['intermediate']}) | FP16 | FP32 | "
+            f"{dtype_row['max_abs']:.3e} | {dtype_row['mean_abs']:.3e} | "
+            f"{dtype_row['relative_l2']:.3e} |",
             "",
-            "## Communication overlap feasibility",
+            "## Deterministic communication overlap",
             "",
-            "The measured implementation deliberately serializes dependencies; the "
-            "numbers above do not claim overlap. A representative 1 MiB "
-            f"deterministic collective costs {min(one_mib_collective_ms):.4f}-"
-            f"{max(one_mib_collective_ms):.4f} ms, while distributed Triton FFN "
-            f"forward costs {min(triton_forward_ms):.4f}-"
-            f"{max(triton_forward_ms):.4f} ms and forward+backward costs "
-            f"{min(triton_training_ms):.4f}-{max(triton_training_ms):.4f} ms.",
+            "The current timing includes the fixed-order communication schedule "
+            "and makes no overlap claim. Forward SP all-gather must finish before "
+            "gate/up projection, and TP reduction consumes the down-projection "
+            "output, so those edges are hard dependencies.",
             "",
-            "- Forward SP all-gather feeds both gate/up GEMMs, and the final TP "
-            "all-reduce or reduce-scatter consumes the down projection. These are "
-            "hard data dependencies.",
-            "- Backward can overlap the fixed-order TP reduction of the gate "
-            "contribution to dHidden with computation of the independent up "
-            "contribution. The final wait and gate-then-up add order must remain "
-            "unchanged to preserve 0 mismatch.",
-            "- CP gathers are prerequisites for weight-gradient GEMMs. Packing them "
-            "in a fixed layout can amortize launch/signature overhead, but they "
-            "cannot be hidden behind the GEMMs that consume them.",
-            "- Coalescing and host-side validation caching should be measured before "
-            "introducing multi-stream scheduling complexity.",
+            "In backward, the gate and up contributions to dHidden are independent "
+            "until their final ordered addition. A future implementation can place "
+            "the fixed-rank reduction of one contribution on a second stream while "
+            "computing the other, but it must preserve rank order, reduction tree, "
+            "wait points, and gate-then-up addition order. Any optimization is "
+            "accepted only if every TP=1 mismatch column remains zero.",
             "",
             "## Figures",
             "",
-            "![Single-GPU overhead](single_gpu_overhead.png)",
+            "![Single-GPU official TP=1 versus Triton speed](single_gpu_overhead.png)",
             "",
-            "![RCCL collective overhead](collective_overhead.png)",
+            "![Topology mismatch versus Triton TP=1](collective_overhead.png)",
             "",
-            "![Distributed FFN overhead](distributed_ffn_overhead.png)",
+            "![Distributed official TP=1 versus Triton speed](distributed_ffn_overhead.png)",
             "",
-            "![Accuracy trade-off](accuracy_tradeoff.png)",
+            "![Official Qwen3MLP FP16 versus FP32](accuracy_tradeoff.png)",
             "",
         )
     )
@@ -1346,198 +997,186 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
 
     matplotlib.use("Agg")
     import matplotlib.pyplot as plt
+    import numpy as np
 
     plt.style.use("seaborn-v0_8-whitegrid")
     plt.rcParams.update(
         {
-            "font.size": 16,
-            "axes.titlesize": 24,
-            "axes.labelsize": 20,
-            "xtick.labelsize": 13,
+            "font.size": 18,
+            "axes.titlesize": 25,
+            "axes.labelsize": 21,
+            "xtick.labelsize": 15,
             "ytick.labelsize": 16,
-            "legend.fontsize": 16,
+            "legend.fontsize": 17,
         }
     )
 
-    gemm_rows = payload["single_gpu"]["gemm"]
-    swiglu_rows = payload["single_gpu"]["swiglu"]
-    ffn_rows = payload["single_gpu"]["ffn"]
-    single_rows = gemm_rows + swiglu_rows + ffn_rows
-    single_labels = (
-        [f"GEMM\n({row['m']},{row['k']},{row['n']})" for row in gemm_rows]
-        + [
-            f"SwiGLU {row['direction']}\n(M,I)=({row['tokens']},12288)"
-            for row in swiglu_rows
-        ]
-        + [
-            f"FFN {'FWD' if row['direction'] == 'forward' else 'FWD+BWD'}"
-            f"\n(M,H,I)=({row['tokens']},4096,12288)"
-            for row in ffn_rows
-        ]
-    )
-    single_ratios = [row["overhead_ratio"] for row in single_rows]
-    figure, axis = plt.subplots(figsize=(26, 10))
-    positions = list(range(len(single_rows)))
-    bars = axis.bar(
-        positions,
-        single_ratios,
-        color="#8b5cf6",
-        label="Deterministic Triton",
+    single_rows = payload["single_gpu"]["speed"]
+    single_labels = [
+        f"M={row['tokens']}\n"
+        f"{'FWD' if row['direction'] == 'forward' else 'FWD+BWD'}"
+        for row in single_rows
+    ]
+    positions = np.arange(len(single_rows))
+    width = 0.37
+    figure, axis = plt.subplots(figsize=(17, 10))
+    official_values = [row["official_tp1"]["median_ms"] for row in single_rows]
+    triton_values = [row["triton"]["median_ms"] for row in single_rows]
+    official_bars = axis.bar(
+        positions - width / 2,
+        official_values,
+        width,
+        label="Official Qwen3MLP TP=1",
+        color="#2563eb",
     )
-    axis.bar_label(
-        bars,
-        labels=[f"{value:.1f}×" for value in single_ratios],
-        padding=4,
-        fontsize=13,
-        rotation=90,
+    triton_bars = axis.bar(
+        positions + width / 2,
+        triton_values,
+        width,
+        label="Deterministic Triton FFN",
+        color="#7c3aed",
     )
-    axis.axhline(1.0, color="black", linewidth=2, label="Native ROCm = 1.0×")
+    for bars, values in ((official_bars, official_values), (triton_bars, triton_values)):
+        axis.bar_label(
+            bars,
+            labels=[f"{value:.2f}" for value in values],
+            padding=4,
+            fontsize=13,
+            rotation=90,
+        )
     axis.set_yscale("log")
-    axis.set_ylim(0.5, max(single_ratios) * 1.8)
-    axis.set_xlabel("Operator and tensor shape")
-    axis.set_ylabel("Median latency ratio (×, log scale)\nTriton / native ROCm")
-    axis.set_title("MI300X single-GPU operator latency by shape")
-    axis.set_xticks(positions, single_labels, rotation=55, ha="right")
+    axis.set_xlabel("Token count M and measured direction\nH=4096, I=12288, BF16")
+    axis.set_ylabel("Median latency (ms, log scale)")
+    axis.set_title("MI300X single-GPU FFN speed: official TP=1 vs Triton")
+    axis.set_xticks(positions, single_labels)
     axis.legend(loc="upper left")
     figure.tight_layout()
     figure.savefig(output_directory / "single_gpu_overhead.png", dpi=180)
     plt.close(figure)
 
-    figure, axes = plt.subplots(1, 3, figsize=(24, 8), sharey=True)
-    for axis, operation in zip(
-        axes, ("all_reduce", "all_gather", "reduce_scatter"), strict=True
-    ):
-        for world_size in (2, 4, 8):
-            rows = [
-                row
-                for row in payload["collectives"]
-                if row["operation"] == operation and row["world_size"] == world_size
-            ]
-            x_values = [row["message_bytes_per_rank"] / _MIB for row in rows]
-            y_values = [row["overhead_ratio"] for row in rows]
-            axis.plot(
-                x_values,
-                y_values,
-                marker="o",
-                linewidth=3,
-                markersize=9,
-                label=f"{world_size} ranks",
+    exactness_rows = _topology_exactness_rows(payload["distributed_ffn"])
+    mismatch_keys = (
+        "forward_output",
+        "training_output",
+        "hidden_gradient",
+        "weight_gradient",
+    )
+    mismatch_labels = (
+        "Forward\noutput",
+        "Training\noutput",
+        "dHidden",
+        "dWeights",
+    )
+    matrix = np.asarray(
+        [[row[key] for key in mismatch_keys] for row in exactness_rows],
+        dtype=float,
+    )
+    figure, axis = plt.subplots(figsize=(16, 11))
+    image = axis.imshow(matrix, cmap="RdYlGn_r", vmin=0, vmax=max(1.0, matrix.max()))
+    for y_position in range(matrix.shape[0]):
+        for x_position in range(matrix.shape[1]):
+            axis.text(
+                x_position,
+                y_position,
+                str(int(matrix[y_position, x_position])),
+                ha="center",
+                va="center",
+                fontsize=18,
+                fontweight="bold",
             )
-            for x_value, y_value in zip(x_values, y_values, strict=True):
-                y_offset = -17 if world_size == 2 else 9
-                axis.annotate(
-                    f"{y_value:.1f}×",
-                    (x_value, y_value),
-                    xytext=(0, y_offset),
-                    textcoords="offset points",
-                    ha="center",
-                    va="top" if y_offset < 0 else "bottom",
-                    fontsize=12,
-                )
-        axis.axhline(1.0, color="black", linewidth=2)
-        axis.set_xscale("log", base=2)
-        axis.set_yscale("log")
-        axis.set_xticks((0.0625, 1, 16), labels=("0.0625", "1", "16"))
-        axis.set_title(operation.replace("_", " ").title())
-        axis.set_xlabel("BF16 input per rank (MiB)")
-    axes[0].set_ylabel(
-        "Median latency ratio (×, log scale)\nDeterministic / native RCCL"
+    axis.set_xticks(range(len(mismatch_labels)), mismatch_labels)
+    axis.set_yticks(
+        range(len(exactness_rows)),
+        [
+            f"TP={row['tp_size']}, CP={row['cp_size']}, "
+            f"SP={'on' if row['sequence_parallel'] else 'off'}"
+            for row in exactness_rows
+        ],
     )
-    axes[-1].legend(title="World size", loc="lower left")
-    figure.suptitle("MI300X collective latency by message size and rank count", y=0.98)
-    figure.tight_layout(rect=(0.0, 0.0, 1.0, 0.94))
+    axis.set_xlabel("Compared tensor category")
+    axis.set_ylabel("Distributed parallel configuration")
+    axis.set_title("Topology mismatch vs Triton TP=1")
+    colorbar = figure.colorbar(image, ax=axis, fraction=0.03, pad=0.03)
+    colorbar.set_label("Mismatched elements")
+    figure.tight_layout()
     figure.savefig(output_directory / "collective_overhead.png", dpi=180)
     plt.close(figure)
 
     rows = payload["distributed_ffn"]
-    labels = [
-        f"TP={row['tp_size']}, CP={row['cp_size']}, "
-        f"SP={'on' if row['sequence_parallel'] else 'off'}\n"
-        f"{'FWD' if row['direction'] == 'forward' else 'FWD+BWD'}"
-        for row in rows
-    ]
-    ratios = [row["overhead_ratio"] for row in rows]
-    figure, axis = plt.subplots(figsize=(24, 9))
-    positions = list(range(len(rows)))
-    bars = axis.bar(
-        positions,
-        ratios,
-        color="#8b5cf6",
-        label="Triton + deterministic RCCL",
-    )
-    axis.bar_label(
-        bars,
-        labels=[f"{value:.1f}×" for value in ratios],
-        padding=5,
-        fontsize=13,
-    )
-    axis.axhline(
-        1.0,
-        color="black",
-        linewidth=2,
-        label="Native ROCm/RCCL = 1.0×",
-    )
-    axis.set_yscale("log")
-    axis.set_ylim(0.8, max(ratios) * 1.4)
-    axis.set_xlabel("Parallel configuration and measured direction")
-    axis.set_ylabel(
-        "Median latency ratio (×, log scale)\n"
-        "Triton + deterministic RCCL / native"
+    figure, axes = plt.subplots(1, 2, figsize=(24, 10), sharey=True)
+    for axis, direction, direction_label in (
+        (axes[0], "forward", "Forward"),
+        (axes[1], "train_fwd_bwd", "Forward + backward"),
+    ):
+        direction_rows = [row for row in rows if row["direction"] == direction]
+        positions = np.arange(len(direction_rows))
+        labels = [row["name"].upper().replace("_", "\n") for row in direction_rows]
+        official_values = [
+            row["official_tp1"]["median_ms"] for row in direction_rows
+        ]
+        triton_values = [row["triton"]["median_ms"] for row in direction_rows]
+        official_bars = axis.bar(
+            positions - width / 2,
+            official_values,
+            width,
+            label="Official Qwen3MLP TP=1",
+            color="#2563eb",
+        )
+        triton_bars = axis.bar(
+            positions + width / 2,
+            triton_values,
+            width,
+            label="Deterministic distributed Triton",
+            color="#7c3aed",
+        )
+        for bars, values in (
+            (official_bars, official_values),
+            (triton_bars, triton_values),
+        ):
+            axis.bar_label(
+                bars,
+                labels=[f"{value:.2f}" for value in values],
+                padding=4,
+                fontsize=12,
+                rotation=90,
+            )
+        axis.set_yscale("log")
+        axis.set_xlabel("Parallel configuration\nM=32, H=4096, I=12288, BF16")
+        axis.set_title(direction_label)
+        axis.set_xticks(positions, labels)
+    axes[0].set_ylabel("Median latency (ms, log scale)")
+    axes[0].legend(loc="upper left")
+    figure.suptitle(
+        "MI300X distributed FFN speed: official TP=1 vs Triton",
+        y=0.99,
     )
-    axis.set_title("MI300X distributed FFN latency by TP / CP / SP configuration")
-    axis.set_xticks(positions, labels, rotation=50, ha="right")
-    axis.legend(loc="upper right")
-    figure.tight_layout()
+    figure.tight_layout(rect=(0.0, 0.0, 1.0, 0.95))
     figure.savefig(output_directory / "distributed_ffn_overhead.png", dpi=180)
     plt.close(figure)
 
-    figure, axis = plt.subplots(figsize=(19, 9))
-    positions = list(range(len(gemm_rows)))
-    width = 0.4
-    native_errors = [
-        row["native_vs_fp32"]["relative_l2"] * 100.0 for row in gemm_rows
-    ]
-    triton_errors = [
-        row["triton_vs_fp32"]["relative_l2"] * 100.0 for row in gemm_rows
-    ]
-    native_bars = axis.bar(
-        [position - width / 2 for position in positions],
-        native_errors,
-        width,
-        label="Native ROCm BF16 vs FP32",
-        color="#3b82f6",
-    )
-    triton_bars = axis.bar(
-        [position + width / 2 for position in positions],
-        triton_errors,
-        width,
-        label="Deterministic Triton BF16 vs FP32",
-        color="#8b5cf6",
-    )
-    for bars, values in (
-        (native_bars, native_errors),
-        (triton_bars, triton_errors),
-    ):
-        axis.bar_label(
-            bars,
-            labels=[f"{value:.3f}%" for value in values],
-            padding=4,
-            fontsize=12,
-            rotation=90,
-        )
-    axis.set_ylim(0.0, max(triton_errors) * 1.3)
-    axis.set_xlabel("GEMM shape (M, K, N)")
-    axis.set_ylabel(
-        "Relative L2 error (%)\nReference: FP32 A.float() @ B.float()"
+    dtype_row = payload["single_gpu"]["dtype_accuracy"][0]
+    relative_l2_percent = dtype_row["relative_l2"] * 100.0
+    figure, axis = plt.subplots(figsize=(11, 9))
+    bars = axis.bar(
+        [0],
+        [relative_l2_percent],
+        width=0.48,
+        color="#0f766e",
+        label="Official Qwen3MLP TP=1",
     )
-    axis.set_title("MI300X BF16 GEMM numerical error by shape and implementation")
-    axis.set_xticks(
-        positions,
-        [f"({row['m']}, {row['k']}, {row['n']})" for row in gemm_rows],
-        rotation=45,
-        ha="right",
+    axis.bar_label(
+        bars,
+        labels=[f"{relative_l2_percent:.4f}%"],
+        padding=7,
+        fontsize=19,
+        fontweight="bold",
     )
-    axis.legend(loc="upper left")
+    axis.set_ylim(0.0, max(0.01, relative_l2_percent * 1.3))
+    axis.set_xticks([0], ["FP16 output"])
+    axis.set_xlabel("Candidate dtype\n(M,H,I)=(8,4096,12288)")
+    axis.set_ylabel("Relative L2 error (%)\nReference: official Qwen3MLP FP32")
+    axis.set_title("MI300X simple FP16 vs FP32 accuracy observation")
+    axis.legend(loc="upper right")
     figure.tight_layout()
     figure.savefig(output_directory / "accuracy_tradeoff.png", dpi=180)
     plt.close(figure)
@@ -1550,12 +1189,13 @@ def _environment() -> dict[str, Any]:
         "gpu_count": torch.cuda.device_count(),
         "architecture": properties.gcnArchName,
         "torch": torch.__version__,
+        "transformers": transformers_version,
         "hip": torch.version.hip,
         "python": os.sys.version.split()[0],
         "git_commit": os.popen("git rev-parse HEAD").read().strip(),
-        "native_gemm": "torch.matmul (ROCm rocBLAS/hipBLASLt dispatch)",
+        "speed_baseline": "Hugging Face Transformers Qwen3MLP, TP=1",
         "deterministic_compute": "ROCm-native Triton",
-        "native_collective": "ProcessGroupNCCL (RCCL on ROCm)",
+        "deterministic_transport": "fixed-order RCCL on ROCm",
         "NCCL_IB_DISABLE": os.environ.get("NCCL_IB_DISABLE", ""),
     }
 
@@ -1602,7 +1242,6 @@ def main() -> None:
             samples=args.samples,
             training_samples=args.training_samples,
         ),
-        "collectives": [],
         "distributed_ffn": [],
     }
     torch.cuda.empty_cache()
@@ -1613,7 +1252,6 @@ def main() -> None:
             samples=args.samples,
             training_samples=args.training_samples,
         )
-        payload["collectives"].extend(result["collectives"])
         payload["distributed_ffn"].extend(result["distributed_ffn"])
     (args.output_dir / "results.json").write_text(
         json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8"

From 981c7ab58223c6d031e13445685e81b67bede7b4 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sun, 23 Aug 2026 09:10:05 +0000
Subject: [PATCH 15/28] docs(bench): report TP1 FFN speed and exactness

---
 .../pr325_rocm_mi300x/accuracy_tradeoff.png   |  Bin 282180 -> 158655 bytes
 .../pr325_rocm_mi300x/collective_overhead.png |  Bin 362192 -> 198637 bytes
 .../distributed_ffn_overhead.png              |  Bin 257182 -> 210813 bytes
 .../results/pr325_rocm_mi300x/report.md       |  223 +--
 .../results/pr325_rocm_mi300x/results.json    | 1670 ++++-------------
 .../pr325_rocm_mi300x/single_gpu_overhead.png |  Bin 460768 -> 167538 bytes
 6 files changed, 409 insertions(+), 1484 deletions(-)

diff --git a/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png b/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png
index 3ca1a2643e3370a830a00ceb6135c982d2872076..98eacd7f28c90cef1b7d27f8fef3de335da087bb 100644
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diff --git a/benchmarks/results/pr325_rocm_mi300x/report.md b/benchmarks/results/pr325_rocm_mi300x/report.md
index b3f7c49d..9e84bda2 100644
--- a/benchmarks/results/pr325_rocm_mi300x/report.md
+++ b/benchmarks/results/pr325_rocm_mi300x/report.md
@@ -1,33 +1,39 @@
-# PR #325 ROCm-native Triton distributed FFN correctness and performance
+# PR #325 ROCm deterministic Triton FFN report
 
-> Operator-only benchmark. No model checkpoint or serving engine was used.
+This is an operator-only MI300X report. It does not load or benchmark a model checkpoint.
+
+## Comparison contract
+
+1. **Determinism:** every Triton TP/CP/SP result is compared bitwise with the same deterministic Triton FFN at **TP=1**. The reported metric is element mismatch count; acceptance requires 0.
+2. **FP16/FP32:** one separate, simple output comparison runs official Hugging Face `Qwen3MLP` at TP=1 in FP16 and FP32. FP32 is the reference.
+3. **Speed:** official Hugging Face `Qwen3MLP` at TP=1 is the only speed baseline. Speed tables and figures intentionally contain no numerical accuracy comparison between official FFN and Triton.
 
 ## Environment
 
-| Item | Value |
+| Field | Value |
 |---|---|
 | gpu | AMD Instinct MI300X |
 | gpu_count | 8 |
 | architecture | gfx942:sramecc+:xnack- |
 | torch | 2.12.0+rocm7.14.0a20260608 |
+| transformers | 5.10.4 |
 | hip | 7.14.60850 |
 | python | 3.12.3 |
-| git_commit | 43a31ae7f6535e2c11efdd50d96b6633805359fd |
-| native_gemm | torch.matmul (ROCm rocBLAS/hipBLASLt dispatch) |
+| git_commit | 08f47d97d0443c5998b8da6b41a22fdf3848da8f |
+| speed_baseline | Hugging Face Transformers Qwen3MLP, TP=1 |
 | deterministic_compute | ROCm-native Triton |
-| native_collective | ProcessGroupNCCL (RCCL on ROCm) |
+| deterministic_transport | fixed-order RCCL on ROCm |
 | NCCL_IB_DISABLE | 1 |
 
 ## Methodology
 
-- BF16 inputs; production FFN dimensions H=4096 and I=12288.
-- Native compute is PyTorch `torch.matmul`/elementwise ROCm dispatch; the deterministic compute path is written directly in Triton.
-- Native communication is ProcessGroupNCCL (RCCL on ROCm); the deterministic transport is a fixed rank-order RCCL all-gather followed by a balanced BF16 reduction tree.
-- Native and Triton distributed FFNs use identical weights, TP/CP/SP sharding, and collective placement.
-- Exactness is accepted separately from native numerical proximity: the topology suite compares Triton TP/CP/SP outputs and all gradients bitwise against Triton TP=1.
+- Operator shape: H=4096, I=12288; BF16 is used for all speed and determinism measurements.
+- Single-GPU shapes use M=1/8/32. Distributed cases use the same full logical M=32 input for TP2/4/8, TP+CP, and sequence parallelism.
+- The official performance baseline is upstream Transformers `Qwen3MLP` with unsharded weights and input (TP=1). Each distributed rank runs that TP=1 reference and the slowest rank/sample is reported.
+- The distributed exactness baseline is the PR's deterministic Triton FFN at TP=1. Local outputs, dHidden, and sharded dWeights are compared against their exact TP=1 slices.
 - Single-GPU timing: GPU events, median and p95; distributed timing: synchronized wall clock, slowest rank/sample.
-- 3 warmups, 10 measured forward/collective samples, and 5 forward+backward samples.
-- `NCCL_IB_DISABLE=1` forces the same intra-node XGMI transport. Raw median, p95, min, and max values are in `results.json`.
+- 3 warmups, 10 measured forward samples, and 5 measured forward+backward samples.
+- `NCCL_IB_DISABLE=1` keeps the distributed run on intra-node XGMI. Median, p95, min, and max values are available in `results.json`.
 
 Reproduce from the repository root:
 
@@ -39,136 +45,85 @@ python benchmarks/benchmark_rocm_ffn.py \
   --output-dir benchmarks/results/pr325_rocm_mi300x
 ```
 
-Reproduce topology exactness (including Qwen3-8B H=4096/I=12288 TP2):
-
-```bash
-NCCL_IB_DISABLE=1 pytest -q \
-  tests/distributed/test_qwen_ffn_topology.py
-```
+## Results summary
+
+- TP=1 exactness baseline: **0 mismatched elements** across topology forward outputs, training outputs, dHidden, and dWeights.
+- Repeat mismatch: **0**; training/inference forward mismatch: **0**.
+- Single-GPU deterministic Triton latency is **7.38-22.86x** the official Qwen3MLP TP=1 latency across M=1/8/32 and forward/training.
+- Distributed deterministic Triton latency is **7.45-15.79x** the official Qwen3MLP TP=1 latency across tested parallel layouts.
+- The separate official-Qwen3MLP FP16 versus FP32 observation has relative-L2 error **6.544e-04** for (M,H,I)=(8,4096,12288).
+
+## Single-GPU FFN speed
+
+Performance only; no official-versus-Triton accuracy metric is reported here.
+
+| Shape / direction | Official Qwen3MLP TP=1 (ms) | Deterministic Triton (ms) | Triton / official TP=1 |
+|---|---:|---:|---:|
+| (M,H,I)=(1,4096,12288), forward | 0.1183 | 1.9102 | 16.15x |
+| (M,H,I)=(1,4096,12288), forward+backward | 0.6008 | 4.4312 | 7.38x |
+| (M,H,I)=(8,4096,12288), forward | 0.2263 | 2.0424 | 9.03x |
+| (M,H,I)=(8,4096,12288), forward+backward | 0.3997 | 4.6210 | 11.56x |
+| (M,H,I)=(32,4096,12288), forward | 0.1108 | 2.5334 | 22.86x |
+| (M,H,I)=(32,4096,12288), forward+backward | 0.7081 | 5.7710 | 8.15x |
+
+## Distributed FFN speed
+
+The baseline remains the full logical M=32 official FFN at TP=1 for every row. Performance only; no numerical accuracy is mixed into this comparison.
+
+| Parallel layout | Direction | Official Qwen3MLP TP=1 (ms) | Deterministic distributed Triton (ms) | Triton / official TP=1 |
+|---|---|---:|---:|---:|
+| tp2 | forward | 0.1575 | 1.9598 | 12.44x |
+| tp2 | train_fwd_bwd | 0.6511 | 5.1613 | 7.93x |
+| tp2_sp | forward | 0.1575 | 2.4866 | 15.79x |
+| tp2_sp | train_fwd_bwd | 0.6511 | 9.0512 | 13.90x |
+| tp4 | forward | 0.1644 | 1.5044 | 9.15x |
+| tp4 | train_fwd_bwd | 0.7244 | 5.3938 | 7.45x |
+| tp2_cp2 | forward | 0.1644 | 1.8202 | 11.07x |
+| tp2_cp2 | train_fwd_bwd | 0.7244 | 10.1842 | 14.06x |
+| tp2_cp2_sp | forward | 0.1644 | 2.3788 | 14.47x |
+| tp2_cp2_sp | train_fwd_bwd | 0.7244 | 9.7862 | 13.51x |
+| tp8 | forward | 0.1692 | 1.7434 | 10.31x |
+| tp8 | train_fwd_bwd | 0.6325 | 5.1244 | 8.10x |
+| tp4_cp2 | forward | 0.1692 | 1.4826 | 8.76x |
+| tp4_cp2 | train_fwd_bwd | 0.6325 | 7.2102 | 11.40x |
+| tp4_cp2_sp | forward | 0.1692 | 2.1856 | 12.92x |
+| tp4_cp2_sp | train_fwd_bwd | 0.6325 | 8.6635 | 13.70x |
+
+## Topology exactness versus Triton TP=1
+
+All columns are element mismatch counts. This table does not compare against the official FFN.
+
+| Parallel layout | Forward output | Training output | dHidden | dWeights | Repeat | Train/infer |
+|---|---:|---:|---:|---:|---:|---:|
+| tp2 | 0 | 0 | 0 | 0 | 0 | 0 |
+| tp2_sp | 0 | 0 | 0 | 0 | 0 | 0 |
+| tp4 | 0 | 0 | 0 | 0 | 0 | 0 |
+| tp2_cp2 | 0 | 0 | 0 | 0 | 0 | 0 |
+| tp2_cp2_sp | 0 | 0 | 0 | 0 | 0 | 0 |
+| tp8 | 0 | 0 | 0 | 0 | 0 | 0 |
+| tp4_cp2 | 0 | 0 | 0 | 0 | 0 | 0 |
+| tp4_cp2_sp | 0 | 0 | 0 | 0 | 0 | 0 |
 
-## Key findings
+## Simple FP16 versus FP32 observation
 
-- Deterministic Triton GEMM costs 5.4-31.5x native ROCm latency over the tested `(M,K,N)` shapes.
-- Triton SwiGLU costs 0.72-1.60x native PyTorch latency; the complete single-GPU FFN costs 7.4-23.1x.
-- Deterministic collectives cost 2.1-19.9x native RCCL latency across 2/4/8 ranks and 64 KiB/1 MiB/16 MiB per rank.
-- Distributed Triton FFN costs 6.5-9.1x native for forward and 4.4-6.8x for forward+backward across TP2/4/8, CP2, and sequence-parallel configurations.
-- Deterministic repeats produced 0 mismatched elements; training and inference produced 0 mismatched elements.
-- TP2/TP4/TP8, TP+CP, and sequence-parallel topology tests produced 0 output/gradient mismatches versus TP=1; a production-dimension Qwen3-8B TP2 smoke test also produced 0 mismatches.
-- Numerical drift means Triton versus native ROCm/RCCL on identical BF16 inputs. GEMM-to-FP32 error is separately labelled and uses `A.float() @ B.float()` as its reference.
+This is an official `Qwen3MLP` TP=1 output comparison only; it is not used to judge deterministic Triton and is not included in speed ratios.
 
-## Accuracy references
+| Shape | Candidate | Reference | Max abs | Mean abs | Relative L2 |
+|---|---|---|---:|---:|---:|
+| (M,H,I)=(8,4096,12288) | FP16 | FP32 | 2.046e-06 | 3.742e-07 | 6.544e-04 |
 
-- `Repeat mismatch`: Triton run 1 versus Triton run 2.
-- `Train/infer mismatch`: Triton training forward versus Triton inference forward.
-- `Topology mismatch`: distributed Triton TP/CP/SP versus Triton TP=1.
-- `vs FP32`: BF16 GEMM output versus `A.float() @ B.float()`.
-- `Triton/native rel-L2`: Triton output or gradient versus the native ROCm/RCCL path with identical BF16 tensors and parallel layout.
+## Deterministic communication overlap
 
-## Single-GPU GEMM
+The current timing includes the fixed-order communication schedule and makes no overlap claim. Forward SP all-gather must finish before gate/up projection, and TP reduction consumes the down-projection output, so those edges are hard dependencies.
 
-| Shape | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat mismatch | Triton rel-L2 vs FP32 | Native rel-L2 vs FP32 |
-|---|---:|---:|---:|---:|---:|---:|
-| (M,K,N)=(1,4096,12288) | 0.0388 | 0.2087 | 5.4x | 0 | 5.003e-03 | 1.651e-03 |
-| (M,K,N)=(1,12288,4096) | 0.0299 | 0.2629 | 8.8x | 0 | 5.769e-03 | 1.635e-03 |
-| (M,K,N)=(8,4096,12288) | 0.0305 | 0.2543 | 8.3x | 0 | 5.018e-03 | 1.663e-03 |
-| (M,K,N)=(8,12288,4096) | 0.0315 | 0.3225 | 10.2x | 0 | 5.679e-03 | 1.654e-03 |
-| (M,K,N)=(32,4096,12288) | 0.0321 | 0.4758 | 14.8x | 0 | 5.019e-03 | 1.654e-03 |
-| (M,K,N)=(32,12288,4096) | 0.0308 | 0.4955 | 16.1x | 0 | 5.604e-03 | 1.662e-03 |
-| (M,K,N)=(128,4096,12288) | 0.0472 | 1.2570 | 26.7x | 0 | 5.019e-03 | 1.657e-03 |
-| (M,K,N)=(128,12288,4096) | 0.0479 | 1.5075 | 31.5x | 0 | 5.623e-03 | 1.659e-03 |
-
-## Single-GPU SwiGLU
-
-| Case | Native PyTorch (ms) | Triton (ms) | Triton/native | Repeat mismatch | Triton/native ROCm rel-L2 |
-|---|---:|---:|---:|---:|---:|
-| (M,I)=(1,12288), forward | 0.0176 | 0.0282 | 1.60x | 0 | 2.877e-03 |
-| (M,I)=(1,12288), backward | 0.0590 | 0.0507 | 0.86x | 0 | 5.159e-03 |
-| (M,I)=(8,12288), forward | 0.0203 | 0.0285 | 1.40x | 0 | 2.816e-03 |
-| (M,I)=(8,12288), backward | 0.0684 | 0.0512 | 0.75x | 0 | 5.074e-03 |
-| (M,I)=(32,12288), forward | 0.0203 | 0.0280 | 1.38x | 0 | 2.825e-03 |
-| (M,I)=(32,12288), backward | 0.0684 | 0.0495 | 0.72x | 0 | 5.071e-03 |
-| (M,I)=(128,12288), forward | 0.0196 | 0.0268 | 1.37x | 0 | 2.828e-03 |
-| (M,I)=(128,12288), backward | 0.0643 | 0.0493 | 0.77x | 0 | 5.059e-03 |
-
-## Single-GPU FFN
-
-| Case | Native ROCm (ms) | Triton (ms) | Triton/native | Repeat mismatch | Train/infer mismatch | Triton/native output rel-L2 | Triton/native max grad rel-L2 |
-|---|---:|---:|---:|---:|---:|---:|---:|
-| (M,H,I)=(1,4096,12288), forward | 0.1052 | 1.8770 | 17.8x | 0 | 0 | 1.008e-02 | nan |
-| (M,H,I)=(1,4096,12288), forward+backward | 0.3984 | 4.4071 | 11.1x | 0 | 0 | 1.008e-02 | 1.037e-02 |
-| (M,H,I)=(8,4096,12288), forward | 0.1081 | 2.0033 | 18.5x | 0 | 0 | 9.930e-03 | nan |
-| (M,H,I)=(8,4096,12288), forward+backward | 0.6354 | 4.6865 | 7.4x | 0 | 0 | 9.930e-03 | 1.017e-02 |
-| (M,H,I)=(32,4096,12288), forward | 0.1085 | 2.5084 | 23.1x | 0 | 0 | 9.952e-03 | nan |
-| (M,H,I)=(32,4096,12288), forward+backward | 0.4620 | 5.7915 | 12.5x | 0 | 0 | 9.952e-03 | 1.024e-02 |
-
-## RCCL collectives
-
-| Operation | Ranks | Input/rank | Native RCCL (ms) | Deterministic (ms) | Overhead | Repeat mismatch |
-|---|---:|---:|---:|---:|---:|---:|
-| all_reduce | 2 | 0.0625 MiB | 0.0746 | 0.5678 | 7.6x | 0 |
-| all_gather | 2 | 0.0625 MiB | 0.0525 | 0.5303 | 10.1x | 0 |
-| reduce_scatter | 2 | 0.0625 MiB | 0.0514 | 0.5667 | 11.0x | 0 |
-| all_reduce | 2 | 1 MiB | 0.0893 | 0.5569 | 6.2x | 0 |
-| all_gather | 2 | 1 MiB | 0.0734 | 0.5396 | 7.4x | 0 |
-| reduce_scatter | 2 | 1 MiB | 0.0694 | 0.5679 | 8.2x | 0 |
-| all_reduce | 2 | 16 MiB | 0.4230 | 0.8854 | 2.1x | 0 |
-| all_gather | 2 | 16 MiB | 0.4086 | 0.8916 | 2.2x | 0 |
-| reduce_scatter | 2 | 16 MiB | 0.2422 | 0.8880 | 3.7x | 0 |
-| all_reduce | 4 | 0.0625 MiB | 0.0794 | 0.6986 | 8.8x | 0 |
-| all_gather | 4 | 0.0625 MiB | 0.0584 | 0.6524 | 11.2x | 0 |
-| reduce_scatter | 4 | 0.0625 MiB | 0.0563 | 0.7090 | 12.6x | 0 |
-| all_reduce | 4 | 1 MiB | 0.0935 | 0.6918 | 7.4x | 0 |
-| all_gather | 4 | 1 MiB | 0.0881 | 0.6801 | 7.7x | 0 |
-| reduce_scatter | 4 | 1 MiB | 0.0906 | 0.7181 | 7.9x | 0 |
-| all_reduce | 4 | 16 MiB | 0.2569 | 1.0410 | 4.1x | 0 |
-| all_gather | 4 | 16 MiB | 0.4229 | 1.0316 | 2.4x | 0 |
-| reduce_scatter | 4 | 16 MiB | 0.1543 | 1.0400 | 6.7x | 0 |
-| all_reduce | 8 | 0.0625 MiB | 0.0517 | 1.0270 | 19.9x | 0 |
-| all_gather | 8 | 0.0625 MiB | 0.0745 | 0.9675 | 13.0x | 0 |
-| reduce_scatter | 8 | 0.0625 MiB | 0.0675 | 1.0467 | 15.5x | 0 |
-| all_reduce | 8 | 1 MiB | 0.0759 | 1.0367 | 13.7x | 0 |
-| all_gather | 8 | 1 MiB | 0.1045 | 1.0074 | 9.6x | 0 |
-| reduce_scatter | 8 | 1 MiB | 0.0928 | 1.0538 | 11.4x | 0 |
-| all_reduce | 8 | 16 MiB | 0.1947 | 1.3829 | 7.1x | 0 |
-| all_gather | 8 | 16 MiB | 0.4276 | 1.4123 | 3.3x | 0 |
-| reduce_scatter | 8 | 16 MiB | 0.1201 | 1.3751 | 11.5x | 0 |
-
-## Distributed FFN
-
-| Topology | Direction | Native ROCm/RCCL (ms) | Triton/deterministic RCCL (ms) | Overhead | Repeat mismatch | Train/infer mismatch | Triton/native output rel-L2 | Triton/native max grad rel-L2 |
-|---|---|---:|---:|---:|---:|---:|---:|---:|
-| tp2 | forward | 0.2178 | 1.9718 | 9.1x | 0 | 0 | 1.012e-02 | nan |
-| tp2 | train_fwd_bwd | 0.8281 | 5.1330 | 6.2x | 0 | 0 | 1.012e-02 | 1.079e-02 |
-| tp2_sp | forward | 0.2785 | 2.5044 | 9.0x | 0 | 0 | 1.015e-02 | nan |
-| tp2_sp | train_fwd_bwd | 0.9735 | 6.3391 | 6.5x | 0 | 0 | 1.015e-02 | 1.083e-02 |
-| tp4 | forward | 0.2179 | 1.4986 | 6.9x | 0 | 0 | 1.031e-02 | nan |
-| tp4 | train_fwd_bwd | 1.0952 | 6.2879 | 5.7x | 0 | 0 | 1.031e-02 | 1.095e-02 |
-| tp2_cp2 | forward | 0.2206 | 1.8216 | 8.3x | 0 | 0 | 1.015e-02 | nan |
-| tp2_cp2 | train_fwd_bwd | 1.1910 | 7.5867 | 6.4x | 0 | 0 | 1.015e-02 | 1.083e-02 |
-| tp2_cp2_sp | forward | 0.2823 | 2.3629 | 8.4x | 0 | 0 | 1.022e-02 | nan |
-| tp2_cp2_sp | train_fwd_bwd | 1.2807 | 8.7284 | 6.8x | 0 | 0 | 1.022e-02 | 1.089e-02 |
-| tp8 | forward | 0.1965 | 1.7419 | 8.9x | 0 | 0 | 1.060e-02 | nan |
-| tp8 | train_fwd_bwd | 1.0607 | 7.2079 | 6.8x | 0 | 0 | 1.060e-02 | 1.126e-02 |
-| tp4_cp2 | forward | 0.2265 | 1.4769 | 6.5x | 0 | 0 | 1.033e-02 | nan |
-| tp4_cp2 | train_fwd_bwd | 1.8629 | 8.3197 | 4.5x | 0 | 0 | 1.033e-02 | 1.099e-02 |
-| tp4_cp2_sp | forward | 0.2949 | 2.1559 | 7.3x | 0 | 0 | 1.045e-02 | nan |
-| tp4_cp2_sp | train_fwd_bwd | 1.9582 | 8.6712 | 4.4x | 0 | 0 | 1.045e-02 | 1.109e-02 |
-
-## Communication overlap feasibility
-
-The measured implementation deliberately serializes dependencies; the numbers above do not claim overlap. A representative 1 MiB deterministic collective costs 0.5396-1.0538 ms, while distributed Triton FFN forward costs 1.4769-2.5044 ms and forward+backward costs 5.1330-8.7284 ms.
-
-- Forward SP all-gather feeds both gate/up GEMMs, and the final TP all-reduce or reduce-scatter consumes the down projection. These are hard data dependencies.
-- Backward can overlap the fixed-order TP reduction of the gate contribution to dHidden with computation of the independent up contribution. The final wait and gate-then-up add order must remain unchanged to preserve 0 mismatch.
-- CP gathers are prerequisites for weight-gradient GEMMs. Packing them in a fixed layout can amortize launch/signature overhead, but they cannot be hidden behind the GEMMs that consume them.
-- Coalescing and host-side validation caching should be measured before introducing multi-stream scheduling complexity.
+In backward, the gate and up contributions to dHidden are independent until their final ordered addition. A future implementation can place the fixed-rank reduction of one contribution on a second stream while computing the other, but it must preserve rank order, reduction tree, wait points, and gate-then-up addition order. Any optimization is accepted only if every TP=1 mismatch column remains zero.
 
 ## Figures
 
-![Single-GPU overhead](single_gpu_overhead.png)
+![Single-GPU official TP=1 versus Triton speed](single_gpu_overhead.png)
 
-![RCCL collective overhead](collective_overhead.png)
+![Topology mismatch versus Triton TP=1](collective_overhead.png)
 
-![Distributed FFN overhead](distributed_ffn_overhead.png)
+![Distributed official TP=1 versus Triton speed](distributed_ffn_overhead.png)
 
-![Accuracy trade-off](accuracy_tradeoff.png)
+![Official Qwen3MLP FP16 versus FP32](accuracy_tradeoff.png)
diff --git a/benchmarks/results/pr325_rocm_mi300x/results.json b/benchmarks/results/pr325_rocm_mi300x/results.json
index a2cd939d..b3e67a28 100644
--- a/benchmarks/results/pr325_rocm_mi300x/results.json
+++ b/benchmarks/results/pr325_rocm_mi300x/results.json
@@ -1,571 +1,31 @@
 {
-  "collectives": [
-    {
-      "deterministic": {
-        "max_ms": 1.1652708053588867,
-        "median_ms": 0.5677836015820503,
-        "min_ms": 0.5442192777991295,
-        "p95_ms": 0.9265512693673367
-      },
-      "dtype": "bfloat16",
-      "message_bytes_per_rank": 65536,
-      "mismatch_count": 0,
-      "native": {
-        "max_ms": 0.09214691817760468,
-        "median_ms": 0.0745758879929781,
-        "min_ms": 0.07288902997970581,
-        "p95_ms": 0.08698240853846072
-      },
-      "operation": "all_reduce",
-      "overhead_ratio": 7.613501050574303,
-      "world_size": 2
-    },
-    {
-      "deterministic": {
-        "max_ms": 0.5525210872292519,
-        "median_ms": 0.5303230136632919,
-        "min_ms": 0.5241190083324909,
-        "p95_ms": 0.5499206017702818
-      },
-      "dtype": "bfloat16",
-      "message_bytes_per_rank": 65536,
-      "mismatch_count": 0,
-      "native": {
-        "max_ms": 0.06032036617398262,
-        "median_ms": 0.05245255306363106,
-        "min_ms": 0.05118595436215401,
-        "p95_ms": 0.05755266174674034
-      },
-      "operation": "all_gather",
-      "overhead_ratio": 10.110528138066956,
-      "world_size": 2
-    },
-    {
-      "deterministic": {
-        "max_ms": 0.5927002057433128,
-        "median_ms": 0.566747272387147,
-        "min_ms": 0.5522910505533218,
-        "p95_ms": 0.5873370449990034
-      },
-      "dtype": "bfloat16",
-      "message_bytes_per_rank": 65536,
-      "mismatch_count": 0,
-      "native": {
-        "max_ms": 0.08604675531387329,
-        "median_ms": 0.051361508667469025,
-        "min_ms": 0.04940386861562729,
-        "p95_ms": 0.07282891310751435
-      },
-      "operation": "reduce_scatter",
-      "overhead_ratio": 11.034474786487516,
-      "world_size": 2
-    },
-    {
-      "deterministic": {
-        "max_ms": 0.6198110058903694,
-        "median_ms": 0.556926941499114,
-        "min_ms": 0.5455012433230877,
-        "p95_ms": 0.5973493913188577
-      },
-      "dtype": "bfloat16",
-      "message_bytes_per_rank": 1048576,
-      "mismatch_count": 0,
-      "native": {
-        "max_ms": 0.09909691289067268,
-        "median_ms": 0.08925795555114746,
-        "min_ms": 0.08755037561058998,
-        "p95_ms": 0.09635707829147577
-      },
-      "operation": "all_reduce",
-      "overhead_ratio": 6.239521598497496,
-      "world_size": 2
-    },
-    {
-      "deterministic": {
-        "max_ms": 0.5778782069683075,
-        "median_ms": 0.5395815242081881,
-        "min_ms": 0.531299039721489,
-        "p95_ms": 0.5648269550874829
-      },
-      "dtype": "bfloat16",
-      "message_bytes_per_rank": 1048576,
-      "mismatch_count": 0,
-      "native": {
-        "max_ms": 0.08441600948572159,
-        "median_ms": 0.07336889393627644,
-        "min_ms": 0.07197679951786995,
-        "p95_ms": 0.08217154536396265
-      },
-      "operation": "all_gather",
-      "overhead_ratio": 7.35436361732309,
-      "world_size": 2
-    },
-    {
-      "deterministic": {
-        "max_ms": 0.5988101474940777,
-        "median_ms": 0.5678634624928236,
-        "min_ms": 0.5560568533837795,
-        "p95_ms": 0.59038654435426
-      },
-      "dtype": "bfloat16",
-      "message_bytes_per_rank": 1048576,
-      "mismatch_count": 0,
-      "native": {
-        "max_ms": 0.07919687777757645,
-        "median_ms": 0.06940285675227642,
-        "min_ms": 0.06794091314077377,
-        "p95_ms": 0.0760065158829093
-      },
-      "operation": "reduce_scatter",
-      "overhead_ratio": 8.182133835206972,
-      "world_size": 2
-    },
-    {
-      "deterministic": {
-        "max_ms": 0.9080991148948669,
-        "median_ms": 0.8853601757436991,
-        "min_ms": 0.8760811761021614,
-        "p95_ms": 0.9046065853908658
-      },
-      "dtype": "bfloat16",
-      "message_bytes_per_rank": 16777216,
-      "mismatch_count": 0,
-      "native": {
-        "max_ms": 0.43065985664725304,
-        "median_ms": 0.4230381455272436,
-        "min_ms": 0.4201037809252739,
-        "p95_ms": 0.42850570753216743
-      },
-      "operation": "all_reduce",
-      "overhead_ratio": 2.092861329656812,
-      "world_size": 2
-    },
-    {
-      "deterministic": {
-        "max_ms": 4.324185196310282,
-        "median_ms": 0.8916144724935293,
-        "min_ms": 0.8772630244493484,
-        "p95_ms": 4.294783575460315
-      },
-      "dtype": "bfloat16",
-      "message_bytes_per_rank": 16777216,
-      "mismatch_count": 0,
-      "native": {
-        "max_ms": 0.41227322071790695,
-        "median_ms": 0.408566789701581,
-        "min_ms": 0.40549319237470627,
-        "p95_ms": 0.41121835820376873
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-      "operation": "all_gather",
-      "overhead_ratio": 2.1822979619679037,
-      "world_size": 2
-    },
-    {
-      "deterministic": {
-        "max_ms": 0.9089908562600613,
-        "median_ms": 0.8880440145730972,
-        "min_ms": 0.8804784156382084,
-        "p95_ms": 0.9036591276526451
-      },
-      "dtype": "bfloat16",
-      "message_bytes_per_rank": 16777216,
-      "mismatch_count": 0,
-      "native": {
-        "max_ms": 0.24792784824967384,
-        "median_ms": 0.24218996986746788,
-        "min_ms": 0.23866398259997368,
-        "p95_ms": 0.24659407790750265
-      },
-      "operation": "reduce_scatter",
-      "overhead_ratio": 3.6667249888963447,
-      "world_size": 2
-    },
-    {
-      "deterministic": {
-        "max_ms": 0.7688519544899464,
-        "median_ms": 0.698622316122055,
-        "min_ms": 0.6783679127693176,
-        "p95_ms": 0.7467786315828562
-      },
-      "dtype": "bfloat16",
-      "message_bytes_per_rank": 65536,
-      "mismatch_count": 0,
-      "native": {
-        "max_ms": 0.10409485548734665,
-        "median_ms": 0.07943762466311455,
-        "min_ms": 0.07669441401958466,
-        "p95_ms": 0.10220620315521955
-      },
-      "operation": "all_reduce",
-      "overhead_ratio": 8.794602294376608,
-      "world_size": 4
-    },
-    {
-      "deterministic": {
-        "max_ms": 0.7039760239422321,
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diff --git a/benchmarks/results/pr325_rocm_mi300x/single_gpu_overhead.png b/benchmarks/results/pr325_rocm_mi300x/single_gpu_overhead.png
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KPyEZLfBHLpqrHRx


From e64abab904880b877d26d04c0cfad020b992aa51 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sun, 23 Aug 2026 11:55:37 +0000
Subject: [PATCH 16/28] docs(bench): simplify ROCm result figures

---
 benchmarks/benchmark_rocm_ffn.py              |  39 ++++--------------
 .../pr325_rocm_mi300x/accuracy_tradeoff.png   | Bin 158655 -> 0 bytes
 .../pr325_rocm_mi300x/collective_overhead.png | Bin 198637 -> 186647 bytes
 .../results/pr325_rocm_mi300x/report.md       |  16 ++++---
 4 files changed, 15 insertions(+), 40 deletions(-)
 delete mode 100644 benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index d3536acf..970a9056 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -983,8 +983,6 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             "",
             "![Distributed official TP=1 versus Triton speed](distributed_ffn_overhead.png)",
             "",
-            "![Official Qwen3MLP FP16 versus FP32](accuracy_tradeoff.png)",
-            "",
         )
     )
     (output_directory / "report.md").write_text(
@@ -1071,8 +1069,14 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
         [[row[key] for key in mismatch_keys] for row in exactness_rows],
         dtype=float,
     )
-    figure, axis = plt.subplots(figsize=(16, 11))
-    image = axis.imshow(matrix, cmap="RdYlGn_r", vmin=0, vmax=max(1.0, matrix.max()))
+    figure, axis = plt.subplots(figsize=(12, 10))
+    image = axis.imshow(
+        matrix,
+        aspect="auto",
+        cmap="RdYlGn_r",
+        vmin=0,
+        vmax=max(1.0, matrix.max()),
+    )
     for y_position in range(matrix.shape[0]):
         for x_position in range(matrix.shape[1]):
             axis.text(
@@ -1154,33 +1158,6 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
     figure.savefig(output_directory / "distributed_ffn_overhead.png", dpi=180)
     plt.close(figure)
 
-    dtype_row = payload["single_gpu"]["dtype_accuracy"][0]
-    relative_l2_percent = dtype_row["relative_l2"] * 100.0
-    figure, axis = plt.subplots(figsize=(11, 9))
-    bars = axis.bar(
-        [0],
-        [relative_l2_percent],
-        width=0.48,
-        color="#0f766e",
-        label="Official Qwen3MLP TP=1",
-    )
-    axis.bar_label(
-        bars,
-        labels=[f"{relative_l2_percent:.4f}%"],
-        padding=7,
-        fontsize=19,
-        fontweight="bold",
-    )
-    axis.set_ylim(0.0, max(0.01, relative_l2_percent * 1.3))
-    axis.set_xticks([0], ["FP16 output"])
-    axis.set_xlabel("Candidate dtype\n(M,H,I)=(8,4096,12288)")
-    axis.set_ylabel("Relative L2 error (%)\nReference: official Qwen3MLP FP32")
-    axis.set_title("MI300X simple FP16 vs FP32 accuracy observation")
-    axis.legend(loc="upper right")
-    figure.tight_layout()
-    figure.savefig(output_directory / "accuracy_tradeoff.png", dpi=180)
-    plt.close(figure)
-
 
 def _environment() -> dict[str, Any]:
     properties = torch.cuda.get_device_properties(0)
diff --git a/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png b/benchmarks/results/pr325_rocm_mi300x/accuracy_tradeoff.png
deleted file mode 100644
index 98eacd7f28c90cef1b7d27f8fef3de335da087bb..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/benchmarks/results/pr325_rocm_mi300x/report.md b/benchmarks/results/pr325_rocm_mi300x/report.md
index 9e84bda2..80b1ec6f 100644
--- a/benchmarks/results/pr325_rocm_mi300x/report.md
+++ b/benchmarks/results/pr325_rocm_mi300x/report.md
@@ -12,18 +12,18 @@ This is an operator-only MI300X report. It does not load or benchmark a model ch
 
 | Field | Value |
 |---|---|
+| NCCL_IB_DISABLE | 1 |
+| architecture | gfx942:sramecc+:xnack- |
+| deterministic_compute | ROCm-native Triton |
+| deterministic_transport | fixed-order RCCL on ROCm |
+| git_commit | 08f47d97d0443c5998b8da6b41a22fdf3848da8f |
 | gpu | AMD Instinct MI300X |
 | gpu_count | 8 |
-| architecture | gfx942:sramecc+:xnack- |
-| torch | 2.12.0+rocm7.14.0a20260608 |
-| transformers | 5.10.4 |
 | hip | 7.14.60850 |
 | python | 3.12.3 |
-| git_commit | 08f47d97d0443c5998b8da6b41a22fdf3848da8f |
 | speed_baseline | Hugging Face Transformers Qwen3MLP, TP=1 |
-| deterministic_compute | ROCm-native Triton |
-| deterministic_transport | fixed-order RCCL on ROCm |
-| NCCL_IB_DISABLE | 1 |
+| torch | 2.12.0+rocm7.14.0a20260608 |
+| transformers | 5.10.4 |
 
 ## Methodology
 
@@ -125,5 +125,3 @@ In backward, the gate and up contributions to dHidden are independent until thei
 ![Topology mismatch versus Triton TP=1](collective_overhead.png)
 
 ![Distributed official TP=1 versus Triton speed](distributed_ffn_overhead.png)
-
-![Official Qwen3MLP FP16 versus FP32](accuracy_tradeoff.png)

From bf36f053414dfc6ea58a0983d531c5ae4f9ed804 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Mon, 24 Aug 2026 12:44:42 +0000
Subject: [PATCH 17/28] docs(bench): add CUDA GPU and CPU comparison

---
 benchmarks/benchmark_rocm_ffn.py              | 383 ++++++++++++++----
 .../cuda_cpu_comparison.json                  |  79 ++++
 .../distributed_ffn_overhead.png              | Bin 210813 -> 274372 bytes
 .../results/pr325_rocm_mi300x/report.md       |  51 ++-
 .../pr325_rocm_mi300x/single_gpu_overhead.png | Bin 167538 -> 263048 bytes
 5 files changed, 436 insertions(+), 77 deletions(-)
 create mode 100644 benchmarks/results/pr325_rocm_mi300x/cuda_cpu_comparison.json

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index 970a9056..b7b63428 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -784,7 +784,20 @@ def _topology_exactness_rows(
     return list(merged.values())
 
 
-def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
+def _load_cuda_cpu_comparison(
+    output_directory: Path,
+) -> dict[str, Any] | None:
+    comparison_path = output_directory / "cuda_cpu_comparison.json"
+    if not comparison_path.exists():
+        return None
+    return json.loads(comparison_path.read_text(encoding="utf-8"))
+
+
+def _write_report(
+    payload: dict[str, Any],
+    output_directory: Path,
+    comparison_payload: dict[str, Any] | None = None,
+) -> None:
     environment = payload["environment"]
     methodology = payload["methodology"]
     single_speed = payload["single_gpu"]["speed"]
@@ -924,6 +937,92 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             f"{row['latency_ratio_vs_official_tp1']:.2f}x |"
         )
 
+    if comparison_payload is not None:
+        comparison_environment = comparison_payload["environment"]
+        comparison_source = comparison_payload["source"]
+        local_single = {
+            (row["tokens"], row["direction"]): row for row in single_speed
+        }
+        local_distributed = {
+            (row["name"], row["direction"]): row for row in distributed_speed
+        }
+        lines.extend(
+            (
+                "",
+                "## CUDA GPU and CPU performance context",
+                "",
+                f"The additional measurements come from [{comparison_source['report']}]"
+                f"({comparison_source['pull_request']}) at CUDA commit "
+                f"`{comparison_source['cuda_commit']}`. H100 Triton replays use "
+                "this PR's code at "
+                f"`{comparison_source['h100_triton_replay_commit']}`.",
+                "",
+                "The same-H100 CUDA/Triton ratio is the hardware-matched comparison. "
+                "CPU and MI300X columns provide absolute-latency context only; they "
+                "are not hardware-normalized speed claims.",
+                "",
+                "| Comparison environment | Value |",
+                "|---|---|",
+                f"| CUDA GPU | {comparison_environment['gpu']} "
+                f"({comparison_environment['architecture']}) |",
+                f"| CUDA / PyTorch | {comparison_environment['cuda']} / "
+                f"{comparison_environment['torch']} |",
+                f"| CPU | {comparison_environment['cpu']}, "
+                f"{comparison_environment['cpu_threads']} intra-op threads |",
+                f"| Transformers | {comparison_environment['transformers']} |",
+                "",
+                "### Single-GPU and CPU absolute latency",
+                "",
+                "| Shape / direction | CPU official (ms) | H100 official TP=1 "
+                "(ms) | H100 Triton replay (ms) | H100 CUDA (ms) | CUDA / "
+                "Triton H100 | MI300X official TP=1 (ms) | MI300X Triton (ms) |",
+                "|---|---:|---:|---:|---:|---:|---:|---:|",
+            )
+        )
+        for row in comparison_payload["single_gpu"]:
+            local = local_single[(row["tokens"], row["direction"])]
+            direction = (
+                "forward"
+                if row["direction"] == "forward"
+                else "forward+backward"
+            )
+            lines.append(
+                f"| M={row['tokens']}, {direction} | "
+                f"{row['official_cpu_ms']:.4f} | "
+                f"{row['official_h100_ms']:.4f} | "
+                f"{row['triton_h100_ms']:.4f} | "
+                f"{row['cuda_h100_ms']:.4f} | "
+                f"{row['cuda_h100_ms'] / row['triton_h100_ms']:.2f}x | "
+                f"{local['official_tp1']['median_ms']:.4f} | "
+                f"{local['triton']['median_ms']:.4f} |"
+            )
+        lines.extend(
+            (
+                "",
+                "### Distributed absolute latency",
+                "",
+                "No distributed CPU or H100 Triton replay measurement was supplied. "
+                "Each deterministic implementation is therefore compared with its "
+                "own hardware's official TP=1 baseline.",
+                "",
+                "| Layout | Direction | H100 official TP=1 (ms) | H100 CUDA "
+                "(ms) | CUDA / H100 official | MI300X official TP=1 (ms) | "
+                "MI300X Triton (ms) | Triton / MI300X official |",
+                "|---|---|---:|---:|---:|---:|---:|---:|",
+            )
+        )
+        for row in comparison_payload["distributed"]:
+            local = local_distributed[(row["name"], row["direction"])]
+            lines.append(
+                f"| {row['name']} | {row['direction']} | "
+                f"{row['official_h100_ms']:.4f} | "
+                f"{row['cuda_h100_ms']:.4f} | "
+                f"{row['cuda_h100_ms'] / row['official_h100_ms']:.2f}x | "
+                f"{local['official_tp1']['median_ms']:.4f} | "
+                f"{local['triton']['median_ms']:.4f} | "
+                f"{local['latency_ratio_vs_official_tp1']:.2f}x |"
+            )
+
     lines.extend(
         (
             "",
@@ -977,11 +1076,12 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
             "",
             "## Figures",
             "",
-            "![Single-GPU official TP=1 versus Triton speed](single_gpu_overhead.png)",
+            "![Single-GPU CUDA, Triton, and CPU latency](single_gpu_overhead.png)",
             "",
             "![Topology mismatch versus Triton TP=1](collective_overhead.png)",
             "",
-            "![Distributed official TP=1 versus Triton speed](distributed_ffn_overhead.png)",
+            "![Distributed H100 CUDA and MI300X Triton latency]"
+            "(distributed_ffn_overhead.png)",
             "",
         )
     )
@@ -990,7 +1090,11 @@ def _write_report(payload: dict[str, Any], output_directory: Path) -> None:
     )
 
 
-def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
+def _write_figures(
+    payload: dict[str, Any],
+    output_directory: Path,
+    comparison_payload: dict[str, Any] | None = None,
+) -> None:
     import matplotlib
 
     matplotlib.use("Agg")
@@ -1010,45 +1114,132 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
     )
 
     single_rows = payload["single_gpu"]["speed"]
-    single_labels = [
-        f"M={row['tokens']}\n"
-        f"{'FWD' if row['direction'] == 'forward' else 'FWD+BWD'}"
-        for row in single_rows
-    ]
-    positions = np.arange(len(single_rows))
-    width = 0.37
-    figure, axis = plt.subplots(figsize=(17, 10))
-    official_values = [row["official_tp1"]["median_ms"] for row in single_rows]
-    triton_values = [row["triton"]["median_ms"] for row in single_rows]
-    official_bars = axis.bar(
-        positions - width / 2,
-        official_values,
-        width,
-        label="Official Qwen3MLP TP=1",
-        color="#2563eb",
-    )
-    triton_bars = axis.bar(
-        positions + width / 2,
-        triton_values,
-        width,
-        label="Deterministic Triton FFN",
-        color="#7c3aed",
-    )
-    for bars, values in ((official_bars, official_values), (triton_bars, triton_values)):
-        axis.bar_label(
-            bars,
-            labels=[f"{value:.2f}" for value in values],
-            padding=4,
-            fontsize=13,
-            rotation=90,
+    if comparison_payload is None:
+        single_labels = [
+            f"M={row['tokens']}\n"
+            f"{'FWD' if row['direction'] == 'forward' else 'FWD+BWD'}"
+            for row in single_rows
+        ]
+        positions = np.arange(len(single_rows))
+        width = 0.37
+        figure, axis = plt.subplots(figsize=(17, 10))
+        official_values = [
+            row["official_tp1"]["median_ms"] for row in single_rows
+        ]
+        triton_values = [row["triton"]["median_ms"] for row in single_rows]
+        official_bars = axis.bar(
+            positions - width / 2,
+            official_values,
+            width,
+            label="Official Qwen3MLP TP=1",
+            color="#2563eb",
         )
-    axis.set_yscale("log")
-    axis.set_xlabel("Token count M and measured direction\nH=4096, I=12288, BF16")
-    axis.set_ylabel("Median latency (ms, log scale)")
-    axis.set_title("MI300X single-GPU FFN speed: official TP=1 vs Triton")
-    axis.set_xticks(positions, single_labels)
-    axis.legend(loc="upper left")
-    figure.tight_layout()
+        triton_bars = axis.bar(
+            positions + width / 2,
+            triton_values,
+            width,
+            label="Deterministic Triton FFN",
+            color="#7c3aed",
+        )
+        for bars, values in (
+            (official_bars, official_values),
+            (triton_bars, triton_values),
+        ):
+            axis.bar_label(
+                bars,
+                labels=[f"{value:.2f}" for value in values],
+                padding=4,
+                fontsize=13,
+                rotation=90,
+            )
+        axis.set_yscale("log")
+        axis.set_xlabel(
+            "Token count M and measured direction\nH=4096, I=12288, BF16"
+        )
+        axis.set_ylabel("Median latency (ms, log scale)")
+        axis.set_title("MI300X single-GPU FFN speed: official TP=1 vs Triton")
+        axis.set_xticks(positions, single_labels)
+        axis.legend(loc="upper left")
+        figure.tight_layout()
+    else:
+        local_lookup = {
+            (row["tokens"], row["direction"]): row for row in single_rows
+        }
+        comparison_lookup = {
+            (row["tokens"], row["direction"]): row
+            for row in comparison_payload["single_gpu"]
+        }
+        figure, axes = plt.subplots(1, 2, figsize=(24, 10), sharey=True)
+        series = (
+            ("CPU official BF16", "official_cpu_ms", "#6b7280"),
+            ("H100 official TP=1", "official_h100_ms", "#60a5fa"),
+            ("H100 Triton replay", "triton_h100_ms", "#06b6d4"),
+            ("H100 deterministic CUDA", "cuda_h100_ms", "#dc2626"),
+            ("MI300X official TP=1", "official_mi300x_ms", "#86efac"),
+            ("MI300X deterministic Triton", "triton_mi300x_ms", "#7c3aed"),
+        )
+        width = 0.13
+        for axis, direction, direction_label in (
+            (axes[0], "forward", "Forward"),
+            (axes[1], "train_fwd_bwd", "Forward + backward"),
+        ):
+            tokens = (1, 8, 32)
+            positions = np.arange(len(tokens))
+            combined_rows = []
+            for token_count in tokens:
+                external = comparison_lookup[(token_count, direction)]
+                local = local_lookup[(token_count, direction)]
+                combined_rows.append(
+                    {
+                        **external,
+                        "official_mi300x_ms": local["official_tp1"]["median_ms"],
+                        "triton_mi300x_ms": local["triton"]["median_ms"],
+                    }
+                )
+            for series_index, (label, key, color) in enumerate(series):
+                values = [row[key] for row in combined_rows]
+                offset = (series_index - (len(series) - 1) / 2) * width
+                bars = axis.bar(
+                    positions + offset,
+                    values,
+                    width,
+                    label=label,
+                    color=color,
+                )
+                axis.bar_label(
+                    bars,
+                    labels=[f"{value:.2f}" for value in values],
+                    padding=3,
+                    fontsize=9,
+                    rotation=90,
+                )
+            axis.set_yscale("log")
+            axis.set_xlabel("Token count M\nH=4096, I=12288, BF16")
+            axis.set_title(direction_label)
+            axis.set_xticks(positions, [f"M={value}" for value in tokens])
+        axes[0].set_ylabel("Median latency (ms, log scale)")
+        axes[0].set_ylim(0.07, 140.0)
+        handles, labels = axes[0].get_legend_handles_labels()
+        figure.legend(
+            handles,
+            labels,
+            loc="upper center",
+            bbox_to_anchor=(0.5, 0.91),
+            ncol=3,
+        )
+        figure.suptitle(
+            "Single-GPU / CPU FFN absolute latency across platforms",
+            y=0.99,
+        )
+        figure.text(
+            0.5,
+            0.01,
+            "Cross-hardware values are context only; H100 CUDA vs H100 Triton "
+            "is the hardware-matched comparison.",
+            ha="center",
+            fontsize=14,
+        )
+        figure.tight_layout(rect=(0.0, 0.06, 1.0, 0.82))
     figure.savefig(output_directory / "single_gpu_overhead.png", dpi=180)
     plt.close(figure)
 
@@ -1107,7 +1298,25 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
     plt.close(figure)
 
     rows = payload["distributed_ffn"]
-    figure, axes = plt.subplots(1, 2, figsize=(24, 10), sharey=True)
+    figure, axes = plt.subplots(1, 2, figsize=(26, 10), sharey=True)
+    if comparison_payload is None:
+        distributed_series = (
+            ("Official Qwen3MLP TP=1", "official_mi300x_ms", "#2563eb"),
+            ("Deterministic distributed Triton", "triton_mi300x_ms", "#7c3aed"),
+        )
+        width = 0.37
+    else:
+        comparison_lookup = {
+            (row["name"], row["direction"]): row
+            for row in comparison_payload["distributed"]
+        }
+        distributed_series = (
+            ("H100 official TP=1", "official_h100_ms", "#60a5fa"),
+            ("H100 deterministic CUDA", "cuda_h100_ms", "#dc2626"),
+            ("MI300X official TP=1", "official_mi300x_ms", "#86efac"),
+            ("MI300X deterministic Triton", "triton_mi300x_ms", "#7c3aed"),
+        )
+        width = 0.19
     for axis, direction, direction_label in (
         (axes[0], "forward", "Forward"),
         (axes[1], "train_fwd_bwd", "Forward + backward"),
@@ -1115,33 +1324,32 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
         direction_rows = [row for row in rows if row["direction"] == direction]
         positions = np.arange(len(direction_rows))
         labels = [row["name"].upper().replace("_", "\n") for row in direction_rows]
-        official_values = [
-            row["official_tp1"]["median_ms"] for row in direction_rows
-        ]
-        triton_values = [row["triton"]["median_ms"] for row in direction_rows]
-        official_bars = axis.bar(
-            positions - width / 2,
-            official_values,
-            width,
-            label="Official Qwen3MLP TP=1",
-            color="#2563eb",
-        )
-        triton_bars = axis.bar(
-            positions + width / 2,
-            triton_values,
-            width,
-            label="Deterministic distributed Triton",
-            color="#7c3aed",
-        )
-        for bars, values in (
-            (official_bars, official_values),
-            (triton_bars, triton_values),
-        ):
+        combined_rows = []
+        for row in direction_rows:
+            combined = {
+                "official_mi300x_ms": row["official_tp1"]["median_ms"],
+                "triton_mi300x_ms": row["triton"]["median_ms"],
+            }
+            if comparison_payload is not None:
+                combined.update(comparison_lookup[(row["name"], direction)])
+            combined_rows.append(combined)
+        for series_index, (label, key, color) in enumerate(distributed_series):
+            values = [row[key] for row in combined_rows]
+            offset = (
+                series_index - (len(distributed_series) - 1) / 2
+            ) * width
+            bars = axis.bar(
+                positions + offset,
+                values,
+                width,
+                label=label,
+                color=color,
+            )
             axis.bar_label(
                 bars,
                 labels=[f"{value:.2f}" for value in values],
-                padding=4,
-                fontsize=12,
+                padding=3,
+                fontsize=9 if comparison_payload is not None else 12,
                 rotation=90,
             )
         axis.set_yscale("log")
@@ -1149,12 +1357,36 @@ def _write_figures(payload: dict[str, Any], output_directory: Path) -> None:
         axis.set_title(direction_label)
         axis.set_xticks(positions, labels)
     axes[0].set_ylabel("Median latency (ms, log scale)")
-    axes[0].legend(loc="upper left")
-    figure.suptitle(
-        "MI300X distributed FFN speed: official TP=1 vs Triton",
-        y=0.99,
-    )
-    figure.tight_layout(rect=(0.0, 0.0, 1.0, 0.95))
+    handles, labels = axes[0].get_legend_handles_labels()
+    if comparison_payload is None:
+        axes[0].legend(loc="upper left")
+        figure.suptitle(
+            "MI300X distributed FFN speed: official TP=1 vs Triton",
+            y=0.99,
+        )
+        figure.tight_layout(rect=(0.0, 0.0, 1.0, 0.95))
+    else:
+        axes[0].set_ylim(0.1, 30.0)
+        figure.legend(
+            handles,
+            labels,
+            loc="upper center",
+            bbox_to_anchor=(0.5, 0.91),
+            ncol=4,
+        )
+        figure.suptitle(
+            "Distributed FFN absolute latency: H100 CUDA vs MI300X Triton",
+            y=0.99,
+        )
+        figure.text(
+            0.5,
+            0.01,
+            "Absolute latency across different hardware; each deterministic path "
+            "uses its own platform's official TP=1 baseline.",
+            ha="center",
+            fontsize=14,
+        )
+        figure.tight_layout(rect=(0.0, 0.06, 1.0, 0.82))
     figure.savefig(output_directory / "distributed_ffn_overhead.png", dpi=180)
     plt.close(figure)
 
@@ -1233,8 +1465,9 @@ def main() -> None:
     (args.output_dir / "results.json").write_text(
         json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8"
     )
-    _write_report(payload, args.output_dir)
-    _write_figures(payload, args.output_dir)
+    comparison_payload = _load_cuda_cpu_comparison(args.output_dir)
+    _write_report(payload, args.output_dir, comparison_payload)
+    _write_figures(payload, args.output_dir, comparison_payload)
     print(json.dumps({"output_dir": str(args.output_dir), "status": "ok"}))
 
 
diff --git a/benchmarks/results/pr325_rocm_mi300x/cuda_cpu_comparison.json b/benchmarks/results/pr325_rocm_mi300x/cuda_cpu_comparison.json
new file mode 100644
index 00000000..44baf8b3
--- /dev/null
+++ b/benchmarks/results/pr325_rocm_mi300x/cuda_cpu_comparison.json
@@ -0,0 +1,79 @@
+{
+  "source": {
+    "report": "PR #321 deterministic CUDA FFN performance report",
+    "pull_request": "https://github.com/RL-Align/RL-Kernel/pull/321",
+    "cuda_commit": "8576fa4bf449734ae99e9b50be8756bb282a8916",
+    "h100_triton_replay_commit": "e64abab904880b877d26d04c0cfad020b992aa51",
+    "note": "User-supplied measurements; cross-hardware values are context only and are not hardware-normalized."
+  },
+  "environment": {
+    "gpu": "NVIDIA H100 80GB HBM3",
+    "gpu_count": 8,
+    "architecture": "sm_90",
+    "cuda": "13.0",
+    "torch": "2.13.0+cu130",
+    "transformers": "5.13.1",
+    "python": "3.11.15",
+    "cpu": "Intel(R) Xeon(R) Platinum 8468",
+    "cpu_threads": 96,
+    "deterministic_compute": "native CUDA kernels",
+    "deterministic_transport": "fixed-order CUDA IPC"
+  },
+  "methodology": {
+    "tokens": [1, 8, 32],
+    "hidden": 4096,
+    "intermediate": 12288,
+    "dtype": "bfloat16",
+    "warmup": 3,
+    "samples": 10,
+    "training_samples": 5,
+    "cpu_timing": "wall clock with 96 PyTorch intra-op threads",
+    "gpu_timing": "GPU events for single GPU; synchronized slowest-rank wall clock for distributed"
+  },
+  "single_gpu": [
+    {"tokens": 1, "direction": "forward", "official_h100_ms": 0.1193, "cuda_h100_ms": 3.9988, "triton_h100_ms": 1.7381, "official_cpu_ms": 12.9558},
+    {"tokens": 1, "direction": "train_fwd_bwd", "official_h100_ms": 0.5184, "cuda_h100_ms": 9.0704, "triton_h100_ms": 4.2997, "official_cpu_ms": 58.5765},
+    {"tokens": 8, "direction": "forward", "official_h100_ms": 0.1239, "cuda_h100_ms": 3.9923, "triton_h100_ms": 1.8293, "official_cpu_ms": 12.7676},
+    {"tokens": 8, "direction": "train_fwd_bwd", "official_h100_ms": 0.5414, "cuda_h100_ms": 9.4500, "triton_h100_ms": 4.5028, "official_cpu_ms": 82.3669},
+    {"tokens": 32, "direction": "forward", "official_h100_ms": 0.1311, "cuda_h100_ms": 4.0277, "triton_h100_ms": 2.2529, "official_cpu_ms": 8.5392},
+    {"tokens": 32, "direction": "train_fwd_bwd", "official_h100_ms": 0.7842, "cuda_h100_ms": 9.1635, "triton_h100_ms": 5.4261, "official_cpu_ms": 65.9689}
+  ],
+  "distributed": [
+    {"name": "tp2", "direction": "forward", "official_h100_ms": 0.1552, "cuda_h100_ms": 2.7896},
+    {"name": "tp2", "direction": "train_fwd_bwd", "official_h100_ms": 0.6301, "cuda_h100_ms": 7.8125},
+    {"name": "tp2_sp", "direction": "forward", "official_h100_ms": 0.1552, "cuda_h100_ms": 3.3310},
+    {"name": "tp2_sp", "direction": "train_fwd_bwd", "official_h100_ms": 0.6301, "cuda_h100_ms": 11.7695},
+    {"name": "tp4", "direction": "forward", "official_h100_ms": 0.1530, "cuda_h100_ms": 2.1687},
+    {"name": "tp4", "direction": "train_fwd_bwd", "official_h100_ms": 0.5714, "cuda_h100_ms": 9.3954},
+    {"name": "tp2_cp2", "direction": "forward", "official_h100_ms": 0.1530, "cuda_h100_ms": 2.8261},
+    {"name": "tp2_cp2", "direction": "train_fwd_bwd", "official_h100_ms": 0.5714, "cuda_h100_ms": 16.1722},
+    {"name": "tp2_cp2_sp", "direction": "forward", "official_h100_ms": 0.1530, "cuda_h100_ms": 3.5814},
+    {"name": "tp2_cp2_sp", "direction": "train_fwd_bwd", "official_h100_ms": 0.5714, "cuda_h100_ms": 16.7894},
+    {"name": "tp8", "direction": "forward", "official_h100_ms": 0.1537, "cuda_h100_ms": 2.3206},
+    {"name": "tp8", "direction": "train_fwd_bwd", "official_h100_ms": 0.5006, "cuda_h100_ms": 10.1375},
+    {"name": "tp4_cp2", "direction": "forward", "official_h100_ms": 0.1537, "cuda_h100_ms": 2.2141},
+    {"name": "tp4_cp2", "direction": "train_fwd_bwd", "official_h100_ms": 0.5006, "cuda_h100_ms": 16.5617},
+    {"name": "tp4_cp2_sp", "direction": "forward", "official_h100_ms": 0.1537, "cuda_h100_ms": 3.2972},
+    {"name": "tp4_cp2_sp", "direction": "train_fwd_bwd", "official_h100_ms": 0.5006, "cuda_h100_ms": 17.1457}
+  ],
+  "dtype_accuracy": {
+    "tokens": 8,
+    "hidden": 4096,
+    "intermediate": 12288,
+    "candidate_dtype": "float16",
+    "reference_dtype": "float32",
+    "max_abs": 2.077e-6,
+    "mean_abs": 3.736e-7,
+    "relative_l2": 6.540e-4
+  },
+  "exactness": {
+    "reference": "deterministic CUDA TP=1",
+    "topologies": ["tp2", "tp2_sp", "tp4", "tp2_cp2", "tp2_cp2_sp", "tp8", "tp4_cp2", "tp4_cp2_sp"],
+    "forward_output_mismatch": 0,
+    "training_output_mismatch": 0,
+    "hidden_gradient_mismatch": 0,
+    "weight_gradient_mismatch": 0,
+    "repeat_mismatch": 0,
+    "train_infer_mismatch": 0
+  }
+}
diff --git a/benchmarks/results/pr325_rocm_mi300x/distributed_ffn_overhead.png b/benchmarks/results/pr325_rocm_mi300x/distributed_ffn_overhead.png
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diff --git a/benchmarks/results/pr325_rocm_mi300x/report.md b/benchmarks/results/pr325_rocm_mi300x/report.md
index 80b1ec6f..9784e34a 100644
--- a/benchmarks/results/pr325_rocm_mi300x/report.md
+++ b/benchmarks/results/pr325_rocm_mi300x/report.md
@@ -89,6 +89,53 @@ The baseline remains the full logical M=32 official FFN at TP=1 for every row. P
 | tp4_cp2_sp | forward | 0.1692 | 2.1856 | 12.92x |
 | tp4_cp2_sp | train_fwd_bwd | 0.6325 | 8.6635 | 13.70x |
 
+## CUDA GPU and CPU performance context
+
+The additional measurements come from [PR #321 deterministic CUDA FFN performance report](https://github.com/RL-Align/RL-Kernel/pull/321) at CUDA commit `8576fa4bf449734ae99e9b50be8756bb282a8916`. H100 Triton replays use this PR's code at `e64abab904880b877d26d04c0cfad020b992aa51`.
+
+The same-H100 CUDA/Triton ratio is the hardware-matched comparison. CPU and MI300X columns provide absolute-latency context only; they are not hardware-normalized speed claims.
+
+| Comparison environment | Value |
+|---|---|
+| CUDA GPU | NVIDIA H100 80GB HBM3 (sm_90) |
+| CUDA / PyTorch | 13.0 / 2.13.0+cu130 |
+| CPU | Intel(R) Xeon(R) Platinum 8468, 96 intra-op threads |
+| Transformers | 5.13.1 |
+
+### Single-GPU and CPU absolute latency
+
+| Shape / direction | CPU official (ms) | H100 official TP=1 (ms) | H100 Triton replay (ms) | H100 CUDA (ms) | CUDA / Triton H100 | MI300X official TP=1 (ms) | MI300X Triton (ms) |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| M=1, forward | 12.9558 | 0.1193 | 1.7381 | 3.9988 | 2.30x | 0.1183 | 1.9102 |
+| M=1, forward+backward | 58.5765 | 0.5184 | 4.2997 | 9.0704 | 2.11x | 0.6008 | 4.4312 |
+| M=8, forward | 12.7676 | 0.1239 | 1.8293 | 3.9923 | 2.18x | 0.2263 | 2.0424 |
+| M=8, forward+backward | 82.3669 | 0.5414 | 4.5028 | 9.4500 | 2.10x | 0.3997 | 4.6210 |
+| M=32, forward | 8.5392 | 0.1311 | 2.2529 | 4.0277 | 1.79x | 0.1108 | 2.5334 |
+| M=32, forward+backward | 65.9689 | 0.7842 | 5.4261 | 9.1635 | 1.69x | 0.7081 | 5.7710 |
+
+### Distributed absolute latency
+
+No distributed CPU or H100 Triton replay measurement was supplied. Each deterministic implementation is therefore compared with its own hardware's official TP=1 baseline.
+
+| Layout | Direction | H100 official TP=1 (ms) | H100 CUDA (ms) | CUDA / H100 official | MI300X official TP=1 (ms) | MI300X Triton (ms) | Triton / MI300X official |
+|---|---|---:|---:|---:|---:|---:|---:|
+| tp2 | forward | 0.1552 | 2.7896 | 17.97x | 0.1575 | 1.9598 | 12.44x |
+| tp2 | train_fwd_bwd | 0.6301 | 7.8125 | 12.40x | 0.6511 | 5.1613 | 7.93x |
+| tp2_sp | forward | 0.1552 | 3.3310 | 21.46x | 0.1575 | 2.4866 | 15.79x |
+| tp2_sp | train_fwd_bwd | 0.6301 | 11.7695 | 18.68x | 0.6511 | 9.0512 | 13.90x |
+| tp4 | forward | 0.1530 | 2.1687 | 14.17x | 0.1644 | 1.5044 | 9.15x |
+| tp4 | train_fwd_bwd | 0.5714 | 9.3954 | 16.44x | 0.7244 | 5.3938 | 7.45x |
+| tp2_cp2 | forward | 0.1530 | 2.8261 | 18.47x | 0.1644 | 1.8202 | 11.07x |
+| tp2_cp2 | train_fwd_bwd | 0.5714 | 16.1722 | 28.30x | 0.7244 | 10.1842 | 14.06x |
+| tp2_cp2_sp | forward | 0.1530 | 3.5814 | 23.41x | 0.1644 | 2.3788 | 14.47x |
+| tp2_cp2_sp | train_fwd_bwd | 0.5714 | 16.7894 | 29.38x | 0.7244 | 9.7862 | 13.51x |
+| tp8 | forward | 0.1537 | 2.3206 | 15.10x | 0.1692 | 1.7434 | 10.31x |
+| tp8 | train_fwd_bwd | 0.5006 | 10.1375 | 20.25x | 0.6325 | 5.1244 | 8.10x |
+| tp4_cp2 | forward | 0.1537 | 2.2141 | 14.41x | 0.1692 | 1.4826 | 8.76x |
+| tp4_cp2 | train_fwd_bwd | 0.5006 | 16.5617 | 33.08x | 0.6325 | 7.2102 | 11.40x |
+| tp4_cp2_sp | forward | 0.1537 | 3.2972 | 21.45x | 0.1692 | 2.1856 | 12.92x |
+| tp4_cp2_sp | train_fwd_bwd | 0.5006 | 17.1457 | 34.25x | 0.6325 | 8.6635 | 13.70x |
+
 ## Topology exactness versus Triton TP=1
 
 All columns are element mismatch counts. This table does not compare against the official FFN.
@@ -120,8 +167,8 @@ In backward, the gate and up contributions to dHidden are independent until thei
 
 ## Figures
 
-![Single-GPU official TP=1 versus Triton speed](single_gpu_overhead.png)
+![Single-GPU CUDA, Triton, and CPU latency](single_gpu_overhead.png)
 
 ![Topology mismatch versus Triton TP=1](collective_overhead.png)
 
-![Distributed official TP=1 versus Triton speed](distributed_ffn_overhead.png)
+![Distributed H100 CUDA and MI300X Triton latency](distributed_ffn_overhead.png)
diff --git a/benchmarks/results/pr325_rocm_mi300x/single_gpu_overhead.png b/benchmarks/results/pr325_rocm_mi300x/single_gpu_overhead.png
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From 7380bc8bfe9ea3e771fab7e987b20188b9bf468f Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Wed, 26 Aug 2026 12:06:23 +0000
Subject: [PATCH 18/28] perf(ffn): eliminate deterministic layout copies

---
 benchmarks/profile_rocm_ffn.py                | 784 ++++++++++++++++++
 rl_engine/kernels/ops/triton/ffn/__init__.py  |   6 +
 rl_engine/kernels/ops/triton/ffn/ffn.py       | 333 +++++++-
 .../kernels/ops/triton/matmul/det_gemm.py     | 100 ++-
 tests/distributed/test_qwen_ffn_topology.py   |   5 +
 tests/test_det_gemm.py                        |  84 ++
 tests/test_qwen_ffn.py                        | 216 +++++
 7 files changed, 1507 insertions(+), 21 deletions(-)
 create mode 100644 benchmarks/profile_rocm_ffn.py

diff --git a/benchmarks/profile_rocm_ffn.py b/benchmarks/profile_rocm_ffn.py
new file mode 100644
index 00000000..099ffe0f
--- /dev/null
+++ b/benchmarks/profile_rocm_ffn.py
@@ -0,0 +1,784 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""Collect torch.profiler data for the strict ROCm Triton Qwen3 FFN.
+
+This script profiles the public ``qwen3_ffn`` API without changing or wrapping
+its internal arithmetic. Profiler timings are intended for attribution only;
+uninstrumented GPU-event samples are saved separately for latency comparisons.
+
+Example:
+
+    python benchmarks/profile_rocm_ffn.py \
+      --direction both \
+      --tokens 32 \
+      --warmup 3 \
+      --active-steps 5 \
+      --latency-samples 20 \
+      --output-dir /tmp/rlk_torch_profiler_m32
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import hashlib
+import json
+import os
+import platform
+import statistics
+import subprocess
+import sys
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import torch
+from torch.profiler import ProfilerActivity, profile, record_function
+
+from rl_engine.kernels.ops.triton.ffn import (
+    Qwen3FFNForwardWeights,
+    pack_qwen3_ffn_forward_weights,
+    qwen3_ffn,
+)
+
+
+_REPO_ROOT = Path(__file__).resolve().parents[1]
+_INPUT_SEEDS = {1: 3010, 8: 3012, 32: 3014}
+_KEY_AVERAGE_FIELDS = (
+    "name",
+    "count",
+    "device_type",
+    "self_cpu_time_us",
+    "cpu_time_us",
+    "self_device_time_us",
+    "device_time_us",
+    "self_cpu_memory_bytes",
+    "cpu_memory_bytes",
+    "self_device_memory_bytes",
+    "device_memory_bytes",
+    "input_shapes",
+    "stack",
+)
+
+
+@dataclass
+class FFNCase:
+    direction: str
+    hidden: torch.Tensor
+    gate_weight: torch.Tensor
+    up_weight: torch.Tensor
+    down_weight: torch.Tensor
+    grad_output: torch.Tensor
+    forward_weights: Qwen3FFNForwardWeights | None
+
+    @property
+    def slug(self) -> str:
+        return self.direction.replace("-", "_")
+
+    @property
+    def training(self) -> bool:
+        return self.direction == "forward-backward"
+
+    def clear_gradients(self) -> None:
+        for tensor in (
+            self.hidden,
+            self.gate_weight,
+            self.up_weight,
+            self.down_weight,
+        ):
+            tensor.grad = None
+
+    def run(self, *, use_forward_weights: bool = True) -> torch.Tensor:
+        forward_weights = self.forward_weights if use_forward_weights else None
+        if not self.training:
+            with torch.no_grad():
+                return qwen3_ffn(
+                    self.hidden,
+                    self.gate_weight,
+                    self.up_weight,
+                    self.down_weight,
+                    forward_weights=forward_weights,
+                )
+
+        self.clear_gradients()
+        output = qwen3_ffn(
+            self.hidden,
+            self.gate_weight,
+            self.up_weight,
+            self.down_weight,
+            forward_weights=forward_weights,
+        )
+        output.backward(self.grad_output)
+        return output
+
+    def result_tensors(self, output: torch.Tensor) -> dict[str, torch.Tensor]:
+        result = {"output": output}
+        if not self.training:
+            return result
+
+        for name, tensor in (
+            ("dhidden", self.hidden),
+            ("dgate_weight", self.gate_weight),
+            ("dup_weight", self.up_weight),
+            ("ddown_weight", self.down_weight),
+        ):
+            if tensor.grad is None:
+                raise RuntimeError(f"{name} was not produced by backward")
+            result[name] = tensor.grad
+        return result
+
+
+def _randn(
+    shape: tuple[int, ...],
+    *,
+    seed: int,
+    device: torch.device,
+    requires_grad: bool,
+) -> 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).requires_grad_(requires_grad)
+
+
+def _build_case(args: argparse.Namespace, direction: str) -> FFNCase:
+    device = torch.device("cuda", args.device)
+    training = direction == "forward-backward"
+    input_seed = (
+        args.input_seed
+        if args.input_seed is not None
+        else _INPUT_SEEDS.get(args.tokens, 3010)
+    )
+    hidden = _randn(
+        (args.tokens, args.hidden_size),
+        seed=input_seed,
+        device=device,
+        requires_grad=training,
+    )
+    gate_weight = _randn(
+        (args.intermediate_size, args.hidden_size),
+        seed=args.weight_seed,
+        device=device,
+        requires_grad=training,
+    )
+    up_weight = _randn(
+        (args.intermediate_size, args.hidden_size),
+        seed=args.weight_seed + 1,
+        device=device,
+        requires_grad=training,
+    )
+    down_weight = _randn(
+        (args.hidden_size, args.intermediate_size),
+        seed=args.weight_seed + 2,
+        device=device,
+        requires_grad=training,
+    )
+    forward_weights = (
+        pack_qwen3_ffn_forward_weights(gate_weight, up_weight, down_weight)
+        if args.weight_layout == "packed"
+        else None
+    )
+    return FFNCase(
+        direction=direction,
+        hidden=hidden,
+        gate_weight=gate_weight,
+        up_weight=up_weight,
+        down_weight=down_weight,
+        grad_output=_randn(
+            (args.tokens, args.hidden_size),
+            seed=input_seed + 1,
+            device=device,
+            requires_grad=False,
+        ),
+        forward_weights=forward_weights,
+    )
+
+
+def _weight_layout_metadata(case: FFNCase) -> dict[str, Any]:
+    metadata: dict[str, Any] = {"additional_forward_weight_bytes": 0}
+    for name, weight in (
+        ("gate_weight", case.gate_weight),
+        ("up_weight", case.up_weight),
+        ("down_weight", case.down_weight),
+    ):
+        metadata[name] = {
+            "shape": list(weight.shape),
+            "stride": list(weight.stride()),
+            "is_contiguous": weight.is_contiguous(),
+        }
+    if case.forward_weights is not None:
+        packed_weights = (
+            ("gate_weight_t", case.forward_weights.gate_weight_t),
+            ("up_weight_t", case.forward_weights.up_weight_t),
+            ("down_weight_t", case.forward_weights.down_weight_t),
+        )
+        packed_tensors = {
+            name: {
+                "shape": list(weight.shape),
+                "stride": list(weight.stride()),
+                "is_contiguous": weight.is_contiguous(),
+                "requires_grad": weight.requires_grad,
+                "nbytes": weight.numel() * weight.element_size(),
+            }
+            for name, weight in packed_weights
+        }
+        additional_bytes = sum(
+            weight.numel() * weight.element_size() for _, weight in packed_weights
+        )
+        metadata["additional_forward_weight_bytes"] = additional_bytes
+        metadata["packed_forward_weights"] = {
+            "additional_bytes": additional_bytes,
+            "tensors": packed_tensors,
+        }
+    return metadata
+
+
+def _git_output(*arguments: str) -> str:
+    try:
+        completed = subprocess.run(
+            ["git", *arguments],
+            cwd=_REPO_ROOT,
+            check=True,
+            capture_output=True,
+            text=True,
+        )
+    except (OSError, subprocess.CalledProcessError):
+        return ""
+    return completed.stdout.strip()
+
+
+def _write_json(path: Path, payload: Any) -> None:
+    path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
+
+
+def _percentile(values: list[float], percentile: float) -> float:
+    ordered = sorted(values)
+    position = (len(ordered) - 1) * percentile
+    lower = int(position)
+    upper = min(lower + 1, len(ordered) - 1)
+    weight = position - lower
+    return ordered[lower] * (1.0 - weight) + ordered[upper] * weight
+
+
+def _latency_summary(samples_ms: list[float]) -> dict[str, Any]:
+    if not samples_ms:
+        return {"samples_ms": []}
+    return {
+        "samples_ms": samples_ms,
+        "median_ms": statistics.median(samples_ms),
+        "p95_ms": _percentile(samples_ms, 0.95),
+        "min_ms": min(samples_ms),
+        "max_ms": max(samples_ms),
+    }
+
+
+def _gpu_event_samples(case: FFNCase, samples: int) -> list[float]:
+    if samples == 0:
+        return []
+    events = [
+        (
+            torch.cuda.Event(enable_timing=True),
+            torch.cuda.Event(enable_timing=True),
+        )
+        for _ in range(samples)
+    ]
+    for start, end in events:
+        start.record()
+        output = case.run()
+        end.record()
+        del output
+    torch.cuda.synchronize()
+    case.clear_gradients()
+    return [float(start.elapsed_time(end)) for start, end in events]
+
+
+def _tensor_fingerprint(tensor: torch.Tensor) -> dict[str, Any]:
+    detached = tensor.detach().contiguous()
+    raw = detached.view(torch.uint8).cpu().numpy()
+    return {
+        "shape": list(detached.shape),
+        "dtype": str(detached.dtype),
+        "nbytes": detached.numel() * detached.element_size(),
+        "sha256_raw_bytes": hashlib.sha256(memoryview(raw)).hexdigest(),
+    }
+
+
+def _run_and_fingerprint(
+    case: FFNCase,
+    *,
+    use_forward_weights: bool = True,
+) -> dict[str, Any]:
+    output = case.run(use_forward_weights=use_forward_weights)
+    torch.cuda.synchronize()
+    fingerprints = {
+        name: _tensor_fingerprint(tensor)
+        for name, tensor in case.result_tensors(output).items()
+    }
+    del output
+    case.clear_gradients()
+    return fingerprints
+
+
+def _json_safe(value: Any) -> Any:
+    if value is None or isinstance(value, (bool, int, float, str)):
+        return value
+    if isinstance(value, (list, tuple)):
+        return [_json_safe(item) for item in value]
+    if isinstance(value, dict):
+        return {str(key): _json_safe(item) for key, item in value.items()}
+    return str(value)
+
+
+def _key_average_rows(profiler: profile, *, group_by_input_shape: bool) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    for event in profiler.key_averages(group_by_input_shape=group_by_input_shape):
+        rows.append(
+            {
+                "name": event.key,
+                "count": event.count,
+                "device_type": str(event.device_type),
+                "self_cpu_time_us": event.self_cpu_time_total,
+                "cpu_time_us": event.cpu_time_total,
+                "self_device_time_us": event.self_device_time_total,
+                "device_time_us": event.device_time_total,
+                "self_cpu_memory_bytes": event.self_cpu_memory_usage,
+                "cpu_memory_bytes": event.cpu_memory_usage,
+                "self_device_memory_bytes": event.self_device_memory_usage,
+                "device_memory_bytes": event.device_memory_usage,
+                "input_shapes": _json_safe(event.input_shapes),
+                "stack": _json_safe(event.stack),
+            }
+        )
+    return sorted(
+        rows,
+        key=lambda row: (
+            float(row["self_device_time_us"]),
+            float(row["self_cpu_time_us"]),
+        ),
+        reverse=True,
+    )
+
+
+def _write_key_averages(output_dir: Path, slug: str, rows: list[dict[str, Any]]) -> None:
+    _write_json(output_dir / f"{slug}.key_averages.json", rows)
+    with (output_dir / f"{slug}.key_averages.csv").open("w", newline="") as handle:
+        writer = csv.DictWriter(handle, fieldnames=_KEY_AVERAGE_FIELDS)
+        writer.writeheader()
+        for row in rows:
+            csv_row = dict(row)
+            csv_row["input_shapes"] = json.dumps(row["input_shapes"])
+            csv_row["stack"] = json.dumps(row["stack"])
+            writer.writerow(csv_row)
+
+
+def _kernel_breakdown(rows: list[dict[str, Any]]) -> dict[str, Any]:
+    device_rows = [
+        row
+        for row in rows
+        if str(row["device_type"]).endswith(("CUDA", "HIP"))
+        # Kineto also emits a synthetic device-side aggregate for a user
+        # annotation. It overlaps all child kernels and must not be summed.
+        and not str(row["name"]).startswith("rl_kernel::")
+    ]
+    categories: dict[str, dict[str, float | int]] = {
+        "layout_copy": {"count": 0, "self_device_time_us": 0.0},
+        "gemm_leaf": {"count": 0, "self_device_time_us": 0.0},
+        "gemm_reduce": {"count": 0, "self_device_time_us": 0.0},
+        "gemm_root_copy": {"count": 0, "self_device_time_us": 0.0},
+        "swiglu": {"count": 0, "self_device_time_us": 0.0},
+        "elementwise_add": {"count": 0, "self_device_time_us": 0.0},
+        "other_device_kernels": {"count": 0, "self_device_time_us": 0.0},
+    }
+    for row in device_rows:
+        name = str(row["name"]).lower()
+        if "direct_copy_kernel" in name:
+            category = "layout_copy"
+        elif "det_gemm_tree_leaf" in name:
+            category = "gemm_leaf"
+        elif "det_gemm_tree_reduce" in name:
+            category = "gemm_reduce"
+        elif "copy_tree_root" in name:
+            category = "gemm_root_copy"
+        elif "swiglu" in name:
+            category = "swiglu"
+        elif "functor_add" in name:
+            category = "elementwise_add"
+        else:
+            category = "other_device_kernels"
+        categories[category]["count"] += int(row["count"])
+        categories[category]["self_device_time_us"] += float(
+            row["self_device_time_us"]
+        )
+
+    total_device_us = sum(
+        float(category["self_device_time_us"]) for category in categories.values()
+    )
+    for category in categories.values():
+        category["percent_of_device_kernel_time"] = (
+            100.0 * float(category["self_device_time_us"]) / total_device_us
+            if total_device_us
+            else 0.0
+        )
+    return {
+        "accounting": (
+            "sum of device kernel events; synthetic rl_kernel:: annotation "
+            "device aggregates are excluded to avoid double counting"
+        ),
+        "total_device_kernel_time_us": total_device_us,
+        "categories": categories,
+        "top_device_kernels": device_rows[:25],
+    }
+
+
+def _environment(args: argparse.Namespace) -> dict[str, Any]:
+    properties = torch.cuda.get_device_properties(args.device)
+    try:
+        import triton
+
+        triton_version = triton.__version__
+    except (ImportError, AttributeError):
+        triton_version = ""
+    status = _git_output("status", "--short")
+    return {
+        "timestamp_utc": datetime.now(timezone.utc).isoformat(),
+        "command": [sys.executable, *sys.argv],
+        "hostname": platform.node(),
+        "python": platform.python_version(),
+        "torch": torch.__version__,
+        "hip": torch.version.hip,
+        "triton": triton_version,
+        "gpu_index": args.device,
+        "gpu": torch.cuda.get_device_name(args.device),
+        "architecture": getattr(properties, "gcnArchName", ""),
+        "total_memory_bytes": properties.total_memory,
+        "gpu_count": torch.cuda.device_count(),
+        "git_commit": _git_output("rev-parse", "HEAD"),
+        "git_branch": _git_output("branch", "--show-current"),
+        "git_dirty": bool(status),
+        "git_status": status.splitlines(),
+        "environment": {
+            name: os.environ.get(name, "")
+            for name in (
+                "HIP_VISIBLE_DEVICES",
+                "ROCR_VISIBLE_DEVICES",
+                "CUDA_VISIBLE_DEVICES",
+                "NCCL_IB_DISABLE",
+            )
+        },
+    }
+
+
+def _profile_case(
+    case: FFNCase,
+    args: argparse.Namespace,
+    output_dir: Path,
+) -> dict[str, Any]:
+    for _ in range(args.warmup):
+        output = case.run()
+        del output
+    torch.cuda.synchronize()
+    case.clear_gradients()
+
+    latency = _latency_summary(_gpu_event_samples(case, args.latency_samples))
+    _write_json(output_dir / f"{case.slug}.latency.json", latency)
+
+    # The reference deliberately reuses the exact canonical tensor objects but
+    # passes no forward-weight cache. In packed mode this exercises the original
+    # per-call transpose path independently of the profiled candidate.
+    standard_reference_fingerprints = (
+        _run_and_fingerprint(case, use_forward_weights=False)
+        if not args.skip_bitwise_hash
+        else {}
+    )
+    before_profile_fingerprints = (
+        _run_and_fingerprint(case) if not args.skip_bitwise_hash else {}
+    )
+    torch.cuda.synchronize()
+
+    device = torch.device("cuda", args.device)
+    torch.cuda.reset_peak_memory_stats(device)
+    baseline_allocated = torch.cuda.memory_allocated(device)
+    baseline_reserved = torch.cuda.memory_reserved(device)
+
+    effective_record_shapes = args.record_shapes or args.profile_memory
+    effective_with_stack = args.with_stack or args.profile_memory
+    with profile(
+        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+        record_shapes=effective_record_shapes,
+        profile_memory=args.profile_memory,
+        with_stack=effective_with_stack,
+        acc_events=True,
+    ) as profiler:
+        for _ in range(args.active_steps):
+            with record_function(f"rl_kernel::{case.direction}"):
+                output = case.run()
+                del output
+            profiler.step()
+        torch.cuda.synchronize()
+
+    peak_allocated = torch.cuda.max_memory_allocated(device)
+    peak_reserved = torch.cuda.max_memory_reserved(device)
+    memory = {
+        "baseline_allocated_bytes": baseline_allocated,
+        "baseline_reserved_bytes": baseline_reserved,
+        "peak_allocated_bytes": peak_allocated,
+        "peak_reserved_bytes": peak_reserved,
+        "peak_allocated_delta_bytes": max(0, peak_allocated - baseline_allocated),
+        "peak_reserved_delta_bytes": max(0, peak_reserved - baseline_reserved),
+    }
+
+    trace_path = output_dir / f"{case.slug}.trace.json.gz"
+    profiler.export_chrome_trace(str(trace_path))
+    rows = _key_average_rows(
+        profiler,
+        group_by_input_shape=effective_record_shapes,
+    )
+    _write_key_averages(output_dir, case.slug, rows)
+    kernel_breakdown = _kernel_breakdown(rows)
+    _write_json(output_dir / f"{case.slug}.kernel_breakdown.json", kernel_breakdown)
+
+    key_averages = profiler.key_averages(
+        group_by_input_shape=effective_record_shapes
+    )
+    summary = "\n\n".join(
+        (
+            "Sorted by self device time\n"
+            + key_averages.table(
+                sort_by="self_device_time_total",
+                row_limit=args.row_limit,
+            ),
+            "Sorted by self CPU time\n"
+            + key_averages.table(
+                sort_by="self_cpu_time_total",
+                row_limit=args.row_limit,
+            ),
+        )
+    )
+    (output_dir / f"{case.slug}.summary.txt").write_text(summary)
+
+    memory_timeline_error = ""
+    if args.profile_memory:
+        try:
+            profiler.export_memory_timeline(
+                str(output_dir / f"{case.slug}.memory.raw.json.gz"),
+                device=str(device),
+            )
+        except (AssertionError, RuntimeError, ValueError) as error:
+            memory_timeline_error = f"{type(error).__name__}: {error}"
+
+    after_profile_fingerprints = (
+        _run_and_fingerprint(case) if not args.skip_bitwise_hash else {}
+    )
+    before_matches_reference = {
+        name: before_profile_fingerprints.get(name) == fingerprint
+        for name, fingerprint in standard_reference_fingerprints.items()
+    }
+    after_matches_reference = {
+        name: after_profile_fingerprints.get(name) == fingerprint
+        for name, fingerprint in standard_reference_fingerprints.items()
+    }
+    after_matches_before = {
+        name: after_profile_fingerprints.get(name) == fingerprint
+        for name, fingerprint in before_profile_fingerprints.items()
+    }
+    all_match = (
+        None
+        if args.skip_bitwise_hash
+        else (
+            all(before_matches_reference.values())
+            and all(after_matches_reference.values())
+            and all(after_matches_before.values())
+        )
+    )
+    correctness = {
+        "comparison": "SHA256 over the contiguous raw tensor bytes",
+        "reference_mode": (
+            "standard qwen3_ffn call with forward_weights=None using the same "
+            "canonical input and weight tensors"
+        ),
+        "candidate_mode": args.weight_layout,
+        "skipped": args.skip_bitwise_hash,
+        "all_match": all_match,
+        "matches_standard_reference_before_profile": before_matches_reference,
+        "matches_standard_reference_after_profile": after_matches_reference,
+        "matches_before_profile_after_profile": after_matches_before,
+        "standard_reference": standard_reference_fingerprints,
+        "before_profile": before_profile_fingerprints,
+        "after_profile": after_profile_fingerprints,
+    }
+    _write_json(output_dir / f"{case.slug}.correctness.json", correctness)
+    case.clear_gradients()
+
+    return {
+        "direction": case.direction,
+        "weight_layout": args.weight_layout,
+        "weight_layout_metadata": _weight_layout_metadata(case),
+        "files": {
+            "trace": trace_path.name,
+            "key_averages_json": f"{case.slug}.key_averages.json",
+            "key_averages_csv": f"{case.slug}.key_averages.csv",
+            "summary": f"{case.slug}.summary.txt",
+            "kernel_breakdown": f"{case.slug}.kernel_breakdown.json",
+            "latency": f"{case.slug}.latency.json",
+            "correctness": f"{case.slug}.correctness.json",
+            "memory_timeline": (
+                f"{case.slug}.memory.raw.json.gz" if args.profile_memory else ""
+            ),
+        },
+        "profiler": {
+            "active_steps": args.active_steps,
+            "record_shapes": effective_record_shapes,
+            "profile_memory": args.profile_memory,
+            "with_stack": effective_with_stack,
+            "trace_size_bytes": trace_path.stat().st_size,
+            "memory_timeline_error": memory_timeline_error,
+        },
+        "latency": latency,
+        "memory": memory,
+        "correctness_all_match": correctness["all_match"],
+        "kernel_breakdown": kernel_breakdown,
+    }
+
+
+def _validate_args(args: argparse.Namespace) -> None:
+    if getattr(torch.version, "hip", None) is None:
+        raise RuntimeError("this profiler requires a ROCm PyTorch build")
+    if not torch.cuda.is_available():
+        raise RuntimeError("no ROCm GPU is available")
+    if args.device < 0 or args.device >= torch.cuda.device_count():
+        raise ValueError(
+            f"--device must be in [0, {torch.cuda.device_count() - 1}], got {args.device}"
+        )
+    for name in (
+        "tokens",
+        "hidden_size",
+        "intermediate_size",
+        "active_steps",
+    ):
+        if getattr(args, name) <= 0:
+            raise ValueError(f"--{name.replace('_', '-')} must be positive")
+    if args.warmup < 0 or args.latency_samples < 0:
+        raise ValueError("--warmup and --latency-samples must be non-negative")
+    if args.profile_memory and args.active_steps > 1:
+        print(
+            "warning: memory/shape/stack profiling adds overhead; "
+            "prefer --active-steps 1 for a memory run",
+            file=sys.stderr,
+        )
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("/tmp/rl_kernel_torch_profiler"),
+    )
+    parser.add_argument(
+        "--direction",
+        choices=("forward", "forward-backward", "both"),
+        default="both",
+    )
+    parser.add_argument("--tokens", type=int, default=32)
+    parser.add_argument("--hidden-size", type=int, default=4096)
+    parser.add_argument("--intermediate-size", type=int, default=12288)
+    parser.add_argument("--device", type=int, default=0)
+    parser.add_argument(
+        "--weight-layout",
+        choices=("standard", "packed"),
+        default="standard",
+        help=(
+            "standard materializes forward transposes in every FFN call; packed "
+            "materializes detached forward-only weights once before measurement"
+        ),
+    )
+    parser.add_argument("--weight-seed", type=int, default=3000)
+    parser.add_argument("--input-seed", type=int)
+    parser.add_argument("--warmup", type=int, default=3)
+    parser.add_argument("--active-steps", type=int, default=5)
+    parser.add_argument("--latency-samples", type=int, default=20)
+    parser.add_argument("--row-limit", type=int, default=100)
+    parser.add_argument("--record-shapes", action="store_true")
+    parser.add_argument("--profile-memory", action="store_true")
+    parser.add_argument("--with-stack", action="store_true")
+    parser.add_argument("--skip-bitwise-hash", action="store_true")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    _validate_args(args)
+    torch.cuda.set_device(args.device)
+    torch.backends.cuda.matmul.allow_tf32 = False
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    directions = (
+        ("forward", "forward-backward")
+        if args.direction == "both"
+        else (args.direction,)
+    )
+    manifest: dict[str, Any] = {
+        "environment": _environment(args),
+        "workload": {
+            "operator": "rl_engine.kernels.ops.triton.ffn.qwen3_ffn",
+            "tokens": args.tokens,
+            "hidden_size": args.hidden_size,
+            "intermediate_size": args.intermediate_size,
+            "dtype": "torch.bfloat16",
+            "weight_layout": args.weight_layout,
+            "weight_seeds": [args.weight_seed + offset for offset in range(3)],
+            "input_seed": (
+                args.input_seed
+                if args.input_seed is not None
+                else _INPUT_SEEDS.get(args.tokens, 3010)
+            ),
+            "grad_output_seed": (
+                args.input_seed + 1
+                if args.input_seed is not None
+                else _INPUT_SEEDS.get(args.tokens, 3010) + 1
+            ),
+            "warmup": args.warmup,
+            "latency_samples": args.latency_samples,
+            "profiler_active_steps": args.active_steps,
+        },
+        "methodology": {
+            "profiler_use": "attribution and launch analysis only",
+            "latency_use": "uninstrumented GPU events",
+            "jit_and_tree_plan": "warmed before profiler starts",
+            "weight_packing": (
+                "performed once during case construction outside all timing"
+            ),
+            "bitwise_fingerprint": "SHA256 over raw BF16 bytes",
+            "packed_bitwise_reference": (
+                "uncached standard qwen3_ffn path using the same canonical tensors"
+            ),
+        },
+        "profiles": [],
+    }
+    _write_json(args.output_dir / "manifest.json", manifest)
+
+    for direction in directions:
+        print(f"profiling {direction} on cuda:{args.device} ...", flush=True)
+        case = _build_case(args, direction)
+        result = _profile_case(case, args, args.output_dir)
+        manifest["profiles"].append(result)
+        _write_json(args.output_dir / "manifest.json", manifest)
+        latency = result["latency"]
+        if latency.get("samples_ms"):
+            print(
+                f"  uninstrumented median={latency['median_ms']:.4f} ms, "
+                f"p95={latency['p95_ms']:.4f} ms",
+                flush=True,
+            )
+        print(
+            f"  raw-bit fingerprints match: {result['correctness_all_match']}",
+            flush=True,
+        )
+        del case
+        torch.cuda.empty_cache()
+
+    print(f"profiler artifacts: {args.output_dir.resolve()}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/rl_engine/kernels/ops/triton/ffn/__init__.py b/rl_engine/kernels/ops/triton/ffn/__init__.py
index 08d59356..16a8565c 100644
--- a/rl_engine/kernels/ops/triton/ffn/__init__.py
+++ b/rl_engine/kernels/ops/triton/ffn/__init__.py
@@ -4,13 +4,19 @@
 from .ffn import (
     QWEN3_8B_HIDDEN_SIZE,
     QWEN3_8B_INTERMEDIATE_SIZE,
+    Qwen3FFNForwardWeights,
+    pack_qwen3_ffn_forward_weights,
     qwen3_ffn,
     qwen3_ffn_triton,
+    refresh_qwen3_ffn_forward_weights,
 )
 
 __all__ = [
     "QWEN3_8B_HIDDEN_SIZE",
     "QWEN3_8B_INTERMEDIATE_SIZE",
+    "Qwen3FFNForwardWeights",
+    "pack_qwen3_ffn_forward_weights",
     "qwen3_ffn",
     "qwen3_ffn_triton",
+    "refresh_qwen3_ffn_forward_weights",
 ]
diff --git a/rl_engine/kernels/ops/triton/ffn/ffn.py b/rl_engine/kernels/ops/triton/ffn/ffn.py
index afe8296e..45caab2a 100644
--- a/rl_engine/kernels/ops/triton/ffn/ffn.py
+++ b/rl_engine/kernels/ops/triton/ffn/ffn.py
@@ -10,6 +10,7 @@
 
 from __future__ import annotations
 
+from dataclasses import dataclass, field
 from typing import Any
 
 import torch
@@ -28,6 +29,41 @@
 _COLLECTIVES: dict[tuple[int, int, int, int], Any] = {}
 
 
+@dataclass(eq=False)
+class Qwen3FFNForwardWeights:
+    """Stable-storage GEMM-ready copies produced after loading or synchronization."""
+
+    gate_weight_t: Tensor
+    up_weight_t: Tensor
+    down_weight_t: Tensor
+    _sources: tuple[Tensor, Tensor, Tensor] = field(repr=False)
+    _source_data_ptrs: tuple[int, int, int] = field(repr=False)
+    _source_versions: tuple[int | None, int | None, int | None] = field(repr=False)
+    _packed_data_ptrs: tuple[int, int, int] = field(repr=False)
+    _packed_versions: tuple[int, int, int] = field(repr=False)
+    _source_shapes: tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]] = field(
+        repr=False
+    )
+    _source_strides: tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]] = field(
+        repr=False
+    )
+
+    def refresh_(
+        self,
+        gate_weight: Tensor,
+        up_weight: Tensor,
+        down_weight: Tensor,
+    ) -> Qwen3FFNForwardWeights:
+        """Refresh values in-place while preserving CUDA Graph-visible addresses."""
+
+        return refresh_qwen3_ffn_forward_weights(
+            self,
+            gate_weight,
+            up_weight,
+            down_weight,
+        )
+
+
 def _require_parallel_group(group: Any, name: str):
     if group is None:
         return None
@@ -99,6 +135,227 @@ def _validate_ffn_inputs(
             )
 
 
+def _tracked_tensor_version(tensor: Tensor) -> int | None:
+    # Inference tensors deliberately have no version counter. Packed buffers are
+    # allocated outside inference mode below, but a loader-owned source may be an
+    # inference tensor and therefore relies on the explicit refresh lifecycle.
+    return None if torch.is_inference(tensor) else int(tensor._version)
+
+
+def _validate_forward_weight_sources(
+    gate_weight: Tensor,
+    up_weight: Tensor,
+    down_weight: Tensor,
+) -> tuple[Tensor, Tensor, Tensor]:
+    weights = {
+        "gate_weight": gate_weight,
+        "up_weight": up_weight,
+        "down_weight": down_weight,
+    }
+    for name, weight in weights.items():
+        if not isinstance(weight, Tensor):
+            raise TypeError(f"{name} must be a torch.Tensor, got {type(weight)!r}.")
+        if weight.dim() != 2:
+            raise ValueError(f"{name} must be 2-D, got shape {tuple(weight.shape)}.")
+        if weight.dtype != torch.bfloat16:
+            raise TypeError(f"{name} must have dtype bfloat16, got {weight.dtype}.")
+        if not weight.is_cuda:
+            raise RuntimeError(
+                f"{name} must be on a CUDA/ROCm GPU device, got '{weight.device}'."
+            )
+
+    if tuple(up_weight.shape) != tuple(gate_weight.shape):
+        raise ValueError(
+            "up_weight must have the same shape as gate_weight, got "
+            f"{tuple(up_weight.shape)} and {tuple(gate_weight.shape)}."
+        )
+    expected_down_shape = (gate_weight.size(1), gate_weight.size(0))
+    if tuple(down_weight.shape) != expected_down_shape:
+        raise ValueError(
+            f"down_weight must have shape {expected_down_shape}, "
+            f"got {tuple(down_weight.shape)}."
+        )
+    if any(weight.device != gate_weight.device for weight in weights.values()):
+        raise RuntimeError("all FFN weights must be on the same device before packing.")
+    return gate_weight, up_weight, down_weight
+
+
+def pack_qwen3_ffn_forward_weights(
+    gate_weight: Tensor,
+    up_weight: Tensor,
+    down_weight: Tensor,
+) -> Qwen3FFNForwardWeights:
+    """Prepare detached forward-only transposes outside the FFN hot path.
+
+    This is analogous to vLLM's backend-specific
+    ``process_weights_after_loading``/kernel-format lifecycle: prepare the
+    kernel-facing layout once per weight load/reload and reuse it at runtime.
+    The canonical weights remain the source of truth for backward and
+    optimization. Call ``refresh_`` after every optimizer update or external
+    synchronization. Freshness checks are best-effort for inference tensors and
+    external writers, so the loader/optimizer must refresh even when it mutates
+    through ``.data``, DLPack, or a custom kernel.
+    """
+
+    sources = _validate_forward_weight_sources(gate_weight, up_weight, down_weight)
+    # Ensure the cached storage has an ordinary version counter even when the
+    # model loader calls us from torch.inference_mode(). A fresh explicit copy
+    # also prevents degenerate transposes from aliasing source storage.
+    with torch.inference_mode(False), torch.no_grad():
+        packed = tuple(
+            torch.empty(
+                (weight.size(1), weight.size(0)),
+                dtype=weight.dtype,
+                device=weight.device,
+            )
+            for weight in sources
+        )
+        for packed_weight, source in zip(packed, sources, strict=True):
+            packed_weight.copy_(source.t())
+    return Qwen3FFNForwardWeights(
+        gate_weight_t=packed[0],
+        up_weight_t=packed[1],
+        down_weight_t=packed[2],
+        _sources=sources,
+        _source_data_ptrs=tuple(weight.data_ptr() for weight in sources),
+        _source_versions=tuple(_tracked_tensor_version(weight) for weight in sources),
+        _packed_data_ptrs=tuple(weight.data_ptr() for weight in packed),
+        _packed_versions=tuple(int(weight._version) for weight in packed),
+        _source_shapes=tuple(tuple(weight.shape) for weight in sources),
+        _source_strides=tuple(tuple(weight.stride()) for weight in sources),
+    )
+
+
+def refresh_qwen3_ffn_forward_weights(
+    forward_weights: Qwen3FFNForwardWeights,
+    gate_weight: Tensor,
+    up_weight: Tensor,
+    down_weight: Tensor,
+) -> Qwen3FFNForwardWeights:
+    """Refresh a forward cache in-place without changing any packed data pointer.
+
+    Stable storage is required by already-captured CUDA Graphs: replacing either
+    the canonical tensors or this bundle would leave graph nodes pointing at old
+    values. Copy reloaded values into the original canonical tensors first, then
+    call this function. Order both copies before graph replay, normally on the
+    same stream or with an explicit stream dependency.
+    """
+
+    if not isinstance(forward_weights, Qwen3FFNForwardWeights):
+        raise TypeError(
+            "forward_weights must be created by "
+            "pack_qwen3_ffn_forward_weights."
+        )
+    sources = _validate_forward_weight_sources(gate_weight, up_weight, down_weight)
+    if any(
+        source is not original
+        for source, original in zip(sources, forward_weights._sources, strict=True)
+    ):
+        raise ValueError(
+            "stable refresh requires the original canonical weight tensors; "
+            "copy new values into those tensors, or repack and recapture CUDA Graphs."
+        )
+    packed = (
+        forward_weights.gate_weight_t,
+        forward_weights.up_weight_t,
+        forward_weights.down_weight_t,
+    )
+    for name, target, source, expected_ptr in zip(
+        ("gate_weight", "up_weight", "down_weight"),
+        packed,
+        sources,
+        forward_weights._packed_data_ptrs,
+        strict=True,
+    ):
+        expected_shape = (source.size(1), source.size(0))
+        if tuple(target.shape) != expected_shape:
+            raise ValueError(
+                f"packed {name} has shape {tuple(target.shape)}, but refresh "
+                f"requires {expected_shape}; repack and recapture CUDA Graphs."
+            )
+        if target.dtype != source.dtype or target.device != source.device:
+            raise RuntimeError(
+                f"packed {name} dtype/device cannot change during stable refresh; "
+                "repack and recapture CUDA Graphs."
+            )
+        if not target.is_contiguous() or target.data_ptr() != expected_ptr:
+            raise RuntimeError(
+                f"packed {name} storage changed; repack and recapture CUDA Graphs."
+            )
+
+    with torch.inference_mode(False), torch.no_grad():
+        for target, source in zip(packed, sources, strict=True):
+            target.copy_(source.t())
+
+    forward_weights._source_versions = tuple(
+        _tracked_tensor_version(weight) for weight in sources
+    )
+    forward_weights._packed_versions = tuple(int(weight._version) for weight in packed)
+    return forward_weights
+
+
+def _validate_forward_weights(
+    forward_weights: Qwen3FFNForwardWeights,
+    gate_weight: Tensor,
+    up_weight: Tensor,
+    down_weight: Tensor,
+) -> None:
+    if not isinstance(forward_weights, Qwen3FFNForwardWeights):
+        raise TypeError(
+            "forward_weights must be created by "
+            "pack_qwen3_ffn_forward_weights."
+        )
+
+    sources = (gate_weight, up_weight, down_weight)
+    names = ("gate_weight", "up_weight", "down_weight")
+    for index, (name, source) in enumerate(zip(names, sources, strict=True)):
+        if forward_weights._sources[index] is not source:
+            raise ValueError(f"forward_weights was not packed from this {name} tensor.")
+        if forward_weights._source_data_ptrs[index] != source.data_ptr():
+            raise RuntimeError(f"{name} storage changed after forward weights were packed.")
+        if forward_weights._source_shapes[index] != tuple(source.shape):
+            raise RuntimeError(f"{name} shape changed after forward weights were packed.")
+        if forward_weights._source_strides[index] != tuple(source.stride()):
+            raise RuntimeError(f"{name} strides changed after forward weights were packed.")
+        if forward_weights._source_versions[index] != _tracked_tensor_version(source):
+            raise RuntimeError(
+                f"{name} changed after forward weights were packed; refresh before FFN."
+            )
+
+    expected = (
+        (gate_weight.size(1), gate_weight.size(0)),
+        (up_weight.size(1), up_weight.size(0)),
+        (down_weight.size(1), down_weight.size(0)),
+    )
+    packed = (
+        forward_weights.gate_weight_t,
+        forward_weights.up_weight_t,
+        forward_weights.down_weight_t,
+    )
+    for name, weight, shape in zip(names, packed, expected, strict=True):
+        if not isinstance(weight, Tensor):
+            raise TypeError(f"packed {name} must be a torch.Tensor.")
+        if tuple(weight.shape) != shape:
+            raise ValueError(
+                f"packed {name} must have shape {shape}, got {tuple(weight.shape)}."
+            )
+        if weight.dtype != torch.bfloat16:
+            raise TypeError(f"packed {name} must have dtype bfloat16, got {weight.dtype}.")
+        if weight.device != gate_weight.device:
+            raise RuntimeError(
+                f"packed {name} must be on {gate_weight.device}, got {weight.device}."
+            )
+        if not weight.is_contiguous():
+            raise ValueError(f"packed {name} must be contiguous.")
+        if weight.requires_grad:
+            raise ValueError(f"packed {name} must be detached from autograd.")
+    for index, (name, weight) in enumerate(zip(names, packed, strict=True)):
+        if forward_weights._packed_data_ptrs[index] != weight.data_ptr():
+            raise RuntimeError(f"packed {name} storage changed; repack before FFN.")
+        if forward_weights._packed_versions[index] != int(weight._version):
+            raise RuntimeError(f"packed {name} changed; refresh before FFN.")
+
+
 def _create_collective(*, group: Any, max_size_bytes: int):
     try:
         from rl_engine.distributed import create_deterministic_collective
@@ -159,7 +416,24 @@ def _gemm(a: Tensor, b: Tensor) -> Tensor:
 
 
 def _gemm_db(a: Tensor, grad_output: Tensor) -> Tensor:
-    return _triton_tree_gemm(a.t().contiguous(), grad_output)
+    grad_weight = torch.empty(
+        (grad_output.size(1), a.size(1)),
+        dtype=torch.bfloat16,
+        device=a.device,
+    )
+    # Mirror only the buffer placement used by Transformer Engine's
+    # fuse_wgrad_accumulation and Megatron's gradient_accumulation_fusion: write
+    # the canonical GEMM root into the final [out, in] gradient buffer. The
+    # strict operand order, K tree, BF16 nodes, and rounding remain unchanged.
+    # Unlike their fused main_grad paths, this returns one fresh autograd dW and
+    # does not fuse or reorder microbatch accumulation.
+    return _triton_tree_gemm(
+        a.t(),
+        grad_output,
+        transpose_output=True,
+        out=grad_weight,
+        preserve_a_strides=True,
+    )
 
 
 class _TritonDeterministicFFNFunction(torch.autograd.Function):
@@ -170,6 +444,9 @@ def forward(
         gate_weight: Tensor,
         up_weight: Tensor,
         down_weight: Tensor,
+        gate_weight_t: Tensor | None,
+        up_weight_t: Tensor | None,
+        down_weight_t: Tensor | None,
         tp_group: Any,
         cp_group: Any,
         sequence_parallel: bool,
@@ -197,10 +474,23 @@ def forward(
         if sequence_parallel:
             rmsnorm_output_2d = _all_gather_tokens(rmsnorm_output_2d, tp_collective)
 
-        gate = _gemm(rmsnorm_output_2d, gate_weight.t().contiguous())
-        up = _gemm(rmsnorm_output_2d, up_weight.t().contiguous())
+        # A loader integration can prepare/cache this backend-specific layout
+        # once per load/reload, analogous to vLLM's
+        # process_weights_after_loading lifecycle. Canonical weights stay
+        # untouched and are saved below for backward.
+        gate = _gemm(
+            rmsnorm_output_2d,
+            gate_weight.t().contiguous() if gate_weight_t is None else gate_weight_t,
+        )
+        up = _gemm(
+            rmsnorm_output_2d,
+            up_weight.t().contiguous() if up_weight_t is None else up_weight_t,
+        )
         activated = _launch_swiglu_fwd(gate, up)
-        output = _gemm(activated, down_weight.t().contiguous())
+        output = _gemm(
+            activated,
+            down_weight.t().contiguous() if down_weight_t is None else down_weight_t,
+        )
 
         if sequence_parallel:
             output = _reduce_scatter_tokens(output, tp_collective)
@@ -242,9 +532,9 @@ def backward(ctx: Any, grad_output: Tensor) -> tuple[Any, ...]:
         if cp_collective is not None:
             activated_full = _all_gather_tokens(activated, cp_collective)
             grad_output_full = _all_gather_tokens(grad_output, cp_collective)
-            grad_down_weight = _gemm_db(activated_full, grad_output_full).t().contiguous()
+            grad_down_weight = _gemm_db(activated_full, grad_output_full)
         else:
-            grad_down_weight = _gemm_db(activated, grad_output).t().contiguous()
+            grad_down_weight = _gemm_db(activated, grad_output)
 
         grad_activated = _gemm(grad_output, down_weight)
         grad_gate, grad_up = _launch_swiglu_bwd(grad_activated, gate, up)
@@ -253,11 +543,11 @@ def backward(ctx: Any, grad_output: Tensor) -> tuple[Any, ...]:
             rmsnorm_full = _all_gather_tokens(rmsnorm_output, cp_collective)
             grad_gate_full = _all_gather_tokens(grad_gate, cp_collective)
             grad_up_full = _all_gather_tokens(grad_up, cp_collective)
-            grad_gate_weight = _gemm_db(rmsnorm_full, grad_gate_full).t().contiguous()
-            grad_up_weight = _gemm_db(rmsnorm_full, grad_up_full).t().contiguous()
+            grad_gate_weight = _gemm_db(rmsnorm_full, grad_gate_full)
+            grad_up_weight = _gemm_db(rmsnorm_full, grad_up_full)
         else:
-            grad_gate_weight = _gemm_db(rmsnorm_output, grad_gate).t().contiguous()
-            grad_up_weight = _gemm_db(rmsnorm_output, grad_up).t().contiguous()
+            grad_gate_weight = _gemm_db(rmsnorm_output, grad_gate)
+            grad_up_weight = _gemm_db(rmsnorm_output, grad_up)
 
         grad_rmsnorm_from_gate = _gemm(grad_gate, gate_weight)
         if ctx.sequence_parallel:
@@ -292,6 +582,9 @@ def backward(ctx: Any, grad_output: Tensor) -> tuple[Any, ...]:
             None,
             None,
             None,
+            None,
+            None,
+            None,
         )
 
 
@@ -301,13 +594,28 @@ def qwen3_ffn(
     up_weight: Tensor,
     down_weight: Tensor,
     *,
+    forward_weights: Qwen3FFNForwardWeights | None = None,
     tp_group: Any = None,
     cp_group: Any = None,
     sequence_parallel: bool = False,
 ) -> Tensor:
-    """Apply the distributed deterministic Qwen3 FFN with Triton kernels."""
+    """Apply the distributed deterministic Qwen3 FFN with Triton kernels.
+
+    ``forward_weights`` is an optional, forward-only cache. Refresh it in-place
+    after every optimizer update or external weight synchronization. The
+    canonical weight arguments always remain the autograd/optimizer source of
+    truth. Refresh is mandatory for inference tensors and external writers,
+    whose mutations cannot always be discovered from a PyTorch version counter.
+    """
 
     _validate_ffn_inputs(rmsnorm_output, gate_weight, up_weight, down_weight)
+    if forward_weights is not None:
+        _validate_forward_weights(
+            forward_weights,
+            gate_weight,
+            up_weight,
+            down_weight,
+        )
     if not isinstance(sequence_parallel, bool):
         raise TypeError(
             f"sequence_parallel must be a bool, got {type(sequence_parallel)!r}."
@@ -317,6 +625,9 @@ def qwen3_ffn(
         gate_weight,
         up_weight,
         down_weight,
+        None if forward_weights is None else forward_weights.gate_weight_t,
+        None if forward_weights is None else forward_weights.up_weight_t,
+        None if forward_weights is None else forward_weights.down_weight_t,
         tp_group,
         cp_group,
         sequence_parallel,
diff --git a/rl_engine/kernels/ops/triton/matmul/det_gemm.py b/rl_engine/kernels/ops/triton/matmul/det_gemm.py
index 4f641dbf..d1946b5f 100644
--- a/rl_engine/kernels/ops/triton/matmul/det_gemm.py
+++ b/rl_engine/kernels/ops/triton/matmul/det_gemm.py
@@ -232,6 +232,36 @@ def _copy_tree_root_kernel(
         values = tl.load(workspace_ptr + root * elements + offsets, mask=mask)
         tl.store(output_ptr + offsets, values, mask=mask)
 
+    @triton.jit
+    def _copy_tree_root_transposed_kernel(
+        workspace_ptr,
+        output_ptr,
+        root,
+        M: tl.constexpr,
+        N: tl.constexpr,
+        BLOCK_M: tl.constexpr,
+        BLOCK_N: tl.constexpr,
+    ):
+        offsets_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
+        offsets_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
+        elements = M * N
+        mask = (offsets_m[:, None] < M) & (offsets_n[None, :] < N)
+        values = tl.load(
+            workspace_ptr
+            + root * elements
+            + offsets_m[:, None] * N
+            + offsets_n[None, :],
+            mask=mask,
+        )
+        # Store the already-rounded BF16 root directly in [N, M] layout.
+        # This is a pure address permutation: the canonical GEMM leaves, tree,
+        # operand order, and every rounding boundary remain unchanged.
+        tl.store(
+            output_ptr + offsets_n[:, None] * M + offsets_m[None, :],
+            tl.trans(values),
+            mask=tl.trans(mask),
+        )
+
     @triton.jit
     def _det_gemm_fp32_kernel(
         a_ptr,
@@ -305,7 +335,14 @@ def _triton_gemm_fp32(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
     return c
 
 
-def _triton_tree_gemm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+def _triton_tree_gemm(
+    a: torch.Tensor,
+    b: torch.Tensor,
+    *,
+    transpose_output: bool = False,
+    out: torch.Tensor | None = None,
+    preserve_a_strides: bool = False,
+) -> torch.Tensor:
     if not _TRITON_AVAILABLE:
         raise RuntimeError("Triton is unavailable")
     if a.dim() != 2 or b.dim() != 2:
@@ -317,10 +354,34 @@ def _triton_tree_gemm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
     if not a.is_cuda or not b.is_cuda or a.device != b.device:
         raise RuntimeError("Triton tree GEMM inputs must share one CUDA/ROCm device")
 
-    a = a.contiguous()
+    # The leaf kernel accepts positive arbitrary strides. Wgrad uses this to
+    # consume an activation transpose view without materializing another copy.
+    # Other callers retain the established contiguous-input behavior.
+    if not preserve_a_strides:
+        a = a.contiguous()
     b = b.contiguous()
     m_size, k_size = a.shape
     n_size = b.size(1)
+    result_shape = (n_size, m_size) if transpose_output else (m_size, n_size)
+    if out is None:
+        result = torch.empty(result_shape, dtype=torch.bfloat16, device=a.device)
+    else:
+        if tuple(out.shape) != result_shape:
+            raise ValueError(
+                f"Triton tree GEMM output must have shape {result_shape}, "
+                f"got {tuple(out.shape)}"
+            )
+        if out.dtype != torch.bfloat16:
+            raise TypeError(f"Triton tree GEMM output must be BF16, got {out.dtype}")
+        if out.device != a.device:
+            raise RuntimeError(
+                f"Triton tree GEMM output must be on {a.device}, got {out.device}"
+            )
+        if not out.is_contiguous():
+            raise ValueError("Triton tree GEMM output buffer must be contiguous")
+        if out.requires_grad:
+            raise ValueError("Triton tree GEMM output buffer must not require gradients")
+        result = out
     plan = _device_tree_plan(k_size, a.device)
     workspace = torch.empty(
         (plan.host.node_count, m_size, n_size),
@@ -367,15 +428,34 @@ def _triton_tree_gemm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
             BLOCK=reduction_block,
         )
 
-    result = torch.empty((m_size, n_size), dtype=torch.bfloat16, device=a.device)
     copy_block = 256
-    _copy_tree_root_kernel[(triton.cdiv(result.numel(), copy_block),)](
-        workspace,
-        result,
-        plan.host.root,
-        result.numel(),
-        BLOCK=copy_block,
-    )
+    if transpose_output:
+        transpose_block = 32
+        transpose_grid = (
+            triton.cdiv(m_size, transpose_block),
+            triton.cdiv(n_size, transpose_block),
+        )
+        _copy_tree_root_transposed_kernel[transpose_grid](
+            workspace,
+            result,
+            plan.host.root,
+            M=m_size,
+            N=n_size,
+            BLOCK_M=transpose_block,
+            BLOCK_N=transpose_block,
+        )
+    else:
+        _copy_tree_root_kernel[(triton.cdiv(result.numel(), copy_block),)](
+            workspace,
+            result,
+            plan.host.root,
+            result.numel(),
+            BLOCK=copy_block,
+        )
+    if out is not None:
+        # Triton mutates caller-owned storage outside PyTorch's dispatcher.
+        # Keep saved-tensor and cache version checks semantically correct.
+        torch.autograd.graph.increment_version(result)
     return result
 
 
diff --git a/tests/distributed/test_qwen_ffn_topology.py b/tests/distributed/test_qwen_ffn_topology.py
index 83d2080b..5cb72a09 100644
--- a/tests/distributed/test_qwen_ffn_topology.py
+++ b/tests/distributed/test_qwen_ffn_topology.py
@@ -24,6 +24,7 @@
 from rl_engine.kernels.ops.triton.ffn import (
     QWEN3_8B_HIDDEN_SIZE,
     QWEN3_8B_INTERMEDIATE_SIZE,
+    pack_qwen3_ffn_forward_weights,
     qwen3_ffn,
 )
 _IS_ROCM = getattr(torch.version, "hip", None) is not None
@@ -226,17 +227,21 @@ def _run_topology(
         up[feature_start:feature_end].contiguous(),
         down[:, feature_start:feature_end].contiguous(),
     )
+    inference_forward_weights = pack_qwen3_ffn_forward_weights(*shard[1:])
 
     with torch.no_grad():
         inference = qwen3_ffn(
             *shard,
+            forward_weights=inference_forward_weights,
             tp_group=tp_group,
             cp_group=cp_group,
             sequence_parallel=sequence_parallel,
         )
     inputs = [value.detach().clone().requires_grad_(True) for value in shard]
+    training_forward_weights = pack_qwen3_ffn_forward_weights(*inputs[1:])
     training = qwen3_ffn(
         *inputs,
+        forward_weights=training_forward_weights,
         tp_group=tp_group,
         cp_group=cp_group,
         sequence_parallel=sequence_parallel,
diff --git a/tests/test_det_gemm.py b/tests/test_det_gemm.py
index ec4bac79..8fe38886 100644
--- a/tests/test_det_gemm.py
+++ b/tests/test_det_gemm.py
@@ -16,6 +16,7 @@
 
 try:
     from rl_engine.kernels.ops.triton.matmul import deterministic_gemm_triton
+    from rl_engine.kernels.ops.triton.matmul.det_gemm import _triton_tree_gemm
 
     _HAS_TRITON = True
 except ImportError:
@@ -43,6 +44,17 @@ def _rand(*shape):
     return torch.randn(*shape, device=DEV, dtype=torch.bfloat16)
 
 
+def _assert_same_raw_bytes(actual: torch.Tensor, expected: torch.Tensor) -> None:
+    assert actual.shape == expected.shape
+    assert actual.dtype == expected.dtype == torch.bfloat16
+    assert actual.is_contiguous()
+    assert expected.is_contiguous()
+    assert torch.equal(
+        actual.reshape(-1).view(torch.uint8),
+        expected.reshape(-1).view(torch.uint8),
+    )
+
+
 _K_TREE_LEAF = 32
 
 
@@ -261,3 +273,75 @@ def test_triton_tree_matches_python_reference_backward_bitwise():
     )
     for expected_grad, triton_input in zip(expected, triton_inputs, strict=True):
         assert torch.equal(expected_grad, triton_input.grad)
+
+
+@pytest.mark.skipif(not _HAS_TRITON, reason="Triton is unavailable")
+@pytest.mark.parametrize(
+    "shape",
+    (
+        (1, 1, 3),
+        (3, 31, 5),
+        (17, 32, 33),
+        (33, 33, 17),
+        (65, 65, 31),
+    ),
+)
+def test_triton_tree_transposed_out_matches_legacy_root_copy_raw_bytes(shape):
+    """Root placement may transpose addresses, but never the BF16 values."""
+
+    torch.manual_seed(49)
+    m_size, k_size, n_size = shape
+    a = _rand(m_size, k_size)
+    b = _rand(k_size, n_size)
+
+    legacy = _triton_tree_gemm(a, b).t().contiguous()
+    output_buffer = torch.empty(
+        (n_size, m_size),
+        dtype=torch.bfloat16,
+        device=a.device,
+    )
+    version_before = output_buffer._version
+    transposed = _triton_tree_gemm(
+        a,
+        b,
+        transpose_output=True,
+        out=output_buffer,
+    )
+
+    assert transposed is output_buffer
+    assert output_buffer._version == version_before + 1
+    assert transposed.stride() == (m_size, 1)
+    _assert_same_raw_bytes(transposed, legacy)
+
+
+@pytest.mark.skipif(not _HAS_TRITON, reason="Triton is unavailable")
+def test_triton_wgrad_reads_positive_stride_transpose_view_raw_bytes():
+    """The copy-free a.T wgrad path must preserve the legacy GEMM bit graph."""
+
+    torch.manual_seed(50)
+    token_count, input_size, output_size = 33, 65, 17
+    activations = _rand(token_count, input_size)
+    grad_output = _rand(token_count, output_size)
+    activation_t = activations.t()
+    assert not activation_t.is_contiguous()
+    assert all(stride > 0 for stride in activation_t.stride())
+
+    legacy = _triton_tree_gemm(
+        activation_t.contiguous(),
+        grad_output,
+    ).t().contiguous()
+    output_buffer = torch.empty(
+        (output_size, input_size),
+        dtype=torch.bfloat16,
+        device=activations.device,
+    )
+    direct = _triton_tree_gemm(
+        activation_t,
+        grad_output,
+        transpose_output=True,
+        out=output_buffer,
+        preserve_a_strides=True,
+    )
+
+    assert direct is output_buffer
+    _assert_same_raw_bytes(direct, legacy)
diff --git a/tests/test_qwen_ffn.py b/tests/test_qwen_ffn.py
index ef28c2db..f0dade06 100644
--- a/tests/test_qwen_ffn.py
+++ b/tests/test_qwen_ffn.py
@@ -14,8 +14,11 @@
 from rl_engine.kernels.ops.triton.ffn import (
     QWEN3_8B_HIDDEN_SIZE,
     QWEN3_8B_INTERMEDIATE_SIZE,
+    Qwen3FFNForwardWeights,
+    pack_qwen3_ffn_forward_weights,
     qwen3_ffn,
     qwen3_ffn_triton,
+    refresh_qwen3_ffn_forward_weights,
 )
 from rl_engine.platforms.device import device_ctx
 
@@ -34,6 +37,19 @@ def _randn(shape, *, seed: int, dtype=torch.float32, device="cpu"):
     return value.to(device=device, dtype=dtype)
 
 
+def _assert_same_raw_bytes(actual: torch.Tensor, expected: torch.Tensor) -> None:
+    actual = actual.detach()
+    expected = expected.detach()
+    assert actual.shape == expected.shape
+    assert actual.dtype == expected.dtype == torch.bfloat16
+    assert actual.is_contiguous()
+    assert expected.is_contiguous()
+    assert torch.equal(
+        actual.reshape(-1).view(torch.uint8),
+        expected.reshape(-1).view(torch.uint8),
+    )
+
+
 def _reference(hidden_states, gate_weight, up_weight, down_weight):
     gate = hidden_states @ gate_weight.t()
     up = hidden_states @ up_weight.t()
@@ -73,10 +89,13 @@ def test_qwen3_ffn_public_api_and_model_dimensions_are_pinned():
         "gate_weight",
         "up_weight",
         "down_weight",
+        "forward_weights",
         "tp_group",
         "cp_group",
         "sequence_parallel",
     )
+    assert signature.parameters["forward_weights"].kind is inspect.Parameter.KEYWORD_ONLY
+    assert signature.parameters["forward_weights"].default is None
     assert signature.parameters["tp_group"].kind is inspect.Parameter.KEYWORD_ONLY
     assert signature.parameters["cp_group"].kind is inspect.Parameter.KEYWORD_ONLY
     assert signature.parameters["sequence_parallel"].default is False
@@ -224,3 +243,200 @@ def test_qwen3_ffn_output_and_hidden_gradient_are_token_slice_invariant():
 
     assert torch.equal(slice_output, full_output[2:4])
     assert torch.equal(slice_hidden.grad, full_hidden.grad[2:4])
+
+
+@requires_rocm
+def test_qwen3_ffn_forward_weight_pack_is_detached_contiguous_and_transposed():
+    device = device_ctx.device
+    gate_weight = _randn((67, 35), seed=50, dtype=torch.bfloat16, device=device)
+    up_weight = _randn((67, 35), seed=51, dtype=torch.bfloat16, device=device)
+    down_weight = _randn((35, 67), seed=52, dtype=torch.bfloat16, device=device)
+
+    packed = pack_qwen3_ffn_forward_weights(gate_weight, up_weight, down_weight)
+
+    assert isinstance(packed, Qwen3FFNForwardWeights)
+    expected = (
+        gate_weight.t().contiguous(),
+        up_weight.t().contiguous(),
+        down_weight.t().contiguous(),
+    )
+    actual = (packed.gate_weight_t, packed.up_weight_t, packed.down_weight_t)
+    assert tuple(value.shape for value in actual) == ((35, 67), (35, 67), (67, 35))
+    for packed_weight, expected_weight in zip(actual, expected, strict=True):
+        assert packed_weight.is_contiguous()
+        assert not packed_weight.requires_grad
+        assert packed_weight.grad_fn is None
+        assert packed_weight.grad is None
+        _assert_same_raw_bytes(packed_weight, expected_weight)
+
+
+@requires_rocm
+def test_qwen3_ffn_standard_and_packed_paths_match_all_raw_bytes():
+    device = device_ctx.device
+    values = (
+        _randn((7, 35), seed=60, dtype=torch.bfloat16, device=device),
+        _randn((67, 35), seed=61, dtype=torch.bfloat16, device=device),
+        _randn((67, 35), seed=62, dtype=torch.bfloat16, device=device),
+        _randn((35, 67), seed=63, dtype=torch.bfloat16, device=device),
+    )
+    grad_output = _randn((7, 35), seed=64, dtype=torch.bfloat16, device=device)
+
+    inference_pack = pack_qwen3_ffn_forward_weights(*values[1:])
+    with torch.no_grad():
+        standard_inference = qwen3_ffn(*values)
+        packed_inference = qwen3_ffn(*values, forward_weights=inference_pack)
+
+    standard_inputs = [value.detach().clone().requires_grad_(True) for value in values]
+    packed_inputs = [value.detach().clone().requires_grad_(True) for value in values]
+    training_pack = pack_qwen3_ffn_forward_weights(*packed_inputs[1:])
+    standard_training = qwen3_ffn(*standard_inputs)
+    packed_training = qwen3_ffn(*packed_inputs, forward_weights=training_pack)
+    standard_training.backward(grad_output)
+    packed_training.backward(grad_output)
+
+    _assert_same_raw_bytes(packed_inference, standard_inference)
+    _assert_same_raw_bytes(standard_training, standard_inference)
+    _assert_same_raw_bytes(packed_training, standard_training)
+    for packed_input, standard_input in zip(packed_inputs, standard_inputs, strict=True):
+        assert packed_input.grad is not None
+        assert standard_input.grad is not None
+        _assert_same_raw_bytes(packed_input.grad, standard_input.grad)
+    for packed_weight in (
+        training_pack.gate_weight_t,
+        training_pack.up_weight_t,
+        training_pack.down_weight_t,
+    ):
+        assert not packed_weight.requires_grad
+        assert packed_weight.grad is None
+
+
+@pytest.mark.parametrize(
+    ("weight_index", "weight_name"),
+    ((0, "gate_weight"), (1, "up_weight"), (2, "down_weight")),
+)
+@requires_rocm
+def test_qwen3_ffn_rejects_stale_forward_weights_after_inplace_source_update(
+    weight_index,
+    weight_name,
+):
+    device = device_ctx.device
+    hidden = _randn((2, 8), seed=70, dtype=torch.bfloat16, device=device)
+    weights = [
+        _randn((12, 8), seed=71, dtype=torch.bfloat16, device=device),
+        _randn((12, 8), seed=72, dtype=torch.bfloat16, device=device),
+        _randn((8, 12), seed=73, dtype=torch.bfloat16, device=device),
+    ]
+    packed = pack_qwen3_ffn_forward_weights(*weights)
+    with torch.no_grad():
+        weights[weight_index].add_(1)
+
+    with pytest.raises(RuntimeError, match=rf"{weight_name} changed after .*refresh"):
+        qwen3_ffn(hidden, *weights, forward_weights=packed)
+
+
+@requires_rocm
+def test_qwen3_ffn_forward_weight_refresh_preserves_storage_and_bits():
+    device = device_ctx.device
+    hidden = _randn((7, 35), seed=80, dtype=torch.bfloat16, device=device)
+    weights = [
+        _randn((67, 35), seed=81, dtype=torch.bfloat16, device=device),
+        _randn((67, 35), seed=82, dtype=torch.bfloat16, device=device),
+        _randn((35, 67), seed=83, dtype=torch.bfloat16, device=device),
+    ]
+    packed = pack_qwen3_ffn_forward_weights(*weights)
+    packed_tensors = (
+        packed.gate_weight_t,
+        packed.up_weight_t,
+        packed.down_weight_t,
+    )
+    data_ptrs = tuple(weight.data_ptr() for weight in packed_tensors)
+
+    with torch.no_grad():
+        for weight in weights:
+            weight.add_(torch.ones_like(weight))
+    refreshed = refresh_qwen3_ffn_forward_weights(packed, *weights)
+
+    assert refreshed is packed
+    assert tuple(weight.data_ptr() for weight in packed_tensors) == data_ptrs
+    with torch.no_grad():
+        standard = qwen3_ffn(hidden, *weights)
+        cached = qwen3_ffn(hidden, *weights, forward_weights=packed)
+    _assert_same_raw_bytes(cached, standard)
+
+
+@requires_rocm
+def test_qwen3_ffn_forward_weight_pack_works_inside_inference_mode():
+    device = device_ctx.device
+    with torch.inference_mode():
+        hidden = _randn((7, 35), seed=90, dtype=torch.bfloat16, device=device)
+        weights = (
+            _randn((67, 35), seed=91, dtype=torch.bfloat16, device=device),
+            _randn((67, 35), seed=92, dtype=torch.bfloat16, device=device),
+            _randn((35, 67), seed=93, dtype=torch.bfloat16, device=device),
+        )
+        assert all(torch.is_inference(weight) for weight in weights)
+        packed = pack_qwen3_ffn_forward_weights(*weights)
+        cached = qwen3_ffn(hidden, *weights, forward_weights=packed)
+        standard = qwen3_ffn(hidden, *weights)
+
+    assert not torch.is_inference(packed.gate_weight_t)
+    assert not torch.is_inference(packed.up_weight_t)
+    assert not torch.is_inference(packed.down_weight_t)
+    _assert_same_raw_bytes(cached, standard)
+
+
+@requires_rocm
+def test_qwen3_ffn_rejects_mutated_packed_forward_weight():
+    device = device_ctx.device
+    hidden = _randn((2, 8), seed=100, dtype=torch.bfloat16, device=device)
+    weights = (
+        _randn((12, 8), seed=101, dtype=torch.bfloat16, device=device),
+        _randn((12, 8), seed=102, dtype=torch.bfloat16, device=device),
+        _randn((8, 12), seed=103, dtype=torch.bfloat16, device=device),
+    )
+    packed = pack_qwen3_ffn_forward_weights(*weights)
+    with torch.no_grad():
+        packed.gate_weight_t.add_(1)
+
+    with pytest.raises(RuntimeError, match="packed gate_weight changed; refresh"):
+        qwen3_ffn(hidden, *weights, forward_weights=packed)
+
+
+@requires_rocm
+def test_qwen3_ffn_refresh_updates_already_captured_cuda_graph():
+    device = device_ctx.device
+    hidden = _randn((7, 35), seed=110, dtype=torch.bfloat16, device=device)
+    weights = [
+        _randn((67, 35), seed=111, dtype=torch.bfloat16, device=device),
+        _randn((67, 35), seed=112, dtype=torch.bfloat16, device=device),
+        _randn((35, 67), seed=113, dtype=torch.bfloat16, device=device),
+    ]
+    packed = pack_qwen3_ffn_forward_weights(*weights)
+    capture_stream = torch.cuda.Stream(device=device)
+    capture_stream.wait_stream(torch.cuda.current_stream(device))
+    with torch.cuda.stream(capture_stream), torch.no_grad():
+        for _ in range(3):
+            qwen3_ffn(hidden, *weights, forward_weights=packed)
+    capture_stream.synchronize()
+
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph, stream=capture_stream), torch.no_grad():
+        static_output = qwen3_ffn(hidden, *weights, forward_weights=packed)
+    graph.replay()
+    torch.cuda.synchronize()
+    before_refresh = static_output.clone()
+
+    with torch.no_grad():
+        weights[0].add_(1)
+    packed.refresh_(*weights)
+    graph.replay()
+    torch.cuda.synchronize()
+    after_refresh = static_output.clone()
+    with torch.no_grad():
+        expected = qwen3_ffn(hidden, *weights)
+
+    assert not torch.equal(
+        before_refresh.view(torch.uint8),
+        after_refresh.view(torch.uint8),
+    )
+    _assert_same_raw_bytes(after_refresh, expected)

From 6e0c2202f9a848020de4e933472fb3aa812e6090 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Wed, 26 Aug 2026 14:47:56 +0000
Subject: [PATCH 19/28] perf(ffn): optimize deterministic gfx942 leaf

---
 benchmarks/benchmark_rocm_det_gemm_leaf.py    | 553 ++++++++++++++++++
 benchmarks/benchmark_rocm_ffn.py              | 149 ++++-
 .../kernels/ops/triton/matmul/det_gemm.py     | 121 +++-
 tests/test_det_gemm.py                        | 172 +++++-
 4 files changed, 959 insertions(+), 36 deletions(-)
 create mode 100644 benchmarks/benchmark_rocm_det_gemm_leaf.py

diff --git a/benchmarks/benchmark_rocm_det_gemm_leaf.py b/benchmarks/benchmark_rocm_det_gemm_leaf.py
new file mode 100644
index 00000000..bfcbc8b7
--- /dev/null
+++ b/benchmarks/benchmark_rocm_det_gemm_leaf.py
@@ -0,0 +1,553 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+"""Offline tile/occupancy sweep for the strict ROCm deterministic GEMM leaf.
+
+The benchmark launches the production Triton kernels directly with preallocated
+buffers. Every candidate is checked against the pinned 64x64/4-warp baseline at
+both the complete leaf workspace and final tree root before timings are reported.
+
+Example:
+
+    python benchmarks/benchmark_rocm_det_gemm_leaf.py \
+      --device 4 \
+      --cases fwd_gate_m32,fwd_down_m32 \
+      --configs 16x128x4,32x64x2xn,32x128x4xn,64x64x4 \
+      --output /tmp/rlk_leaf_sweep.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import platform
+import statistics
+import subprocess
+import sys
+from dataclasses import asdict, dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Callable
+
+import torch
+import triton
+
+from rl_engine.kernels.ops.triton.matmul.det_gemm import (
+    _copy_tree_root_kernel,
+    _copy_tree_root_transposed_kernel,
+    _det_gemm_tree_leaf_kernel,
+    _det_gemm_tree_reduce_kernel,
+    _device_tree_plan,
+)
+
+
+_REPO_ROOT = Path(__file__).resolve().parents[1]
+_KERNEL_SOURCE = _REPO_ROOT / "rl_engine/kernels/ops/triton/matmul/det_gemm.py"
+
+
+@dataclass(frozen=True)
+class LeafConfig:
+    block_m: int
+    block_n: int
+    num_warps: int
+    order: str = "leaf"
+    waves_per_eu: int = 0
+
+    @property
+    def slug(self) -> str:
+        return (
+            f"{self.block_m}x{self.block_n}x{self.num_warps}x"
+            f"{self.order}xw{self.waves_per_eu}"
+        )
+
+
+@dataclass(frozen=True)
+class LeafCase:
+    name: str
+    m_size: int
+    k_size: int
+    n_size: int
+    transposed_a: bool = False
+    transpose_output: bool = False
+
+
+_CASES = {
+    case.name: case
+    for token_count in (1, 8, 32)
+    for case in (
+        LeafCase(f"fwd_gate_m{token_count}", token_count, 4096, 12288),
+        LeafCase(f"fwd_down_m{token_count}", token_count, 12288, 4096),
+        LeafCase(
+            f"wgrad_gate_m{token_count}",
+            4096,
+            token_count,
+            12288,
+            transposed_a=True,
+            transpose_output=True,
+        ),
+        LeafCase(
+            f"wgrad_down_m{token_count}",
+            12288,
+            token_count,
+            4096,
+            transposed_a=True,
+            transpose_output=True,
+        ),
+    )
+}
+
+_BASELINE = LeafConfig(64, 64, 4)
+
+
+def _parse_csv(value: str) -> list[str]:
+    return [item.strip() for item in value.split(",") if item.strip()]
+
+
+def _parse_config(value: str) -> LeafConfig:
+    try:
+        parts = value.split("x")
+        if len(parts) not in (3, 4, 5):
+            raise ValueError
+        block_m, block_n, num_warps = (int(part) for part in parts[:3])
+        order = parts[3] if len(parts) >= 4 else "leaf"
+        waves_per_eu = int(parts[4]) if len(parts) == 5 else 0
+    except (TypeError, ValueError) as error:
+        raise argparse.ArgumentTypeError(
+            "config must be BLOCK_MxBLOCK_NxNUM_WARPS[xORDER[xWAVES_PER_EU]], "
+            f"got {value!r}"
+        ) from error
+    if (
+        block_m <= 0
+        or block_n <= 0
+        or num_warps not in (1, 2, 4, 8)
+        or order not in ("leaf", "n")
+        or waves_per_eu not in (0, 1, 2, 4)
+    ):
+        raise argparse.ArgumentTypeError(f"invalid leaf config {value!r}")
+    return LeafConfig(block_m, block_n, num_warps, order, waves_per_eu)
+
+
+def _git_output(*arguments: str) -> str:
+    try:
+        result = subprocess.run(
+            ["git", *arguments],
+            cwd=_REPO_ROOT,
+            check=True,
+            capture_output=True,
+            text=True,
+        )
+    except (OSError, subprocess.CalledProcessError):
+        return ""
+    return result.stdout.strip()
+
+
+def _command_output(*arguments: str) -> str:
+    try:
+        result = subprocess.run(
+            list(arguments),
+            check=True,
+            capture_output=True,
+            text=True,
+        )
+    except (OSError, subprocess.CalledProcessError):
+        return ""
+    return result.stdout.strip()
+
+
+def _sha256(data: bytes) -> str:
+    return hashlib.sha256(data).hexdigest()
+
+
+def _tensor_sha256(tensor: torch.Tensor) -> str:
+    raw = tensor.detach().contiguous().view(torch.uint8).cpu().numpy()
+    return hashlib.sha256(memoryview(raw)).hexdigest()
+
+
+def _percentile(values: list[float], percentile: float) -> float:
+    ordered = sorted(values)
+    position = (len(ordered) - 1) * percentile
+    lower = int(position)
+    upper = min(lower + 1, len(ordered) - 1)
+    weight = position - lower
+    return ordered[lower] * (1.0 - weight) + ordered[upper] * weight
+
+
+def _summary(samples_ms: list[float]) -> dict[str, float | list[float]]:
+    return {
+        "samples_ms": samples_ms,
+        "median_ms": statistics.median(samples_ms),
+        "p95_ms": _percentile(samples_ms, 0.95),
+        "min_ms": min(samples_ms),
+        "max_ms": max(samples_ms),
+    }
+
+
+def _measure(
+    launch: Callable[[], None],
+    *,
+    warmup: int,
+    samples: int,
+) -> dict[str, float | list[float]]:
+    for _ in range(warmup):
+        launch()
+    torch.cuda.synchronize()
+    events = [
+        (torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True))
+        for _ in range(samples)
+    ]
+    for start, end in events:
+        start.record()
+        launch()
+        end.record()
+    torch.cuda.synchronize()
+    return _summary([float(start.elapsed_time(end)) for start, end in events])
+
+
+def _inputs(case: LeafCase, device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:
+    generator = torch.Generator(device=device).manual_seed(4100 + case.k_size + case.m_size)
+    if case.transposed_a:
+        source = torch.randn(
+            (case.k_size, case.m_size),
+            generator=generator,
+            device=device,
+            dtype=torch.bfloat16,
+        )
+        a = source.t()
+        if any(stride <= 0 for stride in a.stride()):
+            raise RuntimeError("wgrad benchmark requires a positive-stride transpose view")
+        if case.k_size > 1 and a.is_contiguous():
+            raise RuntimeError("non-degenerate wgrad benchmark requires a transpose view")
+    else:
+        a = torch.randn(
+            (case.m_size, case.k_size),
+            generator=generator,
+            device=device,
+            dtype=torch.bfloat16,
+        )
+    b = torch.randn(
+        (case.k_size, case.n_size),
+        generator=generator,
+        device=device,
+        dtype=torch.bfloat16,
+    )
+    return a, b
+
+
+def _launch_leaf(
+    case: LeafCase,
+    config: LeafConfig,
+    a: torch.Tensor,
+    b: torch.Tensor,
+    workspace: torch.Tensor,
+    plan,
+) -> None:
+    tiles_m = triton.cdiv(case.m_size, config.block_m)
+    tiles_n = triton.cdiv(case.n_size, config.block_n)
+    grid = (
+        (tiles_n, tiles_m, len(plan.host.leaf_nodes))
+        if config.order == "n"
+        else (len(plan.host.leaf_nodes), tiles_m, tiles_n)
+    )
+    launch_options = {"num_warps": config.num_warps}
+    if config.waves_per_eu:
+        launch_options["waves_per_eu"] = config.waves_per_eu
+    _det_gemm_tree_leaf_kernel[grid](
+        a,
+        b,
+        workspace,
+        plan.leaf_starts,
+        plan.leaf_lengths,
+        plan.leaf_nodes,
+        M=case.m_size,
+        N=case.n_size,
+        K=case.k_size,
+        stride_am=a.stride(0),
+        stride_ak=a.stride(1),
+        stride_bk=b.stride(0),
+        stride_bn=b.stride(1),
+        BLOCK_M=config.block_m,
+        BLOCK_N=config.block_n,
+        BLOCK_K=32,
+        N_FASTEST=config.order == "n",
+        **launch_options,
+    )
+
+
+def _launch_tree(
+    case: LeafCase,
+    config: LeafConfig,
+    a: torch.Tensor,
+    b: torch.Tensor,
+    workspace: torch.Tensor,
+    output: torch.Tensor,
+    plan,
+) -> None:
+    _launch_leaf(case, config, a, b, workspace, plan)
+    reduction_block = 256
+    for operations, (lower, upper, result) in zip(
+        plan.host.reduction_levels,
+        plan.reduction_levels,
+        strict=True,
+    ):
+        grid = (
+            len(operations),
+            triton.cdiv(case.m_size * case.n_size, reduction_block),
+        )
+        _det_gemm_tree_reduce_kernel[grid](
+            workspace,
+            lower,
+            upper,
+            result,
+            M=case.m_size,
+            N=case.n_size,
+            BLOCK=reduction_block,
+        )
+
+    if case.transpose_output:
+        block = 32
+        grid = (
+            triton.cdiv(case.m_size, block),
+            triton.cdiv(case.n_size, block),
+        )
+        _copy_tree_root_transposed_kernel[grid](
+            workspace,
+            output,
+            plan.host.root,
+            M=case.m_size,
+            N=case.n_size,
+            BLOCK_M=block,
+            BLOCK_N=block,
+        )
+    else:
+        block = 256
+        _copy_tree_root_kernel[(triton.cdiv(output.numel(), block),)](
+            workspace,
+            output,
+            plan.host.root,
+            output.numel(),
+            BLOCK=block,
+        )
+
+
+def _same_raw_bytes(actual: torch.Tensor, expected: torch.Tensor) -> bool:
+    if actual.shape != expected.shape or actual.dtype != expected.dtype:
+        return False
+    return bool(torch.equal(actual.contiguous().view(torch.uint8), expected.view(torch.uint8)))
+
+
+def _run_case(
+    case: LeafCase,
+    configs: list[LeafConfig],
+    *,
+    device: torch.device,
+    warmup: int,
+    samples: int,
+) -> dict[str, object]:
+    print(
+        f"{case.name}: A=({case.m_size}, {case.k_size}), "
+        f"B=({case.k_size}, {case.n_size})",
+        flush=True,
+    )
+    a, b = _inputs(case, device)
+    plan = _device_tree_plan(case.k_size, device)
+    workspace_shape = (plan.host.node_count, case.m_size, case.n_size)
+    output_shape = (
+        (case.n_size, case.m_size)
+        if case.transpose_output
+        else (case.m_size, case.n_size)
+    )
+    reference_workspace = torch.empty(workspace_shape, dtype=torch.bfloat16, device=device)
+    reference_output = torch.empty(output_shape, dtype=torch.bfloat16, device=device)
+    _launch_tree(
+        case,
+        _BASELINE,
+        a,
+        b,
+        reference_workspace,
+        reference_output,
+        plan,
+    )
+    torch.cuda.synchronize()
+    leaf_indices = plan.leaf_nodes.to(torch.int64)
+    reference_leaves = reference_workspace.index_select(0, leaf_indices)
+    reference_fingerprints = {
+        "leaf_workspace_sha256_raw_bytes": _tensor_sha256(reference_leaves),
+        "root_sha256_raw_bytes": _tensor_sha256(reference_output),
+        "leaf_workspace_nbytes": reference_leaves.numel()
+        * reference_leaves.element_size(),
+        "root_nbytes": reference_output.numel() * reference_output.element_size(),
+    }
+
+    results: list[dict[str, object]] = []
+    for config in configs:
+        workspace = torch.empty(workspace_shape, dtype=torch.bfloat16, device=device)
+        output = torch.empty(output_shape, dtype=torch.bfloat16, device=device)
+        _launch_tree(case, config, a, b, workspace, output, plan)
+        _launch_leaf(case, config, a, b, workspace, plan)
+        torch.cuda.synchronize()
+        candidate_leaves = workspace.index_select(0, leaf_indices)
+        leaf_raw_bytes_equal = _same_raw_bytes(candidate_leaves, reference_leaves)
+
+        _launch_tree(case, config, a, b, workspace, output, plan)
+        torch.cuda.synchronize()
+        root_raw_bytes_equal = _same_raw_bytes(output, reference_output)
+        if not leaf_raw_bytes_equal or not root_raw_bytes_equal:
+            raise RuntimeError(
+                f"{case.name}/{config.slug} changed strict GEMM raw bytes: "
+                f"leaf={leaf_raw_bytes_equal}, root={root_raw_bytes_equal}"
+            )
+
+        leaf_timing = _measure(
+            lambda: _launch_leaf(case, config, a, b, workspace, plan),
+            warmup=warmup,
+            samples=samples,
+        )
+        tree_timing = _measure(
+            lambda: _launch_tree(case, config, a, b, workspace, output, plan),
+            warmup=warmup,
+            samples=samples,
+        )
+        result = {
+            "config": asdict(config),
+            "slug": config.slug,
+            "leaf_raw_bytes_equal": leaf_raw_bytes_equal,
+            "root_raw_bytes_equal": root_raw_bytes_equal,
+            "leaf_timing": leaf_timing,
+            "tree_timing": tree_timing,
+        }
+        results.append(result)
+        print(
+            f"  {config.slug}: leaf={leaf_timing['median_ms']:.4f} ms, "
+            f"tree={tree_timing['median_ms']:.4f} ms",
+            flush=True,
+        )
+        del candidate_leaves, workspace, output
+
+    baseline_result = next(result for result in results if result["slug"] == _BASELINE.slug)
+    baseline_leaf = float(baseline_result["leaf_timing"]["median_ms"])
+    baseline_tree = float(baseline_result["tree_timing"]["median_ms"])
+    for result in results:
+        leaf_median = float(result["leaf_timing"]["median_ms"])
+        tree_median = float(result["tree_timing"]["median_ms"])
+        result["leaf_speedup_vs_baseline"] = baseline_leaf / leaf_median
+        result["tree_speedup_vs_baseline"] = baseline_tree / tree_median
+    results.sort(key=lambda result: float(result["leaf_timing"]["median_ms"]))
+    return {
+        "case": asdict(case),
+        "tree": {
+            "leaf_count": len(plan.host.leaf_nodes),
+            "node_count": plan.host.node_count,
+            "reduction_levels": len(plan.host.reduction_levels),
+        },
+        "baseline_fingerprints": reference_fingerprints,
+        "results": results,
+    }
+
+
+def _validate_args(args: argparse.Namespace) -> None:
+    if getattr(torch.version, "hip", None) is None:
+        raise RuntimeError("this benchmark requires a ROCm PyTorch build")
+    if not torch.cuda.is_available():
+        raise RuntimeError("no ROCm GPU is available")
+    if args.device < 0 or args.device >= torch.cuda.device_count():
+        raise ValueError(f"--device must be in [0, {torch.cuda.device_count() - 1}]")
+    if args.warmup < 0 or args.samples <= 0:
+        raise ValueError("--warmup must be non-negative and --samples must be positive")
+    unknown = sorted(set(args.cases) - _CASES.keys())
+    if unknown:
+        raise ValueError(f"unknown cases: {', '.join(unknown)}")
+    if _BASELINE not in args.configs:
+        args.configs.append(_BASELINE)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--device", type=int, default=0)
+    parser.add_argument(
+        "--cases",
+        type=_parse_csv,
+        default=["fwd_gate_m32", "fwd_down_m32"],
+        help=f"comma-separated case names; choices: {','.join(_CASES)}",
+    )
+    parser.add_argument(
+        "--configs",
+        type=lambda value: [_parse_config(item) for item in _parse_csv(value)],
+        default=[
+            LeafConfig(16, 64, 2),
+            LeafConfig(16, 128, 4),
+            LeafConfig(32, 64, 2, "n"),
+            LeafConfig(32, 128, 4, "n"),
+            _BASELINE,
+        ],
+    )
+    parser.add_argument("--warmup", type=int, default=5)
+    parser.add_argument("--samples", type=int, default=30)
+    parser.add_argument("--output", type=Path)
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    _validate_args(args)
+    torch.cuda.set_device(args.device)
+    torch.backends.cuda.matmul.allow_tf32 = False
+    device = torch.device("cuda", args.device)
+    properties = torch.cuda.get_device_properties(args.device)
+    tracked_diff = _git_output("diff", "--binary")
+    benchmark_source = Path(__file__).read_bytes()
+    payload = {
+        "environment": {
+            "timestamp_utc": datetime.now(timezone.utc).isoformat(),
+            "command": [sys.executable, *sys.argv],
+            "hostname": platform.node(),
+            "python": platform.python_version(),
+            "torch": torch.__version__,
+            "hip": torch.version.hip,
+            "triton": triton.__version__,
+            "gpu_index": args.device,
+            "gpu": properties.name,
+            "architecture": getattr(properties, "gcnArchName", ""),
+            "git_commit": _git_output("rev-parse", "HEAD"),
+            "git_status": _git_output("status", "--short").splitlines(),
+            "tracked_diff_sha256": _sha256(tracked_diff.encode()),
+            "kernel_source_sha256": _sha256(_KERNEL_SOURCE.read_bytes()),
+            "benchmark_source_sha256": _sha256(benchmark_source),
+            "rocm_smi_snapshot": _command_output(
+                "rocm-smi",
+                "--showuse",
+                "--showmemuse",
+                "--showtemp",
+                "--showclocks",
+            ),
+        },
+        "methodology": {
+            "timing": "GPU events around preallocated direct kernel launches",
+            "correctness": "raw bytes of every leaf node and final BF16 root",
+            "baseline": asdict(_BASELINE),
+            "warmup": args.warmup,
+            "samples": args.samples,
+        },
+        "cases": [],
+    }
+    for case_name in args.cases:
+        payload["cases"].append(
+            _run_case(
+                _CASES[case_name],
+                args.configs,
+                device=device,
+                warmup=args.warmup,
+                samples=args.samples,
+            )
+        )
+        torch.cuda.empty_cache()
+
+    if args.output is not None:
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        args.output.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
+        print(f"results: {args.output.resolve()}")
+    else:
+        print(json.dumps(payload, indent=2, sort_keys=True))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index b7b63428..11401fc7 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -31,7 +31,11 @@
 from transformers.models.qwen3.modeling_qwen3 import Qwen3MLP
 
 import rl_engine.kernels.ops.triton.ffn.ffn as ffn_module
-from rl_engine.kernels.ops.triton.ffn import qwen3_ffn
+from rl_engine.kernels.ops.triton.ffn import (
+    pack_qwen3_ffn_forward_weights,
+    qwen3_ffn,
+)
+
 _DISTRIBUTED_CONFIGS: dict[int, tuple[tuple[str, int, int, bool], ...]] = {
     2: (("tp2", 2, 1, False), ("tp2_sp", 2, 1, True)),
     4: (
@@ -45,6 +49,7 @@
         ("tp4_cp2_sp", 4, 2, True),
     ),
 }
+_TP1_FORWARD_CACHE_BYTES = 3 * 4096 * 12288 * torch.bfloat16.itemsize
 
 
 def _percentile(values: list[float], percentile: float) -> float:
@@ -179,6 +184,11 @@ def _single_gpu_benchmarks(
     gate_weight = _randn((12288, 4096), seed=3000, device=device)
     up_weight = _randn((12288, 4096), seed=3001, device=device)
     down_weight = _randn((4096, 12288), seed=3002, device=device)
+    forward_weights = pack_qwen3_ffn_forward_weights(
+        gate_weight,
+        up_weight,
+        down_weight,
+    )
     official = _official_qwen3_mlp(gate_weight, up_weight, down_weight)
     for index, tokens in enumerate((1, 8, 32)):
         hidden = _randn((tokens, 4096), seed=3010 + index * 2, device=device)
@@ -195,7 +205,11 @@ def _single_gpu_benchmarks(
         )
         triton_timing = _summary_ms(
             _gpu_event_samples(
-                lambda: qwen3_ffn(hidden, *weights),
+                lambda: qwen3_ffn(
+                    hidden,
+                    *weights,
+                    forward_weights=forward_weights,
+                ),
                 warmup=warmup,
                 samples=samples,
             )
@@ -208,6 +222,7 @@ def _single_gpu_benchmarks(
                 "hidden": 4096,
                 "intermediate": 12288,
                 "dtype": "bfloat16",
+                "weight_layout": "packed_forward_cache",
                 "official_tp1": official_timing,
                 "triton": triton_timing,
                 "latency_ratio_vs_official_tp1": (
@@ -221,6 +236,19 @@ def _single_gpu_benchmarks(
             value.detach().clone().requires_grad_(True)
             for value in (hidden, *weights)
         ]
+        triton_forward_weights = pack_qwen3_ffn_forward_weights(
+            *triton_inputs[1:]
+        )
+
+        def triton_training_step() -> torch.Tensor:
+            return _training_step(
+                lambda *values: qwen3_ffn(
+                    *values,
+                    forward_weights=triton_forward_weights,
+                ),
+                triton_inputs,
+                grad_output,
+            )
 
         def official_training_step() -> torch.Tensor:
             official.zero_grad(set_to_none=True)
@@ -238,7 +266,7 @@ def official_training_step() -> torch.Tensor:
         )
         triton_train_timing = _summary_ms(
             _gpu_event_samples(
-                lambda: _training_step(qwen3_ffn, triton_inputs, grad_output),
+                triton_training_step,
                 warmup=max(1, warmup // 2),
                 samples=training_samples,
             )
@@ -251,6 +279,7 @@ def official_training_step() -> torch.Tensor:
                 "hidden": 4096,
                 "intermediate": 12288,
                 "dtype": "bfloat16",
+                "weight_layout": "packed_forward_cache",
                 "official_tp1": official_train_timing,
                 "triton": triton_train_timing,
                 "latency_ratio_vs_official_tp1": (
@@ -259,11 +288,17 @@ def official_training_step() -> torch.Tensor:
                 ),
             }
         )
-        del hidden, grad_output, official_hidden, triton_inputs
+        del (
+            hidden,
+            grad_output,
+            official_hidden,
+            triton_inputs,
+            triton_forward_weights,
+        )
         torch.cuda.empty_cache()
 
     # This is intentionally separate from the determinism and speed results.
-    del official, gate_weight, up_weight, down_weight
+    del official, forward_weights, gate_weight, up_weight, down_weight
     torch.cuda.empty_cache()
     tokens = 8
     fp32_hidden = _randn(
@@ -449,12 +484,26 @@ def official_training_step() -> torch.Tensor:
 
     # Exactness reference: the same deterministic Triton implementation at TP=1.
     full_values = (hidden_full, gate_full, up_full, down_full)
+    tp1_forward_weights = pack_qwen3_ffn_forward_weights(*full_values[1:])
     with torch.no_grad():
-        tp1_forward = qwen3_ffn(*full_values).detach().clone()
-    tp1_inputs = [value.detach().clone().requires_grad_(True) for value in full_values]
-    tp1_train = _training_step(qwen3_ffn, tp1_inputs, grad_output_full).detach().clone()
+        tp1_forward = qwen3_ffn(
+            *full_values,
+            forward_weights=tp1_forward_weights,
+        ).detach().clone()
+    tp1_inputs = [
+        value.detach().clone().requires_grad_(True) for value in full_values
+    ]
+    tp1_training_weights = pack_qwen3_ffn_forward_weights(*tp1_inputs[1:])
+    tp1_train = _training_step(
+        lambda *values: qwen3_ffn(
+            *values,
+            forward_weights=tp1_training_weights,
+        ),
+        tp1_inputs,
+        grad_output_full,
+    ).detach().clone()
     tp1_grads = [value.grad.detach().clone() for value in tp1_inputs]
-    del tp1_inputs
+    del tp1_inputs, tp1_forward_weights, tp1_training_weights
     torch.cuda.empty_cache()
 
     meshes: dict[tuple[int, int], tuple[list[Any], list[Any]]] = {}
@@ -483,11 +532,13 @@ def official_training_step() -> torch.Tensor:
             up_full[feature_start:feature_end].contiguous(),
             down_full[:, feature_start:feature_end].contiguous(),
         )
+        shard_forward_weights = pack_qwen3_ffn_forward_weights(*shard[1:])
         local_grad_output = grad_output_full[token_start:token_end].contiguous()
 
         def triton_forward():
             return qwen3_ffn(
                 *shard,
+                forward_weights=shard_forward_weights,
                 tp_group=tp_group,
                 cp_group=cp_group,
                 sequence_parallel=sequence_parallel,
@@ -506,14 +557,21 @@ def triton_forward():
             dist.group.WORLD,
         )
 
-        triton_inputs = [value.detach().clone().requires_grad_(True) for value in shard]
-        repeat_inputs = [value.detach().clone().requires_grad_(True) for value in shard]
+        triton_inputs = [
+            value.detach().clone().requires_grad_(True) for value in shard
+        ]
+        repeat_inputs = [
+            value.detach().clone().requires_grad_(True) for value in shard
+        ]
+        triton_forward_weights = pack_qwen3_ffn_forward_weights(*triton_inputs[1:])
+        repeat_forward_weights = pack_qwen3_ffn_forward_weights(*repeat_inputs[1:])
 
-        def triton_training_step(inputs):
+        def triton_training_step(inputs, packed_weights):
             for value in inputs:
                 value.grad = None
             output = qwen3_ffn(
                 *inputs,
+                forward_weights=packed_weights,
                 tp_group=tp_group,
                 cp_group=cp_group,
                 sequence_parallel=sequence_parallel,
@@ -521,13 +579,22 @@ def triton_training_step(inputs):
             output.backward(local_grad_output)
             return output
 
-        triton_train = triton_training_step(triton_inputs).detach().clone()
+        triton_train = triton_training_step(
+            triton_inputs,
+            triton_forward_weights,
+        ).detach().clone()
         triton_grads = [value.grad.detach().clone() for value in triton_inputs]
-        repeat_train = triton_training_step(repeat_inputs).detach().clone()
+        repeat_train = triton_training_step(
+            repeat_inputs,
+            repeat_forward_weights,
+        ).detach().clone()
         repeat_grads = [value.grad.detach().clone() for value in repeat_inputs]
         triton_train_summary = _slowest_rank_summary(
             _distributed_wall_samples(
-                lambda: triton_training_step(triton_inputs),
+                lambda: triton_training_step(
+                    triton_inputs,
+                    triton_forward_weights,
+                ),
                 group=dist.group.WORLD,
                 warmup=max(1, warmup // 2),
                 samples=training_samples,
@@ -576,6 +643,7 @@ def triton_training_step(inputs):
                 "tokens": token_count,
                 "hidden": hidden_size,
                 "intermediate": intermediate_size,
+                "weight_layout": "packed_forward_cache",
             }
             results.extend(
                 (
@@ -629,7 +697,14 @@ def triton_training_step(inputs):
                     },
                 )
             )
-        del shard, triton_inputs, repeat_inputs
+        del (
+            shard,
+            shard_forward_weights,
+            triton_inputs,
+            triton_forward_weights,
+            repeat_inputs,
+            repeat_forward_weights,
+        )
         torch.cuda.empty_cache()
         dist.barrier()
 
@@ -860,6 +935,12 @@ def _write_report(
             "- The official performance baseline is upstream Transformers "
             "`Qwen3MLP` with unsharded weights and input (TP=1). Each distributed "
             "rank runs that TP=1 reference and the slowest rank/sample is reported.",
+            "- Deterministic Triton timings use the explicit prepacked forward-weight "
+            "cache. Packing happens once outside the timed region; canonical source "
+            "weights remain the autograd and optimizer source of truth.",
+            f"- The TP=1 cache adds {_TP1_FORWARD_CACHE_BYTES / 2**20:.0f} MiB; "
+            "each TP rank holds that amount divided by TP size. Refresh cost is "
+            "excluded because the benchmark measures the steady-state FFN call.",
             "- The distributed exactness baseline is the PR's deterministic Triton "
             "FFN at TP=1. Local outputs, dHidden, and sharded dWeights are compared "
             "against their exact TP=1 slices.",
@@ -888,10 +969,10 @@ def _write_report(
             "dHidden, and dWeights.",
             f"- Repeat mismatch: **{total_repeat_mismatch}**; training/inference "
             f"forward mismatch: **{total_train_infer_mismatch}**.",
-            f"- Single-GPU deterministic Triton latency is "
+            f"- Single-GPU deterministic Triton packed-cache latency is "
             f"**{min(single_ratios):.2f}-{max(single_ratios):.2f}x** the official "
             "Qwen3MLP TP=1 latency across M=1/8/32 and forward/training.",
-            f"- Distributed deterministic Triton latency is "
+            f"- Distributed deterministic Triton packed-cache latency is "
             f"**{min(distributed_ratios):.2f}-{max(distributed_ratios):.2f}x** the "
             "official Qwen3MLP TP=1 latency across tested parallel layouts.",
             f"- The separate official-Qwen3MLP FP16 versus FP32 observation has "
@@ -904,7 +985,7 @@ def _write_report(
             "reported here.",
             "",
             "| Shape / direction | Official Qwen3MLP TP=1 (ms) | Deterministic "
-            "Triton (ms) | Triton / official TP=1 |",
+            "Triton, packed (ms) | Triton / official TP=1 |",
             "|---|---:|---:|---:|",
         )
     )
@@ -925,7 +1006,8 @@ def _write_report(
             "this comparison.",
             "",
             "| Parallel layout | Direction | Official Qwen3MLP TP=1 (ms) | "
-            "Deterministic distributed Triton (ms) | Triton / official TP=1 |",
+            "Deterministic distributed Triton, packed (ms) | Triton / official "
+            "TP=1 |",
             "|---|---|---:|---:|---:|",
         )
     )
@@ -1076,11 +1158,12 @@ def _write_report(
             "",
             "## Figures",
             "",
-            "![Single-GPU CUDA, Triton, and CPU latency](single_gpu_overhead.png)",
+            "![Single-GPU CUDA, packed Triton, and CPU latency]"
+            "(single_gpu_overhead.png)",
             "",
             "![Topology mismatch versus Triton TP=1](collective_overhead.png)",
             "",
-            "![Distributed H100 CUDA and MI300X Triton latency]"
+            "![Distributed H100 CUDA and MI300X packed Triton latency]"
             "(distributed_ffn_overhead.png)",
             "",
         )
@@ -1138,7 +1221,7 @@ def _write_figures(
             positions + width / 2,
             triton_values,
             width,
-            label="Deterministic Triton FFN",
+            label="Deterministic Triton FFN (packed)",
             color="#7c3aed",
         )
         for bars, values in (
@@ -1176,7 +1259,11 @@ def _write_figures(
             ("H100 Triton replay", "triton_h100_ms", "#06b6d4"),
             ("H100 deterministic CUDA", "cuda_h100_ms", "#dc2626"),
             ("MI300X official TP=1", "official_mi300x_ms", "#86efac"),
-            ("MI300X deterministic Triton", "triton_mi300x_ms", "#7c3aed"),
+            (
+                "MI300X deterministic Triton (packed)",
+                "triton_mi300x_ms",
+                "#7c3aed",
+            ),
         )
         width = 0.13
         for axis, direction, direction_label in (
@@ -1302,7 +1389,11 @@ def _write_figures(
     if comparison_payload is None:
         distributed_series = (
             ("Official Qwen3MLP TP=1", "official_mi300x_ms", "#2563eb"),
-            ("Deterministic distributed Triton", "triton_mi300x_ms", "#7c3aed"),
+            (
+                "Deterministic distributed Triton (packed)",
+                "triton_mi300x_ms",
+                "#7c3aed",
+            ),
         )
         width = 0.37
     else:
@@ -1314,7 +1405,11 @@ def _write_figures(
             ("H100 official TP=1", "official_h100_ms", "#60a5fa"),
             ("H100 deterministic CUDA", "cuda_h100_ms", "#dc2626"),
             ("MI300X official TP=1", "official_mi300x_ms", "#86efac"),
-            ("MI300X deterministic Triton", "triton_mi300x_ms", "#7c3aed"),
+            (
+                "MI300X deterministic Triton (packed)",
+                "triton_mi300x_ms",
+                "#7c3aed",
+            ),
         )
         width = 0.19
     for axis, direction, direction_label in (
@@ -1445,6 +1540,8 @@ def main() -> None:
             "samples": args.samples,
             "training_samples": args.training_samples,
             "operator_only": True,
+            "triton_weight_layout": "packed_forward_cache_outside_timed_region",
+            "tp1_forward_cache_bytes": _TP1_FORWARD_CACHE_BYTES,
         },
         "single_gpu": _single_gpu_benchmarks(
             warmup=args.warmup,
diff --git a/rl_engine/kernels/ops/triton/matmul/det_gemm.py b/rl_engine/kernels/ops/triton/matmul/det_gemm.py
index d1946b5f..585fb4e2 100644
--- a/rl_engine/kernels/ops/triton/matmul/det_gemm.py
+++ b/rl_engine/kernels/ops/triton/matmul/det_gemm.py
@@ -16,6 +16,7 @@
 
 from __future__ import annotations
 
+import functools
 import threading
 from dataclasses import dataclass
 
@@ -38,6 +39,92 @@
 _TREE_PLAN_LOCK = threading.Lock()
 
 
+@dataclass(frozen=True)
+class _TreeLeafConfig:
+    block_m: int
+    block_n: int
+    num_warps: int
+    n_fastest: bool
+
+
+_DEFAULT_TREE_LEAF_CONFIG = _TreeLeafConfig(_BLOCK_M, _BLOCK_N, 4, False)
+
+# Offline-swept on ROCm gfx942 with Triton 3.7. These entries deliberately
+# cover only the Qwen3-8B TP1 logical shapes and stride modes used by the FFN.
+# Other architectures and shapes retain the established 64x64 specialization.
+_GFX942_QWEN_FORWARD_LEAF_CONFIGS = {
+    1: _TreeLeafConfig(1, 128, 1, True),
+    8: _TreeLeafConfig(8, 128, 2, True),
+    32: _TreeLeafConfig(32, 128, 4, True),
+}
+_GFX942_QWEN_WGRAD_LEAF_CONFIGS = {
+    1: _TreeLeafConfig(128, 64, 2, True),
+    8: _TreeLeafConfig(128, 64, 2, True),
+    32: _TreeLeafConfig(64, 64, 2, True),
+}
+_QWEN_FORWARD_GEMM_SHAPES = {(4096, 12288), (12288, 4096)}
+_QWEN_WGRAD_OUTPUT_SHAPES = {(4096, 12288), (12288, 4096)}
+
+
+def _gfx942_qwen_tree_leaf_config(
+    m_size: int,
+    k_size: int,
+    n_size: int,
+    *,
+    transpose_output: bool,
+    preserve_a_strides: bool,
+) -> _TreeLeafConfig:
+    if (
+        not transpose_output
+        and not preserve_a_strides
+        and (k_size, n_size) in _QWEN_FORWARD_GEMM_SHAPES
+    ):
+        return _GFX942_QWEN_FORWARD_LEAF_CONFIGS.get(
+            m_size,
+            _DEFAULT_TREE_LEAF_CONFIG,
+        )
+    if (
+        transpose_output
+        and preserve_a_strides
+        and (m_size, n_size) in _QWEN_WGRAD_OUTPUT_SHAPES
+    ):
+        return _GFX942_QWEN_WGRAD_LEAF_CONFIGS.get(
+            k_size,
+            _DEFAULT_TREE_LEAF_CONFIG,
+        )
+    return _DEFAULT_TREE_LEAF_CONFIG
+
+
+@functools.lru_cache(maxsize=None)
+def _device_arch(device_index: int) -> str:
+    if getattr(torch.version, "hip", None) is None:
+        return ""
+    properties = torch.cuda.get_device_properties(device_index)
+    return str(getattr(properties, "gcnArchName", "")).partition(":")[0]
+
+
+@functools.lru_cache(maxsize=None)
+def _tree_leaf_config(
+    device: torch.device,
+    m_size: int,
+    k_size: int,
+    n_size: int,
+    *,
+    transpose_output: bool,
+    preserve_a_strides: bool,
+) -> _TreeLeafConfig:
+    device_index = device.index if device.index is not None else torch.cuda.current_device()
+    if _device_arch(device_index) != "gfx942":
+        return _DEFAULT_TREE_LEAF_CONFIG
+    return _gfx942_qwen_tree_leaf_config(
+        m_size,
+        k_size,
+        n_size,
+        transpose_output=transpose_output,
+        preserve_a_strides=preserve_a_strides,
+    )
+
+
 @dataclass(frozen=True)
 class _TreePlan:
     leaf_starts: tuple[int, ...]
@@ -149,10 +236,16 @@ def _det_gemm_tree_leaf_kernel(
         BLOCK_M: tl.constexpr,
         BLOCK_N: tl.constexpr,
         BLOCK_K: tl.constexpr,
+        N_FASTEST: tl.constexpr,
     ):
-        leaf = tl.program_id(0)
-        pid_m = tl.program_id(1)
-        pid_n = tl.program_id(2)
+        if N_FASTEST:
+            pid_n = tl.program_id(0)
+            pid_m = tl.program_id(1)
+            leaf = tl.program_id(2)
+        else:
+            leaf = tl.program_id(0)
+            pid_m = tl.program_id(1)
+            pid_n = tl.program_id(2)
         offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
         offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
         leaf_start = tl.load(leaf_starts_ptr + leaf)
@@ -388,10 +481,20 @@ def _triton_tree_gemm(
         dtype=torch.bfloat16,
         device=a.device,
     )
+    leaf_config = _tree_leaf_config(
+        a.device,
+        m_size,
+        k_size,
+        n_size,
+        transpose_output=transpose_output,
+        preserve_a_strides=preserve_a_strides,
+    )
+    tiles_m = triton.cdiv(m_size, leaf_config.block_m)
+    tiles_n = triton.cdiv(n_size, leaf_config.block_n)
     leaf_grid = (
-        len(plan.host.leaf_nodes),
-        triton.cdiv(m_size, _BLOCK_M),
-        triton.cdiv(n_size, _BLOCK_N),
+        (tiles_n, tiles_m, len(plan.host.leaf_nodes))
+        if leaf_config.n_fastest
+        else (len(plan.host.leaf_nodes), tiles_m, tiles_n)
     )
     _det_gemm_tree_leaf_kernel[leaf_grid](
         a,
@@ -407,9 +510,11 @@ def _triton_tree_gemm(
         stride_ak=a.stride(1),
         stride_bk=b.stride(0),
         stride_bn=b.stride(1),
-        BLOCK_M=_BLOCK_M,
-        BLOCK_N=_BLOCK_N,
+        BLOCK_M=leaf_config.block_m,
+        BLOCK_N=leaf_config.block_n,
         BLOCK_K=_BLOCK_K,
+        N_FASTEST=leaf_config.n_fastest,
+        num_warps=leaf_config.num_warps,
     )
     reduction_block = 256
     for operations, (lower, upper, output) in zip(
diff --git a/tests/test_det_gemm.py b/tests/test_det_gemm.py
index 8fe38886..df53870d 100644
--- a/tests/test_det_gemm.py
+++ b/tests/test_det_gemm.py
@@ -15,8 +15,18 @@
 from rl_engine.kernels.ops.cuda.matmul import deterministic_gemm
 
 try:
+    import triton
+
     from rl_engine.kernels.ops.triton.matmul import deterministic_gemm_triton
-    from rl_engine.kernels.ops.triton.matmul.det_gemm import _triton_tree_gemm
+    from rl_engine.kernels.ops.triton.matmul.det_gemm import (
+        _DEFAULT_TREE_LEAF_CONFIG,
+        _GFX942_QWEN_FORWARD_LEAF_CONFIGS,
+        _GFX942_QWEN_WGRAD_LEAF_CONFIGS,
+        _det_gemm_tree_leaf_kernel,
+        _device_tree_plan,
+        _gfx942_qwen_tree_leaf_config,
+        _triton_tree_gemm,
+    )
 
     _HAS_TRITON = True
 except ImportError:
@@ -28,6 +38,9 @@
 HAS_SUPPORTED_GPU = torch.cuda.is_available() and (
     IS_ROCM or torch.cuda.get_device_capability()[0] >= 8
 )
+IS_GFX942 = IS_ROCM and torch.cuda.is_available() and str(
+    getattr(torch.cuda.get_device_properties(0), "gcnArchName", "")
+).startswith("gfx942")
 
 pytestmark = pytest.mark.skipif(
     not HAS_SUPPORTED_GPU,
@@ -55,6 +68,80 @@ def _assert_same_raw_bytes(actual: torch.Tensor, expected: torch.Tensor) -> None
     )
 
 
+def _leaf_workspace(
+    a: torch.Tensor,
+    b: torch.Tensor,
+    config,
+) -> torch.Tensor:
+    m_size, k_size = a.shape
+    n_size = b.size(1)
+    plan = _device_tree_plan(k_size, a.device)
+    workspace = torch.empty(
+        (plan.host.node_count, m_size, n_size),
+        dtype=torch.bfloat16,
+        device=a.device,
+    )
+    tiles_m = triton.cdiv(m_size, config.block_m)
+    tiles_n = triton.cdiv(n_size, config.block_n)
+    grid = (
+        (tiles_n, tiles_m, len(plan.host.leaf_nodes))
+        if config.n_fastest
+        else (len(plan.host.leaf_nodes), tiles_m, tiles_n)
+    )
+    _det_gemm_tree_leaf_kernel[grid](
+        a,
+        b,
+        workspace,
+        plan.leaf_starts,
+        plan.leaf_lengths,
+        plan.leaf_nodes,
+        M=m_size,
+        N=n_size,
+        K=k_size,
+        stride_am=a.stride(0),
+        stride_ak=a.stride(1),
+        stride_bk=b.stride(0),
+        stride_bn=b.stride(1),
+        BLOCK_M=config.block_m,
+        BLOCK_N=config.block_n,
+        BLOCK_K=_K_TREE_LEAF,
+        N_FASTEST=config.n_fastest,
+        num_warps=config.num_warps,
+    )
+    return workspace.index_select(0, plan.leaf_nodes.to(torch.int64))
+
+
+def _special_bf16(shape: tuple[int, ...], *, offset: int = 0) -> torch.Tensor:
+    bits = torch.tensor(
+        (
+            0x0000,
+            0x8000,
+            0x0001,
+            0x8001,
+            0x007F,
+            0x807F,
+            0x0080,
+            0x8080,
+            0x3F80,
+            0xBF80,
+            0x7F7F,
+            0xFF7F,
+            0x7F80,
+            0xFF80,
+            0x7F81,
+            0x7FC1,
+            0xFF81,
+            0xFFFF,
+        ),
+        dtype=torch.uint16,
+    )
+    elements = 1
+    for size in shape:
+        elements *= size
+    indices = (torch.arange(elements, dtype=torch.int64) + offset) % bits.numel()
+    return bits[indices].view(torch.bfloat16).reshape(shape).to(DEV)
+
+
 _K_TREE_LEAF = 32
 
 
@@ -80,13 +167,94 @@ def _balanced_tree_sum(parts: list[torch.Tensor]) -> torch.Tensor:
     return level[0]
 
 
+@pytest.mark.skipif(not _HAS_TRITON, reason="Triton is unavailable")
+@pytest.mark.parametrize(
+    ("shape", "transpose_output", "preserve_a_strides", "expected"),
+    (
+        ((1, 4096, 12288), False, False, (1, 128, 1, True)),
+        ((8, 4096, 12288), False, False, (8, 128, 2, True)),
+        ((32, 12288, 4096), False, False, (32, 128, 4, True)),
+        ((4096, 1, 12288), True, True, (128, 64, 2, True)),
+        ((12288, 8, 4096), True, True, (128, 64, 2, True)),
+        ((4096, 32, 12288), True, True, (64, 64, 2, True)),
+        ((16, 4096, 12288), False, False, (64, 64, 4, False)),
+        ((32, 4096, 4096), False, False, (64, 64, 4, False)),
+        ((4096, 16, 12288), True, True, (64, 64, 4, False)),
+        ((4096, 8, 12288), True, False, (64, 64, 4, False)),
+    ),
+)
+def test_gfx942_qwen_leaf_config_table_is_exact(
+    shape,
+    transpose_output,
+    preserve_a_strides,
+    expected,
+):
+    config = _gfx942_qwen_tree_leaf_config(
+        *shape,
+        transpose_output=transpose_output,
+        preserve_a_strides=preserve_a_strides,
+    )
+    assert (
+        config.block_m,
+        config.block_n,
+        config.num_warps,
+        config.n_fastest,
+    ) == expected
+
+
+@pytest.mark.skipif(
+    not (_HAS_TRITON and IS_GFX942),
+    reason="leaf specialization raw-byte tests require ROCm gfx942",
+)
+@pytest.mark.parametrize("k_size", (1, 8, 31, 32, 33, 65, 4096, 12288))
+def test_gfx942_leaf_configs_preserve_special_value_workspace_raw_bytes(k_size):
+    a = _special_bf16((3, k_size))
+    b = _special_bf16((k_size, 17), offset=7)
+    expected = _leaf_workspace(a, b, _DEFAULT_TREE_LEAF_CONFIG)
+    configs = tuple(
+        dict.fromkeys(
+            (
+                *_GFX942_QWEN_FORWARD_LEAF_CONFIGS.values(),
+                *_GFX942_QWEN_WGRAD_LEAF_CONFIGS.values(),
+            )
+        )
+    )
+
+    for config in configs:
+        _assert_same_raw_bytes(_leaf_workspace(a, b, config), expected)
+
+
+@pytest.mark.skipif(
+    not (_HAS_TRITON and IS_GFX942),
+    reason="leaf specialization raw-byte tests require ROCm gfx942",
+)
+def test_gfx942_leaf_configs_preserve_transposed_a_workspace_raw_bytes():
+    source = _special_bf16((65, 3))
+    a = source.t()
+    b = _special_bf16((65, 17), offset=11)
+    assert not a.is_contiguous()
+    assert all(stride > 0 for stride in a.stride())
+    expected = _leaf_workspace(a, b, _DEFAULT_TREE_LEAF_CONFIG)
+    configs = tuple(
+        dict.fromkeys(
+            (
+                *_GFX942_QWEN_FORWARD_LEAF_CONFIGS.values(),
+                *_GFX942_QWEN_WGRAD_LEAF_CONFIGS.values(),
+            )
+        )
+    )
+
+    for config in configs:
+        _assert_same_raw_bytes(_leaf_workspace(a, b, config), expected)
+
+
 @pytest.mark.parametrize("tp_size", (2, 4, 8))
 @pytest.mark.skipif(not _HAS_TRITON, reason="Triton is unavailable")
 def test_forward_matches_balanced_contiguous_k_shards_bitwise(tp_size):
     """The GEMM K-tree and the TP collective rank tree must be the same graph."""
 
     torch.manual_seed(8)
-    # Qwen3-8B's down projection uses K=12288.  Its 384 32-wide leaves also
+    # Qwen3-8B's down projection uses K=12288. Its 512 24-wide leaves also
     # exercise the non-power-of-two midpoint tree used by the SM90 kernel.
     m, k, n = 4, 12288, 64
     a, b = _rand(m, k), _rand(k, n)

From caef501101a3906c733076f31f3b5a9870169d16 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sat, 29 Aug 2026 11:46:07 +0000
Subject: [PATCH 20/28] perf(distributed): adopt optimized ROCm collectives

Port the HIP IPC fixed-tree transport and packed reduce-scatter path from PR #357, with RCCL fallback and focused ROCm coverage.
---
 csrc/ops.cpp                                  |   98 ++
 .../distributed/deterministic_collective.hip  | 1099 +++++++++++++++++
 rl_engine/_C.pyi                              |   51 +
 rl_engine/distributed/__init__.py             |    4 +-
 rl_engine/distributed/collectives.py          |  828 +++++++++++++
 .../distributed/transport_collectives.py      |  376 +-----
 .../kernels/ops/cuda/attention/cp_comm.py     |   19 +-
 rl_engine/kernels/ops/triton/ffn/ffn.py       |   22 +-
 setup.py                                      |    7 +
 .../test_deterministic_all_gather.py          |    9 +-
 .../test_deterministic_all_reduce.py          |   23 +-
 .../test_deterministic_reduce_scatter.py      |   47 +-
 .../test_rocm_attention_transport.py          |   95 ++
 ...test_transport_deterministic_collective.py |  176 ++-
 tests/test_build_platform_collectives.py      |   55 +
 15 files changed, 2499 insertions(+), 410 deletions(-)
 create mode 100644 csrc/rocm/distributed/deterministic_collective.hip
 create mode 100644 tests/distributed/test_rocm_attention_transport.py
 create mode 100644 tests/test_build_platform_collectives.py

diff --git a/csrc/ops.cpp b/csrc/ops.cpp
index b1725c50..a759ae93 100644
--- a/csrc/ops.cpp
+++ b/csrc/ops.cpp
@@ -105,6 +105,54 @@ 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_fp32(torch::Tensor a, torch::Tensor b);
@@ -428,6 +476,56 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     m.def("prefix_shared_attention", &prefix_shared_attention, "Prefix-Shared Fused Attention for GRPO");
 #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
+
     // registry Batch-Invariant Deterministic GEMM
     m.def("det_gemm_fwd", &det_gemm_fwd, "Batch-invariant deterministic GEMM forward (C=A@B)");
     m.def("det_gemm_fwd_fp32", &det_gemm_fwd_fp32,
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/rl_engine/_C.pyi b/rl_engine/_C.pyi
index 08d460e3..07588db5 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(
@@ -207,3 +215,46 @@ 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/__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..b9cca962 100644
--- a/rl_engine/distributed/collectives.py
+++ b/rl_engine/distributed/collectives.py
@@ -1,5 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
+"""Deterministic collectives for CUDA IPC and ROCm rank-ordered transport.
+
+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
 
@@ -13,6 +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)
 
 
@@ -221,6 +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."""
 
@@ -323,3 +361,793 @@ 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
+        # 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
+        # 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))
+        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)
+            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(
+        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)
+        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
+
+    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)
+        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)
+            begin = self.rank * 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.
+
+        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
+            self._handle = 0
+
+    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)
+
+    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)
+        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]
+
+    @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):
+    """Single-node ROCm fixed-tree collective using HIP IPC and RCCL."""
+
+    backend_id = "rocm_ipc_fixed_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"
+            )
+        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(
+    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
+    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:
+        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
index e0261f48..b9db44ad 100644
--- a/rl_engine/distributed/transport_collectives.py
+++ b/rl_engine/distributed/transport_collectives.py
@@ -1,375 +1,17 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""Deterministic reductions built on rank-ordered tensor transport.
+"""Compatibility imports for the unified deterministic collectives.
 
-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.
+The implementation now lives in :mod:`rl_engine.distributed.collectives`.
+This module remains as a stable import path for older benchmark and integration
+callers; it contains no separate collective implementation.
 """
 
-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"
-
-    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
-        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
-
-    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)
-            rank_inputs = self._all_gather_transport(input)
-            out.copy_(rank_inputs.reshape(output_shape))
-        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.
-
-        The AllGather implementation owns no persistent device allocation, so
-        closing is an idempotent lifecycle boundary rather than a transport
-        synchronization operation.
-        """
-
-        with self._lock:
-            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) -> 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:
-            return input.clone().reshape((1, *input.shape))
-        input_flat = input.view(-1)
-        gathered_flat = torch.empty(
-            self.world_size * input_flat.numel(),
-            dtype=input.dtype,
-            device=input.device,
-        )
-        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))
-
-    @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")
-        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,
-    )
-
+from rl_engine.distributed.collectives import (
+    RCCLDeterministicCollective,
+    TorchDistributedDeterministicCollective,
+    create_deterministic_collective,
+)
 
 __all__ = [
     "RCCLDeterministicCollective",
diff --git a/rl_engine/kernels/ops/cuda/attention/cp_comm.py b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
index 798b1469..44675437 100644
--- a/rl_engine/kernels/ops/cuda/attention/cp_comm.py
+++ b/rl_engine/kernels/ops/cuda/attention/cp_comm.py
@@ -628,8 +628,13 @@ 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])
+        # Leading-dimension chunks are already contiguous. Avoid materializing
+        # copies for every rank; non-root ranks do not need a scatter list.
+        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_(full)
+            return local
         global_root = self.root
         if self.group is not None:
             get_global_rank = getattr(dist, "get_global_rank", None)
@@ -637,12 +642,8 @@ def scatter(self, full: torch.Tensor) -> torch.Tensor:
                 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,
-        )
+        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:
@@ -680,6 +681,8 @@ class RCCLAGRSAttentionCPCommunication(CUDAAGRSAttentionCPCommunication):
     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:
diff --git a/rl_engine/kernels/ops/triton/ffn/ffn.py b/rl_engine/kernels/ops/triton/ffn/ffn.py
index 45caab2a..f618031a 100644
--- a/rl_engine/kernels/ops/triton/ffn/ffn.py
+++ b/rl_engine/kernels/ops/triton/ffn/ffn.py
@@ -461,8 +461,12 @@ 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 independent gate/up input
+        # gradient lanes in one fixed-tree transport call.
+        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,
@@ -550,24 +554,18 @@ def backward(ctx: Any, grad_output: Tensor) -> tuple[Any, ...]:
             grad_up_weight = _gemm_db(rmsnorm_output, grad_up)
 
         grad_rmsnorm_from_gate = _gemm(grad_gate, gate_weight)
+        grad_rmsnorm_from_up = _gemm(grad_up, up_weight)
         if ctx.sequence_parallel:
-            grad_rmsnorm_from_gate = _reduce_scatter_tokens(
-                grad_rmsnorm_from_gate,
-                tp_collective,
+            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(grad_up, up_weight)
-        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 57b3ebd9..03feb1ab 100644
--- a/setup.py
+++ b/setup.py
@@ -149,6 +149,10 @@ def get_extensions():
                     "csrc/cuda/distributed/deterministic_collective.cu",
                 ]
             )
+        else:
+            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):
@@ -218,6 +222,9 @@ def get_extensions():
         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" and not is_rocm:
+            # CUDA IPC metadata queries use the driver API (cuPointerGetAttribute).
+            extra_link_args.append("-lcuda")
 
         if not is_rocm:
             sm90_srcs = [
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_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
index fb6d4dc3..07478ef7 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,
@@ -35,6 +35,8 @@ 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
     def tensor_transport_calls(self) -> int:
@@ -58,6 +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:
@@ -72,6 +75,7 @@ def all_gather_into_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)
 
@@ -83,10 +87,23 @@ def all_gather(
         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 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],
@@ -104,7 +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",
@@ -180,6 +197,52 @@ def test_nccl_backend_uses_all_gather_into_tensor_transport(
     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_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:
@@ -192,6 +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:
@@ -261,14 +397,14 @@ 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")
@@ -276,15 +412,25 @@ 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")
 
 
+def test_rccl_class_rejects_cpu_before_process_group_exchange(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    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")
+
+
 def test_factory_dispatches_rocm_to_rccl(monkeypatch: pytest.MonkeyPatch) -> None:
     sentinel = object()
     calls: list[dict[str, Any]] = []
@@ -293,11 +439,11 @@ 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,
@@ -315,11 +461,11 @@ 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,
diff --git a/tests/test_build_platform_collectives.py b/tests/test_build_platform_collectives.py
new file mode 100644
index 00000000..19d3a890
--- /dev/null
+++ b/tests/test_build_platform_collectives.py
@@ -0,0 +1,55 @@
+# 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 "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"]
+
+
+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"]

From 19e0ee69b350766ab4ab5dbb7a65fe5de5d8737a Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Sat, 29 Aug 2026 11:46:41 +0000
Subject: [PATCH 21/28] bench(rocm): update distributed FFN results

Record the PR #357 collective measurements, preserve the four-way same-topology comparison, and publish the refreshed MI300X artifacts.
---
 benchmarks/benchmark_rocm_ffn.py              | 609 ++++++++++---
 .../distributed_ffn_overhead.png              | Bin 274372 -> 289737 bytes
 .../results/pr325_rocm_mi300x/report.md       | 142 +--
 .../results/pr325_rocm_mi300x/results.json    | 840 +++++++++++++-----
 .../pr325_rocm_mi300x/single_gpu_overhead.png | Bin 263048 -> 267750 bytes
 docs/benchmarking/README.md                   |  21 +
 6 files changed, 1191 insertions(+), 421 deletions(-)

diff --git a/benchmarks/benchmark_rocm_ffn.py b/benchmarks/benchmark_rocm_ffn.py
index 11401fc7..8b0b342b 100644
--- a/benchmarks/benchmark_rocm_ffn.py
+++ b/benchmarks/benchmark_rocm_ffn.py
@@ -1,11 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # Copyright (c) 2026 RL-Kernel Contributors
-"""ROCm analysis for the deterministic distributed Triton Qwen3 FFN.
+"""ROCm benchmark for the deterministic distributed Triton Qwen3 FFN.
 
-Performance compares the PR's FFN with the official Hugging Face Qwen3MLP at
-TP=1. Determinism compares every Triton TP/CP/SP layout bitwise with Triton
-TP=1. A separate, simple FP16-versus-FP32 observation uses official Qwen3MLP.
-No model checkpoint or serving engine is used.
+Distributed performance compares four paths at the same TP/CP/SP topology:
+H100 official/deterministic and MI300X official/deterministic. Determinism
+compares every Triton TP/CP/SP layout bitwise with Triton TP=1. A separate
+single-GPU section retains the official Hugging Face Qwen3MLP TP=1 context. No
+model checkpoint or serving engine is used.
 """
 
 from __future__ import annotations
@@ -26,6 +27,7 @@
 import torch
 import torch.distributed as dist
 import torch.multiprocessing as mp
+import torch.nn.functional as F
 from transformers import __version__ as transformers_version
 from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
 from transformers.models.qwen3.modeling_qwen3 import Qwen3MLP
@@ -50,6 +52,33 @@
     ),
 }
 _TP1_FORWARD_CACHE_BYTES = 3 * 4096 * 12288 * torch.bfloat16.itemsize
+_COMMUNICATION_CONTRACT = {
+    "forward": {"all_gather": 1, "reduce_scatter": 1, "total": 2},
+    "train_fwd_bwd": {
+        "all_gather": 7,
+        "reduce_scatter_logical_lanes": 3,
+        "logical_total": 10,
+        "collective_invocations": 9,
+    },
+}
+_PREVIOUS_DISTRIBUTED_TRITON_MS = {
+    ("tp2", "forward"): 0.9039933793246746,
+    ("tp2", "train_fwd_bwd"): 2.6996671222150326,
+    ("tp2_sp", "forward"): 1.0561398230493069,
+    ("tp2_sp", "train_fwd_bwd"): 2.9646214097738266,
+    ("tp4", "forward"): 0.8358820341527462,
+    ("tp4", "train_fwd_bwd"): 2.6998785324394703,
+    ("tp2_cp2", "forward"): 0.9015901014208794,
+    ("tp2_cp2", "train_fwd_bwd"): 3.5605919547379017,
+    ("tp2_cp2_sp", "forward"): 1.1296039447188377,
+    ("tp2_cp2_sp", "train_fwd_bwd"): 3.992859274148941,
+    ("tp8", "forward"): 1.0859542526304722,
+    ("tp8", "train_fwd_bwd"): 2.5620022788643837,
+    ("tp4_cp2", "forward"): 1.0536308400332928,
+    ("tp4_cp2", "train_fwd_bwd"): 3.259910736232996,
+    ("tp4_cp2_sp", "forward"): 1.1927778832614422,
+    ("tp4_cp2_sp", "train_fwd_bwd"): 3.7497887387871742,
+}
 
 
 def _percentile(values: list[float], percentile: float) -> float:
@@ -171,6 +200,154 @@ def _training_step(
     return output
 
 
+class _NativeAllReduce(torch.autograd.Function):
+    """Autograd-aware native ProcessGroup all-reduce used by the baseline."""
+
+    @staticmethod
+    def forward(ctx: Any, input: torch.Tensor, group: Any) -> torch.Tensor:
+        ctx.group = group
+        output = input.contiguous().clone()
+        dist.all_reduce(output, group=group)
+        return output
+
+    @staticmethod
+    def backward(ctx: Any, grad_output: torch.Tensor) -> tuple[torch.Tensor, None]:
+        return grad_output, None
+
+
+class _NativeCopyToTensorParallel(torch.autograd.Function):
+    """Identity in forward and native all-reduce in backward."""
+
+    @staticmethod
+    def forward(ctx: Any, input: torch.Tensor, group: Any) -> torch.Tensor:
+        ctx.group = group
+        return input
+
+    @staticmethod
+    def backward(ctx: Any, grad_output: torch.Tensor) -> tuple[torch.Tensor, None]:
+        grad_input = grad_output.contiguous().clone()
+        dist.all_reduce(grad_input, group=ctx.group)
+        return grad_input, None
+
+
+class _NativeAllGather(torch.autograd.Function):
+    """Autograd-aware native first-dimension all-gather baseline."""
+
+    @staticmethod
+    def forward(ctx: Any, input: torch.Tensor, group: Any) -> torch.Tensor:
+        world_size = dist.get_world_size(group=group)
+        ctx.group = group
+        ctx.input_shape = tuple(input.shape)
+        input_flat = input.contiguous().view(-1)
+        output_flat = torch.empty(
+            world_size * input_flat.numel(),
+            dtype=input.dtype,
+            device=input.device,
+        )
+        dist.all_gather_into_tensor(output_flat, input_flat, group=group)
+        return output_flat.view(world_size * input.size(0), *input.shape[1:])
+
+    @staticmethod
+    def backward(ctx: Any, grad_output: torch.Tensor) -> tuple[torch.Tensor, None]:
+        grad_output_flat = grad_output.contiguous().view(-1)
+        grad_input_flat = torch.empty(
+            math.prod(ctx.input_shape),
+            dtype=grad_output.dtype,
+            device=grad_output.device,
+        )
+        dist.reduce_scatter_tensor(
+            grad_input_flat,
+            grad_output_flat,
+            group=ctx.group,
+        )
+        return grad_input_flat.view(ctx.input_shape), None
+
+
+class _NativeReduceScatter(torch.autograd.Function):
+    """Autograd-aware native first-dimension reduce-scatter baseline."""
+
+    @staticmethod
+    def forward(ctx: Any, input: torch.Tensor, group: Any) -> torch.Tensor:
+        world_size = dist.get_world_size(group=group)
+        if input.size(0) % world_size != 0:
+            raise ValueError("native reduce-scatter requires divisible dimension 0")
+        ctx.group = group
+        ctx.input_shape = tuple(input.shape)
+        output_shape = (input.size(0) // world_size, *input.shape[1:])
+        output_flat = torch.empty(
+            math.prod(output_shape),
+            dtype=input.dtype,
+            device=input.device,
+        )
+        dist.reduce_scatter_tensor(
+            output_flat,
+            input.contiguous().view(-1),
+            group=group,
+        )
+        return output_flat.view(output_shape)
+
+    @staticmethod
+    def backward(ctx: Any, grad_output: torch.Tensor) -> tuple[torch.Tensor, None]:
+        grad_output_flat = grad_output.contiguous().view(-1)
+        grad_input_flat = torch.empty(
+            math.prod(ctx.input_shape),
+            dtype=grad_output.dtype,
+            device=grad_output.device,
+        )
+        dist.all_gather_into_tensor(
+            grad_input_flat,
+            grad_output_flat,
+            group=ctx.group,
+        )
+        return grad_input_flat.view(ctx.input_shape), None
+
+
+def _official_distributed_ffn(
+    hidden: torch.Tensor,
+    gate_weight: torch.Tensor,
+    up_weight: torch.Tensor,
+    down_weight: torch.Tensor,
+    *,
+    tp_group: Any,
+    sequence_parallel: bool,
+) -> torch.Tensor:
+    """Upstream Qwen3 FFN math with native distributed collectives."""
+    full_hidden = (
+        _NativeAllGather.apply(hidden, tp_group)
+        if sequence_parallel
+        else _NativeCopyToTensorParallel.apply(hidden, tp_group)
+    )
+    activated = F.silu(F.linear(full_hidden, gate_weight)) * F.linear(
+        full_hidden, up_weight
+    )
+    partial_output = F.linear(activated, down_weight)
+    if sequence_parallel:
+        return _NativeReduceScatter.apply(partial_output, tp_group)
+    return _NativeAllReduce.apply(partial_output, tp_group)
+
+
+def _official_distributed_training_step(
+    inputs: list[torch.Tensor],
+    grad_output: torch.Tensor,
+    *,
+    tp_group: Any,
+    cp_group: Any,
+    sequence_parallel: bool,
+) -> torch.Tensor:
+    for value in inputs:
+        value.grad = None
+    output = _official_distributed_ffn(
+        *inputs,
+        tp_group=tp_group,
+        sequence_parallel=sequence_parallel,
+    )
+    output.backward(grad_output)
+    if cp_group is not None:
+        for weight in inputs[1:]:
+            dist.all_reduce(weight.grad, group=cp_group)
+    return output
+
+
 def _single_gpu_benchmarks(
     *,
     warmup: int,
@@ -446,42 +623,6 @@ def _distributed_ffn_benchmark(
         (token_count, hidden_size), seed=5004, device=device
     )
 
-    # Performance reference: upstream Qwen3MLP, unsharded TP=1, full M=32 input.
-    official = _official_qwen3_mlp(gate_full, up_full, down_full)
-
-    def official_forward() -> torch.Tensor:
-        return _official_inference(official, hidden_full)
-
-    official_forward_summary = _slowest_rank_summary(
-        _distributed_wall_samples(
-            official_forward,
-            group=dist.group.WORLD,
-            warmup=warmup,
-            samples=samples,
-        ),
-        dist.group.WORLD,
-    )
-    official_hidden = hidden_full.detach().clone().requires_grad_(True)
-
-    def official_training_step() -> torch.Tensor:
-        official.zero_grad(set_to_none=True)
-        official_hidden.grad = None
-        output = official(official_hidden)
-        output.backward(grad_output_full)
-        return output
-
-    official_train_summary = _slowest_rank_summary(
-        _distributed_wall_samples(
-            official_training_step,
-            group=dist.group.WORLD,
-            warmup=max(1, warmup // 2),
-            samples=training_samples,
-        ),
-        dist.group.WORLD,
-    )
-    del official, official_hidden
-    torch.cuda.empty_cache()
-
     # Exactness reference: the same deterministic Triton implementation at TP=1.
     full_values = (hidden_full, gate_full, up_full, down_full)
     tp1_forward_weights = pack_qwen3_ffn_forward_weights(*full_values[1:])
@@ -535,6 +676,42 @@ def official_training_step() -> torch.Tensor:
         shard_forward_weights = pack_qwen3_ffn_forward_weights(*shard[1:])
         local_grad_output = grad_output_full[token_start:token_end].contiguous()
 
+        def official_distributed_forward():
+            with torch.no_grad():
+                return _official_distributed_ffn(
+                    *shard,
+                    tp_group=tp_group,
+                    sequence_parallel=sequence_parallel,
+                )
+
+        official_forward_summary = _slowest_rank_summary(
+            _distributed_wall_samples(
+                official_distributed_forward,
+                group=dist.group.WORLD,
+                warmup=warmup,
+                samples=samples,
+            ),
+            dist.group.WORLD,
+        )
+        official_inputs = [
+            value.detach().clone().requires_grad_(True) for value in shard
+        ]
+        official_train_summary = _slowest_rank_summary(
+            _distributed_wall_samples(
+                lambda: _official_distributed_training_step(
+                    official_inputs,
+                    local_grad_output,
+                    tp_group=tp_group,
+                    cp_group=cp_group,
+                    sequence_parallel=sequence_parallel,
+                ),
+                group=dist.group.WORLD,
+                warmup=max(1, warmup // 2),
+                samples=training_samples,
+            ),
+            dist.group.WORLD,
+        )
+
         def triton_forward():
             return qwen3_ffn(
                 *shard,
@@ -650,9 +827,9 @@ def triton_training_step(inputs, packed_weights):
                     {
                         **common,
                         "direction": "forward",
-                        "official_tp1": official_forward_summary,
+                        "official_distributed": official_forward_summary,
                         "triton": triton_forward_summary,
-                        "latency_ratio_vs_official_tp1": (
+                        "latency_ratio_triton_vs_official_distributed": (
                             triton_forward_summary["median_ms"]
                             / official_forward_summary["median_ms"]
                         ),
@@ -671,9 +848,9 @@ def triton_training_step(inputs, packed_weights):
                     {
                         **common,
                         "direction": "train_fwd_bwd",
-                        "official_tp1": official_train_summary,
+                        "official_distributed": official_train_summary,
                         "triton": triton_train_summary,
-                        "latency_ratio_vs_official_tp1": (
+                        "latency_ratio_triton_vs_official_distributed": (
                             triton_train_summary["median_ms"]
                             / official_train_summary["median_ms"]
                         ),
@@ -700,6 +877,7 @@ def triton_training_step(inputs, packed_weights):
         del (
             shard,
             shard_forward_weights,
+            official_inputs,
             triton_inputs,
             triton_forward_weights,
             repeat_inputs,
@@ -724,6 +902,19 @@ def _distributed_worker(
     training_samples: int,
 ) -> None:
     try:
+        try:
+            available_cpus = sorted(os.sched_getaffinity(0))
+            numa_span = max(1, len(available_cpus) // 2)
+            numa_index = 0 if rank < 4 else 1
+            local_rank = rank % 4
+            cpu_index = min(
+                numa_index * numa_span + local_rank,
+                len(available_cpus) - 1,
+            )
+            os.sched_setaffinity(0, {available_cpus[cpu_index]})
+        except (AttributeError, OSError):
+            pass
+        torch.set_num_threads(1)
         torch.cuda.set_device(rank)
         dist.init_process_group(
             backend="nccl",
@@ -868,6 +1059,77 @@ def _load_cuda_cpu_comparison(
     return json.loads(comparison_path.read_text(encoding="utf-8"))
 
 
+def _distributed_platform_comparison_rows(
+    current_rows: list[dict[str, Any]],
+    comparison_payload: dict[str, Any] | None,
+) -> list[dict[str, Any]]:
+    """Join H100 and MI300X distributed timings by topology and direction."""
+    if comparison_payload is None:
+        return []
+    h100_lookup = {
+        (row["name"], row["direction"]): row
+        for row in comparison_payload["distributed"]
+    }
+    rows: list[dict[str, Any]] = []
+    for current in current_rows:
+        key = (current["name"], current["direction"])
+        h100 = h100_lookup.get(key)
+        if h100 is None:
+            continue
+        h100_official_ms = float(h100["official_h100_ms"])
+        h100_deterministic_ms = float(h100["cuda_h100_ms"])
+        mi300x_official_ms = float(
+            current["official_distributed"]["median_ms"]
+        )
+        mi300x_deterministic_ms = float(current["triton"]["median_ms"])
+        rows.append(
+            {
+                "name": current["name"],
+                "direction": current["direction"],
+                "tp_size": current["tp_size"],
+                "cp_size": current["cp_size"],
+                "sequence_parallel": current["sequence_parallel"],
+                "h100_official_distributed_ms": h100_official_ms,
+                "h100_deterministic_cuda_ms": h100_deterministic_ms,
+                "mi300x_official_distributed_ms": mi300x_official_ms,
+                "mi300x_deterministic_triton_ms": mi300x_deterministic_ms,
+                "h100_deterministic_over_official_ratio": (
+                    h100_deterministic_ms / h100_official_ms
+                ),
+                "mi300x_deterministic_over_official_ratio": (
+                    mi300x_deterministic_ms / mi300x_official_ms
+                ),
+                "deterministic_mi300x_over_h100_ratio": (
+                    mi300x_deterministic_ms / h100_deterministic_ms
+                ),
+            }
+        )
+    return rows
+
+
+def _previous_deterministic_comparison_rows(
+    current_rows: list[dict[str, Any]],
+) -> list[dict[str, Any]]:
+    """Compare the current MI300X run with the previous checked report data."""
+    rows: list[dict[str, Any]] = []
+    for current in current_rows:
+        key = (current["name"], current["direction"])
+        previous_ms = _PREVIOUS_DISTRIBUTED_TRITON_MS.get(key)
+        if previous_ms is None:
+            continue
+        current_ms = float(current["triton"]["median_ms"])
+        rows.append(
+            {
+                "name": current["name"],
+                "direction": current["direction"],
+                "previous_ms": previous_ms,
+                "current_ms": current_ms,
+                "latency_reduction_ratio": 1.0 - current_ms / previous_ms,
+            }
+        )
+    return rows
+
+
 def _write_report(
     payload: dict[str, Any],
     output_directory: Path,
@@ -878,12 +1140,22 @@ def _write_report(
     single_speed = payload["single_gpu"]["speed"]
     dtype_rows = payload["single_gpu"]["dtype_accuracy"]
     distributed_speed = payload["distributed_ffn"]
+    platform_comparison = _distributed_platform_comparison_rows(
+        distributed_speed, comparison_payload
+    )
+    previous_comparison = _previous_deterministic_comparison_rows(
+        distributed_speed
+    )
+    previous_reductions = [
+        row["latency_reduction_ratio"] for row in previous_comparison
+    ]
     exactness_rows = _topology_exactness_rows(distributed_speed)
     single_ratios = [
         row["latency_ratio_vs_official_tp1"] for row in single_speed
     ]
-    distributed_ratios = [
-        row["latency_ratio_vs_official_tp1"] for row in distributed_speed
+    platform_ratios = [
+        row["deterministic_mi300x_over_h100_ratio"]
+        for row in platform_comparison
     ]
     total_tp1_mismatch = sum(
         row[key]
@@ -912,9 +1184,9 @@ def _write_report(
         "element mismatch count; acceptance requires 0.",
         "2. **FP16/FP32:** one separate, simple output comparison runs official "
         "Hugging Face `Qwen3MLP` at TP=1 in FP16 and FP32. FP32 is the reference.",
-        "3. **Speed:** official Hugging Face `Qwen3MLP` at TP=1 is the only speed "
-        "baseline. Speed tables and figures intentionally contain no numerical "
-        "accuracy comparison between official FFN and Triton.",
+        "3. **Speed:** single-GPU speed retains the official Qwen3MLP TP=1 "
+        "context. Distributed speed compares four same-topology paths: H100 "
+        "official/deterministic and MI300X official/deterministic.",
         "",
         "## Environment",
         "",
@@ -932,9 +1204,14 @@ def _write_report(
             "determinism measurements.",
             "- Single-GPU shapes use M=1/8/32. Distributed cases use the same full "
             "logical M=32 input for TP2/4/8, TP+CP, and sequence parallelism.",
-            "- The official performance baseline is upstream Transformers "
-            "`Qwen3MLP` with unsharded weights and input (TP=1). Each distributed "
-            "rank runs that TP=1 reference and the slowest rank/sample is reported.",
+            "- The distributed comparison joins rows by topology and direction: "
+            "H100 and MI300X use the same logical M=32 workload and TP/CP/SP layout. "
+            "Official TP=1 latency is neither collected nor used in that distributed "
+            "ratio.",
+            "- MI300X official distributed uses upstream Qwen3 FFN math with native "
+            "PyTorch BF16 GEMMs and native RCCL collectives over the same shards; "
+            "the deterministic path uses the current Triton FFN and fixed-order "
+            "transport.",
             "- Deterministic Triton timings use the explicit prepacked forward-weight "
             "cache. Packing happens once outside the timed region; canonical source "
             "weights remain the autograd and optimizer source of truth.",
@@ -944,8 +1221,18 @@ def _write_report(
             "- The distributed exactness baseline is the PR's deterministic Triton "
             "FFN at TP=1. Local outputs, dHidden, and sharded dWeights are compared "
             "against their exact TP=1 slices.",
+            "- Communication contract for TP+CP+SP: forward uses 1 TP AllGather "
+            "plus 1 TP ReduceScatter (2 calls). Forward+backward retains 7 "
+            "AllGathers plus 3 logical ReduceScatter lanes; PR #357 merges the "
+            "two independent backward gate/up lanes into one "
+            "`reduce_scatter_many` call, for 9 collective invocations.",
+            "- Implementation note: the current ROCm deterministic communication "
+            "operator is adopted from PR #357. This changes the implementation "
+            "under test, not the benchmark comparison contract.",
             f"- Single-GPU timing: {methodology['single_gpu_timing']}; distributed "
             f"timing: {methodology['distributed_timing']}.",
+            f"- Distributed workers: {methodology['distributed_worker_cpu_affinity']} "
+            "to reduce host-scheduler noise in synchronized wall-clock samples.",
             f"- {methodology['warmup']} warmups, {methodology['samples']} measured "
             f"forward samples, and {methodology['training_samples']} measured "
             "forward+backward samples.",
@@ -972,9 +1259,22 @@ def _write_report(
             f"- Single-GPU deterministic Triton packed-cache latency is "
             f"**{min(single_ratios):.2f}-{max(single_ratios):.2f}x** the official "
             "Qwen3MLP TP=1 latency across M=1/8/32 and forward/training.",
-            f"- Distributed deterministic Triton packed-cache latency is "
-            f"**{min(distributed_ratios):.2f}-{max(distributed_ratios):.2f}x** the "
-            "official Qwen3MLP TP=1 latency across tested parallel layouts.",
+            (
+                f"- MI300X deterministic Triton latency is **{min(platform_ratios):.2f}-"
+                f"{max(platform_ratios):.2f}x** the H100 deterministic CUDA latency "
+                "for the same distributed layouts. Both official distributed "
+                "paths are reported alongside them."
+                if platform_ratios
+                else "- No matching H100 distributed rows were supplied."
+            ),
+            (
+                f"- Versus the previous deterministic MI300X benchmark, PR #357 "
+                f"improves **{sum(value > 0 for value in previous_reductions)}/"
+                f"{len(previous_reductions)}** rows, with a mean latency reduction "
+                f"of **{statistics.mean(previous_reductions) * 100:.1f}%**."
+                if previous_reductions
+                else "- No previous deterministic MI300X rows were available."
+            ),
             f"- The separate official-Qwen3MLP FP16 versus FP32 observation has "
             f"relative-L2 error **{dtype_row['relative_l2']:.3e}** for "
             "(M,H,I)=(8,4096,12288).",
@@ -996,28 +1296,70 @@ def _write_report(
             f"{row['latency_ratio_vs_official_tp1']:.2f}x |"
         )
 
-    lines.extend(
-        (
-            "",
-            "## Distributed FFN speed",
-            "",
-            "The baseline remains the full logical M=32 official FFN at TP=1 for "
-            "every row. Performance only; no numerical accuracy is mixed into "
-            "this comparison.",
-            "",
-            "| Parallel layout | Direction | Official Qwen3MLP TP=1 (ms) | "
-            "Deterministic distributed Triton, packed (ms) | Triton / official "
-            "TP=1 |",
-            "|---|---|---:|---:|---:|",
+    lines.extend(("", "## Distributed FFN speed", ""))
+    if platform_comparison:
+        lines.extend(
+            (
+                "Every row compares the same distributed topology and direction. "
+                "No TP=1 latency is used in this table.",
+                "",
+                "| Parallel layout | Direction | H100 official distributed (ms) | "
+                "H100 deterministic CUDA (ms) | MI300X official distributed (ms) | "
+                "MI300X deterministic Triton (ms) | H100 det / official | "
+                "MI300X det / official |",
+                "|---|---|---:|---:|---:|---:|---:|---:|",
+            )
         )
-    )
-    for row in distributed_speed:
-        lines.append(
-            f"| {row['name']} | {row['direction']} | "
-            f"{row['official_tp1']['median_ms']:.4f} | "
-            f"{row['triton']['median_ms']:.4f} | "
-            f"{row['latency_ratio_vs_official_tp1']:.2f}x |"
+        for row in platform_comparison:
+            lines.append(
+                f"| {row['name']} | {row['direction']} | "
+                f"{row['h100_official_distributed_ms']:.4f} | "
+                f"{row['h100_deterministic_cuda_ms']:.4f} | "
+                f"{row['mi300x_official_distributed_ms']:.4f} | "
+                f"{row['mi300x_deterministic_triton_ms']:.4f} | "
+                f"{row['h100_deterministic_over_official_ratio']:.2f}x | "
+                f"{row['mi300x_deterministic_over_official_ratio']:.2f}x |"
+            )
+    else:
+        lines.extend(
+            (
+                "No matching H100 distributed data was supplied; current MI300X "
+                "latency is reported without a TP=1 ratio.",
+                "",
+                "| Parallel layout | Direction | MI300X official distributed (ms) | "
+                "MI300X deterministic Triton (ms) | Deterministic / official |",
+                "|---|---|---:|---:|---:|",
+            )
+        )
+        for row in distributed_speed:
+            lines.append(
+                f"| {row['name']} | {row['direction']} | "
+                f"{row['official_distributed']['median_ms']:.4f} | "
+                f"{row['triton']['median_ms']:.4f} | "
+                f"{row['latency_ratio_triton_vs_official_distributed']:.2f}x |"
+            )
+
+    if previous_comparison:
+        lines.extend(
+            (
+                "",
+                "### PR #357 latency change versus the previous benchmark",
+                "",
+                "This comparison changes only the deterministic ROCm communication "
+                "implementation. It is not included as another series in the main "
+                "four-path figure.",
+                "",
+                "| Parallel layout | Direction | Previous (ms) | Current (ms) | "
+                "Latency reduction |",
+                "|---|---|---:|---:|---:|",
+            )
         )
+        for row in previous_comparison:
+            lines.append(
+                f"| {row['name']} | {row['direction']} | "
+                f"{row['previous_ms']:.4f} | {row['current_ms']:.4f} | "
+                f"{row['latency_reduction_ratio'] * 100:.1f}% |"
+            )
 
     if comparison_payload is not None:
         comparison_environment = comparison_payload["environment"]
@@ -1025,9 +1367,6 @@ def _write_report(
         local_single = {
             (row["tokens"], row["direction"]): row for row in single_speed
         }
-        local_distributed = {
-            (row["name"], row["direction"]): row for row in distributed_speed
-        }
         lines.extend(
             (
                 "",
@@ -1081,29 +1420,12 @@ def _write_report(
         lines.extend(
             (
                 "",
-                "### Distributed absolute latency",
-                "",
-                "No distributed CPU or H100 Triton replay measurement was supplied. "
-                "Each deterministic implementation is therefore compared with its "
-                "own hardware's official TP=1 baseline.",
-                "",
-                "| Layout | Direction | H100 official TP=1 (ms) | H100 CUDA "
-                "(ms) | CUDA / H100 official | MI300X official TP=1 (ms) | "
-                "MI300X Triton (ms) | Triton / MI300X official |",
-                "|---|---|---:|---:|---:|---:|---:|---:|",
+                "Both H100 columns used in the main distributed table are the "
+                "user-supplied distributed timings. They are joined directly with "
+                "MI300X rows of the same topology and direction; TP=1 values are "
+                "excluded from all four columns.",
             )
         )
-        for row in comparison_payload["distributed"]:
-            local = local_distributed[(row["name"], row["direction"])]
-            lines.append(
-                f"| {row['name']} | {row['direction']} | "
-                f"{row['official_h100_ms']:.4f} | "
-                f"{row['cuda_h100_ms']:.4f} | "
-                f"{row['cuda_h100_ms'] / row['official_h100_ms']:.2f}x | "
-                f"{local['official_tp1']['median_ms']:.4f} | "
-                f"{local['triton']['median_ms']:.4f} | "
-                f"{local['latency_ratio_vs_official_tp1']:.2f}x |"
-            )
 
     lines.extend(
         (
@@ -1385,33 +1707,36 @@ def _write_figures(
     plt.close(figure)
 
     rows = payload["distributed_ffn"]
+    platform_comparison = _distributed_platform_comparison_rows(
+        rows, comparison_payload
+    )
     figure, axes = plt.subplots(1, 2, figsize=(26, 10), sharey=True)
-    if comparison_payload is None:
+    if platform_comparison:
+        comparison_lookup = {
+            (row["name"], row["direction"]): row
+            for row in platform_comparison
+        }
         distributed_series = (
-            ("Official Qwen3MLP TP=1", "official_mi300x_ms", "#2563eb"),
+            ("H100 official distributed", "h100_official_distributed_ms", "#60a5fa"),
+            ("H100 deterministic CUDA", "h100_deterministic_cuda_ms", "#dc2626"),
+            ("MI300X official distributed", "mi300x_official_distributed_ms", "#86efac"),
             (
-                "Deterministic distributed Triton (packed)",
-                "triton_mi300x_ms",
+                "MI300X deterministic Triton",
+                "mi300x_deterministic_triton_ms",
                 "#7c3aed",
             ),
         )
-        width = 0.37
+        width = 0.19
     else:
-        comparison_lookup = {
-            (row["name"], row["direction"]): row
-            for row in comparison_payload["distributed"]
-        }
         distributed_series = (
-            ("H100 official TP=1", "official_h100_ms", "#60a5fa"),
-            ("H100 deterministic CUDA", "cuda_h100_ms", "#dc2626"),
-            ("MI300X official TP=1", "official_mi300x_ms", "#86efac"),
+            ("MI300X official distributed", "mi300x_official_distributed_ms", "#86efac"),
             (
-                "MI300X deterministic Triton (packed)",
-                "triton_mi300x_ms",
+                "MI300X deterministic Triton",
+                "mi300x_deterministic_triton_ms",
                 "#7c3aed",
             ),
         )
-        width = 0.19
+        width = 0.34
     for axis, direction, direction_label in (
         (axes[0], "forward", "Forward"),
         (axes[1], "train_fwd_bwd", "Forward + backward"),
@@ -1422,10 +1747,10 @@ def _write_figures(
         combined_rows = []
         for row in direction_rows:
             combined = {
-                "official_mi300x_ms": row["official_tp1"]["median_ms"],
-                "triton_mi300x_ms": row["triton"]["median_ms"],
+                "mi300x_official_distributed_ms": row["official_distributed"]["median_ms"],
+                "mi300x_deterministic_triton_ms": row["triton"]["median_ms"],
             }
-            if comparison_payload is not None:
+            if platform_comparison:
                 combined.update(comparison_lookup[(row["name"], direction)])
             combined_rows.append(combined)
         for series_index, (label, key, color) in enumerate(distributed_series):
@@ -1444,7 +1769,7 @@ def _write_figures(
                 bars,
                 labels=[f"{value:.2f}" for value in values],
                 padding=3,
-                fontsize=9 if comparison_payload is not None else 12,
+                fontsize=10,
                 rotation=90,
             )
         axis.set_yscale("log")
@@ -1453,10 +1778,10 @@ def _write_figures(
         axis.set_xticks(positions, labels)
     axes[0].set_ylabel("Median latency (ms, log scale)")
     handles, labels = axes[0].get_legend_handles_labels()
-    if comparison_payload is None:
+    if not platform_comparison:
         axes[0].legend(loc="upper left")
         figure.suptitle(
-            "MI300X distributed FFN speed: official TP=1 vs Triton",
+            "MI300X distributed FFN latency",
             y=0.99,
         )
         figure.tight_layout(rect=(0.0, 0.0, 1.0, 0.95))
@@ -1470,14 +1795,14 @@ def _write_figures(
             ncol=4,
         )
         figure.suptitle(
-            "Distributed FFN absolute latency: H100 CUDA vs MI300X Triton",
+            "Distributed FFN latency: H100 CUDA vs MI300X Triton",
             y=0.99,
         )
         figure.text(
             0.5,
             0.01,
-            "Absolute latency across different hardware; each deterministic path "
-            "uses its own platform's official TP=1 baseline.",
+            "All four paths use the same M=32 workload, direction, and TP/CP/SP "
+            "topology; no TP=1 latency is included.",
             ha="center",
             fontsize=14,
         )
@@ -1497,9 +1822,10 @@ def _environment() -> dict[str, Any]:
         "hip": torch.version.hip,
         "python": os.sys.version.split()[0],
         "git_commit": os.popen("git rev-parse HEAD").read().strip(),
-        "speed_baseline": "Hugging Face Transformers Qwen3MLP, TP=1",
+        "single_gpu_speed_context": "Hugging Face Transformers Qwen3MLP, TP=1",
+        "distributed_speed_comparison": "four same-topology H100/MI300X paths",
         "deterministic_compute": "ROCm-native Triton",
-        "deterministic_transport": "fixed-order RCCL on ROCm",
+        "deterministic_transport": "fixed-tree HIP IPC with RCCL fallback on ROCm",
         "NCCL_IB_DISABLE": os.environ.get("NCCL_IB_DISABLE", ""),
     }
 
@@ -1520,6 +1846,14 @@ def parse_args() -> argparse.Namespace:
         type=Path,
         default=Path("benchmarks/results/pr325_rocm_mi300x"),
     )
+    parser.add_argument(
+        "--world-sizes",
+        type=int,
+        nargs="+",
+        choices=(2, 4, 8),
+        default=(2, 4, 8),
+        help="distributed world sizes to benchmark (default: 2 4 8)",
+    )
     parser.add_argument("--warmup", type=int, default=3)
     parser.add_argument("--samples", type=int, default=10)
     parser.add_argument("--training-samples", type=int, default=5)
@@ -1536,6 +1870,7 @@ def main() -> None:
         "methodology": {
             "single_gpu_timing": "GPU events, median and p95",
             "distributed_timing": "synchronized wall clock, slowest rank/sample",
+            "distributed_worker_cpu_affinity": "one NUMA-local CPU per GPU rank",
             "warmup": args.warmup,
             "samples": args.samples,
             "training_samples": args.training_samples,
@@ -1543,6 +1878,7 @@ def main() -> None:
             "triton_weight_layout": "packed_forward_cache_outside_timed_region",
             "tp1_forward_cache_bytes": _TP1_FORWARD_CACHE_BYTES,
         },
+        "communication_contract": _COMMUNICATION_CONTRACT,
         "single_gpu": _single_gpu_benchmarks(
             warmup=args.warmup,
             samples=args.samples,
@@ -1551,7 +1887,7 @@ def main() -> None:
         "distributed_ffn": [],
     }
     torch.cuda.empty_cache()
-    for world_size in (2, 4, 8):
+    for world_size in args.world_sizes:
         result = _run_distributed_world(
             world_size,
             warmup=args.warmup,
@@ -1559,10 +1895,27 @@ def main() -> None:
             training_samples=args.training_samples,
         )
         payload["distributed_ffn"].extend(result["distributed_ffn"])
+    comparison_payload = _load_cuda_cpu_comparison(args.output_dir)
+    platform_comparison = _distributed_platform_comparison_rows(
+        payload["distributed_ffn"], comparison_payload
+    )
+    if platform_comparison:
+        payload["distributed_platform_comparison"] = {
+            "source": "cuda_cpu_comparison.json",
+            "contract": "same M=32 workload, direction, and TP/CP/SP topology",
+            "rows": platform_comparison,
+        }
+    previous_comparison = _previous_deterministic_comparison_rows(
+        payload["distributed_ffn"]
+    )
+    if previous_comparison:
+        payload["previous_deterministic_comparison"] = {
+            "source": "previous checked MI300X benchmark before PR #357",
+            "rows": previous_comparison,
+        }
     (args.output_dir / "results.json").write_text(
         json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8"
     )
-    comparison_payload = _load_cuda_cpu_comparison(args.output_dir)
     _write_report(payload, args.output_dir, comparison_payload)
     _write_figures(payload, args.output_dir, comparison_payload)
     print(json.dumps({"output_dir": str(args.output_dir), "status": "ok"}))
diff --git a/benchmarks/results/pr325_rocm_mi300x/distributed_ffn_overhead.png b/benchmarks/results/pr325_rocm_mi300x/distributed_ffn_overhead.png
index 86b9baeb2b99dd81aeb2ee666b42d84d046e27b6..285842652e7fca729909e1909dbe09aa2f568616 100644
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diff --git a/benchmarks/results/pr325_rocm_mi300x/report.md b/benchmarks/results/pr325_rocm_mi300x/report.md
index 9784e34a..2c371bb4 100644
--- a/benchmarks/results/pr325_rocm_mi300x/report.md
+++ b/benchmarks/results/pr325_rocm_mi300x/report.md
@@ -6,7 +6,7 @@ This is an operator-only MI300X report. It does not load or benchmark a model ch
 
 1. **Determinism:** every Triton TP/CP/SP result is compared bitwise with the same deterministic Triton FFN at **TP=1**. The reported metric is element mismatch count; acceptance requires 0.
 2. **FP16/FP32:** one separate, simple output comparison runs official Hugging Face `Qwen3MLP` at TP=1 in FP16 and FP32. FP32 is the reference.
-3. **Speed:** official Hugging Face `Qwen3MLP` at TP=1 is the only speed baseline. Speed tables and figures intentionally contain no numerical accuracy comparison between official FFN and Triton.
+3. **Speed:** single-GPU speed retains the official Qwen3MLP TP=1 context. Distributed speed compares four same-topology paths: H100 official/deterministic and MI300X official/deterministic.
 
 ## Environment
 
@@ -15,13 +15,14 @@ This is an operator-only MI300X report. It does not load or benchmark a model ch
 | NCCL_IB_DISABLE | 1 |
 | architecture | gfx942:sramecc+:xnack- |
 | deterministic_compute | ROCm-native Triton |
-| deterministic_transport | fixed-order RCCL on ROCm |
-| git_commit | 08f47d97d0443c5998b8da6b41a22fdf3848da8f |
+| deterministic_transport | fixed-tree HIP IPC with RCCL fallback on ROCm |
+| distributed_speed_comparison | four same-topology H100/MI300X paths |
+| git_commit | caef501101a3906c733076f31f3b5a9870169d16 |
 | gpu | AMD Instinct MI300X |
 | gpu_count | 8 |
 | hip | 7.14.60850 |
 | python | 3.12.3 |
-| speed_baseline | Hugging Face Transformers Qwen3MLP, TP=1 |
+| single_gpu_speed_context | Hugging Face Transformers Qwen3MLP, TP=1 |
 | torch | 2.12.0+rocm7.14.0a20260608 |
 | transformers | 5.10.4 |
 
@@ -29,19 +30,25 @@ This is an operator-only MI300X report. It does not load or benchmark a model ch
 
 - Operator shape: H=4096, I=12288; BF16 is used for all speed and determinism measurements.
 - Single-GPU shapes use M=1/8/32. Distributed cases use the same full logical M=32 input for TP2/4/8, TP+CP, and sequence parallelism.
-- The official performance baseline is upstream Transformers `Qwen3MLP` with unsharded weights and input (TP=1). Each distributed rank runs that TP=1 reference and the slowest rank/sample is reported.
+- The distributed comparison joins rows by topology and direction: H100 and MI300X use the same logical M=32 workload and TP/CP/SP layout. Official TP=1 latency is neither collected nor used in that distributed ratio.
+- MI300X official distributed uses upstream Qwen3 FFN math with native PyTorch BF16 GEMMs and native RCCL collectives over the same shards; the deterministic path uses the current Triton FFN and fixed-order transport.
+- Deterministic Triton timings use the explicit prepacked forward-weight cache. Packing happens once outside the timed region; canonical source weights remain the autograd and optimizer source of truth.
+- The TP=1 cache adds 288 MiB; each TP rank holds that amount divided by TP size. Refresh cost is excluded because the benchmark measures the steady-state FFN call.
 - The distributed exactness baseline is the PR's deterministic Triton FFN at TP=1. Local outputs, dHidden, and sharded dWeights are compared against their exact TP=1 slices.
+- Communication contract for TP+CP+SP: forward uses 1 TP AllGather plus 1 TP ReduceScatter (2 calls). Forward+backward retains 7 AllGathers plus 3 logical ReduceScatter lanes; PR #357 merges the two independent backward gate/up lanes into one `reduce_scatter_many` call, for 9 collective invocations.
+- Implementation note: the current ROCm deterministic communication operator is adopted from PR #357. This changes the implementation under test, not the benchmark comparison contract.
 - Single-GPU timing: GPU events, median and p95; distributed timing: synchronized wall clock, slowest rank/sample.
-- 3 warmups, 10 measured forward samples, and 5 measured forward+backward samples.
+- Distributed workers: one NUMA-local CPU per GPU rank to reduce host-scheduler noise in synchronized wall-clock samples.
+- 10 warmups, 50 measured forward samples, and 20 measured forward+backward samples.
 - `NCCL_IB_DISABLE=1` keeps the distributed run on intra-node XGMI. Median, p95, min, and max values are available in `results.json`.
 
 Reproduce from the repository root:
 
 ```bash
 python benchmarks/benchmark_rocm_ffn.py \
-  --warmup 3 \
-  --samples 10 \
-  --training-samples 5 \
+  --warmup 10 \
+  --samples 50 \
+  --training-samples 20 \
   --output-dir benchmarks/results/pr325_rocm_mi300x
 ```
 
@@ -49,45 +56,69 @@ python benchmarks/benchmark_rocm_ffn.py \
 
 - TP=1 exactness baseline: **0 mismatched elements** across topology forward outputs, training outputs, dHidden, and dWeights.
 - Repeat mismatch: **0**; training/inference forward mismatch: **0**.
-- Single-GPU deterministic Triton latency is **7.38-22.86x** the official Qwen3MLP TP=1 latency across M=1/8/32 and forward/training.
-- Distributed deterministic Triton latency is **7.45-15.79x** the official Qwen3MLP TP=1 latency across tested parallel layouts.
+- Single-GPU deterministic Triton packed-cache latency is **3.92-7.64x** the official Qwen3MLP TP=1 latency across M=1/8/32 and forward/training.
+- MI300X deterministic Triton latency is **0.14-0.38x** the H100 deterministic CUDA latency for the same distributed layouts. Both official distributed paths are reported alongside them.
+- Versus the previous deterministic MI300X benchmark, PR #357 improves **16/16** rows, with a mean latency reduction of **22.1%**.
 - The separate official-Qwen3MLP FP16 versus FP32 observation has relative-L2 error **6.544e-04** for (M,H,I)=(8,4096,12288).
 
 ## Single-GPU FFN speed
 
 Performance only; no official-versus-Triton accuracy metric is reported here.
 
-| Shape / direction | Official Qwen3MLP TP=1 (ms) | Deterministic Triton (ms) | Triton / official TP=1 |
+| Shape / direction | Official Qwen3MLP TP=1 (ms) | Deterministic Triton, packed (ms) | Triton / official TP=1 |
 |---|---:|---:|---:|
-| (M,H,I)=(1,4096,12288), forward | 0.1183 | 1.9102 | 16.15x |
-| (M,H,I)=(1,4096,12288), forward+backward | 0.6008 | 4.4312 | 7.38x |
-| (M,H,I)=(8,4096,12288), forward | 0.2263 | 2.0424 | 9.03x |
-| (M,H,I)=(8,4096,12288), forward+backward | 0.3997 | 4.6210 | 11.56x |
-| (M,H,I)=(32,4096,12288), forward | 0.1108 | 2.5334 | 22.86x |
-| (M,H,I)=(32,4096,12288), forward+backward | 0.7081 | 5.7710 | 8.15x |
+| (M,H,I)=(1,4096,12288), forward | 0.1004 | 0.6053 | 6.03x |
+| (M,H,I)=(1,4096,12288), forward+backward | 0.4371 | 1.7138 | 3.92x |
+| (M,H,I)=(8,4096,12288), forward | 0.1077 | 0.6054 | 5.62x |
+| (M,H,I)=(8,4096,12288), forward+backward | 0.6476 | 2.8767 | 4.44x |
+| (M,H,I)=(32,4096,12288), forward | 0.1146 | 0.8761 | 7.64x |
+| (M,H,I)=(32,4096,12288), forward+backward | 0.4182 | 2.3188 | 5.54x |
 
 ## Distributed FFN speed
 
-The baseline remains the full logical M=32 official FFN at TP=1 for every row. Performance only; no numerical accuracy is mixed into this comparison.
+Every row compares the same distributed topology and direction. No TP=1 latency is used in this table.
 
-| Parallel layout | Direction | Official Qwen3MLP TP=1 (ms) | Deterministic distributed Triton (ms) | Triton / official TP=1 |
+| Parallel layout | Direction | H100 official distributed (ms) | H100 deterministic CUDA (ms) | MI300X official distributed (ms) | MI300X deterministic Triton (ms) | H100 det / official | MI300X det / official |
+|---|---|---:|---:|---:|---:|---:|---:|
+| tp2 | forward | 0.1552 | 2.7896 | 0.2323 | 0.8759 | 17.97x | 3.77x |
+| tp2 | train_fwd_bwd | 0.6301 | 7.8125 | 0.6984 | 2.0802 | 12.40x | 2.98x |
+| tp2_sp | forward | 0.1552 | 3.3310 | 0.3066 | 0.8885 | 21.46x | 2.90x |
+| tp2_sp | train_fwd_bwd | 0.6301 | 11.7695 | 1.4217 | 2.5187 | 18.68x | 1.77x |
+| tp4 | forward | 0.1530 | 2.1687 | 0.2380 | 0.8324 | 14.17x | 3.50x |
+| tp4 | train_fwd_bwd | 0.5714 | 9.3954 | 0.8280 | 2.2969 | 16.44x | 2.77x |
+| tp2_cp2 | forward | 0.1530 | 2.8261 | 0.2317 | 0.7562 | 18.47x | 3.26x |
+| tp2_cp2 | train_fwd_bwd | 0.5714 | 16.1722 | 4.4830 | 2.5281 | 28.30x | 0.56x |
+| tp2_cp2_sp | forward | 0.1530 | 3.5814 | 0.3262 | 0.8067 | 23.41x | 2.47x |
+| tp2_cp2_sp | train_fwd_bwd | 0.5714 | 16.7894 | 4.5870 | 2.5462 | 29.38x | 0.56x |
+| tp8 | forward | 0.1537 | 2.3206 | 0.2075 | 0.7065 | 15.10x | 3.41x |
+| tp8 | train_fwd_bwd | 0.5006 | 10.1375 | 0.8685 | 2.0507 | 20.25x | 2.36x |
+| tp4_cp2 | forward | 0.1537 | 2.2141 | 0.2424 | 0.8306 | 14.41x | 3.43x |
+| tp4_cp2 | train_fwd_bwd | 0.5006 | 16.5617 | 3.0071 | 2.4620 | 33.08x | 0.82x |
+| tp4_cp2_sp | forward | 0.1537 | 3.2972 | 0.3153 | 0.7618 | 21.45x | 2.42x |
+| tp4_cp2_sp | train_fwd_bwd | 0.5006 | 17.1457 | 2.9346 | 2.4232 | 34.25x | 0.83x |
+
+### PR #357 latency change versus the previous benchmark
+
+This comparison changes only the deterministic ROCm communication implementation. It is not included as another series in the main four-path figure.
+
+| Parallel layout | Direction | Previous (ms) | Current (ms) | Latency reduction |
 |---|---|---:|---:|---:|
-| tp2 | forward | 0.1575 | 1.9598 | 12.44x |
-| tp2 | train_fwd_bwd | 0.6511 | 5.1613 | 7.93x |
-| tp2_sp | forward | 0.1575 | 2.4866 | 15.79x |
-| tp2_sp | train_fwd_bwd | 0.6511 | 9.0512 | 13.90x |
-| tp4 | forward | 0.1644 | 1.5044 | 9.15x |
-| tp4 | train_fwd_bwd | 0.7244 | 5.3938 | 7.45x |
-| tp2_cp2 | forward | 0.1644 | 1.8202 | 11.07x |
-| tp2_cp2 | train_fwd_bwd | 0.7244 | 10.1842 | 14.06x |
-| tp2_cp2_sp | forward | 0.1644 | 2.3788 | 14.47x |
-| tp2_cp2_sp | train_fwd_bwd | 0.7244 | 9.7862 | 13.51x |
-| tp8 | forward | 0.1692 | 1.7434 | 10.31x |
-| tp8 | train_fwd_bwd | 0.6325 | 5.1244 | 8.10x |
-| tp4_cp2 | forward | 0.1692 | 1.4826 | 8.76x |
-| tp4_cp2 | train_fwd_bwd | 0.6325 | 7.2102 | 11.40x |
-| tp4_cp2_sp | forward | 0.1692 | 2.1856 | 12.92x |
-| tp4_cp2_sp | train_fwd_bwd | 0.6325 | 8.6635 | 13.70x |
+| tp2 | forward | 0.9040 | 0.8759 | 3.1% |
+| tp2 | train_fwd_bwd | 2.6997 | 2.0802 | 22.9% |
+| tp2_sp | forward | 1.0561 | 0.8885 | 15.9% |
+| tp2_sp | train_fwd_bwd | 2.9646 | 2.5187 | 15.0% |
+| tp4 | forward | 0.8359 | 0.8324 | 0.4% |
+| tp4 | train_fwd_bwd | 2.6999 | 2.2969 | 14.9% |
+| tp2_cp2 | forward | 0.9016 | 0.7562 | 16.1% |
+| tp2_cp2 | train_fwd_bwd | 3.5606 | 2.5281 | 29.0% |
+| tp2_cp2_sp | forward | 1.1296 | 0.8067 | 28.6% |
+| tp2_cp2_sp | train_fwd_bwd | 3.9929 | 2.5462 | 36.2% |
+| tp8 | forward | 1.0860 | 0.7065 | 34.9% |
+| tp8 | train_fwd_bwd | 2.5620 | 2.0507 | 20.0% |
+| tp4_cp2 | forward | 1.0536 | 0.8306 | 21.2% |
+| tp4_cp2 | train_fwd_bwd | 3.2599 | 2.4620 | 24.5% |
+| tp4_cp2_sp | forward | 1.1928 | 0.7618 | 36.1% |
+| tp4_cp2_sp | train_fwd_bwd | 3.7498 | 2.4232 | 35.4% |
 
 ## CUDA GPU and CPU performance context
 
@@ -106,35 +137,14 @@ The same-H100 CUDA/Triton ratio is the hardware-matched comparison. CPU and MI30
 
 | Shape / direction | CPU official (ms) | H100 official TP=1 (ms) | H100 Triton replay (ms) | H100 CUDA (ms) | CUDA / Triton H100 | MI300X official TP=1 (ms) | MI300X Triton (ms) |
 |---|---:|---:|---:|---:|---:|---:|---:|
-| M=1, forward | 12.9558 | 0.1193 | 1.7381 | 3.9988 | 2.30x | 0.1183 | 1.9102 |
-| M=1, forward+backward | 58.5765 | 0.5184 | 4.2997 | 9.0704 | 2.11x | 0.6008 | 4.4312 |
-| M=8, forward | 12.7676 | 0.1239 | 1.8293 | 3.9923 | 2.18x | 0.2263 | 2.0424 |
-| M=8, forward+backward | 82.3669 | 0.5414 | 4.5028 | 9.4500 | 2.10x | 0.3997 | 4.6210 |
-| M=32, forward | 8.5392 | 0.1311 | 2.2529 | 4.0277 | 1.79x | 0.1108 | 2.5334 |
-| M=32, forward+backward | 65.9689 | 0.7842 | 5.4261 | 9.1635 | 1.69x | 0.7081 | 5.7710 |
-
-### Distributed absolute latency
+| M=1, forward | 12.9558 | 0.1193 | 1.7381 | 3.9988 | 2.30x | 0.1004 | 0.6053 |
+| M=1, forward+backward | 58.5765 | 0.5184 | 4.2997 | 9.0704 | 2.11x | 0.4371 | 1.7138 |
+| M=8, forward | 12.7676 | 0.1239 | 1.8293 | 3.9923 | 2.18x | 0.1077 | 0.6054 |
+| M=8, forward+backward | 82.3669 | 0.5414 | 4.5028 | 9.4500 | 2.10x | 0.6476 | 2.8767 |
+| M=32, forward | 8.5392 | 0.1311 | 2.2529 | 4.0277 | 1.79x | 0.1146 | 0.8761 |
+| M=32, forward+backward | 65.9689 | 0.7842 | 5.4261 | 9.1635 | 1.69x | 0.4182 | 2.3188 |
 
-No distributed CPU or H100 Triton replay measurement was supplied. Each deterministic implementation is therefore compared with its own hardware's official TP=1 baseline.
-
-| Layout | Direction | H100 official TP=1 (ms) | H100 CUDA (ms) | CUDA / H100 official | MI300X official TP=1 (ms) | MI300X Triton (ms) | Triton / MI300X official |
-|---|---|---:|---:|---:|---:|---:|---:|
-| tp2 | forward | 0.1552 | 2.7896 | 17.97x | 0.1575 | 1.9598 | 12.44x |
-| tp2 | train_fwd_bwd | 0.6301 | 7.8125 | 12.40x | 0.6511 | 5.1613 | 7.93x |
-| tp2_sp | forward | 0.1552 | 3.3310 | 21.46x | 0.1575 | 2.4866 | 15.79x |
-| tp2_sp | train_fwd_bwd | 0.6301 | 11.7695 | 18.68x | 0.6511 | 9.0512 | 13.90x |
-| tp4 | forward | 0.1530 | 2.1687 | 14.17x | 0.1644 | 1.5044 | 9.15x |
-| tp4 | train_fwd_bwd | 0.5714 | 9.3954 | 16.44x | 0.7244 | 5.3938 | 7.45x |
-| tp2_cp2 | forward | 0.1530 | 2.8261 | 18.47x | 0.1644 | 1.8202 | 11.07x |
-| tp2_cp2 | train_fwd_bwd | 0.5714 | 16.1722 | 28.30x | 0.7244 | 10.1842 | 14.06x |
-| tp2_cp2_sp | forward | 0.1530 | 3.5814 | 23.41x | 0.1644 | 2.3788 | 14.47x |
-| tp2_cp2_sp | train_fwd_bwd | 0.5714 | 16.7894 | 29.38x | 0.7244 | 9.7862 | 13.51x |
-| tp8 | forward | 0.1537 | 2.3206 | 15.10x | 0.1692 | 1.7434 | 10.31x |
-| tp8 | train_fwd_bwd | 0.5006 | 10.1375 | 20.25x | 0.6325 | 5.1244 | 8.10x |
-| tp4_cp2 | forward | 0.1537 | 2.2141 | 14.41x | 0.1692 | 1.4826 | 8.76x |
-| tp4_cp2 | train_fwd_bwd | 0.5006 | 16.5617 | 33.08x | 0.6325 | 7.2102 | 11.40x |
-| tp4_cp2_sp | forward | 0.1537 | 3.2972 | 21.45x | 0.1692 | 2.1856 | 12.92x |
-| tp4_cp2_sp | train_fwd_bwd | 0.5006 | 17.1457 | 34.25x | 0.6325 | 8.6635 | 13.70x |
+Both H100 columns used in the main distributed table are the user-supplied distributed timings. They are joined directly with MI300X rows of the same topology and direction; TP=1 values are excluded from all four columns.
 
 ## Topology exactness versus Triton TP=1
 
@@ -167,8 +177,8 @@ In backward, the gate and up contributions to dHidden are independent until thei
 
 ## Figures
 
-![Single-GPU CUDA, Triton, and CPU latency](single_gpu_overhead.png)
+![Single-GPU CUDA, packed Triton, and CPU latency](single_gpu_overhead.png)
 
 ![Topology mismatch versus Triton TP=1](collective_overhead.png)
 
-![Distributed H100 CUDA and MI300X Triton latency](distributed_ffn_overhead.png)
+![Distributed H100 CUDA and MI300X packed Triton latency](distributed_ffn_overhead.png)
diff --git a/benchmarks/results/pr325_rocm_mi300x/results.json b/benchmarks/results/pr325_rocm_mi300x/results.json
index b3e67a28..575e685c 100644
--- a/benchmarks/results/pr325_rocm_mi300x/results.json
+++ b/benchmarks/results/pr325_rocm_mi300x/results.json
@@ -1,17 +1,30 @@
 {
+  "communication_contract": {
+    "forward": {
+      "all_gather": 1,
+      "reduce_scatter": 1,
+      "total": 2
+    },
+    "train_fwd_bwd": {
+      "all_gather": 7,
+      "collective_invocations": 9,
+      "logical_total": 10,
+      "reduce_scatter_logical_lanes": 3
+    }
+  },
   "distributed_ffn": [
     {
       "cp_size": 1,
       "direction": "forward",
       "hidden": 4096,
       "intermediate": 12288,
-      "latency_ratio_vs_official_tp1": 12.441502350193632,
+      "latency_ratio_triton_vs_official_distributed": 3.770714554916725,
       "name": "tp2",
-      "official_tp1": {
-        "max_ms": 0.3982321359217167,
-        "median_ms": 0.15751924365758896,
-        "min_ms": 0.15461910516023636,
-        "p95_ms": 0.2975837094709275
+      "official_distributed": {
+        "max_ms": 1.3578161597251892,
+        "median_ms": 0.23228488862514496,
+        "min_ms": 0.2129673957824707,
+        "p95_ms": 0.8294882718473673
       },
       "repeat_mismatch_count": 0,
       "sequence_parallel": false,
@@ -22,11 +35,12 @@
       "tp_size": 2,
       "train_infer_mismatch_count": 0,
       "triton": {
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\ No newline at end of file
+}
diff --git a/benchmarks/results/pr325_rocm_mi300x/single_gpu_overhead.png b/benchmarks/results/pr325_rocm_mi300x/single_gpu_overhead.png
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diff --git a/docs/benchmarking/README.md b/docs/benchmarking/README.md
index 99da15c0..849a82d0 100644
--- a/docs/benchmarking/README.md
+++ b/docs/benchmarking/README.md
@@ -14,9 +14,30 @@ python benchmarks/profiler.py --format json --output reports/profile.json
 python benchmarks/benchmark_sampling.py
 python benchmarks/benchmark_grpo_op.py
 python benchmarks/benchmark_pack.py --smoke
+python benchmarks/benchmark_rocm_ffn.py --help
 python scripts/run_perf.py
 ```
 
+## Unified ROCm deterministic FFN benchmark
+
+The RCCL/FFN benchmark has one entry point. It measures the deterministic
+Triton FFN across TP, CP, and sequence-parallel layouts, checks bitwise
+exactness against deterministic TP=1, and compares four distributed paths at
+the same topology: H100 official/deterministic and MI300X
+official/deterministic. TP=1 latency is not part of the distributed figure.
+
+```bash
+HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 NCCL_IB_DISABLE=1 PYTHONPATH=. \
+  python benchmarks/benchmark_rocm_ffn.py \
+  --warmup 5 --samples 20 --training-samples 10 \
+  --output-dir benchmarks/results/pr325_rocm_mi300x
+```
+
+The checked-in report is
+`benchmarks/results/pr325_rocm_mi300x/report.md`, with the machine-readable
+data in `results.json` and the updated topology figure in
+`distributed_ffn_overhead.png`.
+
 The automated profiler records one row per workload shape with:
 
 - `tokens_per_sec`: active tokens divided by median latency.

From c57a8ae2186d76417f567ce0dded570fcc1468e1 Mon Sep 17 00:00:00 2001
From: lamentropetion <3051000145@qq.com>
Date: Sun, 30 Aug 2026 20:18:35 +0800
Subject: [PATCH 22/28] Optimize deterministic rollout tensor-parallel
 all-reduce

---
 .../distributed/deterministic_collective.cu   | 17 +++--
 rl_engine/integrations/vllm_runtime.py        | 69 ++++++++++++++++++-
 2 files changed, 79 insertions(+), 7 deletions(-)

diff --git a/csrc/cuda/distributed/deterministic_collective.cu b/csrc/cuda/distributed/deterministic_collective.cu
index 72d3f874..41cea442 100644
--- a/csrc/cuda/distributed/deterministic_collective.cu
+++ b/csrc/cuda/distributed/deterministic_collective.cu
@@ -949,7 +949,10 @@ class DeterministicCollectiveState {
     has_staged_input_ = true;
   }
 
-  void all_reduce(torch::Tensor& output, cudaStream_t stream) {
+  void all_reduce(
+      torch::Tensor& output,
+      cudaStream_t stream,
+      bool allow_owner_path = true) {
     check_tensor(output, "output");
     TORCH_CHECK(has_staged_input_, "stage() must be called before all_reduce()");
     TORCH_CHECK(
@@ -960,7 +963,10 @@ class DeterministicCollectiveState {
         "all-reduce output size must match the staged input size");
 
     const int64_t element_count = output.numel();
-    if (staged_owner_path_) {
+    // Graph-replayed calls must avoid the owner-push branch because it writes
+    // to remote IPC frames. Callers that use the fused ABI pass false here;
+    // direct staged collectives retain the eager owner optimization.
+    if (allow_owner_path && staged_owner_path_) {
       if (rank_ == 0) {
         // Logical rank 0 is the topology-favorable reader in both traced TP
         // engines. It evaluates the original fixed tree exactly once, then
@@ -1090,10 +1096,11 @@ class DeterministicCollectiveState {
         output.numel() == input.numel(),
         "all-reduce output size must match the input size");
 
-    // Route both ABI entry points through the same staged protocol so the
-    // topology-aware owner reduction is always applied.
+    // Use the graph-safe staged protocol for every message size. The fused
+    // two-slot protocol is intentionally kept available in the extension for
+    // experiments, but is not safe to replay across vLLM's many graph shapes.
     stage(input, stream);
-    all_reduce(output, stream);
+    all_reduce(output, stream, /*allow_owner_path=*/false);
   }
 
   void all_gather_fused(
diff --git a/rl_engine/integrations/vllm_runtime.py b/rl_engine/integrations/vllm_runtime.py
index c4da28be..ade13cab 100644
--- a/rl_engine/integrations/vllm_runtime.py
+++ b/rl_engine/integrations/vllm_runtime.py
@@ -12,7 +12,11 @@
 
 import torch
 
-from rl_engine.distributed.collectives import DETERMINISTIC_ALL_REDUCE_OP
+from rl_engine.distributed.collectives import (
+    DETERMINISTIC_ALL_REDUCE_OP,
+    collective_for_group,
+    deterministic_all_reduce_inplace,
+)
 from rl_engine.integrations.ablation import (
     Implementation,
     IntegrationPlan,
@@ -40,6 +44,8 @@
 _STRICT_RMS_NORM_INIT_MARKER = "__rl_kernel_original_strict_rms_norm_init__"
 _STRICT_ROTARY_INIT_MARKER = "__rl_kernel_original_strict_rotary_init__"
 _STRICT_LM_HEAD_LINEAR_PATCH_MARKER = "__rl_kernel_original_lm_head_linear_apply__"
+_STRICT_O_PROJ_COLLECTIVE_MARKER = "__rl_kernel_o_proj_collective__"
+_STRICT_ROW_PARALLEL_PATCH_MARKER = "__rl_kernel_original_row_parallel_forward__"
 _RLK_ATTENTION_BACKEND: type[Any] | None = None
 _RLK_ATTENTION_IMPL: type[Any] | None = None
 _RLK_ATTENTION_BUILDER: type[Any] | None = None
@@ -367,6 +373,7 @@ def _patch_qwen3_strict_model(
     rotary_cls: type[Any] | None = None,
     linear_method_cls: type[Any] | None = None,
     attention_cls: type[Any] | None = None,
+    row_parallel_cls: type[Any] | None = None,
     det_gemm: Any | None = None,
 ) -> None:
     """Align vLLM's RMSNorm and Attention projections with Megatron."""
@@ -374,7 +381,7 @@ def _patch_qwen3_strict_model(
     production_classes = rms_norm_cls is None or linear_method_cls is None or attention_cls is None
     if production_classes:
         from vllm.model_executor.layers.layernorm import RMSNorm
-        from vllm.model_executor.layers.linear import UnquantizedLinearMethod
+        from vllm.model_executor.layers.linear import RowParallelLinear, UnquantizedLinearMethod
         from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
         from vllm.model_executor.models.qwen3 import Qwen3Attention
 
@@ -382,6 +389,7 @@ def _patch_qwen3_strict_model(
         rotary_cls = RotaryEmbedding
         linear_method_cls = UnquantizedLinearMethod
         attention_cls = Qwen3Attention
+        row_parallel_cls = RowParallelLinear
     assert rms_norm_cls is not None
     assert linear_method_cls is not None
     assert attention_cls is not None
@@ -435,6 +443,62 @@ def strict_rms_norm_forward_cuda(
             eps=instance.variance_epsilon,
         )
 
+    def bind_o_proj_collective(module: Any) -> None:
+        if int(getattr(module, "tp_size", 1)) <= 1:
+            return
+        from vllm.distributed.parallel_state import get_tp_group
+
+        coordinator = get_tp_group()
+        group = getattr(coordinator, "device_group", coordinator)
+        collective = collective_for_group(group)
+        if collective is None:
+            raise RuntimeError("strict rollout o_proj requires an initialized TP process group")
+        setattr(module, _STRICT_O_PROJ_COLLECTIVE_MARKER, collective)
+
+    if row_parallel_cls is not None and not hasattr(
+        row_parallel_cls, _STRICT_ROW_PARALLEL_PATCH_MARKER
+    ):
+        row_parallel_forward = row_parallel_cls.forward
+
+        def strict_row_parallel_forward(instance: Any, input_: torch.Tensor) -> Any:
+            collective = getattr(instance, _STRICT_O_PROJ_COLLECTIVE_MARKER, None)
+            if collective is None:
+                return row_parallel_forward(instance, input_)
+
+            if instance.input_is_parallel:
+                input_parallel = input_
+            else:
+                from vllm.distributed import split_tensor_along_last_dim
+
+                input_parallel = split_tensor_along_last_dim(
+                    input_, num_partitions=instance.tp_size
+                )[instance.tp_rank].contiguous()
+
+            assert instance.quant_method is not None
+            bias_ = (
+                None
+                if (instance.tp_rank > 0 or instance.skip_bias_add)
+                else instance.bias
+            )
+            output_parallel = instance.quant_method.apply(instance, input_parallel, bias_)
+
+            if instance.reduce_results and instance.tp_size > 1:
+                deterministic_all_reduce_inplace(
+                    output_parallel,
+                    collective_handle=int(collective._handle),
+                )
+                output = output_parallel
+            else:
+                output = output_parallel
+
+            if not instance.return_bias:
+                return output
+            output_bias = instance.bias if instance.skip_bias_add else None
+            return output, output_bias
+
+        setattr(row_parallel_cls, _STRICT_ROW_PARALLEL_PATCH_MARKER, row_parallel_forward)
+        row_parallel_cls.forward = strict_row_parallel_forward
+
     if not hasattr(rms_norm_cls, _STRICT_RMS_NORM_INIT_MARKER):
         rms_norm_init = rms_norm_cls.__init__
 
@@ -463,6 +527,7 @@ def attention_init_wrapped(instance: Any, *args: Any, **kwargs: Any) -> None:
         attention_init(instance, *args, **kwargs)
         setattr(instance.qkv_proj, _STRICT_PROJECTION_MARKER, "qkv")
         setattr(instance.o_proj, _STRICT_PROJECTION_MARKER, "o_proj")
+        bind_o_proj_collective(instance.o_proj)
 
     setattr(attention_cls, _STRICT_MODEL_PATCH_MARKER, attention_init)
     linear_method_cls.apply = deterministic_linear_apply

From 9d5732be1759b08a9ecbab84b5d98092d0b078ef Mon Sep 17 00:00:00 2001
From: Takumi <3051000145@qq.com>
Date: Sun, 30 Aug 2026 20:37:25 +0800
Subject: [PATCH 23/28] Optimize deterministic rollout tensor-parallel
 all-reduce (#365)

---
 .../distributed/deterministic_collective.cu   | 17 +++--
 rl_engine/integrations/vllm_runtime.py        | 69 ++++++++++++++++++-
 2 files changed, 79 insertions(+), 7 deletions(-)

diff --git a/csrc/cuda/distributed/deterministic_collective.cu b/csrc/cuda/distributed/deterministic_collective.cu
index 72d3f874..41cea442 100644
--- a/csrc/cuda/distributed/deterministic_collective.cu
+++ b/csrc/cuda/distributed/deterministic_collective.cu
@@ -949,7 +949,10 @@ class DeterministicCollectiveState {
     has_staged_input_ = true;
   }
 
-  void all_reduce(torch::Tensor& output, cudaStream_t stream) {
+  void all_reduce(
+      torch::Tensor& output,
+      cudaStream_t stream,
+      bool allow_owner_path = true) {
     check_tensor(output, "output");
     TORCH_CHECK(has_staged_input_, "stage() must be called before all_reduce()");
     TORCH_CHECK(
@@ -960,7 +963,10 @@ class DeterministicCollectiveState {
         "all-reduce output size must match the staged input size");
 
     const int64_t element_count = output.numel();
-    if (staged_owner_path_) {
+    // Graph-replayed calls must avoid the owner-push branch because it writes
+    // to remote IPC frames. Callers that use the fused ABI pass false here;
+    // direct staged collectives retain the eager owner optimization.
+    if (allow_owner_path && staged_owner_path_) {
       if (rank_ == 0) {
         // Logical rank 0 is the topology-favorable reader in both traced TP
         // engines. It evaluates the original fixed tree exactly once, then
@@ -1090,10 +1096,11 @@ class DeterministicCollectiveState {
         output.numel() == input.numel(),
         "all-reduce output size must match the input size");
 
-    // Route both ABI entry points through the same staged protocol so the
-    // topology-aware owner reduction is always applied.
+    // Use the graph-safe staged protocol for every message size. The fused
+    // two-slot protocol is intentionally kept available in the extension for
+    // experiments, but is not safe to replay across vLLM's many graph shapes.
     stage(input, stream);
-    all_reduce(output, stream);
+    all_reduce(output, stream, /*allow_owner_path=*/false);
   }
 
   void all_gather_fused(
diff --git a/rl_engine/integrations/vllm_runtime.py b/rl_engine/integrations/vllm_runtime.py
index c4da28be..ade13cab 100644
--- a/rl_engine/integrations/vllm_runtime.py
+++ b/rl_engine/integrations/vllm_runtime.py
@@ -12,7 +12,11 @@
 
 import torch
 
-from rl_engine.distributed.collectives import DETERMINISTIC_ALL_REDUCE_OP
+from rl_engine.distributed.collectives import (
+    DETERMINISTIC_ALL_REDUCE_OP,
+    collective_for_group,
+    deterministic_all_reduce_inplace,
+)
 from rl_engine.integrations.ablation import (
     Implementation,
     IntegrationPlan,
@@ -40,6 +44,8 @@
 _STRICT_RMS_NORM_INIT_MARKER = "__rl_kernel_original_strict_rms_norm_init__"
 _STRICT_ROTARY_INIT_MARKER = "__rl_kernel_original_strict_rotary_init__"
 _STRICT_LM_HEAD_LINEAR_PATCH_MARKER = "__rl_kernel_original_lm_head_linear_apply__"
+_STRICT_O_PROJ_COLLECTIVE_MARKER = "__rl_kernel_o_proj_collective__"
+_STRICT_ROW_PARALLEL_PATCH_MARKER = "__rl_kernel_original_row_parallel_forward__"
 _RLK_ATTENTION_BACKEND: type[Any] | None = None
 _RLK_ATTENTION_IMPL: type[Any] | None = None
 _RLK_ATTENTION_BUILDER: type[Any] | None = None
@@ -367,6 +373,7 @@ def _patch_qwen3_strict_model(
     rotary_cls: type[Any] | None = None,
     linear_method_cls: type[Any] | None = None,
     attention_cls: type[Any] | None = None,
+    row_parallel_cls: type[Any] | None = None,
     det_gemm: Any | None = None,
 ) -> None:
     """Align vLLM's RMSNorm and Attention projections with Megatron."""
@@ -374,7 +381,7 @@ def _patch_qwen3_strict_model(
     production_classes = rms_norm_cls is None or linear_method_cls is None or attention_cls is None
     if production_classes:
         from vllm.model_executor.layers.layernorm import RMSNorm
-        from vllm.model_executor.layers.linear import UnquantizedLinearMethod
+        from vllm.model_executor.layers.linear import RowParallelLinear, UnquantizedLinearMethod
         from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
         from vllm.model_executor.models.qwen3 import Qwen3Attention
 
@@ -382,6 +389,7 @@ def _patch_qwen3_strict_model(
         rotary_cls = RotaryEmbedding
         linear_method_cls = UnquantizedLinearMethod
         attention_cls = Qwen3Attention
+        row_parallel_cls = RowParallelLinear
     assert rms_norm_cls is not None
     assert linear_method_cls is not None
     assert attention_cls is not None
@@ -435,6 +443,62 @@ def strict_rms_norm_forward_cuda(
             eps=instance.variance_epsilon,
         )
 
+    def bind_o_proj_collective(module: Any) -> None:
+        if int(getattr(module, "tp_size", 1)) <= 1:
+            return
+        from vllm.distributed.parallel_state import get_tp_group
+
+        coordinator = get_tp_group()
+        group = getattr(coordinator, "device_group", coordinator)
+        collective = collective_for_group(group)
+        if collective is None:
+            raise RuntimeError("strict rollout o_proj requires an initialized TP process group")
+        setattr(module, _STRICT_O_PROJ_COLLECTIVE_MARKER, collective)
+
+    if row_parallel_cls is not None and not hasattr(
+        row_parallel_cls, _STRICT_ROW_PARALLEL_PATCH_MARKER
+    ):
+        row_parallel_forward = row_parallel_cls.forward
+
+        def strict_row_parallel_forward(instance: Any, input_: torch.Tensor) -> Any:
+            collective = getattr(instance, _STRICT_O_PROJ_COLLECTIVE_MARKER, None)
+            if collective is None:
+                return row_parallel_forward(instance, input_)
+
+            if instance.input_is_parallel:
+                input_parallel = input_
+            else:
+                from vllm.distributed import split_tensor_along_last_dim
+
+                input_parallel = split_tensor_along_last_dim(
+                    input_, num_partitions=instance.tp_size
+                )[instance.tp_rank].contiguous()
+
+            assert instance.quant_method is not None
+            bias_ = (
+                None
+                if (instance.tp_rank > 0 or instance.skip_bias_add)
+                else instance.bias
+            )
+            output_parallel = instance.quant_method.apply(instance, input_parallel, bias_)
+
+            if instance.reduce_results and instance.tp_size > 1:
+                deterministic_all_reduce_inplace(
+                    output_parallel,
+                    collective_handle=int(collective._handle),
+                )
+                output = output_parallel
+            else:
+                output = output_parallel
+
+            if not instance.return_bias:
+                return output
+            output_bias = instance.bias if instance.skip_bias_add else None
+            return output, output_bias
+
+        setattr(row_parallel_cls, _STRICT_ROW_PARALLEL_PATCH_MARKER, row_parallel_forward)
+        row_parallel_cls.forward = strict_row_parallel_forward
+
     if not hasattr(rms_norm_cls, _STRICT_RMS_NORM_INIT_MARKER):
         rms_norm_init = rms_norm_cls.__init__
 
@@ -463,6 +527,7 @@ def attention_init_wrapped(instance: Any, *args: Any, **kwargs: Any) -> None:
         attention_init(instance, *args, **kwargs)
         setattr(instance.qkv_proj, _STRICT_PROJECTION_MARKER, "qkv")
         setattr(instance.o_proj, _STRICT_PROJECTION_MARKER, "o_proj")
+        bind_o_proj_collective(instance.o_proj)
 
     setattr(attention_cls, _STRICT_MODEL_PATCH_MARKER, attention_init)
     linear_method_cls.apply = deterministic_linear_apply

From 8e3231359a15ccfd224c94d720686cd770a6d117 Mon Sep 17 00:00:00 2001
From: lamentropetion <3051000145@qq.com>
Date: Sun, 30 Aug 2026 23:49:22 +0800
Subject: [PATCH 24/28] Enable exact-batch CUDA graphs for strict rollout

---
 .../aligned_python_entrypoint.sh              | 64 ++++++++++++++++++-
 1 file changed, 62 insertions(+), 2 deletions(-)

diff --git a/examples/vime_qwen3_8b_tp2_cp2/aligned_python_entrypoint.sh b/examples/vime_qwen3_8b_tp2_cp2/aligned_python_entrypoint.sh
index 4b3a601c..eb1cf61b 100755
--- a/examples/vime_qwen3_8b_tp2_cp2/aligned_python_entrypoint.sh
+++ b/examples/vime_qwen3_8b_tp2_cp2/aligned_python_entrypoint.sh
@@ -12,14 +12,37 @@ if [[ "${1:-}" == "train.py" || "${1:-}" == */train.py ]]; then
   # vLLM hooks; otherwise it silently falls back to native attention/FFN and
   # loses the R/R performance path. Preserve explicit ablation selections.
   strict_linear_logp=0
+  rollout_batch_size=""
+  n_samples_per_prompt="1"
+  explicit_vllm_execution_config=0
   previous_arg=""
   for current_arg in "$@"; do
     if [[ "${previous_arg}" == "--linear-logp-provider-mode" && "${current_arg}" == "strict" ]]; then
       strict_linear_logp=1
-      break
+    elif [[ "${previous_arg}" == "--rollout-batch-size" ]]; then
+      rollout_batch_size="${current_arg}"
+    elif [[ "${previous_arg}" == "--n-samples-per-prompt" ]]; then
+      n_samples_per_prompt="${current_arg}"
     fi
+
+    case "${current_arg}" in
+      --linear-logp-provider-mode=strict)
+        strict_linear_logp=1
+        ;;
+      --rollout-batch-size=*)
+        rollout_batch_size="${current_arg#*=}"
+        ;;
+      --n-samples-per-prompt=*)
+        n_samples_per_prompt="${current_arg#*=}"
+        ;;
+      --vllm-enforce-eager|--vllm-optimization-level|--vllm-optimization-level=*|--vllm-compilation-config|--vllm-compilation-config=*)
+        explicit_vllm_execution_config=1
+        ;;
+    esac
     previous_arg="${current_arg}"
   done
+
+  strict_cudagraph_args=()
   if [[ "${strict_linear_logp}" == "1" ]]; then
     export RL_KERNEL_VLLM_INTEGRATION="${RL_KERNEL_VLLM_INTEGRATION:-1}"
     export RL_KERNEL_CUDA_ONLY="${RL_KERNEL_CUDA_ONLY:-1}"
@@ -27,6 +50,42 @@ if [[ "${1:-}" == "train.py" || "${1:-}" == */train.py ]]; then
     export RL_KERNEL_ATTENTION_CASE="${RL_KERNEL_ATTENTION_CASE:-R/R}"
     export RL_KERNEL_FFN_CASE="${RL_KERNEL_FFN_CASE:-R/R}"
     export RL_KERNEL_LOGP_CASE="${RL_KERNEL_LOGP_CASE:-R/R}"
+
+    # Strict rollout kernels preserve their arithmetic order under CUDA Graph.
+    # Capturing the complete decode graph removes the per-layer host-launch
+    # gaps that otherwise dominate small decode batches. Capture every exact
+    # batch size: padding a strict custom kernel to a larger sparse graph can
+    # access invalid slots and, more importantly, changes the tested contract.
+    # Explicit vLLM execution flags always win so callers can opt out.
+    if [[ "${explicit_vllm_execution_config}" == "0" ]]; then
+      if [[ "${rollout_batch_size}" =~ ^[1-9][0-9]*$ && "${n_samples_per_prompt}" =~ ^[1-9][0-9]*$ ]]; then
+        max_capture_size=$((rollout_batch_size * n_samples_per_prompt))
+        if [[ -n "${RL_KERNEL_VLLM_CUDAGRAPH_MAX_CAPTURE_SIZE:-}" ]]; then
+          max_capture_size="${RL_KERNEL_VLLM_CUDAGRAPH_MAX_CAPTURE_SIZE}"
+        fi
+        if ! [[ "${max_capture_size}" =~ ^[1-9][0-9]*$ ]]; then
+          echo "RL_KERNEL_VLLM_CUDAGRAPH_MAX_CAPTURE_SIZE must be a positive integer" >&2
+          exit 2
+        fi
+
+        capture_sizes="["
+        for ((batch_size = 1; batch_size <= max_capture_size; batch_size++)); do
+          if ((batch_size > 1)); then
+            capture_sizes+=","
+          fi
+          capture_sizes+="${batch_size}"
+        done
+        capture_sizes+="]"
+        compilation_config="{\"cudagraph_mode\":\"FULL_DECODE_ONLY\",\"cudagraph_capture_sizes\":${capture_sizes},\"max_cudagraph_capture_size\":${max_capture_size}}"
+        strict_cudagraph_args=(
+          --vllm-optimization-level 0
+          --vllm-compilation-config "${compilation_config}"
+        )
+        echo "[RL-Kernel] strict vLLM full-decode CUDA Graph capture sizes: ${capture_sizes}" >&2
+      else
+        echo "[RL-Kernel] strict CUDA Graph disabled: rollout batch size is unavailable" >&2
+      fi
+    fi
   fi
   exec "${REAL_PYTHON}" "$@" \
     --seed 1234 \
@@ -35,7 +94,8 @@ if [[ "${1:-}" == "train.py" || "${1:-}" == */train.py ]]; then
     --vllm-attention-backend flash_attn \
     --vllm-disable-custom-all-reduce \
     --deterministic-mode \
-    --accumulate-allreduce-grads-in-fp32
+    --accumulate-allreduce-grads-in-fp32 \
+    "${strict_cudagraph_args[@]}"
 fi
 
 exec "${REAL_PYTHON}" "$@"

From 9b95e348e26131a0c56d7ddcaa6717d420fd4132 Mon Sep 17 00:00:00 2001
From: Takumi <3051000145@qq.com>
Date: Mon, 31 Aug 2026 19:20:28 +0800
Subject: [PATCH 25/28] Precompile strict FA4 training kernels

Added a new function to precompile strict attention training for better performance when using RL_KERNEL. Modified the integration entry point to include this precompilation step based on the plan's implementation.
---
 rl_engine/integrations/megatron_runtime.py | 44 +++++++++++++++++++++-
 1 file changed, 43 insertions(+), 1 deletion(-)

diff --git a/rl_engine/integrations/megatron_runtime.py b/rl_engine/integrations/megatron_runtime.py
index e296e824..72ac8919 100644
--- a/rl_engine/integrations/megatron_runtime.py
+++ b/rl_engine/integrations/megatron_runtime.py
@@ -6,6 +6,7 @@
 from __future__ import annotations
 
 import importlib
+import os
 from collections.abc import Callable, Iterable
 from types import MethodType
 from typing import Any
@@ -290,7 +291,48 @@ def initialize_from_environment(_args: Any = None) -> MegatronIntegration:
 
     from rl_engine.integrations.ablation import integration_plan_from_environment
 
-    return install_megatron_integration(integration_plan_from_environment())
+    plan = integration_plan_from_environment()
+    integration = install_megatron_integration(plan)
+    if plan.implementation_for("attention", "training") is Implementation.RL_KERNEL:
+        _precompile_strict_attention_training(_args)
+    return integration
+
+
+def _precompile_strict_attention_training(args: Any) -> None:
+    """Warm FA4 CuTe fwd/bwd JIT outside Vime's actor_train timer."""
+
+    if args is None or torch.version.hip is not None or not torch.cuda.is_available():
+        return
+    if os.getenv("RL_KERNEL_PRECOMPILE_FA4", "1") == "0":
+        return
+
+    from rl_engine.kernels.ops.cuda.attention.flash_attn import StrictFlashAttention4Core
+
+    attention_heads = int(getattr(args, "num_attention_heads", 0) or 0)
+    query_groups = int(getattr(args, "num_query_groups", 0) or attention_heads)
+    tp_size = int(getattr(args, "tensor_model_parallel_size", 1) or 1)
+    head_dim = int(
+        getattr(args, "kv_channels", 0)
+        or (int(getattr(args, "hidden_size", 0) or 0) // attention_heads)
+    )
+    if (
+        attention_heads <= 0
+        or query_groups <= 0
+        or tp_size <= 0
+        or attention_heads % tp_size
+        or query_groups % tp_size
+        or head_dim <= 0
+    ):
+        return
+
+    params_dtype = getattr(args, "params_dtype", None)
+    dtype = params_dtype if params_dtype in (torch.float16, torch.bfloat16) else torch.bfloat16
+    StrictFlashAttention4Core.precompile_training(
+        q_heads=attention_heads // tp_size,
+        kv_heads=query_groups // tp_size,
+        head_dim=head_dim,
+        dtype=dtype,
+    )
 
 
 __all__ = ["initialize_from_environment", "install_megatron_integration"]

From 0aa1d63f8362768c29e72ba90909706706bb4e97 Mon Sep 17 00:00:00 2001
From: Takumi <3051000145@qq.com>
Date: Mon, 31 Aug 2026 19:21:28 +0800
Subject: [PATCH 26/28] Add strict FA4 training precompile

---
 .../kernels/ops/cuda/attention/flash_attn.py  | 76 +++++++++++++++++++
 1 file changed, 76 insertions(+)

diff --git a/rl_engine/kernels/ops/cuda/attention/flash_attn.py b/rl_engine/kernels/ops/cuda/attention/flash_attn.py
index cec8d2fe..9ad510b3 100644
--- a/rl_engine/kernels/ops/cuda/attention/flash_attn.py
+++ b/rl_engine/kernels/ops/cuda/attention/flash_attn.py
@@ -109,6 +109,82 @@ def __init__(
         self._op = op
         self._paged_op = paged_op
 
+    @classmethod
+    def precompile_training(
+        cls,
+        *,
+        q_heads: int,
+        kv_heads: int,
+        head_dim: int,
+        device: torch.device | None = None,
+        dtype: torch.dtype = torch.bfloat16,
+        sequence_length: int = 512,
+    ) -> None:
+        """Compile strict training FA4 forward/backward before timing.
+
+        FA4 CuTe compiles its forward kernel on the first invocation and its
+        deterministic backward kernel on the first autograd backward.  A
+        one-step RL workload would otherwise charge both compilations to
+        Vime's ``actor_train`` timer.  These isolated tensors exercise the
+        same Qwen-style GQA and multi-block shape class without touching model
+        tensors, RNG state, or distributed collectives.
+        """
+        if torch.version.hip is not None:
+            raise StrictFlashAttentionUnavailable(
+                "FA4 CUDA precompile is unavailable on ROCm"
+            )
+        if not torch.cuda.is_available():
+            raise StrictFlashAttentionUnavailable(
+                "FA4 CUDA precompile requires an available CUDA device"
+            )
+        if dtype not in (torch.float16, torch.bfloat16):
+            raise ValueError("strict FA4 training precompile requires FP16 or BF16")
+        if q_heads <= 0 or kv_heads <= 0 or q_heads % kv_heads != 0:
+            raise ValueError("Q/KV head counts must be positive and GQA-compatible")
+        if head_dim <= 0 or sequence_length <= 0:
+            raise ValueError("head_dim and sequence_length must be positive")
+
+        target = (
+            torch.device("cuda", torch.cuda.current_device())
+            if device is None
+            else device
+        )
+        if target.type != "cuda":
+            raise ValueError("strict FA4 training precompile requires a CUDA device")
+
+        core = cls()
+        # Zeros deliberately avoid consuming RNG state.  The tensors are
+        # independent from the model and only populate FA4's process-local
+        # JIT caches.
+        q = torch.zeros(
+            (1, sequence_length, q_heads, head_dim),
+            dtype=dtype,
+            device=target,
+            requires_grad=True,
+        )
+        k = torch.zeros(
+            (1, sequence_length, kv_heads, head_dim),
+            dtype=dtype,
+            device=target,
+            requires_grad=True,
+        )
+        v = torch.zeros_like(k, requires_grad=True)
+        positions = torch.arange(sequence_length, dtype=torch.int64, device=target).expand(1, -1)
+        with torch.enable_grad():
+            result = core.forward_bshd_with_lse(
+                q,
+                k,
+                v,
+                causal=True,
+                scale=head_dim**-0.5,
+                query_position_ids=positions,
+                key_position_ids=positions,
+                output_dtype=dtype,
+            )
+            result.out.sum().backward()
+        torch.cuda.synchronize(target)
+        del result, q, k, v, positions, core
+
     @staticmethod
     def _validate_api(op: Callable[..., Any]) -> None:
         try:

From afccecc028d16d227593262fbd107f5868c07e69 Mon Sep 17 00:00:00 2001
From: Takumi <3051000145@qq.com>
Date: Mon, 31 Aug 2026 19:21:54 +0800
Subject: [PATCH 27/28] Optimize small deterministic all-reduce path

---
 .../distributed/deterministic_collective.cu   | 52 +++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/csrc/cuda/distributed/deterministic_collective.cu b/csrc/cuda/distributed/deterministic_collective.cu
index 41cea442..e7d836a3 100644
--- a/csrc/cuda/distributed/deterministic_collective.cu
+++ b/csrc/cuda/distributed/deterministic_collective.cu
@@ -1028,6 +1028,58 @@ class DeterministicCollectiveState {
       return;
     }
 
+    // Small collectives are launch-bound. The fast kernel folds the peer
+    // stage wait, fixed-tree reduction, and completion publication into one
+    // launch while preserving the exact reduction order.
+    if (staged_fast_path_ && staged_bytes_ <= kSingleBlockFastPathMaxBytes) {
+      if (element_count > 0) {
+        switch (output.scalar_type()) {
+          case at::ScalarType::Float:
+            launch_all_reduce_fast<float>(
+                peers_,
+                local_stage_sequence_,
+                local_done_sequence_,
+                static_cast<float*>(output.data_ptr()),
+                element_count,
+                world_size_,
+                stream);
+            break;
+          case at::ScalarType::Half:
+            launch_all_reduce_fast<half>(
+                peers_,
+                local_stage_sequence_,
+                local_done_sequence_,
+                static_cast<half*>(output.data_ptr()),
+                element_count,
+                world_size_,
+                stream);
+            break;
+#if (__CUDA_ARCH__ >= 800 || !defined(__CUDA_ARCH__))
+          case at::ScalarType::BFloat16:
+            launch_all_reduce_fast<nv_bfloat16>(
+                peers_,
+                local_stage_sequence_,
+                local_done_sequence_,
+                static_cast<nv_bfloat16*>(output.data_ptr()),
+                element_count,
+                world_size_,
+                stream);
+            break;
+#endif
+          default:
+            TORCH_CHECK(
+                false,
+                "deterministic all-reduce supports float32, float16, and bfloat16; got ",
+                output.scalar_type());
+        }
+        AT_CUDA_CHECK(cudaGetLastError());
+      } else {
+        publish_done(stream);
+      }
+      has_staged_input_ = false;
+      return;
+    }
+
     wait_for_staged_peers(stream);
     if (element_count == 0) {
       publish_done(stream);

From afede7a3f66179585bf9565d6742099a2d00ec99 Mon Sep 17 00:00:00 2001
From: frank-2077 <siruhe666@gmail.com>
Date: Mon, 31 Aug 2026 11:30:24 +0000
Subject: [PATCH 28/28] perf(ffn): tune gfx942 TP2 shard leaves

---
 benchmarks/benchmark_rocm_det_gemm_leaf.py    | 88 ++++++++++++++-----
 .../kernels/ops/triton/matmul/det_gemm.py     | 29 ++++++
 tests/test_det_gemm.py                        | 19 ++++
 3 files changed, 112 insertions(+), 24 deletions(-)

diff --git a/benchmarks/benchmark_rocm_det_gemm_leaf.py b/benchmarks/benchmark_rocm_det_gemm_leaf.py
index bfcbc8b7..eb00a05b 100644
--- a/benchmarks/benchmark_rocm_det_gemm_leaf.py
+++ b/benchmarks/benchmark_rocm_det_gemm_leaf.py
@@ -71,30 +71,70 @@ class LeafCase:
     transpose_output: bool = False
 
 
-_CASES = {
-    case.name: case
-    for token_count in (1, 8, 32)
-    for case in (
-        LeafCase(f"fwd_gate_m{token_count}", token_count, 4096, 12288),
-        LeafCase(f"fwd_down_m{token_count}", token_count, 12288, 4096),
-        LeafCase(
-            f"wgrad_gate_m{token_count}",
-            4096,
-            token_count,
-            12288,
-            transposed_a=True,
-            transpose_output=True,
-        ),
-        LeafCase(
-            f"wgrad_down_m{token_count}",
-            12288,
-            token_count,
-            4096,
-            transposed_a=True,
-            transpose_output=True,
-        ),
-    )
-}
+def _leaf_cases() -> dict[str, LeafCase]:
+    cases: list[LeafCase] = []
+    for token_count in (1, 8, 16, 32):
+        cases.extend(
+            (
+                LeafCase(f"fwd_gate_m{token_count}", token_count, 4096, 12288),
+                LeafCase(f"fwd_down_m{token_count}", token_count, 12288, 4096),
+                LeafCase(
+                    f"wgrad_gate_m{token_count}",
+                    4096,
+                    token_count,
+                    12288,
+                    transposed_a=True,
+                    transpose_output=True,
+                ),
+                LeafCase(
+                    f"wgrad_down_m{token_count}",
+                    12288,
+                    token_count,
+                    4096,
+                    transposed_a=True,
+                    transpose_output=True,
+                ),
+            )
+        )
+        for tp_size in (2, 4, 8):
+            local_intermediate = 12288 // tp_size
+            suffix = f"tp{tp_size}_m{token_count}"
+            cases.extend(
+                (
+                    LeafCase(
+                        f"fwd_gate_{suffix}",
+                        token_count,
+                        4096,
+                        local_intermediate,
+                    ),
+                    LeafCase(
+                        f"fwd_down_{suffix}",
+                        token_count,
+                        local_intermediate,
+                        4096,
+                    ),
+                    LeafCase(
+                        f"wgrad_gate_{suffix}",
+                        4096,
+                        token_count,
+                        local_intermediate,
+                        transposed_a=True,
+                        transpose_output=True,
+                    ),
+                    LeafCase(
+                        f"wgrad_down_{suffix}",
+                        local_intermediate,
+                        token_count,
+                        4096,
+                        transposed_a=True,
+                        transpose_output=True,
+                    ),
+                )
+            )
+    return {case.name: case for case in cases}
+
+
+_CASES = _leaf_cases()
 
 _BASELINE = LeafConfig(64, 64, 4)
 
diff --git a/rl_engine/kernels/ops/triton/matmul/det_gemm.py b/rl_engine/kernels/ops/triton/matmul/det_gemm.py
index 585fb4e2..078167b0 100644
--- a/rl_engine/kernels/ops/triton/matmul/det_gemm.py
+++ b/rl_engine/kernels/ops/triton/matmul/det_gemm.py
@@ -65,6 +65,24 @@ class _TreeLeafConfig:
 _QWEN_FORWARD_GEMM_SHAPES = {(4096, 12288), (12288, 4096)}
 _QWEN_WGRAD_OUTPUT_SHAPES = {(4096, 12288), (12288, 4096)}
 
+# Qwen3-8B TP2 local shards. TP4/8 candidates improved isolated leaves but did
+# not clear the distributed end-to-end promotion threshold, so they deliberately
+# retain the fallback. These entries were swept independently from the TP1 table
+# because smaller local N/K changes both grid occupancy and the
+# point where tree-reduction launch cost dominates leaf time. Keep the key
+# exact: nearby shapes and non-target token counts retain the established
+# fallback instead of inheriting a configuration from a different TP graph.
+_GFX942_QWEN_TP_SHARD_FORWARD_LEAF_CONFIGS = {
+    (16, 4096, 6144): _TreeLeafConfig(8, 64, 1, True),
+    (16, 6144, 4096): _TreeLeafConfig(8, 64, 1, True),
+    (32, 4096, 6144): _TreeLeafConfig(32, 128, 4, True),
+    (32, 6144, 4096): _TreeLeafConfig(32, 128, 4, True),
+}
+_GFX942_QWEN_TP_SHARD_WGRAD_LEAF_CONFIGS = {
+    (4096, 32, 6144): _TreeLeafConfig(128, 64, 2, True),
+    (6144, 32, 4096): _TreeLeafConfig(128, 64, 2, True),
+}
+
 
 def _gfx942_qwen_tree_leaf_config(
     m_size: int,
@@ -74,6 +92,7 @@ def _gfx942_qwen_tree_leaf_config(
     transpose_output: bool,
     preserve_a_strides: bool,
 ) -> _TreeLeafConfig:
+    logical_shape = (m_size, k_size, n_size)
     if (
         not transpose_output
         and not preserve_a_strides
@@ -83,6 +102,11 @@ def _gfx942_qwen_tree_leaf_config(
             m_size,
             _DEFAULT_TREE_LEAF_CONFIG,
         )
+    if not transpose_output and not preserve_a_strides:
+        return _GFX942_QWEN_TP_SHARD_FORWARD_LEAF_CONFIGS.get(
+            logical_shape,
+            _DEFAULT_TREE_LEAF_CONFIG,
+        )
     if (
         transpose_output
         and preserve_a_strides
@@ -92,6 +116,11 @@ def _gfx942_qwen_tree_leaf_config(
             k_size,
             _DEFAULT_TREE_LEAF_CONFIG,
         )
+    if transpose_output and preserve_a_strides:
+        return _GFX942_QWEN_TP_SHARD_WGRAD_LEAF_CONFIGS.get(
+            logical_shape,
+            _DEFAULT_TREE_LEAF_CONFIG,
+        )
     return _DEFAULT_TREE_LEAF_CONFIG
 
 
diff --git a/tests/test_det_gemm.py b/tests/test_det_gemm.py
index df53870d..dd9e55c1 100644
--- a/tests/test_det_gemm.py
+++ b/tests/test_det_gemm.py
@@ -21,6 +21,8 @@
     from rl_engine.kernels.ops.triton.matmul.det_gemm import (
         _DEFAULT_TREE_LEAF_CONFIG,
         _GFX942_QWEN_FORWARD_LEAF_CONFIGS,
+        _GFX942_QWEN_TP_SHARD_FORWARD_LEAF_CONFIGS,
+        _GFX942_QWEN_TP_SHARD_WGRAD_LEAF_CONFIGS,
         _GFX942_QWEN_WGRAD_LEAF_CONFIGS,
         _det_gemm_tree_leaf_kernel,
         _device_tree_plan,
@@ -181,6 +183,19 @@ def _balanced_tree_sum(parts: list[torch.Tensor]) -> torch.Tensor:
         ((32, 4096, 4096), False, False, (64, 64, 4, False)),
         ((4096, 16, 12288), True, True, (64, 64, 4, False)),
         ((4096, 8, 12288), True, False, (64, 64, 4, False)),
+        ((32, 4096, 6144), False, False, (32, 128, 4, True)),
+        ((32, 6144, 4096), False, False, (32, 128, 4, True)),
+        ((16, 4096, 6144), False, False, (8, 64, 1, True)),
+        ((16, 6144, 4096), False, False, (8, 64, 1, True)),
+        ((32, 4096, 3072), False, False, (64, 64, 4, False)),
+        ((32, 3072, 4096), False, False, (64, 64, 4, False)),
+        ((32, 4096, 1536), False, False, (64, 64, 4, False)),
+        ((32, 1536, 4096), False, False, (64, 64, 4, False)),
+        ((4096, 32, 6144), True, True, (128, 64, 2, True)),
+        ((6144, 32, 4096), True, True, (128, 64, 2, True)),
+        ((4096, 32, 3072), True, True, (64, 64, 4, False)),
+        ((3072, 32, 4096), True, True, (64, 64, 4, False)),
+        ((4096, 16, 6144), True, True, (64, 64, 4, False)),
     ),
 )
 def test_gfx942_qwen_leaf_config_table_is_exact(
@@ -215,6 +230,8 @@ def test_gfx942_leaf_configs_preserve_special_value_workspace_raw_bytes(k_size):
         dict.fromkeys(
             (
                 *_GFX942_QWEN_FORWARD_LEAF_CONFIGS.values(),
+                *_GFX942_QWEN_TP_SHARD_FORWARD_LEAF_CONFIGS.values(),
+                *_GFX942_QWEN_TP_SHARD_WGRAD_LEAF_CONFIGS.values(),
                 *_GFX942_QWEN_WGRAD_LEAF_CONFIGS.values(),
             )
         )
@@ -239,6 +256,8 @@ def test_gfx942_leaf_configs_preserve_transposed_a_workspace_raw_bytes():
         dict.fromkeys(
             (
                 *_GFX942_QWEN_FORWARD_LEAF_CONFIGS.values(),
+                *_GFX942_QWEN_TP_SHARD_FORWARD_LEAF_CONFIGS.values(),
+                *_GFX942_QWEN_TP_SHARD_WGRAD_LEAF_CONFIGS.values(),
                 *_GFX942_QWEN_WGRAD_LEAF_CONFIGS.values(),
             )
         )