[plugin][vllm] dp support#1101
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Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
Signed-off-by: kliuae <kuanfu.liu@embeddedllm.com>
Signed-off-by: zejunchen-zejun <zejun.chen@amd.com>
* Qwen3.5-35B-A3B-FP8: GDN decode lossy fast path + fused MRoPE QK (#838) * add gdn decode fast kernel * resolve gdn code conflicts * resolve gdn code conflicts * solve mispelling error * solve redundant import error * add layernorm and rope optimization * revert non-gdn optimization changes Co-authored-by: Cursor <cursoragent@cursor.com> * revert gdn changes Co-authored-by: Cursor <cursoragent@cursor.com> * add gdn decode lossy fast kernel * revert sglang benchmark file changes Co-authored-by: Cursor <cursoragent@cursor.com> * gate gdn decode lossy fast path Co-authored-by: Cursor <cursoragent@cursor.com> * address gdn decode review comments Co-authored-by: Cursor <cursoragent@cursor.com> * fix(gdn): zero out for PAD_SLOT_ID in lossy fast kernel When ssm_state_indices contains a negative slot id (e.g. SGLang's PAD_SLOT_ID = -1 for idle/padded decode slots) the kernel previously returned early without writing to out, leaving the corresponding positions in the output tensor uninitialized and propagating garbage into downstream ops. Match the safer behavior expected by callers: write zeros to out for the invalid slot and skip the state load/store entirely. Addresses the latest Copilot review comment on PR #838. * style: apply black formatting Fix Check Code Style with Black CI failure on #838. * perf(qwen3.5): add fused MRoPE QK Triton path Merges the MRoPE Q/K fusion work originally in #888 into this PR so the two related Qwen3.5-35B-A3B-FP8 optimizations ship together (per review feedback that #888's stand-alone +1.7% gain is too small to justify a separate PR). Adds: - atom/model_ops/triton_mrope.py: specialized Qwen3.5 MRoPE Q/K Triton kernels (tiled + per-token) with a try_mrope_qk_fused dispatcher decorated with @torch.compiler.disable so Dynamo cannot specialize positions/q/k symbolic dims to constants (was tripping ConstraintViolationError under MMStar dynamic-shape compile). - atom/models/qwen3_next.py: wires try_mrope_qk_fused into Qwen3NextAttention after qk_norm; falls back to the generic rotary_emb path when the shapes don't match. Combined effect over main (MI308X, CONC 224, ISL 4094, OSL 2048, TP/EP 1/1, ATOM_ENABLE_GDN_DECODE_LOSSY_FAST=1): - Total token throughput: 7466.90 -> 8004.41 tok/s (+7.20%) - Mean E2E latency: 176401 -> 164893 ms (-6.52%) - Mean TPOT: 77.44 -> 71.87 ms (-7.19%) GSM8K 5-shot remains on par with main: - flexible-extract: 0.895 (vs 0.8946 baseline) - strict-match: 0.903 (vs 0.9052 baseline) * fix(mrope): early-return under torch.compile instead of graph break Previously try_mrope_qk_fused used @torch.compiler.disable to keep the Python shape branches out of Dynamo. That fixed the original ConstraintViolationError but introduced a new MMStar failure: torch._dynamo.exc.BackendCompilerFailed: backend='...VllmBackend' raised: AssertionError: VllmBackend can only be called once The graph break inserted by @torch.compiler.disable inside the compiled Qwen3NextAttention forward causes Dynamo to invoke ATOM's VllmBackend a second time on the same instance. Switch to torch.compiler.is_compiling() early-return: under compile we skip the fused path entirely (fall back to self.rotary_emb, identical to main), eager mode keeps the fused-path perf gain. No graph break, no double-backend invocation. * perf(mrope): drop tl.constexpr on num_tokens to avoid recompilation num_tokens equals positions.shape[1], which changes every batch (mixed prefill/decode, varying decode batch sizes). With tl.constexpr, Triton specializes and recompiles the kernel for every distinct value, which defeats the perf gain of the fused path. num_tokens is only used in a runtime mask (row_mask = rows < num_tokens), so it does not need constexpr semantics. Drop the annotation so the kernel is compiled once per shape group. Addresses Copilot review r3322237301. --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: zovonoir <zovonoir@users.noreply.github.com> * fix(spec_decode): support DP attention with MTP in Deepseek V4 (#1001) * fix(spec_decode): support DP attention with MTP draft Refresh dp_metadata per draft step (force variable-length DP path) and add num_spec_step + scheduled_spec_decode_tokens to the dummy decode batch so DP+MTP runs stay in lockstep. * style: apply black formatting --------- Co-authored-by: ZhangLirong-amd <ZhangLirong@amd.com> * Remove qkv 256 tok limitation (#999) * [Refactor][ATOM-vLLM][Attention] Refactor ATOM-vLLM Attention (#750) * [feat][Attention Refactor] Reconstruct the Attention arch Signed-off-by: zejunchen-zejun <zejun.chen@amd.com> * ci(benchmark): raise benchmark drain MAX_MIN 30->60 and step timeout 60->80 (#1019) High-concurrency long-context benchmarks (DP-attention 8k/1k c=1024, which runs num_prompts = conc*10 = 10240) need ~48 min wall: ~14 min warmup + ~34 min for the measured run (10 waves of 1024 at ~3:20/wave). The benchmark drain's MAX_MIN=30 cut them off mid-run with exit 4 (timeout), failing the job even though the server was healthy and still making progress. Raise the benchmark drain MAX_MIN 30->60 and the "Run benchmark" step timeout-minutes 60->80 so these runs complete. Fast jobs are unaffected (drain exits on client completion, well before MAX_MIN); genuine hangs/faults still surface quickly via STUCK_POLLS (3 min) and fault detection, not MAX_MIN. The accuracy drain (MAX_MIN=30) is left unchanged. * [atom-vllm-benchmark] Retrieve model case name (#1022) Co-authored-by: root <root@hjbog-srdc-15.amd.com> * ci(accuracy): set Qwen3.5-35B-A3B TP2 baseline to 0.85 (#993) Mean of first 4 valid CI runs after PR #893 (0.8226 / 0.8529 / 0.8620 / 0.8628). Threshold 0.83 unchanged. Co-authored-by: JiaoliangYu <jiaolyu@amd.com> Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com> * fix: support PTPC indexer wk FP8 scales (#1009) * fix: support MTP indexer wk FP8 scales Allow DeepSeek-V3.2 MTP checkpoints to load indexer.wk tensors that use per-channel FP8 scales while preserving the existing block-scale path. Co-authored-by: Cursor <cursoragent@cursor.com> * fix: clarify PTPC indexer wk scale support Describe the per-channel FP8 scale path as PTPC quantization support rather than MTP-specific behavior. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com> * [Fix] Enable dpsk r1 mxfp4 V2 model (#934) * [Fix] Enable dpsk r1 mxfp4 V2 model * [Benchmark] Change model to dpsk v2 model for sglang plugin * [Fix] Move MXFP4 kv_b_proj preservation into SGLang MLA * [Fix] Handle SGLang MXFP4 kv_b_proj postprocess order * Add fused chunk GDN prefill path for Qwen3.5-35B (#921) * Add fused chunk GDN prefill path for Qwen3.5-35B Port AMD HIP fast path from sglang's flash-linear-attention to chunk_gated_delta_rule prefill. Fuses 4 kernels into 3. * remove unused o_16 in fused_merge_recompute_kernel * format NT_16 ternary on single line for black * [fix](attn): fix slot mapping in model runner v2 (#1015) Co-authored-by: perzhang <perzhang@amd.com> * [MoE] adapt to triton_kernels matmul_ogs -> matmul rename (#763) Upstream triton_kernels merged the `matmul_ogs` module into `matmul` and the `matmul_ogs_details` package into `matmul_details`. The `PrecisionConfig` dataclass was also reshaped: `weight_scale` is now `b_mx_scale`, and setting it requires `b_microblock_size` to be provided explicitly (enforced by an assert in the new `matmul()`). - fused_moe_triton: try importing `FnSpecs / FusedActivation / PrecisionConfig / matmul` from `triton_kernels.matmul` first, fall back to the old `triton_kernels.matmul_ogs` path. Alias `matmul as matmul_ogs` so existing call sites stay unchanged. - moe (Mxfp4MoEMethod.process_weights_after_loading): same dual-path import for `FlexCtx / PrecisionConfig`; detect the kwarg name via `dataclasses.fields` so the old `weight_scale=` path keeps working while the new API takes `b_mx_scale=` + `b_microblock_size=`. - Drop the `_amd_smem_safe_tile` workaround that pinned block_m / block_n on gfx950: the underlying LDS-spill is no longer reproducible against current triton / triton_kernels. Co-authored-by: jianlian <jianlian@amd.com> Co-authored-by: Cursor <cursoragent@cursor.com> * CI: Use linux-atom-mi35x-1 in docker release pipeline * [atom-vllm benchmark] set 0 to random range ratio for vllm bench (#1029) * Fix AW benchmark fixed length config (#1020) Co-authored-by: XiaobingSuper <xiaobingzhangupc@gmail.com> * Clarify AW benchmark matrix job name (#1021) * Clarify AW benchmark matrix job name * Use explicit zero ratio for AW benchmark cases --------- Co-authored-by: XiaobingSuper <xiaobingzhangupc@gmail.com> --------- Co-authored-by: wuhuikx <hattie.wu@amd.com> Co-authored-by: XiaobingSuper <xiaobingzhangupc@gmail.com> * [atom-sgl-benchmark] Debug timeout (#977) Co-authored-by: root <root@hjbog-srdc-15.amd.com> * [atom-vllm benchmark] allow P0 benchmarks at 128 and 256 concurrency (#1036) Allow P0 benchmarks at 128 and 256 concurrency (#1030) Co-authored-by: root <root@hjbog-srdc-15.amd.com> * fix: chunk prefill (#1032) * remove disable deepseek v4 chunk prefill flag * fix(scheduler): use num_tokens for preempted seq re-prefill chunk size Preempted seqs keep their decoded token_ids (preempt() only deallocates KV blocks) so seq.num_tokens > seq.num_prompt_tokens on re-admit. Computing num_new_tokens from num_prompt_tokens caused chunk=0 when a fully-cached prefix exhausted num_prompt_tokens, triggering the "chunk must be positive" assert under high concurrency benchmarks. * fix format * fix sparse_attn_v4_paged_prefill for MI308 (#1003) * [ATOM SGLang] SGL plugin Attention Refractory (#863) * add work log * [ATOM-SGL][Attn refrac] Separate model-specific MLA from SGL full attention backend * remove work log * [ATOM-SGL][Attn refrac] Route DeepSeek MLA through an SGLang wrapper Move the SGLang DeepSeek MLA runtime entry from legacy forward glue into SGLangDeepseekMLAAttention while keeping RadixAttention and the full-attention backend as the host/backend layers. Shrink deepseek_mla_forward.py into a helper module and clarify absorbed vs non-absorbed path naming. * [ATOM SGL] runtime extraction * [ATOM-SGL][Runtime] Introduce model adapter specs Co-authored-by: Cursor <cursoragent@cursor.com> * [ATOM-SGL][Runtime] Keep custom wrappers out of generated entries Co-authored-by: Cursor <cursoragent@cursor.com> * [ATOM-SGL][Attn refrac] Split full attention backend helpers Co-authored-by: Cursor <cursoragent@cursor.com> * [ATOM-SGL][Attn refrac] Format refactored attention files Co-authored-by: Cursor <cursoragent@cursor.com> * [ATOM-SGL][Attn refrac] Fix ruff findings in refactored attention code Co-authored-by: Cursor <cursoragent@cursor.com> * [ATOM-SGL][Attn refrac] Avoid DeepSeek MLA wrapper module cycle Co-authored-by: Cursor <cursoragent@cursor.com> * fix rebase issue * precheckin * prepare for sglang only * import error meet in qwen3.5 * qwen3.5 acc fix * [Fix] Limit static FP4 linear kv_b_proj post-processing --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: qichu-yun <qichu@amd.com> * [enable EP] deepseek V4 (#875) * [enable EP] deepseek V4 * update * [KV-events] ZMQ publisher for KV cache events (#869) * feat(kv-events): ZMQ publisher for KV cache events Add a KV cache lifecycle event pipeline so external consumers can track when blocks become resident, are evicted, or move across tiers. - atom/distributed/kv_events.py: EventBatch + tagged-union schema (BlockStored, BlockRemoved, AllBlocksCleared, BlockTransferred); ZMQ PUB publisher with a background sender thread and bounded queue (drops oldest on slow subscriber). - atom/model_engine/block_manager.py: emit BlockStored on prefix-cache coalesced runs, BlockRemoved on lazy LRU eviction, AllBlocksCleared on clear_cache(); record_remote_store() hook for remote-transfer connectors to emit BlockStored(medium=REMOTE). - atom/model_engine/scheduler.py: publish_kv_events() drains the BlockManager event log per scheduler step into one EventBatch; shutdown_kv_events() tears down the publisher on engine shutdown. - atom/model_engine/engine_core.py: publisher lifecycle wiring. - atom/utils/envs.py: ATOM_KV_EVENTS_{ENABLE,PUBLISHER,ENDPOINT,TOPIC, HWM,BUFFER_STEPS} env vars. - atom/config.py: KV-events config knobs. - tests/test_kv_events.py: schema round-trip + tagged-union batch. BlockTransferred and medium in {CPU, DISK} are reserved in the schema but not emitted yet. The hybrid-cache metadata fields on BlockStored (kv_cache_spec_kind, kv_cache_spec_sliding_window) are reserved wire slots emitted as None until a follow-up wires them from the cache-spec coordinator. Review feedback (incorporated): - Make pyzmq an optional runtime dep: import zmq inside ZmqEventPublisher so BlockManager's unconditional import of this module no longer requires pyzmq when KV events are disabled. - Validate buffer_steps >= 1 in ZmqEventPublisher so 0 (which Python's queue.Queue treats as unbounded) can't silently disable backpressure. - Track encode failures in stats (encode_errors counter) instead of swallowing the exception silently. - Add BlockManager.kv_events_enabled property so the scheduler stops reaching into _event_log directly. - Use the MEDIUM_REMOTE constant rather than the "REMOTE" string literal in record_remote_store. - Use pytest.importorskip("zmq") and an inproc:// endpoint in test_zmq_publisher_roundtrip so the test no longer hard-codes a TCP port and can be skipped cleanly when pyzmq is absent. * chore(kv-events): trim verbose comments and docstrings Remove descriptive comments and docstrings that restated what the code already says, leaving only the ones whose WHY is non-obvious (lazy eviction point, coalesced-store parent semantics, sticky cache_miss invariant, drop-on-overflow design, clear_cache live-seq invariant). * fix(kv-events): import MEDIUM_REMOTE for record_remote_store The earlier commit added a MEDIUM_REMOTE reference at the record_remote_store() emit site but the import line was never added, which would have raised NameError on first remote-store callback. Path wasn't exercised in the local smoke run because we never wired a KV-transfer producer. * fix(kv-events): close shutdown race and drop unused _EventBatch * fix(kv-events): align KVEventsConfig defaults with env * fix(kv-events): teardown safety, multipart docstring, parent_hash dedupe * fix(kv-events): no BlockRemoved on cache-hit block reuse * fix(kv-events): chain parent on remote store, atomic drain, longer linger * fix(kv-events): use sub.poll in test_zmq_publisher_roundtrip * Merge branch 'main' into feat/kv-events * fix(kv-events): publish on every step, skip cached blocks on remote-store, safer shutdown * fix(kv-events): default endpoint to loopback for safer opt-in * fix(kv-events): default group_idx to None to match vLLM wire layout * fix(kv-events): call hash_blocks before fwd_output idx-skip main's postprocess() skipped seqs whose idx is None (prefill step pattern) before calling hash_blocks(), so the prefill seq's hashes were never registered and BlockStored was never emitted. Move the hash_blocks call above the idx-None continue so it runs on every prefill step regardless of the fwd_output idx mapping. * test(kv-events): rename test_cache_hit_emits_no_new_store -> only_new_blocks * kv_events: log first encode error, count shutdown drops, hoist event-log check * black format * kv_events: harden finally, add overflow/encode tests * pyproject: add msgspec to deps * [atom-vllm benchmark] enable DeepSeek V3.2 quick reduce envs (#1047) * [atom-vllm] enable DeepSeek V3.2 quick reduce envs Co-authored-by: Cursor <cursoragent@cursor.com> * add accuracy recipe --------- Co-authored-by: perzhang <perzhang@amd.com> Co-authored-by: Cursor <cursoragent@cursor.com> * fix: warmup uses full token budget for DP (#1024) * fix: warmup uses full token budget * only for dp attn --------- Co-authored-by: ZhangLirong-amd <ZhangLirong@amd.com> * feat: support DeepSeek-V4-Flash-Base model on gfx942 device. (#996) * Expose ATOM test base image input (#1053) * [atom-vllm-benchmark] Add model case amd/DeepSeek-V3.2-mtp-ptpc for AW_P0 (#1039) * Add model case amd/DeepSeek-V3.2-mtp-ptpc for AW_P0 * First run non-mtp version * Remove 'MTP' from choice_label * Add model case amd/DeepSeek-V3.2-mtp-ptpc to accuracy and recipe * Add launch params to deepseek v3.2 ptpc --------- Co-authored-by: root <root@hjbog-srdc-15.amd.com> * [atom-vllm-benchmark] Change AW execution logic from one server one job to one server multi jobs (#1005) * Rename to AW (#1000) Co-authored-by: root <root@hjbog-srdc-15.amd.com> * Debug 'no such file or directory benchmark_matrix.json' (#1002) Co-authored-by: root <root@hjbog-srdc-15.amd.com> * [minimax dev_perf] remove qkv token 256 limitation for ar fusion (#1004) * [atom-vllm benchmark] refine model case name (#995) Co-authored-by: root <root@hjbog-srdc-15.amd.com> * Remove qkv 256 tok limitation --------- Co-authored-by: junyyang-amd <junyyang@amd.com> Co-authored-by: root <root@hjbog-srdc-15.amd.com> * Change AW execution logic from one server one job to one server multi jobs * Change the content as suggested * Fix metadata naming after rebase --------- Co-authored-by: root <root@hjbog-srdc-15.amd.com> Co-authored-by: Yutao Xu <xytpai@foxmail.com> * [Feat] Fused qknorm + quant for dpsk v2 model (#963) * [Feat] Fused qknorm + quant for dpsk v2 model * [Fix] Localize SGLang MXFP4 projection preservation --------- Co-authored-by: Cursor <cursoragent@cursor.com> * use ATOM_USE_FP4_NON_SHUFFLE_TRITON_GEMM to enable non shuffle triton gemm (#1031) * use ATOM_USE_FP4_TRITON_GEMM to enable non shuffle triton gemm Signed-off-by: zhuyuhua-v <yuhzhu@amd.com> * update env name and add comments Signed-off-by: zhuyuhua-v <yuhzhu@amd.com> * Apply suggestions from code review Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> --------- Signed-off-by: zhuyuhua-v <yuhzhu@amd.com> Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * fix(v4): drop redundant cu_seqlens_q refill in attention metadata builder (#1058) cu_seqlens_q is already populated in ModelRunner as a variable-length prefix sum over num_scheduled_tokens, with the [scheduled_bs+1:bs+1] tail padded to the boundary value for cudagraph. The DeepseekV4 attention metadata builder re-filled it with a uniform np.arange sized scheduled_bs+1, overwriting ModelRunner's correct values. Remove the redundant fill and copy bs+1 entries so the GPU buffer matches the range ModelRunner populates. Also split a grouped local import into per-line imports (isort). * [ATOM-vLLM] Upgrade vLLM version to v0.22.0 (#1006) upgrade atom-vllm vllm version to 0.22.0 Signed-off-by: whx-sjtu <xiaowang990929@gmail.com> * [feat] Add RLHF rollout integration support (verl) (#549) * [verl] feat: add trust_remote_code arg and compilation_config dict support * [verl] feat: add logprobs and request_id support across sampling pipeline * [verl] feat: weight sync, memory lifecycle and DP isolation for verl integration (TP+DP) * [verl] feat: utility command dispatch and broadcast communication * [verl] feat: basic integration with verl - load_weights, sleep/wake_up API * [atom] fix: rope parameters handling, remove CLI trust_remote_code, and minor fixes * [atom] feat: implement packed weight handling in ModelRunner for FP8 parameters * [verl] refactor: decouple RLHF rollout logic from inference engine into atom/rollout/ * [verl] feat: extend tokenIDProcessor for logprobs support and enhance ModelRunner with DP isolation handling * fix: patch NCCL device binding for DP-isolated ModelRunner * refactor: minimize diff against main by reverting non-functional changes * refactor: improve code readability by formatting and organizing function parameters and comments across multiple files * refactor: extract sleep logic from engine_core busy_loop into helper methods * [verl] refactor: merge logprobs and DP isolation into base ModelRunner, simplify RLHFModelRunner * refactor: rename sleep state variables and update related logic for RL training in EngineCore and ModelRunner * fix: restore mark_trace profiler around cudagraph capture * docs: add veRL + Megatron + ATOM environment setup guide for ROCm * [verl] feat: add logprobs and request_id support across sampling pipeline * [verl] refactor: unify load_weights API with auto mode selection * fix: batch token ID processing in tokenIDProcessor * fix: use process group size instead of config for DP-isolated mode Co-Authored-By: Claude Opus 4 <noreply@anthropic.com> * [rollout, atom] fix: align DP logic with main * [rollout] fix: remove unnecessary DP config overrides and RLHF APIs from LLMEngine --------- Co-authored-by: Claude Opus 4 <noreply@anthropic.com> * fix Signed-off-by: kliuae <kuanfu.liu@embeddedllm.com> * trim decode tensors for moe Signed-off-by: kliuae <kuanfu.liu@embeddedllm.com> * [atom-vllm recipe] align recipe to nightly script (#1040) Co-authored-by: perzhang <perzhang@amd.com> * [sgl-atom][docker]add optional sglang_tag_suffix (#1068) * add docker prefix Signed-off-by: zhuyuhua-v <yuhzhu@amd.com> * Enable standalone DeepSeek NextN draft model (#964) Co-authored-by: zhuyuhua-v <yuhzhu@amd.com> Co-authored-by: Cursor <cursoragent@cursor.com> * [Feat] enable dualstream in mtp (#1049) * [atom-vllm-benchmark] Change matrix cell launches one server for one ISL/OSL pair + all concurrency (#1075) --------- Co-authored-by: Jun Yan Yang * [atom-vllm benchmark] recover warmup to concurrency Co-authored-by: perzhang <perzhang@amd.com> * Update SGLANG accuracy runner (#1084) * [plugin][perf] refine pa dispatch for better perf (#1038) * add pa dispatch for GLM-4.7 and clean code * refine the dispatch * fix minimax acc * revert unnecessary change * clean code --------- Co-authored-by: Guanbao Yu <gyu@amd.com> * fix fused_moe (#1076) * fix non triton routing expert mask in moe Signed-off-by: kliuae <kuanfu.liu@embeddedllm.com> * fold heads to 8 Signed-off-by: kliuae <kuanfu.liu@embeddedllm.com> * black Signed-off-by: kliuae <kuanfu.liu@embeddedllm.com> --------- Signed-off-by: zejunchen-zejun <zejun.chen@amd.com> Signed-off-by: kliuae <kuanfu.liu@embeddedllm.com> Signed-off-by: zhuyuhua-v <yuhzhu@amd.com> Signed-off-by: whx-sjtu <xiaowang990929@gmail.com> Co-authored-by: Zhu Jiale <69138280+zovonoir@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: zovonoir <zovonoir@users.noreply.github.com> Co-authored-by: ZhangLirong <lirzhang@amd.com> Co-authored-by: ZhangLirong-amd <ZhangLirong@amd.com> Co-authored-by: Yutao Xu <xytpai@foxmail.com> Co-authored-by: zejunchen-zejun <zejun.chen@amd.com> Co-authored-by: Lingpeng Jin <103567126+valarLip@users.noreply.github.com> Co-authored-by: junyyang-amd <junyyang@amd.com> Co-authored-by: root <root@hjbog-srdc-15.amd.com> Co-authored-by: JiaoliangYu <Jiaoliang.Yu@amd.com> Co-authored-by: JiaoliangYu <jiaolyu@amd.com> Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com> Co-authored-by: XiaobingZhang <xiaobingzhangupc@gmail.com> Co-authored-by: qichu-yun <qichu@amd.com> Co-authored-by: ningding01 <niding@amd.com> Co-authored-by: PerryZhang01 <Perry.Zhang@amd.com> Co-authored-by: perzhang <perzhang@amd.com> Co-authored-by: jianhao <Jianhao.Liang@amd.com> Co-authored-by: jianlian <jianlian@amd.com> Co-authored-by: Xin Huang <Xin.Huang@amd.com> Co-authored-by: wuhuikx <hattie.wu@amd.com> Co-authored-by: Jiayun <jiayyu@amd.com> Co-authored-by: Wang, Yiting <18916612990@163.com> Co-authored-by: Zhiwei <yanzhw5@mail3.sysu.edu.cn> Co-authored-by: amd-ruitang3 <145657428+amd-ruitang3@users.noreply.github.com> Co-authored-by: Bongwoo Bak <bongwoobak@gmail.com> Co-authored-by: junna2016 <xingjunna.xjn@alibaba-inc.com> Co-authored-by: Zhu Yuhua <yuhzhu@amd.com> Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> Co-authored-by: Hexiang Wang <56632993+whx-sjtu@users.noreply.github.com> Co-authored-by: Sijing Yang <Sijing.Yang@amd.com> Co-authored-by: Ling Zhang <69022634+ZLkanyo009@users.noreply.github.com> Co-authored-by: gbyu-amd <Guanbao.Yu@amd.com> Co-authored-by: Guanbao Yu <gyu@amd.com> Co-authored-by: Wang, Yiting <yitiwang@amd.com>
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