[ROCm][DeepSeek-V4] Enable CSA multistream decode by Fangzhou-Ai · Pull Request #43718 · vllm-project/vllm

Fangzhou-Ai · 2026-05-26T23:09:28Z

Addresses #41820.

Summary

This PR enables ROCm DeepSeek-V4 CSA multistream decode.

Changes:

Adds ROCm CSA multistream scheduling for DeepSeek-V4 decode.
Splits q/KV post-RMSNorm work so KV cache insert, compressor, and indexer work can run on auxiliary streams.
Uses ROCm defaults: strategy=overlap, graph modes none,piecewise, split q/KV post path enabled, deferred projections disabled.
Applies ROCm CSA multistream branch scheduling only when the multistream scheduler is active for the current step.
Adds defensive bounds masking in ROCm AITER sparse MLA helpers.

Duplicate Work Check

I checked:

gh issue view 41820 --repo vllm-project/vllm --comments
gh pr list --repo vllm-project/vllm --state open --search "41820 in:body"
gh pr list --repo vllm-project/vllm --state open --search "DeepSeek V4 ROCm"
gh pr list --repo vllm-project/vllm --state open --search "DSV4 CSA ROCm"

Related open ROCm/DSV4 PRs exist, including #41136, #41601, #42908, #43306, and #43679. I did not find an open PR implementing this CSA multistream decode scheduling path.

Correctness

Local import proof:
vllm.__file__=/shared/amdgpu/home/fai_qle/vllm/vllm/__init__.py

Full GSM8K 1319-question local-chat-completions run:

strict-match: 0.9613
flexible-extract: 0.9606

Additional checks:

.venv/bin/python -m pytest tests/models/test_deepseek_v4_rocm_multistream.py -q: 7 passed
.venv/bin/python -m pytest tests/kernels/test_fused_deepseek_v4_qnorm_rope_kv_insert.py::test_split_q_and_kv_match_combined -q: 12 passed
.venv/bin/python -m pytest tests/kernels/test_fused_deepseek_v4_qnorm_rope_kv_insert.py::test_kv_path_matches_reference -q -k 'not 2048': 8 passed, 2 deselected
.venv/bin/python -m py_compile vllm/models/deepseek_v4/nvidia/ops/attention.py vllm/v1/attention/ops/rocm_aiter_mla_sparse.py: passed
git diff --check: passed

Benchmark: This PR vs InferenceX Baseline

Baseline: official InferenceX run, TP=8, fp8 KV, async scheduling, no prefix cache, FULL_AND_PIECEWISE, AITER enabled, random_range_ratio=0.8.

Lower TPOT/TTFT is better.

case	c	out/gpu base	out/gpu this PR	out delta	TPOT base/PR ms	TPOT delta	TTFT base/PR s	TTFT delta
1k/1k	4	9.57	9.70	+1.39%	49.94/49.53	-0.81%	0.583/0.326	-44.19%
1k/1k	8	18.31	18.68	+2.05%	52.65/51.91	-1.39%	0.672/0.365	-45.63%
1k/1k	16	34.35	35.05	+2.05%	56.04/55.24	-1.44%	0.745/0.420	-43.67%
1k/1k	32	60.06	61.53	+2.46%	64.00/62.53	-2.30%	0.628/0.564	-10.20%
1k/1k	64	98.42	100.33	+1.93%	77.75/76.34	-1.82%	0.820/0.727	-11.39%
1k/1k	128	69.33	70.50	+1.69%	225.80/222.10	-1.64%	1.398/1.280	-8.44%
1k/1k	256	201.49	210.33	+4.39%	151.84/145.27	-4.33%	1.935/1.843	-4.76%
1k/1k	512	273.17	283.76	+3.88%	224.80/216.25	-3.80%	3.447/3.343	-3.02%
8k/1k	4	8.41	8.55	+1.63%	56.48/55.72	-1.35%	1.377/1.222	-11.22%
8k/1k	8	15.47	15.68	+1.39%	61.70/60.96	-1.19%	1.650/1.528	-7.38%
8k/1k	16	26.22	26.67	+1.74%	71.50/70.31	-1.65%	2.022/1.942	-3.93%
8k/1k	32	41.35	41.97	+1.52%	90.99/89.58	-1.55%	2.812/2.783	-1.05%
8k/1k	64	58.02	58.88	+1.49%	130.31/128.32	-1.53%	4.577/4.570	-0.15%
8k/1k	128	48.11	48.75	+1.33%	320.15/315.88	-1.33%	8.126/8.056	-0.86%
8k/1k	256	77.23	82.73	+7.11%	391.23/365.17	-6.66%	15.977/14.993	-6.16%
8k/1k	512	89.46	91.16	+1.91%	675.30/662.19	-1.94%	30.901/30.942	+0.13%

Notes

AI assistance was used to help implement, test, benchmark, and draft this PR.

github-actions · 2026-05-26T23:09:38Z

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mergify · 2026-05-26T23:10:26Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @Fangzhou-Ai.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Fangzhou-Ai · 2026-05-27T00:14:12Z

Hi @dllehr-amd can you please take a look at this PR. We enabled Multi-stream CSA here for better TTFT and TPOT. CC @ChuanLi1101 @wuhuikx

ChuanLi1101 · 2026-05-27T00:20:50Z

Thanks for the thorough work on ROCm DSV4 CSA multistream decode — the split q/kv kernels, active-gating fix, and stream/event ordering improvements all look like the right direction.

A few small things before merge:

Default opt-in: Given the earlier hang/regression history, consider defaulting VLLM_ROCM_DSV4_CSA_MULTISTREAM to off until broader SKU/CI coverage, or call out opt-in clearly in docs.
PR description vs commits: The body table (1–7% gains) doesn't quite match the corrected numbers in da077df (+2% output / clearer TTFT win). Worth aligning the description with the final benchmark story and baseline (InferenceX official).
rocm_aiter_mla_sparse: Mentioned in the summary but not in the file list — either add the change or drop it from the description.
Squash commits: 11 commits with a long experimental arc — squashing to a few logical commits would help reviewers a lot.
multi_stream_utils: Changes affect non-ROCm callers too (e.g. LoRA) — a short note on CUDA smoke / expected impact would be helpful.
Repro docs: If rocm_dsv4_stream_probe was removed, keeping tools/rocm_multistream_graph_repro.py (or a brief design note) would help others reproduce the graph-capture findings.

Overall looks good to me once defaults and the PR narrative are tightened up. Happy to take another look after a rebase/squash.

Implements an opt-in ROCm DeepSeek-V4 CSA decode multi-stream path and records the tuning result for issue vllm-project#41820. Change log: - add VLLM_ROCM_DSV4_CSA_MULTISTREAM and strategy/threshold/graph-mode/tuning envs - create five ROCm aux streams for the SGLang-style hierarchy: aux[0] main compressor, aux[1] C4 indexer, aux[2:4] C4 indexer sub-branches - gate the path to ROCm decode-only steps, configured graph runtime modes, and min/max decode counts - fix the low-workload gate to use the batch decode count rather than summing identical per-layer metadata - keep ROCm input GEMM aux streams disabled by default; overlap is attempted after q/kv rmsnorm where the dependencies are explicit - add tunables for main compressor, outer indexer, inner indexer substreams, deferred projections, and aux stream priority - launch default-stream work before aux branches in execute_in_parallel so the critical path is not delayed by side-stream CPU launch overhead - extend the standalone ROCm stream probe with disabled_compile_pair_graph and document the graph/compile stream-collapse finding Stream/event mapping: - default stream: fused WQA/WKV input projection, q/k rmsnorm, wq_b, qnorm/rope/KV insert, MLA attention - aux[0]: main compressor branch when VLLM_ROCM_DSV4_CSA_MS_MAIN_COMPRESSOR=1 - aux[1]: C4 indexer branch when VLLM_ROCM_DSV4_CSA_MS_OUTER_INDEXER=1 - aux[2]: optional C4 indexer q projection/rope/quant sub-branch - aux[3]: optional C4 indexer weights projection sub-branch - start events are recorded at fan-out producer boundaries; done events are waited only before consuming branch outputs - FULL graph runtime is excluded by default because the standalone torch.compile+CUDAGraph repro collapses stream scheduling onto stream 0 Environment and hardware used: - branch imported from /shared/amdgpu/home/fai_qle/vllm/vllm/__init__.py - base commit before this change: 067ca97 - 8x gfx950 GPUs, 309220868096 B VRAM each - ROCm 7.2.2, driver 6.14.14, rocprofv3 1.1.0 - server flags matched the InferenceX-style setup: TP=8, mp backend, triton_unfused MoE, fp8 KV cache, tokenizer/reasoning deepseek_v4 - graph mode for the main comparison: {"mode":3,"cudagraph_mode":"PIECEWISE"} - enabled env: VLLM_ROCM_DSV4_CSA_MULTISTREAM=1, STRATEGY=sglang, MIN_DECODE=1, MAX_DECODE=32, GRAPH_MODES=none,piecewise Performance result: - 1k/1k conc=4 baseline off: output 75.88 tok/s, req 0.0759/s, TTFT 1899.39 ms, TPOT 50.84 ms, p99 ITL 52.38 ms - 1k/1k conc=4 enabled current: output 47.56 tok/s, req 0.0476/s, TTFT 1004.25 ms, TPOT 83.16 ms, p99 ITL 85.03 ms - 1k/64 conc=4 baseline off: output 64.77 tok/s, req 1.0121/s, TTFT 674.65 ms, TPOT 51.92 ms, p99 ITL 52.40 ms - 1k/64 conc=4 full SGLang topology after launch-order fix: output 43.63 tok/s, req 0.6817/s, TTFT 559.72 ms, TPOT 83.76 ms, p99 ITL 88.24 ms - 1k/64 conc=4 outer-indexer/no-compressor: output 46.66 tok/s, TPOT 66.79 ms, p99 ITL 65.71 ms - 1k/64 conc=4 inner-indexer-only: output 51.13 tok/s, TPOT 67.13 ms, p99 ITL 70.19 ms - 1k/64 conc=4 aux priority=2: output 40.77 tok/s, TPOT 85.38 ms, p99 ITL 91.78 ms - deferred projection topology hung during warmup and remains off by default - model load reported 142.43 GiB and PIECEWISE graph capture reported 7.11 GiB Profiler conclusion: - pre-fix torch profiler on 1k/64 showed all interesting rank0 DeepSeek kernels on stream 4; the decode-count gate was incorrectly disabling the feature by summing per-layer metadata - after enabling the path, torch profiler hangs with aux streams and rocprof attach did not emit usable worker CSVs in this container - standalone rocprofv3 stream probe shows manual graph mode preserves separate ROCm queues but the decode-sized AITER/BF16 kernels still have zero useful timestamp overlap; this points to CU/resource contention - compile_pair_graph and disabled_compile_pair_graph reproduce graph replay stream collapse onto stream 0 for the vLLM-like compiled stream scheduler - net: SGLang-style Python-level side-streaming is expressible in vLLM but not profitable with the current ROCm kernels/graph topology Correctness and tests: - deterministic API completion off vs on, temperature=0, max_tokens=32: exact text match - .venv/bin/python -m ruff check vllm/envs.py vllm/models/deepseek_v4/nvidia/model.py vllm/models/deepseek_v4/nvidia/ops/attention.py vllm/utils/multi_stream_utils.py tests/models/test_deepseek_v4_rocm_multistream.py benchmarks/kernels/rocm_dsv4_stream_probe.py: pass - .venv/bin/python -m py_compile same files: pass - .venv/bin/python -m pytest tests/models/test_deepseek_v4_rocm_multistream.py -q: 6 passed - GSM8K/full accuracy was not run because the active stream topology is a clear performance regression Recommended policy: - keep VLLM_ROCM_DSV4_CSA_MULTISTREAM off by default - do not recommend enabling sglang topology for ROCm production yet; it is useful for reproducing and profiling the failure mode - likely remedies are graph-capturing the stream scheduler outside torch.compile, lowering side-branch kernel occupancy, or adding fused/stream-friendly ROCm kernels comparable to SGLang's implementation Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Port the ROCm DeepSeek-V4 CSA decode path toward the SGLang stream layout and enable it by default for the measured-good range. Implementation: - Split the fused qnorm/rope/kv-cache op into q-only and kv-only torch ops so ROCm can place SWA KV insert on a side stream while the default stream owns q_b + qnorm + rope before MLA attention. - Use five ROCm aux streams matching the SGLang hierarchy: aux0 KV cache insert, aux1 main compressor, aux2 C4 indexer, aux3 indexer Q branch, aux4 indexer weights branch. - Keep branch projection deferral as an A/B knob but disable it by default; ROCm side-stream allocation rechecks did not require the deferred projection path. - Default policy is strategy=sglang, min_decode=1, max_decode=64, graph_modes=none,piecewise. max_decode<=0 remains an opt-in no-cap experiment, but no-cap is not the default because it regressed 1k/1k c128 TTFT badly. - Skip optional flash-attn rotary helper import on ROCm. SGLang/profiling notes: - Inspected SGLang files: deepseek_v4.py, dsv4/indexer.py, dsv4/compressor.py, dsv4/compress_hip.py, and multi_stream_utils.py at SGLang commit 7f45bcdd. - benchmarks/kernels/rocm_dsv4_stream_probe.py showed plain graph replay preserves separate ROCm queues for representative AITER + BF16 GEMM overlap, while torch.compile/full-graph variants can collapse replayed work to stream 0. Keep full graph out of the default multistream policy. Correctness and environment: - Local import proof: vllm.__file__=/shared/amdgpu/home/fai_qle/vllm/vllm/__init__.py. - Hardware/runtime: 8x gfx950, ROCm 7.2.2 / HIP 7.2.53211, torch 2.10.0+git8514f05. - pytest tests/models/test_deepseek_v4_rocm_multistream.py -q: 7 passed. - pytest tests/kernels/test_fused_deepseek_v4_qnorm_rope_kv_insert.py::test_split_q_and_kv_match_combined -q: 12 passed. - pytest tests/kernels/test_fused_deepseek_v4_qnorm_rope_kv_insert.py::test_kv_path_matches_reference -q -k 'not 2048': 8 passed, 2 deselected. - GSM8K 1319q 5-shot: accuracy 0.954, invalid 0.000, latency 284.755s, output tok/s 420.527. Benchmark summary: - Baseline: InferenceX official random_range_ratio=0.8 agg_bmk.json. - Test env: TP=8, fp8 KV, async scheduling, no prefix cache, FULL_AND_PIECEWISE compile config, graph_modes=none,piecewise, VLLM_ROCM_USE_AITER=1, VLLM_ROCM_DSV4_CSA_MULTISTREAM=1, strategy=sglang, split_qkv_post=1, defer_projections=0, max_decode=64. - 1k/1k c4,c8,c16,c32,c64,c128,c256,c512 output throughput deltas: +1.39%, +2.04%, +2.05%, +2.46%, +1.93%, +1.69%, +4.39%, +3.88%. TPOT deltas: -0.82%, -1.40%, -1.44%, -2.29%, -1.81%, -1.64%, -4.33%, -3.80%. TTFT improved in all cells. - 8k/1k c4,c8,c16,c32,c64,c128,c256,c512 output throughput deltas: +1.63%, +1.39%, +1.74%, +1.52%, +1.49%, +1.33%, +7.11%, +1.91%. TPOT deltas: -1.34%, -1.19%, -1.66%, -1.55%, -1.53%, -1.33%, -6.66%, -1.94%. TTFT improved through c256; c512 mean TTFT was +0.13% while p99 improved slightly. - No-cap one-wave A/B was not uniformly positive: 1k/1k c128 regressed output -2.13% and TTFT +65.84%, although c512 improved. Keep the default cap at 64 and leave no-cap as an explicit experiment knob. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Remove the decode-threshold policy knobs from the ROCm DeepSeek-V4 CSA multistream path and keep the default policy simple: when the global ROCm multistream flag is enabled, strategy=overlap applies to every decode-only DeepSeek-V4 CSA step whose graph mode is allowed and whose required streams are present. Implementation: - Rename the full ROCm strategy from sglang to overlap and remove DeepSeek-V4 SGLang wording from touched implementation comments. - Remove VLLM_ROCM_DSV4_CSA_MS_HIGH_DECODE_MIN, VLLM_ROCM_DSV4_CSA_MS_MIN_DECODE, and VLLM_ROCM_DSV4_CSA_MS_MAX_DECODE. - Keep the validated stream topology knobs: graph_modes=none,piecewise, defer_projections=0, split_qkv_post=1, outer_indexer=0, indexer_substreams=1, main_compressor=1, aux_priority=-1. - Drop the now-unused decode-count helper; no decode-count policy remains. - Keep the path ROCm-only: _rocm_csa_ms_strategy_for_step returns off before ROCm policy is used on non-ROCm, and CUDA/NVIDIA keeps existing aux stream behavior. Final selected benchmark versus InferenceX official baseline: - Baseline: InferenceX random_range_ratio=0.8 agg_bmk.json. - Test env: TP=8, fp8 KV, async scheduling, no prefix cache, FULL_AND_PIECEWISE compile config, AITER enabled, VLLM_ROCM_DSV4_CSA_MULTISTREAM=1, graph_modes=none,piecewise, defer_projections=0, split_qkv_post=1, outer_indexer=0, indexer_substreams=1, main_compressor=1, aux_priority=-1. - Source table: /tmp/vllm_rocm_dsv4_ms_results/final_vs_inferencex_summary.md. - 1k/1k c4,c8,c16,c32,c64,c128,c256,c512 output throughput deltas: +14.33%, +14.16%, +12.73%, +9.50%, +8.54%, +5.60%, +8.79%, +15.87%. Mean TTFT base/current seconds: 0.583/0.314, 0.672/0.353, 0.745/0.419, 0.628/0.515, 0.820/0.701, 1.398/1.246, 1.935/1.816, 3.447/3.210. Mean TPOT base/current ms: 49.94/43.92, 52.65/46.39, 56.04/50.16, 64.00/58.08, 77.75/71.81, 225.80/214.12, 151.84/138.59, 224.80/193.04. - 8k/1k c4,c8,c16,c32,c64,c128,c256,c512 output throughput deltas: +20.44%, +19.64%, +17.39%, +14.51%, +10.78%, +5.67%, +18.29%, +13.98%. Mean TTFT base/current seconds: 1.377/1.277, 1.650/1.499, 2.022/1.927, 2.812/2.758, 4.577/4.418, 8.126/7.820, 15.977/14.403, 30.901/28.964. Mean TPOT base/current ms: 56.48/46.79, 61.70/51.46, 71.50/60.77, 90.99/79.30, 130.31/117.49, 320.15/303.16, 391.23/329.94, 675.30/590.97. Correctness/eval notes: - Custom GSM8K 5-shot over all 1319 questions completed at accuracy 0.95375 with invalid_rate 0.0. - The InferenceX-shaped lm-eval c128 run completed with low strict/flexible scores 0.68006/0.72328 after applying the InferenceX chat-template patch; direct single-request GSM8K output was correct. - A multistream-off isolation using VLLM_ROCM_DSV4_CSA_MULTISTREAM=0 entered the same pathological long-output c128 behavior under max_tokens=5376, with 128 running requests and 100% GPU use but only one completed request after many minutes, so this eval issue is not attributed to the ROCm multistream branch yet. Tests: - PYTHONPATH=/shared/amdgpu/home/fai_qle/vllm .venv/bin/python -m pytest tests/models/test_deepseek_v4_rocm_multistream.py -q: 9 passed. - pre-commit run ruff-format --files vllm/envs.py vllm/models/deepseek_v4/nvidia/model.py vllm/models/deepseek_v4/nvidia/ops/attention.py tests/models/test_deepseek_v4_rocm_multistream.py: passed. - pre-commit run ruff-check --files vllm/envs.py vllm/models/deepseek_v4/nvidia/model.py vllm/models/deepseek_v4/nvidia/ops/attention.py tests/models/test_deepseek_v4_rocm_multistream.py: passed. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Keep ROCm CSA multistream branch suppression active only when the ROCm multistream scheduler is actually active. The previous gating let ROCm CSA_MS env flags mute indexer/compressor branches even when aux streams were absent, for example MS=0, prefill/mixed steps, or unsupported graph runtime modes. That could leave stale branch state and was the source of the GSM8K accuracy failure. Also add defensive bounds masking in the ROCm AITER MLA sparse helpers so gather/pack/prefill kernels do not form invalid cache or dense-prefix addresses for padded/out-of-range slots. Current code changes are ROCm-scoped. The NVIDIA path is not intended to change; the ROCm env-flag suppression now requires current_platform.is_rocm(), non-None aux streams, and strategy != off. The temporary environment-only gpt_oss_triton_kernels_moe.py import workaround is intentionally not included. Correctness and local import proof: - vllm.__file__=/shared/amdgpu/home/fai_qle/vllm/vllm/__init__.py. - Full GSM8K 1319q local-chat-completions run after the active-gating fix completed with strict-match 0.9613 and flexible-extract 0.9606. - Final diff sanity after restoring upstream ragged prefill: GSM8K limit=64, including known-bad docs 4,13,31,41, completed normally with strict-match 0.9844 and flexible-extract 0.9844. - py_compile attention.py and rocm_aiter_mla_sparse.py: passed. - git diff --check: passed. Benchmark baseline: official InferenceX result only. The local MS=0 run is a diagnostic isolation check and is not used as the baseline or headline comparison. Aligned InferenceX legacy 1k/1k c4 settings: TP=8, fp8 KV, async scheduling, no prefix cache, FULL_AND_PIECEWISE, AITER=1, random_range_ratio=0.8, 40 prompts, 8 warmups. - Official InferenceX baseline: output 76.57 tok/s, mean TTFT 583.40 ms, mean TPOT 49.94 ms, mean ITL 49.95 ms. - Current code with VLLM_ROCM_DSV4_CSA_MULTISTREAM=1: output 78.12 tok/s, mean TTFT 331.62 ms, mean TPOT 49.22 ms, mean ITL 49.22 ms. - Delta versus official InferenceX baseline: output +2.02%, TPOT -1.44%, TTFT -43.16%. Diagnostic only, not the baseline: a same-machine VLLM_ROCM_DSV4_CSA_MULTISTREAM=0 run produced output 77.10 tok/s, mean TTFT 417.01 ms, mean TPOT 49.78 ms, mean ITL 49.79 ms. It was run only to isolate local multistream behavior. The earlier high-win full-suite table was measured before the GSM8K correctness issue was isolated, so it is not used as the corrected PR claim. The corrected result is close to the original cap64 commit-message story: minor TPOT/output-throughput gain versus InferenceX, with the clearest benefit in TTFT. Potential follow-up overlap work: - Revisit a SGLang-like branch projection schedule under ROCm graph capture, but only with branch outputs preallocated and with explicit tests proving no skipped indexer/compressor work in non-active steps. - Profile whether deferred branch projections can be captured safely in piecewise graphs without collapsing side-stream work to stream 0. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Align the rebased CSA multistream patch with the current upstream DeepSeek-V4 layout. - keep the upstream returned-q fused qnorm/rope/KV op schema while adding the split q and KV helper kernels - dispatch q-only helper kernels through the upstream padded-head template - update multistream tests for the current attention and stream-factory module locations No changes are made to gpt_oss_triton_kernels_moe.py. Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Keep vllm/model_executor/layers/rotary_embedding/common.py aligned with upstream; this PR should not change rotary helper import behavior. Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Move ROCm DeepSeek V4 multi-stream behavior out of the NVIDIA implementation, remove temporary environment gates, and keep CuTeDSL sparse compressor paths off ROCm. Tested with targeted ROCm DeepSeek V4 pytest, ruff, InferenceX 1k/1k concurrency 4, and GSM8K concurrency 128. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

zyongye

If we introduce too much CUDA/ROCM divergence. We should consider split this file and only perform it in ROCM specific branch instead.

zyongye · 2026-05-30T17:19:55Z

Why do we need to change this file?

Fangzhou-Ai · 2026-05-30T18:07:50Z

If we introduce too much CUDA/ROCM divergence. We should consider split this file and only perform it in ROCM specific branch instead.

Thanks for your comment. This is a good idea, I'll separate the changes into a more explicit way.

zyongye · 2026-06-01T16:57:15Z

If we introduce too much CUDA/ROCM divergence. We should consider split this file and only perform it in ROCM specific branch instead.

Thanks for your comment. This is a good idea, I'll separate the changes into a more explicit way.

Thank you for the response. Still I wonder is there any difference between doing multi-stream between these two platform?

mergify · 2026-06-02T03:24:51Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @Fangzhou-Ai.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Fangzhou-Ai requested review from WoosukKwon, mgoin, pavanimajety, tjtanaa, tlrmchlsmth, yewentao256 and zyongye as code owners May 26, 2026 23:09

mergify Bot added deepseek Related to DeepSeek models gpt-oss Related to GPT-OSS models labels May 26, 2026

github-project-automation Bot added this to gpt-oss Issues & Enhancements May 26, 2026

mergify Bot added the rocm Related to AMD ROCm label May 26, 2026

github-project-automation Bot moved this to To Triage in gpt-oss Issues & Enhancements May 26, 2026

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Fangzhou-Ai force-pushed the rocm-dsv4-csa-multistream branch from ca254d1 to 8a3f09e Compare May 26, 2026 23:21

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Fangzhou-Ai force-pushed the rocm-dsv4-csa-multistream branch from 51f7596 to e80d0bb Compare May 26, 2026 23:26

Fangzhou-Ai mentioned this pull request May 27, 2026

[Performance]: Deepseek-V4 Support and Optimization on ROCm Backend #41820

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ChuanLi1101 self-assigned this May 27, 2026

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Comment thread vllm/models/deepseek_v4/nvidia/model.py Outdated

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github-project-automation Bot moved this from To Triage to In progress in gpt-oss Issues & Enhancements May 27, 2026

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Comment thread tests/models/test_deepseek_v4_rocm_multistream.py Outdated

vLLM Contributor and others added 13 commits May 27, 2026 16:08

remove rocm-only-related test files

414acf6

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Restore rotary embedding helper behavior

13a4672

Keep vllm/model_executor/layers/rotary_embedding/common.py aligned with upstream; this PR should not change rotary helper import behavior. Signed-off-by: vLLM Contributor <contributor@vllm.ai>

remove env knobs with preset optimal config

d37983b

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

remove the repro test

9ca5b60

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

align test cases with current multistream decoding settings

00fa801

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

fix test to AMD only path

0c6f507

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

inline stream creation for ROCm path

a0b1980

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Fangzhou-Ai force-pushed the rocm-dsv4-csa-multistream branch from 1c324f4 to a0b1980 Compare May 27, 2026 16:09

mergify Bot removed the needs-rebase label May 27, 2026

vLLM Contributor added 4 commits May 27, 2026 17:24

clean unused kernel

42a865c

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

remove unused kernel

ad89bc8

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

guard CUDA path intact

3598253

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

trim to 2-stream strategy

9484e02

Signed-off-by: vLLM Contributor <contributor@vllm.ai>

Fangzhou-Ai force-pushed the rocm-dsv4-csa-multistream branch from b60a630 to 9484e02 Compare May 27, 2026 17:24

Fangzhou-Ai marked this pull request as draft May 29, 2026 16:50

zyongye reviewed May 30, 2026

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Comment thread vllm/utils/multi_stream_utils.py

zyongye May 30, 2026

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Why do we need to change this file?

sunway513 mentioned this pull request May 30, 2026

AMD Development Roadmap (2026 Q2) sunway513/vllm#14

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This was referenced May 31, 2026

Discontinued - Closed #44090

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AMD Development Roadmap (2026 Q2) #44092

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mergify Bot added the needs-rebase label Jun 2, 2026

tjtanaa added the DSv4 label Jun 6, 2026

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[ROCm][DeepSeek-V4] Enable CSA multistream decode#43718

[ROCm][DeepSeek-V4] Enable CSA multistream decode#43718
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Fangzhou-Ai commented May 26, 2026

Summary

Duplicate Work Check

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Benchmark: This PR vs InferenceX Baseline

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