Enable ROCm DeepSeek V4 decode multi-stream#43491
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Enable the DeepSeek V4 model setup to create the same three attention auxiliary streams on ROCm that CUDA already uses. This activates the existing decode overlap choreography for CSA: c4a layers can overlap the indexer pipeline, main KV compression, and SWA insertion, while c128a layers can overlap main KV compression with SWA insertion. XPU keeps the existing serial fallback, and CUDA behavior remains unchanged. Duplicate-work check: issue vllm-project#41820 remains open; unauthenticated GitHub API searches found no open PR with "41820 in:body" and the closest open PRs from area keyword searches were vllm-project#41136 and vllm-project#41834, which cover ROCm enablement/fallbacks and NVIDIA SM12x support rather than this ROCm aux-stream gate. Tests: .venv/bin/python -m pytest tests/models/test_deepseek_v4_rocm_multistream.py -q (3 passed, 16 warnings); pre-commit run ruff-check --files vllm/models/deepseek_v4/nvidia/model.py tests/models/test_deepseek_v4_rocm_multistream.py (passed); pre-commit run ruff-format --files vllm/models/deepseek_v4/nvidia/model.py tests/models/test_deepseek_v4_rocm_multistream.py (passed). AI assistance was used for implementation and validation.
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Code Review
This pull request refactors the auxiliary stream initialization for DeepSeek V4 by introducing the make_deepseek_v4_aux_streams function and enabling multi-stream support for ROCm, which previously used a serial fallback. It also includes a new test file to verify stream allocation across ROCm, XPU, and CUDA platforms. Review feedback suggests simplifying the logic in the new helper function by merging the identical ROCm and CUDA branches to reduce redundancy and improve maintainability.
| if current_platform.is_rocm(): | ||
| return [torch.cuda.Stream() for _ in range(3)] | ||
| if current_platform.is_xpu(): | ||
| return None | ||
| return [torch.cuda.Stream() for _ in range(3)] |
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The logic in make_deepseek_v4_aux_streams can be simplified. Since the ROCm and default (CUDA) cases both return three streams, you can combine them to reduce redundancy and improve maintainability.
if current_platform.is_xpu():
return None
return [torch.cuda.Stream() for _ in range(3)]1 participant
Enable the DeepSeek V4 model setup to create the same three attention auxiliary streams on ROCm that CUDA already uses. This activates the existing decode overlap choreography for CSA: c4a layers can overlap the indexer pipeline, main KV compression, and SWA insertion, while c128a layers can overlap main KV compression with SWA insertion. XPU keeps the existing serial fallback, and CUDA behavior remains unchanged.
Duplicate-work check: issue #41820 remains open; unauthenticated GitHub API searches found no open PR with "41820 in:body" and the closest open PRs from area keyword searches were #41136 and #41834, which cover ROCm enablement/fallbacks and NVIDIA SM12x support rather than this ROCm aux-stream gate.
Tests: .venv/bin/python -m pytest tests/models/test_deepseek_v4_rocm_multistream.py -q (3 passed, 16 warnings); pre-commit run ruff-check --files vllm/models/deepseek_v4/nvidia/model.py tests/models/test_deepseek_v4_rocm_multistream.py (passed); pre-commit run ruff-format --files vllm/models/deepseek_v4/nvidia/model.py tests/models/test_deepseek_v4_rocm_multistream.py (passed).
AI assistance was used for implementation and validation.
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Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.