[ROCm][FEAT] Enable Full Graph Mode in AITER MLA V1 Attn Backend (Decode Phase only) #20254
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Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
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Summary of Changes
Hello @vllmellm, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly enhances the AITER MLA attention backend by integrating full graph mode support for decode operations on ROCm. The core change involves pre-allocating and persistently managing key attention metadata tensors, which allows for the efficient capture and replay of GPU operations via CUDA graphs, leading to notable performance improvements as demonstrated by the provided benchmarks.
Highlights
- Full Graph Mode Enablement: Enabled full graph mode support for the AITER MLA attention backend, specifically for decode-only operations on ROCm, aiming to improve performance.
- Persistent Buffer Allocation: Introduced pre-allocated, persistent
torch.Tensorbuffers for attention metadata (e.g.,paged_kv_indptr,paged_kv_indices,paged_kv_last_page_len,qo_indptr) within theAiterMLAMetadataBuilder's initialization. This is a critical step for enabling efficient CUDA graph capture by avoiding dynamic memory allocations during graph execution. - Metadata Building Refactor: Refactored the
_build_decodemethod to directly populate these newly introduced persistent buffers. This ensures that the tensors used for attention metadata have consistent shapes, which is a requirement for successful CUDA graph capture and replay, and removes the need for a separate helper function (_get_paged_kv_tensors).
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Code Review
This PR enables full graph mode support for the AITER MLA backend on ROCm, enhancing performance for decode-only workloads. The implementation pre-allocates persistent buffers in AiterMLAMetadataBuilder and populates them at runtime, ensuring CUDA graph compatibility.
| @@ -54,7 +54,7 @@ class AiterMLADecodeMetadata(MLACommonDecodeMetadata): | |||
| # The number of entries in the last page of each request in | |||
| # the paged kv cache, shape: [batch_size] | |||
| paged_kv_last_page_len: Optional[torch.Tensor] = None | |||
| # The query indptr, shape : [num_decode + 1] | |||
| # # The query indptr, shape : [num_decode + 1] | |||
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This comment has an extra # at the beginning. Correct it to a single # for clarity.
| # # The query indptr, shape : [num_decode + 1] | |
| # The query indptr, shape : [num_decode + 1] |
|
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Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
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…nce caused by full graph metadata tesnor buffers Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
…ode Phase only) (vllm-project#20254) Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
…ode Phase only) (vllm-project#20254) Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
…ode Phase only) (vllm-project#20254) Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com> Signed-off-by: avigny <47987522+avigny@users.noreply.github.com>
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.Purpose
This PR adds full graph mode support in AITER MLA backend for decode only.
Test Plan
running lm_eval:
enabling full graph:
VLLM_ROCM_USE_AITER=1 vllm serve deepseek-ai/DeepSeek-V3 -tp 8 --trust-remote-code --gpu_memory_utilization 0.95 --block-size 1 -O '{"full_cuda_graph":true}'piecewise:
VLLM_ROCM_USE_AITER=1 vllm serve deepseek-ai/DeepSeek-V3 -tp 8 --trust-remote-code --gpu_memory_utilization 0.95 --block-size 1lm_eval --model local-completions --tasks gsm8k --model_args model=deepseek-ai/DeepSeek-V3,base_url=http://localhost:8000/v1/completions --trust_remote_code --num_fewshot 5 --batch_size 128Test Result
Accuracy when full graph is enabled:
Accuracy for piecewise:
Benchmarking
Summary Gain of Full Graph (Decode Phase only) over Piecewise Graph
Note: Positive percentages indicate Full Graph outperforms Piecewise Graph. Negative percentages indicate Piecewise Graph performs better in those specific metrics.
command:
serving full graph:
SAFETENSORS_FAST_GPU=1 VLLM_USE_V1=1 VLLM_ROCM_USE_AITER=1 vllm serve deepseek-ai/DeepSeek-V3 -tp 8 --trust-remote-code --gpu_memory_utilization 0.95 --block-size 1 --compilation-config '{\"full_cuda_graph\": true}'serving piecewise graph:
SAFETENSORS_FAST_GPU=1 VLLM_USE_V1=1 VLLM_ROCM_USE_AITER=1 vllm serve deepseek-ai/DeepSeek-V3 -tp 8 --trust-remote-code --gpu_memory_utilization 0.95 --block-size 1python vllm/benchmarks/benchmark_serving.py --backend vllm --model "$model_name" --dataset-name random --num-prompts 50 --random-input-len "$_in_len" --request-rate 10 --random-output-len "$_out_len"