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[Perf] Enable cuda graph for deepepHT, 5.3% throughput improvement, 4.4% TTFT improvement (#29558)

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Signed-off-by: yewentao256 <zhyanwentao@126.com>
This commit is contained in:
Wentao Ye
2025-12-06 23:44:50 -05:00
committed by GitHub
parent dce6d229f7
commit 17eb25e327
4 changed files with 142 additions and 78 deletions
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38
vllm/platforms/cuda.py
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@@ -232,44 +232,6 @@ class CudaPlatformBase(Platform):
logger.info(
"Forcing kv cache block size to 64 for FlashMLASparse backend."
)
# lazy import to avoid circular import
from vllm.config import CUDAGraphMode
compilation_config = vllm_config.compilation_config
if compilation_config.cudagraph_mode.has_full_cudagraphs():
# decode context parallel does not support full cudagraphs
if parallel_config.decode_context_parallel_size > 1:
logger.warning_once(
"Decode context parallel (DCP) is enabled, which is "
"incompatible with full CUDA graphs. "
"Overriding cudagraph_mode to PIECEWISE."
)
compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
# prefill context parallel do not support full cudagraphs
elif parallel_config.prefill_context_parallel_size > 1:
logger.warning_once(
"Prefill context parallel (PCP) is enabled, which is "
"incompatible with full CUDA graphs. "
"Overriding cudagraph_mode to PIECEWISE."
)
compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
if (
parallel_config.all2all_backend == "deepep_high_throughput"
and parallel_config.data_parallel_size > 1
and compilation_config.cudagraph_mode != CUDAGraphMode.NONE
):
# TODO: Piecewise Cuda graph might be enabled
# if torch compile cache key issue fixed
# See https://github.com/vllm-project/vllm/pull/25093
logger.info(
"WideEP: Disabling CUDA Graphs since DeepEP high-throughput "
"kernels are optimized for prefill and are incompatible with "
"CUDA Graphs. "
"In order to use CUDA Graphs for decode-optimized workloads, "
"use --all2all-backend with another option, such as "
"deepep_low_latency, pplx, or allgather_reducescatter."
)
compilation_config.cudagraph_mode = CUDAGraphMode.NONE
@classmethod
def get_current_memory_usage(