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[E2E] Optimize the E2E test time. (#5294)

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### What this PR does / why we need it?
Add cudagraph_capture_sizes for E2E CI test.

- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

Signed-off-by: menogrey <1299267905@qq.com>
This commit is contained in:
zhangyiming
2025-12-26 14:17:50 +08:00
committed by GitHub
parent 29d2fe653d
commit 45c5bcd962
22 changed files with 57 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
tests/e2e/multicard/long_sequence/test_basic.py
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@@ -122,6 +122,7 @@ def test_models_pcp_dcp_piece_wise():
decode_context_parallel_size=2,
max_num_batched_tokens=1024,
enable_expert_parallel=True,
cudagraph_capture_sizes=[1, 2, 4, 8],
block_size=128) as runner:
runner.model.generate(prompts, sampling_params)
@@ -132,6 +133,7 @@ def test_models_pcp_dcp_piece_wise():
prefill_context_parallel_size=2,
decode_context_parallel_size=1,
enable_expert_parallel=True,
cudagraph_capture_sizes=[1, 2, 4, 8],
block_size=128,
quantization="ascend") as runner:
runner.model.generate(prompts, sampling_params)

5
tests/e2e/multicard/test_expert_parallel.py
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@@ -15,11 +15,14 @@ def test_deepseek_correctness_ep(model_name):
max_tokens = 5
# FIXME: Really strange that chunked prefill might lead to different results, investigate further
with VllmRunner(model_name, tensor_parallel_size=2) as vllm_model:
with VllmRunner(model_name,
cudagraph_capture_sizes=[1, 2, 4, 8],
tensor_parallel_size=2) as vllm_model:
tp_output = vllm_model.generate_greedy(example_prompts, max_tokens)
with VllmRunner(model_name,
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
enable_expert_parallel=True) as vllm_model:
ep_output = vllm_model.generate_greedy(example_prompts, max_tokens)

2
tests/e2e/multicard/test_full_graph_mode.py
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@@ -51,6 +51,7 @@ def test_qwen3_moe_full_decode_only_tp2():
with VllmRunner(
model,
max_model_len=1024,
cudagraph_capture_sizes=[4, 8, 24, 48, 60],
tensor_parallel_size=2,
) as runner:
vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
@@ -95,6 +96,7 @@ def test_qwen3_moe_full_graph_tp2():
with VllmRunner(
model,
max_model_len=1024,
cudagraph_capture_sizes=[4, 8, 24, 48, 60],
tensor_parallel_size=2,
) as runner:
vllm_eager_outputs = runner.model.generate(prompts, sampling_params)

1
tests/e2e/multicard/test_ilama_lora_tp2.py
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@@ -16,6 +16,7 @@ def test_ilama_lora_tp2(distributed_executor_backend, ilama_lora_files):
max_model_len=1024,
max_num_seqs=16,
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
distributed_executor_backend=distributed_executor_backend,
) as vllm_model:
output = do_sample(vllm_model.model, ilama_lora_files, lora_id=2)

5
tests/e2e/multicard/test_offline_inference_distributed.py
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@@ -60,6 +60,7 @@ def test_deepseek_multistream_moe_tp2():
"vllm-ascend/DeepSeek-V3-Pruning",
dtype=dtype,
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
distributed_executor_backend="mp",
additional_config={
"enable_multistream_moe": True,
@@ -80,6 +81,7 @@ def test_qwen3_w4a8_dynamic_tp2(model):
max_model_len=8192,
dtype="auto",
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
) as vllm_model:
vllm_model.generate_greedy(prompts, max_tokens)
@@ -120,6 +122,7 @@ def test_deepseek_w4a8_accuracy_tp2(model):
with VllmRunner(snapshot_download(model),
dtype="auto",
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
enable_expert_parallel=True) as vllm_model:
vllm_quant_outputs = vllm_model.model.generate(prompts,
@@ -190,6 +193,7 @@ def test_qwen3_dense_fc1_tp2(model):
max_model_len=8192,
dtype="auto",
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
@@ -208,6 +212,7 @@ def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
max_model_len=8192,
dtype="auto",
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)

1
tests/e2e/multicard/test_pipeline_parallel.py
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@@ -42,6 +42,7 @@ def test_models_pp2(model: str, tp_size: int, pp_size: int,
with VllmRunner(model,
tensor_parallel_size=tp_size,
pipeline_parallel_size=pp_size,
cudagraph_capture_sizes=[1, 2, 4, 8],
distributed_executor_backend=distributed_executor_backend,
gpu_memory_utilization=0.7) as vllm_model:
vllm_model.generate_greedy(prompts, 64)

2
tests/e2e/multicard/test_prefix_caching.py
View File

@@ -64,6 +64,7 @@ def test_models_prefix_cache_tp2(model: str, max_tokens: int) -> None:
with VllmRunner(model,
max_model_len=2048,
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
gpu_memory_utilization=0.7) as vllm_model:
prefix_cache_output = vllm_model.generate_greedy(
INPUT_PROMPTS, max_tokens)
@@ -72,6 +73,7 @@ def test_models_prefix_cache_tp2(model: str, max_tokens: int) -> None:
enable_prefix_caching=False,
max_model_len=2048,
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
gpu_memory_utilization=0.7) as vllm_model:
vllm_output = vllm_model.generate_greedy(INPUT_PROMPTS, max_tokens)

1
tests/e2e/multicard/test_quantization.py
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@@ -33,6 +33,7 @@ def test_qwen2_5_w8a8_external_quantized_tp2():
with VllmRunner(
snapshot_download("neuralmagic/Qwen2.5-3B-quantized.w8a8"),
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
max_model_len=4096,
gpu_memory_utilization=0.8,
) as vllm_model:

3
tests/e2e/multicard/test_qwen3_moe.py
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@@ -43,6 +43,7 @@ def test_qwen3_moe_distributed_mp_tp2_ep():
"Qwen/Qwen3-30B-A3B",
tensor_parallel_size=2,
enable_expert_parallel=True,
cudagraph_capture_sizes=[1, 2, 4, 8],
distributed_executor_backend="mp",
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
@@ -57,6 +58,7 @@ def test_qwen3_moe_w8a8_distributed_tp2():
snapshot_download("vllm-ascend/Qwen3-30B-A3B-W8A8"),
max_model_len=8192,
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
@@ -73,6 +75,7 @@ def test_qwen3_moe_distributed_aiv_tp2():
"Qwen/Qwen3-30B-A3B",
dtype=dtype,
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)

2
tests/e2e/multicard/test_qwen3_next.py
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@@ -36,6 +36,7 @@ def test_qwen3_next_distributed_mp_tp4():
max_tokens = 5
with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
tensor_parallel_size=4,
cudagraph_capture_sizes=[1, 2, 4, 8],
max_model_len=4096,
gpu_memory_utilization=0.8,
distributed_executor_backend="mp") as vllm_model:
@@ -125,6 +126,7 @@ def test_qwen3_next_w8a8dynamic_distributed_tp4_ep():
gpu_memory_utilization=0.4,
max_num_seqs=1,
enable_expert_parallel=True,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)