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[Lint]Style: Convert test/ to ruff format(Batch #1) (#6738)

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### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| `tests/e2e/310p/multicard/test_vl_model_multicard.py` |
| `tests/e2e/310p/singlecard/test_vl_model_singlecard.py` |
| `tests/e2e/310p/test_utils.py` |
| `tests/e2e/conftest.py` |
| `tests/e2e/model_utils.py` |
| `tests/e2e/models/conftest.py` |
| `tests/e2e/models/test_lm_eval_correctness.py` |
| `tests/e2e/multicard/2-cards/spec_decode/test_spec_decode.py` |
| `tests/e2e/multicard/2-cards/test_aclgraph_capture_replay.py` |
| `tests/e2e/multicard/2-cards/test_data_parallel.py` |
| `tests/e2e/multicard/2-cards/test_disaggregated_encoder.py` |
| `tests/e2e/multicard/2-cards/test_expert_parallel.py` |
| `tests/e2e/multicard/2-cards/test_external_launcher.py` |
| `tests/e2e/multicard/2-cards/test_full_graph_mode.py` |
| `tests/e2e/multicard/2-cards/test_ilama_lora_tp2.py` |
| `tests/e2e/multicard/2-cards/test_offline_inference_distributed.py` |
| `tests/e2e/multicard/2-cards/test_offline_weight_load.py` |
| `tests/e2e/multicard/2-cards/test_pipeline_parallel.py` |
| `tests/e2e/multicard/2-cards/test_prefix_caching.py` |
| `tests/e2e/multicard/2-cards/test_quantization.py` |
| `tests/e2e/multicard/2-cards/test_qwen3_moe.py` |
| `tests/e2e/multicard/2-cards/test_qwen3_moe_routing_replay.py` |
| `tests/e2e/multicard/2-cards/test_qwen3_performance.py` |
| `tests/e2e/multicard/2-cards/test_shared_expert_dp.py` |
| `tests/e2e/multicard/2-cards/test_single_request_aclgraph.py` |
| `tests/e2e/multicard/2-cards/test_sp_pass.py` |

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
9562912cea

Signed-off-by: MrZ20 <2609716663@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
SILONG ZENG
2026-03-10 09:52:50 +08:00
committed by GitHub
parent 9216e1b050
commit 43df2cb2fc
27 changed files with 753 additions and 859 deletions
Download Patch File Download Diff File Expand all files Collapse all files

232
tests/e2e/multicard/2-cards/test_offline_inference_distributed.py
View File

@@ -20,8 +20,10 @@
Run `pytest tests/test_offline_inference.py`.
"""
import os
from unittest.mock import patch
import pytest
from vllm import SamplingParams
@@ -51,6 +53,7 @@ GPT_OSS_MODELS = [
"unsloth/gpt-oss-20b-BF16",
]
def test_deepseek_multistream_moe_tp2():
example_prompts = [
"Hello, my name is",
@@ -58,15 +61,15 @@ def test_deepseek_multistream_moe_tp2():
dtype = "half"
max_tokens = 5
with VllmRunner(
"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,
"refresh": True,
},
"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,
"refresh": True,
},
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
@@ -78,12 +81,12 @@ def test_qwen3_w4a8_dynamic_tp2(model):
]
max_tokens = 5
with VllmRunner(
model,
max_model_len=8192,
dtype="auto",
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
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)
@@ -92,20 +95,17 @@ def test_qwen3_moe_sp_tp2() -> None:
example_prompts = [
"Hello, my name is",
]
sampling_params = SamplingParams(max_tokens=5,
temperature=0.0,
top_k=50,
top_p=0.9)
sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)
with VllmRunner("Qwen/Qwen3-30B-A3B",
dtype="auto",
tensor_parallel_size=2,
distributed_executor_backend="mp",
compilation_config={"pass_config": {
"enable_sp": True
}},
enable_expert_parallel=True,
enforce_eager=True) as vllm_model:
with VllmRunner(
"Qwen/Qwen3-30B-A3B",
dtype="auto",
tensor_parallel_size=2,
distributed_executor_backend="mp",
compilation_config={"pass_config": {"enable_sp": True}},
enable_expert_parallel=True,
enforce_eager=True,
) as vllm_model:
vllm_model.generate(example_prompts, sampling_params)
@@ -113,33 +113,34 @@ def test_qwen3_moe_sp_tp2() -> None:
@patch.dict(os.environ, {"HCCL_BUFFSIZE": "2048"})
def test_deepseek_w4a8_accuracy_tp2(model):
prompts = [
"Hello, my name is", "The president of the United States is",
"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs"
]
vllm_ds_w4a8_answers = [
'逍遙而至地去 accrued', '平行于我udo madreHelen', 'ysteepaolis backwards Kj'
"Hello, my name is",
"The president of the United States is",
"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs",
]
vllm_ds_w4a8_answers = ["逍遙而至地去 accrued", "平行于我udo madreHelen", "ysteepaolis backwards Kj"]
sampling_params = SamplingParams(max_tokens=5, temperature=0.0)
with VllmRunner(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,
sampling_params)
with VllmRunner(
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, sampling_params)
vllm_quant_outputs_list = []
for output in vllm_quant_outputs:
vllm_quant_outputs_list.append(
([output.outputs[0].index], output.outputs[0].text))
vllm_quant_outputs_list.append(([output.outputs[0].index], output.outputs[0].text))
vllm_answer_list = []
vllm_answer_list = ([([0], answer) for answer in vllm_ds_w4a8_answers])
vllm_answer_list = [([0], answer) for answer in vllm_ds_w4a8_answers]
check_outputs_equal(outputs_0_lst=vllm_answer_list,
outputs_1_lst=vllm_quant_outputs_list,
name_0="vllm_quant_outputs",
name_1="vllm_answer_outputs")
check_outputs_equal(
outputs_0_lst=vllm_answer_list,
outputs_1_lst=vllm_quant_outputs_list,
name_0="vllm_quant_outputs",
name_1="vllm_answer_outputs",
)
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
@@ -148,17 +149,16 @@ def test_qwen3_moe_fc2_tp2() -> None:
example_prompts = [
"Hello, my name is",
]
sampling_params = SamplingParams(max_tokens=5,
temperature=0.0,
top_k=50,
top_p=0.9)
sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)
with VllmRunner("Qwen/Qwen3-30B-A3B",
dtype="auto",
tensor_parallel_size=2,
distributed_executor_backend="mp",
enable_expert_parallel=True,
enforce_eager=True) as vllm_model:
with VllmRunner(
"Qwen/Qwen3-30B-A3B",
dtype="auto",
tensor_parallel_size=2,
distributed_executor_backend="mp",
enable_expert_parallel=True,
enforce_eager=True,
) as vllm_model:
vllm_model.generate(example_prompts, sampling_params)
@@ -168,20 +168,17 @@ def test_qwen3_moe_fc2_oshard_tp2() -> None:
example_prompts = [
"Hello, my name is",
]
sampling_params = SamplingParams(max_tokens=5,
temperature=0.0,
top_k=50,
top_p=0.9)
sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)
with VllmRunner(
"Qwen/Qwen3-30B-A3B",
dtype="auto",
tensor_parallel_size=2,
distributed_executor_backend="mp",
enable_expert_parallel=True,
enforce_eager=
True, # TODO(Levi-JQ): support graph mode for fc2 in Qwen
additional_config={"layer_sharding": ["o_proj"]}) as vllm_model:
"Qwen/Qwen3-30B-A3B",
dtype="auto",
tensor_parallel_size=2,
distributed_executor_backend="mp",
enable_expert_parallel=True,
enforce_eager=True, # TODO(Levi-JQ): support graph mode for fc2 in Qwen
additional_config={"layer_sharding": ["o_proj"]},
) as vllm_model:
vllm_model.generate(example_prompts, sampling_params)
@@ -190,17 +187,16 @@ def test_deepseek_v2_lite_fc1_tp2() -> None:
example_prompts = [
"test" * 1001,
]
sampling_params = SamplingParams(max_tokens=5,
temperature=0.0,
top_k=50,
top_p=0.9)
with VllmRunner("vllm-ascend/DeepSeek-V2-Lite-W8A8",
dtype="auto",
tensor_parallel_size=2,
distributed_executor_backend="mp",
enable_expert_parallel=True,
enforce_eager=True,
quantization="ascend") as vllm_model:
sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)
with VllmRunner(
"vllm-ascend/DeepSeek-V2-Lite-W8A8",
dtype="auto",
tensor_parallel_size=2,
distributed_executor_backend="mp",
enable_expert_parallel=True,
enforce_eager=True,
quantization="ascend",
) as vllm_model:
vllm_model.generate(example_prompts, sampling_params)
@@ -213,12 +209,12 @@ def test_qwen3_dense_fc1_tp2(model):
max_tokens = 5
with VllmRunner(
model,
max_model_len=8192,
dtype="auto",
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
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)
@@ -232,13 +228,13 @@ def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
max_tokens = 5
with VllmRunner(
model,
max_model_len=8192,
dtype="auto",
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
additional_config={"weight_prefetch_config": {"enabled": True}},
model,
max_model_len=8192,
dtype="auto",
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
additional_config={"weight_prefetch_config": {"enabled": True}},
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
@@ -252,28 +248,20 @@ def test_deepseek3_2_w8a8_pruning_mtp_tp2_ep():
"Hello ",
]
# "max_position_embeddings": 163840,
long_example_prompts = [
"Hello " * (163839 - 500) + "Hello"
]
long_example_prompts = ["Hello " * (163839 - 500) + "Hello"]
max_tokens = 500
with VllmRunner("vllm-ascend/DeepSeek-V3.2-W8A8-Pruning",
tensor_parallel_size=2,
quantization="ascend",
enable_expert_parallel=True,
max_model_len=163840,
compilation_config={
"cudagraph_capture_sizes": [2, 4, 6, 8, 10, 12],
"cudagraph_mode": "FULL_DECODE_ONLY"
},
speculative_config={
"num_speculative_tokens": 1,
"method": "deepseek_mtp"
},
additional_config={
"layer_sharding":["q_b_proj", "o_proj"]
},
reasoning_parser="deepseek_v3",
tokenizer_mode="deepseek_v32") as vllm_model:
with VllmRunner(
"vllm-ascend/DeepSeek-V3.2-W8A8-Pruning",
tensor_parallel_size=2,
quantization="ascend",
enable_expert_parallel=True,
max_model_len=163840,
compilation_config={"cudagraph_capture_sizes": [2, 4, 6, 8, 10, 12], "cudagraph_mode": "FULL_DECODE_ONLY"},
speculative_config={"num_speculative_tokens": 1, "method": "deepseek_mtp"},
additional_config={"layer_sharding": ["q_b_proj", "o_proj"]},
reasoning_parser="deepseek_v3",
tokenizer_mode="deepseek_v32",
) as vllm_model:
vllm_model.generate_greedy(short_example_prompts, max_tokens)
vllm_model.generate_greedy(long_example_prompts, max_tokens)
@@ -285,10 +273,10 @@ def test_qwen3_w4a4_distributed_tp2(model):
]
max_tokens = 5
with VllmRunner(
model,
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
model,
tensor_parallel_size=2,
cudagraph_capture_sizes=[1, 2, 4, 8],
quantization="ascend",
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
@@ -300,8 +288,8 @@ def test_gpt_oss_distributed_tp2(model):
]
max_tokens = 5
with VllmRunner(
model,
tensor_parallel_size=2,
enforce_eager=True,
model,
tensor_parallel_size=2,
enforce_eager=True,
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)