517fd9272d
Reverts vllm-project/vllm-ascend#4498 - vLLM version: v0.11.2 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
139 lines
4.9 KiB
Python
139 lines
4.9 KiB
Python
#
|
|
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
# Copyright 2023 The vLLM team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# This file is a part of the vllm-ascend project.
|
|
# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
|
|
#
|
|
"""Compare the short outputs of HF and vLLM when using greedy sampling.
|
|
|
|
Run `pytest tests/e2e/multicard/test_qwen3_next.py`.
|
|
"""
|
|
|
|
import os
|
|
from unittest.mock import patch
|
|
|
|
from modelscope import snapshot_download # type: ignore
|
|
|
|
from tests.e2e.conftest import VllmRunner
|
|
|
|
|
|
def test_models_distributed_Qwen3_NEXT_TP4():
|
|
example_prompts = [
|
|
"Hello, my name is",
|
|
] * 4
|
|
max_tokens = 5
|
|
with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
|
|
tensor_parallel_size=4,
|
|
max_model_len=4096,
|
|
gpu_memory_utilization=0.8,
|
|
distributed_executor_backend="mp",
|
|
enforce_eager=True) as vllm_model:
|
|
vllm_model.generate_greedy(example_prompts, max_tokens)
|
|
del vllm_model
|
|
|
|
|
|
def test_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
|
|
example_prompts = [
|
|
"Hello, my name is",
|
|
] * 4
|
|
max_tokens = 5
|
|
with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
|
|
tensor_parallel_size=4,
|
|
max_model_len=4096,
|
|
gpu_memory_utilization=0.8,
|
|
distributed_executor_backend="mp",
|
|
enforce_eager=False,
|
|
compilation_config={
|
|
"cudagraph_mode": "FULL_DECODE_ONLY",
|
|
"cudagraph_capture_sizes": [1, 8, 24, 48, 60]
|
|
}) as vllm_model:
|
|
vllm_model.generate_greedy(example_prompts, max_tokens)
|
|
del vllm_model
|
|
|
|
|
|
def test_models_distributed_Qwen3_NEXT_MTP_TP4_SIMILARITY():
|
|
example_prompts = [
|
|
"Hello, my name is",
|
|
"The president of the United States is",
|
|
"The capital of France is",
|
|
"The future of AI is",
|
|
]
|
|
max_tokens = 20
|
|
|
|
with VllmRunner(
|
|
"Qwen/Qwen3-Next-80B-A3B-Instruct",
|
|
tensor_parallel_size=4,
|
|
max_model_len=4096,
|
|
gpu_memory_utilization=0.8,
|
|
distributed_executor_backend="mp",
|
|
enforce_eager=True,
|
|
) as vllm_model:
|
|
ref_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
|
|
del vllm_model
|
|
|
|
with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
|
|
tensor_parallel_size=4,
|
|
max_model_len=4096,
|
|
gpu_memory_utilization=0.8,
|
|
distributed_executor_backend="mp",
|
|
enforce_eager=True,
|
|
additional_config={
|
|
"ascend_scheduler_config": {
|
|
"enabled": True,
|
|
"enable_chunked_prefill": False
|
|
}
|
|
},
|
|
speculative_config={
|
|
"method": "qwen3_next_mtp",
|
|
"num_speculative_tokens": 1
|
|
}) as spec_vllm_model:
|
|
spec_outputs = spec_vllm_model.generate_greedy(example_prompts,
|
|
max_tokens)
|
|
del spec_vllm_model
|
|
|
|
matches = 0
|
|
misses = 0
|
|
for ref_output, spec_output in zip(ref_outputs, spec_outputs):
|
|
ref_token_ids = ref_output[0]
|
|
spec_token_ids = spec_output[0]
|
|
if ref_token_ids == spec_token_ids[:len(ref_token_ids)]:
|
|
matches += 1
|
|
else:
|
|
misses += 1
|
|
print(f"ref_output: {ref_output[1]}")
|
|
print(f"spec_output: {spec_output[1]}")
|
|
|
|
assert matches > int(0.66 * len(ref_outputs))
|
|
|
|
|
|
# TODO: will conduct accuracy verification after the subsequent version becomes stable
|
|
@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
|
|
def test_models_distributed_Qwen3_NEXT_W8A8DYNAMIC_WITH_EP():
|
|
example_prompts = [
|
|
"Hello, my name is",
|
|
]
|
|
max_tokens = 5
|
|
with VllmRunner(
|
|
snapshot_download(
|
|
"vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8-Pruning"),
|
|
max_model_len=4096,
|
|
tensor_parallel_size=2,
|
|
gpu_memory_utilization=0.4,
|
|
max_num_seqs=1,
|
|
enable_expert_parallel=True,
|
|
quantization="ascend",
|
|
) as vllm_model:
|
|
vllm_model.generate_greedy(example_prompts, max_tokens)
|