f10acddb78
Ascend scheduler was added for non chunk prefill case before, since that the npu ops didn't work well with chunked prefill. Now the ops with chunked prefill work better, it's time to remove the ascend scheduler to use vLLM default scheduler. - vLLM version: v0.11.2 --------- Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
136 lines
4.7 KiB
Python
136 lines
4.7 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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#
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"""Compare the short outputs of HF and vLLM when using greedy sampling.
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Run `pytest tests/e2e/multicard/test_qwen3_next.py`.
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"""
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import os
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from unittest.mock import patch
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import pytest
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from modelscope import snapshot_download # type: ignore
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from tests.e2e.conftest import VllmRunner
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def test_models_distributed_Qwen3_NEXT_TP4():
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example_prompts = [
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"Hello, my name is",
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] * 4
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max_tokens = 5
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with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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distributed_executor_backend="mp",
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enforce_eager=True) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model
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def test_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
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example_prompts = [
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"Hello, my name is",
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] * 4
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max_tokens = 5
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with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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distributed_executor_backend="mp",
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enforce_eager=False,
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compilation_config={
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"cudagraph_mode": "FULL_DECODE_ONLY",
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"cudagraph_capture_sizes": [1, 8, 24, 48, 60]
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}) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model
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@pytest.mark.skip(
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reason="Qwen3-Next + MTP doesn't work with chunked prefill. Fix Me")
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def test_models_distributed_Qwen3_NEXT_MTP_TP4_SIMILARITY():
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example_prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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max_tokens = 20
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with VllmRunner(
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"Qwen/Qwen3-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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distributed_executor_backend="mp",
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enforce_eager=True,
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) as vllm_model:
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ref_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model
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with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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distributed_executor_backend="mp",
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enforce_eager=True,
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speculative_config={
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"method": "qwen3_next_mtp",
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"num_speculative_tokens": 1
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}) as spec_vllm_model:
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spec_outputs = spec_vllm_model.generate_greedy(example_prompts,
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max_tokens)
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del spec_vllm_model
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matches = 0
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misses = 0
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for ref_output, spec_output in zip(ref_outputs, spec_outputs):
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ref_token_ids = ref_output[0]
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spec_token_ids = spec_output[0]
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if ref_token_ids == spec_token_ids[:len(ref_token_ids)]:
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matches += 1
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else:
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misses += 1
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print(f"ref_output: {ref_output[1]}")
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print(f"spec_output: {spec_output[1]}")
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assert matches > int(0.66 * len(ref_outputs))
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# TODO: will conduct accuracy verification after the subsequent version becomes stable
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@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
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def test_models_distributed_Qwen3_NEXT_W8A8DYNAMIC_WITH_EP():
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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snapshot_download(
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"vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8-Pruning"),
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max_model_len=4096,
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tensor_parallel_size=2,
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gpu_memory_utilization=0.4,
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max_num_seqs=1,
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enable_expert_parallel=True,
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quantization="ascend",
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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