cc210f46e6
### What this PR does / why we need it?
Now there is no need to calculate `num_draft_tokens` when allocating
slots.
This PR follows the changes in vllm:
https://github.com/vllm-project/vllm/pull/20701
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test
- vLLM version: v0.9.2
- vLLM main:
cc876d0f29
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
100 lines
3.3 KiB
Python
100 lines
3.3 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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#
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"""
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Compare the outputs of vLLM with and without aclgraph.
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Run `pytest tests/compile/test_aclgraph.py`.
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"""
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import os
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import pytest
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import torch
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from vllm import LLM, SamplingParams
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from tests.conftest import VllmRunner
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from tests.model_utils import check_outputs_equal
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MODELS = [
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"Qwen/Qwen2.5-0.5B-Instruct",
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# TODO: REVERT ME when oom is fixed
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# "vllm-ascend/Qwen3-30B-A3B-Puring"
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]
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@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
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reason="aclgraph only support on v1")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [32])
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def test_models(
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model: str,
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max_tokens: int,
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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with monkeypatch.context() as m:
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prompts = [
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"Hello, my name is", "The president of the United States is",
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"The capital of France is", "The future of AI is"
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]
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# aclgraph only support on v1
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m.setenv("VLLM_USE_V1", "1")
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sampling_params = SamplingParams(max_tokens=max_tokens,
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temperature=0.0)
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# TODO: change to use vllmrunner when the registry of custom op is solved
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# while running pytest
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vllm_model = LLM(model)
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vllm_aclgraph_outputs = vllm_model.generate(prompts, sampling_params)
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del vllm_model
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torch.npu.empty_cache()
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vllm_model = LLM(model, enforce_eager=True)
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vllm_eager_outputs = vllm_model.generate(prompts, sampling_params)
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del vllm_model
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torch.npu.empty_cache()
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vllm_aclgraph_outputs_list = []
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for output in vllm_aclgraph_outputs:
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vllm_aclgraph_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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vllm_eager_outputs_list = []
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for output in vllm_eager_outputs:
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vllm_eager_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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check_outputs_equal(
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outputs_0_lst=vllm_eager_outputs_list,
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outputs_1_lst=vllm_aclgraph_outputs_list,
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name_0="vllm_eager_outputs",
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name_1="vllm_aclgraph_outputs",
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)
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@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
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reason="aclgraph only support on v1")
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def test_deepseek_raises_error(monkeypatch: pytest.MonkeyPatch) -> None:
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with monkeypatch.context() as m:
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m.setenv("VLLM_USE_MODELSCOPE", "True")
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m.setenv("VLLM_USE_V1", "1")
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with pytest.raises(NotImplementedError) as excinfo:
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VllmRunner("deepseek-ai/DeepSeek-V2-Lite-Chat",
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max_model_len=1024,
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enforce_eager=False)
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assert "ACL Graph does not support deepseek" in str(excinfo.value)
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