ab5d110fcc
### What this PR does / why we need it? vllm-ascend support chunked prefill for MLA --------- Signed-off-by: fems14 <1804143737@qq.com>
75 lines
2.3 KiB
Python
75 lines
2.3 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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MODELS = ["deepseek-ai/DeepSeek-V2-Lite"]
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@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
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reason="new chunked only support on v1")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [1])
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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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return
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with monkeypatch.context() as m:
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prompts = "The president of the United States is"
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m.setenv("VLLM_USE_V1", "1")
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sampling_params = SamplingParams(
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max_tokens=max_tokens,
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temperature=0.0,
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)
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vllm_model = LLM(model,
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long_prefill_token_threshold=4,
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enforce_eager=True)
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output_chunked = vllm_model.generate(prompts, sampling_params)
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logprobs_chunked = output_chunked.outputs[0].logprobs
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del vllm_model
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torch.npu.empty_cache()
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vllm_model = LLM(model,
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enforce_eager=True,
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additional_config={
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'ascend_scheduler_config': {
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'enabled': True
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},
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})
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output = vllm_model.generate(prompts, sampling_params)
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logprobs = output.outputs[0].logprobs
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del vllm_model
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torch.npu.empty_cache()
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logprobs_similarity = torch.cosine_similarity(
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logprobs_chunked.flatten(), logprobs.flatten(), dim=0)
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assert logprobs_similarity > 0.95
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