c980e68d40
### What this PR does / why we need it?
This PR aims to support aclgraph for model runner v2, please see RFC
#5208. The PR contains these modifications:
- adapt to newest commit of vllm main branch.
- supply a unified interface of extra forward context for both model
runner v1 and model runner v2.
- implement graph mode for main model.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
114 lines
3.4 KiB
Python
114 lines
3.4 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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import os
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from unittest.mock import patch
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import pytest
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from vllm import SamplingParams
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from tests.e2e.conftest import VllmRunner
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MODELS = ["Qwen/Qwen3-0.6B"]
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MAIN_MODELS = ["LLM-Research/Meta-Llama-3.1-8B-Instruct"]
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EGALE_MODELS = ["vllm-ascend/EAGLE-LLaMA3.1-Instruct-8B"]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("enforce_eager", [True])
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@patch.dict(os.environ, {"VLLM_USE_V2_MODEL_RUNNER": "1"})
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def test_qwen3_dense_eager_mode(
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model: str,
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max_tokens: int,
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enforce_eager: bool,
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) -> None:
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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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sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
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with VllmRunner(
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model,
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max_model_len=1024,
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enforce_eager=enforce_eager,
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) as runner:
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runner.model.generate(prompts, sampling_params)
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@pytest.mark.parametrize("model", MAIN_MODELS)
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@pytest.mark.parametrize("eagle_model", EGALE_MODELS)
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("enforce_eager", [True])
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@patch.dict(os.environ, {"VLLM_USE_V2_MODEL_RUNNER": "1"})
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def test_egale_spec_decoding(
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model: str,
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eagle_model: str,
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max_tokens: int,
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enforce_eager: bool,
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) -> None:
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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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sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
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with VllmRunner(
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model,
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max_model_len=1024,
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enforce_eager=enforce_eager,
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async_scheduling=True,
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speculative_config={
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"model": eagle_model,
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"method": "eagle",
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"num_speculative_tokens": 3,
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},
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) as runner:
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runner.model.generate(prompts, sampling_params)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("enforce_eager", [False])
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@pytest.mark.parametrize("compilation_config", [{"cudagraph_mode": "FULL_DECODE_ONLY"}, {}])
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@patch.dict(os.environ, {"VLLM_USE_V2_MODEL_RUNNER": "1"})
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def test_qwen3_dense_graph_mode(
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model: str,
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max_tokens: int,
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enforce_eager: bool,
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) -> None:
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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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sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
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with VllmRunner(
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model,
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max_model_len=1024,
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enforce_eager=enforce_eager,
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) as runner:
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runner.model.generate(prompts, sampling_params)
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