5508a602ed
### What this PR does / why we need it?
Due to the inconsistency between the attention operators used in eager
mode and graph mode, the accumulation order of the operator cannot be
guaranteed to be deterministic. Therefore, we modify the test to compare
with given outputs.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
Co-authored-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
195 lines
7.5 KiB
Python
195 lines
7.5 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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from vllm import SamplingParams
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from tests.e2e.conftest import VllmRunner
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from tests.e2e.model_utils import check_outputs_equal
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MODELS = [
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"Qwen/Qwen3-0.6B",
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"vllm-ascend/DeepSeek-V2-Lite-W8A8",
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]
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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_with_aclgraph(
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model: str,
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max_tokens: int,
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) -> None:
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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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sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
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if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8":
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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=False,
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quantization="ascend",
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) as runner:
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vllm_aclgraph_outputs = runner.model.generate(
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prompts, sampling_params)
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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=True,
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quantization="ascend",
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) as runner:
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vllm_eager_outputs = runner.model.generate(prompts,
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sampling_params)
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else:
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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=False,
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) as runner:
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vllm_aclgraph_outputs = runner.model.generate(
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prompts, sampling_params)
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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=True,
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) as runner:
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vllm_eager_outputs = runner.model.generate(prompts,
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sampling_params)
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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.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [32])
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def test_models_with_aclgraph_full_decode_only(
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model: str,
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max_tokens: int,
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) -> None:
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if 'HCCL_OP_EXPANSION_MODE' in os.environ:
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del os.environ['HCCL_OP_EXPANSION_MODE']
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# NOTE: Randomly fill the prompt with the requested amount for
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# the specified capture shape to prevent accuracy issues caused by padding
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prompts = [
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('Solve the following math problem step by step.'
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'The last line of your response should be of the form Answer: '
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'$Answer (without quotes) where $Answer is the answer to the problem.\n\n'
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'In triangle $ABC$, $\\sin \\angle A = \\frac{4}{5}$ and $\\angle A < 90^\\circ$. Let $D$'
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'be a point outside triangle $ABC$ such that $\\angle BAD = \\angle DAC$,'
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'$\\angle BDC = 90^\\circ$. Suppose $AD = 1$ and $\\frac{BD}{CD} = \\frac{3}{2}$.'
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'If $AB + AC$ can be expressed in the form $\\frac{a\\sqrt{b}}{c}$,'
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'where $a, b, c$ are pairwise relatively prime integers, find $a + b + c$.'
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),
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('Solve the following math problem step by step.'
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'The last line of your response should be of the form Answer: '
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'$Answer (without quotes) where $Answer is the answer to the problem.\n\n'
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'Let $ABCD$ be a unit square in the plane. Points $X$ and $Y$ are chosen'
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'independently and uniformly at random on the perimeter of $ABCD$.'
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'If the expected value of the area of triangle $\\triangle AXY$'
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'can be expressed as $\\frac{m}{n}$, for relatively prime positive'
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'integers $m$ and $n$, compute $m+n$.'),
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('Solve the following math problem step by step.'
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'The last line of your response should be of the form Answer: '
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'$Answer (without quotes) where $Answer is the answer to the problem.\n\n'
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'Let $a, b, c$ be distinct numbers such that the equations $x^2 + ax + 1 = 0$'
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'and $x^2 + bx + c = 0$ have a common real root, and the equations $x^2 + x + a = 0$'
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'and $x^2 + cx + b = 0$ also have a common real root.'
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'Compute the sum $a + b + c$.')
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]
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vllm_aclgraph_qwen_answers = [
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' \n\nTo solve this problem, we need to use the Law of Sines and Law of Cosines. Let me start by drawing triangle $ABC$ with the',
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" \n\nTo solve this problem, we can use the fact that the expected value of the area of a triangle formed by two random points on a square's perimeter is",
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'i$.\n\nLet $a, b, c$ be distinct numbers such that the equations $x^2 + ax + 1 = 0$ and'
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]
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vllm_aclgraph_ds_answers = [
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'\n\nSelect an assignment template',
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'\n\nSelect an assignment template',
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'\n\nSelect an assignment template'
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]
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sampling_params = SamplingParams(max_tokens=max_tokens,
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n=1,
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temperature=0.0,
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top_p=1.0,
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top_k=1)
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if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8":
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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=False,
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compilation_config={"cudagraph_mode": "FULL_DECODE_ONLY"},
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quantization="ascend",
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) as runner:
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vllm_aclgraph_outputs = runner.model.generate(
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prompts, sampling_params)
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else:
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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=False,
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compilation_config={
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"cudagraph_capture_sizes": [4, 8, 32, 64],
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"cudagraph_mode": "FULL_DECODE_ONLY"
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},
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) as runner:
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vllm_aclgraph_outputs = runner.model.generate(
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prompts, sampling_params)
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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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vllm_eager_outputs_list = ([
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([0], answer) for answer in vllm_aclgraph_ds_answers
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] if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8" else [
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([0], answer) for answer in vllm_aclgraph_qwen_answers
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])
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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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