# # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # Copyright 2023 The vLLM team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """ Compare the outputs of vLLM with and without aclgraph. Run `pytest tests/compile/test_aclgraph.py`. """ import os import random import string import pytest from vllm import SamplingParams from tests.e2e.conftest import VllmRunner from tests.e2e.model_utils import check_outputs_equal MODELS = [ "Qwen/Qwen3-0.6B", "vllm-ascend/DeepSeek-V2-Lite-W8A8", ] @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("max_tokens", [32]) def test_models_with_aclgraph( model: str, max_tokens: int, ) -> None: prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is" ] sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0) if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8": with VllmRunner( model, max_model_len=1024, enforce_eager=False, quantization="ascend", ) as runner: vllm_aclgraph_outputs = runner.model.generate( prompts, sampling_params) with VllmRunner( model, max_model_len=1024, enforce_eager=True, quantization="ascend", ) as runner: vllm_eager_outputs = runner.model.generate(prompts, sampling_params) else: with VllmRunner( model, max_model_len=1024, enforce_eager=False, ) as runner: vllm_aclgraph_outputs = runner.model.generate( prompts, sampling_params) with VllmRunner( model, max_model_len=1024, enforce_eager=True, ) as runner: vllm_eager_outputs = runner.model.generate(prompts, sampling_params) vllm_aclgraph_outputs_list = [] for output in vllm_aclgraph_outputs: vllm_aclgraph_outputs_list.append( (output.outputs[0].index, output.outputs[0].text)) vllm_eager_outputs_list = [] for output in vllm_eager_outputs: vllm_eager_outputs_list.append( (output.outputs[0].index, output.outputs[0].text)) check_outputs_equal( outputs_0_lst=vllm_eager_outputs_list, outputs_1_lst=vllm_aclgraph_outputs_list, name_0="vllm_eager_outputs", name_1="vllm_aclgraph_outputs", ) @pytest.mark.skip("Skipping this test for now, " "it fails intermittently and needs investigation.") @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("max_tokens", [5]) def test_models_with_aclgraph_full_decode_only( model: str, max_tokens: int, ) -> None: if 'HCCL_OP_EXPANSION_MODE' in os.environ: del os.environ['HCCL_OP_EXPANSION_MODE'] # NOTE: Randomly fill the prompt with the requested amount for # the specified capture shape to prevent accuracy issues caused by padding random_number = random.choice(list(range(6, 47, 8))) prompts = [ ('Solve the following math problem step by step.' 'The last line of your response should be of the form Answer: ' '$Answer (without quotes) where $Answer is the answer to the problem.\n\n' 'In triangle $ABC$, $\\sin \\angle A = \\frac{4}{5}$ and $\\angle A < 90^\\circ$. Let $D$' 'be a point outside triangle $ABC$ such that $\\angle BAD = \\angle DAC$,' '$\\angle BDC = 90^\\circ$. Suppose $AD = 1$ and $\\frac{BD}{CD} = \\frac{3}{2}$.' 'If $AB + AC$ can be expressed in the form $\\frac{a\\sqrt{b}}{c}$,' 'where $a, b, c$ are pairwise relatively prime integers, find $a + b + c$.' ), ('Solve the following math problem step by step.' 'The last line of your response should be of the form Answer: ' '$Answer (without quotes) where $Answer is the answer to the problem.\n\n' 'Let $ABCD$ be a unit square in the plane. Points $X$ and $Y$ are chosen' 'independently and uniformly at random on the perimeter of $ABCD$.' 'If the expected value of the area of triangle $\\triangle AXY$' 'can be expressed as $\\frac{m}{n}$, for relatively prime positive' 'integers $m$ and $n$, compute $m+n$.'), ('Solve the following math problem step by step.' 'The last line of your response should be of the form Answer: ' '$Answer (without quotes) where $Answer is the answer to the problem.\n\n' 'Let $a, b, c$ be distinct numbers such that the equations $x^2 + ax + 1 = 0$' 'and $x^2 + bx + c = 0$ have a common real root, and the equations $x^2 + x + a = 0$' 'and $x^2 + cx + b = 0$ also have a common real root.' 'Compute the sum $a + b + c$.') ] + [ ''.join(random.choices(string.ascii_lowercase, k=random.randint( 1, 25))) for _ in range(random_number) ] sampling_params = SamplingParams(max_tokens=5, n=1, temperature=0.0, top_p=1.0, top_k=1) if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8": with VllmRunner( model, max_model_len=1024, enforce_eager=False, compilation_config={"cudagraph_mode": "FULL_DECODE_ONLY"}, quantization="ascend", ) as runner: vllm_aclgraph_outputs = runner.model.generate( prompts, sampling_params) with VllmRunner( model, max_model_len=1024, enforce_eager=True, quantization="ascend", ) as runner: vllm_eager_outputs = runner.model.generate(prompts, sampling_params) else: with VllmRunner( model, max_model_len=1024, enforce_eager=False, compilation_config={"cudagraph_mode": "FULL_DECODE_ONLY"}, ) as runner: vllm_aclgraph_outputs = runner.model.generate( prompts, sampling_params) with VllmRunner( model, max_model_len=1024, enforce_eager=True, ) as runner: vllm_eager_outputs = runner.model.generate(prompts, sampling_params) vllm_aclgraph_outputs_list = [] for output in vllm_aclgraph_outputs: vllm_aclgraph_outputs_list.append( (output.outputs[0].index, output.outputs[0].text)) vllm_eager_outputs_list = [] for output in vllm_eager_outputs: vllm_eager_outputs_list.append( (output.outputs[0].index, output.outputs[0].text)) check_outputs_equal( outputs_0_lst=vllm_eager_outputs_list, outputs_1_lst=vllm_aclgraph_outputs_list, name_0="vllm_eager_outputs", name_1="vllm_aclgraph_outputs", )