Files
xc-llm-ascend/tests/e2e/singlecard/test_aclgraph_accuracy.py
zhangxinyuehfad 8ae7fca947 [CI] refect e2e ci test (#5246)
### What this PR does / why we need it?
efect e2e ci test:
1. tests/e2e/singlecard/pooling/test_embedding.py: remove the eager
parameter and rename test case
2. tests/e2e/singlecard/pooling/test_scoring.py: Rename test cases
3. tests/e2e/singlecard/pooling/test_classification.py: Rename test case
4. tests/e2e/singlecard/test_quantization.py: remove the eager parameter
and chage model to vllm-ascend/Qwen2.5-0.6B-W8A8 and Rename test case
5. tests/e2e/multicard/test_shared_expert_dp.py: Rename test cases
6. tests/e2e/singlecard/test_sampler.py: Rename test cases
7. tests/e2e/singlecard/test_aclgraph_accuracy.py: Rename test cases
8. tests/e2e/multicard/test_offline_inference_distributed.py: Rename
test cases and remove the eager parameter
9. tests/e2e/multicard/long_sequence/test_accuracy.py: Rename test cases
and remove the eager parameter
10. tests/e2e/multicard/long_sequence/test_basic.py: Rename test cases
and remove the eager parameter
11.tests/e2e/multicard/test_expert_parallel.py:remove the eager
parameter
12.tests/e2e/multicard/test_full_graph_mode.py:remove the eager
parameter
13.tests/e2e/multicard/test_ilama_lora_tp2.py:remove the eager parameter

14.tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py:remove
the eager parameter
15.tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py:remove the
eager parameter
16.tests/e2e/singlecard/test_aclgraph_accuracy.py:remove the eager
parameter
17.tests/e2e/singlecard/test_camem.py:remove the eager parameter
18.tests/e2e/singlecard/test_ilama_lora.py:remove the eager parameter

19.tests/e2e/singlecard/test_multistream_overlap_shared_expert.py:remove
the eager parameter
20.tests/e2e/singlecard/test_vlm.py:remove the eager parameter
21.tests/e2e/singlecard/test_xli:remove the eager parameter

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

Signed-off-by: hfadzxy <starmoon_zhang@163.com>
2025-12-23 18:42:35 +08:00

305 lines
13 KiB
Python

#
# 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_accuracy.py`.
"""
import os
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_output_between_eager_and_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,
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,
) 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.parametrize("model", MODELS)
@pytest.mark.parametrize("max_tokens", [32])
def test_models_output_between_eager_and_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
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 $a + b + c$.')
]
vllm_aclgraph_qwen_answers = [
' \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',
" \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",
' \n\nTo solve this problem, we can use the following approach: Let $ \\alpha $ be the common real root of the two equations. Then, we can'
]
vllm_aclgraph_ds_answers = [
'\n\nSelect an assignment template',
'\n\nSelect an assignment template',
'\n\nSelect an assignment template'
]
sampling_params = SamplingParams(max_tokens=max_tokens,
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,
compilation_config={"cudagraph_mode": "FULL_DECODE_ONLY"},
quantization="ascend",
) as runner:
vllm_aclgraph_outputs = runner.model.generate(
prompts, sampling_params)
else:
with VllmRunner(
model,
max_model_len=1024,
compilation_config={
"cudagraph_capture_sizes": [4, 8, 32, 64],
"cudagraph_mode": "FULL_DECODE_ONLY"
},
) as runner:
vllm_aclgraph_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 = []
vllm_eager_outputs_list = ([
([0], answer) for answer in vllm_aclgraph_ds_answers
] if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8" else [
([0], answer) for answer in vllm_aclgraph_qwen_answers
])
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.parametrize("model", MODELS)
@pytest.mark.parametrize("max_tokens", [32])
def test_models_output_between_eager_and_fullgraph_npugraph_ex(
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
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 $a + b + c$.')
]
vllm_aclgraph_qwen_answers = [
' \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',
" \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",
' \n\nTo solve this problem, we can use the following approach: Let $ \\alpha $ be the common real root of the two equations. Then, we can'
]
vllm_aclgraph_ds_answers = [
'\n\nSelect an assignment template',
'\n\nSelect an assignment template',
'\n\nSelect an assignment template'
]
sampling_params = SamplingParams(max_tokens=max_tokens,
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,
compilation_config={"cudagraph_mode": "FULL_DECODE_ONLY"},
additional_config={"enable_npugraph_ex": True},
quantization="ascend",
) as runner:
vllm_aclgraph_outputs = runner.model.generate(
prompts, sampling_params)
else:
with VllmRunner(
model,
max_model_len=1024,
compilation_config={
"cudagraph_capture_sizes": [4, 8, 32, 64],
"cudagraph_mode": "FULL_DECODE_ONLY"
},
additional_config={"enable_npugraph_ex": True},
) as runner:
vllm_aclgraph_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 = []
vllm_eager_outputs_list = ([
([0], answer) for answer in vllm_aclgraph_ds_answers
] if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8" else [
([0], answer) for answer in vllm_aclgraph_qwen_answers
])
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",
)
def test_aclgraph_enable():
# Generally, this test is not belong to e2e, but it is a good way to check if
# aclgraph is enabled in real environment
from vllm.config.compilation import CompilationMode, CUDAGraphMode
from vllm.engine.arg_utils import EngineArgs
from vllm_ascend.platform import NPUPlatform
# vLLM default mode is piecewise cudagraph
config = EngineArgs()
VllmConfig = config.create_engine_config()
assert VllmConfig.compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE
# after check_and_update_config, mode should be VLLM_COMPILE and piecewise cudagraph
NPUPlatform.check_and_update_config(VllmConfig)
assert VllmConfig.compilation_config.mode == CompilationMode.VLLM_COMPILE
assert VllmConfig.compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE