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[Test] Remove VLLM_USE_V1 in example and tests (#1733)

Browse Source 
V1 is enabled by default, no need to set it by hand now. This PR remove
the useless setting in example and tests

- vLLM version: v0.9.2
- vLLM main:
9ad0a4588b

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-07-15 12:49:57 +08:00
committed by GitHub
parent eb921d2b6f
commit 787010a637
29 changed files with 186 additions and 291 deletions
Download Patch File Download Diff File Expand all files Collapse all files

69
.github/workflows/vllm_ascend_test.yaml vendored
View File

@ -41,16 +41,10 @@ concurrency:
jobs:
lint:
# Only trigger lint on pull request
if: ${{ github.event_name == 'pull_request' }}
uses: ./.github/workflows/pre-commit.yml
changes:
# Only trigger changes on pull request
if: ${{ github.event_name == 'pull_request' }}
runs-on: ubuntu-latest
permissions:
pull-requests: read
outputs:
e2e_tracker: ${{ steps.filter.outputs.e2e_tracker }}
ut_tracker: ${{ steps.filter.outputs.ut_tracker }}
@ -60,20 +54,24 @@ jobs:
with:
filters: |
e2e_tracker:
- '.github/workflows/vllm_ascend_test.yaml'
- 'vllm_ascend/**'
- 'csrc/**'
- 'cmake/**'
- 'tests/e2e/**'
- 'tests/conftest.py'
- 'tests/model_utils.py'
- 'tests/utils.py'
- 'CMakeLists.txt'
- 'setup.py'
- 'requirements.txt'
- 'requirements-dev.txt'
- 'requirements-lint.txt'
- 'packages.txt'
ut_tracker:
- 'tests/ut/**'
ut:
needs: [lint, changes]
name: unit test
# only trigger unit test after lint passed and the change is e2e and ut related. Or the PR is merged.
if: ${{ github.event_name == 'push' || (needs.lint.result == 'success' && (needs.changes.outputs.e2e_tracker == 'true' || needs.changes.outputs.ut_tracker == 'true')) }}
# only trigger unit test after lint passed and the change is e2e and ut related.
if: ${{ needs.lint.result == 'success' && (needs.changes.outputs.e2e_tracker == 'true' || needs.changes.outputs.ut_tracker == 'true') }}
runs-on: ubuntu-latest
container:
image: quay.io/ascend/cann:8.1.rc1-910b-ubuntu22.04-py3.10
@ -112,9 +110,8 @@ jobs:
python3 -m pip install -r requirements-dev.txt --extra-index https://download.pytorch.org/whl/cpu/
python3 -m pip install -v . --extra-index https://download.pytorch.org/whl/cpu/
- name: Run unit test for V1 Engine
- name: Run unit test
env:
VLLM_USE_V1: 1
VLLM_WORKER_MULTIPROC_METHOD: spawn
TORCH_DEVICE_BACKEND_AUTOLOAD: 0
run: |
@ -133,8 +130,8 @@ jobs:
e2e:
needs: [lint, changes]
# only trigger e2e test after lint passed and the change is e2e related.
if: ${{ needs.lint.result == 'success' && needs.changes.outputs.e2e_tracker == 'true' }}
# only trigger e2e test after lint passed and the change is e2e related with pull request.
if: ${{ github.event_name == 'pull_request' && needs.lint.result == 'success' && needs.changes.outputs.e2e_tracker == 'true' }}
strategy:
max-parallel: 2
matrix:
@ -189,9 +186,8 @@ jobs:
pip install -r requirements-dev.txt
pip install -v -e .
- name: Run e2e test for V1 Engine
- name: Run e2e test
env:
VLLM_USE_V1: 1
VLLM_WORKER_MULTIPROC_METHOD: spawn
VLLM_USE_MODELSCOPE: True
run: |
@ -213,26 +209,6 @@ jobs:
# TODO: revert me when test_v1_spec_decode.py::test_ngram_correctness is fixed
VLLM_USE_MODELSCOPE=True pytest -sv tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py
- name: Run e2e test on V0 engine
if: ${{ github.event_name == 'schedule' }}
env:
VLLM_USE_V1: 0
VLLM_USE_MODELSCOPE: True
run: |
pytest -sv tests/e2e/singlecard/test_offline_inference.py
pytest -sv tests/e2e/singlecard/test_ilama_lora.py
pytest -sv tests/e2e/singlecard/test_guided_decoding.py
pytest -sv tests/e2e/singlecard/test_camem.py
pytest -sv tests/e2e/singlecard/test_prompt_embedding.py
pytest -sv tests/e2e/singlecard/test_embedding.py
pytest -sv tests/e2e/singlecard/ \
--ignore=tests/e2e/singlecard/test_offline_inference.py \
--ignore=tests/e2e/singlecard/test_ilama_lora.py \
--ignore=tests/e2e/singlecard/test_guided_decoding.py \
--ignore=tests/e2e/singlecard/test_camem.py \
--ignore=tests/e2e/singlecard/test_prompt_embedding.py \
--ignore=tests/e2e/singlecard/test_embedding.py
e2e-4-cards:
needs: [e2e]
if: ${{ needs.e2e.result == 'success' }}
@ -290,9 +266,8 @@ jobs:
pip install -r requirements-dev.txt
pip install -v -e .
- name: Run vllm-project/vllm-ascend test for V1 Engine
- name: Run vllm-project/vllm-ascend test
env:
VLLM_USE_V1: 1
VLLM_WORKER_MULTIPROC_METHOD: spawn
VLLM_USE_MODELSCOPE: True
run: |
@ -308,19 +283,3 @@ jobs:
pytest -sv tests/e2e/multicard/ --ignore=tests/e2e/multicard/test_ilama_lora_tp2.py \
--ignore=tests/e2e/multicard/test_offline_inference_distributed.py \
--ignore=tests/e2e/multicard/test_data_parallel.py
- name: Run vllm-project/vllm-ascend test on V0 engine
if: ${{ github.event_name == 'schedule' }}
env:
VLLM_USE_V1: 0
VLLM_USE_MODELSCOPE: True
run: |
pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py
# Fixme: run VLLM_USE_MODELSCOPE=True pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py will raise error.
# To avoid oom, we need to run the test in a single process.
pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_QwQ
pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_W8A8
pytest -sv tests/e2e/multicard/test_data_parallel.py
pytest -sv tests/e2e/multicard/ --ignore=tests/e2e/multicard/test_ilama_lora_tp2.py \
--ignore=tests/e2e/multicard/test_offline_inference_distributed.py \
--ignore=tests/e2e/multicard/test_data_parallel.py

1
examples/offline_data_parallel.py
View File

@ -120,7 +120,6 @@ def main(
trust_remote_code,
):
# DP only support on V1 engine
os.environ["VLLM_USE_V1"] = "1"
os.environ["VLLM_DP_RANK"] = str(global_dp_rank)
os.environ["VLLM_DP_RANK_LOCAL"] = str(local_dp_rank)
os.environ["VLLM_DP_SIZE"] = str(dp_size)

1
examples/offline_dualbatch_overlap_npu.py
View File

@ -5,7 +5,6 @@ from vllm import LLM, SamplingParams
# enable dual-batch overlap for vllm ascend
os.environ["VLLM_ASCEND_ENABLE_DBO"] = "1"
os.environ["VLLM_USE_V1"] = "1"
# Sample prompts.
prompts = ["The president of the United States is"] * 41

1
examples/offline_inference_sleep_mode_npu.py
View File

@ -22,7 +22,6 @@ import torch
from vllm import LLM, SamplingParams
from vllm.utils import GiB_bytes
os.environ["VLLM_USE_V1"] = "1"
os.environ["VLLM_USE_MODELSCOPE"] = "True"
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

1
examples/run_dp_attention_etp16.sh
View File

@ -1,4 +1,3 @@
export VLLM_USE_V1=1
export TASK_QUEUE_ENABLE=1
source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

1
requirements-dev.txt
View File

@ -12,4 +12,5 @@ xgrammar
zmq
types-psutil
pytest-cov
regex
sentence_transformers

1
requirements-lint.txt
View File

@ -4,5 +4,6 @@ pre-commit==4.0.1
# type checking
mypy==1.11.1
types-PyYAML
types-regex
types-requests
types-setuptools

6
tests/conftest.py → tests/e2e/conftest.py
View File

@ -39,8 +39,8 @@ from vllm.sampling_params import BeamSearchParams
from vllm.transformers_utils.utils import maybe_model_redirect
from vllm.utils import is_list_of
from tests.model_utils import (PROMPT_TEMPLATES, TokensTextLogprobs,
TokensTextLogprobsPromptLogprobs)
from tests.e2e.model_utils import (PROMPT_TEMPLATES, TokensTextLogprobs,
TokensTextLogprobsPromptLogprobs)
# TODO: remove this part after the patch merged into vllm, if
# we not explicitly patch here, some of them might be effectiveless
# in pytest scenario
@ -62,7 +62,7 @@ PromptAudioInput = _PromptMultiModalInput[Tuple[np.ndarray, int]]
PromptVideoInput = _PromptMultiModalInput[np.ndarray]
_TEST_DIR = os.path.dirname(__file__)
_TEST_PROMPTS = [os.path.join(_TEST_DIR, "e2e", "prompts", "example.txt")]
_TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
def cleanup_dist_env_and_memory(shutdown_ray: bool = False):

0
tests/model_utils.py → tests/e2e/model_utils.py
View File

15
tests/e2e/multicard/test_fused_moe_allgather_ep.py
View File

@ -26,12 +26,11 @@ from unittest.mock import patch
from modelscope import snapshot_download # type: ignore
from vllm import SamplingParams
from tests.conftest import VllmRunner
from tests.e2e.conftest import VllmRunner
@patch.dict(
os.environ, {
"VLLM_USE_V1": "1",
"VLLM_WORKER_MULTIPROC_METHOD": "spawn",
"TASK_QUEUE_ENABLE": "1",
"VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP": "1"
@ -56,12 +55,10 @@ def test_generate_with_allgather():
vllm_model.generate(example_prompts, sampling_params)
@patch.dict(
os.environ, {
"VLLM_USE_V1": "1",
"VLLM_WORKER_MULTIPROC_METHOD": "spawn",
"TASK_QUEUE_ENABLE": "1"
})
@patch.dict(os.environ, {
"VLLM_WORKER_MULTIPROC_METHOD": "spawn",
"TASK_QUEUE_ENABLE": "1"
})
def test_generate_with_alltoall():
example_prompts = ["Hello, my name is"]
sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
@ -79,4 +76,4 @@ def test_generate_with_alltoall():
},
"expert_tensor_parallel_size": 1
}) as vllm_model:
vllm_model.generate(example_prompts, sampling_params)
vllm_model.generate(example_prompts, sampling_params)

2
tests/e2e/multicard/test_ilama_lora_tp2.py
View File

@ -1,7 +1,7 @@
import pytest
from modelscope import snapshot_download # type: ignore
from tests.conftest import VllmRunner
from tests.e2e.conftest import VllmRunner
from tests.e2e.singlecard.test_ilama_lora import (EXPECTED_LORA_OUTPUT,
MODEL_PATH, do_sample)

2
tests/e2e/multicard/test_offline_inference_distributed.py
View File

@ -27,7 +27,7 @@ from modelscope import snapshot_download # type: ignore
from vllm import SamplingParams
from vllm.model_executor.models.registry import ModelRegistry
from tests.conftest import VllmRunner
from tests.e2e.conftest import VllmRunner
os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"

2
tests/e2e/multicard/test_pipeline_parallel.py
View File

@ -16,7 +16,7 @@
#
import pytest
from tests.conftest import VllmRunner
from tests.e2e.conftest import VllmRunner
MODELS = [
"Qwen/Qwen3-0.6B",

10
tests/e2e/multicard/test_prefix_caching.py
View File

@ -2,12 +2,10 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Compare the with and without prefix caching on V1 scheduler or AscendScheduler."""
import os
import pytest
from tests.conftest import VllmRunner
from tests.model_utils import check_outputs_equal
from tests.e2e.conftest import VllmRunner
from tests.e2e.model_utils import check_outputs_equal
MODELS = [
# for MHA
@ -60,8 +58,6 @@ INPUT_PROMPTS = [
]
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
reason="mtp is not supported on v1")
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("max_tokens", [50])
def test_prefix_cache_with_v1_scheduler(model: str, max_tokens: int) -> None:
@ -89,8 +85,6 @@ def test_prefix_cache_with_v1_scheduler(model: str, max_tokens: int) -> None:
)
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
reason="mtp is not supported on v1")
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("max_tokens", [50])
def test_prefix_cache_with_ascend_scheduler(model: str,

10
tests/e2e/multicard/test_torchair_graph_mode.py
View File

@ -22,9 +22,7 @@ Run `pytest tests/multicard/test_torchair_graph_mode.py`.
import os
from typing import Dict
import pytest
from tests.conftest import VllmRunner
from tests.e2e.conftest import VllmRunner
os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
@ -78,8 +76,6 @@ def _deepseek_torchair_test_fixture(
print(f"Generated text: {vllm_output[i][1]!r}")
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
reason="torchair graph is not supported on v0")
def test_e2e_deepseekv3_with_torchair():
additional_config = {
"torchair_graph_config": {
@ -89,8 +85,6 @@ def test_e2e_deepseekv3_with_torchair():
_deepseek_torchair_test_fixture(additional_config)
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
reason="torchair graph is not supported on v0")
def test_e2e_deepseekv3_with_torchair_ms_mla():
additional_config = {
"torchair_graph_config": {
@ -150,8 +144,6 @@ def _pangu_torchair_test_fixture(
print(f"Generated text: {vllm_output[i][1]!r}")
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
reason="torchair graph is not supported on v0")
def test_e2e_pangu_with_torchair():
additional_config = {
"torchair_graph_config": {

4
tests/e2e/singlecard/core/ascend_scheduler/test_ascend_scheduler_e2e.py
View File

@ -1,15 +1,11 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import gc
import os
import pytest
import torch
from vllm import LLM
if os.getenv("VLLM_USE_V1", "0") != "1":
pytest.skip("Test package requires V1", allow_module_level=True)
MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
PROMPT = "Hello my name is Robert and I"

4
tests/e2e/singlecard/core/ascend_scheduler/test_chunk_prefill.py
View File

@ -9,8 +9,8 @@ Run `pytest tests/e2e/singlecard/core/ascend_scheduler/test_chunk_prefill.py`.
"""
import pytest
from tests.conftest import VllmRunner
from tests.model_utils import check_outputs_equal
from tests.e2e.conftest import VllmRunner
from tests.e2e.model_utils import check_outputs_equal
MODELS = [
"Qwen/Qwen3-0.6B-Base",

54
tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py
View File

@ -53,7 +53,6 @@ def model_name():
@pytest.mark.skipif(
True, reason="TODO: Enable me after test_mtp_correctness is fixed")
def test_mtp_correctness(
monkeypatch: pytest.MonkeyPatch,
test_prompts: list[list[dict[str, Any]]],
sampling_config: SamplingParams,
model_name: str,
@ -62,33 +61,30 @@ def test_mtp_correctness(
Compare the outputs of a original LLM and a speculative LLM
should be the same when using mtp speculative decoding.
'''
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
ref_llm = LLM(model=model_name, max_model_len=256, enforce_eager=True)
ref_outputs = ref_llm.chat(test_prompts, sampling_config)
del ref_llm
ref_llm = LLM(model=model_name, max_model_len=256, enforce_eager=True)
ref_outputs = ref_llm.chat(test_prompts, sampling_config)
del ref_llm
spec_llm = LLM(model=model_name,
trust_remote_code=True,
speculative_config={
"method": "deepseek_mtp",
"num_speculative_tokens": 1,
},
max_model_len=256,
enforce_eager=True)
spec_outputs = spec_llm.chat(test_prompts, sampling_config)
matches = 0
misses = 0
for ref_output, spec_output in zip(ref_outputs, spec_outputs):
if ref_output.outputs[0].text == spec_output.outputs[0].text:
matches += 1
else:
misses += 1
print(f"ref_output: {ref_output.outputs[0].text}")
print(f"spec_output: {spec_output.outputs[0].text}")
spec_llm = LLM(model=model_name,
trust_remote_code=True,
speculative_config={
"method": "deepseek_mtp",
"num_speculative_tokens": 1,
},
max_model_len=256,
enforce_eager=True)
spec_outputs = spec_llm.chat(test_prompts, sampling_config)
matches = 0
misses = 0
for ref_output, spec_output in zip(ref_outputs, spec_outputs):
if ref_output.outputs[0].text == spec_output.outputs[0].text:
matches += 1
else:
misses += 1
print(f"ref_output: {ref_output.outputs[0].text}")
print(f"spec_output: {spec_output.outputs[0].text}")
# Heuristic: expect at least 66% of the prompts to match exactly
# Upon failure, inspect the outputs to check for inaccuracy.
assert matches > int(0.66 * len(ref_outputs))
del spec_llm
# Heuristic: expect at least 66% of the prompts to match exactly
# Upon failure, inspect the outputs to check for inaccuracy.
assert matches > int(0.66 * len(ref_outputs))
del spec_llm

132
tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py
View File

@ -60,7 +60,6 @@ def eagle3_model_name():
def test_ngram_correctness(
monkeypatch: pytest.MonkeyPatch,
test_prompts: list[list[dict[str, Any]]],
sampling_config: SamplingParams,
model_name: str,
@ -70,44 +69,40 @@ def test_ngram_correctness(
should be the same when using ngram speculative decoding.
'''
pytest.skip("Not current support for the test.")
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
ref_llm = LLM(model=model_name, max_model_len=1024, enforce_eager=True)
ref_outputs = ref_llm.chat(test_prompts, sampling_config)
del ref_llm
ref_llm = LLM(model=model_name, max_model_len=1024, enforce_eager=True)
ref_outputs = ref_llm.chat(test_prompts, sampling_config)
del ref_llm
spec_llm = LLM(
model=model_name,
speculative_config={
"method": "ngram",
"prompt_lookup_max": 5,
"prompt_lookup_min": 3,
"num_speculative_tokens": 3,
},
max_model_len=1024,
enforce_eager=True,
)
spec_outputs = spec_llm.chat(test_prompts, sampling_config)
matches = 0
misses = 0
for ref_output, spec_output in zip(ref_outputs, spec_outputs):
if ref_output.outputs[0].text == spec_output.outputs[0].text:
matches += 1
else:
misses += 1
print(f"ref_output: {ref_output.outputs[0].text}")
print(f"spec_output: {spec_output.outputs[0].text}")
spec_llm = LLM(
model=model_name,
speculative_config={
"method": "ngram",
"prompt_lookup_max": 5,
"prompt_lookup_min": 3,
"num_speculative_tokens": 3,
},
max_model_len=1024,
enforce_eager=True,
)
spec_outputs = spec_llm.chat(test_prompts, sampling_config)
matches = 0
misses = 0
for ref_output, spec_output in zip(ref_outputs, spec_outputs):
if ref_output.outputs[0].text == spec_output.outputs[0].text:
matches += 1
else:
misses += 1
print(f"ref_output: {ref_output.outputs[0].text}")
print(f"spec_output: {spec_output.outputs[0].text}")
# Heuristic: expect at least 70% of the prompts to match exactly
# Upon failure, inspect the outputs to check for inaccuracy.
assert matches > int(0.7 * len(ref_outputs))
del spec_llm
# Heuristic: expect at least 70% of the prompts to match exactly
# Upon failure, inspect the outputs to check for inaccuracy.
assert matches > int(0.7 * len(ref_outputs))
del spec_llm
@pytest.mark.parametrize("use_eagle3", [False, True], ids=["eagle", "eagle3"])
def test_eagle_correctness(
monkeypatch: pytest.MonkeyPatch,
test_prompts: list[list[dict[str, Any]]],
sampling_config: SamplingParams,
model_name: str,
@ -119,43 +114,40 @@ def test_eagle_correctness(
'''
if not use_eagle3:
pytest.skip("Not current support for the test.")
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
ref_llm = LLM(model=model_name, max_model_len=2048, enforce_eager=True)
ref_outputs = ref_llm.chat(test_prompts, sampling_config)
del ref_llm
ref_llm = LLM(model=model_name, max_model_len=2048, enforce_eager=True)
ref_outputs = ref_llm.chat(test_prompts, sampling_config)
del ref_llm
spec_model_name = eagle3_model_name(
) if use_eagle3 else eagle_model_name()
spec_llm = LLM(
model=model_name,
trust_remote_code=True,
enable_chunked_prefill=True,
max_num_seqs=1,
max_num_batched_tokens=2048,
gpu_memory_utilization=0.6,
speculative_config={
"method": "eagle3" if use_eagle3 else "eagle",
"model": spec_model_name,
"num_speculative_tokens": 2,
"max_model_len": 128,
},
max_model_len=128,
enforce_eager=True,
)
spec_outputs = spec_llm.chat(test_prompts, sampling_config)
matches = 0
misses = 0
for ref_output, spec_output in zip(ref_outputs, spec_outputs):
if ref_output.outputs[0].text == spec_output.outputs[0].text:
matches += 1
else:
misses += 1
print(f"ref_output: {ref_output.outputs[0].text}")
print(f"spec_output: {spec_output.outputs[0].text}")
spec_model_name = eagle3_model_name() if use_eagle3 else eagle_model_name()
spec_llm = LLM(
model=model_name,
trust_remote_code=True,
enable_chunked_prefill=True,
max_num_seqs=1,
max_num_batched_tokens=2048,
gpu_memory_utilization=0.6,
speculative_config={
"method": "eagle3" if use_eagle3 else "eagle",
"model": spec_model_name,
"num_speculative_tokens": 2,
"max_model_len": 128,
},
max_model_len=128,
enforce_eager=True,
)
spec_outputs = spec_llm.chat(test_prompts, sampling_config)
matches = 0
misses = 0
for ref_output, spec_output in zip(ref_outputs, spec_outputs):
if ref_output.outputs[0].text == spec_output.outputs[0].text:
matches += 1
else:
misses += 1
print(f"ref_output: {ref_output.outputs[0].text}")
print(f"spec_output: {spec_output.outputs[0].text}")
# Heuristic: expect at least 66% of the prompts to match exactly
# Upon failure, inspect the outputs to check for inaccuracy.
assert matches > int(0.66 * len(ref_outputs))
del spec_llm
# Heuristic: expect at least 66% of the prompts to match exactly
# Upon failure, inspect the outputs to check for inaccuracy.
assert matches > int(0.66 * len(ref_outputs))
del spec_llm

47
tests/e2e/singlecard/test_aclgraph.py
View File

@ -20,14 +20,12 @@ Compare the outputs of vLLM with and without aclgraph.
Run `pytest tests/compile/test_aclgraph.py`.
"""
import os
import pytest
import torch
from vllm import LLM, SamplingParams
from tests.conftest import VllmRunner
from tests.model_utils import check_outputs_equal
from tests.e2e.conftest import VllmRunner
from tests.e2e.model_utils import check_outputs_equal
MODELS = [
"Qwen/Qwen2.5-0.5B-Instruct",
@ -36,37 +34,29 @@ MODELS = [
]
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
reason="aclgraph only support on v1")
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("max_tokens", [32])
def test_models(
model: str,
max_tokens: int,
monkeypatch: pytest.MonkeyPatch,
) -> None:
with monkeypatch.context() as m:
prompts = [
"Hello, my name is", "The president of the United States is",
"The capital of France is", "The future of AI is"
]
prompts = [
"Hello, my name is", "The president of the United States is",
"The capital of France is", "The future of AI is"
]
# aclgraph only support on v1
m.setenv("VLLM_USE_V1", "1")
sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
# TODO: change to use vllmrunner when the registry of custom op is solved
# while running pytest
vllm_model = LLM(model)
vllm_aclgraph_outputs = vllm_model.generate(prompts, sampling_params)
del vllm_model
torch.npu.empty_cache()
sampling_params = SamplingParams(max_tokens=max_tokens,
temperature=0.0)
# TODO: change to use vllmrunner when the registry of custom op is solved
# while running pytest
vllm_model = LLM(model)
vllm_aclgraph_outputs = vllm_model.generate(prompts, sampling_params)
del vllm_model
torch.npu.empty_cache()
vllm_model = LLM(model, enforce_eager=True)
vllm_eager_outputs = vllm_model.generate(prompts, sampling_params)
del vllm_model
torch.npu.empty_cache()
vllm_model = LLM(model, enforce_eager=True)
vllm_eager_outputs = vllm_model.generate(prompts, sampling_params)
del vllm_model
torch.npu.empty_cache()
vllm_aclgraph_outputs_list = []
for output in vllm_aclgraph_outputs:
@ -86,12 +76,9 @@ def test_models(
)
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
reason="aclgraph only support on v1")
def test_deepseek_raises_error(monkeypatch: pytest.MonkeyPatch) -> None:
with monkeypatch.context() as m:
m.setenv("VLLM_USE_MODELSCOPE", "True")
m.setenv("VLLM_USE_V1", "1")
with pytest.raises(NotImplementedError) as excinfo:
VllmRunner("deepseek-ai/DeepSeek-V2-Lite-Chat",
max_model_len=1024,

2
tests/e2e/singlecard/test_camem.py
View File

@ -21,7 +21,7 @@ import torch
from vllm import LLM, SamplingParams
from vllm.utils import GiB_bytes
from tests.utils import fork_new_process_for_each_test
from tests.e2e.utils import fork_new_process_for_each_test
from vllm_ascend.device_allocator.camem import CaMemAllocator

57
tests/e2e/singlecard/test_chunked.py
View File

@ -20,8 +20,6 @@ Compare the outputs of vLLM with and without aclgraph.
Run `pytest tests/compile/test_aclgraph.py`.
"""
import os
import pytest
import torch
from vllm import LLM, SamplingParams
@ -29,8 +27,6 @@ from vllm import LLM, SamplingParams
MODELS = ["deepseek-ai/DeepSeek-V2-Lite"]
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
reason="new chunked only support on v1")
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("max_tokens", [1])
def test_models(
@ -39,36 +35,33 @@ def test_models(
monkeypatch: pytest.MonkeyPatch,
) -> None:
return
with monkeypatch.context() as m:
prompts = "The president of the United States is"
m.setenv("VLLM_USE_V1", "1")
prompts = "The president of the United States is"
sampling_params = SamplingParams(
max_tokens=max_tokens,
temperature=0.0,
)
sampling_params = SamplingParams(
max_tokens=max_tokens,
temperature=0.0,
)
vllm_model = LLM(model,
long_prefill_token_threshold=4,
enforce_eager=True)
output_chunked = vllm_model.generate(prompts, sampling_params)
logprobs_chunked = output_chunked.outputs[0].logprobs
del vllm_model
torch.npu.empty_cache()
vllm_model = LLM(model, long_prefill_token_threshold=4, enforce_eager=True)
output_chunked = vllm_model.generate(prompts, sampling_params)
logprobs_chunked = output_chunked.outputs[0].logprobs
del vllm_model
torch.npu.empty_cache()
vllm_model = LLM(model,
enforce_eager=True,
additional_config={
'ascend_scheduler_config': {
'enabled': True
},
})
output = vllm_model.generate(prompts, sampling_params)
logprobs = output.outputs[0].logprobs
del vllm_model
torch.npu.empty_cache()
vllm_model = LLM(model,
enforce_eager=True,
additional_config={
'ascend_scheduler_config': {
'enabled': True
},
})
output = vllm_model.generate(prompts, sampling_params)
logprobs = output.outputs[0].logprobs
del vllm_model
torch.npu.empty_cache()
logprobs_similarity = torch.cosine_similarity(
logprobs_chunked.flatten(), logprobs.flatten(), dim=0)
assert logprobs_similarity > 0.95
logprobs_similarity = torch.cosine_similarity(logprobs_chunked.flatten(),
logprobs.flatten(),
dim=0)
assert logprobs_similarity > 0.95

4
tests/e2e/singlecard/test_embedding.py
View File

@ -21,8 +21,8 @@ from typing import Optional
from modelscope import snapshot_download # type: ignore[import-untyped]
from tests.conftest import HfRunner
from tests.utils import check_embeddings_close, matryoshka_fy
from tests.e2e.conftest import HfRunner
from tests.e2e.utils import check_embeddings_close, matryoshka_fy
def run_embedding_correctness_test(

10
tests/e2e/singlecard/test_guided_decoding.py
View File

@ -18,14 +18,14 @@
#
import json
import os
import re
import jsonschema
import pytest
import regex as re
from vllm.outputs import RequestOutput
from vllm.sampling_params import GuidedDecodingParams, SamplingParams
from tests.conftest import VllmRunner
from tests.e2e.conftest import VllmRunner
os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
@ -85,11 +85,7 @@ def sample_json_schema():
def check_backend(guided_decoding_backend: str):
if guided_decoding_backend not in GuidedDecodingBackendV0 and os.getenv(
"VLLM_USE_V1") == "0":
pytest.skip(f"{guided_decoding_backend} does not support v0, skip it.")
if guided_decoding_backend not in GuidedDecodingBackendV1 and os.getenv(
"VLLM_USE_V1") == "1":
if guided_decoding_backend not in GuidedDecodingBackendV1:
pytest.skip(f"{guided_decoding_backend} does not support v1, skip it.")

2
tests/e2e/singlecard/test_ilama_lora.py
View File

@ -3,7 +3,7 @@ import vllm
from modelscope import snapshot_download # type: ignore
from vllm.lora.request import LoRARequest
from tests.conftest import VllmRunner
from tests.e2e.conftest import VllmRunner
MODEL_PATH = "vllm-ascend/ilama-3.2-1B"

2
tests/e2e/singlecard/test_offline_inference.py
View File

@ -30,7 +30,7 @@ from vllm import SamplingParams
from vllm.assets.image import ImageAsset
import vllm_ascend # noqa: F401
from tests.conftest import VllmRunner
from tests.e2e.conftest import VllmRunner
MODELS = [
"Qwen/Qwen2.5-0.5B-Instruct",

0
tests/utils.py → tests/e2e/utils.py
View File

35
tests/ut/test_ascend_config.py
View File

@ -14,7 +14,6 @@
#
import os
from unittest import mock
from transformers import PretrainedConfig
from vllm.config import ModelConfig, VllmConfig
@ -170,25 +169,23 @@ class TestAscendConfig(TestBase):
init_ascend_config(test_vllm_config)
check_ascend_config(test_vllm_config, False)
# For V1 engine
with mock.patch.dict(os.environ, {"VLLM_USE_V1": "1"}):
test_vllm_config.additional_config = {
"torchair_graph_config": {
"enabled": True,
},
"refresh": True
}
init_ascend_config(test_vllm_config)
check_ascend_config(test_vllm_config, False)
test_vllm_config.additional_config = {
"torchair_graph_config": {
"enabled": True,
},
"refresh": True
}
init_ascend_config(test_vllm_config)
check_ascend_config(test_vllm_config, False)
test_vllm_config.additional_config = {
"torchair_graph_config": {
"enabled": False,
},
"refresh": True
}
init_ascend_config(test_vllm_config)
check_ascend_config(test_vllm_config, False)
test_vllm_config.additional_config = {
"torchair_graph_config": {
"enabled": False,
},
"refresh": True
}
init_ascend_config(test_vllm_config)
check_ascend_config(test_vllm_config, False)
@_clean_up_ascend_config
def test_check_ascend_config_wrong_case(self):

2
tests/ut/test_platform.py
View File

@ -373,7 +373,6 @@ class TestNPUPlatform(TestBase):
@patch("vllm_ascend.utils.is_310p", return_value=False)
@patch("vllm_ascend.ascend_config.check_ascend_config")
@patch("vllm_ascend.ascend_config.init_ascend_config")
@patch("vllm.envs.VLLM_USE_V1", True)
def test_check_and_update_config_v1_worker_class_selection(
self, mock_init_ascend, mock_check_ascend, mock_is_310p):
mock_init_ascend.return_value = self.mock_ascend_config
@ -392,7 +391,6 @@ class TestNPUPlatform(TestBase):
@patch("vllm_ascend.ascend_config.check_ascend_config")
@patch("vllm_ascend.ascend_config.init_ascend_config")
@patch("vllm_ascend.utils.is_310p", return_value=True)
@patch("vllm.envs.VLLM_USE_V1", True)
def test_check_and_update_config_310p_no_custom_ops(
self, mock_is_310p, mock_init_ascend, mock_check_ascend):
mock_init_ascend.return_value = self.mock_ascend_config