f96100fad5
### What this PR does / why we need it?
Remove V0 related codes of test, example, platform.
This PR is a part of
https://github.com/vllm-project/vllm-ascend/issues/1620.
- vLLM version: v0.9.2
- vLLM main:
235bfd5dfe
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
150 lines
5.0 KiB
Python
150 lines
5.0 KiB
Python
#
|
|
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
# This file is a part of the vllm-ascend project.
|
|
# Adapted from vllm/tests/entrypoints/llm/test_guided_generate.py
|
|
# 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.
|
|
#
|
|
import json
|
|
import os
|
|
|
|
import jsonschema
|
|
import pytest
|
|
import regex as re
|
|
from vllm.outputs import RequestOutput
|
|
from vllm.sampling_params import GuidedDecodingParams, SamplingParams
|
|
|
|
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"
|
|
|
|
GuidedDecodingBackend = ["xgrammar", "guidance"]
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def sample_regex():
|
|
return (r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}"
|
|
r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)")
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def sample_json_schema():
|
|
return {
|
|
"type": "object",
|
|
"properties": {
|
|
"name": {
|
|
"type": "string"
|
|
},
|
|
"age": {
|
|
"type": "integer"
|
|
},
|
|
"skills": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "string",
|
|
"maxLength": 10
|
|
},
|
|
"minItems": 3
|
|
},
|
|
"work_history": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "object",
|
|
"properties": {
|
|
"company": {
|
|
"type": "string"
|
|
},
|
|
"duration": {
|
|
"type": "number"
|
|
},
|
|
"position": {
|
|
"type": "string"
|
|
}
|
|
},
|
|
"required": ["company", "position"]
|
|
}
|
|
}
|
|
},
|
|
"required": ["name", "age", "skills", "work_history"]
|
|
}
|
|
|
|
|
|
@pytest.mark.parametrize("guided_decoding_backend", GuidedDecodingBackend)
|
|
def test_guided_json_completion(guided_decoding_backend: str,
|
|
sample_json_schema):
|
|
sampling_params = SamplingParams(
|
|
temperature=1.0,
|
|
max_tokens=500,
|
|
guided_decoding=GuidedDecodingParams(json=sample_json_schema))
|
|
|
|
with VllmRunner(
|
|
MODEL_NAME,
|
|
seed=0,
|
|
dtype="auto",
|
|
guided_decoding_backend=guided_decoding_backend,
|
|
) as vllm_model:
|
|
prompts = [
|
|
f"Give an example JSON for an employee profile "
|
|
f"that fits this schema: {sample_json_schema}"
|
|
] * 2
|
|
inputs = vllm_model.get_inputs(prompts)
|
|
outputs = vllm_model.model.generate(inputs,
|
|
sampling_params=sampling_params)
|
|
|
|
assert outputs is not None
|
|
|
|
for output in outputs:
|
|
assert output is not None
|
|
assert isinstance(output, RequestOutput)
|
|
prompt = output.prompt
|
|
|
|
generated_text = output.outputs[0].text
|
|
assert generated_text is not None
|
|
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|
|
output_json = json.loads(generated_text)
|
|
jsonschema.validate(instance=output_json,
|
|
schema=sample_json_schema)
|
|
|
|
|
|
@pytest.mark.parametrize("guided_decoding_backend", GuidedDecodingBackend)
|
|
def test_guided_regex(guided_decoding_backend: str, sample_regex):
|
|
sampling_params = SamplingParams(
|
|
temperature=0.8,
|
|
top_p=0.95,
|
|
guided_decoding=GuidedDecodingParams(regex=sample_regex))
|
|
|
|
with VllmRunner(
|
|
MODEL_NAME,
|
|
seed=0,
|
|
dtype="auto",
|
|
guided_decoding_backend=guided_decoding_backend,
|
|
) as vllm_model:
|
|
prompts = [
|
|
f"Give an example IPv4 address with this regex: {sample_regex}"
|
|
] * 2
|
|
inputs = vllm_model.get_inputs(prompts)
|
|
outputs = vllm_model.model.generate(inputs,
|
|
sampling_params=sampling_params)
|
|
assert outputs is not None
|
|
for output in outputs:
|
|
assert output is not None
|
|
assert isinstance(output, RequestOutput)
|
|
prompt = output.prompt
|
|
generated_text = output.outputs[0].text
|
|
print(generated_text)
|
|
assert generated_text is not None
|
|
assert re.fullmatch(".*", generated_text) is not None
|
|
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|