mirror of
https://github.com/vllm-project/vllm.git
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375 lines
13 KiB
Python
375 lines
13 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import json
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import re
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from enum import Enum
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from typing import Any
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import jsonschema
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import pytest
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from pydantic import BaseModel
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from vllm.entrypoints.llm import LLM
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from vllm.outputs import RequestOutput
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from vllm.sampling_params import GuidedDecodingParams, SamplingParams
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PARAMS_MODELS_BACKENDS_TOKENIZER_MODE = [
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("mistralai/Ministral-8B-Instruct-2410", "xgrammar:disable-any-whitespace",
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"auto"),
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("mistralai/Ministral-8B-Instruct-2410", "guidance:disable-any-whitespace",
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"auto"),
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("mistralai/Ministral-8B-Instruct-2410", "xgrammar:disable-any-whitespace",
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"mistral"),
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("Qwen/Qwen2.5-1.5B-Instruct", "xgrammar:disable-any-whitespace", "auto"),
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("Qwen/Qwen2.5-1.5B-Instruct", "guidance:disable-any-whitespace", "auto"),
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]
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PARAMS_MODELS_TOKENIZER_MODE = [
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("mistralai/Ministral-8B-Instruct-2410", "auto"),
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("Qwen/Qwen2.5-1.5B-Instruct", "auto"),
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]
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class CarType(str, Enum):
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sedan = "sedan"
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suv = "SUV"
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truck = "Truck"
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coupe = "Coupe"
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class CarDescription(BaseModel):
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brand: str
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model: str
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car_type: CarType
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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize("model_name, guided_decoding_backend, tokenizer_mode",
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PARAMS_MODELS_BACKENDS_TOKENIZER_MODE)
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def test_structured_output(
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monkeypatch: pytest.MonkeyPatch,
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sample_json_schema: dict[str, Any],
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unsupported_json_schema: dict[str, Any],
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sample_sql_ebnf: str,
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sample_sql_lark: str,
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sample_regex: str,
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sample_guided_choice: str,
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guided_decoding_backend: str,
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tokenizer_mode: str,
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model_name: str,
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):
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monkeypatch.setenv("VLLM_USE_V1", "1")
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# Use a single LLM instance for several scenarios to
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# speed up the test suite.
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llm = LLM(model=model_name,
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enforce_eager=True,
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max_model_len=1024,
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guided_decoding_backend=guided_decoding_backend,
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tokenizer_mode=tokenizer_mode)
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#
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# Test 1: Generate JSON output based on a provided schema
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#
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sampling_params = SamplingParams(
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temperature=1.0,
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max_tokens=1000,
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guided_decoding=GuidedDecodingParams(json=sample_json_schema))
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outputs = llm.generate(prompts=[
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f"Give an example JSON for an employee profile "
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f"that fits this schema: {sample_json_schema}"
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] * 2,
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sampling_params=sampling_params,
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use_tqdm=True)
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assert outputs is not None
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for output in outputs:
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assert output is not None
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assert isinstance(output, RequestOutput)
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prompt = output.prompt
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generated_text = output.outputs[0].text
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assert generated_text is not None
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if 'disable-any-whitespace' in guided_decoding_backend:
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assert "\n" not in generated_text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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output_json = json.loads(generated_text)
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jsonschema.validate(instance=output_json, schema=sample_json_schema)
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#
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# Test 2: Generate JSON object without a schema
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#
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sampling_params = SamplingParams(
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temperature=1.0,
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max_tokens=100,
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n=2,
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guided_decoding=GuidedDecodingParams(json_object=True))
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outputs = llm.generate(
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prompts=("Generate a JSON object with curly braces for a person with "
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"name and age fields for John Smith who is 31 years old."),
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sampling_params=sampling_params,
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use_tqdm=True)
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assert outputs is not None
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for output in outputs:
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assert output is not None
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assert isinstance(output, RequestOutput)
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for i in range(2):
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generated_text = output.outputs[i].text
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print(generated_text)
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assert generated_text is not None
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# Parse to verify it is valid JSON
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parsed_json = json.loads(generated_text)
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allowed_types: tuple[type, ...] = (dict, )
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if guided_decoding_backend.startswith("xgrammar"):
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# TODO - we are currently too permissive with xgrammar and
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# allow # any valid json (typically comes back as a list or
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# object). We can fix this by specifying a jsonschema of
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# {"type": "object"}, # but we need this fix in a release
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# first: https://github.com/mlc-ai/xgrammar/pull/264
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allowed_types = (dict, list)
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assert isinstance(parsed_json, allowed_types)
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#
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# Test 3: test a jsonschema incompatible with xgrammar
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#
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sampling_params = SamplingParams(
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temperature=1.0,
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max_tokens=1000,
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guided_decoding=GuidedDecodingParams(json=unsupported_json_schema))
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if guided_decoding_backend.startswith("xgrammar"):
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with pytest.raises(ValueError,
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match="The provided JSON schema contains features "
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"not supported by xgrammar."):
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llm.generate(prompts=[
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f"Give an example JSON for an employee profile "
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f"that fits this schema: {unsupported_json_schema}"
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] * 2,
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sampling_params=sampling_params,
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use_tqdm=True)
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else:
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outputs = llm.generate(
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prompts=("Give an example JSON object for a grade "
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"that fits this schema: "
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f"{unsupported_json_schema}"),
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sampling_params=sampling_params,
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use_tqdm=True)
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assert outputs is not None
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for output in outputs:
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assert output is not None
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assert isinstance(output, RequestOutput)
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generated_text = output.outputs[0].text
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assert generated_text is not None
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print(generated_text)
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# Parse to verify it is valid JSON
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parsed_json = json.loads(generated_text)
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assert isinstance(parsed_json, dict)
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#
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# Test 4: Generate SQL statement using EBNF grammar
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#
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sampling_params = SamplingParams(
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temperature=0.8,
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top_p=0.95,
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max_tokens=1000,
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guided_decoding=GuidedDecodingParams(grammar=sample_sql_ebnf))
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outputs = llm.generate(
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prompts=("Generate a sql statement that selects col_1 from "
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"table_1 where it is equal to 1"),
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sampling_params=sampling_params,
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use_tqdm=True,
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)
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assert outputs is not None
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for output in outputs:
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assert output is not None
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assert isinstance(output, RequestOutput)
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prompt = output.prompt
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generated_text = output.outputs[0].text
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assert generated_text is not None
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# remove spaces for comparison b/c we removed them in the grammar
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ground_truth = "SELECT col_1 from table_1 where col_1 = 1".replace(
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" ", "")
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assert generated_text.strip() == ground_truth
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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#
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# Test 5: Generate SQL statement using Lark grammar
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#
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sampling_params = SamplingParams(
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temperature=0.8,
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top_p=0.95,
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max_tokens=1000,
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guided_decoding=GuidedDecodingParams(grammar=sample_sql_lark))
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outputs = llm.generate(
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prompts=("Generate a sql statement that selects col_1 from "
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"table_1 where it is equal to 1"),
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sampling_params=sampling_params,
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use_tqdm=True,
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)
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assert outputs is not None
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for output in outputs:
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assert output is not None
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assert isinstance(output, RequestOutput)
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prompt = output.prompt
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generated_text = output.outputs[0].text
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assert generated_text is not None
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# use Lark to parse the output, and make sure it's a valid parse tree
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from lark import Lark
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parser = Lark(sample_sql_lark)
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parser.parse(generated_text)
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# remove spaces for comparison b/c we removed them in the grammar
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ground_truth = "SELECT col_1 from table_1 where col_1 = 1".replace(
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" ", "")
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assert generated_text.strip() == ground_truth
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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#
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# Test 6: Test invalid grammar input
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#
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sampling_params = SamplingParams(
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temperature=0.8,
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top_p=0.95,
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max_tokens=1000,
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guided_decoding=GuidedDecodingParams(grammar="not a grammar"))
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with pytest.raises(ValueError, match="Failed to convert the grammar "):
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llm.generate(
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prompts=("Generate a sql statement that selects col_1 from "
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"table_1 where it is equal to 1"),
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sampling_params=sampling_params,
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use_tqdm=True,
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)
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#
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# Test 7: Generate text based on a regex pattern
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#
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sampling_params = SamplingParams(
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temperature=0.8,
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top_p=0.95,
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guided_decoding=GuidedDecodingParams(regex=sample_regex))
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outputs = llm.generate(
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prompts=[
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f"Give an example IPv4 address with this regex: {sample_regex}"
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] * 2,
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sampling_params=sampling_params,
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use_tqdm=True,
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)
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assert outputs is not None
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for output in outputs:
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assert output is not None
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assert isinstance(output, RequestOutput)
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(generated_text)
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assert generated_text is not None
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assert re.fullmatch(sample_regex, generated_text) is not None
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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#
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# Test 8: Generate text based on a choices
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#
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sampling_params = SamplingParams(
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temperature=0.8,
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top_p=0.95,
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guided_decoding=GuidedDecodingParams(choice=sample_guided_choice))
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outputs = llm.generate(
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prompts="The best language for type-safe systems programming is ",
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sampling_params=sampling_params,
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use_tqdm=True)
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assert outputs is not None
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for output in outputs:
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assert output is not None
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assert isinstance(output, RequestOutput)
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(generated_text)
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assert generated_text is not None
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assert generated_text in sample_guided_choice
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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#
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# Test 9: Generate structured output using a Pydantic model with an enum
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#
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json_schema = CarDescription.model_json_schema()
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sampling_params = SamplingParams(
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temperature=1.0,
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max_tokens=1000,
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guided_decoding=GuidedDecodingParams(json=json_schema))
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outputs = llm.generate(
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prompts="Generate a JSON with the brand, model and car_type of"
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"the most iconic car from the 90's",
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sampling_params=sampling_params,
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use_tqdm=True)
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assert outputs is not None
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for output in outputs:
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assert output is not None
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assert isinstance(output, RequestOutput)
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prompt = output.prompt
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generated_text = output.outputs[0].text
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assert generated_text is not None
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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output_json = json.loads(generated_text)
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jsonschema.validate(instance=output_json, schema=json_schema)
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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize("model_name, tokenizer_mode",
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PARAMS_MODELS_TOKENIZER_MODE)
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def test_structured_output_auto_mode(
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monkeypatch: pytest.MonkeyPatch,
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unsupported_json_schema: dict[str, Any],
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model_name: str,
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tokenizer_mode: str,
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):
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monkeypatch.setenv("VLLM_USE_V1", "1")
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llm = LLM(model=model_name,
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max_model_len=1024,
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guided_decoding_backend="auto",
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tokenizer_mode=tokenizer_mode)
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sampling_params = SamplingParams(
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temperature=1.0,
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max_tokens=1000,
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guided_decoding=GuidedDecodingParams(json=unsupported_json_schema))
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# This would fail with the default of "xgrammar", but in "auto"
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# we will handle fallback automatically.
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outputs = llm.generate(prompts=("Give an example JSON object for a grade "
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"that fits this schema: "
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f"{unsupported_json_schema}"),
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sampling_params=sampling_params,
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use_tqdm=True)
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assert outputs is not None
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for output in outputs:
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assert output is not None
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assert isinstance(output, RequestOutput)
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generated_text = output.outputs[0].text
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assert generated_text is not None
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print(generated_text)
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# Parse to verify it is valid JSON
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parsed_json = json.loads(generated_text)
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assert isinstance(parsed_json, dict)
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