311 lines
11 KiB
Python
311 lines
11 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 typing import Any
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import jsonschema
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import pytest
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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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GUIDED_DECODING_BACKENDS_V1 = ["xgrammar"]
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@pytest.fixture
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def model_name():
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return [
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"Qwen/Qwen2.5-1.5B-Instruct", "mistralai/Ministral-8B-Instruct-2410"
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]
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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize("guided_decoding_backend",
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GUIDED_DECODING_BACKENDS_V1)
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def test_guided_json_completion(
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monkeypatch: pytest.MonkeyPatch,
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sample_json_schema: dict[str, Any],
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guided_decoding_backend: 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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llm = LLM(model=model_name, max_model_len=1024)
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sampling_params = SamplingParams(temperature=1.0,
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max_tokens=1000,
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guided_decoding=GuidedDecodingParams(
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json=sample_json_schema,
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backend=guided_decoding_backend))
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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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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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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize("guided_decoding_backend",
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GUIDED_DECODING_BACKENDS_V1)
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def test_guided_json_object(
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monkeypatch: pytest.MonkeyPatch,
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guided_decoding_backend: 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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llm = LLM(model=model_name, max_model_len=1024)
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sampling_params = SamplingParams(temperature=1.0,
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max_tokens=100,
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n=2,
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guided_decoding=GuidedDecodingParams(
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json_object=True,
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backend=guided_decoding_backend))
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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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assert isinstance(parsed_json, dict)
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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize("guided_decoding_backend",
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GUIDED_DECODING_BACKENDS_V1)
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def test_guided_json_unsupported_schema(
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monkeypatch: pytest.MonkeyPatch,
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unsupported_json_schema: dict[str, Any],
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guided_decoding_backend: 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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llm = LLM(model=model_name, max_model_len=1024)
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sampling_params = SamplingParams(temperature=1.0,
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max_tokens=1000,
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guided_decoding=GuidedDecodingParams(
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json=unsupported_json_schema,
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backend=guided_decoding_backend))
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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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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize("guided_decoding_backend",
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GUIDED_DECODING_BACKENDS_V1)
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def test_guided_grammar_ebnf(
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monkeypatch: pytest.MonkeyPatch,
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sample_sql_ebnf: str,
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guided_decoding_backend: 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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llm = LLM(model=model_name, max_model_len=1024)
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sampling_params = SamplingParams(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(
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grammar=sample_sql_ebnf,
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backend=guided_decoding_backend))
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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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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize("guided_decoding_backend",
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GUIDED_DECODING_BACKENDS_V1)
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def test_guided_grammar_lark(
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monkeypatch: pytest.MonkeyPatch,
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sample_sql_lark: str,
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guided_decoding_backend: 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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llm = LLM(model=model_name, max_model_len=1024)
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sampling_params = SamplingParams(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(
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grammar=sample_sql_lark,
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backend=guided_decoding_backend))
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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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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize("guided_decoding_backend",
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GUIDED_DECODING_BACKENDS_V1)
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def test_guided_grammar_ebnf_invalid(
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monkeypatch: pytest.MonkeyPatch,
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guided_decoding_backend: 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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llm = LLM(model=model_name, max_model_len=1024)
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sampling_params = SamplingParams(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(
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grammar="not a grammar",
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backend=guided_decoding_backend))
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with pytest.raises(ValueError,
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match="Failed to convert the grammar "
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"from Lark to EBNF."):
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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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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize("guided_decoding_backend",
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GUIDED_DECODING_BACKENDS_V1)
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def test_guided_regex(
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monkeypatch: pytest.MonkeyPatch,
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sample_regex: str,
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guided_decoding_backend: 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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llm = LLM(model=model_name, max_model_len=1024)
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sampling_params = SamplingParams(temperature=0.8,
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top_p=0.95,
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guided_decoding=GuidedDecodingParams(
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regex=sample_regex,
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backend=guided_decoding_backend))
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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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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize("guided_decoding_backend",
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GUIDED_DECODING_BACKENDS_V1)
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def test_guided_choice_completion(
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monkeypatch: pytest.MonkeyPatch,
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sample_guided_choice: str,
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guided_decoding_backend: 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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llm = LLM(model=model_name, max_model_len=1024)
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sampling_params = SamplingParams(temperature=0.8,
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top_p=0.95,
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guided_decoding=GuidedDecodingParams(
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choice=sample_guided_choice,
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backend=guided_decoding_backend))
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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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