245 lines
8.4 KiB
Python
245 lines
8.4 KiB
Python
import json
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import re
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import weakref
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import jsonschema
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import pytest
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from vllm.distributed import cleanup_dist_env_and_memory
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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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MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
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@pytest.fixture(scope="module")
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def llm():
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# pytest caches the fixture so we use weakref.proxy to
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# enable garbage collection
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llm = LLM(model=MODEL_NAME, max_model_len=1024)
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with llm.deprecate_legacy_api():
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yield weakref.proxy(llm)
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del llm
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cleanup_dist_env_and_memory()
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@pytest.mark.skip_global_cleanup
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def test_guided_regex(sample_regex, llm):
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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(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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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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def test_guided_json_completion(sample_json_schema, llm):
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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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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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def test_guided_complex_json_completion(sample_complex_json_schema, llm):
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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_complex_json_schema))
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outputs = llm.generate(prompts=[
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f"Give an example JSON for an assignment grade "
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f"that fits this schema: {sample_complex_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,
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schema=sample_complex_json_schema)
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@pytest.mark.skip_global_cleanup
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def test_guided_definition_json_completion(sample_definition_json_schema, llm):
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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_definition_json_schema))
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outputs = llm.generate(prompts=[
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f"Give an example JSON for solving 8x + 7 = -23 "
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f"that fits this schema: {sample_definition_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,
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schema=sample_definition_json_schema)
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@pytest.mark.skip_global_cleanup
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def test_guided_choice_completion(sample_guided_choice, llm):
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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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@pytest.mark.skip_global_cleanup
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def test_guided_grammar(sample_sql_statements, llm):
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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_statements))
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outputs = llm.generate(
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prompts=("Generate a sql state that select col_1 from "
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"table_1 where it is equals 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_statements)
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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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def test_guided_options_request_deprecation_warning(sample_regex, llm):
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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with pytest.warns(DeprecationWarning, match="guided_options_request"):
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llm.generate(prompts="This should fail",
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sampling_params=sampling_params,
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use_tqdm=True,
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guided_options_request=dict(guided_regex=sample_regex))
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@pytest.mark.skip_global_cleanup
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def test_validation_against_both_guided_decoding_options(sample_regex, llm):
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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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with pytest.raises(ValueError, match="Cannot set both"):
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llm.generate(prompts="This should fail",
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sampling_params=sampling_params,
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use_tqdm=True,
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guided_options_request=dict(guided_regex=sample_regex))
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@pytest.mark.skip_global_cleanup
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def test_guided_json_object(llm):
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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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guided_decoding=GuidedDecodingParams(json_object=True))
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outputs = llm.generate(
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prompts=("Generate a JSON object describing a person with name "
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"and age 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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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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# 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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