[CI] Add test case with JSON schema using references + use xgrammar by default with OpenAI parse (#10935)
Signed-off-by: mgoin <michael@neuralmagic.com>
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@ -100,6 +100,45 @@ def sample_complex_json_schema():
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}
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@pytest.fixture
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def sample_definition_json_schema():
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return {
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'$defs': {
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'Step': {
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'properties': {
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'explanation': {
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'title': 'Explanation',
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'type': 'string'
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},
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'output': {
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'title': 'Output',
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'type': 'string'
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}
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},
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'required': ['explanation', 'output'],
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'title': 'Step',
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'type': 'object'
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}
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},
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'properties': {
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'steps': {
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'items': {
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'$ref': '#/$defs/Step'
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},
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'title': 'Steps',
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'type': 'array'
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},
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'final_answer': {
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'title': 'Final Answer',
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'type': 'string'
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}
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},
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'required': ['steps', 'final_answer'],
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'title': 'MathReasoning',
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'type': 'object'
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}
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@pytest.fixture
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def sample_guided_choice():
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return [
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@ -104,6 +104,34 @@ def test_guided_complex_json_completion(sample_complex_json_schema, llm):
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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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@ -387,7 +387,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
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assert json_schema is not None
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self.guided_json = json_schema.json_schema
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if self.guided_decoding_backend is None:
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self.guided_decoding_backend = "lm-format-enforcer"
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self.guided_decoding_backend = "xgrammar"
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guided_decoding = GuidedDecodingParams.from_optional(
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json=self._get_guided_json_from_tool() or self.guided_json,
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