9323a3153b
Signed-off-by: Aaron Pham <contact@aarnphm.xyz> Signed-off-by: mgoin <michael@neuralmagic.com> Co-authored-by: mgoin <michael@neuralmagic.com>
87 lines
3.6 KiB
Python
87 lines
3.6 KiB
Python
import pytest
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import torch
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from transformers import AutoTokenizer
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from vllm.model_executor.guided_decoding import (
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get_guided_decoding_logits_processor)
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from vllm.model_executor.guided_decoding.outlines_logits_processors import (
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JSONLogitsProcessor, RegexLogitsProcessor)
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from vllm.sampling_params import GuidedDecodingParams
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def test_guided_logits_processors(sample_regex, sample_json_schema):
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"""Basic unit test for RegexLogitsProcessor and JSONLogitsProcessor."""
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tokenizer = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-beta')
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regex_LP = RegexLogitsProcessor(sample_regex, tokenizer)
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json_LP = JSONLogitsProcessor(sample_json_schema,
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tokenizer,
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whitespace_pattern=None)
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token_ids = tokenizer.encode(
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f"Give an example IPv4 address with this regex: {sample_regex}")
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tensor = torch.rand(32000)
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original_tensor = torch.clone(tensor)
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regex_LP(token_ids, tensor)
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assert tensor.shape == original_tensor.shape
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assert not torch.allclose(tensor, original_tensor)
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token_ids = tokenizer.encode(
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f"Give an employee profile that fits this schema: {sample_json_schema}"
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)
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tensor = torch.rand(32000)
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original_tensor = torch.clone(tensor)
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json_LP(token_ids, tensor)
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assert tensor.shape == original_tensor.shape
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assert not torch.allclose(tensor, original_tensor)
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@pytest.mark.asyncio
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@pytest.mark.parametrize("backend",
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["outlines", "lm-format-enforcer", "xgrammar"])
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async def test_guided_logits_processor_black_box(backend: str, sample_regex,
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sample_json_schema):
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tokenizer = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-beta')
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token_ids = tokenizer.encode(
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f"Give an example IPv4 address with this regex: {sample_regex}")
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regex_request = GuidedDecodingParams(regex=sample_regex, backend=backend)
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regex_lp = await get_guided_decoding_logits_processor(
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regex_request, tokenizer)
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assert regex_lp is not None
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tensor = torch.rand(32000)
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original_tensor = torch.clone(tensor)
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tensor = regex_lp(token_ids, tensor)
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assert tensor.shape == original_tensor.shape
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assert not torch.allclose(tensor, original_tensor)
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token_ids = tokenizer.encode(
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f"Give an employee profile that fits this schema: {sample_json_schema}"
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)
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json_request = GuidedDecodingParams(json=sample_json_schema,
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backend=backend)
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json_lp = await get_guided_decoding_logits_processor(
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json_request, tokenizer)
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assert json_lp is not None
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tensor = torch.rand(32000)
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original_tensor = torch.clone(tensor)
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tensor = json_lp(token_ids, tensor)
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assert tensor.shape == original_tensor.shape
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assert not torch.allclose(tensor, original_tensor)
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def test_multiple_guided_options_not_allowed(sample_json_schema, sample_regex):
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with pytest.raises(ValueError,
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match="You can only use one kind of guided"):
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GuidedDecodingParams(json=sample_json_schema, regex=sample_regex)
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with pytest.raises(ValueError,
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match="You can only use one kind of guided"):
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GuidedDecodingParams(json=sample_json_schema, json_object=True)
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with pytest.raises(ValueError,
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match="You can only use one kind of guided"):
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GuidedDecodingParams(json=sample_json_schema, choice=["a", "b"])
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with pytest.raises(ValueError,
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match="You can only use one kind of guided"):
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GuidedDecodingParams(json=sample_json_schema, grammar="test grammar")
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