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6f79d26442
* Result of black 23.1 * Update target to Python 3.7 * Switch flake8 to ruff * Configure isort * Configure isort * Apply isort with line limit * Put the right black version * adapt black in check copies * Fix copies
63 lines
2.9 KiB
Python
63 lines
2.9 KiB
Python
"""
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Framework agnostic tests for generate()-related methods.
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"""
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import numpy as np
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from transformers import AutoTokenizer
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class GenerationIntegrationTestsMixin:
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# To be populated by the child classes
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framework_dependent_parameters = {
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"AutoModelForSeq2SeqLM": None,
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"LogitsProcessorList": None,
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"MinLengthLogitsProcessor": None,
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"create_tensor_fn": None,
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"return_tensors": None,
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}
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def test_validate_generation_inputs(self):
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model_cls = self.framework_dependent_parameters["AutoModelForSeq2SeqLM"]
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return_tensors = self.framework_dependent_parameters["return_tensors"]
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create_tensor_fn = self.framework_dependent_parameters["create_tensor_fn"]
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
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model = model_cls.from_pretrained("hf-internal-testing/tiny-random-t5")
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encoder_input_str = "Hello world"
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input_ids = tokenizer(encoder_input_str, return_tensors=return_tensors).input_ids
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# typos are quickly detected (the correct argument is `do_sample`)
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with self.assertRaisesRegex(ValueError, "do_samples"):
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model.generate(input_ids, do_samples=True)
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# arbitrary arguments that will not be used anywhere are also not accepted
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with self.assertRaisesRegex(ValueError, "foo"):
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fake_model_kwargs = {"foo": "bar"}
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model.generate(input_ids, **fake_model_kwargs)
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# however, valid model_kwargs are accepted
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valid_model_kwargs = {"attention_mask": create_tensor_fn(np.zeros_like(input_ids))}
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model.generate(input_ids, **valid_model_kwargs)
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def test_custom_logits_processor(self):
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model_cls = self.framework_dependent_parameters["AutoModelForSeq2SeqLM"]
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logits_processor_list_cls = self.framework_dependent_parameters["LogitsProcessorList"]
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min_length_logits_processor_cls = self.framework_dependent_parameters["MinLengthLogitsProcessor"]
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return_tensors = self.framework_dependent_parameters["return_tensors"]
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bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
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article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
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bart_model = model_cls.from_pretrained("hf-internal-testing/tiny-random-bart", min_length=1)
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input_ids = bart_tokenizer(article, return_tensors=return_tensors).input_ids
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logits_processor = logits_processor_list_cls()
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logits_processor.append(min_length_logits_processor_cls(min_length=10, eos_token_id=0))
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# it should not be allowed to both define `min_length` via config and `logits_processor` list
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with self.assertRaises(ValueError):
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bart_model.generate(input_ids, logits_processor=logits_processor)
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bart_model.config.min_length = None
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bart_model.generate(input_ids, logits_processor=logits_processor)
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