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HuggingFace_transformer/tests/generation/test_framework_agnostic.py
2023-01-31 11:33:18 +00:00

42 lines
1.6 KiB
Python

"""
Framework agnostic tests for generate()-related methods.
"""
import numpy as np
from transformers import AutoTokenizer
class GenerationIntegrationTestsMixin:
# To be populated by the child classes
framework_dependent_parameters = {
"AutoModelForSeq2SeqLM": None,
"create_tensor_fn": None,
"return_tensors": None,
}
def test_validate_generation_inputs(self):
model_cls = self.framework_dependent_parameters["AutoModelForSeq2SeqLM"]
return_tensors = self.framework_dependent_parameters["return_tensors"]
create_tensor_fn = self.framework_dependent_parameters["create_tensor_fn"]
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-t5")
encoder_input_str = "Hello world"
input_ids = tokenizer(encoder_input_str, return_tensors=return_tensors).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(ValueError, "do_samples"):
model.generate(input_ids, do_samples=True)
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(ValueError, "foo"):
fake_model_kwargs = {"foo": "bar"}
model.generate(input_ids, **fake_model_kwargs)
# however, valid model_kwargs are accepted
valid_model_kwargs = {"attention_mask": create_tensor_fn(np.zeros_like(input_ids))}
model.generate(input_ids, **valid_model_kwargs)