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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
Text2TextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class Text2TextGenerationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
tf_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def get_test_pipeline(
self,
model,
tokenizer=None,
image_processor=None,
feature_extractor=None,
processor=None,
torch_dtype="float32",
):
generator = Text2TextGenerationPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
image_processor=image_processor,
processor=processor,
torch_dtype=torch_dtype,
max_new_tokens=20,
)
return generator, ["Something to write", "Something else"]
def run_pipeline_test(self, generator, _):
outputs = generator("Something there")
self.assertEqual(outputs, [{"generated_text": ANY(str)}])
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))
outputs = generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True)
self.assertEqual(
outputs,
[
[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
],
)
outputs = generator(
["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True
)
self.assertEqual(
outputs,
[
[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
],
)
with self.assertRaises(ValueError):
generator(4)
@require_torch
def test_small_model_pt(self):
generator = pipeline(
"text2text-generation",
model="patrickvonplaten/t5-tiny-random",
framework="pt",
num_beams=1,
max_new_tokens=9,
)
# do_sample=False necessary for reproducibility
outputs = generator("Something there", do_sample=False)
self.assertEqual(outputs, [{"generated_text": ""}])
num_return_sequences = 3
outputs = generator(
"Something there",
num_return_sequences=num_return_sequences,
num_beams=num_return_sequences,
)
target_outputs = [
{"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
{"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
{"generated_text": ""},
]
self.assertEqual(outputs, target_outputs)
outputs = generator("This is a test", do_sample=True, num_return_sequences=2, return_tensors=True)
self.assertEqual(
outputs,
[
{"generated_token_ids": ANY(torch.Tensor)},
{"generated_token_ids": ANY(torch.Tensor)},
],
)
generator.tokenizer.pad_token_id = generator.model.config.eos_token_id
generator.tokenizer.pad_token = "<pad>"
outputs = generator(
["This is a test", "This is a second test"],
do_sample=True,
num_return_sequences=2,
batch_size=2,
return_tensors=True,
)
self.assertEqual(
outputs,
[
[
{"generated_token_ids": ANY(torch.Tensor)},
{"generated_token_ids": ANY(torch.Tensor)},
],
[
{"generated_token_ids": ANY(torch.Tensor)},
{"generated_token_ids": ANY(torch.Tensor)},
],
],
)
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