128 lines
4.7 KiB
Python
128 lines
4.7 KiB
Python
# 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_tf, 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, processor):
|
|
generator = Text2TextGenerationPipeline(model=model, tokenizer=tokenizer)
|
|
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")
|
|
# 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)},
|
|
],
|
|
],
|
|
)
|
|
|
|
@require_tf
|
|
def test_small_model_tf(self):
|
|
generator = pipeline("text2text-generation", model="patrickvonplaten/t5-tiny-random", framework="tf")
|
|
# do_sample=False necessary for reproducibility
|
|
outputs = generator("Something there", do_sample=False)
|
|
self.assertEqual(outputs, [{"generated_text": ""}])
|