mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 13:20:12 +06:00
a6e36558ef
* Moving `text-generation` pipeline to new testing framework. * Keep check_model_type but log instead of raise Exception. * warning -> error.
109 lines
4.5 KiB
Python
109 lines
4.5 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_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TextGenerationPipeline, pipeline
|
|
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
|
|
|
|
from .test_pipelines_common import ANY, PipelineTestCaseMeta
|
|
|
|
|
|
@is_pipeline_test
|
|
class TextGenerationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
|
|
model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING
|
|
tf_model_mapping = TF_MODEL_FOR_CAUSAL_LM_MAPPING
|
|
|
|
@require_torch
|
|
def test_small_model_pt(self):
|
|
text_generator = pipeline(task="text-generation", model="sshleifer/tiny-ctrl", framework="pt")
|
|
# Using `do_sample=False` to force deterministic output
|
|
outputs = text_generator("This is a test", do_sample=False)
|
|
self.assertEqual(
|
|
outputs,
|
|
[
|
|
{
|
|
"generated_text": "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope. oscope. FiliFili@@"
|
|
}
|
|
],
|
|
)
|
|
|
|
outputs = text_generator(["This is a test", "This is a second test"])
|
|
self.assertEqual(
|
|
outputs,
|
|
[
|
|
[
|
|
{
|
|
"generated_text": "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope. oscope. FiliFili@@"
|
|
}
|
|
],
|
|
[
|
|
{
|
|
"generated_text": "This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope. oscope. FiliFili@@"
|
|
}
|
|
],
|
|
],
|
|
)
|
|
|
|
@require_tf
|
|
def test_small_model_tf(self):
|
|
text_generator = pipeline(task="text-generation", model="sshleifer/tiny-ctrl", framework="tf")
|
|
|
|
# Using `do_sample=False` to force deterministic output
|
|
outputs = text_generator("This is a test", do_sample=False)
|
|
self.assertEqual(
|
|
outputs,
|
|
[
|
|
{
|
|
"generated_text": "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵 please,"
|
|
}
|
|
],
|
|
)
|
|
|
|
outputs = text_generator(["This is a test", "This is a second test"], do_sample=False)
|
|
self.assertEqual(
|
|
outputs,
|
|
[
|
|
[
|
|
{
|
|
"generated_text": "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵 please,"
|
|
}
|
|
],
|
|
[
|
|
{
|
|
"generated_text": "This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵 please,"
|
|
}
|
|
],
|
|
],
|
|
)
|
|
|
|
def run_pipeline_test(self, model, tokenizer, feature_extractor):
|
|
text_generator = TextGenerationPipeline(model=model, tokenizer=tokenizer)
|
|
outputs = text_generator("This is a test")
|
|
self.assertEqual(outputs, [{"generated_text": ANY(str)}])
|
|
self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))
|
|
|
|
outputs = text_generator("This is a test", return_full_text=False)
|
|
self.assertEqual(outputs, [{"generated_text": ANY(str)}])
|
|
self.assertNotIn("This is a test", outputs[0]["generated_text"])
|
|
|
|
text_generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer, return_full_text=False)
|
|
outputs = text_generator("This is a test")
|
|
self.assertEqual(outputs, [{"generated_text": ANY(str)}])
|
|
self.assertNotIn("This is a test", outputs[0]["generated_text"])
|
|
|
|
outputs = text_generator("This is a test", return_full_text=True)
|
|
self.assertEqual(outputs, [{"generated_text": ANY(str)}])
|
|
self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))
|