245 lines
8.9 KiB
Python
245 lines
8.9 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.
|
|
|
|
from typing import List, Optional
|
|
from unittest import mock
|
|
|
|
from transformers import is_tf_available, is_torch_available, pipeline
|
|
from transformers.pipelines import Pipeline
|
|
from transformers.testing_utils import _run_slow_tests, is_pipeline_test, require_tf, require_torch, slow
|
|
from transformers.tokenization_utils_base import to_py_obj
|
|
|
|
|
|
VALID_INPUTS = ["A simple string", ["list of strings"]]
|
|
|
|
|
|
@is_pipeline_test
|
|
class CustomInputPipelineCommonMixin:
|
|
pipeline_task = None
|
|
pipeline_loading_kwargs = {} # Additional kwargs to load the pipeline with
|
|
pipeline_running_kwargs = {} # Additional kwargs to run the pipeline with
|
|
small_models = [] # Models tested without the @slow decorator
|
|
large_models = [] # Models tested with the @slow decorator
|
|
valid_inputs = VALID_INPUTS # Some inputs which are valid to compare fast and slow tokenizers
|
|
|
|
def setUp(self) -> None:
|
|
if not is_tf_available() and not is_torch_available():
|
|
return # Currently no JAX pipelines
|
|
|
|
# Download needed checkpoints
|
|
models = self.small_models
|
|
if _run_slow_tests:
|
|
models = models + self.large_models
|
|
|
|
for model_name in models:
|
|
if is_torch_available():
|
|
pipeline(
|
|
self.pipeline_task,
|
|
model=model_name,
|
|
tokenizer=model_name,
|
|
framework="pt",
|
|
**self.pipeline_loading_kwargs,
|
|
)
|
|
if is_tf_available():
|
|
pipeline(
|
|
self.pipeline_task,
|
|
model=model_name,
|
|
tokenizer=model_name,
|
|
framework="tf",
|
|
**self.pipeline_loading_kwargs,
|
|
)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_pt_defaults(self):
|
|
pipeline(self.pipeline_task, framework="pt", **self.pipeline_loading_kwargs)
|
|
|
|
@require_tf
|
|
@slow
|
|
def test_tf_defaults(self):
|
|
pipeline(self.pipeline_task, framework="tf", **self.pipeline_loading_kwargs)
|
|
|
|
@require_torch
|
|
def test_torch_small(self):
|
|
for model_name in self.small_models:
|
|
nlp = pipeline(
|
|
task=self.pipeline_task,
|
|
model=model_name,
|
|
tokenizer=model_name,
|
|
framework="pt",
|
|
**self.pipeline_loading_kwargs,
|
|
)
|
|
self._test_pipeline(nlp)
|
|
|
|
@require_tf
|
|
def test_tf_small(self):
|
|
for model_name in self.small_models:
|
|
nlp = pipeline(
|
|
task=self.pipeline_task,
|
|
model=model_name,
|
|
tokenizer=model_name,
|
|
framework="tf",
|
|
**self.pipeline_loading_kwargs,
|
|
)
|
|
self._test_pipeline(nlp)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_torch_large(self):
|
|
for model_name in self.large_models:
|
|
nlp = pipeline(
|
|
task=self.pipeline_task,
|
|
model=model_name,
|
|
tokenizer=model_name,
|
|
framework="pt",
|
|
**self.pipeline_loading_kwargs,
|
|
)
|
|
self._test_pipeline(nlp)
|
|
|
|
@require_tf
|
|
@slow
|
|
def test_tf_large(self):
|
|
for model_name in self.large_models:
|
|
nlp = pipeline(
|
|
task=self.pipeline_task,
|
|
model=model_name,
|
|
tokenizer=model_name,
|
|
framework="tf",
|
|
**self.pipeline_loading_kwargs,
|
|
)
|
|
self._test_pipeline(nlp)
|
|
|
|
def _test_pipeline(self, nlp: Pipeline):
|
|
raise NotImplementedError
|
|
|
|
@require_torch
|
|
def test_compare_slow_fast_torch(self):
|
|
for model_name in self.small_models:
|
|
nlp_slow = pipeline(
|
|
task=self.pipeline_task,
|
|
model=model_name,
|
|
tokenizer=model_name,
|
|
framework="pt",
|
|
use_fast=False,
|
|
**self.pipeline_loading_kwargs,
|
|
)
|
|
nlp_fast = pipeline(
|
|
task=self.pipeline_task,
|
|
model=model_name,
|
|
tokenizer=model_name,
|
|
framework="pt",
|
|
use_fast=True,
|
|
**self.pipeline_loading_kwargs,
|
|
)
|
|
self._compare_slow_fast_pipelines(nlp_slow, nlp_fast, method="forward")
|
|
|
|
@require_tf
|
|
def test_compare_slow_fast_tf(self):
|
|
for model_name in self.small_models:
|
|
nlp_slow = pipeline(
|
|
task=self.pipeline_task,
|
|
model=model_name,
|
|
tokenizer=model_name,
|
|
framework="tf",
|
|
use_fast=False,
|
|
**self.pipeline_loading_kwargs,
|
|
)
|
|
nlp_fast = pipeline(
|
|
task=self.pipeline_task,
|
|
model=model_name,
|
|
tokenizer=model_name,
|
|
framework="tf",
|
|
use_fast=True,
|
|
**self.pipeline_loading_kwargs,
|
|
)
|
|
self._compare_slow_fast_pipelines(nlp_slow, nlp_fast, method="call")
|
|
|
|
def _compare_slow_fast_pipelines(self, nlp_slow: Pipeline, nlp_fast: Pipeline, method: str):
|
|
"""We check that the inputs to the models forward passes are identical for
|
|
slow and fast tokenizers.
|
|
"""
|
|
with mock.patch.object(
|
|
nlp_slow.model, method, wraps=getattr(nlp_slow.model, method)
|
|
) as mock_slow, mock.patch.object(nlp_fast.model, method, wraps=getattr(nlp_fast.model, method)) as mock_fast:
|
|
for inputs in self.valid_inputs:
|
|
if isinstance(inputs, dict):
|
|
inputs.update(self.pipeline_running_kwargs)
|
|
_ = nlp_slow(**inputs)
|
|
_ = nlp_fast(**inputs)
|
|
else:
|
|
_ = nlp_slow(inputs, **self.pipeline_running_kwargs)
|
|
_ = nlp_fast(inputs, **self.pipeline_running_kwargs)
|
|
|
|
mock_slow.assert_called()
|
|
mock_fast.assert_called()
|
|
|
|
self.assertEqual(len(mock_slow.call_args_list), len(mock_fast.call_args_list))
|
|
for mock_slow_call_args, mock_fast_call_args in zip(
|
|
mock_slow.call_args_list, mock_slow.call_args_list
|
|
):
|
|
slow_call_args, slow_call_kwargs = mock_slow_call_args
|
|
fast_call_args, fast_call_kwargs = mock_fast_call_args
|
|
|
|
slow_call_args, slow_call_kwargs = to_py_obj(slow_call_args), to_py_obj(slow_call_kwargs)
|
|
fast_call_args, fast_call_kwargs = to_py_obj(fast_call_args), to_py_obj(fast_call_kwargs)
|
|
|
|
self.assertEqual(slow_call_args, fast_call_args)
|
|
self.assertDictEqual(slow_call_kwargs, fast_call_kwargs)
|
|
|
|
|
|
@is_pipeline_test
|
|
class MonoInputPipelineCommonMixin(CustomInputPipelineCommonMixin):
|
|
"""A version of the CustomInputPipelineCommonMixin
|
|
with a predefined `_test_pipeline` method.
|
|
"""
|
|
|
|
mandatory_keys = {} # Keys which should be in the output
|
|
invalid_inputs = [None] # inputs which are not allowed
|
|
expected_multi_result: Optional[List] = None
|
|
expected_check_keys: Optional[List[str]] = None
|
|
|
|
def _test_pipeline(self, nlp: Pipeline):
|
|
self.assertIsNotNone(nlp)
|
|
|
|
mono_result = nlp(self.valid_inputs[0], **self.pipeline_running_kwargs)
|
|
self.assertIsInstance(mono_result, list)
|
|
self.assertIsInstance(mono_result[0], (dict, list))
|
|
|
|
if isinstance(mono_result[0], list):
|
|
mono_result = mono_result[0]
|
|
|
|
for key in self.mandatory_keys:
|
|
self.assertIn(key, mono_result[0])
|
|
|
|
multi_result = [nlp(input, **self.pipeline_running_kwargs) for input in self.valid_inputs]
|
|
self.assertIsInstance(multi_result, list)
|
|
self.assertIsInstance(multi_result[0], (dict, list))
|
|
|
|
if self.expected_multi_result is not None:
|
|
for result, expect in zip(multi_result, self.expected_multi_result):
|
|
for key in self.expected_check_keys or []:
|
|
self.assertEqual(
|
|
set([o[key] for o in result]),
|
|
set([o[key] for o in expect]),
|
|
)
|
|
|
|
if isinstance(multi_result[0], list):
|
|
multi_result = multi_result[0]
|
|
|
|
for result in multi_result:
|
|
for key in self.mandatory_keys:
|
|
self.assertIn(key, result)
|
|
|
|
self.assertRaises(Exception, nlp, self.invalid_inputs)
|