Moving zero-shot-classification pipeline to new testing. (#13299)
* Moving `zero-shot-classification` pipeline to new testing. * Cleaning up old mixins. * Fixing tests `sshleifer/tiny-distilbert-base-uncased-finetuned-sst-2-english` is corrupted in PT. * Adding warning.
This commit is contained in:
Nicolas Patry
@@ -17,21 +17,9 @@ import logging
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import string
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from abc import abstractmethod
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from functools import lru_cache
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from typing import List, Optional
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from unittest import mock, skipIf
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from unittest import skipIf
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from transformers import (
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FEATURE_EXTRACTOR_MAPPING,
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TOKENIZER_MAPPING,
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AutoFeatureExtractor,
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AutoTokenizer,
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is_tf_available,
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is_torch_available,
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pipeline,
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)
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from transformers.file_utils import to_py_obj
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from transformers.pipelines import Pipeline
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from transformers.testing_utils import _run_slow_tests, is_pipeline_test, require_tf, require_torch, slow
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from transformers import FEATURE_EXTRACTOR_MAPPING, TOKENIZER_MAPPING, AutoFeatureExtractor, AutoTokenizer
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logger = logging.getLogger(__name__)
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@@ -189,228 +177,3 @@ class PipelineTestCaseMeta(type):
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dct["test_small_model_tf"] = dct.get("test_small_model_tf", inner)
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return type.__new__(mcs, name, bases, dct)
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VALID_INPUTS = ["A simple string", ["list of strings"]]
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@is_pipeline_test
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class CustomInputPipelineCommonMixin:
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pipeline_task = None
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pipeline_loading_kwargs = {} # Additional kwargs to load the pipeline with
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pipeline_running_kwargs = {} # Additional kwargs to run the pipeline with
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small_models = [] # Models tested without the @slow decorator
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large_models = [] # Models tested with the @slow decorator
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valid_inputs = VALID_INPUTS # Some inputs which are valid to compare fast and slow tokenizers
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def setUp(self) -> None:
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if not is_tf_available() and not is_torch_available():
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return # Currently no JAX pipelines
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# Download needed checkpoints
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models = self.small_models
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if _run_slow_tests:
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models = models + self.large_models
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for model_name in models:
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if is_torch_available():
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pipeline(
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self.pipeline_task,
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model=model_name,
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tokenizer=model_name,
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framework="pt",
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**self.pipeline_loading_kwargs,
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)
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if is_tf_available():
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pipeline(
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self.pipeline_task,
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model=model_name,
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tokenizer=model_name,
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framework="tf",
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**self.pipeline_loading_kwargs,
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)
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@require_torch
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@slow
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def test_pt_defaults(self):
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pipeline(self.pipeline_task, framework="pt", **self.pipeline_loading_kwargs)
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@require_tf
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@slow
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def test_tf_defaults(self):
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pipeline(self.pipeline_task, framework="tf", **self.pipeline_loading_kwargs)
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@require_torch
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def test_torch_small(self):
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for model_name in self.small_models:
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pipe_small = pipeline(
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task=self.pipeline_task,
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model=model_name,
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tokenizer=model_name,
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framework="pt",
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**self.pipeline_loading_kwargs,
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)
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self._test_pipeline(pipe_small)
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@require_tf
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def test_tf_small(self):
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for model_name in self.small_models:
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pipe_small = pipeline(
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task=self.pipeline_task,
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model=model_name,
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tokenizer=model_name,
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framework="tf",
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**self.pipeline_loading_kwargs,
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)
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self._test_pipeline(pipe_small)
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@require_torch
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@slow
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def test_torch_large(self):
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for model_name in self.large_models:
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pipe_large = pipeline(
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task=self.pipeline_task,
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model=model_name,
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tokenizer=model_name,
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framework="pt",
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**self.pipeline_loading_kwargs,
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)
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self._test_pipeline(pipe_large)
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@require_tf
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@slow
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def test_tf_large(self):
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for model_name in self.large_models:
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pipe_large = pipeline(
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task=self.pipeline_task,
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model=model_name,
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tokenizer=model_name,
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framework="tf",
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**self.pipeline_loading_kwargs,
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)
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self._test_pipeline(pipe_large)
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def _test_pipeline(self, pipe: Pipeline):
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raise NotImplementedError
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@require_torch
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def test_compare_slow_fast_torch(self):
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for model_name in self.small_models:
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pipe_slow = pipeline(
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task=self.pipeline_task,
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model=model_name,
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tokenizer=model_name,
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framework="pt",
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use_fast=False,
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**self.pipeline_loading_kwargs,
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)
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pipe_fast = pipeline(
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task=self.pipeline_task,
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model=model_name,
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tokenizer=model_name,
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framework="pt",
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use_fast=True,
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**self.pipeline_loading_kwargs,
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)
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self._compare_slow_fast_pipelines(pipe_slow, pipe_fast, method="forward")
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@require_tf
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def test_compare_slow_fast_tf(self):
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for model_name in self.small_models:
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pipe_slow = pipeline(
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task=self.pipeline_task,
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model=model_name,
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tokenizer=model_name,
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framework="tf",
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use_fast=False,
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**self.pipeline_loading_kwargs,
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)
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pipe_fast = pipeline(
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task=self.pipeline_task,
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model=model_name,
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tokenizer=model_name,
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framework="tf",
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use_fast=True,
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**self.pipeline_loading_kwargs,
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)
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self._compare_slow_fast_pipelines(pipe_slow, pipe_fast, method="call")
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def _compare_slow_fast_pipelines(self, pipe_slow: Pipeline, pipe_fast: Pipeline, method: str):
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"""We check that the inputs to the models forward passes are identical for
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slow and fast tokenizers.
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"""
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with mock.patch.object(
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pipe_slow.model, method, wraps=getattr(pipe_slow.model, method)
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) as mock_slow, mock.patch.object(
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pipe_fast.model, method, wraps=getattr(pipe_fast.model, method)
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) as mock_fast:
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for inputs in self.valid_inputs:
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if isinstance(inputs, dict):
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inputs.update(self.pipeline_running_kwargs)
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_ = pipe_slow(**inputs)
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_ = pipe_fast(**inputs)
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else:
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_ = pipe_slow(inputs, **self.pipeline_running_kwargs)
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_ = pipe_fast(inputs, **self.pipeline_running_kwargs)
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mock_slow.assert_called()
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mock_fast.assert_called()
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self.assertEqual(len(mock_slow.call_args_list), len(mock_fast.call_args_list))
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for mock_slow_call_args, mock_fast_call_args in zip(
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mock_slow.call_args_list, mock_slow.call_args_list
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):
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slow_call_args, slow_call_kwargs = mock_slow_call_args
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fast_call_args, fast_call_kwargs = mock_fast_call_args
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slow_call_args, slow_call_kwargs = to_py_obj(slow_call_args), to_py_obj(slow_call_kwargs)
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fast_call_args, fast_call_kwargs = to_py_obj(fast_call_args), to_py_obj(fast_call_kwargs)
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self.assertEqual(slow_call_args, fast_call_args)
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self.assertDictEqual(slow_call_kwargs, fast_call_kwargs)
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@is_pipeline_test
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class MonoInputPipelineCommonMixin(CustomInputPipelineCommonMixin):
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"""A version of the CustomInputPipelineCommonMixin
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with a predefined `_test_pipeline` method.
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"""
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mandatory_keys = {} # Keys which should be in the output
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invalid_inputs = [None] # inputs which are not allowed
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expected_multi_result: Optional[List] = None
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expected_check_keys: Optional[List[str]] = None
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def _test_pipeline(self, pipe: Pipeline):
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self.assertIsNotNone(pipe)
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mono_result = pipe(self.valid_inputs[0], **self.pipeline_running_kwargs)
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], (dict, list))
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if isinstance(mono_result[0], list):
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mono_result = mono_result[0]
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for key in self.mandatory_keys:
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self.assertIn(key, mono_result[0])
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multi_result = [pipe(input, **self.pipeline_running_kwargs) for input in self.valid_inputs]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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if self.expected_multi_result is not None:
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for result, expect in zip(multi_result, self.expected_multi_result):
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for key in self.expected_check_keys or []:
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self.assertEqual(
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set([o[key] for o in result]),
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set([o[key] for o in expect]),
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)
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in self.mandatory_keys:
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self.assertIn(key, result)
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self.assertRaises(Exception, pipe, self.invalid_inputs)
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