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https://github.com/huggingface/transformers.git
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84caa23301
* Some work to fix the behaviour of DefaultArgumentHandler by removing it. * Fixing specific pipelines argument checking.
275 lines
9.5 KiB
Python
275 lines
9.5 KiB
Python
from typing import List, Optional
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from transformers import is_tf_available, is_torch_available, pipeline
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# from transformers.pipelines import DefaultArgumentHandler, Pipeline
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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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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 = {}
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small_models = None # Models tested without the @slow decorator
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large_models = None # Models tested with the @slow decorator
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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")
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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")
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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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nlp = pipeline(task=self.pipeline_task, model=model_name, tokenizer=model_name, framework="pt")
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self._test_pipeline(nlp)
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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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nlp = pipeline(task=self.pipeline_task, model=model_name, tokenizer=model_name, framework="tf")
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self._test_pipeline(nlp)
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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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nlp = pipeline(task=self.pipeline_task, model=model_name, tokenizer=model_name, framework="pt")
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self._test_pipeline(nlp)
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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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nlp = pipeline(task=self.pipeline_task, model=model_name, tokenizer=model_name, framework="tf")
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self._test_pipeline(nlp)
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def _test_pipeline(self, nlp: Pipeline):
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raise NotImplementedError
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@is_pipeline_test
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class MonoInputPipelineCommonMixin:
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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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mandatory_keys = {} # Keys which should be in the output
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valid_inputs = VALID_INPUTS # inputs which are valid
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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 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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for model_name in self.small_models:
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pipeline(self.pipeline_task, model=model_name, tokenizer=model_name, **self.pipeline_loading_kwargs)
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for model_name in self.large_models:
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pipeline(self.pipeline_task, model=model_name, tokenizer=model_name, **self.pipeline_loading_kwargs)
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@require_torch
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@slow
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def test_pt_defaults_loads(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_loads(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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nlp = 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(nlp)
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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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nlp = 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(nlp)
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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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nlp = 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(nlp)
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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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nlp = 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(nlp)
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def _test_pipeline(self, nlp: Pipeline):
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self.assertIsNotNone(nlp)
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mono_result = nlp(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 = [nlp(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, nlp, self.invalid_inputs)
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# @is_pipeline_test
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# class DefaultArgumentHandlerTestCase(unittest.TestCase):
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# def setUp(self) -> None:
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# self.handler = DefaultArgumentHandler()
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#
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# def test_kwargs_x(self):
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# mono_data = {"X": "This is a sample input"}
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# mono_args = self.handler(**mono_data)
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#
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# self.assertTrue(isinstance(mono_args, list))
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# self.assertEqual(len(mono_args), 1)
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#
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# multi_data = {"x": ["This is a sample input", "This is a second sample input"]}
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# multi_args = self.handler(**multi_data)
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#
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# self.assertTrue(isinstance(multi_args, list))
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# self.assertEqual(len(multi_args), 2)
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#
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# def test_kwargs_data(self):
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# mono_data = {"data": "This is a sample input"}
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# mono_args = self.handler(**mono_data)
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#
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# self.assertTrue(isinstance(mono_args, list))
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# self.assertEqual(len(mono_args), 1)
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#
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# multi_data = {"data": ["This is a sample input", "This is a second sample input"]}
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# multi_args = self.handler(**multi_data)
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#
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# self.assertTrue(isinstance(multi_args, list))
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# self.assertEqual(len(multi_args), 2)
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#
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# def test_multi_kwargs(self):
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# mono_data = {"data": "This is a sample input", "X": "This is a sample input 2"}
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# mono_args = self.handler(**mono_data)
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#
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# self.assertTrue(isinstance(mono_args, list))
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# self.assertEqual(len(mono_args), 2)
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#
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# multi_data = {
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# "data": ["This is a sample input", "This is a second sample input"],
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# "test": ["This is a sample input 2", "This is a second sample input 2"],
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# }
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# multi_args = self.handler(**multi_data)
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#
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# self.assertTrue(isinstance(multi_args, list))
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# self.assertEqual(len(multi_args), 4)
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#
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# def test_args(self):
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# mono_data = "This is a sample input"
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# mono_args = self.handler(mono_data)
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#
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# self.assertTrue(isinstance(mono_args, list))
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# self.assertEqual(len(mono_args), 1)
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#
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# mono_data = ["This is a sample input"]
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# mono_args = self.handler(mono_data)
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#
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# self.assertTrue(isinstance(mono_args, list))
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# self.assertEqual(len(mono_args), 1)
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#
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# multi_data = ["This is a sample input", "This is a second sample input"]
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# multi_args = self.handler(multi_data)
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#
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# self.assertTrue(isinstance(multi_args, list))
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# self.assertEqual(len(multi_args), 2)
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#
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# multi_data = ["This is a sample input", "This is a second sample input"]
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# multi_args = self.handler(*multi_data)
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#
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# self.assertTrue(isinstance(multi_args, list))
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# self.assertEqual(len(multi_args), 2)
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