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Fixed: Better names for nlp variables in pipelines' tests and docs. (#11752)

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* Fixed: Better names for nlp variables in pipelines' tests and docs.

* Fixed: Better variable names
This commit is contained in:
Vyom Pathak
2021-05-18 19:17:28 +05:30
committed by GitHub
parent cebb96f53a
commit fd3b12e8c3
12 changed files with 163 additions and 159 deletions
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56
tests/test_pipelines_common.py
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@@ -73,60 +73,60 @@ class CustomInputPipelineCommonMixin:
@require_torch
def test_torch_small(self):
for model_name in self.small_models:
nlp = pipeline(
pipe_small = pipeline(
task=self.pipeline_task,
model=model_name,
tokenizer=model_name,
framework="pt",
**self.pipeline_loading_kwargs,
)
self._test_pipeline(nlp)
self._test_pipeline(pipe_small)
@require_tf
def test_tf_small(self):
for model_name in self.small_models:
nlp = pipeline(
pipe_small = pipeline(
task=self.pipeline_task,
model=model_name,
tokenizer=model_name,
framework="tf",
**self.pipeline_loading_kwargs,
)
self._test_pipeline(nlp)
self._test_pipeline(pipe_small)
@require_torch
@slow
def test_torch_large(self):
for model_name in self.large_models:
nlp = pipeline(
pipe_large = pipeline(
task=self.pipeline_task,
model=model_name,
tokenizer=model_name,
framework="pt",
**self.pipeline_loading_kwargs,
)
self._test_pipeline(nlp)
self._test_pipeline(pipe_large)
@require_tf
@slow
def test_tf_large(self):
for model_name in self.large_models:
nlp = pipeline(
pipe_large = pipeline(
task=self.pipeline_task,
model=model_name,
tokenizer=model_name,
framework="tf",
**self.pipeline_loading_kwargs,
)
self._test_pipeline(nlp)
self._test_pipeline(pipe_large)
def _test_pipeline(self, nlp: Pipeline):
def _test_pipeline(self, pipe: Pipeline):
raise NotImplementedError
@require_torch
def test_compare_slow_fast_torch(self):
for model_name in self.small_models:
nlp_slow = pipeline(
pipe_slow = pipeline(
task=self.pipeline_task,
model=model_name,
tokenizer=model_name,
@@ -134,7 +134,7 @@ class CustomInputPipelineCommonMixin:
use_fast=False,
**self.pipeline_loading_kwargs,
)
nlp_fast = pipeline(
pipe_fast = pipeline(
task=self.pipeline_task,
model=model_name,
tokenizer=model_name,
@@ -142,12 +142,12 @@ class CustomInputPipelineCommonMixin:
use_fast=True,
**self.pipeline_loading_kwargs,
)
self._compare_slow_fast_pipelines(nlp_slow, nlp_fast, method="forward")
self._compare_slow_fast_pipelines(pipe_slow, pipe_fast, method="forward")
@require_tf
def test_compare_slow_fast_tf(self):
for model_name in self.small_models:
nlp_slow = pipeline(
pipe_slow = pipeline(
task=self.pipeline_task,
model=model_name,
tokenizer=model_name,
@@ -155,7 +155,7 @@ class CustomInputPipelineCommonMixin:
use_fast=False,
**self.pipeline_loading_kwargs,
)
nlp_fast = pipeline(
pipe_fast = pipeline(
task=self.pipeline_task,
model=model_name,
tokenizer=model_name,
@@ -163,23 +163,25 @@ class CustomInputPipelineCommonMixin:
use_fast=True,
**self.pipeline_loading_kwargs,
)
self._compare_slow_fast_pipelines(nlp_slow, nlp_fast, method="call")
self._compare_slow_fast_pipelines(pipe_slow, pipe_fast, method="call")
def _compare_slow_fast_pipelines(self, nlp_slow: Pipeline, nlp_fast: Pipeline, method: str):
def _compare_slow_fast_pipelines(self, pipe_slow: Pipeline, pipe_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:
pipe_slow.model, method, wraps=getattr(pipe_slow.model, method)
) as mock_slow, mock.patch.object(
pipe_fast.model, method, wraps=getattr(pipe_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)
_ = pipe_slow(**inputs)
_ = pipe_fast(**inputs)
else:
_ = nlp_slow(inputs, **self.pipeline_running_kwargs)
_ = nlp_fast(inputs, **self.pipeline_running_kwargs)
_ = pipe_slow(inputs, **self.pipeline_running_kwargs)
_ = pipe_fast(inputs, **self.pipeline_running_kwargs)
mock_slow.assert_called()
mock_fast.assert_called()
@@ -209,10 +211,10 @@ class MonoInputPipelineCommonMixin(CustomInputPipelineCommonMixin):
expected_multi_result: Optional[List] = None
expected_check_keys: Optional[List[str]] = None
def _test_pipeline(self, nlp: Pipeline):
self.assertIsNotNone(nlp)
def _test_pipeline(self, pipe: Pipeline):
self.assertIsNotNone(pipe)
mono_result = nlp(self.valid_inputs[0], **self.pipeline_running_kwargs)
mono_result = pipe(self.valid_inputs[0], **self.pipeline_running_kwargs)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], (dict, list))
@@ -222,7 +224,7 @@ class MonoInputPipelineCommonMixin(CustomInputPipelineCommonMixin):
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]
multi_result = [pipe(input, **self.pipeline_running_kwargs) for input in self.valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
@@ -241,4 +243,4 @@ class MonoInputPipelineCommonMixin(CustomInputPipelineCommonMixin):
for key in self.mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, self.invalid_inputs)
self.assertRaises(Exception, pipe, self.invalid_inputs)