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Ci test tf super slow (#8007)

Browse Source 
* Test TF GPU CI

* Change cache

* Fix missing torch requirement

* Fix some model tests


Style

* LXMERT

* MobileBERT

* Longformer skip test

* XLNet

* The rest of the tests

* RAG goes OOM in multi gpu setup

* YAML test files

* Last fixes

* Skip doctests

* Fill mask tests

* Yaml files

* Last test fix

* Style

* Update cache

* Change ONNX tests to slow + use tiny model
This commit is contained in:
Lysandre Debut
2020-10-30 14:25:48 +00:00
committed by GitHub
parent 7e36deec7a
commit 10f8c63620
25 changed files with 562 additions and 126 deletions
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124
tests/test_pipelines_fill_mask.py
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@@ -96,21 +96,53 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
framework="pt",
topk=2,
)
self._test_mono_column_pipeline(
nlp,
valid_inputs,
mandatory_keys,
expected_multi_result=EXPECTED_FILL_MASK_RESULT,
expected_check_keys=["sequence"],
)
self._test_mono_column_pipeline(
nlp,
valid_inputs[:1],
mandatory_keys,
expected_multi_result=EXPECTED_FILL_MASK_TARGET_RESULT,
expected_check_keys=["sequence"],
targets=valid_targets,
)
mono_result = nlp(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_RESULT):
self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
if isinstance(multi_result[0], list):
multi_result = multi_result[0]
for result in multi_result:
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])
valid_inputs = valid_inputs[:1]
mono_result = nlp(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_TARGET_RESULT):
self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
if isinstance(multi_result[0], list):
multi_result = multi_result[0]
for result in multi_result:
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])
@require_tf
@slow
@@ -123,18 +155,50 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
valid_targets = [" Patrick", " Clara"]
for model_name in self.large_models:
nlp = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf", topk=2)
self._test_mono_column_pipeline(
nlp,
valid_inputs,
mandatory_keys,
expected_multi_result=EXPECTED_FILL_MASK_RESULT,
expected_check_keys=["sequence"],
)
self._test_mono_column_pipeline(
nlp,
valid_inputs[:1],
mandatory_keys,
expected_multi_result=EXPECTED_FILL_MASK_TARGET_RESULT,
expected_check_keys=["sequence"],
targets=valid_targets,
)
mono_result = nlp(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_RESULT):
self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
if isinstance(multi_result[0], list):
multi_result = multi_result[0]
for result in multi_result:
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])
valid_inputs = valid_inputs[:1]
mono_result = nlp(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_TARGET_RESULT):
self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
if isinstance(multi_result[0], list):
multi_result = multi_result[0]
for result in multi_result:
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])