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Moving feature-extraction pipeline to new testing scheme (#12843)

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* Update feature extraction pipelilne.

* Leaving 1 small model for actual values check.

* Fixes tests

- Better support for tokenizer with no pad token
- Increasing PegasusModelTesterConfig for pipelines
- Test of feature extraction are more permissive + don't test Multimodel
models + encoder-decoder.

* Fixing model loading with incorrect shape (+ model with HEAD).

* Update tests/test_pipelines_common.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Revert modeling_utils modification.

* Some corrections.

* Update tests/test_pipelines_common.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update tests/test_pipelines_feature_extraction.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Syntax.

* Fixing text-classification tests.

* Don't modify this file.

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Nicolas Patry
2021-07-29 19:35:55 +02:00
committed by GitHub
parent 640421c0ec
commit e2d22eef14
9 changed files with 184 additions and 55 deletions
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51
tests/test_pipelines_common.py
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@@ -29,7 +29,11 @@ logger = logging.getLogger(__name__)
def get_checkpoint_from_architecture(architecture):
module = importlib.import_module(architecture.__module__)
try:
module = importlib.import_module(architecture.__module__)
except ImportError:
logger.error(f"Ignoring architecture {architecture}")
return
if hasattr(module, "_CHECKPOINT_FOR_DOC"):
return module._CHECKPOINT_FOR_DOC
@@ -46,8 +50,12 @@ def get_tiny_config_from_class(configuration_class):
model_type = configuration_class.model_type
camel_case_model_name = configuration_class.__name__.split("Config")[0]
module = importlib.import_module(f".test_modeling_{model_type.replace('-', '_')}", package="tests")
model_tester_class = getattr(module, f"{camel_case_model_name}ModelTester", None)
try:
module = importlib.import_module(f".test_modeling_{model_type.replace('-', '_')}", package="tests")
model_tester_class = getattr(module, f"{camel_case_model_name}ModelTester", None)
except (ImportError, AttributeError):
logger.error(f"No model tester class for {configuration_class.__name__}")
return
if model_tester_class is None:
logger.warning(f"No model tester class for {configuration_class.__name__}")
@@ -95,7 +103,8 @@ class PipelineTestCaseMeta(type):
model = model.eval()
try:
tokenizer = get_tiny_tokenizer_from_checkpoint(checkpoint)
tokenizer.model_max_length = model.config.max_position_embeddings
if hasattr(model.config, "max_position_embeddings"):
tokenizer.model_max_length = model.config.max_position_embeddings
# Rust Panic exception are NOT Exception subclass
# Some test tokenizer contain broken vocabs or custom PreTokenizer, so we
# provide some default tokenizer and hope for the best.
@@ -107,27 +116,21 @@ class PipelineTestCaseMeta(type):
return test
mapping = dct.get("model_mapping", {})
if mapping:
for configuration, model_architecture in mapping.items():
checkpoint = get_checkpoint_from_architecture(model_architecture)
tiny_config = get_tiny_config_from_class(configuration)
tokenizer_classes = TOKENIZER_MAPPING.get(configuration, [])
for tokenizer_class in tokenizer_classes:
if tokenizer_class is not None and tokenizer_class.__name__.endswith("Fast"):
test_name = f"test_pt_{configuration.__name__}_{model_architecture.__name__}_{tokenizer_class.__name__}"
dct[test_name] = gen_test(model_architecture, checkpoint, tiny_config, tokenizer_class)
for prefix, key in [("pt", "model_mapping"), ("tf", "tf_model_mapping")]:
mapping = dct.get(key, {})
if mapping:
for configuration, model_architectures in mapping.items():
if not isinstance(model_architectures, tuple):
model_architectures = (model_architectures,)
tf_mapping = dct.get("tf_model_mapping", {})
if tf_mapping:
for configuration, model_architecture in tf_mapping.items():
checkpoint = get_checkpoint_from_architecture(model_architecture)
tiny_config = get_tiny_config_from_class(configuration)
tokenizer_classes = TOKENIZER_MAPPING.get(configuration, [])
for tokenizer_class in tokenizer_classes:
if tokenizer_class is not None and tokenizer_class.__name__.endswith("Fast"):
test_name = f"test_tf_{configuration.__name__}_{model_architecture.__name__}_{tokenizer_class.__name__}"
dct[test_name] = gen_test(model_architecture, checkpoint, tiny_config, tokenizer_class)
for model_architecture in model_architectures:
checkpoint = get_checkpoint_from_architecture(model_architecture)
tiny_config = get_tiny_config_from_class(configuration)
tokenizer_classes = TOKENIZER_MAPPING.get(configuration, [])
for tokenizer_class in tokenizer_classes:
if tokenizer_class is not None and tokenizer_class.__name__.endswith("Fast"):
test_name = f"test_{prefix}_{configuration.__name__}_{model_architecture.__name__}_{tokenizer_class.__name__}"
dct[test_name] = gen_test(model_architecture, checkpoint, tiny_config, tokenizer_class)
return type.__new__(mcs, name, bases, dct)