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Migrating conversational pipeline tests to new testing format (#13114)

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* New test format for conversational.

* Putting back old mixin.

* Re-enabling auto tests with LazyLoading.

* Feature extraction tests.

* Remove feature-extraction.

* Feature extraction with feature_extractor (No pun intended).

* Update check_model_type for fill-mask.
This commit is contained in:
Nicolas Patry
2021-08-26 09:50:43 +02:00
committed by GitHub
parent 72eefb34a9
commit 83bfdbdd75
10 changed files with 131 additions and 114 deletions
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47
tests/test_pipelines_common.py
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@@ -19,7 +19,15 @@ from functools import lru_cache
from typing import List, Optional
from unittest import mock, skipIf
from transformers import TOKENIZER_MAPPING, AutoTokenizer, is_tf_available, is_torch_available, pipeline
from transformers import (
FEATURE_EXTRACTOR_MAPPING,
TOKENIZER_MAPPING,
AutoFeatureExtractor,
AutoTokenizer,
is_tf_available,
is_torch_available,
pipeline,
)
from transformers.file_utils import to_py_obj
from transformers.pipelines import Pipeline
from transformers.testing_utils import _run_slow_tests, is_pipeline_test, require_tf, require_torch, slow
@@ -81,6 +89,16 @@ def get_tiny_tokenizer_from_checkpoint(checkpoint):
return tokenizer
def get_tiny_feature_extractor_from_checkpoint(checkpoint, tiny_config):
try:
feature_extractor = AutoFeatureExtractor.from_pretrained(checkpoint)
except Exception:
feature_extractor = None
if hasattr(tiny_config, "image_size") and feature_extractor:
feature_extractor = feature_extractor.__class__(size=tiny_config.image_size, crop_size=tiny_config.image_size)
return feature_extractor
class ANY:
def __init__(self, _type):
self._type = _type
@@ -94,10 +112,14 @@ class ANY:
class PipelineTestCaseMeta(type):
def __new__(mcs, name, bases, dct):
def gen_test(ModelClass, checkpoint, tiny_config, tokenizer_class):
def gen_test(ModelClass, checkpoint, tiny_config, tokenizer_class, feature_extractor_class):
@skipIf(tiny_config is None, "TinyConfig does not exist")
@skipIf(checkpoint is None, "checkpoint does not exist")
def test(self):
if ModelClass.__name__.endswith("ForCausalLM"):
tiny_config.is_encoder_decoder = False
if ModelClass.__name__.endswith("WithLMHead"):
tiny_config.is_decoder = True
model = ModelClass(tiny_config)
if hasattr(model, "eval"):
model = model.eval()
@@ -110,7 +132,8 @@ class PipelineTestCaseMeta(type):
# provide some default tokenizer and hope for the best.
except: # noqa: E722
self.skipTest(f"Ignoring {ModelClass}, cannot create a simple tokenizer")
self.run_pipeline_test(model, tokenizer)
feature_extractor = get_tiny_feature_extractor_from_checkpoint(checkpoint, tiny_config)
self.run_pipeline_test(model, tokenizer, feature_extractor)
return test
@@ -125,10 +148,24 @@ class PipelineTestCaseMeta(type):
checkpoint = get_checkpoint_from_architecture(model_architecture)
tiny_config = get_tiny_config_from_class(configuration)
tokenizer_classes = TOKENIZER_MAPPING.get(configuration, [])
feature_extractor_class = FEATURE_EXTRACTOR_MAPPING.get(configuration, None)
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)
tokenizer_name = tokenizer_class.__name__ if tokenizer_class else "notokenizer"
feature_extractor_name = (
feature_extractor_class.__name__
if feature_extractor_class
else "nofeature_extractor"
)
test_name = f"test_{prefix}_{configuration.__name__}_{model_architecture.__name__}_{tokenizer_name}_{feature_extractor_name}"
dct[test_name] = gen_test(
model_architecture,
checkpoint,
tiny_config,
tokenizer_class,
feature_extractor_class,
)
return type.__new__(mcs, name, bases, dct)