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Add support for Perceiver ONNX export (#17213)

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* Start adding perceiver support for ONNX

* Fix pad token bug for fast tokenizers

* Fix formatting

* Make get_preprocesor more opinionated (processor priority, otherwise tokenizer/feature extractor)

* Clean docs format

* Minor cleanup following @sgugger's comments

* Fix typo in docs

* Fix another docs typo

* Fix one more typo in docs

* Update src/transformers/onnx/utils.py

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

* Update src/transformers/onnx/utils.py

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

* Update src/transformers/onnx/utils.py

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

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Patrick Deutschmann
2022-06-03 13:40:22 +02:00
committed by GitHub
parent 5c17918fe4
commit babeff5524
7 changed files with 178 additions and 30 deletions
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38
tests/onnx/test_onnx_v2.py
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@@ -6,7 +6,7 @@ from unittest.mock import patch
import pytest
from parameterized import parameterized
from transformers import AutoConfig, AutoFeatureExtractor, AutoTokenizer, is_tf_available, is_torch_available
from transformers import AutoConfig, PreTrainedTokenizerBase, is_tf_available, is_torch_available
from transformers.onnx import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
@@ -15,7 +15,11 @@ from transformers.onnx import (
export,
validate_model_outputs,
)
from transformers.onnx.utils import compute_effective_axis_dimension, compute_serialized_parameters_size
from transformers.onnx.utils import (
compute_effective_axis_dimension,
compute_serialized_parameters_size,
get_preprocessor,
)
from transformers.testing_utils import require_onnx, require_rjieba, require_tf, require_torch, require_vision, slow
@@ -189,6 +193,8 @@ PYTORCH_EXPORT_MODELS = {
("deit", "facebook/deit-small-patch16-224"),
("beit", "microsoft/beit-base-patch16-224"),
("data2vec-text", "facebook/data2vec-text-base"),
("perceiver", "deepmind/language-perceiver", ("masked-lm", "sequence-classification")),
("perceiver", "deepmind/vision-perceiver-conv", ("image-classification",)),
}
PYTORCH_EXPORT_WITH_PAST_MODELS = {
@@ -226,10 +232,15 @@ TENSORFLOW_EXPORT_SEQ2SEQ_WITH_PAST_MODELS = {}
def _get_models_to_test(export_models_list):
models_to_test = []
if is_torch_available() or is_tf_available():
for name, model in export_models_list:
for feature, onnx_config_class_constructor in FeaturesManager.get_supported_features_for_model_type(
name
).items():
for name, model, *features in export_models_list:
if features:
feature_config_mapping = {
feature: FeaturesManager.get_config(name, feature) for _ in features for feature in _
}
else:
feature_config_mapping = FeaturesManager.get_supported_features_for_model_type(name)
for feature, onnx_config_class_constructor in feature_config_mapping.items():
models_to_test.append((f"{name}_{feature}", name, model, feature, onnx_config_class_constructor))
return sorted(models_to_test)
else:
@@ -261,16 +272,11 @@ class OnnxExportTestCaseV2(TestCase):
f" {onnx_config.torch_onnx_minimum_version}, got: {torch_version}"
)
# Check the modality of the inputs and instantiate the appropriate preprocessor
if model.main_input_name == "input_ids":
preprocessor = AutoTokenizer.from_pretrained(model_name)
# Useful for causal lm models that do not use pad tokens.
if not getattr(config, "pad_token_id", None):
config.pad_token_id = preprocessor.eos_token_id
elif model.main_input_name == "pixel_values":
preprocessor = AutoFeatureExtractor.from_pretrained(model_name)
else:
raise ValueError(f"Unsupported model input name: {model.main_input_name}")
preprocessor = get_preprocessor(model_name)
# Useful for causal lm models that do not use pad tokens.
if isinstance(preprocessor, PreTrainedTokenizerBase) and not getattr(config, "pad_token_id", None):
config.pad_token_id = preprocessor.eos_token_id
with NamedTemporaryFile("w") as output:
try: