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Unverified Commit 2ab75add authored by Yih-Dar's avatar Yih-Dar Committed by GitHub
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Remove `tests/onnx` (#24868) 



fix
Co-authored-by: default avatarydshieh <ydshieh@users.noreply.github.com>
parent d561408c
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tests/onnx/test_features.py deleted 100644 → 0
View file @ d561408c
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class DetermineFrameworkTest(TestCase):
"""
Test `FeaturesManager.determine_framework`
"""
def setUp(self):
self.test_model = SMALL_MODEL_IDENTIFIER
self.framework_pt = "pt"
self.framework_tf = "tf"
def _setup_pt_ckpt(self, save_dir):
model_pt = AutoModel.from_pretrained(self.test_model)
model_pt.save_pretrained(save_dir)
def _setup_tf_ckpt(self, save_dir):
model_tf = TFAutoModel.from_pretrained(self.test_model, from_pt=True)
model_tf.save_pretrained(save_dir)
def test_framework_provided(self):
"""
Ensure the that the provided framework is returned.
"""
mock_framework = "mock_framework"
# Framework provided - return whatever the user provides
result = FeaturesManager.determine_framework(self.test_model, mock_framework)
self.assertEqual(result, mock_framework)
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(local_pt_ckpt)
result = FeaturesManager.determine_framework(local_pt_ckpt, mock_framework)
self.assertEqual(result, mock_framework)
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(local_tf_ckpt)
result = FeaturesManager.determine_framework(local_tf_ckpt, mock_framework)
self.assertEqual(result, mock_framework)
def test_checkpoint_provided(self):
"""
Ensure that the determined framework is the one used for the local checkpoint.
For the functionality to execute, local checkpoints are provided but framework is not.
"""
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(local_pt_ckpt)
result = FeaturesManager.determine_framework(local_pt_ckpt)
self.assertEqual(result, self.framework_pt)
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(local_tf_ckpt)
result = FeaturesManager.determine_framework(local_tf_ckpt)
self.assertEqual(result, self.framework_tf)
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(FileNotFoundError):
result = FeaturesManager.determine_framework(local_invalid_ckpt)
def test_from_environment(self):
"""
Ensure that the determined framework is the one available in the environment.
For the functionality to execute, framework and local checkpoints are not provided.
"""
# Framework not provided, hub model is used (no local checkpoint directory)
# TensorFlow not in environment -> use PyTorch
mock_tf_available = MagicMock(return_value=False)
with patch("transformers.onnx.features.is_tf_available", mock_tf_available):
result = FeaturesManager.determine_framework(self.test_model)
self.assertEqual(result, self.framework_pt)
# PyTorch not in environment -> use TensorFlow
mock_torch_available = MagicMock(return_value=False)
with patch("transformers.onnx.features.is_torch_available", mock_torch_available):
result = FeaturesManager.determine_framework(self.test_model)
self.assertEqual(result, self.framework_tf)
# Both in environment -> use PyTorch
mock_tf_available = MagicMock(return_value=True)
mock_torch_available = MagicMock(return_value=True)
with patch("transformers.onnx.features.is_tf_available", mock_tf_available), patch(
"transformers.onnx.features.is_torch_available", mock_torch_available
):
result = FeaturesManager.determine_framework(self.test_model)
self.assertEqual(result, self.framework_pt)
# Both not in environment -> raise error
mock_tf_available = MagicMock(return_value=False)
mock_torch_available = MagicMock(return_value=False)
with patch("transformers.onnx.features.is_tf_available", mock_tf_available), patch(
"transformers.onnx.features.is_torch_available", mock_torch_available
):
with self.assertRaises(EnvironmentError):
result = FeaturesManager.determine_framework(self.test_model)
tests/onnx/test_onnx.py deleted 100644 → 0
View file @ d561408c
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class FuncContiguousArgs:
def forward(self, input_ids, token_type_ids, attention_mask):
return None
class FuncNonContiguousArgs:
def forward(self, input_ids, some_other_args, token_type_ids, attention_mask):
return None
class OnnxExportTestCase(unittest.TestCase):
MODEL_TO_TEST = [
# (model_name, model_kwargs)
("bert-base-cased", {}),
("gpt2", {"use_cache": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def test_export_tensorflow(self):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(model, "tf", 12, **model_kwargs)
@require_torch
@slow
def test_export_pytorch(self):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(model, "pt", 12, **model_kwargs)
@require_torch
@slow
def test_export_custom_bert_model(self):
from transformers import BertModel
vocab = ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
with NamedTemporaryFile(mode="w+t") as vocab_file:
vocab_file.write("\n".join(vocab))
vocab_file.flush()
tokenizer = BertTokenizerFast(vocab_file.name)
with TemporaryDirectory() as bert_save_dir:
model = BertModel(BertConfig(vocab_size=len(vocab)))
model.save_pretrained(bert_save_dir)
self._test_export(bert_save_dir, "pt", 12, tokenizer)
@require_tf
@slow
def test_quantize_tf(self):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
path = self._test_export(model, "tf", 12, **model_kwargs)
quantized_path = quantize(Path(path))
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(path).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model")
@require_torch
@slow
def test_quantize_pytorch(self):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
path = self._test_export(model, "pt", 12, **model_kwargs)
quantized_path = quantize(path)
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(path).stat().st_size:
self.fail("Quantized model is bigger than initial ONNX model")
def _test_export(self, model, framework, opset, tokenizer=None, **model_kwargs):
try:
# Compute path
with TemporaryDirectory() as tempdir:
path = Path(tempdir).joinpath("model.onnx")
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(framework, model, path, opset, tokenizer, **model_kwargs)
return path
except Exception as e:
self.fail(e)
@require_torch
@require_tokenizers
@slow
def test_infer_dynamic_axis_pytorch(self):
"""
Validate the dynamic axis generated for each parameters are correct
"""
from transformers import BertModel
model = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"))
tokenizer = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random")
self._test_infer_dynamic_axis(model, tokenizer, "pt")
@require_tf
@require_tokenizers
@slow
def test_infer_dynamic_axis_tf(self):
"""
Validate the dynamic axis generated for each parameters are correct
"""
from transformers import TFBertModel
model = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"))
tokenizer = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random")
self._test_infer_dynamic_axis(model, tokenizer, "tf")
def _test_infer_dynamic_axis(self, model, tokenizer, framework):
feature_extractor = FeatureExtractionPipeline(model, tokenizer)
variable_names = ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
input_vars, output_vars, shapes, tokens = infer_shapes(feature_extractor, framework)
# Assert all variables are present
self.assertEqual(len(shapes), len(variable_names))
self.assertTrue(all(var_name in shapes for var_name in variable_names))
self.assertSequenceEqual(variable_names[:3], input_vars)
self.assertSequenceEqual(variable_names[3:], output_vars)
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name], {0: "batch", 1: "sequence"})
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes["output_0"], {0: "batch", 1: "sequence"})
self.assertDictEqual(shapes["output_1"], {0: "batch"})
def test_ensure_valid_input(self):
"""
Validate parameters are correctly exported
GPT2 has "past" parameter in the middle of input_ids, token_type_ids and attention_mask.
ONNX doesn't support export with a dictionary, only a tuple. Thus we need to ensure we remove
token_type_ids and attention_mask for now to not having a None tensor in the middle
"""
# All generated args are valid
input_names = ["input_ids", "attention_mask", "token_type_ids"]
tokens = {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
ordered_input_names, inputs_args = ensure_valid_input(FuncContiguousArgs(), tokens, input_names)
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(inputs_args), 3)
# Should have exactly the same input names
self.assertEqual(set(ordered_input_names), set(input_names))
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(inputs_args, (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]))
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
ordered_input_names, inputs_args = ensure_valid_input(FuncNonContiguousArgs(), tokens, input_names)
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(inputs_args), 1)
self.assertEqual(len(ordered_input_names), 1)
# Should have only "input_ids"
self.assertEqual(inputs_args[0], tokens["input_ids"])
self.assertEqual(ordered_input_names[0], "input_ids")
def test_generate_identified_name(self):
generated = generate_identified_filename(Path("/home/something/my_fake_model.onnx"), "-test")
self.assertEqual("/home/something/my_fake_model-test.onnx", generated.as_posix())
tests/onnx/test_onnx_v2.py deleted 100644 → 0
View file @ d561408c
import os
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile
from unittest import TestCase
from unittest.mock import patch
import pytest
from packaging import version
from parameterized import parameterized
from transformers import AutoConfig, PreTrainedTokenizerBase, is_tf_available, is_torch_available
from transformers.onnx import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
ParameterFormat,
validate_model_outputs,
)
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
if is_torch_available() or is_tf_available():
from transformers.onnx.features import FeaturesManager
if is_torch_available():
import torch
from transformers.models.deberta import modeling_deberta
@require_onnx
class OnnxUtilsTestCaseV2(TestCase):
"""
Cover all the utilities involved to export ONNX models
"""
def test_compute_effective_axis_dimension(self):
"""
When exporting ONNX model with dynamic axis (batch or sequence) we set batch_size and/or sequence_length = -1.
We cannot generate an effective tensor with axis dim == -1, so we trick by using some "fixed" values
(> 1 to avoid ONNX squeezing the axis).
This test ensure we are correctly replacing generated batch / sequence tensor with axis > 1
"""
# Dynamic axis (batch, no token added by the tokenizer)
self.assertEqual(compute_effective_axis_dimension(-1, fixed_dimension=2, num_token_to_add=0), 2)
# Static axis (batch, no token added by the tokenizer)
self.assertEqual(compute_effective_axis_dimension(0, fixed_dimension=2, num_token_to_add=0), 2)
# Dynamic axis (sequence, token added by the tokenizer 2 (no pair))
self.assertEqual(compute_effective_axis_dimension(0, fixed_dimension=8, num_token_to_add=2), 6)
self.assertEqual(compute_effective_axis_dimension(0, fixed_dimension=8, num_token_to_add=2), 6)
# Dynamic axis (sequence, token added by the tokenizer 3 (pair))
self.assertEqual(compute_effective_axis_dimension(0, fixed_dimension=8, num_token_to_add=3), 5)
self.assertEqual(compute_effective_axis_dimension(0, fixed_dimension=8, num_token_to_add=3), 5)
def test_compute_parameters_serialized_size(self):
"""
This test ensures we compute a "correct" approximation of the underlying storage requirement (size) for all the
parameters for the specified parameter's dtype.
"""
self.assertEqual(compute_serialized_parameters_size(2, ParameterFormat.Float), 2 * ParameterFormat.Float.size)
def test_flatten_output_collection_property(self):
"""
This test ensures we correctly flatten nested collection such as the one we use when returning past_keys.
past_keys = Tuple[Tuple]
ONNX exporter will export nested collections as ${collection_name}.${level_idx_0}.${level_idx_1}...${idx_n}
"""
self.assertEqual(
OnnxConfig.flatten_output_collection_property("past_key", [[0], [1], [2]]),
{
"past_key.0": 0,
"past_key.1": 1,
"past_key.2": 2,
},
)
class OnnxConfigTestCaseV2(TestCase):
"""
Cover the test for models default.
Default means no specific features is being enabled on the model.
"""
@patch.multiple(OnnxConfig, __abstractmethods__=set())
def test_use_external_data_format(self):
"""
External data format is required only if the serialized size of the parameters if bigger than 2Gb
"""
TWO_GB_LIMIT = EXTERNAL_DATA_FORMAT_SIZE_LIMIT
# No parameters
self.assertFalse(OnnxConfig.use_external_data_format(0))
# Some parameters
self.assertFalse(OnnxConfig.use_external_data_format(1))
# Almost 2Gb parameters
self.assertFalse(OnnxConfig.use_external_data_format((TWO_GB_LIMIT - 1) // ParameterFormat.Float.size))
# Exactly 2Gb parameters
self.assertTrue(OnnxConfig.use_external_data_format(TWO_GB_LIMIT))
# More than 2Gb parameters
self.assertTrue(OnnxConfig.use_external_data_format((TWO_GB_LIMIT + 1) // ParameterFormat.Float.size))
class OnnxConfigWithPastTestCaseV2(TestCase):
"""
Cover the tests for model which have use_cache feature (i.e. "with_past" for ONNX)
"""
SUPPORTED_WITH_PAST_CONFIGS = {}
# SUPPORTED_WITH_PAST_CONFIGS = {
# ("BART", BartConfig),
# ("GPT2", GPT2Config),
# # ("T5", T5Config)
# }
@patch.multiple(OnnxConfigWithPast, __abstractmethods__=set())
def test_use_past(self):
"""
Ensure the use_past variable is correctly being set
"""
for name, config in OnnxConfigWithPastTestCaseV2.SUPPORTED_WITH_PAST_CONFIGS:
with self.subTest(name):
self.assertFalse(
OnnxConfigWithPast.from_model_config(config()).use_past,
"OnnxConfigWithPast.from_model_config() should not use_past",
)
self.assertTrue(
OnnxConfigWithPast.with_past(config()).use_past,
"OnnxConfigWithPast.from_model_config() should use_past",
)
@patch.multiple(OnnxConfigWithPast, __abstractmethods__=set())
def test_values_override(self):
"""
Ensure the use_past variable correctly set the `use_cache` value in model's configuration
"""
for name, config in OnnxConfigWithPastTestCaseV2.SUPPORTED_WITH_PAST_CONFIGS:
with self.subTest(name):
# without past
onnx_config_default = OnnxConfigWithPast.from_model_config(config())
self.assertIsNotNone(onnx_config_default.values_override, "values_override should not be None")
self.assertIn("use_cache", onnx_config_default.values_override, "use_cache should be present")
self.assertFalse(
onnx_config_default.values_override["use_cache"], "use_cache should be False if not using past"
)
# with past
onnx_config_default = OnnxConfigWithPast.with_past(config())
self.assertIsNotNone(onnx_config_default.values_override, "values_override should not be None")
self.assertIn("use_cache", onnx_config_default.values_override, "use_cache should be present")
self.assertTrue(
onnx_config_default.values_override["use_cache"], "use_cache should be False if not using past"
)
PYTORCH_EXPORT_MODELS = {
("albert", "hf-internal-testing/tiny-random-AlbertModel"),
("bert", "hf-internal-testing/tiny-random-BertModel"),
("beit", "microsoft/beit-base-patch16-224"),
("big-bird", "hf-internal-testing/tiny-random-BigBirdModel"),
("camembert", "camembert-base"),
("clip", "hf-internal-testing/tiny-random-CLIPModel"),
("convbert", "hf-internal-testing/tiny-random-ConvBertModel"),
("codegen", "hf-internal-testing/tiny-random-CodeGenModel"),
("data2vec-text", "hf-internal-testing/tiny-random-Data2VecTextModel"),
("data2vec-vision", "facebook/data2vec-vision-base"),
("deberta", "hf-internal-testing/tiny-random-DebertaModel"),
("deberta-v2", "hf-internal-testing/tiny-random-DebertaV2Model"),
("deit", "facebook/deit-small-patch16-224"),
("convnext", "facebook/convnext-tiny-224"),
("detr", "facebook/detr-resnet-50"),
("distilbert", "hf-internal-testing/tiny-random-DistilBertModel"),
("electra", "hf-internal-testing/tiny-random-ElectraModel"),
("groupvit", "nvidia/groupvit-gcc-yfcc"),
("ibert", "kssteven/ibert-roberta-base"),
("imagegpt", "openai/imagegpt-small"),
("levit", "facebook/levit-128S"),
("layoutlm", "hf-internal-testing/tiny-random-LayoutLMModel"),
("layoutlmv3", "microsoft/layoutlmv3-base"),
("longformer", "allenai/longformer-base-4096"),
("mobilebert", "hf-internal-testing/tiny-random-MobileBertModel"),
("mobilenet_v1", "google/mobilenet_v1_0.75_192"),
("mobilenet_v2", "google/mobilenet_v2_0.35_96"),
("mobilevit", "apple/mobilevit-small"),
("owlvit", "google/owlvit-base-patch32"),
("perceiver", "hf-internal-testing/tiny-random-PerceiverModel", ("masked-lm", "sequence-classification")),
("perceiver", "hf-internal-testing/tiny-random-PerceiverModel", ("image-classification",)),
("poolformer", "sail/poolformer_s12"),
("rembert", "google/rembert"),
("resnet", "microsoft/resnet-50"),
("roberta", "hf-internal-testing/tiny-random-RobertaModel"),
("roformer", "hf-internal-testing/tiny-random-RoFormerModel"),
("segformer", "nvidia/segformer-b0-finetuned-ade-512-512"),
("squeezebert", "hf-internal-testing/tiny-random-SqueezeBertModel"),
("swin", "microsoft/swin-tiny-patch4-window7-224"),
("vit", "google/vit-base-patch16-224"),
("yolos", "hustvl/yolos-tiny"),
("whisper", "openai/whisper-tiny.en"),
("xlm", "hf-internal-testing/tiny-random-XLMModel"),
("xlm-roberta", "hf-internal-testing/tiny-random-XLMRobertaXLModel"),
}
PYTORCH_EXPORT_ENCODER_DECODER_MODELS = {
("vision-encoder-decoder", "nlpconnect/vit-gpt2-image-captioning"),
}
PYTORCH_EXPORT_WITH_PAST_MODELS = {
("bloom", "hf-internal-testing/tiny-random-BloomModel"),
("gpt2", "hf-internal-testing/tiny-random-GPT2Model"),
("gpt-neo", "hf-internal-testing/tiny-random-GPTNeoModel"),
}
PYTORCH_EXPORT_SEQ2SEQ_WITH_PAST_MODELS = {
("bart", "hf-internal-testing/tiny-random-BartModel"),
("bigbird-pegasus", "hf-internal-testing/tiny-random-BigBirdPegasusModel"),
("blenderbot-small", "facebook/blenderbot_small-90M"),
("blenderbot", "hf-internal-testing/tiny-random-BlenderbotModel"),
("longt5", "hf-internal-testing/tiny-random-LongT5Model"),
("marian", "Helsinki-NLP/opus-mt-en-de"),
("mbart", "sshleifer/tiny-mbart"),
("mt5", "google/mt5-base"),
("m2m-100", "hf-internal-testing/tiny-random-M2M100Model"),
("t5", "hf-internal-testing/tiny-random-T5Model"),
}
# TODO(lewtun): Include the same model types in `PYTORCH_EXPORT_MODELS` once TensorFlow has parity with the PyTorch model implementations.
TENSORFLOW_EXPORT_DEFAULT_MODELS = {
("albert", "hf-internal-testing/tiny-albert"),
("bert", "hf-internal-testing/tiny-random-BertModel"),
("camembert", "camembert-base"),
("distilbert", "hf-internal-testing/tiny-random-DistilBertModel"),
("roberta", "hf-internal-testing/tiny-random-RobertaModel"),
}
# TODO(lewtun): Include the same model types in `PYTORCH_EXPORT_WITH_PAST_MODELS` once TensorFlow has parity with the PyTorch model implementations.
TENSORFLOW_EXPORT_WITH_PAST_MODELS = {}
# TODO(lewtun): Include the same model types in `PYTORCH_EXPORT_SEQ2SEQ_WITH_PAST_MODELS` once TensorFlow has parity with the PyTorch model implementations.
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, *features in export_models_list:
if features:
feature_config_mapping = {
feature: FeaturesManager.get_config(name, feature) for _ in features for feature in _
}
else:
# pre-process the model names
model_type = name.replace("_", "-")
model_name = getattr(model, "name", "")
feature_config_mapping = FeaturesManager.get_supported_features_for_model_type(
model_type, model_name=model_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:
# Returning some dummy test that should not be ever called because of the @require_torch / @require_tf
# decorators.
# The reason for not returning an empty list is because parameterized.expand complains when it's empty.
return [("dummy", "dummy", "dummy", "dummy", OnnxConfig.from_model_config)]
class OnnxExportTestCaseV2(TestCase):
"""
Integration tests ensuring supported models are correctly exported
"""
def _onnx_export(
self, test_name, name, model_name, feature, onnx_config_class_constructor, device="cpu", framework="pt"
):
from transformers.onnx import export
model_class = FeaturesManager.get_model_class_for_feature(feature, framework=framework)
config = AutoConfig.from_pretrained(model_name)
model = model_class.from_config(config)
# Dynamic axes aren't supported for YOLO-like models. This means they cannot be exported to ONNX on CUDA devices.
# See: https://github.com/ultralytics/yolov5/pull/8378
if model.__class__.__name__.startswith("Yolos") and device != "cpu":
return
# ONNX inference fails with the following name, feature, framework parameterizations
# See: https://github.com/huggingface/transformers/issues/19357
if (name, feature, framework) in {
("deberta-v2", "question-answering", "pt"),
("deberta-v2", "multiple-choice", "pt"),
("roformer", "multiple-choice", "pt"),
("groupvit", "default", "pt"),
("perceiver", "masked-lm", "pt"),
("perceiver", "sequence-classification", "pt"),
("perceiver", "image-classification", "pt"),
("bert", "multiple-choice", "tf"),
("camembert", "multiple-choice", "tf"),
("roberta", "multiple-choice", "tf"),
}:
return
onnx_config = onnx_config_class_constructor(model.config)
if is_torch_available():
from transformers.utils import get_torch_version
if version.parse(get_torch_version()) < onnx_config.torch_onnx_minimum_version:
pytest.skip(
"Skipping due to incompatible PyTorch version. Minimum required is"
f" {onnx_config.torch_onnx_minimum_version}, got: {get_torch_version()}"
)
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:
onnx_inputs, onnx_outputs = export(
preprocessor, model, onnx_config, onnx_config.default_onnx_opset, Path(output.name), device=device
)
validate_model_outputs(
onnx_config,
preprocessor,
model,
Path(output.name),
onnx_outputs,
onnx_config.atol_for_validation,
)
except (RuntimeError, ValueError) as e:
self.fail(f"{name}, {feature} -> {e}")
def _onnx_export_encoder_decoder_models(
self, test_name, name, model_name, feature, onnx_config_class_constructor, device="cpu"
):
from transformers import AutoFeatureExtractor, AutoTokenizer
from transformers.onnx import export
model_class = FeaturesManager.get_model_class_for_feature(feature)
config = AutoConfig.from_pretrained(model_name)
model = model_class.from_config(config)
onnx_config = onnx_config_class_constructor(model.config)
if is_torch_available():
from transformers.utils import get_torch_version
if version.parse(get_torch_version()) < onnx_config.torch_onnx_minimum_version:
pytest.skip(
"Skipping due to incompatible PyTorch version. Minimum required is"
f" {onnx_config.torch_onnx_minimum_version}, got: {get_torch_version()}"
)
encoder_model = model.get_encoder()
decoder_model = model.get_decoder()
encoder_onnx_config = onnx_config.get_encoder_config(encoder_model.config)
decoder_onnx_config = onnx_config.get_decoder_config(encoder_model.config, decoder_model.config, feature)
preprocessor = AutoFeatureExtractor.from_pretrained(model_name)
onnx_opset = max(encoder_onnx_config.default_onnx_opset, decoder_onnx_config.default_onnx_opset)
with NamedTemporaryFile("w") as encoder_output:
onnx_inputs, onnx_outputs = export(
preprocessor, encoder_model, encoder_onnx_config, onnx_opset, Path(encoder_output.name), device=device
)
validate_model_outputs(
encoder_onnx_config,
preprocessor,
encoder_model,
Path(encoder_output.name),
onnx_outputs,
encoder_onnx_config.atol_for_validation,
)
preprocessor = AutoTokenizer.from_pretrained(model_name)
with NamedTemporaryFile("w") as decoder_output:
_, onnx_outputs = export(
preprocessor,
decoder_model,
decoder_onnx_config,
onnx_config.default_onnx_opset,
Path(decoder_output.name),
device=device,
)
validate_model_outputs(
decoder_onnx_config,
preprocessor,
decoder_model,
Path(decoder_output.name),
onnx_outputs,
decoder_onnx_config.atol_for_validation,
)
@parameterized.expand(_get_models_to_test(PYTORCH_EXPORT_MODELS))
@slow
@require_torch
@require_vision
@require_rjieba
def test_pytorch_export(self, test_name, name, model_name, feature, onnx_config_class_constructor):
self._onnx_export(test_name, name, model_name, feature, onnx_config_class_constructor)
@parameterized.expand(_get_models_to_test(PYTORCH_EXPORT_MODELS))
@slow
@require_torch
@require_vision
@require_rjieba
def test_pytorch_export_on_cuda(self, test_name, name, model_name, feature, onnx_config_class_constructor):
self._onnx_export(test_name, name, model_name, feature, onnx_config_class_constructor, device="cuda")
@parameterized.expand(_get_models_to_test(PYTORCH_EXPORT_ENCODER_DECODER_MODELS))
@slow
@require_torch
@require_vision
@require_rjieba
def test_pytorch_export_encoder_decoder_models(
self, test_name, name, model_name, feature, onnx_config_class_constructor
):
self._onnx_export_encoder_decoder_models(test_name, name, model_name, feature, onnx_config_class_constructor)
@parameterized.expand(_get_models_to_test(PYTORCH_EXPORT_ENCODER_DECODER_MODELS))
@slow
@require_torch
@require_vision
@require_rjieba
def test_pytorch_export_encoder_decoder_models_on_cuda(
self, test_name, name, model_name, feature, onnx_config_class_constructor
):
self._onnx_export_encoder_decoder_models(
test_name, name, model_name, feature, onnx_config_class_constructor, device="cuda"
)
@parameterized.expand(_get_models_to_test(PYTORCH_EXPORT_WITH_PAST_MODELS))
@slow
@require_torch
def test_pytorch_export_with_past(self, test_name, name, model_name, feature, onnx_config_class_constructor):
self._onnx_export(test_name, name, model_name, feature, onnx_config_class_constructor)
@parameterized.expand(_get_models_to_test(PYTORCH_EXPORT_SEQ2SEQ_WITH_PAST_MODELS))
@slow
@require_torch
def test_pytorch_export_seq2seq_with_past(
self, test_name, name, model_name, feature, onnx_config_class_constructor
):
self._onnx_export(test_name, name, model_name, feature, onnx_config_class_constructor)
@parameterized.expand(_get_models_to_test(TENSORFLOW_EXPORT_DEFAULT_MODELS))
@slow
@require_tf
@require_vision
def test_tensorflow_export(self, test_name, name, model_name, feature, onnx_config_class_constructor):
self._onnx_export(test_name, name, model_name, feature, onnx_config_class_constructor, framework="tf")
@parameterized.expand(_get_models_to_test(TENSORFLOW_EXPORT_WITH_PAST_MODELS), skip_on_empty=True)
@slow
@require_tf
def test_tensorflow_export_with_past(self, test_name, name, model_name, feature, onnx_config_class_constructor):
self._onnx_export(test_name, name, model_name, feature, onnx_config_class_constructor, framework="tf")
@parameterized.expand(_get_models_to_test(TENSORFLOW_EXPORT_SEQ2SEQ_WITH_PAST_MODELS), skip_on_empty=True)
@slow
@require_tf
def test_tensorflow_export_seq2seq_with_past(
self, test_name, name, model_name, feature, onnx_config_class_constructor
):
self._onnx_export(test_name, name, model_name, feature, onnx_config_class_constructor, framework="tf")
class StableDropoutTestCase(TestCase):
"""Tests export of StableDropout module."""
@unittest.skip("torch 2.0.0 gives `torch.onnx.errors.OnnxExporterError: Module onnx is not installed!`.")
@require_torch
@pytest.mark.filterwarnings("ignore:.*Dropout.*:UserWarning:torch.onnx.*") # torch.onnx is spammy.
def test_training(self):
"""Tests export of StableDropout in training mode."""
devnull = open(os.devnull, "wb")
# drop_prob must be > 0 for the test to be meaningful
sd = modeling_deberta.StableDropout(0.1)
# Avoid warnings in training mode
do_constant_folding = False
# Dropout is a no-op in inference mode
training = torch.onnx.TrainingMode.PRESERVE
input = (torch.randn(2, 2),)
torch.onnx.export(
sd,
input,
devnull,
opset_version=12, # Minimum supported
do_constant_folding=do_constant_folding,
training=training,
)
# Expected to fail with opset_version < 12
with self.assertRaises(Exception):
torch.onnx.export(
sd,
input,
devnull,
opset_version=11,
do_constant_folding=do_constant_folding,
training=training,
)
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