def _pytorch_export(self, test_name, name, model_name, feature,
onnx_config_class_constructor):
from transformers.onnx import export
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.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 = tokenizer.eos_token_id
model_class = FeaturesManager.get_model_class_for_feature(feature)
model = model_class.from_config(config)
onnx_config = onnx_config_class_constructor(model.config)
with NamedTemporaryFile("w") as output:
try:
onnx_inputs, onnx_outputs = export(
tokenizer, model, onnx_config,
onnx_config.default_onnx_opset, Path(output.name))
validate_model_outputs(
onnx_config,
tokenizer,
model,
Path(output.name),
onnx_outputs,
onnx_config.atol_for_validation,
)
except (RuntimeError, ValueError) as e:
self.fail(f"{name}, {feature} -> {e}")
def test_pytorch_export_with_past(self):
from transformers.onnx import export
for name, model, model_class, config_class, onnx_config_class in PYTORCH_EXPORT_WITH_PAST_MODELS:
with self.subTest(name):
self.assertTrue(hasattr(onnx_config_class, "with_past"),
"OnnxConfigWithPast should have with_past()")
tokenizer = AutoTokenizer.from_pretrained(model)
model = model_class(config_class())
onnx_config = onnx_config_class.with_past(model.config)
self.assertTrue(
hasattr(onnx_config, "use_past"),
"OnnxConfigWithPast should have use_past attribute.")
self.assertTrue(
onnx_config.use_past,
"OnnxConfigWithPast.use_past should be if called with with_past()"
)
with NamedTemporaryFile("w") as output:
output = Path(output.name)
onnx_inputs, onnx_outputs = export(tokenizer, model,
onnx_config,
DEFAULT_ONNX_OPSET,
output)
try:
validate_model_outputs(onnx_config, tokenizer, model,
output, onnx_outputs, 1e-5)
except ValueError as ve:
self.fail(f"{name} -> {ve}")
def _onnx_export(self,
test_name,
name,
model_name,
feature,
onnx_config_class_constructor,
device="cpu"):
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)
# 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_config = onnx_config_class_constructor(model.config)
if is_torch_available():
from transformers.utils import torch_version
if 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: {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(self,
test_name,
name,
model_name,
feature,
onnx_config_class_constructor,
device="cpu"):
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 torch_version
if 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: {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}")
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 test_pytorch_export_default(self):
from transformers.onnx import export
for name, model, model_class, config_class, onnx_config_class in PYTORCH_EXPORT_DEFAULT_MODELS:
with self.subTest(name):
self.assertTrue(hasattr(onnx_config_class, "default"))
tokenizer = AutoTokenizer.from_pretrained(model)
model = model_class(config_class())
onnx_config = onnx_config_class.default(model.config)
with NamedTemporaryFile("w") as output:
onnx_inputs, onnx_outputs = export(tokenizer, model,
onnx_config,
DEFAULT_ONNX_OPSET,
Path(output.name))
try:
validate_model_outputs(onnx_config, tokenizer, model,
Path(output.name), onnx_outputs,
1e-5)
except ValueError as ve:
self.fail(f"{name} -> {ve}")
