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# 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.
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def convert_command_factory(args: Namespace):
"""
Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint.
Returns: ServeCommand
"""
return ConvertCommand(
args.model_type, args.tf_checkpoint, args.pytorch_dump_output, args.config, args.finetuning_task_name
)
IMPORT_ERROR_MESSAGE = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class ConvertCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
"""
train_parser = parser.add_parser(
"convert",
help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.",
)
train_parser.add_argument("--model_type", type=str, required=True, help="Model's type.")
train_parser.add_argument(
"--tf_checkpoint", type=str, required=True, help="TensorFlow checkpoint path or folder."
)
train_parser.add_argument(
"--pytorch_dump_output", type=str, required=True, help="Path to the PyTorch saved model output."
)
train_parser.add_argument("--config", type=str, default="", help="Configuration file path or folder.")
train_parser.add_argument(
"--finetuning_task_name",
type=str,
default=None,
help="Optional fine-tuning task name if the TF model was a finetuned model.",
)
train_parser.set_defaults(func=convert_command_factory)
def __init__(
self,
model_type: str,
tf_checkpoint: str,
pytorch_dump_output: str,
config: str,
finetuning_task_name: str,
*args,
):
self._logger = logging.get_logger("transformers-cli/converting")
self._logger.info(f"Loading model {model_type}")
self._model_type = model_type
self._tf_checkpoint = tf_checkpoint
self._pytorch_dump_output = pytorch_dump_output
self._config = config
self._finetuning_task_name = finetuning_task_name
def run(self):
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "t5":
try:
from ..models.t5.convert_t5_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
if "ckpt" in self._tf_checkpoint.lower():
TF_CHECKPOINT = self._tf_checkpoint
TF_DATASET_FILE = ""
else:
TF_DATASET_FILE = self._tf_checkpoint
TF_CHECKPOINT = ""
convert_transfo_xl_checkpoint_to_pytorch(
TF_CHECKPOINT, self._config, self._pytorch_dump_output, TF_DATASET_FILE
)
elif self._model_type == "gpt2":
try:
from ..models.gpt2.convert_gpt2_original_tf_checkpoint_to_pytorch import (
convert_gpt2_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_gpt2_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(IMPORT_ERROR_MESSAGE)
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint, self._config, self._pytorch_dump_output, self._finetuning_task_name
)
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint, self._pytorch_dump_output)
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint, self._pytorch_dump_output)
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"
)
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