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# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from argparse import ArgumentParser, Namespace
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from ..utils import logging
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from . import BaseTransformersCLICommand
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MAX_ERROR = 5e-5 # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
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def convert_command_factory(args: Namespace):
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"""
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Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint.
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Returns: ServeCommand
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"""
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return PTtoTFCommand(
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args.model_name,
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args.local_dir,
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args.max_error,
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args.new_weights,
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args.no_pr,
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args.push,
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args.extra_commit_description,
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args.override_model_class,
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)
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class PTtoTFCommand(BaseTransformersCLICommand):
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@staticmethod
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def register_subcommand(parser: ArgumentParser):
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"""
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Register this command to argparse so it's available for the transformer-cli
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Args:
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parser: Root parser to register command-specific arguments
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"""
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train_parser = parser.add_parser(
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"pt-to-tf",
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help=(
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"CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
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" Can also be used to validate existing weights without opening PRs, with --no-pr."
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),
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)
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train_parser.add_argument(
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"--model-name",
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type=str,
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required=True,
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help="The model name, including owner/organization, as seen on the hub.",
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)
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train_parser.add_argument(
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"--local-dir",
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type=str,
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default="",
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help="Optional local directory of the model repository. Defaults to /tmp/{model_name}",
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)
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train_parser.add_argument(
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"--max-error",
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type=float,
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default=MAX_ERROR,
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help=(
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f"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
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),
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)
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train_parser.add_argument(
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"--new-weights",
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action="store_true",
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help="Optional flag to create new TensorFlow weights, even if they already exist.",
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)
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train_parser.add_argument(
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"--no-pr", action="store_true", help="Optional flag to NOT open a PR with converted weights."
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)
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train_parser.add_argument(
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"--push",
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action="store_true",
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help="Optional flag to push the weights directly to `main` (requires permissions)",
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)
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train_parser.add_argument(
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"--extra-commit-description",
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type=str,
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default="",
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help="Optional additional commit description to use when opening a PR (e.g. to tag the owner).",
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)
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train_parser.add_argument(
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"--override-model-class",
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type=str,
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default=None,
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help="If you think you know better than the auto-detector, you can specify the model class here. "
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"Can be either an AutoModel class or a specific model class like BertForSequenceClassification.",
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)
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train_parser.set_defaults(func=convert_command_factory)
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def __init__(
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self,
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model_name: str,
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local_dir: str,
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max_error: float,
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new_weights: bool,
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no_pr: bool,
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push: bool,
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extra_commit_description: str,
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override_model_class: str,
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*args,
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):
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self._logger = logging.get_logger("transformers-cli/pt_to_tf")
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self._model_name = model_name
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self._local_dir = local_dir if local_dir else os.path.join("/tmp", model_name)
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self._max_error = max_error
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self._new_weights = new_weights
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self._no_pr = no_pr
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self._push = push
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self._extra_commit_description = extra_commit_description
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self._override_model_class = override_model_class
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def run(self):
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# TODO (joao): delete file in v4.47
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raise NotImplementedError(
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"\n\nConverting PyTorch weights to TensorFlow weights was removed in v4.43. "
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"Instead, we recommend that you convert PyTorch weights to Safetensors, an improved "
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"format that can be loaded by any framework, including TensorFlow. For more information, "
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"please see the Safetensors conversion guide: "
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"https://huggingface.co/docs/safetensors/en/convert-weights\n\n"
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)
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Joao Gante