426 lines
20 KiB
Python
426 lines
20 KiB
Python
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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 inspect
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import os
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from argparse import ArgumentParser, Namespace
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from importlib import import_module
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import huggingface_hub
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import numpy as np
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from packaging import version
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from .. import (
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FEATURE_EXTRACTOR_MAPPING,
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IMAGE_PROCESSOR_MAPPING,
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PROCESSOR_MAPPING,
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TOKENIZER_MAPPING,
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AutoConfig,
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AutoFeatureExtractor,
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AutoImageProcessor,
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AutoProcessor,
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AutoTokenizer,
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is_datasets_available,
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is_tf_available,
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is_torch_available,
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)
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from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
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from . import BaseTransformersCLICommand
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if is_tf_available():
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import tensorflow as tf
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tf.config.experimental.enable_tensor_float_32_execution(False)
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if is_torch_available():
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import torch
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if is_datasets_available():
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from datasets import load_dataset
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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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@staticmethod
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def find_pt_tf_differences(pt_outputs, tf_outputs):
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"""
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Compares the TensorFlow and PyTorch outputs, returning a dictionary with all tensor differences.
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"""
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# 1. All output attributes must be the same
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pt_out_attrs = set(pt_outputs.keys())
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tf_out_attrs = set(tf_outputs.keys())
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if pt_out_attrs != tf_out_attrs:
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raise ValueError(
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f"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
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f" {tf_out_attrs})"
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)
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# 2. For each output attribute, computes the difference
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def _find_pt_tf_differences(pt_out, tf_out, differences, attr_name=""):
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# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
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# recursivelly, keeping the name of the attribute.
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if isinstance(pt_out, torch.Tensor):
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tensor_difference = np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
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differences[attr_name] = tensor_difference
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else:
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root_name = attr_name
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for i, pt_item in enumerate(pt_out):
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# If it is a named attribute, we keep the name. Otherwise, just its index.
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if isinstance(pt_item, str):
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branch_name = root_name + pt_item
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tf_item = tf_out[pt_item]
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pt_item = pt_out[pt_item]
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else:
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branch_name = root_name + f"[{i}]"
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tf_item = tf_out[i]
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differences = _find_pt_tf_differences(pt_item, tf_item, differences, branch_name)
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return differences
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return _find_pt_tf_differences(pt_outputs, tf_outputs, {})
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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 get_inputs(self, pt_model, tf_dummy_inputs, config):
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"""
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Returns the right inputs for the model, based on its signature.
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"""
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def _get_audio_input():
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ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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speech_samples = ds.sort("id").select(range(2))[:2]["audio"]
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raw_samples = [x["array"] for x in speech_samples]
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return raw_samples
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model_config_class = type(pt_model.config)
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if model_config_class in PROCESSOR_MAPPING:
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processor = AutoProcessor.from_pretrained(self._local_dir)
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if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
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processor.tokenizer.pad_token = processor.tokenizer.eos_token
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elif model_config_class in IMAGE_PROCESSOR_MAPPING:
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processor = AutoImageProcessor.from_pretrained(self._local_dir)
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elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
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processor = AutoFeatureExtractor.from_pretrained(self._local_dir)
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elif model_config_class in TOKENIZER_MAPPING:
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processor = AutoTokenizer.from_pretrained(self._local_dir)
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if processor.pad_token is None:
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processor.pad_token = processor.eos_token
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else:
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raise ValueError(f"Unknown data processing type (model config type: {model_config_class})")
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model_forward_signature = set(inspect.signature(pt_model.forward).parameters.keys())
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processor_inputs = {}
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if "input_ids" in model_forward_signature:
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processor_inputs.update(
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{
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"text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
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"padding": True,
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"truncation": True,
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}
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)
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if "pixel_values" in model_forward_signature:
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sample_images = load_dataset("cifar10", "plain_text", split="test")[:2]["img"]
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processor_inputs.update({"images": sample_images})
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if "input_features" in model_forward_signature:
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feature_extractor_signature = inspect.signature(processor.feature_extractor).parameters
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# Pad to the largest input length by default but take feature extractor default
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# padding value if it exists e.g. "max_length" and is not False or None
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if "padding" in feature_extractor_signature:
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default_strategy = feature_extractor_signature["padding"].default
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if default_strategy is not False and default_strategy is not None:
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padding_strategy = default_strategy
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else:
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padding_strategy = True
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else:
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padding_strategy = True
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processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy})
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if "input_values" in model_forward_signature: # Wav2Vec2 audio input
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processor_inputs.update({"audio": _get_audio_input(), "padding": True})
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pt_input = processor(**processor_inputs, return_tensors="pt")
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tf_input = processor(**processor_inputs, return_tensors="tf")
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# Extra input requirements, in addition to the input modality
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if (
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config.is_encoder_decoder
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or (hasattr(pt_model, "encoder") and hasattr(pt_model, "decoder"))
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or "decoder_input_ids" in tf_dummy_inputs
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):
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decoder_input_ids = np.asarray([[1], [1]], dtype=int) * (pt_model.config.decoder_start_token_id or 0)
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pt_input.update({"decoder_input_ids": torch.tensor(decoder_input_ids)})
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tf_input.update({"decoder_input_ids": tf.convert_to_tensor(decoder_input_ids)})
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return pt_input, tf_input
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def run(self):
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# hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
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if version.parse(huggingface_hub.__version__) < version.parse("0.9.0"):
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raise ImportError(
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"The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
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" installation."
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)
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else:
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from huggingface_hub import Repository, create_commit
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from huggingface_hub._commit_api import CommitOperationAdd
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# Fetch remote data
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repo = Repository(local_dir=self._local_dir, clone_from=self._model_name)
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# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
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config = AutoConfig.from_pretrained(self._local_dir)
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architectures = config.architectures
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if self._override_model_class is not None:
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if self._override_model_class.startswith("TF"):
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architectures = [self._override_model_class[2:]]
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else:
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architectures = [self._override_model_class]
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try:
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pt_class = getattr(import_module("transformers"), architectures[0])
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except AttributeError:
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raise ValueError(f"Model class {self._override_model_class} not found in transformers.")
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try:
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tf_class = getattr(import_module("transformers"), "TF" + architectures[0])
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except AttributeError:
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raise ValueError(f"TF model class TF{self._override_model_class} not found in transformers.")
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elif architectures is None: # No architecture defined -- use auto classes
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pt_class = getattr(import_module("transformers"), "AutoModel")
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tf_class = getattr(import_module("transformers"), "TFAutoModel")
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self._logger.warning("No detected architecture, using AutoModel/TFAutoModel")
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else: # Architecture defined -- use it
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if len(architectures) > 1:
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raise ValueError(f"More than one architecture was found, aborting. (architectures = {architectures})")
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self._logger.warning(f"Detected architecture: {architectures[0]}")
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pt_class = getattr(import_module("transformers"), architectures[0])
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try:
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tf_class = getattr(import_module("transformers"), "TF" + architectures[0])
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except AttributeError:
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raise AttributeError(f"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.")
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# Check the TF dummy inputs to see what keys we need in the forward pass
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tf_from_pt_model = tf_class.from_config(config)
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tf_dummy_inputs = tf_from_pt_model.dummy_inputs
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del tf_from_pt_model # Try to keep only one model in memory at a time
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# Load the model and get some basic inputs
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pt_model = pt_class.from_pretrained(self._local_dir)
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pt_model.eval()
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pt_input, tf_input = self.get_inputs(pt_model, tf_dummy_inputs, config)
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with torch.no_grad():
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pt_outputs = pt_model(**pt_input, output_hidden_states=True)
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del pt_model # will no longer be used, and may have a large memory footprint
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tf_from_pt_model = tf_class.from_pretrained(self._local_dir, from_pt=True)
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tf_from_pt_outputs = tf_from_pt_model(**tf_input, output_hidden_states=True, training=False)
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# Confirms that cross loading PT weights into TF worked.
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crossload_differences = self.find_pt_tf_differences(pt_outputs, tf_from_pt_outputs)
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output_differences = {k: v for k, v in crossload_differences.items() if "hidden" not in k}
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hidden_differences = {k: v for k, v in crossload_differences.items() if "hidden" in k}
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if len(output_differences) == 0 and architectures is not None:
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raise ValueError(
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f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
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" output was found. All outputs start with 'hidden'"
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)
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max_crossload_output_diff = max(output_differences.values()) if output_differences else 0.0
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max_crossload_hidden_diff = max(hidden_differences.values())
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if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
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||
|
|
raise ValueError(
|
||
|
|
"The cross-loaded TensorFlow model has different outputs, something went wrong!\n"
|
||
|
|
+ f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
|
||
|
|
+ "\n".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error])
|
||
|
|
+ f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
|
||
|
|
+ "\n".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error])
|
||
|
|
)
|
||
|
|
|
||
|
|
# Save the weights in a TF format (if needed) and confirms that the results are still good
|
||
|
|
tf_weights_path = os.path.join(self._local_dir, TF2_WEIGHTS_NAME)
|
||
|
|
tf_weights_index_path = os.path.join(self._local_dir, TF2_WEIGHTS_INDEX_NAME)
|
||
|
|
if (not os.path.exists(tf_weights_path) and not os.path.exists(tf_weights_index_path)) or self._new_weights:
|
||
|
|
tf_from_pt_model.save_pretrained(self._local_dir)
|
||
|
|
del tf_from_pt_model # will no longer be used, and may have a large memory footprint
|
||
|
|
|
||
|
|
tf_model = tf_class.from_pretrained(self._local_dir)
|
||
|
|
tf_outputs = tf_model(**tf_input, output_hidden_states=True)
|
||
|
|
|
||
|
|
conversion_differences = self.find_pt_tf_differences(pt_outputs, tf_outputs)
|
||
|
|
output_differences = {k: v for k, v in conversion_differences.items() if "hidden" not in k}
|
||
|
|
hidden_differences = {k: v for k, v in conversion_differences.items() if "hidden" in k}
|
||
|
|
if len(output_differences) == 0 and architectures is not None:
|
||
|
|
raise ValueError(
|
||
|
|
f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
|
||
|
|
" output was found. All outputs start with 'hidden'"
|
||
|
|
)
|
||
|
|
max_conversion_output_diff = max(output_differences.values()) if output_differences else 0.0
|
||
|
|
max_conversion_hidden_diff = max(hidden_differences.values())
|
||
|
|
if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
|
||
|
|
raise ValueError(
|
||
|
|
"The converted TensorFlow model has different outputs, something went wrong!\n"
|
||
|
|
+ f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
|
||
|
|
+ "\n".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error])
|
||
|
|
+ f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
|
||
|
|
+ "\n".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error])
|
||
|
|
)
|
||
|
|
|
||
|
|
commit_message = "Update TF weights" if self._new_weights else "Add TF weights"
|
||
|
|
if self._push:
|
||
|
|
repo.git_add(auto_lfs_track=True)
|
||
|
|
repo.git_commit(commit_message)
|
||
|
|
repo.git_push(blocking=True) # this prints a progress bar with the upload
|
||
|
|
self._logger.warning(f"TF weights pushed into {self._model_name}")
|
||
|
|
elif not self._no_pr:
|
||
|
|
self._logger.warning("Uploading the weights into a new PR...")
|
||
|
|
commit_descrition = (
|
||
|
|
"Model converted by the [`transformers`' `pt_to_tf`"
|
||
|
|
" CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). "
|
||
|
|
"All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"
|
||
|
|
f"Maximum crossload output difference={max_crossload_output_diff:.3e}; "
|
||
|
|
f"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"
|
||
|
|
f"Maximum conversion output difference={max_conversion_output_diff:.3e}; "
|
||
|
|
f"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"
|
||
|
|
)
|
||
|
|
if self._max_error > MAX_ERROR:
|
||
|
|
commit_descrition += (
|
||
|
|
f"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"
|
||
|
|
)
|
||
|
|
if self._extra_commit_description:
|
||
|
|
commit_descrition += "\n\n" + self._extra_commit_description
|
||
|
|
|
||
|
|
# sharded model -> adds all related files (index and .h5 shards)
|
||
|
|
if os.path.exists(tf_weights_index_path):
|
||
|
|
operations = [
|
||
|
|
CommitOperationAdd(path_in_repo=TF2_WEIGHTS_INDEX_NAME, path_or_fileobj=tf_weights_index_path)
|
||
|
|
]
|
||
|
|
for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"):
|
||
|
|
operations += [
|
||
|
|
CommitOperationAdd(path_in_repo=os.path.basename(shard_path), path_or_fileobj=shard_path)
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
operations = [CommitOperationAdd(path_in_repo=TF2_WEIGHTS_NAME, path_or_fileobj=tf_weights_path)]
|
||
|
|
|
||
|
|
hub_pr_url = create_commit(
|
||
|
|
repo_id=self._model_name,
|
||
|
|
operations=operations,
|
||
|
|
commit_message=commit_message,
|
||
|
|
commit_description=commit_descrition,
|
||
|
|
repo_type="model",
|
||
|
|
create_pr=True,
|
||
|
|
).pr_url
|
||
|
|
self._logger.warning(f"PR open in {hub_pr_url}")
|