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Grounded-Segment-Anything / transformers_4_35_0 /commands /pt_to_tf.py

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	# Copyright 2022 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 inspect
	import os
	from argparse import ArgumentParser, Namespace
	from importlib import import_module

	import huggingface_hub
	import numpy as np
	from packaging import version

	from .. import (
	FEATURE_EXTRACTOR_MAPPING,
	IMAGE_PROCESSOR_MAPPING,
	PROCESSOR_MAPPING,
	TOKENIZER_MAPPING,
	AutoConfig,
	AutoFeatureExtractor,
	AutoImageProcessor,
	AutoProcessor,
	AutoTokenizer,
	is_datasets_available,
	is_tf_available,
	is_torch_available,
	)
	from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
	from . import BaseTransformersCLICommand


	if is_tf_available():
	import tensorflow as tf

	tf.config.experimental.enable_tensor_float_32_execution(False)

	if is_torch_available():
	import torch

	if is_datasets_available():
	from datasets import load_dataset


	MAX_ERROR = 5e-5 # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


	def convert_command_factory(args: Namespace):
	"""
	Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint.

	Returns: ServeCommand
	"""
	return PTtoTFCommand(
	args.model_name,
	args.local_dir,
	args.max_error,
	args.new_weights,
	args.no_pr,
	args.push,
	args.extra_commit_description,
	args.override_model_class,
	)


	class PTtoTFCommand(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(
	"pt-to-tf",
	help=(
	"CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
	" Can also be used to validate existing weights without opening PRs, with --no-pr."
	),
	)
	train_parser.add_argument(
	"--model-name",
	type=str,
	required=True,
	help="The model name, including owner/organization, as seen on the hub.",
	)
	train_parser.add_argument(
	"--local-dir",
	type=str,
	default="",
	help="Optional local directory of the model repository. Defaults to /tmp/{model_name}",
	)
	train_parser.add_argument(
	"--max-error",
	type=float,
	default=MAX_ERROR,
	help=(
	f"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
	),
	)
	train_parser.add_argument(
	"--new-weights",
	action="store_true",
	help="Optional flag to create new TensorFlow weights, even if they already exist.",
	)
	train_parser.add_argument(
	"--no-pr", action="store_true", help="Optional flag to NOT open a PR with converted weights."
	)
	train_parser.add_argument(
	"--push",
	action="store_true",
	help="Optional flag to push the weights directly to `main` (requires permissions)",
	)
	train_parser.add_argument(
	"--extra-commit-description",
	type=str,
	default="",
	help="Optional additional commit description to use when opening a PR (e.g. to tag the owner).",
	)
	train_parser.add_argument(
	"--override-model-class",
	type=str,
	default=None,
	help="If you think you know better than the auto-detector, you can specify the model class here. "
	"Can be either an AutoModel class or a specific model class like BertForSequenceClassification.",
	)
	train_parser.set_defaults(func=convert_command_factory)

	@staticmethod
	def find_pt_tf_differences(pt_outputs, tf_outputs):
	"""
	Compares the TensorFlow and PyTorch outputs, returning a dictionary with all tensor differences.
	"""
	# 1. All output attributes must be the same
	pt_out_attrs = set(pt_outputs.keys())
	tf_out_attrs = set(tf_outputs.keys())
	if pt_out_attrs != tf_out_attrs:
	raise ValueError(
	f"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
	f" {tf_out_attrs})"
	)

	# 2. For each output attribute, computes the difference
	def _find_pt_tf_differences(pt_out, tf_out, differences, attr_name=""):
	# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
	# recursivelly, keeping the name of the attribute.
	if isinstance(pt_out, torch.Tensor):
	tensor_difference = np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
	differences[attr_name] = tensor_difference
	else:
	root_name = attr_name
	for i, pt_item in enumerate(pt_out):
	# If it is a named attribute, we keep the name. Otherwise, just its index.
	if isinstance(pt_item, str):
	branch_name = root_name + pt_item
	tf_item = tf_out[pt_item]
	pt_item = pt_out[pt_item]
	else:
	branch_name = root_name + f"[{i}]"
	tf_item = tf_out[i]
	differences = _find_pt_tf_differences(pt_item, tf_item, differences, branch_name)

	return differences

	return _find_pt_tf_differences(pt_outputs, tf_outputs, {})

	def __init__(
	self,
	model_name: str,
	local_dir: str,
	max_error: float,
	new_weights: bool,
	no_pr: bool,
	push: bool,
	extra_commit_description: str,
	override_model_class: str,
	*args,
	):
	self._logger = logging.get_logger("transformers-cli/pt_to_tf")
	self._model_name = model_name
	self._local_dir = local_dir if local_dir else os.path.join("/tmp", model_name)
	self._max_error = max_error
	self._new_weights = new_weights
	self._no_pr = no_pr
	self._push = push
	self._extra_commit_description = extra_commit_description
	self._override_model_class = override_model_class

	def get_inputs(self, pt_model, tf_dummy_inputs, config):
	"""
	Returns the right inputs for the model, based on its signature.
	"""

	def _get_audio_input():
	ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
	speech_samples = ds.sort("id").select(range(2))[:2]["audio"]
	raw_samples = [x["array"] for x in speech_samples]
	return raw_samples

	model_config_class = type(pt_model.config)
	if model_config_class in PROCESSOR_MAPPING:
	processor = AutoProcessor.from_pretrained(self._local_dir)
	if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
	processor.tokenizer.pad_token = processor.tokenizer.eos_token
	elif model_config_class in IMAGE_PROCESSOR_MAPPING:
	processor = AutoImageProcessor.from_pretrained(self._local_dir)
	elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
	processor = AutoFeatureExtractor.from_pretrained(self._local_dir)
	elif model_config_class in TOKENIZER_MAPPING:
	processor = AutoTokenizer.from_pretrained(self._local_dir)
	if processor.pad_token is None:
	processor.pad_token = processor.eos_token
	else:
	raise ValueError(f"Unknown data processing type (model config type: {model_config_class})")

	model_forward_signature = set(inspect.signature(pt_model.forward).parameters.keys())
	processor_inputs = {}
	if "input_ids" in model_forward_signature:
	processor_inputs.update(
	{
	"text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
	"padding": True,
	"truncation": True,
	}
	)
	if "pixel_values" in model_forward_signature:
	sample_images = load_dataset("cifar10", "plain_text", split="test")[:2]["img"]
	processor_inputs.update({"images": sample_images})
	if "input_features" in model_forward_signature:
	feature_extractor_signature = inspect.signature(processor.feature_extractor).parameters
	# Pad to the largest input length by default but take feature extractor default
	# padding value if it exists e.g. "max_length" and is not False or None
	if "padding" in feature_extractor_signature:
	default_strategy = feature_extractor_signature["padding"].default
	if default_strategy is not False and default_strategy is not None:
	padding_strategy = default_strategy
	else:
	padding_strategy = True
	else:
	padding_strategy = True
	processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy})
	if "input_values" in model_forward_signature: # Wav2Vec2 audio input
	processor_inputs.update({"audio": _get_audio_input(), "padding": True})
	pt_input = processor(**processor_inputs, return_tensors="pt")
	tf_input = processor(**processor_inputs, return_tensors="tf")

	# Extra input requirements, in addition to the input modality
	if (
	config.is_encoder_decoder
	or (hasattr(pt_model, "encoder") and hasattr(pt_model, "decoder"))
	or "decoder_input_ids" in tf_dummy_inputs
	):
	decoder_input_ids = np.asarray([[1], [1]], dtype=int) * (pt_model.config.decoder_start_token_id or 0)
	pt_input.update({"decoder_input_ids": torch.tensor(decoder_input_ids)})
	tf_input.update({"decoder_input_ids": tf.convert_to_tensor(decoder_input_ids)})

	return pt_input, tf_input

	def run(self):
	# hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
	if version.parse(huggingface_hub.__version__) < version.parse("0.9.0"):
	raise ImportError(
	"The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
	" installation."
	)
	else:
	from huggingface_hub import Repository, create_commit
	from huggingface_hub._commit_api import CommitOperationAdd

	# Fetch remote data
	repo = Repository(local_dir=self._local_dir, clone_from=self._model_name)

	# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
	config = AutoConfig.from_pretrained(self._local_dir)
	architectures = config.architectures
	if self._override_model_class is not None:
	if self._override_model_class.startswith("TF"):
	architectures = [self._override_model_class[2:]]
	else:
	architectures = [self._override_model_class]
	try:
	pt_class = getattr(import_module("transformers"), architectures[0])
	except AttributeError:
	raise ValueError(f"Model class {self._override_model_class} not found in transformers.")
	try:
	tf_class = getattr(import_module("transformers"), "TF" + architectures[0])
	except AttributeError:
	raise ValueError(f"TF model class TF{self._override_model_class} not found in transformers.")
	elif architectures is None: # No architecture defined -- use auto classes
	pt_class = getattr(import_module("transformers"), "AutoModel")
	tf_class = getattr(import_module("transformers"), "TFAutoModel")
	self._logger.warning("No detected architecture, using AutoModel/TFAutoModel")
	else: # Architecture defined -- use it
	if len(architectures) > 1:
	raise ValueError(f"More than one architecture was found, aborting. (architectures = {architectures})")
	self._logger.warning(f"Detected architecture: {architectures[0]}")
	pt_class = getattr(import_module("transformers"), architectures[0])
	try:
	tf_class = getattr(import_module("transformers"), "TF" + architectures[0])
	except AttributeError:
	raise AttributeError(f"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.")

	# Check the TF dummy inputs to see what keys we need in the forward pass
	tf_from_pt_model = tf_class.from_config(config)
	tf_dummy_inputs = tf_from_pt_model.dummy_inputs

	del tf_from_pt_model # Try to keep only one model in memory at a time

	# Load the model and get some basic inputs
	pt_model = pt_class.from_pretrained(self._local_dir)
	pt_model.eval()

	pt_input, tf_input = self.get_inputs(pt_model, tf_dummy_inputs, config)

	with torch.no_grad():
	pt_outputs = pt_model(**pt_input, output_hidden_states=True)
	del pt_model # will no longer be used, and may have a large memory footprint

	tf_from_pt_model = tf_class.from_pretrained(self._local_dir, from_pt=True)
	tf_from_pt_outputs = tf_from_pt_model(**tf_input, output_hidden_states=True, training=False)

	# Confirms that cross loading PT weights into TF worked.
	crossload_differences = self.find_pt_tf_differences(pt_outputs, tf_from_pt_outputs)
	output_differences = {k: v for k, v in crossload_differences.items() if "hidden" not in k}
	hidden_differences = {k: v for k, v in crossload_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_crossload_output_diff = max(output_differences.values()) if output_differences else 0.0
	max_crossload_hidden_diff = max(hidden_differences.values())
	if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
	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}")