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https://github.com/huggingface/transformers.git
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Create dummy models (#19901)
* create dummy models * quality * update * update * Make Wav2Vec2Conformer work * style * deal with models with text_config and vision_config * apply suggestions * Composite models * style * style * fix shape issue * fix shape issue * For VisionTextDualEncoderModel * show_progress=False when converting tokenizers * Fix for OwlViT * Fix for VisualBert * Update * final Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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Yih-Dar
GitHub
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commit
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@ -49,29 +49,35 @@ CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {
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}
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def get_checkpoint_from_config_class(config_class):
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checkpoint = None
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# source code of `config_class`
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config_source = inspect.getsource(config_class)
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checkpoints = _re_checkpoint.findall(config_source)
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for checkpoint in checkpoints:
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# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
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# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
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ckpt_name, ckpt_link = checkpoint
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# verify the checkpoint name corresponds to the checkpoint link
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ckpt_link_from_name = f"https://huggingface.co/{ckpt_name}"
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if ckpt_link == ckpt_link_from_name:
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checkpoint = ckpt_name
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break
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return checkpoint
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def check_config_docstrings_have_checkpoints():
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configs_without_checkpoint = []
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for config_class in list(CONFIG_MAPPING.values()):
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checkpoint_found = False
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# source code of `config_class`
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config_source = inspect.getsource(config_class)
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checkpoints = _re_checkpoint.findall(config_source)
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for checkpoint in checkpoints:
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# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
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# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
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ckpt_name, ckpt_link = checkpoint
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# verify the checkpoint name corresponds to the checkpoint link
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ckpt_link_from_name = f"https://huggingface.co/{ckpt_name}"
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if ckpt_link == ckpt_link_from_name:
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checkpoint_found = True
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break
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checkpoint = get_checkpoint_from_config_class(config_class)
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name = config_class.__name__
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if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
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if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
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configs_without_checkpoint.append(name)
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if len(configs_without_checkpoint) > 0:
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915
utils/create_dummy_models.py
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915
utils/create_dummy_models.py
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@ -0,0 +1,915 @@
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# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. 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 argparse
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import collections.abc
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import importlib
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import inspect
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import json
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import os
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import shutil
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import sys
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from pathlib import Path
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from datasets import load_dataset
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from check_config_docstrings import get_checkpoint_from_config_class
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from transformers import (
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CONFIG_MAPPING,
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FEATURE_EXTRACTOR_MAPPING,
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PROCESSOR_MAPPING,
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TOKENIZER_MAPPING,
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AutoTokenizer,
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LayoutLMv3TokenizerFast,
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PreTrainedTokenizerFast,
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logging,
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)
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from transformers.feature_extraction_utils import FeatureExtractionMixin
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from transformers.file_utils import is_tf_available, is_torch_available
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from transformers.models.auto.configuration_auto import AutoConfig, model_type_to_module_name
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from transformers.processing_utils import ProcessorMixin, transformers_module
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from transformers.tokenization_utils_base import PreTrainedTokenizerBase
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logging.set_verbosity_error()
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logger = logging.get_logger(__name__)
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sys.path.append(".")
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
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if not is_torch_available():
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raise ValueError("Please install PyTorch.")
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if not is_tf_available():
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raise ValueError("Please install TensorFlow.")
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FRAMEWORKS = ["pytorch", "tensorflow"]
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INVALID_ARCH = []
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TARGET_VOCAB_SIZE = 1024
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def get_processor_types_from_config_class(config_class, allowed_mappings=None):
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"""Return a tuple of processors for `config_class`.
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We use `tuple` here to include (potentially) both slow & fast tokenizers.
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"""
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if allowed_mappings is None:
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allowed_mappings = ["processor", "tokenizer", "feature_extractor"]
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processor_types = ()
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# Check first if a model has `ProcessorMixin`. Otherwise, check if it has tokenizers or a feature extractor.
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if config_class in PROCESSOR_MAPPING and "processor" in allowed_mappings:
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processor_types = PROCESSOR_MAPPING[config_class]
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elif config_class in TOKENIZER_MAPPING and "tokenizer" in allowed_mappings:
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processor_types = TOKENIZER_MAPPING[config_class]
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elif config_class in FEATURE_EXTRACTOR_MAPPING and "feature_extractor" in allowed_mappings:
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processor_types = FEATURE_EXTRACTOR_MAPPING[config_class]
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else:
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# Some configurations have no processor at all. For example, generic composite models like
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# `EncoderDecoderModel` is used for any (compatible) text models. Also, `DecisionTransformer` doesn't
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# require any processor.
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pass
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# make a uniform return type
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if not isinstance(processor_types, collections.abc.Sequence):
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processor_types = (processor_types,)
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else:
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processor_types = tuple(processor_types)
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# We might get `None` for some tokenizers - remove them here.
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processor_types = tuple(p for p in processor_types if p is not None)
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return processor_types
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def get_architectures_from_config_class(config_class, arch_mappings):
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"""Return a tuple of all possible architectures attributed to a configuration class `config_class`.
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For example, BertConfig -> [BertModel, BertForMaskedLM, ..., BertForQuestionAnswering].
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"""
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# A model architecture could appear in several mappings. For example, `BartForConditionalGeneration` is in
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# - MODEL_FOR_PRETRAINING_MAPPING_NAMES
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# - MODEL_WITH_LM_HEAD_MAPPING_NAMES
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# - MODEL_FOR_MASKED_LM_MAPPING_NAMES
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# - MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
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# We avoid the duplication.
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architectures = set()
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for mapping in arch_mappings:
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if config_class in mapping:
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models = mapping[config_class]
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models = tuple(models) if isinstance(models, collections.abc.Sequence) else (models,)
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for model in models:
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if model.__name__ not in unexportable_model_architectures:
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architectures.add(model)
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architectures = tuple(architectures)
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return architectures
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def get_config_class_from_processor_class(processor_class):
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"""Get the config class from a processor class.
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Some config/model classes use tokenizers/feature_extractors from other models. For example, `GPT-J` uses
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`GPT2Tokenizer`. If no checkpoint is found for a config class, or a checkpoint is found without necessary file(s) to
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create the processor for `processor_class`, we get the config class that corresponds to `processor_class` and use it
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to find a checkpoint in order to create the processor.
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"""
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processor_prefix = processor_class.__name__
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for postfix in ["Processor", "TokenizerFast", "Tokenizer", "FeatureExtractor"]:
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processor_prefix = processor_prefix.replace(postfix, "")
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# `Wav2Vec2CTCTokenizer` -> `Wav2Vec2Config`
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if processor_prefix == "Wav2Vec2CTC":
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processor_prefix = "Wav2Vec2"
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# Find the new configuration class
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new_config_name = f"{processor_prefix}Config"
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new_config_class = getattr(transformers_module, new_config_name)
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return new_config_class
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def build_processor(config_class, processor_class):
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"""Create a processor for `processor_class`.
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If a processor is not able to be built with the original arguments, this method tries to change the arguments and
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call itself recursively, by inferring a new `config_class` or a new `processor_class` from another one, in order to
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find a checkpoint containing the necessary files to build a processor.
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The processor is not saved here. Instead, it will be saved in `convert_processors` after further changes in
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`convert_processors`. For each model architecture`, a copy will be created and saved along the built model.
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"""
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# Currently, this solely uses the docstring in the source file of `config_class` to find a checkpoint.
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checkpoint = get_checkpoint_from_config_class(config_class)
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if checkpoint is None:
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# try to get the checkpoint from the config class for `processor_class`.
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# This helps cases like `XCLIPConfig` and `VideoMAEFeatureExtractor` to find a checkpoint from `VideoMAEConfig`.
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config_class_from_processor_class = get_config_class_from_processor_class(processor_class)
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checkpoint = get_checkpoint_from_config_class(config_class_from_processor_class)
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processor = None
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try:
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processor = processor_class.from_pretrained(checkpoint)
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except Exception as e:
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logger.error(e)
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pass
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# Try to get a new processor class from checkpoint. This is helpful for a checkpoint without necessary file to load
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# processor while `processor_class` is an Auto class. For example, `sew` has `Wav2Vec2Processor` in
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# `PROCESSOR_MAPPING_NAMES`, its `tokenizer_class` is `AutoTokenizer`, and the checkpoint
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# `https://huggingface.co/asapp/sew-tiny-100k` has no tokenizer file, but we can get
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# `tokenizer_class: Wav2Vec2CTCTokenizer` from the config file. (The new processor class won't be able to load from
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# `checkpoint`, but it helps this recursive method to find a way to build a processor).
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if (
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processor is None
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and checkpoint is not None
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and issubclass(processor_class, (PreTrainedTokenizerBase, AutoTokenizer))
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):
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try:
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config = AutoConfig.from_pretrained(checkpoint)
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except Exception as e:
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logger.error(e)
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config = None
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if config is not None:
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assert isinstance(config, config_class)
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tokenizer_class = config.tokenizer_class
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new_processor_class = None
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if tokenizer_class is not None:
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new_processor_class = getattr(transformers_module, tokenizer_class)
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if new_processor_class != processor_class:
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processor = build_processor(config_class, new_processor_class)
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# If `tokenizer_class` is not specified in `config`, let's use `config` to get the process class via auto
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# mappings, but only allow the tokenizer mapping being used. This is to make `Wav2Vec2Conformer` build
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if processor is None:
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new_processor_classes = get_processor_types_from_config_class(
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config.__class__, allowed_mappings=["tokenizer"]
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)
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# Used to avoid infinite recursion between a pair of fast/slow tokenizer types
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names = [
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x.__name__.replace("Fast", "") for x in [processor_class, new_processor_class] if x is not None
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]
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new_processor_classes = [
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x for x in new_processor_classes if x is not None and x.__name__.replace("Fast", "") not in names
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]
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if len(new_processor_classes) > 0:
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new_processor_class = new_processor_classes[0]
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processor = build_processor(config_class, new_processor_class)
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if processor is None:
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# Try to build each component (tokenizer & feature extractor) of a `ProcessorMixin`.
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if issubclass(processor_class, ProcessorMixin):
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attrs = {}
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for attr_name in processor_class.attributes:
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attrs[attr_name] = []
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# This could be a tuple (for tokenizers). For example, `CLIPProcessor` has
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# - feature_extractor_class = "CLIPFeatureExtractor"
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# - tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
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attr_class_names = getattr(processor_class, f"{attr_name}_class")
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if not isinstance(attr_class_names, tuple):
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attr_class_names = (attr_class_names,)
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for name in attr_class_names:
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attr_class = getattr(transformers_module, name)
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attr = build_processor(config_class, attr_class)
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if attr is not None:
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attrs[attr_name].append(attr)
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# try to build a `ProcessorMixin`, so we can return a single value
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if all(len(v) > 0 for v in attrs.values()):
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try:
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processor = processor_class(**{k: v[0] for k, v in attrs.items()})
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except Exception as e:
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logger.error(e)
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pass
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else:
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# `checkpoint` might lack some file(s) to load a processor. For example, `facebook/hubert-base-ls960`
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# has no tokenizer file to load `Wav2Vec2CTCTokenizer`. In this case, we try to build a processor
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# with the configuration class (for example, `Wav2Vec2Config`) corresponding to `processor_class`.
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config_class_from_processor_class = get_config_class_from_processor_class(processor_class)
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if config_class_from_processor_class != config_class:
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processor = build_processor(config_class_from_processor_class, processor_class)
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# validation
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if processor is not None:
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assert isinstance(processor, processor_class) or processor_class.__name__.startswith("Auto")
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return processor
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def get_tiny_config(config_class):
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"""Retrieve a tiny configuration from `config_class` using each model's `ModelTester`.
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Args:
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config_class: Subclass of `PreTrainedConfig`.
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Returns:
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An instance of `config_class` with tiny hyperparameters
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"""
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model_type = config_class.model_type
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# For model type like `data2vec-vision` and `donut-swin`, we can't get the config/model file name directly via
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# `model_type` as it would be sth. like `configuration_data2vec_vision.py`.
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# A simple way is to use `inspect.getsourcefile(config_class)`.
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config_source_file = inspect.getsourcefile(config_class)
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# The modeling file name without prefix (`modeling_`) and postfix (`.py`)
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modeling_name = config_source_file.split("/")[-1].replace("configuration_", "").replace(".py", "")
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try:
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print("Importing", model_type_to_module_name(model_type))
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module_name = model_type_to_module_name(model_type)
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assert modeling_name.startswith(module_name)
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module = importlib.import_module(f".models.{module_name}.test_modeling_{modeling_name}", package="tests")
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camel_case_model_name = config_class.__name__.split("Config")[0]
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model_tester_class = getattr(module, f"{camel_case_model_name}ModelTester", None)
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except ModuleNotFoundError as e:
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error = f"Tiny config not created for {model_type} - cannot find the testing module from the model name."
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raise ValueError(f"{error}: {str(e)}")
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if model_tester_class is None:
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error = f"Tiny config not created for {model_type} - no model tester is found in the testing module."
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raise ValueError(error)
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# `parent` is an instance of `unittest.TestCase`, but we don't need it here.
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model_tester = model_tester_class(parent=None)
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if hasattr(model_tester, "get_pipeline_config"):
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return model_tester.get_pipeline_config()
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elif hasattr(model_tester, "prepare_config_and_inputs"):
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# `PoolFormer` has no `get_config` defined. Furthermore, it's better to use `prepare_config_and_inputs` even if
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# `get_config` is defined, since there might be some extra changes in `prepare_config_and_inputs`.
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return model_tester.prepare_config_and_inputs()[0]
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elif hasattr(model_tester, "get_config"):
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return model_tester.get_config()
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else:
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error = (
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f"Tiny config not created for {model_type} - the model tester {model_tester_class.__name__} lacks"
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" necessary method to create config."
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)
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raise ValueError(error)
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def convert_tokenizer(tokenizer_fast: PreTrainedTokenizerFast):
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new_tokenizer = tokenizer_fast.train_new_from_iterator(training_ds["text"], TARGET_VOCAB_SIZE, show_progress=False)
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# Make sure it at least runs
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if not isinstance(new_tokenizer, LayoutLMv3TokenizerFast):
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new_tokenizer(testing_ds["text"])
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return new_tokenizer
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def convert_feature_extractor(feature_extractor, tiny_config):
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to_convert = False
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kwargs = {}
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if hasattr(tiny_config, "image_size"):
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kwargs["size"] = tiny_config.image_size
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kwargs["crop_size"] = tiny_config.image_size
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to_convert = True
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elif (
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hasattr(tiny_config, "vision_config")
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and tiny_config.vision_config is not None
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and hasattr(tiny_config.vision_config, "image_size")
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):
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kwargs["size"] = tiny_config.vision_config.image_size
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kwargs["crop_size"] = tiny_config.vision_config.image_size
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to_convert = True
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# Speech2TextModel specific.
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if hasattr(tiny_config, "input_feat_per_channel"):
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kwargs["feature_size"] = tiny_config.input_feat_per_channel
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kwargs["num_mel_bins"] = tiny_config.input_feat_per_channel
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to_convert = True
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if to_convert:
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feature_extractor = feature_extractor.__class__(**kwargs)
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return feature_extractor
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def convert_processors(processors, tiny_config, output_folder, result):
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"""Change a processor to work with smaller inputs.
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For tokenizers, we try to reduce their vocabulary size.
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For feature extractor, we use smaller image size or change
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other attributes using the values from `tiny_config`. See `convert_feature_extractor`.
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This method should not fail: we catch the errors and put them in `result["warnings"]` with descriptive messages.
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"""
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tokenizers = []
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feature_extractors = []
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for processor in processors:
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if isinstance(processor, PreTrainedTokenizerBase):
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tokenizers.append(processor)
|
||||
elif isinstance(processor, FeatureExtractionMixin):
|
||||
feature_extractors.append(processor)
|
||||
elif isinstance(processor, ProcessorMixin):
|
||||
# Currently, we only have these 2 possibilities
|
||||
tokenizers.append(processor.tokenizer)
|
||||
feature_extractors.append(processor.feature_extractor)
|
||||
|
||||
# check the built processors have the unique type
|
||||
assert len(set([x.__class__.__name__ for x in feature_extractors])) < 2
|
||||
assert len(set([x.__class__.__name__.replace("Fast", "") for x in tokenizers])) < 2
|
||||
|
||||
fast_tokenizer = None
|
||||
slow_tokenizer = None
|
||||
for tokenizer in tokenizers:
|
||||
if isinstance(tokenizer, PreTrainedTokenizerFast):
|
||||
if fast_tokenizer is None:
|
||||
fast_tokenizer = tokenizer
|
||||
try:
|
||||
# Wav2Vec2ForCTC , ByT5Tokenizer etc. all are already small enough and have no fast version that can
|
||||
# be retrained
|
||||
if fast_tokenizer.vocab_size > TARGET_VOCAB_SIZE:
|
||||
fast_tokenizer = convert_tokenizer(tokenizer)
|
||||
except Exception as e:
|
||||
result["warnings"].append(
|
||||
f"Failed to convert the fast tokenizer for {fast_tokenizer.__class__.__name__}: {e}"
|
||||
)
|
||||
continue
|
||||
elif slow_tokenizer is None:
|
||||
slow_tokenizer = tokenizer
|
||||
|
||||
# Make sure the fast tokenizer can be saved
|
||||
if fast_tokenizer:
|
||||
try:
|
||||
fast_tokenizer.save_pretrained(output_folder)
|
||||
except Exception as e:
|
||||
result["warnings"].append(
|
||||
f"Failed to save the fast tokenizer for {fast_tokenizer.__class__.__name__}: {e}"
|
||||
)
|
||||
fast_tokenizer = None
|
||||
|
||||
# Make sure the slow tokenizer (if any) corresponds to the fast version (as it might be converted above)
|
||||
if fast_tokenizer:
|
||||
try:
|
||||
slow_tokenizer = AutoTokenizer.from_pretrained(output_folder, use_fast=False)
|
||||
except Exception as e:
|
||||
result["warnings"].append(
|
||||
f"Failed to load the slow tokenizer saved from {fast_tokenizer.__class__.__name__}: {e}"
|
||||
)
|
||||
# Let's just keep the fast version
|
||||
slow_tokenizer = None
|
||||
|
||||
# If the fast version can't be created and saved, let's use the slow version
|
||||
if not fast_tokenizer and slow_tokenizer:
|
||||
try:
|
||||
slow_tokenizer.save_pretrained(output_folder)
|
||||
except Exception as e:
|
||||
result["warnings"].append(
|
||||
f"Failed to save the slow tokenizer for {slow_tokenizer.__class__.__name__}: {e}"
|
||||
)
|
||||
slow_tokenizer = None
|
||||
|
||||
# update feature extractors using the tiny config
|
||||
try:
|
||||
feature_extractors = [convert_feature_extractor(p, tiny_config) for p in feature_extractors]
|
||||
except Exception as e:
|
||||
result["warnings"].append(f"Failed to convert feature extractors: {e}")
|
||||
feature_extractors = []
|
||||
|
||||
processors = [fast_tokenizer, slow_tokenizer] + feature_extractors
|
||||
processors = [p for p in processors if p is not None]
|
||||
for p in processors:
|
||||
p.save_pretrained(output_folder)
|
||||
|
||||
return processors
|
||||
|
||||
|
||||
def get_checkpoint_dir(output_dir, model_arch):
|
||||
"""Get framework-agnostic architecture name. Used to save all PT/TF/Flax models into the same directory."""
|
||||
|
||||
arch_name = model_arch.__name__
|
||||
if arch_name.startswith("TF"):
|
||||
arch_name = arch_name[2:]
|
||||
elif arch_name.startswith("Flax"):
|
||||
arch_name = arch_name[4:]
|
||||
|
||||
return os.path.join(output_dir, arch_name)
|
||||
|
||||
|
||||
def build_model(model_arch, tiny_config, output_dir):
|
||||
"""Create and save a model for `model_arch`.
|
||||
|
||||
Also copy the set of processors to each model (under the same model type) output folder.
|
||||
"""
|
||||
|
||||
checkpoint_dir = get_checkpoint_dir(output_dir, model_arch)
|
||||
|
||||
processor_output_dir = os.path.join(output_dir, "processors")
|
||||
# copy the (same set of) processors (for a model type) to the model arch. specific folder
|
||||
if os.path.isdir(processor_output_dir):
|
||||
shutil.copytree(processor_output_dir, checkpoint_dir, dirs_exist_ok=True)
|
||||
|
||||
model = model_arch(config=tiny_config)
|
||||
model.save_pretrained(checkpoint_dir)
|
||||
model.from_pretrained(checkpoint_dir)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
def fill_result_with_error(result, error, models_to_create):
|
||||
"""Fill `result` with errors for all target model arch if we can't build processor"""
|
||||
|
||||
result["error"] = error
|
||||
for framework in FRAMEWORKS:
|
||||
if framework in models_to_create:
|
||||
result[framework] = {}
|
||||
for model_arch in models_to_create[framework]:
|
||||
result[framework][model_arch.__name__] = {"model": None, "checkpoint": None, "error": error}
|
||||
|
||||
|
||||
def build_composite_models(config_class, output_dir):
|
||||
|
||||
import tempfile
|
||||
|
||||
from transformers import (
|
||||
BertConfig,
|
||||
BertLMHeadModel,
|
||||
BertModel,
|
||||
BertTokenizer,
|
||||
BertTokenizerFast,
|
||||
EncoderDecoderModel,
|
||||
GPT2Config,
|
||||
GPT2LMHeadModel,
|
||||
GPT2Tokenizer,
|
||||
GPT2TokenizerFast,
|
||||
SpeechEncoderDecoderModel,
|
||||
TFEncoderDecoderModel,
|
||||
TFVisionEncoderDecoderModel,
|
||||
VisionEncoderDecoderModel,
|
||||
VisionTextDualEncoderModel,
|
||||
ViTConfig,
|
||||
ViTFeatureExtractor,
|
||||
ViTModel,
|
||||
Wav2Vec2Config,
|
||||
Wav2Vec2Model,
|
||||
Wav2Vec2Processor,
|
||||
)
|
||||
|
||||
# These will be removed at the end if they are empty
|
||||
result = {"error": None, "warnings": []}
|
||||
|
||||
if config_class.model_type == "encoder-decoder":
|
||||
encoder_config_class = BertConfig
|
||||
decoder_config_class = BertConfig
|
||||
encoder_processor = (BertTokenizerFast, BertTokenizer)
|
||||
decoder_processor = (BertTokenizerFast, BertTokenizer)
|
||||
encoder_class = BertModel
|
||||
decoder_class = BertLMHeadModel
|
||||
model_class = EncoderDecoderModel
|
||||
tf_model_class = TFEncoderDecoderModel
|
||||
elif config_class.model_type == "vision-encoder-decoder":
|
||||
encoder_config_class = ViTConfig
|
||||
decoder_config_class = GPT2Config
|
||||
encoder_processor = (ViTFeatureExtractor,)
|
||||
decoder_processor = (GPT2TokenizerFast, GPT2Tokenizer)
|
||||
encoder_class = ViTModel
|
||||
decoder_class = GPT2LMHeadModel
|
||||
model_class = VisionEncoderDecoderModel
|
||||
tf_model_class = TFVisionEncoderDecoderModel
|
||||
elif config_class.model_type == "speech-encoder-decoder":
|
||||
encoder_config_class = Wav2Vec2Config
|
||||
decoder_config_class = BertConfig
|
||||
encoder_processor = (Wav2Vec2Processor,)
|
||||
decoder_processor = (BertTokenizerFast, BertTokenizer)
|
||||
encoder_class = Wav2Vec2Model
|
||||
decoder_class = BertLMHeadModel
|
||||
model_class = SpeechEncoderDecoderModel
|
||||
tf_model_class = None
|
||||
elif config_class.model_type == "vision-text-dual-encoder":
|
||||
# Not encoder-decoder, but encoder-encoder. We just keep the same name as above to make code easier
|
||||
encoder_config_class = ViTConfig
|
||||
decoder_config_class = BertConfig
|
||||
encoder_processor = (ViTFeatureExtractor,)
|
||||
decoder_processor = (BertTokenizerFast, BertTokenizer)
|
||||
encoder_class = ViTModel
|
||||
decoder_class = BertModel
|
||||
model_class = VisionTextDualEncoderModel
|
||||
tf_model_class = None
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
|
||||
try:
|
||||
# build encoder
|
||||
models_to_create = {"processor": encoder_processor, "pytorch": (encoder_class,), "tensorflow": []}
|
||||
encoder_output_dir = os.path.join(tmpdir, "encoder")
|
||||
build(encoder_config_class, models_to_create, encoder_output_dir)
|
||||
|
||||
# build decoder
|
||||
models_to_create = {"processor": decoder_processor, "pytorch": (decoder_class,), "tensorflow": []}
|
||||
decoder_output_dir = os.path.join(tmpdir, "decoder")
|
||||
build(decoder_config_class, models_to_create, decoder_output_dir)
|
||||
|
||||
# build encoder-decoder
|
||||
encoder_path = os.path.join(encoder_output_dir, encoder_class.__name__)
|
||||
decoder_path = os.path.join(decoder_output_dir, decoder_class.__name__)
|
||||
|
||||
if config_class.model_type != "vision-text-dual-encoder":
|
||||
# Specify these explicitly for encoder-decoder like models, but not for `vision-text-dual-encoder` as it
|
||||
# has no decoder.
|
||||
decoder_config = decoder_config_class.from_pretrained(decoder_path)
|
||||
decoder_config.is_decoder = True
|
||||
decoder_config.add_cross_attention = True
|
||||
model = model_class.from_encoder_decoder_pretrained(
|
||||
encoder_path,
|
||||
decoder_path,
|
||||
decoder_config=decoder_config,
|
||||
)
|
||||
elif config_class.model_type == "vision-text-dual-encoder":
|
||||
model = model_class.from_vision_text_pretrained(encoder_path, decoder_path)
|
||||
|
||||
model_path = os.path.join(
|
||||
output_dir,
|
||||
f"{model_class.__name__}-{encoder_config_class.model_type}-{decoder_config_class.model_type}",
|
||||
)
|
||||
model.save_pretrained(model_path)
|
||||
|
||||
if tf_model_class is not None:
|
||||
model = tf_model_class.from_pretrained(model_path, from_pt=True)
|
||||
model.save_pretrained(model_path)
|
||||
|
||||
# copy the processors
|
||||
encoder_processor_path = os.path.join(encoder_output_dir, "processors")
|
||||
decoder_processor_path = os.path.join(decoder_output_dir, "processors")
|
||||
if os.path.isdir(encoder_processor_path):
|
||||
shutil.copytree(encoder_processor_path, model_path, dirs_exist_ok=True)
|
||||
if os.path.isdir(decoder_processor_path):
|
||||
shutil.copytree(decoder_processor_path, model_path, dirs_exist_ok=True)
|
||||
|
||||
# fill `result`
|
||||
result["processor"] = tuple(set([x.__name__ for x in encoder_processor + decoder_processor]))
|
||||
|
||||
result["pytorch"] = {model_class.__name__: {"model": model_class.__name__, "checkpoint": model_path}}
|
||||
|
||||
result["tensorflow"] = {}
|
||||
if tf_model_class is not None:
|
||||
result["tensorflow"] = {
|
||||
tf_model_class.__name__: {"model": tf_model_class.__name__, "checkpoint": model_path}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
result["error"] = f"Failed to build models for {config_class.__name__}: {e}"
|
||||
|
||||
if not result["error"]:
|
||||
del result["error"]
|
||||
if not result["warnings"]:
|
||||
del result["warnings"]
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def build(config_class, models_to_create, output_dir):
|
||||
"""Create all models for a certain model type.
|
||||
|
||||
Args:
|
||||
config_class (`PretrainedConfig`):
|
||||
A subclass of `PretrainedConfig` that is used to determine `models_to_create`.
|
||||
models_to_create (`dict`):
|
||||
A dictionary containing the processor/model classes that we want to create the instances. These models are
|
||||
of the same model type which is associated to `config_class`.
|
||||
output_dir (`str`):
|
||||
The directory to save all the checkpoints. Each model architecture will be saved in a subdirectory under
|
||||
it. Models in different frameworks with the same architecture will be saved in the same subdirectory.
|
||||
"""
|
||||
|
||||
if config_class.model_type in [
|
||||
"encoder-decoder",
|
||||
"vision-encoder-decoder",
|
||||
"speech-encoder-decoder",
|
||||
"vision-text-dual-encoder",
|
||||
]:
|
||||
return build_composite_models(config_class, output_dir)
|
||||
|
||||
result = {k: {} for k in models_to_create}
|
||||
|
||||
# These will be removed at the end if they are empty
|
||||
result["error"] = None
|
||||
result["warnings"] = []
|
||||
|
||||
# Build processors
|
||||
processor_classes = models_to_create["processor"]
|
||||
|
||||
if len(processor_classes) == 0:
|
||||
error = f"No processor class could be found in {config_class.__name__}."
|
||||
fill_result_with_error(result, error, models_to_create)
|
||||
logger.error(result["error"])
|
||||
return result
|
||||
|
||||
for processor_class in processor_classes:
|
||||
processor = build_processor(config_class, processor_class)
|
||||
if processor is not None:
|
||||
result["processor"][processor_class] = processor
|
||||
|
||||
if len(result["processor"]) == 0:
|
||||
error = f"No processor could be built for {config_class.__name__}."
|
||||
fill_result_with_error(result, error, models_to_create)
|
||||
logger.error(result["error"])
|
||||
return result
|
||||
|
||||
try:
|
||||
tiny_config = get_tiny_config(config_class)
|
||||
except Exception as e:
|
||||
# Let's still return the processors, so we know they could be built.
|
||||
result["processor"] = {type(p).__name__: p.__class__.__name__ for p in result["processor"]}
|
||||
error = str(e)
|
||||
fill_result_with_error(result, error, models_to_create)
|
||||
logger.error(result["error"])
|
||||
return result
|
||||
|
||||
# Convert the processors (reduce vocabulary size, smaller image size, etc.)
|
||||
processors = list(result["processor"].values())
|
||||
processor_output_folder = os.path.join(output_dir, "processors")
|
||||
processors = convert_processors(processors, tiny_config, processor_output_folder, result)
|
||||
# update `result["processor"]`
|
||||
result["processor"] = {type(p).__name__: p.__class__.__name__ for p in processors}
|
||||
|
||||
if len(result["processor"]) == 0:
|
||||
error = f"No processor could be converted for {config_class.__name__}."
|
||||
fill_result_with_error(result, error, models_to_create)
|
||||
logger.error(result["error"])
|
||||
return result
|
||||
|
||||
# Update the config with the properties of the converted processors (smaller vocab size, image size, etc.)
|
||||
for processor in processors:
|
||||
if isinstance(processor, PreTrainedTokenizerBase):
|
||||
vocab_size = processor.vocab_size
|
||||
result["vocab_size"] = vocab_size
|
||||
config_overrides = {k: v for k, v in result.items() if k in ["vocab_size"] and v is not None}
|
||||
# Update `vocab_size`
|
||||
for k, v in config_overrides.items():
|
||||
if hasattr(tiny_config, k):
|
||||
setattr(tiny_config, k, v)
|
||||
# currently only `vocab_size` is involved, so let's just look `text_config`
|
||||
elif (
|
||||
hasattr(tiny_config, "text_config")
|
||||
and tiny_config.text_config is not None
|
||||
and hasattr(tiny_config.text_config, k)
|
||||
):
|
||||
setattr(tiny_config.text_config, k, v)
|
||||
# If `text_config_dict` exists, we need to update its value here too in order to # make
|
||||
# `save_pretrained -> from_pretrained` work.
|
||||
if hasattr(tiny_config, "text_config_dict"):
|
||||
tiny_config.text_config_dict[k] = v
|
||||
|
||||
if result["warnings"]:
|
||||
logger.warning(result["warnings"])
|
||||
|
||||
for pytorch_arch in models_to_create["pytorch"]:
|
||||
result["pytorch"][pytorch_arch.__name__] = {}
|
||||
error = None
|
||||
try:
|
||||
model = build_model(pytorch_arch, tiny_config, output_dir=output_dir)
|
||||
except Exception as e:
|
||||
model = None
|
||||
error = f"Failed to create the pytorch model for {pytorch_arch}: {e}"
|
||||
|
||||
result["pytorch"][pytorch_arch.__name__]["model"] = model.__class__.__name__ if model is not None else None
|
||||
result["pytorch"][pytorch_arch.__name__]["checkpoint"] = (
|
||||
get_checkpoint_dir(output_dir, pytorch_arch) if model is not None else None
|
||||
)
|
||||
if error:
|
||||
result["pytorch"][pytorch_arch.__name__]["error"] = error
|
||||
logger.error(f"{pytorch_arch.__name__}: {error}")
|
||||
|
||||
for tensorflow_arch in models_to_create["tensorflow"]:
|
||||
# Make PT/TF weights compatible
|
||||
pt_arch_name = tensorflow_arch.__name__[2:] # Remove `TF`
|
||||
pt_arch = getattr(transformers_module, pt_arch_name)
|
||||
|
||||
result["tensorflow"][tensorflow_arch.__name__] = {}
|
||||
error = None
|
||||
if pt_arch.__name__ in result["pytorch"] and result["pytorch"][pt_arch.__name__]["checkpoint"] is not None:
|
||||
ckpt = get_checkpoint_dir(output_dir, pt_arch)
|
||||
# Use the same weights from PyTorch.
|
||||
try:
|
||||
model = tensorflow_arch.from_pretrained(ckpt, from_pt=True)
|
||||
model.save_pretrained(ckpt)
|
||||
except Exception as e:
|
||||
# Conversion may fail. Let's not create a model with different weights to avoid confusion (for now).
|
||||
model = None
|
||||
error = f"Failed to convert the pytorch model to the tensorflow model for {pt_arch}: {e}"
|
||||
else:
|
||||
try:
|
||||
model = build_model(tensorflow_arch, tiny_config, output_dir=output_dir)
|
||||
except Exception as e:
|
||||
model = None
|
||||
error = f"Failed to create the tensorflow model for {tensorflow_arch}: {e}"
|
||||
|
||||
result["tensorflow"][tensorflow_arch.__name__]["model"] = (
|
||||
model.__class__.__name__ if model is not None else None
|
||||
)
|
||||
result["tensorflow"][tensorflow_arch.__name__]["checkpoint"] = (
|
||||
get_checkpoint_dir(output_dir, tensorflow_arch) if model is not None else None
|
||||
)
|
||||
if error:
|
||||
result["tensorflow"][tensorflow_arch.__name__]["error"] = error
|
||||
logger.error(f"{tensorflow_arch.__name__}: {error}")
|
||||
|
||||
if not result["error"]:
|
||||
del result["error"]
|
||||
if not result["warnings"]:
|
||||
del result["warnings"]
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def build_failed_report(results, include_warning=True):
|
||||
|
||||
failed_results = {}
|
||||
for config_name in results:
|
||||
if "error" in results[config_name]:
|
||||
if config_name not in failed_results:
|
||||
failed_results[config_name] = {}
|
||||
failed_results[config_name] = {"error": results[config_name]["error"]}
|
||||
|
||||
if include_warning and "warnings" in results[config_name]:
|
||||
if config_name not in failed_results:
|
||||
failed_results[config_name] = {}
|
||||
failed_results[config_name]["warnings"] = results[config_name]["warnings"]
|
||||
|
||||
for framework in FRAMEWORKS:
|
||||
if framework not in results[config_name]:
|
||||
continue
|
||||
for arch_name in results[config_name][framework]:
|
||||
if "error" in results[config_name][framework][arch_name]:
|
||||
if config_name not in failed_results:
|
||||
failed_results[config_name] = {}
|
||||
if framework not in failed_results[config_name]:
|
||||
failed_results[config_name][framework] = {}
|
||||
if arch_name not in failed_results[config_name][framework]:
|
||||
failed_results[config_name][framework][arch_name] = {}
|
||||
error = results[config_name][framework][arch_name]["error"]
|
||||
failed_results[config_name][framework][arch_name]["error"] = error
|
||||
|
||||
return failed_results
|
||||
|
||||
|
||||
def build_simple_report(results):
|
||||
|
||||
text = ""
|
||||
failed_text = ""
|
||||
for config_name in results:
|
||||
for framework in FRAMEWORKS:
|
||||
if framework not in results[config_name]:
|
||||
continue
|
||||
for arch_name in results[config_name][framework]:
|
||||
if "error" in results[config_name][framework][arch_name]:
|
||||
result = results[config_name][framework][arch_name]["error"]
|
||||
failed_text += f"{arch_name}: {result}\n"
|
||||
else:
|
||||
result = "OK"
|
||||
text += f"{arch_name}: {result}\n"
|
||||
|
||||
return text, failed_text
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
clone_path = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
|
||||
if os.getcwd() != clone_path:
|
||||
raise ValueError(f"This script should be run from the root of the clone of `transformers` {clone_path}")
|
||||
|
||||
_pytorch_arch_mappings = [
|
||||
x
|
||||
for x in dir(transformers_module)
|
||||
if x.startswith("MODEL_") and x.endswith("_MAPPING") and x != "MODEL_NAMES_MAPPING"
|
||||
]
|
||||
_tensorflow_arch_mappings = [
|
||||
x for x in dir(transformers_module) if x.startswith("TF_MODEL_") and x.endswith("_MAPPING")
|
||||
]
|
||||
# _flax_arch_mappings = [x for x in dir(transformers_module) if x.startswith("FLAX_MODEL_") and x.endswith("_MAPPING")]
|
||||
|
||||
pytorch_arch_mappings = [getattr(transformers_module, x) for x in _pytorch_arch_mappings]
|
||||
tensorflow_arch_mappings = [getattr(transformers_module, x) for x in _tensorflow_arch_mappings]
|
||||
# flax_arch_mappings = [getattr(transformers_module, x) for x in _flax_arch_mappings]
|
||||
|
||||
unexportable_model_architectures = []
|
||||
|
||||
ds = load_dataset("wikitext", "wikitext-2-raw-v1")
|
||||
training_ds = ds["train"]
|
||||
testing_ds = ds["test"]
|
||||
|
||||
def list_str(values):
|
||||
return values.split(",")
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--all", action="store_true", help="Will create all tiny models.")
|
||||
parser.add_argument(
|
||||
"--no_check",
|
||||
action="store_true",
|
||||
help="If set, will not check the validity of architectures. Use with caution.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-m",
|
||||
"--model_types",
|
||||
type=list_str,
|
||||
help="Comma-separated list of model type(s) from which the tiny models will be created.",
|
||||
)
|
||||
parser.add_argument("output_path", type=Path, help="Path indicating where to store generated model.")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if not args.all and not args.model_types:
|
||||
raise ValueError("Please provide at least one model type or pass `--all` to export all architectures.")
|
||||
|
||||
config_classes = CONFIG_MAPPING.values()
|
||||
if not args.all:
|
||||
config_classes = [CONFIG_MAPPING[model_type] for model_type in args.model_types]
|
||||
|
||||
# A map from config classes to tuples of processors (tokenizer, feature extractor, processor) classes
|
||||
processor_type_map = {c: get_processor_types_from_config_class(c) for c in config_classes}
|
||||
|
||||
to_create = {
|
||||
c: {
|
||||
"processor": processor_type_map[c],
|
||||
"pytorch": get_architectures_from_config_class(c, pytorch_arch_mappings),
|
||||
"tensorflow": get_architectures_from_config_class(c, tensorflow_arch_mappings),
|
||||
# "flax": get_architectures_from_config_class(c, flax_arch_mappings),
|
||||
}
|
||||
for c in config_classes
|
||||
}
|
||||
|
||||
results = {}
|
||||
for c, models_to_create in list(to_create.items()):
|
||||
print(f"Create models for {c.__name__} ...")
|
||||
result = build(c, models_to_create, output_dir=os.path.join(args.output_path, c.model_type))
|
||||
results[c.__name__] = result
|
||||
print("=" * 40)
|
||||
|
||||
with open("tiny_model_creation_report.json", "w") as fp:
|
||||
json.dump(results, fp, indent=4)
|
||||
|
||||
# Build the failure report
|
||||
failed_results = build_failed_report(results)
|
||||
with open("failed_report.json", "w") as fp:
|
||||
json.dump(failed_results, fp, indent=4)
|
||||
|
||||
# Build the failure report
|
||||
simple_report, failed_report = build_simple_report(results)
|
||||
with open("simple_report.txt", "w") as fp:
|
||||
fp.write(simple_report)
|
||||
|
||||
with open("simple_failed_report.txt", "w") as fp:
|
||||
fp.write(failed_report)
|
||||
Loading…
Reference in New Issue
Block a user