# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This script can be used to convert a head-less TF2.x Bert model to PyTorch, as published on the official (now deprecated) GitHub: https://github.com/tensorflow/models/tree/v2.3.0/official/nlp/bert TF2.x uses different variable names from the original BERT (TF 1.4) implementation. The script re-maps the TF2.x Bert weight names to the original names, so the model can be imported with Huggingface/transformer. You may adapt this script to include classification/MLM/NSP/etc. heads. Note: This script is only working with an older version of the TensorFlow models repository (<= v2.3.0). Models trained with never versions are not compatible with this script. """ import argparse import os import re import tensorflow as tf import torch from transformers import BertConfig, BertModel from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def load_tf2_weights_in_bert(model, tf_checkpoint_path, config): tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] layer_depth = [] for full_name, shape in init_vars: # logger.info(f"Loading TF weight {name} with shape {shape}") name = full_name.split("/") if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]: logger.info(f"Skipping non-model layer {full_name}") continue if "optimizer" in full_name: logger.info(f"Skipping optimization layer {full_name}") continue if name[0] == "model": # ignore initial 'model' name = name[1:] # figure out how many levels deep the name is depth = 0 for _name in name: if _name.startswith("layer_with_weights"): depth += 1 else: break layer_depth.append(depth) # read data array = tf.train.load_variable(tf_path, full_name) names.append("/".join(name)) arrays.append(array) logger.info(f"Read a total of {len(arrays):,} layers") # Sanity check if len(set(layer_depth)) != 1: raise ValueError(f"Found layer names with different depths (layer depth {list(set(layer_depth))})") layer_depth = list(set(layer_depth))[0] if layer_depth != 1: raise ValueError( "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP" " heads." ) # convert layers logger.info("Converting weights...") for full_name, array in zip(names, arrays): name = full_name.split("/") pointer = model trace = [] for i, m_name in enumerate(name): if m_name == ".ATTRIBUTES": # variable names end with .ATTRIBUTES/VARIABLE_VALUE break if m_name.startswith("layer_with_weights"): layer_num = int(m_name.split("-")[-1]) if layer_num <= 2: # embedding layers # layer_num 0: word_embeddings # layer_num 1: position_embeddings # layer_num 2: token_type_embeddings continue elif layer_num == 3: # embedding LayerNorm trace.extend(["embeddings", "LayerNorm"]) pointer = getattr(pointer, "embeddings") pointer = getattr(pointer, "LayerNorm") elif layer_num > 3 and layer_num < config.num_hidden_layers + 4: # encoder layers trace.extend(["encoder", "layer", str(layer_num - 4)]) pointer = getattr(pointer, "encoder") pointer = getattr(pointer, "layer") pointer = pointer[layer_num - 4] elif layer_num == config.num_hidden_layers + 4: # pooler layer trace.extend(["pooler", "dense"]) pointer = getattr(pointer, "pooler") pointer = getattr(pointer, "dense") elif m_name == "embeddings": trace.append("embeddings") pointer = getattr(pointer, "embeddings") if layer_num == 0: trace.append("word_embeddings") pointer = getattr(pointer, "word_embeddings") elif layer_num == 1: trace.append("position_embeddings") pointer = getattr(pointer, "position_embeddings") elif layer_num == 2: trace.append("token_type_embeddings") pointer = getattr(pointer, "token_type_embeddings") else: raise ValueError(f"Unknown embedding layer with name {full_name}") trace.append("weight") pointer = getattr(pointer, "weight") elif m_name == "_attention_layer": # self-attention layer trace.extend(["attention", "self"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "self") elif m_name == "_attention_layer_norm": # output attention norm trace.extend(["attention", "output", "LayerNorm"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "output") pointer = getattr(pointer, "LayerNorm") elif m_name == "_attention_output_dense": # output attention dense trace.extend(["attention", "output", "dense"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "output") pointer = getattr(pointer, "dense") elif m_name == "_output_dense": # output dense trace.extend(["output", "dense"]) pointer = getattr(pointer, "output") pointer = getattr(pointer, "dense") elif m_name == "_output_layer_norm": # output dense trace.extend(["output", "LayerNorm"]) pointer = getattr(pointer, "output") pointer = getattr(pointer, "LayerNorm") elif m_name == "_key_dense": # attention key trace.append("key") pointer = getattr(pointer, "key") elif m_name == "_query_dense": # attention query trace.append("query") pointer = getattr(pointer, "query") elif m_name == "_value_dense": # attention value trace.append("value") pointer = getattr(pointer, "value") elif m_name == "_intermediate_dense": # attention intermediate dense trace.extend(["intermediate", "dense"]) pointer = getattr(pointer, "intermediate") pointer = getattr(pointer, "dense") elif m_name == "_output_layer_norm": # output layer norm trace.append("output") pointer = getattr(pointer, "output") # weights & biases elif m_name in ["bias", "beta"]: trace.append("bias") pointer = getattr(pointer, "bias") elif m_name in ["kernel", "gamma"]: trace.append("weight") pointer = getattr(pointer, "weight") else: logger.warning(f"Ignored {m_name}") # for certain layers reshape is necessary trace = ".".join(trace) if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)", trace) or re.match( r"(\S+)\.attention\.output\.dense\.weight", trace ): array = array.reshape(pointer.data.shape) if "kernel" in full_name: array = array.transpose() if pointer.shape == array.shape: pointer.data = torch.from_numpy(array) else: raise ValueError( f"Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:" f" {array.shape}" ) logger.info(f"Successfully set variable {full_name} to PyTorch layer {trace}") return model def convert_tf2_checkpoint_to_pytorch(tf_checkpoint_path, config_path, pytorch_dump_path): # Instantiate model logger.info(f"Loading model based on config from {config_path}...") config = BertConfig.from_json_file(config_path) model = BertModel(config) # Load weights from checkpoint logger.info(f"Loading weights from checkpoint {tf_checkpoint_path}...") load_tf2_weights_in_bert(model, tf_checkpoint_path, config) # Save pytorch-model logger.info(f"Saving PyTorch model to {pytorch_dump_path}...") torch.save(model.state_dict(), pytorch_dump_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x checkpoint path." ) parser.add_argument( "--bert_config_file", type=str, required=True, help="The config json file corresponding to the BERT model. This specifies the model architecture.", ) parser.add_argument( "--pytorch_dump_path", type=str, required=True, help="Path to the output PyTorch model (must include filename).", ) args = parser.parse_args() convert_tf2_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)