# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # 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. """Convert T5/LongT5X checkpoints from the original repository to JAX/FLAX model. This script is an extension of 'src/transformers/models/t5/convert_t5x_checkpoint_to_flax. """ import argparse from t5x import checkpoints from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path): config = AutoConfig.from_pretrained(config_name) flax_model = FlaxAutoModelForSeq2SeqLM.from_config(config=config) t5x_model = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path) split_mlp_wi = "wi_0" in t5x_model["target"]["encoder"]["layers_0"]["mlp"] if config.model_type == "t5": encoder_attn_name = "SelfAttention" if config.model_type == "longt5" and config.encoder_attention_type == "local": encoder_attn_name = "LocalSelfAttention" elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global": encoder_attn_name = "TransientGlobalSelfAttention" else: raise ValueError( "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`" " attribute with a value from ['local', 'transient-global]." ) # Encoder for layer_index in range(config.num_layers): layer_name = f"layers_{str(layer_index)}" # Self-Attention t5x_attention_key = t5x_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"] t5x_attention_out = t5x_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"] t5x_attention_query = t5x_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"] t5x_attention_value = t5x_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"] # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": t5x_global_layer_norm = t5x_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"] # Layer Normalization t5x_attention_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"] if split_mlp_wi: t5x_mlp_wi_0 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"] t5x_mlp_wi_1 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: t5x_mlp_wi = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"] t5x_mlp_wo = t5x_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization t5x_mlp_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning flax_model_encoder_layer_block = flax_model.params["encoder"]["block"][str(layer_index)]["layer"] flax_model_encoder_layer_block["0"][encoder_attn_name]["k"]["kernel"] = t5x_attention_key flax_model_encoder_layer_block["0"][encoder_attn_name]["o"]["kernel"] = t5x_attention_out flax_model_encoder_layer_block["0"][encoder_attn_name]["q"]["kernel"] = t5x_attention_query flax_model_encoder_layer_block["0"][encoder_attn_name]["v"]["kernel"] = t5x_attention_value flax_model_encoder_layer_block["0"]["layer_norm"]["weight"] = t5x_attention_layer_norm # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": flax_model_encoder_layer_block["0"][encoder_attn_name]["global_input_layer_norm"]["weight"] = ( t5x_global_layer_norm ) if split_mlp_wi: flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0 flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1 else: flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi flax_model_encoder_layer_block["1"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo flax_model_encoder_layer_block["1"]["layer_norm"]["weight"] = t5x_mlp_layer_norm flax_model.params["encoder"]["block"][str(layer_index)]["layer"] = flax_model_encoder_layer_block # Only for layer 0: t5x_encoder_rel_embedding = t5x_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["relative_attention_bias"][ "embedding" ] = t5x_encoder_rel_embedding # Side/global relative position_bias + layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": t5x_encoder_global_rel_embedding = t5x_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["global_relative_attention_bias"][ "embedding" ] = t5x_encoder_global_rel_embedding # Assigning t5x_encoder_norm = t5x_model["target"]["encoder"]["encoder_norm"]["scale"] flax_model.params["encoder"]["final_layer_norm"]["weight"] = t5x_encoder_norm # Decoder for layer_index in range(config.num_layers): layer_name = f"layers_{str(layer_index)}" # Self-Attention t5x_attention_key = t5x_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"] t5x_attention_out = t5x_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"] t5x_attention_query = t5x_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"] t5x_attention_value = t5x_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"] # Layer Normalization t5x_pre_attention_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][ "scale" ] # Encoder-Decoder-Attention t5x_enc_dec_attention_module = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"] t5x_enc_dec_attention_key = t5x_enc_dec_attention_module["key"]["kernel"] t5x_enc_dec_attention_out = t5x_enc_dec_attention_module["out"]["kernel"] t5x_enc_dec_attention_query = t5x_enc_dec_attention_module["query"]["kernel"] t5x_enc_dec_attention_value = t5x_enc_dec_attention_module["value"]["kernel"] # Layer Normalization t5x_cross_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"] # MLP if split_mlp_wi: t5x_mlp_wi_0 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"] t5x_mlp_wi_1 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: t5x_mlp_wi = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"] t5x_mlp_wo = t5x_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization tx5_mlp_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning flax_model_decoder_layer_block = flax_model.params["decoder"]["block"][str(layer_index)]["layer"] flax_model_decoder_layer_block["0"]["SelfAttention"]["k"]["kernel"] = t5x_attention_key flax_model_decoder_layer_block["0"]["SelfAttention"]["o"]["kernel"] = t5x_attention_out flax_model_decoder_layer_block["0"]["SelfAttention"]["q"]["kernel"] = t5x_attention_query flax_model_decoder_layer_block["0"]["SelfAttention"]["v"]["kernel"] = t5x_attention_value flax_model_decoder_layer_block["0"]["layer_norm"]["weight"] = t5x_pre_attention_layer_norm flax_model_decoder_layer_block["1"]["EncDecAttention"]["k"]["kernel"] = t5x_enc_dec_attention_key flax_model_decoder_layer_block["1"]["EncDecAttention"]["o"]["kernel"] = t5x_enc_dec_attention_out flax_model_decoder_layer_block["1"]["EncDecAttention"]["q"]["kernel"] = t5x_enc_dec_attention_query flax_model_decoder_layer_block["1"]["EncDecAttention"]["v"]["kernel"] = t5x_enc_dec_attention_value flax_model_decoder_layer_block["1"]["layer_norm"]["weight"] = t5x_cross_layer_norm if split_mlp_wi: flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0 flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1 else: flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi flax_model_decoder_layer_block["2"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo flax_model_decoder_layer_block["2"]["layer_norm"]["weight"] = tx5_mlp_layer_norm flax_model.params["decoder"]["block"][str(layer_index)]["layer"] = flax_model_decoder_layer_block # Decoder Normalization tx5_decoder_norm = t5x_model["target"]["decoder"]["decoder_norm"]["scale"] flax_model.params["decoder"]["final_layer_norm"]["weight"] = tx5_decoder_norm # Only for layer 0: t5x_decoder_rel_embedding = t5x_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T flax_model.params["decoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][ "embedding" ] = t5x_decoder_rel_embedding # Token Embeddings tx5_token_embeddings = t5x_model["target"]["token_embedder"]["embedding"] flax_model.params["shared"]["embedding"] = tx5_token_embeddings # LM Head (only in v1.1 and LongT5 checkpoints) if "logits_dense" in t5x_model["target"]["decoder"]: flax_model.params["lm_head"]["kernel"] = t5x_model["target"]["decoder"]["logits_dense"]["kernel"] flax_model.save_pretrained(flax_dump_folder_path) print("T5X Model was sucessfully converted!") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint." ) parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.") parser.add_argument( "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model." ) args = parser.parse_args() convert_t5x_checkpoint_to_flax(args.t5x_checkpoint_path, args.config_name, args.flax_dump_folder_path)