convert_t5_checkpoint_to_flax.py

import argparse

from t5x import checkpoints
from transformers import T5Config, FlaxT5Model


def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path):
    config = T5Config.from_pretrained(config_name)
    flax_model = FlaxT5Model(config=config)
    t5x_model = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path)
   

    #breakpoint()
    # 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"]

        ## Layer Normalization
        t5x_attention_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]
        
        # MLP
        #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"]
        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.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["k"]["kernel"] = t5x_attention_key
        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["o"]["kernel"] = t5x_attention_out
        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["q"]["kernel"] = t5x_attention_query
        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["v"]["kernel"] = t5x_attention_value
        
        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["layer_norm"]["weight"] = t5x_attention_layer_norm
        
        #flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0
        #flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1
        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi
        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo
        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["layer_norm"]["weight"] = t5x_mlp_layer_norm

    t5x_encoder_norm = t5x_model["target"]["encoder"]["encoder_norm"]["scale"]

    # Only for layer 0:
    t5x_encoder_rel_embedding = t5x_model["target"]["encoder"]["relpos_bias"]["rel_embedding"]
    x, y = t5x_encoder_rel_embedding.shape

    # Assigning
    flax_model.params["encoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"]["embedding"] = t5x_encoder_rel_embedding.reshape(y, x)
    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_key = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"]["kernel"]
        t5x_enc_dec_attention_out = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"]["kernel"]
        t5x_enc_dec_attention_query = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"]["kernel"]
        t5x_enc_dec_attention_value = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"]["kernel"]

        ## Layer Normalization
        t5x_cross_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

        # MLP
        #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"]
        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.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["k"]["kernel"] = t5x_attention_key
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["o"]["kernel"] = t5x_attention_out
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["q"]["kernel"] = t5x_attention_query
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["v"]["kernel"] = t5x_attention_value

        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["layer_norm"]["weight"] = t5x_pre_attention_layer_norm
        
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["k"]["kernel"] = t5x_enc_dec_attention_key
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["o"]["kernel"] = t5x_enc_dec_attention_out
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["q"]["kernel"] = t5x_enc_dec_attention_query
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["v"]["kernel"] = t5x_enc_dec_attention_value
        
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["layer_norm"]["weight"] = t5x_cross_layer_norm
        
        #flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0
        #flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo
        
        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["layer_norm"]["weight"] = tx5_mlp_layer_norm

    # 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"]
    x, y = t5x_decoder_rel_embedding.shape

    flax_model.params["decoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"] = t5x_decoder_rel_embedding.reshape(y, x)

    # Token Embeddings
    tx5_token_embeddings = t5x_model["target"]["token_embedder"]["embedding"]
    flax_model.params["shared"]["embedding"] = tx5_token_embeddings

    flax_model.save_pretrained(flax_dump_folder_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
    )
    parser.add_argument(
        "--config_name", default=None, type=str, required=True, help="Config name of 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)