codingutf8 2018 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 orglicenseslicense2 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 note if you intend to run this script make sure you look under scriptsfsmt to locate the appropriate script to do the work correctly there is a set of scripts to download and prepare data and run the conversion script perform eval to get the best hparam into the config generate modelcards useful if you have multiple models from the same paper based on the results of a search on a range of numbeams lengthpenalty and earlystopping values against wmt19 test data to obtain the best bleu scores we will use the following defaults numbeams 5 higher scores better but requires more memoryis slower can be adjusted by users earlystopping false consistently scored better lengthpenalty varied so will assign the best one depending on the model fairseq allenai this remaps the different models to their organization names 1 remove word breaking symbol 2 add word ending symbol where the word is not broken up e g d le 5 tt 6 er 7 le 5 tt 6 erw 7 restore the special tokens prep handle various types of models note since the model dump is old fairseq has upgraded its model some time later and it does a whole lot of rewrites and splits on the saved weights therefore we can t use torch load directly on the model file see upgradestatedictstatedict in fairseqmodel py dicts detect whether this is a dolowercase situation which can be derived by checking whether we have at least one uppercase letter in the source vocab mergesfile bpecodes model config validate bpetokenizer config as currently it s hardcoded to mosesfastbpe may have to modify the tokenizer if a different type is used by a future model good hparam defaults to start with tokenizer config model rename keys to start with model remove unneeded keys check that it loads ok save required parameters coding utf 8 2018 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 note if you intend to run this script make sure you look under scripts fsmt to locate the appropriate script to do the work correctly there is a set of scripts to download and prepare data and run the conversion script perform eval to get the best hparam into the config generate model_cards useful if you have multiple models from the same paper based on the results of a search on a range of num_beams length_penalty and early_stopping values against wmt19 test data to obtain the best bleu scores we will use the following defaults num_beams 5 higher scores better but requires more memory is slower can be adjusted by users early_stopping false consistently scored better length_penalty varied so will assign the best one depending on the model fairseq allenai this remaps the different models to their organization names 1 remove word breaking symbol 2 add word ending symbol where the word is not broken up e g d le 5 tt 6 er 7 le 5 tt 6 er w 7 restore the special tokens restore prep handle various types of models note since the model dump is old fairseq has upgraded its model some time later and it does a whole lot of rewrites and splits on the saved weights therefore we can t use torch load directly on the model file see upgrade_state_dict state_dict in fairseq_model py dicts detect whether this is a do_lower_case situation which can be derived by checking whether we have at least one uppercase letter in the source vocab merges_file bpecodes older fairseq called the merges file code remove frequency number model config validate bpe tokenizer config as currently it s hardcoded to moses fastbpe may have to modify the tokenizer if a different type is used by a future model good hparam defaults to start with tokenizer config model rename keys to start with model remove unneeded keys check that it loads ok save required parameters
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

json_indent = 2







best_score_hparams = {
    
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}


org_names = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
    org_names[m] = "facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
    org_names[m] = "allenai"


def rewrite_dict_keys(d):
    
    
    d2 = dict((re.sub(r"@@$", "", k), v) if k.endswith("@@") else (re.sub(r"$", "</w>", k), v) for k, v in d.items())
    keep_keys = "<s> <pad> </s> <unk>".split()
    
    for k in keep_keys:
        del d2[f"{k}</w>"]
        d2[k] = d[k]  
    return d2


def convert_fsmt_checkpoint_to_pytorch(fsmt_checkpoint_path, pytorch_dump_folder_path):
    
    assert os.path.exists(fsmt_checkpoint_path)
    os.makedirs(pytorch_dump_folder_path, exist_ok=True)
    print(f"Writing results to {pytorch_dump_folder_path}")

    

    checkpoint_file = basename(fsmt_checkpoint_path)
    fsmt_folder_path = dirname(fsmt_checkpoint_path)

    cls = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
    models = cls.hub_models()
    kwargs = {"bpe": "fastbpe", "tokenizer": "moses"}
    data_name_or_path = "."
    
    
    
    
    print(f"using checkpoint {checkpoint_file}")
    chkpt = hub_utils.from_pretrained(
        fsmt_folder_path, checkpoint_file, data_name_or_path, archive_map=models, **kwargs
    )

    args = vars(chkpt["args"]["model"])

    src_lang = args["source_lang"]
    tgt_lang = args["target_lang"]

    data_root = dirname(pytorch_dump_folder_path)
    model_dir = basename(pytorch_dump_folder_path)

    
    src_dict_file = os.path.join(fsmt_folder_path, f"dict.{src_lang}.txt")
    tgt_dict_file = os.path.join(fsmt_folder_path, f"dict.{tgt_lang}.txt")

    src_dict = Dictionary.load(src_dict_file)
    src_vocab = rewrite_dict_keys(src_dict.indices)
    src_vocab_size = len(src_vocab)
    src_vocab_file = os.path.join(pytorch_dump_folder_path, "vocab-src.json")
    print(f"Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records")
    with open(src_vocab_file, "w", encoding="utf-8") as f:
        f.write(json.dumps(src_vocab, ensure_ascii=False, indent=json_indent))

    
    
    do_lower_case = True
    for k in src_vocab.keys():
        if not k.islower():
            do_lower_case = False
            break

    tgt_dict = Dictionary.load(tgt_dict_file)
    tgt_vocab = rewrite_dict_keys(tgt_dict.indices)
    tgt_vocab_size = len(tgt_vocab)
    tgt_vocab_file = os.path.join(pytorch_dump_folder_path, "vocab-tgt.json")
    print(f"Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records")
    with open(tgt_vocab_file, "w", encoding="utf-8") as f:
        f.write(json.dumps(tgt_vocab, ensure_ascii=False, indent=json_indent))

    
    merges_file = os.path.join(pytorch_dump_folder_path, VOCAB_FILES_NAMES["merges_file"])
    for fn in ["bpecodes", "code"]:  
        fsmt_merges_file = os.path.join(fsmt_folder_path, fn)
        if os.path.exists(fsmt_merges_file):
            break
    with open(fsmt_merges_file, encoding="utf-8") as fin:
        merges = fin.read()
    merges = re.sub(r" \d+$", "", merges, 0, re.M)  
    print(f"Generating {merges_file}")
    with open(merges_file, "w", encoding="utf-8") as fout:
        fout.write(merges)

    
    fsmt_model_config_file = os.path.join(pytorch_dump_folder_path, "config.json")

    
    
    assert args["bpe"] == "fastbpe", f"need to extend tokenizer to support bpe={args['bpe']}"
    assert args["tokenizer"] == "moses", f"need to extend tokenizer to support bpe={args['tokenizer']}"

    model_conf = {
        "architectures": ["FSMTForConditionalGeneration"],
        "model_type": "fsmt",
        "activation_dropout": args["activation_dropout"],
        "activation_function": "relu",
        "attention_dropout": args["attention_dropout"],
        "d_model": args["decoder_embed_dim"],
        "dropout": args["dropout"],
        "init_std": 0.02,
        "max_position_embeddings": args["max_source_positions"],
        "num_hidden_layers": args["encoder_layers"],
        "src_vocab_size": src_vocab_size,
        "tgt_vocab_size": tgt_vocab_size,
        "langs": [src_lang, tgt_lang],
        "encoder_attention_heads": args["encoder_attention_heads"],
        "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
        "encoder_layerdrop": args["encoder_layerdrop"],
        "encoder_layers": args["encoder_layers"],
        "decoder_attention_heads": args["decoder_attention_heads"],
        "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
        "decoder_layerdrop": args["decoder_layerdrop"],
        "decoder_layers": args["decoder_layers"],
        "bos_token_id": 0,
        "pad_token_id": 1,
        "eos_token_id": 2,
        "is_encoder_decoder": True,
        "scale_embedding": not args["no_scale_embedding"],
        "tie_word_embeddings": args["share_all_embeddings"],
    }

    
    model_conf["num_beams"] = 5
    model_conf["early_stopping"] = False
    if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
        model_conf["length_penalty"] = best_score_hparams[model_dir]["length_penalty"]
    else:
        model_conf["length_penalty"] = 1.0

    print(f"Generating {fsmt_model_config_file}")
    with open(fsmt_model_config_file, "w", encoding="utf-8") as f:
        f.write(json.dumps(model_conf, ensure_ascii=False, indent=json_indent))

    
    fsmt_tokenizer_config_file = os.path.join(pytorch_dump_folder_path, TOKENIZER_CONFIG_FILE)

    tokenizer_conf = {
        "langs": [src_lang, tgt_lang],
        "model_max_length": 1024,
        "do_lower_case": do_lower_case,
    }

    print(f"Generating {fsmt_tokenizer_config_file}")
    with open(fsmt_tokenizer_config_file, "w", encoding="utf-8") as f:
        f.write(json.dumps(tokenizer_conf, ensure_ascii=False, indent=json_indent))

    
    model = chkpt["models"][0]
    model_state_dict = model.state_dict()

    
    model_state_dict = OrderedDict(("model." + k, v) for k, v in model_state_dict.items())

    
    ignore_keys = [
        "model.model",
        "model.encoder.version",
        "model.decoder.version",
        "model.encoder_embed_tokens.weight",
        "model.decoder_embed_tokens.weight",
        "model.encoder.embed_positions._float_tensor",
        "model.decoder.embed_positions._float_tensor",
    ]
    for k in ignore_keys:
        model_state_dict.pop(k, None)

    config = FSMTConfig.from_pretrained(pytorch_dump_folder_path)
    model_new = FSMTForConditionalGeneration(config)

    
    model_new.load_state_dict(model_state_dict, strict=False)

    
    pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
    print(f"Generating {pytorch_weights_dump_path}")
    torch.save(model_state_dict, pytorch_weights_dump_path)

    print("Conversion is done!")
    print("\nLast step is to upload the files to s3")
    print(f"cd {data_root}")
    print(f"transformers-cli upload {model_dir}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    
    parser.add_argument(
        "--fsmt_checkpoint_path",
        default=None,
        type=str,
        required=True,
        help=(
            "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
            " bpecodes, etc."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    args = parser.parse_args()
    convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)