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R11484/source_code/transformers/src/transformers/models/mbart/convert_mbart_original_checkpoint_to_pytorch.py
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convert_mbart_original_checkpoint_to_pytorch.py
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# 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.
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def remove_ignore_keys_(state_dict):
ignore_keys = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"_float_tensor",
"decoder.output_projection.weight",
]
for k in ignore_keys:
state_dict.pop(k, None)
def make_linear_from_emb(emb):
vocab_size, emb_size = emb.weight.shape
lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
lin_layer.weight.data = emb.weight.data
return lin_layer
def convert_fairseq_mbart_checkpoint_from_disk(
checkpoint_path, hf_config_path="facebook/mbart-large-en-ro", finetuned=False, mbart_50=False
):
state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
remove_ignore_keys_(state_dict)
vocab_size = state_dict["encoder.embed_tokens.weight"].shape[0]
mbart_config = MBartConfig.from_pretrained(hf_config_path, vocab_size=vocab_size)
if mbart_50 and finetuned:
mbart_config.activation_function = "relu"
state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
model = MBartForConditionalGeneration(mbart_config)
model.model.load_state_dict(state_dict)
if finetuned:
model.lm_head = make_linear_from_emb(model.model.shared)
return model
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
)
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--hf_config",
default="facebook/mbart-large-cc25",
type=str,
help="Which huggingface architecture to use: mbart-large",
)
parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
args = parser.parse_args()
model = convert_fairseq_mbart_checkpoint_from_disk(
args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_50=args.mbart_50
)
model.save_pretrained(args.pytorch_dump_folder_path)
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