# What does this PR do?
1. Fixes an issue explained in https://github.com/pytorch/pytorch/issues/105840 when using FSDP for training very large models. Should be merged after https://github.com/huggingface/accelerate/pull/1777

Currently, when using FSDP, the model is loaded for each of the N processes completely on CPU leading to huge CPU RAM usage. When training models like Flacon-40B with FSDP on a dgx node with 8 GPUs, it would lead to CPU RAM getting out of memory because each process is loading 160GB (40B x 4Bytes (FP32)) in CPU RAM for a total of 160*8=1280GB requirement which results in script getting killed due to out of CPU RAM.

To combat this, we load the model only on rank 0 and have it on meta device when rank!=0. Then use no-op param_init_fn along with sync_module_states=True for FSDP to properly init the weights on other ranks and broadcast the params from rank 0 to other ranks. 

Usage:

**No user-facing changes**:

Post this PR:
```
accelerator.process_index=0 GPU Memory before entering the loading : 0
accelerator.process_index=0 GPU Memory consumed at the end of the loading (end-begin): 0
accelerator.process_index=0 GPU Peak Memory consumed during the loading (max-begin): 0
accelerator.process_index=0 GPU Total Peak Memory consumed during the loading (max): 0
accelerator.process_index=0 CPU Memory before entering the loading : 926
accelerator.process_index=0 CPU Memory consumed at the end of the loading (end-begin): 26415
accelerator.process_index=0 CPU Peak Memory consumed during the loading (max-begin): 31818
accelerator.process_index=0 CPU Total Peak Memory consumed during the loading (max): 32744
accelerator.process_index=0 model.lm_head.weight=Parameter containing:
tensor([[-0.0179,  0.0201, -0.0273,  ..., -0.0275, -0.0396, -0.0131],
        [-0.0510, -0.0079, -0.0383,  ..., -0.0481,  0.0581,  0.0282],
        [-0.0217, -0.0216, -0.0064,  ..., -0.0508,  0.0554, -0.0013],
        ...,
        [ 0.0425,  0.0452, -0.0131,  ...,  0.0019,  0.0476,  0.0342],
        [-0.0170, -0.0085,  0.0449,  ..., -0.0074,  0.0178,  0.0043],
        [-0.0439, -0.0859, -0.0820,  ...,  0.0130,  0.0669,  0.0884]],
       requires_grad=True)
accelerator.process_index=1 GPU Memory before entering the loading : 0
accelerator.process_index=1 GPU Memory consumed at the end of the loading (end-begin): 0
accelerator.process_index=1 GPU Peak Memory consumed during the loading (max-begin): 0
accelerator.process_index=1 GPU Total Peak Memory consumed during the loading (max): 0
accelerator.process_index=1 CPU Memory before entering the loading : 933
accelerator.process_index=1 CPU Memory consumed at the end of the loading (end-begin): 10
accelerator.process_index=1 CPU Peak Memory consumed during the loading (max-begin): 573
accelerator.process_index=1 CPU Total Peak Memory consumed during the loading (max): 1506
accelerator.process_index=1 model.lm_head.weight=Parameter containing:
tensor([[0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        ...,
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.]], requires_grad=True)
accelerator.process_index=0 GPU Memory before entering the prepare : 0
accelerator.process_index=0 GPU Memory consumed at the end of the prepare (end-begin): 13202
accelerator.process_index=0 GPU Peak Memory consumed during the prepare (max-begin): 15458
accelerator.process_index=0 GPU Total Peak Memory consumed during the prepare (max): 15458
accelerator.process_index=0 CPU Memory before entering the prepare : 27345
accelerator.process_index=0 CPU Memory consumed at the end of the prepare (end-begin): -26394
accelerator.process_index=0 CPU Peak Memory consumed during the prepare (max-begin): 0
accelerator.process_index=0 CPU Total Peak Memory consumed during the prepare (max): 27345
FullyShardedDataParallel(
  (_fsdp_wrapped_module): RWForCausalLM(
    (transformer): RWModel(
      (word_embeddings): Embedding(65024, 4544)
      (h): ModuleList(
        (0-31): 32 x FullyShardedDataParallel(
          (_fsdp_wrapped_module): DecoderLayer(
            (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
            (self_attention): Attention(
              (maybe_rotary): RotaryEmbedding()
              (query_key_value): Linear(in_features=4544, out_features=4672, bias=False)
              (dense): Linear(in_features=4544, out_features=4544, bias=False)
              (attention_dropout): Dropout(p=0.0, inplace=False)
            )
            (mlp): MLP(
              (dense_h_to_4h): Linear(in_features=4544, out_features=18176, bias=False)
              (act): GELU(approximate='none')
              (dense_4h_to_h): Linear(in_features=18176, out_features=4544, bias=False)
            )
          )
        )
      )
      (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
    )
    (lm_head): Linear(in_features=4544, out_features=65024, bias=False)
  )
)
accelerator.process_index=1 GPU Memory before entering the prepare : 0
accelerator.process_index=1 GPU Memory consumed at the end of the prepare (end-begin): 13202
accelerator.process_index=1 GPU Peak Memory consumed during the prepare (max-begin): 15458
accelerator.process_index=1 GPU Total Peak Memory consumed during the prepare (max): 15458
accelerator.process_index=1 CPU Memory before entering the prepare : 945
accelerator.process_index=1 CPU Memory consumed at the end of the prepare (end-begin): 4
accelerator.process_index=1 CPU Peak Memory consumed during the prepare (max-begin): 4
accelerator.process_index=1 CPU Total Peak Memory consumed during the prepare (max): 949
accelerator.process_index=1 model.lm_head.weight=Parameter containing:
tensor([[-0.0179,  0.0201, -0.0273,  ..., -0.0275, -0.0396, -0.0131],
        [-0.0510, -0.0079, -0.0383,  ..., -0.0481,  0.0581,  0.0282],
        [-0.0217, -0.0216, -0.0064,  ..., -0.0508,  0.0554, -0.0013],
        ...,
        [ 0.0425,  0.0452, -0.0131,  ...,  0.0019,  0.0476,  0.0342],
        [-0.0170, -0.0085,  0.0449,  ..., -0.0074,  0.0178,  0.0043],
        [-0.0439, -0.0859, -0.0820,  ...,  0.0130,  0.0669,  0.0884]],
       device='cuda:1', requires_grad=True)
accelerator.process_index=0 model.lm_head.weight=Parameter containing:
tensor([[-0.0179,  0.0201, -0.0273,  ..., -0.0275, -0.0396, -0.0131],
        [-0.0510, -0.0079, -0.0383,  ..., -0.0481,  0.0581,  0.0282],
        [-0.0217, -0.0216, -0.0064,  ..., -0.0508,  0.0554, -0.0013],
        ...,
        [ 0.0425,  0.0452, -0.0131,  ...,  0.0019,  0.0476,  0.0342],
        [-0.0170, -0.0085,  0.0449,  ..., -0.0074,  0.0178,  0.0043],
        [-0.0439, -0.0859, -0.0820,  ...,  0.0130,  0.0669,  0.0884]],
       device='cuda:0', requires_grad=True)
```

**So you can see that during loading Rank 1 doesn't take any more CPU RAM. And the performance between both setups matches.**

To Do:

- [ ] Add docs in the FSDP section

add util for ram efficient loading of model when using fsdp. # What does this PR do?
1. Fixes an issue explained in https://github.com/pytorch/pytorch/issues/105840 when using FSDP for training very large models. Should be merged after https://github.com/huggingface/accelerate/pull/1777

Currently, when using FSDP, the model is loaded for each of the N processes completely on CPU leading to huge CPU RAM usage. When training models like Flacon-40B with FSDP on a dgx node with 8 GPUs, it would lead to CPU RAM getting out of memory because each process is loading 160GB (40B x 4Bytes (FP32)) in CPU RAM for a total of 160*8=1280GB requirement which results in script getting killed due to out of CPU RAM.

To combat this, we load the model only on rank 0 and have it on meta device when rank!=0. Then use no-op param_init_fn along with sync_module_states=True for FSDP to properly init the weights on other ranks and broadcast the params from rank 0 to other ranks. 

Usage:

**No user-facing changes**:

Post this PR:
```
accelerator.process_index=0 GPU Memory before entering the loading : 0
accelerator.process_index=0 GPU Memory consumed at the end of the loading (end-begin): 0
accelerator.process_index=0 GPU Peak Memory consumed during the loading (max-begin): 0
accelerator.process_index=0 GPU Total Peak Memory consumed during the loading (max): 0
accelerator.process_index=0 CPU Memory before entering the loading : 926
accelerator.process_index=0 CPU Memory consumed at the end of the loading (end-begin): 26415
accelerator.process_index=0 CPU Peak Memory consumed during the loading (max-begin): 31818
accelerator.process_index=0 CPU Total Peak Memory consumed during the loading (max): 32744
accelerator.process_index=0 model.lm_head.weight=Parameter containing:
tensor([[-0.0179,  0.0201, -0.0273,  ..., -0.0275, -0.0396, -0.0131],
        [-0.0510, -0.0079, -0.0383,  ..., -0.0481,  0.0581,  0.0282],
        [-0.0217, -0.0216, -0.0064,  ..., -0.0508,  0.0554, -0.0013],
        ...,
        [ 0.0425,  0.0452, -0.0131,  ...,  0.0019,  0.0476,  0.0342],
        [-0.0170, -0.0085,  0.0449,  ..., -0.0074,  0.0178,  0.0043],
        [-0.0439, -0.0859, -0.0820,  ...,  0.0130,  0.0669,  0.0884]],
       requires_grad=True)
accelerator.process_index=1 GPU Memory before entering the loading : 0
accelerator.process_index=1 GPU Memory consumed at the end of the loading (end-begin): 0
accelerator.process_index=1 GPU Peak Memory consumed during the loading (max-begin): 0
accelerator.process_index=1 GPU Total Peak Memory consumed during the loading (max): 0
accelerator.process_index=1 CPU Memory before entering the loading : 933
accelerator.process_index=1 CPU Memory consumed at the end of the loading (end-begin): 10
accelerator.process_index=1 CPU Peak Memory consumed during the loading (max-begin): 573
accelerator.process_index=1 CPU Total Peak Memory consumed during the loading (max): 1506
accelerator.process_index=1 model.lm_head.weight=Parameter containing:
tensor([[0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        ...,
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.]], requires_grad=True)
accelerator.process_index=0 GPU Memory before entering the prepare : 0
accelerator.process_index=0 GPU Memory consumed at the end of the prepare (end-begin): 13202
accelerator.process_index=0 GPU Peak Memory consumed during the prepare (max-begin): 15458
accelerator.process_index=0 GPU Total Peak Memory consumed during the prepare (max): 15458
accelerator.process_index=0 CPU Memory before entering the prepare : 27345
accelerator.process_index=0 CPU Memory consumed at the end of the prepare (end-begin): -26394
accelerator.process_index=0 CPU Peak Memory consumed during the prepare (max-begin): 0
accelerator.process_index=0 CPU Total Peak Memory consumed during the prepare (max): 27345
FullyShardedDataParallel(
  (_fsdp_wrapped_module): RWForCausalLM(
    (transformer): RWModel(
      (word_embeddings): Embedding(65024, 4544)
      (h): ModuleList(
        (0-31): 32 x FullyShardedDataParallel(
          (_fsdp_wrapped_module): DecoderLayer(
            (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
            (self_attention): Attention(
              (maybe_rotary): RotaryEmbedding()
              (query_key_value): Linear(in_features=4544, out_features=4672, bias=False)
              (dense): Linear(in_features=4544, out_features=4544, bias=False)
              (attention_dropout): Dropout(p=0.0, inplace=False)
            )
            (mlp): MLP(
              (dense_h_to_4h): Linear(in_features=4544, out_features=18176, bias=False)
              (act): GELU(approximate='none')
              (dense_4h_to_h): Linear(in_features=18176, out_features=4544, bias=False)
            )
          )
        )
      )
      (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
    )
    (lm_head): Linear(in_features=4544, out_features=65024, bias=False)
  )
)
accelerator.process_index=1 GPU Memory before entering the prepare : 0
accelerator.process_index=1 GPU Memory consumed at the end of the prepare (end-begin): 13202
accelerator.process_index=1 GPU Peak Memory consumed during the prepare (max-begin): 15458
accelerator.process_index=1 GPU Total Peak Memory consumed during the prepare (max): 15458
accelerator.process_index=1 CPU Memory before entering the prepare : 945
accelerator.process_index=1 CPU Memory consumed at the end of the prepare (end-begin): 4
accelerator.process_index=1 CPU Peak Memory consumed during the prepare (max-begin): 4
accelerator.process_index=1 CPU Total Peak Memory consumed during the prepare (max): 949
accelerator.process_index=1 model.lm_head.weight=Parameter containing:
tensor([[-0.0179,  0.0201, -0.0273,  ..., -0.0275, -0.0396, -0.0131],
        [-0.0510, -0.0079, -0.0383,  ..., -0.0481,  0.0581,  0.0282],
        [-0.0217, -0.0216, -0.0064,  ..., -0.0508,  0.0554, -0.0013],
        ...,
        [ 0.0425,  0.0452, -0.0131,  ...,  0.0019,  0.0476,  0.0342],
        [-0.0170, -0.0085,  0.0449,  ..., -0.0074,  0.0178,  0.0043],
        [-0.0439, -0.0859, -0.0820,  ...,  0.0130,  0.0669,  0.0884]],
       device='cuda:1', requires_grad=True)
accelerator.process_index=0 model.lm_head.weight=Parameter containing:
tensor([[-0.0179,  0.0201, -0.0273,  ..., -0.0275, -0.0396, -0.0131],
        [-0.0510, -0.0079, -0.0383,  ..., -0.0481,  0.0581,  0.0282],
        [-0.0217, -0.0216, -0.0064,  ..., -0.0508,  0.0554, -0.0013],
        ...,
        [ 0.0425,  0.0452, -0.0131,  ...,  0.0019,  0.0476,  0.0342],
        [-0.0170, -0.0085,  0.0449,  ..., -0.0074,  0.0178,  0.0043],
        [-0.0439, -0.0859, -0.0820,  ...,  0.0130,  0.0669,  0.0884]],
       device='cuda:0', requires_grad=True)
```

**So you can see that during loading Rank 1 doesn't take any more CPU RAM. And the performance between both setups matches.**

To Do:

- [ ] Add docs in the FSDP section

# coding=utf-8
# Copyright 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.
"""Convert Transformer XL checkpoint and datasets."""


import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
data_utils.Vocab = data_utils.TransfoXLTokenizer
data_utils.Corpus = data_utils.TransfoXLCorpus
sys.modules["data_utils"] = data_utils
sys.modules["vocabulary"] = data_utils


def convert_transfo_xl_checkpoint_to_pytorch(
    tf_checkpoint_path, transfo_xl_config_file, pytorch_dump_folder_path, transfo_xl_dataset_file
):
    if transfo_xl_dataset_file:
        # Convert a pre-processed corpus (see original TensorFlow repo)
        with open(transfo_xl_dataset_file, "rb") as fp:
            corpus = pickle.load(fp, encoding="latin1")
        # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
        pytorch_vocab_dump_path = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
        print(f"Save vocabulary to {pytorch_vocab_dump_path}")
        corpus_vocab_dict = corpus.vocab.__dict__
        torch.save(corpus_vocab_dict, pytorch_vocab_dump_path)

        corpus_dict_no_vocab = corpus.__dict__
        corpus_dict_no_vocab.pop("vocab", None)
        pytorch_dataset_dump_path = pytorch_dump_folder_path + "/" + CORPUS_NAME
        print(f"Save dataset to {pytorch_dataset_dump_path}")
        torch.save(corpus_dict_no_vocab, pytorch_dataset_dump_path)

    if tf_checkpoint_path:
        # Convert a pre-trained TensorFlow model
        config_path = os.path.abspath(transfo_xl_config_file)
        tf_path = os.path.abspath(tf_checkpoint_path)

        print(f"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.")
        # Initialise PyTorch model
        if transfo_xl_config_file == "":
            config = TransfoXLConfig()
        else:
            config = TransfoXLConfig.from_json_file(transfo_xl_config_file)
        print(f"Building PyTorch model from configuration: {config}")
        model = TransfoXLLMHeadModel(config)

        model = load_tf_weights_in_transfo_xl(model, config, tf_path)
        # Save pytorch-model
        pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
        pytorch_config_dump_path = os.path.join(pytorch_dump_folder_path, CONFIG_NAME)
        print(f"Save PyTorch model to {os.path.abspath(pytorch_weights_dump_path)}")
        torch.save(model.state_dict(), pytorch_weights_dump_path)
        print(f"Save configuration file to {os.path.abspath(pytorch_config_dump_path)}")
        with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
            f.write(config.to_json_string())


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to store the PyTorch model or dataset/vocab.",
    )
    parser.add_argument(
        "--tf_checkpoint_path",
        default="",
        type=str,
        help="An optional path to a TensorFlow checkpoint path to be converted.",
    )
    parser.add_argument(
        "--transfo_xl_config_file",
        default="",
        type=str,
        help=(
            "An optional config json file corresponding to the pre-trained BERT model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--transfo_xl_dataset_file",
        default="",
        type=str,
        help="An optional dataset file to be converted in a vocabulary.",
    )
    args = parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )

# coding=utf-8
# Copyright 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.
"""Convert Transformer XL checkpoint and datasets."""


import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
data_utils.Vocab = data_utils.TransfoXLTokenizer
data_utils.Corpus = data_utils.TransfoXLCorpus
sys.modules["data_utils"] = data_utils
sys.modules["vocabulary"] = data_utils


def convert_transfo_xl_checkpoint_to_pytorch(
    tf_checkpoint_path, transfo_xl_config_file, pytorch_dump_folder_path, transfo_xl_dataset_file
):
    if transfo_xl_dataset_file:
        # Convert a pre-processed corpus (see original TensorFlow repo)
        with open(transfo_xl_dataset_file, "rb") as fp:
            corpus = pickle.load(fp, encoding="latin1")
        # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
        pytorch_vocab_dump_path = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
        print(f"Save vocabulary to {pytorch_vocab_dump_path}")
        corpus_vocab_dict = corpus.vocab.__dict__
        torch.save(corpus_vocab_dict, pytorch_vocab_dump_path)

        corpus_dict_no_vocab = corpus.__dict__
        corpus_dict_no_vocab.pop("vocab", None)
        pytorch_dataset_dump_path = pytorch_dump_folder_path + "/" + CORPUS_NAME
        print(f"Save dataset to {pytorch_dataset_dump_path}")
        torch.save(corpus_dict_no_vocab, pytorch_dataset_dump_path)

    if tf_checkpoint_path:
        # Convert a pre-trained TensorFlow model
        config_path = os.path.abspath(transfo_xl_config_file)
        tf_path = os.path.abspath(tf_checkpoint_path)

        print(f"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.")
        # Initialise PyTorch model
        if transfo_xl_config_file == "":
            config = TransfoXLConfig()
        else:
            config = TransfoXLConfig.from_json_file(transfo_xl_config_file)
        print(f"Building PyTorch model from configuration: {config}")
        model = TransfoXLLMHeadModel(config)

        model = load_tf_weights_in_transfo_xl(model, config, tf_path)
        # Save pytorch-model
        pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
        pytorch_config_dump_path = os.path.join(pytorch_dump_folder_path, CONFIG_NAME)
        print(f"Save PyTorch model to {os.path.abspath(pytorch_weights_dump_path)}")
        torch.save(model.state_dict(), pytorch_weights_dump_path)
        print(f"Save configuration file to {os.path.abspath(pytorch_config_dump_path)}")
        with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
            f.write(config.to_json_string())


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to store the PyTorch model or dataset/vocab.",
    )
    parser.add_argument(
        "--tf_checkpoint_path",
        default="",
        type=str,
        help="An optional path to a TensorFlow checkpoint path to be converted.",
    )
    parser.add_argument(
        "--transfo_xl_config_file",
        default="",
        type=str,
        help=(
            "An optional config json file corresponding to the pre-trained BERT model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--transfo_xl_dataset_file",
        default="",
        type=str,
        help="An optional dataset file to be converted in a vocabulary.",
    )
    args = parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )
