# What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

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-->

<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

add VITS model. # What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

####################################################################################################

# Copyright (c) 2021-, NVIDIA CORPORATION.  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.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPT2Config


####################################################################################################


def recursive_print(name, val, spaces=0):
    # Format the message.
    if name is None:
        msg = None
    else:
        fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
        msg = fmt.format(name)

    # Print and recurse (if needed).
    if isinstance(val, dict):
        if msg is not None:
            print(msg)
        for k in val.keys():
            recursive_print(k, val[k], spaces + 2)
    elif isinstance(val, torch.Tensor):
        print(msg, ":", val.size())
    else:
        print(msg, ":", val)


def fix_query_key_value_ordering(param, checkpoint_version, num_splits, num_heads, hidden_size):
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    input_shape = param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        saved_shape = (num_heads, hidden_size, num_splits) + input_shape[1:]
        param = param.view(*saved_shape)
        param = param.transpose(0, 2)
        param = param.transpose(1, 2).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        saved_shape = (num_heads, num_splits, hidden_size) + input_shape[1:]
        param = param.view(*saved_shape)
        param = param.transpose(0, 1).contiguous()
    param = param.view(*input_shape)
    return param


####################################################################################################


def convert_megatron_checkpoint(args, input_state_dict, config):
    # The converted output model.
    output_state_dict = {}

    # old versions did not store training args
    ds_args = input_state_dict.get("args", None)
    if ds_args is not None:
        # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
        # from pprint import pprint
        # pprint(vars(ds_args))

        config.vocab_size = ds_args.padded_vocab_size
        config.n_positions = ds_args.max_position_embeddings
        config.n_embd = ds_args.hidden_size
        config.n_layer = ds_args.num_layers
        config.n_head = ds_args.num_attention_heads
        config.n_inner = ds_args.ffn_hidden_size
        # pprint(config)

    # The number of heads.
    heads = config.n_head
    # The hidden_size per head.
    hidden_size_per_head = config.n_embd // config.n_head
    # Megatron-LM checkpoint version
    if "checkpoint_version" in input_state_dict.keys():
        checkpoint_version = input_state_dict["checkpoint_version"]
    else:
        checkpoint_version = 0.0

    # The model.
    model = input_state_dict["model"]
    # The language model.
    lm = model["language_model"]
    # The embeddings.
    embeddings = lm["embedding"]

    # The word embeddings.
    word_embeddings = embeddings["word_embeddings"]["weight"]
    # Truncate the embedding table to vocab_size rows.
    word_embeddings = word_embeddings[: config.vocab_size, :]
    output_state_dict["transformer.wte.weight"] = word_embeddings

    # The position embeddings.
    pos_embeddings = embeddings["position_embeddings"]["weight"]
    # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
    n_positions = pos_embeddings.size(0)
    if n_positions != config.n_positions:
        raise ValueError(
            f"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"
        )
    # Store the position embeddings.
    output_state_dict["transformer.wpe.weight"] = pos_embeddings

    # The transformer.
    transformer = lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

    # The regex to extract layer names.
    layer_re = re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")

    # The simple map of names for "automated" rules.
    megatron_to_transformers = {
        "attention.dense": ".attn.c_proj.",
        "self_attention.dense": ".attn.c_proj.",
        "mlp.dense_h_to_4h": ".mlp.c_fc.",
        "mlp.dense_4h_to_h": ".mlp.c_proj.",
    }

    # Extract the layers.
    for key, val in transformer.items():
        # Match the name.
        m = layer_re.match(key)

        # Stop if that's not a layer
        if m is None:
            break

        # The index of the layer.
        layer_idx = int(m.group(1))
        # The name of the operation.
        op_name = m.group(2)
        # Is it a weight or a bias?
        weight_or_bias = m.group(3)

        # The name of the layer.
        layer_name = f"transformer.h.{layer_idx}"

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith("layernorm"):
            ln_name = "ln_1" if op_name.startswith("input") else "ln_2"
            output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = val

        # Transpose the QKV matrix.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "weight":
            # Insert a tensor of 1x1xDxD bias.
            causal_mask = torch.tril(torch.ones((n_positions, n_positions), dtype=torch.float16)).view(
                1, 1, n_positions, n_positions
            )
            output_state_dict[layer_name + ".attn.bias"] = causal_mask

            # Insert a "dummy" tensor for masked_bias.
            masked_bias = torch.tensor(-1e4, dtype=torch.float16)
            output_state_dict[layer_name + ".attn.masked_bias"] = masked_bias

            out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
            # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
            out_val = out_val.transpose(0, 1).contiguous()
            # Store.
            output_state_dict[layer_name + ".attn.c_attn.weight"] = out_val

        # Transpose the bias.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "bias":
            out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
            # Store. No change of shape.
            output_state_dict[layer_name + ".attn.c_attn.bias"] = out_val

        # Transpose the weights.
        elif weight_or_bias == "weight":
            out_name = megatron_to_transformers[op_name]
            output_state_dict[layer_name + out_name + "weight"] = val.transpose(0, 1)

        # Copy the bias.
        elif weight_or_bias == "bias":
            out_name = megatron_to_transformers[op_name]
            output_state_dict[layer_name + out_name + "bias"] = val

    # DEBUG.
    assert config.n_layer == layer_idx + 1

    # The final layernorm.
    output_state_dict["transformer.ln_f.weight"] = transformer["final_layernorm.weight"]
    output_state_dict["transformer.ln_f.bias"] = transformer["final_layernorm.bias"]

    # For LM head, transformers' wants the matrix to weight embeddings.
    output_state_dict["lm_head.weight"] = word_embeddings

    # It should be done!
    return output_state_dict


####################################################################################################


def main():
    # Create the argument parser.
    parser = argparse.ArgumentParser()
    parser.add_argument("--print-checkpoint-structure", action="store_true")
    parser.add_argument(
        "path_to_checkpoint",
        type=str,
        help="Path to the checkpoint file (.zip archive or direct .pt file)",
    )
    parser.add_argument(
        "--config_file",
        default="",
        type=str,
        help="An optional config json file describing the pre-trained model.",
    )
    args = parser.parse_args()

    # Extract the basename.
    basename = os.path.dirname(args.path_to_checkpoint)

    # Load the model.
    # the .zip is very optional, let's keep it for backward compatibility
    print(f"Extracting PyTorch state dictionary from {args.path_to_checkpoint}")
    if args.path_to_checkpoint.endswith(".zip"):
        with zipfile.ZipFile(args.path_to_checkpoint, "r") as checkpoint:
            with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict:
                input_state_dict = torch.load(pytorch_dict, map_location="cpu")
    else:
        input_state_dict = torch.load(args.path_to_checkpoint, map_location="cpu")

    ds_args = input_state_dict.get("args", None)

    # Read the config, or default to the model released by NVIDIA.
    if args.config_file == "":
        if ds_args is not None:
            if ds_args.bias_gelu_fusion:
                activation_function = "gelu_fast"
            elif ds_args.openai_gelu:
                activation_function = "gelu_new"
            else:
                activation_function = "gelu"
        else:
            # in the very early days this used to be "gelu_new"
            activation_function = "gelu_new"

        # Spell out all parameters in case the defaults change.
        config = GPT2Config(
            vocab_size=50257,
            n_positions=1024,
            n_embd=1024,
            n_layer=24,
            n_head=16,
            n_inner=4096,
            activation_function=activation_function,
            resid_pdrop=0.1,
            embd_pdrop=0.1,
            attn_pdrop=0.1,
            layer_norm_epsilon=1e-5,
            initializer_range=0.02,
            summary_type="cls_index",
            summary_use_proj=True,
            summary_activation=None,
            summary_proj_to_labels=True,
            summary_first_dropout=0.1,
            scale_attn_weights=True,
            use_cache=True,
            bos_token_id=50256,
            eos_token_id=50256,
        )
    else:
        config = GPT2Config.from_json_file(args.config_file)

    config.architectures = ["GPT2LMHeadModel"]

    # Convert.
    print("Converting")
    output_state_dict = convert_megatron_checkpoint(args, input_state_dict, config)

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(None, output_state_dict)

    # Add tokenizer class info to config
    # see https://github.com/huggingface/transformers/issues/13906)
    if ds_args is not None:
        tokenizer_type = ds_args.tokenizer_type
        if tokenizer_type == "GPT2BPETokenizer":
            tokenizer_model_name = "gpt2"
        elif tokenizer_type == "PretrainedFromHF":
            tokenizer_model_name = ds_args.tokenizer_name_or_path
        else:
            raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}")
    else:
        tokenizer_model_name = "gpt2"

    tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_name)
    tokenizer_class = type(tokenizer).__name__
    config.tokenizer_class = tokenizer_class

    # Store the config to file.
    print("Saving config")
    config.save_pretrained(basename)

    # Save tokenizer based on args
    print(f"Adding {tokenizer_class} tokenizer files")
    tokenizer.save_pretrained(basename)

    # Store the state_dict to file.
    output_checkpoint_file = os.path.join(basename, "pytorch_model.bin")
    print(f'Saving checkpoint to "{output_checkpoint_file}"')
    torch.save(output_state_dict, output_checkpoint_file)


####################################################################################################

if __name__ == "__main__":
    main()

####################################################################################################

####################################################################################################

# Copyright (c) 2021-, NVIDIA CORPORATION.  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.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPT2Config


####################################################################################################


def recursive_print(name, val, spaces=0):
    # Format the message.
    if name is None:
        msg = None
    else:
        fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
        msg = fmt.format(name)

    # Print and recurse (if needed).
    if isinstance(val, dict):
        if msg is not None:
            print(msg)
        for k in val.keys():
            recursive_print(k, val[k], spaces + 2)
    elif isinstance(val, torch.Tensor):
        print(msg, ":", val.size())
    else:
        print(msg, ":", val)


def fix_query_key_value_ordering(param, checkpoint_version, num_splits, num_heads, hidden_size):
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    input_shape = param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        saved_shape = (num_heads, hidden_size, num_splits) + input_shape[1:]
        param = param.view(*saved_shape)
        param = param.transpose(0, 2)
        param = param.transpose(1, 2).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        saved_shape = (num_heads, num_splits, hidden_size) + input_shape[1:]
        param = param.view(*saved_shape)
        param = param.transpose(0, 1).contiguous()
    param = param.view(*input_shape)
    return param


####################################################################################################


def convert_megatron_checkpoint(args, input_state_dict, config):
    # The converted output model.
    output_state_dict = {}

    # old versions did not store training args
    ds_args = input_state_dict.get("args", None)
    if ds_args is not None:
        # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
        # from pprint import pprint
        # pprint(vars(ds_args))

        config.vocab_size = ds_args.padded_vocab_size
        config.n_positions = ds_args.max_position_embeddings
        config.n_embd = ds_args.hidden_size
        config.n_layer = ds_args.num_layers
        config.n_head = ds_args.num_attention_heads
        config.n_inner = ds_args.ffn_hidden_size
        # pprint(config)

    # The number of heads.
    heads = config.n_head
    # The hidden_size per head.
    hidden_size_per_head = config.n_embd // config.n_head
    # Megatron-LM checkpoint version
    if "checkpoint_version" in input_state_dict.keys():
        checkpoint_version = input_state_dict["checkpoint_version"]
    else:
        checkpoint_version = 0.0

    # The model.
    model = input_state_dict["model"]
    # The language model.
    lm = model["language_model"]
    # The embeddings.
    embeddings = lm["embedding"]

    # The word embeddings.
    word_embeddings = embeddings["word_embeddings"]["weight"]
    # Truncate the embedding table to vocab_size rows.
    word_embeddings = word_embeddings[: config.vocab_size, :]
    output_state_dict["transformer.wte.weight"] = word_embeddings

    # The position embeddings.
    pos_embeddings = embeddings["position_embeddings"]["weight"]
    # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
    n_positions = pos_embeddings.size(0)
    if n_positions != config.n_positions:
        raise ValueError(
            f"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"
        )
    # Store the position embeddings.
    output_state_dict["transformer.wpe.weight"] = pos_embeddings

    # The transformer.
    transformer = lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

    # The regex to extract layer names.
    layer_re = re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")

    # The simple map of names for "automated" rules.
    megatron_to_transformers = {
        "attention.dense": ".attn.c_proj.",
        "self_attention.dense": ".attn.c_proj.",
        "mlp.dense_h_to_4h": ".mlp.c_fc.",
        "mlp.dense_4h_to_h": ".mlp.c_proj.",
    }

    # Extract the layers.
    for key, val in transformer.items():
        # Match the name.
        m = layer_re.match(key)

        # Stop if that's not a layer
        if m is None:
            break

        # The index of the layer.
        layer_idx = int(m.group(1))
        # The name of the operation.
        op_name = m.group(2)
        # Is it a weight or a bias?
        weight_or_bias = m.group(3)

        # The name of the layer.
        layer_name = f"transformer.h.{layer_idx}"

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith("layernorm"):
            ln_name = "ln_1" if op_name.startswith("input") else "ln_2"
            output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = val

        # Transpose the QKV matrix.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "weight":
            # Insert a tensor of 1x1xDxD bias.
            causal_mask = torch.tril(torch.ones((n_positions, n_positions), dtype=torch.float16)).view(
                1, 1, n_positions, n_positions
            )
            output_state_dict[layer_name + ".attn.bias"] = causal_mask

            # Insert a "dummy" tensor for masked_bias.
            masked_bias = torch.tensor(-1e4, dtype=torch.float16)
            output_state_dict[layer_name + ".attn.masked_bias"] = masked_bias

            out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
            # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
            out_val = out_val.transpose(0, 1).contiguous()
            # Store.
            output_state_dict[layer_name + ".attn.c_attn.weight"] = out_val

        # Transpose the bias.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "bias":
            out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
            # Store. No change of shape.
            output_state_dict[layer_name + ".attn.c_attn.bias"] = out_val

        # Transpose the weights.
        elif weight_or_bias == "weight":
            out_name = megatron_to_transformers[op_name]
            output_state_dict[layer_name + out_name + "weight"] = val.transpose(0, 1)

        # Copy the bias.
        elif weight_or_bias == "bias":
            out_name = megatron_to_transformers[op_name]
            output_state_dict[layer_name + out_name + "bias"] = val

    # DEBUG.
    assert config.n_layer == layer_idx + 1

    # The final layernorm.
    output_state_dict["transformer.ln_f.weight"] = transformer["final_layernorm.weight"]
    output_state_dict["transformer.ln_f.bias"] = transformer["final_layernorm.bias"]

    # For LM head, transformers' wants the matrix to weight embeddings.
    output_state_dict["lm_head.weight"] = word_embeddings

    # It should be done!
    return output_state_dict


####################################################################################################


def main():
    # Create the argument parser.
    parser = argparse.ArgumentParser()
    parser.add_argument("--print-checkpoint-structure", action="store_true")
    parser.add_argument(
        "path_to_checkpoint",
        type=str,
        help="Path to the checkpoint file (.zip archive or direct .pt file)",
    )
    parser.add_argument(
        "--config_file",
        default="",
        type=str,
        help="An optional config json file describing the pre-trained model.",
    )
    args = parser.parse_args()

    # Extract the basename.
    basename = os.path.dirname(args.path_to_checkpoint)

    # Load the model.
    # the .zip is very optional, let's keep it for backward compatibility
    print(f"Extracting PyTorch state dictionary from {args.path_to_checkpoint}")
    if args.path_to_checkpoint.endswith(".zip"):
        with zipfile.ZipFile(args.path_to_checkpoint, "r") as checkpoint:
            with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict:
                input_state_dict = torch.load(pytorch_dict, map_location="cpu")
    else:
        input_state_dict = torch.load(args.path_to_checkpoint, map_location="cpu")

    ds_args = input_state_dict.get("args", None)

    # Read the config, or default to the model released by NVIDIA.
    if args.config_file == "":
        if ds_args is not None:
            if ds_args.bias_gelu_fusion:
                activation_function = "gelu_fast"
            elif ds_args.openai_gelu:
                activation_function = "gelu_new"
            else:
                activation_function = "gelu"
        else:
            # in the very early days this used to be "gelu_new"
            activation_function = "gelu_new"

        # Spell out all parameters in case the defaults change.
        config = GPT2Config(
            vocab_size=50257,
            n_positions=1024,
            n_embd=1024,
            n_layer=24,
            n_head=16,
            n_inner=4096,
            activation_function=activation_function,
            resid_pdrop=0.1,
            embd_pdrop=0.1,
            attn_pdrop=0.1,
            layer_norm_epsilon=1e-5,
            initializer_range=0.02,
            summary_type="cls_index",
            summary_use_proj=True,
            summary_activation=None,
            summary_proj_to_labels=True,
            summary_first_dropout=0.1,
            scale_attn_weights=True,
            use_cache=True,
            bos_token_id=50256,
            eos_token_id=50256,
        )
    else:
        config = GPT2Config.from_json_file(args.config_file)

    config.architectures = ["GPT2LMHeadModel"]

    # Convert.
    print("Converting")
    output_state_dict = convert_megatron_checkpoint(args, input_state_dict, config)

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(None, output_state_dict)

    # Add tokenizer class info to config
    # see https://github.com/huggingface/transformers/issues/13906)
    if ds_args is not None:
        tokenizer_type = ds_args.tokenizer_type
        if tokenizer_type == "GPT2BPETokenizer":
            tokenizer_model_name = "gpt2"
        elif tokenizer_type == "PretrainedFromHF":
            tokenizer_model_name = ds_args.tokenizer_name_or_path
        else:
            raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}")
    else:
        tokenizer_model_name = "gpt2"

    tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_name)
    tokenizer_class = type(tokenizer).__name__
    config.tokenizer_class = tokenizer_class

    # Store the config to file.
    print("Saving config")
    config.save_pretrained(basename)

    # Save tokenizer based on args
    print(f"Adding {tokenizer_class} tokenizer files")
    tokenizer.save_pretrained(basename)

    # Store the state_dict to file.
    output_checkpoint_file = os.path.join(basename, "pytorch_model.bin")
    print(f'Saving checkpoint to "{output_checkpoint_file}"')
    torch.save(output_state_dict, output_checkpoint_file)


####################################################################################################

if __name__ == "__main__":
    main()

####################################################################################################
