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# 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.

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#
# 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_bert/convert_megatron_bert_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 MegatronBertConfig


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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 BERT.
    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


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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.tokenizer_type = ds_args.tokenizer_type
        config.vocab_size = ds_args.padded_vocab_size
        config.max_position_embeddings = ds_args.max_position_embeddings
        config.hidden_size = ds_args.hidden_size
        config.num_hidden_layers = ds_args.num_layers
        config.num_attention_heads = ds_args.num_attention_heads
        config.intermediate_size = ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
        # pprint(config)

    # The number of heads.
    heads = config.num_attention_heads
    # The hidden_size per head.
    hidden_size_per_head = config.hidden_size // heads
    # 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, :]
    # Store the word embeddings.
    output_state_dict["bert.embeddings.word_embeddings.weight"] = word_embeddings

    # The position embeddings.
    pos_embeddings = embeddings["position_embeddings"]["weight"]
    assert pos_embeddings.size(0) == config.max_position_embeddings and pos_embeddings.size(1) == config.hidden_size
    # Store the position embeddings.
    output_state_dict["bert.embeddings.position_embeddings.weight"] = pos_embeddings

    # The token-type embeddings.
    tokentype_embeddings = embeddings["tokentype_embeddings"]["weight"]
    # Store the position embeddings.
    output_state_dict["bert.embeddings.token_type_embeddings.weight"] = tokentype_embeddings

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

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

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

    # Keep track of the attention/query/value tensor.
    attention_qkv_weight = None

    # 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"bert.encoder.layer.{layer_idx}"

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith("layernorm"):
            ln_name = "attention.ln" if op_name.startswith("input") else "ln"
            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":
            # Make sure the QKV pointer is nil.
            assert attention_qkv_weight is None, ""

            out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
            # Store the tensor as we need the bias as well to interleave QKV and biases.
            attention_qkv_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":
            # Make sure we read the weight tensor.
            assert attention_qkv_weight is not None, ""

            # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
            q = attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
            k = attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
            v = attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

            out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
            # Split the bias.
            q_bias = out_val[0 * config.hidden_size : 1 * config.hidden_size]
            k_bias = out_val[1 * config.hidden_size : 2 * config.hidden_size]
            v_bias = out_val[2 * config.hidden_size : 3 * config.hidden_size]

            # Store.
            output_state_dict[f"{layer_name}.attention.self.query.weight"] = q
            output_state_dict[f"{layer_name}.attention.self.query.bias"] = q_bias
            output_state_dict[f"{layer_name}.attention.self.key.weight"] = k
            output_state_dict[f"{layer_name}.attention.self.key.bias"] = k_bias
            output_state_dict[f"{layer_name}.attention.self.value.weight"] = v
            output_state_dict[f"{layer_name}.attention.self.value.bias"] = v_bias

            # Clear the stored tensor.
            attention_qkv_weight = None

        # Copy weights and biases as is.
        elif weight_or_bias in ["weight", "bias"]:
            out_name = megatron_to_transformers[op_name]
            output_state_dict[layer_name + out_name + weight_or_bias] = val

    # The final layernorm.
    output_state_dict["bert.encoder.ln.weight"] = transformer["final_layernorm.weight"]
    output_state_dict["bert.encoder.ln.bias"] = transformer["final_layernorm.bias"]

    # The pooler.
    pooler = lm["pooler"]

    # Store the matrix and the bias.
    output_state_dict["bert.pooler.dense.weight"] = pooler["dense.weight"]
    output_state_dict["bert.pooler.dense.bias"] = pooler["dense.bias"]

    # The LM head from Megatron (for RACE).
    lm_head = model["lm_head"]

    # The transform matrix.
    output_state_dict["cls.predictions.transform.dense.weight"] = lm_head["dense.weight"]
    output_state_dict["cls.predictions.transform.dense.bias"] = lm_head["dense.bias"]

    # The transform LN.
    output_state_dict["cls.predictions.transform.LayerNorm.weight"] = lm_head["layernorm.weight"]
    output_state_dict["cls.predictions.transform.LayerNorm.bias"] = lm_head["layernorm.bias"]

    # For the decoder, we replicate the weights.
    output_state_dict["cls.predictions.decoder.weight"] = word_embeddings
    output_state_dict["cls.predictions.bias"] = lm_head["bias"]

    # The classifier from Megatron (for MLNI).
    binary_head = model["binary_head"]

    # Store the classifier.
    output_state_dict["cls.seq_relationship.weight"] = binary_head["weight"]
    output_state_dict["cls.seq_relationship.bias"] = binary_head["bias"]

    # It should be done!
    return output_state_dict


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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 ZIP file containing the checkpoint")
    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")

    if args.config_file == "":
        # Default config of megatron-bert 345m
        config = MegatronBertConfig()

        # different megatron-bert-*-345m models have different vocab sizes, so override the default
        # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
        config.vocab_size = input_state_dict["model"]["lm_head"]["bias"].numel()
    else:
        config = MegatronBertConfig.from_json_file(args.config_file)

    # 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)

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


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if __name__ == "__main__":
    main()

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