



# 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, GPTaConfig



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       Optional[Any] ,       UpperCamelCase_				:       Union[str, Any]=0	)->  str:
    # Format the message.
    if name is None:
        A__		 =					None
    else:
        A__		 =					'''.''' * max(0 ,       spaces - 2	) + '''# {:''' + str(5_0 - spaces	) + '''s}'''
        A__		 =					fmt.format(UpperCamelCase_	)

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any ,       UpperCamelCase_				:       Tuple ,       UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       Dict ,       UpperCamelCase_				:       Tuple	)->  List[str]:
    # 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.
    A__		 =					param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        A__		 =					(num_heads, hidden_size, num_splits) + input_shape[1:]
        A__		 =					param.view(*UpperCamelCase_	)
        A__		 =					param.transpose(0 ,       2	)
        A__		 =					param.transpose(1 ,       2	).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        A__		 =					(num_heads, num_splits, hidden_size) + input_shape[1:]
        A__		 =					param.view(*UpperCamelCase_	)
        A__		 =					param.transpose(0 ,       1	).contiguous()
    A__		 =					param.view(*UpperCamelCase_	)
    return param



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       str ,       UpperCamelCase_				:       List[str]	)->  List[str]:
    # The converted output model.
    A__		 =					{}

    # old versions did not store training args
    A__		 =					input_state_dict.get('''args''' ,       UpperCamelCase_	)
    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))

        A__		 =					ds_args.padded_vocab_size
        A__		 =					ds_args.max_position_embeddings
        A__		 =					ds_args.hidden_size
        A__		 =					ds_args.num_layers
        A__		 =					ds_args.num_attention_heads
        A__		 =					ds_args.ffn_hidden_size
        # pprint(config)

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

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

    # The word embeddings.
    A__		 =					embeddings['''word_embeddings''']['''weight''']
    # Truncate the embedding table to vocab_size rows.
    A__		 =					word_embeddings[: config.vocab_size, :]
    A__		 =					word_embeddings

    # The position embeddings.
    A__		 =					embeddings['''position_embeddings''']['''weight''']
    # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
    A__		 =					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.
    A__		 =					pos_embeddings

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

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

    # The simple map of names for "automated" rules.
    A__		 =					{
        '''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.
        A__		 =					layer_re.match(UpperCamelCase_	)

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

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

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

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith('''layernorm'''	):
            A__		 =					'''ln_1''' if op_name.startswith('''input'''	) else '''ln_2'''
            A__		 =					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.
            A__		 =					torch.tril(torch.ones((n_positions, n_positions) ,       dtype=torch.floataa	)	).view(
                1 ,       1 ,       UpperCamelCase_ ,       UpperCamelCase_	)
            A__		 =					causal_mask

            # Insert a "dummy" tensor for masked_bias.
            A__		 =					torch.tensor(-1E4 ,       dtype=torch.floataa	)
            A__		 =					masked_bias

            A__		 =					fix_query_key_value_ordering(UpperCamelCase_ ,       UpperCamelCase_ ,       3 ,       UpperCamelCase_ ,       UpperCamelCase_	)
            # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
            A__		 =					out_val.transpose(0 ,       1	).contiguous()
            # Store.
            A__		 =					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":
            A__		 =					fix_query_key_value_ordering(UpperCamelCase_ ,       UpperCamelCase_ ,       3 ,       UpperCamelCase_ ,       UpperCamelCase_	)
            # Store. No change of shape.
            A__		 =					out_val

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

        # Copy the bias.
        elif weight_or_bias == "bias":
            A__		 =					megatron_to_transformers[op_name]
            A__		 =					val

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

    # The final layernorm.
    A__		 =					transformer['''final_layernorm.weight''']
    A__		 =					transformer['''final_layernorm.bias''']

    # For LM head, transformers' wants the matrix to weight embeddings.
    A__		 =					word_embeddings

    # It should be done!
    return output_state_dict



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

    # Extract the basename.
    A__		 =					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:
                A__		 =					torch.load(UpperCamelCase_ ,       map_location='''cpu'''	)
    else:
        A__		 =					torch.load(args.path_to_checkpoint ,       map_location='''cpu'''	)

    A__		 =					input_state_dict.get('''args''' ,       UpperCamelCase_	)

    # 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:
                A__		 =					'''gelu_fast'''
            elif ds_args.openai_gelu:
                A__		 =					'''gelu_new'''
            else:
                A__		 =					'''gelu'''
        else:
            # in the very early days this used to be "gelu_new"
            A__		 =					'''gelu_new'''

        # Spell out all parameters in case the defaults change.
        A__		 =					GPTaConfig(
            vocab_size=5_0_2_5_7 ,       n_positions=1_0_2_4 ,       n_embd=1_0_2_4 ,       n_layer=2_4 ,       n_head=1_6 ,       n_inner=4_0_9_6 ,       activation_function=UpperCamelCase_ ,       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=UpperCamelCase_ ,       summary_activation=UpperCamelCase_ ,       summary_proj_to_labels=UpperCamelCase_ ,       summary_first_dropout=0.1 ,       scale_attn_weights=UpperCamelCase_ ,       use_cache=UpperCamelCase_ ,       bos_token_id=5_0_2_5_6 ,       eos_token_id=5_0_2_5_6 ,       )
    else:
        A__		 =					GPTaConfig.from_json_file(args.config_file	)

    A__		 =					['''GPT2LMHeadModel''']

    # Convert.
    print('''Converting'''	)
    A__		 =					convert_megatron_checkpoint(UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(UpperCamelCase_ ,       UpperCamelCase_	)

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

    A__		 =					AutoTokenizer.from_pretrained(UpperCamelCase_	)
    A__		 =					type(UpperCamelCase_	).__name__
    A__		 =					tokenizer_class

    # Store the config to file.
    print('''Saving config'''	)
    config.save_pretrained(UpperCamelCase_	)

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

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


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

if __name__ == "__main__":
   main()

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





import numpy as np



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.array	)->  np.array:
    return 1 / (1 + np.exp(-vector	))



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.array	)->  np.array:
    return vector * sigmoid(1.702 * vector	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
