





# 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   __snake_case    (       _lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			Tuple					,					_lowerCAmelCase			:			Union[str, Any]=0						)			->    int:
   # Format the message.
   if name is None:
      A_       :					Optional[Any]        =      None
   else:
      A_       :					int        =      "." * max(0					,					spaces - 2						) + "# {:" + str(50 - spaces						) + "s}"
      A_       :					int        =      fmt.format(_lowerCAmelCase						)

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

def   __snake_case    (       _lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			Union[str, Any]					,					_lowerCAmelCase			:			Dict					,					_lowerCAmelCase			:			str					,					_lowerCAmelCase			:			List[Any]						)			->    Union[str, Any]:
   # 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_       :					str        =      param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      A_       :					List[str]        =      (num_heads, hidden_size, num_splits) + input_shape[1:]
      A_       :					List[str]        =      param.view(*_lowerCAmelCase						)
      A_       :					int        =      param.transpose(0					,					2						)
      A_       :					Optional[int]        =      param.transpose(1					,					2						).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      A_       :					Optional[Any]        =      (num_heads, num_splits, hidden_size) + input_shape[1:]
      A_       :					List[str]        =      param.view(*_lowerCAmelCase						)
      A_       :					List[str]        =      param.transpose(0					,					1						).contiguous()
   A_       :					Union[str, Any]        =      param.view(*_lowerCAmelCase						)
   return param

def   __snake_case    (       _lowerCAmelCase			:			str					,					_lowerCAmelCase			:			str					,					_lowerCAmelCase			:			str						)			->    Any:
   # The converted output model.
   A_       :					List[str]        =      {}

   # old versions did not store training args
   A_       :					List[str]        =      input_state_dict.get("args"					,					_lowerCAmelCase						)
   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_       :					Dict        =      ds_args.padded_vocab_size
      A_       :					List[str]        =      ds_args.max_position_embeddings
      A_       :					List[str]        =      ds_args.hidden_size
      A_       :					List[Any]        =      ds_args.num_layers
      A_       :					Tuple        =      ds_args.num_attention_heads
      A_       :					List[str]        =      ds_args.ffn_hidden_size
      # pprint(config)

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

   # The model.
   A_       :					Optional[int]        =      input_state_dict["model"]
   # The language model.
   A_       :					List[str]        =      model["language_model"]
   # The embeddings.
   A_       :					Union[str, Any]        =      lm["embedding"]

   # The word embeddings.
   A_       :					Union[str, Any]        =      embeddings["word_embeddings"]["weight"]
   # Truncate the embedding table to vocab_size rows.
   A_       :					int        =      word_embeddings[: config.vocab_size, :]
   A_       :					Union[str, Any]        =      word_embeddings

   # The position embeddings.
   A_       :					str        =      embeddings["position_embeddings"]["weight"]
   # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
   A_       :					str        =      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_       :					Any        =      pos_embeddings

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

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

   # The simple map of names for "automated" rules.
   A_       :					str        =      {
       "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_       :					Optional[Any]        =      layer_re.match(_lowerCAmelCase						)

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

      # The index of the layer.
      A_       :					List[str]        =      int(m.group(1						)						)
      # The name of the operation.
      A_       :					List[Any]        =      m.group(2						)
      # Is it a weight or a bias?
      A_       :					List[str]        =      m.group(3						)

      # The name of the layer.
      A_       :					Tuple        =      f"transformer.h.{layer_idx}"

      # For layernorm(s), simply store the layer norm.
      if op_name.endswith("layernorm"						):
         A_       :					Optional[Any]        =      "ln_1" if op_name.startswith("input"						) else "ln_2"
         A_       :					List[str]        =      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_       :					Dict        =      torch.tril(torch.ones((n_positions, n_positions)					,					dtype=torch.floataa						)						).view(
             1					,					1					,					_lowerCAmelCase					,					_lowerCAmelCase						)
         A_       :					Optional[int]        =      causal_mask

         # Insert a "dummy" tensor for masked_bias.
         A_       :					int        =      torch.tensor(-1e4					,					dtype=torch.floataa						)
         A_       :					Union[str, Any]        =      masked_bias

         A_       :					int        =      fix_query_key_value_ordering(_lowerCAmelCase					,					_lowerCAmelCase					,					3					,					_lowerCAmelCase					,					_lowerCAmelCase						)
         # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
         A_       :					Optional[Any]        =      out_val.transpose(0					,					1						).contiguous()
         # Store.
         A_       :					List[str]        =      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_       :					Any        =      fix_query_key_value_ordering(_lowerCAmelCase					,					_lowerCAmelCase					,					3					,					_lowerCAmelCase					,					_lowerCAmelCase						)
         # Store. No change of shape.
         A_       :					int        =      out_val

      # Transpose the weights.
      elif weight_or_bias == "weight":
         A_       :					Optional[int]        =      megatron_to_transformers[op_name]
         A_       :					Union[str, Any]        =      val.transpose(0					,					1						)

      # Copy the bias.
      elif weight_or_bias == "bias":
         A_       :					Optional[Any]        =      megatron_to_transformers[op_name]
         A_       :					List[Any]        =      val

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

   # The final layernorm.
   A_       :					Any        =      transformer["final_layernorm.weight"]
   A_       :					Dict        =      transformer["final_layernorm.bias"]

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

   # It should be done!
   return output_state_dict

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

   # Extract the basename.
   A_       :					int        =      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_       :					str        =      torch.load(_lowerCAmelCase					,					map_location="cpu"						)
   else:
      A_       :					Optional[int]        =      torch.load(args.path_to_checkpoint					,					map_location="cpu"						)

   A_       :					int        =      input_state_dict.get("args"					,					_lowerCAmelCase						)

   # 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_       :					Optional[int]        =      "gelu_fast"
         elif ds_args.openai_gelu:
            A_       :					Tuple        =      "gelu_new"
         else:
            A_       :					List[Any]        =      "gelu"
      else:
         # in the very early days this used to be "gelu_new"
         A_       :					int        =      "gelu_new"

      # Spell out all parameters in case the defaults change.
      A_       :					int        =      GPTaConfig(
          vocab_size=50257					,					n_positions=1024					,					n_embd=1024					,					n_layer=24					,					n_head=16					,					n_inner=4096					,					activation_function=_lowerCAmelCase					,					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=_lowerCAmelCase					,					summary_activation=_lowerCAmelCase					,					summary_proj_to_labels=_lowerCAmelCase					,					summary_first_dropout=0.1					,					scale_attn_weights=_lowerCAmelCase					,					use_cache=_lowerCAmelCase					,					bos_token_id=50256					,					eos_token_id=50256					,					)
   else:
      A_       :					Any        =      GPTaConfig.from_json_file(args.config_file						)

   A_       :					Dict        =      ["GPT2LMHeadModel"]

   # Convert.
   print("Converting"						)
   A_       :					Union[str, Any]        =      convert_megatron_checkpoint(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)

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

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

   A_       :					Dict        =      AutoTokenizer.from_pretrained(_lowerCAmelCase						)
   A_       :					Dict        =      type(_lowerCAmelCase						).__name__
   A_       :					Optional[Any]        =      tokenizer_class

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

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

   # Store the state_dict to file.
   A_       :					Any        =      os.path.join(_lowerCAmelCase					,					"pytorch_model.bin"						)
   print(f"Saving checkpoint to \"{output_checkpoint_file}\""						)
   torch.save(_lowerCAmelCase					,					_lowerCAmelCase						)


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

if __name__ == "__main__":
 main()

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







def   __snake_case    (       _lowerCAmelCase			:			int						)			->    bool:
   if num < 0:
      return False

   A_       :					int        =      num
   A_       :					int        =      0
   while num > 0:
      A_       :					str        =      rev_num * 10 + (num % 10)
      num //= 10

   return num_copy == rev_num


if __name__ == "__main__":
 import doctest

 doctest.testmod()
