





"""simple docstring"""





# 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 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=0     )   ->Dict:





  """simple docstring"""


  if name is None:
    lowerCAmelCase__     :Dict											=				None
  else:
    lowerCAmelCase__     :List[Any]											=				'.' * max(0	,  spaces - 2     ) + '# {:' + str(50 - spaces     ) + 's}'
    lowerCAmelCase__     :Union[str, Any]											=				fmt.format(_SCREAMING_SNAKE_CASE     )

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





  """simple docstring"""


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





  """simple docstring"""


  lowerCAmelCase__     :str											=				{}

  # old versions did not store training args
  lowerCAmelCase__     :List[str]											=				input_state_dict.get('args'	,  _SCREAMING_SNAKE_CASE     )
  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))

    lowerCAmelCase__     :Optional[Any]											=				ds_args.padded_vocab_size
    lowerCAmelCase__     :str											=				ds_args.max_position_embeddings
    lowerCAmelCase__     :str											=				ds_args.hidden_size
    lowerCAmelCase__     :int											=				ds_args.num_layers
    lowerCAmelCase__     :List[str]											=				ds_args.num_attention_heads
    lowerCAmelCase__     :Dict											=				ds_args.ffn_hidden_size
    # pprint(config)

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

  # The model.
  lowerCAmelCase__     :int											=				input_state_dict['model']
  # The language model.
  lowerCAmelCase__     :Any											=				model['language_model']
  # The embeddings.
  lowerCAmelCase__     :List[Any]											=				lm['embedding']

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

  # The position embeddings.
  lowerCAmelCase__     :Optional[int]											=				embeddings['position_embeddings']['weight']
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  lowerCAmelCase__     :Optional[int]											=				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.
  lowerCAmelCase__     :List[Any]											=				pos_embeddings

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

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

  # The simple map of names for "automated" rules.
  lowerCAmelCase__     :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.
    lowerCAmelCase__     :Tuple											=				layer_re.match(_SCREAMING_SNAKE_CASE     )

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

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

    # The name of the layer.
    lowerCAmelCase__     :Optional[Any]											=				F"transformer.h.{layer_idx}"

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

      # Insert a "dummy" tensor for masked_bias.
      lowerCAmelCase__     :List[Any]											=				torch.tensor(-1e4	,  dtype=torch.floataa     )
      lowerCAmelCase__     :Dict											=				masked_bias

      lowerCAmelCase__     :Tuple											=				fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  3	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
      # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
      lowerCAmelCase__     :Tuple											=				out_val.transpose(0	,  1     ).contiguous()
      # Store.
      lowerCAmelCase__     :Optional[Any]											=				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":
      lowerCAmelCase__     :Optional[Any]											=				fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  3	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
      # Store. No change of shape.
      lowerCAmelCase__     :Dict											=				out_val

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

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

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

  # The final layernorm.
  lowerCAmelCase__     :Optional[Any]											=				transformer['final_layernorm.weight']
  lowerCAmelCase__     :int											=				transformer['final_layernorm.bias']

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

  # It should be done!
  return output_state_dict
def 					__A  ()   ->int:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				argparse.ArgumentParser()
  parser.add_argument('--print-checkpoint-structure'	,  action='store_true'     )
  parser.add_argument(
      'path_to_checkpoint'	,  type=_SCREAMING_SNAKE_CASE	,  help='Path to the checkpoint file (.zip archive or direct .pt file)'	,  )
  parser.add_argument(
      '--config_file'	,  default=''	,  type=_SCREAMING_SNAKE_CASE	,  help='An optional config json file describing the pre-trained model.'	,  )
  lowerCAmelCase__     :str											=				parser.parse_args()

  # Extract the basename.
  lowerCAmelCase__     :Union[str, Any]											=				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:
        lowerCAmelCase__     :int											=				torch.load(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )
  else:
    lowerCAmelCase__     :Optional[int]											=				torch.load(args.path_to_checkpoint	,  map_location='cpu'     )

  lowerCAmelCase__     :Tuple											=				input_state_dict.get('args'	,  _SCREAMING_SNAKE_CASE     )

  # 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:
        lowerCAmelCase__     :Optional[Any]											=				'gelu_fast'
      elif ds_args.openai_gelu:
        lowerCAmelCase__     :Any											=				'gelu_new'
      else:
        lowerCAmelCase__     :str											=				'gelu'
    else:
      # in the very early days this used to be "gelu_new"
      lowerCAmelCase__     :Union[str, Any]											=				'gelu_new'

    # Spell out all parameters in case the defaults change.
    lowerCAmelCase__     :int											=				GPTaConfig(
        vocab_size=5_0257	,  n_positions=1024	,  n_embd=1024	,  n_layer=24	,  n_head=16	,  n_inner=4096	,  activation_function=_SCREAMING_SNAKE_CASE	,  resid_pdrop=0.1	,  embd_pdrop=0.1	,  attn_pdrop=0.1	,  layer_norm_epsilon=1e-5	,  initializer_range=0.0_2	,  summary_type='cls_index'	,  summary_use_proj=_SCREAMING_SNAKE_CASE	,  summary_activation=_SCREAMING_SNAKE_CASE	,  summary_proj_to_labels=_SCREAMING_SNAKE_CASE	,  summary_first_dropout=0.1	,  scale_attn_weights=_SCREAMING_SNAKE_CASE	,  use_cache=_SCREAMING_SNAKE_CASE	,  bos_token_id=5_0256	,  eos_token_id=5_0256	,  )
  else:
    lowerCAmelCase__     :Dict											=				GPTaConfig.from_json_file(args.config_file     )

  lowerCAmelCase__     :List[Any]											=				['GPT2LMHeadModel']

  # Convert.
  print('Converting'     )
  lowerCAmelCase__     :Union[str, Any]											=				convert_megatron_checkpoint(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

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

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

  lowerCAmelCase__     :Optional[int]											=				AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :List[Any]											=				type(_SCREAMING_SNAKE_CASE     ).__name__
  lowerCAmelCase__     :Optional[Any]											=				tokenizer_class

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

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

  # Store the state_dict to file.
  lowerCAmelCase__     :Optional[Any]											=				os.path.join(_SCREAMING_SNAKE_CASE	,  'pytorch_model.bin'     )
  print(F"Saving checkpoint to \"{output_checkpoint_file}\""     )
  torch.save(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )


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

if __name__ == "__main__":
  main()

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




# Copyright 2021 The HuggingFace Team. 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.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async





def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]=None    )			-> Union[str, Any]:





	"""simple docstring"""







	if subparsers is not None:
		A             =					subparsers.add_parser("""test"""    )
	else:
		A             =					argparse.ArgumentParser("""Accelerate test command"""    )

	parser.add_argument(
	    """--config_file"""				,       default=UpperCamelCase__				,       help=(
	        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
	        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
	        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
	        """with 'huggingface'."""
	    )				,       )

	if subparsers is not None:
		parser.set_defaults(func=UpperCamelCase__    )
	return parser





def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Any:





	"""simple docstring"""







	A             =					os.path.sep.join(__file__.split(os.path.sep    )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""]    )

	if args.config_file is None:
		A             =					script_name
	else:
		A             =					f'--config_file={args.config_file} {script_name}'

	A             =					["""accelerate-launch"""] + test_args.split()
	A             =					execute_subprocess_async(UpperCamelCase__				,       env=os.environ.copy()    )
	if result.returncode == 0:
		print("""Test is a success! You are ready for your distributed training!"""    )





def 	_lowerCAmelCase					( )			-> List[Any]:





	"""simple docstring"""







	A             =					test_command_parser()
	A             =					parser.parse_args()
	test_command(UpperCamelCase__    )


if __name__ == "__main__":
				main()



