




# 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 __UpperCamelCase			(       _A ,  _A ,  _A=0  ):
						# Format the message.
						if name is None:
												lowerCAmelCase_      			=  None
						else:
												lowerCAmelCase_      			=  '''.''' * max(0 ,  spaces - 2  ) + '''# {:''' + str(50 - spaces  ) + '''s}'''
												lowerCAmelCase_      			=  fmt.format(_A  )

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





def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A ,  _A  ):
						# 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.
						lowerCAmelCase_      			=  param.size()
						if checkpoint_version == 1.0:
												# version 1.0 stores [num_heads * hidden_size * num_splits, :]
												lowerCAmelCase_      			=  (num_heads, hidden_size, num_splits) + input_shape[1:]
												lowerCAmelCase_      			=  param.view(*_A  )
												lowerCAmelCase_      			=  param.transpose(0 ,  2  )
												lowerCAmelCase_      			=  param.transpose(1 ,  2  ).contiguous()
						elif checkpoint_version >= 2.0:
												# other versions store [num_heads * num_splits * hidden_size, :]
												lowerCAmelCase_      			=  (num_heads, num_splits, hidden_size) + input_shape[1:]
												lowerCAmelCase_      			=  param.view(*_A  )
												lowerCAmelCase_      			=  param.transpose(0 ,  1  ).contiguous()
						lowerCAmelCase_      			=  param.view(*_A  )
						return param





def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						# The converted output model.
						lowerCAmelCase_      			=  {}

						# old versions did not store training args
						lowerCAmelCase_      			=  input_state_dict.get('''args''' ,  _A  )
						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_      			=  ds_args.padded_vocab_size
												lowerCAmelCase_      			=  ds_args.max_position_embeddings
												lowerCAmelCase_      			=  ds_args.hidden_size
												lowerCAmelCase_      			=  ds_args.num_layers
												lowerCAmelCase_      			=  ds_args.num_attention_heads
												lowerCAmelCase_      			=  ds_args.ffn_hidden_size
												# pprint(config)

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

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

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

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

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

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

						# The simple map of names for "automated" rules.
						lowerCAmelCase_      			=  {
						    '''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_      			=  layer_re.match(_A  )

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

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

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

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

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

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

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

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

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

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

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

						# It should be done!
						return output_state_dict





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

						# Extract the basename.
						lowerCAmelCase_      			=  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_      			=  torch.load(_A ,  map_location='''cpu'''  )
						else:
												lowerCAmelCase_      			=  torch.load(args.path_to_checkpoint ,  map_location='''cpu'''  )

						lowerCAmelCase_      			=  input_state_dict.get('''args''' ,  _A  )

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

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

						lowerCAmelCase_      			=  ['''GPT2LMHeadModel''']

						# Convert.
						print('''Converting'''  )
						lowerCAmelCase_      			=  convert_megatron_checkpoint(_A ,  _A ,  _A  )

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

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

						lowerCAmelCase_      			=  AutoTokenizer.from_pretrained(_A  )
						lowerCAmelCase_      			=  type(_A  ).__name__
						lowerCAmelCase_      			=  tokenizer_class

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

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

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


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

if __name__ == "__main__":
						main()

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










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(_A ,  _A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  emb.weight.shape
						lowerCAmelCase_      			=  nn.Linear(_A ,  _A ,  bias=_A  )
						lowerCAmelCase_      			=  emb.weight.data
						return lin_layer





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )
						lowerCAmelCase_      			=  Namespace(**checkpoint['''cfg''']['''model''']  )
						lowerCAmelCase_      			=  checkpoint['''model''']
						remove_ignore_keys_(_A  )
						lowerCAmelCase_      			=  state_dict['''decoder.embed_tokens.weight'''].shape[0]

						lowerCAmelCase_      			=  {key.replace('''decoder''' ,  '''model'''  ): val for key, val in state_dict.items()}

						lowerCAmelCase_      			=  XGLMConfig(
						    vocab_size=_A ,  max_position_embeddings=args.max_target_positions ,  num_layers=args.decoder_layers ,  attention_heads=args.decoder_attention_heads ,  ffn_dim=args.decoder_ffn_embed_dim ,  d_model=args.decoder_embed_dim ,  layerdrop=args.decoder_layerdrop ,  dropout=args.dropout ,  attention_dropout=args.attention_dropout ,  activation_dropout=args.activation_dropout ,  activation_function='''gelu''' ,  scale_embedding=not args.no_scale_embedding ,  tie_word_embeddings=args.share_decoder_input_output_embed ,  )

						lowerCAmelCase_      			=  XGLMForCausalLM(_A  )
						lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						print(_A  )
						lowerCAmelCase_      			=  make_linear_from_emb(model.model.embed_tokens  )

						return model


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						_A     =			parser.parse_args()
						_A     =			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)




