




# 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_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]=0     ):
					# Format the message.
					if name is None:
										__lowerCAmelCase				   =       None
					else:
										__lowerCAmelCase				   =       "." * max(0      ,      spaces - 2     ) + "# {:" + str(50 - spaces     ) + "s}"
										__lowerCAmelCase				   =       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_							:				Any      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					# 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(*SCREAMING_SNAKE_CASE_     )
										__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(*SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       param.transpose(0      ,      1     ).contiguous()
					__lowerCAmelCase				   =       param.view(*SCREAMING_SNAKE_CASE_     )
					return param







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Dict     ):
					# The converted output model.
					__lowerCAmelCase				   =       {}

					# old versions did not store training args
					__lowerCAmelCase				   =       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				   =       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(SCREAMING_SNAKE_CASE_     )

										# 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      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															__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(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				   =       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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      3      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															# 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 	_a      (						):
					# Create the argument parser.
					__lowerCAmelCase				   =       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				   =       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(SCREAMING_SNAKE_CASE_      ,      map_location="cpu"     )
					else:
										__lowerCAmelCase				   =       torch.load(args.path_to_checkpoint      ,      map_location="cpu"     )

					__lowerCAmelCase				   =       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				   =       "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=5_02_57      ,      n_positions=10_24      ,      n_embd=10_24      ,      n_layer=24      ,      n_head=16      ,      n_inner=40_96      ,      activation_function=SCREAMING_SNAKE_CASE_      ,      resid_pdrop=0.1      ,      embd_pdrop=0.1      ,      attn_pdrop=0.1      ,      layer_norm_epsilon=1E-5      ,      initializer_range=0.02      ,      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_02_56      ,      eos_token_id=5_02_56      ,      )
					else:
										__lowerCAmelCase				   =       GPTaConfig.from_json_file(args.config_file     )

					__lowerCAmelCase				   =       ["GPT2LMHeadModel"]

					# Convert.
					print("Converting"     )
					__lowerCAmelCase				   =       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				   =       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(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       type(SCREAMING_SNAKE_CASE_     ).__name__
					__lowerCAmelCase				   =       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				   =       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()

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





def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Dict =					0
 for ch in input_str:
  UpperCamelCase_					:						Tuple =					ord(lowerCamelCase	)
  UpperCamelCase_					:						str =					pow(2  ,  lowerCamelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()



