




# 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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase=0					)  ->       str:
   # Format the message.
   if name is None:
      snake_case				:    List[str]           =	None
   else:
      snake_case				:    Tuple           =	""".""" * max(0						,spaces - 2					) + """# {:""" + str(50 - spaces					) + """s}"""
      snake_case				:    Tuple           =	fmt.format(lowercase					)

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




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




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Tuple:
   # The converted output model.
   snake_case				:    List[Any]           =	{}

   # old versions did not store training args
   snake_case				:    Optional[int]           =	input_state_dict.get("""args"""						,lowercase					)
   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))

      snake_case				:    List[Any]           =	ds_args.padded_vocab_size
      snake_case				:    List[Any]           =	ds_args.max_position_embeddings
      snake_case				:    Any           =	ds_args.hidden_size
      snake_case				:    Tuple           =	ds_args.num_layers
      snake_case				:    Any           =	ds_args.num_attention_heads
      snake_case				:    Union[str, Any]           =	ds_args.ffn_hidden_size
      # pprint(config)

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

   # The model.
   snake_case				:    Any           =	input_state_dict["""model"""]
   # The language model.
   snake_case				:    List[Any]           =	model["""language_model"""]
   # The embeddings.
   snake_case				:    Dict           =	lm["""embedding"""]

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

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

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

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

   # The simple map of names for "automated" rules.
   snake_case				:    Dict           =	{
       """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.
      snake_case				:    int           =	layer_re.match(lowercase					)

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

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

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

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

         # Insert a "dummy" tensor for masked_bias.
         snake_case				:    Tuple           =	torch.tensor(-1E4						,dtype=torch.floataa					)
         snake_case				:    Dict           =	masked_bias

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

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

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

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

   # The final layernorm.
   snake_case				:    List[Any]           =	transformer["""final_layernorm.weight"""]
   snake_case				:    Union[str, Any]           =	transformer["""final_layernorm.bias"""]

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

   # It should be done!
   return output_state_dict




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

   # Extract the basename.
   snake_case				:    Tuple           =	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:
            snake_case				:    Any           =	torch.load(lowercase						,map_location="""cpu"""					)
   else:
      snake_case				:    int           =	torch.load(args.path_to_checkpoint						,map_location="""cpu"""					)

   snake_case				:    Optional[Any]           =	input_state_dict.get("""args"""						,lowercase					)

   # 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:
            snake_case				:    Any           =	"""gelu_fast"""
         elif ds_args.openai_gelu:
            snake_case				:    str           =	"""gelu_new"""
         else:
            snake_case				:    List[Any]           =	"""gelu"""
      else:
         # in the very early days this used to be "gelu_new"
         snake_case				:    str           =	"""gelu_new"""

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

   snake_case				:    Tuple           =	["""GPT2LMHeadModel"""]

   # Convert.
   print("""Converting"""					)
   snake_case				:    Tuple           =	convert_megatron_checkpoint(lowercase						,lowercase						,lowercase					)

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

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

   snake_case				:    str           =	AutoTokenizer.from_pretrained(lowercase					)
   snake_case				:    int           =	type(lowercase					).__name__
   snake_case				:    Dict           =	tokenizer_class

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

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

   # Store the state_dict to file.
   snake_case				:    Union[str, Any]           =	os.path.join(lowercase						,"""pytorch_model.bin"""					)
   print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""					)
   torch.save(lowercase						,lowercase					)


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

if __name__ == "__main__":
       main()

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











import warnings
from functools import wraps
from typing import Callable




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Callable:

   @wraps(lowercase					)
   def _inner_fn(*lowercase						,**lowercase					):
      warnings.warn(
          (f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")						,lowercase						,)
      return fn(*lowercase						,**lowercase					)

   return _inner_fn





