




# 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       (						lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: str=0      ):
     """simple docstring"""

     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       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: str       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     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       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: str       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     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       (						):
     """simple docstring"""

     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_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=0.02       ,    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_0256       ,    eos_token_id=5_0256       ,    )
     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()

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






from statistics import mean, stdev



def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int = 3      ):
     """simple docstring"""

     lowerCAmelCase__          =			min(lowerCAmelCase_      )
     lowerCAmelCase__          =			max(lowerCAmelCase_      )
     # normalize data
     return [round((x - x_min) / (x_max - x_min)       ,    lowerCAmelCase_      ) for x in data]






def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int = 3      ):
     """simple docstring"""

     lowerCAmelCase__          =			mean(lowerCAmelCase_      )
     lowerCAmelCase__          =			stdev(lowerCAmelCase_      )
     # standardize data
     return [round((x - mu) / (sigma)       ,    lowerCAmelCase_      ) for x in data]
