



# 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(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=0 )	->						Dict:
				# Format the message.
				if name is None:
								a__	:							Dict      						= None
				else:
								a__	:							List[Any]      						= "." * max(0	,				spaces - 2 ) + "# {:" + str(50 - spaces ) + "s}"
								a__	:							Optional[Any]      						= fmt.format(__UpperCamelCase )

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






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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Tuple:
				# The converted output model.
				a__	:							Dict      						= {}

				# old versions did not store training args
				a__	:							Union[str, Any]      						= input_state_dict.get("args"	,				__UpperCamelCase )
				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))

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

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

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

				# The word embeddings.
				a__	:							int      						= embeddings["word_embeddings"]["weight"]
				# Truncate the embedding table to vocab_size rows.
				a__	:							List[str]      						= word_embeddings[: config.vocab_size, :]
				a__	:							int      						= word_embeddings

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

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

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

				# The simple map of names for "automated" rules.
				a__	:							Any      						= {
				    "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.
								a__	:							int      						= layer_re.match(__UpperCamelCase )

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

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

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

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

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

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

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

								# Copy the bias.
								elif weight_or_bias == "bias":
												a__	:							str      						= megatron_to_transformers[op_name]
												a__	:							Union[str, Any]      						= val

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

				# The final layernorm.
				a__	:							str      						= transformer["final_layernorm.weight"]
				a__	:							int      						= transformer["final_layernorm.bias"]

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

				# It should be done!
				return output_state_dict






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

				# Extract the basename.
				a__	:							List[str]      						= 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:
																a__	:							Tuple      						= torch.load(__UpperCamelCase	,				map_location="cpu" )
				else:
								a__	:							Tuple      						= torch.load(args.path_to_checkpoint	,				map_location="cpu" )

				a__	:							int      						= input_state_dict.get("args"	,				__UpperCamelCase )

				# 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:
																a__	:							int      						= "gelu_fast"
												elif ds_args.openai_gelu:
																a__	:							int      						= "gelu_new"
												else:
																a__	:							Tuple      						= "gelu"
								else:
												# in the very early days this used to be "gelu_new"
												a__	:							str      						= "gelu_new"

								# Spell out all parameters in case the defaults change.
								a__	:							Dict      						= 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=__UpperCamelCase	,				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=__UpperCamelCase	,				summary_activation=__UpperCamelCase	,				summary_proj_to_labels=__UpperCamelCase	,				summary_first_dropout=0.1	,				scale_attn_weights=__UpperCamelCase	,				use_cache=__UpperCamelCase	,				bos_token_id=5_02_56	,				eos_token_id=5_02_56	,				)
				else:
								a__	:							Optional[Any]      						= GPTaConfig.from_json_file(args.config_file )

				a__	:							str      						= ["GPT2LMHeadModel"]

				# Convert.
				print("Converting" )
				a__	:							Optional[int]      						= convert_megatron_checkpoint(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )

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

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

				a__	:							str      						= AutoTokenizer.from_pretrained(__UpperCamelCase )
				a__	:							Union[str, Any]      						= type(__UpperCamelCase ).__name__
				a__	:							Tuple      						= tokenizer_class

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

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

				# Store the state_dict to file.
				a__	:							Tuple      						= os.path.join(__UpperCamelCase	,				"pytorch_model.bin" )
				print(F'Saving checkpoint to "{output_checkpoint_file}"' )
				torch.save(__UpperCamelCase	,				__UpperCamelCase )


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

if __name__ == "__main__":
						main()

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







from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCamelCase		       =      logging.get_logger(__name__)


lowerCamelCase		       =      {
    """Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
    """Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
    """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
    """Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
    """Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
    """Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
    """Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
    """Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
    """Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
    """Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
    """Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
    """Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:str =       "codegen"
							A				:Tuple =       {
							    "max_position_embeddings": "n_positions",
							    "hidden_size": "n_embd",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}



							def __init__(							self ,					__UpperCAmelCase=5_0400 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=4096 ,					__UpperCAmelCase=28 ,					__UpperCAmelCase=16 ,					__UpperCAmelCase=64 ,					__UpperCAmelCase=None ,					__UpperCAmelCase="gelu_new" ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=1E-5 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=5_0256 ,					__UpperCAmelCase=5_0256 ,					__UpperCAmelCase=False ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Dict      						= vocab_size
											a__	:							int      						= n_ctx
											a__	:							str      						= n_positions
											a__	:							Union[str, Any]      						= n_embd
											a__	:							Union[str, Any]      						= n_layer
											a__	:							Optional[int]      						= n_head
											a__	:							Dict      						= n_inner
											a__	:							Optional[Any]      						= rotary_dim
											a__	:							str      						= activation_function
											a__	:							List[Any]      						= resid_pdrop
											a__	:							str      						= embd_pdrop
											a__	:							List[str]      						= attn_pdrop
											a__	:							str      						= layer_norm_epsilon
											a__	:							Any      						= initializer_range
											a__	:							List[str]      						= use_cache

											a__	:							Tuple      						= bos_token_id
											a__	:							List[Any]      						= eos_token_id

											super().__init__(
											    bos_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					tie_word_embeddings=__UpperCAmelCase ,					**__UpperCAmelCase				)





class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = "default" ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False ,					):







											"""simple docstring"""



											super().__init__(__UpperCAmelCase ,					task=__UpperCAmelCase ,					patching_specs=__UpperCAmelCase ,					use_past=__UpperCAmelCase				)
											if not getattr(self._config ,					"pad_token_id" ,					__UpperCAmelCase				):
															# TODO: how to do that better?
															a__	:							Union[str, Any]      						= 0



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
											if self.use_past:
															self.fill_with_past_key_values_(__UpperCAmelCase ,					direction="inputs"				)
															a__	:							Any      						= {0: "batch", 1: "past_sequence + sequence"}
											else:
															a__	:							List[Any]      						= {0: "batch", 1: "sequence"}

											return common_inputs



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._config.n_layer



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._config.n_head



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = -1 ,					__UpperCAmelCase = -1 ,					__UpperCAmelCase = False ,					__UpperCAmelCase = None ,					):







											"""simple docstring"""



											a__	:							Dict      						= super(__UpperCAmelCase ,					self				).generate_dummy_inputs(
											    __UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					seq_length=__UpperCAmelCase ,					is_pair=__UpperCAmelCase ,					framework=__UpperCAmelCase				)

											# We need to order the input in the way they appears in the forward()
											a__	:							Union[str, Any]      						= OrderedDict({"input_ids": common_inputs["input_ids"]}				)

											# Need to add the past_keys
											if self.use_past:
															if not is_torch_available():
																			raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
															else:
																			import torch

																			a__						,   a__	:							Union[str, Any]      						= common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			a__	:							Any      						= seqlen + 2
																			a__	:							str      						= (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			a__	:							str      						= [
																			    (torch.zeros(__UpperCAmelCase				), torch.zeros(__UpperCAmelCase				)) for _ in range(self.num_layers				)
																			]

											a__	:							Union[str, Any]      						= common_inputs["attention_mask"]
											if self.use_past:
															a__	:							str      						= ordered_inputs["attention_mask"].dtype
															a__	:							Union[str, Any]      						= torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(__UpperCAmelCase ,					__UpperCAmelCase ,					dtype=__UpperCAmelCase				)] ,					dim=1				)

											return ordered_inputs




							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return 13


