






import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


a__       =     logging.getLogger(__name__)

a__       =     5_0  # max width of layer names
a__       =     7_0  # max width of quantizer names

def _UpperCAmelCase   (	a						:	Optional[Any] ):
							snake_case__	  =       parser.add_argument_group("""quant_trainer arguments""" )
							group.add_argument("""--wprec"""  ,					type=a  ,					default=8  ,					help="""weight precision""" )
							group.add_argument("""--aprec"""  ,					type=a  ,					default=8  ,					help="""activation precision""" )
							group.add_argument("""--quant-per-tensor"""  ,					action="""store_true"""  ,					help="""per tensor weight scaling""" )
							group.add_argument("""--quant-disable"""  ,					action="""store_true"""  ,					help="""disable all quantizers""" )
							group.add_argument("""--quant-disable-embeddings"""  ,					action="""store_true"""  ,					help="""disable all embeddings quantizers""" )
							group.add_argument("""--quant-disable-keyword"""  ,					type=a  ,					nargs="""+"""  ,					help="""disable quantizers by keyword""" )
							group.add_argument("""--quant-disable-layer-module"""  ,					type=a  ,					help="""disable quantizers by keyword under layer.""" )
							group.add_argument("""--quant-enable-layer-module"""  ,					type=a  ,					help="""enable quantizers by keyword under layer""" )
							group.add_argument("""--calibrator"""  ,					default="""max"""  ,					help="""which quantization range calibrator to use""" )
							group.add_argument("""--percentile"""  ,					default=a  ,					type=a  ,					help="""percentile for PercentileCalibrator""" )
							group.add_argument("""--fuse-qkv"""  ,					action="""store_true"""  ,					help="""use the same scale factor for qkv""" )
							group.add_argument("""--clip-gelu"""  ,					metavar="""N"""  ,					type=a  ,					help="""clip gelu output maximum value to N""" )
							group.add_argument(
							    """--recalibrate-weights"""  ,					action="""store_true"""  ,					help=(
							        """recalibrate weight amaxes by taking the max of the weights."""
							        """ amaxes will be computed with the current quantization granularity (axis)."""
							    )  ,					)

def _UpperCAmelCase   (	a						:	List[Any] ):

							if args.calibrator == "max":
														snake_case__	  =       """max"""
							elif args.calibrator == "percentile":
														if args.percentile is None:
																					raise ValueError("""Specify --percentile when using percentile calibrator""" )
														snake_case__	  =       """histogram"""
							elif args.calibrator == "mse":
														snake_case__	  =       """histogram"""
							else:
														raise ValueError(F'''Invalid calibrator {args.calibrator}''' )

							snake_case__	  =       QuantDescriptor(num_bits=args.aprec  ,					calib_method=a )
							snake_case__	  =       QuantDescriptor(num_bits=args.wprec  ,					axis=(None if args.quant_per_tensor else (0,)) )
							quant_nn.QuantLinear.set_default_quant_desc_input(a )
							quant_nn.QuantLinear.set_default_quant_desc_weight(a )

def _UpperCAmelCase   (	a						:	Dict  ,					a						:	Union[str, Any]  ,					a						:	Dict=False  ,					a						:	int=False ):
							logger.info("""Configuring Model for Quantization""" )
							logger.info(F'''using quantization package {pytorch_quantization.__file__}''' )

							if not calib:
														if args.quant_disable_embeddings:
																					set_quantizer_by_name(a  ,					["""embeddings"""]  ,					which="""weight"""  ,					_disabled=a )

														if args.quant_disable:
																					set_quantizer_by_name(a  ,					[""""""]  ,					_disabled=a )

														if args.quant_disable_keyword:
																					set_quantizer_by_name(a  ,					args.quant_disable_keyword  ,					_disabled=a )

														if args.quant_disable_layer_module:
																					set_quantizer_by_name(a  ,					[r"""layer.\d+.""" + args.quant_disable_layer_module]  ,					_disabled=a )

														if args.quant_enable_layer_module:
																					set_quantizer_by_name(a  ,					[r"""layer.\d+.""" + args.quant_enable_layer_module]  ,					_disabled=a )

														if args.recalibrate_weights:
																					recalibrate_weights(a )

														if args.fuse_qkv:
																					fuse_qkv(a  ,					a )

							if args.clip_gelu:
														clip_gelu(a  ,					args.clip_gelu )

							# if args.local_rank in [-1, 0] and not calib:
							print_quant_summary(a )

def _UpperCAmelCase   (	a						:	Any ):
							logger.info("""Enabling Calibration""" )
							for name, module in model.named_modules():
														if name.endswith("""_quantizer""" ):
																					if module._calibrator is not None:
																												module.disable_quant()
																												module.enable_calib()
																					else:
																												module.disable()
																					logger.info(F'''{name:80}: {module}''' )

def _UpperCAmelCase   (	a						:	int  ,					a						:	Dict ):
							logger.info("""Loading calibrated amax""" )
							for name, module in model.named_modules():
														if name.endswith("""_quantizer""" ):
																					if module._calibrator is not None:
																												if isinstance(module._calibrator  ,					calib.MaxCalibrator ):
																																			module.load_calib_amax()
																												else:
																																			module.load_calib_amax("""percentile"""  ,					percentile=args.percentile )
																												module.enable_quant()
																												module.disable_calib()
																					else:
																												module.enable()
							model.cuda()
							print_quant_summary(a )

def _UpperCAmelCase   (	a						:	Dict  ,					a						:	Dict ):

							def fusea(a						:	Dict  ,					a						:	Union[str, Any]  ,					a						:	Union[str, Any] ):
														for mod in [qq, qk, qv]:
																					if not hasattr(a  ,					"""_amax""" ):
																												print("""          WARNING: NO AMAX BUFFER""" )
																												return
														snake_case__	  =       qq._amax.detach().item()
														snake_case__	  =       qk._amax.detach().item()
														snake_case__	  =       qv._amax.detach().item()

														snake_case__	  =       max(a  ,					a  ,					a )
														qq._amax.fill_(a )
														qk._amax.fill_(a )
														qv._amax.fill_(a )
														logger.info(F'''          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )

							for name, mod in model.named_modules():
														if name.endswith(""".attention.self""" ):
																					logger.info(F'''FUSE_QKV: {name:{name_width}}''' )
																					fusea(mod.matmul_q_input_quantizer  ,					mod.matmul_k_input_quantizer  ,					mod.matmul_v_input_quantizer )
																					if args.quant_per_tensor:
																												fusea(mod.query._weight_quantizer  ,					mod.key._weight_quantizer  ,					mod.value._weight_quantizer )

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	str ):

							for name, mod in model.named_modules():
														if name.endswith(""".output.dense""" ) and not name.endswith("""attention.output.dense""" ):
																					snake_case__	  =       mod._input_quantizer._amax.data.detach().item()
																					mod._input_quantizer._amax.data.detach().clamp_(max=a )
																					snake_case__	  =       mod._input_quantizer._amax.data.detach().item()
																					logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )

def _UpperCAmelCase   (	a						:	str ):

							for name, mod in model.named_modules():
														if hasattr(a  ,					"""_weight_quantizer""" ) and mod._weight_quantizer.axis is not None:
																					snake_case__	  =       mod.weight.shape[0]
																					snake_case__	  =       mod._weight_quantizer._amax.detach()
																					snake_case__	  =       torch.ones(a  ,					dtype=amax.dtype  ,					device=amax.device ) * amax
																					print(F'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )

def _UpperCAmelCase   (	a						:	Dict ):

							for name, mod in model.named_modules():
														if hasattr(a  ,					"""_weight_quantizer""" ):
																					if not hasattr(mod.weight_quantizer  ,					"""_amax""" ):
																												print("""RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER""" )
																												continue

																					# determine which axes to reduce across
																					# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
																					snake_case__	  =       set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
																					snake_case__	  =       set(range(len(mod.weight.size() ) ) ) - axis_set
																					snake_case__	  =       pytorch_quantization.utils.reduce_amax(mod.weight  ,					axis=a  ,					keepdims=a ).detach()
																					logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
																					snake_case__	  =       amax

def _UpperCAmelCase   (	a						:	Tuple  ,					a						:	str=25  ,					a						:	List[str]=180  ,					a						:	Tuple=None ):

							if ignore is None:
														snake_case__	  =       []
							elif not isinstance(a  ,					a ):
														snake_case__	  =       [ignore]

							snake_case__	  =       0
							for name, mod in model.named_modules():
														if not hasattr(a  ,					"""weight""" ):
																					continue
														snake_case__	  =       max(a  ,					len(a ) )

							for name, mod in model.named_modules():
														snake_case__	  =       getattr(a  ,					"""_input_quantizer"""  ,					a )
														snake_case__	  =       getattr(a  ,					"""_weight_quantizer"""  ,					a )
														if not hasattr(a  ,					"""weight""" ):
																					continue
														if type(a ) in ignore:
																					continue
														if [True for s in ignore if type(a ) is str and s in name]:
																					continue
														snake_case__	  =       F'''Act:{input_q.extra_repr()}'''
														snake_case__	  =       F'''Wgt:{weight_q.extra_repr()}'''
														snake_case__	  =       F'''{name:{name_width}} {act_str} {wgt_str}'''
														if len(a ) <= line_width:
																					logger.info(a )
														else:
																					logger.info(F'''{name:{name_width}} {act_str}''' )
																					logger.info(F'''{'  ':{name_width}} {wgt_str}''' )

def _UpperCAmelCase   (	a						:	List[Any] ):
							snake_case__	  =       0
							for name, mod in model.named_modules():
														if isinstance(a  ,					pytorch_quantization.nn.TensorQuantizer ):
																					print(F'''{name:80} {mod}''' )
																					count += 1
							print(F'''{count} TensorQuantizers found in model''' )

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	int  ,					a						:	Optional[int]  ,					a						:	Any  ,					a						:	Optional[Any] ):
							snake_case__	  =       getattr(a  ,					a  ,					a )
							if quantizer_mod is not None:
														assert hasattr(a  ,					a )
														setattr(a  ,					a  ,					a )
							else:
														logger.warning(F'''{name} has no {quantizer}''' )

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Optional[Any]  ,					a						:	Optional[Any]="both"  ,					**a						:	Any ):
							snake_case__	  =       F'''Warning: changing {which} quantizers of {name:{qname_width}}'''
							for k, v in kwargs.items():
														s += F''' {k}={v}'''
														if which in ["input", "both"]:
																					set_quantizer(a  ,					a  ,					"""_input_quantizer"""  ,					a  ,					a )
														if which in ["weight", "both"]:
																					set_quantizer(a  ,					a  ,					"""_weight_quantizer"""  ,					a  ,					a )
							logger.info(a )



def _UpperCAmelCase   (	a						:	str  ,					a						:	Union[str, Any]  ,					**a						:	List[Any] ):

							for name, mod in model.named_modules():
														if hasattr(a  ,					"""_input_quantizer""" ) or hasattr(a  ,					"""_weight_quantizer""" ):
																					for n in names:
																												if re.search(a  ,					a ):
																																			set_quantizers(a  ,					a  ,					**a )
														elif name.endswith("""_quantizer""" ):
																					for n in names:
																												if re.search(a  ,					a ):
																																			snake_case__	  =       F'''Warning: changing {name:{name_width}}'''
																																			for k, v in kwargs.items():
																																										s += F''' {k}={v}'''
																																										setattr(a  ,					a  ,					a )
																																			logger.info(a )








import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel

def _UpperCAmelCase   (	a						:	int=32  ,					a						:	int=10  ,					a						:	Tuple=100  ,					a						:	str=1026  ,					a						:	Union[str, Any]=True  ,					a						:	Optional[int]="data/tokenized_stories_train_wikitext103.jbl"  ,					a						:	Tuple="igf_context_pairs.jbl"  ,					):
							set_seed(3 )
							# generate train_data and objective_set
							snake_case__  , snake_case__	  =       generate_datasets(
							    a  ,					a  ,					number=a  ,					min_len=1026  ,					trim=a )
							# keeps model same across runs
							set_seed(4 )
							# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
							# can we train on GPU?
							snake_case__	  =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )

							# load pretrained model
							snake_case__	  =       load_gpta("""gpt2""" ).to(a )
							print("""computing perplexity on objective set""" )
							snake_case__	  =       compute_perplexity(a  ,					a  ,					a ).item()
							print("""perplexity on objective set:"""  ,					a )

							# collect igf pairs and save to file demo.jbl
							collect_objective_set(a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a )

							# clean up, delete model and data we don't need anymore
							del model, train_data, objective_set
							torch.cuda.empty_cache()

def _UpperCAmelCase   (	a						:	int  ,					a						:	Dict=15  ,					a						:	Optional[int]=128  ,					a						:	str=100  ,					a						:	Optional[int]="igf_model.pt"  ,					):
							set_seed(42 )

							# Load pre-trained model
							snake_case__	  =       GPTaLMHeadModel.from_pretrained("""gpt2""" )

							# Initialize secondary learner to use embedding weights of model
							snake_case__	  =       SecondaryLearner(a )

							# Train secondary learner
							snake_case__	  =       train_secondary_learner(
							    a  ,					a  ,					max_epochs=a  ,					batch_size=a  ,					eval_freq=100  ,					igf_model_path=a  ,					)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner

def _UpperCAmelCase   (	a						:	Tuple  ,					a						:	List[Any]  ,					a						:	str  ,					a						:	List[Any]=32  ,					a						:	Any=1000  ,					a						:	str=16  ,					a						:	Tuple=1.0  ,					a						:	str=recopy_gpta  ,					a						:	Any=None  ,					a						:	Optional[int]=10  ,					a						:	Tuple="gpt2_finetuned.pt"  ,					):
							snake_case__	  =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
							snake_case__	  =       RandomSampler(a )
							snake_case__	  =       DataLoader(a  ,					sampler=a )

							snake_case__	  =       max_steps // (len(a )) + 1
							snake_case__	  =       0
							snake_case__	  =       torch.zeros((1, context_len)  ,					dtype=torch.long  ,					device=a )
							snake_case__  , snake_case__  , snake_case__	  =       recopy_model(a  ,					a  ,					a )

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(a )
														secondary_learner.eval()
							snake_case__	  =       []
							snake_case__	  =       0

							snake_case__	  =       []
							snake_case__	  =       []

							# Compute the performance of the transformer model at the beginning
							snake_case__	  =       compute_perplexity(a  ,					a  ,					a )
							test_perps.append(a )
							print("""Test perplexity, step"""  ,					a  ,					""":"""  ,					a )
							for epoch in range(int(a ) ):
														for step, example in enumerate(a ):
																					torch.cuda.empty_cache()
																					snake_case__	  =       random.randint(0  ,					example.size(2 ) - context_len - 1 )
																					snake_case__	  =       example[0, 0, start : start + context_len]
																					lm_optimizer.zero_grad()
																					snake_case__	  =       model(a  ,					labels=a )
																					snake_case__	  =       True

																					if secondary_learner is not None:
																												snake_case__	  =       secondary_learner.forward(
																												    torch.tensor(a  ,					dtype=torch.long  ,					device=a ).unsqueeze(0 ) )[0].item()
																												observed_qs.append(float(a ) )

																												# Here we implement the simple non-constant threshold for the predicted IG(X) value
																												# We will decay the selectivity of our secondary learner filter from
																												# 1 standard deviation above average to 1 below average after 10 batches.

																												if global_step == 10:
																																			snake_case__	  =       -1
																												if predicted_q < threshold:
																																			snake_case__	  =       False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu() ) )
																												snake_case__	  =       outputs[0]
																												lm_loss.backward()
																												examples += 1

																					del outputs

																					# Once the batch is filled with enough contexts, backprop on the batch.
																					if examples == batch_size:
																												torch.cuda.empty_cache()
																												snake_case__	  =       0
																												# Do LM backprop
																												torch.nn.utils.clip_grad_norm_(model.parameters()  ,					3.0 )
																												lm_optimizer.step()
																												lm_scheduler.step()  # Update learning rate schedule
																												global_step += 1
																												# Compute the performance of the transformer model at this batch
																												if global_step % eval_interval == 0:
																																			snake_case__	  =       compute_perplexity(a  ,					a  ,					a )
																																			test_perps.append(a )

																																			print("""Test perplexity, step"""  ,					a  ,					""":"""  ,					a )
            # Break out of the loop after 60 batches
																					if max_steps > 0 and global_step > 60:
																												break
														if max_steps > 0 and global_step > 60:
																					break

    # save finetuned transformer model
							torch.save(model.state_dict()  ,					a )
							torch.cuda.empty_cache()
							# Do some cleaning up so we can reinitialize for the next run of this function
							del lm_optimizer
							del lm_scheduler
							return model

def _UpperCAmelCase   (	):
							snake_case__	  =       argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task""" )

							# Required parameters
							parser.add_argument(
							    """--data_dir"""  ,					default=a  ,					type=a  ,					required=a  ,					help="""The input data dir. Should contain data files for WikiText."""  ,					)
							parser.add_argument(
							    """--model_name_or_path"""  ,					default=a  ,					type=a  ,					required=a  ,					help="""Path to pretrained model or model identifier from huggingface.co/models"""  ,					)
							parser.add_argument(
							    """--data_file"""  ,					type=a  ,					default=a  ,					help=(
							        """A jbl file containing tokenized data which can be split as objective dataset, """
							        """train_dataset and test_dataset."""
							    )  ,					)

							parser.add_argument(
							    """--igf_data_file"""  ,					type=a  ,					default=a  ,					help="""A jbl file containing the context and information gain pairs to train secondary learner."""  ,					)

							parser.add_argument(
							    """--output_dir"""  ,					default=a  ,					type=a  ,					required=a  ,					help="""The output directory where the final fine-tuned model is stored."""  ,					)

							parser.add_argument(
							    """--tokenizer_name"""  ,					default=a  ,					type=a  ,					help="""Pretrained tokenizer name or path if not the same as model_name"""  ,					)
							parser.add_argument("""--seed"""  ,					type=a  ,					default=a  ,					help="""A seed for reproducible training.""" )

							parser.add_argument(
							    """--context_len"""  ,					default=32  ,					type=a  ,					help=(
							        """The maximum total input sequence length after tokenization. Sequences longer """
							        """than this will be truncated, sequences shorter will be padded."""
							    )  ,					)

							parser.add_argument(
							    """--size_objective_set"""  ,					default=100  ,					type=a  ,					help="""number of articles that are long enough to be used as our objective set"""  ,					)
							parser.add_argument(
							    """--eval_freq"""  ,					default=100  ,					type=a  ,					help="""secondary model evaluation is triggered at eval_freq""" )

							parser.add_argument("""--max_steps"""  ,					default=1000  ,					type=a  ,					help="""To calculate training epochs""" )

							parser.add_argument(
							    """--secondary_learner_batch_size"""  ,					default=128  ,					type=a  ,					help="""batch size of training data for secondary learner"""  ,					)

							parser.add_argument(
							    """--batch_size"""  ,					default=16  ,					type=a  ,					help="""batch size of training data of language model(gpt2) """ )

							parser.add_argument(
							    """--eval_interval"""  ,					default=10  ,					type=a  ,					help=(
							        """decay the selectivity of our secondary learner filter from"""
							        """1 standard deviation above average to 1 below average after 10 batches"""
							    )  ,					)

							parser.add_argument(
							    """--number"""  ,					default=100  ,					type=a  ,					help="""The number of examples split to be used as objective_set/test_data""" )

							parser.add_argument(
							    """--min_len"""  ,					default=1026  ,					type=a  ,					help="""The minimum length of the article to be used as objective set""" )

							parser.add_argument(
							    """--secondary_learner_max_epochs"""  ,					default=15  ,					type=a  ,					help="""number of epochs to train secondary learner""" )

							parser.add_argument("""--trim"""  ,					default=a  ,					type=a  ,					help="""truncate the example if it exceeds context length""" )

							parser.add_argument(
							    """--threshold"""  ,					default=1.0  ,					type=a  ,					help=(
							        """The threshold value used by secondary learner to filter the train_data and allow only"""
							        """ informative data as input to the model"""
							    )  ,					)

							parser.add_argument("""--finetuned_model_name"""  ,					default="""gpt2_finetuned.pt"""  ,					type=a  ,					help="""finetuned_model_name""" )

							parser.add_argument(
							    """--recopy_model"""  ,					default=a  ,					type=a  ,					help="""Reset the model to the original pretrained GPT-2 weights after each iteration"""  ,					)

							# function calls
							# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
							generate_n_pairs(
							    context_len=32  ,					max_steps=10  ,					size_objective_set=100  ,					min_len=1026  ,					trim=a  ,					data_file="""data/tokenized_stories_train_wikitext103.jbl"""  ,					igf_data_file="""igf_context_pairs.jbl"""  ,					)

							# Load train data for secondary learner
							snake_case__	  =       joblib.load("""data/IGF_values.jbl""" )

							# Train secondary learner
							snake_case__	  =       training_secondary_learner(
							    a  ,					secondary_learner_max_epochs=15  ,					secondary_learner_batch_size=128  ,					eval_freq=100  ,					igf_model_path="""igf_model.pt"""  ,					)

							# load pretrained gpt2 model
							snake_case__	  =       GPTaLMHeadModel.from_pretrained("""gpt2""" )
							set_seed(42 )

							# Generate train and test data to train and evaluate gpt2 model
							snake_case__  , snake_case__	  =       generate_datasets(
							    context_len=32  ,					file="""data/tokenized_stories_train_wikitext103.jbl"""  ,					number=100  ,					min_len=1026  ,					trim=a )

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    a  ,					a  ,					a  ,					context_len=32  ,					max_steps=1000  ,					batch_size=16  ,					threshold=1.0  ,					recopy_model=a  ,					secondary_learner=a  ,					eval_interval=10  ,					finetuned_model_name="""gpt2_finetuned.pt"""  ,					)


if __name__ == "__main__":
	main()
