





'''simple docstring'''

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


__lowerCAmelCase							:	List[Any]			=logging.getLogger(__name__)

__lowerCAmelCase							:	int			=50  # max width of layer names
__lowerCAmelCase							:	Optional[Any]			=70  # max width of quantizer names





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]       ):
    A__				=			parser.add_argument_group("quant_trainer arguments"       )
    group.add_argument("--wprec"						,       type=lowerCAmelCase__						,       default=8						,       help="weight precision"       )
    group.add_argument("--aprec"						,       type=lowerCAmelCase__						,       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=lowerCAmelCase__						,       nargs="+"						,       help="disable quantizers by keyword"       )
    group.add_argument("--quant-disable-layer-module"						,       type=lowerCAmelCase__						,       help="disable quantizers by keyword under layer."       )
    group.add_argument("--quant-enable-layer-module"						,       type=lowerCAmelCase__						,       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=lowerCAmelCase__						,       type=lowerCAmelCase__						,       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=lowerCAmelCase__						,       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				(							_lowerCamelCase					:					Optional[Any]       ):




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

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





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Optional[Any]						,       _lowerCamelCase					:					Optional[int]=False						,       _lowerCamelCase					:					Optional[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(lowerCAmelCase__						,       ["embeddings"]						,       which="weight"						,       _disabled=lowerCAmelCase__       )

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

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

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

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

        if args.recalibrate_weights:
            recalibrate_weights(lowerCAmelCase__       )

        if args.fuse_qkv:
            fuse_qkv(lowerCAmelCase__						,       lowerCAmelCase__       )

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

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





def 						UpperCamelCase				(							_lowerCamelCase					:					str       ):
    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				(							_lowerCamelCase					:					int						,       _lowerCamelCase					:					Tuple       ):
    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(lowerCAmelCase__       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Optional[int]       ):




    def fusea(_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					Any       ):
        for mod in [qq, qk, qv]:
            if not hasattr(lowerCAmelCase__						,       "_amax"       ):
                print("          WARNING: NO AMAX BUFFER"       )
                return
        A__				=			qq._amax.detach().item()
        A__				=			qk._amax.detach().item()
        A__				=			qv._amax.detach().item()

        A__				=			max(lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__       )
        qq._amax.fill_(lowerCAmelCase__       )
        qk._amax.fill_(lowerCAmelCase__       )
        qv._amax.fill_(lowerCAmelCase__       )
        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				(							_lowerCamelCase					:					Optional[Any]						,       _lowerCamelCase					:					Optional[int]       ):




    for name, mod in model.named_modules():
        if name.endswith(".output.dense"       ) and not name.endswith("attention.output.dense"       ):
            A__				=			mod._input_quantizer._amax.data.detach().item()
            mod._input_quantizer._amax.data.detach().clamp_(max=lowerCAmelCase__       )
            A__				=			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				(							_lowerCamelCase					:					Optional[Any]       ):




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





def 						UpperCamelCase				(							_lowerCamelCase					:					List[Any]       ):




    for name, mod in model.named_modules():
        if hasattr(lowerCAmelCase__						,       "_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)
            A__				=			set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis       )
            A__				=			set(range(len(mod.weight.size()       )       )       ) - axis_set
            A__				=			pytorch_quantization.utils.reduce_amax(mod.weight						,       axis=lowerCAmelCase__						,       keepdims=lowerCAmelCase__       ).detach()
            logger.info(F"RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"       )
            A__				=			amax





def 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Optional[int]=25						,       _lowerCamelCase					:					List[str]=1_80						,       _lowerCamelCase					:					Any=None       ):




    if ignore is None:
        A__				=			[]
    elif not isinstance(lowerCAmelCase__						,       lowerCAmelCase__       ):
        A__				=			[ignore]

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

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





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple       ):
    A__				=			0
    for name, mod in model.named_modules():
        if isinstance(lowerCAmelCase__						,       pytorch_quantization.nn.TensorQuantizer       ):
            print(F"{name:80} {mod}"       )
            count += 1
    print(F"{count} TensorQuantizers found in model"       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[Any]						,       _lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					List[Any]       ):
    A__				=			getattr(lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__       )
    if quantizer_mod is not None:
        assert hasattr(lowerCAmelCase__						,       lowerCAmelCase__       )
        setattr(lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__       )
    else:
        logger.warning(F"{name} has no {quantizer}"       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					Dict="both"						,       **_lowerCamelCase					:					Union[str, Any]       ):
    A__				=			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(lowerCAmelCase__						,       lowerCAmelCase__						,       "_input_quantizer"						,       lowerCAmelCase__						,       lowerCAmelCase__       )
        if which in ["weight", "both"]:
            set_quantizer(lowerCAmelCase__						,       lowerCAmelCase__						,       "_weight_quantizer"						,       lowerCAmelCase__						,       lowerCAmelCase__       )
    logger.info(lowerCAmelCase__       )





def 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Optional[int]						,       **_lowerCamelCase					:					str       ):




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








"""simple docstring"""







from typing import Union

import fire
import torch
from tqdm import tqdm

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str = "cpu"    ,   lowerCAmelCase__      :				Union[str, None] = None						)						->   None:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= torch.load(lowerCAmelCase__    ,   map_location=lowerCAmelCase__						)
  for k, v in tqdm(state_dict.items()						):
    if not isinstance(lowerCAmelCase__    ,   torch.Tensor						):
      raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'						)
    lowerCAmelCase_   :				str								= v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_   :				Dict								= src_path
  torch.save(lowerCAmelCase__    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     fire.Fire(convert)





