post_training_quantization.py

#   Copyright (c) 2018 PaddlePaddle Authors. 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.
import math
import os
import re
import logging
import numpy as np
import shutil
try:
    from tqdm import tqdm
except:
    from .utils import tqdm
from inspect import isgeneratorfunction
from .... import io
from .... import core
from .... import framework
from .... import unique_name
from ....executor import global_scope, Executor
from ....framework import IrGraph
from ....log_helper import get_logger
from .quantization_pass import QuantizationTransformPass, QuantizationTransformPassV2, QuantizationFreezePass, QuantWeightPass, AddQuantDequantPass, AddQuantDequantPassV2
from .cal_kl_threshold import cal_kl_threshold
from .adaround import run_adaround
from . import utils

__all__ = ['PostTrainingQuantization', 'WeightQuantization']

_logger = get_logger(__name__,
                     logging.INFO,
                     fmt='%(asctime)s-%(levelname)s: %(message)s')


def _all_persistable_var_names(program):
    persistable_var_names = []
    for var in program.list_vars():
        if var.persistable:
            persistable_var_names.append(var.name)
    return persistable_var_names


def _remove_unused_var_nodes(graph):
    all_used_vars = set()
    ops = graph.all_op_nodes()
    for op_node in ops:
        for input_node in op_node.inputs:
            all_used_vars.add(input_node)
        for output_node in op_node.outputs:
            all_used_vars.add(output_node)

    all_used_vars = {n.node for n in all_used_vars}
    all_unused_vars = {
        n
        for n in filter(lambda node: node.node not in all_used_vars,
                        graph.all_var_nodes())
    }
    graph.safe_remove_nodes(all_unused_vars)
    return graph


def _remove_ctrl_vars(graph):
    remove_ctr_vars = set()
    for node in graph.all_var_nodes():
        if node.is_ctrl_var():
            remove_ctr_vars.add(node)
    graph.safe_remove_nodes(remove_ctr_vars)
    return graph


def _apply_pass(scope,
                graph,
                pass_name,
                attrs=None,
                attr_values=None,
                debug=False):
    ir_pass = core.get_pass(pass_name)
    cpp_graph = graph.graph
    if not cpp_graph.has('__param_scope__'):
        cpp_graph.set_not_owned('__param_scope__', scope)
    if attrs:
        assert attr_values and len(attrs) == len(
            attr_values
        ), "Different number of pass attributes and their values."
        for attr, value in zip(attrs, attr_values):
            ir_pass.set(attr, value)
    ir_pass.apply(cpp_graph)
    if debug:
        graph.draw('.', 'qat_fp32_{}'.format(pass_name), graph.all_op_nodes())
    _remove_unused_var_nodes(graph)
    return graph


class PostTrainingQuantization(object):
    """
    Utilizing post training quantization methon to quantize the FP32 model,
    and it uses calibrate data to get the quantization information for all 
    quantized variables.
    """

    def __init__(self,
                 executor=None,
                 scope=None,
                 model_dir=None,
                 model_filename=None,
                 params_filename=None,
                 batch_generator=None,
                 sample_generator=None,
                 data_loader=None,
                 batch_size=10,
                 batch_nums=None,
                 algo="KL",
                 hist_percent=0.99999,
                 quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
                 round_type='round',
                 learning_rate=0.001,
                 is_full_quantize=False,
                 bias_correction=False,
                 activation_bits=8,
                 weight_bits=8,
                 activation_quantize_type='range_abs_max',
                 weight_quantize_type='channel_wise_abs_max',
                 onnx_format=False,
                 optimize_model=False,
                 is_use_cache_file=False,
                 skip_tensor_list=None,
                 cache_dir=None):
        '''
        Constructor.

        Args:
            executor(fluid.Executor): The executor to load, run and save the
                quantized model.
            scope(fluid.Scope, optional): The scope of the program, use it to load 
                and save variables. If scope=None, get scope by global_scope(). 
            model_dir(str): The path of the fp32 model that will be quantized, 
                and the model and params files are under the path.
            model_filename(str, optional): The name of file to load the inference 
                program. If it is None, the default filename '__model__' will 
                be used. Default is 'None'.
            params_filename(str, optional): The name of file to load all parameters.
                When all parameters were saved in a single binary file, set it 
                as the real filename. If parameters were saved in separate files, 
                set it as 'None'. Default is 'None'.
            batch_generator(Python Generator): The batch generator provides 
                calibrate data for DataLoader, and it returns a batch every
                time. Note that, sample_generator and batch_generator, only one
                should be set. Beisdes, batch_generator supports lod tensor.
            sample_generator(Python Generator): The sample generator provides
                calibrate data for DataLoader, and it only returns a sample every
                time. Note that, sample_generator and batch_generator, only one
                should be set. Beisdes, sample_generator dose not support lod tensor.
            data_loader(Python Generator, Paddle.io.DataLoader, optional): The
                Generator or Dataloader provides calibrate data, and it could
                return a batch every time.
            batch_size(int, optional): The batch size of DataLoader. Default is 10.
            batch_nums(int, optional): If batch_nums is not None, the number of 
                calibrate data is batch_size*batch_nums. If batch_nums is None, use 
                all data provided by sample_generator as calibrate data.
            algo(str, optional): If algo='KL', use KL-divergenc method to
                get the KL threshold for quantized activations and get the abs_max
                value for quantized weights. If algo='abs_max', get the abs max 
                value for activations and weights. If algo= 'min_max', get the min 
                and max value for quantized activations and weights. If algo='avg',
                get the average value among the max values for activations. If 
                algo= 'hist', get the value of 'hist_percent' quantile as the threshold.
                If algo='mse', get the value which makes the quantization mse loss 
                minimal. Default is KL.
            hist_percent(float, optional): The threshold of algo 'hist' for activations.
                Default is 0.99999.
            quantizable_op_type(list[str], optional): List the type of ops 
                that will be quantized. Default is ["conv2d", "depthwise_conv2d", 
                "mul"].
            round_type(str, optional): The method of converting the quantized weights
                value float->int. Currently supports ['round', 'adaround'] methods.
                Default is `round`, which is rounding nearest to the nearest whole number.
            learning_rate(float, optional): The learning rate of adaround method.
            is_full_quantized(bool, optional): If set is_full_quantized as True, 
                apply quantization to all supported quantizable op type. If set
                is_full_quantized as False, only apply quantization to the op type 
                according to the input quantizable_op_type.
            bias_correction(bool, optional): If set as True, use the bias correction
                method of https://arxiv.org/abs/1810.05723. Default is False.
            activation_bits(int): quantization bit number for activation.
            weight_bits(int, optional): quantization bit number for weights.
            activation_quantize_type(str): quantization type for activation,
                now support 'range_abs_max', 'moving_average_abs_max' and 'abs_max'.
                This param only specifies the fake ops in saving quantized model.
                If it is 'range_abs_max' or 'moving_average_abs_max', we save the scale
                obtained by post training quantization in fake ops. Note that, if it
                is 'abs_max', the scale will not be saved in fake ops.
            weight_quantize_type(str): quantization type for weights,
                support 'abs_max' and 'channel_wise_abs_max'. This param only specifies
                the fake ops in saving quantized model, and we save the scale obtained
                by post training quantization in fake ops. Compared to 'abs_max',
                the model accuracy is usually higher when it is 'channel_wise_abs_max'.
            onnx_format(bool): Whether to export the quantized model with format of ONNX.
                Default is False.
            skip_tensor_list(list): List of skip quant tensor name.
            optimize_model(bool, optional): If set optimize_model as True, it applies
                some passes to the model before quantization, and it supports
                `conv2d/depthwise_conv2d + bn` pass so far. Some targets require the
                weights are quantized by tensor-wise method, which means the weights
                scale for all channel are the same. However, if fuse
                `conv2d/depthwise_conv2d + bn`, the weights scale for all channel will
                be different. In address this problem, fuse the pattern before
                quantization. Default False.
            is_use_cache_file(bool, optional): This param is deprecated.
            cache_dir(str, optional): This param is deprecated.
        Returns:
            None

        Examples:
        .. code-block:: python
            import paddle.fluid as fluid
            from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization
            
            exe = fluid.Executor(fluid.CPUPlace())
            model_dir = path/to/fp32_model_params
            # set model_filename as None when the filename is __model__, 
            # otherwise set it as the real filename
            model_filename = None 
            # set params_filename as None when all parameters were saved in 
            # separate files, otherwise set it as the real filename
            params_filename = None
            save_model_path = path/to/save_model_path
            # prepare the sample generator according to the model, and the 
            # sample generator must return a sample every time. The reference
            # document: https://www.paddlepaddle.org.cn/documentation/docs/zh
            # /user_guides/howto/prepare_data/use_py_reader.html
            sample_generator = your_sample_generator
            batch_size = 10
            batch_nums = 10
            algo = "KL"
            quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
            ptq = PostTrainingQuantization(
                        executor=exe,
                        sample_generator=sample_generator,
                        model_dir=model_dir,
                        model_filename=model_filename,
                        params_filename=params_filename,
                        batch_size=batch_size,
                        batch_nums=batch_nums,
                        algo=algo,
                        quantizable_op_type=quantizable_op_type)
            ptq.quantize()
            ptq.save_quantized_model(save_model_path)
        '''

        self._support_activation_quantize_type = [
            'range_abs_max', 'moving_average_abs_max', 'abs_max'
        ]
        self._support_weight_quantize_type = ['abs_max', 'channel_wise_abs_max']
        self._support_algo_type = [
            'KL', 'hist', 'avg', 'mse', 'emd', 'abs_max', 'min_max'
        ]
        assert round_type in ['adaround', 'round']
        self._round_type = round_type
        self._learning_rate = learning_rate
        self._dynamic_quantize_op_type = ['lstm']
        self._support_quantize_op_type = \
            list(set(utils._weight_supported_quantizable_op_type +
                utils._act_supported_quantizable_op_type +
                self._dynamic_quantize_op_type))

        # Check inputs
        assert executor is not None, "The executor cannot be None."
        assert model_dir is not None, "The model_dir cannot be None."
        assert any([gen is not None] for gen in [sample_generator,
            batch_generator, data_loader]), "The sample_generator, batch_generator " \
            "and data_loader cannot be None in the same time."
        if data_loader is not None:
            assert isinstance(data_loader, (io.DataLoader, type(isgeneratorfunction))), \
                "data_loader only accepts `paddle.io.DataLoader` or Generator instance."
        assert batch_size > 0, "The batch_size should be greater than 0."
        assert algo in self._support_algo_type, \
            "The algo should be KL, hist, mse, avg, abs_max or min_max."
        assert activation_quantize_type in self._support_activation_quantize_type, \
            "The activation_quantize_type ({}) should in ({}).".format(
            activation_quantize_type, self._support_activation_quantize_type)
        assert weight_quantize_type in self._support_weight_quantize_type, \
            "The weight_quantize_type ({}) shoud in ({}).".format(
            weight_quantize_type, self._support_weight_quantize_type)

        # Save input params
        self._bias_correction = bias_correction
        self._executor = executor
        self._scope = global_scope() if scope == None else scope
        self._model_dir = model_dir
        self._model_filename = model_filename
        self._params_filename = params_filename
        self._sample_generator = sample_generator
        self._batch_generator = batch_generator
        self._batch_size = batch_size
        self._batch_nums = batch_nums
        self._algo = algo
        self._hist_percent = hist_percent
        self._activation_bits = activation_bits
        self._weight_bits = weight_bits
        self._activation_quantize_type = activation_quantize_type
        self._weight_quantize_type = weight_quantize_type
        self._onnx_format = onnx_format
        self._skip_tensor_list = skip_tensor_list
        self._is_full_quantize = is_full_quantize
        if is_full_quantize:
            self._quantizable_op_type = self._support_quantize_op_type
        else:
            self._quantizable_op_type = quantizable_op_type
            for op_type in self._quantizable_op_type:
                assert op_type in self._support_quantize_op_type, \
                    op_type + " is not supported for quantization."
        self._optimize_model = optimize_model

        # Define variables
        self._place = self._executor.place
        self._program = None
        self._feed_list = None
        self._fetch_list = None
        self._data_loader = data_loader

        self._out_scale_op_list = utils._out_scale_op_list
        self._quantized_weight_var_name = set()
        self._quantized_act_var_name = set()
        self._weight_op_pairs = {}
        # The vars for alog = KL or hist
        self._sampling_act_abs_min_max = {}
        self._sampling_act_histogram = {}
        self._sampling_data = {}
        self._quantized_var_threshold = {}
        self._histogram_bins = 2048
        # The vars for algo = min_max
        self._quantized_var_min = {}
        self._quantized_var_max = {}
        # The vars for algo = avg
        self._quantized_var_avg = {}
        # The best loss of algo = mse
        self._best_calibration_loss = {}
        # The threshold for algo = abs_max, mse or avg
        self._quantized_threshold = {}

    def quantize(self):
        '''
        Load the FP32 model, and use the calibrate data to calculate the forward-stage.
        Based on the sample data, we can get the quantization information, and obtain
        the final quantized model.

        Args:
            None
        Returns:
            the program of quantized model.
        '''
        self._load_model_data()
        self._collect_target_varnames()
        self._set_activation_persistable()

        if self._algo in ["KL", "hist"]:
            batch_id = 0
            with tqdm(
                    total=self._batch_nums,
                    bar_format=
                    'Preparation stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
                    ncols=80) as t:
                for data in self._data_loader():
                    self._executor.run(program=self._program,
                                       feed=data,
                                       fetch_list=self._fetch_list,
                                       return_numpy=False,
                                       scope=self._scope)
                    self._collect_activation_abs_min_max()
                    batch_id += 1
                    t.update()
                    if self._batch_nums and batch_id >= self._batch_nums:
                        break
            self._init_sampling_act_histogram()

        batch_id = 0
        with tqdm(total=self._batch_nums,
                  bar_format=
                  'Sampling stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
                  ncols=80) as t:
            for data in self._data_loader():
                self._executor.run(program=self._program,
                                   feed=data,
                                   fetch_list=self._fetch_list,
                                   return_numpy=False,
                                   scope=self._scope)
                self._sampling()
                batch_id += 1
                t.update()
                if self._batch_nums and batch_id >= self._batch_nums:
                    break

        if self._algo == 'avg':
            for var_name in self._quantized_act_var_name:
                self._quantized_threshold[var_name] = \
                np.array(self._quantized_var_avg[var_name]).mean()
        if self._algo in ["KL", "hist"]:
            self._calculate_kl_hist_threshold()

        if self._round_type == 'adaround':
            self._adaround_apply()

        self._reset_activation_persistable()

        if self._algo is 'min_max':
            self._save_input_threhold()
        else:
            self._update_program()

        # save out_threshold for quantized ops.
        if not self._onnx_format:
            self._save_output_threshold()

        if any(op_type in self._quantizable_op_type
               for op_type in self._dynamic_quantize_op_type):
            self._collect_dynamic_quantize_op_threshold(
                self._dynamic_quantize_op_type)

        # Move sub blocks persistable var to global block
        global_block = self._program.global_block()
        for _op in global_block.ops:
            if _op.type == "while":
                _block_id = _op.attr("sub_block").id
                _block = self._program.block(_block_id)
                persistables = []
                for _name, _var in _block.vars.items():
                    if _var.persistable:
                        global_block._clone_variable(_var)
                        persistables.append(_name)
                for _name in persistables:
                    _block._remove_var(_name)
                persistables.extend(_op.input('X'))
                _op.desc.set_input("X", persistables)

        return self._program

    def _adaround_apply(self):
        assert self._algo != "min_max", "The algo should not be min_max."
        if self._algo in ["KL", "hist"]:
            scale_dict = self._quantized_var_threshold
        else:
            scale_dict = self._quantized_threshold
        run_adaround(self._data_loader,
                     self._program,
                     self._fetch_list,
                     self._executor,
                     self._scope,
                     self._place,
                     self._quantized_op_pairs,
                     self._weight_op_pairs,
                     scale_dict,
                     num_iterations=self._batch_nums,
                     lr=self._learning_rate)

    def save_quantized_model(self,
                             save_model_path,
                             model_filename=None,
                             params_filename=None):
        '''
        Save the quantized model to the disk.

        Args:
            save_model_path(str): The path to save the quantized model.
            model_filename(str, optional): If the model_filename is None,
                save the model to '__model__'. Otherwise, save the model
                to the specified filename. Default: None.
            params_filename(str, optional): If the params_filename is None,
                save params to separted files. Otherwise, save all params
                to the specified filename.
        Returns:
            None
        '''
        clip_extra = True if self._onnx_format else False
        io.save_inference_model(dirname=save_model_path,
                                model_filename=model_filename,
                                params_filename=params_filename,
                                feeded_var_names=self._feed_list,
                                target_vars=self._fetch_list,
                                executor=self._executor,
                                main_program=self._program,
                                clip_extra=clip_extra)
        _logger.info("The quantized model is saved in " + save_model_path)

    def _load_model_data(self):
        '''
        Load model and set data loader.
        '''
        _logger.info("Load model and set data loader ...")
        [self._program, self._feed_list, self._fetch_list] = \
            io.load_inference_model(dirname=self._model_dir,
                                    executor=self._executor,
                                    model_filename=self._model_filename,
                                    params_filename=self._params_filename)

        if self._optimize_model:
            self._optimize_fp32_model()

        feed_vars = [framework._get_var(str(var_name), self._program) \
            for var_name in self._feed_list]

        if self._data_loader is not None:
            return
        self._data_loader = io.DataLoader.from_generator(feed_list=feed_vars,
                                                         capacity=3 *
                                                         self._batch_size,
                                                         iterable=True)
        if self._sample_generator is not None:
            self._data_loader.set_sample_generator(self._sample_generator,
                                                   batch_size=self._batch_size,
                                                   drop_last=True,
                                                   places=self._place)
        elif self._batch_generator is not None:
            self._data_loader.set_batch_generator(self._batch_generator,
                                                  places=self._place)

    def _optimize_fp32_model(self):
        '''
        Fuse the `conv2d/depthwise_conv2d + bn` in FP32 model.
        '''
        _logger.info("Optimize FP32 model ...")
        graph = IrGraph(core.Graph(self._program.desc), for_test=True)
        graph = _remove_ctrl_vars(graph)
        graph = _apply_pass(self._scope, graph, 'conv_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph, 'depthwise_conv_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph, 'conv_transpose_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph, 'conv_eltwiseadd_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph,
                            'depthwise_conv_eltwiseadd_bn_fuse_pass')

        self._program = graph.to_program()

    def _collect_target_varnames(self):
        '''
        Collect the variable names for sampling, and set activation
        variables to be persistable.
        '''
        # TODO(juncaipeng), consider the name_scope of skip_quant
        _logger.info("Collect quantized variable names ...")
        self._quantized_op_pairs = {}

        def collect_var_name(var_name_list, persistable_var_names, op_type):
            for var_name in var_name_list:
                if var_name in persistable_var_names:
                    self._quantized_weight_var_name.add(var_name)
                    self._weight_op_pairs[var_name] = op_type
                else:
                    self._quantized_act_var_name.add(var_name)

        persistable_var_names = _all_persistable_var_names(self._program)
        for block_id in range(len(self._program.blocks)):
            for op in self._program.blocks[block_id].ops:
                # skip quant form self._skip_tensor_list
                if self._skip_tensor_list is not None:
                    for inp_name in utils._get_op_input_var_names(op):
                        if inp_name in self._skip_tensor_list:
                            op._set_attr("op_namescope", "skip_quant")

                op_type = op.type
                if self._is_full_quantize and \
                    op_type not in self._quantizable_op_type:
                    _logger.warning(op_type +
                                    " is not supported for quantization.")
                # For quantized ops, sample inputs and outputs
                if op_type in self._quantizable_op_type:
                    collect_var_name(utils._get_op_input_var_names(op),
                                     persistable_var_names, op_type)
                    collect_var_name(utils._get_op_output_var_names(op),
                                     persistable_var_names, op_type)
                    # collect quanted op output var name
                    for out_var_name in utils._get_op_output_var_names(op):
                        for in_var_name in utils._get_op_input_var_names(op):
                            if in_var_name in persistable_var_names:
                                self._quantized_op_pairs[
                                    in_var_name] = out_var_name
                # For other op, only sample output scale
                elif op_type in self._out_scale_op_list:
                    collect_var_name(utils._get_op_output_var_names(op),
                                     persistable_var_names, op_type)

    def _set_activation_persistable(self):
        '''
        Set activation variables to be persistable, so can obtain 
        the tensor data in sample_data
        '''
        for var in self._program.list_vars():
            if var.name in self._quantized_act_var_name:
                var.persistable = True

    def _reset_activation_persistable(self):
        '''
        Reset activations to be not persistable.
        '''
        to_erase = []
        for var in self._program.list_vars():
            if var.name in self._quantized_act_var_name:
                var.persistable = False
                to_erase.append(var.name)
        self._scope.erase(to_erase)

    def _sampling(self):
        '''
        Sample the min/max, abs_max or histogram in every iterations.
        '''
        if self._algo == "abs_max":
            self._sample_abs_max()
        elif self._algo == "avg":
            self._sample_avg()
        elif self._algo == "min_max":
            self._sample_min_max()
        elif self._algo == "mse":
            self._sample_mse()
        elif self._algo == "emd":
            self._sample_emd()
        elif self._algo in ["KL", "hist"]:
            self._sample_histogram()

    def _sample_mse(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if self._weight_op_pairs[
                            var_name] in utils._channelwise_quant_axis1_ops:
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i]))))
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i]))))
                self._quantized_threshold[var_name] = abs_max_value
        _logger.info("MSE searching stage ...")
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor = var_tensor.flatten()
            abs_max_value = float(np.max(np.abs(var_tensor)))
            abs_max_value = 1e-8 if abs_max_value == 0.0 else abs_max_value
            s = 0.3
            if var_name not in self._best_calibration_loss:
                self._best_calibration_loss[var_name] = float('inf')
            while s <= 1.0:
                scale = s * abs_max_value
                s += 0.02
                bins = 2**(self._activation_bits - 1) - 1
                quant_dequant_var = np.round(
                    np.clip(var_tensor, 0.0, scale) / scale *
                    bins) / bins * scale
                mse_loss = ((var_tensor - quant_dequant_var)**2).mean()
                if mse_loss <= self._best_calibration_loss[var_name]:
                    self._best_calibration_loss[var_name] = mse_loss
                    self._quantized_threshold[var_name] = scale

    def _sample_emd(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if self._weight_op_pairs[
                            var_name] in utils._channelwise_quant_axis1_ops:
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i]))))
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i]))))
                self._quantized_threshold[var_name] = abs_max_value
        _logger.info("EMD searching stage ...")
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor = var_tensor.flatten()
            abs_max_value = float(np.max(np.abs(var_tensor)))
            abs_max_value = 1e-8 if abs_max_value == 0.0 else abs_max_value
            s = 0.3
            if var_name not in self._best_calibration_loss:
                self._best_calibration_loss[var_name] = float('inf')
            while s <= 1.0:
                scale = s * abs_max_value
                s += 0.02
                bins = 2**(self._activation_bits - 1) - 1
                quant_dequant_var = np.round(
                    np.clip(var_tensor, 0.0, scale) / scale *
                    bins) / bins * scale
                emd_loss = np.abs(
                    np.mean(var_tensor) - np.mean(quant_dequant_var)) + np.abs(
                        np.std(var_tensor) - np.std(quant_dequant_var))
                if emd_loss <= self._best_calibration_loss[var_name]:
                    self._best_calibration_loss[var_name] = emd_loss
                    self._quantized_threshold[var_name] = scale

    def _sample_avg(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if self._weight_op_pairs[
                            var_name] in utils._channelwise_quant_axis1_ops:
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i]))))
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i]))))
                self._quantized_threshold[var_name] = abs_max_value

        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            abs_max_value = float(np.max(np.abs(var_tensor)))
            if (var_name not in self._quantized_var_avg):
                self._quantized_var_avg[var_name] = []
            abs_avg_value = float(np.mean(np.max(  \
            np.abs(var_tensor.reshape(var_tensor.shape[0], -1)), axis=(1))))
            self._quantized_var_avg[var_name].append(abs_avg_value)
            continue

    def _sample_abs_max(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if self._weight_op_pairs[
                            var_name] in utils._channelwise_quant_axis1_ops:
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i]))))
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i]))))
                self._quantized_threshold[var_name] = abs_max_value

        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            abs_max_value = float(np.max(np.abs(var_tensor)))
            if (var_name not in self._quantized_threshold) or \
                (abs_max_value > self._quantized_threshold[var_name]):
                self._quantized_threshold[var_name] = abs_max_value

    def _sample_min_max(self):
        if self._quantized_var_min == {} and self._quantized_var_max == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    min_value = float(np.min(var_tensor))
                    max_value = float(np.max(var_tensor))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    min_value = []
                    max_value = []
                    if self._weight_op_pairs[
                            var_name] in utils._channelwise_quant_axis1_ops:
                        for i in range(var_tensor.shape[1]):
                            min_value.append(float(np.min(var_tensor[:, i])))
                            max_value.append(float(np.max(var_tensor[:, i])))
                    else:
                        for i in range(var_tensor.shape[0]):
                            min_value.append(float(np.min(var_tensor[i])))
                            max_value.append(float(np.max(var_tensor[i])))
                self._quantized_var_min[var_name] = min_value
                self._quantized_var_max[var_name] = max_value

        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            min_value = float(np.min(var_tensor))
            max_value = float(np.max(var_tensor))
            if (var_name not in self._quantized_var_min) or \
                (min_value < self._quantized_var_min[var_name]):
                self._quantized_var_min[var_name] = min_value
            if (var_name not in self._quantized_var_max) or \
                (max_value > self._quantized_var_max[var_name]):
                self._quantized_var_max[var_name] = max_value

    def _sample_histogram(self):
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor_abs = np.abs(var_tensor)
            bins = self._sampling_act_histogram[var_name][1]
            hist, _ = np.histogram(var_tensor_abs, bins=bins)
            self._sampling_act_histogram[var_name][0] += hist

    def _save_input_threhold(self):
        '''
        Save input threshold to the quantized op.
        '''
        assert self._algo == "min_max", \
            "The algo should be min_max to save input threshold."
        for block_id in range(len(self._program.blocks)):
            for op in self._program.blocks[block_id].ops:
                if op.type in self._quantizable_op_type:
                    for var_name in utils._get_op_input_var_names(op):
                        assert var_name in self._quantized_var_min
                        assert var_name in self._quantized_var_max
                        op._set_attr(var_name + ".min",
                                     self._quantized_var_min[var_name])
                        op._set_attr(var_name + ".max",
                                     self._quantized_var_max[var_name])
                        op._set_attr("with_quant_attr", True)

    def _collect_activation_abs_min_max(self):
        '''
        Collect the abs_min and abs_max for all activation. When algo = KL,
        get the min and max value, and then calculate the threshold.
        '''
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor = np.abs(var_tensor)
            min_value = float(np.min(var_tensor))
            max_value = float(np.max(var_tensor))
            if var_name not in self._sampling_act_abs_min_max:
                self._sampling_act_abs_min_max[var_name] = [
                    min_value, max_value
                ]
            else:
                if min_value < self._sampling_act_abs_min_max[var_name][0]:
                    self._sampling_act_abs_min_max[var_name][0] = min_value
                if max_value > self._sampling_act_abs_min_max[var_name][1]:
                    self._sampling_act_abs_min_max[var_name][1] = max_value

    def _init_sampling_act_histogram(self):
        '''
        Based on the min/max value, init the sampling_act_histogram.
        '''
        for var_name in self._quantized_act_var_name:
            if var_name not in self._sampling_act_histogram:
                min_val = self._sampling_act_abs_min_max[var_name][0]
                max_val = self._sampling_act_abs_min_max[var_name][1]
                hist, hist_edeges = np.histogram([],
                                                 bins=self._histogram_bins,
                                                 range=(min_val, max_val))
                self._sampling_act_histogram[var_name] = [hist, hist_edeges]

    def _calculate_kl_hist_threshold(self):
        '''
        Calculate the KL or hist threshold of quantized variables.
        '''
        _logger.info("Calculate {} threshold ...".format(self._algo))
        assert self._algo in ["KL", "hist"], "The algo should be KL or hist."

        # Abs_max threshold for weights
        for var_name in self._quantized_weight_var_name:
            weight_data = utils.load_variable_data(self._scope, var_name)
            if self._weight_quantize_type == "abs_max":
                weight_threshold = float(np.max(np.abs(weight_data)))
            elif self._weight_quantize_type == "channel_wise_abs_max":
                weight_threshold = []
                if self._weight_op_pairs[
                        var_name] in utils._channelwise_quant_axis1_ops:
                    for i in range(weight_data.shape[1]):
                        weight_threshold.append(
                            float(np.max(np.abs(weight_data[:, i]))))
                else:
                    for i in range(weight_data.shape[0]):
                        weight_threshold.append(
                            float(np.max(np.abs(weight_data[i]))))
            self._quantized_var_threshold[var_name] = weight_threshold

        for var_name in self._quantized_act_var_name:
            hist, hist_edeges = self._sampling_act_histogram[var_name]
            if self._algo == "KL":
                bin_width = hist_edeges[1] - hist_edeges[0]
                self._quantized_var_threshold[var_name] = \
                    cal_kl_threshold(hist, bin_width, self._activation_bits)
            elif self._algo == "hist":
                self._quantized_var_threshold[var_name] = \
                    self._get_hist_scaling_factor(hist, hist_edeges)

    def _update_program(self):
        '''
        Use QuantizationTransformPass and AddQuantDequantPass to insert 
        fake_quantize, fake_dequantize and fake_quant_dequant op. 
        Besides, save all threshold to the scale var node.
        '''
        _logger.info("Update the program ...")
        graph = IrGraph(core.Graph(self._program.desc), for_test=True)

        # use QuantizationTransformPass to insert fake_quant/fake_dequantize op
        major_quantizable_op_types = []
        for op_type in utils._weight_supported_quantizable_op_type:
            if op_type in self._quantizable_op_type:
                major_quantizable_op_types.append(op_type)
        if not self._onnx_format:
            transform_pass = QuantizationTransformPass(
                scope=self._scope,
                place=self._place,
                weight_bits=self._weight_bits,
                activation_bits=self._activation_bits,
                activation_quantize_type=self._activation_quantize_type,
                weight_quantize_type=self._weight_quantize_type,
                quantizable_op_type=major_quantizable_op_types)
        else:
            transform_pass = QuantizationTransformPassV2(
                scope=self._scope,
                place=self._place,
                weight_bits=self._weight_bits,
                activation_bits=self._activation_bits,
                activation_quantize_type=self._activation_quantize_type,
                weight_quantize_type=self._weight_quantize_type,
                quantizable_op_type=major_quantizable_op_types)

        for sub_graph in graph.all_sub_graphs():
            # Insert fake_quant/fake_dequantize op must in test graph, so
            # set per graph's _for_test is True.
            sub_graph._for_test = True
            transform_pass.apply(sub_graph)

        # use AddQuantDequantPass to insert fake_quant_dequant op
        minor_quantizable_op_types = []
        for op_type in utils._act_supported_quantizable_op_type:
            if op_type in self._quantizable_op_type:
                minor_quantizable_op_types.append(op_type)
        if not self._onnx_format:
            add_quant_dequant_pass = AddQuantDequantPass(
                scope=self._scope,
                place=self._place,
                quantizable_op_type=minor_quantizable_op_types)
        else:
            add_quant_dequant_pass = AddQuantDequantPassV2(
                scope=self._scope,
                place=self._place,
                quantizable_op_type=minor_quantizable_op_types,
                is_full_quantized=self._is_full_quantize)

        for sub_graph in graph.all_sub_graphs():
            sub_graph._for_test = True
            add_quant_dequant_pass.apply(sub_graph)

        # save threshold to scale var node
        if self._algo in ["KL", "hist"]:
            scale_dict = self._quantized_var_threshold
        else:
            scale_dict = self._quantized_threshold
        for key, val in scale_dict.items():
            utils.set_variable_data(self._scope, self._place, key + ".scale",
                                    np.array([val], dtype=np.float32))
            utils.set_variable_data(self._scope, self._place,
                                    key + ".quant_dequant.scale",
                                    np.array([val], dtype=np.float32))

        if not self._onnx_format:
            # apply QuantizationFreezePass, and obtain the final quant model
            freeze_pass = QuantizationFreezePass(
                scope=self._scope,
                place=self._place,
                bias_correction=self._bias_correction,
                weight_bits=self._weight_bits,
                round_type=self._round_type,
                activation_bits=self._activation_bits,
                weight_quantize_type=self._weight_quantize_type,
                quantizable_op_type=major_quantizable_op_types)

            for sub_graph in graph.all_sub_graphs():
                sub_graph._for_test = True
                freeze_pass.apply(sub_graph)
        else:
            quant_weight_pass = QuantWeightPass(self._scope, self._place)
            for sub_graph in graph.all_sub_graphs():
                sub_graph._for_test = True
                quant_weight_pass.apply(sub_graph)

        self._program = graph.to_program()

    def _save_output_threshold(self):
        '''
        Save output threshold to the quantized op.
        '''

        def save_info(op_node, out_var_name, threshold_map, out_info_name,
                      quantized_type):
            assert out_var_name in threshold_map, \
                "The output ({}) of {} node does not have threshold.".format(
                out_var_name, op_node.type)
            op_node._set_attr(out_info_name, threshold_map[var_name])
            op_node._set_attr("with_quant_attr", True)
            if op_node.type in self._quantizable_op_type:
                op._set_attr("quantization_type", quantized_type)

        def analysis_and_save_info(op_node, out_var_name):
            argname_index = utils._get_output_name_index(op_node, out_var_name)
            assert argname_index is not None, \
                out_var_name + " is not the output of the op"
            if self._algo == "KL":
                # For compatibility, we save output threshold by two methods.
                save_info(op_node, out_var_name, self._quantized_var_threshold,
                          "out_threshold", "post_kl")
                save_info(
                    op_node, out_var_name, self._quantized_var_threshold,
                    argname_index[0] + str(argname_index[1]) + "_threshold",
                    "post_kl")
            elif self._algo == "hist":
                # For compatibility, we save output threshold by two methods.
                save_info(op_node, out_var_name, self._quantized_var_threshold,
                          "out_threshold", "post_hist")
                save_info(
                    op_node, out_var_name, self._quantized_var_threshold,
                    argname_index[0] + str(argname_index[1]) + "_threshold",
                    "post_hist")

            elif self._algo in ["avg", "abs_max", "mse", "emd"]:
                save_info(op_node, out_var_name, self._quantized_threshold,
                          "out_threshold", "post_" + str(self._algo))
                save_info(
                    op_node, out_var_name, self._quantized_threshold,
                    argname_index[0] + str(argname_index[1]) + "_threshold",
                    "post_" + str(self._algo))
            elif self._algo == "min_max":
                save_info(op_node, out_var_name, self._quantized_var_min,
                          "out_min", "post_min_max")
                save_info(op_node, out_var_name, self._quantized_var_max,
                          "out_max", "post_min_max")

        for block_id in range(len(self._program.blocks)):
            for op in self._program.blocks[block_id].ops:
                if op.type in (self._quantizable_op_type +
                               self._out_scale_op_list):
                    out_var_names = utils._get_op_output_var_names(op)
                    for var_name in out_var_names:
                        analysis_and_save_info(op, var_name)

    def _collect_dynamic_quantize_op_threshold(self, target_ops_type):
        """
        Collect and save the weight threshold for dynamic quantize ops,
        such as lstm and gru.
        Args:
            target_ops_type(list): the op type of target ops
        Returns:
            None
        """

        target_ops = []
        for index in range(self._program.num_blocks):
            for op in self._program.block(index).ops:
                if op.type in target_ops_type:
                    target_ops.append(op)

        quantization_type = str("post_" + self._algo).lower()
        persistable_var_names = _all_persistable_var_names(self._program)
        for op in target_ops:
            for var_name in utils._get_op_input_var_names(op):
                if var_name in persistable_var_names:
                    var_data = utils.load_variable_data(self._scope, var_name)
                    threshold = float(np.max(np.abs(var_data)))
                    argname, index = utils._get_input_name_index(op, var_name)
                    op._set_attr(argname + str(index) + "_threshold", threshold)
                    op._set_attr("quantization_type", quantization_type)
                    op._set_attr("bit_length", self._weight_bits)
                    op._set_attr("with_quant_attr", True)

    def _get_hist_scaling_factor(self, hist, hist_edges):
        '''
        Using the hist method to get the scaling factor.
        '''
        threshold_rate = self._hist_percent
        hist = hist / float(sum(hist))
        hist_sum = 0
        hist_index = 0
        for i in range(len(hist)):
            hist_sum += hist[i]
            if hist_sum >= threshold_rate:
                hist_index = i + 1
                break
        bin_width = hist_edges[1] - hist_edges[0]
        return (hist_index - 0.5) * bin_width


class WeightQuantization(object):
    _supported_quantizable_op_type = ['conv2d', 'depthwise_conv2d', 'mul']
    _supported_weight_quantize_type = ['channel_wise_abs_max', 'abs_max']

    def __init__(self, model_dir, model_filename=None, params_filename=None):
        '''
        This class quantizes the weight of some ops to reduce the size of model
        or improve the perforemace.

        Args:
            model_dir(str): The path of the fp32 model that will be quantized,
                and the model and params files are under the path.
            model_filename(str, optional): The name of file to load the inference
                program. If it is None, the default filename '__model__' will
                be used. Default is 'None'.
            params_filename(str, optional): The name of file to load all parameters.
                When all parameters were saved in a single binary file, set it
                as the real filename. If parameters were saved in separate files,
                set it as 'None'. Default is 'None'.
        '''
        self._model_dir = model_dir
        self._model_filename = model_filename
        self._params_filename = params_filename

    def quantize_weight_to_int(self,
                               save_model_dir,
                               save_model_filename=None,
                               save_params_filename=None,
                               quantizable_op_type=["conv2d", "mul"],
                               weight_bits=8,
                               weight_quantize_type="channel_wise_abs_max",
                               generate_test_model=False,
                               threshold_rate=0.0):
        '''
        In order to reduce the size of model, this api quantizes the weight
        of some ops from float32 to int8/16. In the inference stage, the 
        quantized weight will be dequantized to float32 again.
        
        Args:
            save_model_dir(str): The path to save the quantized model.
            save_model_filename(str, optional): The name of file to 
                save the inference program. If it is None, the default 
                filename '__model__' will be used. Default is 'None'.
            save_params_filename(str, optional): The name of file to 
                save all parameters. If it is None, parameters were 
                saved in separate files. If it is not None, all 
                parameters were saved in a single binary file.
            quantizable_op_type(list[str], optional): The list of ops 
                that will be quantized, and the quantized ops should be
                contained in ["conv2d", "depthwise_conv2d", "mul"]. 
                Default is ["conv2d","mul"].
            weight_bits(int, optional): The bits for the quantized weight, 
                and it should be 8 or 16. Default is 8.
            weight_quantize_type(str, optional): quantization type for weights,
                support 'channel_wise_abs_max' and 'abs_max'. Set it as
                'channel_wise_abs_max', the accuracy performs better.
            generate_test_model(bool, optional): If set generate_test_model 
                as True, it saves a fake quantized model, in which the weights 
                are quantized and dequantized. We can use PaddlePaddle to load 
                the fake quantized model and test the accuracy on GPU or CPU.
            threshold_rate(float, optional): This api uses abs_max methd to 
                quantize the weight from float32 to int8/16, and the abs max 
                value is important for quantization diff. When the abs_max 
                value is far away from the center of the numerical distribution, 
                we can set threshold_rate between 1e-6 and 1e-8, so the abs max 
                value will be optimized. Default is 0.0.
        '''
        for op_type in quantizable_op_type:
            assert op_type in self._supported_quantizable_op_type, \
                "Input error:" + op_type + \
                " is not supported for weight quantization."
        assert weight_bits in [8, 16], \
            "Input error: weight_bits should be 8 or 16."
        assert weight_quantize_type in self._supported_weight_quantize_type, \
            "Input error: weight_quantize_type should in {}".format(
                self._supported_weight_quantize_type)

        quantized_model_dir = os.path.join(save_model_dir, "quantized_model")
        self._quantize_weight_to_int(quantized_model_dir, save_model_filename,
                                     save_params_filename, quantizable_op_type,
                                     weight_bits, weight_quantize_type, False,
                                     threshold_rate)

        if generate_test_model:
            test_model_dir = os.path.join(save_model_dir, "test_model")
            self._quantize_weight_to_int(test_model_dir, save_model_filename,
                                         save_params_filename,
                                         quantizable_op_type, weight_bits,
                                         weight_quantize_type, True,
                                         threshold_rate)

    def convert_weight_to_fp16(self, save_model_dir):
        """
        Convert all presistable vars from fp32 to fp16.
        Note that, this api only changes the data type of variables in
        __params__ file, and the __model__ file remains unchanged. 

        Args:
            save_model_dir(str): The path to save the fp16 model.
        """

        # Load model
        place = core.CPUPlace()
        exe = Executor(place)
        scope = global_scope()
        [infer_program, feed_list, fetch_list] = \
            io.load_inference_model(dirname=self._model_dir,
                                    executor=exe,
                                    model_filename=self._model_filename,
                                    params_filename=self._params_filename)

        # Clone and save fp16 weights
        save_program = framework.Program()
        save_block = save_program.global_block()
        save_var_map = {}

        for var in infer_program.list_vars():
            if (var.type == core.VarDesc.VarType.RAW) or \
                (not var.persistable) or (var.name in ['feed', 'fetch']) \
                or (var.dtype != core.VarDesc.VarType.FP32):
                continue

            #new_var = _clone_var_to_block_(var, save_block)
            new_var = save_block._clone_variable(var)
            if self._params_filename is not None:
                save_var_map[new_var.name] = new_var
            else:
                save_file_path = os.path.join(os.path.normpath(save_model_dir),
                                              new_var.name)
                save_block.append_op(type='save',
                                     inputs={'X': [new_var]},
                                     outputs={},
                                     attrs={
                                         'file_path':
                                         os.path.normpath(save_file_path),
                                         'save_as_fp16':
                                         True
                                     })

        if self._params_filename is not None:
            save_var_list = []
            for name in sorted(save_var_map.keys()):
                save_var_list.append(save_var_map[name])

            saved_params_var = save_block.create_var(
                type=core.VarDesc.VarType.RAW,
                name=unique_name.generate("saved_params"))
            saved_params_var.desc.set_persistable(True)

            save_path = os.path.join(os.path.normpath(save_model_dir),
                                     self._params_filename)
            save_block.append_op(type='save_combine',
                                 inputs={'X': save_var_list},
                                 outputs={'Y': saved_params_var},
                                 attrs={
                                     'file_path': save_path,
                                     'save_as_fp16': True
                                 })

        save_program._sync_with_cpp()
        exe.run(save_program)

        # Copy model
        model_filename = "__model__" if self._model_filename is None \
                    else self._model_filename
        src_model = os.path.join(self._model_dir, model_filename)
        dest_model = os.path.join(save_model_dir, model_filename)
        shutil.copyfile(src_model, dest_model)

    def _quantize_weight_to_int(self, save_model_dir, save_model_filename,
                                save_params_filename, quantizable_op_type,
                                weight_bits, weight_quantize_type, for_test,
                                threshold_rate):
        """
        Generate quantized model or fake quantized model.
        """
        # Load model
        place = core.CPUPlace()
        exe = Executor(place)
        scope = global_scope()
        [program, feed_list, fetch_list] = \
            io.load_inference_model(dirname=self._model_dir,
                                    executor=exe,
                                    model_filename=self._model_filename,
                                    params_filename=self._params_filename)

        quantized_ops = []
        for index in range(program.num_blocks):
            block = program.block(index)
            for op in block.ops:
                if op.type in quantizable_op_type:
                    quantized_ops.append(op)

        # Quantize weights
        persistable_var_names = _all_persistable_var_names(program)
        for op in quantized_ops:
            for var_name in op.input_arg_names:
                if var_name in persistable_var_names:
                    if weight_quantize_type == "abs_max":
                        self._weight_abs_max_quantization(
                            scope, place, weight_bits, threshold_rate, op,
                            var_name, for_test)
                    elif weight_quantize_type == "channel_wise_abs_max":
                        self._weight_channel_wise_abs_max_quantization(
                            scope, place, weight_bits, op, var_name, for_test)

        io.save_inference_model(dirname=save_model_dir,
                                feeded_var_names=feed_list,
                                target_vars=fetch_list,
                                executor=exe,
                                main_program=program,
                                model_filename=save_model_filename,
                                params_filename=save_params_filename)

    def _weight_abs_max_quantization(self, scope, place, weight_bits,
                                     threshold_rate, op, var_name, for_test):
        '''
        Use abs_max method to quantize weight.
        '''
        quantize_range = (1 << (weight_bits - 1)) - 1
        save_weight_dtype = np.int8 if weight_bits == 8 else np.int16

        # Get quantized scale and weight data
        weight_data = utils.load_variable_data(scope, var_name)
        if abs(threshold_rate) < 1e-10:
            threshold_value = np.max(np.abs(weight_data))
        else:
            threshold_value = self._calculate_threshold(\
                weight_data, threshold_rate)
            weight_data[weight_data > threshold_value] = threshold_value
            weight_data[weight_data < -threshold_value] = -threshold_value
        scale = threshold_value / quantize_range
        quantized_weight_data = \
            np.around(weight_data / scale).astype(save_weight_dtype)

        # Set weight data
        if not for_test:
            utils.set_variable_data(scope, place, var_name,
                                    quantized_weight_data)
        else:
            dequantized_weight_data = \
                (quantized_weight_data * scale).astype(np.float32)
            utils.set_variable_data(scope, place, var_name,
                                    dequantized_weight_data)

        # Save info
        op._set_attr('quantization_type', 'post_weight_abs_max')
        op._set_attr('quantize_weight_bits', weight_bits)
        op._set_attr(var_name + "_quant_scale", [scale])  # Save as list
        op._set_attr("with_quant_attr", True)

    def _weight_channel_wise_abs_max_quantization(self, scope, place,
                                                  weight_bits, op, var_name,
                                                  for_test):
        ''' 
        Use channel_wise_abs_max method to quantize weight.
        '''
        quantize_range = (1 << (weight_bits - 1)) - 1
        save_weight_dtype = np.int8 if weight_bits == 8 else np.int16

        # Get quantized scale and weight data
        weight_data = utils.load_variable_data(scope, var_name)
        if op.type == "mul":
            scales, quantized_weight_data = \
                self._mul_channel_wise_quantization(weight_data,
                    quantize_range, save_weight_dtype)
        elif op.type in ["conv2d", "depthwise_conv2d"]:
            scales, quantized_weight_data = \
                self._conv_channel_wise_quantization(weight_data,
                    quantize_range, save_weight_dtype)
        else:
            _logger.error(op.type + " is not supported by weight quantization")

        # Set weight data
        if not for_test:
            utils.set_variable_data(scope, place, var_name,
                                    quantized_weight_data)
        else:
            if op.type == "mul":
                dequantized_weight_data = \
                    self._mul_channel_wise_dequantization(quantized_weight_data, scales)
            elif op.type in ["conv2d", "depthwise_conv2d"]:
                dequantized_weight_data = \
                    self._conv_channel_wise_dequantization(quantized_weight_data, scales)
            else:
                _logger.error(op.type +
                              " is not supported by weight quantization")
            utils.set_variable_data(scope, place, var_name,
                                    dequantized_weight_data)

        # Save info
        op._set_attr('quantization_type', 'post_weight_channel_wise_abs_max')
        op._set_attr('quantize_weight_bits', weight_bits)
        op._set_attr(var_name + "_quant_scale", scales)
        op._set_attr("with_quant_attr", True)

    def _conv_channel_wise_quantization(self, weight_data, quantize_range,
                                        save_weight_dtype):
        '''
        Get channel wise scale for the weights of conv2d and depthwise_conv2d,
        and quantize the weights.
        '''
        scales = []
        quantized_weight_data = np.zeros_like(weight_data,
                                              dtype=save_weight_dtype)
        channel_num = weight_data.shape[0]
        for i in range(channel_num):
            scale = np.max(np.abs(weight_data[i])) / quantize_range
            scales.append(scale)
            quantized_weight_data[i] = \
                np.around(weight_data[i] / scale).astype(save_weight_dtype)
        return scales, quantized_weight_data

    def _conv_channel_wise_dequantization(self, quantized_weight_data, scales):
        '''
        For conv2d and depthwise_conv2d, dequantize the weights to fp32.
        '''
        dequantized_weight_data = np.zeros_like(quantized_weight_data,
                                                dtype=np.float32)
        for i in range(len(scales)):
            dequantized_weight_data[i] = \
                (quantized_weight_data[i] * scales[i]).astype(np.float32)
        return dequantized_weight_data

    def _mul_channel_wise_quantization(self, weight_data, quantize_range,
                                       save_weight_dtype):
        '''
        Get channel wise scale for the weights of conv2d and depthwise_conv2d,
        and quantize the weights.
        '''
        scales = []
        quantized_weight_data = np.zeros_like(weight_data,
                                              dtype=save_weight_dtype)
        channel_num = weight_data.shape[-1]
        for i in range(channel_num):
            scale = np.max(np.abs(weight_data[:, i])) / quantize_range
            scales.append(scale)
            quantized_weight_data[:, i] = \
                np.around(weight_data[:, i] / scale).astype(save_weight_dtype)
        return scales, quantized_weight_data

    def _mul_channel_wise_dequantization(self, quantized_weight_data, scales):
        '''
        For mul, dequantize the weights to fp32.
        '''
        dequantized_weight_data = np.zeros_like(quantized_weight_data,
                                                dtype=np.float32)
        for i in range(len(scales)):
            dequantized_weight_data[:, i] = \
                (quantized_weight_data[:, i] * scales[i]).astype(np.float32)
        return dequantized_weight_data

    def _calculate_threshold(self, input, threshold_rate, histogram_bins=5000):
        input_abs = np.abs(input)
        hist, hist_edeges = np.histogram(input_abs,
                                         bins=histogram_bins,
                                         range=(0, np.max(input_abs)))
        hist = hist / float(sum(hist))
        hist_sum = 0
        hist_index = 0
        for i in range(len(hist)):
            hist_sum += hist[i]
            if hist_sum >= 1.0 - threshold_rate:
                hist_index = i + 1
                break
        bin_width = hist_edeges[1] - hist_edeges[0]
        return hist_index * bin_width