quant.py

# Copyright (c) 2020 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
sys.path.append(
    os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))

import yaml
import paddle
import paddle.distributed as dist

paddle.seed(2)

from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model
from ppocr.losses import build_loss
from ppocr.optimizer import build_optimizer
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
from ppocr.utils.save_load import load_model
import tools.program as program
from paddleslim.dygraph.quant import QAT

dist.get_world_size()


class PACT(paddle.nn.Layer):
    def __init__(self):
        super(PACT, self).__init__()
        alpha_attr = paddle.ParamAttr(
            name=self.full_name() + ".pact",
            initializer=paddle.nn.initializer.Constant(value=20),
            learning_rate=1.0,
            regularizer=paddle.regularizer.L2Decay(2e-5))

        self.alpha = self.create_parameter(
            shape=[1], attr=alpha_attr, dtype='float32')

    def forward(self, x):
        out_left = paddle.nn.functional.relu(x - self.alpha)
        out_right = paddle.nn.functional.relu(-self.alpha - x)
        x = x - out_left + out_right
        return x


quant_config = {
    # weight preprocess type, default is None and no preprocessing is performed. 
    'weight_preprocess_type': None,
    # activation preprocess type, default is None and no preprocessing is performed.
    'activation_preprocess_type': None,
    # weight quantize type, default is 'channel_wise_abs_max'
    'weight_quantize_type': 'channel_wise_abs_max',
    # activation quantize type, default is 'moving_average_abs_max'
    'activation_quantize_type': 'moving_average_abs_max',
    # weight quantize bit num, default is 8
    'weight_bits': 8,
    # activation quantize bit num, default is 8
    'activation_bits': 8,
    # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
    'dtype': 'int8',
    # window size for 'range_abs_max' quantization. default is 10000
    'window_size': 10000,
    # The decay coefficient of moving average, default is 0.9
    'moving_rate': 0.9,
    # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
    'quantizable_layer_type': ['Conv2D', 'Linear'],
}


def main(config, device, logger, vdl_writer):
    # init dist environment
    if config['Global']['distributed']:
        dist.init_parallel_env()

    global_config = config['Global']

    # build dataloader
    train_dataloader = build_dataloader(config, 'Train', device, logger)
    if config['Eval']:
        valid_dataloader = build_dataloader(config, 'Eval', device, logger)
    else:
        valid_dataloader = None

    # build post process
    post_process_class = build_post_process(config['PostProcess'],
                                            global_config)

    # build model
    # for rec algorithm
    if hasattr(post_process_class, 'character'):
        char_num = len(getattr(post_process_class, 'character'))
        if config['Architecture']["algorithm"] in ["Distillation",
                                                   ]:  # distillation model
            for key in config['Architecture']["Models"]:
                config['Architecture']["Models"][key]["Head"][
                    'out_channels'] = char_num
        else:  # base rec model
            config['Architecture']["Head"]['out_channels'] = char_num
    model = build_model(config['Architecture'])

    pre_best_model_dict = dict()
    # load fp32 model to begin quantization
    if config["Global"]["pretrained_model"] is not None:
        pre_best_model_dict = load_model(config, model)

    quanter = QAT(config=quant_config, act_preprocess=PACT)
    quanter.quantize(model)

    if config['Global']['distributed']:
        model = paddle.DataParallel(model)

    # build loss
    loss_class = build_loss(config['Loss'])

    # build optim
    optimizer, lr_scheduler = build_optimizer(
        config['Optimizer'],
        epochs=config['Global']['epoch_num'],
        step_each_epoch=len(train_dataloader),
        parameters=model.parameters())

    # resume PACT training process
    if config["Global"]["checkpoints"] is not None:
        pre_best_model_dict = load_model(config, model, optimizer)

    # build metric
    eval_class = build_metric(config['Metric'])

    logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
                format(len(train_dataloader), len(valid_dataloader)))

    # start train
    program.train(config, train_dataloader, valid_dataloader, device, model,
                  loss_class, optimizer, lr_scheduler, post_process_class,
                  eval_class, pre_best_model_dict, logger, vdl_writer)


if __name__ == '__main__':
    config, device, logger, vdl_writer = program.preprocess(is_train=True)
    main(config, device, logger, vdl_writer)