#   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.

import os
import unittest
import json
import random
import numpy as np
import six
import paddle.fluid as fluid
import paddle
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
from paddle.fluid.contrib.slim.quantization import OutScaleForTrainingPass
from paddle.fluid.contrib.slim.quantization import OutScaleForInferencePass
from paddle.fluid.contrib.slim.quantization import AddQuantDequantPass
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper

os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["CPU_NUM"] = "1"


def conv_net(img, label):
    conv_pool_1 = fluid.nets.simple_img_conv_pool(
        input=img,
        filter_size=5,
        num_filters=20,
        pool_size=2,
        pool_stride=2,
        pool_type='max',
        act="relu")
    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
    conv_pool_2 = fluid.nets.simple_img_conv_pool(
        input=conv_pool_1,
        filter_size=5,
        num_filters=50,
        pool_size=2,
        pool_stride=2,
        pool_type='avg',
        act="relu")
    hidden = fluid.layers.fc(input=conv_pool_2, size=100, act='relu')
    prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
    loss = fluid.layers.cross_entropy(input=prediction, label=label)
    avg_loss = fluid.layers.mean(loss)
    return avg_loss


def pact(x, name=None):
    helper = LayerHelper("pact", **locals())
    dtype = 'float32'
    init_thres = 20
    u_param_attr = fluid.ParamAttr(
        name=x.name + '_pact',
        initializer=fluid.initializer.ConstantInitializer(value=init_thres),
        regularizer=fluid.regularizer.L2Decay(0.0001),
        learning_rate=1)
    u_param = helper.create_parameter(attr=u_param_attr, shape=[1], dtype=dtype)
    x = fluid.layers.elementwise_sub(
        x, fluid.layers.relu(fluid.layers.elementwise_sub(x, u_param)))
    x = fluid.layers.elementwise_add(
        x, fluid.layers.relu(fluid.layers.elementwise_sub(-u_param, x)))

    return x


class TestUserDefinedQuantization(unittest.TestCase):
    def quantization_scale(self,
                           use_cuda,
                           seed,
                           activation_quant_type,
                           weight_quant_type='abs_max',
                           for_ci=False,
                           act_preprocess_func=None,
                           weight_preprocess_func=None,
                           act_quantize_func=None,
                           weight_quantize_func=None):
        def build_program(main, startup, is_test):
            main.random_seed = seed
            startup.random_seed = seed
            with fluid.unique_name.guard():
                with fluid.program_guard(main, startup):
                    img = fluid.layers.data(
                        name='image', shape=[1, 28, 28], dtype='float32')
                    img.stop_gradient = False
                    label = fluid.layers.data(
                        name='label', shape=[1], dtype='int64')
                    loss = conv_net(img, label)
                    if not is_test:
                        opt = fluid.optimizer.SGD(learning_rate=0.0001)
                        opt.minimize(loss)
            return [img, label], loss

        def get_optimizer():
            return fluid.optimizer.MomentumOptimizer(0.0001, 0.9)

        def load_dict():
            with open('mapping_table_for_saving_inference_model', 'r') as file:
                data = file.read()
                data = json.loads(data)
                return data

        def save_dict(Dict):
            with open('mapping_table_for_saving_inference_model', 'w') as file:
                file.write(json.dumps(Dict))

        random.seed(0)
        np.random.seed(0)

        main = fluid.Program()
        startup = fluid.Program()
        test_program = fluid.Program()
        feeds, loss = build_program(main, startup, False)
        build_program(test_program, startup, True)
        test_program = test_program.clone(for_test=True)
        main_graph = IrGraph(core.Graph(main.desc), for_test=False)
        test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)

        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
        exe = fluid.Executor(place)
        scope = fluid.Scope()
        with fluid.scope_guard(scope):
            exe.run(startup)
        train_transform_pass = QuantizationTransformPass(
            scope=scope,
            place=place,
            activation_quantize_type=activation_quant_type,
            weight_quantize_type=weight_quant_type,
            act_preprocess_func=act_preprocess_func,
            weight_preprocess_func=weight_preprocess_func,
            act_quantize_func=act_quantize_func,
            weight_quantize_func=weight_quantize_func,
            optimizer_func=get_optimizer,
            executor=exe)
        train_transform_pass.apply(main_graph)
        test_transform_pass = QuantizationTransformPass(
            scope=scope,
            place=place,
            activation_quantize_type=activation_quant_type,
            weight_quantize_type=weight_quant_type,
            act_preprocess_func=act_preprocess_func,
            weight_preprocess_func=weight_preprocess_func,
            act_quantize_func=act_quantize_func,
            weight_quantize_func=weight_quantize_func,
            optimizer_func=get_optimizer,
            executor=exe)

        test_transform_pass.apply(test_graph)
        save_dict(test_graph.out_node_mapping_table)

        add_quant_dequant_pass = AddQuantDequantPass(scope=scope, place=place)
        add_quant_dequant_pass.apply(main_graph)
        add_quant_dequant_pass.apply(test_graph)

        scale_training_pass = OutScaleForTrainingPass(scope=scope, place=place)
        scale_training_pass.apply(main_graph)

        dev_name = '_gpu' if use_cuda else '_cpu'

        build_strategy = fluid.BuildStrategy()
        build_strategy.memory_optimize = False
        build_strategy.enable_inplace = False
        build_strategy.fuse_all_reduce_ops = False
        binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
            loss_name=loss.name, build_strategy=build_strategy)
        iters = 5
        batch_size = 8

        train_reader = paddle.batch(
            paddle.reader.shuffle(
                paddle.dataset.mnist.train(), buf_size=500),
            batch_size=batch_size)
        feeder = fluid.DataFeeder(feed_list=feeds, place=place)
        with fluid.scope_guard(scope):
            for _ in range(iters):
                data = next(train_reader())
                loss_v = exe.run(binary,
                                 feed=feeder.feed(data),
                                 fetch_list=[loss])

        out_scale_infer_pass = OutScaleForInferencePass(scope=scope)
        out_scale_infer_pass.apply(test_graph)

        freeze_pass = QuantizationFreezePass(
            scope=scope,
            place=place,
            weight_bits=8,
            activation_bits=8,
            weight_quantize_type=weight_quant_type)

        mapping_table = load_dict()
        test_graph.out_node_mapping_table = mapping_table
        if act_quantize_func == None and weight_quantize_func == None:
            freeze_pass.apply(test_graph)

    def test_act_preprocess_cuda(self):
        if fluid.core.is_compiled_with_cuda():
            with fluid.unique_name.guard():
                self.quantization_scale(
                    True,
                    seed=1,
                    activation_quant_type='moving_average_abs_max',
                    weight_quant_type='channel_wise_abs_max',
                    for_ci=True,
                    act_preprocess_func=pact)

    def test_act_preprocess_cpu(self):
        with fluid.unique_name.guard():
            self.quantization_scale(
                False,
                seed=2,
                activation_quant_type='moving_average_abs_max',
                weight_quant_type='channel_wise_abs_max',
                for_ci=True,
                act_preprocess_func=pact)

    def test_weight_preprocess_cuda(self):
        if fluid.core.is_compiled_with_cuda():
            with fluid.unique_name.guard():
                self.quantization_scale(
                    True,
                    seed=1,
                    activation_quant_type='moving_average_abs_max',
                    weight_quant_type='channel_wise_abs_max',
                    for_ci=True,
                    weight_preprocess_func=pact)

    def test_weight_preprocess_cpu(self):
        with fluid.unique_name.guard():
            self.quantization_scale(
                False,
                seed=2,
                activation_quant_type='moving_average_abs_max',
                weight_quant_type='channel_wise_abs_max',
                for_ci=True,
                weight_preprocess_func=pact)

    def test_act_quantize_cuda(self):
        if fluid.core.is_compiled_with_cuda():
            with fluid.unique_name.guard():
                self.quantization_scale(
                    True,
                    seed=1,
                    activation_quant_type='moving_average_abs_max',
                    weight_quant_type='channel_wise_abs_max',
                    for_ci=True,
                    act_quantize_func=pact)

    def test_act_quantize_cpu(self):
        with fluid.unique_name.guard():
            self.quantization_scale(
                False,
                seed=2,
                activation_quant_type='moving_average_abs_max',
                weight_quant_type='channel_wise_abs_max',
                for_ci=True,
                act_quantize_func=pact)

    def test_weight_quantize_cuda(self):
        if fluid.core.is_compiled_with_cuda():
            with fluid.unique_name.guard():
                self.quantization_scale(
                    True,
                    seed=1,
                    activation_quant_type='moving_average_abs_max',
                    weight_quant_type='channel_wise_abs_max',
                    for_ci=True,
                    weight_quantize_func=pact)

    def test_weight_quantize_cpu(self):
        with fluid.unique_name.guard():
            self.quantization_scale(
                False,
                seed=2,
                activation_quant_type='moving_average_abs_max',
                weight_quant_type='channel_wise_abs_max',
                for_ci=True,
                weight_quantize_func=pact)


if __name__ == '__main__':
    unittest.main()
