test_imperative_qat.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.

from __future__ import print_function

import os
import numpy as np
import random
import shutil
import time
import unittest
import logging
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.optimizer import AdamOptimizer
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
from paddle.fluid.dygraph.container import Sequential
from paddle.nn import Linear, Conv2D, Softmax
from paddle.fluid.dygraph.nn import Pool2D
from paddle.fluid.log_helper import get_logger
from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
from paddle.fluid.contrib.slim.quantization.imperative.quant_nn import QuantizedConv2D

paddle.enable_static()

os.environ["CPU_NUM"] = "1"
if core.is_compiled_with_cuda():
    fluid.set_flags({"FLAGS_cudnn_deterministic": True})

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


def StaticLenet(data, num_classes=10):
    conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1")
    conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2")
    fc_w1_attr = fluid.ParamAttr(name="fc_w_1")
    fc_w2_attr = fluid.ParamAttr(name="fc_w_2")
    fc_w3_attr = fluid.ParamAttr(name="fc_w_3")
    conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1")
    conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2")
    fc_b1_attr = fluid.ParamAttr(name="fc_b_1")
    fc_b2_attr = fluid.ParamAttr(name="fc_b_2")
    fc_b3_attr = fluid.ParamAttr(name="fc_b_3")
    conv1 = fluid.layers.conv2d(
        data,
        num_filters=6,
        filter_size=3,
        stride=1,
        padding=1,
        param_attr=conv2d_w1_attr,
        bias_attr=conv2d_b1_attr)
    pool1 = fluid.layers.pool2d(
        conv1, pool_size=2, pool_type='max', pool_stride=2)
    conv2 = fluid.layers.conv2d(
        pool1,
        num_filters=16,
        filter_size=5,
        stride=1,
        padding=0,
        param_attr=conv2d_w2_attr,
        bias_attr=conv2d_b2_attr)
    pool2 = fluid.layers.pool2d(
        conv2, pool_size=2, pool_type='max', pool_stride=2)

    fc1 = fluid.layers.fc(input=pool2,
                          size=120,
                          param_attr=fc_w1_attr,
                          bias_attr=fc_b1_attr)
    fc2 = fluid.layers.fc(input=fc1,
                          size=84,
                          param_attr=fc_w2_attr,
                          bias_attr=fc_b2_attr)
    fc3 = fluid.layers.fc(input=fc2,
                          size=num_classes,
                          param_attr=fc_w3_attr,
                          bias_attr=fc_b3_attr)
    fc4 = fluid.layers.softmax(fc3, use_cudnn=True)

    return fc4


class ImperativeLenet(fluid.dygraph.Layer):
    def __init__(self, num_classes=10):
        super(ImperativeLenet, self).__init__()
        conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1")
        conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2")
        fc_w1_attr = fluid.ParamAttr(name="fc_w_1")
        fc_w2_attr = fluid.ParamAttr(name="fc_w_2")
        fc_w3_attr = fluid.ParamAttr(name="fc_w_3")
        conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1")
        conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2")
        fc_b1_attr = fluid.ParamAttr(name="fc_b_1")
        fc_b2_attr = fluid.ParamAttr(name="fc_b_2")
        fc_b3_attr = fluid.ParamAttr(name="fc_b_3")
        self.features = Sequential(
            Conv2D(
                in_channels=1,
                out_channels=6,
                kernel_size=3,
                stride=1,
                padding=1,
                weight_attr=conv2d_w1_attr,
                bias_attr=conv2d_b1_attr),
            Pool2D(
                pool_size=2, pool_type='max', pool_stride=2),
            Conv2D(
                in_channels=6,
                out_channels=16,
                kernel_size=5,
                stride=1,
                padding=0,
                weight_attr=conv2d_w2_attr,
                bias_attr=conv2d_b2_attr),
            Pool2D(
                pool_size=2, pool_type='max', pool_stride=2))

        self.fc = Sequential(
            Linear(
                in_features=400,
                out_features=120,
                weight_attr=fc_w1_attr,
                bias_attr=fc_b1_attr),
            Linear(
                in_features=120,
                out_features=84,
                weight_attr=fc_w2_attr,
                bias_attr=fc_b2_attr),
            Linear(
                in_features=84,
                out_features=num_classes,
                weight_attr=fc_w3_attr,
                bias_attr=fc_b3_attr),
            Softmax())

    def forward(self, inputs):
        x = self.features(inputs)
        x = fluid.layers.flatten(x, 1)
        x = self.fc(x)
        return x


class TestImperativeQat(unittest.TestCase):
    """
    QAT = quantization-aware training
    """

    @classmethod
    def setUpClass(cls):
        timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
        cls.root_path = os.path.join(os.getcwd(), "imperative_qat_" + timestamp)
        cls.save_path = os.path.join(cls.root_path, "lenet")
        cls.dynamic_root_path = os.path.join(os.getcwd(),
                                             "dynamic_mnist_" + timestamp)
        cls.dynamic_save_path = os.path.join(cls.dynamic_root_path, "model")

    @classmethod
    def tearDownClass(cls):
        shutil.rmtree(cls.root_path)
        shutil.rmtree(cls.dynamic_root_path)

    def test_qat_save(self):
        imperative_qat = ImperativeQuantAware(
            weight_quantize_type='abs_max',
            activation_quantize_type='moving_average_abs_max')
        with fluid.dygraph.guard():
            # For CI coverage
            conv1 = Conv2D(
                in_channels=3,
                out_channels=2,
                kernel_size=3,
                stride=1,
                padding=1,
                padding_mode='replicate')
            quant_conv1 = QuantizedConv2D(conv1)
            data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
            quant_conv1(fluid.dygraph.to_variable(data))

            lenet = ImperativeLenet()
            imperative_qat.quantize(lenet)
            adam = AdamOptimizer(
                learning_rate=0.001, parameter_list=lenet.parameters())
            train_reader = paddle.batch(
                paddle.dataset.mnist.train(), batch_size=32, drop_last=True)
            test_reader = paddle.batch(
                paddle.dataset.mnist.test(), batch_size=32)

            epoch_num = 1
            for epoch in range(epoch_num):
                lenet.train()
                for batch_id, data in enumerate(train_reader()):
                    x_data = np.array([x[0].reshape(1, 28, 28)
                                       for x in data]).astype('float32')
                    y_data = np.array(
                        [x[1] for x in data]).astype('int64').reshape(-1, 1)

                    img = fluid.dygraph.to_variable(x_data)
                    label = fluid.dygraph.to_variable(y_data)
                    out = lenet(img)
                    acc = fluid.layers.accuracy(out, label)
                    loss = fluid.layers.cross_entropy(out, label)
                    avg_loss = fluid.layers.mean(loss)
                    avg_loss.backward()
                    adam.minimize(avg_loss)
                    lenet.clear_gradients()
                    if batch_id % 100 == 0:
                        _logger.info(
                            "Train | At epoch {} step {}: loss = {:}, acc= {:}".
                            format(epoch, batch_id,
                                   avg_loss.numpy(), acc.numpy()))
                    if batch_id == 500:  # For shortening CI time
                        break

                lenet.eval()
                for batch_id, data in enumerate(test_reader()):
                    x_data = np.array([x[0].reshape(1, 28, 28)
                                       for x in data]).astype('float32')
                    y_data = np.array(
                        [x[1] for x in data]).astype('int64').reshape(-1, 1)

                    img = fluid.dygraph.to_variable(x_data)
                    label = fluid.dygraph.to_variable(y_data)

                    out = lenet(img)
                    acc_top1 = fluid.layers.accuracy(
                        input=out, label=label, k=1)
                    acc_top5 = fluid.layers.accuracy(
                        input=out, label=label, k=5)

                    if batch_id % 100 == 0:
                        _logger.info(
                            "Test | At epoch {} step {}: acc1 = {:}, acc5 = {:}".
                            format(epoch, batch_id,
                                   acc_top1.numpy(), acc_top5.numpy()))

            # save weights
            model_dict = lenet.state_dict()
            fluid.save_dygraph(model_dict, "save_temp")

            # test the correctness of `paddle.jit.save`
            data = next(test_reader())
            test_data = np.array([x[0].reshape(1, 28, 28)
                                  for x in data]).astype('float32')
            test_img = fluid.dygraph.to_variable(test_data)
            lenet.eval()
            before_save = lenet(test_img)

        # save inference quantized model
        paddle.jit.save(
            layer=lenet,
            path=TestImperativeQat.save_path,
            input_spec=[
                paddle.static.InputSpec(
                    shape=[None, 1, 28, 28], dtype='float32')
            ])

        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        exe = fluid.Executor(place)
        [inference_program, feed_target_names,
         fetch_targets] = fluid.io.load_inference_model(
             dirname=TestImperativeQat.root_path,
             executor=exe,
             model_filename="lenet" + INFER_MODEL_SUFFIX,
             params_filename="lenet" + INFER_PARAMS_SUFFIX)
        after_save, = exe.run(inference_program,
                              feed={feed_target_names[0]: test_data},
                              fetch_list=fetch_targets)

        self.assertTrue(
            np.allclose(after_save, before_save.numpy()),
            msg='Failed to save the inference quantized model.')

    def test_qat_acc(self):
        def _build_static_lenet(main, startup, is_test=False, seed=1000):
            with fluid.unique_name.guard():
                with fluid.program_guard(main, startup):
                    main.random_seed = seed
                    startup.random_seed = seed
                    img = fluid.layers.data(
                        name='image', shape=[1, 28, 28], dtype='float32')
                    label = fluid.layers.data(
                        name='label', shape=[1], dtype='int64')
                    prediction = StaticLenet(img)
                    if not is_test:
                        loss = fluid.layers.cross_entropy(
                            input=prediction, label=label)
                        avg_loss = fluid.layers.mean(loss)
                    else:
                        avg_loss = prediction
            return img, label, avg_loss

        reader = paddle.batch(
            paddle.dataset.mnist.test(), batch_size=32, drop_last=True)
        weight_quantize_type = 'abs_max'
        activation_quant_type = 'moving_average_abs_max'
        param_init_map = {}
        seed = 1000
        lr = 0.01

        # imperative train
        _logger.info(
            "--------------------------dynamic graph qat--------------------------"
        )
        imperative_qat = ImperativeQuantAware(
            weight_quantize_type=weight_quantize_type,
            activation_quantize_type=activation_quant_type)

        with fluid.dygraph.guard():
            np.random.seed(seed)
            fluid.default_main_program().random_seed = seed
            fluid.default_startup_program().random_seed = seed
            lenet = ImperativeLenet()
            fixed_state = {}
            for name, param in lenet.named_parameters():
                p_shape = param.numpy().shape
                p_value = param.numpy()
                if name.endswith("bias"):
                    value = np.zeros_like(p_value).astype('float32')
                else:
                    value = np.random.normal(
                        loc=0.0, scale=0.01, size=np.product(p_shape)).reshape(
                            p_shape).astype('float32')
                fixed_state[name] = value
                param_init_map[param.name] = value
            lenet.set_dict(fixed_state)

            imperative_qat.quantize(lenet)
            adam = AdamOptimizer(
                learning_rate=lr, parameter_list=lenet.parameters())
            dynamic_loss_rec = []
            lenet.train()
            for batch_id, data in enumerate(reader()):
                x_data = np.array([x[0].reshape(1, 28, 28)
                                   for x in data]).astype('float32')
                y_data = np.array(
                    [x[1] for x in data]).astype('int64').reshape(-1, 1)

                img = fluid.dygraph.to_variable(x_data)
                label = fluid.dygraph.to_variable(y_data)

                out = lenet(img)
                loss = fluid.layers.cross_entropy(out, label)
                avg_loss = fluid.layers.mean(loss)
                avg_loss.backward()
                adam.minimize(avg_loss)
                lenet.clear_gradients()
                dynamic_loss_rec.append(avg_loss.numpy()[0])
                if batch_id % 100 == 0:
                    _logger.info('{}: {}'.format('loss', avg_loss.numpy()))

        paddle.jit.save(
            layer=lenet,
            path=TestImperativeQat.dynamic_save_path,
            input_spec=[
                paddle.static.InputSpec(
                    shape=[None, 1, 28, 28], dtype='float32')
            ])

        # static graph train
        _logger.info(
            "--------------------------static graph qat--------------------------"
        )
        static_loss_rec = []
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        exe = fluid.Executor(place)

        main = fluid.Program()
        infer = fluid.Program()
        startup = fluid.Program()
        static_img, static_label, static_loss = _build_static_lenet(
            main, startup, False, seed)
        infer_img, _, infer_pre = _build_static_lenet(infer, startup, True,
                                                      seed)
        with fluid.unique_name.guard():
            with fluid.program_guard(main, startup):
                opt = AdamOptimizer(learning_rate=lr)
                opt.minimize(static_loss)

        scope = core.Scope()
        with fluid.scope_guard(scope):
            exe.run(startup)
        for param in main.all_parameters():
            param_tensor = scope.var(param.name).get_tensor()
            param_tensor.set(param_init_map[param.name], place)

        main_graph = IrGraph(core.Graph(main.desc), for_test=False)
        infer_graph = IrGraph(core.Graph(infer.desc), for_test=True)
        transform_pass = QuantizationTransformPass(
            scope=scope,
            place=place,
            activation_quantize_type=activation_quant_type,
            weight_quantize_type=weight_quantize_type,
            quantizable_op_type=['conv2d', 'depthwise_conv2d', 'mul'])
        transform_pass.apply(main_graph)
        transform_pass.apply(infer_graph)
        build_strategy = fluid.BuildStrategy()
        build_strategy.fuse_all_reduce_ops = False
        binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
            loss_name=static_loss.name, build_strategy=build_strategy)

        feeder = fluid.DataFeeder(
            feed_list=[static_img, static_label], place=place)
        with fluid.scope_guard(scope):
            for batch_id, data in enumerate(reader()):
                loss_v, = exe.run(binary,
                                  feed=feeder.feed(data),
                                  fetch_list=[static_loss])
                static_loss_rec.append(loss_v[0])
                if batch_id % 100 == 0:
                    _logger.info('{}: {}'.format('loss', loss_v))

        save_program = infer_graph.to_program()
        with fluid.scope_guard(scope):
            fluid.io.save_inference_model("./static_mnist", [infer_img.name],
                                          [infer_pre], exe, save_program)
        rtol = 1e-05
        atol = 1e-08
        for i, (loss_d,
                loss_s) in enumerate(zip(dynamic_loss_rec, static_loss_rec)):
            diff = np.abs(loss_d - loss_s)
            if diff > (atol + rtol * np.abs(loss_s)):
                _logger.info(
                    "diff({}) at {}, dynamic loss = {}, static loss = {}".
                    format(diff, i, loss_d, loss_s))
                break

        self.assertTrue(
            np.allclose(
                np.array(dynamic_loss_rec),
                np.array(static_loss_rec),
                rtol=rtol,
                atol=atol,
                equal_nan=True),
            msg='Failed to do the imperative qat.')


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