#   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 unittest
import os
import tempfile
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
import paddle.fluid.layers as layers
import paddle.fluid.core as core
from paddle.fluid import compiler, Program, program_guard
import paddle.fluid.proto.profiler.profiler_pb2 as profiler_pb2


class TestProfiler(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        os.environ['CPU_NUM'] = str(4)

    def net_profiler(self,
                     state,
                     option,
                     iter_range=None,
                     use_parallel_executor=False):
        profile_path = os.path.join(tempfile.gettempdir(), "profile")
        open(profile_path, "w").write("")
        startup_program = fluid.Program()
        main_program = fluid.Program()

        with fluid.program_guard(main_program, startup_program):
            image = fluid.layers.data(name='x', shape=[784], dtype='float32')
            hidden1 = fluid.layers.fc(input=image, size=64, act='relu')
            i = layers.zeros(shape=[1], dtype='int64')
            counter = fluid.layers.zeros(
                shape=[1], dtype='int64', force_cpu=True)
            until = layers.fill_constant([1], dtype='int64', value=10)
            data_arr = layers.array_write(hidden1, i)
            cond = fluid.layers.less_than(x=counter, y=until)
            while_op = fluid.layers.While(cond=cond)
            with while_op.block():
                hidden_n = fluid.layers.fc(input=hidden1, size=64, act='relu')
                layers.array_write(hidden_n, i, data_arr)
                fluid.layers.increment(x=counter, value=1, in_place=True)
                layers.less_than(x=counter, y=until, cond=cond)

            hidden_n = layers.array_read(data_arr, i)
            hidden2 = fluid.layers.fc(input=hidden_n, size=64, act='relu')
            predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
            label = fluid.layers.data(name='y', shape=[1], dtype='int64')
            cost = fluid.layers.cross_entropy(input=predict, label=label)
            avg_cost = fluid.layers.mean(cost)
            batch_size = fluid.layers.create_tensor(dtype='int64')
            batch_acc = fluid.layers.accuracy(
                input=predict, label=label, total=batch_size)

        optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
        opts = optimizer.minimize(avg_cost, startup_program=startup_program)

        place = fluid.CPUPlace() if state == 'CPU' else fluid.CUDAPlace(0)
        exe = fluid.Executor(place)
        exe.run(startup_program)
        if use_parallel_executor:
            pe = fluid.ParallelExecutor(
                state != 'CPU',
                loss_name=avg_cost.name,
                main_program=main_program)

        pass_acc_calculator = fluid.average.WeightedAverage()
        with profiler.profiler(state, 'total', profile_path, option) as prof:
            for iter in range(10):
                if iter == 2:
                    profiler.reset_profiler()
                x = np.random.random((32, 784)).astype("float32")
                y = np.random.randint(0, 10, (32, 1)).astype("int64")

                if use_parallel_executor:
                    pe.run(feed={'x': x, 'y': y}, fetch_list=[avg_cost.name])
                    continue
                outs = exe.run(main_program,
                               feed={'x': x,
                                     'y': y},
                               fetch_list=[avg_cost, batch_acc, batch_size])
                acc = np.array(outs[1])
                b_size = np.array(outs[2])
                pass_acc_calculator.add(value=acc, weight=b_size)
                pass_acc = pass_acc_calculator.eval()
        data = open(profile_path, 'rb').read()
        if (len(data) > 0):
            profile_pb = profiler_pb2.Profile()
            profile_pb.ParseFromString(data)
            self.assertGreater(len(profile_pb.events), 0)
            for event in profile_pb.events:
                if event.type == profiler_pb2.Event.GPUKernel:
                    if not event.detail_info and not event.name.startswith(
                            "MEM"):
                        raise Exception(
                            "Kernel %s missing event. Has this kernel been recorded by RecordEvent?"
                            % event.name)
                elif event.type == profiler_pb2.Event.CPU and (
                        event.name.startswith("Driver API") or
                        event.name.startswith("Runtime API")):
                    print("Warning: unregister", event.name)

    def test_cpu_profiler(self):
        self.net_profiler('CPU', "Default")
        self.net_profiler('CPU', "Default", use_parallel_executor=True)

    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "profiler is enabled only with GPU")
    def test_cuda_profiler(self):
        self.net_profiler('GPU', "OpDetail")
        self.net_profiler('GPU', "OpDetail", use_parallel_executor=True)

    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "profiler is enabled only with GPU")
    def test_all_profiler(self):
        self.net_profiler('All', "AllOpDetail")
        self.net_profiler('All', "AllOpDetail", use_parallel_executor=True)


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