#   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 numpy as np

import paddle.fluid as fluid
from paddle.fluid.transpiler.distribute_transpiler import delete_ops

from transpiler_test import TranspilerTest


class TestSimpleDistTranspiler(TranspilerTest):
    def setUp(self):
        self.current_pserver_ep = "127.0.0.1:6175"

    def test_simple_transpiler(self):
        np.random.seed(1)

        trainer = self.get_trainer()
        pserver, startup = self.get_pserver(self.current_pserver_ep)
        self.assertEqual([op.type for op in trainer.global_block().ops],
                         self.get_expect_trainer_ops())

        self.assertEqual(len(pserver.blocks), 2)
        # block0: listen_and_serv
        self.assertEqual([op.type for op in pserver.blocks[0].ops],
                         ["listen_and_serv"])
        # block1: optimize pass
        self.assertEqual([op.type for op in pserver.blocks[1].ops],
                         ["sum", "scale", "sgd"])

        # confirm startup program
        self.assertEqual([op.type for op in startup.global_block().ops],
                         ["fill_constant", "uniform_random", "uniform_random"])

        # the variable #fc_w will NOT be splited
        fc_w_var = startup.global_block().var("fc_w@GRAD")
        self.assertEqual(fc_w_var.shape, (1000, 1000))

        fc_w_var = startup.global_block().var("fc_w@GRAD.trainer_0")
        self.assertEqual(fc_w_var.shape, (1000, 1000))

    def get_expect_trainer_ops(self):
        trainer = fluid.Program()

        with fluid.program_guard(trainer):
            optimize_ops, params_grads = self.net_conf()

        delete_ops(trainer.global_block(), optimize_ops)
        ops = [op.type for op in trainer.global_block().ops] + [
            "send_vars", "send_barrier", "recv", "recv", "fetch_barrier"
        ]
        ops.insert(ops.index("elementwise_add_grad") + 1, "send_vars")
        return ops

    def _transpiler_instance(self):
        main = self.get_main_program()
        t = fluid.DistributeTranspiler()
        t.transpile(
            self.trainer_id,
            program=main,
            pservers=self.pserver_eps,
            trainers=self.trainers,
            slice_var_up=False)
        return t


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