#  Copyright (c) 2022 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 unittest

import numpy as np
from op_test_ipu import IPUOpTest

import paddle
import paddle.static


class TestWeightSharing(IPUOpTest):
    def setUp(self):
        self.set_atol()
        self.set_training()
        self.set_data_feed()
        self.set_feed_attr()
        self.set_op_attrs()

    def set_atol(self):
        self.atol = 1e-6
        self.rtol = 1e-5
        self.atol_fp16 = 1e-2
        self.rtol_fp16 = 1e-3

    def set_data_feed(self):
        x = np.random.randint(0, 768, size=(128, 1)).astype(np.int32)
        self.feed_cpu = {"x": x.astype(np.int64)}
        self.feed_ipu = {
            "x": np.tile(x.astype(np.int64)[np.newaxis, :], [3, 1, 1])
        }

    def set_feed_attr(self):
        self.feed_shape = [x.shape for x in self.feed_cpu.values()]
        self.feed_list = list(self.feed_cpu.keys())

    def set_op_attrs(self):
        self.attrs = {}

    @IPUOpTest.static_graph
    def build_model(self):
        x = paddle.static.data(
            name=self.feed_list[0], shape=self.feed_shape[0], dtype='int64'
        )
        with paddle.static.ipu_shard_guard(index=0, stage=0):
            y = paddle.static.nn.embedding(
                input=x,
                size=[768, 768],
                dtype='float32',
                param_attr=paddle.base.ParamAttr(name='word_embedding'),
                is_sparse=False,
            )
        with paddle.static.ipu_shard_guard(index=1, stage=1):
            z = paddle.static.nn.fc(
                x=y, size=768, weight_attr=paddle.base.ParamAttr(name="fc")
            )
        with paddle.static.ipu_shard_guard(index=0, stage=2):
            out = paddle.matmul(
                x=z,
                y=self.main_prog.global_block().var('word_embedding'),
                transpose_y=True,
            )
        self.feed_list = [x.name]
        self.fetch_list = [out.name]

    def run_model(self, run_ipu):
        self.build_model()
        if run_ipu:
            place = paddle.IPUPlace()
        else:
            place = paddle.CPUPlace()
        exe = paddle.static.Executor(place)
        exe.run(self.startup_prog)
        if run_ipu:
            ipu_strategy = paddle.static.IpuStrategy()
            ipu_strategy.set_graph_config(
                num_ipus=2,
                is_training=self.is_training,
                enable_manual_shard=True,
            )
            ipu_strategy.set_pipelining_config(
                enable_pipelining=True, batches_per_step=3
            )
            program = paddle.static.IpuCompiledProgram(
                self.main_prog, ipu_strategy=ipu_strategy
            ).compile(self.feed_list, self.fetch_list)
        else:
            program = self.main_prog

        feed = self.feed_ipu if run_ipu else self.feed_cpu
        result = exe.run(program, feed=feed, fetch_list=self.fetch_list)
        return result[0]

    def test_base(self):
        res0 = self.run_model(False)
        res1 = self.run_model(True)

        np.testing.assert_allclose(
            res0.flatten(), res1[0].flatten(), rtol=1e-05, atol=self.atol
        )


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