# 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 paddle
import numpy as np
import paddle.nn as nn
import paddle.fluid as fluid
import paddle.static as static
import paddle.nn.functional as F
from paddle.distributed.fleet import auto

from paddle.distributed import fleet
from paddle.distributed.auto_parallel.completion import Completer
from paddle.distributed.auto_parallel.partitioner import Partitioner
from paddle.distributed.auto_parallel.utils import make_data_unshard
from paddle.distributed.auto_parallel.dist_context import (
    get_default_distributed_context,
)

paddle.enable_static()

batch_size = 4
epoch_num = 10
hidden_size = 1024
sequence_len = 512
_g_process_mesh = auto.ProcessMesh([0, 1], dim_names=['x'])


def get_random_inputs_and_labels(input_shape, label_shape):
    input = np.random.random(size=input_shape).astype('float32')
    label = np.random.random(size=label_shape).astype('float32')
    return input, label


def batch_generator_creator():
    def __reader__():
        for _ in range(batch_size):
            batch_input, batch_label = get_random_inputs_and_labels(
                [batch_size, sequence_len, hidden_size],
                [batch_size, sequence_len, 1],
            )
            yield batch_input, batch_label

    return __reader__


class MLPLayer(nn.Layer):
    def __init__(
        self,
        hidden_size=1024,
        intermediate_size=4 * 1024,
        dropout_ratio=0.1,
        initializer_range=0.02,
    ):
        super(MLPLayer, self).__init__()
        d_model = hidden_size
        dim_feedforward = intermediate_size
        param_initializer = nn.initializer.Normal(
            mean=0.0, std=initializer_range
        )

        self.norm = nn.LayerNorm(d_model, epsilon=1e-5)
        self.linear0 = nn.Linear(
            d_model,
            dim_feedforward,
            weight_attr=paddle.ParamAttr(initializer=param_initializer),
            bias_attr=None,
        )
        self.linear1 = nn.Linear(
            dim_feedforward,
            d_model,
            weight_attr=paddle.ParamAttr(initializer=param_initializer),
            bias_attr=None,
        )

    def forward(self, input):

        auto.shard_tensor(self.norm.weight, _g_process_mesh, [None])
        auto.shard_tensor(self.norm.bias, _g_process_mesh, [None])
        auto.shard_tensor(self.linear0.weight, _g_process_mesh, [None, 'x'])
        auto.shard_tensor(self.linear0.bias, _g_process_mesh, ['x'])
        auto.shard_tensor(self.linear1.weight, _g_process_mesh, ['x', None])
        auto.shard_tensor(self.linear1.bias, _g_process_mesh, [None])

        out = self.norm(input)
        auto.shard_tensor(out, _g_process_mesh, [None, None, None])
        out = self.linear0(out)
        auto.shard_tensor(out, _g_process_mesh, [None, None, 'x'])
        out = F.gelu(out, approximate=True)
        auto.shard_tensor(out, _g_process_mesh, [None, None, 'x'])
        out = self.linear1(out)
        auto.shard_tensor(out, _g_process_mesh, [None, None, None])

        return out


def get_program():
    dist_strategy = fleet.DistributedStrategy()
    dist_strategy.semi_auto = True
    # fleet.init(is_collective=True, strategy=dist_strategy)

    train_program = static.Program()
    start_program = static.Program()
    with fluid.program_guard(train_program, start_program):

        # 循环计数器
        i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
        auto.shard_tensor(i, _g_process_mesh, [None])

        # 循环次数
        loop_len = fluid.layers.fill_constant(
            shape=[1], dtype='int64', value=epoch_num
        )
        auto.shard_tensor(loop_len, _g_process_mesh, [None])

        # input
        input = static.data(
            name="input",
            shape=[batch_size, sequence_len, hidden_size],
            dtype='float32',
        )
        label = static.data(
            name="label", shape=[batch_size, sequence_len, 1], dtype='float32'
        )

        data_holder = [input, label]
        # dataloader
        dataloader = paddle.io.DataLoader.from_generator(
            feed_list=data_holder, capacity=4 * batch_size, iterable=False
        )
        dataloader.set_batch_generator(
            batch_generator_creator(), places=paddle.static.cuda_places()
        )
        # data dist_attr
        auto.shard_tensor(input, _g_process_mesh, [None, None, None])
        auto.shard_tensor(label, _g_process_mesh, [None, None, None])

        # fill constant bsz like
        tmp = paddle.fluid.layers.fill_constant_batch_size_like(
            input=input, shape=[-1, 16, 0, 48], dtype='float32', value=0
        )
        auto.shard_tensor(tmp, _g_process_mesh, [None, 'x', None, None])

        # model
        mlp_start = MLPLayer(
            hidden_size=hidden_size,
            intermediate_size=4 * hidden_size,
            dropout_ratio=0.1,
            initializer_range=0.02,
        )
        pred = mlp_start(input)

        input_array = fluid.layers.array_write(pred, i)
        # TODO: check whether this annotation is needed
        # auto.shard_tensor(input_array,
        #                   dist_attr={
        #                       "process_mesh": _g_process_mesh,
        #                       "dims_mapping": [-1, -1, -1]
        #                   })

        cond = fluid.layers.less_than(x=i, y=loop_len)
        auto.shard_tensor(cond, _g_process_mesh, [None])

        while_op = fluid.layers.While(cond=cond)
        with while_op.block():

            pre_input = fluid.layers.array_read(array=input_array, i=i)
            auto.shard_tensor(pre_input, _g_process_mesh, [None, None, None])

            mlp_while = MLPLayer(
                hidden_size=hidden_size,
                intermediate_size=4 * hidden_size,
                dropout_ratio=0.1,
                initializer_range=0.02,
            )
            cur_pred = mlp_while(pre_input)

            # 更新循环条件
            i = fluid.layers.increment(x=i, value=1, in_place=True)
            fluid.layers.array_write(cur_pred, array=input_array, i=i)
            fluid.layers.less_than(x=i, y=loop_len, cond=cond)

        end_pred = fluid.layers.array_read(array=input_array, i=i)
        auto.shard_tensor(end_pred, _g_process_mesh, [None, None, None])

        mlp_end = MLPLayer(
            hidden_size=hidden_size,
            intermediate_size=4 * hidden_size,
            dropout_ratio=0.1,
            initializer_range=0.02,
        )
        pred = mlp_end(end_pred)

        error_cost = paddle.nn.functional.square_error_cost(pred, label)
        auto.shard_tensor(error_cost, _g_process_mesh, [None, None, None])

        loss = paddle.mean(error_cost)
        auto.shard_tensor(loss, _g_process_mesh, [None])

    return train_program, start_program, dataloader, i, loss


def completion(train_program, start_program, dist_context):
    # blocks = train_program.blocks
    # # completion tensors
    # for block in blocks:
    #     for op in block.ops:
    #         if op.type == "layer_norm":
    #             for out_name in op.output_arg_names:
    #                 out_var = block.vars[out_name]
    #                 tensor_dist_attr = dist_context.get_tensor_dist_attr_for_program(
    #                     out_var)
    #                 if tensor_dist_attr:
    #                     continue
    #                 tensor_dist_attr = TensorDistributedAttribute()
    #                 tensor_dist_attr.process_mesh = _g_process_mesh
    #                 tensor_dist_attr.dims_mapping = [-1]
    #                 dist_context.set_tensor_dist_attr_for_program(
    #                     out_var, tensor_dist_attr)

    #         elif op.type == "elementwise_sub":
    #             for out_name in op.output_arg_names:
    #                 out_var = block.vars[out_name]
    #                 tensor_dist_attr = TensorDistributedAttribute()
    #                 tensor_dist_attr.process_mesh = _g_process_mesh
    #                 tensor_dist_attr.dims_mapping = [-1, -1, -1]
    #                 dist_context.set_tensor_dist_attr_for_program(
    #                     out_var, tensor_dist_attr)

    #         elif op.type == "matmul_v2":
    #             col = False
    #             for in_name in op.input_arg_names:
    #                 if ".w_" not in in_name:
    #                     continue
    #                 if in_name not in block.vars:
    #                     in_var = blocks[0].vars[in_name]
    #                 else:
    #                     in_var = block.vars[in_name]
    #                 tensor_dist_attr = dist_context.get_tensor_dist_attr_for_program(
    #                     in_var)
    #                 assert tensor_dist_attr is not None
    #                 if tensor_dist_attr.dims_mapping == [-1, 0]:
    #                     col = True
    #             for out_name in op.output_arg_names:
    #                 out_var = block.vars[out_name]
    #                 tensor_dist_attr = dist_context.get_tensor_dist_attr_for_program(
    #                     out_var)
    #                 if tensor_dist_attr:
    #                     continue
    #                 tensor_dist_attr = TensorDistributedAttribute()
    #                 tensor_dist_attr.process_mesh = _g_process_mesh
    #                 if col:
    #                     tensor_dist_attr.dims_mapping = [-1, -1, 0]
    #                 else:
    #                     tensor_dist_attr.dims_mapping = [-1, -1, -1]
    #                 dist_context.set_tensor_dist_attr_for_program(
    #                     out_var, tensor_dist_attr)
    #         elif op.type == "while":
    #             out_name = op.desc.output("StepScopes")[0]
    #             out_var = block.vars[out_name]
    #             tensor_dist_attr = TensorDistributedAttribute()
    #             tensor_dist_attr.process_mesh = _g_process_mesh
    #             tensor_dist_attr.dims_mapping = [-1]
    #             dist_context.set_tensor_dist_attr_for_program(out_var,
    #                                                           tensor_dist_attr)

    # # completion ops
    # for block in blocks:
    #     for op in block.ops:
    #         op_dist_attr = OperatorDistributedAttribute()
    #         op_dist_attr.process_mesh = _g_process_mesh
    #         if op.type == "create_by_read" or op.type == "create_double_buffer_reader":
    #             for in_name in op.input_arg_names:
    #                 op_dist_attr.set_input_dims_mapping(in_name, [])
    #             for out_name in op.output_arg_names:
    #                 op_dist_attr.set_output_dims_mapping(out_name, [])
    #         elif op.type == "read":
    #             for in_name in op.input_arg_names:
    #                 op_dist_attr.set_output_dims_mapping(in_name, [])
    #             for out_name in op.output_arg_names:
    #                 out_var = block.vars[out_name]
    #                 out_dist_attr = dist_context.get_tensor_dist_attr_for_program(
    #                     out_var)
    #                 op_dist_attr.set_output_dist_attr(out_name, out_dist_attr)
    #         elif op.type == "while":
    #             for in_name in op.input_arg_names:
    #                 in_var = block.vars[in_name]
    #                 in_dist_attr = dist_context.get_tensor_dist_attr_for_program(
    #                     in_var)
    #                 op_dist_attr.set_input_dist_attr(in_name, in_dist_attr)
    #             for out_name in op.output_arg_names:
    #                 if out_name == op.desc.output("StepScopes")[0]:
    #                     op_dist_attr.set_output_dims_mapping(out_name, [])
    #                 else:
    #                     out_var = block.vars[out_name]
    #                     out_dist_attr = dist_context.get_tensor_dist_attr_for_program(
    #                         out_var)
    #                     op_dist_attr.set_output_dist_attr(out_name,
    #                                                       out_dist_attr)
    #         else:
    #             for in_name in op.input_arg_names:
    #                 if in_name == "lod_tensor_blocking_queue_0":
    #                     continue
    #                 if in_name not in block.vars:
    #                     in_var = blocks[0].vars[in_name]
    #                 else:
    #                     in_var = block.vars[in_name]
    #                 in_dist_attr = dist_context.get_tensor_dist_attr_for_program(
    #                     in_var)
    #                 op_dist_attr.set_input_dist_attr(in_name, in_dist_attr)
    #             for out_name in op.output_arg_names:
    #                 if out_name not in block.vars:
    #                     out_var = blocks[0].vars[out_name]
    #                 else:
    #                     out_var = block.vars[out_name]
    #                 out_dist_attr = dist_context.get_tensor_dist_attr_for_program(
    #                     out_var)
    #                 op_dist_attr.set_output_dist_attr(out_name, out_dist_attr)

    #         if op.type == "matmul_v2":
    #             op_dist_attr.impl_type = "matmul_v2"
    #             for in_name in op_dist_attr.inputs_dist_attrs.keys():
    #                 in_dist_attr = op_dist_attr.inputs_dist_attrs[in_name]
    #                 if ".w_" in in_name and in_dist_attr.dims_mapping[-1] == 0:
    #                     op_dist_attr.impl_idx = 0
    #                 else:
    #                     op_dist_attr.impl_idx = 1
    #         elif op.type == "fill_constant_batch_size_like":
    #             op_dist_attr.impl_type = "fill_constant_batch_size_like"
    #             op_dist_attr.impl_idx = 0
    #         else:
    #             op_dist_attr.impl_type = "default"
    #             op_dist_attr.impl_idx = 0

    #         dist_context.set_op_dist_attr_for_program(op, op_dist_attr)
    #         make_data_unshard(train_program, start_program, dist_context)

    completer = Completer(dist_context)
    train_program = completer.complete_forward_annotation(train_program)
    make_data_unshard(train_program, start_program, dist_context)

    return train_program, start_program


def partition(train_program, start_program, dist_context):

    # optimizer = paddle.optimizer.SGD(learning_rate=0.00001)
    rank = paddle.distributed.get_rank()
    partitioner = Partitioner(dist_context, rank)
    dist_main_prog, dist_startup_prog, _ = partitioner.partition(
        train_program, start_program, []
    )

    return dist_main_prog, dist_startup_prog


class TestMLP(unittest.TestCase):
    def test_partitioner(self):

        train_program, start_program, dataloader, i, loss = get_program()
        dist_context = get_default_distributed_context()
        train_program, start_program = completion(
            train_program, start_program, dist_context
        )
        dist_context.block_state.parse_forward_blocks(train_program)

        dist_main_prog, dist_startup_prog = partition(
            train_program, start_program, dist_context
        )
        global_block_ops = dist_main_prog.blocks[0].ops

        fill_op = None
        for op in global_block_ops:
            if op.type == "fill_constant_batch_size_like":
                fill_op = op

        global_block_ops = [op.type for op in global_block_ops]
        sub_block_ops = dist_main_prog.blocks[1].ops
        sub_block_ops = [op.type for op in sub_block_ops]

        self.assertTrue("c_allreduce_sum" in global_block_ops)
        self.assertTrue("c_allreduce_sum" in sub_block_ops)

        # test fill_constant_batch_size_like

        self.assertIsNotNone(fill_op)
        ref_shape = [-1, 8, 0, 48]
        shape = fill_op.attr("shape")
        self.assertTrue(ref_shape == shape)


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