# Copyright (c) 2021 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 paddle
import paddle.nn as nn
import paddle.static as static
import paddle.nn.functional as F
import paddle.utils as utils
import paddle.distributed.auto_parallel as auto
from paddle.distributed.auto_parallel.completion import Completer
from paddle.distributed.auto_parallel.dist_context import DistributedContext
from paddle.distributed import fleet
from paddle.distributed.auto_parallel.parallelizer import AutoParallelizer
from paddle.distributed.auto_parallel.partitioner import Partitioner
from paddle.distributed.auto_parallel.reshard import Resharder
from paddle.distributed.auto_parallel.process_group import _g_process_group_map
from paddle.distributed.auto_parallel.utils import print_program_with_dist_attr

paddle.enable_static()
_global_parallel_strategy = None
_global_process_mesh = None
PP_MESH_0 = None
PP_MESH_1 = None


class MLPLayer(nn.Layer):

    def __init__(self,
                 hidden_size=1024,
                 intermediate_size=4 * 1024,
                 initializer_range=0.02):
        super(MLPLayer, self).__init__()
        d_model = hidden_size
        dim_feedforward = intermediate_size
        weight_attr = paddle.ParamAttr(
            initializer=nn.initializer.Normal(mean=0.0, std=initializer_range))
        bias_attr = None

        self.linear0 = nn.Linear(d_model,
                                 dim_feedforward,
                                 weight_attr,
                                 bias_attr=bias_attr)
        self.linear1 = nn.Linear(dim_feedforward,
                                 d_model,
                                 weight_attr,
                                 bias_attr=bias_attr)
        self.norm = nn.LayerNorm(d_model, epsilon=1e-5)

    def forward(self, input):
        if _global_parallel_strategy == "pp":
            auto.shard_tensor(self.linear0.weight,
                              dist_attr={
                                  "process_mesh": PP_MESH_0,
                                  "dims_mapping": [-1, -1]
                              })
            auto.shard_tensor(self.linear1.weight,
                              dist_attr={
                                  "process_mesh": PP_MESH_1,
                                  "dims_mapping": [-1, -1]
                              })
        else:
            auto.shard_tensor(self.linear0.weight,
                              dist_attr={
                                  "process_mesh": _global_process_mesh,
                                  "dims_mapping": [-1, -1]
                              })
            auto.shard_tensor(self.linear1.weight,
                              dist_attr={
                                  "process_mesh": _global_process_mesh,
                                  "dims_mapping": [-1, -1]
                              })

        out = self.norm(input)
        out = self.linear0(out)
        out = F.gelu(out, approximate=True)
        out = self.linear1(out)

        return out


def mlp_forward(train_program, start_program):
    with static.program_guard(train_program,
                              start_program), utils.unique_name.guard():
        batch_size = 4
        hidden_size = 1024
        sequence_len = 512
        input = static.data(name="input",
                            shape=[batch_size, hidden_size],
                            dtype='float32')
        label = static.data(name="label",
                            shape=[batch_size, 1],
                            dtype='float32')

        if _global_parallel_strategy == "pp":
            auto.shard_tensor(input,
                              dist_attr={
                                  "process_mesh": PP_MESH_0,
                                  "dims_mapping": [-1, -1]
                              })
            auto.shard_tensor(label,
                              dist_attr={
                                  "process_mesh": PP_MESH_1,
                                  "dims_mapping": [-1, -1]
                              })
        elif _global_parallel_strategy == "dp":
            auto.shard_tensor(input,
                              dist_attr={
                                  "process_mesh": _global_process_mesh,
                                  "dims_mapping": [0, -1]
                              })
        else:
            auto.shard_tensor(input,
                              dist_attr={
                                  "process_mesh": _global_process_mesh,
                                  "dims_mapping": [-1, -1]
                              })

        mlp = MLPLayer(hidden_size=hidden_size,
                       intermediate_size=4 * hidden_size,
                       initializer_range=0.02)

        predict = mlp(input)
        error_cost = paddle.nn.functional.square_error_cost(predict, label)
        loss = paddle.mean(error_cost)

    return loss, train_program, start_program


def get_dist_prog(train_program,
                  startup_program,
                  dist_context,
                  rank_id,
                  change_process_mesh=False):
    loss, train_program, startup_program = mlp_forward(train_program,
                                                       startup_program)

    fleet._user_defined_strategy = fleet.DistributedStrategy()
    fleet.user_defined_optimizer = paddle.fluid.optimizer.AdamOptimizer()
    parallelizer = AutoParallelizer(fleet)
    parallelizer._dist_context = dist_context

    # serial forward & backward completion
    completer = Completer(dist_context)
    complete_train_program = completer.complete_forward_annotation(
        train_program)
    dist_context.block_state.parse_forward_blocks(complete_train_program)
    if change_process_mesh:
        global PP_MESH_1
        dist_context.get_tensor_dist_attr_for_program(
            train_program.global_block(
            ).vars["gelu_0.tmp_0"]).process_mesh = PP_MESH_1

    params_grads = parallelizer._generate_backward(complete_train_program,
                                                   startup_program,
                                                   loss,
                                                   parameter_list=None,
                                                   no_grad_set=None,
                                                   callbacks=None)

    # logical partition
    partitioner = Partitioner(dist_context, rank_id)
    auto_parallel_main_prog, auto_parallel_startup_prog, dist_params_grads = partitioner.partition(
        complete_train_program, startup_program, params_grads)

    partitioned_optimize_ops = parallelizer._apply_optimize(
        auto_parallel_main_prog, auto_parallel_startup_prog, dist_params_grads)

    return auto_parallel_main_prog, auto_parallel_startup_prog, dist_params_grads


def check_backward_dist_attr(dist_context, dist_main_prog, op_need_check):
    has_dist_attr = True
    vars = dist_main_prog.global_block().vars

    op_dist_attr = dist_context.get_op_dist_attr_for_program(op_need_check)
    if not op_dist_attr or not op_dist_attr.process_mesh:
        has_dist_attr = False

    for var_name in op_need_check.input_arg_names:
        if not op_dist_attr.get_input_dims_mapping(var_name) or \
        not dist_context.get_tensor_dist_attr_for_program(vars[var_name]).dims_mapping or \
        not dist_context.get_tensor_dist_attr_for_program(vars[var_name]).process_mesh:
            has_dist_attr = False
            break

    if has_dist_attr:
        for var_name in op_need_check.output_arg_names:
            if not dist_context.get_tensor_dist_attr_for_program(vars[var_name]).dims_mapping or \
            not dist_context.get_tensor_dist_attr_for_program(vars[var_name]).process_mesh:
                has_dist_attr = False
                break

    return has_dist_attr


def check_send_recv_result(dist_main_prog, rank_id):
    send_result = False
    recv_result = False
    ops = dist_main_prog.global_block().ops

    if rank_id == 0:
        for idx, op in enumerate(ops):
            if op.type == "send_v2" and "gelu_0.tmp_0" in op.input_arg_names:
                send_result = True
            if op.type == "recv_v2" and "gelu_0.tmp_0@GRAD" in op.output_arg_names[
                    0]:
                recv_result = True
    else:
        for idx, op in enumerate(ops):
            if op.type == "send_v2" and "gelu_0.tmp_0@GRAD" in op.input_arg_names:
                send_result = True
            if op.type == "recv_v2" and "gelu_0.tmp_0" in op.output_arg_names[0]:
                recv_result = True

    return send_result and recv_result


def check_initialization(dist_startup_prog, rank_id):
    if rank_id == 0:
        need_check_params = [
            "layer_norm_0.b_0", "layer_norm_0.w_0", "linear_0.w_0",
            "linear_0.b_0"
        ]
    else:
        need_check_params = ['linear_1.w_0', 'linear_1.b_0']

    params = []
    for var_name, var in dist_startup_prog.global_block().vars.items():
        if var.is_parameter:
            params.append(var_name)

    return params == need_check_params


def check_initialization_for_dp(dist_startup_prog):
    need_check_params = [
        "layer_norm_0.b_0", "layer_norm_0.w_0", "linear_0.w_0", "linear_0.b_0"
    ] + ['linear_1.w_0', 'linear_1.b_0']
    params = []
    for var_name, var in dist_startup_prog.global_block().vars.items():
        if var.is_parameter:
            params.append(var_name)
    broadcast_varnames = []
    for op in dist_startup_prog.global_block().ops:
        if op.type == "c_broadcast":
            broadcast_varnames.append(op.output_arg_names[0])

    return sorted(params) == sorted(need_check_params) == sorted(
        broadcast_varnames)


class TestMLPReshard(unittest.TestCase):

    def test_complete_backward_annotation(self):
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(mesh=[0, 1])

        train_program = paddle.static.Program()
        startup_program = paddle.static.Program()
        dist_context = DistributedContext()
        rank_id = 0
        dist_main_prog, dist_startup_prog, dist_params_grads = get_dist_prog(
            train_program, startup_program, dist_context, 0)

        op_need_check = None
        for op in dist_main_prog.global_block().ops:
            if op.type == "gelu_grad":
                op_need_check = op
                break
        # print_program_with_dist_attr(dist_main_prog, dist_context)

        # grad op should have dist attr
        self.assertTrue(
            check_backward_dist_attr(dist_context, dist_main_prog,
                                     op_need_check))

    def test_mlp_pp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "pp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(mesh=[0, 1])
        global PP_MESH_0
        PP_MESH_0 = auto.ProcessMesh(mesh=[0])
        global PP_MESH_1
        PP_MESH_1 = auto.ProcessMesh(mesh=[1])

        train_program = paddle.static.Program()
        startup_program = paddle.static.Program()
        dist_context = DistributedContext()
        rank_id = 1
        dist_main_prog, dist_startup_prog, dist_params_grads = get_dist_prog(
            train_program, startup_program, dist_context, rank_id)
        for key in list(_g_process_group_map.keys()):
            del _g_process_group_map[key]
        resharder = Resharder(dist_main_prog, dist_startup_prog, rank_id,
                              dist_context, dist_params_grads)
        resharder.reshard()

        # check send and recv result
        self.assertTrue(check_send_recv_result(dist_main_prog, rank_id))

        # parameter initialization of every rank should be different in the pipeline scene
        self.assertTrue(check_initialization(dist_startup_prog, rank_id))

    def test_mlp_pp_diff_process_mesh(self):
        train_program = paddle.static.Program()
        startup_program = paddle.static.Program()
        dist_context = DistributedContext()
        rank_id = 1
        dist_main_prog, dist_startup_prog, dist_params_grads = get_dist_prog(
            train_program, startup_program, dist_context, rank_id, True)
        for key in list(_g_process_group_map.keys()):
            del _g_process_group_map[key]
        resharder = Resharder(dist_main_prog, dist_startup_prog, rank_id,
                              dist_context, dist_params_grads)
        resharder.reshard()

        # check send and recv result
        self.assertTrue(check_send_recv_result(dist_main_prog, rank_id))
        self.assertTrue(check_initialization(dist_startup_prog, rank_id))

    def test_mlp_dp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "dp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(mesh=[0, 1])

        train_program = paddle.static.Program()
        startup_program = paddle.static.Program()
        dist_context = DistributedContext()
        rank_id = 0
        dist_main_prog, dist_startup_prog, dist_params_grads = get_dist_prog(
            train_program, startup_program, dist_context, rank_id)
        resharder = Resharder(dist_main_prog, dist_startup_prog, rank_id,
                              dist_context, dist_params_grads)
        resharder.reshard()
        # send and recv should not exist in dp scene.
        self.assertFalse(check_send_recv_result(dist_main_prog, rank_id))

        # all parameters should be initialized in dp scene
        self.assertTrue(check_initialization_for_dp(dist_startup_prog))


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