#   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

import os
import operator
import functools
import json
import paddle
from collections import deque
from .graph import Node
from .graph import Edge
from .graph import Graph
from .cluster import DeviceType
from .process_group import get_process_group


def is_collective_comm_op(op):
    comm_list = [
        "c_allreduce_sum", "c_allreduce_min", "c_allreduce_max",
        "c_allreduce_prod", "c_reduce_sum", "c_reduce_min", "c_reduce_max",
        "c_reduce_prod", "c_broadcast", "c_allgather"
    ]
    if op.type in comm_list:
        return True
    else:
        return False


def is_p2p_comm_op(op):
    comm_list = ["send_v2", "recv_v2"]
    if op.type in comm_list:
        return True
    else:
        return False


def get_dtype_bytes(dtype):
    num_bytes = 0
    if dtype == paddle.float64:
        num_bytes = 8
    elif dtype == paddle.float32:
        num_bytes = 4
    elif dtype == paddle.float16:
        num_bytes = 2
    elif dtype == paddle.bfloat16:
        num_bytes = 2
    elif dtype == paddle.int64:
        num_bytes = 8
    elif dtype == paddle.int32:
        num_bytes = 4
    elif dtype == paddle.int16:
        num_bytes = 2
    elif dtype == paddle.int8:
        num_bytes = 1
    elif dtype == paddle.uint8:
        num_bytes = 1
    else:
        raise ValueError("Unrecognized dtype {}.".format(dtype))
    return num_bytes


def get_comm_volume(comm_op, src_rank, tgt_rank):
    comm_volume = None
    if src_rank == tgt_rank:
        return comm_volume
    comm_op_type = comm_op.type
    if comm_op_type != "recv_v2":
        tensor_name = comm_op.input_arg_names[0]
    else:
        tensor_name = comm_op.output_arg_names[0]
    tensor = comm_op.block._find_var_recursive(tensor_name)
    assert tensor is not None
    tensor_shape = tensor.shape
    # Skip the batch dim
    new_tensor_shape = []
    for val in tensor_shape:
        if val == -1:
            print("Warning: -1 in the tensor shape.")
            new_tensor_shape.append(1)
        else:
            new_tensor_shape.append(val)
    tensor_size = functools.reduce(operator.mul, new_tensor_shape, 1)
    tensor_bytes = tensor_size * get_dtype_bytes(tensor.dtype)
    if "c_allreduce" in comm_op_type:
        comm_volume = 2 * tensor_bytes
    elif "c_allgather" in comm_op_type:
        comm_volume = tensor_bytes
    elif "c_broadcast" in comm_op_type:
        if comm_op.attr("root") == src_rank:
            comm_volume = tensor_bytes
        else:
            comm_volume = None
    elif "c_reduce" in comm_op_type:
        if comm_op.attr("root_id") == src_rank:
            comm_volume = None
        else:
            comm_volume = tensor_bytes
    elif "send_v2" in comm_op_type:
        if comm_op.attr("peer") == tgt_rank:
            comm_volume = tensor_bytes
        else:
            comm_volume = None
    elif "recv_v2" in comm_op_type:
        comm_volume = None
    else:
        raise ValueError("Unrecognized communication operator.")
    return comm_volume


def analyze_comm_requirements_from_op(op, rank, g_process_group_map):
    comm_requirements_to_ranks = {}
    if is_collective_comm_op(op):
        process_group_id = op.attr("ring_id")
        process_group = get_process_group(process_group_id, g_process_group_map)
        if rank not in process_group.ranks:
            return comm_requirements_to_ranks
        for tgt_rank in process_group.ranks:
            comm_volume = get_comm_volume(op, rank, tgt_rank)
            if comm_volume is not None:
                comm_requirements_to_ranks[tgt_rank] = {}
                comm_requirements_to_ranks[tgt_rank][
                    "comm_volume"] = comm_volume
    elif is_p2p_comm_op(op):
        tgt_rank = op.attr("peer")
        comm_volume = get_comm_volume(op, rank, tgt_rank)
        if comm_volume is not None:
            comm_requirements_to_ranks[tgt_rank] = {}
            comm_requirements_to_ranks[tgt_rank]["comm_volume"] = comm_volume
    else:
        comm_requirements_to_ranks = {}
    return comm_requirements_to_ranks


def analyze_requirements_for_program(src_info, rank):
    program = src_info[0]
    g_process_group_map = src_info[1]
    resource_requirements = {}
    comm_requirements_to_ranks = {}
    # only support device_type and only support GPU for now
    resource_requirements["device_type"] = DeviceType.GPU
    for block in program.blocks:
        for op in block.ops:
            cur_comm_requirements_to_ranks = analyze_comm_requirements_from_op(
                op, rank, g_process_group_map)
            for tgt_rank, link_info in cur_comm_requirements_to_ranks.items():
                if tgt_rank in comm_requirements_to_ranks:
                    comm_requirements_to_ranks[tgt_rank][
                        "comm_volume"] += link_info["comm_volume"]
                else:
                    comm_requirements_to_ranks[tgt_rank] = {}
                    comm_requirements_to_ranks[tgt_rank][
                        "comm_volume"] = link_info["comm_volume"]
    return resource_requirements, comm_requirements_to_ranks


def build_process_graph(distributed_program):
    graph = Graph()
    for src_rank, src_info in distributed_program.items():
        resource_requirements, comm_requirements_to_ranks = analyze_requirements_for_program(
            src_info, src_rank)
        graph.add_node(src_rank, resource_requirements=resource_requirements)
        for tgt_rank, comm_requirements in comm_requirements_to_ranks.items():
            graph.add_edge(src_rank,
                           tgt_rank,
                           comm_requirements=comm_requirements)
    return graph


def build_cluster_graph(cluster):
    graph = Graph()
    cuda_visible_devices_env = os.getenv("CUDA_VISIBLE_DEVICES")
    cuda_visible_devices = []
    if cuda_visible_devices_env is not None and cuda_visible_devices_env != "":
        cuda_visible_devices = [
            int(d.strip()) for d in cuda_visible_devices_env.split(",")
        ]
    for machine in cluster.machines.values():
        for device in machine.devices.values():
            graph.add_node(device.global_id, device=device)
            if cuda_visible_devices and device.local_id not in cuda_visible_devices:
                graph.nodes[device.global_id]["occupied"] = True
            else:
                graph.nodes[device.global_id]["occupied"] = False
        for link in machine.links.values():
            graph.add_edge(link.source.global_id,
                           link.target.global_id,
                           link=link)
    return graph


def mapping(distributed_program, cluster):
    # A very simple mapping algorithm only for GPUs.
    # Here we assume one process will be mapped to one GPU.
    # In the future, more mapping configurations and algorithms will be supported.
    process_graph = build_process_graph(distributed_program)

    cluster_graph = build_cluster_graph(cluster)

    for cur_rank_node in process_graph:
        cur_rank_node["visited"] = False

    def sort_by_comm_volume(rank_edge):
        return rank_edge["comm_requirements"]["comm_volume"]

    def sort_by_comm_bandwidth(device_edge):
        return device_edge["link"].bandwidth

    def select_unvisited_rank_node(rank_node_list):
        selected_rank_node = None
        for rank_node in rank_node_list:
            if rank_node["visited"] is False:
                selected_rank_node = rank_node
        return selected_rank_node

    queue = deque()
    root_rank_node = select_unvisited_rank_node(
        list(process_graph.nodes.values()))
    while root_rank_node is not None:
        queue.append(root_rank_node)
        while queue:
            cur_rank_node = queue.popleft()
            if cur_rank_node["visited"]:
                continue
            device_type = cur_rank_node["resource_requirements"]["device_type"]
            cur_device_node = None
            for device_node in cluster_graph.nodes.values():
                if (device_node["device"].type
                        == device_type) and (not device_node["occupied"]):
                    device_node["occupied"] = True
                    cur_rank_node["visited"] = True
                    cur_rank_node["device"] = device_node["device"]
                    cur_device_node = device_node
                    break
            assert cur_device_node, "Cannot find a device to satisfy the requirement."

            nbr_rank_edges = []
            for nbr_rank_node_id, nbr_rank_edge in process_graph.adjs[
                    cur_rank_node.id].items():
                assert nbr_rank_edge.src_id == cur_rank_node.id and nbr_rank_edge.tgt_id == nbr_rank_node_id
                queue.append(process_graph.nodes[nbr_rank_node_id])
                nbr_rank_edges.append(nbr_rank_edge)
            nbr_rank_edges.sort(key=sort_by_comm_volume)

            nbr_device_edges = []
            for nbr_device_edge in cluster_graph.adjs[
                    cur_device_node.id].values():
                nbr_device_edges.append(nbr_device_edge)
            nbr_device_edges.sort(key=sort_by_comm_bandwidth)

            for nbr_rank_edge in nbr_rank_edges:
                src_rank_node = process_graph.nodes[
                    nbr_rank_edge.src_id]["visited"]
                if src_rank_node:
                    continue
                device_type = src_rank_node["resource_requirements"][
                    "device_type"]
                nbr_rank_node = process_graph.nodes[nbr_rank_edge.tgt_id]
                for nbr_device_edge in nbr_device_edges:
                    nbr_device_node = cluster_graph.nodes[
                        nbr_device_edge.tgt_id]
                    if (nbr_device_node["device"].type == device_type) and (
                            not nbr_device_node["occupied"]):
                        nbr_device_node["occupied"] = True
                        nbr_rank_node["visited"] = True
                        nbr_rank_node["device"] = nbr_device_node["device"]
                        break
        root_rank_node = select_unvisited_rank_node(
            list(process_graph.nodes.values()))

    rank_mapping = {}
    for rank, rank_node in process_graph.nodes.items():
        device = rank_node["device"]
        machine = device.machine
        if machine.id in rank_mapping:
            rank_mapping[machine.id]["hostname"] = machine.hostname
            rank_mapping[machine.id]["addr"] = machine.addr
            rank_mapping[machine.id]["port"] = machine.port
            if rank not in rank_mapping[machine.id]["ranks"]:
                rank_mapping[machine.id]["ranks"][rank] = []
                rank_mapping[machine.id]["ranks"][rank].append(device.local_id)
            else:
                rank_mapping[machine.id]["ranks"][rank].append(device.local_id)
        else:
            rank_mapping[machine.id] = {}
            rank_mapping[machine.id]["hostname"] = machine.hostname
            rank_mapping[machine.id]["addr"] = machine.addr
            rank_mapping[machine.id]["port"] = machine.port
            rank_mapping[machine.id]["ranks"] = {}
            rank_mapping[machine.id]["ranks"][rank] = []
            rank_mapping[machine.id]["ranks"][rank].append(device.local_id)
    for machine_mapping in rank_mapping.values():
        for rank_devices in machine_mapping["ranks"].values():
            rank_devices.sort()

    return rank_mapping