#   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 six
import numpy as np
import warnings

from paddle import framework
import paddle
from paddle.fluid import core
from paddle.fluid.dygraph.parallel import _split_tensors, sync_params_buffers, build_groups
from paddle.fluid.framework import in_dygraph_mode, _in_legacy_dygraph
from collections import OrderedDict
from .log_util import logger

__all__ = []


def _apply_collective_grads(parameters, comm_group):
    grad_var_set = set()
    grad_vars = []
    sparse_grad_vars = []

    for param in parameters:
        if param.trainable and (param._grad_ivar() is not None):
            g_var = param._grad_ivar()
            assert not g_var._is_sparse(
            ), "Now, it doesn't support sparse parameters"
            grad_vars.append(g_var)
            assert g_var not in grad_var_set
            grad_var_set.add(g_var)

    coalesced_grads_and_vars = build_groups(grad_vars, 128 * 1024 * 1024)

    for coalesced_grad, _, _ in coalesced_grads_and_vars:
        # need to div nranks
        nranks = paddle.distributed.get_world_size(
        ) if comm_group is None else comm_group.nranks
        div_factor = paddle.to_tensor(nranks, dtype=coalesced_grad.dtype)
        paddle.distributed.all_reduce(coalesced_grad, group=comm_group)
        paddle.fluid.framework._dygraph_tracer().trace_op(
            type="elementwise_div",
            inputs={'X': coalesced_grad,
                    'Y': div_factor},
            outputs={'Out': coalesced_grad},
            attrs={'axis': -1})

    _split_tensors(coalesced_grads_and_vars)


def _apply_collective_grads_eager(parameters, comm_group):
    grad_var_set = set()
    grad_vars = []

    for param in parameters:
        if param.trainable and (param._grad_ivar() is not None):
            g_var = param._grad_ivar()
            assert not g_var.is_sparse(
            ), "Now, it doesn't support sparse parameters"
            grad_vars.append(g_var)
            assert g_var not in grad_var_set
            grad_var_set.add(g_var)

    coalesced_grads_and_vars = build_groups(grad_vars, 128 * 1024 * 1024)

    div_factor = 1.0 / comm_group.nranks
    for coalesced_grad, _, _ in coalesced_grads_and_vars:
        # need to div nranks 
        coalesced_grad.scale_(div_factor)
        paddle.distributed.all_reduce(coalesced_grad, group=comm_group)

    _split_tensors(coalesced_grads_and_vars)


def _broadcast_data_help(data, shape, dtype, hcg):
    model_parallel_group = hcg.get_model_parallel_group()
    src_rank = hcg.get_model_parallel_group_src_rank()
    mp_rank = hcg.get_model_parallel_rank()

    shape_gpu = paddle.to_tensor(shape, dtype="int32")
    paddle.distributed.broadcast(
        shape_gpu,
        src=src_rank,
        group=model_parallel_group,
        use_calc_stream=True)

    if mp_rank != 0:
        input_data = paddle.zeros(shape_gpu, dtype=dtype)
    else:
        input_data = data

    paddle.distributed.broadcast(
        input_data,
        src=src_rank,
        group=model_parallel_group,
        use_calc_stream=True)


def broadcast_input_data(hcg, *inputs, **kwargs):
    for v in inputs:
        if isinstance(v, core.VarBase):
            with framework.no_grad():
                _broadcast_data_help(v, v.shape, v.dtype, hcg)
        else:
            logger.error("it doesn't support data type {}".format(type(v)))

    for k, v in kwargs.items():
        if isinstance(v, core.VarBase):
            with framework.no_grad():
                _broadcast_data_help(v, v.shape, v.dtype, hcg)
            kwargs[k] = v
        else:
            logger.error("it doesn't support data type {}".format(type(v)))
    return inputs, kwargs


def broadcast_mp_parameters(model, hcg):
    model_parallel_group = hcg.get_model_parallel_group()
    src_rank = hcg.get_model_parallel_group_src_rank()
    sync_params_buffers(
        model, model_parallel_group, src_rank, is_model_parallel=True)


def broadcast_dp_parameters(model, hcg):
    data_parallel_group = hcg.get_data_parallel_group()
    src_rank = hcg.get_data_parallel_group_src_rank()
    sync_params_buffers(
        model, data_parallel_group, src_rank, is_model_parallel=False)


def fused_allreduce_gradients(parameter_list, hcg):
    if _in_legacy_dygraph():
        data_parallel_group = None if hcg is None else hcg.get_data_parallel_group(
        )
        logger.debug("dp start fuse allreduce gradients")
        with framework.no_grad():
            _apply_collective_grads(parameter_list, data_parallel_group)
    elif in_dygraph_mode():
        assert hcg is None, "It's not support to use hcg in EagerDygraph now."
        data_parallel_group = paddle.distributed.collective._get_default_group()
        with framework.no_grad():
            _apply_collective_grads_eager(parameter_list, data_parallel_group)


def sharding_reduce_gradients(parameter_list, hcg):
    # TODO allreduce --> reduce
    # TODO merge grad / nrank with dp 
    logger.debug("sharding start gradients sync")
    with framework.no_grad():

        sharding_nrank = hcg.get_sharding_parallel_group().nranks
        for param in parameter_list:
            if param.trainable and (param._grad_ivar() is not None):

                g_var = param._grad_ivar()

                # need use trace_op to allreduce 
                # paddle.distributed.all_reduce(
                #     g_var, group=hcg.get_sharding_parallel_group(), use_calc_stream=True)
                paddle.fluid.framework._dygraph_tracer().trace_op(
                    type="c_allreduce_sum",
                    inputs={'X': g_var},
                    outputs={'Out': g_var},
                    attrs={
                        'ring_id': hcg.get_sharding_parallel_group().id,
                        'use_calc_stream': True
                    })

                # grad / sharding_rank
                div_factor = paddle.to_tensor(sharding_nrank, dtype=g_var.dtype)
                paddle.fluid.framework._dygraph_tracer().trace_op(
                    type="elementwise_div",
                    inputs={'X': g_var,
                            'Y': div_factor},
                    outputs={'Out': g_var},
                    attrs={'axis': -1})


def broadcast_sharding_parameters(model, hcg):
    # TODO TO save memory, use un-fused broadcast to avoid potentional OOM
    logger.debug("sharding start init parameters sync")
    sharding_parallel_group = hcg.get_sharding_parallel_group()
    src_rank = hcg.get_sharding_parallel_group_src_rank()
    sync_params_buffers(
        model, sharding_parallel_group, src_rank, is_model_parallel=False)
