#   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 os
import contextlib
from enum import Enum
import numpy as np
from types import MethodType

import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid import core
from paddle.fluid import layers
from paddle.fluid.dygraph import to_variable
from paddle.fluid.framework import dygraph_only


class Taskflow:
    """
    Task flows, one way linked list for task acquisition.
    """

    def __init__(self, task, callback):
        self.task = task
        self.callback = callback


class Type(Enum):
    """
    Type of trainable parameters
    """
    fp16 = paddle.float16
    bf16 = paddle.bfloat16
    fp32 = paddle.float32


class GroupShardedClipGrad:

    def __init__(self, clip, device, group):
        self._clip = clip
        self._device = device
        self._group = group

    @paddle.autograd.no_grad()
    def _dygraph_clip(self, params_grads):
        sum_square_fp32, sum_square_fp16 = [], []
        unslice_params_fp32, unslice_params_fp16 = [], []

        for p, g in params_grads:
            p_slice = True  # using for slice parameter in sharding stage3
            if g is None or getattr(p, 'need_clip', True) is False:
                continue
            if hasattr(p, "unslice"):
                p_slice = False

            merge_grad = g
            if g.type == core.VarDesc.VarType.SELECTED_ROWS:
                merge_grad = layers.get_tensor_from_selected_rows(
                    layers.merge_selected_rows(g))
            square = layers.square(merge_grad)
            sum_square = layers.reduce_sum(square)

            if p.dtype == paddle.float16:
                if p_slice: sum_square_fp16.append(sum_square)
                else: unslice_params_fp16.append(sum_square)
            elif p.dtype == paddle.float32:
                if p_slice: sum_square_fp32.append(sum_square)
                else: unslice_params_fp32.append(sum_square)

        # global norm of non-distributed FP16 params_and_grads
        if len(sum_square_fp16) == 0:
            global_norm_fp16 = paddle.to_tensor([0.], dtype=paddle.float32)
        else:
            global_norm_fp16 = layers.concat(sum_square_fp16)
            global_norm_fp16 = layers.reduce_sum(global_norm_fp16)
            global_norm_fp16 = paddle.cast(global_norm_fp16,
                                           dtype=paddle.float32)

        # global norm of non-distributed FP16 params_and_grads for unslice parameters
        if len(unslice_params_fp16) == 0:
            global_unslice_fp16 = paddle.to_tensor([0.], dtype=paddle.float32)
        else:
            global_unslice_fp16 = layers.concat(unslice_params_fp16)
            global_unslice_fp16 = layers.reduce_sum(global_unslice_fp16)
            global_unslice_fp16 = paddle.cast(global_unslice_fp16,
                                              dtype=paddle.float32)

        # global norm of non-distributed FP32 params_and_grads
        global_norm_fp32 = layers.concat(
            sum_square_fp32) if len(sum_square_fp32) != 0 else paddle.to_tensor(
                [0.], dtype=paddle.float32)
        global_norm_fp32 = layers.reduce_sum(global_norm_fp32)

        # global norm of non-distributed FP32 params_and_grads for unslice parameters
        global_unslice_fp32 = layers.concat(unslice_params_fp32) if len(
            unslice_params_fp32) != 0 else paddle.to_tensor(
                [0.], dtype=paddle.float32)
        global_unslice_fp32 = layers.reduce_sum(global_unslice_fp32)
        global_unslice_var = global_unslice_fp16 + global_unslice_fp32

        global_norm_var = global_norm_fp16 + global_norm_fp32 + 1.0 / self._group.nranks * global_unslice_var

        # add all reduce to get global norm of distributed params_and_grads
        dev_id = int(self._device.split(":")[1])
        if paddle.device.get_device() == "cpu":
            global_norm_var = global_norm_var.cuda(dev_id)

        with device_guard(dev_id, "gpu"):
            paddle.distributed.all_reduce(global_norm_var, group=self._group)

        global_norm_var = layers.sqrt(global_norm_var)
        max_global_norm = layers.fill_constant(shape=[1],
                                               dtype=global_norm_var.dtype,
                                               value=self.clip_norm)

        clip_var = layers.elementwise_div(x=max_global_norm,
                                          y=layers.elementwise_max(
                                              x=global_norm_var,
                                              y=max_global_norm))
        clip_var_fp16 = paddle.cast(clip_var, paddle.float16)

        for p, g in params_grads:
            if getattr(p, 'need_clip', True) is False or g is None:
                continue
            origin_state = g.stop_gradient
            g.stop_gradient = True
            if p.dtype == paddle.float16:
                g.scale_(clip_var_fp16.item())
            else:
                g.scale_(clip_var.item())
            g.stop_gradient = origin_state
            # p._reset_grad_inplace_version(True)

        return params_grads

    def __getattr__(self, item):
        return getattr(self._clip, item)

    def __call__(self, params_grads):
        return self._dygraph_clip(params_grads)


@contextlib.contextmanager
def device_guard(dev_id=0, device="cpu"):
    origin_device = paddle.device.get_device()
    if device == "cpu":
        paddle.set_device(device)
    elif device == "gpu":
        paddle.set_device("gpu:{}".format(dev_id))
    try:
        yield
    finally:
        paddle.set_device(origin_device)


@dygraph_only
def GroupShardedScaler(scaler):

    def unscale_method(self, optimizer):
        if not self._enable:
            return
        param_grads = []
        param_grads_fp16 = []
        param_grads_fp32 = []
        if hasattr(optimizer, "update_slice"):
            optimizer.update_slice()
            optimizer.update_scaler = True

        if getattr(optimizer._optim, '_param_groups', None) and isinstance(
                optimizer._optim._param_groups[0], dict):

            for group in optimizer._optim._param_groups:
                for param in group['params']:
                    if param.grad is not None:
                        param_grads.append(param.grad)
                        if param.grad.dtype in [
                                core.VarDesc.VarType.FP16, paddle.float16
                        ]:
                            param_grads_fp16.append(param.grad)
                        else:
                            param_grads_fp32.append(param.grad)
        else:
            for param in optimizer._optim._parameter_list:
                if param.grad is not None:
                    param_grads.append(param.grad)
                    if param.grad.dtype in [
                            core.VarDesc.VarType.FP16, paddle.float16
                    ]:
                        param_grads_fp16.append(param.grad)
                    else:
                        param_grads_fp32.append(param.grad)

        temp_found_inf_fp16 = to_variable(np.array([0]).astype(np.bool_))
        temp_found_inf_fp32 = to_variable(np.array([0]).astype(np.bool_))

        device = "cpu" if optimizer.offload else "gpu"
        dev_id = 0 if device == "cpu" else int(
            paddle.get_device().split(":")[1])

        with device_guard(dev_id, device):
            if len(param_grads_fp16):
                _legacy_C_ops.check_finite_and_unscale(param_grads_fp16,
                                                       self._scale,
                                                       param_grads_fp16,
                                                       temp_found_inf_fp16)
            if len(param_grads_fp32):
                _legacy_C_ops.check_finite_and_unscale(param_grads_fp32,
                                                       self._scale,
                                                       param_grads_fp32,
                                                       temp_found_inf_fp32)

        self._found_inf = 1 if temp_found_inf_fp16 or temp_found_inf_fp32 else 0
        is_found_inf = paddle.to_tensor([self._found_inf], dtype="int32")

        paddle.distributed.all_reduce(is_found_inf,
                                      op=paddle.distributed.ReduceOp.MAX,
                                      group=optimizer._group)
        self._found_inf = is_found_inf.numpy()[0]

    scaler._unscale = MethodType(unscale_method, scaler)
    return scaler
