# 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 paddle
from paddle.fluid import core
from paddle.autograd import PyLayer
from paddle.autograd.py_layer import LegacyPyLayer

from paddle.fluid import framework
import contextlib
from paddle.fluid.framework import in_dygraph_mode

from ..utils.log_util import logger

__all__ = []


def detach_variable(inputs):
    out = []
    for inp in inputs:
        if not isinstance(inp, (core.eager.Tensor, core.VarBase)):
            out.append(inp)
            continue

        x = inp.detach()
        x.stop_gradient = inp.stop_gradient
        out.append(x)
    return tuple(out)


def check_recompute_necessary(inputs):
    if not any(input_.stop_gradient == False for input_ in inputs
               if isinstance(input_, (core.eager.Tensor, paddle.Tensor))):
        logger.warning(
            "[Recompute]: None of the inputs to current recompute block need grad, "
            "therefore there is NO need to recompute this block in backward !")


@contextlib.contextmanager
def swith_rng_state_tracker(rng_state, tracker):
    from paddle.distributed.fleet.meta_parallel.parallel_layers.random import get_rng_state_tracker
    orig_cuda_rng_state = paddle.get_cuda_rng_state()
    orig_cuda_rng_tracker = get_rng_state_tracker().get_states_tracker()

    paddle.set_cuda_rng_state(rng_state)
    get_rng_state_tracker().set_states_tracker(tracker)
    try:
        yield
    finally:
        paddle.set_cuda_rng_state(orig_cuda_rng_state)
        get_rng_state_tracker().set_states_tracker(orig_cuda_rng_tracker)


class LegacyRecomputeFunction(LegacyPyLayer):

    @staticmethod
    def forward(ctx, run_function, preserve_rng_state, *args):
        from paddle.distributed.fleet.meta_parallel.parallel_layers.random import get_rng_state_tracker

        # store for recomputing
        ctx.run_function = run_function
        ctx.preserve_rng_state = preserve_rng_state

        # NOTE the number of outputs of backward() should be equal to the number of tensors in forward()'s input
        # the order of tensors in backward()'s output should be the same as tensors in forward()'s input
        # None tensor inputs will be filtered in backward inputs.

        # save input for backward
        ctx.inputs = []
        ctx.tensor_indices = []
        tensor_inputs = []
        for i, arg in enumerate(args):
            if paddle.is_tensor(arg):
                tensor_inputs.append(arg)
                ctx.tensor_indices.append(i)
                ctx.inputs.append(None)
            else:
                ctx.inputs.append(arg)
        ctx.save_for_backward(*tensor_inputs)

        # NOTE recompute with restore RNG only support one senario where one process for one cuda gpu.
        # one process with multiple gpu and mix-gpu-cpu senarios are not support
        if ctx.preserve_rng_state:
            cur_device = paddle.get_device()
            if 'gpu:' not in cur_device:
                raise RuntimeError(
                    "Recompute with RNG perserve is not support current device: {}."
                    .format(cur_device))
            ctx.fw_cuda_rng_state = paddle.get_cuda_rng_state()
            ctx.fwd_cuda_rng_state_tracker = get_rng_state_tracker(
            ).get_states_tracker()

        # TODO support AMP
        tracer = framework._dygraph_tracer()
        ctx.is_fw_autocast = False if tracer._amp_level == core.AmpLevel.O0 else True
        if tracer._amp_level == core.AmpLevel.O2:
            ctx.amp_level = 'O2'
        elif tracer._amp_level in (core.AmpLevel.O1, core.AmpLevel.O0):
            ctx.amp_level = 'O1'
        else:
            raise ValueError("unsupported amp level: {}".format(
                tracer._amp_level))

        if tracer._amp_dtype == 'float16':
            ctx.amp_dtype = 'float16'
        elif tracer._amp_dtype in ('bfloat16', 'float32'):
            ctx.amp_dtype = 'bfloat16'
        else:
            raise ValueError("unsupported amp dtype: {}".format(
                tracer._amp_dtype))

        ctx.amp_white_list, ctx.amp_black_list = tracer._get_amp_op_list()

        with paddle.no_grad():
            outputs = run_function(*args)
        return outputs

    @staticmethod
    def backward(ctx, *args):
        with paddle.fluid.dygraph.guard():
            # TODO need to check the recompute calling is vaild or not

            # Restore inputs
            inputs = list(ctx.inputs)
            tensor_indices = ctx.tensor_indices
            tensors = ctx.saved_tensor()
            for i, idx in enumerate(tensor_indices):
                inputs[idx] = tensors[i]

            # paddle.enable_grad()
            tracer = framework._dygraph_tracer()
            tracer._has_grad = True

            # NOTE support AMP
            # need restore auto_cast state as well as w/b list
            if ctx.preserve_rng_state:
                with swith_rng_state_tracker(ctx.fw_cuda_rng_state,
                                             ctx.fwd_cuda_rng_state_tracker):
                    with paddle.amp.auto_cast(
                            enable=ctx.is_fw_autocast,
                            custom_white_list=ctx.amp_white_list,
                            custom_black_list=ctx.amp_black_list,
                            level=ctx.amp_level,
                            dtype=ctx.amp_dtype):
                        detached_inputs = detach_variable(tuple(inputs))
                        outputs = ctx.run_function(*detached_inputs)
            else:
                with paddle.amp.auto_cast(enable=ctx.is_fw_autocast,
                                          custom_white_list=ctx.amp_white_list,
                                          custom_black_list=ctx.amp_black_list,
                                          level=ctx.amp_level,
                                          dtype=ctx.amp_dtype):
                    detached_inputs = detach_variable(tuple(inputs))
                    outputs = ctx.run_function(*detached_inputs)

            if isinstance(outputs, core.VarBase):
                outputs = (outputs, )
            assert len(outputs) == len(args)

            # run backward() with only tensor that requires grad
            forward_outputs_with_grad = []
            # NOTE In Transformer-like network, if user put the attention mask into the recompute segment output,
            # pylayer will force the stop_gradient of attention mask to be False, which will make the number of
            # tensor that need grad does not match.
            # the following backward_inputs_with_grad is used to avoid this case.
            backward_inputs_with_grad = []
            for i in range(len(outputs)):
                if isinstance(outputs[i],
                              core.VarBase) and not outputs[i].stop_gradient:
                    forward_outputs_with_grad.append(outputs[i])
                    backward_inputs_with_grad.append(args[i])

            if len(forward_outputs_with_grad) == 0:
                raise RuntimeError(
                    "none of output has requires_grad=True, this recompute() is not necessary"
                )

            # actually backward
            with paddle.amp.auto_cast(enable=False):
                paddle.autograd.backward(forward_outputs_with_grad,
                                         backward_inputs_with_grad)

            grads = list(inp._grad_ivar() for inp in detached_inputs
                         if isinstance(inp, core.VarBase))
            return grads


class RecomputeFunction(PyLayer):

    @staticmethod
    def forward(ctx, run_function, preserve_rng_state, *args, **kwargs):
        from paddle.distributed.fleet.meta_parallel.parallel_layers.random import get_rng_state_tracker

        # store for recomputing
        ctx.run_function = run_function
        ctx.preserve_rng_state = preserve_rng_state
        ctx.kwargs = kwargs

        # NOTE the number of outputs of backward() should be equal to the number of tensors in forward()'s input
        # the order of tensors in backward()'s output should be the same as tensors in forward()'s input
        # None tensor inputs will be filtered in backward inputs.

        # save input for backward
        ctx.inputs = []
        ctx.tensor_indices = []
        tensor_inputs = []
        for i, arg in enumerate(args):
            if paddle.is_tensor(arg):
                tensor_inputs.append(arg)
                ctx.tensor_indices.append(i)
                ctx.inputs.append(None)
            else:
                ctx.inputs.append(arg)
        ctx.save_for_backward(*tensor_inputs)

        # NOTE recompute with restore RNG only support one senario where one process for one cuda gpu.
        # one process with multiple gpu and mix-gpu-cpu senarios are not support
        if ctx.preserve_rng_state:
            cur_device = paddle.get_device()
            if 'gpu:' not in cur_device:
                raise RuntimeError(
                    "Recompute with RNG perserve is not support current device: {}."
                    .format(cur_device))
            ctx.fw_cuda_rng_state = paddle.get_cuda_rng_state()
            ctx.fwd_cuda_rng_state_tracker = get_rng_state_tracker(
            ).get_states_tracker()

        # TODO support AMP
        tracer = framework._dygraph_tracer()
        ctx.is_fw_autocast = False if tracer._amp_level == core.AmpLevel.O0 else True
        if tracer._amp_level == core.AmpLevel.O2:
            ctx.amp_level = 'O2'
        elif tracer._amp_level in (core.AmpLevel.O1, core.AmpLevel.O0):
            ctx.amp_level = 'O1'
        else:
            raise ValueError("unsupported amp level: {}".format(
                tracer._amp_level))

        if tracer._amp_dtype == 'float16':
            ctx.amp_dtype = 'float16'
        elif tracer._amp_dtype in ('bfloat16', 'float32'):
            ctx.amp_dtype = 'bfloat16'
        else:
            raise ValueError("unsupported amp dtype: {}".format(
                tracer._amp_dtype))

        ctx.amp_white_list, ctx.amp_black_list = tracer._get_amp_op_list()

        with paddle.no_grad():
            outputs = run_function(*args, **kwargs)
        return outputs

    @staticmethod
    def backward(ctx, *args):
        with paddle.fluid.dygraph.guard():
            # TODO need to check the recompute calling is vaild or not

            # Restore inputs
            inputs = list(ctx.inputs)
            tensor_indices = ctx.tensor_indices
            tensors = ctx.saved_tensor()
            for i, idx in enumerate(tensor_indices):
                inputs[idx] = tensors[i]

            # paddle.enable_grad()
            tracer = framework._dygraph_tracer()
            tracer._has_grad = True

            # NOTE support AMP
            # need restore auto_cast state as well as w/b list
            if ctx.preserve_rng_state:
                with swith_rng_state_tracker(ctx.fw_cuda_rng_state,
                                             ctx.fwd_cuda_rng_state_tracker):
                    with paddle.amp.auto_cast(
                            enable=ctx.is_fw_autocast,
                            custom_white_list=ctx.amp_white_list,
                            custom_black_list=ctx.amp_black_list,
                            level=ctx.amp_level,
                            dtype=ctx.amp_dtype):
                        detached_inputs = detach_variable(tuple(inputs))
                        outputs = ctx.run_function(*detached_inputs,
                                                   **ctx.kwargs)
            else:
                with paddle.amp.auto_cast(enable=ctx.is_fw_autocast,
                                          custom_white_list=ctx.amp_white_list,
                                          custom_black_list=ctx.amp_black_list,
                                          level=ctx.amp_level,
                                          dtype=ctx.amp_dtype):
                    detached_inputs = detach_variable(tuple(inputs))
                    outputs = ctx.run_function(*detached_inputs, **ctx.kwargs)

            if isinstance(outputs, (core.VarBase, core.eager.Tensor)):
                outputs = (outputs, )
            assert len(outputs) == len(args)

            # run backward() with only tensor that requires grad
            forward_outputs_with_grad = []
            # NOTE In Transformer-like network, if user put the attention mask into the recompute segment output,
            # pylayer will force the stop_gradient of attention mask to be False, which will make the number of
            # tensor that need grad does not match.
            # the following backward_inputs_with_grad is used to avoid this case.
            backward_inputs_with_grad = []
            for i in range(len(outputs)):
                if isinstance(
                        outputs[i],
                    (core.VarBase,
                     core.eager.Tensor)) and not outputs[i].stop_gradient:
                    forward_outputs_with_grad.append(outputs[i])
                    backward_inputs_with_grad.append(args[i])

            if len(forward_outputs_with_grad) == 0:
                raise RuntimeError(
                    "none of output has requires_grad=True, this recompute() is not necessary"
                )

            # actually backward
            with paddle.amp.auto_cast(enable=False):
                paddle.autograd.backward(forward_outputs_with_grad,
                                         backward_inputs_with_grad)

            if in_dygraph_mode():
                grads = tuple(
                    inp._grad_ivar() for inp in detached_inputs
                    if isinstance(inp, (core.VarBase, core.eager.Tensor)))
            else:
                grads = list(
                    inp._grad_ivar() for inp in detached_inputs
                    if isinstance(inp, (core.VarBase, core.eager.Tensor)))
            return grads


def recompute(function, *args, **kwargs):
    """
    recompute intermediate activations to save then memory.

    Parameters:
        function(paddle.nn.Layer): layer of sequence of layers that describes part of forward pass of the model
              whose intermediate activations will be released to save memory in forward stage and will be recomputed
              in backward stage for gradient calculation.
        *args(Tensor): inputs to the function.
        **kwargs(Dict): Kwargs should only contain the key-value pair of preserve_rng_state, which is used to
              indicate whether to save the forward rng. If it is True, then the last forward rng value will be
              restored when the forward recalculation of backpropagation is performed. The default
              preserve_rng_state is True.

    Returns:
        Output of function on args.

    Examples:
        .. code-block:: python

            import numpy as np
            import paddle
            from paddle.distributed.fleet.utils import recompute
            import random

            # required: gpu

            def get_fc_block(block_idx, input_size, is_last=False):
                block_name = "block_" + str(block_idx)
                block = paddle.nn.Sequential(
                    (block_name + "_fc_0", paddle.nn.Linear(input_size, input_size, bias_attr=False)),
                    (block_name + "_dropout", paddle.nn.Dropout(p=0.5)),
                    (block_name + "_relu_1", paddle.nn.ReLU()),
                    (block_name + "_fc_1", paddle.nn.Linear(input_size, input_size, bias_attr=False)),
                    (block_name + "_relu_2", paddle.nn.ReLU()),
                )
                if is_last:
                    block.add_sublayer(
                        block_name + "_fc_2",
                        paddle.nn.Linear(
                            input_size, 1, bias_attr=False
                        )
                    )
                else:
                    block.add_sublayer(
                        block_name + "_fc_2",
                        paddle.nn.Linear(input_size, input_size, bias_attr=False)
                    )

                return block


            class Naive_fc_net(paddle.nn.Layer):
                def __init__(self, input_size=10,
                            recompute_blocks=[1, 3],
                            recompute_kwargs={}):
                    super(Naive_fc_net, self).__init__()
                    self.recompute_blocks = recompute_blocks
                    self.recompute_kwargs = recompute_kwargs
                    self.runfunc0 = get_fc_block(0, input_size, is_last=False)
                    self.runfunc1 = get_fc_block(1, input_size, is_last=False)
                    self.runfunc2 = get_fc_block(2, input_size, is_last=False)
                    self.runfunc3 = get_fc_block(3, input_size, is_last=False)
                    self.runfunc4 = get_fc_block(4, input_size, is_last=True)
                    self.total_func = [self.runfunc0, self.runfunc1, self.runfunc2, self.runfunc3, self.runfunc4]

                def forward(self, inputs):
                    nums = len(self.total_func)
                    for i in range(nums):
                        if i in self.recompute_blocks:
                            inputs = recompute(self.total_func[i], inputs, **{"preserve_rng_state": True})
                        else:
                            inputs = self.total_func[i](inputs)
                    return inputs

            def run_model(cuda_state, recompute_block=[], recompute_kwargs={}):
                gen = paddle.seed(10)
                gen.manual_seed(10)
                np.random.seed(10)
                random.seed(10)
                if cuda_state:
                    paddle.set_cuda_rng_state(cuda_state)

                batch_size, input_size = 1, 10
                model = Naive_fc_net(
                    input_size,
                    recompute_blocks=recompute_block,
                    recompute_kwargs=recompute_kwargs)
                optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters())
                loss_ = []
                param_ = []
                grad_ = []
                for _ in range(5):
                    x_data = np.random.randn(batch_size, input_size).astype(np.float32)
                    x = paddle.to_tensor(x_data)
                    y_pred = model(x)
                    loss = y_pred.mean()
                    loss_.append(np.asarray(loss).tolist())
                    loss.backward()
                    optimizer.step()
                    param_.append(np.asarray(model.parameters()[9]).tolist())
                    grad_.append(np.asarray(model.parameters()[3]._grad_ivar()).tolist())
                    optimizer.clear_grad()

                return loss_, param_, grad_

            cuda_state = paddle.get_cuda_rng_state()
            # without recompute
            loss_ref, param_ref, grad_ref = run_model(
                cuda_state, recompute_block=[]
            )

            loss, param, grad = run_model(cuda_state, recompute_block=[1, 2])
            print("normal_loss: {}, recompute_loss: {}".format(loss_ref, loss))
            # The result of the recompute_loss should be the same as the normal_loss.

    """
    # Hack to mix *args with **kwargs in a python 2.7-compliant way
    preserve = kwargs.pop('preserve_rng_state', True)

    if framework._dygraph_tracer()._has_grad:
        check_recompute_necessary(args)

    return RecomputeFunction.apply(function, preserve, *args, **kwargs)


def recompute_sequential(ctx, functions, *args, **kwargs):
    """
    recompute intermediate activations to save then memory for 'Sequential' models.

    Parameters:
        ctx(dict): include 'segments' and  'preserve_rng_state' keys, the key 'segments' (int, default 1), represents the number of chunks to create in the model,
                   the key 'preserve_rng_state' (bool, optional, default=True) indicate whether to save the forward rng. If it is True, then the last forward rng value will be
                   restored when the forward recalculation of backpropagation is performed. and some keys such as 'mp_group', 'offload' and 'partition' are invalid here,
                   they are useful in 'recompute_hybrid' API.
        functions(paddle.nn.Sequential): layer of sequence of layers that describes part of forward pass of the model
              whose intermediate activations will be released to save memory in forward stage and will be recomputed
              in backward stage for gradient calculation.
        *args(Tensor): inputs(tuple) to the function.
        **kwargs(Dict): inputs(dict) to the function.

    Returns:
        Output of function on args and kwargs.

    Examples:
        .. code-block:: python

            model = paddle.nn.Sequential(...)
            input = recompute_sequential({'segments' : 1}, model, input)
    """
    segments = ctx.get('segments', 1)
    preserve_rng_state = ctx.get('preserve_rng_state', True)

    def _run_func(begin, end, funcs):

        def do_run(input):
            for i in range(begin, end + 1):
                input = funcs[i](input)
            return input

        return do_run

    if isinstance(functions, paddle.nn.Sequential):
        functions = list(functions.children())

    segment_size = len(functions) // segments

    end = -1
    for begin in range(0, segment_size * (segments - 1), segment_size):
        end = begin + segment_size - 1
        args = recompute(_run_func(begin, end, functions),
                         *args,
                         preserve_rng_state=preserve_rng_state,
                         **kwargs)
    return _run_func(end + 1, len(functions) - 1, functions)(args)
