recompute_hybrid.py

# 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.autograd import PyLayer
from paddle.fluid import core, framework

from ..meta_parallel.parallel_layers.random import get_rng_state_tracker
from ..meta_parallel.pp_utils import utils
from .recompute import (
    check_recompute_necessary,
    detach_variable,
    swith_rng_state_tracker,
)

__all__ = []


def _split_activation(tensor, mp_group):

    mp_degree = mp_group.nranks
    mp_rank = mp_group.rank
    if mp_degree < 2:
        return tensor

    tensor_numel = paddle.numel(tensor)
    assert tensor_numel != 0, "can't recompute zero element"
    assert (
        tensor_numel % mp_degree == 0
    ), "The capacity of the activation ({}) cannot be divisible by mp_degree({})".format(
        tensor_numel, mp_degree
    )

    # use inplace operation to save memory
    data = tensor.flatten_()

    part_size = tensor_numel // mp_degree
    start = part_size * mp_rank
    end = start + part_size
    return data[start:end]


def _merge_activation(tensor, mp_group):
    mp_degree = mp_group.nranks
    mp_rank = mp_group.rank
    if mp_degree < 2:
        return tensor

    # adapt to new dygraph
    tensor_shape = list(tensor.shape)
    tensor_shape[0] *= mp_group.nranks
    out = paddle.empty(tensor_shape, tensor.dtype)
    task = mp_group.process_group.all_gather(tensor.cuda(), out)
    task.wait()
    return out


class _HPRecomputeFunction(PyLayer):
    """
    Compared with paddle.distributed.fleet.utils.recompute, there are the following differences:
    1. In order to support PipeLineParallel, the input of recompute is modified to ensure that the input can be tuple type.
    2. Offload support for activation
    3. Support MP segmentation of activation to further reduce cuda memory
    4. Adapt to the random state of MP
    """

    @staticmethod
    def forward(
        ctx,
        run_function,
        all_outputs,
        mp_group,
        offload,
        partition,
        *args,
        **kwargs
    ):

        # store for recomputing
        ctx.run_function = run_function

        ctx.kwargs = kwargs

        # store the rng states
        ctx.fwd_cuda_rng_state = paddle.get_cuda_rng_state()
        ctx.fwd_cuda_rng_state_tracker = (
            get_rng_state_tracker().get_states_tracker()
        )

        # save config info
        ctx.mp_group = mp_group
        ctx.offload = offload
        ctx.partition = partition

        # save input for backward
        ctx.inputs = []
        ctx.tensor_indices = []
        ctx.tensor_shapes = []
        tensor_inputs = []

        cur_device = paddle.get_device()
        assert (
            'gpu:' in paddle.get_device()
        ), "Recompute with RNG is not support current device: {}.".format(
            cur_device
        )

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

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

        for i, arg in enumerate(args):
            if paddle.is_tensor(arg):
                state = arg.stop_gradient
                if partition:
                    ctx.tensor_shapes.append(arg.shape)
                    partition = _split_activation(
                        arg.detach(), mp_group
                    ).clone()
                    # TODO(shenliang03) not use calculate stream to D2H to speed
                    arg = partition.cpu() if offload else partition
                else:
                    arg = arg.cpu() if offload else arg
                arg.stop_gradient = state
                tensor_inputs.append(arg)
                ctx.tensor_indices.append(i)
                ctx.inputs.append(None)
            else:
                ctx.inputs.append(arg)

        ctx.save_for_backward(*tensor_inputs)

        if paddle.is_tensor(outputs):
            all_outputs += [outputs]
            return outputs
        else:
            all_outputs += outputs
            return tuple(outputs)

    @staticmethod
    def backward(ctx, *args):
        with paddle.fluid.dygraph.guard():
            # Restore inputs
            inputs = list(ctx.inputs)
            tensor_indices = ctx.tensor_indices
            tensor_shapes = ctx.tensor_shapes
            tensors = list(ctx.saved_tensor())

            device_id = paddle.distributed.ParallelEnv().device_id
            for i, idx in enumerate(tensor_indices):
                if ctx.partition:
                    state = tensors[i].stop_gradient
                    tensors[i] = (
                        _merge_activation(tensors[i], ctx.mp_group)
                        .detach()
                        .reshape_(tensor_shapes[i])
                    )
                    tensors[i].stop_gradient = state
                inputs[idx] = (
                    tensors[i].cuda(device_id) if ctx.offload else tensors[i]
                )

            tracer = framework._dygraph_tracer()
            tracer._has_grad = True

            # need restore auto_cast state as well as w/b list
            with swith_rng_state_tracker(
                ctx.fwd_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,
                ):
                    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)

            forward_outputs_with_grad = []
            backward_inputs = []

            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.append(args[i])

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

            # actually backward
            paddle.autograd.backward(forward_outputs_with_grad, backward_inputs)
            grads = tuple(
                inp._grad_ivar()
                for inp in detached_inputs
                if isinstance(inp, (core.VarBase, core.eager.Tensor))
            )
            return grads


def recompute_hybrid(ctx, function, *args, **kwargs):
    """
    # NODTE(shenliang03)The current hybrid parallel recompute has limitations.
    # It cannot handle the following situations:
    # 1. The calculation output of recompute, there are tensors that do not require gradients.
    # 2. The forward output tensor has no gradient. This problem can be solved temporarily by detach().
    # 3. Here, we only use float dtype to distinguish whether a gradient is needed in output tensor

    Parameters:
        ctx(dict): include 'mp_group', 'offload', and 'partition' keys. the key 'mp_group' (Group), represents the avtivations are splitted
                   in which group. the key 'offload' (bool, optional, default=False), represents whether to offload to cpu. the key 'partition' (bool, optional, default=False),
                   represents whether to split activations in the mp_group. and some keys such as 'segments' and 'preserve_rng_state' are invalid here, they are useful in
                   'recompute_sequential' API.
        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(tuple) to the function.

        **kwargs(Dict): inputs(dict) to the function.

    Returns:
        Output of function on args and kwargs.

    """
    mp_group = ctx.get('mp_group', None)
    assert (
        mp_group is not None
    ), "ctx must contains mp_group and mp_group can not be None."

    offload = ctx.get('offload', False)
    partition = ctx.get('partition', False)

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

    all_outputs = []
    _HPRecomputeFunction.apply(
        function, all_outputs, mp_group, offload, partition, *args, **kwargs
    )

    if len(all_outputs) == 1:
        return all_outputs[0]
    else:
        for output in all_outputs:
            if paddle.is_tensor(output) and not utils.is_float_tensor(output):
                output.stop_gradient = True

        return tuple(all_outputs)