fairseq-a54021305d6b3c4c5959ac9395135f63202db8f1/fairseq/modules/checkpoint_activations.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import functools
from typing import Any, Dict, List, Tuple, Union

import torch
import torch.utils.checkpoint as checkpoint
from fairseq import utils


def checkpoint_wrapper(m, offload_to_cpu=False):
    """
    A friendlier wrapper for performing activation checkpointing.

    Compared to the PyTorch version, this version:
    - wraps an nn.Module, so that all subsequent calls will use checkpointing
    - handles keyword arguments in the forward
    - handles non-Tensor outputs from the forward

    Usage::

        checkpointed_module = checkpoint_wrapper(my_module, offload_to_cpu=True)
        a, b = checkpointed_module(x, y=3, z=torch.Tensor([1]))
    """
    # should I check whether original_forward has already been set?
    assert not hasattr(
        m, "precheckpoint_forward"
    ), "checkpoint function has already been applied?"
    m.precheckpoint_forward = m.forward
    m.forward = functools.partial(
        _checkpointed_forward,
        m.precheckpoint_forward,  # original_forward
        offload_to_cpu,
    )
    return m


def unwrap_checkpoint(m: torch.nn.Module):
    """
    unwrap a module and its children from checkpoint_wrapper
    """
    for module in m.modules():
        if hasattr(module, "precheckpoint_forward"):
            module.forward = module.precheckpoint_forward
            del module.precheckpoint_forward
        if hasattr(module, "old_deepcopy_method"):
            module.__deepcopy__ = module.old_deepcopy_method
            del module.old_deepcopy_method
    return m


def _checkpointed_forward(original_forward, offload_to_cpu, *args, **kwargs):
    # Autograd Functions in PyTorch work best with positional args, since
    # the backward must return gradients (or None) for every input argument.
    # We can flatten keyword arguments to make this easier.
    kwarg_keys, flat_args = pack_kwargs(*args, **kwargs)
    parent_ctx_dict = {"offload": offload_to_cpu}
    output = CheckpointFunction.apply(
        original_forward, parent_ctx_dict, kwarg_keys, *flat_args
    )
    if isinstance(output, torch.Tensor):
        return output
    else:
        packed_non_tensor_outputs = parent_ctx_dict["packed_non_tensor_outputs"]
        if packed_non_tensor_outputs:
            output = unpack_non_tensors(output, packed_non_tensor_outputs)
        return output


def pack_kwargs(*args, **kwargs) -> Tuple[List[str], List[Any]]:
    """
    Usage::

        kwarg_keys, flat_args = pack_kwargs(1, 2, a=3, b=4)
        args, kwargs = unpack_kwargs(kwarg_keys, flat_args)
        assert args == [1, 2]
        assert kwargs == {"a": 3, "b": 4}
    """
    kwarg_keys = []
    flat_args = list(args)
    for k, v in kwargs.items():
        kwarg_keys.append(k)
        flat_args.append(v)
    return kwarg_keys, flat_args


def unpack_kwargs(
    kwarg_keys: List[str], flat_args: List[Any]
) -> Tuple[List[Any], Dict[str, Any]]:
    if len(kwarg_keys) == 0:
        return flat_args, {}
    args = flat_args[: -len(kwarg_keys)]
    kwargs = {k: v for k, v in zip(kwarg_keys, flat_args[-len(kwarg_keys) :])}
    return args, kwargs


def split_non_tensors(
    mixed: Union[torch.Tensor, Tuple[Any]]
) -> Tuple[Tuple[torch.Tensor], Dict[str, List[Any]]]:
    """
    Usage::

        x = torch.Tensor([1])
        y = torch.Tensor([2])
        tensors, packed_non_tensors = split_non_tensors((x, y, None, 3))
        recon = unpack_non_tensors(tensors, packed_non_tensors)
        assert recon == (x, y, None, 3)
    """
    if isinstance(mixed, torch.Tensor):
        return (mixed,), None
    tensors = []
    packed_non_tensors = {"is_tensor": [], "objects": []}
    for o in mixed:
        if isinstance(o, torch.Tensor):
            packed_non_tensors["is_tensor"].append(True)
            tensors.append(o)
        else:
            packed_non_tensors["is_tensor"].append(False)
            packed_non_tensors["objects"].append(o)
    return tuple(tensors), packed_non_tensors


def unpack_non_tensors(
    tensors: Tuple[torch.Tensor],
    packed_non_tensors: Dict[str, List[Any]],
) -> Tuple[Any]:
    if packed_non_tensors is None:
        return tensors
    assert isinstance(packed_non_tensors, dict)
    mixed = []
    is_tensor_list = packed_non_tensors["is_tensor"]
    objects = packed_non_tensors["objects"]
    assert len(tensors) + len(objects) == len(is_tensor_list)
    obj_i = tnsr_i = 0
    for is_tensor in is_tensor_list:
        if is_tensor:
            mixed.append(tensors[tnsr_i])
            tnsr_i += 1
        else:
            mixed.append(objects[obj_i])
            obj_i += 1
    return tuple(mixed)


class CheckpointFunction(torch.autograd.Function):
    """Similar to the torch version, but support non-Tensor outputs.

    The caller is expected to provide a dict (*parent_ctx_dict*) that will hold
    the non-Tensor outputs. These should be combined with the Tensor *outputs*
    by calling ``unpack_non_tensors``.
    """

    @staticmethod
    def forward(ctx, run_function, parent_ctx_dict, kwarg_keys, *args):
        if torch.is_grad_enabled():  # grad may be disabled, e.g., during validation
            checkpoint.check_backward_validity(args)

        ctx.run_function = run_function
        ctx.kwarg_keys = kwarg_keys
        ctx.fwd_rng_state = utils.get_rng_state()

        tensor_inputs, packed_non_tensor_inputs = split_non_tensors(args)
        if parent_ctx_dict["offload"]:
            ctx.fwd_device = tuple(x.device for x in tensor_inputs)
            ctx.grad_requirements = tuple(x.requires_grad for x in tensor_inputs)
            tensor_inputs = tuple(
                x.to(torch.device("cpu"), non_blocking=True) for x in tensor_inputs
            )

        else:
            ctx.fwd_device, ctx.grad_requirements = None, None

        ctx.save_for_backward(*tensor_inputs)
        ctx.packed_non_tensor_inputs = packed_non_tensor_inputs

        with torch.no_grad():
            unpacked_args, unpacked_kwargs = unpack_kwargs(kwarg_keys, args)
            outputs = run_function(*unpacked_args, **unpacked_kwargs)

        if isinstance(outputs, torch.Tensor):
            return outputs
        else:
            # Autograd Functions don't like non-Tensor outputs. We can split the
            # non-Tensor and Tensor outputs, returning the former by reference
            # through *parent_ctx_dict* and returning the latter directly.
            outputs, packed_non_tensor_outputs = split_non_tensors(outputs)
            parent_ctx_dict["packed_non_tensor_outputs"] = packed_non_tensor_outputs
            return outputs

    @staticmethod
    def backward(ctx, *args):
        if not torch.autograd._is_checkpoint_valid():
            raise RuntimeError(
                "Checkpointing is not compatible with .grad(), please use .backward() if possible"
            )

        tensor_inputs: Tuple = ctx.saved_tensors
        tensor_inputs = checkpoint.detach_variable(tensor_inputs)
        if ctx.fwd_device is not None:
            tensor_inputs = [
                t.to(ctx.fwd_device[i], non_blocking=True)
                for i, t in enumerate(tensor_inputs)
            ]
            for i, need_grad in enumerate(ctx.grad_requirements):
                tensor_inputs[i].requires_grad = need_grad
        inputs = unpack_non_tensors(tensor_inputs, ctx.packed_non_tensor_inputs)

        # Store the current states.
        bwd_rng_state = utils.get_rng_state()

        # Set the states to what it used to be before the forward pass.
        utils.set_rng_state(ctx.fwd_rng_state)

        with torch.enable_grad():
            unpacked_args, unpacked_kwargs = unpack_kwargs(ctx.kwarg_keys, inputs)
            outputs = ctx.run_function(*unpacked_args, **unpacked_kwargs)
            tensor_outputs, _ = split_non_tensors(outputs)
        # Set the states back to what it was at the start of this function.
        utils.set_rng_state(bwd_rng_state)

        # Run backward() with only Tensors that require grad
        outputs_with_grad = []
        args_with_grad = []
        for i in range(len(tensor_outputs)):
            if tensor_outputs[i].requires_grad:
                outputs_with_grad.append(tensor_outputs[i])
                args_with_grad.append(args[i])
        if len(outputs_with_grad) == 0:
            raise RuntimeError(
                "None of the outputs have requires_grad=True, "
                "this checkpoint() is not necessary"
            )

        torch.autograd.backward(outputs_with_grad, args_with_grad)

        grads = tuple(
            inp.grad if isinstance(inp, torch.Tensor) else None for inp in inputs
        )
        return (None, None, None) + grads