graph.py

import torch
import contextlib
from typing import Callable, Any, Dict, Tuple, Optional, Sequence, List, Set
from torch.utils.hooks import RemovableHandle
from torch.utils._python_dispatch import TorchDispatchMode
from collections import defaultdict
import weakref

__all__ = [
    "saved_tensors_hooks",
    "save_on_cpu",
    "disable_saved_tensors_hooks",
    "register_multi_grad_hook",
    "allow_mutation_on_saved_tensors",
]

class saved_tensors_hooks():
    """Context-manager that sets a pair of pack / unpack hooks for saved tensors.

    Use this context-manager to define how intermediary results of an operation
    should be packed before saving, and unpacked on retrieval.

    In that context, the ``pack_hook`` function will be called everytime an
    operation saves a tensor for backward (this includes intermediary results
    saved using
    :func:`~torch.autograd.function._ContextMethodMixin.save_for_backward` but
    also those recorded by a PyTorch-defined operation). The output of
    ``pack_hook`` is then stored in the computation graph instead of the
    original tensor.

    The ``unpack_hook`` is called when the saved tensor needs to be accessed,
    namely when executing :func:`torch.Tensor.backward()` or
    :func:`torch.autograd.grad()`. It takes as argument the *packed* object
    returned by ``pack_hook`` and should return a tensor which has the same
    content as the original tensor (passed as input to the corresponding
    ``pack_hook``).

    The hooks should have the following signatures:

        pack_hook(tensor: Tensor) -> Any

        unpack_hook(Any) -> Tensor

    where the return value of ``pack_hook`` is a valid input to ``unpack_hook``.

    In general, you want ``unpack_hook(pack_hook(t))`` to be equal to ``t`` in terms
    of value, size, dtype and device.

    Example::

        >>> def pack_hook(x):
        ...     print("Packing", x)
        ...     return x
        >>>
        >>> def unpack_hook(x):
        ...     print("Unpacking", x)
        ...     return x
        >>>
        >>> a = torch.ones(5, requires_grad=True)
        >>> b = torch.ones(5, requires_grad=True) * 2
        >>> with torch.autograd.graph.saved_tensors_hooks(pack_hook, unpack_hook):
        ...     y = a * b
        Packing tensor([1., 1., 1., 1., 1.], requires_grad=True)
        Packing tensor([2., 2., 2., 2., 2.], grad_fn=<MulBackward0>)
        >>> y.sum().backward()
        Unpacking tensor([1., 1., 1., 1., 1.], requires_grad=True)
        Unpacking tensor([2., 2., 2., 2., 2.], grad_fn=<MulBackward0>)

    .. warning ::
        Performing an inplace operation on the input to either hooks may lead
        to undefined behavior.

    .. warning ::
        Only one pair of hooks is allowed at a time. When recursively nesting this
        context-manager, only the inner-most pair of hooks will be applied.
    """
    def __init__(self, pack_hook: Callable[[torch.Tensor], Any], unpack_hook: Callable[[Any], torch.Tensor]):
        self.pack_hook = pack_hook
        self.unpack_hook = unpack_hook

    def __enter__(self):
        torch._C._autograd._push_saved_tensors_default_hooks(self.pack_hook, self.unpack_hook)

    def __exit__(self, *args: Any):
        torch._C._autograd._pop_saved_tensors_default_hooks()


class save_on_cpu(saved_tensors_hooks):
    """Context-manager under which tensors saved by the forward pass will be
    stored on cpu, then retrieved for backward.

    When performing operations within this context manager, intermediary
    results saved in the graph during the forward pass will be moved to CPU,
    then copied back to the original device when needed for the backward pass.
    If the graph was already on CPU, no tensor copy is performed.

    Use this context-manager to trade compute for GPU memory usage (e.g.
    when your model doesn't fit in GPU memory during training).

    Args:
        pin_memory (bool): If ``True`` tensors will be saved to CPU pinned memory
                           during packing and copied to GPU asynchronously during unpacking.
                           Defaults to ``False``.
                           Also see :ref:`cuda-memory-pinning`.


    Example::

        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
        >>> a = torch.randn(5, requires_grad=True, device="cuda")
        >>> b = torch.randn(5, requires_grad=True, device="cuda")
        >>> c = torch.randn(5, requires_grad=True, device="cuda")
        >>>
        >>> def f(a, b, c):
        ...     prod_1 = a * b           # a and b are saved on GPU
        ...     with torch.autograd.graph.save_on_cpu():
        ...         prod_2 = prod_1 * c  # prod_1 and c are saved on CPU
        ...     y = prod_2 * a           # prod_2 and a are saved on GPU
        ...     return y
        >>>
        >>> y = f(a, b, c)
        >>> del a, b, c  # for illustration only
        >>> # the content of a, b, and prod_2 are still alive on GPU
        >>> # the content of prod_1 and c only live on CPU
        >>> y.sum().backward()  # all CPU tensors are moved back to GPU, for backward
        >>> # all intermediary tensors are released (deleted) after the call to backward

    """
    def __init__(self, pin_memory=False):
        def pack_to_cpu(tensor):
            if not pin_memory:
                return (tensor.device, tensor.cpu())

            packed = torch.empty(
                tensor.size(),
                dtype=tensor.dtype,
                layout=tensor.layout,
                pin_memory=(torch.cuda.is_available() and not tensor.is_sparse))
            packed.copy_(tensor)
            return (tensor.device, packed)

        def unpack_from_cpu(packed):
            device, tensor = packed
            return tensor.to(device, non_blocking=pin_memory)

        super().__init__(pack_to_cpu, unpack_from_cpu)

# NOTE: [new checkpoint mechanism]
#
# Contents:
# - Definition
# - Mechanism design
# - Example
#
# Definition: Checkpointing
# =========================
#
# We define checkpoint as a context manager such that any variables that
# were saved by forward under this context AND remain saved at the point
# in time when we exit the context are cleared and marked as needed during
# recomputation.
#
# In other words, if saved tensors are manually cleared, e.g. by running
# backward, checkpoint will ignore those tensors because it only cares about
# the set of tensors saved at the point in time when the context exits.
#
# with checkpoint():
#    y = x.sin()                # saves x
#    z = y.exp()                # saves z
#    torch.autograd.grad(z, z)  # clears z, only x remains saved
#                               # As we exit, clears x only
#
# This may also mean that we cannot simply halt execution early as soon as we've
# saved the right number of buffers.
#
# Special handling of input for the nested case
# ---------------------------------------------
#
# There is some specially handling for the nested case: the inputs to
# are treated as saved variables in the parent context.
#
# with checkpoint0():
#   with checkpoint1():          # saves `y` in check0
#     y = f(x)                   # f's saved variables are cleared by check1
#   with checkpoint2():          # saves `z` in check0
#     z = g(y)                   # g's saved variables are cleared by check1
#                                # exiting check0, clears `y` and `z`
#                                # whatever f and g save are hidden
#
# NB: We never need to recompute function until we have finished running it
#
# TODO: Handling of free variables.
# The current stack was created to recompute values for some call to checkpoint.
# If that checkpointed function calls checkpoint one or more times (possibly
# in a nested way), the inputs to the top-most checkpoints are cleared, so if we
# detect that we are the direct children to the ambient frame, we save its inputs
#
# We can register as many hooks as we want, they all do the same thing
# Backward usually happens outside of the context of any checkpoint anyway
# so we'll at least need to call this once per recomputation stack
#
# We can reuse the checkpoint code because what we are doing is very similar
# to checkpointing, we want to manage the tensors saved and collect the ones
# that remain alive at the very end.
# Creating a new CheckpointStack creates an ambient frame which manages this
#
# Sketch of the mechanism
# =======================
# TODO: Why we need a stack of stacks
#
# Demonstrating the mechanism with an example
# -------------------------------------------
# TODO: reference cycle concerns
#

class CheckpointFrame():
    def __init__(self, parent, fn):
        self.parent = weakref.ref(parent) if parent is not None else parent
        self.fn = fn
        self.exited = False
        self.needed_counter = 0
        # Assume that entries are added/removed determinsitically
        #        and that removal preserves order
        self.saved_tensors: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
        self.child_inputs: List[Tuple[Any, ...]] = []
        # Set this when we leave
        self.num_needs_recompute = 0
        self.idx_of_input_in_parent = None
        self.num_child_inputs_need_recompute = 0

        # During recomputation
        self.recomputed: List[torch.Tensor] = []
        self.recomputed_child_inputs: List[Tuple[Any, ...]] = []

class CheckpointStack():
    def __init__(self, parent, target_frame=None):
        self.parent: CheckpointStack = parent
        self.target_frame: CheckpointFrame = target_frame
        self.frames = [CheckpointFrame(parent=None, fn=None)]

checkpoint_stacks: List[CheckpointStack] = []

class _checkpoint_hook(saved_tensors_hooks):
    def __init__(self):
        def pack_hook(x):
            # Snapshot the state of the current checkpoint stack
            handle = _Handle()
            frames = tuple(checkpoint_stacks[-1].frames)
            frames[-1].saved_tensors[handle] = x
            frames[-1].needed_counter += 1
            return handle, frames, frames[-1].needed_counter - 1

        def unpack_hook(saved):
            handle, frames, idx = saved

            # backward was called before leaving checkpoint context
            if not frames[-1].exited:
                assert handle in frames[-1].saved_tensors
                return frames[-1].saved_tensors[handle]
            # TODO: give a nice error for when we backwarded during the context

            assert len(frames[-1].recomputed) <= frames[-1].num_needs_recompute
            if len(frames[-1].recomputed) < frames[-1].num_needs_recompute:
                # The first frame is always the ambient frame
                for frame in frames[1:]:
                    if (frame.exited is False
                            or (len(frame.recomputed) == frame.num_needs_recompute
                                and len(frame.recomputed_child_inputs) == frame.num_child_inputs_need_recompute)):
                        continue
                    assert frame.parent is not None and frame.parent() is not None
                    parent = frame.parent()
                    inps = parent.recomputed_child_inputs if parent.exited else parent.child_inputs
                    args, kwargs = inps[frame.idx_of_input_in_parent]

                    with torch.autograd.enable_grad():
                        # Do recomputation in a fresh checkpoint stack
                        _checkpoint(frame.fn, *args, target_frame=frame, **kwargs)

            ret = frames[-1].recomputed[idx]
            return ret

        super().__init__(pack_hook, unpack_hook)

def get_wrapped_fn(fn):
    # Capture the current context, so we can replay it
    def wrapped(*args, **kwargs):
        return fn(*args, **kwargs)
    return wrapped

def _checkpoint(fn, *args, target_frame: CheckpointFrame = None, **kwargs):
    needs_to_pop = False
    if len(checkpoint_stacks) == 0 or target_frame is not None:
        needs_to_pop = True
        parent = None if len(checkpoint_stacks) == 0 else checkpoint_stacks[-1]
        checkpoint_stacks.append(CheckpointStack(parent=parent, target_frame=target_frame))

    curr_stack = checkpoint_stacks[-1]

    if target_frame is None:
        # Create a proper checkpoint frame and append it to the current stack
        wrapped_fn = get_wrapped_fn(fn)
        curr_frame = CheckpointFrame(parent=curr_stack.frames[-1], fn=wrapped_fn)
        curr_stack.frames.append(curr_frame)
    else:
        # Don't need a checkpoint frame if we're starting a stack for recomputation
        assert len(curr_stack.frames) == 1
        curr_frame = curr_stack.frames[0]

    if (curr_stack.target_frame is not None
            and curr_frame.parent is not None
            and curr_frame.parent() is curr_stack.frames[0]):
        # Top-level checkpoints save their inputs to the target frame during recomputation
        assert len(curr_stack.frames) == 2
        curr_stack.target_frame.recomputed_child_inputs.append((args, kwargs))

    with _checkpoint_hook():
        # We can register this hook as many times as we want, it only reads global state
        ret = fn(*args, **kwargs)

    curr_frame.num_needs_recompute = len(curr_frame.saved_tensors)
    if target_frame is None:
        # Children register their inputs to the parent checkpoint to be cleared
        # when the parent exits and restored when the parent is recomputed
        inputs = (args, kwargs)
        assert curr_frame.parent is not None
        parent = curr_frame.parent()
        assert parent is not None
        curr_frame.idx_of_input_in_parent = len(parent.child_inputs)
        parent.child_inputs.append(inputs)
        parent.num_child_inputs_need_recompute += 1
    else:
        # Stack for recomputation is getting destroyed, save into the target frame
        assert len(target_frame.recomputed) == 0
        detached_saved = [t.detach() for t in curr_frame.saved_tensors.values()]
        target_frame.recomputed.extend(detached_saved)

    curr_frame.child_inputs.clear()
    curr_frame.saved_tensors.clear()
    curr_frame.exited = True

    curr_stack.frames.pop()
    if needs_to_pop:
        checkpoint_stacks.pop()

    return ret


@contextlib.contextmanager
def disable_saved_tensors_hooks(error_message):
    """Context-manager that disables the saved tensors default hooks feature.

    Useful for if you are creating a feature that does not work with saved
    tensors default hooks.

    Args:
        error_message (str): When saved tensors default hooks are used when they
                             have been are disabled, a RuntimeError with this
                             error message gets raised.

    Example::

        >>> message = "saved tensors default hooks are disabled"
        >>> with torch.autograd.graph.disable_saved_tensors_hooks(message):
        ...     # Raises RuntimeError: saved tensors default hooks are disabled
        ...     with torch.autograd.graph.save_on_cpu():
        ...         pass

    """
    yield
    return
    try:
        maybe_prev_message = torch._C._autograd._saved_tensors_hooks_get_disabled_error_message()
        torch._C._autograd._saved_tensors_hooks_disable(error_message)
        yield
    finally:
        # See NOTE: [disabled_error_message invariant]
        if maybe_prev_message is None:
            torch._C._autograd._saved_tensors_hooks_enable()
        else:
            torch._C._autograd._saved_tensors_hooks_disable(maybe_prev_message)


def register_multi_grad_hook(tensors: Sequence[torch.Tensor], fn: Callable[[Sequence[Optional[torch.Tensor]]], None]):
    r"""Registers a multi-grad backward hook.

    The hook will be called after gradients with respect to every tensor in
    :attr:`tensors` have been computed. If a tensor is in :attr:`tensors` but
    is not part of the graph, or if a tensor is not needed to compute the gradients
    for any ``inputs`` specified for the current ``.backward()`` or ``.grad()`` call,
    this tensor will be ignored and the hook will not wait for its gradient to be
    computed.

    After every non-ignored tensor's gradient has been computed, :attr:`fn` will be
    called with those gradients. ``None`` will be passed for tensors that did not
    have their gradients computed.

    The hook should not modify its arguments.

    This function returns a handle with a method ``handle.remove()`` that removes the hook.

    Example::

        >>> import torch
        >>>
        >>> a = torch.rand(2, 3, requires_grad=True)
        >>> b = torch.rand(2, 3, requires_grad=True)
        >>> c = a * b
        >>> d = a * b
        >>>
        >>> def fn(grads):
        ...     print([g is not None for g in grads])
        ...
        >>> torch.autograd.graph.register_multi_grad_hook((a, b, c, d), fn)
        >>>
        >>> c.sum().backward(retain_graph=True)
        [True, True, True, False]
        >>> c.sum().backward(inputs=(a,), retain_graph=True)
        [True, False, True, False]
        >>>
    """
    count: Dict[int, int] = dict()
    nb_calls = None
    buffer: Dict[int, List[Optional[torch.Tensor]]] = dict()

    def get_grad_fn(t):
        # or grad accumulator
        if t.requires_grad and t.grad_fn is None:
            return t.clone().grad_fn.next_functions[0][0]
        else:
            return t.grad_fn

    grad_fns = list(map(get_grad_fn, tensors))

    def get_inner_hook(idx):
        def inner_hook(grad: torch.Tensor):
            nonlocal count, nb_calls, buffer
            id = torch._C._current_graph_task_id()
            assert id != -1, "expected this hook to be called inside a backward call"
            count[id] = count.get(id, 0)
            buffer[id] = buffer.get(id, [None] * len(tensors))

            if count[id] == 0:
                # On the first call, compute the actual nb_calls and buffer
                nb_calls = sum(torch._C._will_engine_execute_node(g) for g in grad_fns)  # type: ignore[attr-defined]

            buffer[id][idx] = grad
            count[id] += 1

            if count[id] == nb_calls:
                fn(buffer[id])
                del count[id]
                del buffer[id]
        return inner_hook

    class Handle(RemovableHandle):
        handles: Tuple[RemovableHandle, ...]

        def __init__(self, handles: Tuple[RemovableHandle, ...]):
            self.handles = handles

        def remove(self):
            for handle in self.handles:
                handle.remove()

        def __getstate__(self):
            return self.handles

        def __setstate__(self, state):
            self.handles = state

    handles: List[RemovableHandle] = []
    for i, t in enumerate(tensors):
        handles.append(t.register_hook(get_inner_hook(i)))

    return Handle(tuple(handles))


# NOTE [Allow mutation on tensors saved for backward]
#
# 1. Tensor gets saved for backward
#    - remember the python object id and the version of the tensor
#    - remember aliasing information (data_ptr of base + version)
#    - save the original so we control its lifetime
# 2. Any time a tensor gets in-placed
#    - for each tensor aliased to it:
#      - check using its object id and version to see if it has been saved
#      - if it has been saved, clone it
#      - delete the reference to the original
# 3. during backward
#    - if the clone exists, the tensor must've been modified in-place
_allow_mutation_on_saved_tensors_enabled = False

def _get_tid(t) -> Tuple[int, int, int]:
    return (id(t), t.data_ptr(), t._version)

def _get_sid(t) -> Tuple[int, int]:
    return (t.data_ptr(), t._version)

class _Handle():
    pass

class _swap_with_cloned(saved_tensors_hooks):
    def __init__(self, ctx):
        def pack_hook(t):
            tid = _get_tid(t)
            sid = _get_sid(t)
            # Tensors saved for backward have an entry in _tid_to_weakhandle
            handle: Optional[_Handle] = None

            # Save aliasing information
            ctx.sid_to_tid[sid].add(tid)

            # NB: The same tensor (of the same version) can be saved multiple times
            if tid not in ctx.tid_to_weakhandle:
                handle = _Handle()
                ctx.tid_to_weakhandle[tid] = handle
                ctx.original[handle] = t
            else:
                # Store an additional strong reference to the handle
                handle = ctx.tid_to_weakhandle[tid]
            return handle

        def unpack_hook(tup):
            handle = tup
            error_msg = (
                "Trying to backward outside of the 'allow_mutation_on_saved_tensors' context"
                "in which the graph was originally recorded.")
            assert _allow_mutation_on_saved_tensors_enabled, error_msg
            if handle in ctx.cloned:
                res = ctx.cloned[handle]
            else:
                assert handle in ctx.original, error_msg
                res = ctx.original[handle]
            return res

        super().__init__(pack_hook, unpack_hook)

class _CloneArgBeforeMutateMode(TorchDispatchMode):
    def __init__(self, ctx):
        self.ctx = ctx

    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
        kwargs = kwargs or {}

        for idx, arg in enumerate(func._schema.arguments):
            if arg.alias_info is not None and arg.alias_info.is_write:
                t = kwargs["out"] if arg.is_out else args[idx]
                tid = _get_tid(t)
                sid = _get_sid(t)
                ctx = self.ctx
                if sid in ctx.sid_to_tid:
                    for tid in ctx.sid_to_tid[sid]:
                        if tid not in ctx.tid_to_weakhandle:
                            # We know that if tid is in sid_to_tid, then it must also be in
                            # tid_to_weakhandle. However, it is possible for the tensor to be
                            # saved at one point, but cleared by backward before it is modified
                            # in-place. Consider the following example:
                            #
                            # >>> a = torch.randn(2, 3, requires_grad=True).clone()
                            # >>> out = (a**2).sum()
                            # >>> out.backward()
                            # >>> a.sin_()
                            continue
                        handle = ctx.tid_to_weakhandle[tid]
                        if handle in ctx.cloned:
                            # The same exact tensor has been cloned already
                            continue
                        ctx.cloned[handle] = ctx.original[handle].clone()
                        del ctx.original[handle]

        rs = func(*args, **kwargs)
        return rs

class _AllowMutationOnSavedContext():
    def __init__(self):
        self.cloned: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
        self.original: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
        self.tid_to_weakhandle: weakref.WeakValueDictionary = weakref.WeakValueDictionary()
        self.sid_to_tid: Dict[Tuple[int, int], Set[Tuple[int, int, int]]] = defaultdict(set)

    def clear(self):
        self.cloned.clear()
        self.original.clear()
        self.tid_to_weakhandle.clear()
        self.sid_to_tid.clear()

@contextlib.contextmanager
def allow_mutation_on_saved_tensors():
    """Context manager under which mutating tensors saved for backward is allowed

    Under this context manager, tensors saved for backward are cloned on mutation,
    so the original version can still be used during backward. Normally, mutating a tensor
    saved for backward will result in an error raised when it's used during backward.

    To ensure the correct behavior, both the forward and backward should be run under
    the same context manager.

    returns:
        An _AllowMutationOnSavedContext object storing the state managed by this
        context manager. This object can be useful for debugging purposes. The state
        managed by the context manager is automatically cleared upon exiting.

    Example::

        >>> import torch
        >>> with torch.autograd.graph.allow_mutation_on_saved_tensors():
        ...     # forward
        ...     a = torch.ones(2, 3, requires_grad=True)
        ...     b = a.clone()
        ...     out = (b**2).sum()
        ...     b.sin_()
        ...     # backward
        ...     out.sum().backward()
        ...
        tensor([[0.8415, 0.8415, 0.8415],
                [0.8415, 0.8415, 0.8415]], grad_fn=<SinBackward0>)
    """
    global _allow_mutation_on_saved_tensors_enabled

    ctx = _AllowMutationOnSavedContext()

    with _swap_with_cloned(ctx), _CloneArgBeforeMutateMode(ctx):
        try:
            if _allow_mutation_on_saved_tensors_enabled:
                raise RuntimeError("allow_mutation_on_saved_tensors contexts cannot be nested")
            _allow_mutation_on_saved_tensors_enabled = True
            yield ctx
        finally:
            ctx.clear()
            _allow_mutation_on_saved_tensors_enabled = False