BackwardCFunction¶
- class torch.autograd.function.BackwardCFunction[source]¶
This class is used for internal autograd work. Do not use.
- mark_dirty(*args)¶
Mark given tensors as modified in an in-place operation.
This should be called at most once, in either the
setup_context()
orforward()
methods, and all arguments should be inputs.Every tensor that’s been modified in-place in a call to
forward()
should be given to this function, to ensure correctness of our checks. It doesn’t matter whether the function is called before or after modification.- Examples::
>>> class Inplace(Function): >>> @staticmethod >>> def forward(ctx, x): >>> x_npy = x.numpy() # x_npy shares storage with x >>> x_npy += 1 >>> ctx.mark_dirty(x) >>> return x >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, grad_output): >>> return grad_output >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone() >>> b = a * a >>> Inplace.apply(a) # This would lead to wrong gradients! >>> # but the engine would not know unless we mark_dirty >>> b.backward() # RuntimeError: one of the variables needed for gradient >>> # computation has been modified by an inplace operation
- mark_non_differentiable(*args)¶
Mark outputs as non-differentiable.
This should be called at most once, in either the
setup_context()
orforward()
methods, and all arguments should be tensor outputs.This will mark outputs as not requiring gradients, increasing the efficiency of backward computation. You still need to accept a gradient for each output in
backward()
, but it’s always going to be a zero tensor with the same shape as the shape of a corresponding output.- This is used e.g. for indices returned from a sort. See example::
>>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x): >>> sorted, idx = x.sort() >>> ctx.mark_non_differentiable(idx) >>> ctx.save_for_backward(x, idx) >>> return sorted, idx >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): # still need to accept g2 >>> x, idx = ctx.saved_tensors >>> grad_input = torch.zeros_like(x) >>> grad_input.index_add_(0, idx, g1) >>> return grad_input
- save_for_backward(*tensors)¶
Save given tensors for a future call to
backward()
.save_for_backward
should be called at most once, in either thesetup_context()
orforward()
methods, and only with tensors.All tensors intended to be used in the backward pass should be saved with
save_for_backward
(as opposed to directly onctx
) to prevent incorrect gradients and memory leaks, and enable the application of saved tensor hooks. Seetorch.autograd.graph.saved_tensors_hooks
.Note that if intermediary tensors, tensors that are neither inputs nor outputs of
forward()
, are saved for backward, your custom Function may not support double backward. Custom Functions that do not support double backward should decorate theirbackward()
method with@once_differentiable
so that performing double backward raises an error. If you’d like to support double backward, you can either recompute intermediaries based on the inputs during backward or return the intermediaries as the outputs of the custom Function. See the double backward tutorial for more details.In
backward()
, saved tensors can be accessed through thesaved_tensors
attribute. Before returning them to the user, a check is made to ensure they weren’t used in any in-place operation that modified their content.Arguments can also be
None
. This is a no-op.See Extending torch.autograd for more details on how to use this method.
- Example::
>>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int): >>> w = x * z >>> out = x * y + y * z + w * y >>> ctx.save_for_backward(x, y, w, out) >>> ctx.z = z # z is not a tensor >>> return out >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, grad_out): >>> x, y, w, out = ctx.saved_tensors >>> z = ctx.z >>> gx = grad_out * (y + y * z) >>> gy = grad_out * (x + z + w) >>> gz = None >>> return gx, gy, gz >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double) >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double) >>> c = 4 >>> d = Func.apply(a, b, c)
- save_for_forward(*tensors)¶
Save given tensors for a future call to
jvp()
.save_for_forward
should be called at most once, in either thesetup_context()
orforward()
methods, and all arguments should be tensors.In
jvp()
, saved objects can be accessed through thesaved_tensors
attribute.Arguments can also be
None
. This is a no-op.See Extending torch.autograd for more details on how to use this method.
- Example::
>>> class Func(torch.autograd.Function): >>> @staticmethod >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int): >>> ctx.save_for_backward(x, y) >>> ctx.save_for_forward(x, y) >>> ctx.z = z >>> return x * y * z >>> >>> @staticmethod >>> def jvp(ctx, x_t, y_t, _): >>> x, y = ctx.saved_tensors >>> z = ctx.z >>> return z * (y * x_t + x * y_t) >>> >>> @staticmethod >>> def vjp(ctx, grad_out): >>> x, y = ctx.saved_tensors >>> z = ctx.z >>> return z * grad_out * y, z * grad_out * x, None >>> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double) >>> t = torch.tensor(1., dtype=torch.double) >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double) >>> c = 4 >>> >>> with fwAD.dual_level(): >>> a_dual = fwAD.make_dual(a, t) >>> d = Func.apply(a_dual, b, c)
- set_materialize_grads(value)¶
Set whether to materialize grad tensors. Default is
True
.This should be called only from either the
setup_context()
orforward()
methods.If
True
, undefined grad tensors will be expanded to tensors full of zeros prior to calling thebackward()
andjvp()
methods.- Example::
>>> class SimpleFunc(Function): >>> @staticmethod >>> def forward(ctx, x): >>> return x.clone(), x.clone() >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): >>> return g1 + g2 # No check for None necessary >>> >>> # We modify SimpleFunc to handle non-materialized grad outputs >>> class Func(Function): >>> @staticmethod >>> def forward(ctx, x): >>> ctx.set_materialize_grads(False) >>> ctx.save_for_backward(x) >>> return x.clone(), x.clone() >>> >>> @staticmethod >>> @once_differentiable >>> def backward(ctx, g1, g2): >>> x, = ctx.saved_tensors >>> grad_input = torch.zeros_like(x) >>> if g1 is not None: # We must check for None now >>> grad_input += g1 >>> if g2 is not None: >>> grad_input += g2 >>> return grad_input >>> >>> a = torch.tensor(1., requires_grad=True) >>> b, _ = Func.apply(a) # induces g2 to be undefined