BackwardCFunction

class torch.autograd.function.BackwardCFunction

This class is used for internal autograd work. Do not use.

apply(*args)

Apply method used when executing this Node during the backward.

Called by the autograd engine (non-boxed path) and by direct grad_fn.apply() calls. When boxed_grads_call is True, boxes grads into a mutable list before calling user’s backward.

apply_boxed(*args)

Apply method called by the autograd engine when boxed_grads_call is True. Grads arrive as a single mutable list argument, allowing backward to free individual grads mid-execution.

apply_jvp(*args)

Apply method used when executing forward mode AD during the forward

property input_grad_buffers: tuple[Tensor | None, ...]

Return existing buffers for accumulating gradients of this Function’s inputs.

Each entry corresponds to an argument passed to forward(), in the same order. Each entry is None or the autograd engine’s current InputBuffer for that input. A non-None buffer contains gradient contributions already produced during the current backward. A custom backward may accumulate its contribution directly into the buffer and return None for that input, fusing gradient computation with accumulation and avoiding a separate gradient tensor.

Availability follows backward execution order and is independent for each input. An entry is None when no earlier producer has contributed to that input, or when the existing buffer is aliased or cannot safely be updated in place.

For example, x has another forward use while weight does not. The custom backward conditionally fuses accumulation only for grad_x:

>>> class Matmul(torch.autograd.Function):
>>>     @staticmethod
>>>     def forward(ctx, x, weight):
>>>         ctx.save_for_backward(x, weight)
>>>         return x @ weight
>>>
>>>     @staticmethod
>>>     def backward(ctx, grad_output):
>>>         x, weight = ctx.saved_tensors
>>>         x_buffer, _ = ctx.input_grad_buffers
>>>         if x_buffer is not None:
>>>             # Computes grad_x and adds it to the existing buffer.
>>>             matmul_backward_input_acc(
>>>                 grad_output, weight, acc_into=x_buffer
>>>             )
>>>             grad_x = None
>>>         else:
>>>             grad_x = matmul_backward_input(grad_output, weight)
>>>         grad_weight = matmul_backward_weight(grad_output, x)
>>>         return grad_x, grad_weight
>>>
>>> loss = Matmul.apply(x, weight).sum() + other_op(x).sum()

If other_op produces its contribution first, x_buffer can expose that partial sum. If Matmul runs first, x_buffer is None and it returns a separate tensor instead. The fallback lets the function work under either ordering.

Warning

A returned buffer is valid only while the current custom backward invocation is running. Mutate it synchronously and do not retain it. A later producer may replace the engine’s buffer, making a retained tensor stale.

After receiving a non-None buffer, calling backward or grad before the custom backward returns raises an error.

All engine-scheduled producers that use or subsequently update an exposed buffer must execute on the same device, autograd engine thread, and stream.

This property is available only while a Python custom backward is executing during an eager, first-order backward(), torch.autograd.backward(), or torch.autograd.grad() call. It is unavailable with create_graph=True, anomaly detection, a post-hook on the producing autograd node, or stale capture stream overrides.

Note

For a leaf input, a non-None entry exposes its execution-local InputBuffer, not its existing .grad. All contributions are first combined in that buffer. During backward, AccumulateGrad then runs once with the completed buffer to update .grad and run its usual hooks. torch.autograd.grad() instead returns the completed buffer without updating .grad.

During backward, a custom backward that instead accumulates directly into a leaf .grad and returns None does not use this interface. It is responsible for managing .grad state, including initialization and lifetime, synchronization with all other producers, and any AccumulateGrad hook behavior bypassed by the direct write.

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() or forward() 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() or forward() 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 the setup_context() or forward() 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 on ctx) to prevent incorrect gradients and memory leaks, and enable the application of saved tensor hooks. See torch.autograd.graph.saved_tensors_hooks. See Extending torch.autograd for more details.

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 their backward() 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 the saved_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 the setup_context() or forward() methods, and all arguments should be tensors.

In jvp(), saved objects can be accessed through the saved_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() or forward() methods.

If True, undefined grad tensors will be expanded to tensors full of zeros prior to calling the backward() and jvp() 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

set_output_grad_dtype(*dtypes)

Declare the gradient dtype for each of this Function’s outputs.

This should be called at most once, from either the setup_context() or forward() methods. The number of declarations must match the number of returned values, and each argument corresponds positionally to the output at the same index.

For each output, pass the dtype your backward should receive its gradient in:

• Pass a torch.dtype and the engine guarantees the gradient handed to backward has that dtype. This is only valid for a differentiable Tensor output.

• Pass None and the gradient is handed to backward with whatever dtype it already has. This is also the only valid choice for a non-Tensor or non-differentiable output, which has no gradient.

• Omit this call (or pass the output’s own dtype) and the gradient is handed to backward in the output’s dtype, which is the default.

For example:

>>> @staticmethod
>>> def forward(ctx, x):
>>>     t1 = x.sin()
>>>     t2 = x.cos()
>>>     t3 = x.tan()
>>>     ctx.set_output_grad_dtype(torch.float32, t2.dtype, None, None)
>>>     return t1, t2, t3, "not a tensor"

This ensures that backward receives t1’s gradient in float32, keeps the default behavior for t2’s gradient via t2.dtype, passes t3’s gradient through uncast with None, and uses None as the placeholder for the trailing non-Tensor output.