torch.Tensor.backward

Tensor.backward(gradient=None, retain_graph=None, create_graph=False, inputs=None)

Computes the gradient of current tensor wrt graph leaves.

The graph is differentiated using the chain rule. If the tensor is non-scalar (i.e. its data has more than one element) and requires gradient, the function additionally requires specifying a gradient. It should be a tensor of matching type and shape, that represents the gradient of the differentiated function w.r.t. self.

This function accumulates gradients in the leaves - you might need to zero .grad attributes or set them to None before calling it. See Default gradient layouts for details on the memory layout of accumulated gradients.

Note

If you run any forward ops, create gradient, and/or call backward in a user-specified CUDA stream context, see Stream semantics of backward passes.

Note

When inputs are provided and a given input is not a leaf, the current implementation will call its grad_fn (though it is not strictly needed to get this gradients). It is an implementation detail on which the user should not rely. See https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780 for more details.

Parameters

• gradient (Tensor, optional) – The gradient of the function being differentiated w.r.t. self. This argument can be omitted if self is a scalar.

• retain_graph (bool, optional) – If False, the graph used to compute the grads will be freed. Note that in nearly all cases setting this option to True is not needed and often can be worked around in a much more efficient way. Defaults to the value of create_graph.

• create_graph (bool, optional) – If True, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to False.

• inputs (sequence of Tensor, optional) – Inputs w.r.t. which the gradient will be accumulated into .grad. All other tensors will be ignored. If not provided, the gradient is accumulated into all the leaf Tensors that were used to compute the tensors.