While using the deep learning library PyTorch I came across definitions like this. Does ctx
have the same behavior as self
?
class LinearFunction(Function):
@staticmethod
def forward(ctx, input, weight, bias=None):
ctx.save_for_backward(input, weight, bias)
output = input.mm(weight.t())
if bias is not None:
output += bias.unsqueeze(0).expand_as(output)
return output
@staticmethod
def backward(ctx, grad_output):
input, weight, bias = ctx.saved_variables
grad_input = grad_weight = grad_bias = None
if ctx.needs_input_grad[0]:
grad_input = grad_output.mm(weight)
if ctx.needs_input_grad[1]:
grad_weight = grad_output.t().mm(input)
if bias is not None and ctx.needs_input_grad[2]:
grad_bias = grad_output.sum(0).squeeze(0)
return grad_input, grad_weight, grad_bias
A static method (@staticmethod
) is called using the class type directly, not an instance of this class:
LinearFunction.backward(x, y)
Since you have no instance, it does not make sense to use self
in a static method.
Here, ctx
is just a regular argument that you'll have to pass when calling your methods.
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