def backward(ctx, gradgrad_input, gradgrad_bias): out, = ctx.saved_tensors gradgrad_out = fused.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale) return gradgrad_out, None, None, None, None
def forward(ctx, input, bias, negative_slope, scale): empty = input.new_empty(0) out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) ctx.save_for_backward(out) ctx.negative_slope = negative_slope ctx.scale = scale return out
def forward(ctx, grad_output, out, negative_slope, scale): ctx.save_for_backward(out) ctx.negative_slope = negative_slope ctx.scale = scale empty = grad_output.new_empty(0) grad_input = fused.fused_bias_act(grad_output, empty, out, 3, 1, negative_slope, scale) dim = [0] if grad_input.ndim > 2: dim += list(range(2, grad_input.ndim)) grad_bias = grad_input.sum(dim).detach() return grad_input, grad_bias