Exact python implementation of Linear function class

For the purposes of fundamental research I need to modify the nn.Linear function class (both forward and backward functions). I require an exact version of the LinearFunction class in python, that does not suffer from miscellaneous errors. The example provided of a custom Linear class throws a broadcasting error when compared to the default C++ implementation (nn.functional.linear - torch._C._nn.linear);


class LinearFunction(Function):
	output = input.mm(weight.t()) -> "RuntimeError: self must be a matrix"

You could check the backend implementation for linear here and use similar logic in your custom code.

Thanks I had a look at Linear.cpp but was not confident converting it. Here is a version of the extending LinearFunction example function that supports 3D matrix multiplication (replaces instances of mm with matmul, t with swapaxes);

class LinearFunction(Function):
	# ctx is the first argument to forward
	def forward(ctx, input, weight, bias):
		# The forward pass can use ctx.
		ctx.save_for_backward(input, weight, bias)
		output = input.matmul(weight.t())
		if bias is not None:
			output += bias.unsqueeze(0).expand_as(output)
		return output

	def backward(ctx, grad_output):
		input, weight, bias = ctx.saved_tensors
		grad_input = grad_weight = grad_bias = None

		if ctx.needs_input_grad[0]:
			grad_input = grad_output.matmul(weight)
		if ctx.needs_input_grad[1]:
			grad_weight = grad_output.swapaxes(-1, -2).matmul(input)
		if bias is not None and ctx.needs_input_grad[2]:
			grad_bias = grad_output.sum(0)

		return grad_input, grad_weight, grad_bias	

Note nn.linear apparently supports 1-dimensional input, in which case the following check may be required;

if(len(grad_output.shape) > 1):
	grad_outputT = grad_output.transpose(-1, -2)
	grad_outputT = grad_output
grad_weight = grad_outputT.matmul(input)