TensorComprehensions

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Hi,

I'm currently trying to implement a custom convolution operation (foward and backward passes). I am starting from the code provided here

I am getting an error with the backward pass, as d_o and I don't have the same Height H and Width W (when the kernel size is > 1).

Is there currently a way to define the classic convolution operator with backward and a filter size > 1 pass using Tensor Comprehension ?

(Note that I'm not using TC.make_autograd, as this functionality does not seem to be available with the currently released conda package)

Thanks, Lucas

Yes, you can definitely do it. I have a working solution using a different set of variables for the W and H of d_O, eg. float(N,M,OH, OW) d_O. I also had to wrap the gradient formulation with the ternary operator to handle some cases when the kernel is operating on the edges of its input.

Great news!

Would you mind explaining how to implement handling of border cases ? If possible, could you share code ?

Best, Lucas

My code ended up looking something like follows. You absolutely want to check it for errors before using it.

d_I(n, c, h, w) +=! ((h - r_kh >= 0) && (w - r_kw >= 0) && (w - r_kw + KW <= W) && (h - r_kh +KH <= H)) ? d_O( n, r_m, h - r_kh, w - r_kw) * W1(r_m, c, r_kh, r_kw) : 0 where r_kh in 0:KH, r_kw in 0:KW, h in 0:H, w in 0:W, r_m in 0:M, n in 0:N, c in 0:C

d_W1(m, c, kh, kw) +=! ((r_h >= kh) && (r_w >=kw) && (r_w - kw + KW <= W) && (r_h - kh +KH <= H) ) ? d_O( r_n, m, r_h-kh, r_w-kw) * I(r_n, c, r_h, r_w) : 0 where kh in 0:KH, kw in 0:KW, r_h in 0:H, r_w in 0:W, m in 0:M, c in 0:C

ah smart! Thanks a lot for your solution :)

Hello @pclucas14 @kevjshih

My apologies for the long delay, I am sorry to say that the core TC team has moved on to other things. As far as I know the project is not maintained anymore but someone from Facebook should voice in, I am not in a position to speak for them.

Glad to see you have found a solution though! There are indeed a lot of stuff that you can do by just injecting some predicated control flow in your TCs, in particular triangular and sparser data structures.

Best,

N