MVM not being decomposed properly when targeting VTA

Questions
#1

I’m attempting to implement a 32x32 matrix-vector-multiplier (MVM), written in TensorFlow, on the VTA back-end.
I’m expecting to see the design get decomposed into 4 16x16 MVM operations, since the default configuration of the VTA uses a 16-element accumulator (and I haven’t fiddled with it).
However, I’m seeing the graph before and after the call to vta.graph.pack() remain unchanged:

Graph(%W, %x) {
  %1 = transpose(%W, axes='(0, 1)')
  %2 = reshape(%1, shape='(32, 32)')
  %4 = transpose(%x, axes='(0,)')
  %5 = reshape(%4, shape='(32, 1)')
  %6 = transpose(%5, axes='(1, 0)')
  %7 = dense(%2, %6, use_bias='False', units='1')
  %8 = reshape(%7, shape='(32,)')
  ret %8
}

Graph(%W, %x) {
  %1 = transpose(%W, axes='(0, 1)')
  %2 = reshape(%1, shape='(32, 32)')
  %4 = transpose(%x, axes='(0,)')
  %5 = reshape(%4, shape='(32, 1)')
  %6 = transpose(%5, axes='(1, 0)')
  %7 = dense(%2, %6, use_bias='False', units='1')
  %8 = reshape(%7, shape='(32,)')
  ret %8
}
graph_attr_keys = [shape_num_unknown_nodes, shape]

I was expecting to see “16” dimensions in the second graph above.

Can anyone help me understand what’s going on here?

Thanks!
:slight_smile:

#2

Can you point to a simple program that runs the compilation steps?

#3

Hi,

Thanks for replying!

Here is my program:
https://htmlpreview.github.io/?https://github.com/capn-freako/public/blob/master/TensorFlow_to_VTA.html

Thanks,
-db

#4

Hi @thierry,

Have you had a chance, yet, to look at my code?

Thanks!
-db

#5

Not yet, a little busy with other code changes, but it’s on my TODO list

#6

Okay, thanks for the reply.

Do you think you could point me to certain areas of the documentation that might hold some hope for me, so that I can try to solve this one, while I wait for you?

Thanks!
-db

#7

we don’t have comprehensive documentation on the VTA specific passes, as they are quite in flux. We’re getting a lot of demand to support new models and operators, so I’ll do my best to address this need in the near future so it’s easier for people to extend compilation support.