[GitHub] [tvm] comaniac commented on pull request #7675: [Torch] Remove unnecessary reshapes for batch_matmul

[GitBox <gi...@apache.org>]

comaniac commented on pull request #7675:
URL: https://github.com/apache/tvm/pull/7675#issuecomment-800626924


   Pushed a new commit to also reorder the reshape_b and transpose so that the simplify expression can be used.
   
   Before this PR:
   
   ```
   fn (%input0: Tensor[(10, 3, 4), float32], %input1: Tensor[(10, 4, 5), float32]) -> Tensor[(10, 3, 5), float32] {
     %0 = reshape(%input0, newshape=[-1, 3, 4]) /* ty=Tensor[(10, 3, 4), float32] */;
     %1 = reshape(%input1, newshape=[-1, 4, 5]) /* ty=Tensor[(10, 4, 5), float32] */;
     %2 = transpose(%1, axes=[0, 2, 1]) /* ty=Tensor[(10, 5, 4), float32] */;
     %3 = nn.batch_matmul(%0, %2, meta[relay.attrs.BatchMatmulAttrs][0]) /* ty=Tensor[(10, 3, 5), float32] */;
     reshape(%3, newshape=[10, 3, 5]) /* ty=Tensor[(10, 3, 5), float32] */
   }
   
   fn (%input0: Tensor[(10, 3, 4), float32], %input1: Tensor[(4, 5), float32]) -> Tensor[(10, 3, 5), float32] {
     %0 = reshape(%input0, newshape=[-1, 3, 4]) /* ty=Tensor[(10, 3, 4), float32] */;
     %1 = reshape(%input1, newshape=[-1, 4, 5]) /* ty=Tensor[(1, 4, 5), float32] */;
     %2 = transpose(%1, axes=[0, 2, 1]) /* ty=Tensor[(1, 5, 4), float32] */;
     %3 = nn.batch_matmul(%0, %2, meta[relay.attrs.BatchMatmulAttrs][0]) /* ty=Tensor[(10, 3, 5), float32] */;
     reshape(%3, newshape=[10, 3, 5]) /* ty=Tensor[(10, 3, 5), float32] */
   }
   
   fn (%input0: Tensor[(1, 12, 14, 64), float32], %input1: Tensor[(1, 12, 64, 14), float32]) -> Tensor[(1, 12, 14, 14), float32] {
     %0 = reshape(%input0, newshape=[-1, 14, 64]) /* ty=Tensor[(12, 14, 64), float32] */;
     %1 = reshape(%input1, newshape=[-1, 64, 14]) /* ty=Tensor[(12, 64, 14), float32] */;
     %2 = transpose(%1, axes=[0, 2, 1]) /* ty=Tensor[(12, 14, 64), float32] */;
     %3 = nn.batch_matmul(%0, %2, meta[relay.attrs.BatchMatmulAttrs][0]) /* ty=Tensor[(12, 14, 14), float32] */;
     reshape(%3, newshape=[1, 12, 14, 14]) /* ty=Tensor[(1, 12, 14, 14), float32] */
   }
   ```
   
   After this PR:
   
   ```
   fn (%input0: Tensor[(10, 3, 4), float32], %input1: Tensor[(10, 4, 5), float32]) -> Tensor[(10, 3, 5), float32] {
     %0 = transpose(%input1, axes=[0, 2, 1]) /* ty=Tensor[(10, 5, 4), float32] */;
     nn.batch_matmul(%input0, %0, meta[relay.attrs.BatchMatmulAttrs][0]) /* ty=Tensor[(10, 3, 5), float32] */
   }
   
   fn (%input0: Tensor[(10, 3, 4), float32], %input1: Tensor[(4, 5), float32]) -> Tensor[(10, 3, 5), float32] {
     %0 = transpose(%input1, axes=[1, 0]) /* ty=Tensor[(5, 4), float32] */;
     %1 = reshape(%0, newshape=[-1, 5, 4]) /* ty=Tensor[(1, 5, 4), float32] */;
     nn.batch_matmul(%input0, %1, meta[relay.attrs.BatchMatmulAttrs][0]) /* ty=Tensor[(10, 3, 5), float32] */
   }
   
   fn (%input0: Tensor[(1, 12, 14, 64), float32], %input1: Tensor[(1, 12, 64, 14), float32]) -> Tensor[(1, 12, 14, 14), float32] {
     %0 = reshape(%input0, newshape=[-1, 14, 64]) /* ty=Tensor[(12, 14, 64), float32] */;
     %1 = transpose(%input1, axes=[0, 1, 3, 2]) /* ty=Tensor[(1, 12, 14, 64), float32] */;
     %2 = reshape(%1, newshape=[-1, 14, 64]) /* ty=Tensor[(12, 14, 64), float32] */;
     %3 = nn.batch_matmul(%0, %2, meta[relay.attrs.BatchMatmulAttrs][0]) /* ty=Tensor[(12, 14, 14), float32] */;
     reshape(%3, newshape=[1, 12, 14, 14]) /* ty=Tensor[(1, 12, 14, 14), float32] */
   }
   ```
   
   In particular, since the weights in most PyTorch models have to be transposed when converting to Relay, the second case, for example, could be:
   
   ```
   fn (%input0: Tensor[(10, 3, 4), float32], %input1: Tensor[(5, 4), float32]) -> Tensor[(10, 3, 5), float32] {
     %0 = transpose(%input1, axes=[1, 0]) /* ty=Tensor[(4, 5), float32] */; <- Not added by matmul
     %1 = transpose(%0, axes=[1, 0]) /* ty=Tensor[(5, 4), float32] */; <- Added by matmul
     %2 = reshape(%1, newshape=[-1, 5, 4]) /* ty=Tensor[(1, 5, 4), float32] */;
     nn.batch_matmul(%input0, %2, meta[relay.attrs.BatchMatmulAttrs][0]) /* ty=Tensor[(10, 3, 5), float32] */
   }
   ```
   
   By applying SimplifyExpr to cancel unnecessary `transpose`, we could have:
   
   ```
   fn (%input0: Tensor[(10, 3, 4), float32], %input1: Tensor[(5, 4), float32]) -> Tensor[(10, 3, 5), float32] {
     %0 = reshape(%input1, newshape=[-1, 5, 4]) /* ty=Tensor[(1, 5, 4), float32] */;
     nn.batch_matmul(%input0, %0, meta[relay.attrs.BatchMatmulAttrs][0]) /* ty=Tensor[(10, 3, 5), float32] */
   }
   ```


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