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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