Before this PR, a statically shaped aten.convolution would generate
dynamically shaped linalg IR, and even `-canonicalize` would not be able
to fold it back into static shapes. This PR ensure that shape
calculations are folded on construction to directly generate statically
shaped linalg IR.
We achieve that by ensuring that `arith` ops involved in computing
shapes are created via `createOrFold`, so that later uses of
`getAsOpFoldResult` see constants instead of those ops.
For example
```
module {
func.func @forward(%arg0: !torch.vtensor<[32,336,112,112],f32>,
%arg1: !torch.vtensor<[336,168,3,3],f32>,
%arg2: !torch.vtensor<[336],f32>)
-> !torch.vtensor<[32,336,56,56],f32> {
%false = torch.constant.bool false
%int2 = torch.constant.int 2
%int1 = torch.constant.int 1
%0 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%1 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
%2 = torch.prim.ListConstruct : () -> !torch.list<int>
%3 = torch.aten.convolution %arg0, %arg1, %arg2, %1, %0, %0, %false, %2, %int2
: !torch.vtensor<[32,336,112,112],f32>, !torch.vtensor<[336,168,3,3],f32>, !torch.vtensor<[336],f32>, !torch.list<int>,
!torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int
-> !torch.vtensor<[32,336,56,56],f32>
return %3 : !torch.vtensor<[32,336,56,56],f32>
}
}
```
would result in
```
[...]
%padded = tensor.pad %2 low[%14, %15, %16, %17] high[%14, %15, %16, %17] {
^bb0(%arg3: index, %arg4: index, %arg5: index, %arg6: index):
tensor.yield %cst : f32
} : tensor<32x336x112x112xf32> to tensor<?x?x?x?xf32>
[...]
%45 = linalg.conv_2d_ngchw_gfchw {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
ins(%expanded, %expanded_37 : tensor<?x2x?x?x?xf32>, tensor<2x168x168x3x3xf32>)
outs(%expanded_44 : tensor<32x2x168x?x?xf32>) -> tensor<32x2x168x?x?xf32>
[...]
```
and with this PR all shapes are static.
Matthias Gehre
Peiming Liu
Aart Bik
Zhekun Zhang
Yuanqiang Liu