mirror of https://github.com/llvm/torch-mlir
1259e8a00a
### Changes
1. Folders for view-like ops: `aten.view`, `aten.flatten.using_ints`,
and `aten.unflatten.int`
2. Folder for transpose
3. Extended support for the `aten.slice.Tensor` op folder to include
negative strides.
### Motivation
The biggest motivation for this patch is to fold the extremely
convoluted ir that gets generated when exporting a pytorch model with an
`aten.pad` op to ONNX, then re-importing and lowering back to torch. For
example, the verbose output of the e2e test `PadModule_basic` with `-c
onnx`:
```mlir
module {
func.func @main_graph(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "2.5.0"} {
%none = torch.constant.none
%0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64>
%1 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1> : tensor<4xsi64>} : () -> !torch.vtensor<[4],si64>
%2 = torch.operator "onnx.ConstantOfShape"(%0) {torch.onnx.value = dense_resource<__2> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[4],si64>
%3 = torch.operator "onnx.Concat"(%1, %2) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[4],si64>) -> !torch.vtensor<[8],si64>
%4 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64>
%5 = torch.operator "onnx.Reshape"(%3, %4) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[8],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[4,2],si64>
%6 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__4> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64>
%7 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__5> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64>
%8 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__6> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64>
%9 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__7> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64>
%10 = torch.operator "onnx.Slice"(%5, %7, %8, %6, %9) : (!torch.vtensor<[4,2],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4,2],si64>
%11 = torch.operator "onnx.Transpose"(%10) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[4,2],si64>) -> !torch.vtensor<[2,4],si64>
%12 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__8> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64>
%13 = torch.operator "onnx.Reshape"(%11, %12) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,4],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[8],si64>
%14 = torch.operator "onnx.Cast"(%13) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[8],si64>) -> !torch.vtensor<[8],si64>
%15 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__9> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%16 = torch.operator "onnx.Pad"(%arg0, %14, %15) {torch.onnx.mode = "constant"} : (!torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[8],si64>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?,?,?],f32>
return %16 : !torch.vtensor<[?,?,?,?],f32>
}
}
{-#
dialect_resources: {
builtin: {
_: "0x080000000400000000000000",
__1: "0x080000000000000000000000010000000000000002000000000000000300000000000000",
__2: "0x080000000000000000000000",
__3: "0x08000000FFFFFFFFFFFFFFFF0200000000000000",
__4: "0x080000000000000000000000",
__5: "0x08000000FFFFFFFFFFFFFFFF",
__6: "0x080000000100000000000080",
__7: "0x08000000FFFFFFFFFFFFFFFF",
__8: "0x08000000FFFFFFFFFFFFFFFF",
__9: "0x080000000000C03F"
}
}
#-}
```
Get's converted to the torch IR:
```mlir
module {
func.func @main_graph(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "2.5.0"} {
%float1.500000e00 = torch.constant.float 1.500000e+00
%int-9223372036854775807 = torch.constant.int -9223372036854775807
%int-1 = torch.constant.int -1
%int7 = torch.constant.int 7
%int6 = torch.constant.int 6
%int5 = torch.constant.int 5
%int3 = torch.constant.int 3
%int8 = torch.constant.int 8
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int4 = torch.constant.int 4
%int0 = torch.constant.int 0
%0 = torch.vtensor.literal(dense<[0, 1, 2, 3, 0, 0, 0, 0]> : tensor<8xsi64>) : !torch.vtensor<[8],si64>
%1 = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
%2 = torch.aten.view %0, %1 : !torch.vtensor<[8],si64>, !torch.list<int> -> !torch.vtensor<[4,2],si64>
%3 = torch.aten.slice.Tensor %2, %int0, %int-1, %int-9223372036854775807, %int-1 : !torch.vtensor<[4,2],si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[4,2],si64>
%4 = torch.aten.transpose.int %3, %int0, %int1 : !torch.vtensor<[4,2],si64>, !torch.int, !torch.int -> !torch.vtensor<[2,4],si64>
%5 = torch.prim.ListConstruct %int-1 : (!torch.int) -> !torch.list<int>
%6 = torch.aten.view %4, %5 : !torch.vtensor<[2,4],si64>, !torch.list<int> -> !torch.vtensor<[8],si64>
%7 = torch.aten.slice.Tensor %6, %int0, %int0, %int1, %int1 : !torch.vtensor<[8],si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%8 = torch.aten.item %7 : !torch.vtensor<[1],si64> -> !torch.int
%9 = torch.aten.slice.Tensor %6, %int0, %int1, %int2, %int1 : !torch.vtensor<[8],si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%10 = torch.aten.item %9 : !torch.vtensor<[1],si64> -> !torch.int
%11 = torch.aten.slice.Tensor %6, %int0, %int2, %int3, %int1 : !torch.vtensor<[8],si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%12 = torch.aten.item %11 : !torch.vtensor<[1],si64> -> !torch.int
%13 = torch.aten.slice.Tensor %6, %int0, %int3, %int4, %int1 : !torch.vtensor<[8],si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%14 = torch.aten.item %13 : !torch.vtensor<[1],si64> -> !torch.int
%15 = torch.aten.slice.Tensor %6, %int0, %int4, %int5, %int1 : !torch.vtensor<[8],si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%16 = torch.aten.item %15 : !torch.vtensor<[1],si64> -> !torch.int
%17 = torch.aten.slice.Tensor %6, %int0, %int5, %int6, %int1 : !torch.vtensor<[8],si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%18 = torch.aten.item %17 : !torch.vtensor<[1],si64> -> !torch.int
%19 = torch.aten.slice.Tensor %6, %int0, %int6, %int7, %int1 : !torch.vtensor<[8],si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%20 = torch.aten.item %19 : !torch.vtensor<[1],si64> -> !torch.int
%21 = torch.aten.slice.Tensor %6, %int0, %int7, %int8, %int1 : !torch.vtensor<[8],si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%22 = torch.aten.item %21 : !torch.vtensor<[1],si64> -> !torch.int
%23 = torch.prim.ListConstruct %14, %22, %12, %20, %10, %18, %8, %16 : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%24 = torch.aten.constant_pad_nd %arg0, %23, %float1.500000e00 : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.float -> !torch.vtensor<[?,?,?,?],f32>
return %24 : !torch.vtensor<[?,?,?,?],f32>
}
}
```
***All of these operations are useless***. It is literally the result of
needing to reverse (and change the lexicographic order hierarchy of)
padding ints provided via torch vs. ONNX pad ops, which is then
subsequently UNDONE by our ONNX->Torch lowering (represented in the
ordering of the generated list construct).
With the added folders in this patch, the torch IR becomes:
```
module {
func.func @main_graph(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "2.5.0"} {
%float1.500000e00 = torch.constant.float 1.500000e+00
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%int1 = torch.constant.int 1
%0 = torch.prim.ListConstruct %int0, %int1, %int2, %int3, %int0, %int0, %int0, %int0 : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.constant_pad_nd %arg0, %0, %float1.500000e00 : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.float -> !torch.vtensor<[?,?,?,?],f32>
return %1 : !torch.vtensor<[?,?,?,?],f32>
}
}
```
|
||
|---|---|---|
| .. | ||
| torch-mlir | ||
| torch-mlir-c | ||
| torch-mlir-dialects | ||
| CMakeLists.txt | ||