convert-torch-onnx-to-torch generates invalid IR for onnx.Resize where scaling is in the first two dimensions

[mgehre-amd]

The code in that pass seems to silently assume the the first two dimensions are not scaled, but ONNX has no such restriction.

With input

func.func @test_resize_middle(%arg0: !torch.vtensor<[1,36,42,384],f32>) -> !torch.vtensor<[1,72,84,384],f32> 
  attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
 %none = torch.constant.none
 %12 = torch.vtensor.literal(dense<[1.000000e+00, 2.000000e+00, 2.000000e+00, 1.000000e+00]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
 %19 = torch.operator "onnx.Resize"(%arg0, %none, %12) {
  torch.onnx.coordinate_transformation_mode = "half_pixel",
  torch.onnx.mode = "nearest",
  torch.onnx.nearest_mode = "round_prefer_floor"} : (!torch.vtensor<[1,36,42,384],f32>, !torch.none, !torch.vtensor<[4],f32>) -> !torch.vtensor<[1,72,84,384],f32>
 return %19 : !torch.vtensor<[1,72,84,384],f32>
}

we get

module {
  func.func @test_resize_middle(%arg0: !torch.vtensor<[1,36,42,384],f32>) -> !torch.vtensor<[1,72,84,384],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
    %none = torch.constant.none
    %0 = torch.vtensor.literal(dense<[1.000000e+00, 2.000000e+00, 2.000000e+00, 1.000000e+00]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
    %none_0 = torch.constant.none
    %int0 = torch.constant.int 0
    %false = torch.constant.bool false
    %true = torch.constant.bool true
    %str = torch.constant.str "nearest_half_pixel,round_prefer_floor"
    %int2 = torch.constant.int 2
    %1 = torch.aten.select.int %0, %int0, %int2 : !torch.vtensor<[4],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
    %2 = torch.aten.item %1 : !torch.vtensor<[1],f32> -> !torch.float
    %int3 = torch.constant.int 3
    %3 = torch.aten.select.int %0, %int0, %int3 : !torch.vtensor<[4],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
    %4 = torch.aten.item %3 : !torch.vtensor<[1],f32> -> !torch.float
    %5 = torch.prim.ListConstruct %2, %4 : (!torch.float, !torch.float) -> !torch.list<float>
    %6 = torch.aten.__interpolate.size_list_scale_list %arg0, %none_0, %5, %str, %false, %none_0, %false : !torch.vtensor<[1,36,42,384],f32>, !torch.none, !torch.list<float>, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[1,72,84,384],f32>
    return %6 : !torch.vtensor<[1,72,84,384],f32>
  }
}

Here, %1 and %3 only read the last two elements out of %0. When lowering this IR to linalg (-convert-torch-to-linalg), we get
error: unexpected error: 'tensor.cast' op operand type 'tensor<1x36x?x?xf32>' and result type 'tensor<1x72x84x384xf32>' are cast incompatible because the scales used by torch.aten.__interpolate.size_list_scale_list are not matching the output shape anymore.

[arnavmehta1]

I can work on this.

[zjgarvey]

@arnavmehta1 , Since this is going stale, I'm going to unassign you from this task and hand it off to someone else. Please let me know if you have some active work on this issue.

[bjacobgordon]

@zjgarvey Taking it!

[bjacobgordon]

See #3945