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Fix reducesum onnx lit test to linalg lowering fails (#3218) 

fixes https://github.com/nod-ai/SHARK-Turbine/issues/653

---------

Co-authored-by: Xida Ren <xida.ren.dev@gmail.com>
pull/3436/head
Xida Ren (Cedar) 2024-06-03 13:36:09 -07:00 committed by GitHub
parent 948981a773
commit 11c3281a8a
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2 changed files with 7 additions and 5 deletions
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4
test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
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@ -1649,8 +1649,8 @@ func.func @ints_constant() -> !torch.vtensor<[2], si64> attributes {torch.onnx_m
// -----
// CHECK-LABEL: @dense_constant
func.func @dense_constant() -> () attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64} {
// CHECK-LABEL: @dense_resource_constant
func.func @dense_resource_constant() -> () attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64} {
// CHECK: torch.vtensor.literal(dense<[0, 10, 128, 17000]> : tensor<4xsi32>) : !torch.vtensor<[4],si32>
%0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_int32> : tensor<4xsi32>} : () -> !torch.vtensor<[4],si32>
// CHECK: torch.vtensor.literal(dense<[0.000000e+00, 1.000000e+01, 1.280000e+02, 1.700000e+04]> : tensor<4xf32>) : !torch.vtensor<[4],f32>

8
test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
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@ -1085,15 +1085,17 @@ func.func @test_reduce_sum_empty_set_non_reduced_axis_zero(%arg0: !torch.vtensor
// -----
// CHECK-LABEL: func.func @test_reduce_sum_keepdims_example
func.func @test_reduce_sum_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,1,2],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
func.func @test_reduce_sum_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>) -> !torch.vtensor<[3,1,2],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[VAL_1:.*]] = torch.vtensor.literal(dense<2> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
// CHECK: %[[INT0:.+]] = torch.constant.int 0
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
// CHECK: %[[SELECT:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
// CHECK: %[[SELECT:.+]] = torch.aten.select.int %[[VAL_1]], %[[INT0]], %[[INT0_0]] : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
// CHECK: %[[DIM:.+]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %1 : (!torch.int) -> !torch.list<int>
// CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[DIM]] : (!torch.int) -> !torch.list<int>
// CHECK: %[[TRUE:.+]] = torch.constant.bool true
// CHECK: %[[NONE:.+]] = torch.constant.none
// CHECK: torch.aten.sum.dim_IntList %arg0, %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,1,2],f32>
%arg1 = torch.vtensor.literal(dense<2> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
%0 = torch.operator "onnx.ReduceSum"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,1,2],f32>
return %0 : !torch.vtensor<[3,1,2],f32>
}