140 lines
6.5 KiB
MLIR
140 lines
6.5 KiB
MLIR
// RUN: onnx-mlir-opt --decompose-onnx %s -split-input-file | FileCheck %s
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// -----
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// CHECK-LABEL: @test_reducel1(%{{.*}}: tensor<?x?x?xf32>) -> tensor<*xf32>
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func @test_reducel1(%arg0 : tensor<?x?x?xf32>) -> tensor<*xf32> {
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%0 ="onnx.ReduceL1"(%arg0) {axes=[1], keepdims = 0 : i64} : (tensor<?x?x?xf32>)-> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-NEXT: [[ABS:%.+]] = "onnx.Abs"(%arg0) : (tensor<?x?x?xf32>) -> tensor<*xf32>
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// CHECK-NEXT: %{{[0-9]+}} = "onnx.ReduceSum"([[ABS]]) {axes = [1], keepdims = 0 : i64} : (tensor<*xf32>) -> tensor<*xf32>
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}
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// -----
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// CHECK-LABEL: @test_reducel2(%{{.*}}: tensor<?x?x?xf32>) -> tensor<*xf32>
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func @test_reducel2(%arg0 : tensor<?x?x?xf32>) -> tensor<*xf32> {
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%0 ="onnx.ReduceL2"(%arg0) {axes=[1], keepdims = 0 : i64} : (tensor<?x?x?xf32>)-> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-NEXT: [[MUL:%.+]] = "onnx.Mul"(%arg0, %arg0) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>) -> tensor<*xf32>
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// CHECK-NEXT: [[REDUCE_SUM:%.+]] = "onnx.ReduceSum"([[MUL]]) {axes = [1], keepdims = 0 : i64} : (tensor<*xf32>) -> tensor<*xf32>
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// CHECK-NEXT: [[SQRT:%.+]] = "onnx.Sqrt"([[REDUCE_SUM]]) : (tensor<*xf32>) -> tensor<*xf32>
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}
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// -----
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// CHECK-LABEL: @test_reducelogsum(%{{.*}}: tensor<?x?x?xf32>) -> tensor<*xf32>
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func @test_reducelogsum(%arg0 : tensor<?x?x?xf32>) -> tensor<*xf32> {
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%0 ="onnx.ReduceLogSum"(%arg0) {axes=[1], keepdims = 0 : i64} : (tensor<?x?x?xf32>)-> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-NEXT: [[REDUCE_SUM:%.+]] = "onnx.ReduceSum"(%arg0) {axes = [1], keepdims = 0 : i64} : (tensor<?x?x?xf32>) -> tensor<*xf32>
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// CHECK-NEXT: [[LOG:%.+]] = "onnx.Log"([[REDUCE_SUM]]) : (tensor<*xf32>) -> tensor<*xf32>
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}
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// -----
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// CHECK-LABEL: @test_reducelogsumexp(%{{.*}}: tensor<?x?x?xf32>) -> tensor<*xf32>
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func @test_reducelogsumexp(%arg0 : tensor<?x?x?xf32>) -> tensor<*xf32> {
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%0 ="onnx.ReduceLogSumExp"(%arg0) {axes=[1], keepdims = 0 : i64} : (tensor<?x?x?xf32>)-> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-NEXT: [[EXP:%.+]] = "onnx.Exp"(%arg0) : (tensor<?x?x?xf32>) -> tensor<*xf32>
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// CHECK-NEXT: [[REDUCE_SUM:%.+]] = "onnx.ReduceSum"([[EXP]]) {axes = [1], keepdims = 0 : i64} : (tensor<*xf32>) -> tensor<*xf32>
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// CHECK-NEXT: [[LOG:%.+]] = "onnx.Log"([[REDUCE_SUM]]) : (tensor<*xf32>) -> tensor<*xf32>
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}
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// -----
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// CHECK-LABEL: @test_reducesumsquare(%{{.*}}: tensor<?x?x?xf32>) -> tensor<*xf32>
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func @test_reducesumsquare(%arg0 : tensor<?x?x?xf32>) -> tensor<*xf32> {
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%0 ="onnx.ReduceSumSquare"(%arg0) {axes=[1], keepdims = 0 : i64} : (tensor<?x?x?xf32>)-> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-NEXT: [[SQUARE:%.+]] = "onnx.Mul"(%arg0, %arg0) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>) -> tensor<*xf32>
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// CHECK-NEXT: %{{[0-9]+}} = "onnx.ReduceSum"([[SQUARE]]) {axes = [1], keepdims = 0 : i64} : (tensor<*xf32>) -> tensor<*xf32>
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}
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// -----
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// Scaler Pattern test
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// -----
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// null
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// CHECK-LABEL: func @test_scaler_null_float(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_null_float(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: return %arg0 : tensor<3xf32>
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}
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// -----
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// null not float
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// CHECK-LABEL: func @test_scaler_null(%{{.*}}: tensor<3xi32>) -> tensor<3xf32> {
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func @test_scaler_null(%arg0: tensor<3xi32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) : (tensor<3xi32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Cast"(%arg0) {to = 1 : i64} : (tensor<3xi32>) -> tensor<3xf32>
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// CHECK-NEXT: return %0 : tensor<3xf32>
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}
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// -----
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// scaler no offset
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// CHECK-LABEL: func @test_scaler_no_offset(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_no_offset(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Mul"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %1 : tensor<3xf32>
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}
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// -----
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// scaler no scale
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// CHECK-LABEL: func @test_scaler_no_scale(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_no_scale(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %1 : tensor<3xf32>
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}
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// -----
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// normal scaler
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// CHECK-LABEL: func @test_scaler_normal(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_normal(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32], scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %3 : tensor<3xf32>
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}
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// -----
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// normal scaler with constant offset and scale
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// CHECK-LABEL: func @test_scaler_constant(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_constant(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32], scale = [3.125000e-02 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<1986.99939> : tensor<1xf32>} : () -> tensor<1xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
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// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<3.125000e-02> : tensor<1xf32>} : () -> tensor<1xf32>
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// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %3 : tensor<3xf32>
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}
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