87 lines
5.8 KiB
MLIR
87 lines
5.8 KiB
MLIR
// RUN: onnf-opt --canonicalize %s -split-input-file | FileCheck %s
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// CHECK-LABEL: func @test_matmul_add_fused(%{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>) -> tensor<10x10xf32> {
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func @test_matmul_add_fused(%a0: tensor<10x10xf32>, %a1: tensor<10x10xf32>, %a2: tensor<10x10xf32>) -> tensor<10x10xf32> {
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// CHECK-NEXT: %{{[0-9]+}} = "onnx.Gemm"(%{{.*}}, %{{.*}}, %{{.*}}) {alpha = 1.000000e+00 : f32, beta = 1.000000e+00 : f32, transA = 0 : i64, transB = 0 : i64} : (tensor<10x10xf32>, tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%0 = "onnx.MatMul"(%a0, %a1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%1 = "onnx.Add"(%0, %a2) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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"std.return"(%1) : (tensor<10x10xf32>) -> ()
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}
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// onnx.MatMul ops for non 2-D matrices should not get fused because Gemm only supports 2-D matrices.
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// CHECK-LABEL: func @test_matmul_add_not_fused(%{{.*}}: tensor<10x10x10xf32>, %{{.*}}: tensor<10x10x10xf32>, %{{.*}}: tensor<10x10x10xf32>) -> tensor<10x10x10xf32> {
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func @test_matmul_add_not_fused(%a0: tensor<10x10x10xf32>, %a1: tensor<10x10x10xf32>, %a2: tensor<10x10x10xf32>) -> tensor<10x10x10xf32> {
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// CHECK-NEXT: %{{[0-9]+}} = "onnx.MatMul"(%{{.*}}, %{{.*}}) : (tensor<10x10x10xf32>, tensor<10x10x10xf32>) -> tensor<10x10x10xf32>
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%0 = "onnx.MatMul"(%a0, %a1) : (tensor<10x10x10xf32>, tensor<10x10x10xf32>) -> tensor<10x10x10xf32>
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%1 = "onnx.Add"(%0, %a2) : (tensor<10x10x10xf32>, tensor<10x10x10xf32>) -> tensor<10x10x10xf32>
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"std.return"(%1) : (tensor<10x10x10xf32>) -> ()
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}
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// onnx.MatMul ops with more than one result uses should not get fused.
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// CHECK-LABEL: func @test_sigmoid_add(%{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>) -> tensor<10x10xf32>
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func @test_sigmoid_add(%a0: tensor<10x10xf32>, %a1: tensor<10x10xf32>, %a2: tensor<10x10xf32>) -> tensor<10x10xf32> {
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// CHECK-NEXT: %{{[0-9]+}} = "onnx.MatMul"(%{{.*}}, %{{.*}}) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%0 = "onnx.MatMul"(%a0, %a1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%1 = "onnx.Add"(%0, %a2) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%2 = "onnx.Add"(%0, %a1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%3 = "onnx.Add"(%1, %2) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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"std.return"(%3) : (tensor<10x10xf32>) -> ()
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}
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// CHECK-LABEL: @test_identity_identity(%{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>) -> tensor<10x10xf32>
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func @test_identity_identity(%a0: tensor<10x10xf32>, %a1: tensor<10x10xf32>) -> tensor<10x10xf32> {
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// CHECK-NEXT: %{{[0-9]+}} = "onnx.Add"(%{{.*}}, %{{.*}}) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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%0 = "onnx.Identity"(%a0) : (tensor<10x10xf32>) -> tensor<10x10xf32>
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%1 = "onnx.Identity"(%a1) : (tensor<10x10xf32>) -> tensor<10x10xf32>
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%2 = "onnx.Add"(%0, %1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
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"std.return"(%2) : (tensor<10x10xf32>) -> ()
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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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// 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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// 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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// 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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// 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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