24 lines
1.3 KiB
MLIR
24 lines
1.3 KiB
MLIR
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// RUN: onnf-opt --shape-inference --lower-frontend %s -split-input-file | FileCheck %s
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module {
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func @test_sigmoid(%a1 : tensor<?x10xf32>, %a2 : tensor<?x10xf32>) -> tensor<*xf32> {
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%0 = "onnx.Add"(%a1, %a2) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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}
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}
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// CHECK: func @test_sigmoid([[ARG0:%.+]]: memref<?x10xf32>, [[ARG1:%.+]]: memref<?x10xf32>) -> memref<?x10xf32> {
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// CHECK: [[DIM_0:%.+]] = dim [[ARG0]], 0 : memref<?x10xf32>
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// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
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// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
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// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
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// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
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// CHECK: } : () -> (!krnl.loop, !krnl.loop)
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// CHECK: [[DIM_2:%.+]] = dim [[ARG0]], 0 : memref<?x10xf32>
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// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
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// CHECK: [[LOAD1:%.+]] = load [[ARG0]][%arg2, %arg3] : memref<?x10xf32>
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// CHECK: [[LOAD2:%.+]] = load [[ARG1]][%arg2, %arg3] : memref<?x10xf32>
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// CHECK: [[ADDF:%.+]] = addf [[LOAD1]], [[LOAD2]] : f32
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// CHECK: store [[ADDF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
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// CHECK: return [[RES]] : memref<?x10xf32>
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