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comparison mlir/test/Examples/Toy/Ch7/shape_inference.mlir @ 173:0572611fdcc8 llvm10 llvm12

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reorgnization done
author Shinji KONO <kono@ie.u-ryukyu.ac.jp>
date Mon, 25 May 2020 11:55:54 +0900
parents 1d019706d866
children 2e18cbf3894f
comparison
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172:9fbae9c8bf63 173:0572611fdcc8
2 2
3 // Check the result of inlining+shape inference on an input module. 3 // Check the result of inlining+shape inference on an input module.
4 4
5 func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> 5 func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64>
6 attributes { sym_visibility = "private" } { 6 attributes { sym_visibility = "private" } {
7 %0 = "toy.transpose"(%arg0) : (tensor<*xf64>) -> tensor<*xf64> 7 %0 = toy.transpose(%arg0 : tensor<*xf64>) to tensor<*xf64>
8 %1 = "toy.transpose"(%arg1) : (tensor<*xf64>) -> tensor<*xf64> 8 %1 = toy.transpose(%arg1 : tensor<*xf64>) to tensor<*xf64>
9 %2 = "toy.mul"(%0, %1) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*xf64> 9 %2 = toy.mul %0, %1 : tensor<*xf64>
10 "toy.return"(%2) : (tensor<*xf64>) -> () 10 toy.return %2 : tensor<*xf64>
11 } 11 }
12 func @main() { 12 func @main() {
13 %0 = "toy.constant"() {value = dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>} : () -> tensor<2x3xf64> 13 %0 = toy.constant dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>
14 %1 = "toy.reshape"(%0) : (tensor<2x3xf64>) -> tensor<2x3xf64> 14 %1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64>
15 %2 = "toy.constant"() {value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64>} : () -> tensor<6xf64> 15 %2 = toy.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64>
16 %3 = "toy.reshape"(%2) : (tensor<6xf64>) -> tensor<2x3xf64> 16 %3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64>
17 %4 = "toy.generic_call"(%1, %3) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> 17 %4 = toy.generic_call @multiply_transpose(%1, %3) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
18 %5 = "toy.generic_call"(%3, %1) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> 18 %5 = toy.generic_call @multiply_transpose(%3, %1) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
19 "toy.print"(%5) : (tensor<*xf64>) -> () 19 toy.print %5 : tensor<*xf64>
20 "toy.return"() : () -> () 20 toy.return
21 } 21 }
22 22
23 // CHECK-NOT: func @multiply_transpose 23 // CHECK-NOT: func @multiply_transpose
24 // CHECK-NOT: tensor<*xf64> 24 // CHECK-NOT: tensor<*xf64>
25 25
26 // CHECK-LABEL: func @main() 26 // CHECK-LABEL: func @main()
27 // CHECK: [[VAL_0:%.*]] = "toy.constant"() {value = dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>} : () -> tensor<2x3xf64> 27 // CHECK: [[VAL_0:%.*]] = toy.constant dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>
28 // CHECK: [[VAL_1:%.*]] = "toy.transpose"([[VAL_0]]) : (tensor<2x3xf64>) -> tensor<3x2xf64> 28 // CHECK: [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<2x3xf64>) to tensor<3x2xf64>
29 // CHECK: [[VAL_2:%.*]] = "toy.mul"([[VAL_1]], [[VAL_1]]) : (tensor<3x2xf64>, tensor<3x2xf64>) -> tensor<3x2xf64> 29 // CHECK: [[VAL_2:%.*]] = toy.mul [[VAL_1]], [[VAL_1]] : tensor<3x2xf64>
30 // CHECK: "toy.print"([[VAL_2]]) : (tensor<3x2xf64>) -> () 30 // CHECK: toy.print [[VAL_2]] : tensor<3x2xf64>
31 // CHECK: "toy.return"() : () -> () 31 // CHECK: toy.return