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annotate 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
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1 // RUN: toyc-ch7 %s -emit=mlir -opt 2>&1 | FileCheck %s
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2
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3 // Check the result of inlining+shape inference on an input module.
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4
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5 func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64>
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6 attributes { sym_visibility = "private" } {
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7 %0 = toy.transpose(%arg0 : tensor<*xf64>) to tensor<*xf64>
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8 %1 = toy.transpose(%arg1 : tensor<*xf64>) to tensor<*xf64>
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9 %2 = toy.mul %0, %1 : tensor<*xf64>
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10 toy.return %2 : tensor<*xf64>
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11 }
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12 func @main() {
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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>
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14 %1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64>
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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>
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16 %3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64>
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17 %4 = toy.generic_call @multiply_transpose(%1, %3) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
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18 %5 = toy.generic_call @multiply_transpose(%3, %1) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
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19 toy.print %5 : tensor<*xf64>
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20 toy.return
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21 }
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22
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23 // CHECK-NOT: func @multiply_transpose
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24 // CHECK-NOT: tensor<*xf64>
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25
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26 // CHECK-LABEL: func @main()
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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>
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28 // CHECK: [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<2x3xf64>) to tensor<3x2xf64>
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29 // CHECK: [[VAL_2:%.*]] = toy.mul [[VAL_1]], [[VAL_1]] : tensor<3x2xf64>
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30 // CHECK: toy.print [[VAL_2]] : tensor<3x2xf64>
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31 // CHECK: toy.return