| // RUN: toyc-ch5 %s -emit=mlir -opt 2>&1 | FileCheck %s |
| |
| // Check the result of inlining+shape inference on an input module. |
| |
| func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> |
| attributes { sym_visibility = "private" } { |
| %0 = toy.transpose(%arg0 : tensor<*xf64>) to tensor<*xf64> |
| %1 = toy.transpose(%arg1 : tensor<*xf64>) to tensor<*xf64> |
| %2 = toy.mul %0, %1 : tensor<*xf64> |
| toy.return %2 : tensor<*xf64> |
| } |
| func @main() { |
| %0 = toy.constant dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64> |
| %1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64> |
| %2 = toy.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64> |
| %3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64> |
| %4 = toy.generic_call @multiply_transpose(%1, %3) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> |
| %5 = toy.generic_call @multiply_transpose(%3, %1) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> |
| toy.print %5 : tensor<*xf64> |
| toy.return |
| } |
| |
| // CHECK-NOT: func @multiply_transpose |
| // CHECK-NOT: tensor<*xf64> |
| |
| // CHECK-LABEL: func @main() |
| // 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> |
| // CHECK: [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<2x3xf64>) to tensor<3x2xf64> |
| // CHECK: [[VAL_2:%.*]] = toy.mul [[VAL_1]], [[VAL_1]] : tensor<3x2xf64> |
| // CHECK: toy.print [[VAL_2]] : tensor<3x2xf64> |
| // CHECK: toy.return |
