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// RUN: toyc-ch4 %s -emit=mlir -opt 2>&1 | FileCheck %s
// Check the result of inlining+shape inference on an input module.
toy.func private @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> {
%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>
}
toy.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: toy.func private @multiply_transpose
// CHECK-NOT: tensor<*xf64>
// CHECK-LABEL: toy.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
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