21 lines
1.1 KiB
MLIR
21 lines
1.1 KiB
MLIR
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module {
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func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> {
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%0 = toy.transpose(%arg0 : tensor<*xf64>) to tensor<*xf64>
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%1 = toy.transpose(%arg1 : tensor<*xf64>) to tensor<*xf64>
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%2 = toy.mul %0, %1 : tensor<*xf64>
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toy.return %2 : tensor<*xf64>
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}
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func @main() {
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%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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%1 = 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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%2 = toy.reshape(%1 : tensor<6xf64>) to tensor<2x3xf64>
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%3 = toy.generic_call @multiply_transpose(%0, %2) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
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%4 = toy.generic_call @multiply_transpose(%2, %0) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
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%5 = toy.generic_call @multiply_transpose(%2, %3) : (tensor<2x3xf64>, tensor<*xf64>) -> tensor<*xf64>
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%6 = toy.transpose(%0 : tensor<2x3xf64>) to tensor<*xf64>
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%7 = toy.generic_call @multiply_transpose(%6, %3) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*xf64>
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toy.return
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}
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}
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