1// RUN: mlir-opt %s --canonicalize -split-input-file | FileCheck %s 2 3// CHECK-LABEL: func.func @test_cancel_transpose_transpose( 4// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x2x3xi32>) -> tensor<1x2x3xi32> { 5// CHECK: return %[[VAL_0]] : tensor<1x2x3xi32> 6// CHECK: } 7 8func.func @test_cancel_transpose_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1x2x3xi32>) { 9 %0 = arith.constant dense<[1, 2, 0]> : tensor<3xi32> 10 %1 = tosa.transpose %arg0, %0 : (tensor<1x2x3xi32>, tensor<3xi32>) -> tensor<2x3x1xi32> 11 %2 = arith.constant dense<[2, 0, 1]> : tensor<3xi32> 12 %3 = tosa.transpose %1, %2 : (tensor<2x3x1xi32>, tensor<3xi32>) -> tensor<1x2x3xi32> 13 return %3 : tensor<1x2x3xi32> 14} 15 16// ----- 17 18// CHECK-LABEL: func.func @test_remove_identity_transpose( 19// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x2x3xi32>) -> tensor<1x2x3xi32> { 20// CHECK: return %[[VAL_0]] : tensor<1x2x3xi32> 21// CHECK: } 22 23func.func @test_remove_identity_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1x2x3xi32>) { 24 %0 = arith.constant dense<[0, 1, 2]> : tensor<3xi32> 25 %1 = tosa.transpose %arg0, %0 : (tensor<1x2x3xi32>, tensor<3xi32>) -> tensor<1x2x3xi32> 26 return %1 : tensor<1x2x3xi32> 27} 28 29// ----- 30 31// CHECK-LABEL: func.func @test_do_not_cancel_different_transpose( 32// CHECK-SAME: %[[VAL_0:.*]]: tensor<2x3x4x5xi32>) -> tensor<5x4x3x2xi32> { 33// CHECK: %[[VAL_1:.*]] = arith.constant dense<[3, 2, 1, 0]> : tensor<4xi32> 34// CHECK: %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_1]] : (tensor<2x3x4x5xi32>, tensor<4xi32>) -> tensor<5x4x3x2xi32> 35// CHECK: return %[[VAL_2]] : tensor<5x4x3x2xi32> 36// CHECK: } 37 38func.func @test_do_not_cancel_different_transpose(%arg0: tensor<2x3x4x5xi32>) -> (tensor<5x4x3x2xi32>) { 39 %0 = arith.constant dense<[1, 2, 0, 3]> : tensor<4xi32> 40 %1 = tosa.transpose %arg0, %0 : (tensor<2x3x4x5xi32>, tensor<4xi32>) -> tensor<3x4x2x5xi32> 41 %2 = arith.constant dense<[3, 1, 0, 2]> : tensor<4xi32> 42 %3 = tosa.transpose %1, %2 : (tensor<3x4x2x5xi32>, tensor<4xi32>) -> tensor<5x4x3x2xi32> 43 return %3 : tensor<5x4x3x2xi32> 44} 45 46// ----- 47 48// CHECK-LABEL: func.func @test_prefer_compose_transpose( 49// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x2x3x4xi32>) -> tensor<4x3x2x1xi32> { 50// CHECK: %[[VAL_1:.*]] = arith.constant dense<[3, 2, 1, 0]> : tensor<4xi32> 51// CHECK: %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_1]] : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<4x3x2x1xi32> 52// CHECK: return %[[VAL_2]] : tensor<4x3x2x1xi32> 53// CHECK: } 54 55func.func @test_prefer_compose_transpose(%arg0: tensor<1x2x3x4xi32>) -> (tensor<4x3x2x1xi32>) { 56 %0 = arith.constant dense<[1, 2, 0, 3]> : tensor<4xi32> 57 %1 = tosa.transpose %arg0, %0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<2x3x1x4xi32> 58 %2 = arith.constant dense<[3, 1, 0, 2]> : tensor<4xi32> 59 %3 = tosa.transpose %1, %2 : (tensor<2x3x1x4xi32>, tensor<4xi32>) -> tensor<4x3x2x1xi32> 60 return %3 : tensor<4x3x2x1xi32> 61} 62