597 lines
21 KiB
MLIR
597 lines
21 KiB
MLIR
// RUN: mlir-hlo-opt %s -hlo-legalize-to-linalg -split-input-file | FILECHECK_OPTS="" FileCheck %s
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// CHECK: #map0 = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-LABEL: func @float_add
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func @float_add(%lhs: tensor<2x2xf32>,
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%rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: ^{{[a-z0-9_]*}}
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: f32
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// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: f32
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// CHECK: %[[RESULT:[a-zA-Z0-9_]*]] = addf %[[ARG0]], %[[ARG1]]
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// CHECK: linalg.yield %[[RESULT]]
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%0 = "mhlo.add"(%lhs, %rhs) : (tensor<2x2xf32>,
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tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: integer_add
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func @integer_add(%lhs: tensor<2x2xi32>,
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%rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
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// CHECK: linalg.generic
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// CHECK: addi
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%0 = "mhlo.add"(%lhs, %rhs) : (tensor<2x2xi32>,
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tensor<2x2xi32>) -> tensor<2x2xi32>
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return %0 : tensor<2x2xi32>
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}
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// -----
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// CHECK-LABEL: func @float_mul
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func @float_mul(%lhs: tensor<2x2xf32>,
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%rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: mulf
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%0 = "mhlo.multiply"(%lhs, %rhs) : (tensor<2x2xf32>,
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tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @integer_mul
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func @integer_mul(%lhs: tensor<2x2xi32>,
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%rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
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// CHECK: linalg.generic
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// CHECK: muli
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%0 = "mhlo.multiply"(%lhs, %rhs) : (tensor<2x2xi32>,
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tensor<2x2xi32>) -> tensor<2x2xi32>
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return %0 : tensor<2x2xi32>
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}
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// -----
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// CHECK-LABEL: func @float_remainder
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func @float_remainder(%lhs: tensor<2x2xf32>,
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%rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: remf
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%0 = "mhlo.remainder"(%lhs, %rhs) : (tensor<2x2xf32>,
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tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @integer_remainder
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func @integer_remainder(%lhs: tensor<2x2xi32>,
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%rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
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// CHECK: linalg.generic
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// CHECK: remi_signed
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%0 = "mhlo.remainder"(%lhs, %rhs) : (tensor<2x2xi32>,
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tensor<2x2xi32>) -> tensor<2x2xi32>
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return %0 : tensor<2x2xi32>
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}
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// -----
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// CHECK-LABEL: func @float_rsqrt
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func @float_rsqrt(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
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%tensor_result = "mhlo.rsqrt"(%operand)
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: (tensor<2x2xf32>) -> tensor<2x2xf32>
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// CHECK: linalg.generic
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// CHECK: rsqrt
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return %tensor_result : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @float_sub
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func @float_sub(%lhs: tensor<2x2xf32>,
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%rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: subf
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%0 = "mhlo.subtract"(%lhs, %rhs) : (tensor<2x2xf32>,
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tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @integer_sub
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func @integer_sub(%lhs: tensor<2x2xi32>,
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%rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
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// CHECK: linalg.generic
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// CHECK: subi
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%0 = "mhlo.subtract"(%lhs, %rhs) : (tensor<2x2xi32>,
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tensor<2x2xi32>) -> tensor<2x2xi32>
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return %0 : tensor<2x2xi32>
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}
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// -----
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// CHECK-LABEL: func @float_abs
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func @float_abs(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: absf
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%0 = "mhlo.abs"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @float_exp
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func @float_exp(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: exp
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%0 = "mhlo.exponential"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @float_log
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func @float_log(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: log
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%0 = "mhlo.log"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @float_ceil
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func @float_ceil(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: ceilf
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%0 = "mhlo.ceil"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @floor
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func @floor(%input: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: floorf
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%0 = "mhlo.floor"(%input) : (tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @float_neg
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func @float_neg(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: negf
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%0 = "mhlo.negate"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @float_tanh
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func @float_tanh(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: tanh
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%0 = "mhlo.tanh"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @integer_and
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func @integer_and(%lhs: tensor<2x2xi32>,
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%rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
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// CHECK: linalg.generic
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// CHECK: and
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%0 = "mhlo.and"(%lhs, %rhs) : (tensor<2x2xi32>,
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tensor<2x2xi32>) -> tensor<2x2xi32>
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return %0 : tensor<2x2xi32>
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}
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// -----
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// CHECK-LABEL: func @float_cmp
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func @float_cmp(%lhs: tensor<2x2xf32>,
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%rhs: tensor<2x2xf32>) -> (tensor<2x2xi1>) {
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%0 = "mhlo.compare"(%lhs, %rhs) {comparison_direction = "EQ"}
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: (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xi1>
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return %0 : tensor<2x2xi1>
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}
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// CHECK: linalg.generic
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// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: f32, %[[RHS_IN:.*]]: f32):
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// CHECK-NEXT: %[[RESULT:.*]] = cmpf "oeq", %[[LHS_IN]], %[[RHS_IN]] : f32
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// CHECK-NEXT: linalg.yield %[[RESULT]] : i1
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// -----
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// CHECK-LABEL: func @int_cmp
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func @int_cmp(%lhs: tensor<2x2xi32>,
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%rhs: tensor<2x2xi32>) -> tensor<2x2xi1> {
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%0 = "mhlo.compare"(%lhs, %rhs) {comparison_direction = "LT"}
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: (tensor<2x2xi32>, tensor<2x2xi32>) -> (tensor<2x2xi1>)
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return %0 : tensor<2x2xi1>
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}
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// CHECK: linalg.generic
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// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i32, %[[RHS_IN:.*]]: i32):
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// CHECK-NEXT: %[[RESULT:.*]] = cmpi "slt", %[[LHS_IN]], %[[RHS_IN]] : i32
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// CHECK-NEXT: linalg.yield %[[RESULT]] : i1
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// -----
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// CHECK-LABEL: func @float_cos
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func @float_cos(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: cos
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%0 = "mhlo.cosine"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @float_sin
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func @float_sin(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
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// CHECK: linalg.generic
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// CHECK: sin
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%0 = "mhlo.sine"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
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return %0 : tensor<2x2xf32>
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}
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// -----
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// CHECK-LABEL: func @copy
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// CHECK-SAME: [[ARG:%[a-zA-Z0-9]+]]
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func @copy(%input: tensor<2x4x8xf32>) -> tensor<2x4x8xf32> {
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%0 = "mhlo.copy"(%input) : (tensor<2x4x8xf32>) -> (tensor<2x4x8xf32>)
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return %0 : tensor<2x4x8xf32>
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}
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// CHECK: return [[ARG]] : tensor<2x4x8xf32>
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// -----
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// CHECK-LABEL: func @select
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func @select(%pred: tensor<2x2xi1>, %lhs: tensor<2x2xf32>,
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%rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
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%0 = "mhlo.select"(%pred, %lhs, %rhs)
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: (tensor<2x2xi1>, tensor<2x2xf32>, tensor<2x2xf32>) -> (tensor<2x2xf32>)
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return %0 : tensor<2x2xf32>
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}
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// CHECK: linalg.generic
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// CHECK-NEXT: ^bb0(%[[PRED_IN:.*]]: i1, %[[LHS_IN:.*]]: f32, %[[RHS_IN:.*]]: f32):
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// CHECK-NEXT: %[[RESULT:.*]] = select %[[PRED_IN]], %[[LHS_IN]], %[[RHS_IN]] : f32
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// CHECK-NEXT: linalg.yield %[[RESULT]] : f32
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// -----
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// CHECK-DAG: #[[OPERAND_MAP:.+]] = affine_map<(d0, d1, d2) -> ()>
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// CHECK-DAG: #[[RESULT_MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-LABEL: func @broadcast_scalar
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func @broadcast_scalar(%arg: tensor<f32>) -> tensor<4x2x1xf32> {
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%0 = "mhlo.broadcast"(%arg) {broadcast_sizes = dense<[4, 2, 1]> : tensor<3xi64>} : (tensor<f32>) -> tensor<4x2x1xf32>
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return %0: tensor<4x2x1xf32>
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}
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// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
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// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32):
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// CHECK-NEXT: linalg.yield %[[OPERAND]] : f32
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// -----
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// CHECK-DAG: #[[OPERAND_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4, d5)>
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// CHECK-DAG: #[[RESULT_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d3, d4, d5)>
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// CHECK-LABEL: func @broadcast
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func @broadcast(%arg: tensor<4x?x16xf32>) -> tensor<4x2x1x4x?x16xf32> {
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%0 = "mhlo.broadcast"(%arg) {broadcast_sizes = dense<[4, 2, 1]> : tensor<3xi64>} : (tensor<4x?x16xf32>) -> tensor<4x2x1x4x?x16xf32>
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return %0: tensor<4x2x1x4x?x16xf32>
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}
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// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
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// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32):
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// CHECK-NEXT: linalg.yield %[[OPERAND]] : f32
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// -----
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// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0, d1, d2, d3, d4) -> (d4, d0, 0)>
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// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
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// CHECK-LABEL: func @broadcast_in_dim
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func @broadcast_in_dim(%operand: tensor<5x7x1xf32>) -> tensor<7x10x6x4x5xf32> {
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%0 = "mhlo.broadcast_in_dim"(%operand)
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{broadcast_dimensions = dense<[4,0,2]> : tensor<3xi64>}
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: (tensor<5x7x1xf32>) -> tensor<7x10x6x4x5xf32>
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return %0 : tensor<7x10x6x4x5xf32>
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}
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// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
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// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32):
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// CHECK-NEXT: linalg.yield %[[OPERAND]] : f32
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// -----
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// CHECK-DAG: #[[OPERAND_MAP:.+]] = affine_map<(d0, d1) -> (d0)>
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// CHECK-DAG: #[[RESULT_MAP:.+]] = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-LABEL: func @broadcast_in_dim_with_one_to_one
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func @broadcast_in_dim_with_one_to_one(
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%operand: tensor<1xf32>) -> tensor<1x5xf32> {
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%0 = "mhlo.broadcast_in_dim"(%operand)
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{broadcast_dimensions = dense<[0]> : tensor<1xi64>}
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: (tensor<1xf32>) -> tensor<1x5xf32>
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return %0 : tensor<1x5xf32>
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}
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// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
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// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32):
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// CHECK-NEXT: linalg.yield %[[OPERAND]] : f32
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// -----
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// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0, d1, d2) -> ()>
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// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-LABEL: func @broadcast_scalar
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func @broadcast_scalar(%operand: tensor<f32>) -> tensor<7x10x6xf32> {
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%0 = "mhlo.broadcast_in_dim"(%operand)
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{broadcast_dimensions = dense<[]> : tensor<0xi64>}
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: (tensor<f32>) -> tensor<7x10x6xf32>
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return %0 : tensor<7x10x6xf32>
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}
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// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
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// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32):
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// CHECK-NEXT: linalg.yield %[[OPERAND]] : f32
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// -----
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// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d0, d3, d2)>
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// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
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// CHECK-LABEL: func @transpose
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func @transpose(%arg0: tensor<2x3x9x5xi32>) -> tensor<3x2x5x9xi32> {
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%0 = "mhlo.transpose"(%arg0) {permutation = dense<[1, 0, 3, 2]> : tensor<4xi64>}
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: (tensor<2x3x9x5xi32>) -> tensor<3x2x5x9xi32>
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return %0 : tensor<3x2x5x9xi32>
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}
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// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
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// -----
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// CHECK-DAG: #[[RESHAPE_MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
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// CHECK-DAG: #[[RESHAPE_MAP2:.*]] = affine_map<(d0, d1, d2) -> (d2)>
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// CHECK-LABEL: func @reshape_3D_2D
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func @reshape_3D_2D(%arg0: tensor<12x1x42xi32>) -> tensor<12x42xi32> {
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%0 = "mhlo.reshape"(%arg0) : (tensor<12x1x42xi32>) -> tensor<12x42xi32>
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return %0 : tensor<12x42xi32>
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}
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// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP1]], #[[RESHAPE_MAP2]]]
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// -----
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// CHECK-DAG: #[[RESHAPE_MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0)>
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// CHECK-DAG: #[[RESHAPE_MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d2, d3)>
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// CHECK-LABEL: func @reshape_4D_2D
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func @reshape_4D_2D(%arg0: tensor<12x42x1x1xi32>) -> tensor<12x42xi32> {
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%0 = "mhlo.reshape"(%arg0) : (tensor<12x42x1x1xi32>) -> tensor<12x42xi32>
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return %0 : tensor<12x42xi32>
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}
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// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP1]], #[[RESHAPE_MAP2]]]
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// -----
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// CHECK-DAG: #[[RESHAPE_MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1)>
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// CHECK-DAG: #[[RESHAPE_MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d3)>
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// CHECK-LABEL: func @reshape_2D_4D
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func @reshape_2D_4D(%arg0: tensor<12x42xi32>) -> tensor<12x1x42x1xi32> {
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%0 = "mhlo.reshape"(%arg0) : (tensor<12x42xi32>) -> tensor<12x1x42x1xi32>
|
|
return %0 : tensor<12x1x42x1xi32>
|
|
}
|
|
// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP1]], #[[RESHAPE_MAP2]]]
|
|
|
|
// -----
|
|
|
|
// CHECK-DAG: #[[RESHAPE_MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
|
|
// CHECK-DAG: #[[RESHAPE_MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
|
|
// CHECK-LABEL: func @reshape_3D_4D
|
|
func @reshape_3D_4D(%arg0: tensor<1x49x16xf32>) -> tensor<1x784x1x1xf32> {
|
|
%0 = "mhlo.reshape"(%arg0) : (tensor<1x49x16xf32>) -> tensor<1x784x1x1xf32>
|
|
return %0 : tensor<1x784x1x1xf32>
|
|
}
|
|
// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP1]]]
|
|
// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP2]]]
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @minf
|
|
func @minf(%lhs: tensor<2x2xf32>, %rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
|
%0 = "mhlo.minimum"(%lhs, %rhs)
|
|
: (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
|
|
return %0 : tensor<2x2xf32>
|
|
}
|
|
// CHECK: linalg.generic
|
|
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: f32, %[[RHS_IN:.*]]: f32):
|
|
// CHECK-NEXT: %[[CMP:.*]] = cmpf "olt", %[[LHS_IN]], %[[RHS_IN]] : f32
|
|
// CHECK-NEXT: %[[RESULT:.*]] = select %[[CMP]], %[[LHS_IN]], %[[RHS_IN]] : f32
|
|
// CHECK-NEXT: linalg.yield %[[RESULT]] : f32
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @maxi
|
|
func @maxi(%lhs: tensor<2x2xi32>, %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
|
|
%0 = "mhlo.maximum"(%lhs, %rhs)
|
|
: (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
|
|
return %0 : tensor<2x2xi32>
|
|
}
|
|
// CHECK: linalg.generic
|
|
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i32, %[[RHS_IN:.*]]: i32):
|
|
// CHECK-NEXT: %[[CMP:.*]] = cmpi "sgt", %[[LHS_IN]], %[[RHS_IN]] : i32
|
|
// CHECK-NEXT: %[[RESULT:.*]] = select %[[CMP]], %[[LHS_IN]], %[[RHS_IN]] : i32
|
|
// CHECK-NEXT: linalg.yield %[[RESULT]] : i32
|
|
|
|
// -----
|
|
|
|
// CHECK-DAG: #[[MAP:.*]] = affine_map<() -> ()>
|
|
// CHECK-LABEL: func @add_scalar
|
|
func @add_scalar(%lhs: tensor<f32>, %rhs: tensor<f32>) -> tensor<f32> {
|
|
%0 = "mhlo.add"(%lhs, %rhs) : (tensor<f32>, tensor<f32>) -> tensor<f32>
|
|
return %0 : tensor<f32>
|
|
}
|
|
// CHECK: linalg.generic
|
|
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
|
|
// CHECK-NEXT: ^bb0(%[[LHS:.*]]: f32, %[[RHS:.*]]: f32):
|
|
// CHECK: %[[RESULT:.*]] = addf %[[LHS]], %[[RHS]]
|
|
// CHECK-NEXT: linalg.yield %[[RESULT]] : f32
|
|
|
|
// -----
|
|
|
|
func @reshape_collapse_single_dim
|
|
(%arg0: tensor<1x28x28x1xf32>) -> tensor<1x784xf32> {
|
|
%0 = "mhlo.reshape"(%arg0) : (tensor<1x28x28x1xf32>) -> tensor<1x784xf32>
|
|
return %0 : tensor<1x784xf32>
|
|
}
|
|
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d0)>
|
|
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d2, d3)>
|
|
// CHECK-LABEL: func @reshape_collapse_single_dim
|
|
// CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP0]], #[[MAP1]]]
|
|
|
|
// -----
|
|
|
|
func @reshape_collapse(%arg0: tensor<2x2x2x3xf32>) -> tensor<2x4x3xf32> {
|
|
%0 = "mhlo.reshape"(%arg0) : (tensor<2x2x2x3xf32>) -> tensor<2x4x3xf32>
|
|
return %0 : tensor<2x4x3xf32>
|
|
}
|
|
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d0)>
|
|
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d2)>
|
|
// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
|
|
// CHECK-LABEL: func @reshape_collapse
|
|
// CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
|
|
|
|
// -----
|
|
|
|
func @reshape_expand(%arg0: tensor<2x8xf32>) -> tensor<2x4x2xf32> {
|
|
%0 = "mhlo.reshape"(%arg0) : (tensor<2x8xf32>) -> tensor<2x4x2xf32>
|
|
return %0 : tensor<2x4x2xf32>
|
|
}
|
|
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1, d2) -> (d0)>
|
|
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2) -> (d1, d2)>
|
|
// CHECK-LABEL: func @reshape_expand
|
|
// CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP0]], #[[MAP1]]]
|
|
|
|
// -----
|
|
|
|
func @reshape_single_expand(%arg0 : tensor<8xf32>) -> tensor<1x4x2xf32> {
|
|
%0 = "mhlo.reshape"(%arg0) : (tensor<8xf32>) -> tensor<1x4x2xf32>
|
|
return %0 : tensor<1x4x2xf32>
|
|
}
|
|
// CHECK: #[[MAP0:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
|
|
// CHECK-LABEL: func @reshape_single_expand
|
|
// CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP0]]]
|
|
|
|
// -----
|
|
|
|
func @reshape_multiple_collapse
|
|
(%arg0 : tensor<1x2x2x5x3x2xf32>) -> tensor<1x4x5x6xf32> {
|
|
%0 = "mhlo.reshape"(%arg0) : (tensor<1x2x2x5x3x2xf32>) -> tensor<1x4x5x6xf32>
|
|
return %0 : tensor<1x4x5x6xf32>
|
|
}
|
|
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0)>
|
|
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d1, d2)>
|
|
// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3)>
|
|
// CHECK-DAG: #[[MAP3:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d4, d5)>
|
|
// CHECK-LABEL: func @reshape_multiple_collapse
|
|
// CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP0]], #[[MAP1]], #[[MAP2]], #[[MAP3]]]
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @convert_i32_to_f32
|
|
func @convert_i32_to_f32(%input: tensor<2x2xi32>) -> tensor<2x2xf32> {
|
|
%result = "mhlo.convert"(%input) : (tensor<2x2xi32>) -> tensor<2x2xf32>
|
|
return %result : tensor<2x2xf32>
|
|
}
|
|
// CHECK: linalg.generic
|
|
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: i32):
|
|
// CHECK-NEXT: %[[RESULT:.*]] = sitofp %[[OPERAND_IN]] : i32 to f32
|
|
// CHECK-NEXT: linalg.yield %[[RESULT]] : f32
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @convert_i16_to_i32
|
|
func @convert_i16_to_i32(%input: tensor<2x2xi16>) -> tensor<2x2xi32> {
|
|
%result = "mhlo.convert"(%input) : (tensor<2x2xi16>) -> tensor<2x2xi32>
|
|
return %result : tensor<2x2xi32>
|
|
}
|
|
// CHECK: linalg.generic
|
|
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: i16):
|
|
// CHECK-NEXT: %[[RESULT:.*]] = zexti %[[OPERAND_IN]] : i16 to i32
|
|
// CHECK-NEXT: linalg.yield %[[RESULT]] : i32
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @convert_i32_to_i16
|
|
func @convert_i32_to_i16(%input: tensor<2x2xi32>) -> tensor<2x2xi16> {
|
|
%result = "mhlo.convert"(%input) : (tensor<2x2xi32>) -> tensor<2x2xi16>
|
|
return %result : tensor<2x2xi16>
|
|
}
|
|
// CHECK: linalg.generic
|
|
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: i32):
|
|
// CHECK-NEXT: %[[RESULT:.*]] = trunci %[[OPERAND_IN]] : i32 to i16
|
|
// CHECK-NEXT: linalg.yield %[[RESULT]] : i16
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @convert_f32_to_f64
|
|
func @convert_f32_to_f64(%input: tensor<2x2xf32>) -> tensor<2x2xf64> {
|
|
%result = "mhlo.convert"(%input) : (tensor<2x2xf32>) -> tensor<2x2xf64>
|
|
return %result : tensor<2x2xf64>
|
|
}
|
|
// CHECK: linalg.generic
|
|
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: f32):
|
|
// CHECK-NEXT: %[[RESULT:.*]] = fpext %[[OPERAND_IN]] : f32 to f64
|
|
// CHECK-NEXT: linalg.yield %[[RESULT]] : f64
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @convert_f64_to_f32
|
|
func @convert_f64_to_f32(%input: tensor<2x2xf64>) -> tensor<2x2xf32> {
|
|
%result = "mhlo.convert"(%input) : (tensor<2x2xf64>) -> tensor<2x2xf32>
|
|
return %result : tensor<2x2xf32>
|
|
}
|
|
// CHECK: linalg.generic
|
|
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: f64):
|
|
// CHECK-NEXT: %[[RESULT:.*]] = fptrunc %[[OPERAND_IN]] : f64 to f32
|
|
// CHECK-NEXT: linalg.yield %[[RESULT]] : f32
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @convert_f32_to_i32
|
|
func @convert_f32_to_i32(%input: tensor<2x2xf32>) -> tensor<2x2xi32> {
|
|
%result = "mhlo.convert"(%input) : (tensor<2x2xf32>) -> tensor<2x2xi32>
|
|
return %result : tensor<2x2xi32>
|
|
}
|
|
// CHECK: linalg.generic
|
|
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: f32):
|
|
// CHECK-NEXT: %[[RESULT:.*]] = fptosi %[[OPERAND_IN]] : f32 to i32
|
|
// CHECK-NEXT: linalg.yield %[[RESULT]] : i32
|
|
|
|
// -----
|
|
|
|
// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0, d1) -> (d0, -d1 + 2)>
|
|
// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0, d1) -> (d0, d1)>
|
|
// CHECK-LABEL: func @reverse
|
|
func @reverse(%input: tensor<2x3xf32>) -> tensor<2x3xf32> {
|
|
%result = "mhlo.reverse"(%input) {
|
|
dimensions = dense<1> : tensor<1xi64>
|
|
} : (tensor<2x3xf32>) -> tensor<2x3xf32>
|
|
return %result : tensor<2x3xf32>
|
|
}
|
|
// CHECK: linalg.generic
|
|
// CHECK-SAME: indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
|
|
|
|
// -----
|
|
|
|
// CHECK: #[[RESULT_MAP:.*]] = affine_map<(d0, d1) -> (d0, d1)>
|
|
// CHECK-LABEL: func @iota
|
|
func @iota() -> tensor<7x10xf32> {
|
|
%result = "mhlo.iota"() {iota_dimension = 1 : i64} : () -> (tensor<7x10xf32>)
|
|
return %result : tensor<7x10xf32>
|
|
}
|
|
// CHECK: linalg.indexed_generic
|
|
// CHECK-SAME: indexing_maps = [#[[RESULT_MAP]]]
|
|
// CHECK-NEXT: ^bb0(%[[D0:.*]]: index, %[[D1:.*]]: index):
|
|
// CHECK-NEXT: %[[INT_CAST:.*]] = index_cast %[[D1]] : index to i32
|
|
// CHECK-NEXT: %[[FLOAT_CAST:.*]] = sitofp %[[INT_CAST]] : i32 to f32
|
|
// CHECK-NEXT: linalg.yield %[[FLOAT_CAST]] : f32
|