38 lines
1.9 KiB
MLIR
38 lines
1.9 KiB
MLIR
// RUN: mlir-hlo-opt --chlo-legalize-to-hlo --split-input-file %s | FileCheck %s
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// Lower statically shaped `constant_like` to constant.
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// CHECK-LABEL: @constant_like_static_shape
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func @constant_like_static_shape(%arg : tensor<1x2xi64>) -> tensor<1x2xf32> {
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// CHECK: %[[RESULT:.*]] = mhlo.constant dense<3.200000e+00> : tensor<1x2xf32>
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// CHECK: return %[[RESULT]]
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%result = "chlo.constant_like"(%arg) { value = 3.2 : f32 }
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: (tensor<1x2xi64>) -> tensor<1x2xf32>
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return %result : tensor<1x2xf32>
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}
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// Lower dynamically shaped `constant_like` to broadcasted constant.
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// CHECK-LABEL: constant_like_dynamic_shape
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// CHECK-SAME: (%[[ARG:.*]]: tensor<?x?xi64>)
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func @constant_like_dynamic_shape(%arg : tensor<?x?xi64>) -> tensor<?x?xf32> {
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// CHECK: %[[CONSTANT:.*]] = mhlo.constant dense<3.200000e+00> : tensor<f32>
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// CHECK: %[[UNCASTED_SHAPE:.*]] = shape.shape_of %[[ARG]] : tensor<?x?xi64> -> tensor<?xindex>
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// CHECK: %[[SHAPE:.*]] = tensor_cast %[[UNCASTED_SHAPE]] : tensor<?xindex> to tensor<2xindex>
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// CHECK: %[[BROADCASTED_CONSTANT:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[CONSTANT]], %[[SHAPE]]) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>, tensor<2xindex>) -> tensor<?x?xf32>
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// CHECK: return %[[BROADCASTED_CONSTANT]] : tensor<?x?xf32>
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%result = "chlo.constant_like"(%arg) { value = 3.2 : f32 }
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: (tensor<?x?xi64>) -> tensor<?x?xf32>
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return %result : tensor<?x?xf32>
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}
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// CHECK-LABEL: func @conj
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func @conj(%arg0: tensor<3xcomplex<f32>>) -> tensor<3xcomplex<f32>> {
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// CHECK-SAME: ([[INPUT:%.*]]: tensor
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// CHECK-NEXT: [[R1:%.*]] = "mhlo.real"([[INPUT]])
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// CHECK-NEXT: [[R2:%.*]] = "mhlo.imag"([[INPUT]])
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// CHECK-NEXT: [[R3:%.*]] = "mhlo.negate"([[R2]])
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// CHECK-NEXT: [[R4:%.*]] = "mhlo.complex"([[R1]], [[R3]])
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%1 = "chlo.conj"(%arg0) : (tensor<3xcomplex<f32>>) -> tensor<3xcomplex<f32>>
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return %1 : tensor<3xcomplex<f32>>
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}
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