89 lines
4.1 KiB
MLIR
89 lines
4.1 KiB
MLIR
// RUN: mlir-hlo-opt -transform-unranked-hlo -split-input-file %s | FileCheck %s
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// Check the validity of expected IR.
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// CHECK-LABEL: @sqr_transform_result
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func @sqr_transform_result(%a: tensor<*xf32>) -> tensor<*xf32> {
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// Flatten operand shape.
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%shape = shape.shape_of %a : tensor<*xf32>
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%num_elements = shape.num_elements %shape
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%num_elements_as_index = shape.size_to_index %num_elements
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%flat_shape = tensor_from_elements(%num_elements_as_index) : tensor<1xindex>
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%flat_a = "mhlo.dynamic_reshape"(%a, %flat_shape)
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: (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
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// Apply operation.
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%flat_b = "mhlo.sqrt"(%flat_a) : (tensor<?xf32>) -> tensor<?xf32>
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// Restore original shape.
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%shape_as_extent_tensor = shape.to_extent_tensor %shape : tensor<?xindex>
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%b = "mhlo.dynamic_reshape"(%flat_b, %shape_as_extent_tensor)
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: (tensor<?xf32>, tensor<?xindex>) -> tensor<*xf32>
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return %b : tensor<*xf32>
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}
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// -----
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// Check transformation of unranked code.
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// CHECK-LABEL: @sqrt
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// CHECK-SAME: (%[[A:.*]]: tensor<*xf32>)
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func @sqrt(%a: tensor<*xf32>) -> tensor<*xf32> {
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// CHECK-NEXT: %[[SHAPE:.*]] = shape.shape_of %[[A]] : tensor<*xf32>
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// CHECK-NEXT: %[[NUM_ELEMENTS:.*]] = shape.num_elements %[[SHAPE]]
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// CHECK-NEXT: %[[NUM_ELEMENTS_AS_INDEX:.*]] = shape.size_to_index %[[NUM_ELEMENTS]]
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// CHECK-NEXT: %[[FLAT_SHAPE:.*]] = tensor_from_elements(%[[NUM_ELEMENTS_AS_INDEX]]) : tensor<1xindex>
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// CHECK-NEXT: %[[FLAT_A:.*]] = "mhlo.dynamic_reshape"(%[[A]], %[[FLAT_SHAPE]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
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// CHECK-NEXT: %[[FLAT_B:.*]] = "mhlo.sqrt"(%[[FLAT_A]]) : (tensor<?xf32>) -> tensor<?xf32>
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// CHECK-NEXT: %[[SHAPE_AS_EXTENT_TENSOR:.*]] = shape.to_extent_tensor %[[SHAPE]] : tensor<?xindex>
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// CHECK-NEXT: %[[B:.*]] = "mhlo.dynamic_reshape"(%[[FLAT_B]], %[[SHAPE_AS_EXTENT_TENSOR]]) : (tensor<?xf32>, tensor<?xindex>) -> tensor<*xf32>
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// CHECK-NEXT: return %[[B]] : tensor<*xf32>
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%b = "mhlo.sqrt"(%a) : (tensor<*xf32>) -> tensor<*xf32>
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return %b : tensor<*xf32>
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}
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// -----
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// Not transformed when ranked.
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// CHECK-LABEL: @sqrt_ranked
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// CHECK-SAME: (%[[A:.*]]: tensor<3x?xf32>)
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func @sqrt_ranked(%a: tensor<3x?xf32>) -> tensor<3x?xf32> {
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// CHECK-NEXT: %[[B:.*]] = "mhlo.sqrt"(%[[A]]) : (tensor<3x?xf32>) -> tensor<3x?xf32>
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// CHECK-NEXT: return %[[B]] : tensor<3x?xf32>
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%b = "mhlo.sqrt"(%a) : (tensor<3x?xf32>) -> tensor<3x?xf32>
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return %b : tensor<3x?xf32>
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}
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// -----
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// Not transformed when statically shaped.
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// CHECK-LABEL: @sqrt_static
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// CHECK-SAME: (%[[A:.*]]: tensor<2x3xf32>)
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func @sqrt_static(%a: tensor<2x3xf32>) -> tensor<2x3xf32> {
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// CHECK-NEXT: %[[B:.*]] = "mhlo.sqrt"(%[[A]]) : (tensor<2x3xf32>) -> tensor<2x3xf32>
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// CHECK-NEXT: return %[[B]] : tensor<2x3xf32>
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%b = "mhlo.sqrt"(%a) : (tensor<2x3xf32>) -> tensor<2x3xf32>
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return %b : tensor<2x3xf32>
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}
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// -----
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// CHECK-LABEL: @add_unranked
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// CHECK-SAME: (%[[A:.*]]: tensor<*xf32>, %[[B:.*]]: tensor<*xf32>) -> tensor<*xf32>
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func @add_unranked(%a : tensor<*xf32>, %b : tensor<*xf32>) -> tensor<*xf32> {
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// CHECK: %[[SHAPE_A:.*]] = shape.shape_of %[[A]]
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// CHECK: %[[SHAPE_B:.*]] = shape.shape_of %[[B]]
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// CHECK: %[[SHAPE:.*]] = shape.any %[[SHAPE_A]], %[[SHAPE_B]]
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// CHECK: %[[NUM_ELEMENTS:.*]] = shape.num_elements %[[SHAPE]]
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// CHECK: %[[NUM_ELEMENTS_AS_INDEX:.*]] = shape.size_to_index %[[NUM_ELEMENTS]]
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// CHECK: %[[FLAT_SHAPE:.*]] = tensor_from_elements(%[[NUM_ELEMENTS_AS_INDEX]]) : tensor<1xindex>
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// CHECK: %[[FLAT_A:.*]] = "mhlo.dynamic_reshape"(%[[A]], %[[FLAT_SHAPE]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
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// CHECK: %[[FLAT_B:.*]] = "mhlo.dynamic_reshape"(%[[B]], %[[FLAT_SHAPE]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
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// CHECK: %[[FLAT_RESULT:.*]] = mhlo.add %[[FLAT_A]], %[[FLAT_B]] : tensor<?xf32>
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// CHECK: %[[SHAPE_AS_EXTENT_TENSOR:.*]] = shape.to_extent_tensor %[[SHAPE]] : tensor<?xindex>
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// CHECK: %[[RESULT:.*]] = "mhlo.dynamic_reshape"(%[[FLAT_RESULT]], %[[SHAPE_AS_EXTENT_TENSOR]]) : (tensor<?xf32>, tensor<?xindex>) -> tensor<*xf32>
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// CHECK: return %[[RESULT]] : tensor<*xf32>
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%result = mhlo.add %a, %b : tensor<*xf32>
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return %result : tensor<*xf32>
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}
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