35 lines
1.3 KiB
MLIR
35 lines
1.3 KiB
MLIR
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// RUN: mlir-opt --transform-interpreter %s | FileCheck %s
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// CHECK-LABEL: func.func @generalize_unary
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func.func @generalize_unary(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
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// CHECK-NOT: linalg.elemwise_unary
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// CHECK: linalg.generic
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%0 = linalg.elemwise_unary ins(%arg0 : tensor<?x?xf32>)
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outs(%arg1: tensor<?x?xf32>) -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// CHECK-LABEL: func @map_no_inputs(
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func.func @map_no_inputs(%input: tensor<16x32x64xf32>,
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%init: tensor<16x64xf32>) -> tensor<16x64xf32> {
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// CHECK-NOT: linalg.map
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// CHECK: linalg.generic
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%reduce = linalg.reduce
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ins(%input:tensor<16x32x64xf32>)
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outs(%init:tensor<16x64xf32>)
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dimensions = [1]
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(%in: f32, %out: f32) {
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%0 = arith.addf %out, %in: f32
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linalg.yield %0: f32
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}
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func.return %reduce : tensor<16x64xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
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%1 = transform.structured.generalize %0 : (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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