35 lines
1.4 KiB
MLIR
35 lines
1.4 KiB
MLIR
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// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification -cse | FileCheck %s
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#Dense = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : dense, d1 : dense)
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}>
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#trait_scale = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel"],
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doc = "X(i,j) = X(i,j) * 2.0"
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}
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// CHECK-LABEL: func.func @sparse_scale(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x1xf32, #sparse{{[0-9]*}}>)
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// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f32
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// CHECK: %[[VAL_3:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1x1xf32, #sparse{{[0-9]*}}> to memref<?xf32>
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// CHECK: %[[VAL_4:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xf32>
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// CHECK: %[[VAL_5:.*]] = arith.mulf %[[VAL_4]], %[[VAL_2]] : f32
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// CHECK: memref.store %[[VAL_5]], %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xf32>
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<1x1xf32, #sparse{{[0-9]*}}>
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// CHECK: return %[[VAL_6]] : tensor<1x1xf32, #sparse{{[0-9]*}}>
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func.func @sparse_scale(%argx: tensor<1x1xf32, #Dense>) -> tensor<1x1xf32, #Dense> {
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%c = arith.constant 2.0 : f32
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%0 = linalg.generic #trait_scale
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outs(%argx: tensor<1x1xf32, #Dense>) {
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^bb(%x: f32):
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%1 = arith.mulf %x, %c : f32
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linalg.yield %1 : f32
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} -> tensor<1x1xf32, #Dense>
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return %0 : tensor<1x1xf32, #Dense>
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}
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