47 lines
1.9 KiB
MLIR
47 lines
1.9 KiB
MLIR
// RUN: mlir-opt %s --canonicalize --cse | FileCheck %s
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#DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"]}>
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#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
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// CHECK-LABEL: func @sparse_nop_convert(
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// CHECK-SAME: %[[A:.*]]: tensor<64xf32, #{{.*}}>)
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// CHECK: return %[[A]] : tensor<64xf32, #{{.*}}>
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func @sparse_nop_convert(%arg0: tensor<64xf32, #SparseVector>) -> tensor<64xf32, #SparseVector> {
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%0 = sparse_tensor.convert %arg0 : tensor<64xf32, #SparseVector> to tensor<64xf32, #SparseVector>
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return %0 : tensor<64xf32, #SparseVector>
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}
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// CHECK-LABEL: func @sparse_dce_convert(
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// CHECK-SAME: %[[A:.*]]: tensor<64xf32>)
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// CHECK-NOT: sparse_tensor.convert
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// CHECK: return
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func @sparse_dce_convert(%arg0: tensor<64xf32>) {
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%0 = sparse_tensor.convert %arg0 : tensor<64xf32> to tensor<64xf32, #SparseVector>
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return
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}
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// CHECK-LABEL: func @sparse_dce_getters(
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// CHECK-SAME: %[[A:.*]]: tensor<64xf32, #sparse_tensor.encoding<{{{.*}}}>>)
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// CHECK-NOT: sparse_tensor.pointers
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// CHECK-NOT: sparse_tensor.indices
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// CHECK-NOT: sparse_tensor.values
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// CHECK: return
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func @sparse_dce_getters(%arg0: tensor<64xf32, #SparseVector>) {
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%c = constant 0 : index
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%0 = sparse_tensor.pointers %arg0, %c : tensor<64xf32, #SparseVector> to memref<?xindex>
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%1 = sparse_tensor.indices %arg0, %c : tensor<64xf32, #SparseVector> to memref<?xindex>
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%2 = sparse_tensor.values %arg0 : tensor<64xf32, #SparseVector> to memref<?xf32>
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return
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}
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// CHECK-LABEL: func @sparse_dce_reconstruct(
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// CHECK-SAME: %[[A:.*]]: tensor<64xf32, #sparse_tensor.encoding<{{{.*}}}>>)
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// CHECK-NOT: sparse_tensor.values
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// CHECK-NOT: sparse_tensor.tensor
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// CHECK: return
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func @sparse_dce_reconstruct(%arg0: tensor<64xf32, #DenseVector>) {
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%0 = sparse_tensor.values %arg0 : tensor<64xf32, #DenseVector> to memref<?xf32>
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%1 = sparse_tensor.tensor %0 : memref<?xf32> to tensor<64xf32, #DenseVector>
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return
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}
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