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llvm-project/mlir/test/Dialect/SparseTensor/sparse_perm_lower.mlir
Batzorig Zorigoo d186277e6b
[MLIR][Bufferization] Fold LoadOp only when the buffer is read only (#172595) 
When we `memref.load` from a buffer, it folded to `tensor.extract` even
when the buffer was writable, causing unexpected results. For example:

```mlir
func.func @load_after_write_from_buffer_cast(%arg0: index, %arg1: index,
                            %arg2: tensor<?x?xf32>) -> f32 {
  %0 = bufferization.to_buffer %arg2 : tensor<?x?xf32> to memref<?x?xf32>
  linalg.ceil ins(%0 : memref<?x?xf32>) outs(%0 : memref<?x?xf32>)
  %1 = memref.load %0[%arg0, %arg1] : memref<?x?xf32>
  return %1 : f32
}
```
would fold into
```mlir
module {
  func.func @load_after_write_from_buffer_cast(%arg0: index, %arg1: index, %arg2: tensor<?x?xf32>) -> f32 {
    %0 = bufferization.to_buffer %arg2 : tensor<?x?xf32> to memref<?x?xf32>
    linalg.ceil ins(%0 : memref<?x?xf32>) outs(%0 : memref<?x?xf32>)
    %extracted = tensor.extract %arg2[%arg0, %arg1] : tensor<?x?xf32>
    return %extracted : f32
  }
}
```
2026-01-14 07:13:31 +01:00

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// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification --canonicalize | FileCheck %s --check-prefix=CHECK-HIR
//
// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification --sparse-tensor-conversion --canonicalize | \
// RUN: FileCheck %s --check-prefix=CHECK-MIR
#X = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d2 : dense, d0 : dense, d1 : dense)
}>
#trait = {
indexing_maps = [
affine_map<(i,j,k) -> (k,i,j)>, // A (in)
affine_map<(i,j,k) -> ()> // X (out)
],
iterator_types = ["reduction", "reduction", "reduction"]
}
// CHECK-HIR-LABEL: func @sparse_dynamic_dims(
// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse{{[0-9]*}}>,
// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK-HIR-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 2 : index
// CHECK-HIR: %[[DEMAP:. *]] = sparse_tensor.reinterpret_map %[[VAL_0]]
// CHECK-HIR-DAG: %[[VAL_5:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}>
// CHECK-HIR-DAG: %[[VAL_6:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}>
// CHECK-HIR-DAG: %[[VAL_7:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}>
// CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[DEMAP]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}>
// CHECK-HIR-DAG: %[[VAL_10:.*]] = bufferization.to_buffer %[[VAL_1]] : tensor<f32> to memref<f32>
// CHECK-HIR: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32>
// CHECK-HIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) {
// CHECK-HIR: %[[VAL_18:.*]] = arith.muli %[[VAL_13]], %[[VAL_6]] : index
// CHECK-HIR: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_2]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) {
// CHECK-HIR: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_18]] : index
// CHECK-HIR: %[[VAL_23:.*]] = arith.muli %[[VAL_19]], %[[VAL_7]] : index
// CHECK-HIR: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_2]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) {
// CHECK-HIR: %[[VAL_24:.*]] = arith.addi %[[VAL_21]], %[[VAL_23]] : index
// CHECK-HIR: %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32>
// CHECK-HIR: %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f32
// CHECK-HIR: scf.yield %[[VAL_26]] : f32
// CHECK-HIR: }
// CHECK-HIR: scf.yield %[[VAL_20]] : f32
// CHECK-HIR: }
// CHECK-HIR: scf.yield %[[VAL_15]] : f32
// CHECK-HIR: }
// CHECK-HIR: memref.store %[[VAL_12]], %[[VAL_10]][] : memref<f32>
// CHECK-HIR: %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<f32>
// CHECK-HIR: return %[[VAL_30]] : tensor<f32>
// CHECK-HIR: }
//
// CHECK-MIR-LABEL: func @sparse_dynamic_dims(
// CHECK-MIR-SAME: %[[ARGA:.*]]: !llvm.ptr,
// CHECK-MIR-SAME: %[[ARGX:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK-MIR-DAG: %[[I0:.*]] = arith.constant 0 : index
// CHECK-MIR-DAG: %[[I1:.*]] = arith.constant 1 : index
// CHECK-MIR-DAG: %[[I2:.*]] = arith.constant 2 : index
// CHECK-MIR-DAG: %[[DimSize0:.*]] = call @sparseLvlSize(%[[ARGA]], %[[I0]])
// CHECK-MIR-DAG: %[[DimSize1:.*]] = call @sparseLvlSize(%[[ARGA]], %[[I1]])
// CHECK-MIR-DAG: %[[DimSize2:.*]] = call @sparseLvlSize(%[[ARGA]], %[[I2]])
// CHECK-MIR-DAG: %[[VAL_8:.*]] = call @sparseValuesF32(%[[ARGA]]) : (!llvm.ptr) -> memref<?xf32>
// CHECK-MIR-DAG: %[[VAL_10:.*]] = bufferization.to_buffer %[[ARGX]] : tensor<f32> to memref<f32>
// CHECK-MIR: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32>
// CHECK-MIR: %[[VAL_12:.*]] = scf.for %[[D2:.*]] = %[[I0]] to %[[DimSize0]] step %[[I1]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) {
// CHECK-MIR: %[[VAL_18:.*]] = arith.muli %[[D2]], %[[DimSize1]] : index
// CHECK-MIR: %[[VAL_15:.*]] = scf.for %[[D0:.*]] = %[[I0]] to %[[DimSize1]] step %[[I1]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) {
// CHECK-MIR: %[[VAL_19:.*]] = arith.addi %[[D0]], %[[VAL_18]] : index
// CHECK-MIR: %[[VAL_23:.*]] = arith.muli %[[VAL_19]], %[[DimSize2]] : index
// CHECK-MIR: %[[VAL_20:.*]] = scf.for %[[D1:.*]] = %[[I0]] to %[[DimSize2]] step %[[I1]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) {
// CHECK-MIR: %[[VAL_24:.*]] = arith.addi %[[D1]], %[[VAL_23]] : index
// CHECK-MIR: %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32>
// CHECK-MIR: %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f32
// CHECK-MIR: scf.yield %[[VAL_26]] : f32
// CHECK-MIR: }
// CHECK-MIR: scf.yield %[[VAL_20]] : f32
// CHECK-MIR: }
// CHECK-MIR: scf.yield %[[VAL_15]] : f32
// CHECK-MIR: }
// CHECK-MIR: memref.store %[[VAL_12]], %[[VAL_10]][] : memref<f32>
// CHECK-MIR: %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<f32>
// CHECK-MIR: return %[[VAL_30]] : tensor<f32>
// CHECK-MIR: }
func.func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>,
%argx: tensor<f32>) -> tensor<f32> {
%0 = linalg.generic #trait
ins(%arga: tensor<?x?x?xf32, #X>)
outs(%argx: tensor<f32>) {
^bb(%a : f32, %x: f32):
%0 = arith.addf %x, %a : f32
linalg.yield %0 : f32
} -> tensor<f32>
return %0 : tensor<f32>
}
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