[MLIR][Bufferization] Choose default memory space in tensor copy inse…

…rtion (#88500) Tensor copy insertion currently uses memory_space = 0 when creating a tensor copy using alloc_tensor. This memory space should instead be the default memory space provided in bufferization options.
llvm · Apr 12, 2024 · 6f1e23b · 6f1e23b
1 parent e0a6287
commit 6f1e23b
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Showing 3 changed files with 8 additions and 7 deletions.
diff --git a/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp b/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
@@ -193,10 +193,11 @@ FailureOr<Value> bufferization::allocateTensorForShapedValue(
   FailureOr<BaseMemRefType> copyBufferType = getBufferType(tensor, options);
   if (failed(copyBufferType))
     return failure();
-  Attribute memorySpace = copyBufferType->getMemorySpace();
+  std::optional<Attribute> memorySpace = copyBufferType->getMemorySpace();
   if (!memorySpace)
-    memorySpace = b.getI64IntegerAttr(0);
-  allocTensorOp.setMemorySpaceAttr(memorySpace);
+    memorySpace = options.defaultMemorySpaceFn(tensorType);
+  if (memorySpace.has_value())
+    allocTensorOp.setMemorySpaceAttr(memorySpace.value());
   return allocTensorOp.getResult();
 }
 

diff --git a/mlir/test/Dialect/Bufferization/Transforms/tensor-copy-insertion.mlir b/mlir/test/Dialect/Bufferization/Transforms/tensor-copy-insertion.mlir
@@ -34,7 +34,7 @@ func.func @do_not_copy_undefined_tensor(%f: f32, %idx: index)
 {
   // The second alloc_tensor should not have a copy operand.
   // CHECK: bufferization.alloc_tensor() : tensor<5xf32>
-  // CHECK: bufferization.alloc_tensor() {memory_space = 0 : i64} : tensor<5xf32>
+  // CHECK: bufferization.alloc_tensor() : tensor<5xf32>
   %0 = bufferization.alloc_tensor() : tensor<5xf32>
   %1 = tensor.insert %f into %0[%idx] : tensor<5xf32>
   return %0, %1 : tensor<5xf32>, tensor<5xf32>
@@ -46,7 +46,7 @@ func.func @do_not_copy_undefined_tensor(%f: f32, %idx: index)
 func.func @do_not_copy_when_overwritten(%t: tensor<5xf32>, %f: f32)
   -> (tensor<5xf32>, tensor<5xf32>)
 {
-  // CHECK: %[[alloc:.*]] = bufferization.alloc_tensor() {memory_space = 0 : i64} : tensor<5xf32>
+  // CHECK: %[[alloc:.*]] = bufferization.alloc_tensor() : tensor<5xf32>
   // CHECK: linalg.generic {{.*}} outs(%[[alloc]] : tensor<5xf32>)
   %r = linalg.generic {
     indexing_maps = [affine_map<(d0) -> (d0)>],
@@ -65,7 +65,7 @@ func.func @do_not_copy_when_result_not_read(%t: tensor<5xf32>, %f: f32)
   -> (tensor<3xf32>)
 {
   %0 = tensor.extract_slice %t[0][3][1] : tensor<5xf32> to tensor<3xf32>
-  // CHECK: %[[alloc:.*]] = bufferization.alloc_tensor() {memory_space = 0 : i64} : tensor<3xf32>
+  // CHECK: %[[alloc:.*]] = bufferization.alloc_tensor() : tensor<3xf32>
   // CHECK: linalg.generic {{.*}} outs(%[[alloc]] : tensor<3xf32>)
   %r = linalg.generic {
     indexing_maps = [affine_map<(d0) -> (d0)>],

diff --git a/mlir/test/Dialect/SparseTensor/sparse_sddmm.mlir b/mlir/test/Dialect/SparseTensor/sparse_sddmm.mlir
@@ -63,7 +63,7 @@ func.func @fold_yield_direct_zero() -> tensor<32xf64> {
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant dense<0.000000e+00> : tensor<8x8xf64>
 // CHECK-DAG:       %[[VAL_7:.*]] = bufferization.alloc_tensor() copy(%[[VAL_6]]) : tensor<8x8xf64>
-// CHECK-DAG:       %[[VAL_8:.*]] = bufferization.alloc_tensor() copy(%[[VAL_6]]) {memory_space = 0 : i64} : tensor<8x8xf64>
+// CHECK-DAG:       %[[VAL_8:.*]] = bufferization.alloc_tensor() copy(%[[VAL_6]]) : tensor<8x8xf64>
 // CHECK-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64>
 // CHECK-DAG:       %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xindex>