LogicalResult
matchAndRewrite(tensor::DimOp op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const override {
- if (!operands[0].getType().isa<LLVM::LLVMPointerType>())
- return failure();
Type resType = op.getType();
+ auto enc = getSparseTensorEncoding(op.source().getType());
+ if (!enc)
+ return failure();
+ // Permute the dim index.
+ Optional<int64_t> index = op.getConstantIndex();
+ if (!index.hasValue())
+ return failure();
+ int64_t idx = index.getValue();
+ AffineMap p = enc.getDimOrdering();
+ if (p) {
+ assert(p.isPermutation());
+ for (unsigned i = 0, sz = p.getNumResults(); i < sz; i++) {
+ if (p.getDimPosition(i) == idx) {
+ idx = i;
+ break;
+ }
+ }
+ }
+ // Generate the call.
StringRef name = "sparseDimSize";
+ SmallVector<Value, 2> params;
+ params.push_back(operands[0]);
+ params.push_back(
+ rewriter.create<ConstantOp>(op.getLoc(), rewriter.getIndexAttr(idx)));
rewriter.replaceOpWithNewOp<CallOp>(
- op, resType, getFunc(op, name, resType, operands), operands);
+ op, resType, getFunc(op, name, resType, params), params);
return success();
}
};
dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
}>
-// CHECK-LABEL: func @sparse_dim(
+// CHECK-LABEL: func @sparse_dim1d(
// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
// CHECK: %[[C:.*]] = constant 0 : index
// CHECK: %[[D:.*]] = call @sparseDimSize(%[[A]], %[[C]])
// CHECK: return %[[D]] : index
-func @sparse_dim(%arg0: tensor<?xf64, #SparseVector>) -> index {
+func @sparse_dim1d(%arg0: tensor<?xf64, #SparseVector>) -> index {
%c = constant 0 : index
%0 = tensor.dim %arg0, %c : tensor<?xf64, #SparseVector>
return %0 : index
}
+// CHECK-LABEL: func @sparse_dim3d(
+// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
+// CHECK: %[[C:.*]] = constant 2 : index
+// CHECK: %[[D:.*]] = call @sparseDimSize(%[[A]], %[[C]])
+// CHECK: return %[[D]] : index
+func @sparse_dim3d(%arg0: tensor<?x?x?xf64, #SparseTensor>) -> index {
+ // Needs permuting 1 into 2.
+ %c = constant 1 : index
+ %0 = tensor.dim %arg0, %c : tensor<?x?x?xf64, #SparseTensor>
+ return %0 : index
+}
+
// CHECK-LABEL: func @sparse_new1d(
// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8>
// CHECK-DAG: %[[U:.*]] = constant dense<1> : tensor<1xi8>
--- /dev/null
+// RUN: mlir-opt %s -sparsification --canonicalize | FileCheck %s --check-prefix=CHECK-HIR
+//
+// RUN: mlir-opt %s -sparsification --sparse-tensor-conversion --canonicalize | \
+// RUN: FileCheck %s --check-prefix=CHECK-MIR
+
+#X = #sparse_tensor.encoding<{
+ dimLevelType = [ "dense", "dense", "dense" ],
+ dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
+}>
+
+#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: builtin.func @sparse_dynamic_dims(
+// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>,
+// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
+// CHECK-HIR-DAG: %[[C0:.*]] = constant 0 : index
+// CHECK-HIR-DAG: %[[C1:.*]] = constant 1 : index
+// CHECK-HIR-DAG: %[[C2:.*]] = constant 2 : index
+// CHECK-HIR: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[C2]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-HIR: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[C0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-HIR: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[C1]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-HIR: %[[VAL_9:.*]] = memref.buffer_cast %[[VAL_1]] : memref<f32>
+// CHECK-HIR: %[[VAL_10:.*]] = memref.alloc() : memref<f32>
+// CHECK-HIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32>
+// CHECK-HIR: scf.for %[[VAL_11:.*]] = %[[C0]] to %[[VAL_5]] step %[[C1]] {
+// CHECK-HIR: scf.for %[[VAL_12:.*]] = %[[C0]] to %[[VAL_6]] step %[[C1]] {
+// CHECK-HIR: %[[VAL_13:.*]] = muli %[[VAL_6]], %[[VAL_11]] : index
+// CHECK-HIR: %[[VAL_14:.*]] = addi %[[VAL_13]], %[[VAL_12]] : index
+// CHECK-HIR: %[[VAL_15:.*]] = memref.load %[[VAL_10]][] : memref<f32>
+// CHECK-HIR: %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[C0]] to %[[VAL_7]] step %[[C1]] iter_args(%[[VAL_18:.*]] = %[[VAL_15]]) -> (f32) {
+// CHECK-HIR: %[[VAL_19:.*]] = muli %[[VAL_7]], %[[VAL_14]] : index
+// CHECK-HIR: %[[VAL_20:.*]] = addi %[[VAL_19]], %[[VAL_17]] : index
+// CHECK-HIR: %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xf32>
+// CHECK-HIR: %[[VAL_22:.*]] = addf %[[VAL_18]], %[[VAL_21]] : f32
+// CHECK-HIR: scf.yield %[[VAL_22]] : f32
+// CHECK-HIR: }
+// CHECK-HIR: memref.store %[[VAL_23:.*]], %[[VAL_10]][] : memref<f32>
+// CHECK-HIR: }
+// CHECK-HIR: }
+// CHECK-HIR: %[[VAL_24:.*]] = memref.tensor_load %[[VAL_10]] : memref<f32>
+// CHECK-HIR: return %[[VAL_24]] : tensor<f32>
+// CHECK-HIR: }
+//
+// CHECK-MIR-LABEL: builtin.func @sparse_dynamic_dims(
+// CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
+// CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
+// CHECK-MIR-DAG: %[[C0:.*]] = constant 0 : index
+// CHECK-MIR-DAG: %[[C1:.*]] = constant 1 : index
+// CHECK-MIR-DAG: %[[C2:.*]] = constant 2 : index
+// CHECK-MIR: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[C0]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK-MIR: %[[VAL_6:.*]] = call @sparseDimSize(%[[VAL_0]], %[[C1]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK-MIR: %[[VAL_7:.*]] = call @sparseDimSize(%[[VAL_0]], %[[C2]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK-MIR: %[[VAL_8:.*]] = call @sparseValuesF32(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf32>
+// CHECK-MIR: %[[VAL_9:.*]] = memref.buffer_cast %[[VAL_1]] : memref<f32>
+// CHECK-MIR: %[[VAL_10:.*]] = memref.alloc() : memref<f32>
+// CHECK-MIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32>
+// CHECK-MIR: scf.for %[[VAL_11:.*]] = %[[C0]] to %[[VAL_5]] step %[[C1]] {
+// CHECK-MIR: scf.for %[[VAL_12:.*]] = %[[C0]] to %[[VAL_6]] step %[[C1]] {
+// CHECK-MIR: %[[VAL_13:.*]] = muli %[[VAL_6]], %[[VAL_11]] : index
+// CHECK-MIR: %[[VAL_14:.*]] = addi %[[VAL_13]], %[[VAL_12]] : index
+// CHECK-MIR: %[[VAL_15:.*]] = memref.load %[[VAL_10]][] : memref<f32>
+// CHECK-MIR: %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[C0]] to %[[VAL_7]] step %[[C1]] iter_args(%[[VAL_18:.*]] = %[[VAL_15]]) -> (f32) {
+// CHECK-MIR: %[[VAL_19:.*]] = muli %[[VAL_7]], %[[VAL_14]] : index
+// CHECK-MIR: %[[VAL_20:.*]] = addi %[[VAL_19]], %[[VAL_17]] : index
+// CHECK-MIR: %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xf32>
+// CHECK-MIR: %[[VAL_22:.*]] = addf %[[VAL_18]], %[[VAL_21]] : f32
+// CHECK-MIR: scf.yield %[[VAL_22]] : f32
+// CHECK-MIR: }
+// CHECK-MIR: memref.store %[[VAL_23:.*]], %[[VAL_10]][] : memref<f32>
+// CHECK-MIR: }
+// CHECK-MIR: }
+// CHECK-MIR: %[[VAL_24:.*]] = memref.tensor_load %[[VAL_10]] : memref<f32>
+// CHECK-MIR: return %[[VAL_24]] : tensor<f32>
+// CHECK-MIR: }
+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 = addf %x, %a : f32
+ linalg.yield %0 : f32
+ } -> tensor<f32>
+ return %0 : tensor<f32>
+}