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[mlir][sparse] codegen for sparse alloc
Reviewed By: Peiming Differential Revision: https://reviews.llvm.org/D133241
1 parent 5d30565 commit 0c7abd3

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‎mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp

Lines changed: 141 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -34,6 +34,16 @@ namespace {
3434
// Helper methods.
3535
//===----------------------------------------------------------------------===//
3636

37+
/// Reorders stored dimension to original dimension.
38+
static unsigned toOrig(const SparseTensorEncodingAttr &enc, unsigned i) {
39+
auto order = enc.getDimOrdering();
40+
if (order) {
41+
assert(order.isPermutation());
42+
return order.getDimPosition(i);
43+
}
44+
return i;
45+
}
46+
3747
/// Reorders original dimension to stored dimension.
3848
static unsigned toStored(const SparseTensorEncodingAttr &enc, unsigned i) {
3949
auto order = enc.getDimOrdering();
@@ -87,7 +97,7 @@ static Optional<Type> convertSparseTensorType(Type type) {
8797
// tensor type.
8898
switch (enc.getDimLevelType()[r]) {
8999
case SparseTensorEncodingAttr::DimLevelType::Dense:
90-
break;
100+
break; // no fields
91101
case SparseTensorEncodingAttr::DimLevelType::Compressed:
92102
case SparseTensorEncodingAttr::DimLevelType::CompressedNu:
93103
case SparseTensorEncodingAttr::DimLevelType::CompressedNo:
@@ -111,7 +121,7 @@ static Optional<Type> convertSparseTensorType(Type type) {
111121
return TupleType::get(context, fields);
112122
}
113123

114-
// Returns field index for pointers (d), indices (d) for set field.
124+
// Returns field index of sparse tensor type for pointers/indices, when set.
115125
static unsigned getFieldIndex(Type type, unsigned ptrDim, unsigned idxDim) {
116126
auto enc = getSparseTensorEncoding(type);
117127
assert(enc);
@@ -161,6 +171,94 @@ static Value createTupleGet(OpBuilder &builder, Location loc, Value tuple,
161171
builder.getIntegerAttr(indexType, field));
162172
}
163173

174+
/// Creates tuple.
175+
static Value createTupleMake(OpBuilder &builder, Location loc, Type type,
176+
ValueRange values) {
177+
return builder.create<StorageNewOp>(loc, type, values);
178+
}
179+
180+
/// Create allocation operation.
181+
static Value createAllocation(OpBuilder &builder, Location loc, Type type,
182+
Value sz) {
183+
auto memType = MemRefType::get({ShapedType::kDynamicSize}, type);
184+
return builder.create<memref::AllocOp>(loc, memType, sz);
185+
}
186+
187+
/// Creates allocation tuple for sparse tensor type.
188+
///
189+
/// TODO: for efficiency, we will need heuristis to make educated guesses
190+
/// on the required final sizes; also, we will need an improved
191+
/// memory allocation scheme with capacity and reallocation
192+
///
193+
static Value createAllocTuple(OpBuilder &builder, Location loc, Type type,
194+
ValueRange dynSizes) {
195+
auto enc = getSparseTensorEncoding(type);
196+
assert(enc);
197+
// Construct the basic types.
198+
unsigned idxWidth = enc.getIndexBitWidth();
199+
unsigned ptrWidth = enc.getPointerBitWidth();
200+
RankedTensorType rType = type.cast<RankedTensorType>();
201+
Type indexType = builder.getIndexType();
202+
Type idxType = idxWidth ? builder.getIntegerType(idxWidth) : indexType;
203+
Type ptrType = ptrWidth ? builder.getIntegerType(ptrWidth) : indexType;
204+
Type eltType = rType.getElementType();
205+
// Build the allocation tuple, using heuristics for pre-allocation.
206+
auto shape = rType.getShape();
207+
unsigned rank = shape.size();
208+
SmallVector<Value, 8> fields;
209+
bool allDense = true;
210+
Value one = constantIndex(builder, loc, 1);
211+
Value linear = one;
212+
Value heuristic = one; // FIX, see TODO above
213+
// Build original sizes.
214+
SmallVector<Value, 8> sizes;
215+
for (unsigned r = 0, o = 0; r < rank; r++) {
216+
if (ShapedType::isDynamic(shape[r]))
217+
sizes.push_back(dynSizes[o++]);
218+
else
219+
sizes.push_back(constantIndex(builder, loc, shape[r]));
220+
}
221+
// The dimSizes array.
222+
Value dimSizes =
223+
builder.create<memref::AllocOp>(loc, MemRefType::get({rank}, indexType));
224+
fields.push_back(dimSizes);
225+
// Per-dimension storage.
226+
for (unsigned r = 0; r < rank; r++) {
227+
// Get the original dimension (ro) for the current stored dimension.
228+
unsigned ro = toOrig(enc, r);
229+
builder.create<memref::StoreOp>(loc, sizes[ro], dimSizes,
230+
constantIndex(builder, loc, r));
231+
linear = builder.create<arith::MulIOp>(loc, linear, sizes[ro]);
232+
// Allocate fiels.
233+
switch (enc.getDimLevelType()[r]) {
234+
case SparseTensorEncodingAttr::DimLevelType::Dense:
235+
break; // no fields
236+
case SparseTensorEncodingAttr::DimLevelType::Compressed:
237+
case SparseTensorEncodingAttr::DimLevelType::CompressedNu:
238+
case SparseTensorEncodingAttr::DimLevelType::CompressedNo:
239+
case SparseTensorEncodingAttr::DimLevelType::CompressedNuNo:
240+
fields.push_back(createAllocation(builder, loc, ptrType, heuristic));
241+
fields.push_back(createAllocation(builder, loc, idxType, heuristic));
242+
allDense = false;
243+
break;
244+
case SparseTensorEncodingAttr::DimLevelType::Singleton:
245+
case SparseTensorEncodingAttr::DimLevelType::SingletonNu:
246+
case SparseTensorEncodingAttr::DimLevelType::SingletonNo:
247+
case SparseTensorEncodingAttr::DimLevelType::SingletonNuNo:
248+
fields.push_back(createAllocation(builder, loc, idxType, heuristic));
249+
allDense = false;
250+
break;
251+
}
252+
}
253+
// The values array. For all-dense, the full length is required.
254+
// In all other case, we resort to the heuristical initial value.
255+
Value valuesSz = allDense ? linear : heuristic;
256+
fields.push_back(createAllocation(builder, loc, eltType, valuesSz));
257+
// Construct tuple allocation.
258+
Type tupleType = *convertSparseTensorType(type);
259+
return createTupleMake(builder, loc, tupleType, fields);
260+
}
261+
164262
/// Returns integral constant, if defined.
165263
static Optional<int64_t> getConstantInt(Value val) {
166264
if (auto constantOp = val.getDefiningOp<arith::ConstantOp>())
@@ -233,6 +331,28 @@ class SparseCastConverter : public OpConversionPattern<tensor::CastOp> {
233331
}
234332
};
235333

334+
/// Sparse codgen rule for the alloc operator.
335+
class SparseTensorAllocConverter
336+
: public OpConversionPattern<bufferization::AllocTensorOp> {
337+
public:
338+
using OpConversionPattern::OpConversionPattern;
339+
LogicalResult
340+
matchAndRewrite(bufferization::AllocTensorOp op, OpAdaptor adaptor,
341+
ConversionPatternRewriter &rewriter) const override {
342+
RankedTensorType resType = op.getType();
343+
auto enc = getSparseTensorEncoding(resType);
344+
if (!enc)
345+
return failure();
346+
if (op.getCopy())
347+
return rewriter.notifyMatchFailure(op, "tensor copy not implemented");
348+
// Construct allocation tuple.
349+
Value tuple = createAllocTuple(rewriter, op->getLoc(), resType,
350+
adaptor.getOperands());
351+
rewriter.replaceOp(op, tuple);
352+
return success();
353+
}
354+
};
355+
236356
/// Sparse codegen rule for the dealloc operator.
237357
class SparseTensorDeallocConverter
238358
: public OpConversionPattern<bufferization::DeallocTensorOp> {
@@ -311,6 +431,22 @@ class SparseToValuesConverter : public OpConversionPattern<ToValuesOp> {
311431
}
312432
};
313433

434+
/// Sparse codegen rule for tensor rematerialization.
435+
class SparseTensorLoadConverter : public OpConversionPattern<LoadOp> {
436+
public:
437+
using OpConversionPattern::OpConversionPattern;
438+
LogicalResult
439+
matchAndRewrite(LoadOp op, OpAdaptor adaptor,
440+
ConversionPatternRewriter &rewriter) const override {
441+
if (op.getHasInserts()) {
442+
// Finalize any pending insertions.
443+
// TODO: implement
444+
}
445+
rewriter.replaceOp(op, adaptor.getOperands());
446+
return success();
447+
}
448+
};
449+
314450
} // namespace
315451

316452
//===----------------------------------------------------------------------===//
@@ -331,7 +467,8 @@ mlir::SparseTensorTypeToBufferConverter::SparseTensorTypeToBufferConverter() {
331467
void mlir::populateSparseTensorCodegenPatterns(TypeConverter &typeConverter,
332468
RewritePatternSet &patterns) {
333469
patterns.add<SparseReturnConverter, SparseDimOpConverter, SparseCastConverter,
334-
SparseTensorDeallocConverter, SparseToPointersConverter,
335-
SparseToIndicesConverter, SparseToValuesConverter>(
470+
SparseTensorAllocConverter, SparseTensorDeallocConverter,
471+
SparseToPointersConverter, SparseToIndicesConverter,
472+
SparseToValuesConverter, SparseTensorLoadConverter>(
336473
typeConverter, patterns.getContext());
337474
}

‎mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorPasses.cpp

Lines changed: 5 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -156,7 +156,7 @@ struct SparseTensorCodegenPass
156156
ConversionTarget target(*ctx);
157157
// Almost everything in the sparse dialect must go!
158158
target.addIllegalDialect<SparseTensorDialect>();
159-
target.addLegalOp<StorageGetOp, StorageSetOp>();
159+
target.addLegalOp<StorageGetOp, StorageSetOp, StorageNewOp>();
160160
// All dynamic rules below accept new function, call, return, and various
161161
// tensor and bufferization operations as legal output of the rewriting
162162
// provided that all sparse tensor types have been fully rewritten.
@@ -169,6 +169,10 @@ struct SparseTensorCodegenPass
169169
target.addDynamicallyLegalOp<func::ReturnOp>([&](func::ReturnOp op) {
170170
return converter.isLegal(op.getOperandTypes());
171171
});
172+
target.addDynamicallyLegalOp<bufferization::AllocTensorOp>(
173+
[&](bufferization::AllocTensorOp op) {
174+
return converter.isLegal(op.getType());
175+
});
172176
target.addDynamicallyLegalOp<bufferization::DeallocTensorOp>(
173177
[&](bufferization::DeallocTensorOp op) {
174178
return converter.isLegal(op.getTensor().getType());

‎mlir/test/Dialect/SparseTensor/codegen.mlir

Lines changed: 58 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,6 @@
11
// RUN: mlir-opt %s --sparse-tensor-codegen --canonicalize --cse | FileCheck %s --check-prefix=CHECK-CODEGEN
2-
// RUN: mlir-opt %s --sparse-tensor-codegen --sparse-tensor-storage-expansion --canonicalize --cse | FileCheck %s --check-prefix=CHECK-STORAGE
3-
2+
// FIXME:
3+
// R_U_N: mlir-opt %s --sparse-tensor-codegen --sparse-tensor-storage-expansion --canonicalize --cse | FileCheck %s --check-prefix=CHECK-STORAGE
44

55
#SparseVector = #sparse_tensor.encoding<{
66
dimLevelType = [ "compressed" ],
@@ -26,6 +26,11 @@
2626
pointerBitWidth = 32
2727
}>
2828

29+
#CSC = #sparse_tensor.encoding<{
30+
dimLevelType = [ "dense", "compressed" ],
31+
dimOrdering = affine_map<(i, j) -> (j, i)>
32+
}>
33+
2934
#DCSR = #sparse_tensor.encoding<{
3035
dimLevelType = [ "compressed", "compressed" ],
3136
indexBitWidth = 64,
@@ -45,7 +50,7 @@
4550
// CHECK-STORAGE-SAME: %[[A0:.*0]]: memref<1xindex>,
4651
// CHECK-STORAGE-SAME: %[[A1:.*1]]: memref<?xi32>,
4752
// CHECK-STORAGE-SAME: %[[A2:.*2]]: memref<?xi64>,
48-
// CHECK-STORAGE-SAME: %[[A3:.*3]]: memref<?xf64>)
53+
// CHECK-STORAGE-SAME: %[[A3:.*3]]: memref<?xf64>)
4954
// CHECK-STORAGE: return %[[A0]], %[[A1]], %[[A2]], %[[A3]] : memref<1xindex>, memref<?xi32>, memref<?xi64>, memref<?xf64>
5055
func.func @sparse_nop(%arg0: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
5156
return %arg0 : tensor<?xf64, #SparseVector>
@@ -59,7 +64,7 @@ func.func @sparse_nop(%arg0: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #Spa
5964
// CHECK-STORAGE-SAME: %[[A0:.*0]]: memref<1xindex>,
6065
// CHECK-STORAGE-SAME: %[[A1:.*1]]: memref<?xi32>,
6166
// CHECK-STORAGE-SAME: %[[A2:.*2]]: memref<?xi64>,
62-
// CHECK-STORAGE-SAME: %[[A3:.*3]]: memref<?xf32>)
67+
// CHECK-STORAGE-SAME: %[[A3:.*3]]: memref<?xf32>)
6368
// CHECK-STORAGE: return %[[A0]], %[[A1]], %[[A2]], %[[A3]] : memref<1xindex>, memref<?xi32>, memref<?xi64>, memref<?xf32>
6469
func.func @sparse_nop_cast(%arg0: tensor<64xf32, #SparseVector>) -> tensor<?xf32, #SparseVector> {
6570
%0 = tensor.cast %arg0 : tensor<64xf32, #SparseVector> to tensor<?xf32, #SparseVector>
@@ -72,7 +77,7 @@ func.func @sparse_nop_cast(%arg0: tensor<64xf32, #SparseVector>) -> tensor<?xf32
7277
//
7378
// CHECK-STORAGE-LABEL: func @sparse_nop_cast_3d(
7479
// CHECK-STORAGE-SAME: %[[A0:.*0]]: memref<3xindex>,
75-
// CHECK-STORAGE-SAME: %[[A1:.*1]]: memref<?xf32>)
80+
// CHECK-STORAGE-SAME: %[[A1:.*1]]: memref<?xf32>)
7681
// CHECK-STORAGE: return %[[A0]], %[[A1]] : memref<3xindex>, memref<?xf32>
7782
func.func @sparse_nop_cast_3d(%arg0: tensor<10x20x30xf32, #Dense3D>) -> tensor<?x?x?xf32, #Dense3D> {
7883
%0 = tensor.cast %arg0 : tensor<10x20x30xf32, #Dense3D> to tensor<?x?x?xf32, #Dense3D>
@@ -142,7 +147,7 @@ func.func @sparse_dcsr(%arg0: tensor<?x?xf64, #DCSR>) {
142147
//
143148
// CHECK-STORAGE-LABEL: func @sparse_dense_3d(
144149
// CHECK-STORAGE-SAME: %[[A0:.*0]]: memref<3xindex>,
145-
// CHECK-STORAGE-SAME: %[[A1:.*1]]: memref<?xf64>)
150+
// CHECK-STORAGE-SAME: %[[A1:.*1]]: memref<?xf64>)
146151
// CHECK-STORAGE: %[[C:.*]] = arith.constant 20 : index
147152
// CHECK-STORAGE: return %[[C]] : index
148153
func.func @sparse_dense_3d(%arg0: tensor<10x20x30xf64, #Dense3D>) -> index {
@@ -165,7 +170,7 @@ func.func @sparse_dense_3d(%arg0: tensor<10x20x30xf64, #Dense3D>) -> index {
165170
//
166171
// CHECK-STORAGE-LABEL: func @sparse_dense_3d_dyn(
167172
// CHECK-STORAGE-SAME: %[[A0:.*0]]: memref<3xindex>,
168-
// CHECK-STORAGE-SAME: %[[A1:.*1]]: memref<?xf64>)
173+
// CHECK-STORAGE-SAME: %[[A1:.*1]]: memref<?xf64>)
169174
// CHECK-STORAGE: %[[C:.*]] = arith.constant 2 : index
170175
// CHECK-STORAGE: %[[L:.*]] = memref.load %[[A0]][%[[C]]] : memref<3xindex>
171176
// CHECK-STORAGE: return %[[L]] : index
@@ -186,7 +191,7 @@ func.func @sparse_dense_3d_dyn(%arg0: tensor<?x?x?xf64, #Dense3D>) -> index {
186191
// CHECK-STORAGE-SAME: %[[A2:.*2]]: memref<?xi64>,
187192
// CHECK-STORAGE-SAME: %[[A3:.*3]]: memref<?xi32>,
188193
// CHECK-STORAGE-SAME: %[[A4:.*4]]: memref<?xi64>,
189-
// CHECK-STORAGE-SAME: %[[A5:.*5]]: memref<?xf64>)
194+
// CHECK-STORAGE-SAME: %[[A5:.*5]]: memref<?xf64>)
190195
// CHECK-STORAGE: return %[[A3]] : memref<?xi32>
191196
func.func @sparse_pointers_dcsr(%arg0: tensor<?x?xf64, #DCSR>) -> memref<?xi32> {
192197
%c = arith.constant 1 : index
@@ -205,7 +210,7 @@ func.func @sparse_pointers_dcsr(%arg0: tensor<?x?xf64, #DCSR>) -> memref<?xi32>
205210
// CHECK-STORAGE-SAME: %[[A2:.*2]]: memref<?xi64>,
206211
// CHECK-STORAGE-SAME: %[[A3:.*3]]: memref<?xi32>,
207212
// CHECK-STORAGE-SAME: %[[A4:.*4]]: memref<?xi64>,
208-
// CHECK-STORAGE-SAME: %[[A5:.*5]]: memref<?xf64>)
213+
// CHECK-STORAGE-SAME: %[[A5:.*5]]: memref<?xf64>)
209214
// CHECK-STORAGE: return %[[A4]] : memref<?xi64>
210215
func.func @sparse_indices_dcsr(%arg0: tensor<?x?xf64, #DCSR>) -> memref<?xi64> {
211216
%c = arith.constant 1 : index
@@ -224,7 +229,7 @@ func.func @sparse_indices_dcsr(%arg0: tensor<?x?xf64, #DCSR>) -> memref<?xi64> {
224229
// CHECK-STORAGE-SAME: %[[A2:.*2]]: memref<?xi64>,
225230
// CHECK-STORAGE-SAME: %[[A3:.*3]]: memref<?xi32>,
226231
// CHECK-STORAGE-SAME: %[[A4:.*4]]: memref<?xi64>,
227-
// CHECK-STORAGE-SAME: %[[A5:.*5]]: memref<?xf64>)
232+
// CHECK-STORAGE-SAME: %[[A5:.*5]]: memref<?xf64>)
228233
// CHECK-STORAGE: return %[[A5]] : memref<?xf64>
229234
func.func @sparse_values_dcsr(%arg0: tensor<?x?xf64, #DCSR>) -> memref<?xf64> {
230235
%0 = sparse_tensor.values %arg0 : tensor<?x?xf64, #DCSR> to memref<?xf64>
@@ -257,3 +262,46 @@ func.func @sparse_dealloc_csr(%arg0: tensor<?x?xf64, #CSR>) {
257262
bufferization.dealloc_tensor %arg0 : tensor<?x?xf64, #CSR>
258263
return
259264
}
265+
266+
// CHECK-CODEGEN-LABEL: func @sparse_alloc_csc(
267+
// CHECK-CODEGEN-SAME: %[[A:.*]]: index)
268+
// CHECK-CODEGEN-DAG: %[[C0:.*]] = arith.constant 0 : index
269+
// CHECK-CODEGEN-DAG: %[[C1:.*]] = arith.constant 1 : index
270+
// CHECK-CODEGEN-DAG: %[[C10:.*]] = arith.constant 10 : index
271+
// CHECK-CODEGEN: %[[T0:.*]] = memref.alloc() : memref<2xindex>
272+
// CHECK-CODEGEN: memref.store %[[A]], %[[T0]][%[[C0]]] : memref<2xindex>
273+
// CHECK-CODEGEN: memref.store %[[C10]], %[[T0]][%[[C1]]] : memref<2xindex>
274+
// CHECK-CODEGEN: %[[T1:.*]] = memref.alloc() : memref<1xindex>
275+
// CHECK-CODEGEN: %[[T2:.*]] = memref.cast %[[T1]] : memref<1xindex> to memref<?xindex>
276+
// CHECK-CODEGEN: %[[T3:.*]] = memref.alloc() : memref<1xindex>
277+
// CHECK-CODEGEN: %[[T4:.*]] = memref.cast %[[T3]] : memref<1xindex> to memref<?xindex>
278+
// CHECK-CODEGEN: %[[T5:.*]] = memref.alloc() : memref<1xf64>
279+
// CHECK-CODEGEN: %[[T6:.*]] = memref.cast %[[T5]] : memref<1xf64> to memref<?xf64>
280+
// CHECK-CODEGEN: %[[T:.*]] = sparse_tensor.storage(%[[T0]], %[[T2]], %[[T4]], %[[T6]])
281+
// CHECK-CODEGEN: return %[[T]] : tuple<memref<2xindex>, memref<?xindex>, memref<?xindex>, memref<?xf64>>
282+
func.func @sparse_alloc_csc(%arg0: index) -> tensor<10x?xf64, #CSC> {
283+
%0 = bufferization.alloc_tensor(%arg0) : tensor<10x?xf64, #CSC>
284+
%1 = sparse_tensor.load %0 : tensor<10x?xf64, #CSC>
285+
return %1 : tensor<10x?xf64, #CSC>
286+
}
287+
288+
// CHECK-CODEGEN-LABEL: func @sparse_alloc_3d() -> tuple<memref<3xindex>, memref<?xf64>>
289+
// CHECK-CODEGEN-DAG: %[[C0:.*]] = arith.constant 0 : index
290+
// CHECK-CODEGEN-DAG: %[[C1:.*]] = arith.constant 1 : index
291+
// CHECK-CODEGEN-DAG: %[[C2:.*]] = arith.constant 2 : index
292+
// CHECK-CODEGEN-DAG: %[[C10:.*]] = arith.constant 10 : index
293+
// CHECK-CODEGEN-DAG: %[[C20:.*]] = arith.constant 20 : index
294+
// CHECK-CODEGEN-DAG: %[[C30:.*]] = arith.constant 30 : index
295+
// CHECK-CODEGEN: %[[A0:.*]] = memref.alloc() : memref<3xindex>
296+
// CHECK-CODEGEN: memref.store %[[C30]], %[[A0]][%[[C0]]] : memref<3xindex>
297+
// CHECK-CODEGEN: memref.store %[[C10]], %[[A0]][%[[C1]]] : memref<3xindex>
298+
// CHECK-CODEGEN: memref.store %[[C20]], %[[A0]][%[[C2]]] : memref<3xindex>
299+
// CHECK-CODEGEN: %[[A:.*]] = memref.alloc() : memref<6000xf64>
300+
// CHECK-CODEGEN: %[[A1:.*]] = memref.cast %[[A]] : memref<6000xf64> to memref<?xf64>
301+
// CHECK-CODEGEN: %[[T:.*]] = sparse_tensor.storage(%[[A0]], %[[A1]])
302+
// CHECK-CODEGEN: return %[[T]] : tuple<memref<3xindex>, memref<?xf64>>
303+
func.func @sparse_alloc_3d() -> tensor<10x20x30xf64, #Dense3D> {
304+
%0 = bufferization.alloc_tensor() : tensor<10x20x30xf64, #Dense3D>
305+
%1 = sparse_tensor.load %0 : tensor<10x20x30xf64, #Dense3D>
306+
return %1 : tensor<10x20x30xf64, #Dense3D>
307+
}

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