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Mar 6, 2024
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[mlir][sparse] use a consistent order between [dis]assembleOp and sto… #84079

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[mlir][sparse] use a consistent order between [dis]assembleOp and sto…
Mar 5, 2024
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31 changes: 17 additions & 14 deletions mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td
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Expand Up @@ -55,8 +55,8 @@ def SparseTensor_NewOp : SparseTensor_Op<"new", [Pure]>,
}

def SparseTensor_AssembleOp : SparseTensor_Op<"assemble", [Pure]>,
Arguments<(ins TensorOf<[AnyType]>:$values,
Variadic<TensorOf<[AnySignlessIntegerOrIndex]>>:$levels)>,
Arguments<(ins Variadic<TensorOf<[AnySignlessIntegerOrIndex]>>:$levels,
TensorOf<[AnyType]>:$values)>,
Results<(outs AnySparseTensor: $result)> {
let summary = "Returns a sparse tensor assembled from the given values and levels";

Expand Down Expand Up @@ -96,20 +96,20 @@ def SparseTensor_AssembleOp : SparseTensor_Op<"assemble", [Pure]>,
}];

let assemblyFormat =
"$values `,` $levels attr-dict"
"`:` type($values) `,` type($levels) `to` type($result)";
"` ` `(` $levels `)` `,` $values attr-dict"
" `:` `(` type($levels) `)` `,` type($values) `to` type($result)";

let hasVerifier = 1;
}

def SparseTensor_DisassembleOp : SparseTensor_Op<"disassemble", [Pure, SameVariadicResultSize]>,
Arguments<(ins AnySparseTensor:$tensor,
TensorOf<[AnyType]>:$out_values,
Variadic<TensorOf<[AnySignlessIntegerOrIndex]>>:$out_levels)>,
Results<(outs TensorOf<[AnyType]>:$ret_values,
Variadic<TensorOf<[AnySignlessIntegerOrIndex]>>:$ret_levels,
AnyIndexingScalarLike:$val_len,
Variadic<AnyIndexingScalarLike>:$lvl_lens)> {
Variadic<TensorOf<[AnySignlessIntegerOrIndex]>>:$out_levels,
TensorOf<[AnyType]>:$out_values)>,
Results<(outs Variadic<TensorOf<[AnySignlessIntegerOrIndex]>>:$ret_levels,
TensorOf<[AnyType]>:$ret_values,
Variadic<AnyIndexingScalarLike>:$lvl_lens,
AnyIndexingScalarLike:$val_len)> {
let summary = "Returns the (values, coordinates) pair disassembled from the input tensor";

let description = [{
Expand All @@ -134,8 +134,9 @@ def SparseTensor_DisassembleOp : SparseTensor_Op<"disassemble", [Pure, SameVaria
// |0.0, 0.0, 0.0, 0.0|
%v, %p, %c, %v_len, %p_len, %c_len =
sparse_tensor.disassemble %sp : tensor<3x4xf64, #COO>
outs(%od, %op, %oi : tensor<3xf64>, tensor<2xindex>, tensor<3x2xindex>)
-> tensor<3xf64>, (tensor<2xindex>, tensor<3x2xindex>), index, (index, index)
out_lvls(%op, %oi) : tensor<2xindex>, tensor<3x2xindex>,
out_vals(%od) : tensor<3xf64> ->
tensor<3xf64>, (tensor<2xindex>, tensor<3x2xindex>), index, (index, index)
// %v = arith.constant dense<[ 1.1, 2.2, 3.3 ]> : tensor<3xf64>
// %p = arith.constant dense<[ 0, 3 ]> : tensor<2xindex>
// %c = arith.constant dense<[[0,0], [1,2], [1,3]]> : tensor<3x2xindex>
Expand All @@ -147,8 +148,10 @@ def SparseTensor_DisassembleOp : SparseTensor_Op<"disassemble", [Pure, SameVaria

let assemblyFormat =
"$tensor `:` type($tensor) "
"`outs` `(` $out_values `,` $out_levels `:` type($out_values) `,` type($out_levels) `)` attr-dict"
"`->` type($ret_values) `,` `(` type($ret_levels) `)` `,` type($val_len) `,` `(` type($lvl_lens) `)`";
"`out_lvls` `(` $out_levels `:` type($out_levels) `)` "
"`out_vals` `(` $out_values `:` type($out_values) `)` attr-dict"
"`->` `(` type($ret_levels) `)` `,` type($ret_values) `,` "
"`(` type($lvl_lens) `)` `,` type($val_len)";

let hasVerifier = 1;
}
Expand Down
33 changes: 10 additions & 23 deletions mlir/lib/Dialect/SparseTensor/Transforms/SparseAssembler.cpp
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Expand Up @@ -31,22 +31,16 @@ static void convTypes(TypeRange types, SmallVectorImpl<Type> &convTypes,
convTypes.push_back(type);
continue;
}
// Convert the external representation of the values array.

// Convert the external representation of the position/coordinate array
const SparseTensorType stt(cast<RankedTensorType>(type));
auto shape = stt.getBatchLvlShape();
shape.push_back(ShapedType::kDynamic);
auto vtp = RankedTensorType::get(shape, stt.getElementType());
convTypes.push_back(vtp);
if (extraTypes)
extraTypes->push_back(vtp);

// Convert the external representation of the position/coordinate array.
foreachFieldAndTypeInSparseTensor(stt, [&convTypes, extraTypes](
Type t, FieldIndex,
SparseTensorFieldKind kind,
Level, LevelType) {
if (kind == SparseTensorFieldKind::CrdMemRef ||
kind == SparseTensorFieldKind::PosMemRef) {
kind == SparseTensorFieldKind::PosMemRef ||
kind == SparseTensorFieldKind::ValMemRef) {
ShapedType st = t.cast<ShapedType>();
auto rtp = RankedTensorType::get(st.getShape(), st.getElementType());
convTypes.push_back(rtp);
Expand All @@ -70,37 +64,30 @@ static void convVals(OpBuilder &builder, Location loc, TypeRange types,
toVals.push_back(fromVals[idx++]);
continue;
}
// Convert the external representation of the values array.
// Handle sparse data.
auto rtp = cast<RankedTensorType>(type);
const SparseTensorType stt(rtp);
auto shape = stt.getBatchLvlShape();
shape.push_back(ShapedType::kDynamic);
SmallVector<Value> inputs;
SmallVector<Type> retTypes;
SmallVector<Type> cntTypes;
// Collect the external representation of the values array for
// input or the outgoing sparse tensor for output.
inputs.push_back(fromVals[idx++]);
if (!isIn) {
inputs.push_back(extraVals[extra++]);
retTypes.push_back(RankedTensorType::get(shape, stt.getElementType()));
cntTypes.push_back(builder.getIndexType()); // nnz
}
if (!isIn)
inputs.push_back(fromVals[idx++]); // The sparse tensor to disassemble

// Collect the external representations of the pos/crd arrays.
foreachFieldAndTypeInSparseTensor(stt, [&, isIn](Type t, FieldIndex,
SparseTensorFieldKind kind,
Level, LevelType) {
if (kind == SparseTensorFieldKind::CrdMemRef ||
kind == SparseTensorFieldKind::PosMemRef) {
kind == SparseTensorFieldKind::PosMemRef ||
kind == SparseTensorFieldKind::ValMemRef) {
if (isIn) {
inputs.push_back(fromVals[idx++]);
} else {
ShapedType st = t.cast<ShapedType>();
auto rtp = RankedTensorType::get(st.getShape(), st.getElementType());
inputs.push_back(extraVals[extra++]);
retTypes.push_back(rtp);
cntTypes.push_back(rtp.getElementType());
cntTypes.push_back(builder.getIndexType());
}
}
return true;
Expand Down
4 changes: 2 additions & 2 deletions mlir/lib/Dialect/SparseTensor/Transforms/SparseGPUCodegen.cpp
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Expand Up @@ -928,8 +928,8 @@ static LogicalResult rewriteSpGEMM(PatternRewriter &rewriter,
Value vt = rewriter.create<bufferization::ToTensorOp>(loc, valH);
Value rt = rewriter.create<bufferization::ToTensorOp>(loc, rowH);
Value ct = rewriter.create<bufferization::ToTensorOp>(loc, colH);
rewriter.replaceOpWithNewOp<AssembleOp>(op, c.getType(), vt,
ValueRange{rt, ct});
rewriter.replaceOpWithNewOp<AssembleOp>(op, c.getType(), ValueRange{rt, ct},
vt);
return success();
}

Expand Down
10 changes: 3 additions & 7 deletions mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp
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Expand Up @@ -1409,14 +1409,10 @@ struct SparseDisassembleOpConverter
sz = desc.getValMemSize(rewriter, loc);
src = desc.getValMemRef();
dst = genToMemref(rewriter, loc, op.getOutValues());
// Values is the last field in descriptor, but it is the first
// operand in unpack operation.
// TODO: maybe change unpack/pack operation instead to be
// consistent.
retMem.insert(retMem.begin(), dst);

retMem.push_back(dst);
Type valLenTp = op.getValLen().getType();
retLen.insert(retLen.begin(),
genScalarToTensor(rewriter, loc, sz, valLenTp));
retLen.push_back(genScalarToTensor(rewriter, loc, sz, valLenTp));
} else {
assert(fKind == SparseTensorFieldKind::PosMemRef ||
fKind == SparseTensorFieldKind::CrdMemRef);
Expand Down
27 changes: 14 additions & 13 deletions mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp
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Expand Up @@ -738,13 +738,7 @@ class SparseTensorDisassembleConverter
auto stt = getSparseTensorType(op.getTensor());
SmallVector<Value> retVal;
SmallVector<Value> retLen;
// Get the values buffer first.
auto vals = genValuesCall(rewriter, loc, stt, adaptor.getTensor());
auto valLenTp = op.getValLen().getType();
auto valLen = linalg::createOrFoldDimOp(rewriter, loc, vals, 0);
retVal.push_back(vals);
retLen.push_back(genScalarToTensor(rewriter, loc, valLen, valLenTp));
// Then get the positions and coordinates buffers.
// Get the positions and coordinates buffers.
const Level lvlRank = stt.getLvlRank();
Level trailCOOLen = 0;
for (Level l = 0; l < lvlRank; l++) {
Expand All @@ -761,15 +755,15 @@ class SparseTensorDisassembleConverter
auto poss =
genPositionsCall(rewriter, loc, stt, adaptor.getTensor(), l);
auto posLen = linalg::createOrFoldDimOp(rewriter, loc, poss, 0);
auto posLenTp = op.getLvlLens().getTypes()[retLen.size() - 1];
auto posLenTp = op.getLvlLens().getTypes()[retLen.size()];
retVal.push_back(poss);
retLen.push_back(genScalarToTensor(rewriter, loc, posLen, posLenTp));
}
if (stt.isWithCrd(l)) {
auto crds =
genCoordinatesCall(rewriter, loc, stt, adaptor.getTensor(), l);
auto crdLen = linalg::createOrFoldDimOp(rewriter, loc, crds, 0);
auto crdLenTp = op.getLvlLens().getTypes()[retLen.size() - 1];
auto crdLenTp = op.getLvlLens().getTypes()[retLen.size()];
retVal.push_back(crds);
retLen.push_back(genScalarToTensor(rewriter, loc, crdLen, crdLenTp));
}
Expand All @@ -784,14 +778,13 @@ class SparseTensorDisassembleConverter
auto poss = genPositionsCall(rewriter, loc, stt, adaptor.getTensor(),
cooStartLvl);
auto posLen = linalg::createOrFoldDimOp(rewriter, loc, poss, 0);
auto posLenTp = op.getLvlLens().getTypes()[retLen.size() - 1];
auto posLenTp = op.getLvlLens().getTypes()[retLen.size()];
retVal.push_back(poss);
retLen.push_back(genScalarToTensor(rewriter, loc, posLen, posLenTp));
// Coordinates, copied over with:
// for (i = 0; i < crdLen; i++)
// buf[i][0] = crd0[i]; buf[i][1] = crd1[i];
auto buf =
genToMemref(rewriter, loc, op.getOutLevels()[retLen.size() - 1]);
auto buf = genToMemref(rewriter, loc, op.getOutLevels()[retLen.size()]);
auto crds0 = genCoordinatesCall(rewriter, loc, stt, adaptor.getTensor(),
cooStartLvl);
auto crds1 = genCoordinatesCall(rewriter, loc, stt, adaptor.getTensor(),
Expand All @@ -814,17 +807,25 @@ class SparseTensorDisassembleConverter
args[1] = one;
rewriter.create<memref::StoreOp>(loc, c1, buf, args);
rewriter.setInsertionPointAfter(forOp);
auto bufLenTp = op.getLvlLens().getTypes()[retLen.size() - 1];
auto bufLenTp = op.getLvlLens().getTypes()[retLen.size()];
retVal.push_back(buf);
retLen.push_back(genScalarToTensor(rewriter, loc, bufLen, bufLenTp));
}
// Get the values buffer last.
auto vals = genValuesCall(rewriter, loc, stt, adaptor.getTensor());
auto valLenTp = op.getValLen().getType();
auto valLen = linalg::createOrFoldDimOp(rewriter, loc, vals, 0);
retVal.push_back(vals);
retLen.push_back(genScalarToTensor(rewriter, loc, valLen, valLenTp));

// Converts MemRefs back to Tensors.
assert(retVal.size() + retLen.size() == op.getNumResults());
for (unsigned i = 0, sz = retVal.size(); i < sz; i++) {
auto tensor = rewriter.create<bufferization::ToTensorOp>(loc, retVal[i]);
retVal[i] =
rewriter.create<tensor::CastOp>(loc, op.getResultTypes()[i], tensor);
}

// Appends the actual memory length used in each buffer returned.
retVal.append(retLen.begin(), retLen.end());
rewriter.replaceOp(op, retVal);
Expand Down
2 changes: 1 addition & 1 deletion mlir/test/Dialect/SparseTensor/GPU/gpu_spgemm_lib.mlir
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Expand Up @@ -85,7 +85,7 @@
// CHECK: %[[VAL_a2:.*]] = bufferization.to_tensor %[[VAL_83]] : memref<?xf32>
// CHECK: %[[VAL_a3:.*]] = bufferization.to_tensor %[[VAL_81]] : memref<?xindex>
// CHECK: %[[VAL_a4:.*]] = bufferization.to_tensor %[[VAL_82]] : memref<?xindex>
// CHECK: %[[VAL_a5:.*]] = sparse_tensor.assemble %[[VAL_a2]], %[[VAL_a3]], %[[VAL_a4]] : tensor<?xf32>, tensor<?xindex>, tensor<?xindex> to tensor<8x8xf32, #{{.*}}>
// CHECK: %[[VAL_a5:.*]] = sparse_tensor.assemble (%[[VAL_a3]], %[[VAL_a4]]), %[[VAL_a2]] : (tensor<?xindex>, tensor<?xindex>), tensor<?xf32> to tensor<8x8xf32, #{{.*}}>
// CHECK: return %[[VAL_a5]] : tensor<8x8xf32, #{{.*}}>
// CHECK: }
func.func @matmulCSR(%A: tensor<8x8xf32, #CSR>,
Expand Down
66 changes: 33 additions & 33 deletions mlir/test/Dialect/SparseTensor/external.mlir
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Expand Up @@ -13,10 +13,10 @@ func.func @nop(%arg0: tensor<100xf32>) -> tensor<100xf32> {
// -----

// CHECK-LABEL: func.func @sparse_in(
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>,
// CHECK-SAME: %[[B:.*]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*]]: tensor<?xindex>) -> tensor<64x64xf32> {
// CHECK: %[[I:.*]] = sparse_tensor.assemble %[[A]], %[[B]], %[[C]]
// CHECK-SAME: %[[B:.*0]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*1]]: tensor<?xindex>,
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>) -> tensor<64x64xf32> {
// CHECK: %[[I:.*]] = sparse_tensor.assemble (%[[B]], %[[C]]), %[[A]]
// CHECK: %[[F:.*]] = call @_internal_sparse_in(%[[I]])
// CHECK: return %[[F]] : tensor<64x64xf32>
// CHECK: }
Expand All @@ -30,11 +30,11 @@ func.func @sparse_in(%arg0: tensor<64x64xf32, #sparse>) -> tensor<64x64xf32> {
// -----

// CHECK-LABEL: func.func @sparse_in2(
// CHECK-SAME: %[[X:.*]]: tensor<100xf32>,
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>,
// CHECK-SAME: %[[B:.*]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*]]: tensor<?xindex>) -> tensor<64x64xf32> {
// CHECK: %[[I:.*]] = sparse_tensor.assemble %[[A]], %[[B]], %[[C]]
// CHECK-SAME: %[[X:.*0]]: tensor<100xf32>,
// CHECK-SAME: %[[B:.*1]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*2]]: tensor<?xindex>,
// CHECK-SAME: %[[A:.*3]]: tensor<?xf32>) -> tensor<64x64xf32> {
// CHECK: %[[I:.*]] = sparse_tensor.assemble (%[[B]], %[[C]]), %[[A]]
// CHECK: %[[F:.*]] = call @_internal_sparse_in2(%[[X]], %[[I]])
// CHECK: return %[[F]] : tensor<64x64xf32>
// CHECK: }
Expand All @@ -48,10 +48,10 @@ func.func @sparse_in2(%arg0: tensor<100xf32>, %arg1: tensor<64x64xf32, #sparse>)
// -----

// CHECK-LABEL: func.func @sparse_out(
// CHECK-SAME: %[[X:.*]]: tensor<64x64xf32>,
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>,
// CHECK-SAME: %[[B:.*]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*]]: tensor<?xindex>) -> (tensor<?xf32>, tensor<?xindex>, tensor<?xindex>) {
// CHECK-SAME: %[[X:.*0]]: tensor<64x64xf32>,
// CHECK-SAME: %[[B:.*1]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*2]]: tensor<?xindex>,
// CHECK-SAME: %[[A:.*3]]: tensor<?xf32>)
// CHECK: %[[F:.*]] = call @_internal_sparse_out(%[[X]])
// CHECK: sparse_tensor.disassemble %[[F]]
// CHECK: return
Expand All @@ -66,10 +66,10 @@ func.func @sparse_out(%arg0: tensor<64x64xf32>) -> tensor<64x64xf32, #sparse> {
// -----

// CHECK-LABEL: func.func @sparse_out2(
// CHECK-SAME: %[[X:.*]]: tensor<64x64xf32>,
// CHECK-SAME: %[[A:.*]]: tensor<?xf32>,
// CHECK-SAME: %[[B:.*]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*]]: tensor<?xindex>) -> (tensor<64x64xf32>, tensor<?xf32>, tensor<?xindex>, tensor<?xindex>) {
// CHECK-SAME: %[[X:.*0]]: tensor<64x64xf32>,
// CHECK-SAME: %[[B:.*1]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*2]]: tensor<?xindex>,
// CHECK-SAME: %[[A:.*3]]: tensor<?xf32>)
// CHECK: %[[F:.*]]:2 = call @_internal_sparse_out2(%[[X]])
// CHECK: sparse_tensor.disassemble %[[F]]#1
// CHECK: return %[[F]]#0
Expand All @@ -84,13 +84,13 @@ func.func @sparse_out2(%arg0: tensor<64x64xf32>) -> (tensor<64x64xf32>, tensor<6
// -----

// CHECK-LABEL: func.func @sparse_inout(
// CHECK-SAME: %[[A:.*0]]: tensor<?xf32>,
// CHECK-SAME: %[[B:.*1]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*2]]: tensor<?xindex>,
// CHECK-SAME: %[[D:.*3]]: tensor<?xf32>,
// CHECK-SAME: %[[E:.*4]]: tensor<?xindex>,
// CHECK-SAME: %[[F:.*5]]: tensor<?xindex>) -> (tensor<?xf32>, tensor<?xindex>, tensor<?xindex>) {
// CHECK: %[[I:.*]] = sparse_tensor.assemble %[[A]], %[[B]], %[[C]]
// CHECK-SAME: %[[B:.*0]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*1]]: tensor<?xindex>,
// CHECK-SAME: %[[A:.*2]]: tensor<?xf32>,
// CHECK-SAME: %[[E:.*3]]: tensor<?xindex>,
// CHECK-SAME: %[[F:.*4]]: tensor<?xindex>,
// CHECK-SAME: %[[D:.*5]]: tensor<?xf32>)
// CHECK: %[[I:.*]] = sparse_tensor.assemble (%[[B]], %[[C]]), %[[A]]
// CHECK: %[[F:.*]] = call @_internal_sparse_inout(%[[I]])
// CHECK: sparse_tensor.disassemble %[[F]]
// CHECK: return
Expand All @@ -104,15 +104,15 @@ func.func @sparse_inout(%arg0: tensor<64x64xf32, #sparse>) -> tensor<64x64xf32,
// -----

// CHECK-LABEL: func.func @sparse_inout_coo_soa(
// CHECK-SAME: %[[A:.*0]]: tensor<?xf32>,
// CHECK-SAME: %[[B:.*1]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*2]]: tensor<?xindex>,
// CHECK-SAME: %[[D:.*3]]: tensor<?xindex>,
// CHECK-SAME: %[[E:.*4]]: tensor<?xf32>,
// CHECK-SAME: %[[F:.*5]]: tensor<?xindex>,
// CHECK-SAME: %[[G:.*6]]: tensor<?xindex>,
// CHECK-SAME: %[[H:.*7]]: tensor<?xindex>) -> (tensor<?xf32>, tensor<?xindex>, tensor<?xindex>, tensor<?xindex>) {
// CHECK: %[[I:.*]] = sparse_tensor.assemble %[[A]], %[[B]], %[[C]], %[[D]]
// CHECK-SAME: %[[B:.*0]]: tensor<?xindex>,
// CHECK-SAME: %[[C:.*1]]: tensor<?xindex>,
// CHECK-SAME: %[[D:.*2]]: tensor<?xindex>,
// CHECK-SAME: %[[A:.*3]]: tensor<?xf32>,
// CHECK-SAME: %[[F:.*4]]: tensor<?xindex>,
// CHECK-SAME: %[[G:.*5]]: tensor<?xindex>,
// CHECK-SAME: %[[H:.*6]]: tensor<?xindex>,
// CHECK-SAME: %[[E:.*7]]: tensor<?xf32>)
// CHECK: %[[I:.*]] = sparse_tensor.assemble (%[[B]], %[[C]], %[[D]]), %[[A]]
// CHECK: %[[F:.*]] = call @_internal_sparse_inout_coo_soa(%[[I]])
// CHECK: sparse_tensor.disassemble %[[F]]
// CHECK: return
Expand Down
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