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Revert "[mlir][sparse] fix sparse tensor rewriting patterns that do not propagate sparse tensor SSA properly."

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This reverts commit 70508b614e.

This change depends on a reverted change that broke the windows mlir buildbot; reverting to bring remaining mlir bots to green
This commit is contained in:
Stella Stamenova
2022-11-07 09:00:08 -08:00
parent 058f727a98
commit ec224e3b68
8 changed files with 120 additions and 181 deletions
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5
mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
View File

@@ -603,12 +603,9 @@ void ForeachOp::build(
std::fill_n(std::back_inserter(blockArgTypes), rank, builder.getIndexType());
// Followed by one value.
blockArgTypes.push_back(rtp.getElementType());
// Followed by reduction variable.
blockArgTypes.append(initArgs.getTypes().begin(), initArgs.getTypes().end());
SmallVector<Location, 4> blockArgLocs;
std::fill_n(std::back_inserter(blockArgLocs), blockArgTypes.size(),
tensor.getLoc());
std::fill_n(std::back_inserter(blockArgLocs), rank + 1, tensor.getLoc());
OpBuilder::InsertionGuard guard(builder);
auto &region = *result.regions.front();

3
mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp
View File

@@ -880,9 +880,6 @@ Value mlir::sparse_tensor::genValueForDense(OpBuilder &builder, Location loc,
return val;
}
// FIXME:
// 1. Dense tensors loop should be generated by loop emitter.
// 2. Support reduction variables to propagate SSA chains properly.
void mlir::sparse_tensor::genDenseTensorOrSparseConstantIterLoop(
OpBuilder &builder, Location loc, Value src, unsigned rank,
function_ref<void(OpBuilder &, Location, Value, ValueRange)> bodyBuilder) {

119
mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
View File

@@ -356,8 +356,8 @@ public:
RankedTensorType cooTp = getUnorderedCOOFromType(dstTp);
auto cooBuffer =
rewriter.create<AllocTensorOp>(loc, cooTp, dstDynSizes).getResult();
ForeachOp foreachOp = rewriter.create<ForeachOp>(
loc, srcTensor, cooBuffer,
rewriter.create<ForeachOp>(
loc, srcTensor, llvm::None,
[&](OpBuilder &builder, Location loc, ValueRange args, Value v,
ValueRange reduc) {
SmallVector<Value, 4> srcIndices;
@@ -368,11 +368,11 @@ public:
}
translateIndicesArray(builder, loc, op.getReassociationIndices(),
srcIndices, srcSizes, dstSizes, dstIndices);
auto t = builder.create<InsertOp>(loc, v, reduc.front(), dstIndices);
builder.create<sparse_tensor::YieldOp>(loc, t);
builder.create<InsertOp>(loc, v, cooBuffer, dstIndices);
builder.create<sparse_tensor::YieldOp>(loc);
});
auto t = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
rewriter.replaceOpWithNewOp<ConvertOp>(op, dstTp, t);
rewriter.replaceOpWithNewOp<ConvertOp>(op, dstTp, cooBuffer);
return success();
}
};
@@ -442,14 +442,13 @@ struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
rewriter.create<AllocTensorOp>(loc, cooTp, ValueRange()).getResult();
Value offset = constantIndex(rewriter, loc, 0);
ForeachOp foreachOp;
for (Value input : op.getInputs()) {
// Builds the indexing map.
// Build a for op for each input tensor to append new values into the
// output tensor.
foreachOp = rewriter.create<ForeachOp>(
loc, input, cooBuffer,
rewriter.create<ForeachOp>(
loc, input, llvm::None,
[&](OpBuilder &builder, Location loc, ValueRange args, Value v,
ValueRange reduc) {
SmallVector<Value, 4> indices;
@@ -462,8 +461,8 @@ struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
idx = builder.create<arith::AddIOp>(loc, idx, offset);
indices.push_back(idx);
}
auto t = builder.create<InsertOp>(loc, v, reduc.front(), indices);
builder.create<sparse_tensor::YieldOp>(loc, t);
builder.create<InsertOp>(loc, v, cooBuffer, indices);
builder.create<sparse_tensor::YieldOp>(loc);
});
// Accumulates the offset. Note that only static-shaped inputs are allowed
// by concatenate op verifier, which saves us from computing the offset
@@ -472,10 +471,7 @@ struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
assert(!ShapedType::isDynamic(d));
offset = rewriter.create<arith::AddIOp>(loc, offset,
constantIndex(rewriter, loc, d));
cooBuffer = foreachOp.getResult(0);
}
cooBuffer = rewriter.create<LoadOp>(loc, cooBuffer, true);
rewriter.replaceOpWithNewOp<ConvertOp>(op, rtp, cooBuffer);
return success();
}
@@ -606,8 +602,8 @@ private:
srcTp = getUnorderedCOOFromType(srcTp);
tmpCoo =
rewriter.create<AllocTensorOp>(loc, srcTp, dynSrcSizes).getResult();
auto foreachOp = rewriter.create<ForeachOp>(
loc, src, tmpCoo,
rewriter.create<ForeachOp>(
loc, src, llvm::None,
[&](OpBuilder &builder, Location loc, ValueRange args, Value v,
ValueRange reduc) {
SmallVector<Value, 4> indices;
@@ -615,10 +611,10 @@ private:
uint64_t dim = toStoredDim(encSrc, i);
indices.push_back(args[dim]);
}
auto t = builder.create<InsertOp>(loc, v, reduc.front(), indices);
builder.create<sparse_tensor::YieldOp>(loc, t);
builder.create<InsertOp>(loc, v, tmpCoo, indices);
builder.create<sparse_tensor::YieldOp>(loc);
});
src = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
src = tmpCoo;
}
// Sort the COO tensor so that its elements are ordered via increasing
@@ -657,31 +653,29 @@ private:
getDynamicSizes(dstTp, srcSizes, dynDstSizes);
Value dst =
rewriter.create<AllocTensorOp>(loc, dstTp, dynDstSizes).getResult();
auto foreachOp = rewriter.create<ForeachOp>(
loc, src, dst,
[&](OpBuilder &builder, Location loc, ValueRange args, Value v,
ValueRange reduc) {
SmallVector<Value, 4> indices;
for (int64_t i = 0, e = srcTp.getRank(); i < e; i++) {
uint64_t dim = toStoredDim(encDst, i);
indices.push_back(args[dim]);
}
auto t = builder.create<InsertOp>(loc, v, reduc.front(), indices);
builder.create<sparse_tensor::YieldOp>(loc, t);
});
rewriter.create<ForeachOp>(loc, src, llvm::None,
[&](OpBuilder &builder, Location loc,
ValueRange args, Value v, ValueRange reduc) {
SmallVector<Value, 4> indices;
for (int64_t i = 0, e = srcTp.getRank(); i < e;
i++) {
uint64_t dim = toStoredDim(encDst, i);
indices.push_back(args[dim]);
}
builder.create<InsertOp>(loc, v, dst, indices);
builder.create<sparse_tensor::YieldOp>(loc);
});
// Release the temporary COO if it is created. Note that tmpCoo is
// invalidated due to foreach and updated to src.
// Release the temporary COO if it is created.
if (tmpCoo)
rewriter.create<DeallocTensorOp>(loc, src);
rewriter.create<DeallocTensorOp>(loc, tmpCoo);
// Directly replace op with dst results in bufferization error message
// "sparse tensor allocation should not escape function".
// As such, we insert a trivial tensor convert which will be removed by
// codegen.
rewriter.setInsertionPointAfter(op);
auto t = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
rewriter.replaceOpWithNewOp<ConvertOp>(op, dstTp, t);
rewriter.replaceOpWithNewOp<ConvertOp>(op, dstTp, dst);
return success();
}
};
@@ -700,8 +694,6 @@ public:
int64_t rank = rtp.getRank();
auto enc = getSparseTensorEncoding(rtp);
SmallVector<Value> reduc = op.getInitArgs();
// 1. Generates loop for the sparse input.
SparseTensorLoopEmitter loopEmitter(ValueRange{input});
loopEmitter.initializeLoopEmit(rewriter, loc);
@@ -709,9 +701,7 @@ public:
// TODO: provide utility function for loop sequences that only contains
// one for loop?
loopEmitter.enterNewLoopSeq(rewriter, loc, 0, static_cast<size_t>(i));
// Note that reduc will be taken care of by loop emitter and get updated
// in place.
loopEmitter.enterLoopOverTensorAtDim(rewriter, loc, 0, i, reduc);
loopEmitter.enterLoopOverTensorAtDim(rewriter, loc, 0, i);
}
SmallVector<Value, 4> coords;
@@ -726,7 +716,15 @@ public:
: rewriter.create<memref::LoadOp>(loc, vals, coords);
// 2. Inline the block in the foreach operator.
Block::iterator inlinePos = rewriter.getInsertionPoint();
Block *srcBlock = op.getBody();
// Remove sparse_tensor.yield.
rewriter.eraseOp(srcBlock->getTerminator());
for (int64_t i = 0; i < rank; i++) {
loopEmitter.exitCurrentLoop(rewriter, loc);
loopEmitter.exitCurrentLoopSeq();
}
SmallVector<Value, 4> args;
// Remap coordinates.
@@ -736,33 +734,11 @@ public:
}
// Remap value.
args.push_back(val);
// Remap reduction variables.
args.append(reduc);
// Remove sparse_tensor.yield.
SmallVector<Value> reducValue = srcBlock->getTerminator()->getOperands();
rewriter.eraseOp(srcBlock->getTerminator());
// Inline body.
if (!reducValue.empty()) {
rewriter.mergeBlocks(srcBlock, rewriter.getBlock(), args);
} else {
// This is annoying, since scf.for inserts a implicit yield op when
// there is no reduction variable upon creation, in this case we need to
// merge the block *before* the yield op.
rewriter.mergeBlockBefore(srcBlock, &*rewriter.getInsertionPoint(), args);
}
for (int64_t i = 0; i < rank; i++) {
// Link the reduction chain. Note that loop emitter update the reducValue
// in place.
loopEmitter.exitCurrentLoop(rewriter, loc, reducValue);
loopEmitter.exitCurrentLoopSeq();
}
// Replace the foreach operator with the value returned by the outtermost
// for loop.
rewriter.replaceOp(op, reducValue);
rewriter.mergeBlockBefore(srcBlock, &*inlinePos, args);
// delete the foreach operator.
rewriter.eraseOp(op);
return success();
}
};
@@ -825,8 +801,7 @@ struct NewRewriter : public OpRewritePattern<NewOp> {
.getResult(0);
Type eltTp = dstTp.getElementType();
Value value = genAllocaScalar(rewriter, loc, eltTp);
scf::ForOp forOp = rewriter.create<scf::ForOp>(loc, c0, nnz, c1,
ArrayRef<Value>(cooBuffer));
scf::ForOp forOp = rewriter.create<scf::ForOp>(loc, c0, nnz, c1);
rewriter.setInsertionPointToStart(forOp.getBody());
SmallString<18> getNextFuncName{"getSparseTensorReaderNext",
@@ -841,17 +816,13 @@ struct NewRewriter : public OpRewritePattern<NewOp> {
loc, indices, constantIndex(rewriter, loc, i)));
}
Value v = rewriter.create<memref::LoadOp>(loc, value);
auto t = rewriter.create<InsertOp>(loc, v, forOp.getRegionIterArg(0),
indicesArray);
rewriter.create<scf::YieldOp>(loc, ArrayRef<Value>(t));
rewriter.create<InsertOp>(loc, v, cooBuffer, indicesArray);
rewriter.setInsertionPointAfter(forOp);
// Link SSA chain.
cooBuffer = forOp.getResult(0);
// Release the sparse tensor reader.
createFuncCall(rewriter, loc, "delSparseTensorReader", {}, {reader},
EmitCInterface::Off);
cooBuffer = rewriter.create<LoadOp>(loc, cooBuffer, true);
Value newOp = rewriter.replaceOpWithNewOp<ConvertOp>(op, dstTp, cooBuffer);
// Release the unordered COO tensor buffer.

22
mlir/test/Dialect/SparseTensor/convert_dense2sparse.mlir
View File

@@ -116,7 +116,6 @@ func.func @sparse_convert_complex(%arg0: tensor<100xcomplex<f64>>) -> tensor<100
// CHECK-RWT: %[[V:.*]] = tensor.extract %[[A]]{{\[}}%[[FI]], %[[FJ]]] : tensor<2x4xf64>
// CHECK-RWT: %[[NZ:.*]] = arith.cmpf une, %[[V]], %[[F0]] : f64
// CHECK-RWT: scf.if %[[NZ]] {
// // FIXME: the SSA chain is broken here!
// CHECK-RWT: %{{.*}} = sparse_tensor.insert %[[V]] into %[[COO]]{{\[}}%[[FI]], %[[FJ]]]
// CHECK-RWT: }
// CHECK-RWT: }
@@ -127,13 +126,11 @@ func.func @sparse_convert_complex(%arg0: tensor<100xcomplex<f64>>) -> tensor<100
// CHECK-RWT: %[[V2:.*]] = sparse_tensor.values %[[COO]]
// CHECK-RWT: sparse_tensor.sort %[[NNZ]], %[[I0]], %[[I1]] jointly %[[V2]]
// CHECK-RWT: %[[DST:.*]] = bufferization.alloc_tensor()
// CHECK-RWT: %[[NEW_T:.*]] = sparse_tensor.foreach in %[[COO]] init(%[[DST]])
// CHECK-RWT: ^bb0(%[[FI0:.*]]: index, %[[FI1:.*]]: index, %[[FV:.*]]: f64, %[[R0:.*]]: tensor
// CHECK-RWT: %[[RET:.*]] = sparse_tensor.insert %[[FV]] into %[[R0]]{{\[}}%[[FI0]], %[[FI1]]]
// CHECK-RWT: sparse_tensor.yield %[[RET]]
// CHECK-RWT: sparse_tensor.foreach in %[[COO]]
// CHECK-RWT: ^bb0(%[[FI0:.*]]: index, %[[FI1:.*]]: index, %[[FV:.*]]: f64):
// CHECK-RWT: sparse_tensor.insert %[[FV]] into %[[DST]]{{\[}}%[[FI0]], %[[FI1]]]
// CHECK-RWT: }
// CHECK-RWT: %[[NT:.*]] = sparse_tensor.load %[[NEW_T]] hasInserts
// CHECK-RWT: %[[R:.*]] = sparse_tensor.convert %[[NT]]
// CHECK-RWT: %[[R:.*]] = sparse_tensor.convert %[[DST]]
// CHECK-RWT: bufferization.dealloc_tensor %[[COO]]
// CHECK-RWT: return %[[R]] : tensor<2x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>
func.func @sparse_convert_2d(%arg0: tensor<2x4xf64>) -> tensor<2x4xf64, #CSR> {
@@ -182,7 +179,6 @@ func.func @sparse_convert_2d(%arg0: tensor<2x4xf64>) -> tensor<2x4xf64, #CSR> {
// CHECK-RWT: %[[I1r:.*]] = tensor.extract %[[SI]]{{\[}}%[[FI]], %[[C1]]] : tensor<2x2xi64>
// CHECK-RWT: %[[I1:.*]] = arith.index_cast %[[I1r]] : i64 to index
// CHECK-RWT: %[[V:.*]] = tensor.extract %[[SV]]{{\[}}%[[FI]]] : tensor<2xf32>
// // FIXME: the SSA chain is broken here!
// CHECK-RWT: sparse_tensor.insert %[[V]] into %[[COO]]{{\[}}%[[I0]], %[[I1]]]
// CHECK-RWT: }
// CHECK-RWT: %[[TI0:.*]] = sparse_tensor.indices %[[COO]] {dimension = 0 : index}
@@ -191,13 +187,11 @@ func.func @sparse_convert_2d(%arg0: tensor<2x4xf64>) -> tensor<2x4xf64, #CSR> {
// CHECK-RWT: %[[TV:.*]] = sparse_tensor.values %[[COO]]
// CHECK-RWT: sparse_tensor.sort %[[NNZ]], %[[TI0]], %[[TI1]] jointly %[[TV]]
// CHECK-RWT: %[[DST:.*]] = bufferization.alloc_tensor()
// CHECK-RWT: %[[RET:.*]] = sparse_tensor.foreach in %[[COO]] init(%[[DST]])
// CHECK-RWT: ^bb0(%[[F2I0:.*]]: index, %[[F2I1:.*]]: index, %[[F2V:.*]]: f32, %[[R0:.*]]: tensor
// CHECK-RWT: %[[NEW_T:.*]] = sparse_tensor.insert %[[F2V]] into %[[R0]]{{\[}}%[[F2I0]], %[[F2I1]]]
// CHECK-RWT: sparse_tensor.yield %[[NEW_T]]
// CHECK-RWT: sparse_tensor.foreach in %[[COO]]
// CHECK-RWT: ^bb0(%[[F2I0:.*]]: index, %[[F2I1:.*]]: index, %[[F2V:.*]]: f32):
// CHECK-RWT: sparse_tensor.insert %[[F2V]] into %[[DST]]{{\[}}%[[F2I0]], %[[F2I1]]]
// CHECK-RWT: }
// CHECK-RWT: %[[T:.*]] = sparse_tensor.load %[[RET]] hasInserts
// CHECK-RWT: %[[R:.*]] = sparse_tensor.convert %[[T]]
// CHECK-RWT: %[[R:.*]] = sparse_tensor.convert %[[DST]]
// CHECK-RWT: bufferization.dealloc_tensor %[[COO]]
// CHECK-RWT: return %[[R]] : tensor<8x7xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>
func.func @sparse_constant() -> tensor<8x7xf32, #CSR>{

10
mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
View File

@@ -94,13 +94,11 @@ func.func @sparse_convert_1d_ss(%arg0: tensor<?xf32, #SparseVector64>) -> tensor
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[A]]
// CHECK-RWT: sparse_tensor.sort %[[NNZ]], %[[I0]] jointly %[[V]]
// CHECK-RWT: %[[DST:.*]] = bufferization.alloc_tensor(%[[D]])
// CHECK-RWT: %[[RET:.*]] = sparse_tensor.foreach in %[[A]] init(%[[DST]])
// CHECK-RWT: ^bb0(%[[FI2:.*]]: index, %[[FV2:.*]]: f32, %[[T:.*]]: tensor<?xf32,
// CHECK-RWT: %[[I:.*]] = sparse_tensor.insert %[[FV2]] into %[[T]]{{\[}}%[[FI2]]]
// CHECK-RWT: sparse_tensor.yield %[[I]]
// CHECK-RWT: sparse_tensor.foreach in %[[A]]
// CHECK-RWT: ^bb0(%[[FI2:.*]]: index, %[[FV2:.*]]: f32):
// CHECK-RWT: sparse_tensor.insert %[[FV2]] into %[[DST]]{{\[}}%[[FI2]]]
// CHECK-RWT: }
// CHECK-RWT: %[[T:.*]] = sparse_tensor.load %[[RET]] hasInserts
// CHECK-RWT: %[[R:.*]] = sparse_tensor.convert %[[T]]
// CHECK-RWT: %[[R:.*]] = sparse_tensor.convert %[[DST]]
// CHECK-RWT: return %[[R]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 32, indexBitWidth = 32 }>>
func.func @sparse_convert(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32, #SparseVector32> {
%0 = sparse_tensor.convert %arg0 : tensor<?xf32, #SparseVector64> to tensor<?xf32, #SparseVector32>

11
mlir/test/Dialect/SparseTensor/rewriting_for_codegen.mlir
View File

@@ -18,19 +18,18 @@
// CHECK: %[[T:.*]] = bufferization.alloc_tensor(%[[D0]], %[[D1]])
// CHECK: %[[N:.*]] = call @getSparseTensorReaderNNZ(%[[R]])
// CHECK: %[[VB:.*]] = memref.alloca()
// CHECK: %[[T2:.*]] = scf.for %{{.*}} = %[[C0]] to %[[N]] step %[[C1]] iter_args(%[[A2:.*]] = %[[T]])
// CHECK: scf.for %{{.*}} = %[[C0]] to %[[N]] step %[[C1]] {
// CHECK: func.call @getSparseTensorReaderNextF32(%[[R]], %[[DS]], %[[VB]])
// CHECK: %[[E0:.*]] = memref.load %[[DS]]{{\[}}%[[C0]]]
// CHECK: %[[E1:.*]] = memref.load %[[DS]]{{\[}}%[[C1]]]
// CHECK: %[[V:.*]] = memref.load %[[VB]][]
// CHECK: %[[T1:.*]] = sparse_tensor.insert %[[V]] into %[[A2]]{{\[}}%[[E0]], %[[E1]]]
// CHECK: scf.yield %[[T1]]
// CHECK: sparse_tensor.insert %[[V]] into %[[T]]{{\[}}%[[E0]], %[[E1]]]
// CHECK: }
// CHECK: call @delSparseTensorReader(%[[R]])
// CHECK: %[[T3:.*]] = sparse_tensor.load %[[T2]] hasInserts
// CHECK: %[[R:.*]] = sparse_tensor.convert %[[T3]]
// CHECK: bufferization.dealloc_tensor %[[T3]]
// CHECK: %[[R:.*]] = sparse_tensor.convert %[[T]]
// CHECK: bufferization.dealloc_tensor %[[T]]
// CHECK: return %[[R]]
// CHECK: }
func.func @sparse_new(%arg0: !llvm.ptr<i8>) -> tensor<?x?xf32, #CSR> {
%0 = sparse_tensor.new %arg0 : !llvm.ptr<i8> to tensor<?x?xf32, #CSR>
return %0 : tensor<?x?xf32, #CSR>

27
mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir
View File

@@ -19,18 +19,16 @@
// CHECK: %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] : tensor<2x4xf64, #sparse_tensor
// CHECK: %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
// CHECK: %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
// CHECK: %[[RET_1:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] iter_args(%[[A0:.*]] = %[[TMP_0]])
// CHECK: scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] {
// CHECK: %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK-DAG: %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK-DAG: %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
// CHECK: %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
// CHECK: %[[RET_4:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A1:.*]] = %[[A0]])
// CHECK: scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] {
// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_4]][%[[TMP_arg4]]] : memref<?xindex>
// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_5]][%[[TMP_arg4]]] : memref<?xf64>
// CHECK: %[[NEW_1:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A1]][%[[TMP_23]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
// CHECK: scf.yield %[[NEW_1]]
// CHECK: sparse_tensor.insert %[[TMP_28]] into %[[TMP_0]][%[[TMP_23]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
// CHECK: }
// CHECK: scf.yield %[[RET_4]]
// CHECK: }
// CHECK: %[[TMP_8:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
// CHECK: %[[TMP_9:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
@@ -39,19 +37,17 @@
// CHECK: %[[TMP_12:.*]] = sparse_tensor.values %[[TMP_arg1]] : tensor<3x4xf64, #sparse_tensor
// CHECK: %[[TMP_13:.*]] = memref.load %[[TMP_8]][%[[TMP_c0]]] : memref<?xindex>
// CHECK: %[[TMP_14:.*]] = memref.load %[[TMP_8]][%[[TMP_c1]]] : memref<?xindex>
// CHECK: %[[RET_2:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]] iter_args(%[[A2:.*]] = %[[RET_1]])
// CHECK: scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]] {
// CHECK: %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK-DAG: %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK-DAG: %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
// CHECK: %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
// CHECK: %[[RET_5:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A3:.*]] = %[[A2]])
// CHECK: scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] {
// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_11]][%[[TMP_arg4]]] : memref<?xindex>
// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_12]][%[[TMP_arg4]]] : memref<?xf64>
// CHECK: %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c2]] : index
// CHECK: %[[NEW_2:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A3]][%[[TMP_29]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
// CHECK: scf.yield %[[NEW_2]]
// CHECK: sparse_tensor.insert %[[TMP_28]] into %[[TMP_0]][%[[TMP_29]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
// CHECK: }
// CHECK: scf.yield %[[RET_5]]
// CHECK: }
// CHECK: %[[TMP_15:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
// CHECK: %[[TMP_16:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
@@ -60,22 +56,19 @@
// CHECK: %[[TMP_19:.*]] = sparse_tensor.values %[[TMP_arg2]] : tensor<4x4xf64, #sparse_tensor
// CHECK: %[[TMP_20:.*]] = memref.load %[[TMP_15]][%[[TMP_c0]]] : memref<?xindex>
// CHECK: %[[TMP_21:.*]] = memref.load %[[TMP_15]][%[[TMP_c1]]] : memref<?xindex>
// CHECK: %[[RET_3:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]] iter_args(%[[A4:.*]] = %[[RET_2]])
// CHECK: scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]] {
// CHECK: %[[TMP_23:.*]] = memref.load %[[TMP_16]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK: %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK: %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
// CHECK: %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>
// CHECK: %[[RET_6:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A5:.*]] = %[[A4]])
// CHECK: scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] {
// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_18]][%[[TMP_arg4]]] : memref<?xindex>
// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_19]][%[[TMP_arg4]]] : memref<?xf64>
// CHECK: %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c5]] : index
// CHECK: %[[NEW_3:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A5]][%[[TMP_29]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
// CHECK: scf.yield %[[NEW_3]]
// CHECK: sparse_tensor.insert %[[TMP_28]] into %[[TMP_0]][%[[TMP_29]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
// CHECK: }
// CHECK: scf.yield %[[RET_6]]
// CHECK: }
// CHECK: %[[TMP_23:.*]] = sparse_tensor.load %[[RET_3]] hasInserts
// CHECK: %[[TMP_22:.*]] = sparse_tensor.convert %[[TMP_23]] : tensor<9x4xf64, #sparse_tensor
// CHECK: %[[TMP_22:.*]] = sparse_tensor.convert %[[TMP_0]] : tensor<9x4xf64, #sparse_tensor
// CHECK: return %[[TMP_22]] : tensor<9x4xf64, #sparse_tensor
func.func @concat_sparse_sparse(%arg0: tensor<2x4xf64, #DCSR>,
%arg1: tensor<3x4xf64, #DCSR>,

104
mlir/test/Dialect/SparseTensor/sparse_reshape.mlir
View File

@@ -52,16 +52,14 @@
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[S]]
// CHECK-RWT: %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK-RWT: %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK-RWT: %[[RET:.*]] = scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] iter_args(%[[R:.*]] = %[[B]])
// CHECK-RWT: scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] {
// CHECK-RWT: %[[SI:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT: %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[I]]] : memref<?xf64>
// CHECK-RWT: %[[DI0:.*]] = arith.divui %[[SI]], %[[C10]] : index
// CHECK-RWT: %[[DI1:.*]] = arith.remui %[[SI]], %[[C10]] : index
// CHECK-RWT: %[[NT:.*]] = sparse_tensor.insert %[[SV]] into %[[R]]{{\[}}%[[DI0]], %[[DI1]]]
// CHECK-RWT: scf.yield %[[NT:.*]]
// CHECK-RWT: sparse_tensor.insert %[[SV]] into %[[B]]{{\[}}%[[DI0]], %[[DI1]]]
// CHECK-RWT: }
// CHECK-RWT: %[[NT1:.*]] = sparse_tensor.load %[[RET]] hasInserts
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[NT1]]
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[B]]
// CHECK-RWT: return %[[T]] : tensor<10x10xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
//
func.func @sparse_expand(%arg0: tensor<100xf64, #SparseVector>) -> tensor<10x10xf64, #SparseMatrix> {
@@ -113,28 +111,25 @@ func.func @sparse_expand(%arg0: tensor<100xf64, #SparseVector>) -> tensor<10x10x
// CHECK-RWT: %[[B:.*]] = bufferization.alloc_tensor()
// CHECK-RWT: %[[P0:.*]] = sparse_tensor.pointers %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[I0:.*]] = sparse_tensor.indices %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[P1:.*]] = sparse_tensor.pointers %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[I1:.*]] = sparse_tensor.indices %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[S]]
// CHECK-RWT: %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK-RWT: %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK-RWT: %[[RET:.*]] = scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] iter_args(%[[A0:.*]] = %[[B]])
// CHECK-RWT: %[[SI0:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT-DAG: %[[S1:.*]] = memref.load %[[P1]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT-DAG: %[[PE1:.*]] = arith.addi %[[I]], %[[C1]] : index
// CHECK-RWT: %[[E1:.*]] = memref.load %[[P1]]{{\[}}%[[PE1]]] : memref<?xindex>
// CHECK-RWT: %[[RET_1:.*]] = scf.for %[[J:.*]] = %[[S1]] to %[[E1]] step %[[C1]] iter_args(%[[A1:.*]] = %[[A0]])
// CHECK-RWT: %[[SI1:.*]] = memref.load %[[I1]]{{\[}}%[[J]]] : memref<?xindex>
// CHECK-RWT: %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[J]]] : memref<?xf64>
// CHECK-RWT: %[[T:.*]] = arith.muli %[[SI0]], %[[C10]] : index
// CHECK-RWT: %[[DI:.*]] = arith.addi %[[T]], %[[SI1]] : index
// CHECK-RWT: %[[R1:.*]] = sparse_tensor.insert %[[SV]] into %[[A1]]{{\[}}%[[DI]]]
// CHECK-RWT scf.yield %[[R1]]
// CHECK-RWT }
// CHECK-RWT scf.yield %[[RET_1]]
// CHECK-RWT: }
// CHECK-RWT: %[[NT1:.*]] = sparse_tensor.load %[[RET]] hasInserts
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[NT1]]
// CHECK-RWT: %[[P1:.*]] = sparse_tensor.pointers %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[I1:.*]] = sparse_tensor.indices %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[S]]
// CHECK-RWT: %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK-RWT: %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK-RWT: scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] {
// CHECK-RWT: %[[SI0:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT-DAG: %[[S1:.*]] = memref.load %[[P1]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT-DAG: %[[PE1:.*]] = arith.addi %[[I]], %[[C1]] : index
// CHECK-RWT: %[[E1:.*]] = memref.load %[[P1]]{{\[}}%[[PE1]]] : memref<?xindex>
// CHECK-RWT: scf.for %[[J:.*]] = %[[S1]] to %[[E1]] step %[[C1]] {
// CHECK-RWT: %[[SI1:.*]] = memref.load %[[I1]]{{\[}}%[[J]]] : memref<?xindex>
// CHECK-RWT: %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[J]]] : memref<?xf64>
// CHECK-RWT: %[[T:.*]] = arith.muli %[[SI0]], %[[C10]] : index
// CHECK-RWT: %[[DI:.*]] = arith.addi %[[T]], %[[SI1]] : index
// CHECK-RWT: sparse_tensor.insert %[[SV]] into %[[B]]{{\[}}%[[DI]]]
// CHECK-RWT }
// CHECK-RWT: }
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[B]]
// CHECK-RWT: return %[[T]] : tensor<100xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
//
func.func @sparse_collapse(%arg0: tensor<10x10xf64, #SparseMatrix>) -> tensor<100xf64, #SparseVector> {
@@ -196,7 +191,7 @@ func.func @sparse_collapse(%arg0: tensor<10x10xf64, #SparseMatrix>) -> tensor<10
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[S]]
// CHECK-RWT: %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK-RWT: %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK-RWT: %[[RET:.*]] = scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] iter_args(%[[R:.*]] = %[[B]])
// CHECK-RWT: scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] {
// CHECK-RWT: %[[SI:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT: %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[I]]] : memref<?xf64>
// CHECK-RWT: %[[T1:.*]] = arith.muli %[[DD0]], %[[C10]] : index
@@ -205,11 +200,9 @@ func.func @sparse_collapse(%arg0: tensor<10x10xf64, #SparseMatrix>) -> tensor<10
// CHECK-RWT: %[[T3:.*]] = arith.remui %[[SI]], %[[T2]] : index
// CHECK-RWT: %[[T4:.*]] = arith.divui %[[T2]], %[[C10]] : index
// CHECK-RWT: %[[DI1:.*]] = arith.divui %[[T3]], %[[T4]] : index
// CHECK-RWT: %[[NT:.*]] = sparse_tensor.insert %[[SV]] into %[[R]]{{\[}}%[[DI0]], %[[DI1]]]
// CHECK-RWT: scf.yield %[[NT]]
// CHECK-RWT: sparse_tensor.insert %[[SV]] into %[[B]]{{\[}}%[[DI0]], %[[DI1]]]
// CHECK-RWT: }
// CHECK-RWT: %[[NT1:.*]] = sparse_tensor.load %[[RET]] hasInserts
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[NT1]]
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[B]]
// CHECK-RWT: return %[[T]] : tensor<?x10xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
//
func.func @dynamic_sparse_expand(%arg0: tensor<?xf64, #SparseVector>) -> tensor<?x10xf64, #SparseMatrix> {
@@ -267,31 +260,28 @@ func.func @dynamic_sparse_expand(%arg0: tensor<?xf64, #SparseVector>) -> tensor<
// CHECK-RWT: %[[B:.*]] = bufferization.alloc_tensor(%[[DD0]])
// CHECK-RWT: %[[P0:.*]] = sparse_tensor.pointers %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[I0:.*]] = sparse_tensor.indices %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[P1:.*]] = sparse_tensor.pointers %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[I1:.*]] = sparse_tensor.indices %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[S]]
// CHECK-RWT: %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK-RWT: %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK-RWT: %[[RET:.*]] = scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] iter_args(%[[R0:.*]] = %[[B]])
// CHECK-RWT: %[[SI0:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT-DAG: %[[S1:.*]] = memref.load %[[P1]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT-DAG: %[[PE1:.*]] = arith.addi %[[I]], %[[C1]] : index
// CHECK-RWT: %[[E1:.*]] = memref.load %[[P1]]{{\[}}%[[PE1]]] : memref<?xindex>
// CHECK-RWT: %[[RET_1:.*]] = scf.for %[[J:.*]] = %[[S1]] to %[[E1]] step %[[C1]] iter_args(%[[R1:.*]] = %[[R0]])
// CHECK-RWT: %[[SI1:.*]] = memref.load %[[I1]]{{\[}}%[[J]]] : memref<?xindex>
// CHECK-RWT: %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[J]]] : memref<?xf64>
// CHECK-RWT: %[[T1:.*]] = arith.divui %[[DD0]], %[[C10]] : index
// CHECK-RWT: %[[T2:.*]] = arith.muli %[[SI0]], %[[T1]] : index
// CHECK-RWT: %[[T3:.*]] = arith.divui %[[T1]], %[[SD1]] : index
// CHECK-RWT: %[[T4:.*]] = arith.muli %[[SI1]], %[[T3]] : index
// CHECK-RWT: %[[DI:.*]] = arith.addi %[[T2]], %[[T4]] : index
// CHECK-RWT: %[[NT:.*]] = sparse_tensor.insert %[[SV]] into %[[R1]]{{\[}}%[[DI]]]
// CHECK-RWT scf.yield %[[NT]]
// CHECK-RWT }
// CHECK-RWT scf.yield %[[RET_1]]
// CHECK-RWT: }
// CHECK-RWT: %[[NT1:.*]] = sparse_tensor.load %[[RET]] hasInserts
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[NT1]]
// CHECK-RWT: %[[P1:.*]] = sparse_tensor.pointers %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[I1:.*]] = sparse_tensor.indices %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[S]]
// CHECK-RWT: %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK-RWT: %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK-RWT: scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] {
// CHECK-RWT: %[[SI0:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT-DAG: %[[S1:.*]] = memref.load %[[P1]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT-DAG: %[[PE1:.*]] = arith.addi %[[I]], %[[C1]] : index
// CHECK-RWT: %[[E1:.*]] = memref.load %[[P1]]{{\[}}%[[PE1]]] : memref<?xindex>
// CHECK-RWT: scf.for %[[J:.*]] = %[[S1]] to %[[E1]] step %[[C1]] {
// CHECK-RWT: %[[SI1:.*]] = memref.load %[[I1]]{{\[}}%[[J]]] : memref<?xindex>
// CHECK-RWT: %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[J]]] : memref<?xf64>
// CHECK-RWT: %[[T1:.*]] = arith.divui %[[DD0]], %[[C10]] : index
// CHECK-RWT: %[[T2:.*]] = arith.muli %[[SI0]], %[[T1]] : index
// CHECK-RWT: %[[T3:.*]] = arith.divui %[[T1]], %[[SD1]] : index
// CHECK-RWT: %[[T4:.*]] = arith.muli %[[SI1]], %[[T3]] : index
// CHECK-RWT: %[[DI:.*]] = arith.addi %[[T2]], %[[T4]] : index
// CHECK-RWT: sparse_tensor.insert %[[SV]] into %[[B]]{{\[}}%[[DI]]]
// CHECK-RWT }
// CHECK-RWT: }
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[B]]
// CHECK-RWT: return %[[T]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
//
func.func @dynamic_sparse_collapse(%arg0: tensor<10x?xf64, #SparseMatrix>) -> tensor<?xf64, #SparseVector> {