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[mlir][sparse] Add rewriting rules for concatente using foreach opera…
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…tor.

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D134895
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@PeimingLiu
PeimingLiu committed Sep 30, 2022
1 parent 550288c commit 00ad065
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6 changes: 6 additions & 0 deletions mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td
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Expand Up @@ -815,6 +815,12 @@ def SparseTensor_ForeachOp : SparseTensor_Op<"foreach",
```
}];

let builders = [
OpBuilder<(
ins "Value":$tensor,
"function_ref<void(OpBuilder &, Location, ValueRange)>")>
];

let regions = (region AnyRegion:$region);
let assemblyFormat = "`in` $tensor attr-dict `:` type($tensor) `do` $region";
let hasVerifier = 1;
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26 changes: 26 additions & 0 deletions mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
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Expand Up @@ -597,6 +597,32 @@ LogicalResult CompressOp::verify() {
return success();
}

void ForeachOp::build(
OpBuilder &builder, OperationState &result, Value tensor,
function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuilder) {
build(builder, result, tensor);
if (!bodyBuilder)
return;

auto rtp = tensor.getType().cast<RankedTensorType>();
int64_t rank = rtp.getRank();

SmallVector<Type, 4> blockArgTypes;
// Starts with n index.
std::fill_n(std::back_inserter(blockArgTypes), rank, builder.getIndexType());
// Followed by one value.
blockArgTypes.push_back(rtp.getElementType());

SmallVector<Location, 4> blockArgLocs;
std::fill_n(std::back_inserter(blockArgLocs), rank + 1, tensor.getLoc());

OpBuilder::InsertionGuard guard(builder);
auto &region = *result.regions.front();
Block *bodyBlock =
builder.createBlock(&region, region.end(), blockArgTypes, blockArgLocs);
bodyBuilder(builder, result.location, bodyBlock->getArguments());
}

LogicalResult ForeachOp::verify() {
auto t = getTensor().getType().cast<RankedTensorType>();
auto args = getBody()->getArguments();
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83 changes: 83 additions & 0 deletions mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
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Expand Up @@ -111,6 +111,32 @@ static bool isZeroYield(GenericOp op) {
return isZeroValue(yieldOp.getOperand(0));
}

// TODO: The dim level property of the COO type relies on input tensors, the
// shape relies on the output tensor
// Helpers to setup a COO type.
static RankedTensorType getUnorderedCOOFromType(RankedTensorType src) {
auto *ctx = src.getContext();
auto rank = src.getRank();
SmallVector<SparseTensorEncodingAttr::DimLevelType, 4> dims;

// An unordered and non-unique compressed dim at beginning.
dims.push_back(SparseTensorEncodingAttr::DimLevelType::CompressedNuNo);
// TODO: it is actually ordered at the level for ordered input.
// Followed by unordered non-unique n-2 singleton levels.
std::fill_n(std::back_inserter(dims), rank - 2,
SparseTensorEncodingAttr::DimLevelType::SingletonNuNo);
// TODO: only if all the inputs (for concatentate) are unique at the last
// level should the COO has a unique level at the end. Ends by a unordered
// unique singleton level.
dims.push_back(SparseTensorEncodingAttr::DimLevelType::SingletonNo);
// TODO: Maybe pick the bitwidth based on input/output tensors (probably the
// largest one among them) in the original operation instead of using the
// default value.
auto enc = SparseTensorEncodingAttr::get(
ctx, dims, AffineMap::getMultiDimIdentityMap(rank, ctx), 0, 0);
return RankedTensorType::get(src.getShape(), src.getElementType(), enc);
}

//===---------------------------------------------------------------------===//
// The actual sparse tensor rewriting rules.
//===---------------------------------------------------------------------===//
Expand Down Expand Up @@ -296,6 +322,61 @@ struct ReshapeRewriter : public OpRewritePattern<ReshapeOp> {
}
};

struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(ConcatenateOp op,
PatternRewriter &rewriter) const override {
auto loc = op.getLoc();
auto rtp = op.getType().cast<RankedTensorType>();
// TODO: Build the output shape if needed.
assert(rtp.hasStaticShape());
auto rank = rtp.getRank();
size_t conDim = op.getDimension().getZExtValue();
// %t = concatenate %s1, %s2, %s3 {dim = 1}
// ==>
// %tmp = bufferization.alloc_tensor : unordered COO
// foreach in %s1 : insert d0, d1, %tmp
// foreach in %s2 : insert d0, d1 + size(s1), %tmp
// foreach in %s3 : insert d0, d1 + size(s1) + size(s2), %tmp
// %t = sparse_tensor.cast %tmp
auto cooTp = getUnorderedCOOFromType(rtp);
auto cooBuffer =
rewriter.create<AllocTensorOp>(loc, cooTp, ValueRange()).getResult();

Value offset = constantIndex(rewriter, loc, 0);
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.
rewriter.create<ForeachOp>(
loc, input, [&](OpBuilder &builder, Location loc, ValueRange args) {
SmallVector<Value, 4> indices;
for (int64_t i = 0; i < rank; i++) {
uint64_t dim =
toStoredDim(getSparseTensorEncoding(input.getType()), i);
Value idx = args[dim];
if (i == static_cast<int64_t>(conDim))
// transform coordinates on matching dim
idx = builder.create<arith::AddIOp>(loc, idx, offset);
indices.push_back(idx);
}
builder.create<InsertOp>(loc, args.back(), 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
// dynamically.
auto d = input.getType().cast<RankedTensorType>().getShape()[conDim];
assert(!ShapedType::isDynamic(d));
offset = rewriter.create<arith::AddIOp>(loc, offset,
constantIndex(rewriter, loc, d));
}
rewriter.replaceOpWithNewOp<ConvertOp>(op, rtp, cooBuffer);
return success();
}
};

/// Sparse rewriting rule for the foreach operator.
struct ForeachRewriter : public OpRewritePattern<ForeachOp> {
public:
Expand Down Expand Up @@ -363,4 +444,6 @@ void mlir::populateSparseTensorRewriting(RewritePatternSet &patterns,
ReshapeRewriter<tensor::CollapseShapeOp>, ForeachRewriter>(
patterns.getContext());
// TODO: If RT not enabled, rewrite concatenate ops, etc here.
if (!enableRT)
patterns.add<ConcatenateRewriter>(patterns.getContext());
}
81 changes: 81 additions & 0 deletions mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir
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@@ -0,0 +1,81 @@
// RUN: mlir-opt %s --sparsification=enable-runtime-library=false | FileCheck %s

#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>

// CHECK-LABEL: @concat_sparse_sparse(
// CHECK-SAME: %[[TMP_arg0:.*]]: tensor<2x4xf64, #sparse_tensor
// CHECK-SAME: %[[TMP_arg1:.*]]: tensor<3x4xf64, #sparse_tensor
// CHECK-SAME: %[[TMP_arg2:.*]]: tensor<4x4xf64, #sparse_tensor
// CHECK: %[[TMP_c0:.*]] = arith.constant 0 : index
// CHECK: %[[TMP_c1:.*]] = arith.constant 1 : index
// CHECK: %[[TMP_c5:.*]] = arith.constant 5 : index
// CHECK: %[[TMP_c2:.*]] = arith.constant 2 : index
// CHECK: %[[TMP_0:.*]] = bufferization.alloc_tensor() : tensor<9x4xf64, #sparse_tensor
// CHECK: %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
// CHECK: %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
// CHECK: %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
// CHECK: %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
// 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: scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] {
// CHECK: %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK: %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
// CHECK: %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK: %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
// 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: sparse_tensor.insert %[[TMP_28]] into %[[TMP_0]][%[[TMP_23]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
// CHECK: }
// 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
// CHECK: %[[TMP_10:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
// CHECK: %[[TMP_11:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
// 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: scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]] {
// CHECK: %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK: %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
// CHECK: %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK: %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
// 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: sparse_tensor.insert %[[TMP_28]] into %[[TMP_0]][%[[TMP_29]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
// CHECK: }
// 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
// CHECK: %[[TMP_17:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
// CHECK: %[[TMP_18:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
// 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: 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_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
// CHECK: %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK: %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>
// 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: sparse_tensor.insert %[[TMP_28]] into %[[TMP_0]][%[[TMP_29]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
// CHECK: }
// CHECK: }
// 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>,
%arg2: tensor<4x4xf64, #DCSR>)
-> tensor<9x4xf64, #DCSR> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #DCSR>,
tensor<3x4xf64, #DCSR>,
tensor<4x4xf64, #DCSR> to tensor<9x4xf64, #DCSR>
return %0 : tensor<9x4xf64, #DCSR>
}

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