[Mlir-commits] [mlir] 00ad065 - [mlir][sparse] Add rewriting rules for concatente using foreach operator.
Peiming Liu
llvmlistbot at llvm.org
Fri Sep 30 14:57:04 PDT 2022
Author: Peiming Liu
Date: 2022-09-30T21:56:55Z
New Revision: 00ad065548c566088c937c17f627dbe15b91cf2a
URL: https://github.com/llvm/llvm-project/commit/00ad065548c566088c937c17f627dbe15b91cf2a
DIFF: https://github.com/llvm/llvm-project/commit/00ad065548c566088c937c17f627dbe15b91cf2a.diff
LOG: [mlir][sparse] Add rewriting rules for concatente using foreach operator.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D134895
Added:
mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir
Modified:
mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td
mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td b/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td
index a9f6c18d9d112..8cd1a01a2e330 100644
--- a/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td
+++ b/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td
@@ -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;
diff --git a/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp b/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
index 48bb5585d4d8c..d12ecb9d023f6 100644
--- a/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
+++ b/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
@@ -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 ®ion = *result.regions.front();
+ Block *bodyBlock =
+ builder.createBlock(®ion, region.end(), blockArgTypes, blockArgLocs);
+ bodyBuilder(builder, result.location, bodyBlock->getArguments());
+}
+
LogicalResult ForeachOp::verify() {
auto t = getTensor().getType().cast<RankedTensorType>();
auto args = getBody()->getArguments();
diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
index f0c7f599e2853..b110657740680 100644
--- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
@@ -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.
//===---------------------------------------------------------------------===//
@@ -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:
@@ -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());
}
diff --git a/mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir b/mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir
new file mode 100644
index 0000000000000..018f391122079
--- /dev/null
+++ b/mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir
@@ -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>
+}
More information about the Mlir-commits
mailing list