315 lines
11 KiB
C++
315 lines
11 KiB
C++
//===- SparseTensorLowering.cpp - Sparse tensor primitives conversion -----===//
|
|
//
|
|
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
|
|
// See https://llvm.org/LICENSE.txt for license information.
|
|
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
//
|
|
// Convert sparse tensor primitives to calls into a runtime support library.
|
|
// Note that this is a current implementation choice to keep the conversion
|
|
// simple. In principle, these primitives could also be converted to actual
|
|
// elaborate IR code that implements the primitives on the selected sparse
|
|
// tensor storage schemes.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "mlir/Dialect/LLVMIR/LLVMTypes.h"
|
|
#include "mlir/Dialect/MemRef/IR/MemRef.h"
|
|
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
|
|
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
|
|
#include "mlir/Dialect/StandardOps/IR/Ops.h"
|
|
#include "mlir/Dialect/Tensor/IR/Tensor.h"
|
|
#include "mlir/Transforms/DialectConversion.h"
|
|
|
|
using namespace mlir;
|
|
using namespace mlir::sparse_tensor;
|
|
|
|
namespace {
|
|
|
|
/// Returns internal type encoding for overhead storage.
|
|
static unsigned getOverheadTypeEncoding(unsigned width) {
|
|
switch (width) {
|
|
default:
|
|
return 1;
|
|
case 32:
|
|
return 2;
|
|
case 16:
|
|
return 3;
|
|
case 8:
|
|
return 4;
|
|
}
|
|
}
|
|
|
|
/// Returns internal dimension level type encoding.
|
|
static unsigned
|
|
getDimLevelTypeEncoding(SparseTensorEncodingAttr::DimLevelType dlt) {
|
|
switch (dlt) {
|
|
case SparseTensorEncodingAttr::DimLevelType::Dense:
|
|
return 0;
|
|
case SparseTensorEncodingAttr::DimLevelType::Compressed:
|
|
return 1;
|
|
case SparseTensorEncodingAttr::DimLevelType::Singleton:
|
|
return 2;
|
|
}
|
|
llvm_unreachable("Unknown SparseTensorEncodingAttr::DimLevelType");
|
|
}
|
|
|
|
/// Returns integers of given width and values as a constant tensor.
|
|
/// We cast the static shape into a dynamic shape to ensure that the
|
|
/// method signature remains uniform accross different tensor dimensions.
|
|
static Value getTensor(ConversionPatternRewriter &rewriter, unsigned width,
|
|
Location loc, ArrayRef<APInt> values) {
|
|
Type etp = rewriter.getIntegerType(width);
|
|
unsigned sz = values.size();
|
|
RankedTensorType tt1 = RankedTensorType::get({sz}, etp);
|
|
RankedTensorType tt2 = RankedTensorType::get({ShapedType::kDynamicSize}, etp);
|
|
auto elts =
|
|
rewriter.create<ConstantOp>(loc, DenseElementsAttr::get(tt1, values));
|
|
return rewriter.create<tensor::CastOp>(loc, tt2, elts);
|
|
}
|
|
|
|
/// Returns function reference (first hit also inserts into module).
|
|
static FlatSymbolRefAttr getFunc(Operation *op, StringRef name, Type result,
|
|
ValueRange operands) {
|
|
MLIRContext *context = op->getContext();
|
|
auto module = op->getParentOfType<ModuleOp>();
|
|
auto func = module.lookupSymbol<FuncOp>(name);
|
|
if (!func) {
|
|
OpBuilder moduleBuilder(module.getBodyRegion());
|
|
moduleBuilder
|
|
.create<FuncOp>(op->getLoc(), name,
|
|
FunctionType::get(context, operands.getTypes(), result))
|
|
.setPrivate();
|
|
}
|
|
return SymbolRefAttr::get(context, name);
|
|
}
|
|
|
|
/// Sparse conversion rule for returns.
|
|
class SparseReturnConverter : public OpConversionPattern<ReturnOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ReturnOp op, ArrayRef<Value> operands,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
rewriter.replaceOpWithNewOp<ReturnOp>(op, operands);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse conversion rule for dimension accesses.
|
|
class SparseTensorToDimSizeConverter
|
|
: public OpConversionPattern<tensor::DimOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(tensor::DimOp op, ArrayRef<Value> operands,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
if (!operands[0].getType().isa<LLVM::LLVMPointerType>())
|
|
return failure();
|
|
Type resType = op.getType();
|
|
StringRef name = "sparseDimSize";
|
|
rewriter.replaceOpWithNewOp<CallOp>(
|
|
op, resType, getFunc(op, name, resType, operands), operands);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse conversion rule for the new operator.
|
|
class SparseTensorNewConverter : public OpConversionPattern<NewOp> {
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(NewOp op, ArrayRef<Value> operands,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
Location loc = op.getLoc();
|
|
Type resType = op.getType();
|
|
Type eltType = resType.cast<ShapedType>().getElementType();
|
|
MLIRContext *context = op->getContext();
|
|
SmallVector<Value, 5> params;
|
|
// Sparse encoding.
|
|
auto enc = getSparseTensorEncoding(resType);
|
|
if (!enc)
|
|
return failure();
|
|
// User pointer.
|
|
params.push_back(operands[0]);
|
|
// Sparsity annotations in tensor constant form.
|
|
SmallVector<APInt, 4> attrs;
|
|
unsigned sz = enc.getDimLevelType().size();
|
|
for (unsigned i = 0; i < sz; i++)
|
|
attrs.push_back(
|
|
APInt(8, getDimLevelTypeEncoding(enc.getDimLevelType()[i])));
|
|
params.push_back(getTensor(rewriter, 8, loc, attrs));
|
|
// Dimension order permutation array. This is the "identity"
|
|
// permutation by default, or otherwise the "reverse" permutation
|
|
// of a given ordering, so that indices can be mapped quickly
|
|
// to the right position.
|
|
SmallVector<APInt, 4> perm(sz);
|
|
AffineMap p = enc.getDimOrdering();
|
|
if (p) {
|
|
assert(p.isPermutation() && p.getNumResults() == sz);
|
|
for (unsigned i = 0; i < sz; i++)
|
|
perm[p.getDimPosition(i)] = APInt(64, i);
|
|
} else {
|
|
for (unsigned i = 0; i < sz; i++)
|
|
perm[i] = APInt(64, i);
|
|
}
|
|
params.push_back(getTensor(rewriter, 64, loc, perm));
|
|
// Secondary and primary types encoding.
|
|
unsigned secPtr = getOverheadTypeEncoding(enc.getPointerBitWidth());
|
|
unsigned secInd = getOverheadTypeEncoding(enc.getIndexBitWidth());
|
|
unsigned primary;
|
|
if (eltType.isF64())
|
|
primary = 1;
|
|
else if (eltType.isF32())
|
|
primary = 2;
|
|
else if (eltType.isInteger(32))
|
|
primary = 3;
|
|
else if (eltType.isInteger(16))
|
|
primary = 4;
|
|
else if (eltType.isInteger(8))
|
|
primary = 5;
|
|
else
|
|
return failure();
|
|
params.push_back(
|
|
rewriter.create<ConstantOp>(loc, rewriter.getI64IntegerAttr(secPtr)));
|
|
params.push_back(
|
|
rewriter.create<ConstantOp>(loc, rewriter.getI64IntegerAttr(secInd)));
|
|
params.push_back(
|
|
rewriter.create<ConstantOp>(loc, rewriter.getI64IntegerAttr(primary)));
|
|
// Generate the call to create new tensor.
|
|
Type ptrType = LLVM::LLVMPointerType::get(IntegerType::get(context, 8));
|
|
StringRef name = "newSparseTensor";
|
|
rewriter.replaceOpWithNewOp<CallOp>(
|
|
op, ptrType, getFunc(op, name, ptrType, params), params);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse conversion rule for pointer accesses.
|
|
class SparseTensorToPointersConverter
|
|
: public OpConversionPattern<ToPointersOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ToPointersOp op, ArrayRef<Value> operands,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
Type resType = op.getType();
|
|
Type eltType = resType.cast<ShapedType>().getElementType();
|
|
StringRef name;
|
|
if (eltType.isIndex())
|
|
name = "sparsePointers";
|
|
else if (eltType.isInteger(64))
|
|
name = "sparsePointers64";
|
|
else if (eltType.isInteger(32))
|
|
name = "sparsePointers32";
|
|
else if (eltType.isInteger(16))
|
|
name = "sparsePointers16";
|
|
else if (eltType.isInteger(8))
|
|
name = "sparsePointers8";
|
|
else
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<CallOp>(
|
|
op, resType, getFunc(op, name, resType, operands), operands);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse conversion rule for index accesses.
|
|
class SparseTensorToIndicesConverter : public OpConversionPattern<ToIndicesOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ToIndicesOp op, ArrayRef<Value> operands,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
Type resType = op.getType();
|
|
Type eltType = resType.cast<ShapedType>().getElementType();
|
|
StringRef name;
|
|
if (eltType.isIndex())
|
|
name = "sparseIndices";
|
|
else if (eltType.isInteger(64))
|
|
name = "sparseIndices64";
|
|
else if (eltType.isInteger(32))
|
|
name = "sparseIndices32";
|
|
else if (eltType.isInteger(16))
|
|
name = "sparseIndices16";
|
|
else if (eltType.isInteger(8))
|
|
name = "sparseIndices8";
|
|
else
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<CallOp>(
|
|
op, resType, getFunc(op, name, resType, operands), operands);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse conversion rule for value accesses.
|
|
class SparseTensorToValuesConverter : public OpConversionPattern<ToValuesOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ToValuesOp op, ArrayRef<Value> operands,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
Type resType = op.getType();
|
|
Type eltType = resType.cast<ShapedType>().getElementType();
|
|
StringRef name;
|
|
if (eltType.isF64())
|
|
name = "sparseValuesF64";
|
|
else if (eltType.isF32())
|
|
name = "sparseValuesF32";
|
|
else if (eltType.isInteger(32))
|
|
name = "sparseValuesI32";
|
|
else if (eltType.isInteger(16))
|
|
name = "sparseValuesI16";
|
|
else if (eltType.isInteger(8))
|
|
name = "sparseValuesI8";
|
|
else
|
|
return failure();
|
|
rewriter.replaceOpWithNewOp<CallOp>(
|
|
op, resType, getFunc(op, name, resType, operands), operands);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse conversion rule for tensor reconstruction.
|
|
class SparseTensorToTensorConverter : public OpConversionPattern<ToTensorOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
// Simply fold the operator into the pointer to the sparse storage scheme.
|
|
matchAndRewrite(ToTensorOp op, ArrayRef<Value> operands,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
// Check that all arguments of the tensor reconstruction operators are calls
|
|
// into the support library that query exactly the same opaque pointer.
|
|
Value ptr;
|
|
for (Value op : operands) {
|
|
if (auto call = op.getDefiningOp<CallOp>()) {
|
|
Value arg = call.getOperand(0);
|
|
if (!arg.getType().isa<LLVM::LLVMPointerType>())
|
|
return failure();
|
|
if (!ptr)
|
|
ptr = arg;
|
|
else if (arg != ptr)
|
|
return failure();
|
|
}
|
|
}
|
|
// If a single opaque pointer is found, perform the folding.
|
|
if (!ptr)
|
|
return failure();
|
|
rewriter.replaceOp(op, ptr);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
/// Populates the given patterns list with conversion rules required for
|
|
/// the sparsification of linear algebra operations.
|
|
void mlir::populateSparseTensorConversionPatterns(TypeConverter &typeConverter,
|
|
RewritePatternSet &patterns) {
|
|
patterns.add<SparseReturnConverter, SparseTensorToDimSizeConverter,
|
|
SparseTensorNewConverter, SparseTensorToPointersConverter,
|
|
SparseTensorToIndicesConverter, SparseTensorToValuesConverter,
|
|
SparseTensorToTensorConverter>(typeConverter,
|
|
patterns.getContext());
|
|
}
|