b54c724be0
This has been a TODO for a long time, and it brings about many advantages (namely nice accessors, and less fragile code). The existing overloads that accept ArrayRef are now treated as deprecated and will be removed in a followup (after a small grace period). Most of the upstream MLIR usages have been fixed by this commit, the rest will be handled in a followup. Differential Revision: https://reviews.llvm.org/D110293
522 lines
20 KiB
C++
522 lines
20 KiB
C++
//===- SparseTensorLowering.cpp - Sparse tensor primitives conversion -----===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// Convert sparse tensor primitives to calls into a runtime support library.
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// Note that this is a current implementation choice to keep the conversion
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// simple. In principle, these primitives could also be converted to actual
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// elaborate IR code that implements the primitives on the selected sparse
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// tensor storage schemes.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
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#include "mlir/Dialect/Linalg/Utils/Utils.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/Dialect/SCF/SCF.h"
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#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
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#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Transforms/DialectConversion.h"
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using namespace mlir;
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using namespace mlir::sparse_tensor;
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namespace {
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//===----------------------------------------------------------------------===//
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// Helper methods.
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//===----------------------------------------------------------------------===//
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/// Returns internal type encoding for primary storage. Keep these
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/// values consistent with the sparse runtime support library.
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static unsigned getPrimaryTypeEncoding(Type tp) {
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if (tp.isF64())
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return 1;
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if (tp.isF32())
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return 2;
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if (tp.isInteger(64))
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return 3;
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if (tp.isInteger(32))
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return 4;
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if (tp.isInteger(16))
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return 5;
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if (tp.isInteger(8))
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return 6;
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return 0;
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}
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/// Returns internal type encoding for overhead storage. Keep these
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/// values consistent with the sparse runtime support library.
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static unsigned getOverheadTypeEncoding(unsigned width) {
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switch (width) {
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default:
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return 1;
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case 32:
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return 2;
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case 16:
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return 3;
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case 8:
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return 4;
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}
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}
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/// Returns internal dimension level type encoding. Keep these
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/// values consistent with the sparse runtime support library.
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static unsigned
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getDimLevelTypeEncoding(SparseTensorEncodingAttr::DimLevelType dlt) {
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switch (dlt) {
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case SparseTensorEncodingAttr::DimLevelType::Dense:
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return 0;
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case SparseTensorEncodingAttr::DimLevelType::Compressed:
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return 1;
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case SparseTensorEncodingAttr::DimLevelType::Singleton:
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return 2;
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}
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llvm_unreachable("Unknown SparseTensorEncodingAttr::DimLevelType");
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}
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/// Returns integers of given width and values as a constant tensor.
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/// We cast the static shape into a dynamic shape to ensure that the
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/// method signature remains uniform across different tensor dimensions.
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static Value getTensor(ConversionPatternRewriter &rewriter, unsigned width,
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Location loc, ArrayRef<APInt> values) {
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Type etp = rewriter.getIntegerType(width);
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unsigned sz = values.size();
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RankedTensorType tt1 = RankedTensorType::get({sz}, etp);
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RankedTensorType tt2 = RankedTensorType::get({ShapedType::kDynamicSize}, etp);
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auto elts =
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rewriter.create<ConstantOp>(loc, DenseElementsAttr::get(tt1, values));
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return rewriter.create<tensor::CastOp>(loc, tt2, elts);
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}
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/// Returns a function reference (first hit also inserts into module). Sets
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/// the "_emit_c_interface" on the function declaration when requested,
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/// so that LLVM lowering generates a wrapper function that takes care
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/// of ABI complications with passing in and returning MemRefs to C functions.
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static FlatSymbolRefAttr getFunc(Operation *op, StringRef name, Type resultType,
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ValueRange operands,
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bool emitCInterface = false) {
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MLIRContext *context = op->getContext();
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auto module = op->getParentOfType<ModuleOp>();
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auto result = SymbolRefAttr::get(context, name);
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auto func = module.lookupSymbol<FuncOp>(result.getAttr());
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if (!func) {
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OpBuilder moduleBuilder(module.getBodyRegion());
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func = moduleBuilder.create<FuncOp>(
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op->getLoc(), name,
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FunctionType::get(context, operands.getTypes(), resultType));
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func.setPrivate();
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if (emitCInterface)
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func->setAttr("llvm.emit_c_interface", UnitAttr::get(context));
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}
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return result;
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}
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/// Generates a call into the "swiss army knife" method of the sparse runtime
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/// support library for materializing sparse tensors into the computation. The
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/// method returns the call value and assigns the permutation to 'perm'.
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static Value genNewCall(ConversionPatternRewriter &rewriter, Operation *op,
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SparseTensorEncodingAttr &enc, uint32_t action,
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Value &perm, Value ptr = Value()) {
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Location loc = op->getLoc();
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ShapedType resType = op->getResult(0).getType().cast<ShapedType>();
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SmallVector<Value, 8> params;
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// Sparsity annotations in tensor constant form.
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SmallVector<APInt, 4> attrs;
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unsigned sz = enc.getDimLevelType().size();
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for (unsigned i = 0; i < sz; i++)
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attrs.push_back(
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APInt(8, getDimLevelTypeEncoding(enc.getDimLevelType()[i])));
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params.push_back(getTensor(rewriter, 8, loc, attrs));
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// Dimension sizes array of the enveloping *dense* tensor. Useful for either
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// verification of external data, or for construction of internal data.
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auto shape = resType.getShape();
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SmallVector<APInt, 4> sizes;
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for (unsigned i = 0; i < sz; i++) {
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uint64_t s = shape[i] == ShapedType::kDynamicSize ? 0 : shape[i];
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sizes.push_back(APInt(64, s));
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}
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params.push_back(getTensor(rewriter, 64, loc, sizes));
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// Dimension order permutation array. This is the "identity" permutation by
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// default, or otherwise the "reverse" permutation of a given ordering, so
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// that indices can be mapped quickly to the right position.
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SmallVector<APInt, 4> rev(sz);
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if (AffineMap p = enc.getDimOrdering()) {
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for (unsigned i = 0; i < sz; i++)
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rev[p.getDimPosition(i)] = APInt(64, i);
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} else {
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for (unsigned i = 0; i < sz; i++)
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rev[i] = APInt(64, i);
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}
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perm = getTensor(rewriter, 64, loc, rev);
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params.push_back(perm);
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// Secondary and primary types encoding.
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unsigned secPtr = getOverheadTypeEncoding(enc.getPointerBitWidth());
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unsigned secInd = getOverheadTypeEncoding(enc.getIndexBitWidth());
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unsigned primary = getPrimaryTypeEncoding(resType.getElementType());
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assert(primary);
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params.push_back(
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rewriter.create<ConstantOp>(loc, rewriter.getI64IntegerAttr(secPtr)));
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params.push_back(
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rewriter.create<ConstantOp>(loc, rewriter.getI64IntegerAttr(secInd)));
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params.push_back(
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rewriter.create<ConstantOp>(loc, rewriter.getI64IntegerAttr(primary)));
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// User action and pointer.
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Type pTp = LLVM::LLVMPointerType::get(IntegerType::get(op->getContext(), 8));
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if (!ptr)
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ptr = rewriter.create<LLVM::NullOp>(loc, pTp);
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params.push_back(
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rewriter.create<ConstantOp>(loc, rewriter.getI32IntegerAttr(action)));
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params.push_back(ptr);
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// Generate the call to create new tensor.
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StringRef name = "newSparseTensor";
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auto call = rewriter.create<CallOp>(
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loc, pTp, getFunc(op, name, pTp, params, /*emitCInterface=*/true),
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params);
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return call.getResult(0);
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}
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/// Generates the comparison `v != 0` where `v` is of numeric type `t`.
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/// For floating types, we use the "unordered" comparator (i.e., returns
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/// true if `v` is NaN).
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static Value genIsNonzero(ConversionPatternRewriter &rewriter, Location loc,
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Type t, Value v) {
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Value zero = rewriter.create<ConstantOp>(loc, rewriter.getZeroAttr(t));
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if (t.isa<FloatType>())
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return rewriter.create<CmpFOp>(loc, CmpFPredicate::UNE, v, zero);
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if (t.isIntOrIndex())
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return rewriter.create<CmpIOp>(loc, CmpIPredicate::ne, v, zero);
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llvm_unreachable("Unknown element type");
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}
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/// Generates a call that adds one element to a coordinate scheme.
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/// In particular, this generates code like the following:
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/// val = a[i1,..,ik];
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/// if val != 0
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/// t->add(val, [i1,..,ik], [p1,..,pk]);
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static void genAddEltCall(ConversionPatternRewriter &rewriter, Operation *op,
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Value ptr, Value tensor, Value ind, Value perm,
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ValueRange ivs) {
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StringRef name;
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Type eltType = tensor.getType().cast<ShapedType>().getElementType();
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if (eltType.isF64())
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name = "addEltF64";
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else if (eltType.isF32())
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name = "addEltF32";
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else if (eltType.isInteger(64))
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name = "addEltI64";
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else if (eltType.isInteger(32))
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name = "addEltI32";
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else if (eltType.isInteger(16))
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name = "addEltI16";
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else if (eltType.isInteger(8))
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name = "addEltI8";
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else
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llvm_unreachable("Unknown element type");
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Location loc = op->getLoc();
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Value val = rewriter.create<tensor::ExtractOp>(loc, tensor, ivs);
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Value cond = genIsNonzero(rewriter, loc, eltType, val);
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scf::IfOp ifOp = rewriter.create<scf::IfOp>(loc, cond, /*else*/ false);
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rewriter.setInsertionPointToStart(&ifOp.thenRegion().front());
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unsigned i = 0;
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for (auto iv : ivs) {
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Value idx = rewriter.create<ConstantOp>(loc, rewriter.getIndexAttr(i++));
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rewriter.create<memref::StoreOp>(loc, iv, ind, idx);
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}
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SmallVector<Value, 8> params;
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params.push_back(ptr);
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params.push_back(val);
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params.push_back(ind);
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params.push_back(perm);
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Type pTp = LLVM::LLVMPointerType::get(IntegerType::get(op->getContext(), 8));
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rewriter.create<CallOp>(
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loc, pTp, getFunc(op, name, pTp, params, /*emitCInterface=*/true),
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params);
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}
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//===----------------------------------------------------------------------===//
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// Conversion rules.
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//===----------------------------------------------------------------------===//
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/// Sparse conversion rule for returns.
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class SparseReturnConverter : public OpConversionPattern<ReturnOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(ReturnOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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rewriter.replaceOpWithNewOp<ReturnOp>(op, adaptor.getOperands());
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return success();
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}
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};
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/// Sparse conversion rule for dimension accesses.
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class SparseTensorToDimSizeConverter
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: public OpConversionPattern<tensor::DimOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(tensor::DimOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Type resType = op.getType();
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auto enc = getSparseTensorEncoding(op.source().getType());
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if (!enc)
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return failure();
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// Permute the dim index.
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Optional<int64_t> index = op.getConstantIndex();
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if (!index.hasValue())
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return failure();
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int64_t idx = index.getValue();
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if (AffineMap p = enc.getDimOrdering())
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idx = p.getPermutedPosition(idx);
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// Generate the call.
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StringRef name = "sparseDimSize";
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SmallVector<Value, 2> params;
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params.push_back(adaptor.getOperands()[0]);
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params.push_back(
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rewriter.create<ConstantOp>(op.getLoc(), rewriter.getIndexAttr(idx)));
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rewriter.replaceOpWithNewOp<CallOp>(
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op, resType, getFunc(op, name, resType, params), params);
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return success();
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}
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};
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/// Sparse conversion rule for the new operator.
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class SparseTensorNewConverter : public OpConversionPattern<NewOp> {
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(NewOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Type resType = op.getType();
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auto enc = getSparseTensorEncoding(resType);
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if (!enc)
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return failure();
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Value perm;
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rewriter.replaceOp(
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op, genNewCall(rewriter, op, enc, 0, perm, adaptor.getOperands()[0]));
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return success();
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}
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};
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/// Sparse conversion rule for the convert operator.
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class SparseTensorConvertConverter : public OpConversionPattern<ConvertOp> {
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(ConvertOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Type resType = op.getType();
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auto encDst = getSparseTensorEncoding(resType);
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auto encSrc = getSparseTensorEncoding(op.source().getType());
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if (encDst && encSrc) {
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// This is a sparse => sparse conversion, which is handled as follows:
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// t = src->asCOO(); ; src to COO in dst order
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// dst = newSparseTensor(t)
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// Using the coordinate scheme as an intermediate does not always
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// yield the fastest conversion but avoids the need for a full
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// O(N^2) conversion matrix.
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Value perm;
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Value coo =
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genNewCall(rewriter, op, encDst, 3, perm, adaptor.getOperands()[0]);
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rewriter.replaceOp(op, genNewCall(rewriter, op, encDst, 1, perm, coo));
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return success();
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}
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if (!encDst || encSrc) {
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// TODO: sparse => dense
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return failure();
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}
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// This is a dense => sparse conversion, which is handled as follows:
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// t = newSparseCOO()
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// for i1 in dim1
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// ..
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// for ik in dimk
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// val = a[i1,..,ik]
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// if val != 0
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// t->add(val, [i1,..,ik], [p1,..,pk])
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// s = newSparseTensor(t)
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// Note that the dense tensor traversal code is actually implemented
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// using MLIR IR to avoid having to expose too much low-level
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// memref traversal details to the runtime support library.
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// Also note that the code below only generates the "new" ops and
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// the loop-nest per se; whereas the entire body of the innermost
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// loop is generated by genAddElt().
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Location loc = op->getLoc();
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ShapedType shape = resType.cast<ShapedType>();
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auto memTp =
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MemRefType::get({ShapedType::kDynamicSize}, rewriter.getIndexType());
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Value perm;
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Value ptr = genNewCall(rewriter, op, encDst, 2, perm);
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Value tensor = adaptor.getOperands()[0];
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Value arg = rewriter.create<ConstantOp>(
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loc, rewriter.getIndexAttr(shape.getRank()));
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Value ind = rewriter.create<memref::AllocaOp>(loc, memTp, ValueRange{arg});
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SmallVector<Value> lo;
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SmallVector<Value> hi;
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SmallVector<Value> st;
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Value zero = rewriter.create<ConstantOp>(loc, rewriter.getIndexAttr(0));
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Value one = rewriter.create<ConstantOp>(loc, rewriter.getIndexAttr(1));
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for (unsigned i = 0, rank = shape.getRank(); i < rank; i++) {
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lo.push_back(zero);
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hi.push_back(linalg::createOrFoldDimOp(rewriter, loc, tensor, i));
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st.push_back(one);
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}
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scf::buildLoopNest(rewriter, op.getLoc(), lo, hi, st, {},
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[&](OpBuilder &builder, Location loc, ValueRange ivs,
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ValueRange args) -> scf::ValueVector {
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genAddEltCall(rewriter, op, ptr, tensor, ind, perm,
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ivs);
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return {};
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});
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rewriter.replaceOp(op, genNewCall(rewriter, op, encDst, 1, perm, ptr));
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return success();
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}
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};
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/// Sparse conversion rule for pointer accesses.
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class SparseTensorToPointersConverter
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: public OpConversionPattern<ToPointersOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(ToPointersOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Type resType = op.getType();
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Type eltType = resType.cast<ShapedType>().getElementType();
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StringRef name;
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if (eltType.isIndex())
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name = "sparsePointers";
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else if (eltType.isInteger(64))
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name = "sparsePointers64";
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else if (eltType.isInteger(32))
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name = "sparsePointers32";
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else if (eltType.isInteger(16))
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name = "sparsePointers16";
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else if (eltType.isInteger(8))
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name = "sparsePointers8";
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else
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return failure();
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rewriter.replaceOpWithNewOp<CallOp>(op, resType,
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getFunc(op, name, resType,
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adaptor.getOperands(),
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/*emitCInterface=*/true),
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adaptor.getOperands());
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return success();
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}
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};
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/// Sparse conversion rule for index accesses.
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class SparseTensorToIndicesConverter : public OpConversionPattern<ToIndicesOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(ToIndicesOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Type resType = op.getType();
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Type eltType = resType.cast<ShapedType>().getElementType();
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StringRef name;
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if (eltType.isIndex())
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name = "sparseIndices";
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else if (eltType.isInteger(64))
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name = "sparseIndices64";
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else if (eltType.isInteger(32))
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name = "sparseIndices32";
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else if (eltType.isInteger(16))
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name = "sparseIndices16";
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else if (eltType.isInteger(8))
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name = "sparseIndices8";
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else
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return failure();
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rewriter.replaceOpWithNewOp<CallOp>(op, resType,
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getFunc(op, name, resType,
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adaptor.getOperands(),
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/*emitCInterface=*/true),
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adaptor.getOperands());
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return success();
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}
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};
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/// Sparse conversion rule for value accesses.
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class SparseTensorToValuesConverter : public OpConversionPattern<ToValuesOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(ToValuesOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Type resType = op.getType();
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Type eltType = resType.cast<ShapedType>().getElementType();
|
|
StringRef name;
|
|
if (eltType.isF64())
|
|
name = "sparseValuesF64";
|
|
else if (eltType.isF32())
|
|
name = "sparseValuesF32";
|
|
else if (eltType.isInteger(64))
|
|
name = "sparseValuesI64";
|
|
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,
|
|
adaptor.getOperands(),
|
|
/*emitCInterface=*/true),
|
|
adaptor.getOperands());
|
|
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, OpAdaptor adaptor,
|
|
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 : adaptor.getOperands()) {
|
|
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
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Public method for populating conversion rules.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// 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, SparseTensorConvertConverter,
|
|
SparseTensorToPointersConverter, SparseTensorToIndicesConverter,
|
|
SparseTensorToValuesConverter, SparseTensorToTensorConverter>(
|
|
typeConverter, patterns.getContext());
|
|
}
|