d37d72eaf8
This shares more code with existing utilities. Also, to be consistent, we moved dimension permutation on the DimOp to the tensor lowering phase. This way, both pre-existing DimOps on sparse tensors (not likely but possible) as well as compiler generated DimOps are handled consistently. Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D108309
436 lines
16 KiB
C++
436 lines
16 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/LLVMTypes.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.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 accross 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 function reference (first hit also inserts into module).
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static FlatSymbolRefAttr getFunc(Operation *op, StringRef name, Type result,
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ValueRange operands) {
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MLIRContext *context = op->getContext();
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auto module = op->getParentOfType<ModuleOp>();
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auto func = module.lookupSymbol<FuncOp>(name);
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if (!func) {
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OpBuilder moduleBuilder(module.getBodyRegion());
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moduleBuilder
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.create<FuncOp>(op->getLoc(), name,
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FunctionType::get(context, operands.getTypes(), result))
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.setPrivate();
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}
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return SymbolRefAttr::get(context, name);
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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.
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static void genNewCall(ConversionPatternRewriter &rewriter, Operation *op,
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SparseTensorEncodingAttr &enc, uint32_t action,
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Value ptr) {
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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> perm(sz);
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AffineMap p = enc.getDimOrdering();
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if (p) {
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assert(p.isPermutation() && p.getNumResults() == sz);
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for (unsigned i = 0; i < sz; i++)
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perm[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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perm[i] = APInt(64, i);
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}
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params.push_back(getTensor(rewriter, 64, loc, 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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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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Type ptrType =
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LLVM::LLVMPointerType::get(IntegerType::get(op->getContext(), 8));
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StringRef name = "newSparseTensor";
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rewriter.replaceOpWithNewOp<CallOp>(
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op, ptrType, getFunc(op, name, ptrType, params), params);
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}
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/// Generates a call that exposes the data pointer as a void pointer.
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// TODO: probing the data pointer directly is a bit raw; we should replace
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// this with proper memref util calls once they become available.
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static bool genPtrCall(ConversionPatternRewriter &rewriter, Operation *op,
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Value val, Value &ptr) {
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Location loc = op->getLoc();
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ShapedType sType = op->getResult(0).getType().cast<ShapedType>();
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Type eltType = sType.getElementType();
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// Specialize name for the data type. Even though the final buffferized
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// version only operates on pointers, different names are required to
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// ensure type correctness for all intermediate states.
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StringRef name;
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if (eltType.isF64())
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name = "getPtrF64";
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else if (eltType.isF32())
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name = "getPtrF32";
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else if (eltType.isInteger(64))
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name = "getPtrI64";
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else if (eltType.isInteger(32))
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name = "getPtrI32";
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else if (eltType.isInteger(16))
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name = "getPtrI16";
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else if (eltType.isInteger(8))
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name = "getPtrI8";
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else
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return false;
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auto memRefTp = MemRefType::get(sType.getShape(), eltType);
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auto unrankedTp = UnrankedMemRefType::get(eltType, 0);
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Value c = rewriter.create<memref::BufferCastOp>(loc, memRefTp, val);
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Value d = rewriter.create<memref::CastOp>(loc, unrankedTp, c);
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Type ptrType =
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LLVM::LLVMPointerType::get(IntegerType::get(op->getContext(), 8));
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auto call =
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rewriter.create<CallOp>(loc, ptrType, getFunc(op, name, ptrType, d), d);
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ptr = call.getResult(0);
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return true;
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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, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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rewriter.replaceOpWithNewOp<ReturnOp>(op, operands);
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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, ArrayRef<Value> operands,
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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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AffineMap p = enc.getDimOrdering();
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if (p) {
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assert(p.isPermutation());
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for (unsigned i = 0, sz = p.getNumResults(); i < sz; i++) {
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if (p.getDimPosition(i) == idx) {
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idx = i;
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break;
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}
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}
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}
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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(operands[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, ArrayRef<Value> operands,
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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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genNewCall(rewriter, op, enc, 0, operands[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, ArrayRef<Value> operands,
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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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// TODO: implement sparse => sparse
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// and sparse => dense
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if (!encDst || encSrc)
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return failure();
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// This is a dense => sparse conversion.
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Value ptr;
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if (!genPtrCall(rewriter, op, operands[0], ptr))
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return failure();
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genNewCall(rewriter, op, encDst, 1, 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, ArrayRef<Value> operands,
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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>(
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op, resType, getFunc(op, name, resType, operands), operands);
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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, ArrayRef<Value> operands,
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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>(
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op, resType, getFunc(op, name, resType, operands), operands);
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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, ArrayRef<Value> operands,
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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.isF64())
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name = "sparseValuesF64";
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else if (eltType.isF32())
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name = "sparseValuesF32";
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else if (eltType.isInteger(64))
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name = "sparseValuesI64";
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else if (eltType.isInteger(32))
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name = "sparseValuesI32";
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else if (eltType.isInteger(16))
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name = "sparseValuesI16";
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else if (eltType.isInteger(8))
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name = "sparseValuesI8";
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else
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return failure();
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rewriter.replaceOpWithNewOp<CallOp>(
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op, resType, getFunc(op, name, resType, operands), operands);
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return success();
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}
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};
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/// Sparse conversion rule for tensor reconstruction.
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class SparseTensorToTensorConverter : public OpConversionPattern<ToTensorOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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// Simply fold the operator into the pointer to the sparse storage scheme.
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matchAndRewrite(ToTensorOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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// Check that all arguments of the tensor reconstruction operators are calls
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// into the support library that query exactly the same opaque pointer.
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Value ptr;
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for (Value op : operands) {
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if (auto call = op.getDefiningOp<CallOp>()) {
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Value arg = call.getOperand(0);
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if (!arg.getType().isa<LLVM::LLVMPointerType>())
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return failure();
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if (!ptr)
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ptr = arg;
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else if (arg != ptr)
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return failure();
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}
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}
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// If a single opaque pointer is found, perform the folding.
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if (!ptr)
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return failure();
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rewriter.replaceOp(op, ptr);
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return success();
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}
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};
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} // namespace
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//===----------------------------------------------------------------------===//
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// Public method for populating conversion rules.
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//===----------------------------------------------------------------------===//
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/// Populates the given patterns list with conversion rules required for
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/// the sparsification of linear algebra operations.
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void mlir::populateSparseTensorConversionPatterns(TypeConverter &typeConverter,
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RewritePatternSet &patterns) {
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patterns.add<SparseReturnConverter, SparseTensorToDimSizeConverter,
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SparseTensorNewConverter, SparseTensorConvertConverter,
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SparseTensorToPointersConverter, SparseTensorToIndicesConverter,
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SparseTensorToValuesConverter, SparseTensorToTensorConverter>(
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typeConverter, patterns.getContext());
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}
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