5167c36ab4
Depends On D111763 Reviewed By: aartbik Differential Revision: https://reviews.llvm.org/D111766
646 lines
25 KiB
C++
646 lines
25 KiB
C++
//===- SparseTensorConversion.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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/// Generates a constant zero of the given type.
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inline static Value constantZero(ConversionPatternRewriter &rewriter,
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Location loc, Type t) {
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return rewriter.create<arith::ConstantOp>(loc, t, rewriter.getZeroAttr(t));
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}
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/// Generates a constant of `index` type.
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inline static Value constantIndex(ConversionPatternRewriter &rewriter,
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Location loc, unsigned i) {
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return rewriter.create<arith::ConstantIndexOp>(loc, i);
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}
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/// Generates a constant of `i64` type.
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inline static Value constantI64(ConversionPatternRewriter &rewriter,
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Location loc, int64_t i) {
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return rewriter.create<arith::ConstantIntOp>(loc, i, 64);
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}
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/// Generates a constant of `i32` type.
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inline static Value constantI32(ConversionPatternRewriter &rewriter,
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Location loc, int32_t i) {
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return rewriter.create<arith::ConstantIntOp>(loc, i, 32);
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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 = rewriter.create<arith::ConstantOp>(
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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,
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TypeRange resultType, 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(constantI64(rewriter, loc, secPtr));
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params.push_back(constantI64(rewriter, loc, secInd));
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params.push_back(constantI64(rewriter, loc, primary));
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// User action and pointer.
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Type pTp = LLVM::LLVMPointerType::get(rewriter.getI8Type());
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if (!ptr)
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ptr = rewriter.create<LLVM::NullOp>(loc, pTp);
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params.push_back(constantI32(rewriter, loc, 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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Value v) {
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Type t = v.getType();
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Value zero = constantZero(rewriter, loc, t);
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if (t.isa<FloatType>())
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return rewriter.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UNE, v,
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zero);
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if (t.isIntOrIndex())
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return rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ne, v,
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zero);
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llvm_unreachable("Unknown element type");
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}
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/// Generates the code to read the value from tensor[ivs], and conditionally
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/// stores the indices ivs to the memory in ind. The generated code looks like
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/// the following and the insertion point after this routine is inside the
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/// if-then branch behind the assignment to ind. This is to ensure that the
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/// addEltX call generated after is inside the if-then branch.
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/// if (tensor[ivs]!=0) {
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/// ind = ivs
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static Value genIndexAndValueForDense(ConversionPatternRewriter &rewriter,
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Location loc, Value tensor, Value ind,
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ValueRange ivs) {
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Value val = rewriter.create<tensor::ExtractOp>(loc, tensor, ivs);
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Value cond = genIsNonzero(rewriter, loc, 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 = constantIndex(rewriter, loc, i++);
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rewriter.create<memref::StoreOp>(loc, iv, ind, idx);
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}
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return val;
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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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Type eltType, Value ptr, Value val, Value ind,
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Value perm) {
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Location loc = op->getLoc();
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StringRef name;
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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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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(rewriter.getI8Type());
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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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/// If the tensor is a sparse constant, generates and returns the pair of
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/// the constants for the indices and the values.
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static Optional<std::pair<Value, Value>>
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genSplitSparseConstant(ConversionPatternRewriter &rewriter, Location loc,
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Value tensor) {
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if (auto constOp = tensor.getDefiningOp<arith::ConstantOp>()) {
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if (auto attr = constOp.value().dyn_cast<SparseElementsAttr>()) {
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DenseElementsAttr indicesAttr = attr.getIndices();
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Value indices = rewriter.create<arith::ConstantOp>(loc, indicesAttr);
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DenseElementsAttr valuesAttr = attr.getValues();
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Value values = rewriter.create<arith::ConstantOp>(loc, valuesAttr);
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return std::make_pair(indices, values);
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}
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}
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return {};
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}
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/// Generates the code to copy the index at indices[ivs] to ind, and return
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/// the value at value[ivs].
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static Value genIndexAndValueForSparse(ConversionPatternRewriter &rewriter,
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Location loc, Value indices,
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Value values, Value ind, ValueRange ivs,
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unsigned rank) {
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for (unsigned i = 0; i < rank; i++) {
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Value idx = constantIndex(rewriter, loc, i);
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Value val = rewriter.create<tensor::ExtractOp>(loc, indices,
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ValueRange{ivs[0], idx});
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val =
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rewriter.create<arith::IndexCastOp>(loc, val, rewriter.getIndexType());
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rewriter.create<memref::StoreOp>(loc, val, ind, idx);
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}
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return rewriter.create<tensor::ExtractOp>(loc, values, ivs[0]);
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}
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/// Generates code to stack-allocate a `memref<?xindex>` where the `?`
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/// is the given `rank`. This array is intended to serve as a reusable
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/// buffer for storing the indices of a single tensor element, to avoid
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/// allocation in the body of loops.
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static Value allocaIndices(ConversionPatternRewriter &rewriter, Location loc,
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int64_t rank) {
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auto indexTp = rewriter.getIndexType();
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auto memTp = MemRefType::get({ShapedType::kDynamicSize}, indexTp);
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Value arg = constantIndex(rewriter, loc, rank);
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return rewriter.create<memref::AllocaOp>(loc, memTp, ValueRange{arg});
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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(constantIndex(rewriter, op.getLoc(), 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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auto src = adaptor.getOperands()[0];
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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->toCOO(); ; 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 = genNewCall(rewriter, op, encDst, 3, perm, src);
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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 or a sparse constant in COO =>
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// sparse conversion, which is handled as follows:
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// t = newSparseCOO()
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// ...code to fill the COO tensor t...
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// s = newSparseTensor(t)
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//
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// To fill the COO tensor from a dense tensor:
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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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//
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// To fill the COO tensor from a sparse constant in COO format:
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// for i in range(NNZ)
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// val = values[i]
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// [i1,..,ik] = indices[i]
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// t->add(val, [i1,..,ik], [p1,..,pk])
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//
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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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Value perm;
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Value ptr = genNewCall(rewriter, op, encDst, 2, perm);
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Value ind = allocaIndices(rewriter, loc, shape.getRank());
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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 = constantIndex(rewriter, loc, 0);
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Value one = constantIndex(rewriter, loc, 1);
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auto indicesValues = genSplitSparseConstant(rewriter, loc, src);
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bool isCOOConstant = indicesValues.hasValue();
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Value indices;
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|
Value values;
|
|
if (isCOOConstant) {
|
|
indices = indicesValues->first;
|
|
values = indicesValues->second;
|
|
lo.push_back(zero);
|
|
hi.push_back(linalg::createOrFoldDimOp(rewriter, loc, values, 0));
|
|
st.push_back(one);
|
|
} else {
|
|
for (unsigned i = 0, rank = shape.getRank(); i < rank; i++) {
|
|
lo.push_back(zero);
|
|
hi.push_back(linalg::createOrFoldDimOp(rewriter, loc, src, i));
|
|
st.push_back(one);
|
|
}
|
|
}
|
|
Type eltType = shape.getElementType();
|
|
unsigned rank = shape.getRank();
|
|
scf::buildLoopNest(
|
|
rewriter, op.getLoc(), lo, hi, st, {},
|
|
[&](OpBuilder &builder, Location loc, ValueRange ivs,
|
|
ValueRange args) -> scf::ValueVector {
|
|
Value val;
|
|
if (isCOOConstant)
|
|
val = genIndexAndValueForSparse(rewriter, loc, indices, values, ind,
|
|
ivs, rank);
|
|
else
|
|
val = genIndexAndValueForDense(rewriter, loc, src, ind, ivs);
|
|
genAddEltCall(rewriter, op, eltType, ptr, val, ind, perm);
|
|
return {};
|
|
});
|
|
rewriter.replaceOp(op, genNewCall(rewriter, op, encDst, 1, perm, ptr));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse conversion rule for the release operator.
|
|
class SparseTensorReleaseConverter : public OpConversionPattern<ReleaseOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ReleaseOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
StringRef name = "delSparseTensor";
|
|
TypeRange none;
|
|
rewriter.create<CallOp>(op.getLoc(), none,
|
|
getFunc(op, name, none, adaptor.getOperands()),
|
|
adaptor.getOperands());
|
|
rewriter.eraseOp(op);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse conversion rule for pointer accesses.
|
|
class SparseTensorToPointersConverter
|
|
: public OpConversionPattern<ToPointersOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ToPointersOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
Type resType = op.getType();
|
|
Type eltType = resType.cast<ShapedType>().getElementType();
|
|
StringRef name;
|
|
if (eltType.isIndex())
|
|
name = "sparsePointers"; // 64-bit, but its own name for unique signature
|
|
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,
|
|
adaptor.getOperands(),
|
|
/*emitCInterface=*/true),
|
|
adaptor.getOperands());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse conversion rule for index accesses.
|
|
class SparseTensorToIndicesConverter : public OpConversionPattern<ToIndicesOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ToIndicesOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
Type resType = op.getType();
|
|
Type eltType = resType.cast<ShapedType>().getElementType();
|
|
StringRef name;
|
|
if (eltType.isIndex())
|
|
name = "sparseIndices"; // 64-bit, but its own name for unique signature
|
|
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,
|
|
adaptor.getOperands(),
|
|
/*emitCInterface=*/true),
|
|
adaptor.getOperands());
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse conversion rule for value accesses.
|
|
class SparseTensorToValuesConverter : public OpConversionPattern<ToValuesOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(ToValuesOp op, OpAdaptor adaptor,
|
|
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(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,
|
|
SparseTensorReleaseConverter, SparseTensorToPointersConverter,
|
|
SparseTensorToIndicesConverter, SparseTensorToValuesConverter,
|
|
SparseTensorToTensorConverter>(typeConverter,
|
|
patterns.getContext());
|
|
}
|