654 lines
26 KiB
C++
654 lines
26 KiB
C++
//===- SparseTensorCodegen.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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// A pass that converts sparse tensor types and primitives to actual compiler
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// visible buffers and actual compiler IR that implements these primitives on
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// the selected sparse tensor storage schemes. This pass provides an alternative
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// to the SparseTensorConversion pass, eliminating the dependence on a runtime
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// support library, and providing much more opportunities for subsequent
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// compiler optimization of the generated code.
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//
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//===----------------------------------------------------------------------===//
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#include "CodegenUtils.h"
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#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
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#include "mlir/Dialect/Func/IR/FuncOps.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/SparseTensor/IR/SparseTensor.h"
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#include "mlir/Dialect/SparseTensor/Transforms/Passes.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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/// Flatten a list of operands that may contain sparse tensors.
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static void flattenOperands(ValueRange operands,
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SmallVectorImpl<Value> &flattened) {
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// In case of
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// sparse_tensor, c, sparse_tensor
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// ==>
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// memref ..., c, memref ...
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for (auto operand : operands) {
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if (auto cast =
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dyn_cast<UnrealizedConversionCastOp>(operand.getDefiningOp());
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cast && getSparseTensorEncoding(cast->getResultTypes()[0]))
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// An unrealized_conversion_cast will be inserted by type converter to
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// inter-mix the gap between 1:N conversion between sparse tensors and
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// fields. In this case, take the operands in the cast and replace the
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// sparse tensor output with the flattened type array.
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flattened.append(cast.getOperands().begin(), cast.getOperands().end());
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else
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flattened.push_back(operand);
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}
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}
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/// Gets the dimension size for the given sparse tensor at the given dim.
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/// Returns None if no sparse encoding is attached to the tensor type.
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static Optional<Value> sizeFromTensorAtDim(OpBuilder &rewriter, Location loc,
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RankedTensorType tensorTp,
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Value adaptedValue, unsigned dim) {
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auto enc = getSparseTensorEncoding(tensorTp);
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if (!enc)
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return llvm::None;
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// Access into static dimension can query original type directly.
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// Note that this is typically already done by DimOp's folding.
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auto shape = tensorTp.getShape();
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if (!ShapedType::isDynamic(shape[dim]))
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return constantIndex(rewriter, loc, shape[dim]);
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// Any other query can consult the dimSizes array at field 0 using,
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// accounting for the reordering applied to the sparse storage.
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auto tuple =
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llvm::cast<UnrealizedConversionCastOp>(adaptedValue.getDefiningOp());
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Value idx = constantIndex(rewriter, loc, toStoredDim(tensorTp, dim));
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return rewriter.create<memref::LoadOp>(loc, tuple.getInputs().front(), idx)
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.getResult();
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}
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/// Returns field index of sparse tensor type for pointers/indices, when set.
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static unsigned getFieldIndex(Type type, unsigned ptrDim, unsigned idxDim) {
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assert(getSparseTensorEncoding(type));
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RankedTensorType rType = type.cast<RankedTensorType>();
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unsigned field = 2; // start past sizes
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unsigned ptr = 0;
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unsigned idx = 0;
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for (unsigned r = 0, rank = rType.getShape().size(); r < rank; r++) {
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if (isCompressedDim(rType, r)) {
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if (ptr++ == ptrDim)
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return field;
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field++;
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if (idx++ == idxDim)
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return field;
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field++;
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} else if (isSingletonDim(rType, r)) {
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if (idx++ == idxDim)
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return field;
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field++;
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} else {
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assert(isDenseDim(rType, r)); // no fields
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}
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}
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assert(ptrDim == -1u && idxDim == -1u);
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return field + 1; // return values field index
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}
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/// Maps a sparse tensor type to the appropriate compounded buffers.
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static Optional<LogicalResult>
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convertSparseTensorType(Type type, SmallVectorImpl<Type> &fields) {
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auto enc = getSparseTensorEncoding(type);
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if (!enc)
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return llvm::None;
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// Construct the basic types.
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auto *context = type.getContext();
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unsigned idxWidth = enc.getIndexBitWidth();
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unsigned ptrWidth = enc.getPointerBitWidth();
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RankedTensorType rType = type.cast<RankedTensorType>();
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Type indexType = IndexType::get(context);
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Type idxType = idxWidth ? IntegerType::get(context, idxWidth) : indexType;
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Type ptrType = ptrWidth ? IntegerType::get(context, ptrWidth) : indexType;
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Type eltType = rType.getElementType();
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//
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// Sparse tensor storage for rank-dimensional tensor is organized as a
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// single compound type with the following fields. Note that every
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// memref with ? size actually behaves as a "vector", i.e. the stored
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// size is the capacity and the used size resides in the memSizes array.
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//
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// struct {
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// memref<rank x index> dimSizes ; size in each dimension
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// memref<n x index> memSizes ; sizes of ptrs/inds/values
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// ; per-dimension d:
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// ; if dense:
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// <nothing>
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// ; if compresed:
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// memref<? x ptr> pointers-d ; pointers for sparse dim d
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// memref<? x idx> indices-d ; indices for sparse dim d
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// ; if singleton:
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// memref<? x idx> indices-d ; indices for singleton dim d
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// memref<? x eltType> values ; values
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// };
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//
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unsigned rank = rType.getShape().size();
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// The dimSizes array.
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fields.push_back(MemRefType::get({rank}, indexType));
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// The memSizes array.
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unsigned lastField = getFieldIndex(type, -1u, -1u);
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fields.push_back(MemRefType::get({lastField - 2}, indexType));
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// Per-dimension storage.
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for (unsigned r = 0; r < rank; r++) {
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// Dimension level types apply in order to the reordered dimension.
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// As a result, the compound type can be constructed directly in the given
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// order. Clients of this type know what field is what from the sparse
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// tensor type.
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if (isCompressedDim(rType, r)) {
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fields.push_back(MemRefType::get({ShapedType::kDynamicSize}, ptrType));
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fields.push_back(MemRefType::get({ShapedType::kDynamicSize}, idxType));
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} else if (isSingletonDim(rType, r)) {
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fields.push_back(MemRefType::get({ShapedType::kDynamicSize}, idxType));
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} else {
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assert(isDenseDim(rType, r)); // no fields
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}
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}
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// The values array.
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fields.push_back(MemRefType::get({ShapedType::kDynamicSize}, eltType));
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assert(fields.size() == lastField);
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return success();
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}
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/// Create allocation operation.
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static Value createAllocation(OpBuilder &builder, Location loc, Type type,
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Value sz) {
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auto memType = MemRefType::get({ShapedType::kDynamicSize}, type);
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return builder.create<memref::AllocOp>(loc, memType, sz);
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}
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/// Creates allocation for each field in sparse tensor type. Note that
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/// for all dynamic memrefs, the memory size is really the capacity of
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/// the "vector", while the actual size resides in the sizes array.
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///
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/// TODO: for efficiency, we will need heuristis to make educated guesses
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/// on the required capacities
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///
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static void createAllocFields(OpBuilder &builder, Location loc, Type type,
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ValueRange dynSizes,
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SmallVectorImpl<Value> &fields) {
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auto enc = getSparseTensorEncoding(type);
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assert(enc);
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// Construct the basic types.
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unsigned idxWidth = enc.getIndexBitWidth();
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unsigned ptrWidth = enc.getPointerBitWidth();
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RankedTensorType rType = type.cast<RankedTensorType>();
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Type indexType = builder.getIndexType();
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Type idxType = idxWidth ? builder.getIntegerType(idxWidth) : indexType;
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Type ptrType = ptrWidth ? builder.getIntegerType(ptrWidth) : indexType;
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Type eltType = rType.getElementType();
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auto shape = rType.getShape();
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unsigned rank = shape.size();
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bool allDense = true;
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Value one = constantIndex(builder, loc, 1);
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Value linear = one;
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Value heuristic = one; // FIX, see TODO above
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// Build original sizes.
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SmallVector<Value, 8> sizes;
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for (unsigned r = 0, o = 0; r < rank; r++) {
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if (ShapedType::isDynamic(shape[r]))
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sizes.push_back(dynSizes[o++]);
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else
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sizes.push_back(constantIndex(builder, loc, shape[r]));
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}
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// The dimSizes array.
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Value dimSizes =
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builder.create<memref::AllocOp>(loc, MemRefType::get({rank}, indexType));
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fields.push_back(dimSizes);
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// The sizes array.
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unsigned lastField = getFieldIndex(type, -1u, -1u);
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Value memSizes = builder.create<memref::AllocOp>(
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loc, MemRefType::get({lastField - 2}, indexType));
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fields.push_back(memSizes);
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// Per-dimension storage.
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for (unsigned r = 0; r < rank; r++) {
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// Get the original dimension (ro) for the current stored dimension.
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unsigned ro = toOrigDim(rType, r);
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builder.create<memref::StoreOp>(loc, sizes[ro], dimSizes,
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constantIndex(builder, loc, r));
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linear = builder.create<arith::MulIOp>(loc, linear, sizes[ro]);
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// Allocate fields.
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if (isCompressedDim(rType, r)) {
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fields.push_back(createAllocation(builder, loc, ptrType, heuristic));
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fields.push_back(createAllocation(builder, loc, idxType, heuristic));
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allDense = false;
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} else if (isSingletonDim(rType, r)) {
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fields.push_back(createAllocation(builder, loc, idxType, heuristic));
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allDense = false;
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} else {
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assert(isDenseDim(rType, r)); // no fields
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}
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}
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// The values array. For all-dense, the full length is required.
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// In all other case, we resort to the heuristical initial value.
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Value valuesSz = allDense ? linear : heuristic;
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fields.push_back(createAllocation(builder, loc, eltType, valuesSz));
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// Set memSizes.
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if (allDense)
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builder.create<memref::StoreOp>(
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loc, valuesSz, memSizes,
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constantIndex(builder, loc, 0)); // TODO: avoid memSizes in this case?
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else
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builder.create<linalg::FillOp>(
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loc, ValueRange{constantZero(builder, loc, indexType)},
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ValueRange{memSizes});
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assert(fields.size() == lastField);
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}
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/// Creates a straightforward counting for-loop.
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static scf::ForOp createFor(OpBuilder &builder, Location loc, Value count) {
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Type indexType = builder.getIndexType();
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Value zero = constantZero(builder, loc, indexType);
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Value one = constantOne(builder, loc, indexType);
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scf::ForOp forOp = builder.create<scf::ForOp>(loc, zero, count, one);
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builder.setInsertionPointToStart(forOp.getBody());
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return forOp;
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}
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//===----------------------------------------------------------------------===//
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// Codegen rules.
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//===----------------------------------------------------------------------===//
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/// Sparse tensor storage conversion rule for returns.
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class SparseReturnConverter : public OpConversionPattern<func::ReturnOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(func::ReturnOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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SmallVector<Value, 8> flattened;
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flattenOperands(adaptor.getOperands(), flattened);
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// Create a return with the flattened value extracted from sparse tensors.
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rewriter.replaceOpWithNewOp<func::ReturnOp>(op, flattened);
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return success();
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}
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};
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/// Sparse tensor storage conversion rule for calls.
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class SparseCallConverter : public OpConversionPattern<func::CallOp> {
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public:
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// The default CallOp converter can not handle 1:N type conversion.
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(func::CallOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Location loc = op.getLoc();
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// In case of:
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// sparse_tensor, f, sparse_tensor = call @foo(...)
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// ==>
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// memref..., f, memref = call @foo(...) replace with
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// cast(memref...)->sparse_tensor, f, cast(memref...)->sparse_tensor
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SmallVector<Type, 8> finalRetTy;
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if (failed(typeConverter->convertTypes(op.getResultTypes(), finalRetTy)))
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return failure();
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// (1) Genereates new call with flattened return value.
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SmallVector<Value, 8> flattened;
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flattenOperands(adaptor.getOperands(), flattened);
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auto newCall = rewriter.create<func::CallOp>(loc, op.getCallee(),
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finalRetTy, flattened);
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// (2) Create cast operation for sparse tensor returns.
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SmallVector<Value, 4> castedRet;
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// Tracks the offset of current return value (of the orignal call)
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// relative to the new call (after sparse tensor flattening);
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unsigned retOffset = 0;
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// Temporal buffer to hold the flattened list of type for
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// a sparse tensor.
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SmallVector<Type, 8> sparseFlat;
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for (auto ret : op.getResults()) {
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assert(retOffset < newCall.getNumResults());
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auto retType = ret.getType();
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if (failed(typeConverter->convertType(retType, sparseFlat)))
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// This should never happen.
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llvm_unreachable("Failed to convert type in sparse tensor codegen");
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// Converted types can not be empty when the type conversion succeed.
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assert(!sparseFlat.empty());
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if (sparseFlat.size() > 1) {
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auto flatSize = sparseFlat.size();
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ValueRange sparseElem(iterator_range<ResultRange::iterator>(
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newCall.result_begin() + retOffset,
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newCall.result_begin() + retOffset + flatSize));
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auto castOp = rewriter.create<UnrealizedConversionCastOp>(
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loc, TypeRange({retType}), sparseElem);
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castedRet.push_back(castOp.getResult(0));
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retOffset += flatSize;
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} else {
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// If this is an 1:1 conversion, no need for casting.
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castedRet.push_back(newCall.getResult(retOffset));
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retOffset++;
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}
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sparseFlat.clear();
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}
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assert(castedRet.size() == op.getNumResults());
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rewriter.replaceOp(op, castedRet);
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return success();
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}
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};
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/// Sparse codegen rule for dimension accesses.
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class SparseDimOpConverter : 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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Optional<int64_t> index = op.getConstantIndex();
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if (!index)
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return failure();
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auto sz =
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sizeFromTensorAtDim(rewriter, op.getLoc(),
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op.getSource().getType().cast<RankedTensorType>(),
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adaptor.getSource(), *index);
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if (!sz)
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return failure();
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rewriter.replaceOp(op, *sz);
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return success();
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}
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};
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/// Sparse codegen rule for trivial tensor casts.
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class SparseCastConverter : public OpConversionPattern<tensor::CastOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(tensor::CastOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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// Only rewrite identically annotated source/dest.
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auto encDst = getSparseTensorEncoding(op.getType());
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auto encSrc = getSparseTensorEncoding(op.getSource().getType());
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if (!encDst || encDst != encSrc)
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return failure();
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rewriter.replaceOp(op, adaptor.getOperands());
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return success();
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}
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};
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/// Sparse codgen rule for the alloc operator.
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class SparseTensorAllocConverter
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: public OpConversionPattern<bufferization::AllocTensorOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(bufferization::AllocTensorOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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RankedTensorType 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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if (op.getCopy())
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return rewriter.notifyMatchFailure(op, "tensor copy not implemented");
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// Construct allocation for each field.
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Location loc = op.getLoc();
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SmallVector<Value, 8> fields;
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createAllocFields(rewriter, loc, resType, adaptor.getOperands(), fields);
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rewriter.replaceOpWithNewOp<UnrealizedConversionCastOp>(
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op, TypeRange{resType}, fields);
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return success();
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}
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};
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/// Sparse codegen rule for the dealloc operator.
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class SparseTensorDeallocConverter
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: public OpConversionPattern<bufferization::DeallocTensorOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(bufferization::DeallocTensorOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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auto enc = getSparseTensorEncoding(op.getTensor().getType());
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if (!enc)
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return failure();
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// Replace the sparse tensor deallocation with field deallocations.
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Location loc = op.getLoc();
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auto tuple = llvm::cast<UnrealizedConversionCastOp>(
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adaptor.getTensor().getDefiningOp());
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for (auto input : tuple.getInputs())
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// Deallocate every buffer used to store the sparse tensor handler.
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rewriter.create<memref::DeallocOp>(loc, input);
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rewriter.eraseOp(op);
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return success();
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}
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};
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/// Sparse codegen rule for tensor rematerialization.
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class SparseTensorLoadConverter : public OpConversionPattern<LoadOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(LoadOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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if (op.getHasInserts()) {
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// Finalize any pending insertions.
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// TODO: implement
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}
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rewriter.replaceOp(op, adaptor.getOperands());
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return success();
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}
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};
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/// Sparse codegen rule for the expand op.
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class SparseExpandConverter : public OpConversionPattern<ExpandOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(ExpandOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Location loc = op->getLoc();
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RankedTensorType srcType =
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op.getTensor().getType().cast<RankedTensorType>();
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Type eltType = srcType.getElementType();
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Type boolType = rewriter.getIntegerType(1);
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Type idxType = rewriter.getIndexType();
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|
// All initialization should be done on entry of the loop nest.
|
|
rewriter.setInsertionPointAfter(op.getTensor().getDefiningOp());
|
|
// Determine the size for access expansion (always the innermost stored
|
|
// dimension size, translated back to original dimension). Note that we
|
|
// recursively rewrite the new DimOp on the **original** tensor.
|
|
unsigned innerDim = toOrigDim(srcType, srcType.getRank() - 1);
|
|
auto sz = sizeFromTensorAtDim(rewriter, loc, srcType, adaptor.getTensor(),
|
|
innerDim);
|
|
assert(sz); // This for sure is a sparse tensor
|
|
// Generate a memref for `sz` elements of type `t`.
|
|
auto genAlloc = [&](Type t) {
|
|
auto memTp = MemRefType::get({ShapedType::kDynamicSize}, t);
|
|
return rewriter.create<memref::AllocOp>(loc, memTp, ValueRange{*sz});
|
|
};
|
|
// Allocate temporary buffers for values/filled-switch and added.
|
|
// We do not use stack buffers for this, since the expanded size may
|
|
// be rather large (as it envelops a single expanded dense dimension).
|
|
Value values = genAlloc(eltType);
|
|
Value filled = genAlloc(boolType);
|
|
Value added = genAlloc(idxType);
|
|
Value zero = constantZero(rewriter, loc, idxType);
|
|
// Reset the values/filled-switch to all-zero/false. Note that this
|
|
// introduces an O(N) operation into the computation, but this reset
|
|
// operation is amortized over the innermost loops for the access
|
|
// pattern expansion. As noted in the operation doc, we would like
|
|
// to amortize this setup cost even between kernels.
|
|
rewriter.create<linalg::FillOp>(
|
|
loc, ValueRange{constantZero(rewriter, loc, eltType)},
|
|
ValueRange{values});
|
|
rewriter.create<linalg::FillOp>(
|
|
loc, ValueRange{constantZero(rewriter, loc, boolType)},
|
|
ValueRange{filled});
|
|
// Replace expansion op with these buffers and initial index.
|
|
assert(op.getNumResults() == 4);
|
|
rewriter.replaceOp(op, {values, filled, added, zero});
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse codegen rule for the compress operator.
|
|
class SparseCompressConverter : public OpConversionPattern<CompressOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(CompressOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
Location loc = op->getLoc();
|
|
RankedTensorType dstType =
|
|
op.getTensor().getType().cast<RankedTensorType>();
|
|
Type eltType = dstType.getElementType();
|
|
Value values = adaptor.getValues();
|
|
Value filled = adaptor.getFilled();
|
|
Value added = adaptor.getAdded();
|
|
Value count = adaptor.getCount();
|
|
// If the innermost dimension is ordered, we need to sort the indices
|
|
// in the "added" array prior to applying the compression.
|
|
unsigned rank = dstType.getShape().size();
|
|
if (isOrderedDim(dstType, rank - 1))
|
|
rewriter.create<SortOp>(loc, count, ValueRange{added}, ValueRange{});
|
|
// While performing the insertions, we also need to reset the elements
|
|
// of the values/filled-switch by only iterating over the set elements,
|
|
// to ensure that the runtime complexity remains proportional to the
|
|
// sparsity of the expanded access pattern.
|
|
//
|
|
// Generate
|
|
// for (i = 0; i < count; i++) {
|
|
// index = added[i];
|
|
// value = values[index];
|
|
//
|
|
// TODO: insert prev_indices, index, value
|
|
//
|
|
// values[index] = 0;
|
|
// filled[index] = false;
|
|
// }
|
|
Value i = createFor(rewriter, loc, count).getInductionVar();
|
|
Value index = rewriter.create<memref::LoadOp>(loc, added, i);
|
|
rewriter.create<memref::LoadOp>(loc, values, index);
|
|
// TODO: insert
|
|
rewriter.create<memref::StoreOp>(loc, constantZero(rewriter, loc, eltType),
|
|
values, index);
|
|
rewriter.create<memref::StoreOp>(loc, constantI1(rewriter, loc, false),
|
|
filled, index);
|
|
|
|
// Deallocate the buffers on exit of the full loop nest.
|
|
Operation *parent = op;
|
|
for (; isa<scf::ForOp>(parent->getParentOp()) ||
|
|
isa<scf::WhileOp>(parent->getParentOp()) ||
|
|
isa<scf::ParallelOp>(parent->getParentOp()) ||
|
|
isa<scf::IfOp>(parent->getParentOp());
|
|
parent = parent->getParentOp())
|
|
;
|
|
rewriter.setInsertionPointAfter(parent);
|
|
rewriter.create<memref::DeallocOp>(loc, values);
|
|
rewriter.create<memref::DeallocOp>(loc, filled);
|
|
rewriter.create<memref::DeallocOp>(loc, added);
|
|
rewriter.eraseOp(op);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Base class for getter-like operations, e.g., to_indices, to_pointers.
|
|
template <typename SourceOp, typename Base>
|
|
class SparseGetterOpConverter : public OpConversionPattern<SourceOp> {
|
|
public:
|
|
using OpAdaptor = typename SourceOp::Adaptor;
|
|
using OpConversionPattern<SourceOp>::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(SourceOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
// Replace the requested pointer access with corresponding field.
|
|
// The cast_op is inserted by type converter to intermix 1:N type
|
|
// conversion.
|
|
auto tuple = llvm::cast<UnrealizedConversionCastOp>(
|
|
adaptor.getTensor().getDefiningOp());
|
|
unsigned idx = Base::getIndexForOp(tuple, op);
|
|
auto fields = tuple.getInputs();
|
|
assert(idx < fields.size());
|
|
rewriter.replaceOp(op, fields[idx]);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Sparse codegen rule for pointer accesses.
|
|
class SparseToPointersConverter
|
|
: public SparseGetterOpConverter<ToPointersOp, SparseToPointersConverter> {
|
|
public:
|
|
using SparseGetterOpConverter::SparseGetterOpConverter;
|
|
// Callback for SparseGetterOpConverter.
|
|
static unsigned getIndexForOp(UnrealizedConversionCastOp /*tuple*/,
|
|
ToPointersOp op) {
|
|
uint64_t dim = op.getDimension().getZExtValue();
|
|
return getFieldIndex(op.getTensor().getType(), /*ptrDim=*/dim, -1u);
|
|
}
|
|
};
|
|
|
|
/// Sparse codegen rule for index accesses.
|
|
class SparseToIndicesConverter
|
|
: public SparseGetterOpConverter<ToIndicesOp, SparseToIndicesConverter> {
|
|
public:
|
|
using SparseGetterOpConverter::SparseGetterOpConverter;
|
|
// Callback for SparseGetterOpConverter.
|
|
static unsigned getIndexForOp(UnrealizedConversionCastOp /*tuple*/,
|
|
ToIndicesOp op) {
|
|
uint64_t dim = op.getDimension().getZExtValue();
|
|
return getFieldIndex(op.getTensor().getType(), -1u, /*idxDim=*/dim);
|
|
}
|
|
};
|
|
|
|
/// Sparse codegen rule for value accesses.
|
|
class SparseToValuesConverter
|
|
: public SparseGetterOpConverter<ToValuesOp, SparseToValuesConverter> {
|
|
public:
|
|
using SparseGetterOpConverter::SparseGetterOpConverter;
|
|
// Callback for SparseGetterOpConverter.
|
|
static unsigned getIndexForOp(UnrealizedConversionCastOp tuple,
|
|
ToValuesOp /*op*/) {
|
|
// The last field holds the value buffer.
|
|
return tuple.getInputs().size() - 1;
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Sparse tensor type conversion into an actual buffer.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
mlir::SparseTensorTypeToBufferConverter::SparseTensorTypeToBufferConverter() {
|
|
addConversion([](Type type) { return type; });
|
|
addConversion(convertSparseTensorType);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Public method for populating conversion rules.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Populates the given patterns list with conversion rules required for
|
|
/// the sparsification of linear algebra operations.
|
|
void mlir::populateSparseTensorCodegenPatterns(TypeConverter &typeConverter,
|
|
RewritePatternSet &patterns) {
|
|
patterns.add<SparseReturnConverter, SparseCallConverter, SparseDimOpConverter,
|
|
SparseCastConverter, SparseTensorAllocConverter,
|
|
SparseTensorDeallocConverter, SparseTensorLoadConverter,
|
|
SparseExpandConverter, SparseCompressConverter,
|
|
SparseToPointersConverter, SparseToIndicesConverter,
|
|
SparseToValuesConverter>(typeConverter, patterns.getContext());
|
|
}
|