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fuchsia / third_party / llvm-project / 69d757c0e8ffc5b49fda10df38e470a56d616ef4 / . / mlir / lib / Dialect / VectorOps / VectorTransforms.cpp
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//===- VectorToLoops.cpp - Conversion within the Vector dialect -----------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file implements target-independent rewrites as 1->N patterns.
//
//===----------------------------------------------------------------------===//
#include <type_traits>
#include "mlir/Dialect/AffineOps/AffineOps.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/Dialect/Utils/StructuredOpsUtils.h"
#include "mlir/Dialect/VectorOps/VectorOps.h"
#include "mlir/Dialect/VectorOps/VectorTransforms.h"
#include "mlir/Dialect/VectorOps/VectorUtils.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/Attributes.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/Function.h"
#include "mlir/IR/Location.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/Module.h"
#include "mlir/IR/OperationSupport.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/Types.h"
#include "mlir/Support/Functional.h"
#include "mlir/Support/STLExtras.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
#define DEBUG_TYPE "vector-to-vector"
using namespace mlir;
using llvm::dbgs;
using mlir::functional::zipMap;
/// Given a shape with sizes greater than 0 along all dimensions,
/// returns the distance, in number of elements, between a slice in a dimension
/// and the next slice in the same dimension.
/// e.g. shape[3, 4, 5] -> linearization_basis[20, 5, 1]
static SmallVector<int64_t, 8> computeStrides(ArrayRef<int64_t> shape) {
if (shape.empty())
return {};
SmallVector<int64_t, 8> tmp;
tmp.reserve(shape.size());
int64_t running = 1;
for (auto size : llvm::reverse(shape)) {
assert(size > 0 && "size must be nonnegative");
tmp.push_back(running);
running *= size;
}
return SmallVector<int64_t, 8>(tmp.rbegin(), tmp.rend());
}
static int64_t computeMaxLinearIndex(ArrayRef<int64_t> basis) {
if (basis.empty())
return 0;
int64_t res = 1;
for (auto b : basis)
res *= b;
return res;
}
/// Computes and returns the linearized index of 'offsets' w.r.t. 'basis'.
static int64_t linearize(ArrayRef<int64_t> offsets, ArrayRef<int64_t> basis) {
assert(offsets.size() == basis.size());
int64_t linearIndex = 0;
for (unsigned idx = 0, e = basis.size(); idx < e; ++idx)
linearIndex += offsets[idx] * basis[idx];
return linearIndex;
}
// Clones `op` into a new operations that takes `operands` and returns
// `resultTypes`.
static Operation *cloneOpWithOperandsAndTypes(PatternRewriter &builder,
Location loc, Operation *op,
ArrayRef<Value> operands,
ArrayRef<Type> resultTypes) {
OperationState res(loc, op->getName().getStringRef(), operands, resultTypes,
op->getAttrs());
return builder.createOperation(res);
}
// Populates 'resultElements[indexMap[i]]' with elements from 'inputElements[i]'
// for each index 'i' in inputElements with a valid mapping in 'indexMap'.
static void getMappedElements(const DenseMap<int64_t, int64_t> &indexMap,
ArrayRef<int64_t> inputElements,
SmallVectorImpl<int64_t> &resultElements) {
assert(indexMap.size() == resultElements.size());
assert(inputElements.size() >= resultElements.size());
for (unsigned i = 0, e = inputElements.size(); i < e; ++i) {
auto it = indexMap.find(i);
if (it != indexMap.end())
resultElements[it->second] = inputElements[i];
}
}
// Returns a tuple type with vector element types for each resulting slice
// of 'vectorType' unrolled by 'sizes' and 'strides'.
// TODO(andydavis) Move this to a utility function and share it with
// Extract/InsertSlicesOp verification.
static TupleType generateExtractSlicesOpResultType(VectorType vectorType,
ArrayRef<int64_t> sizes,
ArrayRef<int64_t> strides,
PatternRewriter &builder) {
assert(llvm::all_of(strides, [](int64_t s) { return s == 1; }));
assert(static_cast<int64_t>(sizes.size()) == vectorType.getRank());
assert(static_cast<int64_t>(strides.size()) == vectorType.getRank());
// Compute shape ratio of 'shape' and 'sizes'.
auto shape = vectorType.getShape();
auto maybeDimSliceCounts = shapeRatio(shape, sizes);
assert(maybeDimSliceCounts.hasValue());
auto sliceDimCounts = *maybeDimSliceCounts;
// Compute strides w.r.t number of slices in each dimension.
auto sliceStrides = computeStrides(sliceDimCounts);
int64_t sliceCount = computeMaxLinearIndex(sliceDimCounts);
SmallVector<Type, 4> vectorTypes(sliceCount);
for (unsigned i = 0; i < sliceCount; ++i) {
auto vectorOffsets = delinearize(sliceStrides, i);
auto elementOffsets =
computeElementOffsetsFromVectorSliceOffsets(sizes, vectorOffsets);
auto sliceSizes = computeSliceSizes(shape, sizes, elementOffsets);
// Create Vector type and add to 'vectorTypes[i]'.
vectorTypes[i] = VectorType::get(sliceSizes, vectorType.getElementType());
}
return TupleType::get(vectorTypes, builder.getContext());
}
// UnrolledVectorState aggregates per-operand/result vector state required for
// unrolling.
struct UnrolledVectorState {
SmallVector<int64_t, 4> unrolledShape;
SmallVector<int64_t, 4> unrollFactors;
SmallVector<int64_t, 8> basis;
int64_t numInstances;
Value slicesTuple;
};
// Populates 'state' with unrolled shape, unroll factors, basis and
// num unrolled instances for 'vectorType'.
static void initUnrolledVectorState(VectorType vectorType, Value initValue,
const DenseMap<int64_t, int64_t> &indexMap,
ArrayRef<int64_t> targetShape,
UnrolledVectorState &state,
PatternRewriter &builder) {
// Compute unrolled shape of 'vectorType'.
state.unrolledShape.resize(vectorType.getRank());
getMappedElements(indexMap, targetShape, state.unrolledShape);
// Compute unroll factors for unrolled shape.
auto maybeUnrollFactors =
shapeRatio(vectorType.getShape(), state.unrolledShape);
assert(maybeUnrollFactors.hasValue());
state.unrollFactors = *maybeUnrollFactors;
// Compute 'basis' and 'numInstances' based on 'state.unrollFactors'.
state.basis = computeStrides(state.unrollFactors);
state.numInstances = computeMaxLinearIndex(state.unrollFactors);
state.slicesTuple = nullptr;
if (initValue != nullptr) {
// Create ExtractSlicesOp.
SmallVector<int64_t, 4> sizes(state.unrolledShape);
SmallVector<int64_t, 4> strides(state.unrollFactors.size(), 1);
auto tupleType =
generateExtractSlicesOpResultType(vectorType, sizes, strides, builder);
state.slicesTuple = builder.create<vector::ExtractSlicesOp>(
initValue.getLoc(), tupleType, initValue, sizes, strides);
}
}
// Computes and returns the linear index of the unrolled vector at
// 'vectorOffsets' within the vector represented by 'state'.
static int64_t
getUnrolledVectorLinearIndex(UnrolledVectorState &state,
ArrayRef<int64_t> vectorOffsets,
DenseMap<int64_t, int64_t> &indexMap) {
// Compute vector offsets.
SmallVector<int64_t, 4> sliceOffsets(state.unrolledShape.size());
getMappedElements(indexMap, vectorOffsets, sliceOffsets);
// Compute and return linear index of 'sliceOffsets' w.r.t 'state.basis'.
return linearize(sliceOffsets, state.basis);
}
// Returns an unrolled vector at 'vectorOffsets' within the vector
// represented by 'state'. The vector is created from a slice of 'initValue'
// if not present in 'cache'.
static Value getOrCreateUnrolledVectorSlice(
Location loc, UnrolledVectorState &state, ArrayRef<int64_t> vectorOffsets,
ArrayRef<int64_t> offsets, DenseMap<int64_t, int64_t> &indexMap,
Value initValue, SmallVectorImpl<Value> &cache, PatternRewriter &builder) {
// Compute slice offsets.
SmallVector<int64_t, 4> sliceOffsets(state.unrolledShape.size());
getMappedElements(indexMap, offsets, sliceOffsets);
// TODO(b/144845578) Support non-1 strides.
SmallVector<int64_t, 4> sliceStrides(state.unrolledShape.size(), 1);
// Compute linear index of 'sliceOffsets' w.r.t 'state.basis'.
int64_t sliceLinearIndex =
getUnrolledVectorLinearIndex(state, vectorOffsets, indexMap);
assert(sliceLinearIndex < static_cast<int64_t>(cache.size()));
auto valueSlice = cache[sliceLinearIndex];
if (valueSlice == nullptr) {
// Return tuple element at 'sliceLinearIndex'.
auto tupleIndex = builder.getI64IntegerAttr(sliceLinearIndex);
auto initValueType = initValue.getType().cast<VectorType>();
auto vectorType =
VectorType::get(state.unrolledShape, initValueType.getElementType());
// Initialize 'cache' with slice from 'initValue'.
valueSlice = builder.create<vector::TupleGetOp>(
loc, vectorType, state.slicesTuple, tupleIndex);
// Store value back to 'cache'.
cache[sliceLinearIndex] = valueSlice;
}
return valueSlice;
}
// VectorState aggregates per-operand/result vector state required for
// creating slices of vector operands, and clones of the operation being
// unrolled.
struct VectorState {
// The type of this vector.
VectorType type;
// Map from iteration space index to vector dimension index.
DenseMap<int64_t, int64_t> indexMap;
// Index of this value in operation's operand list (-1 if not an operand).
int64_t operandIndex = -1;
// Accumulator iterator flag.
bool isAcc = false;
};
//
// unrollSingleResultStructuredOp
//
// Returns a value representing the result of structured operation 'op'
// with iteration bounds 'iterationBounds' unrolled to 'targetShape'.
// A list of VectorState objects must be specified in 'vectors', where
// each VectorState in the list represents a vector operand or vector result
// (if the operation does not have an accumulator operand).
// The VectorState at index 'resultIndex' in the list must be the state
// associated with the operations single result (i.e. either its accumulator
// operand or vector result value).
//
// Example:
//
// // Before unrolling
//
// operand0 operand1 operand2
// \ | /
// -------------------- opA --------------------
//
// // After unrolling by 2
//
// operand0 operand1 operand2
// / \ / \ / \
// slice00 slice01 slice10 slice11 slice20 slice21
// \ | | | / |
// -------------------- opA0 -------------------- |
// | | | |
// \ | | /
// -------------------- opA1 -------------------
// | |
// \ /
// insertslice
// |
// TODO(andydavis) Add the following canonicalization/simplifcation patterns:
// *) Add pattern which matches InsertStridedSlice -> StridedSlice and forwards
// InsertStridedSlice operand to StridedSlice.
// *) Add pattern which matches SourceOp -> StridedSlice -> UserOp which checks
// if there are duplicate identical StridedSlice ops from SourceOp, and
// rewrites itself to use the first duplicate. This transformation should
// cause users of identifical StridedSlice ops to reuse the same StridedSlice
// operation, and leave the duplicate StridedSlice ops with no users
// (removable with DCE).
// TODO(andydavis) Generalize this to support structured ops beyond
// vector ContractionOp, and merge it with 'unrollSingleResultOpMatchingType'
static Value unrollSingleResultStructuredOp(Operation *op,
ArrayRef<int64_t> iterationBounds,
std::vector<VectorState> &vectors,
unsigned resultIndex,
ArrayRef<int64_t> targetShape,
PatternRewriter &builder) {
auto shapedType = op->getResult(0).getType().dyn_cast_or_null<ShapedType>();
if (!shapedType || !shapedType.hasStaticShape())
assert(false && "Expected a statically shaped result type");
// Compute unroll factors for 'iterationBounds' based on 'targetShape'
auto maybeUnrollFactors = shapeRatio(iterationBounds, targetShape);
if (!maybeUnrollFactors.hasValue())
assert(false && "Failed to compute unroll factors for target shape");
auto unrollFactors = *maybeUnrollFactors;
// Compute unrolled vector state for each vector in 'vectors'.
unsigned numVectors = vectors.size();
SmallVector<UnrolledVectorState, 3> unrolledVectorState(numVectors);
for (unsigned i = 0; i < numVectors; ++i) {
int64_t operandIndex = vectors[i].operandIndex;
auto operand = operandIndex >= 0 ? op->getOperand(operandIndex) : nullptr;
initUnrolledVectorState(vectors[i].type, operand, vectors[i].indexMap,
targetShape, unrolledVectorState[i], builder);
}
// Compute number of total unrolled instances.
auto numUnrolledInstances = computeMaxLinearIndex(unrollFactors);
auto sliceStrides = computeStrides(unrollFactors);
auto &resultValueState = unrolledVectorState[resultIndex];
auto unrolledResultType = VectorType::get(resultValueState.unrolledShape,
shapedType.getElementType());
// Initialize caches for intermediate vector results.
std::vector<SmallVector<Value, 4>> caches(numVectors);
for (unsigned i = 0; i < numVectors; ++i)
caches[i].resize(unrolledVectorState[i].numInstances);
// Unroll 'numUnrolledInstances' of 'op', storing results in 'caches'.
for (unsigned i = 0; i < numUnrolledInstances; ++i) {
auto vectorOffsets = delinearize(sliceStrides, i);
auto elementOffsets =
computeElementOffsetsFromVectorSliceOffsets(targetShape, vectorOffsets);
// Get cached slice (or create slice) for each operand at 'offsets'.
SmallVector<Value, 3> operands;
operands.resize(op->getNumOperands());
for (unsigned i = 0; i < numVectors; ++i) {
int64_t operandIndex = vectors[i].operandIndex;
if (operandIndex < 0)
continue; // Output
auto operand = op->getOperand(operandIndex);
operands[operandIndex] = getOrCreateUnrolledVectorSlice(
op->getLoc(), unrolledVectorState[i], vectorOffsets, elementOffsets,
vectors[i].indexMap, operand, caches[i], builder);
}
// Create op on sliced vector arguments.
auto resultVector =
cloneOpWithOperandsAndTypes(builder, op->getLoc(), op, operands,
unrolledResultType)
->getResult(0);
// Compute linear result index.
int64_t linearIndex = getUnrolledVectorLinearIndex(
resultValueState, vectorOffsets, vectors[resultIndex].indexMap);
// Update result cache at 'linearIndex'.
caches[resultIndex][linearIndex] = resultVector;
}
// Create TupleOp of unrolled result vectors.
SmallVector<Type, 4> vectorTupleTypes(resultValueState.numInstances);
SmallVector<Value, 4> vectorTupleValues(resultValueState.numInstances);
for (unsigned i = 0; i < resultValueState.numInstances; ++i) {
vectorTupleTypes[i] = caches[resultIndex][i].getType().cast<VectorType>();
vectorTupleValues[i] = caches[resultIndex][i];
}
TupleType tupleType = builder.getTupleType(vectorTupleTypes);
Value tupleOp = builder.create<vector::TupleOp>(op->getLoc(), tupleType,
vectorTupleValues);
// Create InsertSlicesOp(Tuple(result_vectors)).
auto resultVectorType = op->getResult(0).getType().cast<VectorType>();
SmallVector<int64_t, 4> sizes(resultValueState.unrolledShape);
SmallVector<int64_t, 4> strides(resultValueState.unrollFactors.size(), 1);
Value insertSlicesOp = builder.create<vector::InsertSlicesOp>(
op->getLoc(), resultVectorType, tupleOp, builder.getI64ArrayAttr(sizes),
builder.getI64ArrayAttr(strides));
return insertSlicesOp;
}
static void getVectorContractionOpUnrollState(
vector::ContractionOp contractionOp, ArrayRef<int64_t> targetShape,
SmallVectorImpl<int64_t> &iterationBounds,
std::vector<VectorState> &vectors, unsigned &resultIndex) {
// Get contraction op iteration bounds.
contractionOp.getIterationBounds(iterationBounds);
assert(iterationBounds.size() == targetShape.size());
// Get map from iteration space index to lhs/rhs/result shape index.
std::vector<DenseMap<int64_t, int64_t>> iterationIndexMapList;
contractionOp.getIterationIndexMap(iterationIndexMapList);
unsigned numIterators = iterationIndexMapList.size();
vectors.resize(numIterators);
unsigned accOperandIndex = vector::ContractionOp::getAccOperandIndex();
for (unsigned i = 0; i < numIterators; ++i) {
vectors[i].type = contractionOp.getOperand(i).getType().cast<VectorType>();
vectors[i].indexMap = iterationIndexMapList[i];
vectors[i].operandIndex = i;
vectors[i].isAcc = i == accOperandIndex ? true : false;
}
if (llvm::size(contractionOp.masks()) == 2) {
// Add vectors for lhs/rhs vector mask arguments. Masks have the
// same vector shape lhs/rhs args, so copy their index maps.
vectors.push_back({contractionOp.getLHSVectorMaskType(),
vectors[0].indexMap, accOperandIndex + 1, false});
vectors.push_back({contractionOp.getRHSVectorMaskType(),
vectors[1].indexMap, accOperandIndex + 2, false});
}
// Unroll 'op' 'iterationBounds' to 'targetShape'.
// TODO(andydavis) Use linalg style 'args_in'/'args_out' to partition
// 'vectors' instead of 'resultIndex'.
resultIndex = accOperandIndex;
}
static void
getVectorElementwiseOpUnrollState(Operation *op, ArrayRef<int64_t> targetShape,
SmallVectorImpl<int64_t> &iterationBounds,
std::vector<VectorState> &vectors,
unsigned &resultIndex) {
// Verify that operation and operands all have the same vector shape.
auto resultType = op->getResult(0).getType().dyn_cast_or_null<VectorType>();
assert(resultType && "Expected op with vector result type");
auto resultShape = resultType.getShape();
// Verify that all operands have the same vector type as result.
assert(llvm::all_of(op->getOperandTypes(),
[=](Type type) { return type == resultType; }));
// Populate 'iterationBounds' with 'resultShape' for elementwise operations.
iterationBounds.assign(resultShape.begin(), resultShape.end());
// Create trivial elementwise identity index map based on 'resultShape'.
DenseMap<int64_t, int64_t> indexMap;
indexMap.reserve(resultShape.size());
for (unsigned i = 0; i < resultShape.size(); ++i)
indexMap[i] = i;
// Create VectorState each operand and single result.
unsigned numVectors = op->getNumOperands() + op->getNumResults();
vectors.resize(numVectors);
for (unsigned i = 0; i < op->getNumOperands(); ++i)
vectors[i] = {resultType, indexMap, i, false};
vectors[numVectors - 1] = {resultType, indexMap, -1, false};
resultIndex = numVectors - 1;
}
// Entry point for unrolling declarative pattern rewrites.
SmallVector<Value, 1> mlir::vector::unrollSingleResultOpMatchingType(
PatternRewriter &builder, Operation *op, ArrayRef<int64_t> targetShape) {
assert(op->getNumResults() == 1 && "Expected single result operation");
// Populate 'iterationBounds', 'vectors' and 'resultIndex' to unroll 'op'.
SmallVector<int64_t, 6> iterationBounds;
std::vector<VectorState> vectors;
unsigned resultIndex;
if (auto contractionOp = dyn_cast<vector::ContractionOp>(op)) {
// Populate state for vector ContractionOp.
getVectorContractionOpUnrollState(contractionOp, targetShape,
iterationBounds, vectors, resultIndex);
} else {
// Populate state for vector elementwise op.
getVectorElementwiseOpUnrollState(op, targetShape, iterationBounds, vectors,
resultIndex);
}
// Unroll 'op' with 'iterationBounds' to 'targetShape'.
return SmallVector<Value, 1>{unrollSingleResultStructuredOp(
op, iterationBounds, vectors, resultIndex, targetShape, builder)};
}
/// Generates slices of 'vectorType' according to 'sizes' and 'strides, and
/// calls 'fn' with linear index and indices for each slice.
static void
generateTransferOpSlices(Type memrefElementType, VectorType vectorType,
TupleType tupleType, ArrayRef<int64_t> sizes,
ArrayRef<int64_t> strides, ArrayRef<Value> indices,
PatternRewriter &rewriter,
function_ref<void(unsigned, ArrayRef<Value>)> fn) {
// Compute strides w.r.t. to slice counts in each dimension.
auto maybeDimSliceCounts = shapeRatio(vectorType.getShape(), sizes);
assert(maybeDimSliceCounts.hasValue());
auto sliceDimCounts = *maybeDimSliceCounts;
auto sliceStrides = computeStrides(sliceDimCounts);
int64_t numSlices = tupleType.size();
unsigned numSliceIndices = indices.size();
// Compute 'indexOffset' at which to update 'indices', which is equal
// to the memref rank (indices.size) minus the effective 'vectorRank'.
// The effective 'vectorRank', is equal to the rank of the vector type
// minus the rank of the memref vector element type (if it has one).
//
// For example:
//
// Given memref type 'memref<6x2x1xvector<2x4xf32>>' and vector
// transfer_read/write ops which read/write vectors of type
// 'vector<2x1x2x4xf32>'. The memref rank is 3, and the effective
// vector rank is 4 - 2 = 2, and so 'indexOffset' = 3 - 2 = 1.
//
unsigned vectorRank = vectorType.getRank();
if (auto memrefVectorElementType = memrefElementType.dyn_cast<VectorType>()) {
assert(vectorRank >= memrefVectorElementType.getRank());
vectorRank -= memrefVectorElementType.getRank();
}
unsigned indexOffset = numSliceIndices - vectorRank;
auto *ctx = rewriter.getContext();
for (unsigned i = 0; i < numSlices; ++i) {
auto vectorOffsets = delinearize(sliceStrides, i);
auto elementOffsets =
computeElementOffsetsFromVectorSliceOffsets(sizes, vectorOffsets);
// Compute 'sliceIndices' by adding 'sliceOffsets[i]' to 'indices[i]'.
SmallVector<Value, 4> sliceIndices(numSliceIndices);
for (unsigned j = 0; j < numSliceIndices; ++j) {
if (j < indexOffset) {
sliceIndices[j] = indices[j];
} else {
auto expr = getAffineDimExpr(0, ctx) +
getAffineConstantExpr(elementOffsets[j - indexOffset], ctx);
auto map = AffineMap::get(/*dimCount=*/1, /*symbolCount=*/0, expr);
sliceIndices[j] = rewriter.create<AffineApplyOp>(
indices[j].getLoc(), map, ArrayRef<Value>(indices[j]));
}
}
// Call 'fn' to generate slice 'i' at 'sliceIndices'.
fn(i, sliceIndices);
}
}
/// Returns true if 'map' is a suffix of an identity affine map, false
/// otherwise. Example: affine_map<(d0, d1, d2, d3) -> (d2, d3)>
static bool isIdentitySuffix(AffineMap map) {
if (map.getNumDims() < map.getNumResults())
return false;
ArrayRef<AffineExpr> results = map.getResults();
Optional<int> lastPos;
for (unsigned i = 0, e = map.getNumResults(); i < e; ++i) {
auto expr = results[i].dyn_cast<AffineDimExpr>();
if (!expr)
return false;
int currPos = static_cast<int>(expr.getPosition());
if (lastPos.hasValue() && currPos != lastPos.getValue() + 1)
return false;
lastPos = currPos;
}
return true;
}
namespace {
// Splits vector TransferReadOp into smaller TransferReadOps based on slicing
// scheme of its unique ExtractSlicesOp user.
struct SplitTransferReadOp : public OpRewritePattern<vector::TransferReadOp> {
using OpRewritePattern<vector::TransferReadOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(vector::TransferReadOp xferReadOp,
PatternRewriter &rewriter) const override {
// TODO(andydavis, ntv) Support splitting TransferReadOp with non-identity
// permutation maps. Repurpose code from MaterializeVectors transformation.
if (!isIdentitySuffix(xferReadOp.permutation_map()))
return matchFailure();
// Return unless the unique 'xferReadOp' user is an ExtractSlicesOp.
Value xferReadResult = xferReadOp.getResult();
auto extractSlicesOp =
dyn_cast<vector::ExtractSlicesOp>(*xferReadResult.getUsers().begin());
if (!xferReadResult.hasOneUse() || !extractSlicesOp)
return matchFailure();
// Get 'sizes' and 'strides' parameters from ExtractSlicesOp user.
auto sourceVectorType = extractSlicesOp.getSourceVectorType();
auto resultTupleType = extractSlicesOp.getResultTupleType();
SmallVector<int64_t, 4> sizes;
extractSlicesOp.getSizes(sizes);
SmallVector<int64_t, 4> strides;
extractSlicesOp.getStrides(strides);
assert(llvm::all_of(strides, [](int64_t s) { return s == 1; }));
Location loc = xferReadOp.getLoc();
auto memrefElementType =
xferReadOp.memref().getType().cast<MemRefType>().getElementType();
int64_t numSlices = resultTupleType.size();
SmallVector<Value, 4> vectorTupleValues(numSlices);
SmallVector<Value, 4> indices(xferReadOp.indices().begin(),
xferReadOp.indices().end());
auto createSlice = [&](unsigned index, ArrayRef<Value> sliceIndices) {
// Get VectorType for slice 'i'.
auto sliceVectorType = resultTupleType.getType(index);
// Create split TransferReadOp for 'sliceUser'.
vectorTupleValues[index] = rewriter.create<vector::TransferReadOp>(
loc, sliceVectorType, xferReadOp.memref(), sliceIndices,
xferReadOp.permutation_map(), xferReadOp.padding());
};
generateTransferOpSlices(memrefElementType, sourceVectorType,
resultTupleType, sizes, strides, indices, rewriter,
createSlice);
// Create tuple of splice xfer read operations.
Value tupleOp = rewriter.create<vector::TupleOp>(loc, resultTupleType,
vectorTupleValues);
// Replace 'xferReadOp' with result 'insertSlicesResult'.
rewriter.replaceOpWithNewOp<vector::InsertSlicesOp>(
xferReadOp, sourceVectorType, tupleOp, extractSlicesOp.sizes(),
extractSlicesOp.strides());
return matchSuccess();
}
};
// Splits vector TransferWriteOp into smaller TransferWriteOps for each source.
struct SplitTransferWriteOp : public OpRewritePattern<vector::TransferWriteOp> {
using OpRewritePattern<vector::TransferWriteOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(vector::TransferWriteOp xferWriteOp,
PatternRewriter &rewriter) const override {
// TODO(andydavis, ntv) Support splitting TransferWriteOp with non-identity
// permutation maps. Repurpose code from MaterializeVectors transformation.
if (!isIdentitySuffix(xferWriteOp.permutation_map()))
return matchFailure();
// Return unless the 'xferWriteOp' 'vector' operand is an 'InsertSlicesOp'.
auto *vectorDefOp = xferWriteOp.vector().getDefiningOp();
auto insertSlicesOp = dyn_cast_or_null<vector::InsertSlicesOp>(vectorDefOp);
if (!insertSlicesOp)
return matchFailure();
// Get TupleOp operand of 'insertSlicesOp'.
auto tupleOp = dyn_cast_or_null<vector::TupleOp>(
insertSlicesOp.vectors().getDefiningOp());
if (!tupleOp)
return matchFailure();
// Get 'sizes' and 'strides' parameters from InsertSlicesOp user.
auto sourceTupleType = insertSlicesOp.getSourceTupleType();
auto resultVectorType = insertSlicesOp.getResultVectorType();
SmallVector<int64_t, 4> sizes;
insertSlicesOp.getSizes(sizes);
SmallVector<int64_t, 4> strides;
insertSlicesOp.getStrides(strides);
Location loc = xferWriteOp.getLoc();
auto memrefElementType =
xferWriteOp.memref().getType().cast<MemRefType>().getElementType();
SmallVector<Value, 4> indices(xferWriteOp.indices().begin(),
xferWriteOp.indices().end());
auto createSlice = [&](unsigned index, ArrayRef<Value> sliceIndices) {
// Create split TransferWriteOp for source vector 'tupleOp.operand[i]'.
rewriter.create<vector::TransferWriteOp>(
loc, tupleOp.getOperand(index), xferWriteOp.memref(), sliceIndices,
xferWriteOp.permutation_map());
};
generateTransferOpSlices(memrefElementType, resultVectorType,
sourceTupleType, sizes, strides, indices, rewriter,
createSlice);
// Erase old 'xferWriteOp'.
rewriter.eraseOp(xferWriteOp);
return matchSuccess();
}
};
/// Decomposes ShapeCastOp on tuple-of-vectors to multiple ShapeCastOps, each
/// on vector types.
struct ShapeCastOpDecomposer : public OpRewritePattern<vector::ShapeCastOp> {
using OpRewritePattern<vector::ShapeCastOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(vector::ShapeCastOp shapeCastOp,
PatternRewriter &rewriter) const override {
// Check if 'shapeCastOp' has tuple source/result type.
auto sourceTupleType =
shapeCastOp.source().getType().dyn_cast_or_null<TupleType>();
auto resultTupleType =
shapeCastOp.result().getType().dyn_cast_or_null<TupleType>();
if (!sourceTupleType || !resultTupleType)
return matchFailure();
assert(sourceTupleType.size() == resultTupleType.size());
// Create single-vector ShapeCastOp for each source tuple element.
Location loc = shapeCastOp.getLoc();
SmallVector<Value, 8> resultElements;
resultElements.reserve(resultTupleType.size());
for (unsigned i = 0, e = sourceTupleType.size(); i < e; ++i) {
auto sourceElement = rewriter.create<vector::TupleGetOp>(
loc, sourceTupleType.getType(i), shapeCastOp.source(),
rewriter.getI64IntegerAttr(i));
resultElements.push_back(rewriter.create<vector::ShapeCastOp>(
loc, resultTupleType.getType(i), sourceElement));
}
// Replace 'shapeCastOp' with tuple of 'resultElements'.
rewriter.replaceOpWithNewOp<vector::TupleOp>(shapeCastOp, resultTupleType,
resultElements);
return matchSuccess();
}
};
/// ShapeCastOpFolder folds cancelling ShapeCastOps away.
//
// Example:
//
// The following MLIR with cancelling ShapeCastOps:
//
// %0 = source : vector<5x4x2xf32>
// %1 = shape_cast %0 : vector<5x4x2xf32> to vector<20x2xf32>
// %2 = shape_cast %1 : vector<20x2xf32> to vector<5x4x2xf32>
// %3 = user %2 : vector<5x4x2xf32>
//
// Should canonicalize to the following:
//
// %0 = source : vector<5x4x2xf32>
// %1 = user %0 : vector<5x4x2xf32>
//
struct ShapeCastOpFolder : public OpRewritePattern<vector::ShapeCastOp> {
using OpRewritePattern<vector::ShapeCastOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(vector::ShapeCastOp shapeCastOp,
PatternRewriter &rewriter) const override {
// Check if 'shapeCastOp' has vector source/result type.
auto sourceVectorType =
shapeCastOp.source().getType().dyn_cast_or_null<VectorType>();
auto resultVectorType =
shapeCastOp.result().getType().dyn_cast_or_null<VectorType>();
if (!sourceVectorType || !resultVectorType)
return matchFailure();
// Check if shape cast op source operand is also a shape cast op.
auto sourceShapeCastOp = dyn_cast_or_null<vector::ShapeCastOp>(
shapeCastOp.source().getDefiningOp());
if (!sourceShapeCastOp)
return matchFailure();
auto operandSourceVectorType =
sourceShapeCastOp.source().getType().cast<VectorType>();
auto operandResultVectorType =
sourceShapeCastOp.result().getType().cast<VectorType>();
// Check if shape cast operations invert each other.
if (operandSourceVectorType != resultVectorType ||
operandResultVectorType != sourceVectorType)
return matchFailure();
rewriter.replaceOp(shapeCastOp, sourceShapeCastOp.source());
return matchSuccess();
}
};
// Patter rewrite which forward tuple elements to their users.
// User(TupleGetOp(ExtractSlicesOp(InsertSlicesOp(TupleOp(Producer)))))
// -> User(Producer)
struct TupleGetFolderOp : public OpRewritePattern<vector::TupleGetOp> {
using OpRewritePattern<vector::TupleGetOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(vector::TupleGetOp tupleGetOp,
PatternRewriter &rewriter) const override {
// Return if 'tupleGetOp.vectors' arg was not defined by ExtractSlicesOp.
auto extractSlicesOp = dyn_cast_or_null<vector::ExtractSlicesOp>(
tupleGetOp.vectors().getDefiningOp());
if (!extractSlicesOp)
return matchFailure();
// Return if 'extractSlicesOp.vector' arg was not defined by InsertSlicesOp.
auto insertSlicesOp = dyn_cast_or_null<vector::InsertSlicesOp>(
extractSlicesOp.vector().getDefiningOp());
if (!insertSlicesOp)
return matchFailure();
// Return if 'insertSlicesOp.vectors' arg was not defined by TupleOp.
auto tupleOp = dyn_cast_or_null<vector::TupleOp>(
insertSlicesOp.vectors().getDefiningOp());
if (!tupleOp)
return matchFailure();
// Forward Value from 'tupleOp' at 'tupleGetOp.index'.
Value tupleValue = tupleOp.getOperand(tupleGetOp.getIndex());
rewriter.replaceOp(tupleGetOp, tupleValue);
return matchSuccess();
}
};
/// Progressive lowering of ExtractSlicesOp to tuple of StridedSliceOp.
/// One:
/// %x = vector.extract_slices %0
/// is replaced by:
/// %a = vector.strided_slice %0
/// %b = vector.strided_slice %0
/// ..
/// %x = vector.tuple %a, %b, ..
class ExtractSlicesOpLowering
: public OpRewritePattern<vector::ExtractSlicesOp> {
public:
using OpRewritePattern<vector::ExtractSlicesOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(vector::ExtractSlicesOp op,
PatternRewriter &rewriter) const override {
auto loc = op.getLoc();
VectorType vectorType = op.getSourceVectorType();
auto shape = vectorType.getShape();
SmallVector<int64_t, 4> sizes;
op.getSizes(sizes);
SmallVector<int64_t, 4> strides;
op.getStrides(strides); // all-ones at the moment
// For each element in the tuple, generate the proper strided slice.
TupleType tupleType = op.getResultTupleType();
int64_t tupleSize = tupleType.size();
SmallVector<Value, 4> tupleValues(tupleSize);
auto sliceStrides = computeStrides(shape, sizes);
for (int64_t i = 0; i < tupleSize; ++i) {
auto vectorOffsets = delinearize(sliceStrides, i);
auto elementOffsets =
computeElementOffsetsFromVectorSliceOffsets(sizes, vectorOffsets);
auto sliceSizes = computeSliceSizes(shape, sizes, elementOffsets);
// Insert in tuple.
tupleValues[i] = rewriter.create<vector::StridedSliceOp>(
loc, op.vector(), elementOffsets, sliceSizes, strides);
}
rewriter.replaceOpWithNewOp<vector::TupleOp>(op, tupleType, tupleValues);
return matchSuccess();
}
};
/// Progressive lowering of InsertSlicesOp to series of InsertStridedSliceOp.
/// One:
/// %x = vector.insert_slices %0
/// is replaced by:
/// %r0 = vector.splat 0
// %t1 = vector.tuple_get %0, 0
/// %r1 = vector.insert_strided_slice %r0, %t1
// %t2 = vector.tuple_get %0, 1
/// %r2 = vector.insert_strided_slice %r1, %t2
/// ..
/// %x = ..
class InsertSlicesOpLowering : public OpRewritePattern<vector::InsertSlicesOp> {
public:
using OpRewritePattern<vector::InsertSlicesOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(vector::InsertSlicesOp op,
PatternRewriter &rewriter) const override {
auto loc = op.getLoc();
VectorType vectorType = op.getResultVectorType();
auto shape = vectorType.getShape();
SmallVector<int64_t, 4> sizes;
op.getSizes(sizes);
SmallVector<int64_t, 4> strides;
op.getStrides(strides); // all-ones at the moment
// Prepare result.
auto elemType = vectorType.getElementType();
Value zero = rewriter.create<ConstantOp>(loc, elemType,
rewriter.getZeroAttr(elemType));
Value result = rewriter.create<SplatOp>(loc, vectorType, zero);
// For each element in the tuple, extract the proper strided slice.
TupleType tupleType = op.getSourceTupleType();
int64_t tupleSize = tupleType.size();
auto sliceStrides = computeStrides(shape, sizes);
for (int64_t i = 0; i < tupleSize; ++i) {
auto vectorOffsets = delinearize(sliceStrides, i);
auto elementOffsets =
computeElementOffsetsFromVectorSliceOffsets(sizes, vectorOffsets);
// Extract from tuple into the result.
auto index = rewriter.getI64IntegerAttr(i);
auto tupleGet = rewriter.create<vector::TupleGetOp>(
loc, tupleType.getType(i), op.getOperand(), index);
result = rewriter.create<vector::InsertStridedSliceOp>(
loc, tupleGet, result, elementOffsets, strides);
}
rewriter.replaceOp(op, result);
return matchSuccess();
}
};
/// Progressive lowering of ConstractionOp.
/// One:
/// %x = vector.contract with at least one free/batch dimension
/// is replaced by:
/// %a = vector.contract with one less free/batch dimension
/// %b = vector.contract with one less free/batch dimension
/// ..
/// %x = combine %a %b ..
/// until a pure contraction is reached (no free/batch dimensions),
/// which is replaced by a fma/reduction op.
///
/// TODO(ajcbik): break down into transpose/reshape/cast ops
/// when they become available to avoid code dup
/// TODO(ajcbik): investigate lowering order impact on performance
class ContractionOpLowering : public OpRewritePattern<vector::ContractionOp> {
public:
using OpRewritePattern<vector::ContractionOp>::OpRewritePattern;
PatternMatchResult matchAndRewrite(vector::ContractionOp op,
PatternRewriter &rewriter) const override {
// TODO(ajcbik): implement masks
if (llvm::size(op.masks()) != 0)
return matchFailure();
// Find first batch dimension in LHS/RHS, and lower when found.
std::vector<std::pair<int64_t, int64_t>> batchDimMap = op.getBatchDimMap();
if (!batchDimMap.empty()) {
int64_t lhsIndex = batchDimMap[0].first;
int64_t rhsIndex = batchDimMap[0].second;
rewriter.replaceOp(op, lowerParallel(op, lhsIndex, rhsIndex, rewriter));
return matchSuccess();
}
// Collect contracting dimensions.
std::vector<std::pair<int64_t, int64_t>> contractingDimMap =
op.getContractingDimMap();
DenseSet<int64_t> lhsContractingDimSet;
DenseSet<int64_t> rhsContractingDimSet;
for (auto &dimPair : contractingDimMap) {
lhsContractingDimSet.insert(dimPair.first);
rhsContractingDimSet.insert(dimPair.second);
}
// Find first free dimension in LHS, and lower when found.
VectorType lhsType = op.getLhsType();
for (int64_t lhsIndex = 0, e = lhsType.getRank(); lhsIndex < e;
++lhsIndex) {
if (lhsContractingDimSet.count(lhsIndex) == 0) {
rewriter.replaceOp(
op, lowerParallel(op, lhsIndex, /*rhsIndex=*/-1, rewriter));
return matchSuccess();
}
}
// Find first free dimension in RHS, and lower when found.
VectorType rhsType = op.getRhsType();
for (int64_t rhsIndex = 0, e = rhsType.getRank(); rhsIndex < e;
++rhsIndex) {
if (rhsContractingDimSet.count(rhsIndex) == 0) {
rewriter.replaceOp(
op, lowerParallel(op, /*lhsIndex=*/-1, rhsIndex, rewriter));
return matchSuccess();
}
}
// Lower the first remaining reduction dimension.
if (!contractingDimMap.empty()) {
rewriter.replaceOp(op, lowerReduction(op, rewriter));
return matchSuccess();
}
return matchFailure();
}
private:
// Lower one parallel dimension.
// TODO(ajcbik): consider reusing existing contract unrolling
Value lowerParallel(vector::ContractionOp op, int64_t lhsIndex,
int64_t rhsIndex, PatternRewriter &rewriter) const {
VectorType lhsType = op.getLhsType();
VectorType rhsType = op.getRhsType();
VectorType resType = op.getResultType().cast<VectorType>();
// Find the iterator type index and result index.
SmallVector<AffineMap, 4> iMap = op.getIndexingMaps();
int64_t iterIndex = -1;
int64_t dimSize = -1;
if (lhsIndex >= 0) {
iterIndex =
iMap[0].getResult(lhsIndex).cast<AffineDimExpr>().getPosition();
assert((rhsIndex < 0 || iterIndex == iMap[1]
.getResult(rhsIndex)
.cast<AffineDimExpr>()
.getPosition()) &&
"parallel index should be free in LHS or batch in LHS/RHS");
dimSize = lhsType.getDimSize(lhsIndex);
} else {
assert(rhsIndex >= 0 && "missing parallel index");
iterIndex =
iMap[1].getResult(rhsIndex).cast<AffineDimExpr>().getPosition();
dimSize = rhsType.getDimSize(rhsIndex);
}
assert(iterIndex >= 0 && "parallel index not listed in operand mapping");
Optional<int64_t> lookup = getResultIndex(iMap[2], iterIndex);
assert(lookup.hasValue() && "parallel index not listed in reduction");
int64_t resIndex = lookup.getValue();
// Construct new iterator types and affine map array attribute.
SmallVector<AffineMap, 4> lowIndexingMaps;
lowIndexingMaps.push_back(adjustMap(iMap[0], iterIndex, rewriter));
lowIndexingMaps.push_back(adjustMap(iMap[1], iterIndex, rewriter));
lowIndexingMaps.push_back(adjustMap(iMap[2], iterIndex, rewriter));
auto lowAffine = rewriter.getAffineMapArrayAttr(lowIndexingMaps);
auto lowIter =
rewriter.getArrayAttr(adjustIter(op.iterator_types(), iterIndex));
// Unroll into a series of lower dimensional vector.contract ops.
Location loc = op.getLoc();
Value result = zeroVector(loc, resType, rewriter);
for (int64_t d = 0; d < dimSize; ++d) {
auto lhs = reshapeLoad(loc, op.lhs(), lhsType, lhsIndex, d, rewriter);
auto rhs = reshapeLoad(loc, op.rhs(), rhsType, rhsIndex, d, rewriter);
auto acc = reshapeLoad(loc, op.acc(), resType, resIndex, d, rewriter);
Value lowContract = rewriter.create<vector::ContractionOp>(
loc, lhs, rhs, acc, lowAffine, lowIter);
result = reshapeStore(loc, lowContract, result, resType, resIndex, d,
rewriter);
}
return result;
}
// Lower one reduction dimension.
Value lowerReduction(vector::ContractionOp op,
PatternRewriter &rewriter) const {
auto loc = op.getLoc();
VectorType lhsType = op.getLhsType();
VectorType rhsType = op.getRhsType();
Type resType = op.getResultType();
assert(!resType.isa<VectorType>());
// Use iterator index 0.
int64_t iterIndex = 0;
SmallVector<AffineMap, 4> iMap = op.getIndexingMaps();
Optional<int64_t> lookupLhs = getResultIndex(iMap[0], iterIndex);
Optional<int64_t> lookupRhs = getResultIndex(iMap[1], iterIndex);
assert(lookupLhs.hasValue() && "missing LHS parallel index");
assert(lookupRhs.hasValue() && "missing RHS parallel index");
int64_t lhsIndex = lookupLhs.getValue();
int64_t rhsIndex = lookupRhs.getValue();
int64_t dimSize = lhsType.getDimSize(lhsIndex);
assert(dimSize == rhsType.getDimSize(rhsIndex) && "corrupt shape");
// Base case.
if (lhsType.getRank() == 1) {
assert(rhsType.getRank() == 1 && "corrupt contraction");
Value zero = zeroVector(loc, lhsType, rewriter);
Value fma = rewriter.create<vector::FMAOp>(loc, op.lhs(), op.rhs(), zero);
StringAttr kind = rewriter.getStringAttr("add");
return rewriter.create<vector::ReductionV2Op>(loc, resType, kind, fma,
op.acc());
}
// Construct new iterator types and affine map array attribute.
SmallVector<AffineMap, 4> lowIndexingMaps;
lowIndexingMaps.push_back(adjustMap(iMap[0], iterIndex, rewriter));
lowIndexingMaps.push_back(adjustMap(iMap[1], iterIndex, rewriter));
lowIndexingMaps.push_back(adjustMap(iMap[2], iterIndex, rewriter));
auto lowAffine = rewriter.getAffineMapArrayAttr(lowIndexingMaps);
auto lowIter =
rewriter.getArrayAttr(adjustIter(op.iterator_types(), iterIndex));
// Unroll into a series of lower dimensional vector.contract ops.
// By feeding the initial accumulator into the first contraction,
// and the result of each contraction into the next, eventually
// the sum of all reductions is computed.
Value result = op.acc();
for (int64_t d = 0; d < dimSize; ++d) {
auto lhs = reshapeLoad(loc, op.lhs(), lhsType, lhsIndex, d, rewriter);
auto rhs = reshapeLoad(loc, op.rhs(), rhsType, rhsIndex, d, rewriter);
result = rewriter.create<vector::ContractionOp>(loc, lhs, rhs, result,
lowAffine, lowIter);
}
return result;
}
// Helper method to construct a zero vector.
static Value zeroVector(Location loc, VectorType vType,
PatternRewriter &rewriter) {
Type eltType = vType.getElementType();
Value zero = rewriter.create<ConstantOp>(loc, eltType,
rewriter.getZeroAttr(eltType));
return rewriter.create<SplatOp>(loc, vType, zero);
}
// Helper to find an index in an affine map.
static Optional<int64_t> getResultIndex(AffineMap map, int64_t index) {
for (int64_t i = 0, e = map.getNumResults(); i < e; ++i) {
int64_t idx = map.getResult(i).cast<AffineDimExpr>().getPosition();
if (idx == index)
return i;
}
return None;
}
// Helper to construct iterator types with one index removed.
static SmallVector<Attribute, 4> adjustIter(ArrayAttr iteratorTypes,
int64_t index) {
SmallVector<Attribute, 4> results;
for (auto it : llvm::enumerate(iteratorTypes)) {
int64_t idx = it.index();
if (idx == index) {
continue;
}
results.push_back(it.value());
}
return results;
}
// Helper to construct an affine map with one index removed.
static AffineMap adjustMap(AffineMap map, int64_t index,
PatternRewriter &rewriter) {
SmallVector<AffineExpr, 4> results;
for (int64_t i = 0, e = map.getNumResults(); i < e; ++i) {
int64_t idx = map.getResult(i).cast<AffineDimExpr>().getPosition();
if (idx == index)
continue;
// Re-insert remaining indices, but renamed when occurring
// after the removed index.
auto targetExpr =
getAffineDimExpr(idx < index ? idx : idx - 1, rewriter.getContext());
results.push_back(targetExpr);
}
// Since (...) -> () cannot be represented properly,
// we resort to an empty map when this situation happens.
return results.empty() ? AffineMap::get(rewriter.getContext())
: AffineMap::get(map.getNumDims() - 1, 0, results);
}
// Helper to drop dimension from vector type.
static Type adjustType(VectorType tp, int64_t index) {
int64_t rank = tp.getRank();
Type eltType = tp.getElementType();
if (rank == 1) {
assert(index == 0 && "index for scalar result out of bounds");
return eltType;
}
SmallVector<int64_t, 4> adjustedShape;
for (int64_t i = 0; i < rank; ++i) {
// Omit dimension at the given index.
if (i == index)
continue;
// Otherwise, add dimension back.
adjustedShape.push_back(tp.getDimSize(i));
}
return VectorType::get(adjustedShape, eltType);
}
// Helper method to possibly drop a dimension in a load.
// TODO(ajcbik): use a reshaping vector load (and share lowering code)
static Value reshapeLoad(Location loc, Value val, VectorType type,
int64_t index, int64_t pos,
PatternRewriter &rewriter) {
if (index == -1)
return val;
Type lowType = adjustType(type, 0);
// At extraction dimension?
if (index == 0) {
auto posAttr = rewriter.getI64ArrayAttr(pos);
return rewriter.create<vector::ExtractOp>(loc, lowType, val, posAttr);
}
// Unroll leading dimensions.
VectorType vType = lowType.cast<VectorType>();
VectorType resType = adjustType(type, index).cast<VectorType>();
Value result = zeroVector(loc, resType, rewriter);
for (int64_t d = 0, e = resType.getDimSize(0); d < e; d++) {
auto posAttr = rewriter.getI64ArrayAttr(d);
Value ext = rewriter.create<vector::ExtractOp>(loc, vType, val, posAttr);
Value load = reshapeLoad(loc, ext, vType, index - 1, pos, rewriter);
result = rewriter.create<vector::InsertOp>(loc, resType, load, result,
posAttr);
}
return result;
}
// Helper method to possibly drop a dimension in a store.
// TODO(ajcbik): use a reshaping vector store (and share lowering code)
static Value reshapeStore(Location loc, Value val, Value result,
VectorType type, int64_t index, int64_t pos,
PatternRewriter &rewriter) {
// Unmodified?
if (index == -1)
return val;
// At insertion dimension?
if (index == 0) {
auto posAttr = rewriter.getI64ArrayAttr(pos);
return rewriter.create<vector::InsertOp>(loc, type, val, result, posAttr);
}
// Unroll leading dimensions.
Type lowType = adjustType(type, 0);
VectorType vType = lowType.cast<VectorType>();
Type insType = adjustType(vType, 0);
for (int64_t d = 0, e = type.getDimSize(0); d < e; d++) {
auto posAttr = rewriter.getI64ArrayAttr(d);
Value ext =
rewriter.create<vector::ExtractOp>(loc, vType, result, posAttr);
Value ins =
rewriter.create<vector::ExtractOp>(loc, insType, val, posAttr);
Value sto = reshapeStore(loc, ins, ext, vType, index - 1, pos, rewriter);
result =
rewriter.create<vector::InsertOp>(loc, type, sto, result, posAttr);
}
return result;
}
};
} // namespace
// TODO(andydavis) Add pattern to rewrite ExtractSlices(ConstantMaskOp).
// TODO(andydavis) Add this as DRR pattern.
void mlir::vector::populateVectorToVectorTransformationPatterns(
OwningRewritePatternList &patterns, MLIRContext *context) {
patterns.insert<ShapeCastOpDecomposer, ShapeCastOpFolder, SplitTransferReadOp,
SplitTransferWriteOp, TupleGetFolderOp>(context);
}
void mlir::vector::populateVectorSlicesLoweringPatterns(
OwningRewritePatternList &patterns, MLIRContext *context) {
patterns.insert<ExtractSlicesOpLowering, InsertSlicesOpLowering>(context);
}
void mlir::vector::populateVectorContractLoweringPatterns(
OwningRewritePatternList &patterns, MLIRContext *context) {
patterns.insert<ContractionOpLowering>(context);
}