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opensecura / 3p / openxla / iree / dec8a174742e9205fb2733cabd56bf768874bf0b / . / iree / compiler / Dialect / Flow / Transforms / DispatchLinalgOnTensors.cpp
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// Copyright 2020 The IREE Authors
//
// Licensed 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
#include "iree/compiler/Dialect/Flow/IR/FlowDialect.h"
#include "iree/compiler/Dialect/Flow/IR/FlowOps.h"
#include "iree/compiler/Dialect/Flow/IR/FlowTypes.h"
#include "iree/compiler/Dialect/Flow/Transforms/DestructiveUpdateUtils.h"
#include "iree/compiler/Dialect/Flow/Transforms/PassDetail.h"
#include "iree/compiler/Dialect/Flow/Transforms/Passes.h"
#include "iree/compiler/Dialect/Shape/IR/Builders.h"
#include "iree/compiler/Dialect/Shape/IR/ShapeDialect.h"
#include "iree/compiler/Dialect/Shape/IR/ShapeOps.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/Support/CommandLine.h"
#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/IR/Block.h"
#include "mlir/IR/BlockAndValueMapping.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinAttributes.h"
#include "mlir/IR/MLIRContext.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeRange.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Pass/PassManager.h"
#include "mlir/Support/LLVM.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "mlir/Transforms/Passes.h"
#include "mlir/Transforms/RegionUtils.h"
#define DEBUG_TYPE "iree-flow-dispatch-linalg-on-tensors"
// TODO(ravishankarm): Prune this list. These flags should go away ASAP!!
static llvm::cl::list<int64_t> clLinalgOnTensorsTileSizes(
"iree-flow-dispatch-linalg-on-tensors-tile-sizes",
llvm::cl::desc("Comma-separated list of tile sizes for tiling on tensors"),
llvm::cl::CommaSeparated);
// TODO(#5040): This works for the most part but the downstream bufferization
// needs to be sorted out before this can be made the default. Remove after
// making this default.
static llvm::cl::opt<bool> clEnableOperandFusion(
"iree-flow-dispatch-formation-enable-operand-fusion",
llvm::cl::desc(
"Enable fusing operand producers during dispatch region formation"),
llvm::cl::init(false));
static const char kRootOpAttr[] = "__root_op__";
static const char kFusionGroupsAttr[] = "__fused_op__";
namespace mlir {
namespace iree_compiler {
namespace IREE {
namespace Flow {
static unsigned kNumMaxParallelDims = 3;
namespace {
/// PatternRewriter that allows replacing only a subset of uses.
/// Since this only adds a method, it can just be static_cast'ed to when
/// applying a rewrite.
/// TODO(nicolasvasilache): upstream support for this is landing, rebase on that
struct PatternRewriterWithScopedReplaceOp : public PatternRewriter {
void replaceOpWithinScope(Operation *op, ValueRange newValues, Block *block) {
// Notify the rewriter subclass that we're about to replace this root.
notifyRootReplaced(op);
assert(op->getNumResults() == newValues.size() &&
"incorrect # of replacement values");
bool erase = true;
SmallVector<Operation *, 4> ops;
SmallVector<Value, 4> operands, repls;
for (auto &use : op->getUses()) {
if (!block->getParentOp()->isProperAncestor(use.getOwner())) {
erase = false;
continue;
}
OpResult opResult = use.get().cast<OpResult>();
ops.push_back(use.getOwner());
operands.push_back(use.get());
repls.push_back(newValues[opResult.getResultNumber()]);
}
// Perform the actual replacements.
for (auto it : llvm::zip(ops, operands, repls))
std::get<0>(it)->replaceUsesOfWith(std::get<1>(it), std::get<2>(it));
if (erase) {
notifyOperationRemoved(op);
op->erase();
}
}
};
struct DispatchLinalgOnTensorsPass
: public DispatchLinalgOnTensorsBase<DispatchLinalgOnTensorsPass> {
void getDependentDialects(DialectRegistry &registry) const override {
registry
.insert<AffineDialect, IREE::Flow::FlowDialect, linalg::LinalgDialect,
memref::MemRefDialect, scf::SCFDialect, ShapeDialect>();
}
DispatchLinalgOnTensorsPass() = default;
DispatchLinalgOnTensorsPass(const DispatchLinalgOnTensorsPass &pass) {}
void runOnOperation() override;
private:
Statistic numDispatches{this, "number of dispatches",
"Number of Flow dispatches created"};
};
} // namespace
//===----------------------------------------------------------------------===//
// Utility methods
//===----------------------------------------------------------------------===//
/// Returns the number of consecutive outer loops that are "parallel". This is a
/// copy of the function from
/// iree/compiler/Conversion/CodegenUtils/FunctionUtils.h that is duplicated
/// here to avoid adding an build dependency.
static size_t getNumOuterParallelLoops(linalg::LinalgOp op) {
return op.iterator_types()
.getValue()
.take_while([](Attribute attr) -> bool {
return linalg::isParallelIteratorType(attr);
})
.size();
}
/// Returns the number of loops of the operation that are to be tiled.
static size_t getNumTilableLoops(linalg::LinalgOp op) {
return std::min<size_t>(getNumOuterParallelLoops(op), kNumMaxParallelDims);
}
/// Given the `shape` of the computation with the first element being the
/// slowest varying and last element being the fastest warying returns the
/// workload value with
/// - fastest varying dimension first, i.e., x, y, z order
/// - the workload padded to `kNumMaxParallelDims` with ones if needed.
/// The `shape` is expected to be of size less than or equal to
/// `kNumMaxParallelDims`.
static SmallVector<Value, 4> convertToWorkload(OpBuilder &b, Location loc,
ArrayRef<Value> shape) {
assert(shape.size() <= kNumMaxParallelDims &&
"workload cannot be more than 3D for now");
SmallVector<Value, 4> workload = llvm::to_vector<4>(llvm::reverse(shape));
Value one = b.create<ConstantIndexOp>(loc, 1);
workload.resize(kNumMaxParallelDims, one);
return workload;
}
/// Returns the fusion groups for the given `op`.
static SmallVector<int64_t, 1> getFusionGroups(Operation *op) {
SmallVector<int64_t, 1> fusionGroups = {};
if (auto fusionGroupsAttr = op->getAttrOfType<ArrayAttr>(kFusionGroupsAttr)) {
fusionGroups = llvm::to_vector<1>(llvm::map_range(
fusionGroupsAttr,
[](Attribute attr) { return attr.cast<IntegerAttr>().getInt(); }));
}
return fusionGroups;
}
/// Appends the given `op` to the `newGroups` fusion groups.
static void appendToFusionGroup(Operation *op, ArrayRef<int64_t> newGroups) {
SmallVector<int64_t, 1> fusionGroups = getFusionGroups(op);
fusionGroups.append(newGroups.begin(), newGroups.end());
op->setAttr(kFusionGroupsAttr, Builder(op).getI64ArrayAttr(fusionGroups));
}
/// Returns true if the given `op` is in the `targetGroup` fusion group.
static bool isInFusionGroup(Operation *op, unsigned targetGroup) {
if (ArrayAttr opGroupAttr = op->getAttrOfType<ArrayAttr>(kFusionGroupsAttr)) {
return llvm::any_of(opGroupAttr, [&targetGroup](Attribute attr) {
return attr.cast<IntegerAttr>().getInt() == targetGroup;
});
}
return false;
}
//===----------------------------------------------------------------------===//
// Op property charecterizations
//===----------------------------------------------------------------------===//
/// The current fusion algorithm has some embedded heuristics that are meant to
/// be a first simple start, and can be adapted over time. Note hoever that it
/// is better to have a simple default strategy and use some search-based
/// techniques for actual heuristics. Current heuristics classify operations in
/// this heirarchy
/// - Root Op : These are ops that are computationally intensive and most
/// probably dominate model execution time. These are in general named ops
/// like linalg.matmul, linalg.conv, etc. These are tiled and distributed
/// across workgroups.
/// - Dispatchable ops : These are ops that are not root operations, but still
/// perform some "meaningful" computation. Typically, fused element-wise
/// operations, represented as linalg.generic. These could be fused with root
/// operations using tile + fuse, or could be in their own dispatch regions.
/// - Always fused dispatchable ops : These are ops that are chosen to always be
/// fused into dispatch regions that use their values, since when bufferized
/// they can be converted into being no-copy/aliasing operations. Examples of
/// this is linalg.tensor_reshape that can be converted to a linalg.reshape on
/// bufferization. These are different from dispatchable ops in that they are
/// never in their own dispatch region unless there is no consumer to fuse
/// them with. Typically when the result of the operation is the
/// output.
/// - Always cloned into dispatch op : These are operations that are operations
/// that are always cloned into their consuming dispatch regions and never end
/// up in their own dispatch regions. Typical examples are splat constants and
/// linalg.init_tensor operations.
static bool isRootOp(Operation *op) {
if (auto contractionOp = dyn_cast<linalg::ContractionOpInterface>(op)) {
if (contractionOp.isRowMajorMatmul() ||
contractionOp.isColumnMajorMatmul() ||
contractionOp.isRowMajorBatchMatmul()) {
return true;
}
}
return isa<linalg::ConvInputNHWCFilterHWCFOp,
linalg::DepthwiseConvInputNHWCFilterHWCOp,
linalg::DepthwiseConvInputNHWCFilterHWCFOp,
linalg::PoolingNHWCMaxI8Op, linalg::PoolingNHWCMaxI16Op,
linalg::PoolingNHWCMaxI32Op, linalg::PoolingNHWCSumFOp,
linalg::PoolingNHWCMaxFOp, linalg::PoolingNHWCMinFOp>(op);
}
static bool isAlwaysClonedIntoDispatchOp(Operation *op) {
if (isa<IndexCastOp, linalg::InitTensorOp, tensor::ExtractOp>(op)) {
return true;
}
if (auto constantOp = dyn_cast<ConstantOp>(op)) {
return constantOp.getResult().getType().isIntOrIndexOrFloat();
}
if (llvm::all_of(op->getOperands(),
[&](Value v) { return v.getType().isIntOrFloat(); }) &&
llvm::all_of(op->getResults(),
[&](Value v) { return v.getType().isIntOrFloat(); })) {
return true;
}
return false;
}
static bool isDispatchableOp(Operation *op) {
// Ignore operations already in dispatch regions.
if (op->getParentOfType<IREE::Flow::DispatchWorkgroupsOp>()) {
return false;
}
// Linalg ops are marked dispatchable.
if ((op->getDialect() !=
op->getContext()->getLoadedDialect<linalg::LinalgDialect>()) &&
!isa<SubTensorOp, SubTensorInsertOp>(op)) {
return false;
}
return !isAlwaysClonedIntoDispatchOp(op);
}
static bool isAlwaysFusedIntoDispatchOp(Operation *op) {
return isDispatchableOp(op) &&
(isa<linalg::TensorCollapseShapeOp, SubTensorOp>(op) ||
isa<linalg::TensorExpandShapeOp, SubTensorOp>(op));
}
//===----------------------------------------------------------------------===//
// Methods that help creating the dispatch regions
//===----------------------------------------------------------------------===//
// Creates a flow.dispatch.workgroup op without arguments.
// All the necessary operands are transiently captured and rewritten late as
// operands. This greatly simplifies transformations into the resulting op.
static std::pair<IREE::Flow::DispatchWorkgroupsOp, Operation *>
buildOperandLessFlowDispatchWorkgroupOp(PatternRewriter &rewriter, Location loc,
ArrayRef<Value> count, Operation *op) {
auto dispatchOp = rewriter.create<IREE::Flow::DispatchWorkgroupsOp>(
loc, count, op->getResultTypes(), /*result_dims=*/ValueRange{},
/*operands=*/ValueRange{},
/*operand_dims=*/ValueRange{},
/*tied_operands=*/ArrayRef<int64_t>{});
Region &region = dispatchOp.body();
Block *block = &region.front();
Operation *clonedOp;
{
OpBuilder::InsertionGuard g(rewriter);
rewriter.setInsertionPointToStart(block);
clonedOp = rewriter.clone(*op);
for (auto it : llvm::zip(clonedOp->getResults(),
dispatchOp.body().getArguments().take_back(
clonedOp->getNumResults()))) {
rewriter.create<IREE::Flow::DispatchTensorStoreOp>(
loc, std::get<0>(it), std::get<1>(it), llvm::None, llvm::None,
llvm::None, rewriter.getArrayAttr({}), rewriter.getArrayAttr({}),
rewriter.getArrayAttr({}));
}
rewriter.create<IREE::Flow::ReturnOp>(loc);
}
DEBUG_WITH_TYPE(DEBUG_TYPE, llvm::dbgs() << "Created dispatchOp shell "
<< *dispatchOp << "\n");
return {dispatchOp, clonedOp};
}
// Fuses producers marked in the same group recursively.
//
// The impl does not worry about the dispatchOp, operands and arguments are set
// in a post-pattern `legalizeDispatchWorkgroupOperands` function.
// To simplify the implementation of the dispatch region formation, we just
// clone the op that needs to be fused inside the dispatch region and just fuse
// that one. This avoid any concerns related to tensor operands that are only
// used for their DimOp. This is a canonicalization that is more involved than
// necessary across the boundary of regions without captures.
//
// TODO(nicolasvasilache): This implementation jumps an abstraction gap as it
// knows that `clonedLinalgOp` has been tiled into `tiledLinalgOp`. In the case
// where a `rootOp`, i.e. the untiled original operation used to create the
// dispatch region, can be fused with its producer, this allows calling into a
// `fuseProducerOfTensor` to which we provide the producer by construction. This
// avoids an analysis that would need to reconstruct a destructive update from
// the loop nest + operations in order to get the producer of an `out` tensor.
// In the future, this analysis should be implemented in core but for now it is
// IREE-only.
//
// TODO(antiagainst): Right now this function requires taking all shaped
// operands of the tiled op to inspect them. This should probably be changed to
// just take one operand we know that need to be fused.
static void pullInProducersInSameGroup(
PatternRewriter &rewriter, IREE::Flow::DispatchWorkgroupsOp dispatchOp,
linalg::LinalgOp tiledOp, ValueRange tiledOpOperands,
ArrayRef<Operation *> tiledLoops, int64_t groupNum) {
DEBUG_WITH_TYPE(DEBUG_TYPE, llvm::dbgs() << "pull in producers for tiled op: "
<< tiledOp << "\n");
// Scoped within DispatchWorkgroupOp.
OpBuilder::InsertionGuard g(rewriter);
rewriter.setInsertionPointToStart(&dispatchOp.getRegion().front());
for (auto en : llvm::enumerate(tiledOpOperands)) {
if (auto producer = en.value().getDefiningOp<linalg::LinalgOp>()) {
if (!isInFusionGroup(producer, groupNum)) continue;
DEBUG_WITH_TYPE(DEBUG_TYPE,
llvm::dbgs() << "current producer: " << producer << "\n");
Operation *clonedOpToFuse = rewriter.clone(*producer);
linalg::LinalgOp fusedProducer;
static_cast<PatternRewriterWithScopedReplaceOp &>(rewriter)
.replaceOpWithinScope(producer, clonedOpToFuse->getResults(),
&dispatchOp.getRegion().front());
if (tiledLoops.empty()) {
DEBUG_WITH_TYPE(DEBUG_TYPE, llvm::dbgs()
<< "no loops; just copy over the op\n");
// The root op wasn't tiled. We are done then; just to remove the
// attribute.
clonedOpToFuse->removeAttr(kFusionGroupsAttr);
fusedProducer = cast<linalg::LinalgOp>(clonedOpToFuse);
} else {
// TODO: this is incorrect on general pattern failures, try pattern
// within pattern.
OpResult opResult = en.value().cast<OpResult>();
auto maybeFusionInfo = linalg::fuseProducerOfTensor(
rewriter, clonedOpToFuse->getResult(opResult.getResultNumber()),
*tiledOp.getInputAndOutputOperands()[en.index()]);
if (!maybeFusionInfo.hasValue()) {
DEBUG_WITH_TYPE(DEBUG_TYPE, llvm::dbgs()
<< "failed to fuse with tensor\n");
rewriter.replaceOp(clonedOpToFuse, producer->getResults());
} else {
DEBUG_WITH_TYPE(DEBUG_TYPE, llvm::dbgs()
<< "succeeded to fuse with tensor\n");
maybeFusionInfo->fusedProducer.getOperation()->removeAttr(
kFusionGroupsAttr);
fusedProducer = maybeFusionInfo->fusedProducer;
}
}
// If the producer is successfully fused, go recursive over the current
// producer's operands and pull them in if they are marked to be fused
// into the current group.
if (fusedProducer) {
SmallVector<Value> producerOperands =
cast<linalg::LinalgOp>(clonedOpToFuse).getInputAndOutputOperands();
pullInProducersInSameGroup(rewriter, dispatchOp, fusedProducer,
producerOperands, tiledLoops, groupNum);
}
}
}
}
template <typename OpTy>
static Value buildFlowWorkgroupInfoOp(OpBuilder &b, unsigned dim) {
return b.template create<OpTy>(b.getInsertionPoint()->getLoc(), dim);
}
/// Reorders the operations in `ops` such that they could be inlined into the
/// dispatch region in that order to satisfy dependencies.
static SmallVector<Operation *> orderOperations(ArrayRef<Operation *> ops) {
DEBUG_WITH_TYPE(DEBUG_TYPE, {
llvm::dbgs() << "Ops to be inlined :\n";
for (auto op : ops) {
llvm::dbgs() << "\t";
op->print(llvm::dbgs());
llvm::dbgs() << "\n";
}
});
llvm::SmallMapVector<Operation *, SmallVector<Operation *>, 16>
insertAfterMap;
llvm::SetVector<Operation *> opSet(ops.begin(), ops.end());
llvm::SetVector<Operation *> leafOps(ops.begin(), ops.end());
// For each operation compute the list of operations in `ops` that use its
// results. Also compute the operations that form the leafs of the DAG of
// operations in `ops`.
for (auto op : ops) {
for (auto operand : op->getOperands()) {
auto definingOp = operand.getDefiningOp();
if (!definingOp || !opSet.count(definingOp)) continue;
insertAfterMap[definingOp].push_back(op);
if (leafOps.count(op)) leafOps.remove(op);
}
}
// The leaves are at the head of the ordered list.
SmallVector<Operation *> orderedOps(leafOps.begin(), leafOps.end());
orderedOps.reserve(ops.size());
llvm::SmallPtrSet<Operation *, 16> processed;
processed.insert(leafOps.begin(), leafOps.end());
// `readyOps` contains the list of operations that have been just added to the
// `orderedOps` list. With these marked ready, they might make further
// operations in `ops` ready as well.
// The complexity of the algorithm is driven by these
// - Each operations is added to `readyOps` list at most once, and is removed
// after being processed
// - For every operation in `readyOps` every use of its results (within `ops`)
// is looked at once.
// - For every use, the operands of the user are processed.
// Assuming operands is O(1), i.e. constant order, the complexity is O(sum of
// number of uses of each operation). Given that the size of `ops` is at max
// O(10), and not O(100), this is assumed to be reasonable.
ArrayRef<Operation *> readyOps(orderedOps);
size_t startPos = 0;
while (!readyOps.empty()) {
auto op = readyOps.front();
startPos++;
// Check all uses of `op` within `ops`. If all of the operations that define
// the operands of the user have been added to `orderedOps`, then the user
// is ready to be scheduled.
for (auto insertAfterOp : insertAfterMap[op]) {
if (processed.count(insertAfterOp)) continue;
if (llvm::all_of(insertAfterOp->getOperands(), [&](Value operand) {
Operation *operandDefiningOp = operand.getDefiningOp();
return !operandDefiningOp || !opSet.count(operandDefiningOp) ||
processed.count(operandDefiningOp);
})) {
// readyOps.push_back(insertAfterOp);
orderedOps.push_back(insertAfterOp);
processed.insert(insertAfterOp);
}
}
readyOps = ArrayRef<Operation *>(orderedOps).drop_front(startPos);
}
DEBUG_WITH_TYPE(DEBUG_TYPE, {
llvm::dbgs() << "Ops to be inlined (sorted) : \n";
for (auto op : orderedOps) {
llvm::dbgs() << "\t";
op->print(llvm::dbgs());
llvm::dbgs() << "\n";
}
});
assert(orderedOps.size() == ops.size() &&
"ordering of inlined operations failed");
return orderedOps;
}
/// Computes the values that will be eventually be used within the dispatch
/// workgroup op but defined outside the op after all clonable operations are
/// cloned into the region. Returns (by reference) the clonable operations too,
/// in order in which they can be cloned within the region to satisfy use-def
/// relationships between them.
static void getUsedValuesDefinedAboveAfterCloningOps(
IREE::Flow::DispatchWorkgroupsOp dispatchOp,
llvm::SetVector<Value> &valuesDefinedAbove,
llvm::SmallVector<Operation *> &clonedOps) {
llvm::SetVector<Value> visited;
SmallVector<Value, 4> worklist;
worklist.assign(valuesDefinedAbove.begin(), valuesDefinedAbove.end());
valuesDefinedAbove.clear();
while (!worklist.empty()) {
Value outsideValue = worklist.pop_back_val();
if (visited.count(outsideValue)) continue;
visited.insert(outsideValue);
Operation *definingOp = outsideValue.getDefiningOp();
if (!definingOp || !(isAlwaysClonedIntoDispatchOp(definingOp) ||
isAlwaysFusedIntoDispatchOp(definingOp))) {
valuesDefinedAbove.insert(outsideValue);
continue;
}
clonedOps.push_back(definingOp);
worklist.append(definingOp->operand_begin(), definingOp->operand_end());
}
// The cloned operations form a DAG. Return the cloned operations so the
// leaves come first, and can be cloned in-order into the dispatch region.
clonedOps = orderOperations(clonedOps);
// Reverse the values. This is not for correctness, but more for readability
// of the IR.
llvm::SetVector<Value> reversedValues;
reversedValues.insert(valuesDefinedAbove.rbegin(), valuesDefinedAbove.rend());
std::swap(reversedValues, valuesDefinedAbove);
}
/// Modifies `dispatchOp` to attach operand-result tie information when
/// possible.
static void tryToTieOperandsAndResults(
IREE::Flow::DispatchWorkgroupsOp dispatchOp) {
Block *block = dispatchOp.getBody(0);
unsigned numResults = dispatchOp.getNumResults();
auto inputs = block->getArguments().drop_back(numResults);
auto outputs = block->getArguments().take_back(numResults);
// Returns the tied operand for the given `resultArg`. Returns nullptr
// if error or not found.
auto getTiedOperandBlockArgument =
[](BlockArgument resultArg) -> BlockArgument {
// Each output block argument should just have one use.
if (!llvm::hasSingleElement(resultArg.getUses())) return nullptr;
// And that's a flow.dispatch.output.store op.
auto storeOp = dyn_cast<IREE::Flow::DispatchTensorStoreOp>(
(*resultArg.getUses().begin()).getOwner());
if (!storeOp) return nullptr;
Operation *tieOp = storeOp.value().getDefiningOp();
// TODO(antiagainst): use TiedOpInterface here instead of hardcoding ops
// when it's available in MLIR core in some form.
if (auto insertOp = dyn_cast_or_null<SubTensorInsertOp>(tieOp)) {
auto loadOp =
insertOp.dest().getDefiningOp<IREE::Flow::DispatchTensorLoadOp>();
if (!loadOp) return nullptr;
return loadOp.source().cast<BlockArgument>();
} else if (auto linalgOp = dyn_cast_or_null<linalg::LinalgOp>(tieOp)) {
unsigned resultIndex = storeOp.value().cast<OpResult>().getResultNumber();
auto loadOp = linalgOp.getOutputTensorOperands()[resultIndex]
->get()
.getDefiningOp<IREE::Flow::DispatchTensorLoadOp>();
if (!loadOp) return nullptr;
return loadOp.source().cast<BlockArgument>();
}
return nullptr;
};
SmallVector<BlockArgument, 4> tiedOperands;
tiedOperands.reserve(numResults);
// Collect all result argument's tied operand arguments.
for (BlockArgument &arg : outputs) {
tiedOperands.push_back(getTiedOperandBlockArgument(arg));
}
// Go over each result to tie operand when possible, by:
// 1. Update the tied operand argument to take readwrite tensors.
// 2. Erase the result argument.
// 3. Attach the tie information to the DispatchWorkgroupsOp.
for (int i = outputs.size() - 1; i >= 0; --i) {
BlockArgument inputArg = tiedOperands[i];
if (!inputArg) continue;
auto oldType = inputArg.getType().cast<IREE::Flow::DispatchTensorType>();
inputArg.setType(IREE::Flow::DispatchTensorType::get(
IREE::Flow::TensorAccess::ReadWrite, oldType.getShape(),
oldType.getElementType()));
BlockArgument outputArg = block->getArgument(inputs.size() + i);
outputArg.replaceAllUsesWith(inputArg);
block->eraseArgument(inputs.size() + i);
dispatchOp.setTiedResultOperandIndex(i, inputArg.getArgNumber());
}
}
static void replaceAllUsesWithinDispatchOp(
IREE::Flow::DispatchWorkgroupsOp dispatchOp, Value value,
Value replacement) {
SmallPtrSet<Operation *, 4> usesOutsideDispatch;
for (Operation *user : value.getUsers()) {
if (isa<IREE::Flow::DispatchWorkgroupsOp>(user) ||
!dispatchOp->isAncestor(user)) {
usesOutsideDispatch.insert(user);
}
}
value.replaceAllUsesExcept(replacement, usesOutsideDispatch);
}
// After outlining in dispatch region we can rewrite the dispatch ops with
// proper captures.
// A later RematerializeDispatchConstants should be called to avoid passing
// unnecessary constant arguments.
static LogicalResult legalizeDispatchWorkgroupOperands(
IREE::Flow::DispatchWorkgroupsOp dispatchOp) {
Location loc = dispatchOp.getLoc();
Region &region = dispatchOp.body();
Block &block = region.front();
unsigned numOldBBArgs = block.getNumArguments();
OpBuilder b = OpBuilder::atBlockBegin(&block);
llvm::SetVector<Value> valuesDefinedAbove;
llvm::SmallVector<Operation *> clonedOps;
mlir::getUsedValuesDefinedAbove(region, valuesDefinedAbove);
if (valuesDefinedAbove.empty()) return success();
getUsedValuesDefinedAboveAfterCloningOps(dispatchOp, valuesDefinedAbove,
clonedOps);
BlockAndValueMapping map;
SmallVector<Value> toReplaceWithinRegion;
// Replace valuesDefinedAbove by new BB args (including the op's operands).
for (Value operand : valuesDefinedAbove) {
if (auto rt = operand.getType().dyn_cast<RankedTensorType>()) {
block.addArgument(IREE::Flow::DispatchTensorType::get(
TensorAccess::ReadOnly, rt.getShape(), rt.getElementType()));
} else {
block.addArgument(operand.getType());
}
Value bbArg = block.getArguments().back();
Value repl = bbArg;
if (bbArg.getType().isa<IREE::Flow::DispatchTensorType>()) {
repl = b.create<IREE::Flow::DispatchTensorLoadOp>(
loc, operand.getType().cast<RankedTensorType>(), bbArg);
}
map.map(operand, repl);
toReplaceWithinRegion.push_back(operand);
}
// The only existing arguments are for the outputs. Just need to add a new
// argument for the outputs and remap the value to use the new argument.
for (unsigned argNum : llvm::seq<unsigned>(0, numOldBBArgs)) {
BlockArgument arg = block.getArgument(argNum);
assert(arg.getType().isa<IREE::Flow::DispatchTensorType>());
arg.replaceAllUsesWith(block.addArgument(arg.getType()));
}
// Drop old BB args.
block.eraseArguments(
llvm::to_vector<4>(llvm::seq<unsigned>(0, numOldBBArgs)));
// Clone the marked operations.
for (Operation *op : clonedOps) {
b.clone(*op, map);
toReplaceWithinRegion.append(op->result_begin(), op->result_end());
}
// Make the region isolated from above.
for (auto value : toReplaceWithinRegion) {
replaceAllUsesWithinDispatchOp(dispatchOp, value, map.lookup(value));
}
// Gather the dynamic dimensions for all operands.
SmallVector<Value, 4> operandDynamicDims;
OpBuilder builder(dispatchOp);
for (Value operand : valuesDefinedAbove) {
if (auto rt = operand.getType().dyn_cast<RankedTensorType>()) {
for (unsigned i = 0; i < rt.getRank(); ++i) {
if (!rt.isDynamicDim(i)) continue;
auto dim = builder.createOrFold<memref::DimOp>(dispatchOp.getLoc(),
operand, i);
operandDynamicDims.push_back(dim);
}
}
}
// Set the values captured from above as the new operands.
dispatchOp.operandsMutable().assign(llvm::to_vector<4>(valuesDefinedAbove));
dispatchOp.operand_dimsMutable().assign(operandDynamicDims);
// Now try to see if we can tie certain results to operands in order to
// indicate sharing storage. This need to happen here because it needs to
// access region block arguments for input/output tensors, which aren't
// available until now.
tryToTieOperandsAndResults(dispatchOp);
return success();
}
/// Computes the shape of the output. This is used to get the workload of the
/// dispatch region if a dispatch region contains a single "Dispatchable op"
static Optional<SmallVector<SmallVector<Value, 4>, 1>> computeOutputShape(
OpBuilder &builder, Operation *op) {
SmallVector<SmallVector<Value, 4>, 1> outputShapes;
for (auto outputType : op->getResultTypes()) {
// Add empty shape for scalar values.
if (outputType.isIntOrFloat()) {
outputShapes.push_back({});
continue;
}
// TODO(ravishankarm): For now only handle static shapes. For dynamic
// shapes, the shape of the output needs to be resolved using tie shapes,
// etc.
if (auto shapedType = outputType.dyn_cast<ShapedType>()) {
if (!shapedType.hasStaticShape()) return llvm::None;
outputShapes.push_back(llvm::to_vector<4>(
llvm::map_range(shapedType.getShape(), [&](int64_t dim) -> Value {
return builder.create<ConstantIndexOp>(op->getLoc(), dim);
})));
continue;
}
return llvm::None;
}
return outputShapes;
}
static bool hasOnlyDimUses(Operation *op) {
return llvm::all_of(op->getUsers(), [&](Operation *user) {
return isa<memref::DimOp>(user);
});
}
//===----------------------------------------------------------------------===//
// Patterns that create the dispatch region.
//===----------------------------------------------------------------------===//
namespace {
// Rewrite pattern to ensure only ops with tensor semantics are tiled.
struct TileAndDistributeOnTensorsPattern
: public linalg::LinalgBaseTilingPattern {
using Base = linalg::LinalgBaseTilingPattern;
TileAndDistributeOnTensorsPattern(MLIRContext *context,
linalg::LinalgTilingOptions options,
linalg::LinalgTransformationFilter marker,
PatternBenefit benefit = 1)
: Base(context, options, marker, benefit) {}
LogicalResult matchAndRewrite(Operation *op,
PatternRewriter &rewriter) const override {
auto linalgOp = dyn_cast<linalg::LinalgOp>(op);
if (!linalgOp || !linalgOp.hasTensorSemantics()) return failure();
IntegerAttr rootOpAttr = op->getAttrOfType<IntegerAttr>(kRootOpAttr);
if (!rootOpAttr) return failure();
// TODO(ravishankarm): It is getting strange to track when to apply this
// pattern and when not to. Need to revisit this, with dynamic shape cases
// in mind.
if (hasOnlyDimUses(linalgOp)) return failure();
// Compute workgroup count to use for the dispatch op. These are the ranges
// of the outermost parallel loops that can be distributed.
Location loc = op->getLoc();
SmallVector<Value, 4> count = llvm::to_vector<4>(
llvm::map_range(linalgOp.createLoopRanges(rewriter, loc),
[](Range r) { return r.size; }));
size_t numParallelLoops = getNumOuterParallelLoops(op);
if (numParallelLoops > kNumMaxParallelDims) {
count.erase(
count.begin(),
std::next(count.begin(), numParallelLoops - kNumMaxParallelDims));
}
count.resize(getNumTilableLoops(op));
auto workload = convertToWorkload(rewriter, loc, count);
// Capture dynamic result dimensions.
SmallVector<Value, 4> resultDynamicDims;
for (auto result : linalgOp.outputs()) {
resultDynamicDims.append(Shape::buildOrFindDynamicDimsForValue(
linalgOp.getLoc(), result, rewriter));
}
// Note: DispatchTensorStoreOp generated by the
// `buildOperandLessFlowDispatchWorkgroupOp` is an abstraction jump that
// consumes the SSA value produced by `clonedOp` but it does not comply with
// the semantics of DispatchWorkgroupsOp which explicitly states: "behavior
// is undefined if multiple workgroups store to the same regions of the
// output tensors". Similarly to sequentialized SPMD loops, the semantics
// is valid assuming a sequential ordering of execution. After destructive
// update rewrites, the abstraction gap disappears.
auto en = buildOperandLessFlowDispatchWorkgroupOp(rewriter, loc, workload,
linalgOp);
IREE::Flow::DispatchWorkgroupsOp dispatchOp = en.first;
linalg::LinalgOp clonedLinalgOp = cast<linalg::LinalgOp>(en.second);
dispatchOp.result_dimsMutable().assign(resultDynamicDims);
// Scoped within DispatchWorkgroupOp.
OpBuilder::InsertionGuard g(rewriter);
rewriter.setInsertionPoint(clonedLinalgOp);
linalg::TiledLinalgOp tiledLinalgOp;
LogicalResult tilingResult =
Base::matchAndRewriteBase(clonedLinalgOp, rewriter, tiledLinalgOp);
if (failed(tilingResult)) {
// GreedyPatternRewriter is not transactional and does not stop on
// failure. Must explicitly delete on all failure paths.
rewriter.eraseOp(dispatchOp);
return failure();
}
// Keep track of the tiledOpOperands for fusion.
SmallVector<Value> tiledOperands =
clonedLinalgOp.getInputAndOutputOperands();
rewriter.replaceOp(clonedLinalgOp, tiledLinalgOp.tensorResults);
pullInProducersInSameGroup(rewriter, dispatchOp, tiledLinalgOp.op,
tiledOperands, tiledLinalgOp.loops,
rootOpAttr.getInt());
tiledLinalgOp.op.getOperation()->removeAttr(kRootOpAttr);
rewriter.replaceOpWithIf(op, dispatchOp.getResults(),
[&](OpOperand &operand) {
return !isa<memref::DimOp>(operand.getOwner());
});
return success();
}
};
/// Given a list of shapes, returns whether it is statically provable that all
/// shapes are the same. For now checks if
/// 1) Each dimension has the same dynamic value, or,
/// 2) The defining op for each dimension is a `constant` op with the same
/// scalar value.
static bool areAllShapesEqual(ArrayRef<SmallVector<Value>> shapes) {
assert(!shapes.empty());
if (shapes.size() == 1) return true;
auto isSameShape = [&](ArrayRef<Value> lhsShape,
ArrayRef<Value> rhsShape) -> bool {
if (lhsShape.size() != rhsShape.size()) return false;
return llvm::all_of(
llvm::zip(lhsShape, rhsShape), [&](std::tuple<Value, Value> vals) {
APInt lhsInt, rhsInt;
Value lhs = std::get<0>(vals);
Value rhs = std::get<1>(vals);
return lhs == rhs || (matchPattern(lhs, m_ConstantInt(&lhsInt)) &&
matchPattern(rhs, m_ConstantInt(&rhsInt)) &&
lhsInt == rhsInt);
});
};
return llvm::all_of(
llvm::make_range(std::next(shapes.begin()), shapes.end()),
[&](ArrayRef<Value> shape) { return isSameShape(shapes[0], shape); });
}
/// The workload is computed based on the problem size. For a given operation,
/// return the shape of all its results.
static Optional<SmallVector<SmallVector<Value>>> getResultShapes(
PatternRewriter &rewriter, Operation *op) {
if (op->getNumResults() == 0) return llvm::None;
SmallVector<SmallVector<Value>> resultShapes;
// Check if the op implements the shape interface.
if (auto shapedOp = dyn_cast<InferShapedTypeOpInterface>(op)) {
if (failed(shapedOp.reifyReturnTypeShapesPerResultDim(rewriter,
resultShapes))) {
return llvm::None;
}
return resultShapes;
}
// Fallback is to get the shape using `dim` of the outputs. Since the
// workload depends on the output shape, set the insertion point to after
// the operation. After dim canonicalization, the original operation should
// become dead.
rewriter.setInsertionPointAfter(op);
Location loc = op->getLoc();
auto getShapeOfShapedTypeVal = [&](Value v) -> SmallVector<Value> {
SmallVector<Value> shape;
for (auto dim :
llvm::seq<int64_t>(0, v.getType().cast<ShapedType>().getRank())) {
shape.push_back(rewriter.createOrFold<memref::DimOp>(loc, v, dim));
}
return shape;
};
for (OpResult result : op->getResults()) {
auto resultType = result.getType().dyn_cast<ShapedType>();
if (!resultType) return llvm::None;
rewriter.setInsertionPointAfter(op);
auto resultShape = getShapeOfShapedTypeVal(result);
resultShapes.emplace_back(std::move(resultShape));
}
return resultShapes;
}
/// Puts ops that are not-tilable or arent tiled into a
/// `flow.dispatch.workgroups` operation. For example tile and distribute of
/// element-wise operations is not beneficial. These are handled appropriately
/// by the backends.
struct MakeDispatchWorkgroupsOp : public RewritePattern {
MakeDispatchWorkgroupsOp(MLIRContext *context, PatternBenefit benefit = 1)
: RewritePattern(MatchAnyOpTypeTag(), benefit, context) {}
LogicalResult matchAndRewrite(Operation *op,
PatternRewriter &rewriter) const override {
if (!isDispatchableOp(op) || hasOnlyDimUses(op)) return failure();
// If this is a dispatchable op that is to be fused into dispatch ops, and
// all its uses are dispatchable ops, don't do anything.
if ((op->getAttrOfType<ArrayAttr>(kFusionGroupsAttr) ||
isAlwaysFusedIntoDispatchOp(op)) &&
llvm::all_of(op->getUsers(), [](Operation *user) {
return isDispatchableOp(user) ||
user->getParentOfType<IREE::Flow::DispatchWorkgroupsOp>() ||
isa<IREE::Flow::DispatchWorkgroupsOp, memref::DimOp>(user);
})) {
return failure();
}
// The workgroup count is based on the result shape.
Optional<SmallVector<SmallVector<Value>>> resultShapesOpt =
getResultShapes(rewriter, op);
if (!resultShapesOpt) return failure();
ArrayRef<SmallVector<Value>> resultShapes = *resultShapesOpt;
if (resultShapes.size() != op->getNumResults() ||
!areAllShapesEqual(resultShapes))
return failure();
// TODO(ravishankarm): For now the Flow -> HAL conversion only handles
// workload count of 3, though it should be generalized. For now making sure
// the flow has three elements of workload size (x, y, z) by linearizing the
// workloads for all higher dimensions greater than or equal to
// kNumMaxParallelDims.
Location loc = op->getLoc();
SmallVector<Value, 4> count(resultShapes[0].begin(), resultShapes[0].end());
if (count.size() > kNumMaxParallelDims) {
unsigned numSymbols = 0;
AffineExpr expr = rewriter.getAffineSymbolExpr(numSymbols++);
for (int64_t i = 1; i < count.size() - kNumMaxParallelDims + 1; i++) {
expr = expr * rewriter.getAffineSymbolExpr(numSymbols++);
}
count[count.size() - kNumMaxParallelDims] = linalg::applyMapToValues(
rewriter, loc, AffineMap::get(0, numSymbols, expr),
ArrayRef<Value>(count).take_front(count.size() - kNumMaxParallelDims +
1))[0];
count = llvm::to_vector<4>(
ArrayRef<Value>(count).take_back(kNumMaxParallelDims));
}
auto workload = convertToWorkload(rewriter, loc, count);
// Capture dynamic result dimensions.
SmallVector<Value, 4> resultDynamicDims;
for (auto result : llvm::enumerate(op->getResults())) {
auto resultType = result.value().getType().cast<ShapedType>();
for (unsigned i = 0; i < resultType.getRank(); ++i) {
if (resultType.isDynamicDim(i)) {
resultDynamicDims.push_back(resultShapes[result.index()][i]);
}
}
}
auto en = buildOperandLessFlowDispatchWorkgroupOp(rewriter, op->getLoc(),
workload, op);
IREE::Flow::DispatchWorkgroupsOp dispatchOp = en.first;
dispatchOp.result_dimsMutable().assign(resultDynamicDims);
// If this is a root op for fusion, try to pull in the ops to be fused
// together with it.
if (auto rootOpAttr = op->getAttrOfType<IntegerAttr>(kRootOpAttr)) {
linalg::LinalgOp clonedLinalgOp = cast<linalg::LinalgOp>(en.second);
SmallVector<Value> tiledOperands =
clonedLinalgOp.getInputAndOutputOperands();
pullInProducersInSameGroup(
rewriter, dispatchOp, clonedLinalgOp, tiledOperands,
/*tiledLoops=*/ArrayRef<Operation *>(), rootOpAttr.getInt());
clonedLinalgOp->removeAttr(kRootOpAttr);
}
rewriter.replaceOpWithIf(op, dispatchOp.getOperation()->getResults(),
[&](OpOperand &operand) {
Operation *user = operand.getOwner();
return !isa<memref::DimOp>(user);
});
return success();
}
};
}; // namespace
//===----------------------------------------------------------------------===//
// Heuristics for fusing dispatchble ops with root ops using tile + fuse.
//===----------------------------------------------------------------------===//
/// Some heuristic is needed to fuse a dispatchble op with root operations using
/// tile + fuse. Using some heuristic, each root operation is tagged with an ID
/// (using an IntegerAttr with name `kRootOpAttr`) and all dispatchable ops to
/// be fused with it is tagged with the same ID (using a list of IntegerAttr
/// with name `kFusionGroupsAttr`). Each dispatchable operation can be marked to
/// fuse with multiple root operations (i.e. replicated). For now a very simple
/// heuristic is used below, but the mechanism should be general enough to
/// capture any heuristic.
/// Sets elementwise operations as root operations.
// TODO(#5045): After the regression issue on CPU side is addressed, this can be
// folded into the main logic of fusion.
template <typename GenericOpTy>
static unsigned makeElementwiseOpsRootOps(FuncOp funcOp, unsigned numRoots) {
MLIRContext *context = funcOp.getContext();
OpBuilder builder(context);
for (Block &block : funcOp) {
auto linalgOps = block.getOps<linalg::LinalgOp>();
for (linalg::LinalgOp linalgOp : llvm::reverse(linalgOps)) {
Operation *op = linalgOp.getOperation();
if (op->getAttrOfType<IntegerAttr>(kRootOpAttr) ||
op->getAttrOfType<ArrayAttr>(kFusionGroupsAttr))
continue;
if (!isa<GenericOpTy>(op) ||
!llvm::all_of(
cast<linalg::LinalgOp>(op).getIndexingMaps(),
[](AffineMap map) { return map.isProjectedPermutation(); })) {
continue;
}
unsigned newGroup = numRoots++;
op->setAttr(kRootOpAttr, builder.getI64IntegerAttr(newGroup));
for (OpOperand *operand : linalgOp.getOutputTensorOperands()) {
auto producer = operand->get().getDefiningOp<linalg::LinalgOp>();
if (!producer) continue;
if (producer.getNumLoops() != producer.getNumParallelLoops()) continue;
appendToFusionGroup(producer, newGroup);
}
}
}
return numRoots;
}
/// For a given block partition the LinalgOps in the block into fusable
/// groups. All analysis of what to fuse happens here. For now this is just
/// hard-wiring from basic heuristic but this could be adapted to have 1) better
/// heuristics and 2) use a search approach to decide what all should be fused.
static unsigned decideFusableLinalgOps(FuncOp funcOp) {
unsigned numRootOps = 0;
MLIRContext *context = funcOp.getContext();
OpBuilder builder(context);
for (Block &block : funcOp) {
auto linalgOps = block.getOps<linalg::LinalgOp>();
// Tiling and fusion in linalg works by tiling the last operation in the
// fusion group and then pull producer ops into the tiled loops. So go in
// the reverse order here.
for (linalg::LinalgOp linalgOp : llvm::reverse(linalgOps)) {
// Start with a root operation and fuse its producers.
Operation *op = linalgOp.getOperation();
if (!isRootOp(op)) continue;
unsigned newGroup = numRootOps++;
op->setAttr(kRootOpAttr, builder.getI64IntegerAttr(newGroup));
for (OpOperand *operand : linalgOp.getOutputTensorOperands()) {
auto producer = operand->get().getDefiningOp<linalg::LinalgOp>();
if (!producer) continue;
if (producer.getNumLoops() != producer.getNumParallelLoops()) continue;
appendToFusionGroup(producer, newGroup);
}
}
if (clEnableOperandFusion) {
// To fuse root operations with their consumers, for all root ops chosen.
// If, 1) The root op has a single use 2) The consumer is an elementwise
// operation 3) The indexing map in the producer and consumer are identity
// maps The root operation can be fused with its consumer. To do this,
// mark the consumer as the root and add the operation to the fusion
// group.
for (linalg::LinalgOp linalgOp : linalgOps) {
Operation *op = linalgOp.getOperation();
IntegerAttr rootOpAttr = op->getAttrOfType<IntegerAttr>(kRootOpAttr);
if (!rootOpAttr) continue;
if (op->getNumResults() != 1 || !op->hasOneUse()) continue;
OpOperand &use = *op->use_begin();
Operation *user = use.getOwner();
if (user->getAttrOfType<IntegerAttr>(kRootOpAttr) ||
user->getAttrOfType<IntegerAttr>(kFusionGroupsAttr))
continue;
linalg::LinalgOp consumer = dyn_cast<linalg::LinalgOp>(use.getOwner());
if (!consumer ||
consumer.getNumLoops() != consumer.getNumParallelLoops())
continue;
AffineMap consumerIndexingMap = consumer.getTiedIndexingMap(&use);
AffineMap producerIndexingMap =
linalgOp.getTiedIndexingMap(linalgOp.getOutputOperand(0));
if (!consumerIndexingMap.isIdentity() ||
producerIndexingMap.getResults() !=
consumerIndexingMap.getResults()) {
continue;
}
user->setAttr(kRootOpAttr, rootOpAttr);
op->removeAttr(kRootOpAttr);
appendToFusionGroup(op, rootOpAttr.getInt());
}
}
}
return numRootOps;
}
void DispatchLinalgOnTensorsPass::runOnOperation() {
FuncOp funcOp = getOperation();
MLIRContext *context = funcOp->getContext();
context->allowUnregisteredDialects(true);
unsigned numRoots = decideFusableLinalgOps(funcOp);
makeElementwiseOpsRootOps<linalg::GenericOp>(funcOp, numRoots);
DEBUG_WITH_TYPE(DEBUG_TYPE, {
llvm::dbgs() << "\n--- After annotating linalg op fusion scheme ---\n";
funcOp.print(llvm::dbgs(), OpPrintingFlags().useLocalScope());
llvm::dbgs() << "\n\n";
});
// Distribution strategy along at most 3 dimensions with WorkgroupIdOp in
// range [0, WorkgroupSizeOp).
static linalg::LinalgLoopDistributionOptions workgroupDistributionOptions = {
[](OpBuilder &builder, Location loc, ArrayRef<Range> parallelLoopRanges) {
auto numParallelDims = parallelLoopRanges.size();
SmallVector<linalg::ProcInfo, 3> procInfo(numParallelDims);
for (size_t dim = 0; dim < numParallelDims; ++dim) {
procInfo[numParallelDims - dim - 1] = {
buildFlowWorkgroupInfoOp<Flow::DispatchWorkgroupIDOp>(builder,
dim),
buildFlowWorkgroupInfoOp<Flow::DispatchWorkgroupCountOp>(builder,
dim)};
}
return procInfo;
},
{linalg::DistributionMethod::Cyclic, linalg::DistributionMethod::Cyclic,
linalg::DistributionMethod::Cyclic},
DenseMap<StringRef,
std::function<linalg::ProcInfo(OpBuilder &, Location)>>()};
auto tileSizeFn = [&](OpBuilder &builder,
Operation *op) -> SmallVector<Value, 4> {
auto numParallelDims = getNumOuterParallelLoops(cast<linalg::LinalgOp>(op));
auto numTiledLoops = getNumTilableLoops(cast<linalg::LinalgOp>(op));
// Default to zero to skip tiling.
auto zero = builder.create<ConstantIndexOp>(op->getLoc(), 0);
SmallVector<Value, 4> useTileSizes(numParallelDims, zero);
if (!clLinalgOnTensorsTileSizes.empty()) {
SmallVector<int64_t, 2> tileSizes(clLinalgOnTensorsTileSizes.begin(),
clLinalgOnTensorsTileSizes.end());
useTileSizes.resize(std::min<size_t>(tileSizes.size(), numParallelDims));
return llvm::to_vector<4>(llvm::map_range(
ArrayRef<int64_t>(tileSizes).take_front(
std::min<size_t>(tileSizes.size(), numParallelDims)),
[&](int64_t t) -> Value {
return builder.create<ConstantIndexOp>(op->getLoc(), t);
}));
}
// For ops with more than 3 parallel dimensions, we want to ignore the
// higher dimension and tile along last three dimensions.
for (size_t dim = 0; dim < numTiledLoops; ++dim) {
useTileSizes[numParallelDims - dim - 1] =
buildFlowWorkgroupInfoOp<Flow::DispatchWorkgroupSizeOp>(builder, dim);
}
return useTileSizes;
};
{
// Use the workgroup size as a proxy for tile size here. At the flow level
// this represents the "workload" per processors and is not necessarily tied
// to the workgroup size specified by the backend.
OwningRewritePatternList patterns(&getContext());
auto linalgTilingOptions =
linalg::LinalgTilingOptions()
.setDistributionOptions(workgroupDistributionOptions)
.setLoopType(linalg::LinalgTilingLoopType::Loops)
.setTileSizeComputationFunction(tileSizeFn);
assert(linalgTilingOptions.distribution.hasValue());
patterns.insert<TileAndDistributeOnTensorsPattern>(
context, linalgTilingOptions,
// TODO(nicolavasilache): use refactored `getWorkgroupMarker()`
linalg::LinalgTransformationFilter(
ArrayRef<Identifier>(), Identifier::get("workgroup", context)));
// Add canonicalization patterns.
linalg::populateLinalgTilingCanonicalizationPatterns(patterns);
patterns.insert<linalg::AffineMinSCFCanonicalizationPattern>(context);
(void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
}
// If elementwise operations are not tiled and distributed, the wont be marked
// as root ops previously. Mark them so here to allow fusion of `fill` etc.
numRoots = makeElementwiseOpsRootOps<linalg::GenericOp>(funcOp, numRoots);
DEBUG_WITH_TYPE(DEBUG_TYPE, {
llvm::dbgs()
<< "\n--- After annotating linalg op fusion scheme for fallback ---\n";
funcOp.print(llvm::dbgs(), OpPrintingFlags().useLocalScope());
llvm::dbgs() << "\n\n";
});
// After outlining in dispatch region we can rewrite the dispatch ops with
// proper captures.
if (funcOp
.walk([&](IREE::Flow::DispatchWorkgroupsOp op) -> WalkResult {
return legalizeDispatchWorkgroupOperands(op);
})
.wasInterrupted()) {
return signalPassFailure();
}
// Move other operations into their own dispatch regions.
{
OwningRewritePatternList patterns(context);
patterns.insert<MakeDispatchWorkgroupsOp>(context);
(void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
}
// After outlining in dispatch region we can rewrite the dispatch ops with
// proper captures.
if (funcOp
.walk([&](IREE::Flow::DispatchWorkgroupsOp op) -> WalkResult {
numDispatches++;
return legalizeDispatchWorkgroupOperands(op);
})
.wasInterrupted()) {
return signalPassFailure();
}
// Run necessary canonicalization patterns before destructive updates.
{
OwningRewritePatternList patterns(&getContext());
// This is needed because tiling and distribution may create
// subtensor_insert ops whose source operands come from tensor.cast ops.
// Those tensor.cast ops cast tensors into a more dynamic shape, in order
// to guarantee type match during transformation. Later in destructive
// update subtensor_insert ops will be turned into flow dispatch output
// store ops.
SubTensorInsertOp::getCanonicalizationPatterns(patterns, context);
(void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
}
// Rewrite destructive updates and ensure no remaining store remains to the
// full output.
if (funcOp
.walk([&](IREE::Flow::DispatchWorkgroupsOp op) {
if (failed(rewriteLinalgDestructiveUpdates(op))) {
funcOp.emitError("Failed to rewrite destructive updates in:\n")
<< *op.getOperation();
return WalkResult::interrupt();
}
return WalkResult::advance();
})
.wasInterrupted()) {
signalPassFailure();
}
}
std::unique_ptr<OperationPass<FuncOp>> createDispatchLinalgOnTensorsPass() {
return std::make_unique<DispatchLinalgOnTensorsPass>();
}
} // namespace Flow
} // namespace IREE
} // namespace iree_compiler
} // namespace mlir