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llvm-project/mlir/lib/Dialect/Async/Transforms/AsyncParallelFor.cpp

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//===- AsyncParallelFor.cpp - Implementation of Async Parallel For --------===//
//
// 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 scf.parallel to scf.for + async.execute conversion pass.
//
//===----------------------------------------------------------------------===//
#include "PassDetail.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/Async/IR/Async.h"
#include "mlir/Dialect/Async/Passes.h"
#include "mlir/Dialect/Async/Transforms.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/IR/BlockAndValueMapping.h"
#include "mlir/IR/ImplicitLocOpBuilder.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "mlir/Transforms/RegionUtils.h"
using namespace mlir;
using namespace mlir::async;
#define DEBUG_TYPE "async-parallel-for"
namespace {
// Rewrite scf.parallel operation into multiple concurrent async.execute
// operations over non overlapping subranges of the original loop.
//
// Example:
//
// scf.parallel (%i, %j) = (%lbi, %lbj) to (%ubi, %ubj) step (%si, %sj) {
// "do_some_compute"(%i, %j): () -> ()
// }
//
// Converted to:
//
// // Parallel compute function that executes the parallel body region for
// // a subset of the parallel iteration space defined by the one-dimensional
// // compute block index.
// func parallel_compute_function(%block_index : index, %block_size : index,
// <parallel operation properties>, ...) {
// // Compute multi-dimensional loop bounds for %block_index.
// %block_lbi, %block_lbj = ...
// %block_ubi, %block_ubj = ...
//
// // Clone parallel operation body into the scf.for loop nest.
// scf.for %i = %blockLbi to %blockUbi {
// scf.for %j = block_lbj to %block_ubj {
// "do_some_compute"(%i, %j): () -> ()
// }
// }
// }
//
// And a dispatch function depending on the `asyncDispatch` option.
//
// When async dispatch is on: (pseudocode)
//
// %block_size = ... compute parallel compute block size
// %block_count = ... compute the number of compute blocks
//
// func @async_dispatch(%block_start : index, %block_end : index, ...) {
// // Keep splitting block range until we reached a range of size 1.
// while (%block_end - %block_start > 1) {
// %mid_index = block_start + (block_end - block_start) / 2;
// async.execute { call @async_dispatch(%mid_index, %block_end); }
// %block_end = %mid_index
// }
//
// // Call parallel compute function for a single block.
// call @parallel_compute_fn(%block_start, %block_size, ...);
// }
//
// // Launch async dispatch for [0, block_count) range.
// call @async_dispatch(%c0, %block_count);
//
// When async dispatch is off:
//
// %block_size = ... compute parallel compute block size
// %block_count = ... compute the number of compute blocks
//
// scf.for %block_index = %c0 to %block_count {
// call @parallel_compute_fn(%block_index, %block_size, ...)
// }
//
struct AsyncParallelForPass
: public AsyncParallelForBase<AsyncParallelForPass> {
AsyncParallelForPass() = default;
AsyncParallelForPass(bool asyncDispatch, int32_t numWorkerThreads,
int32_t minTaskSize) {
this->asyncDispatch = asyncDispatch;
this->numWorkerThreads = numWorkerThreads;
this->minTaskSize = minTaskSize;
}
void runOnOperation() override;
};
struct AsyncParallelForRewrite : public OpRewritePattern<scf::ParallelOp> {
public:
AsyncParallelForRewrite(
MLIRContext *ctx, bool asyncDispatch, int32_t numWorkerThreads,
AsyncMinTaskSizeComputationFunction computeMinTaskSize)
: OpRewritePattern(ctx), asyncDispatch(asyncDispatch),
numWorkerThreads(numWorkerThreads),
computeMinTaskSize(computeMinTaskSize) {}
LogicalResult matchAndRewrite(scf::ParallelOp op,
PatternRewriter &rewriter) const override;
private:
bool asyncDispatch;
int32_t numWorkerThreads;
AsyncMinTaskSizeComputationFunction computeMinTaskSize;
};
struct ParallelComputeFunctionType {
FunctionType type;
SmallVector<Value> captures;
};
// Helper struct to parse parallel compute function argument list.
struct ParallelComputeFunctionArgs {
BlockArgument blockIndex();
BlockArgument blockSize();
ArrayRef<BlockArgument> tripCounts();
ArrayRef<BlockArgument> lowerBounds();
ArrayRef<BlockArgument> upperBounds();
ArrayRef<BlockArgument> steps();
ArrayRef<BlockArgument> captures();
unsigned numLoops;
ArrayRef<BlockArgument> args;
};
struct ParallelComputeFunctionBounds {
SmallVector<IntegerAttr> tripCounts;
SmallVector<IntegerAttr> lowerBounds;
SmallVector<IntegerAttr> upperBounds;
SmallVector<IntegerAttr> steps;
};
struct ParallelComputeFunction {
unsigned numLoops;
FuncOp func;
llvm::SmallVector<Value> captures;
};
} // namespace
BlockArgument ParallelComputeFunctionArgs::blockIndex() { return args[0]; }
BlockArgument ParallelComputeFunctionArgs::blockSize() { return args[1]; }
ArrayRef<BlockArgument> ParallelComputeFunctionArgs::tripCounts() {
return args.drop_front(2).take_front(numLoops);
}
ArrayRef<BlockArgument> ParallelComputeFunctionArgs::lowerBounds() {
return args.drop_front(2 + 1 * numLoops).take_front(numLoops);
}
ArrayRef<BlockArgument> ParallelComputeFunctionArgs::upperBounds() {
return args.drop_front(2 + 2 * numLoops).take_front(numLoops);
}
ArrayRef<BlockArgument> ParallelComputeFunctionArgs::steps() {
return args.drop_front(2 + 3 * numLoops).take_front(numLoops);
}
ArrayRef<BlockArgument> ParallelComputeFunctionArgs::captures() {
return args.drop_front(2 + 4 * numLoops);
}
template <typename ValueRange>
static SmallVector<IntegerAttr> integerConstants(ValueRange values) {
SmallVector<IntegerAttr> attrs(values.size());
for (unsigned i = 0; i < values.size(); ++i)
matchPattern(values[i], m_Constant(&attrs[i]));
return attrs;
}
// Converts one-dimensional iteration index in the [0, tripCount) interval
// into multidimensional iteration coordinate.
static SmallVector<Value> delinearize(ImplicitLocOpBuilder &b, Value index,
ArrayRef<Value> tripCounts) {
SmallVector<Value> coords(tripCounts.size());
assert(!tripCounts.empty() && "tripCounts must be not empty");
for (ssize_t i = tripCounts.size() - 1; i >= 0; --i) {
coords[i] = b.create<arith::RemSIOp>(index, tripCounts[i]);
index = b.create<arith::DivSIOp>(index, tripCounts[i]);
}
return coords;
}
// Returns a function type and implicit captures for a parallel compute
// function. We'll need a list of implicit captures to setup block and value
// mapping when we'll clone the body of the parallel operation.
static ParallelComputeFunctionType
getParallelComputeFunctionType(scf::ParallelOp op, PatternRewriter &rewriter) {
// Values implicitly captured by the parallel operation.
llvm::SetVector<Value> captures;
getUsedValuesDefinedAbove(op.region(), op.region(), captures);
SmallVector<Type> inputs;
inputs.reserve(2 + 4 * op.getNumLoops() + captures.size());
Type indexTy = rewriter.getIndexType();
// One-dimensional iteration space defined by the block index and size.
inputs.push_back(indexTy); // blockIndex
inputs.push_back(indexTy); // blockSize
// Multi-dimensional parallel iteration space defined by the loop trip counts.
for (unsigned i = 0; i < op.getNumLoops(); ++i)
inputs.push_back(indexTy); // loop tripCount
// Parallel operation lower bound, upper bound and step. Lower bound, upper
// bound and step passed as contiguous arguments:
// call @compute(%lb0, %lb1, ..., %ub0, %ub1, ..., %step0, %step1, ...)
for (unsigned i = 0; i < op.getNumLoops(); ++i) {
inputs.push_back(indexTy); // lower bound
inputs.push_back(indexTy); // upper bound
inputs.push_back(indexTy); // step
}
// Types of the implicit captures.
for (Value capture : captures)
inputs.push_back(capture.getType());
// Convert captures to vector for later convenience.
SmallVector<Value> capturesVector(captures.begin(), captures.end());
return {rewriter.getFunctionType(inputs, TypeRange()), capturesVector};
}
// Create a parallel compute fuction from the parallel operation.
static ParallelComputeFunction createParallelComputeFunction(
scf::ParallelOp op, ParallelComputeFunctionBounds bounds,
unsigned numBlockAlignedInnerLoops, PatternRewriter &rewriter) {
OpBuilder::InsertionGuard guard(rewriter);
ImplicitLocOpBuilder b(op.getLoc(), rewriter);
ModuleOp module = op->getParentOfType<ModuleOp>();
ParallelComputeFunctionType computeFuncType =
getParallelComputeFunctionType(op, rewriter);
FunctionType type = computeFuncType.type;
FuncOp func = FuncOp::create(op.getLoc(),
numBlockAlignedInnerLoops > 0
? "parallel_compute_fn_with_aligned_loops"
: "parallel_compute_fn",
type);
func.setPrivate();
// Insert function into the module symbol table and assign it unique name.
SymbolTable symbolTable(module);
symbolTable.insert(func);
rewriter.getListener()->notifyOperationInserted(func);
// Create function entry block.
Block *block = b.createBlock(&func.getBody(), func.begin(), type.getInputs());
b.setInsertionPointToEnd(block);
ParallelComputeFunctionArgs args = {op.getNumLoops(), func.getArguments()};
// Block iteration position defined by the block index and size.
BlockArgument blockIndex = args.blockIndex();
BlockArgument blockSize = args.blockSize();
// Constants used below.
Value c0 = b.create<arith::ConstantIndexOp>(0);
Value c1 = b.create<arith::ConstantIndexOp>(1);
// Materialize known constants as constant operation in the function body.
auto values = [&](ArrayRef<BlockArgument> args, ArrayRef<IntegerAttr> attrs) {
return llvm::to_vector(
llvm::map_range(llvm::zip(args, attrs), [&](auto tuple) -> Value {
if (IntegerAttr attr = std::get<1>(tuple))
return b.create<ConstantOp>(attr);
return std::get<0>(tuple);
}));
};
// Multi-dimensional parallel iteration space defined by the loop trip counts.
auto tripCounts = values(args.tripCounts(), bounds.tripCounts);
// Parallel operation lower bound and step.
auto lowerBounds = values(args.lowerBounds(), bounds.lowerBounds);
auto steps = values(args.steps(), bounds.steps);
// Remaining arguments are implicit captures of the parallel operation.
ArrayRef<BlockArgument> captures = args.captures();
// Compute a product of trip counts to get the size of the flattened
// one-dimensional iteration space.
Value tripCount = tripCounts[0];
for (unsigned i = 1; i < tripCounts.size(); ++i)
tripCount = b.create<arith::MulIOp>(tripCount, tripCounts[i]);
// Find one-dimensional iteration bounds: [blockFirstIndex, blockLastIndex]:
// blockFirstIndex = blockIndex * blockSize
Value blockFirstIndex = b.create<arith::MulIOp>(blockIndex, blockSize);
// The last one-dimensional index in the block defined by the `blockIndex`:
// blockLastIndex = min(blockFirstIndex + blockSize, tripCount) - 1
Value blockEnd0 = b.create<arith::AddIOp>(blockFirstIndex, blockSize);
Value blockEnd1 = b.create<arith::MinSIOp>(blockEnd0, tripCount);
Value blockLastIndex = b.create<arith::SubIOp>(blockEnd1, c1);
// Convert one-dimensional indices to multi-dimensional coordinates.
auto blockFirstCoord = delinearize(b, blockFirstIndex, tripCounts);
auto blockLastCoord = delinearize(b, blockLastIndex, tripCounts);
// Compute loops upper bounds derived from the block last coordinates:
// blockEndCoord[i] = blockLastCoord[i] + 1
//
// Block first and last coordinates can be the same along the outer compute
// dimension when inner compute dimension contains multiple blocks.
SmallVector<Value> blockEndCoord(op.getNumLoops());
for (size_t i = 0; i < blockLastCoord.size(); ++i)
blockEndCoord[i] = b.create<arith::AddIOp>(blockLastCoord[i], c1);
// Construct a loop nest out of scf.for operations that will iterate over
// all coordinates in [blockFirstCoord, blockLastCoord] range.
using LoopBodyBuilder =
std::function<void(OpBuilder &, Location, Value, ValueRange)>;
using LoopNestBuilder = std::function<LoopBodyBuilder(size_t loopIdx)>;
// Parallel region induction variables computed from the multi-dimensional
// iteration coordinate using parallel operation bounds and step:
//
// computeBlockInductionVars[loopIdx] =
// lowerBound[loopIdx] + blockCoord[loopIdx] * step[loopIdx]
SmallVector<Value> computeBlockInductionVars(op.getNumLoops());
// We need to know if we are in the first or last iteration of the
// multi-dimensional loop for each loop in the nest, so we can decide what
// loop bounds should we use for the nested loops: bounds defined by compute
// block interval, or bounds defined by the parallel operation.
//
// Example: 2d parallel operation
// i j
// loop sizes: [50, 50]
// first coord: [25, 25]
// last coord: [30, 30]
//
// If `i` is equal to 25 then iteration over `j` should start at 25, when `i`
// is between 25 and 30 it should start at 0. The upper bound for `j` should
// be 50, except when `i` is equal to 30, then it should also be 30.
//
// Value at ith position specifies if all loops in [0, i) range of the loop
// nest are in the first/last iteration.
SmallVector<Value> isBlockFirstCoord(op.getNumLoops());
SmallVector<Value> isBlockLastCoord(op.getNumLoops());
// Builds inner loop nest inside async.execute operation that does all the
// work concurrently.
LoopNestBuilder workLoopBuilder = [&](size_t loopIdx) -> LoopBodyBuilder {
return [&, loopIdx](OpBuilder &nestedBuilder, Location loc, Value iv,
ValueRange args) {
ImplicitLocOpBuilder nb(loc, nestedBuilder);
// Compute induction variable for `loopIdx`.
computeBlockInductionVars[loopIdx] = nb.create<arith::AddIOp>(
lowerBounds[loopIdx], nb.create<arith::MulIOp>(iv, steps[loopIdx]));
// Check if we are inside first or last iteration of the loop.
isBlockFirstCoord[loopIdx] = nb.create<arith::CmpIOp>(
arith::CmpIPredicate::eq, iv, blockFirstCoord[loopIdx]);
isBlockLastCoord[loopIdx] = nb.create<arith::CmpIOp>(
arith::CmpIPredicate::eq, iv, blockLastCoord[loopIdx]);
// Check if the previous loop is in its first or last iteration.
if (loopIdx > 0) {
isBlockFirstCoord[loopIdx] = nb.create<arith::AndIOp>(
isBlockFirstCoord[loopIdx], isBlockFirstCoord[loopIdx - 1]);
isBlockLastCoord[loopIdx] = nb.create<arith::AndIOp>(
isBlockLastCoord[loopIdx], isBlockLastCoord[loopIdx - 1]);
}
// Keep building loop nest.
if (loopIdx < op.getNumLoops() - 1) {
if (loopIdx + 1 >= op.getNumLoops() - numBlockAlignedInnerLoops) {
// For block aligned loops we always iterate starting from 0 up to
// the loop trip counts.
nb.create<scf::ForOp>(c0, tripCounts[loopIdx + 1], c1, ValueRange(),
workLoopBuilder(loopIdx + 1));
} else {
// Select nested loop lower/upper bounds depending on our position in
// the multi-dimensional iteration space.
auto lb = nb.create<SelectOp>(isBlockFirstCoord[loopIdx],
blockFirstCoord[loopIdx + 1], c0);
auto ub = nb.create<SelectOp>(isBlockLastCoord[loopIdx],
blockEndCoord[loopIdx + 1],
tripCounts[loopIdx + 1]);
nb.create<scf::ForOp>(lb, ub, c1, ValueRange(),
workLoopBuilder(loopIdx + 1));
}
nb.create<scf::YieldOp>(loc);
return;
}
// Copy the body of the parallel op into the inner-most loop.
BlockAndValueMapping mapping;
mapping.map(op.getInductionVars(), computeBlockInductionVars);
mapping.map(computeFuncType.captures, captures);
for (auto &bodyOp : op.getLoopBody().getOps())
nb.clone(bodyOp, mapping);
};
};
b.create<scf::ForOp>(blockFirstCoord[0], blockEndCoord[0], c1, ValueRange(),
workLoopBuilder(0));
b.create<ReturnOp>(ValueRange());
return {op.getNumLoops(), func, std::move(computeFuncType.captures)};
}
// Creates recursive async dispatch function for the given parallel compute
// function. Dispatch function keeps splitting block range into halves until it
// reaches a single block, and then excecutes it inline.
//
// Function pseudocode (mix of C++ and MLIR):
//
// func @async_dispatch(%block_start : index, %block_end : index, ...) {
//
// // Keep splitting block range until we reached a range of size 1.
// while (%block_end - %block_start > 1) {
// %mid_index = block_start + (block_end - block_start) / 2;
// async.execute { call @async_dispatch(%mid_index, %block_end); }
// %block_end = %mid_index
// }
//
// // Call parallel compute function for a single block.
// call @parallel_compute_fn(%block_start, %block_size, ...);
// }
//
static FuncOp createAsyncDispatchFunction(ParallelComputeFunction &computeFunc,
PatternRewriter &rewriter) {
OpBuilder::InsertionGuard guard(rewriter);
Location loc = computeFunc.func.getLoc();
ImplicitLocOpBuilder b(loc, rewriter);
ModuleOp module = computeFunc.func->getParentOfType<ModuleOp>();
ArrayRef<Type> computeFuncInputTypes =
computeFunc.func.type().cast<FunctionType>().getInputs();
// Compared to the parallel compute function async dispatch function takes
// additional !async.group argument. Also instead of a single `blockIndex` it
// takes `blockStart` and `blockEnd` arguments to define the range of
// dispatched blocks.
SmallVector<Type> inputTypes;
inputTypes.push_back(async::GroupType::get(rewriter.getContext()));
inputTypes.push_back(rewriter.getIndexType()); // add blockStart argument
inputTypes.append(computeFuncInputTypes.begin(), computeFuncInputTypes.end());
FunctionType type = rewriter.getFunctionType(inputTypes, TypeRange());
FuncOp func = FuncOp::create(loc, "async_dispatch_fn", type);
func.setPrivate();
// Insert function into the module symbol table and assign it unique name.
SymbolTable symbolTable(module);
symbolTable.insert(func);
rewriter.getListener()->notifyOperationInserted(func);
// Create function entry block.
Block *block = b.createBlock(&func.getBody(), func.begin(), type.getInputs());
b.setInsertionPointToEnd(block);
Type indexTy = b.getIndexType();
Value c1 = b.create<arith::ConstantIndexOp>(1);
Value c2 = b.create<arith::ConstantIndexOp>(2);
// Get the async group that will track async dispatch completion.
Value group = block->getArgument(0);
// Get the block iteration range: [blockStart, blockEnd)
Value blockStart = block->getArgument(1);
Value blockEnd = block->getArgument(2);
// Create a work splitting while loop for the [blockStart, blockEnd) range.
SmallVector<Type> types = {indexTy, indexTy};
SmallVector<Value> operands = {blockStart, blockEnd};
// Create a recursive dispatch loop.
scf::WhileOp whileOp = b.create<scf::WhileOp>(types, operands);
Block *before = b.createBlock(&whileOp.before(), {}, types);
Block *after = b.createBlock(&whileOp.after(), {}, types);
// Setup dispatch loop condition block: decide if we need to go into the
// `after` block and launch one more async dispatch.
{
b.setInsertionPointToEnd(before);
Value start = before->getArgument(0);
Value end = before->getArgument(1);
Value distance = b.create<arith::SubIOp>(end, start);
Value dispatch =
b.create<arith::CmpIOp>(arith::CmpIPredicate::sgt, distance, c1);
b.create<scf::ConditionOp>(dispatch, before->getArguments());
}
// Setup the async dispatch loop body: recursively call dispatch function
// for the seconds half of the original range and go to the next iteration.
{
b.setInsertionPointToEnd(after);
Value start = after->getArgument(0);
Value end = after->getArgument(1);
Value distance = b.create<arith::SubIOp>(end, start);
Value halfDistance = b.create<arith::DivSIOp>(distance, c2);
Value midIndex = b.create<arith::AddIOp>(start, halfDistance);
// Call parallel compute function inside the async.execute region.
auto executeBodyBuilder = [&](OpBuilder &executeBuilder,
Location executeLoc, ValueRange executeArgs) {
// Update the original `blockStart` and `blockEnd` with new range.
SmallVector<Value> operands{block->getArguments().begin(),
block->getArguments().end()};
operands[1] = midIndex;
operands[2] = end;
executeBuilder.create<CallOp>(executeLoc, func.sym_name(),
func.getCallableResults(), operands);
executeBuilder.create<async::YieldOp>(executeLoc, ValueRange());
};
// Create async.execute operation to dispatch half of the block range.
auto execute = b.create<ExecuteOp>(TypeRange(), ValueRange(), ValueRange(),
executeBodyBuilder);
b.create<AddToGroupOp>(indexTy, execute.token(), group);
b.create<scf::YieldOp>(ValueRange({start, midIndex}));
}
// After dispatching async operations to process the tail of the block range
// call the parallel compute function for the first block of the range.
b.setInsertionPointAfter(whileOp);
// Drop async dispatch specific arguments: async group, block start and end.
auto forwardedInputs = block->getArguments().drop_front(3);
SmallVector<Value> computeFuncOperands = {blockStart};
computeFuncOperands.append(forwardedInputs.begin(), forwardedInputs.end());
b.create<CallOp>(computeFunc.func.sym_name(),
computeFunc.func.getCallableResults(), computeFuncOperands);
b.create<ReturnOp>(ValueRange());
return func;
}
// Launch async dispatch of the parallel compute function.
static void doAsyncDispatch(ImplicitLocOpBuilder &b, PatternRewriter &rewriter,
ParallelComputeFunction &parallelComputeFunction,
scf::ParallelOp op, Value blockSize,
Value blockCount,
const SmallVector<Value> &tripCounts) {
MLIRContext *ctx = op->getContext();
// Add one more level of indirection to dispatch parallel compute functions
// using async operations and recursive work splitting.
FuncOp asyncDispatchFunction =
createAsyncDispatchFunction(parallelComputeFunction, rewriter);
Value c0 = b.create<arith::ConstantIndexOp>(0);
Value c1 = b.create<arith::ConstantIndexOp>(1);
// Appends operands shared by async dispatch and parallel compute functions to
// the given operands vector.
auto appendBlockComputeOperands = [&](SmallVector<Value> &operands) {
operands.append(tripCounts);
operands.append(op.lowerBound().begin(), op.lowerBound().end());
operands.append(op.upperBound().begin(), op.upperBound().end());
operands.append(op.step().begin(), op.step().end());
operands.append(parallelComputeFunction.captures);
};
// Check if the block size is one, in this case we can skip the async dispatch
// completely. If this will be known statically, then canonicalization will
// erase async group operations.
Value isSingleBlock =
b.create<arith::CmpIOp>(arith::CmpIPredicate::eq, blockCount, c1);
auto syncDispatch = [&](OpBuilder &nestedBuilder, Location loc) {
ImplicitLocOpBuilder nb(loc, nestedBuilder);
// Call parallel compute function for the single block.
SmallVector<Value> operands = {c0, blockSize};
appendBlockComputeOperands(operands);
nb.create<CallOp>(parallelComputeFunction.func.sym_name(),
parallelComputeFunction.func.getCallableResults(),
operands);
nb.create<scf::YieldOp>();
};
auto asyncDispatch = [&](OpBuilder &nestedBuilder, Location loc) {
// Create an async.group to wait on all async tokens from the concurrent
// execution of multiple parallel compute function. First block will be
// executed synchronously in the caller thread.
Value groupSize = b.create<arith::SubIOp>(blockCount, c1);
Value group = b.create<CreateGroupOp>(GroupType::get(ctx), groupSize);
ImplicitLocOpBuilder nb(loc, nestedBuilder);
// Launch async dispatch function for [0, blockCount) range.
SmallVector<Value> operands = {group, c0, blockCount, blockSize};
appendBlockComputeOperands(operands);
nb.create<CallOp>(asyncDispatchFunction.sym_name(),
asyncDispatchFunction.getCallableResults(), operands);
// Wait for the completion of all parallel compute operations.
b.create<AwaitAllOp>(group);
nb.create<scf::YieldOp>();
};
// Dispatch either single block compute function, or launch async dispatch.
b.create<scf::IfOp>(TypeRange(), isSingleBlock, syncDispatch, asyncDispatch);
}
// Dispatch parallel compute functions by submitting all async compute tasks
// from a simple for loop in the caller thread.
static void
doSequentialDispatch(ImplicitLocOpBuilder &b, PatternRewriter &rewriter,
ParallelComputeFunction &parallelComputeFunction,
scf::ParallelOp op, Value blockSize, Value blockCount,
const SmallVector<Value> &tripCounts) {
MLIRContext *ctx = op->getContext();
FuncOp compute = parallelComputeFunction.func;
Value c0 = b.create<arith::ConstantIndexOp>(0);
Value c1 = b.create<arith::ConstantIndexOp>(1);
// Create an async.group to wait on all async tokens from the concurrent
// execution of multiple parallel compute function. First block will be
// executed synchronously in the caller thread.
Value groupSize = b.create<arith::SubIOp>(blockCount, c1);
Value group = b.create<CreateGroupOp>(GroupType::get(ctx), groupSize);
// Call parallel compute function for all blocks.
using LoopBodyBuilder =
std::function<void(OpBuilder &, Location, Value, ValueRange)>;
// Returns parallel compute function operands to process the given block.
auto computeFuncOperands = [&](Value blockIndex) -> SmallVector<Value> {
SmallVector<Value> computeFuncOperands = {blockIndex, blockSize};
computeFuncOperands.append(tripCounts);
computeFuncOperands.append(op.lowerBound().begin(), op.lowerBound().end());
computeFuncOperands.append(op.upperBound().begin(), op.upperBound().end());
computeFuncOperands.append(op.step().begin(), op.step().end());
computeFuncOperands.append(parallelComputeFunction.captures);
return computeFuncOperands;
};
// Induction variable is the index of the block: [0, blockCount).
LoopBodyBuilder loopBuilder = [&](OpBuilder &loopBuilder, Location loc,
Value iv, ValueRange args) {
ImplicitLocOpBuilder nb(loc, loopBuilder);
// Call parallel compute function inside the async.execute region.
auto executeBodyBuilder = [&](OpBuilder &executeBuilder,
Location executeLoc, ValueRange executeArgs) {
executeBuilder.create<CallOp>(executeLoc, compute.sym_name(),
compute.getCallableResults(),
computeFuncOperands(iv));
executeBuilder.create<async::YieldOp>(executeLoc, ValueRange());
};
// Create async.execute operation to launch parallel computate function.
auto execute = nb.create<ExecuteOp>(TypeRange(), ValueRange(), ValueRange(),
executeBodyBuilder);
nb.create<AddToGroupOp>(rewriter.getIndexType(), execute.token(), group);
nb.create<scf::YieldOp>();
};
// Iterate over all compute blocks and launch parallel compute operations.
b.create<scf::ForOp>(c1, blockCount, c1, ValueRange(), loopBuilder);
// Call parallel compute function for the first block in the caller thread.
b.create<CallOp>(compute.sym_name(), compute.getCallableResults(),
computeFuncOperands(c0));
// Wait for the completion of all async compute operations.
b.create<AwaitAllOp>(group);
}
LogicalResult
AsyncParallelForRewrite::matchAndRewrite(scf::ParallelOp op,
PatternRewriter &rewriter) const {
// We do not currently support rewrite for parallel op with reductions.
if (op.getNumReductions() != 0)
return failure();
ImplicitLocOpBuilder b(op.getLoc(), rewriter);
// Computing minTaskSize emits IR and can be implemented as executing a cost
// model on the body of the scf.parallel. Thus it needs to be computed before
// the body of the scf.parallel has been manipulated.
Value minTaskSize = computeMinTaskSize(b, op);
// Make sure that all constants will be inside the parallel operation body to
// reduce the number of parallel compute function arguments.
cloneConstantsIntoTheRegion(op.getLoopBody(), rewriter);
// Compute trip count for each loop induction variable:
// tripCount = ceil_div(upperBound - lowerBound, step);
SmallVector<Value> tripCounts(op.getNumLoops());
for (size_t i = 0; i < op.getNumLoops(); ++i) {
auto lb = op.lowerBound()[i];
auto ub = op.upperBound()[i];
auto step = op.step()[i];
auto range = b.createOrFold<arith::SubIOp>(ub, lb);
tripCounts[i] = b.createOrFold<arith::CeilDivSIOp>(range, step);
}
// Compute a product of trip counts to get the 1-dimensional iteration space
// for the scf.parallel operation.
Value tripCount = tripCounts[0];
for (size_t i = 1; i < tripCounts.size(); ++i)
tripCount = b.create<arith::MulIOp>(tripCount, tripCounts[i]);
// Short circuit no-op parallel loops (zero iterations) that can arise from
// the memrefs with dynamic dimension(s) equal to zero.
Value c0 = b.create<arith::ConstantIndexOp>(0);
Value isZeroIterations =
b.create<arith::CmpIOp>(arith::CmpIPredicate::eq, tripCount, c0);
// Do absolutely nothing if the trip count is zero.
auto noOp = [&](OpBuilder &nestedBuilder, Location loc) {
nestedBuilder.create<scf::YieldOp>(loc);
};
// Compute the parallel block size and dispatch concurrent tasks computing
// results for each block.
auto dispatch = [&](OpBuilder &nestedBuilder, Location loc) {
ImplicitLocOpBuilder nb(loc, nestedBuilder);
// Collect statically known constants defining the loop nest in the parallel
// compute function. LLVM can't always push constants across the non-trivial
// async dispatch call graph, by providing these values explicitly we can
// choose to build more efficient loop nest, and rely on a better constant
// folding, loop unrolling and vectorization.
ParallelComputeFunctionBounds staticBounds = {
integerConstants(tripCounts),
integerConstants(op.lowerBound()),
integerConstants(op.upperBound()),
integerConstants(op.step()),
};
// Find how many inner iteration dimensions are statically known, and their
// product is smaller than the `512`. We align the parallel compute block
// size by the product of statically known dimensions, so that we can
// guarantee that the inner loops executes from 0 to the loop trip counts
// and we can elide dynamic loop boundaries, and give LLVM an opportunity to
// unroll the loops. The constant `512` is arbitrary, it should depend on
// how many iterations LLVM will typically decide to unroll.
static constexpr int64_t maxIterations = 512;
// The number of inner loops with statically known number of iterations less
// than the `maxIterations` value.
int numUnrollableLoops = 0;
auto getInt = [](IntegerAttr attr) { return attr ? attr.getInt() : 0; };
SmallVector<int64_t> numIterations(op.getNumLoops());
numIterations.back() = getInt(staticBounds.tripCounts.back());
for (int i = op.getNumLoops() - 2; i >= 0; --i) {
int64_t tripCount = getInt(staticBounds.tripCounts[i]);
int64_t innerIterations = numIterations[i + 1];
numIterations[i] = tripCount * innerIterations;
// Update the number of inner loops that we can potentially unroll.
if (innerIterations > 0 && innerIterations <= maxIterations)
numUnrollableLoops++;
}
// With large number of threads the value of creating many compute blocks
// is reduced because the problem typically becomes memory bound. For small
// number of threads it helps with stragglers.
float overshardingFactor = numWorkerThreads <= 4 ? 8.0
: numWorkerThreads <= 8 ? 4.0
: numWorkerThreads <= 16 ? 2.0
: numWorkerThreads <= 32 ? 1.0
: numWorkerThreads <= 64 ? 0.8
: 0.6;
// Do not overload worker threads with too many compute blocks.
Value maxComputeBlocks = b.create<arith::ConstantIndexOp>(
std::max(1, static_cast<int>(numWorkerThreads * overshardingFactor)));
// Compute parallel block size from the parallel problem size:
// blockSize = min(tripCount,
// max(ceil_div(tripCount, maxComputeBlocks),
// minTaskSize))
Value bs0 = b.create<arith::CeilDivSIOp>(tripCount, maxComputeBlocks);
Value bs1 = b.create<arith::MaxSIOp>(bs0, minTaskSize);
Value blockSize = b.create<arith::MinSIOp>(tripCount, bs1);
ParallelComputeFunction notUnrollableParallelComputeFunction =
createParallelComputeFunction(op, staticBounds, 0, rewriter);
// Dispatch parallel compute function using async recursive work splitting,
// or by submitting compute task sequentially from a caller thread.
auto doDispatch = asyncDispatch ? doAsyncDispatch : doSequentialDispatch;
// Create a parallel compute function that takes a block id and computes
// the parallel operation body for a subset of iteration space.
// Compute the number of parallel compute blocks.
Value blockCount = b.create<arith::CeilDivSIOp>(tripCount, blockSize);
// Unroll when numUnrollableLoops > 0 && blockSize >= maxIterations.
bool staticShouldUnroll = numUnrollableLoops > 0;
auto dispatchNotUnrollable = [&](OpBuilder &nestedBuilder, Location loc) {
ImplicitLocOpBuilder nb(loc, nestedBuilder);
doDispatch(b, rewriter, notUnrollableParallelComputeFunction, op,
blockSize, blockCount, tripCounts);
nb.create<scf::YieldOp>();
};
if (staticShouldUnroll) {
Value dynamicShouldUnroll = b.create<arith::CmpIOp>(
arith::CmpIPredicate::sge, blockSize,
b.create<arith::ConstantIndexOp>(maxIterations));
ParallelComputeFunction unrollableParallelComputeFunction =
createParallelComputeFunction(op, staticBounds, numUnrollableLoops,
rewriter);
auto dispatchUnrollable = [&](OpBuilder &nestedBuilder, Location loc) {
ImplicitLocOpBuilder nb(loc, nestedBuilder);
// Align the block size to be a multiple of the statically known
// number of iterations in the inner loops.
Value numIters = nb.create<arith::ConstantIndexOp>(
numIterations[op.getNumLoops() - numUnrollableLoops]);
Value alignedBlockSize = nb.create<arith::MulIOp>(
nb.create<arith::CeilDivSIOp>(blockSize, numIters), numIters);
doDispatch(b, rewriter, unrollableParallelComputeFunction, op,
alignedBlockSize, blockCount, tripCounts);
nb.create<scf::YieldOp>();
};
b.create<scf::IfOp>(TypeRange(), dynamicShouldUnroll, dispatchUnrollable,
dispatchNotUnrollable);
nb.create<scf::YieldOp>();
} else {
dispatchNotUnrollable(nb, loc);
}
};
// Replace the `scf.parallel` operation with the parallel compute function.
b.create<scf::IfOp>(TypeRange(), isZeroIterations, noOp, dispatch);
// Parallel operation was replaced with a block iteration loop.
rewriter.eraseOp(op);
return success();
}
void AsyncParallelForPass::runOnOperation() {
MLIRContext *ctx = &getContext();
RewritePatternSet patterns(ctx);
populateAsyncParallelForPatterns(
patterns, asyncDispatch, numWorkerThreads,
[&](ImplicitLocOpBuilder builder, scf::ParallelOp op) {
return builder.create<arith::ConstantIndexOp>(minTaskSize);
});
if (failed(applyPatternsAndFoldGreedily(getOperation(), std::move(patterns))))
signalPassFailure();
}
std::unique_ptr<Pass> mlir::createAsyncParallelForPass() {
return std::make_unique<AsyncParallelForPass>();
}
std::unique_ptr<Pass> mlir::createAsyncParallelForPass(bool asyncDispatch,
int32_t numWorkerThreads,
int32_t minTaskSize) {
return std::make_unique<AsyncParallelForPass>(asyncDispatch, numWorkerThreads,
minTaskSize);
}
void mlir::async::populateAsyncParallelForPatterns(
RewritePatternSet &patterns, bool asyncDispatch, int32_t numWorkerThreads,
AsyncMinTaskSizeComputationFunction computeMinTaskSize) {
MLIRContext *ctx = patterns.getContext();
patterns.add<AsyncParallelForRewrite>(ctx, asyncDispatch, numWorkerThreads,
computeMinTaskSize);
}
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