268 lines
11 KiB
C++
268 lines
11 KiB
C++
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//===- GPUHeuristics.cpp - Heuristics Implementation for Transforms -------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Linalg/TransformOps/GPUHeuristics.h"
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#include "mlir/Dialect/GPU/IR/GPUDialect.h"
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#include "mlir/Support/MathExtras.h"
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#include "llvm/ADT/ArrayRef.h"
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#include "llvm/ADT/STLExtras.h"
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#include "llvm/Support/CommandLine.h"
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#include "llvm/Support/Debug.h"
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#include "llvm/Support/raw_ostream.h"
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#include <cmath>
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#include <numeric>
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using namespace mlir;
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#define DEBUG_TYPE "linalg-transforms"
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#define DBGS() (llvm::dbgs() << "[" DEBUG_TYPE "]: ")
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#define LDBG(X) LLVM_DEBUG(DBGS() << X << "\n")
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static Attribute linearId0(MLIRContext *ctx) {
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return gpu::GPUThreadMappingAttr::get(ctx, gpu::MappingId::LinearDim0);
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}
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static Attribute linearId1(MLIRContext *ctx) {
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return gpu::GPUThreadMappingAttr::get(ctx, gpu::MappingId::LinearDim1);
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}
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static Attribute linearId2(MLIRContext *ctx) {
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return gpu::GPUThreadMappingAttr::get(ctx, gpu::MappingId::LinearDim2);
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}
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transform::gpu::CopyMappingInfo::CopyMappingInfo(MLIRContext *ctx,
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int totalNumThreads,
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int64_t desiredBitAlignment,
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ArrayRef<int64_t> copySizes,
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bool favorPredication,
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int64_t elementalBitwidth) {
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assert(!copySizes.empty() && copySizes.size() <= 3 &&
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"only 1,2,3-D copies are supported for now");
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LDBG("START CopyMappingInfo, favorPredication: " << favorPredication);
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LLVM_DEBUG(llvm::interleaveComma(copySizes, DBGS() << "--copy shape: ");
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llvm::dbgs() << "\n";);
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// Greedily find the largest vector size that can be used to copy the most
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// minor dimension: we are in the business of filling kMaxVectorLoadBitWidth
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// contiguous memory transactions with as few threads as possible.
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int64_t desiredVectorSize = CopyMappingInfo::maxContiguousElementsToTransfer(
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desiredBitAlignment, copySizes.back(), elementalBitwidth);
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LDBG("--greedily determined vectorSize: "
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<< desiredVectorSize << " elements of " << elementalBitwidth
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<< "b each -> " << (desiredVectorSize * elementalBitwidth)
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<< "b total out of a max of " << kMaxVectorLoadBitWidth << "b");
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status = inferNumThreads(totalNumThreads, copySizes, desiredVectorSize,
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favorPredication);
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if (status == Status::Invalid)
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return;
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LLVM_DEBUG(llvm::interleaveComma(copySizes, DBGS() << "--copy: ");
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llvm::dbgs() << "\n"; llvm::interleaveComma(
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this->numThreads, DBGS() << "--numThreads: ");
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llvm::dbgs() << "\n";);
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LDBG("--vectorSize: " << this->vectorSize);
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assert(this->numThreads.size() == copySizes.size() &&
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"compute copy mapping expected same number of threads and copy sizes");
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// Compute the smallest bounding box.
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this->smallestBoundingTileSizes = llvm::to_vector(
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llvm::map_range(llvm::zip(copySizes, this->numThreads), [](auto &&pair) {
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int64_t size, numThreads;
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std::tie(size, numThreads) = pair;
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return mlir::ceilDiv(size, numThreads);
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}));
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SmallVector<Attribute> allThreadMappings{linearId2(ctx), linearId1(ctx),
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linearId0(ctx)};
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// Set the thread mapping.
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this->threadMapping =
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llvm::to_vector(ArrayRef(allThreadMappings)
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.take_back(this->smallestBoundingTileSizes.size()));
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LLVM_DEBUG(this->print(DBGS()); llvm::dbgs() << "\n");
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}
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int64_t transform::gpu::CopyMappingInfo::maxContiguousElementsToTransfer(
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int64_t desiredBitAlignment, int64_t numContiguousElements,
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int64_t elementalBitwidth) {
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assert(kMaxVectorLoadBitWidth % elementalBitwidth == 0 &&
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"elemental bitwidth does not divide kMaxVectorLoadBitWidth");
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assert(desiredBitAlignment % elementalBitwidth == 0 &&
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"elemental bitwidth does not divide desired bit alignment");
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return std::gcd(
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std::gcd(desiredBitAlignment / elementalBitwidth, numContiguousElements),
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kMaxVectorLoadBitWidth / elementalBitwidth);
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}
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/// Get the list of all factors that divide `val`, not just the prime factors.
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static SmallVector<int64_t> getFactors(int64_t val) {
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SmallVector<int64_t> factors;
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factors.reserve(val);
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for (int64_t factor = 1; factor <= val; ++factor) {
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if (val % factor != 0)
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continue;
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factors.push_back(factor);
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}
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factors.push_back(val);
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return factors;
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}
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static int64_t product(ArrayRef<int64_t> vals) {
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int64_t res = 1;
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for (auto val : vals)
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res *= val;
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return res;
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}
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/// Extract `result` from `sizes` with the following constraints:
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/// 1. sizes[i] % result[i] for all i
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/// 2. product_of_threadsPerDim <= maxNumThreads
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/// 3. if `currentIndex` is sizes.size() - 1, then threadsPerDim[currentIndex]
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/// must be sizes[currentIndex].
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/// This is used to greedily extract the maximum number of threads usable for
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/// mapping a copy of size `sizes`, while being bounded by `totalNumThreads` and
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/// ensuring coalesced access along the most minor dimension.
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/// Return the number of threads used in the range:
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/// threadsPerDim[currentIndex .. sizes.end()]
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// The implementation uses a dynamic programming approach to greedily extract
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// the best combination under the constraints.
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// TODO: Implementation details can be improved but putting effort there is a
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// tradeoffs: `sizes` is expected to be of small rank and contain small values.
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static SmallVector<int64_t> maximizeNumThreads(ArrayRef<int64_t> sizes,
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int64_t currentIndex,
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int64_t maxNumThreads) {
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assert(static_cast<size_t>(currentIndex) < sizes.size() &&
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"currentIndex out of bounds");
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std::string indent(2 * currentIndex, '-');
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if (static_cast<size_t>(currentIndex) == sizes.size() - 1) {
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LDBG(indent << "mandated globalBest: " << sizes[currentIndex]);
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return SmallVector<int64_t>{sizes[currentIndex]};
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}
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int64_t best = 0;
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int64_t s = sizes[currentIndex];
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SmallVector<int64_t> factors = getFactors(s);
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SmallVector<int64_t> localThreadsPerDim;
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localThreadsPerDim.reserve(sizes.size());
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LDBG(indent << "maximizeNumThreads in " << s
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<< " with limit: " << maxNumThreads);
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for (auto factor : factors) {
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auto nestedThreadsPerDim =
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maximizeNumThreads(sizes, currentIndex + 1, maxNumThreads / factor);
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int64_t localBest = factor * product(nestedThreadsPerDim);
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if (localBest > best && localBest <= maxNumThreads) {
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LDBG(indent << "new localBest: " << localBest);
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LLVM_DEBUG(
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llvm::interleaveComma(nestedThreadsPerDim,
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DBGS() << indent << "nestedThreadsPerDim: ");
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llvm::dbgs() << "\n";);
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localThreadsPerDim.clear();
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localThreadsPerDim.push_back(factor);
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llvm::append_range(localThreadsPerDim, nestedThreadsPerDim);
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best = localBest;
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}
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}
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LDBG(indent << "found globalBest: " << best);
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LLVM_DEBUG(llvm::interleaveComma(localThreadsPerDim,
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DBGS() << indent << "numThreads: ");
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llvm::dbgs() << "\n";);
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return localThreadsPerDim;
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}
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transform::gpu::CopyMappingInfo::Status
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transform::gpu::CopyMappingInfo::inferNumThreads(int64_t totalNumThreads,
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ArrayRef<int64_t> sizes,
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int64_t desiredVectorSize,
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bool favorPredication) {
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if (!favorPredication) {
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int64_t localVectorSize = desiredVectorSize;
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for (; localVectorSize >= 1; localVectorSize /= 2) {
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// Attempt to map the copy with predication and current fixed vector size:
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// 1. if the status is Success, we are done.
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// 2. if the status is Invalid, we fail immediately, no amount of
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// vector size reduction can offset the bad tile size selection from the
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// higher-level.
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// 3. if the status is RequiresPredication, we try again with a smaller
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// vector size.
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Status status =
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inferNumThreadsImpl(totalNumThreads, sizes, localVectorSize);
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if (status == Status::Success || status == Status::Invalid)
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return status;
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LDBG("requires predication, try reducing vector size to "
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<< (localVectorSize / 2));
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}
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}
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// If we have not yet returned, it means that we have tried all vector sizes
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// and we still require predication. Restart from the original vector size and
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// do not attempt to
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return inferNumThreadsImpl(totalNumThreads, sizes, desiredVectorSize);
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}
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transform::gpu::CopyMappingInfo::Status
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transform::gpu::CopyMappingInfo::inferNumThreadsImpl(
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int64_t totalNumThreads, ArrayRef<int64_t> sizes,
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int64_t desiredVectorSize) {
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assert(sizes.back() % desiredVectorSize == 0 &&
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"most-minor size not divisible by actualVectorSize");
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LDBG("inferNumThreadsImpl with totalNumThreads: "
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<< totalNumThreads << " and vectorSize: " << desiredVectorSize);
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// Scale the most minor size to account for the chosen vector size and
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// maximize the number of threads without exceeding the total number of
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// threads.
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SmallVector<int64_t> scaledSizes{sizes};
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scaledSizes.back() /= desiredVectorSize;
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if (scaledSizes.back() > totalNumThreads) {
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LDBG("--Too few threads given the required vector size -> FAIL");
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return Status::Invalid;
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}
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SmallVector<int64_t> inferredNumThreads =
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maximizeNumThreads(scaledSizes, 0, totalNumThreads);
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LLVM_DEBUG(llvm::interleaveComma(inferredNumThreads,
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DBGS() << "inferred numThreads: ");
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llvm::dbgs() << "\n";
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LDBG("computed actualVectorSize: " << desiredVectorSize););
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// Corner case: we cannot use more threads than available. If the dimension of
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// the copy is so bad it is because higher-level tiling did not do its job, we
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// do not try to recover from it here.
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int64_t totalNumThreadsUsed = product(inferredNumThreads);
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LDBG("--totalNumThreadsUsed: " << totalNumThreadsUsed);
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if (totalNumThreadsUsed == 0 || totalNumThreadsUsed > totalNumThreads) {
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LDBG("--Too few threads given the required vector size -> FAIL");
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return Status::Invalid;
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}
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this->vectorSize = desiredVectorSize;
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this->numThreads = inferredNumThreads;
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if (totalNumThreadsUsed == totalNumThreads)
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return Status::Success;
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return Status::RequiresPredication;
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}
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void transform::gpu::CopyMappingInfo::print(llvm::raw_ostream &os) const {
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os << "MappingInfo{";
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os << "CopyMappingInfo: ";
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os << "valid: " << (status != Status::Invalid) << ", ";
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os << "vectorSize: " << vectorSize << ", ";
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llvm::interleaveComma(numThreads, os << ", numThreads: {");
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llvm::interleaveComma(smallestBoundingTileSizes,
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os << "}, smallestBoundingTileSizes: {");
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llvm::interleaveComma(threadMapping, os << "}, threadMapping: {");
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os << "}}";
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}
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