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[Triton-MLIR][BACKEND] Add argmin / argmax implementation for ReduceOp (#918)

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This commit is contained in:
Qingyi Liu
2022-11-28 14:59:27 +08:00
committed by GitHub
parent 04ec5deb41
commit 9d31998a9d
12 changed files with 341 additions and 75 deletions
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37
lib/Analysis/Allocation.cpp
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@@ -88,25 +88,6 @@ getScratchConfigForCvtLayout(triton::gpu::ConvertLayoutOp op, unsigned &inVec,
return paddedRepShape;
}
SmallVector<unsigned> getScratchConfigForReduce(triton::ReduceOp op) {
ReduceOpHelper helper(op);
SmallVector<unsigned> smemShape;
auto srcShape = helper.getSrcShape();
for (auto d : srcShape)
smemShape.push_back(d);
auto axis = op.axis();
if (helper.isFastReduction()) {
smemShape[axis] = helper.getInterWarpSize();
} else {
smemShape[axis] =
std::min(smemShape[axis], helper.getThreadsReductionAxis());
}
return smemShape;
}
// TODO: extend beyond scalars
SmallVector<unsigned> getScratchConfigForAtomicRMW(triton::AtomicRMWOp op) {
SmallVector<unsigned> smemShape;
@@ -173,21 +154,9 @@ private:
/// Initializes temporary shared memory for a given operation.
void getScratchValueSize(Operation *op) {
if (auto reduceOp = dyn_cast<triton::ReduceOp>(op)) {
// TODO(Keren): Reduce with index is not supported yet.
auto value = op->getOperand(0);
if (auto tensorType = value.getType().dyn_cast<RankedTensorType>()) {
bool fastReduce = ReduceOpHelper(reduceOp).isFastReduction();
auto smemShape = getScratchConfigForReduce(reduceOp);
unsigned elems = std::accumulate(smemShape.begin(), smemShape.end(), 1,
std::multiplies{});
if (fastReduce) {
auto mod = op->getParentOfType<ModuleOp>();
unsigned numWarps = triton::gpu::TritonGPUDialect::getNumWarps(mod);
elems = std::max<unsigned>(elems, numWarps * 32);
}
auto bytes = elems * tensorType.getElementTypeBitWidth() / 8;
allocation->addBuffer<BufferT::BufferKind::Scratch>(op, bytes);
}
ReduceOpHelper helper(reduceOp);
unsigned bytes = helper.getScratchSizeInBytes();
allocation->addBuffer<BufferT::BufferKind::Scratch>(op, bytes);
} else if (auto cvtLayout = dyn_cast<triton::gpu::ConvertLayoutOp>(op)) {
auto srcTy = cvtLayout.src().getType().cast<RankedTensorType>();
auto dstTy = cvtLayout.result().getType().cast<RankedTensorType>();