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[BACKEND] Keren/shared memory barrier (#59)

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This commit is contained in:
Keren Zhou
2022-08-18 12:32:57 -07:00
committed by GitHub
parent 8776ad1a0e
commit e0bedeb44c
13 changed files with 904 additions and 278 deletions
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2
bin/triton-opt.cpp
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@@ -14,6 +14,7 @@ namespace mlir {
namespace test { namespace test {
void registerTestAlignmentPass(); void registerTestAlignmentPass();
void registerTestAllocationPass(); void registerTestAllocationPass();
void registerTestMembarPass();
} // namespace test } // namespace test
} // namespace mlir } // namespace mlir
@@ -23,6 +24,7 @@ int main(int argc, char **argv) {
mlir::registerTritonGPUPasses(); mlir::registerTritonGPUPasses();
mlir::test::registerTestAlignmentPass(); mlir::test::registerTestAlignmentPass();
mlir::test::registerTestAllocationPass(); mlir::test::registerTestAllocationPass();
mlir::test::registerTestMembarPass();
mlir::triton::registerConvertTritonToTritonGPUPass(); mlir::triton::registerConvertTritonToTritonGPUPass();
mlir::triton::registerConvertTritonGPUToLLVMPass(); mlir::triton::registerConvertTritonGPUToLLVMPass();

168
include/triton/Analysis/Allocation.h
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@@ -4,23 +4,28 @@
#include "llvm/ADT/DenseMap.h" #include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/MapVector.h" #include "llvm/ADT/MapVector.h"
#include "llvm/Support/raw_ostream.h" #include "llvm/Support/raw_ostream.h"
#include <limits>
#include "triton/Dialect/TritonGPU/IR/Dialect.h" #include "triton/Dialect/TritonGPU/IR/Dialect.h"
#include <atomic>
#include <limits>
namespace mlir { namespace mlir {
namespace triton {
class AllocationAnalysis;
}
/// Modified from llvm-15.0: llvm/ADT/AddressRanges.h /// Modified from llvm-15.0: llvm/ADT/AddressRanges.h
/// A class that represents an address range. The range is specified using /// A class that represents a range, specified using a start and an end values:
/// a start and an end address: [Start, End). /// [Start, End).
template <typename AddrT> class Range { template <typename T> class Range {
public: public:
Range() {} Range() {}
Range(AddrT S, AddrT E) : Start(S), End(E) { assert(Start <= End); } Range(T S, T E) : Start(S), End(E) { assert(Start <= End); }
AddrT start() const { return Start; } T start() const { return Start; }
AddrT end() const { return End; } T end() const { return End; }
AddrT size() const { return End - Start; } T size() const { return End - Start; }
bool contains(AddrT Addr) const { return Start <= Addr && Addr < End; } bool contains(T Addr) const { return Start <= Addr && Addr < End; }
bool intersects(const Range &R) const { bool intersects(const Range &R) const {
return Start < R.End && R.Start < End; return Start < R.End && R.Start < End;
} }
@@ -33,83 +38,122 @@ public:
} }
private: private:
AddrT Start = std::numeric_limits<AddrT>::min(); T Start = std::numeric_limits<T>::min();
AddrT End = std::numeric_limits<AddrT>::max(); T End = std::numeric_limits<T>::max();
}; };
//===----------------------------------------------------------------------===// class Allocation {
// Shared Memory Allocation Analysis
//===----------------------------------------------------------------------===//
class AllocationAnalysis {
public: public:
using ValueSizeMapT = llvm::DenseMap<Value, size_t>; /// A unique identifier for shared memory buffers
using BufferId = size_t;
static constexpr BufferId InvalidBufferId =
std::numeric_limits<BufferId>::max();
public:
/// Creates a new Allocation analysis that computes the shared memory /// Creates a new Allocation analysis that computes the shared memory
/// information for all associated shared memory values. /// information for all associated shared memory values.
AllocationAnalysis(Operation *operation) : operation(operation) { run(); } Allocation(Operation *operation) : operation(operation) { run(); }
/// Returns the operation this analysis was constructed from. /// Returns the operation this analysis was constructed from.
Operation *getOperation() const { return operation; } Operation *getOperation() const { return operation; }
/// Returns the offset of the given value in the shared memory. /// Returns the offset of the given buffer in the shared memory.
size_t getOffset(Value value) const { return valueOffset.lookup(value); } size_t getOffset(BufferId bufferId) const {
return bufferSet.lookup(bufferId).offset;
}
/// Returns the size of the given value in the shared memory. /// Returns the size of the given buffer in the shared memory.
size_t getAllocatedSize(Value value) const { return valueSize.lookup(value); } size_t getAllocatedSize(BufferId bufferId) const {
return bufferSet.lookup(bufferId).size;
}
/// Returns the buffer id of the given value.
BufferId getBufferId(Value value) const {
if (valueBuffer.count(value)) {
return valueBuffer.lookup(value)->id;
} else {
return InvalidBufferId;
}
}
/// Returns the scratch buffer id of the given value.
BufferId getBufferId(Operation *operation) const {
if (opScratch.count(operation)) {
return opScratch.lookup(operation)->id;
} else {
return InvalidBufferId;
}
}
/// Returns the size of total shared memory allocated /// Returns the size of total shared memory allocated
size_t getSharedMemorySize() const { return sharedMemorySize; } size_t getSharedMemorySize() const { return sharedMemorySize; }
private: bool isIntersected(BufferId lhsId, BufferId rhsId) const {
/// Value -> Range if (lhsId == InvalidBufferId || rhsId == InvalidBufferId)
/// Use MapVector to ensure determinism. return false;
using ValueRangeMapT = llvm::MapVector<Value, Range<size_t>>; auto lhsBuffer = bufferSet.lookup(lhsId);
/// Start -> Range auto rhsBuffer = bufferSet.lookup(rhsId);
using TripleMapT = std::multimap<size_t, Range<size_t>>; return lhsBuffer.intersects(rhsBuffer);
/// Nodes -> Nodes }
using GraphT = DenseMap<Value, DenseSet<Value>>;
private:
/// A class that represents a shared memory buffer
struct BufferT {
enum class BufferKind { Explicit, Scratch };
/// MT: thread-safe
inline static std::atomic<BufferId> nextId = 0;
BufferKind kind;
BufferId id;
size_t size;
size_t offset;
bool operator==(const BufferT &other) const { return id == other.id; }
bool operator<(const BufferT &other) const { return id < other.id; }
BufferT() : BufferT(BufferKind::Explicit) {}
BufferT(BufferKind kind) : BufferT(kind, 0, 0) {}
BufferT(BufferKind kind, size_t size) : BufferT(kind, size, 0) {}
BufferT(BufferKind kind, size_t size, size_t offset)
: kind(kind), size(size), offset(offset), id(nextId++) {}
bool intersects(const BufferT &other) const {
return Range<size_t>(offset, offset + size)
.intersects(Range<size_t>(other.offset, other.offset + other.size));
}
};
/// Op -> Scratch Buffer
using OpScratchMapT = DenseMap<Operation *, BufferT *>;
/// Value -> Explicit Buffer
using ValueBufferMapT = DenseMap<Value, BufferT *>;
/// BufferId -> Buffer
using BufferSetT = DenseMap<BufferId, BufferT>;
/// Runs allocation analysis on the given top-level operation. /// Runs allocation analysis on the given top-level operation.
void run(); void run();
/// Resolves liveness of all values involved under the root operation. private:
void resolveLiveness(ValueRangeMapT &valueRangeMap); template <BufferT::BufferKind Kind, typename KeyType, typename... Args>
void addBuffer(KeyType &key, Args &&... args) {
/// Computes the shared memory offsets for all related values. auto buffer = BufferT(Kind, std::forward<Args>(args)...);
/// Paper: Algorithms for Compile-Time Memory Optimization bufferSet[buffer.id] = std::move(buffer);
/// (https://www.cs.utexas.edu/users/harrison/papers/compile-time.pdf) if constexpr (Kind == BufferT::BufferKind::Explicit) {
void computeOffsets(const ValueRangeMapT &valueRangeMap); valueBuffer[key] = &bufferSet[buffer.id];
} else {
/// Gets shared memory value and size from valueRangeMap. opScratch[key] = &bufferSet[buffer.id];
void getSharedMemoryValuesAndSizes(const ValueRangeMapT &valueRangeMap, }
SmallVector<Value> &sharedMemoryValues); }
/// Computes the initial shared memory offsets.
void calculateSharedMemoryStarts(const ValueRangeMapT &valueRangeMap,
const SmallVector<Value> &sharedMemoryValues,
ValueSizeMapT &sharedMemoryStart);
/// Builds a graph of all shared memory values. Edges are created between
/// between shared memory values that are overlapping.
void buildInterferenceGraph(const ValueRangeMapT &valueRangeMap,
const SmallVector<Value> &sharedMemoryValues,
const ValueSizeMapT &sharedMemoryStart,
GraphT &interference);
/// Finalizes shared memory offsets considering interference.
void allocateSharedMemory(const ValueRangeMapT &valueRangeMap,
const SmallVector<Value> &sharedMemoryValues,
const ValueSizeMapT &sharedMemoryStart,
const GraphT &interference);
private: private:
Operation *operation; Operation *operation;
ValueSizeMapT valueOffset; OpScratchMapT opScratch;
ValueSizeMapT valueSize; ValueBufferMapT valueBuffer;
BufferSetT bufferSet;
size_t sharedMemorySize = 0; size_t sharedMemorySize = 0;
friend class triton::AllocationAnalysis;
}; };
} // namespace mlir } // namespace mlir
#endif #endif // TRITON_ANALYSIS_ALLOCATION_H

115
include/triton/Analysis/Membar.h Normal file
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@@ -0,0 +1,115 @@
#ifndef TRITON_ANALYSIS_MEMBAR_H
#define TRITON_ANALYSIS_MEMBAR_H
#include "Allocation.h"
#include "llvm/ADT/SmallPtrSet.h"
namespace mlir {
class OpBuilder;
//===----------------------------------------------------------------------===//
// Shared Memory Barrier Analysis
//===----------------------------------------------------------------------===//
class MembarAnalysis {
public:
/// Creates a new Membar analysis that generates the shared memory barrier
/// in the following circumstances:
/// - RAW: If a shared memory write is followed by a shared memory read, and
/// their addresses are intersected, a barrier is inserted.
/// - WAR: If a shared memory read is followed by a shared memory read, and
/// their addresses are intersected, a barrier is inserted.
/// The following circumstances do not require a barrier:
/// - WAW: not possible because overlapped memory allocation is not allowed.
/// - RAR: no write is performed.
/// Temporary storage of operations such as Reduce are considered as both
/// a shared memory read. If the temporary storage is written but not read,
/// it is considered as the problem of the operation itself but not the membar
/// analysis.
/// The following circumstances are not considered yet:
/// - Double buffers
/// - N buffers
MembarAnalysis(Allocation *allocation) : allocation(allocation) { run(); }
private:
struct RegionInfo {
using BufferIdSetT = DenseSet<Allocation::BufferId>;
BufferIdSetT syncReadBuffers;
BufferIdSetT syncWriteBuffers;
RegionInfo() = default;
RegionInfo(const BufferIdSetT &syncReadBuffers,
const BufferIdSetT &syncWriteBuffers)
: syncReadBuffers(syncReadBuffers), syncWriteBuffers(syncWriteBuffers) {
}
/// Unions two RegionInfo objects.
void join(const RegionInfo &other) {
syncReadBuffers.insert(other.syncReadBuffers.begin(),
other.syncReadBuffers.end());
syncWriteBuffers.insert(other.syncWriteBuffers.begin(),
other.syncWriteBuffers.end());
}
/// Returns true if buffers in two RegionInfo objects are intersected.
bool isIntersected(const RegionInfo &other, Allocation *allocation) const {
return /*RAW*/ isIntersected(syncWriteBuffers, other.syncReadBuffers,
allocation) ||
/*WAR*/ isIntersected(syncReadBuffers, other.syncWriteBuffers,
allocation);
}
/// Clears the buffers because a barrier is inserted.
void sync() {
syncReadBuffers.clear();
syncWriteBuffers.clear();
}
private:
/// Returns true if buffers in two sets are intersected.
bool isIntersected(const BufferIdSetT &lhs, const BufferIdSetT &rhs,
Allocation *allocation) const {
return std::any_of(lhs.begin(), lhs.end(), [&](auto lhsId) {
return std::any_of(rhs.begin(), rhs.end(), [&](auto rhsId) {
return allocation->isIntersected(lhsId, rhsId);
});
});
}
};
/// Runs the membar analysis to the given operation, inserts a barrier if
/// necessary.
void run();
/// Applies the barrier analysis based on the SCF dialect, in which each
/// region has a single basic block only.
/// Example:
/// region1
/// op1
/// op2 (scf.if)
/// region2
/// op3
/// op4
/// region3
/// op5
/// op6
/// op7
/// region2 and region3 started with the information of region1.
/// Each region is analyzed separately and keeps their own copy of the
/// information. At op7, we union the information of the region2 and region3
/// and update the information of region1.
void dfsOperation(Operation *operation, RegionInfo *blockInfo,
OpBuilder *builder);
/// Updates the RegionInfo operation based on the operation.
void transfer(Operation *operation, RegionInfo *blockInfo,
OpBuilder *builder);
private:
Allocation *allocation;
};
} // namespace mlir
#endif // TRITON_ANALYSIS_MEMBAR_H

1
include/triton/Dialect/Triton/IR/Dialect.h
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@@ -1,6 +1,7 @@
#ifndef TRITON_DIALECT_TRITON_IR_DIALECT_H_ #ifndef TRITON_DIALECT_TRITON_IR_DIALECT_H_
#define TRITON_DIALECT_TRITON_IR_DIALECT_H_ #define TRITON_DIALECT_TRITON_IR_DIALECT_H_
#include "mlir/Dialect/GPU/GPUDialect.h"
#include "mlir/Dialect/SCF/SCF.h" #include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h" #include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/IR/BuiltinOps.h" #include "mlir/IR/BuiltinOps.h"

10
include/triton/Dialect/Triton/IR/TritonOps.td
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@@ -228,13 +228,13 @@ def TT_DotOp : TT_Op<"dot", [NoSideEffect,
def TT_ReduceOp : TT_Op<"reduce"> { def TT_ReduceOp : TT_Op<"reduce"> {
let summary = "reduce"; let summary = "reduce";
let arguments = (ins TT_RedOpAttr:$redOp, TT_Type:$operand, I32Attr:$axis); let arguments = (ins TT_RedOpAttr:$redOp, TT_Tensor:$operand, I32Attr:$axis);
let results = (outs TT_Type:$result); let results = (outs TT_Tensor:$result);
// let builders = [ let builders = [
// OpBuilder<(ins "triton::RedOp":$redOp, "value":$operand, "int":$axis)>, OpBuilder<(ins "triton::RedOp":$redOp, "Value":$operand, "int":$axis)>,
// ]; ];
let assemblyFormat = "$operand attr-dict `:` type($operand) `->` type($result)"; let assemblyFormat = "$operand attr-dict `:` type($operand) `->` type($result)";
} }

387
lib/Analysis/Allocation.cpp
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@@ -1,5 +1,6 @@
#include "triton/Analysis/Allocation.h" #include "triton/Analysis/Allocation.h"
#include "mlir/Analysis/Liveness.h" #include "mlir/Analysis/Liveness.h"
#include "mlir/Analysis/SliceAnalysis.h"
#include "triton/Dialect/TritonGPU/IR/Dialect.h" #include "triton/Dialect/TritonGPU/IR/Dialect.h"
#include "llvm/ADT/DenseMap.h" #include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/DenseSet.h" #include "llvm/ADT/DenseSet.h"
@@ -10,191 +11,255 @@
namespace mlir { namespace mlir {
void AllocationAnalysis::run() { //===----------------------------------------------------------------------===//
ValueRangeMapT valueRange; // Shared Memory Allocation Analysis
resolveLiveness(valueRange); //===----------------------------------------------------------------------===//
computeOffsets(valueRange); namespace triton {
} class AllocationAnalysis {
public:
AllocationAnalysis(Operation *operation, Allocation *allocation)
: operation(operation), allocation(allocation) {
run();
}
void AllocationAnalysis::resolveLiveness( private:
AllocationAnalysis::ValueRangeMapT &valueRange) { using BufferT = Allocation::BufferT;
Liveness liveness(operation);
DenseMap<Operation *, size_t> operationIds;
operation->walk<WalkOrder::PreOrder>([&](Operation *op) {
operationIds.insert({op, operationIds.size()});
});
operation->walk<WalkOrder::PreOrder>([&](Operation *op) { /// Value -> Liveness Range
/// Use MapVector to ensure determinism.
using BufferRangeMapT = llvm::MapVector<BufferT *, Range<size_t>>;
/// Nodes -> Nodes
using GraphT = DenseMap<BufferT *, DenseSet<BufferT *>>;
void run() {
getValuesAndSizes();
resolveLiveness();
computeOffsets();
}
/// Initializes explicitly defined shared memory values for a given operation.
void getExplicitValueSize(Operation *op) {
for (Value result : op->getResults()) { for (Value result : op->getResults()) {
auto liveOperations = liveness.resolveLiveness(result); auto type = result.getType();
auto minId = std::numeric_limits<size_t>::max(); if (auto tensorType = type.dyn_cast<RankedTensorType>()) {
auto maxId = std::numeric_limits<size_t>::min(); auto encoding = tensorType.getEncoding();
std::for_each(liveOperations.begin(), liveOperations.end(), if (encoding &&
[&](Operation *liveOp) { encoding.isa<triton::gpu::TritonGPUSharedEncodingAttr>()) {
if (operationIds[liveOp] < minId) { // Bytes could be a different value once we support padding or other
minId = operationIds[liveOp]; // allocation policies.
} auto bytes = tensorType.getNumElements() *
if (operationIds[liveOp] > maxId) { tensorType.getElementTypeBitWidth() / 8;
maxId = operationIds[liveOp]; allocation->addBuffer<BufferT::BufferKind::Explicit>(result, bytes);
} }
}); }
valueRange.insert({result, Range(minId, maxId + 1)});
} }
}); }
}
void AllocationAnalysis::getSharedMemoryValuesAndSizes( /// Initializes temporary shared memory for a given operation.
const AllocationAnalysis::ValueRangeMapT &valueRange, void getScratchValueSize(Operation *op) {
SmallVector<Value> &sharedMemoryValues) { // TODO(Keren): Add atomic ops
for (auto &valueRange : valueRange) { // TODO(Keren): Add convert ops
auto value = valueRange.first; if (auto reduceOp = dyn_cast<triton::ReduceOp>(op)) {
auto type = value.getType(); // TODO(Keren): Reduce with index is not supported yet.
if (auto tensorType = type.dyn_cast<RankedTensorType>()) { auto value = op->getOperand(0);
auto encoding = tensorType.getEncoding(); if (auto tensorType = value.getType().dyn_cast<RankedTensorType>()) {
if (encoding &&
encoding.isa<triton::gpu::TritonGPUSharedEncodingAttr>()) {
// Bytes could be a different value once we support padding or other
// allocation policies.
auto bytes = tensorType.getNumElements() * auto bytes = tensorType.getNumElements() *
tensorType.getElementTypeBitWidth() / 8; tensorType.getElementTypeBitWidth() / 8;
sharedMemoryValues.emplace_back(value); allocation->addBuffer<BufferT::BufferKind::Scratch>(op, bytes);
valueSize.insert({value, bytes});
} }
} }
} }
}
void AllocationAnalysis::calculateSharedMemoryStarts( /// Extract all shared memory values and their sizes
const AllocationAnalysis::ValueRangeMapT &valueRange, void getValuesAndSizes() {
const SmallVector<Value> &sharedMemoryValues, operation->walk<WalkOrder::PreOrder>([&](Operation *op) {
ValueSizeMapT &sharedMemoryStart) { getExplicitValueSize(op);
// v = values in shared memory getScratchValueSize(op);
// t = triplet of (size, start, end)
// shared memory space
// -
// | *******t4
// | /|\ v2 inserts t4, t5, and t6
// | |
// | ******t5 ************t6
// | ^^^^^v2^^^^^^
// | | *********************t2
// | \|/ v2 erases t1
// | ******t1 ^^^^^^^^^v1^^^^^^^^^ ************t3
// |---------------------------------------------| liveness range
// 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
TripleMapT tripleMap;
tripleMap.insert(std::make_pair(0, Range<size_t>()));
SmallVector<Value> values = sharedMemoryValues;
while (!values.empty()) {
auto tripleIt = tripleMap.begin();
auto size = tripleIt->first;
auto range = tripleIt->second;
tripleMap.erase(tripleIt);
auto valueIt = std::find_if(values.begin(), values.end(), [&](Value value) {
auto xRange = valueRange.lookup(value);
bool res = xRange.intersects(range);
for (auto val : tripleMap)
res = res && !val.second.intersects(xRange);
return res;
}); });
if (valueIt != values.end()) {
auto value = *valueIt;
auto xSize = valueSize.lookup(value);
auto xRange = valueRange.lookup(value);
sharedMemoryStart[value] = size;
tripleMap.insert(
{size + xSize, Range{std::max(range.start(), xRange.start()),
std::min(range.end(), xRange.end())}});
if (range.start() < xRange.start())
tripleMap.insert({size, Range{range.start(), xRange.end()}});
if (xRange.end() < range.end())
tripleMap.insert({size, Range{xRange.start(), range.end()}});
values.erase(valueIt);
}
} }
}
void AllocationAnalysis::buildInterferenceGraph( /// Resolves liveness of all values involved under the root operation.
const AllocationAnalysis::ValueRangeMapT &valueRange, void resolveLiveness() {
const SmallVector<Value> &sharedMemoryValues, // In the SCF dialect, we always have a sequentially nested structure of
const ValueSizeMapT &sharedMemoryStart, GraphT &interference) { // blocks
for (auto x : sharedMemoryValues) { DenseMap<Operation *, size_t> operationId;
for (auto y : sharedMemoryValues) { operation->walk<WalkOrder::PreOrder>(
if (x == y) [&](Operation *op) { operationId[op] = operationId.size(); });
continue;
auto xStart = sharedMemoryStart.lookup(x); Liveness liveness(operation);
auto yStart = sharedMemoryStart.lookup(y); operation->walk<WalkOrder::PreOrder>([&](Operation *op) {
auto xSize = valueSize.lookup(x); for (Value result : op->getResults()) {
auto ySize = valueSize.lookup(y); auto liveOperations = liveness.resolveLiveness(result);
Range xSizeRange = {xStart, xStart + xSize}; auto minId = std::numeric_limits<size_t>::max();
Range ySizeRange = {yStart, yStart + ySize}; auto maxId = std::numeric_limits<size_t>::min();
auto xOpRange = valueRange.lookup(x); std::for_each(liveOperations.begin(), liveOperations.end(),
auto yOpRange = valueRange.lookup(y); [&](Operation *liveOp) {
if (xOpRange.intersects(yOpRange) && xSizeRange.intersects(ySizeRange)) { if (operationId[liveOp] < minId) {
interference[x].insert(y); minId = operationId[liveOp];
}
if (operationId[liveOp] > maxId) {
maxId = operationId[liveOp];
}
});
if (allocation->valueBuffer.count(result)) {
auto *buffer = allocation->valueBuffer[result];
bufferRange.insert({buffer, Range(minId, maxId + 1)});
}
}
if (allocation->opScratch.count(op)) {
// Any scratch memory's live range is the current operation's live
// range.
auto *buffer = allocation->opScratch[op];
bufferRange.insert(
{buffer, Range(operationId[op], operationId[op] + 1)});
}
});
}
/// Computes the shared memory offsets for all related values.
/// Paper: Algorithms for Compile-Time Memory Optimization
/// (https://www.cs.utexas.edu/users/harrison/papers/compile-time.pdf)
void computeOffsets() {
SmallVector<BufferT *> buffers;
for (auto bufferIter : bufferRange) {
buffers.emplace_back(bufferIter.first);
}
DenseMap<BufferT *, size_t> bufferStart;
calculateStarts(buffers, bufferStart);
GraphT interference;
buildInterferenceGraph(buffers, bufferStart, interference);
allocate(buffers, bufferStart, interference);
}
/// Computes the initial shared memory offsets.
void calculateStarts(const SmallVector<BufferT *> &buffers,
DenseMap<BufferT *, size_t> &bufferStart) {
// v = values in shared memory
// t = triplet of (size, start, end)
// shared memory space
// -
// | *******t4
// | /|\ v2 inserts t4, t5, and t6
// | |
// | ******t5 ************t6
// | ^^^^^v2^^^^^^
// | | *********************t2
// | \|/ v2 erases t1
// | ******t1 ^^^^^^^^^v1^^^^^^^^^ ************t3
// |---------------------------------------------| liveness range
// 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
/// Start -> Liveness Range
using TripleMapT = std::multimap<size_t, Range<size_t>>;
TripleMapT tripleMap;
tripleMap.insert(std::make_pair(0, Range<size_t>()));
SmallVector<BufferT *> xBuffers = buffers;
while (!xBuffers.empty()) {
auto tripleIt = tripleMap.begin();
auto size = tripleIt->first;
auto range = tripleIt->second;
tripleMap.erase(tripleIt);
auto bufferIt =
std::find_if(xBuffers.begin(), xBuffers.end(), [&](auto *buffer) {
auto xRange = bufferRange[buffer];
bool res = xRange.intersects(range);
for (auto val : tripleMap)
res = res && !val.second.intersects(xRange);
return res;
});
if (bufferIt != xBuffers.end()) {
auto buffer = *bufferIt;
auto xSize = buffer->size;
auto xRange = bufferRange.lookup(buffer);
bufferStart[buffer] = size;
tripleMap.insert(
{size + xSize, Range{std::max(range.start(), xRange.start()),
std::min(range.end(), xRange.end())}});
if (range.start() < xRange.start())
tripleMap.insert({size, Range{range.start(), xRange.end()}});
if (xRange.end() < range.end())
tripleMap.insert({size, Range{xRange.start(), range.end()}});
xBuffers.erase(bufferIt);
} }
} }
} }
}
void AllocationAnalysis::allocateSharedMemory( /// Builds a graph of all shared memory values. Edges are created between
const AllocationAnalysis::ValueRangeMapT &valueRangeMap, /// shared memory values that are overlapping.
const SmallVector<Value> &sharedMemoryValues, void buildInterferenceGraph(const SmallVector<BufferT *> &buffers,
const AllocationAnalysis::ValueSizeMapT &sharedMemoryStart, const DenseMap<BufferT *, size_t> &bufferStart,
const AllocationAnalysis::GraphT &interference) { GraphT &interference) {
// First-fit graph coloring for (auto x : buffers) {
// Neighbors are nodes that interfere with each other. for (auto y : buffers) {
// We color a node by finding the index of the first available non-neighboring if (x == y)
// node or the first neighboring node without any color. continue;
// Nodes with the same color do not interfere with each other. auto xStart = bufferStart.lookup(x);
DenseMap<Value, int> colors; auto yStart = bufferStart.lookup(y);
for (auto value : sharedMemoryValues) { auto xSize = x->size;
colors[value] = (value == sharedMemoryValues[0]) ? 0 : -1; auto ySize = y->size;
} Range xSizeRange = {xStart, xStart + xSize};
SmallVector<bool> available(sharedMemoryValues.size()); Range ySizeRange = {yStart, yStart + ySize};
for (auto x : sharedMemoryValues) { auto xOpRange = bufferRange.lookup(x);
std::fill(available.begin(), available.end(), true); auto yOpRange = bufferRange.lookup(y);
for (auto y : interference.lookup(x)) { if (xOpRange.intersects(yOpRange) &&
int color = colors[y]; xSizeRange.intersects(ySizeRange)) {
if (color >= 0) { interference[x].insert(y);
available[color] = false; }
} }
} }
auto it = std::find(available.begin(), available.end(), true);
colors[x] = std::distance(available.begin(), it);
} }
// Finalize allocation
// color0: [0, 7), [0, 8), [0, 15) -> [0, 7), [0, 8), [0, 15) /// Finalizes shared memory offsets considering interference.
// color1: [7, 9) -> [0 + 1 * 15, 9 + 1 * 15) -> [15, 24) void allocate(const SmallVector<BufferT *> &buffers,
// color2: [8, 12) -> [8 + 2 * 15, 12 + 2 * 15) -> [38, 42) const DenseMap<BufferT *, size_t> &bufferStart,
// TODO(Keren): We are wasting memory here. const GraphT &interference) {
// Nodes with color2 can actually start with 24. // First-fit graph coloring
for (auto x : sharedMemoryValues) { // Neighbors are nodes that interfere with each other.
size_t adj = 0; // We color a node by finding the index of the first available
for (auto y : interference.lookup(x)) { // non-neighboring node or the first neighboring node without any color.
adj = std::max(adj, sharedMemoryStart.lookup(y) + valueSize.lookup(y)); // Nodes with the same color do not interfere with each other.
DenseMap<BufferT *, int> colors;
for (auto value : buffers) {
colors[value] = (value == buffers[0]) ? 0 : -1;
}
SmallVector<bool> available(buffers.size());
for (auto x : buffers) {
std::fill(available.begin(), available.end(), true);
for (auto y : interference.lookup(x)) {
int color = colors[y];
if (color >= 0) {
available[color] = false;
}
}
auto it = std::find(available.begin(), available.end(), true);
colors[x] = std::distance(available.begin(), it);
}
// Finalize allocation
// color0: [0, 7), [0, 8), [0, 15) -> [0, 7), [0, 8), [0, 15)
// color1: [7, 9) -> [0 + 1 * 15, 9 + 1 * 15) -> [15, 24)
// color2: [8, 12) -> [8 + 2 * 15, 12 + 2 * 15) -> [38, 42)
// TODO(Keren): We are wasting memory here.
// Nodes with color2 can actually start with 24.
for (auto x : buffers) {
size_t adj = 0;
for (auto y : interference.lookup(x)) {
adj = std::max(adj, bufferStart.lookup(y) + y->size);
}
x->offset = bufferStart.lookup(x) + colors.lookup(x) * adj;
allocation->sharedMemorySize =
std::max(allocation->sharedMemorySize, x->offset + x->size);
} }
valueOffset[x] = sharedMemoryStart.lookup(x) + colors.lookup(x) * adj;
sharedMemorySize =
std::max(sharedMemorySize, valueOffset[x] + valueSize.lookup(x));
} }
}
void AllocationAnalysis::computeOffsets( private:
const AllocationAnalysis::ValueRangeMapT &valueRange) { Operation *operation;
SmallVector<Value> sharedMemoryValues; Allocation *allocation;
getSharedMemoryValuesAndSizes(valueRange, sharedMemoryValues); BufferRangeMapT bufferRange;
};
} // namespace triton
ValueSizeMapT sharedMemoryStart; void Allocation::run() { triton::AllocationAnalysis(getOperation(), this); }
calculateSharedMemoryStarts(valueRange, sharedMemoryValues,
sharedMemoryStart);
GraphT interference;
buildInterferenceGraph(valueRange, sharedMemoryValues, sharedMemoryStart,
interference);
allocateSharedMemory(valueRange, sharedMemoryValues, sharedMemoryStart,
interference);
}
} // namespace mlir } // namespace mlir

1
lib/Analysis/CMakeLists.txt
View File

@@ -1,6 +1,7 @@
add_mlir_library(TritonAnalysis add_mlir_library(TritonAnalysis
AxisInfo.cpp AxisInfo.cpp
Allocation.cpp Allocation.cpp
Membar.cpp
DEPENDS DEPENDS
TritonGPUAttrDefsIncGen TritonGPUAttrDefsIncGen

95
lib/Analysis/Membar.cpp Normal file
View File

@@ -0,0 +1,95 @@
#include "triton/Analysis/Membar.h"
#include "triton/Dialect/TritonGPU/IR/Dialect.h"
#include "mlir/Dialect/GPU/GPUDialect.h"
namespace mlir {
void MembarAnalysis::run() {
auto *operation = allocation->getOperation();
operation->getContext()->getOrLoadDialect<mlir::gpu::GPUDialect>();
RegionInfo regionInfo;
OpBuilder builder(operation);
dfsOperation(operation, &regionInfo, &builder);
}
void MembarAnalysis::dfsOperation(Operation *operation,
RegionInfo *parentRegionInfo,
OpBuilder *builder) {
transfer(operation, parentRegionInfo, builder);
if (operation->getNumRegions()) {
// If there's any nested regions, we need to visit them.
// scf.if and scf.else: two regions
// scf.if only: two regions
// scf.for: one region
RegionInfo curRegionInfo;
for (auto &region : operation->getRegions()) {
// Copy the parent info as the current info.
RegionInfo regionInfo = *parentRegionInfo;
for (auto &block : region.getBlocks()) {
assert(region.getBlocks().size() == 1 &&
"Multiple blocks in a region is not supported");
for (auto &op : block.getOperations()) {
// Traverse the nested operation.
dfsOperation(&op, &regionInfo, builder);
}
}
curRegionInfo.join(regionInfo);
}
// Set the parent region info as the union of the nested region info.
*parentRegionInfo = curRegionInfo;
}
}
void MembarAnalysis::transfer(Operation *op, RegionInfo *regionInfo,
OpBuilder *builder) {
if (op->getNumResults() < 1)
return;
if (dyn_cast<gpu::BarrierOp>(op)) {
// If the current op is a barrier, we sync previous reads and writes
regionInfo->sync();
return;
}
if (dyn_cast<triton::gpu::AsyncWaitOp>(op)) {
// If the current op is an async wait, we insert a barrier op and sync
// previous reads and writes.
OpBuilder::InsertionGuard g(*builder);
builder->setInsertionPointAfter(op);
builder->create<gpu::BarrierOp>(op->getLoc());
regionInfo->sync();
return;
}
auto addBuffer = [&](RegionInfo::BufferIdSetT &bufferSet,
Allocation::BufferId bufferId) {
if (bufferId != Allocation::InvalidBufferId) {
bufferSet.insert(bufferId);
}
};
RegionInfo curRegionInfo;
for (Value value : op->getOperands()) {
// ConvertLayoutOp: shared memory -> registers
addBuffer(curRegionInfo.syncReadBuffers, allocation->getBufferId(value));
}
for (Value value : op->getResults()) {
// ConvertLayoutOp: registers -> shared memory
addBuffer(curRegionInfo.syncWriteBuffers, allocation->getBufferId(value));
}
// Scratch buffer is considered as a shared memory read
addBuffer(curRegionInfo.syncReadBuffers, allocation->getBufferId(op));
if (regionInfo->isIntersected(curRegionInfo, allocation)) {
OpBuilder::InsertionGuard g(*builder);
builder->setInsertionPoint(op);
builder->create<gpu::BarrierOp>(op->getLoc());
regionInfo->sync();
}
// Update the region info, even if barrier is inserted, we have to maintain
// the current op's read/write buffers.
regionInfo->join(curRegionInfo);
}
} // namespace mlir

147
test/Analysis/test-allocation.mlir
View File

@@ -1,4 +1,4 @@
// RUN: triton-opt %s --mlir-disable-threading -test-print-allocation 2>&1 | FileCheck %s // RUN: triton-opt %s -split-input-file --mlir-disable-threading -test-print-allocation 2>&1 | FileCheck %s
#AL = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [4, 8], warpsPerCTA = [4, 1], order = [1, 0]}> #AL = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [4, 8], warpsPerCTA = [4, 1], order = [1, 0]}>
#BL = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [1, 32], warpsPerCTA = [4, 1], order = [1, 0]}> #BL = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [1, 32], warpsPerCTA = [4, 1], order = [1, 0]}>
@@ -6,6 +6,7 @@
#B = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [1, 0]}> #B = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [1, 0]}>
#C = #triton_gpu.mma<{version = 2, warpsPerCTA = [4, 1]}> #C = #triton_gpu.mma<{version = 2, warpsPerCTA = [4, 1]}>
// CHECK-LABEL: matmul_loop
func @matmul_loop(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B : !tt.ptr<f16>) { func @matmul_loop(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B : !tt.ptr<f16>) {
%a_ptr_init = tt.broadcast %A : (!tt.ptr<f16>) -> tensor<128x32x!tt.ptr<f16>, #AL> %a_ptr_init = tt.broadcast %A : (!tt.ptr<f16>) -> tensor<128x32x!tt.ptr<f16>, #AL>
%b_ptr_init = tt.broadcast %B : (!tt.ptr<f16>) -> tensor<32x128x!tt.ptr<f16>, #BL> %b_ptr_init = tt.broadcast %B : (!tt.ptr<f16>) -> tensor<32x128x!tt.ptr<f16>, #BL>
@@ -24,7 +25,7 @@ func @matmul_loop(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B
// CHECK: offset = 0, size = 8192 // CHECK: offset = 0, size = 8192
%a = triton_gpu.convert_layout %a_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A> %a = triton_gpu.convert_layout %a_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
%b_ = tt.load %b_ptr, %b_mask, %b_other {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<32x128xf16, #BL> %b_ = tt.load %b_ptr, %b_mask, %b_other {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<32x128xf16, #BL>
// CHECK: offset = 8192, size = 8192 // CHECK-NEXT: offset = 8192, size = 8192
%b = triton_gpu.convert_layout %b_ : (tensor<32x128xf16, #BL>) -> tensor<32x128xf16, #B> %b = triton_gpu.convert_layout %b_ : (tensor<32x128xf16, #BL>) -> tensor<32x128xf16, #B>
%c = tt.dot %a, %b, %prev_c {allowTF32 = true} : tensor<128x32xf16, #A> * tensor<32x128xf16, #B> -> tensor<128x128xf32, #C> %c = tt.dot %a, %b, %prev_c {allowTF32 = true} : tensor<128x32xf16, #A> * tensor<32x128xf16, #B> -> tensor<128x128xf32, #C>
@@ -34,11 +35,12 @@ func @matmul_loop(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B
scf.yield %next_a_ptr, %next_b_ptr, %c : tensor<128x32x!tt.ptr<f16>, #AL>, tensor<32x128x!tt.ptr<f16>, #BL>, tensor<128x128xf32, #C> scf.yield %next_a_ptr, %next_b_ptr, %c : tensor<128x32x!tt.ptr<f16>, #AL>, tensor<32x128x!tt.ptr<f16>, #BL>, tensor<128x128xf32, #C>
} }
return return
// CHECK: size = 16384 // CHECK-NEXT: size = 16384
} }
// Shared memory is available after a tensor's liveness range ends // Shared memory is available after a tensor's liveness range ends
func @synthesized_reusable(%A : !tt.ptr<f16>) { // CHECK-LABEL: reusable
func @reusable(%A : !tt.ptr<f16>) {
%cst1 = arith.constant dense<true> : tensor<128x32xi1, #AL> %cst1 = arith.constant dense<true> : tensor<128x32xi1, #AL>
%cst2 = arith.constant dense<0.000000e+00> : tensor<128x32xf16, #AL> %cst2 = arith.constant dense<0.000000e+00> : tensor<128x32xf16, #AL>
%cst3 = arith.constant dense<true> : tensor<32x128xi1, #AL> %cst3 = arith.constant dense<true> : tensor<32x128xi1, #AL>
@@ -51,95 +53,162 @@ func @synthesized_reusable(%A : !tt.ptr<f16>) {
// CHECK: offset = 0, size = 8192 // CHECK: offset = 0, size = 8192
%a1 = triton_gpu.convert_layout %a1_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A> %a1 = triton_gpu.convert_layout %a1_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
%a2_ = tt.load %b_ptr, %cst3, %cst4 {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<32x128xf16, #AL> %a2_ = tt.load %b_ptr, %cst3, %cst4 {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<32x128xf16, #AL>
// CHECK: offset = 8192, size = 8192 // CHECK-NEXT: offset = 8192, size = 8192
%a2 = triton_gpu.convert_layout %a2_ : (tensor<32x128xf16, #AL>) -> tensor<32x128xf16, #A> %a2 = triton_gpu.convert_layout %a2_ : (tensor<32x128xf16, #AL>) -> tensor<32x128xf16, #A>
%a3_ = tt.load %a_ptr, %cst1, %cst2 {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<128x32xf16, #AL> %a3_ = tt.load %a_ptr, %cst1, %cst2 {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<128x32xf16, #AL>
// CHECK: offset = 16384, size = 8192 // CHECK-NEXT: offset = 16384, size = 8192
%a3 = triton_gpu.convert_layout %a3_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A> %a3 = triton_gpu.convert_layout %a3_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
%c = tt.dot %a1, %a2, %c_init {allowTF32 = true} : tensor<128x32xf16, #A> * tensor<32x128xf16, #B> -> tensor<128x128xf32, #C> %c = tt.dot %a1, %a2, %c_init {allowTF32 = true} : tensor<128x32xf16, #A> * tensor<32x128xf16, #B> -> tensor<128x128xf32, #C>
%a4_ = tt.load %b_ptr, %cst3, %cst4 {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<32x128xf16, #AL> %a4_ = tt.load %b_ptr, %cst3, %cst4 {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<32x128xf16, #AL>
// CHECK: offset = 0, size = 8192 // CHECK-NEXT: offset = 0, size = 8192
%a4 = triton_gpu.convert_layout %a4_ : (tensor<32x128xf16, #AL>) -> tensor<32x128xf16, #A> %a4 = triton_gpu.convert_layout %a4_ : (tensor<32x128xf16, #AL>) -> tensor<32x128xf16, #A>
%c1 = tt.dot %a3, %a4, %c {allowTF32 = true} : tensor<128x32xf16, #A> * tensor<32x128xf16, #B> -> tensor<128x128xf32, #C> %c1 = tt.dot %a3, %a4, %c {allowTF32 = true} : tensor<128x32xf16, #A> * tensor<32x128xf16, #B> -> tensor<128x128xf32, #C>
return return
// CHECK: size = 24576 // CHECK-NEXT: size = 24576
} }
// A tensor's shared memory offset is larger than it needs to accommodate further tensors // A tensor's shared memory offset is larger than it needs to accommodate further tensors
// %cst0->%c // %cst0->%c
// %cst1->%cst4 // %cst1->%cst4
// %cst3->%g->%h->%i // %cst3->%g->%h->%i
func @synthesize_preallocate(%A : !tt.ptr<f16>) { // CHECK-LABEL: preallocate
func @preallocate(%A : !tt.ptr<f16>) {
// CHECK: offset = 0, size = 512 // CHECK: offset = 0, size = 512
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 1024, size = 512 // CHECK-NEXT: offset = 1024, size = 512
%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 1536, size = 512 // CHECK-NEXT: offset = 1536, size = 512
%cst2 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst2 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 2048, size = 1024 // CHECK-NEXT: offset = 2048, size = 1024
%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A> %a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
// CHECK: offset = 3072, size = 1024 // CHECK-NEXT: offset = 3072, size = 1024
%b = tt.cat %cst0, %cst2 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A> %b = tt.cat %cst0, %cst2 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
// CHECK: offset = 0, size = 1024 // CHECK-NEXT: offset = 0, size = 1024
%c = tt.cat %cst1, %cst2 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A> %c = tt.cat %cst1, %cst2 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
// CHECK: offset = 1024, size = 1024 // CHECK-NEXT: offset = 1024, size = 1024
%cst4 = arith.constant dense<0.000000e+00> : tensor<32x16xf16, #A> %cst4 = arith.constant dense<0.000000e+00> : tensor<32x16xf16, #A>
// CHECK: offset = 6144, size = 2048 // CHECK-NEXT: offset = 6144, size = 2048
%e = tt.cat %a, %cst4 {axis = 0} : (tensor<32x16xf16, #A>, tensor<32x16xf16, #A>) -> tensor<64x16xf16, #A> %e = tt.cat %a, %cst4 {axis = 0} : (tensor<32x16xf16, #A>, tensor<32x16xf16, #A>) -> tensor<64x16xf16, #A>
// CHECK: offset = 8192, size = 2048 // CHECK-NEXT: offset = 8192, size = 2048
%d = tt.cat %b, %cst4 {axis = 0} : (tensor<32x16xf16, #A>, tensor<32x16xf16, #A>) -> tensor<64x16xf16, #A> %d = tt.cat %b, %cst4 {axis = 0} : (tensor<32x16xf16, #A>, tensor<32x16xf16, #A>) -> tensor<64x16xf16, #A>
// CHECK: offset = 10240, size = 2048 // CHECK-NEXT: offset = 10240, size = 2048
%f = tt.cat %c, %cst4 {axis = 0} : (tensor<32x16xf16, #A>, tensor<32x16xf16, #A>) -> tensor<64x16xf16, #A> %f = tt.cat %c, %cst4 {axis = 0} : (tensor<32x16xf16, #A>, tensor<32x16xf16, #A>) -> tensor<64x16xf16, #A>
// CHECK: offset = 0, size = 2048 // CHECK-NEXT: offset = 0, size = 2048
%cst5 = arith.constant dense<0.000000e+00> : tensor<64x16xf16, #A> %cst5 = arith.constant dense<0.000000e+00> : tensor<64x16xf16, #A>
// CHECK: offset = 2048, size = 4096 // CHECK-NEXT: offset = 2048, size = 4096
%g = tt.cat %e, %cst5 {axis = 0} : (tensor<64x16xf16, #A>, tensor<64x16xf16, #A>) -> tensor<128x16xf16, #A> %g = tt.cat %e, %cst5 {axis = 0} : (tensor<64x16xf16, #A>, tensor<64x16xf16, #A>) -> tensor<128x16xf16, #A>
// CHECK: offset = 2048, size = 4096 // CHECK-NEXT: offset = 2048, size = 4096
%h = tt.cat %d, %cst5 {axis = 0} : (tensor<64x16xf16, #A>, tensor<64x16xf16, #A>) -> tensor<128x16xf16, #A> %h = tt.cat %d, %cst5 {axis = 0} : (tensor<64x16xf16, #A>, tensor<64x16xf16, #A>) -> tensor<128x16xf16, #A>
// CHECK: offset = 2048, size = 4096 // CHECK-NEXT: offset = 2048, size = 4096
%i = tt.cat %f, %cst5 {axis = 0} : (tensor<64x16xf16, #A>, tensor<64x16xf16, #A>) -> tensor<128x16xf16, #A> %i = tt.cat %f, %cst5 {axis = 0} : (tensor<64x16xf16, #A>, tensor<64x16xf16, #A>) -> tensor<128x16xf16, #A>
return return
// CHECK: size = 12288 // CHECK-NEXT: size = 12288
} }
// Unused tensors are immediately released // Unused tensors are immediately released
func @synthesize_unused(%A : !tt.ptr<f16>) { // CHECK-LABEL: unused
func @unused(%A : !tt.ptr<f16>) {
// CHECK: offset = 0, size = 1024 // CHECK: offset = 0, size = 1024
%cst0 = arith.constant dense<0.000000e+00> : tensor<32x16xf16, #A> %cst0 = arith.constant dense<0.000000e+00> : tensor<32x16xf16, #A>
// CHECK: offset = 0, size = 512 // CHECK-NEXT: offset = 0, size = 512
%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 512, size = 512 // CHECK-NEXT: offset = 512, size = 512
%cst2 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst2 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 1024, size = 1024 // CHECK-NEXT: offset = 1024, size = 1024
%a = tt.cat %cst1, %cst2 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A> %a = tt.cat %cst1, %cst2 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
return return
// CHECK: size = 2048 // CHECK: size = 2048
} }
// cst0 is alive through the entire function, it cannot be released before the end of the function // cst0 is alive through the entire function, it cannot be released before the end of the function
func @synthesize_longlive(%A : !tt.ptr<f16>) { // CHECK-LABEL: longlive
func @longlive(%A : !tt.ptr<f16>) {
// CHECK: offset = 0, size = 512 // CHECK: offset = 0, size = 512
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 512, size = 512 // CHECK-NEXT: offset = 512, size = 512
%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 1024, size = 512 // CHECK-NEXT: offset = 1024, size = 512
%cst2 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst2 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 1536, size = 1024 // CHECK-NEXT: offset = 1536, size = 1024
%a = tt.cat %cst1, %cst2 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A> %a = tt.cat %cst1, %cst2 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
// CHECK: offset = 512, size = 512 // CHECK-NEXT: offset = 512, size = 512
%cst3 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst3 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 1024, size = 512 // CHECK-NEXT: offset = 1024, size = 512
%cst4 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst4 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 1536, size = 1024 // CHECK-NEXT: offset = 1536, size = 1024
%b = tt.cat %cst3, %cst4 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A> %b = tt.cat %cst3, %cst4 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
// CHECK: offset = 1536, size = 512 // CHECK-NEXT: offset = 1536, size = 512
%cst5 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst5 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 1536, size = 512 // CHECK-NEXT: offset = 1536, size = 512
%cst6 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A> %cst6 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: offset = 1536, size = 1024 // CHECK-NEXT: offset = 1536, size = 1024
%c = tt.cat %cst3, %cst4 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A> %c = tt.cat %cst3, %cst4 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
// CHECK: offset = 512, size = 1024 // CHECK-NEXT: offset = 512, size = 1024
%d = tt.cat %cst0, %cst0 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A> %d = tt.cat %cst0, %cst0 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
return return
// CHECK: size = 2560 // CHECK-NEXT: size = 2560
}
// CHECK-LABEL: scratch
func @scratch() {
// CHECK: offset = 0, size = 512
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK-NEXT: offset = 1056, size = 1024
%a = tt.cat %cst0, %cst0 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
// CHECK-NEXT: scratch offset = 32, size = 1024
// CHECK-NEXT: offset = 0, size = 32
%b = tt.reduce %a {redOp = 1 : i32, axis = 0 : i32} : tensor<32x16xf16, #A> -> tensor<16xf16, #A>
return
// CHECK-NEXT: size = 2080
}
// B0 -> (B1) -> B0
// Memory used by B1 can be reused by B0.
// CHECK-LABEL: multi_blocks_reuse
func @multi_blocks_reuse(%i1 : i1) {
// CHECK: offset = 0, size = 512
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK-NEXT: offset = 512, size = 512
%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
scf.if %i1 {
// CHECK-NEXT: offset = 1024, size = 1024
%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
// CHECK-NEXT: offset = 1024, size = 1024
%b = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
}
// CHECK-NEXT: offset = 0, size = 512
%cst2 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK-NEXT: offset = 512, size = 512
%cst3 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK-NEXT: offset = 1024, size = 1024
%a = tt.cat %cst2, %cst3 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
return
// CHECK-NEXT: size = 2048
}
// B0 -> (B1) -> (B2) -> B0
// Memory used by B0 cannot be reused by B1 or B2.
// CHECK-LABEL: multi_blocks_noreuse
func @multi_blocks_noreuse(%i1 : i1) {
// CHECK: offset = 0, size = 512
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK-NEXT: offset = 512, size = 512
%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
scf.if %i1 {
// CHECK-NEXT: offset = 1024, size = 1024
%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
// CHECK-NEXT: offset = 1024, size = 1024
%b = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
} else {
// CHECK-NEXT: offset = 1024, size = 512
%cst2 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK-NEXT: offset = 1536, size = 512
%cst3 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK-NEXT: offset = 2048, size = 1024
%a = tt.cat %cst2, %cst3 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
}
// CHECK-NEXT: offset = 1024, size = 1024
%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
return
// CHECK-NEXT: size = 3072
} }

178
test/Analysis/test-membar.mlir Normal file
View File

@@ -0,0 +1,178 @@
// RUN: triton-opt %s -split-input-file --mlir-disable-threading -test-print-membar 2>&1 | FileCheck %s
#AL = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [4, 8], warpsPerCTA = [4, 1], order = [1, 0]}>
#BL = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [1, 32], warpsPerCTA = [4, 1], order = [1, 0]}>
#A = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [1, 0]}>
#B = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [1, 0]}>
#C = #triton_gpu.mma<{version = 2, warpsPerCTA = [4, 1]}>
// CHECK-LABEL: matmul_loop
func @matmul_loop(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B : !tt.ptr<f16>) {
%a_ptr_init = tt.broadcast %A : (!tt.ptr<f16>) -> tensor<128x32x!tt.ptr<f16>, #AL>
%b_ptr_init = tt.broadcast %B : (!tt.ptr<f16>) -> tensor<32x128x!tt.ptr<f16>, #BL>
%a_mask = arith.constant dense<true> : tensor<128x32xi1, #AL>
%a_other = arith.constant dense<0.00e+00> : tensor<128x32xf16, #AL>
%b_mask = arith.constant dense<true> : tensor<32x128xi1, #BL>
%b_other = arith.constant dense<0.00e+00> : tensor<32x128xf16, #BL>
%c_init = arith.constant dense<0.00e+00> : tensor<128x128xf32, #C>
%a_off = arith.constant dense<4> : tensor<128x32xi32, #AL>
%b_off = arith.constant dense<4> : tensor<32x128xi32, #BL>
scf.for %iv = %lb to %ub step %step iter_args(%a_ptr = %a_ptr_init, %b_ptr = %b_ptr_init, %prev_c = %c_init) -> (tensor<128x32x!tt.ptr<f16>, #AL>, tensor<32x128x!tt.ptr<f16>, #BL>, tensor<128x128xf32, #C>) {
%a_ = tt.load %a_ptr, %a_mask, %a_other {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<128x32xf16, #AL>
%a = triton_gpu.convert_layout %a_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
%b_ = tt.load %b_ptr, %b_mask, %b_other {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<32x128xf16, #BL>
%b = triton_gpu.convert_layout %b_ : (tensor<32x128xf16, #BL>) -> tensor<32x128xf16, #B>
// CHECK: Membar 13
%c = tt.dot %a, %b, %prev_c {allowTF32 = true} : tensor<128x32xf16, #A> * tensor<32x128xf16, #B> -> tensor<128x128xf32, #C>
%next_a_ptr = tt.getelementptr %a_ptr, %a_off : tensor<128x32x!tt.ptr<f16>, #AL>
%next_b_ptr = tt.getelementptr %b_ptr, %b_off : tensor<32x128x!tt.ptr<f16>, #BL>
scf.yield %next_a_ptr, %next_b_ptr, %c : tensor<128x32x!tt.ptr<f16>, #AL>, tensor<32x128x!tt.ptr<f16>, #BL>, tensor<128x128xf32, #C>
}
return
}
// CHECK-LABEL: raw_single_block
func @raw_single_block(%A : !tt.ptr<f16>) {
%cst1 = arith.constant dense<true> : tensor<128x32xi1, #AL>
%cst2 = arith.constant dense<0.000000e+00> : tensor<128x32xf16, #AL>
%a_ptr = tt.broadcast %A : (!tt.ptr<f16>) -> tensor<128x32x!tt.ptr<f16>, #AL>
%a1_ = tt.load %a_ptr, %cst1, %cst2 {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<128x32xf16, #AL>
%a1 = triton_gpu.convert_layout %a1_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
// CHECK: Membar 5
%a2 = triton_gpu.convert_layout %a1 : (tensor<128x32xf16, #A>) -> tensor<128x32xf16, #A>
return
}
// CHECK-LABEL: war_single_block
func @war_single_block(%A : !tt.ptr<f16>) {
%cst1 = arith.constant dense<true> : tensor<128x32xi1, #AL>
%cst2 = arith.constant dense<0.000000e+00> : tensor<128x32xf16, #AL>
%a_ptr = tt.broadcast %A : (!tt.ptr<f16>) -> tensor<128x32x!tt.ptr<f16>, #AL>
%a1_ = tt.load %a_ptr, %cst1, %cst2 {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<128x32xf16, #AL>
%a1 = triton_gpu.convert_layout %a1_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
// CHECK: Membar 5
%a2 = triton_gpu.convert_layout %a1 : (tensor<128x32xf16, #A>) -> tensor<128x32xf16, #AL>
// CHECK-NEXT: Membar 7
%a3 = triton_gpu.convert_layout %a1_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
return
}
// CHECK-LABEL: scratch
func @scratch() {
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: Membar 1
%a = tt.cat %cst0, %cst0 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
// CHECK-NEXT: Membar 3
%b = tt.reduce %a {redOp = 1 : i32, axis = 0 : i32} : tensor<32x16xf16, #A> -> tensor<16xf16, #A>
return
}
// CHECK-LABEL: async_wait
func @async_wait() {
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK: Membar 1
%a = tt.cat %cst0, %cst0 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
triton_gpu.async_wait {num = 4 : i32}
// CHECK-NEXT: Membar 4
%a_ = triton_gpu.convert_layout %a : (tensor<32x16xf16, #A>) -> tensor<32x16xf16, #AL>
return
}
// If branch inserted a barrier for %cst0 and %cst1, but else didn't, then the barrier should be inserted in the parent region
// CHECK-LABEL: multi_blocks
func @multi_blocks(%i1 : i1) {
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
scf.if %i1 {
// CHECK: Membar 2
%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
scf.yield
} else {
%cst2 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
%cst3 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
// CHECK-NEXT: Membar 7
%b = tt.cat %cst2, %cst3 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
scf.yield
}
// CHECK-NEXT: Membar 10
%c = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
return
}
// Both branches inserted a barrier for %cst0 and %cst1, then the barrier doesn't need to be inserted in the parent region
// CHECK-LABEL: multi_blocks_join_barrier
func @multi_blocks_join_barrier(%i1 : i1) {
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
scf.if %i1 {
// CHECK: Membar 2
%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
scf.yield
} else {
// CHECK-NEXT: Membar 5
%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
scf.yield
}
%a_ = triton_gpu.convert_layout %cst0 : (tensor<16x16xf16, #A>) -> tensor<16x16xf16, #AL>
return
}
// Read yielded tensor requires a barrier
// CHECK-LABEL: multi_blocks_yield
func @multi_blocks_yield(%i1 : i1) {
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
%a = scf.if %i1 -> (tensor<32x16xf16, #A>) {
// CHECK: Membar 2
%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
scf.yield %a : tensor<32x16xf16, #A>
} else {
// CHECK-NEXT: Membar 5
%b = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
scf.yield %b : tensor<32x16xf16, #A>
}
%a_ = triton_gpu.convert_layout %cst0 : (tensor<16x16xf16, #A>) -> tensor<16x16xf16, #AL>
// CHECK-NEXT: Membar 9
%b = tt.cat %a, %a {axis = 0} : (tensor<32x16xf16, #A>, tensor<32x16xf16, #A>) -> tensor<64x16xf16, #A>
return
}
// Conservatively add a barrier as if the branch (%i1) is never taken
// CHECK-LABEL: multi_blocks_noelse
func @multi_blocks_noelse(%i1 : i1) {
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
scf.if %i1 {
// CHECK: Membar 2
%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
scf.yield
}
%a_ = triton_gpu.convert_layout %cst0 : (tensor<16x16xf16, #A>) -> tensor<16x16xf16, #AL>
return
}
// Conservatively add a barrier as if the branch (%i2) is never taken
// CHECK-LABEL: multi_blocks_nested_scf
func @multi_blocks_nested_scf(%i1 : i1, %i2 : i1) {
%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
scf.if %i1 {
scf.if %i2 {
// CHECK: Membar 2
%b = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
scf.yield
}
scf.yield
} else {
// CHECK-NEXT: Membar 6
%b = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
scf.yield
}
// CHECK-NEXT: Membar 9
%a_ = triton_gpu.convert_layout %cst0 : (tensor<16x16xf16, #A>) -> tensor<16x16xf16, #AL>
return
}

1
test/lib/Analysis/CMakeLists.txt
View File

@@ -1,6 +1,7 @@
add_mlir_library(TritonTestAnalysis add_mlir_library(TritonTestAnalysis
TestAxisInfo.cpp TestAxisInfo.cpp
TestAllocation.cpp TestAllocation.cpp
TestMembar.cpp
LINK_LIBS PUBLIC LINK_LIBS PUBLIC
TritonAnalysis TritonAnalysis

27
test/lib/Analysis/TestAllocation.cpp
View File

@@ -19,24 +19,29 @@ struct TestAllocationPass
void runOnOperation() override { void runOnOperation() override {
Operation *operation = getOperation(); Operation *operation = getOperation();
auto &os = llvm::errs(); auto &os = llvm::errs();
os << "Testing: " << operation->getName() << "\n"; // Convert to std::string can remove quotes from op_name
AllocationAnalysis analysis(operation); auto op_name = SymbolTable::getSymbolName(operation).getValue().str();
os << op_name << "\n";
Allocation allocation(operation);
operation->walk([&](Operation *op) { operation->walk([&](Operation *op) {
auto scratchBufferId = allocation.getBufferId(op);
if (scratchBufferId != Allocation::InvalidBufferId) {
size_t offset = allocation.getOffset(scratchBufferId);
size_t size = allocation.getAllocatedSize(scratchBufferId);
os << "scratch offset = " << offset << ", size = " << size << "\n";
}
if (op->getNumResults() < 1) if (op->getNumResults() < 1)
return; return;
for (Value result : op->getResults()) { for (Value result : op->getResults()) {
Type type = result.getType(); auto bufferId = allocation.getBufferId(result);
if (auto tensorType = type.dyn_cast<RankedTensorType>()) { if (bufferId != Allocation::InvalidBufferId) {
Attribute encoding = tensorType.getEncoding(); size_t offset = allocation.getOffset(bufferId);
if (encoding.isa<triton::gpu::TritonGPUSharedEncodingAttr>()) { size_t size = allocation.getAllocatedSize(bufferId);
size_t offset = analysis.getOffset(result); os << "offset = " << offset << ", size = " << size << "\n";
size_t size = analysis.getAllocatedSize(result);
os << "offset = " << offset << ", size = " << size << "\n";
}
} }
} }
}); });
os << "size = " << analysis.getSharedMemorySize() << "\n"; os << "size = " << allocation.getSharedMemorySize() << "\n";
} }
}; };

50
test/lib/Analysis/TestMembar.cpp Normal file
View File

@@ -0,0 +1,50 @@
#include "mlir/Dialect/GPU/GPUDialect.h"
#include "mlir/IR/Dialect.h"
#include "mlir/Pass/Pass.h"
#include "triton/Analysis/Allocation.h"
#include "triton/Analysis/Membar.h"
using namespace mlir;
namespace {
struct TestMembarPass
: public PassWrapper<TestMembarPass, OperationPass<FuncOp>> {
// LLVM15+
// MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(TestMembarPass);
StringRef getArgument() const final { return "test-print-membar"; }
StringRef getDescription() const final {
return "print the result of the allocation pass";
}
void runOnOperation() override {
Operation *operation = getOperation();
auto &os = llvm::errs();
// Convert to std::string can remove quotes from op_name
auto op_name = SymbolTable::getSymbolName(operation).getValue().str();
os << op_name << "\n";
Allocation allocation(operation);
MembarAnalysis analysis(&allocation);
size_t operationId = 0;
operation->walk<WalkOrder::PreOrder>([&](Operation *op) {
if (dyn_cast<gpu::BarrierOp>(op)) {
os << "Membar " << operationId << "\n";
}
if (op->getNumRegions() == 0) {
// Don't count parent Operation to simplify the test.
operationId++;
}
return;
});
}
};
} // namespace
namespace mlir {
namespace test {
void registerTestMembarPass() { PassRegistration<TestMembarPass>(); }
} // namespace test
} // namespace mlir