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3 Commits

Author SHA1 Message Date
NiccoloN 8d95c604a6 automatic code formatting
Validate Operations / validate-operations (push) Has been cancelled
2026-05-13 21:51:19 +02:00
NiccoloN 55eda487dc use seed in validate.py for deterministic tests 2026-05-13 21:49:36 +02:00
NiccoloN 061139aefb fix wrong send/receive reordering in post dcp merge instructions compaction 2026-05-13 21:48:49 +02:00
16 changed files with 296 additions and 144 deletions
+1 -2
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@@ -1,7 +1,6 @@
#include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
#include "mlir/IR/BuiltinTypeInterfaces.h"
#include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
#include "src/Accelerators/PIM/Common/PimCommon.hpp"
using namespace mlir;
+1 -2
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@@ -1,8 +1,7 @@
#include "src/Accelerators/PIM/Common/Support/ReportUtils.hpp"
#include "llvm/Support/Format.h"
#include "src/Accelerators/PIM/Common/Support/FileSystemUtils.hpp"
#include "src/Accelerators/PIM/Common/Support/ReportUtils.hpp"
namespace onnx_mlir {
+1 -2
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@@ -1,10 +1,9 @@
#pragma once
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/Support/raw_ostream.h"
#include <cstdint>
#include <fstream>
#include <limits>
#include <string>
+36 -43
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@@ -70,9 +70,7 @@ inline void writeUint32LE(llvm::raw_ostream& os, uint32_t value) {
os.write(bytes.data(), bytes.size());
}
inline void writeInt32LE(llvm::raw_ostream& os, int32_t value) {
writeUint32LE(os, static_cast<uint32_t>(value));
}
inline void writeInt32LE(llvm::raw_ostream& os, int32_t value) { writeUint32LE(os, static_cast<uint32_t>(value)); }
inline void writeHeader(llvm::raw_ostream& os) {
os.write(kMagic, sizeof(kMagic));
@@ -186,39 +184,39 @@ inline Opcode opcodeFromString(llvm::StringRef opName) {
inline llvm::StringRef opcodeToString(Opcode opcode) {
switch (opcode) {
case Opcode::nop: return "nop";
case Opcode::sldi: return "sldi";
case Opcode::sld: return "sld";
case Opcode::sadd: return "sadd";
case Opcode::ssub: return "ssub";
case Opcode::smul: return "smul";
case Opcode::saddi: return "saddi";
case Opcode::smuli: return "smuli";
case Opcode::setbw: return "setbw";
case Opcode::mvmul: return "mvmul";
case Opcode::vvadd: return "vvadd";
case Opcode::vvsub: return "vvsub";
case Opcode::vvmul: return "vvmul";
case Opcode::vvdmul: return "vvdmul";
case Opcode::vvmax: return "vvmax";
case Opcode::vvsll: return "vvsll";
case Opcode::vvsra: return "vvsra";
case Opcode::vavg: return "vavg";
case Opcode::vrelu: return "vrelu";
case Opcode::vtanh: return "vtanh";
case Opcode::vsigm: return "vsigm";
case Opcode::nop: return "nop";
case Opcode::sldi: return "sldi";
case Opcode::sld: return "sld";
case Opcode::sadd: return "sadd";
case Opcode::ssub: return "ssub";
case Opcode::smul: return "smul";
case Opcode::saddi: return "saddi";
case Opcode::smuli: return "smuli";
case Opcode::setbw: return "setbw";
case Opcode::mvmul: return "mvmul";
case Opcode::vvadd: return "vvadd";
case Opcode::vvsub: return "vvsub";
case Opcode::vvmul: return "vvmul";
case Opcode::vvdmul: return "vvdmul";
case Opcode::vvmax: return "vvmax";
case Opcode::vvsll: return "vvsll";
case Opcode::vvsra: return "vvsra";
case Opcode::vavg: return "vavg";
case Opcode::vrelu: return "vrelu";
case Opcode::vtanh: return "vtanh";
case Opcode::vsigm: return "vsigm";
case Opcode::vsoftmax: return "vsoftmax";
case Opcode::vmv: return "vmv";
case Opcode::vrsu: return "vrsu";
case Opcode::vrsl: return "vrsl";
case Opcode::ld: return "ld";
case Opcode::st: return "st";
case Opcode::lldi: return "lldi";
case Opcode::lmv: return "lmv";
case Opcode::send: return "send";
case Opcode::recv: return "recv";
case Opcode::wait: return "wait";
case Opcode::sync: return "sync";
case Opcode::vmv: return "vmv";
case Opcode::vrsu: return "vrsu";
case Opcode::vrsl: return "vrsl";
case Opcode::ld: return "ld";
case Opcode::st: return "st";
case Opcode::lldi: return "lldi";
case Opcode::lmv: return "lmv";
case Opcode::send: return "send";
case Opcode::recv: return "recv";
case Opcode::wait: return "wait";
case Opcode::sync: return "sync";
}
llvm_unreachable("Unsupported PIM binary opcode");
}
@@ -235,9 +233,7 @@ inline InstructionRecord makeInstructionRecord(const llvm::json::Object& instruc
case Opcode::sldi:
case Opcode::saddi:
case Opcode::smuli:
case Opcode::lldi:
record.r2OrImm = getOptionalInt(instruction, "imm");
break;
case Opcode::lldi: record.r2OrImm = getOptionalInt(instruction, "imm"); break;
case Opcode::mvmul:
record.r2OrImm = getOptionalInt(instruction, "mbiw");
record.generic1 = getOptionalInt(instruction, "relu");
@@ -252,9 +248,7 @@ inline InstructionRecord makeInstructionRecord(const llvm::json::Object& instruc
record.r2OrImm = getOptionalInt(instruction, "core");
record.generic3 = getOptionalInt(instruction, "size");
break;
default:
record.r2OrImm = getOptionalInt(instruction, "rs2");
break;
default: record.r2OrImm = getOptionalInt(instruction, "rs2"); break;
}
if (record.opcode != Opcode::mvmul && record.opcode != Opcode::setbw) {
@@ -371,8 +365,7 @@ inline llvm::json::Object makeInstructionJson(const InstructionRecord& record) {
break;
case Opcode::wait:
case Opcode::sync:
case Opcode::nop:
break;
case Opcode::nop: break;
}
return instruction;
+1 -1
View File
@@ -367,7 +367,7 @@ void PimCodeGen::emitMemCopyOp(StringRef opName,
instruction.generic1 = 0;
instruction.generic2 = 0;
instruction.generic3 = static_cast<int32_t>(size);
(void)sizeFieldName;
(void) sizeFieldName;
emitInstruction(instruction);
}
@@ -1,5 +1,4 @@
#include "src/Accelerators/PIM/Conversion/SpatialToPim/TensorPackingPatterns.hpp"
#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
using namespace mlir;
@@ -75,16 +74,14 @@ struct PackSpatialConcatInputsPattern final : OpRewritePattern<spatial::SpatConc
return failure();
auto outputType = cast<ShapedType>(concatOp.getOutput().getType());
auto newConcat = pim::PimConcatOp::create(rewriter,
concatOp.getLoc(),
concatOp.getOutput().getType(),
concatOp.getAxisAttr(),
ValueRange(packedInputs),
tensor::EmptyOp::create(rewriter,
concatOp.getLoc(),
outputType.getShape(),
outputType.getElementType())
.getResult());
auto newConcat = pim::PimConcatOp::create(
rewriter,
concatOp.getLoc(),
concatOp.getOutput().getType(),
concatOp.getAxisAttr(),
ValueRange(packedInputs),
tensor::EmptyOp::create(rewriter, concatOp.getLoc(), outputType.getShape(), outputType.getElementType())
.getResult());
rewriter.replaceOp(concatOp, newConcat.getOutput());
return success();
}
@@ -1,7 +1,7 @@
#pragma once
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/PatternMatch.h"
#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -1,15 +1,15 @@
#include "src/Accelerators/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.hpp"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Interfaces/DestinationStyleOpInterface.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/STLExtras.h"
#include <limits>
#include "src/Accelerators/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.hpp"
using namespace mlir;
namespace onnx_mlir {
@@ -29,9 +29,8 @@ static uint64_t getTypeSizeBytes(MemRefType type) {
return static_cast<uint64_t>(type.getNumElements() * type.getElementTypeBitWidth() / 8);
}
static FailureOr<uint64_t> getLastUseInstruction(memref::AllocOp allocOp,
Block& body,
const DenseMap<Operation*, uint64_t>& opOrder) {
static FailureOr<uint64_t>
getLastUseInstruction(memref::AllocOp allocOp, Block& body, const DenseMap<Operation*, uint64_t>& opOrder) {
uint64_t endInstruction = opOrder.lookup(allocOp);
SmallPtrSet<Operation*, 16> visited;
SmallVector<Value> pendingValues;
@@ -45,10 +44,9 @@ static FailureOr<uint64_t> getLastUseInstruction(memref::AllocOp allocOp,
if (!visited.insert(user).second)
continue;
if (isSupportedAliasOp(user)) {
if (isSupportedAliasOp(user))
for (Value result : user->getResults())
pendingValues.push_back(result);
}
if (auto dpsOp = dyn_cast<DestinationStyleOpInterface>(user)) {
for (OpResult result : user->getResults()) {
@@ -2,7 +2,6 @@
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/Operation.h"
#include "llvm/ADT/SmallVector.h"
@@ -45,9 +45,7 @@ struct CoalescingReportEntry {
CoalescingReportRow row;
};
static std::string formatMemory(uint64_t bytes) {
return formatReportMemory(bytes);
}
static std::string formatMemory(uint64_t bytes) { return formatReportMemory(bytes); }
static SmallVector<int32_t> getBatchCoreIds(pim::PimCoreBatchOp coreBatchOp) {
auto coreIdsAttr = coreBatchOp->getAttrOfType<DenseI32ArrayAttr>(onnx_mlir::kCoreIdsAttrName);
@@ -58,9 +56,10 @@ static SmallVector<int32_t> getBatchCoreIds(pim::PimCoreBatchOp coreBatchOp) {
static void printReportRow(raw_ostream& os, const CoalescingReportRow& row) {
llvm::SmallVector<ReportField, 4> fields = {
{"Number of candidates", std::to_string(row.numCandidates)},
{"Skipped allocations", std::to_string(row.numSkipped)},
{"Removed allocations", std::to_string(row.numRemoved)},
{"Saved memory", formatMemory(row.savedBytes)}};
{"Skipped allocations", std::to_string(row.numSkipped) },
{"Removed allocations", std::to_string(row.numRemoved) },
{"Saved memory", formatMemory(row.savedBytes) }
};
printReportFlatFields(os, fields);
}
@@ -87,10 +86,12 @@ static void emitReport(ArrayRef<CoalescingReportEntry> entries) {
totalRow.savedBytes += entryTotal.savedBytes;
}
llvm::SmallVector<ReportField, 4> totalFields = {{"Number of candidates", std::to_string(totalRow.numCandidates)},
{"Skipped allocations", std::to_string(totalRow.numSkipped)},
{"Removed allocations", std::to_string(totalRow.numRemoved)},
{"Saved memory", formatMemory(totalRow.savedBytes)}};
llvm::SmallVector<ReportField, 4> totalFields = {
{"Number of candidates", std::to_string(totalRow.numCandidates)},
{"Skipped allocations", std::to_string(totalRow.numSkipped) },
{"Removed allocations", std::to_string(totalRow.numRemoved) },
{"Saved memory", formatMemory(totalRow.savedBytes) }
};
printReportTotalsBlock(os, totalFields);
if (!entries.empty())
os << "\n";
@@ -127,15 +128,17 @@ static void emitReport(ArrayRef<CoalescingReportEntry> entries) {
if (sortedEntries[index].kind == CoalescingReportEntry::Kind::Batch) {
llvm::SmallVector<ReportField, 4> perCoreFields = {
{"Number of candidates", std::to_string(sortedEntries[index].row.numCandidates)},
{"Skipped allocations", std::to_string(sortedEntries[index].row.numSkipped)},
{"Removed allocations", std::to_string(sortedEntries[index].row.numRemoved)},
{"Saved memory", formatMemory(sortedEntries[index].row.savedBytes)}};
{"Skipped allocations", std::to_string(sortedEntries[index].row.numSkipped) },
{"Removed allocations", std::to_string(sortedEntries[index].row.numRemoved) },
{"Saved memory", formatMemory(sortedEntries[index].row.savedBytes) }
};
CoalescingReportRow totalRow = getTotalRow(sortedEntries[index]);
llvm::SmallVector<ReportField, 4> totalFields = {
{"Number of candidates", std::to_string(totalRow.numCandidates)},
{"Skipped allocations", std::to_string(totalRow.numSkipped)},
{"Removed allocations", std::to_string(totalRow.numRemoved)},
{"Saved memory", formatMemory(totalRow.savedBytes)}};
{"Skipped allocations", std::to_string(totalRow.numSkipped) },
{"Removed allocations", std::to_string(totalRow.numRemoved) },
{"Saved memory", formatMemory(totalRow.savedBytes) }
};
printReportPerCoreAndTotalFields(os, perCoreFields, totalFields);
}
else {
@@ -196,8 +199,6 @@ struct StaticMemoryCoalescingPass : PassWrapper<StaticMemoryCoalescingPass, Oper
} // namespace
std::unique_ptr<Pass> createPimStaticMemoryCoalescingPass() {
return std::make_unique<StaticMemoryCoalescingPass>();
}
std::unique_ptr<Pass> createPimStaticMemoryCoalescingPass() { return std::make_unique<StaticMemoryCoalescingPass>(); }
} // namespace onnx_mlir
@@ -818,13 +818,14 @@ void generateReport(func::FuncOp funcOp, const std::string& name, size_t usedCpu
}
}
llvm::SmallVector<ReportField, 6> totalFields = {{"Used cores", std::to_string(usedCpuCount)},
{"Number of top-level compute ops", std::to_string(totalComputeOps)},
{"Number of logical computes", std::to_string(totalLogicalComputes)},
{"Number of top-level batch compute ops",
std::to_string(totalBatchComputeOps)},
{"Number of instructions", std::to_string(totalInstructionCount)},
{"Number of used crossbars", std::to_string(totalWeightCount)}};
llvm::SmallVector<ReportField, 6> totalFields = {
{"Used cores", std::to_string(usedCpuCount) },
{"Number of top-level compute ops", std::to_string(totalComputeOps) },
{"Number of logical computes", std::to_string(totalLogicalComputes) },
{"Number of top-level batch compute ops", std::to_string(totalBatchComputeOps) },
{"Number of instructions", std::to_string(totalInstructionCount)},
{"Number of used crossbars", std::to_string(totalWeightCount) }
};
printReportTotalsBlock(os, totalFields);
if (!collectedData.empty())
os << "\n";
@@ -876,13 +877,15 @@ void generateReport(func::FuncOp funcOp, const std::string& name, size_t usedCpu
llvm::SmallVector<ReportField, 3> perCoreFields = {
{"Number of logical computes", std::to_string(perCoreLogicalComputeCount)},
{"Number of instructions", std::to_string(perCoreInstructionCount)},
{"Number of used crossbars", std::to_string(perCoreWeightCount)}};
{"Number of instructions", std::to_string(perCoreInstructionCount) },
{"Number of used crossbars", std::to_string(perCoreWeightCount) }
};
if (current.isRebatched) {
llvm::SmallVector<ReportField, 3> totalEntryFields = {
{"Number of logical computes", std::to_string(current.logicalComputeCount)},
{"Number of instructions", std::to_string(totalEntryInstructionCount)},
{"Number of used crossbars", std::to_string(current.weightCount)}};
{"Number of instructions", std::to_string(totalEntryInstructionCount) },
{"Number of used crossbars", std::to_string(current.weightCount) }
};
printReportPerCoreAndTotalFields(os, perCoreFields, totalEntryFields);
}
else {
@@ -1003,6 +1006,23 @@ public:
DenseMap<Value, Value> externalInputMap;
DenseMap<Value, size_t> weightToIndex;
};
struct RemoteSendInfo {
ChannelInfo channelInfo;
ComputeInstance consumer;
size_t inputIndex = 0;
size_t consumerOrder = 0;
size_t sourceOrder = 0;
};
struct RemoteReceiveEntry {
ChannelInfo channelInfo;
ComputeInstance consumer;
size_t inputIndex = 0;
size_t sourceOrder = 0;
};
auto getRemoteSendPairKey = [](const ChannelInfo& channelInfo) {
return (static_cast<uint64_t>(static_cast<uint32_t>(channelInfo.sourceCoreId)) << 32)
| static_cast<uint32_t>(channelInfo.targetCoreId);
};
auto getTaskInputs = [&](const ScheduledTask& task) {
SmallVector<Value> inputs;
@@ -1143,7 +1163,7 @@ public:
}
};
DenseMap<ComputeInstance, SmallVector<SmallVector<ChannelInfo>>> remoteSendsByTask;
DenseMap<ComputeInstance, SmallVector<SmallVector<RemoteSendInfo>>> remoteSendsByTask;
DenseMap<ComputeInstance, SmallVector<std::optional<ChannelInfo>>> remoteInputsByTask;
DenseMap<size_t, SmallVector<Value>> cpuExternalInputs;
DenseMap<size_t, SmallVector<Value>> cpuWeights;
@@ -1176,7 +1196,7 @@ public:
auto& perResultChannels = remoteSendsByTask[producerRef->instance];
if (perResultChannels.empty())
perResultChannels.resize(getTaskOutputTypes(producerIt->second).size());
perResultChannels[producerRef->resultIndex].push_back(info);
perResultChannels[producerRef->resultIndex].push_back({info, task.key, inputIndex, task.order, 0});
}
continue;
}
@@ -1201,6 +1221,79 @@ public:
}
}
DenseSet<uint64_t> pairsNeedingReceiveReorder;
for (size_t cpu : orderedCpus) {
DenseMap<uint64_t, size_t> nextSourceOrderByPair;
DenseMap<uint64_t, size_t> lastConsumerOrderByPair;
for (const ScheduledTask& task : tasksByCpu[cpu]) {
auto sendsIt = remoteSendsByTask.find(task.key);
if (sendsIt == remoteSendsByTask.end())
continue;
for (auto& sendInfos : sendsIt->second) {
for (RemoteSendInfo& sendInfo : sendInfos) {
uint64_t pairKey = getRemoteSendPairKey(sendInfo.channelInfo);
sendInfo.sourceOrder = nextSourceOrderByPair[pairKey]++;
auto [it, inserted] = lastConsumerOrderByPair.try_emplace(pairKey, sendInfo.consumerOrder);
if (!inserted) {
if (sendInfo.consumerOrder < it->second)
pairsNeedingReceiveReorder.insert(pairKey);
it->second = sendInfo.consumerOrder;
}
}
}
}
}
DenseMap<uint64_t, SmallVector<RemoteSendInfo*>> reorderedSendsByPair;
for (auto& taskSends : remoteSendsByTask) {
for (auto& sendInfos : taskSends.second) {
for (RemoteSendInfo& sendInfo : sendInfos) {
uint64_t pairKey = getRemoteSendPairKey(sendInfo.channelInfo);
if (pairsNeedingReceiveReorder.contains(pairKey))
reorderedSendsByPair[pairKey].push_back(&sendInfo);
}
}
}
for (auto& pairSends : reorderedSendsByPair) {
llvm::stable_sort(pairSends.second, [](const RemoteSendInfo* lhs, const RemoteSendInfo* rhs) {
if (lhs->sourceOrder != rhs->sourceOrder)
return lhs->sourceOrder < rhs->sourceOrder;
return lhs->channelInfo.channelId < rhs->channelInfo.channelId;
});
for (RemoteSendInfo* sendInfo : pairSends.second) {
int64_t channelId = nextChannelId++;
sendInfo->channelInfo.channelId = channelId;
auto remoteInputsIt = remoteInputsByTask.find(sendInfo->consumer);
assert(remoteInputsIt != remoteInputsByTask.end() && "missing remote input for reordered send");
assert(sendInfo->inputIndex < remoteInputsIt->second.size() && "remote input index out of range");
assert(remoteInputsIt->second[sendInfo->inputIndex] && "missing reordered remote input channel");
remoteInputsIt->second[sendInfo->inputIndex]->channelId = channelId;
}
}
DenseMap<size_t, DenseMap<uint64_t, SmallVector<RemoteReceiveEntry>>> receiveQueuesByCpu;
for (auto& taskSends : remoteSendsByTask) {
for (const auto& sendInfos : taskSends.second) {
for (const RemoteSendInfo& sendInfo : sendInfos) {
uint64_t pairKey = getRemoteSendPairKey(sendInfo.channelInfo);
if (!pairsNeedingReceiveReorder.contains(pairKey))
continue;
size_t targetCpu = static_cast<size_t>(sendInfo.channelInfo.targetCoreId - 1);
receiveQueuesByCpu[targetCpu][pairKey].push_back(
{sendInfo.channelInfo, sendInfo.consumer, sendInfo.inputIndex, sendInfo.sourceOrder});
}
}
}
for (auto& cpuQueues : receiveQueuesByCpu) {
for (auto& pairQueue : cpuQueues.second) {
llvm::stable_sort(pairQueue.second, [](const RemoteReceiveEntry& lhs, const RemoteReceiveEntry& rhs) {
if (lhs.sourceOrder != rhs.sourceOrder)
return lhs.sourceOrder < rhs.sourceOrder;
return lhs.channelInfo.channelId < rhs.channelInfo.channelId;
});
}
}
auto returnOp = cast<func::ReturnOp>(func.getBody().front().getTerminator());
IRRewriter rewriter(&getContext());
DenseMap<size_t, CpuProgram> cpuPrograms;
@@ -1255,6 +1348,59 @@ public:
CpuProgram& program = cpuPrograms[cpu];
IRRewriter cpuRewriter(&getContext());
cpuRewriter.setInsertionPointToEnd(program.block);
DenseMap<uint64_t, size_t> receiveQueueIndices;
DenseMap<ComputeInstance, SmallVector<Value>> preReceivedInputsByTask;
auto lookupPreReceivedInput = [&](ComputeInstance consumer, size_t inputIndex) -> std::optional<Value> {
auto inputsIt = preReceivedInputsByTask.find(consumer);
if (inputsIt == preReceivedInputsByTask.end() || inputsIt->second.size() <= inputIndex)
return std::nullopt;
Value value = inputsIt->second[inputIndex];
if (!value)
return std::nullopt;
return value;
};
auto receiveThroughInput = [&](const ChannelInfo& requestedChannelInfo,
ComputeInstance requestedConsumer,
size_t requestedInputIndex) -> std::optional<Value> {
uint64_t pairKey = getRemoteSendPairKey(requestedChannelInfo);
auto cpuQueuesIt = receiveQueuesByCpu.find(cpu);
if (cpuQueuesIt == receiveQueuesByCpu.end())
return std::nullopt;
auto queueIt = cpuQueuesIt->second.find(pairKey);
if (queueIt == cpuQueuesIt->second.end())
return std::nullopt;
auto& queue = queueIt->second;
size_t& queueIndex = receiveQueueIndices[pairKey];
while (queueIndex < queue.size()) {
const RemoteReceiveEntry& entry = queue[queueIndex++];
auto consumerTaskIt = taskByKey.find(entry.consumer);
if (consumerTaskIt == taskByKey.end())
return std::nullopt;
SmallVector<Value> consumerInputs = getTaskInputs(consumerTaskIt->second);
if (consumerInputs.size() <= entry.inputIndex)
return std::nullopt;
Type inputType = consumerInputs[entry.inputIndex].getType();
auto receive =
spatial::SpatChannelReceiveOp::create(cpuRewriter,
loc,
inputType,
cpuRewriter.getI64IntegerAttr(entry.channelInfo.channelId),
cpuRewriter.getI32IntegerAttr(entry.channelInfo.sourceCoreId),
cpuRewriter.getI32IntegerAttr(entry.channelInfo.targetCoreId));
auto& receivedInputs = preReceivedInputsByTask[entry.consumer];
if (receivedInputs.size() <= entry.inputIndex)
receivedInputs.resize(entry.inputIndex + 1);
receivedInputs[entry.inputIndex] = receive.getResult();
if (entry.consumer == requestedConsumer && entry.inputIndex == requestedInputIndex)
return receive.getResult();
}
return std::nullopt;
};
for (const ScheduledTask& task : tasksByCpu[cpu]) {
SmallVector<Value> taskInputs = getTaskInputs(task);
@@ -1284,6 +1430,24 @@ public:
continue;
}
const ChannelInfo& channelInfo = *remoteInputsIt->second[inputIndex];
uint64_t pairKey = getRemoteSendPairKey(channelInfo);
if (pairsNeedingReceiveReorder.contains(pairKey)) {
if (std::optional<Value> preReceived = lookupPreReceivedInput(task.key, inputIndex)) {
resolvedInputs.push_back(*preReceived);
continue;
}
std::optional<Value> received = receiveThroughInput(channelInfo, task.key, inputIndex);
if (!received) {
task.sourceOp->emitOpError("failed to materialize reordered remote receive")
<< " consumerCpu=" << cpu << " consumerSlot=" << task.slot
<< " sourceCoreId=" << channelInfo.sourceCoreId << " targetCoreId=" << channelInfo.targetCoreId
<< " channelId=" << channelInfo.channelId;
signalPassFailure();
return;
}
resolvedInputs.push_back(*received);
continue;
}
auto receive =
spatial::SpatChannelReceiveOp::create(cpuRewriter,
loc,
@@ -1367,13 +1531,14 @@ public:
if (sendInfos.empty())
continue;
Value producedValue = taskYieldValues[resultIndex];
for (const ChannelInfo& sendInfo : sendInfos)
for (const RemoteSendInfo& sendInfo : sendInfos) {
spatial::SpatChannelSendOp::create(cpuRewriter,
loc,
cpuRewriter.getI64IntegerAttr(sendInfo.channelId),
cpuRewriter.getI32IntegerAttr(sendInfo.sourceCoreId),
cpuRewriter.getI32IntegerAttr(sendInfo.targetCoreId),
cpuRewriter.getI64IntegerAttr(sendInfo.channelInfo.channelId),
cpuRewriter.getI32IntegerAttr(sendInfo.channelInfo.sourceCoreId),
cpuRewriter.getI32IntegerAttr(sendInfo.channelInfo.targetCoreId),
producedValue);
}
}
}
}
@@ -1666,23 +1831,21 @@ private:
IRRewriter rewriter(context);
rewriter.setInsertionPointAfter(producerOp);
auto savedSendInsertPoint = rewriter.saveInsertionPoint();
auto insertNew = [this, savedSendInsertPoint, context, loc, computeValueResults, producerCpu](size_t resultIndex,
size_t targetCpu) {
auto insertNew = [this, context, loc, computeValueResults, producerCpu](size_t resultIndex, size_t targetCpu) {
auto channelId = nextChannelId++;
LazyInsertComputeResult::ChannelInfo channelInfo {
channelId, getPhysicalCoreId(producerCpu), getPhysicalCoreId(targetCpu)};
auto insertVal = [&context, loc, computeValueResults, channelInfo, resultIndex, savedSendInsertPoint](
mlir::IRRewriter::InsertPoint) {
IRRewriter rewriter(context);
rewriter.restoreInsertionPoint(savedSendInsertPoint);
spatial::SpatChannelSendOp::create(rewriter,
loc,
rewriter.getI64IntegerAttr(channelInfo.channelId),
rewriter.getI32IntegerAttr(channelInfo.sourceCoreId),
rewriter.getI32IntegerAttr(channelInfo.targetCoreId),
computeValueResults.getOuter(resultIndex));
};
auto insertVal =
[&context, loc, computeValueResults, channelInfo, resultIndex](mlir::IRRewriter::InsertPoint insertPoint) {
IRRewriter rewriter(context);
rewriter.restoreInsertionPoint(insertPoint);
spatial::SpatChannelSendOp::create(rewriter,
loc,
rewriter.getI64IntegerAttr(channelInfo.channelId),
rewriter.getI32IntegerAttr(channelInfo.sourceCoreId),
rewriter.getI32IntegerAttr(channelInfo.targetCoreId),
computeValueResults.getOuter(resultIndex));
};
std::pair<LazyInsertComputeResult::ChannelInfo, std::function<void(mlir::IRRewriter::InsertPoint)>> ret {
channelInfo, insertVal};
return ret;
@@ -10,8 +10,6 @@
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
#include <tuple>
#include "RegularOpCompaction.hpp"
#include "src/Accelerators/PIM/Conversion/SpatialToPim/TensorPackingPatterns.hpp"
#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -340,7 +338,18 @@ void compactScalarChannelRuns(func::FuncOp funcOp, int64_t& nextChannelId) {
++runIt;
}
if (run.size() > 1) {
bool hasRepeatedEndpoint = false;
for (size_t lhs = 0; lhs < run.size() && !hasRepeatedEndpoint; ++lhs) {
for (size_t rhs = lhs + 1; rhs < run.size(); ++rhs) {
if (run[lhs].getSourceCoreId() == run[rhs].getSourceCoreId()
&& run[lhs].getTargetCoreId() == run[rhs].getTargetCoreId()) {
hasRepeatedEndpoint = true;
break;
}
}
}
if (run.size() > 1 && !hasRepeatedEndpoint) {
struct ReceiveEntry {
spatial::SpatChannelReceiveOp op;
size_t originalIndex = 0;
@@ -352,10 +361,6 @@ void compactScalarChannelRuns(func::FuncOp funcOp, int64_t& nextChannelId) {
sortedEntries.reserve(run.size());
for (auto [originalIndex, op] : llvm::enumerate(run))
sortedEntries.push_back({op, originalIndex, op.getSourceCoreId(), op.getTargetCoreId(), op.getChannelId()});
llvm::stable_sort(sortedEntries, [](const ReceiveEntry& lhs, const ReceiveEntry& rhs) {
return std::tuple(lhs.sourceCoreId, lhs.targetCoreId, lhs.channelId)
< std::tuple(rhs.sourceCoreId, rhs.targetCoreId, rhs.channelId);
});
SmallVector<int64_t> channelIds;
SmallVector<int32_t> sourceCoreIds;
@@ -436,10 +441,6 @@ void compactScalarChannelRuns(func::FuncOp funcOp, int64_t& nextChannelId) {
sortedEntries.reserve(run.size());
for (auto op : run)
sortedEntries.push_back({op, op.getSourceCoreId(), op.getTargetCoreId(), op.getChannelId()});
llvm::stable_sort(sortedEntries, [](const SendEntry& lhs, const SendEntry& rhs) {
return std::tuple(lhs.sourceCoreId, lhs.targetCoreId, lhs.channelId)
< std::tuple(rhs.sourceCoreId, rhs.targetCoreId, rhs.channelId);
});
SmallVector<int64_t> channelIds;
SmallVector<int32_t> sourceCoreIds;
@@ -66,8 +66,10 @@ static Value buildSubviewChunk(const StaticSubviewInfo& info,
return memref::SubViewOp::create(rewriter, loc, info.source, chunkOffsets, chunkSizes, chunkStrides);
}
static SmallVector<Value>
delinearizeIndexValue(Value linearIndex, ArrayRef<int64_t> shape, ArrayRef<int64_t> strides, PatternRewriter& rewriter) {
static SmallVector<Value> delinearizeIndexValue(Value linearIndex,
ArrayRef<int64_t> shape,
ArrayRef<int64_t> strides,
PatternRewriter& rewriter) {
SmallVector<Value> indices;
indices.reserve(shape.size());
@@ -112,7 +114,8 @@ static Value buildDynamicSubviewChunk(const StaticSubviewInfo& info,
assert(info.strides[dim] == 1 && "loop-based subview rewrite requires unit strides");
chunkOffsets.push_back(addDynamicOffset(info.offsets[dim], outerIndices[dim], rewriter));
chunkSizes.push_back(rewriter.getIndexAttr(1));
} else {
}
else {
chunkOffsets.push_back(info.offsets[dim]);
chunkSizes.push_back(rewriter.getIndexAttr(info.sizes.back()));
}
@@ -122,11 +125,8 @@ static Value buildDynamicSubviewChunk(const StaticSubviewInfo& info,
return memref::SubViewOp::create(rewriter, loc, info.source, chunkOffsets, chunkSizes, chunkStrides);
}
static Value buildContiguousChunk(Value source,
ArrayRef<int64_t> copyShape,
ArrayRef<Value> outerIndices,
Location loc,
PatternRewriter& rewriter) {
static Value buildContiguousChunk(
Value source, ArrayRef<int64_t> copyShape, ArrayRef<Value> outerIndices, Location loc, PatternRewriter& rewriter) {
SmallVector<OpFoldResult> chunkOffsets;
SmallVector<OpFoldResult> chunkSizes;
SmallVector<OpFoldResult> chunkStrides;
@@ -203,7 +203,8 @@ static LogicalResult rewriteSubviewCopyLikeOp(CopyOp copyOp,
rewriter.setInsertionPointToStart(loop.getBody());
SmallVector<Value> outerIndices =
outerShape.empty() ? SmallVector<Value> {} : delinearizeIndexValue(loop.getInductionVar(), outerShape, outerStrides, rewriter);
outerShape.empty() ? SmallVector<Value> {}
: delinearizeIndexValue(loop.getInductionVar(), outerShape, outerStrides, rewriter);
Value chunkDst = splitDst ? buildDynamicSubviewChunk(*dstSubview, outerIndices, copyOp.getLoc(), rewriter)
: buildContiguousChunk(dst, copyShape, outerIndices, copyOp.getLoc(), rewriter);
Value chunkSrc = splitSrc ? buildDynamicSubviewChunk(*srcSubview, outerIndices, copyOp.getLoc(), rewriter)
+1 -1
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@@ -6,10 +6,10 @@
#include "llvm/ADT/STLExtras.h"
#include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
#include "src/Accelerators/PIM/Common/PimCommon.hpp"
#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
#include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
using namespace mlir;
+2
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@@ -60,6 +60,7 @@ def main():
ap.add_argument("--simulator-dir", default=None,
help="Path to pim-simulator crate root (default: auto-detected relative to script).")
ap.add_argument("--threshold", type=float, default=1e-3, help="Max allowed diff per output element.")
ap.add_argument("--seed", type=int, default=0, help="RNG seed for generated validation inputs.")
ap.add_argument("--crossbar-size", type=int, default=64)
ap.add_argument("--crossbar-count", type=int, default=8)
ap.add_argument("--core-count", type=int, default=None,
@@ -117,6 +118,7 @@ def main():
onnx_path, a.raptor_path, a.onnx_include_dir, simulator_dir,
crossbar_size=a.crossbar_size, crossbar_count=a.crossbar_count, core_count=a.core_count,
threshold=a.threshold,
seed=a.seed,
reporter=reporter,
model_index=index,
model_total=len(onnx_files),
+2 -2
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@@ -268,7 +268,7 @@ def validate_outputs(sim_arrays, runner_out_dir, outputs_descriptor, threshold=1
def validate_network(network_onnx_path, raptor_path, onnx_include_dir,
simulator_dir, crossbar_size=64, crossbar_count=8, core_count=None, threshold=1e-3,
reporter=None, model_index=1, model_total=1, verbose=False):
seed=0, reporter=None, model_index=1, model_total=1, verbose=False):
network_onnx_path = Path(network_onnx_path).resolve()
raptor_path = Path(raptor_path).resolve()
onnx_include_dir = Path(onnx_include_dir).resolve()
@@ -306,7 +306,7 @@ def validate_network(network_onnx_path, raptor_path, onnx_include_dir,
print_stage(reporter, model_index, model_total, network_onnx_path.name, "Generate Inputs")
inputs_descriptor, outputs_descriptor = onnx_io(network_onnx_path)
inputs_list, _inputs_dict = gen_random_inputs(inputs_descriptor)
inputs_list, _inputs_dict = gen_random_inputs(inputs_descriptor, seed=seed)
flags, _files = save_inputs_to_files(network_onnx_path, inputs_list, out_dir=workspace_dir / "inputs")
print_info(reporter, f"Saved {len(inputs_list)} input file(s) to {workspace_dir / 'inputs'}")
reporter.advance()