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refactor
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NiccoloN
2026-06-29 14:00:10 +02:00
parent e8f09fd67f
commit f492400eda
37 changed files with 1407 additions and 1898 deletions
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src/PIM/Conversion/ONNXToSpatial/Patterns/Math/Conv.cpp
+1 -141
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@@ -26,6 +26,7 @@
#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/PlanLowering.hpp"
#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns/Math/ConvGeometry.hpp"
#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
#include "src/Dialect/ONNX/ONNXOps.hpp"
@@ -42,59 +43,6 @@ struct ConvToGemm : OpConversionPattern<ONNXConvOp> {
ConversionPatternRewriter& rewriter) const override;
};
struct ConvLoweringState {
Value x;
Value w;
Value b;
RankedTensorType xType;
RankedTensorType wType;
RankedTensorType outType;
int64_t batchSize;
int64_t numChannelsIn;
int64_t xHeight;
int64_t xWidth;
int64_t numChannelsOut;
int64_t wHeight;
int64_t wWidth;
int64_t outHeight;
int64_t outWidth;
int64_t group;
int64_t numChannelsInPerGroup;
int64_t numChannelsOutPerGroup;
int64_t padHeightBegin;
int64_t padHeightEnd;
int64_t padWidthBegin;
int64_t padWidthEnd;
int64_t strideHeight;
int64_t strideWidth;
int64_t dilationHeight;
int64_t dilationWidth;
bool hasBias;
};
struct ConvGeometry {
int64_t batchSize;
int64_t numChannelsIn;
int64_t xHeight;
int64_t xWidth;
int64_t numChannelsOut;
int64_t wHeight;
int64_t wWidth;
int64_t outHeight;
int64_t outWidth;
int64_t group;
int64_t numChannelsInPerGroup;
int64_t numChannelsOutPerGroup;
int64_t k;
int64_t c;
int64_t p;
int64_t xbarSize;
int64_t pack;
uint64_t im2colElements;
bool hasBias;
bool isDepthwise;
};
struct ConvLoweringDecision {
PimConvLoweringType strategy;
std::string reason;
@@ -108,19 +56,6 @@ struct PreparedConvInput {
RankedTensorType type;
};
struct RowInterval {
int64_t begin = 0;
int64_t end = 0;
};
struct ConvRowDemand {
RowInterval outputRows;
RowInterval neededInputRows;
RowInterval acquiredInputRows;
int64_t topHaloRows = 0;
int64_t bottomHaloRows = 0;
};
struct ConvStrategyEstimate {
uint64_t estimatedMvmCount = 0;
uint64_t estimatedReductionVAddCount = 0;
@@ -291,9 +226,6 @@ static FailureOr<Value> createRowStripPackedRows(Value rows,
PatternRewriter& rewriter,
Location loc);
static bool
isDepthwiseConv(int64_t group, int64_t numChannelsIn, int64_t numChannelsOut, int64_t numChannelsInPerGroup);
static uint64_t chooseStreamChunkPositions(const ConvGeometry& geo, int64_t packFactor);
static FailureOr<ConvLoweringState> analyzeConvLoweringState(ONNXConvOp convOp, Value x, Value w, Value b);
static StringRef stringifyDistributedConvBarrierKind(DistributedConvBarrierKind kind) {
@@ -391,34 +323,6 @@ static ConvStrategyEstimate estimateConvStrategy(const ConvGeometry& geo,
return estimate;
}
static ConvGeometry buildConvGeometry(const ConvLoweringState& state) {
ConvGeometry geo {
state.batchSize,
state.numChannelsIn,
state.xHeight,
state.xWidth,
state.numChannelsOut,
state.wHeight,
state.wWidth,
state.outHeight,
state.outWidth,
state.group,
state.numChannelsInPerGroup,
state.numChannelsOutPerGroup,
state.numChannelsInPerGroup * state.wHeight * state.wWidth,
state.numChannelsOutPerGroup,
state.batchSize * state.outHeight * state.outWidth,
static_cast<int64_t>(crossbarSize.getValue()),
1,
0,
state.hasBias,
isDepthwiseConv(state.group, state.numChannelsIn, state.numChannelsOut, state.numChannelsInPerGroup),
};
geo.pack = std::max<int64_t>(1, geo.xbarSize / std::max<int64_t>(geo.k, geo.c));
geo.im2colElements = static_cast<uint64_t>(std::max<int64_t>(0, geo.p)) * static_cast<uint64_t>(std::max<int64_t>(0, geo.k));
return geo;
}
static std::string formatShape(ArrayRef<int64_t> dims) {
std::string text;
llvm::raw_string_ostream os(text);
@@ -563,36 +467,10 @@ classifyDistributedBinaryConsumer(Operation* user,
return std::nullopt;
}
static RowInterval computeConvInputRowsForOutputRows(RowInterval outputRows,
int64_t inputHeight,
int64_t kernelH,
int64_t strideH,
int64_t dilationH,
int64_t padTop) {
const int64_t rawBegin = outputRows.begin * strideH - padTop;
const int64_t rawEnd = (outputRows.end - 1) * strideH - padTop + dilationH * (kernelH - 1) + 1;
return {std::max<int64_t>(0, rawBegin), std::min<int64_t>(inputHeight, rawEnd)};
}
static bool covers(RowInterval acquired, RowInterval needed) {
return acquired.begin <= needed.begin && acquired.end >= needed.end;
}
static ConvRowDemand buildConvRowDemand(RowInterval outputRows, const ConvLoweringState& state) {
const int64_t rawBegin = outputRows.begin * state.strideHeight - state.padHeightBegin;
const int64_t rawEnd =
(outputRows.end - 1) * state.strideHeight - state.padHeightBegin + state.dilationHeight * (state.wHeight - 1) + 1;
RowInterval neededInputRows = computeConvInputRowsForOutputRows(
outputRows, state.xHeight, state.wHeight, state.strideHeight, state.dilationHeight, state.padHeightBegin);
ConvRowDemand demand;
demand.outputRows = outputRows;
demand.neededInputRows = neededInputRows;
demand.acquiredInputRows = neededInputRows;
demand.topHaloRows = std::max<int64_t>(0, -rawBegin);
demand.bottomHaloRows = std::max<int64_t>(0, rawEnd - state.xHeight);
return demand;
}
static bool canConsumeRowStripHwcInput(const ConvLoweringState& state, StringRef& failureReason) {
if (state.batchSize != 1) {
failureReason = "unsupported_batch";
@@ -1250,19 +1128,6 @@ static void reportConvLoweringDecision(ONNXConvOp convOp,
rewriteConvLoweringReport(reportEntries);
}
static uint64_t chooseStreamChunkPositions(const ConvGeometry& geo, int64_t packFactor) {
const uint64_t patchElements = static_cast<uint64_t>(std::max<int64_t>(1, geo.k));
uint64_t chunkPositions = std::max<uint64_t>(1, pimConvIm2colMaxElements / patchElements);
chunkPositions = std::min<uint64_t>(chunkPositions, static_cast<uint64_t>(std::max<int64_t>(1, geo.p)));
chunkPositions = std::min<uint64_t>(chunkPositions, std::max<uint64_t>(1, pimConvStreamChunkPositions));
if (packFactor > 1 && chunkPositions > static_cast<uint64_t>(packFactor)) {
chunkPositions -= chunkPositions % static_cast<uint64_t>(packFactor);
chunkPositions = std::max<uint64_t>(chunkPositions, static_cast<uint64_t>(packFactor));
}
return std::max<uint64_t>(1, chunkPositions);
}
static Value expandBiasIfNeeded(Value bias, PatternRewriter& rewriter, Location loc) {
auto biasType = cast<RankedTensorType>(bias.getType());
if (biasType.getRank() != 1)
@@ -1278,11 +1143,6 @@ static Value expandBiasIfNeeded(Value bias, PatternRewriter& rewriter, Location
});
}
static bool
isDepthwiseConv(int64_t group, int64_t numChannelsIn, int64_t numChannelsOut, int64_t numChannelsInPerGroup) {
return group == numChannelsIn && numChannelsInPerGroup == 1 && numChannelsOut % group == 0;
}
static int64_t findLargestDivisorAtMost(int64_t value, int64_t limit) {
assert(value > 0 && "expected positive value");
limit = std::min(value, limit);