584 lines
22 KiB
C++
584 lines
22 KiB
C++
#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Tosa/IR/TosaOps.h"
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#include "mlir/IR/Block.h"
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#include "mlir/IR/BuiltinAttributes.h"
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#include "mlir/IR/BuiltinTypeInterfaces.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/IRMapping.h"
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#include "mlir/IR/Location.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/Types.h"
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#include "mlir/IR/Value.h"
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#include "mlir/Support/LLVM.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "llvm/ADT/SmallVector.h"
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#include "llvm/Support/LogicalResult.h"
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#include <cstddef>
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#include <memory>
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#include <unordered_map>
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#include <vector>
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#include "src/Accelerators/PIM/Common/PIMCommon.hpp"
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#include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
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#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ONNXToSpatialCommon.hpp"
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#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
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#include "src/Dialect/ONNX/ONNXOps.hpp"
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using namespace mlir;
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using namespace std;
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namespace onnx_mlir {
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// NOTE:
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// This might be useful to re-implement this considering for loops.
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// neededXbars = krn_h * krn_w * inputTileCount * outputTileCount;
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/**
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* @brief A momentary representation of a core, to be used within the tiling of
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* a convolution operation.
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*/
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class Core {
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public:
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Core(const size_t coreId, ConversionPatternRewriter& rewriter)
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: coreId(coreId), rewriter(rewriter) {}
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/**
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* @brief Add a MVM operation to the core.
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*
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* @param inputTile The input tile to the MVM operation.
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* @param xbarIndex The index of the crossbar weight to use.
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* @param outputTileId The id of the output tile.
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* @param mvmOutType The result's shape.
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* @return Value The result of the MVM operation.
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*/
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Value addMVM(Value inputTile, size_t xbarIndex, size_t outputTileId, Type mvmOutType) {
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// Use the inputTile as the reference location for the MVM operation.
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Location loc = inputTile.getLoc();
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// Move the insertion point to the end of the block.
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rewriter.setInsertionPointToEnd(block.get());
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// Add the inputTile to the block arguments, and to the operands.
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Value operand = operandMap.lookupOrNull(inputTile);
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if (not operand) {
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operand = block->addArgument(inputTile.getType(), loc);
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operands.push_back(inputTile);
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operandMap.map(inputTile, operand);
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}
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// TODO: Compute the output type using the matrix, and check if `mvmOutType`
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// is correct.
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// Construct the MVM operation
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Value result = rewriter.create<spatial::SpatWeightedMVMOp>(loc, mvmOutType, xbarIndex, operand);
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// Since we are within the same core and no computation can happen in
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// paralllel, we can just apply a linear reduction in case we have multiple
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// MVM operations for the same outputTile.
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auto lastMVM = outputTileToMVM.find(outputTileId);
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// If an entry for this outputTile already exists, apply reduction.
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if (lastMVM != outputTileToMVM.end()) {
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// MVM results should have the same type for reduction.
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assert(lastMVM->second.getType() == result.getType());
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result = rewriter.create<spatial::SpatVAddOp>(loc, mvmOutType, lastMVM->second, result);
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}
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outputTileToMVM[outputTileId] = result;
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return result;
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}
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/**
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* @brief Mark a result as remappable, and return a shared pointer to it.
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*
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* This function marks a result as remappable, and returns a shared pointer to
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* it. We need to keep track of these values to generate the YieldOp at a
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* later stage.
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*
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* @param result A result to track, for later remapping.
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* @return shared_ptr<Value> A shared pointer to the result.
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*/
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shared_ptr<Value> makeResultRemappable(Value result) {
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// Verify that the result is present in the block.
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assert(result.getDefiningOp()->getBlock() == block.get());
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shared_ptr<mlir::Value> remappableResult = make_shared<Value>(result);
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resultsToRemap.push_back(remappableResult);
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results.push_back(result);
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return remappableResult;
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}
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/**
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* @brief Add a remappable operand to the core, to merge partial results
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* inter-core.
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*
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* @param remappableOperand The operand to add.
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* @return Value The block argument representing the operand.
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*/
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Value addRemappableOperand(std::shared_ptr<Value> operand) {
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// Check that the operand is not already there.
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assert(not operandMap.contains(*operand));
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Value argument = block->addArgument(operand->getType(), operand->getLoc());
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remappableOperands.push_back(operand);
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return argument;
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}
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/**
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* @brief Generate a spatial::SpatWeightedCompute operation from the core.
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*
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* @param loc The location of the operation.
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* @return spatial::SpatWeightedCompute
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*/
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spatial::SpatWeightedCompute createWComputeOp(Location loc) {
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// Get the shape of the results.
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SmallVector<Type> resultTypes;
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for (const auto& value : results)
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resultTypes.push_back(value.getType());
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// Create the WComputeOp, with non-remappable operands only.
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wcomputeOp = rewriter.create<spatial::SpatWeightedCompute>(loc, resultTypes, xbarWeights, operands);
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// Add the body to the WComputeOp.
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Block* releasedBlock = block.release();
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wcomputeOp.getBody().push_back(releasedBlock);
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// Add the `yieldOp` at the end, with the results.
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rewriter.setInsertionPointToEnd(releasedBlock);
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rewriter.create<spatial::SpatYieldOp>(loc, results);
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return wcomputeOp;
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}
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/**
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* @brief Remap the results to the WComputeOp results.
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*/
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void remapResults() {
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// Remap all the results to the WComputeOp results.
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assert(resultsToRemap.size() == wcomputeOp->getNumResults());
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for (size_t i = 0; i < resultsToRemap.size(); i++)
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*resultsToRemap[i] = wcomputeOp.getResult(i);
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}
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void addRemappedOperands() {
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// Insert the remappableOperands (which were remapped in
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// `addRemappableOperand` of another Core)
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for (auto remappedValue : remappableOperands)
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wcomputeOp->insertOperands(wcomputeOp->getNumOperands(), *remappedValue);
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// Update the wcomputeOp operandSegmentSize
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incrementWeightedComputeInputsSegmentSize(wcomputeOp, static_cast<int>(remappableOperands.size()));
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}
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size_t addXbarWeight(Value weight) {
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assert(!isXbarsFull());
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xbarWeights.push_back(weight);
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return xbarWeights.size() - 1;
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}
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bool isXbarsFull() {
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assert(xbarWeights.size() <= crossbarCountInCore);
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return xbarWeights.size() == crossbarCountInCore;
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}
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bool isCoreEmpty() { return block->empty(); }
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void dump() {
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// Print the coreId
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llvm::outs() << "Core " << coreId << ":\n";
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// Print the weights
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llvm::outs() << "Xbar Weights:\n";
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for (auto weight : xbarWeights)
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weight.dump();
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// Print the operands
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llvm::outs() << "Operands:\n";
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for (auto operand : operands)
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llvm::outs() << operand << "\n";
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// Dump the body block
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for (auto& op : block->getOperations())
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op.dump();
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// Print the results
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llvm::outs() << "Results:\n";
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for (auto result : results)
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llvm::outs() << result << "\n";
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}
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const size_t coreId;
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private:
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ConversionPatternRewriter& rewriter;
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// Should these be set<Value> instead? But I need to keep the order
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vector<Value> operands;
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vector<std::shared_ptr<Value>> remappableOperands;
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vector<Value> results;
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vector<std::shared_ptr<Value>> resultsToRemap;
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// Maps from input tiles to the block operand
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IRMapping operandMap;
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// Map from outputTileId to MVM operation producing it
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unordered_map<size_t, Value> outputTileToMVM;
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vector<Value> xbarWeights;
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unique_ptr<mlir::Block> block = make_unique<Block>();
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spatial::SpatWeightedCompute wcomputeOp;
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};
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struct ONNXConvOpTile : public OpConversionPattern<ONNXConvOp> {
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ONNXConvOpTile(MLIRContext* ctx)
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: OpConversionPattern(ctx) {}
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struct Producer_t {
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Value value;
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shared_ptr<Core> core;
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};
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LogicalResult
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matchAndRewrite(ONNXConvOp conv, ONNXConvOpAdaptor convAdaptor, ConversionPatternRewriter& rewriter) const final {
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ShapedType xShape = mlir::cast<ShapedType>(convAdaptor.getX().getType());
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ShapedType wShape = mlir::cast<ShapedType>(convAdaptor.getW().getType());
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ShapedType bShape = mlir::cast<ShapedType>(convAdaptor.getB().getType());
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ShapedType yShape = mlir::cast<ShapedType>(conv.getY().getType());
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size_t stride_x, stride_y, dilation_x, dilation_y, pad_x, pad_y;
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unpackOptionalPairVector(conv.getStrides(), stride_x, stride_y);
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unpackOptionalPairVector(conv.getDilations(), dilation_x, dilation_y);
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auto padUnpackError = unpackOptionalPadsVector(convAdaptor.getPads(), pad_x, pad_y);
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if (padUnpackError.has_value())
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return rewriter.notifyMatchFailure(conv, padUnpackError.value());
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// TODO: Pad value at beginning and end of each dimension could be
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// different. We should handle this case.
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// MapOperations mapOperation = MapOperations::None;
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//
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// // If we have just one user, and it is an activation funcion (or more in
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// // general a mapping operation) just inline it in the computeOps
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// auto firstUserOp = *conv->getUsers().begin();
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// if (conv->hasOneUse()) {
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// mapOperation = mlirOpToMapOperationEnum(firstUserOp);
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//
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// if (mapOperation == MapOperations::ONNXSoftmaxOp) {
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// return rewriter.notifyMatchFailure(
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// conv, "Softmax not supported as activation for convolutions.");
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// }
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// }
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size_t input_h = GET_IMAGE_HEIGHT(xShape);
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size_t input_w = GET_IMAGE_WIDTH(xShape);
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size_t output_h = GET_IMAGE_HEIGHT(yShape);
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size_t output_w = GET_IMAGE_WIDTH(yShape);
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size_t krn_h = GET_KERNEL_HEIGHT(wShape);
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size_t krn_w = GET_KERNEL_WIDTH(wShape);
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Location loc = conv.getLoc();
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size_t inputTileCount = ceilIntegerDivide(GET_IMAGE_CHANNEL(xShape), crossbarSize.getValue());
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size_t inputTileRemainder = GET_IMAGE_CHANNEL(xShape) % crossbarSize;
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size_t outputTileCount = ceilIntegerDivide(GET_IMAGE_CHANNEL(yShape), crossbarSize.getValue());
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size_t outputTileRemainder = GET_IMAGE_CHANNEL(yShape) % crossbarSize;
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// Tile the input tensor
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// Input tiles need to be indexed by:
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// a. Channel Tile
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// b. Pixel `x` position
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// c. Pixel `y` position
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// For example: inputTiles[channelTile][x][y]
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// Example complete input tensor: tensor<1x3x6x6xf32> (NxCxWxH)
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SmallVector<SmallVector<SmallVector<Value>>> inputTiles(
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inputTileCount, SmallVector<SmallVector<Value>>(input_w, SmallVector<Value>(input_h)));
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auto resolveErrorOpt = resolveImgInputTiles(
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convAdaptor.getX(), inputTiles, inputTileCount, inputTileRemainder, input_h, input_h, rewriter);
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if (resolveErrorOpt.has_value())
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return rewriter.notifyMatchFailure(conv, *resolveErrorOpt);
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SmallVector<OpFoldResult> strides = SmallVector<OpFoldResult>(4, rewriter.getIndexAttr(1));
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SmallVector<OpFoldResult> offsets = SmallVector<OpFoldResult>(4, rewriter.getIndexAttr(0));
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SmallVector<OpFoldResult> sizes = SmallVector<OpFoldResult> {rewriter.getIndexAttr(1),
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rewriter.getIndexAttr(crossbarSize),
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rewriter.getIndexAttr(1),
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rewriter.getIndexAttr(1)};
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// Tile the weight tensor
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// Weight tiles need to be indexed by:
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// a. Filter Tile
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// b. Channel Tile
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// c. Kernel `x` position
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// d. Kernel `y` position
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// For example: weightTiles[filterTile][channelTile][x][y]
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// Example complete weight tensor: tensor<32x3x3x3xf32> (FxCxWxH)
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SmallVector<SmallVector<SmallVector<SmallVector<Value>>>> weightTiles(
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outputTileCount,
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SmallVector<SmallVector<SmallVector<Value>>>(inputTileCount,
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SmallVector<SmallVector<Value>>(krn_w, SmallVector<Value>(krn_h))));
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strides = SmallVector<OpFoldResult>(4, rewriter.getIndexAttr(1));
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offsets = SmallVector<OpFoldResult>(4, rewriter.getIndexAttr(0));
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sizes = {rewriter.getIndexAttr(crossbarSize),
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rewriter.getIndexAttr(crossbarSize),
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rewriter.getIndexAttr(1),
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rewriter.getIndexAttr(1)};
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for (size_t i = 0; i < outputTileCount; i++) {
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if (i == outputTileCount - 1 && outputTileRemainder != 0)
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sizes[0] = rewriter.getIndexAttr(outputTileRemainder);
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sizes[1] = rewriter.getIndexAttr(crossbarSize);
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offsets[0] = rewriter.getIndexAttr(i * crossbarSize);
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for (size_t j = 0; j < inputTileCount; j++) {
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if (j == inputTileCount - 1 && inputTileRemainder != 0)
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sizes[1] = rewriter.getIndexAttr(inputTileRemainder);
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for (size_t x = 0; x < krn_w; x++) {
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for (size_t y = 0; y < krn_h; y++) {
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offsets[1] = rewriter.getIndexAttr(j * crossbarSize);
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offsets[2] = rewriter.getIndexAttr(x);
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offsets[3] = rewriter.getIndexAttr(y);
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weightTiles[i][j][x][y] =
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rewriter.create<tensor::ExtractSliceOp>(loc, convAdaptor.getW(), offsets, sizes, strides);
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}
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}
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}
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}
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/* Distribute the computation among many compute cores
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* Try to compute in-core the computation for each output tile, and reduce
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* over as few cores as possible
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*/
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// Tile the output tensor
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// Output tiles need to be indexed by:
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// a. Filter Tile
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// b. Pixel `x` position
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// c. Pixel `y` position
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// For example: outputTiles[filterTile][x][y]
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// Example complete output tensor: tensor<1x32x3x3xf32> (NxFxWxH)
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SmallVector<SmallVector<SmallVector<shared_ptr<Value>>>> outputTiles(
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outputTileCount,
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SmallVector<SmallVector<shared_ptr<Value>>>(output_w, SmallVector<shared_ptr<Value>>(output_h, nullptr)));
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size_t replicationFactor;
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if (!conv->hasAttr(REPLICATION_ATTR_NAME))
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replicationFactor = 1;
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else
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replicationFactor = conv->getAttrOfType<IntegerAttr>(REPLICATION_ATTR_NAME).getInt();
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// producers[outTile][out_x][out_y][producerIndex]
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vector<vector<vector<vector<Producer_t>>>> producers = vector<vector<vector<vector<Producer_t>>>>(
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outputTileCount,
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vector<vector<vector<Producer_t>>>(output_w, vector<vector<Producer_t>>(output_h, vector<Producer_t>())));
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// Schedule in cores
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size_t coreId = 0;
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vector<shared_ptr<Core>> curCores(replicationFactor);
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for (size_t i = 0; i < replicationFactor; i++)
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curCores[i] = make_shared<Core>(coreId++, rewriter);
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vector<shared_ptr<Core>> cores;
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const size_t replicationSliceSize = ceilIntegerDivide(input_w, replicationFactor);
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for (size_t krn_x = 0; krn_x < krn_h; krn_x++) {
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for (size_t krn_y = 0; krn_y < krn_w; krn_y++) {
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RankedTensorType mvmOutType =
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RankedTensorType::get({1, static_cast<long>(crossbarSize), 1, 1}, bShape.getElementType());
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for (size_t outTile = 0; outTile < outputTileCount; outTile++) {
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if (outTile == outputTileCount - 1 && outputTileRemainder != 0)
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mvmOutType = mvmOutType.clone({1, static_cast<long>(outputTileRemainder), 1, 1});
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for (size_t inTile = 0; inTile < inputTileCount; inTile++) {
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vector<size_t> xbarIndexes(replicationFactor);
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for (size_t i = 0; i < replicationFactor; i++)
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xbarIndexes[i] = curCores[i]->addXbarWeight(weightTiles[outTile][inTile][krn_x][krn_y]);
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size_t out_x = 0;
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for (size_t in_x = 0; in_x < input_w; in_x += stride_x) {
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size_t out_y = 0;
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// I use `replicationFactor` cores. I divide the input_w into
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// `replicationFactor` slices, and each slice is distributed to a
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// core. `coreIndex` is the index of the core that will be used
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// for this slice
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size_t coreIndex = in_x / replicationSliceSize;
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assert(coreIndex < replicationFactor);
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for (size_t in_y = 0; in_y < input_h; in_y += stride_y) {
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// Adjust the input based on the kernel
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int actual_in_x = in_x - ((int) krn_w / 2) + krn_x * dilation_x;
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int actual_in_y = in_y - ((int) krn_h / 2) + krn_y * dilation_y;
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// Check if we are within the input image
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if (verifyWithinBoundsAndPaddings(input_w, input_h, actual_in_x, actual_in_y, pad_x, pad_y).failed()) {
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out_y++;
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continue;
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}
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size_t outTileId = outTile * output_w * output_h + out_x * output_h + out_y;
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auto mvm = curCores[coreIndex]->addMVM(
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inputTiles[inTile][actual_in_x][actual_in_y], xbarIndexes[coreIndex], outTileId, mvmOutType);
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producers[outTile][out_x][out_y].push_back({mvm, curCores[coreIndex]});
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out_y++;
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}
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out_x++;
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}
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// Computations for these crossbars are done, check if the cores
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// crossbars are fully used. If full, swap with new core
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for (size_t i = 0; i < replicationFactor; i++) {
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if (curCores[i]->isXbarsFull()) {
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cores.emplace_back(std::move(curCores[i]));
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curCores[i] = make_shared<Core>(coreId++, rewriter);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
for (auto& curCore : curCores)
|
|
if (curCore->isCoreEmpty() == false)
|
|
cores.emplace_back(std::move(curCore));
|
|
curCores.clear();
|
|
// Now, do the reduction of each output pixel tile
|
|
for (size_t outTile = 0; outTile < outputTileCount; outTile++) {
|
|
for (size_t out_x = 0; out_x < output_w; out_x++) {
|
|
for (size_t out_y = 0; out_y < output_h; out_y++) {
|
|
// First, check if some producers are within the same core. If this is
|
|
// true, `Core::addMVM` have already done the reduction within-core.
|
|
// This means that we only need to consider the last producer for that
|
|
// core.
|
|
|
|
std::unordered_map<size_t, Producer_t> withinCoreReducedProducers;
|
|
for (auto producer : producers[outTile][out_x][out_y])
|
|
withinCoreReducedProducers[producer.core->coreId] = producer;
|
|
|
|
// Now, we need to apply inter-core reduction
|
|
|
|
// Base case with one producer
|
|
if (withinCoreReducedProducers.size() == 1) {
|
|
// TODO: Add the bias and apply mapping (if present)
|
|
|
|
auto singleProducer = withinCoreReducedProducers.begin()->second;
|
|
// Use last producer as the final result
|
|
auto reducedValue = singleProducer.core->makeResultRemappable(singleProducer.value);
|
|
outputTiles[outTile][out_x][out_y] = reducedValue;
|
|
continue;
|
|
}
|
|
|
|
// TODO: This is a linear reduction, not a tree reduction. We can do
|
|
// better: a tree reduction would make more computations happen in
|
|
// parallel.
|
|
|
|
Producer_t lastProducer = withinCoreReducedProducers.begin()->second;
|
|
|
|
auto it = withinCoreReducedProducers.begin();
|
|
it++;
|
|
while (it != withinCoreReducedProducers.end()) {
|
|
|
|
Producer_t curProducer = it->second;
|
|
|
|
shared_ptr<Core> core1;
|
|
shared_ptr<Core> core2;
|
|
Value core1Value;
|
|
Value core2Value;
|
|
|
|
auto lastProducerCoreId = lastProducer.core->coreId;
|
|
auto curProducerCoreId = curProducer.core->coreId;
|
|
|
|
assert(lastProducerCoreId != curProducerCoreId
|
|
&& "We should have already applied within-core reduction, how "
|
|
"could we have same cores here?");
|
|
|
|
// Sort the cores by coreId
|
|
if (curProducerCoreId < lastProducerCoreId) {
|
|
core1 = curProducer.core;
|
|
core1Value = curProducer.value;
|
|
core2 = lastProducer.core;
|
|
core2Value = lastProducer.value;
|
|
}
|
|
else {
|
|
core1 = lastProducer.core;
|
|
core1Value = lastProducer.value;
|
|
core2 = curProducer.core;
|
|
core2Value = curProducer.value;
|
|
}
|
|
|
|
auto newCoreRes = core1->makeResultRemappable(core1Value);
|
|
auto secondCoreBlockArg = core2->addRemappableOperand(newCoreRes);
|
|
|
|
rewriter.setInsertionPointAfterValue(core2Value);
|
|
Value vaddRes = rewriter.create<spatial::SpatVAddOp>(
|
|
core2Value.getLoc(), core2Value.getType(), core2Value, secondCoreBlockArg);
|
|
|
|
lastProducer = {vaddRes, core2};
|
|
|
|
it++;
|
|
}
|
|
|
|
// TODO: Add the bias and apply mapping (if present)
|
|
|
|
// Use last producer as the final result
|
|
auto reducedValue = lastProducer.core->makeResultRemappable(lastProducer.value);
|
|
outputTiles[outTile][out_x][out_y] = reducedValue;
|
|
}
|
|
}
|
|
}
|
|
|
|
// Now, we need to turn the cores into a spatial::SpatWeightedCompute.
|
|
rewriter.setInsertionPointAfter(conv);
|
|
spatial::SpatWeightedCompute lastWComputeOp;
|
|
for (auto& core : cores) {
|
|
lastWComputeOp = core->createWComputeOp(loc);
|
|
core->remapResults();
|
|
rewriter.setInsertionPointAfter(lastWComputeOp);
|
|
}
|
|
|
|
for (auto& core : cores)
|
|
core->addRemappedOperands();
|
|
|
|
// Set the insertion point after the last WComputeOp.
|
|
rewriter.setInsertionPointAfter(lastWComputeOp);
|
|
SmallVector<Value> tilesToConcat;
|
|
tilesToConcat.reserve(output_h * output_w * outputTileCount * crossbarSize);
|
|
for (size_t outX = 0; outX < output_h; outX++)
|
|
for (size_t outY = 0; outY < output_w; outY++)
|
|
for (size_t outTile = 0; outTile < outputTileCount; outTile++)
|
|
tilesToConcat.push_back(*outputTiles[outTile][outX][outY]);
|
|
|
|
Value outputImage = rewriter.create<spatial::SpatImgConcatOp>(loc, conv.getY().getType(), tilesToConcat);
|
|
|
|
// Value outputImage =
|
|
// createImgConcatOp(outputTiles, rewriter, loc, Y.getType());
|
|
|
|
// If no mapping (activation) was applied, just replace ConvOp
|
|
// if (mapOperation == MapOperations::None) {
|
|
// rewriter.replaceOp(conv, outputImage);
|
|
// } else {
|
|
// // If mapping was applied, erase ConvOp and replace the mapping op
|
|
// rewriter.eraseOp(conv);
|
|
// rewriter.replaceOp(firstUserOp, outputImage);
|
|
// }
|
|
|
|
return success();
|
|
}
|
|
};
|
|
|
|
void populateTilingConvOpPattern(RewritePatternSet& patterns, MLIRContext* ctx) {
|
|
patterns.insert<ONNXConvOpTile>(ctx);
|
|
}
|
|
|
|
} // namespace onnx_mlir
|