fix mergeTriviallyConnectedComputes

moderate refactor

src/PIM/Compiler/PimCompilerUtils.cpp

@@ -33,7 +33,7 @@ void addPassesPim(OwningOpRef<ModuleOp>& module,
   }
 
   if (pimEmissionTarget >= EmitPim) {
-    pm.addPass(createMergeComputeNodePass());
+    pm.addPass(createMergeComputeNodesPass());
     pm.addPass(createSpatialToPimPass());
     // pm.addPass(createCountInstructionPass());
     pm.addPass(createMessagePass("Spatial lowered to Pim"));
@@ -46,9 +46,9 @@ void addPassesPim(OwningOpRef<ModuleOp>& module,
   }
 
   if (pimEmissionTarget >= EmitPimCodegen) {
-    pm.addPass(createPimConstantFoldingPass());
-    pm.addPass(createMessagePass("Pim constants folded"));
-    pm.addPass(createPimMaterializeConstantsPass());
+    pm.addPass(createPimHostConstantFoldingPass());
+    pm.addPass(createMessagePass("Pim host constants folded"));
+    pm.addPass(createPimMaterializeHostConstantsPass());
     pm.addPass(createPimVerificationPass());
     pm.addPass(createMessagePass("Pim verified"));
     pm.addPass(createEmitPimJsonPass());

src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp

@@ -8,6 +8,7 @@
 #include "mlir/Transforms/Passes.h"
 
 #include "llvm/ADT/STLExtras.h"
+#include "llvm/ADT/SmallSet.h"
 #include "llvm/ADT/SmallVector.h"
 #include "llvm/Support/Casting.h"
 #include "llvm/Support/Debug.h"
@@ -22,8 +23,8 @@
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
-#include "src/Accelerators/PIM/Dialect/Spatial/DCPGraph/DCPAnalysis.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/DCPGraph/DCPAnalysis.hpp"
 #include "src/Accelerators/PIM/Pass/PIMPasses.h"
 #include "src/Compiler/CompilerOptions.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
@@ -51,7 +52,7 @@ struct ONNXToSpatialPass : PassWrapper<ONNXToSpatialPass, OperationPass<ModuleOp
 private:
   void annotateWeightsConstants(func::FuncOp funcOp) const;
   void encapsulateGlobalInstruction(func::FuncOp funcOp);
-  void mergeSingleChildCompute(func::FuncOp funcOp);
+  void mergeTriviallyConnectedComputes(func::FuncOp funcOp);
 };
 
 } // namespace
@@ -148,10 +149,10 @@ void ONNXToSpatialPass::runOnOperation() {
 
   annotateWeightsConstants(*entryFunc);
   encapsulateGlobalInstruction(*entryFunc);
-  mergeSingleChildCompute(*entryFunc);
+  mergeTriviallyConnectedComputes(*entryFunc);
 
   // Dump to file for debug
-  dumpModule(moduleOp, "spatial");
+  dumpModule(moduleOp, "spatial0");
 }
 
 template <typename T>
@@ -230,66 +231,61 @@ void ONNXToSpatialPass::encapsulateGlobalInstruction(func::FuncOp funcOp) {
   }
 }
 
-void ONNXToSpatialPass::mergeSingleChildCompute(func::FuncOp funcOp) {
-  llvm::SmallVector<spatial::SpatWeightedCompute> computeSingleChild;
+void ONNXToSpatialPass::mergeTriviallyConnectedComputes(func::FuncOp funcOp) {
   Location loc = funcOp.getLoc();
   IRRewriter rewriter(&getContext());
+  SmallVector<spatial::SpatWeightedCompute> trivialComputes;
+  llvm::SmallSet<spatial::SpatWeightedCompute, 8> toErase;
+
   for (auto compute : funcOp.getOps<spatial::SpatWeightedCompute>())
-    if (std::distance(compute->getUses().begin(), compute->getUses().end()) == 1) {
+    if (compute->hasOneUse()) {
       auto user = *compute->getUsers().begin();
-      if (user->getNumOperands() == 1)
-        if (llvm::isa<spatial::SpatWeightedCompute>(user))
-          computeSingleChild.push_back(compute);
+      if (llvm::isa<spatial::SpatWeightedCompute>(user) && user->getNumOperands() == 1)
+        trivialComputes.push_back(compute);
     }
 
-  IRMapping mapper;
-  while (!computeSingleChild.empty()) {
-    auto compute = computeSingleChild.front();
-    auto child = dyn_cast_if_present<spatial::SpatWeightedCompute>(*compute->getUsers().begin());
-    assert(child && "Child required!");
+  while (!trivialComputes.empty()) {
+    auto compute = trivialComputes.front();
+
+    if (compute.use_empty()) {
+      std::swap(trivialComputes.front(), trivialComputes.back());
+      trivialComputes.pop_back();
+      continue;
+    }
+    auto child = cast<spatial::SpatWeightedCompute>(*compute->getUsers().begin());
+
     rewriter.setInsertionPointAfter(compute.getOperation());
     auto newCompute =
       spatial::SpatWeightedCompute::create(rewriter, loc, child.getResultTypes(), compute.getOperands());
     newCompute.getProperties().setOperandSegmentSizes(
-      {(int) compute.getWeights().size(), (int) compute.getInputs().size()});
-    llvm::dbgs() << "After Creation\n";
-    newCompute.dump();
+      {static_cast<int>(compute.getWeights().size()), static_cast<int>(compute.getInputs().size())});
 
+    IRMapping mapper;
     compute.getBodyRegion().cloneInto(&newCompute.getBodyRegion(), mapper);
-    llvm::dbgs() << "After Clone\n";
-    newCompute.dump();
     auto newTerminator = newCompute.getBody().front().getTerminator();
     mapper.map(*child.getBody().front().getArguments().begin(), newTerminator->getOperand(0));
     newTerminator->erase();
-    llvm::dbgs() << "After terminator\n";
-    newCompute.dump();
     rewriter.setInsertionPoint(&newCompute.getBody().front(), newCompute.getBody().front().end());
-
     for (auto& op : child.getBody().front())
       rewriter.clone(op, mapper);
 
     child.replaceAllUsesWith(newCompute);
-    assert(child->getUses().empty() && "It's not obvius");
-    llvm::dbgs() << "Node\n";
-    newCompute.dump();
+    toErase.insert(child);
 
-    llvm::dbgs() << "Parent\n";
-    compute.dump();
+    std::swap(trivialComputes.front(), trivialComputes.back());
+    trivialComputes.pop_back();
+    toErase.insert(compute);
 
-    llvm::dbgs() << "Child\n";
-    child.dump();
-
-    child.erase();
-    compute.erase();
-
-    if (std::distance(newCompute->getUses().begin(), newCompute->getUses().end()) == 1) {
+    if (newCompute->hasOneUse()) {
       auto user = *newCompute->getUsers().begin();
-      if (user->getNumOperands() == 1)
-        if (llvm::isa<spatial::SpatWeightedCompute>(user))
-          computeSingleChild.push_back(newCompute);
+      if (llvm::isa<spatial::SpatWeightedCompute>(user) && user->getNumOperands() == 1)
+        trivialComputes.push_back(newCompute);
     }
-    std::swap(computeSingleChild.front(), computeSingleChild.back());
-    computeSingleChild.pop_back();
+  }
+
+  for (auto compute : toErase) {
+    compute.getResult(0).dropAllUses();
+    compute.erase();
   }
 }
 

src/PIM/Conversion/ONNXToSpatial/Patterns/Math/Conv.cpp

@@ -1,12 +1,16 @@
 #include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/Dialect/SCF/IR/SCF.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/IR/BuiltinAttributes.h"
 #include "mlir/IR/BuiltinTypes.h"
 
 #include "llvm/ADT/SmallVector.h"
 
+#include <algorithm>
 #include <cassert>
 
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
@@ -24,122 +28,150 @@ struct ConvToGemm : OpConversionPattern<ONNXConvOp> {
                                 ConversionPatternRewriter& rewriter) const override;
 };
 
-} // namespace
+static DenseElementsAttr getDenseConstantAttr(Value value) {
+  if (auto constantOp = value.getDefiningOp<arith::ConstantOp>())
+    return dyn_cast<DenseElementsAttr>(constantOp.getValue());
 
-LogicalResult ConvToGemm::matchAndRewrite(ONNXConvOp convOp,
-                                          ONNXConvOpAdaptor convOpAdaptor,
-                                          ConversionPatternRewriter& rewriter) const {
-  Location loc = convOp.getLoc();
-  Value x = convOpAdaptor.getX();
-  Value w = convOpAdaptor.getW();
-  Value b = convOpAdaptor.getB();
+  if (auto constantOp = value.getDefiningOp<ONNXConstantOp>())
+    return dyn_cast_or_null<DenseElementsAttr>(constantOp.getValueAttr());
 
-  auto xType = cast<RankedTensorType>(x.getType());
-  auto wType = cast<RankedTensorType>(w.getType());
-  auto outType = cast<RankedTensorType>(convOp.getY().getType());
+  return nullptr;
+}
 
-  assert("Only support static shapes" && xType.hasStaticShape() && wType.hasStaticShape() && outType.hasStaticShape());
-  assert("Only support 2D convolution" && xType.getRank() == 4);
+static int64_t getI64FromArrayAttr(ArrayAttr arr, size_t idx) { return cast<IntegerAttr>(arr[idx]).getInt(); }
 
-  // We need to understand what is group
-  assert("Only support group=1" && convOp.getGroup() == 1);
+static Value expandBiasIfNeeded(Value bias, ConversionPatternRewriter& rewriter, Location loc) {
+  auto biasType = cast<RankedTensorType>(bias.getType());
+  if (biasType.getRank() != 1)
+    return bias;
 
-  const int64_t batchSize = xType.getDimSize(0);
-  const int64_t numChannelsIn = xType.getDimSize(1);
-  const int64_t xHeight = xType.getDimSize(2);
-  const int64_t xWidth = xType.getDimSize(3);
-  const int64_t numChannelsOut = wType.getDimSize(0);
-  const int64_t wHeight = wType.getDimSize(2);
-  const int64_t wWidth = wType.getDimSize(3);
-  const int64_t outHeight = outType.getDimSize(2);
-  const int64_t outWidth = outType.getDimSize(3);
+  auto expandedBiasType = RankedTensorType::get({1, biasType.getDimSize(0)}, biasType.getElementType());
+  return tensor::ExpandShapeOp::create(rewriter,
+                                       loc,
+                                       expandedBiasType,
+                                       bias,
+                                       SmallVector<ReassociationIndices> {
+                                         {0, 1}
+  });
+}
 
-  // Read optional conv attributes (ONNX defaults: stride=1, dilation=1, pad=0)
-  auto getI64 = [](ArrayAttr arr, size_t idx) -> int64_t { return cast<IntegerAttr>(arr[idx]).getInt(); };
+static Value createPaddedRows(Value tensorValue,
+                              RankedTensorType tensorType,
+                              int64_t paddedRows,
+                              ConversionPatternRewriter& rewriter,
+                              Location loc) {
+  if (tensorType.getDimSize(0) == paddedRows)
+    return tensorValue;
 
-  const auto stridesAttr = convOp.getStrides();
-  const auto dilationsAttr = convOp.getDilations();
-  const auto padsAttr = convOp.getPads();
+  auto paddedType = RankedTensorType::get({paddedRows, tensorType.getDimSize(1)}, tensorType.getElementType());
+  SmallVector<OpFoldResult> lowPads = {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
+  SmallVector<OpFoldResult> highPads = {rewriter.getIndexAttr(paddedRows - tensorType.getDimSize(0)),
+                                        rewriter.getIndexAttr(0)};
+  auto padOp = tensor::PadOp::create(rewriter, loc, paddedType, tensorValue, lowPads, highPads);
+  auto* padBlock = new Block();
+  for (int i = 0; i < 2; i++)
+    padBlock->addArgument(rewriter.getIndexType(), loc);
+  padOp.getRegion().push_back(padBlock);
+  rewriter.setInsertionPointToStart(padBlock);
+  auto zero = arith::ConstantOp::create(
+    rewriter, loc, tensorType.getElementType(), rewriter.getZeroAttr(tensorType.getElementType()));
+  tensor::YieldOp::create(rewriter, loc, zero.getResult());
+  rewriter.setInsertionPointAfter(padOp);
+  return padOp.getResult();
+}
 
-  const int64_t strideHeight = stridesAttr ? getI64(*stridesAttr, 0) : 1;
-  const int64_t strideWidth = stridesAttr ? getI64(*stridesAttr, 1) : 1;
-  const int64_t dilationHeight = dilationsAttr ? getI64(*dilationsAttr, 0) : 1;
-  const int64_t dilationWidth = dilationsAttr ? getI64(*dilationsAttr, 1) : 1;
+static Value buildPackedWeight(DenseElementsAttr wDenseAttr,
+                               Value wTrans,
+                               RankedTensorType wType,
+                               int64_t numChannelsIn,
+                               int64_t numChannelsOut,
+                               int64_t wHeight,
+                               int64_t wWidth,
+                               int64_t patchSize,
+                               int64_t packFactor,
+                               ConversionPatternRewriter& rewriter,
+                               Location loc) {
+  if (packFactor == 1)
+    return wTrans;
 
-  int64_t padHeightBegin = 0;
-  int64_t padHeightEnd = 0;
-  int64_t padWidthBegin = 0;
-  int64_t padWidthEnd = 0;
+  auto packedWeightType =
+    RankedTensorType::get({packFactor * patchSize, packFactor * numChannelsOut}, wType.getElementType());
+  SmallVector<Attribute> sourceValues(wDenseAttr.getValues<Attribute>());
+  SmallVector<Attribute> packedValues(packedWeightType.getNumElements(),
+                                      cast<Attribute>(rewriter.getZeroAttr(wType.getElementType())));
 
-  if (padsAttr) {
-    padHeightBegin = getI64(*padsAttr, 0);
-    padWidthBegin = getI64(*padsAttr, 1);
-    padHeightEnd = getI64(*padsAttr, 2);
-    padWidthEnd = getI64(*padsAttr, 3);
-  }
-  else {
-    // Compute padding from auto_pad attribute
-    const auto autoPad = convOp.getAutoPad();
-    if (autoPad == "SAME_UPPER" || autoPad == "SAME_LOWER") {
-      const int64_t effectiveKernelH = (wHeight - 1) * dilationHeight + 1;
-      const int64_t effectiveKernelW = (wWidth - 1) * dilationWidth + 1;
-      const int64_t totalPadH =
-        std::max(static_cast<int64_t>(0), (outHeight - 1) * strideHeight + effectiveKernelH - xHeight);
-      const int64_t totalPadW =
-        std::max(static_cast<int64_t>(0), (outWidth - 1) * strideWidth + effectiveKernelW - xWidth);
-
-      if (autoPad == "SAME_UPPER") {
-        padHeightBegin = totalPadH / 2;
-        padHeightEnd = totalPadH - padHeightBegin;
-        padWidthBegin = totalPadW / 2;
-        padWidthEnd = totalPadW - padWidthBegin;
-      }
-      else { // SAME_LOWER
-        padHeightEnd = totalPadH / 2;
-        padHeightBegin = totalPadH - padHeightEnd;
-        padWidthEnd = totalPadW / 2;
-        padWidthBegin = totalPadW - padWidthEnd;
+  for (int64_t copyId = 0; copyId < packFactor; copyId++) {
+    for (int64_t outChannel = 0; outChannel < numChannelsOut; outChannel++) {
+      for (int64_t inChannel = 0; inChannel < numChannelsIn; inChannel++) {
+        for (int64_t kernelH = 0; kernelH < wHeight; kernelH++) {
+          for (int64_t kernelW = 0; kernelW < wWidth; kernelW++) {
+            const int64_t sourceFlatIndex =
+              (((outChannel * numChannelsIn) + inChannel) * wHeight + kernelH) * wWidth + kernelW;
+            const int64_t patchIndex = ((inChannel * wHeight) + kernelH) * wWidth + kernelW;
+            const int64_t targetRow = copyId * patchSize + patchIndex;
+            const int64_t targetCol = copyId * numChannelsOut + outChannel;
+            packedValues[targetRow * (packFactor * numChannelsOut) + targetCol] = sourceValues[sourceFlatIndex];
+          }
+        }
       }
     }
-    // "NOTSET" or "VALID" -> all pads stay 0
   }
 
-  // im2col layout (flipped with respect to the standard, so filters sit in B = crossbar):
-  //   A (im2col):  [numPatches, patchSize]  -- one row per output spatial position
-  //   B (weights): [patchSize, cOut]        -- W^T, stored in crossbar columns
-  //   Gemm output: [numPatches, cOut]
-  const int64_t patchSize = numChannelsIn * wHeight * wWidth;
-  const int64_t numPatchesPerBatch = outHeight * outWidth;
-  const int64_t numPatches = batchSize * numPatchesPerBatch;
+  auto packedAttr = DenseElementsAttr::get(packedWeightType, packedValues);
+  return arith::ConstantOp::create(rewriter, loc, packedWeightType, packedAttr);
+}
 
+static Value buildPackedBias(bool hasBias,
+                             Value gemmBias,
+                             Value biasMatrix,
+                             DenseElementsAttr biasDenseAttr,
+                             RankedTensorType outType,
+                             int64_t numChannelsOut,
+                             int64_t packFactor,
+                             ConversionPatternRewriter& rewriter,
+                             Location loc) {
+  if (!hasBias)
+    return gemmBias;
+
+  if (packFactor == 1)
+    return biasMatrix;
+
+  SmallVector<Attribute> sourceValues(biasDenseAttr.getValues<Attribute>());
+  SmallVector<Attribute> packedValues;
+  packedValues.reserve(packFactor * numChannelsOut);
+  for (int64_t copyId = 0; copyId < packFactor; copyId++)
+    packedValues.append(sourceValues.begin(), sourceValues.end());
+
+  auto packedBiasType = RankedTensorType::get({1, packFactor * numChannelsOut}, outType.getElementType());
+  auto packedBiasAttr = DenseElementsAttr::get(packedBiasType, packedValues);
+  return arith::ConstantOp::create(rewriter, loc, packedBiasType, packedBiasAttr).getResult();
+}
+
+static Value createIm2colCompute(Value x,
+                                 RankedTensorType xType,
+                                 RankedTensorType im2colType,
+                                 RankedTensorType rowType,
+                                 int64_t batchSize,
+                                 int64_t numChannelsIn,
+                                 int64_t xHeight,
+                                 int64_t xWidth,
+                                 int64_t wHeight,
+                                 int64_t wWidth,
+                                 int64_t padHeightBegin,
+                                 int64_t padHeightEnd,
+                                 int64_t padWidthBegin,
+                                 int64_t padWidthEnd,
+                                 int64_t strideHeight,
+                                 int64_t strideWidth,
+                                 int64_t dilationHeight,
+                                 int64_t dilationWidth,
+                                 int64_t outWidth,
+                                 int64_t patchSize,
+                                 int64_t numPatches,
+                                 int64_t numPatchesPerBatch,
+                                 ConversionPatternRewriter& rewriter,
+                                 Location loc) {
   auto elemType = xType.getElementType();
-  auto im2colType = RankedTensorType::get({numPatches, patchSize}, elemType);
-  auto rowType = RankedTensorType::get({1, patchSize}, elemType);
-  auto wFlatType = RankedTensorType::get({numChannelsOut, patchSize}, wType.getElementType());
-  auto wTransType = RankedTensorType::get({patchSize, numChannelsOut}, wType.getElementType());
-  auto gemmOutType = RankedTensorType::get({numPatches, numChannelsOut}, outType.getElementType());
-  auto nhwcType = RankedTensorType::get({batchSize, outHeight, outWidth, numChannelsOut}, outType.getElementType());
-
-  // Prepare weight matrix W for crossbar storage:
-  // W: [numChannelsOut, numChannelsIn, wHeight, wWidth] -> [numChannelsOut, patchSize] -> [patchSize, numChannelsOut]
-  Value wFlat = tensor::CollapseShapeOp::create(rewriter,
-                                                loc,
-                                                wFlatType,
-                                                w,
-                                                SmallVector<ReassociationIndices> {
-                                                  {0},
-                                                  {1, 2, 3}
-  });
-  Value wTrans = ONNXTransposeOp::create(rewriter, loc, wTransType, wFlat, rewriter.getI64ArrayAttr({1, 0}));
-
-  // Pass bias through directly; Gemm handles rank-1 C canonicalization.
-  bool hasB = !isa<ONNXNoneOp>(b.getDefiningOp());
-  Value gemmC;
-  if (hasB)
-    gemmC = b;
-  else
-    gemmC = ONNXNoneOp::create(rewriter, loc, rewriter.getNoneType());
-
   constexpr size_t numInputs = 1;
   auto im2colComputeOp = createSpatCompute<numInputs>(rewriter, loc, im2colType, {}, x, [&](Value xArg) {
     Value paddedInput = xArg;
@@ -226,23 +258,104 @@ LogicalResult ConvToGemm::matchAndRewrite(ONNXConvOp convOp,
     Value im2col = im2colLoop.getResult(0);
     spatial::SpatYieldOp::create(rewriter, loc, im2col);
   });
+  return im2colComputeOp.getResult(0);
+}
 
-  // Gemm: A @ B + C = im2col @ W^T + b
-  // [numPatches, patchSize] @ [patchSize, numChannelsOut] + [1, numChannelsOut] -> [numPatches, numChannelsOut]
-  auto gemmOp = ONNXGemmOp::create(rewriter,
-                                   loc,
-                                   gemmOutType,
-                                   im2colComputeOp.getResult(0),
-                                   wTrans,
-                                   gemmC,
-                                   rewriter.getF32FloatAttr(1.0f),
-                                   rewriter.getF32FloatAttr(1.0f),
-                                   rewriter.getBoolAttr(false),
-                                   rewriter.getBoolAttr(false));
-  Value gemmOut = gemmOp.getY();
+static Value createPackedIm2colRows(Value im2col,
+                                    RankedTensorType im2colType,
+                                    Type elemType,
+                                    int64_t numPatches,
+                                    int64_t patchSize,
+                                    int64_t packFactor,
+                                    ConversionPatternRewriter& rewriter,
+                                    Location loc) {
+  if (packFactor == 1)
+    return im2col;
 
+  const int64_t packedNumRows = ceilIntegerDivide(numPatches, packFactor);
+  const int64_t paddedNumPatches = packedNumRows * packFactor;
+  auto groupedType = RankedTensorType::get({packedNumRows, packFactor, patchSize}, elemType);
+  auto packedType = RankedTensorType::get({packedNumRows, packFactor * patchSize}, elemType);
+  auto packedComputeOp = createSpatCompute<1>(rewriter, loc, packedType, {}, im2col, [&](Value im2colArg) {
+    Value paddedIm2col = createPaddedRows(im2colArg, im2colType, paddedNumPatches, rewriter, loc);
+    Value groupedIm2col = tensor::ExpandShapeOp::create(rewriter,
+                                                        loc,
+                                                        groupedType,
+                                                        paddedIm2col,
+                                                        SmallVector<ReassociationIndices> {
+                                                          {0, 1},
+                                                          {2}
+    });
+    Value packedIm2col = tensor::CollapseShapeOp::create(rewriter,
+                                                         loc,
+                                                         packedType,
+                                                         groupedIm2col,
+                                                         SmallVector<ReassociationIndices> {
+                                                           {0},
+                                                           {1, 2}
+    });
+    spatial::SpatYieldOp::create(rewriter, loc, packedIm2col);
+  });
+  return packedComputeOp.getResult(0);
+}
+
+static Value createUnpackedOutput(Value packedOutput,
+                                  RankedTensorType gemmOutType,
+                                  RankedTensorType outType,
+                                  int64_t numPatches,
+                                  int64_t numChannelsOut,
+                                  int64_t packFactor,
+                                  ConversionPatternRewriter& rewriter,
+                                  Location loc) {
+  if (packFactor == 1)
+    return packedOutput;
+
+  const int64_t packedNumRows = ceilIntegerDivide(numPatches, packFactor);
+  const int64_t paddedNumPatches = packedNumRows * packFactor;
+  auto expandedType = RankedTensorType::get({packedNumRows, packFactor, numChannelsOut}, outType.getElementType());
+  auto paddedType = RankedTensorType::get({paddedNumPatches, numChannelsOut}, outType.getElementType());
+  auto unpackComputeOp = createSpatCompute<1>(rewriter, loc, gemmOutType, {}, packedOutput, [&](Value packedOutputArg) {
+    Value expandedOutput = tensor::ExpandShapeOp::create(rewriter,
+                                                         loc,
+                                                         expandedType,
+                                                         packedOutputArg,
+                                                         SmallVector<ReassociationIndices> {
+                                                           {0},
+                                                           {1, 2}
+    });
+    Value paddedOutput = tensor::CollapseShapeOp::create(rewriter,
+                                                         loc,
+                                                         paddedType,
+                                                         expandedOutput,
+                                                         SmallVector<ReassociationIndices> {
+                                                           {0, 1},
+                                                           {2}
+    });
+
+    Value unpackedOutput = paddedOutput;
+    if (paddedNumPatches != numPatches) {
+      SmallVector<OpFoldResult> offsets = {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
+      SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(numPatches), rewriter.getIndexAttr(numChannelsOut)};
+      SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+      unpackedOutput =
+        tensor::ExtractSliceOp::create(rewriter, loc, gemmOutType, paddedOutput, offsets, sizes, strides);
+    }
+
+    spatial::SpatYieldOp::create(rewriter, loc, unpackedOutput);
+  });
+  return unpackComputeOp.getResult(0);
+}
+
+static Value createCollectedConvOutput(Value gemmOut,
+                                       Type convType,
+                                       RankedTensorType nhwcType,
+                                       RankedTensorType outType,
+                                       ConversionPatternRewriter& rewriter,
+                                       Location loc) {
   auto collectComputeOp =
-    createSpatCompute<numInputs>(rewriter, loc, convOp.getType(), {}, ValueRange {gemmOut}, [&](Value gemmOutArg) {
+    createSpatCompute(rewriter, loc, convType, {}, ValueRange {gemmOut}, [&](ValueRange gemmOutArgs) {
+      Value gemmOutArg = gemmOutArgs.front();
+
       // Restore to NCHW layout:
       // [numPatches, numChannelsOut]
       // -> [1, outHeight, outWidth, numChannelsOut]
@@ -256,11 +369,225 @@ LogicalResult ConvToGemm::matchAndRewrite(ONNXConvOp convOp,
                                                       {3}
       });
       Value nchwOut = ONNXTransposeOp::create(rewriter, loc, outType, nhwcOut, rewriter.getI64ArrayAttr({0, 3, 1, 2}));
-
       spatial::SpatYieldOp::create(rewriter, loc, nchwOut);
     });
+  return collectComputeOp.getResult(0);
+}
 
-  rewriter.replaceOp(convOp, collectComputeOp.getResult(0));
+} // namespace
+
+LogicalResult ConvToGemm::matchAndRewrite(ONNXConvOp convOp,
+                                          ONNXConvOpAdaptor convOpAdaptor,
+                                          ConversionPatternRewriter& rewriter) const {
+  Location loc = convOp.getLoc();
+  Value x = convOpAdaptor.getX();
+  Value w = convOpAdaptor.getW();
+  Value b = convOpAdaptor.getB();
+
+  auto xType = cast<RankedTensorType>(x.getType());
+  auto wType = cast<RankedTensorType>(w.getType());
+  auto outType = cast<RankedTensorType>(convOp.getY().getType());
+
+  assert("Only support static shapes" && xType.hasStaticShape() && wType.hasStaticShape() && outType.hasStaticShape());
+  assert("Only support 2D convolution" && xType.getRank() == 4);
+
+  // We need to understand what is group
+  assert("Only support group=1" && convOp.getGroup() == 1);
+
+  const int64_t batchSize = xType.getDimSize(0);
+  const int64_t numChannelsIn = xType.getDimSize(1);
+  const int64_t xHeight = xType.getDimSize(2);
+  const int64_t xWidth = xType.getDimSize(3);
+  const int64_t numChannelsOut = wType.getDimSize(0);
+  const int64_t wHeight = wType.getDimSize(2);
+  const int64_t wWidth = wType.getDimSize(3);
+  const int64_t outHeight = outType.getDimSize(2);
+  const int64_t outWidth = outType.getDimSize(3);
+
+  // Read optional conv attributes (ONNX defaults: stride=1, dilation=1, pad=0)
+  const auto stridesAttr = convOp.getStrides();
+  const auto dilationsAttr = convOp.getDilations();
+  const auto padsAttr = convOp.getPads();
+
+  const int64_t strideHeight = stridesAttr ? getI64FromArrayAttr(*stridesAttr, 0) : 1;
+  const int64_t strideWidth = stridesAttr ? getI64FromArrayAttr(*stridesAttr, 1) : 1;
+  const int64_t dilationHeight = dilationsAttr ? getI64FromArrayAttr(*dilationsAttr, 0) : 1;
+  const int64_t dilationWidth = dilationsAttr ? getI64FromArrayAttr(*dilationsAttr, 1) : 1;
+
+  int64_t padHeightBegin = 0;
+  int64_t padHeightEnd = 0;
+  int64_t padWidthBegin = 0;
+  int64_t padWidthEnd = 0;
+
+  if (padsAttr) {
+    padHeightBegin = getI64FromArrayAttr(*padsAttr, 0);
+    padWidthBegin = getI64FromArrayAttr(*padsAttr, 1);
+    padHeightEnd = getI64FromArrayAttr(*padsAttr, 2);
+    padWidthEnd = getI64FromArrayAttr(*padsAttr, 3);
+  }
+  else {
+    // Compute padding from auto_pad attribute
+    const auto autoPad = convOp.getAutoPad();
+    if (autoPad == "SAME_UPPER" || autoPad == "SAME_LOWER") {
+      const int64_t effectiveKernelH = (wHeight - 1) * dilationHeight + 1;
+      const int64_t effectiveKernelW = (wWidth - 1) * dilationWidth + 1;
+      const int64_t totalPadH =
+        std::max(static_cast<int64_t>(0), (outHeight - 1) * strideHeight + effectiveKernelH - xHeight);
+      const int64_t totalPadW =
+        std::max(static_cast<int64_t>(0), (outWidth - 1) * strideWidth + effectiveKernelW - xWidth);
+
+      if (autoPad == "SAME_UPPER") {
+        padHeightBegin = totalPadH / 2;
+        padHeightEnd = totalPadH - padHeightBegin;
+        padWidthBegin = totalPadW / 2;
+        padWidthEnd = totalPadW - padWidthBegin;
+      }
+      else { // SAME_LOWER
+        padHeightEnd = totalPadH / 2;
+        padHeightBegin = totalPadH - padHeightEnd;
+        padWidthEnd = totalPadW / 2;
+        padWidthBegin = totalPadW - padWidthEnd;
+      }
+    }
+    // "NOTSET" or "VALID" -> all pads stay 0
+  }
+
+  // im2col layout (flipped with respect to the standard, so filters sit in B = crossbar):
+  //   A (im2col):  [numPatches, patchSize]  -- one row per output spatial position
+  //   B (weights): [patchSize, cOut]        -- W^T, stored in crossbar columns
+  //   Gemm output: [numPatches, cOut]
+  const int64_t patchSize = numChannelsIn * wHeight * wWidth;
+  const int64_t numPatchesPerBatch = outHeight * outWidth;
+  const int64_t numPatches = batchSize * numPatchesPerBatch;
+
+  auto elemType = xType.getElementType();
+  auto im2colType = RankedTensorType::get({numPatches, patchSize}, elemType);
+  auto rowType = RankedTensorType::get({1, patchSize}, elemType);
+  auto wFlatType = RankedTensorType::get({numChannelsOut, patchSize}, wType.getElementType());
+  auto wTransType = RankedTensorType::get({patchSize, numChannelsOut}, wType.getElementType());
+  auto gemmOutType = RankedTensorType::get({numPatches, numChannelsOut}, outType.getElementType());
+  auto nhwcType = RankedTensorType::get({batchSize, outHeight, outWidth, numChannelsOut}, outType.getElementType());
+
+  const int64_t xbarSize = static_cast<int64_t>(crossbarSize.getValue());
+  const int64_t wMaxDim = std::max(patchSize, numChannelsOut);
+  const int64_t maxParallelPixels = std::max<int64_t>(1, xbarSize / wMaxDim);
+  auto wDenseAttr = getDenseConstantAttr(w);
+
+  // Prepare weight matrix W for crossbar storage:
+  // W: [numChannelsOut, numChannelsIn, wHeight, wWidth] -> [numChannelsOut, patchSize] -> [patchSize, numChannelsOut]
+  Value wFlat = tensor::CollapseShapeOp::create(rewriter,
+                                                loc,
+                                                wFlatType,
+                                                w,
+                                                SmallVector<ReassociationIndices> {
+                                                  {0},
+                                                  {1, 2, 3}
+  });
+  Value wTrans = ONNXTransposeOp::create(rewriter, loc, wTransType, wFlat, rewriter.getI64ArrayAttr({1, 0}));
+
+  // Pass bias through directly; Gemm handles rank-1 C canonicalization.
+  bool hasB = !isa<ONNXNoneOp>(b.getDefiningOp());
+  Value gemmC = ONNXNoneOp::create(rewriter, loc, rewriter.getNoneType());
+  Value biasMatrix;
+  DenseElementsAttr biasDenseAttr;
+  if (hasB) {
+    gemmC = b;
+    biasDenseAttr = getDenseConstantAttr(b);
+    biasMatrix = expandBiasIfNeeded(b, rewriter, loc);
+  }
+  const bool canPackWeightsAsConstants = static_cast<bool>(wDenseAttr);
+  const bool canPackBiasAsConstants = !hasB || static_cast<bool>(biasDenseAttr);
+  const int64_t effectiveMaxParallelPixels =
+    (canPackWeightsAsConstants && canPackBiasAsConstants) ? maxParallelPixels : 1;
+
+  Value im2col = createIm2colCompute(x,
+                                     xType,
+                                     im2colType,
+                                     rowType,
+                                     batchSize,
+                                     numChannelsIn,
+                                     xHeight,
+                                     xWidth,
+                                     wHeight,
+                                     wWidth,
+                                     padHeightBegin,
+                                     padHeightEnd,
+                                     padWidthBegin,
+                                     padWidthEnd,
+                                     strideHeight,
+                                     strideWidth,
+                                     dilationHeight,
+                                     dilationWidth,
+                                     outWidth,
+                                     patchSize,
+                                     numPatches,
+                                     numPatchesPerBatch,
+                                     rewriter,
+                                     loc);
+
+  Value gemmOut;
+  if (effectiveMaxParallelPixels == 1) {
+    // Fallback to the plain im2col GEMM when a single crossbar cannot fit multiple pixels.
+    gemmOut = ONNXGemmOp::create(rewriter,
+                                 loc,
+                                 gemmOutType,
+                                 im2col,
+                                 wTrans,
+                                 gemmC,
+                                 rewriter.getF32FloatAttr(1.0f),
+                                 rewriter.getF32FloatAttr(1.0f),
+                                 rewriter.getBoolAttr(false),
+                                 rewriter.getBoolAttr(false))
+                .getY();
+  }
+  else {
+    // Keep the standard im2col view of convolution:
+    //   A (im2col):  [numPatches, patchSize]  -- one row per output spatial position
+    //   B (weights): [patchSize, cOut]        -- W^T, stored in crossbar columns
+    // but repack several old rows into one new row so we use the available crossbar size better.
+    //
+    // We want to process N spatial pixels at the exact same time. Instead of doing N separate
+    // operations of (1 x patchSize) x (patchSize x cOut), we construct a block-diagonal weight matrix
+    // containing N copies of W^T and concatenate N im2col rows into one longer row:
+    //   A_packed: [ceil(numPatches / N), N * patchSize]
+    //   B_packed: [N * patchSize, N * cOut]
+    //   Y_packed: [ceil(numPatches / N), N * cOut]
+    // The downstream GemmToManyGemv pass still splits by row, but now there are fewer, longer rows.
+    const int64_t packedNumRows = ceilIntegerDivide(numPatches, effectiveMaxParallelPixels);
+    auto packedOutType =
+      RankedTensorType::get({packedNumRows, effectiveMaxParallelPixels * numChannelsOut}, outType.getElementType());
+
+    Value packedA = createPackedIm2colRows(
+      im2col, im2colType, elemType, numPatches, patchSize, effectiveMaxParallelPixels, rewriter, loc);
+    Value packedB = buildPackedWeight(wDenseAttr,
+                                      wTrans,
+                                      wType,
+                                      numChannelsIn,
+                                      numChannelsOut,
+                                      wHeight,
+                                      wWidth,
+                                      patchSize,
+                                      effectiveMaxParallelPixels,
+                                      rewriter,
+                                      loc);
+    Value packedC = buildPackedBias(
+      hasB, gemmC, biasMatrix, biasDenseAttr, outType, numChannelsOut, effectiveMaxParallelPixels, rewriter, loc);
+    Value packedOut = ONNXGemmOp::create(rewriter,
+                                         loc,
+                                         packedOutType,
+                                         packedA,
+                                         packedB,
+                                         packedC,
+                                         rewriter.getF32FloatAttr(1.0f),
+                                         rewriter.getF32FloatAttr(1.0f),
+                                         rewriter.getBoolAttr(false),
+                                         rewriter.getBoolAttr(false))
+                        .getY();
+    gemmOut = createUnpackedOutput(
+      packedOut, gemmOutType, outType, numPatches, numChannelsOut, effectiveMaxParallelPixels, rewriter, loc);
+  }
+
+  rewriter.replaceOp(convOp, createCollectedConvOutput(gemmOut, convOp.getType(), nhwcType, outType, rewriter, loc));
   return success();
 }
 

src/PIM/Conversion/SpatialToPim/Common.cpp

@@ -1,17 +1,29 @@
 #include "mlir/IR/ValueRange.h"
 
 #include "llvm/ADT/STLExtras.h"
+#include "llvm/ADT/StringRef.h"
 
 #include <cassert>
 #include <cstddef>
 
 #include "Common.hpp"
+#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
 using namespace llvm;
 using namespace mlir;
 
 namespace onnx_mlir {
 
+namespace {
+
+IntegerAttr getRequiredI32Attr(Builder& builder, Operation* op, llvm::StringRef attrName) {
+  auto attr = op->getAttrOfType<IntegerAttr>(attrName);
+  assert(attr && "required precomputed channel attr is missing");
+  return IntegerAttr::get(builder.getI32Type(), attr.getInt());
+}
+
+} // namespace
+
 size_t getSliceActualOffset(tensor::ExtractSliceOp& sliceOp, ShapedType& inputShape) {
   /*
     EXAMPLE RUN:
@@ -54,6 +66,45 @@ size_t getSliceActualOffset(tensor::ExtractSliceOp& sliceOp, ShapedType& inputSh
   return returnValue;
 }
 
+size_t getShapedTypeSizeInBytes(ShapedType shapedType) {
+  return shapedType.getNumElements() * shapedType.getElementTypeBitWidth() / 8;
+}
+
+IntegerAttr getTensorSizeInBytesAttr(Builder& builder, mlir::Value value) {
+  return builder.getI32IntegerAttr(static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(value.getType()))));
+}
+
+IntegerAttr getSpatialChannelSourceCoreIdAttr(Builder& builder, mlir::Value channel) {
+  auto channelNewOp = channel.getDefiningOp<spatial::SpatChannelNewOp>();
+  assert(channelNewOp && "spatial channel value must come from spat.channel_new");
+  return getRequiredI32Attr(builder, channelNewOp, kChannelSourceCoreIdAttrName);
+}
+
+IntegerAttr getSpatialChannelTargetCoreIdAttr(Builder& builder, mlir::Value channel) {
+  auto channelNewOp = channel.getDefiningOp<spatial::SpatChannelNewOp>();
+  assert(channelNewOp && "spatial channel value must come from spat.channel_new");
+  return getRequiredI32Attr(builder, channelNewOp, kChannelTargetCoreIdAttrName);
+}
+
+bool hasSpatialChannelSourceCoreIdAttr(mlir::Value channel) {
+  auto channelNewOp = channel.getDefiningOp<spatial::SpatChannelNewOp>();
+  return channelNewOp && channelNewOp->hasAttr(kChannelSourceCoreIdAttrName);
+}
+
+bool hasSpatialChannelTargetCoreIdAttr(mlir::Value channel) {
+  auto channelNewOp = channel.getDefiningOp<spatial::SpatChannelNewOp>();
+  return channelNewOp && channelNewOp->hasAttr(kChannelTargetCoreIdAttrName);
+}
+
+mlir::Value createPimReceiveFromSpatialChannel(
+  PatternRewriter& rewriter, Location loc, mlir::Value output, mlir::Value channel) {
+  mlir::Value outputBuffer = getBestOutputTensorFromOperandsOrAllocate(rewriter, output.getDefiningOp());
+  auto sizeAttr = getTensorSizeInBytesAttr(rewriter, output);
+  auto sourceCoreIdAttr = getSpatialChannelSourceCoreIdAttr(rewriter, channel);
+  return pim::PimReceiveOp::create(rewriter, loc, outputBuffer.getType(), outputBuffer, sizeAttr, sourceCoreIdAttr)
+    .getOutput();
+}
+
 Operation* getEarliestUserWithinBlock(mlir::Value value) {
   auto users = value.getUsers();
 

src/PIM/Conversion/SpatialToPim/Common.hpp

@@ -2,11 +2,16 @@
 
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 
+#include "llvm/ADT/StringRef.h"
+
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
 namespace onnx_mlir {
 
+inline constexpr llvm::StringLiteral kChannelSourceCoreIdAttrName = "precomp_source_core_id";
+inline constexpr llvm::StringLiteral kChannelTargetCoreIdAttrName = "precomp_target_core_id";
+
 /**
  * \brief Get the offset of the ExtractSliceOp based on its static offsets and
  * its static tensor input.
@@ -21,6 +26,21 @@ namespace onnx_mlir {
  */
 size_t getSliceActualOffset(mlir::tensor::ExtractSliceOp& sliceOp, mlir::ShapedType& inputShape);
 
+size_t getShapedTypeSizeInBytes(mlir::ShapedType shapedType);
+
+mlir::IntegerAttr getTensorSizeInBytesAttr(mlir::Builder& builder, mlir::Value value);
+
+mlir::IntegerAttr getSpatialChannelSourceCoreIdAttr(mlir::Builder& builder, mlir::Value channel);
+
+mlir::IntegerAttr getSpatialChannelTargetCoreIdAttr(mlir::Builder& builder, mlir::Value channel);
+
+bool hasSpatialChannelSourceCoreIdAttr(mlir::Value channel);
+
+bool hasSpatialChannelTargetCoreIdAttr(mlir::Value channel);
+
+mlir::Value createPimReceiveFromSpatialChannel(
+  mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value output, mlir::Value channel);
+
 template <class T>
 size_t rangeLength(const mlir::iterator_range<T> range) {
   return std::distance(range.begin(), range.end());

src/PIM/Conversion/SpatialToPim/SpatialToPim.td

@@ -9,6 +9,17 @@ include "src/Accelerators/PIM/Dialect/Spatial/Spatial.td"
 include "src/Accelerators/PIM/Dialect/Pim/Pim.td"
 #endif // OP_BASE
 
+def HasSpatialChannelSourceCoreIdAttr: Constraint<
+  CPred<"onnx_mlir::hasSpatialChannelSourceCoreIdAttr($0)">,
+  "spatial channel has precomputed source core id">;
+
+def HasSpatialChannelTargetCoreIdAttr: Constraint<
+  CPred<"onnx_mlir::hasSpatialChannelTargetCoreIdAttr($0)">,
+  "spatial channel has precomputed target core id">;
+
+def createPimReceiveFromSpatialChannelValue: NativeCodeCall<
+  "onnx_mlir::createPimReceiveFromSpatialChannel($_builder, $_loc, $0, $1)">;
+
 def onnxToPimTranspose : Pat<
   (ONNXTransposeOp:$srcOpRes $data, $perms),
   (PimTransposeOp $data, $perms,
@@ -69,4 +80,18 @@ def spatToPimVSoftmax : Pat<
     (NativeCodeCall<"onnx_mlir::getBestOutputTensorFromOperandsOrAllocate($_builder, $0.getDefiningOp())"> $srcOpRes))
 >;
 
+def spatChannelSendToPimSend : Pat<
+  (SpatChannelSendOp $channel, $input),
+  (PimSendOp $input,
+    (NativeCodeCall<"onnx_mlir::getTensorSizeInBytesAttr($_builder, $0)"> $input),
+    (NativeCodeCall<"onnx_mlir::getSpatialChannelTargetCoreIdAttr($_builder, $0)"> $channel)),
+  [(HasSpatialChannelTargetCoreIdAttr $channel)]
+>;
+
+def spatChannelReceiveToPimReceive : Pat<
+  (SpatChannelReceiveOp:$srcOpRes $channel),
+  (createPimReceiveFromSpatialChannelValue $srcOpRes, $channel),
+  [(HasSpatialChannelSourceCoreIdAttr $channel)]
+>;
+
 #endif // SPATIAL_TO_PIM

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.cpp

@@ -10,8 +10,10 @@
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/Interfaces/FunctionInterfaces.h"
 #include "mlir/Pass/Pass.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 
 #include "llvm/ADT/SmallSet.h"
+#include "llvm/ADT/StringRef.h"
 #include "llvm/Support/Casting.h"
 #include "llvm/Support/raw_os_ostream.h"
 
@@ -68,6 +70,8 @@ private:
                                       bool useBroadcastOp,
                                       IRRewriter& rewriter);
   void markOpToRemove(Operation* op);
+  void annotateChannelCoreIds(func::FuncOp funcOp);
+  void lowerBroadcastChannelOps(func::FuncOp funcOp, IRRewriter& rewriter);
 
   void runOnComputeOp(spatial::SpatWeightedCompute computeOp, IRRewriter& rewriter);
 
@@ -175,6 +179,16 @@ void SpatialToPimPass::runOnOperation() {
     runOnComputeOp(computeOp, rewriter);
   }
 
+  annotateChannelCoreIds(funcOp);
+  lowerBroadcastChannelOps(funcOp, rewriter);
+
+  RewritePatternSet channelPatterns(ctx);
+  populateWithGenerated(channelPatterns);
+  if (failed(applyPatternsGreedily(funcOp, std::move(channelPatterns)))) {
+    signalPassFailure();
+    return;
+  }
+
   enlargeVMMOutTensorsToCrossbarSize(funcOp, rewriter);
   replaceReturnOpOperands(returnOp, rewriter);
 
@@ -623,6 +637,94 @@ void SpatialToPimPass::markOpToRemove(Operation* op) {
     operationsToRemove.push_back(op);
 }
 
+void SpatialToPimPass::annotateChannelCoreIds(func::FuncOp funcOp) {
+  funcOp.walk([&](spatial::SpatChannelNewOp channelNewOp) {
+    markOpToRemove(channelNewOp);
+
+    if (channelNewOp->use_empty())
+      return;
+
+    spatial::SpatChannelSendOp sendOp;
+    spatial::SpatChannelReceiveOp receiveOp;
+    spatial::SpatChannelBroadcastSendOp broadcastSendOp;
+
+    for (Operation* user : channelNewOp->getUsers()) {
+      if (auto op = dyn_cast<spatial::SpatChannelSendOp>(user)) {
+        sendOp = op;
+        continue;
+      }
+      if (auto op = dyn_cast<spatial::SpatChannelReceiveOp>(user)) {
+        receiveOp = op;
+        continue;
+      }
+      if (auto op = dyn_cast<spatial::SpatChannelBroadcastSendOp>(user)) {
+        broadcastSendOp = op;
+        continue;
+      }
+      if (auto op = dyn_cast<spatial::SpatChannelBroadcastReceiveOp>(user)) {
+        continue;
+      }
+      llvm_unreachable("Unexpected user of spat.channel_new during Spatial-to-PIM lowering");
+    }
+
+    if (broadcastSendOp) {
+      auto sourceCoreIdAttr = cast<pim::PimCoreOp>(broadcastSendOp->getParentOp()).getCoreIdAttr();
+      channelNewOp->setAttr(kChannelSourceCoreIdAttrName, sourceCoreIdAttr);
+      return;
+    }
+
+    if (!sendOp || !receiveOp)
+      llvm_unreachable("spat.channel_new must connect exactly one send and one receive");
+
+    auto sourceCoreIdAttr = cast<pim::PimCoreOp>(sendOp->getParentOp()).getCoreIdAttr();
+    auto targetCoreIdAttr = cast<pim::PimCoreOp>(receiveOp->getParentOp()).getCoreIdAttr();
+    channelNewOp->setAttr(kChannelSourceCoreIdAttrName, sourceCoreIdAttr);
+    channelNewOp->setAttr(kChannelTargetCoreIdAttrName, targetCoreIdAttr);
+  });
+}
+
+void SpatialToPimPass::lowerBroadcastChannelOps(func::FuncOp funcOp, IRRewriter& rewriter) {
+  SmallVector<spatial::SpatChannelBroadcastSendOp> broadcastSendOps;
+  funcOp.walk([&](spatial::SpatChannelBroadcastSendOp op) { broadcastSendOps.push_back(op); });
+
+  for (auto sendOp : broadcastSendOps) {
+    auto channelNewOp = cast<spatial::SpatChannelNewOp>(sendOp.getChannel().getDefiningOp());
+    auto sizeAttr = getTensorSizeInBytesAttr(rewriter, sendOp.getInput());
+
+    rewriter.setInsertionPoint(sendOp);
+    bool foundReceiver = false;
+    for (Operation* user : channelNewOp->getUsers()) {
+      auto receiveOp = dyn_cast<spatial::SpatChannelBroadcastReceiveOp>(user);
+      if (!receiveOp)
+        continue;
+
+      foundReceiver = true;
+      auto targetCoreIdAttr = cast<pim::PimCoreOp>(receiveOp->getParentOp()).getCoreIdAttr();
+      PimSendOp::create(rewriter, sendOp.getLoc(), sendOp.getInput(), sizeAttr, targetCoreIdAttr);
+    }
+
+    if (!foundReceiver)
+      llvm_unreachable("spat.channel_broadcast_send has no matching broadcast receive");
+
+    rewriter.eraseOp(sendOp);
+  }
+
+  SmallVector<spatial::SpatChannelBroadcastReceiveOp> broadcastReceiveOps;
+  funcOp.walk([&](spatial::SpatChannelBroadcastReceiveOp op) { broadcastReceiveOps.push_back(op); });
+
+  for (auto receiveOp : broadcastReceiveOps) {
+    rewriter.setInsertionPoint(receiveOp);
+    auto outputType = cast<ShapedType>(receiveOp.getResult().getType());
+    Value outputBuffer = createEmptyTensorFromShaped(rewriter, receiveOp.getLoc(), outputType);
+    auto sizeAttr = getTensorSizeInBytesAttr(rewriter, receiveOp.getResult());
+    auto sourceCoreIdAttr = getSpatialChannelSourceCoreIdAttr(rewriter, receiveOp.getChannel());
+    Value receivedValue =
+      PimReceiveOp::create(rewriter, receiveOp.getLoc(), outputBuffer.getType(), outputBuffer, sizeAttr, sourceCoreIdAttr)
+        .getOutput();
+    rewriter.replaceOp(receiveOp, receivedValue);
+  }
+}
+
 void SpatialToPimPass::replaceReturnOpOperands(func::ReturnOp& returnOp, IRRewriter& rewriter) {
   SmallVector<Value> originalOperands(returnOp.getOperands().begin(), returnOp.getOperands().end());
   for (auto it : llvm::enumerate(originalOperands)) {

src/PIM/Dialect/Pim/Transforms/Bufferization/OpBufferizationInterfaces.cpp

@@ -97,6 +97,31 @@ struct MemCopyDevToHostOpInterface
   }
 };
 
+struct ReceiveOpInterface : DstBufferizableOpInterfaceExternalModel<ReceiveOpInterface, PimReceiveOp> {
+  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
+    return !cast<DestinationStyleOpInterface>(op).isDpsInit(&opOperand);
+  }
+
+  LogicalResult bufferize(Operation* op,
+                          RewriterBase& rewriter,
+                          const BufferizationOptions& options,
+                          BufferizationState& state) const {
+    auto receiveOp = cast<PimReceiveOp>(op);
+
+    auto outputBufferOpt = getBuffer(rewriter, receiveOp.getOutputBuffer(), options, state);
+    if (failed(outputBufferOpt))
+      return failure();
+
+    replaceOpWithNewBufferizedOp<PimReceiveOp>(rewriter,
+                                               op,
+                                               outputBufferOpt->getType(),
+                                               *outputBufferOpt,
+                                               receiveOp.getSizeAttr(),
+                                               receiveOp.getSourceCoreIdAttr());
+    return success();
+  }
+};
+
 struct TransposeOpInterface : DstBufferizableOpInterfaceExternalModel<TransposeOpInterface, PimTransposeOp> {
   bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
     return !cast<DestinationStyleOpInterface>(op).isDpsInit(&opOperand);
@@ -258,6 +283,7 @@ struct UnaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<UnaryDstOpI
 
 void registerOpBufferizationInterfaces(DialectRegistry& registry) {
   registry.addExtension(+[](MLIRContext* ctx, PimDialect* dialect) {
+    PimReceiveOp::attachInterface<ReceiveOpInterface>(*ctx);
     PimMemCopyHostToDevOp::attachInterface<MemCopyHostToDevOpInterface>(*ctx);
     PimMemCopyDevToHostOp::attachInterface<MemCopyDevToHostOpInterface>(*ctx);
     PimTransposeOp::attachInterface<TransposeOpInterface>(*ctx);

src/PIM/Dialect/Spatial/CMakeLists.txt

@@ -3,11 +3,10 @@ add_onnx_mlir_dialect_doc(spat Spatial.td)
 
 add_pim_library(SpatialOps
   SpatialOps.cpp
-  Transforms/SpatialBufferizableOpInterface.cpp
-  Transforms/MergeComputeNode/MergeComputeNodePass.cpp
-  DCPGraph/Graph.cpp
-  DCPGraph/Task.cpp
-  DCPGraph/DCPAnalysis.cpp
+  Transforms/MergeComputeNodes/MergeComputeNodesPass.cpp
+  Transforms/MergeComputeNodes/DCPGraph/Graph.cpp
+  Transforms/MergeComputeNodes/DCPGraph/Task.cpp
+  Transforms/MergeComputeNodes/DCPGraph/DCPAnalysis.cpp
 
   EXCLUDE_FROM_OM_LIBS
 

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/DCPGraph/DCPAnalysis.cpp

@@ -8,7 +8,6 @@
 
 #include <iterator>
 
-#include "../SpatialOps.hpp"
 #include "DCPAnalysis.hpp"
 #include "Graph.hpp"
 #include "src/Support/TypeUtilities.hpp"

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/DCPGraph/DCPAnalysis.hpp

@@ -6,7 +6,7 @@
 
 #include <vector>
 
-#include "../SpatialOps.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
 struct DCPAnalysisResult {
   std::vector<onnx_mlir::spatial::SpatWeightedCompute> dominanceOrderCompute;

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/DCPGraph/Graph.cpp

@@ -7,11 +7,11 @@
 #include <fstream>
 #include <vector>
 
-#include "../../../Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "DCPAnalysis.hpp"
 #include "Graph.hpp"
 #include "Task.hpp"
-#include "Uniqueworklist.hpp"
+#include "UniqueWorklist.hpp"
 #include "Utils.hpp"
 
 std::optional<Edge_pair> addEdge(TaskDCP* parent, TaskDCP* child, Weight_t weight) {
@@ -420,7 +420,7 @@ void GraphDCP::to_dot() {
   std::string outputDir = onnx_mlir::getOutputDir();
   if (outputDir.empty())
     return;
-  std::string graphDir = outputDir + "/DCPGraph";
+  std::string graphDir = outputDir + "/dcp_graph";
   onnx_mlir::createDirectory(graphDir);
   std::fstream file(graphDir + "/graph_" + std::to_string(index++) + ".dot", std::ios::out);
   file << "digraph G {\n";

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/DCPGraph/Task.cpp

@@ -2,7 +2,7 @@
 
 #include "Graph.hpp"
 #include "Task.hpp"
-#include "Uniqueworklist.hpp"
+#include "UniqueWorklist.hpp"
 
 std::optional<Edge_t> TaskDCP::addChild(TaskDCP* child, Weight_t weight) {
   std::optional<Edge_t> oldEdge = std::nullopt;

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/DCPGraph/Task.hpp

@@ -5,7 +5,7 @@
 #include <optional>
 #include <vector>
 
-#include "../SpatialOps.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "Utils.hpp"
 
 std::optional<Edge_pair> addEdge(TaskDCP* parent, TaskDCP* child, Weight_t weight);

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/DCPGraph/Utils.hpp

@@ -9,7 +9,7 @@
 #include <utility>
 #include <vector>
 
-#include "../SpatialOps.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Support/TypeUtilities.hpp"
 
 using CPU = int;

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/MergeComputeNodesPass.cpp

@@ -20,7 +20,7 @@
 #include <memory>
 
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
-#include "src/Accelerators/PIM/Dialect/Spatial/DCPGraph/DCPAnalysis.hpp"
+#include "DCPGraph/DCPAnalysis.hpp"
 
 using namespace mlir;
 
@@ -81,7 +81,7 @@ public:
   ChannelOrLocalOp getAsChannelValueAndInsertSender() { return getAsChannelValueAndInsertSender({}); }
 };
 
-struct MergeComputeNodePass : PassWrapper<MergeComputeNodePass, OperationPass<func::FuncOp>> {
+struct MergeComputeNodesPass : PassWrapper<MergeComputeNodesPass, OperationPass<func::FuncOp>> {
 
 private:
   DenseMap<SpatWeightedCompute, LazyInsertComputeResult> newComputeNodeResults;
@@ -89,11 +89,11 @@ private:
   DenseMap<int64_t, SpatWeightedCompute> cputToNewComputeMap;
 
 public:
-  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(MergeComputeNodePass)
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(MergeComputeNodesPass)
 
-  StringRef getArgument() const override { return "pim-merge-node-pass"; }
+  StringRef getArgument() const override { return "pim-merge-compute-nodes-pass"; }
   StringRef getDescription() const override {
-    return "Merge Spatial-Weighted-Compute-Node in order to reduce the total "
+    return "Merge Spatial-Weighted-Compute-Nodes in order to reduce the total "
            "execution time";
   }
 
@@ -133,7 +133,7 @@ public:
       computeNodetoRemove.erase();
     }
     func::FuncOp func = getOperation();
-    dumpModule(cast<ModuleOp>(func->getParentOp()), "SpatialDCPMerged");
+    dumpModule(cast<ModuleOp>(func->getParentOp()), "spatial1_dcp_merged");
   }
 
 private:
@@ -346,6 +346,6 @@ private:
 
 } // namespace
 
-std::unique_ptr<Pass> createMergeComputeNodePass() { return std::make_unique<MergeComputeNodePass>(); }
+std::unique_ptr<Pass> createMergeComputeNodesPass() { return std::make_unique<MergeComputeNodesPass>(); }
 
 } // namespace onnx_mlir

src/PIM/Dialect/Spatial/Transforms/SpatialBufferizableOpInterface.cpp

@@ -1,532 +0,0 @@
-#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
-#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
-#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
-#include "mlir/Dialect/MemRef/IR/MemRef.h"
-#include "mlir/Dialect/Tensor/IR/Tensor.h"
-#include "mlir/Dialect/Tosa/IR/TosaOps.h"
-#include "mlir/IR/BuiltinAttributes.h"
-#include "mlir/IR/BuiltinTypeInterfaces.h"
-#include "mlir/IR/BuiltinTypes.h"
-#include "mlir/Support/LLVM.h"
-
-#include "llvm/ADT/SmallVector.h"
-#include "llvm/ADT/StringRef.h"
-#include "llvm/Support/Casting.h"
-#include "llvm/Support/LogicalResult.h"
-#include "llvm/Support/raw_ostream.h"
-
-#include <cstdint>
-
-#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/Dialect/Spatial/Transforms/SpatialBufferizableOpInterface.hpp"
-#include "src/Dialect/ONNX/ONNXOps.hpp"
-
-using namespace mlir;
-using namespace bufferization;
-
-namespace onnx_mlir {
-namespace spatial {
-
-memref::AllocOp createEmptyFromType(Type resultType, Location loc, RewriterBase& rewriter) {
-  auto resultShape = cast<ShapedType>(resultType);
-  auto memrefResultType = MemRefType::get(resultShape.getShape(), resultShape.getElementType());
-
-  // Alloc an output memref
-  return memref::AllocOp::create(rewriter, loc, memrefResultType);
-}
-
-Value materializeContiguousMemRef(Value memrefValue, Location loc, RewriterBase& rewriter) {
-  if (succeeded(resolveContiguousAddress(memrefValue)))
-    return memrefValue;
-
-  auto shapedType = cast<ShapedType>(memrefValue.getType());
-  auto contiguousBuffer = createEmptyFromType(memrefValue.getType(), loc, rewriter);
-  auto sizeInBytes = shapedType.getNumElements() * shapedType.getElementTypeBitWidth() / 8;
-
-  return pim::PimMemCopyOp::create(rewriter,
-                                   loc,
-                                   contiguousBuffer.getType(),
-                                   contiguousBuffer,
-                                   memrefValue,
-                                   rewriter.getI32IntegerAttr(0),
-                                   rewriter.getI32IntegerAttr(0),
-                                   rewriter.getI32IntegerAttr(sizeInBytes))
-    .getOutput();
-}
-
-const llvm::StringRef PRECOMPUTED_OTHER_CORE_ID_ATTR_NAME("precomp_other_core_id");
-
-static FailureOr<Operation*> getOtherEndOfChannel(Operation* op, bool opIsReceive) {
-  auto channelNewOp = op->getOperand(0).getDefiningOp<spatial::SpatChannelNewOp>();
-  if (!channelNewOp) {
-    op->emitError("User of Channel must have the first operand created by ChannelNewOp.");
-    return failure();
-  }
-
-  auto channelUsers = channelNewOp->getUsers();
-  auto usersIterator = channelUsers.begin();
-  auto firstUser = *usersIterator;
-  ++usersIterator;
-  if (usersIterator == channelUsers.end()) {
-    op->emitError("Operand generated by ChannelNewOp must have two users, only one found.");
-    channelNewOp->dump();
-    op->dump();
-    channelNewOp->getParentOp()->dump();
-    return failure();
-  }
-
-  auto secondUser = *usersIterator;
-  ++usersIterator;
-  if (usersIterator != channelUsers.end()) {
-    op->emitError("Operand generated by ChannelNewOp must have two users, more than two found.");
-    return failure();
-  }
-
-  Operation* otherUser = nullptr;
-  if (firstUser == op)
-    otherUser = secondUser;
-  else if (secondUser == op)
-    otherUser = firstUser;
-  else {
-    op->emitError("Operand generated by ChannelNewOp must have two users and one of them must be the current op.");
-    return failure();
-  }
-
-  if (opIsReceive && !isa<spatial::SpatChannelSendOp>(otherUser)) {
-    op->emitError("Operand generated by ChannelNewOp has two users, but the other one is not a ChannelSendOp.");
-    return failure();
-  }
-
-  if (!opIsReceive && !isa<spatial::SpatChannelReceiveOp>(otherUser)) {
-    op->emitError("Operand generated by ChannelNewOp has two users, but the other one is not a ChannelReceiveOp.");
-    return failure();
-  }
-
-  return otherUser;
-}
-
-llvm::FailureOr<uint32_t> getCoreIdOfOtherEndOfChannel(Operation* op, bool opIsReceive, RewriterBase& rewriter) {
-
-  // This function requires the existence of ChannelNewOp and the other
-  // Receive/Send operation. However, during bufferization, the first of the
-  // Receive/Send operation that is processed gets removed. As such, we need to
-  // "precompute" the coreId needed for the other op, and save it as attribute
-  auto precomputedOtherCoreId = op->getAttr(PRECOMPUTED_OTHER_CORE_ID_ATTR_NAME);
-  if (precomputedOtherCoreId)
-    return cast<IntegerAttr>(precomputedOtherCoreId).getInt();
-
-  auto notOpUserOpt = getOtherEndOfChannel(op, opIsReceive);
-  if (failed(notOpUserOpt))
-    return failure();
-  Operation* notOpUser = *notOpUserOpt;
-
-  // Save the coreId for this op into the other op as attribute
-  auto opCoreIdAttr = cast<pim::PimCoreOp>(op->getParentOp()).getCoreIdAttr();
-  notOpUser->setAttr(PRECOMPUTED_OTHER_CORE_ID_ATTR_NAME, opCoreIdAttr);
-
-  return cast<pim::PimCoreOp>(notOpUser->getParentOp()).getCoreId();
-}
-
-struct WComputeOpInterface : BufferizableOpInterface::ExternalModel<WComputeOpInterface, SpatWeightedCompute> {
-
-  // Input tensor to the compute OP are always read into its local memory
-  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return true; }
-
-  // Input tensor to the compute OP are _never_ written into its local memory
-  bool bufferizesToMemoryWrite(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return false; }
-
-  // In general, no tensor is aliased with any other tensor in the compute OP
-  AliasingValueList getAliasingValues(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-    // TODO: Is it an empty list or a list of "UNKNOWN" values?
-    return {};
-  }
-
-  LogicalResult bufferize(Operation* op,
-                          RewriterBase& rewriter,
-                          const BufferizationOptions& options,
-                          BufferizationState& state) const {
-    // Bufferize its block
-
-    auto& block = op->getRegion(0).front();
-
-    return bufferizeBlockSignature(&block, rewriter, options, state);
-  }
-};
-
-/*
- * This can be used for operation that have a single argument, which is a
- * variadic of tensors, and a single output with the same same shape
- * Example: VAdd, VSub, VExp
- */
-template <typename InterfaceName, typename OpTy, typename ToTy>
-struct VariadicArgumentElementWiseOpInterface : BufferizableOpInterface::ExternalModel<InterfaceName, OpTy> {
-
-  // Input tensors to the OP are always read
-  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return true; }
-
-  // Input tensors to the OP are _never_ written
-  bool bufferizesToMemoryWrite(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return false; }
-
-  // In general, no tensor is aliased with any other tensor in the OP
-  AliasingValueList getAliasingValues(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-    return {};
-  }
-
-  // Cast tensor values into memref values
-  LogicalResult bufferize(Operation* op,
-                          RewriterBase& rewriter,
-                          const BufferizationOptions& options,
-                          BufferizationState& state) const {
-
-    // Turn Tensor Operands into Memref Operands
-    SmallVector<Value> memrefOperands;
-    memrefOperands.reserve(op->getNumOperands());
-    for (auto operand : op->getOperands()) {
-      auto memref = getBuffer(rewriter, operand, options, state);
-      if (failed(memref))
-        return failure();
-      memrefOperands.push_back(materializeContiguousMemRef(*memref, op->getLoc(), rewriter));
-    }
-
-    // TODO: Support addiction with more than 2 operands
-    if (memrefOperands.size() > 2) {
-      op->emitError("VariadicArgumentElementWiseOpInterface only supports OPs "
-                    "with 1 or 2 operands, for now.");
-      return failure();
-    }
-
-    // Alloc an output memref
-    Value outputTensor = createEmptyFromType(op->getResult(0).getType(), op->getLoc(), rewriter);
-
-    memrefOperands.push_back(outputTensor);
-
-    Value newValue = ToTy::create(rewriter, op->getLoc(), outputTensor.getType(), memrefOperands).getOutput();
-
-    replaceOpWithBufferizedValues(rewriter, op, newValue);
-
-    return success();
-  }
-};
-
-template <typename InterfaceName, typename OpTy, typename ToTy>
-struct WeightedMultiplicationsOpInterface : BufferizableOpInterface::ExternalModel<InterfaceName, OpTy> {
-
-  // Input tensors to the OP are always read
-  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return true; }
-
-  // Input tensors to the OP are _never_ written
-  bool bufferizesToMemoryWrite(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return false; }
-
-  // In general, no tensor is aliased with any other tensor in the OP
-  AliasingValueList getAliasingValues(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-    return {};
-  }
-
-  // Cast tensor value into memref value
-  LogicalResult bufferize(Operation* op,
-                          RewriterBase& rewriter,
-                          const BufferizationOptions& options,
-                          BufferizationState& state) const {
-    auto memrefOperandOpt = getBuffer(rewriter, op->getOperand(0), options, state);
-    if (failed(memrefOperandOpt))
-      return failure();
-    auto memrefOperand = *memrefOperandOpt;
-
-    // Alloc an output memref
-    Value outputTensor = createEmptyFromType(op->getResult(0).getType(), op->getLoc(), rewriter);
-
-    Value newValue = ToTy::create(rewriter,
-                                  op->getLoc(),
-                                  outputTensor.getType(),
-                                  cast<OpTy>(op).getWeightIndexAttr(),
-                                  memrefOperand,
-                                  outputTensor)
-                       .getOutput();
-
-    replaceOpWithBufferizedValues(rewriter, op, newValue);
-
-    return success();
-  }
-};
-
-struct ChannelReceiveOpInterface
-: BufferizableOpInterface::ExternalModel<ChannelReceiveOpInterface, SpatChannelReceiveOp> {
-
-  // Input value is the channel (not read/written, its more of an attribute)
-  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return false; }
-
-  // See above
-  bool bufferizesToMemoryWrite(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return false; }
-
-  // See above
-  AliasingValueList getAliasingValues(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-    // TODO: Is it an empty list or a list of "UNKNOWN" values?
-    return {};
-  }
-
-  /*
-   * Turn the channel receive to pim.recv
-   */
-  LogicalResult bufferize(Operation* op,
-                          RewriterBase& rewriter,
-                          const BufferizationOptions& options,
-                          BufferizationState& state) const {
-
-    auto outputTensor = createEmptyFromType(op->getResult(0).getType(), op->getLoc(), rewriter);
-
-    auto numElements = cast<ShapedType>(outputTensor.getType()).getNumElements();
-    auto elementSize = cast<ShapedType>(outputTensor.getType()).getElementTypeBitWidth() / 8;
-
-    auto srcCoreId = getCoreIdOfOtherEndOfChannel(op, true, rewriter);
-    if (failed(srcCoreId))
-      return failure();
-
-    Value newValue = pim::PimReceiveOp::create(rewriter,
-                                               op->getLoc(),
-                                               outputTensor.getType(),
-                                               outputTensor,
-                                               rewriter.getI32IntegerAttr(numElements * elementSize),
-                                               rewriter.getI32IntegerAttr(srcCoreId.value()))
-                       .getOutput();
-
-    replaceOpWithBufferizedValues(rewriter, op, newValue);
-
-    return success();
-  }
-};
-
-struct ChannelSendOpInterface : BufferizableOpInterface::ExternalModel<ChannelSendOpInterface, SpatChannelSendOp> {
-
-  // First input is channel (not read/writter) second input is Tensor to send,
-  // which is read
-  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-    return opOperand.getOperandNumber() == 2;
-  }
-
-  // See above (both non-written)
-  bool bufferizesToMemoryWrite(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return false; }
-
-  // See above
-  AliasingValueList getAliasingValues(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-    // TODO: Is it an empty list or a list of "UNKNOWN" values?
-    return {};
-  }
-
-  /*
-   * Turn the channel send to pim.send
-   */
-  LogicalResult bufferize(Operation* op,
-                          RewriterBase& rewriter,
-                          const BufferizationOptions& options,
-                          BufferizationState& state) const {
-    auto srcTensor = op->getOperand(1);
-
-    auto srcTensorOpt = getBuffer(rewriter, srcTensor, options, state);
-    if (failed(srcTensorOpt))
-      return failure();
-    auto srcMemRef = *srcTensorOpt;
-
-    auto numElements = cast<ShapedType>(srcTensor.getType()).getNumElements();
-    auto elementSize = cast<ShapedType>(srcTensor.getType()).getElementTypeBitWidth() / 8;
-
-    auto dstCoreId = getCoreIdOfOtherEndOfChannel(op, false, rewriter);
-    if (failed(dstCoreId))
-      return failure();
-
-    replaceOpWithNewBufferizedOp<pim::PimSendOp>(rewriter,
-                                                 op,
-                                                 srcMemRef,
-                                                 rewriter.getI32IntegerAttr(numElements * elementSize),
-                                                 rewriter.getI32IntegerAttr(dstCoreId.value()));
-
-    return success();
-  }
-};
-
-struct ChannelBroadcastReceiveOpInterface
-: BufferizableOpInterface::ExternalModel<ChannelBroadcastReceiveOpInterface, SpatChannelBroadcastReceiveOp> {
-
-  // Input value is the channel (not read/written, its more of an attribute)
-  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return false; }
-
-  // See above
-  bool bufferizesToMemoryWrite(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return false; }
-
-  // See above
-  AliasingValueList getAliasingValues(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-    // TODO: Is it an empty list or a list of "UNKNOWN" values?
-    return {};
-  }
-
-  /*
-   * Turn the broadcast receive into a regular pim.receive from the broadcaster.
-   */
-  LogicalResult bufferize(Operation* op,
-                          RewriterBase& rewriter,
-                          const BufferizationOptions& options,
-                          BufferizationState& state) const {
-
-    auto outputTensor = createEmptyFromType(op->getResult(0).getType(), op->getLoc(), rewriter);
-
-    auto numElements = cast<ShapedType>(outputTensor.getType()).getNumElements();
-    auto elementSize = cast<ShapedType>(outputTensor.getType()).getElementTypeBitWidth() / 8;
-
-    auto precomputedOtherCoreId = op->getAttr(PRECOMPUTED_OTHER_CORE_ID_ATTR_NAME);
-    if (precomputedOtherCoreId) {
-      Value newValue = pim::PimReceiveOp::create(rewriter,
-                                                 op->getLoc(),
-                                                 outputTensor.getType(),
-                                                 outputTensor,
-                                                 rewriter.getI32IntegerAttr(numElements * elementSize),
-                                                 cast<IntegerAttr>(precomputedOtherCoreId))
-                         .getOutput();
-      replaceOpWithBufferizedValues(rewriter, op, newValue);
-      return success();
-    }
-
-    auto channelNewOp = op->getOperand(0).getDefiningOp<SpatChannelNewOp>();
-    if (!channelNewOp) {
-      op->emitError("ChannelBroadcastReceiveOp does not use a channel as operand");
-      return failure();
-    }
-
-    auto srcCoreId = [&]() -> FailureOr<uint32_t> {
-      for (Operation* user : channelNewOp->getUsers()) {
-        auto sendOp = dyn_cast<SpatChannelBroadcastSendOp>(user);
-        if (!sendOp)
-          continue;
-        auto sendCoreIdAttr = cast<pim::PimCoreOp>(sendOp->getParentOp()).getCoreIdAttr();
-        op->setAttr(PRECOMPUTED_OTHER_CORE_ID_ATTR_NAME, sendCoreIdAttr);
-        return cast<pim::PimCoreOp>(sendOp->getParentOp()).getCoreId();
-      }
-      op->emitError("ChannelBroadcastReceiveOp has no matching ChannelBroadcastSendOp");
-      return failure();
-    }();
-    if (failed(srcCoreId))
-      return failure();
-
-    Value newValue = pim::PimReceiveOp::create(rewriter,
-                                               op->getLoc(),
-                                               outputTensor.getType(),
-                                               outputTensor,
-                                               rewriter.getI32IntegerAttr(numElements * elementSize),
-                                               rewriter.getI32IntegerAttr(srcCoreId.value()))
-                       .getOutput();
-
-    replaceOpWithBufferizedValues(rewriter, op, newValue);
-
-    return success();
-  }
-};
-
-struct ChannelBroadcastSendOpInterface
-: BufferizableOpInterface::ExternalModel<ChannelBroadcastSendOpInterface, SpatChannelBroadcastSendOp> {
-
-  // First input is channel (not read/writter) second input is Tensor to send,
-  // which is read
-  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-    return opOperand.getOperandNumber() == 2;
-  }
-
-  // See above (both non-written)
-  bool bufferizesToMemoryWrite(Operation* op, OpOperand& opOperand, const AnalysisState& state) const { return false; }
-
-  // See above
-  AliasingValueList getAliasingValues(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-    // TODO: Is it an empty list or a list of "UNKNOWN" values?
-    return {};
-  }
-
-  /*
-   * Turn the broadcast send into one pim.send per broadcast receiver.
-   */
-  LogicalResult bufferize(Operation* op,
-                          RewriterBase& rewriter,
-                          const BufferizationOptions& options,
-                          BufferizationState& state) const {
-    auto srcTensor = op->getOperand(1);
-
-    auto srcTensorOpt = getBuffer(rewriter, srcTensor, options, state);
-    if (failed(srcTensorOpt))
-      return failure();
-    auto srcMemRef = *srcTensorOpt;
-
-    auto channelNewOp = op->getOperand(0).getDefiningOp<SpatChannelNewOp>();
-    if (!channelNewOp) {
-      op->emitError("SpatChannelBroadcastSendOp does not use a channel as operand");
-      return failure();
-    }
-
-    auto srcType = cast<ShapedType>(srcTensor.getType());
-    auto sizeInBytes = srcType.getNumElements() * srcType.getElementTypeBitWidth() / 8;
-    auto srcCoreIdAttr = cast<pim::PimCoreOp>(op->getParentOp()).getCoreIdAttr();
-
-    rewriter.setInsertionPoint(op);
-    bool foundReceiver = false;
-    for (Operation* user : channelNewOp->getUsers()) {
-      auto receiveOp = dyn_cast<SpatChannelBroadcastReceiveOp>(user);
-      if (!receiveOp)
-        continue;
-
-      foundReceiver = true;
-      auto dstCoreId = cast<pim::PimCoreOp>(receiveOp->getParentOp()).getCoreId();
-      receiveOp->setAttr(PRECOMPUTED_OTHER_CORE_ID_ATTR_NAME, srcCoreIdAttr);
-      pim::PimSendOp::create(rewriter,
-                             op->getLoc(),
-                             srcMemRef,
-                             rewriter.getI32IntegerAttr(sizeInBytes),
-                             rewriter.getI32IntegerAttr(dstCoreId));
-    }
-
-    if (!foundReceiver) {
-      op->emitError("SpatChannelBroadcastSendOp has no matching ChannelBroadcastReceiveOp");
-      return failure();
-    }
-
-    rewriter.eraseOp(op);
-    return success();
-  }
-};
-
-struct VAddOpInterfaceFromTemplate
-: VariadicArgumentElementWiseOpInterface<VAddOpInterfaceFromTemplate, SpatVAddOp, pim::PimVVAddOp> {};
-
-struct WVMMOpInterface : WeightedMultiplicationsOpInterface<WVMMOpInterface, SpatWeightedVMMOp, pim::PimVMMOp> {};
-
-struct WMVMOpInterface : WeightedMultiplicationsOpInterface<WMVMOpInterface, SpatWeightedMVMOp, pim::PimMVMOp> {};
-
-struct VMaxOpInterface : VariadicArgumentElementWiseOpInterface<VMaxOpInterface, SpatVMaxOp, pim::PimVVMaxOp> {};
-
-void registerBufferizableOpInterfaceExternalModels(DialectRegistry& registry) {
-  registry.addExtension(+[](MLIRContext* ctx, SpatialDialect* dialect) {
-    SpatWeightedCompute::attachInterface<WComputeOpInterface>(*ctx);
-    SpatVAddOp::attachInterface<VAddOpInterfaceFromTemplate>(*ctx);
-    SpatWeightedVMMOp::attachInterface<WVMMOpInterface>(*ctx);
-    SpatWeightedMVMOp::attachInterface<WMVMOpInterface>(*ctx);
-    SpatVMaxOp::attachInterface<VMaxOpInterface>(*ctx);
-    SpatChannelReceiveOp::attachInterface<ChannelReceiveOpInterface>(*ctx);
-    SpatChannelSendOp::attachInterface<ChannelSendOpInterface>(*ctx);
-    SpatChannelBroadcastReceiveOp::attachInterface<ChannelBroadcastReceiveOpInterface>(*ctx);
-    SpatChannelBroadcastSendOp::attachInterface<ChannelBroadcastSendOpInterface>(*ctx);
-  });
-}
-
-struct ONNXReluInterface : VariadicArgumentElementWiseOpInterface<ONNXReluInterface, ONNXReluOp, pim::PimVReluOp> {};
-
-struct ONNXTanhInterface : VariadicArgumentElementWiseOpInterface<ONNXTanhInterface, ONNXTanhOp, pim::PimVTanhOp> {};
-
-struct ONNXSigmoidInterface
-: VariadicArgumentElementWiseOpInterface<ONNXSigmoidInterface, ONNXSigmoidOp, pim::PimVSigmOp> {};
-
-void registerONNXBufferizableOpInterfaceExternalModels(DialectRegistry& registry) {
-  registry.addExtension(+[](MLIRContext* ctx, ONNXDialect* dialect) {
-    ONNXReluOp::attachInterface<ONNXReluInterface>(*ctx);
-    ONNXTanhOp::attachInterface<ONNXTanhInterface>(*ctx);
-    ONNXSigmoidOp::attachInterface<ONNXSigmoidInterface>(*ctx);
-  });
-}
-
-} // namespace spatial
-} // namespace onnx_mlir

src/PIM/Dialect/Spatial/Transforms/SpatialBufferizableOpInterface.hpp

@@ -1,15 +0,0 @@
-#pragma once
-
-#include "mlir/IR/DialectRegistry.h"
-
-#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
-
-namespace onnx_mlir {
-namespace spatial {
-
-void registerBufferizableOpInterfaceExternalModels(mlir::DialectRegistry& registry);
-
-void registerONNXBufferizableOpInterfaceExternalModels(mlir::DialectRegistry& registry);
-
-} // namespace spatial
-} // namespace onnx_mlir

src/PIM/Pass/CMakeLists.txt

@@ -1,13 +1,13 @@
 add_pim_library(OMPimPasses
   CountInstructionPass.cpp
   MessagePass.cpp
-  Pim/ConstantFolding/Common.cpp
-  Pim/ConstantFolding/Patterns/Constant.cpp
-  Pim/ConstantFolding/ConstantFoldingPass.cpp
-  Pim/ConstantFolding/Patterns/Subview.cpp
-  Pim/MaterializeConstantsPass.cpp
-  Pim/VerificationPass.cpp
-  Pim/EmitPimJsonPass.cpp
+  PimCodegen/HostConstantFolding/Common.cpp
+  PimCodegen/HostConstantFolding/Patterns/Constant.cpp
+  PimCodegen/HostConstantFolding/HostConstantFoldingPass.cpp
+  PimCodegen/HostConstantFolding/Patterns/Subview.cpp
+  PimCodegen/MaterializeHostConstantsPass.cpp
+  PimCodegen/VerificationPass.cpp
+  PimCodegen/EmitPimJsonPass.cpp
 
   EXCLUDE_FROM_OM_LIBS
 

src/PIM/Pass/PIMPasses.h

@@ -15,11 +15,11 @@ std::unique_ptr<mlir::Pass> createSpatialToPimPass();
 
 std::unique_ptr<mlir::Pass> createPimBufferizationPass();
 
-std::unique_ptr<mlir::Pass> createMergeComputeNodePass();
+std::unique_ptr<mlir::Pass> createMergeComputeNodesPass();
 
-std::unique_ptr<mlir::Pass> createPimConstantFoldingPass();
+std::unique_ptr<mlir::Pass> createPimHostConstantFoldingPass();
 
-std::unique_ptr<mlir::Pass> createPimMaterializeConstantsPass();
+std::unique_ptr<mlir::Pass> createPimMaterializeHostConstantsPass();
 
 std::unique_ptr<mlir::Pass> createPimVerificationPass();
 

src/PIM/Pass/PimCodegen/HostConstantFolding/HostConstantFoldingPass.cpp

@@ -11,10 +11,10 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
-struct ConstantFoldingPass : PassWrapper<ConstantFoldingPass, OperationPass<ModuleOp>> {
-  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(ConstantFoldingPass)
+struct HostConstantFoldingPass : PassWrapper<HostConstantFoldingPass, OperationPass<ModuleOp>> {
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(HostConstantFoldingPass)
 
-  StringRef getArgument() const override { return "pim-constant-folding-pass"; }
+  StringRef getArgument() const override { return "pim-host-constant-folding-pass"; }
   StringRef getDescription() const override { return "Fold host-side constant expressions before PIM verification"; }
 
   LogicalResult initialize(MLIRContext* context) override {
@@ -47,6 +47,6 @@ struct ConstantFoldingPass : PassWrapper<ConstantFoldingPass, OperationPass<Modu
 
 } // namespace
 
-std::unique_ptr<Pass> createPimConstantFoldingPass() { return std::make_unique<ConstantFoldingPass>(); }
+std::unique_ptr<Pass> createPimHostConstantFoldingPass() { return std::make_unique<HostConstantFoldingPass>(); }
 
 } // namespace onnx_mlir

src/PIM/Pass/PimCodegen/MaterializeHostConstantsPass.cpp

@@ -31,10 +31,10 @@ static int64_t getValueSizeInBytes(Value value) {
   return type.getNumElements() * type.getElementTypeBitWidth() / 8;
 }
 
-struct MaterializeConstantsPass : PassWrapper<MaterializeConstantsPass, OperationPass<ModuleOp>> {
-  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(MaterializeConstantsPass)
+struct MaterializeHostConstantsPass : PassWrapper<MaterializeHostConstantsPass, OperationPass<ModuleOp>> {
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(MaterializeHostConstantsPass)
 
-  StringRef getArgument() const override { return "materialize-pim-constants"; }
+  StringRef getArgument() const override { return "materialize-pim-host-constants"; }
   StringRef getDescription() const override {
     return "Materialize explicit host-to-device copies for constant globals used by PIM runtime ops";
   }
@@ -126,6 +126,8 @@ struct MaterializeConstantsPass : PassWrapper<MaterializeConstantsPass, Operatio
 
 } // namespace
 
-std::unique_ptr<Pass> createPimMaterializeConstantsPass() { return std::make_unique<MaterializeConstantsPass>(); }
+std::unique_ptr<Pass> createPimMaterializeHostConstantsPass() {
+  return std::make_unique<MaterializeHostConstantsPass>();
+}
 
 } // namespace onnx_mlir

src/PIM/PimAccelerator.cpp

@@ -18,7 +18,6 @@
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/Transforms/Bufferization/OpBufferizationInterfaces.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
-#include "src/Accelerators/PIM/Dialect/Spatial/Transforms/SpatialBufferizableOpInterface.hpp"
 #include "src/Accelerators/PIM/Pass/PIMPasses.h"
 #include "src/Accelerators/PIM/PimAccelerator.hpp"
 #include "src/Compiler/CompilerUtils.hpp"
@@ -67,8 +66,6 @@ void PimAccelerator::registerDialects(mlir::DialectRegistry& registry) const {
   mlir::arith::registerBufferizableOpInterfaceExternalModels(registry);
   mlir::bufferization::func_ext::registerBufferizableOpInterfaceExternalModels(registry);
   mlir::scf::registerBufferizableOpInterfaceExternalModels(registry);
-  spatial::registerBufferizableOpInterfaceExternalModels(registry);
-  spatial::registerONNXBufferizableOpInterfaceExternalModels(registry);
   pim::registerOpBufferizationInterfaces(registry);
 }
 
@@ -78,9 +75,9 @@ void PimAccelerator::registerPasses(int optLevel) const {
   registerPass(createSpatialToGraphvizPass);
   registerPass(createSpatialToPimPass);
   registerPass(createPimBufferizationPass);
-  registerPass(createMergeComputeNodePass);
-  registerPass(createPimConstantFoldingPass);
-  registerPass(createPimMaterializeConstantsPass);
+  registerPass(createMergeComputeNodesPass);
+  registerPass(createPimHostConstantFoldingPass);
+  registerPass(createPimMaterializeHostConstantsPass);
   registerPass(createPimVerificationPass);
   registerPass(createEmitPimJsonPass);
 }

src/PIM/TODO.md

@@ -1,5 +1,3 @@
-
-Rimuovere la logica di bufferizazione da spatial,
 Rimuovere la gestione delle send e recive da sptaialtopim (nuovo mergeNode)
 
 AnalisiDCP

validation/validate_one.py

@@ -12,7 +12,16 @@ from gen_network_runner import gen_network_runner
 from subprocess_utils import run_command_with_reporter
 
 
-STAGE_COUNT = 6
+STAGE_TITLES = (
+    "Compile ONNX",
+    "Build Runner",
+    "Generate Inputs",
+    "Run Reference",
+    "Compile PIM",
+    "Run Simulator",
+    "Compare Outputs",
+)
+STAGE_COUNT = len(STAGE_TITLES)
 GENERATED_DIR_NAMES = ("inputs", "outputs", "raptor", "runner", "simulation")
 
 
@@ -22,6 +31,8 @@ class ProgressReporter:
         self.stages_per_model = stages_per_model
         self.total_steps = max(1, total_models * stages_per_model)
         self.completed_steps = 0
+        self.passed_models = 0
+        self.failed_models = 0
         self.current_label = ""
         self.enabled = True
         self.columns = shutil.get_terminal_size((100, 20)).columns
@@ -36,21 +47,42 @@ class ProgressReporter:
             return
         bar_width = 24
         filled = int(bar_width * self.completed_steps / self.total_steps)
+        counts_text = f"P:{self.passed_models} F:{self.failed_models}"
         prefix_text = f"[{'#' * filled}{'-' * (bar_width - filled)}] {self.completed_steps}/{self.total_steps}"
         if len(prefix_text) > self.columns:
             prefix_text = f"{self.completed_steps}/{self.total_steps}"
 
-        label = f" {self.current_label}" if self.current_label else ""
-        available_label_width = max(0, self.columns - len(prefix_text))
-        label = label[:available_label_width]
-
         if prefix_text.startswith("["):
             bar = Fore.GREEN + ("#" * filled) + Fore.CYAN + ("-" * (bar_width - filled))
             prefix = Fore.CYAN + f"[{bar}{Fore.CYAN}] {self.completed_steps}/{self.total_steps}" + Style.RESET_ALL
         else:
             prefix = Fore.CYAN + prefix_text + Style.RESET_ALL
 
-        sys.stdout.write("\r" + prefix + label + Style.RESET_ALL)
+        counts = (
+            " "
+            + Style.BRIGHT
+            + Fore.GREEN
+            + f"P:{self.passed_models}"
+            + Style.RESET_ALL
+            + " "
+            + Style.BRIGHT
+            + Fore.RED
+            + f"F:{self.failed_models}"
+            + Style.RESET_ALL
+        )
+        model_counter = ""
+        label = ""
+        if self.current_label.startswith("[") and "] " in self.current_label:
+            model_counter, label = self.current_label.split("] ", 1)
+            model_counter = f" {model_counter}]"
+            label = f" {label}"
+        elif self.current_label:
+            label = f" {self.current_label}"
+
+        available_label_width = max(0, self.columns - len(prefix_text) - len(model_counter) - len(counts_text) - 3)
+        label = label[:available_label_width]
+
+        sys.stdout.write("\r" + prefix + model_counter + counts + label + Style.RESET_ALL)
         sys.stdout.flush()
 
     def log(self, message="", color=None):
@@ -70,6 +102,13 @@ class ProgressReporter:
         self.completed_steps = min(self.total_steps, self.completed_steps + 1)
         self._render()
 
+    def record_result(self, passed):
+        if passed:
+            self.passed_models += 1
+        else:
+            self.failed_models += 1
+        self._render()
+
     def suspend(self):
         self.suspended = True
         self._clear()
@@ -112,13 +151,13 @@ def clean_workspace_artifacts(workspace_dir, model_stem):
 
 def print_stage(reporter, model_index, model_total, model_name, title):
     stage_colors = {
-        "Compile ONNX": Fore.BLUE,
-        "Build Runner": Fore.MAGENTA,
-        "Generate Inputs": Fore.YELLOW,
-        "Run Reference": Fore.GREEN,
-        "Compile PIM": Fore.CYAN,
-        "Run Simulator": Fore.MAGENTA,
-        "Compare Outputs": Fore.YELLOW,
+        STAGE_TITLES[0]: Fore.BLUE,
+        STAGE_TITLES[1]: Fore.MAGENTA,
+        STAGE_TITLES[2]: Fore.YELLOW,
+        STAGE_TITLES[3]: Fore.GREEN,
+        STAGE_TITLES[4]: Fore.CYAN,
+        STAGE_TITLES[5]: Fore.MAGENTA,
+        STAGE_TITLES[6]: Fore.YELLOW,
     }
     color = stage_colors.get(title, Fore.WHITE)
     reporter.log(Style.BRIGHT + color + f"[{title}]" + Style.RESET_ALL)
@@ -284,11 +323,13 @@ def validate_network(network_onnx_path, raptor_path, onnx_include_dir,
         passed = validate_outputs(sim_arrays, out_dir, outputs_descriptor, threshold)
         reporter.resume()
         reporter.advance()
+        reporter.record_result(passed)
         status = Fore.GREEN + "PASS" + Style.RESET_ALL if passed else Fore.RED + "FAIL" + Style.RESET_ALL
         reporter.log(Style.BRIGHT + f"Result: {status}" + Style.RESET_ALL)
         return passed
     except Exception:
         failed_with_exception = True
+        reporter.record_result(False)
         reporter.log(Style.BRIGHT + Fore.RED + "Result: FAIL" + Style.RESET_ALL)
         reporter.suspend()
         raise