multiple-output spat computes

src/PIM/Conversion/ONNXToSpatial/Common.hpp

@@ -182,7 +182,7 @@ auto createSpatCompute(RewriterT& rewriter,
                        mlir::ValueRange inputs,
                        BodyFn&& body) {
   assert(inputs.size() == NumInputs && "NumInputs must match the number of input values");
-  auto computeOp = spatial::SpatWeightedCompute::create(rewriter, loc, resultTypes, weights, inputs);
+  auto computeOp = spatial::SpatCompute::create(rewriter, loc, resultTypes, weights, inputs);
 
   auto* block = new mlir::Block();
   for (mlir::Value input : inputs)
@@ -198,10 +198,10 @@ auto createSpatCompute(RewriterT& rewriter,
     if (mlir::failed(bodyResult)) {
       rewriter.setInsertionPointAfter(computeOp);
       rewriter.eraseOp(computeOp);
-      return mlir::FailureOr<spatial::SpatWeightedCompute>(mlir::failure());
+      return mlir::FailureOr<spatial::SpatCompute>(mlir::failure());
     }
     rewriter.setInsertionPointAfter(computeOp);
-    return mlir::FailureOr<spatial::SpatWeightedCompute>(computeOp);
+    return mlir::FailureOr<spatial::SpatCompute>(computeOp);
   }
   else {
     static_assert(std::is_same_v<BodyResult, void>, "createSpatCompute body must return void or mlir::LogicalResult");
@@ -219,7 +219,7 @@ auto createSpatCompute(RewriterT& rewriter,
                        mlir::ValueRange weights,
                        mlir::ValueRange inputs,
                        BodyFn&& body) {
-  auto computeOp = spatial::SpatWeightedCompute::create(rewriter, loc, resultTypes, weights, inputs);
+  auto computeOp = spatial::SpatCompute::create(rewriter, loc, resultTypes, weights, inputs);
 
   auto* block = new mlir::Block();
   for (mlir::Value input : inputs)
@@ -234,10 +234,10 @@ auto createSpatCompute(RewriterT& rewriter,
     if (mlir::failed(bodyResult)) {
       rewriter.setInsertionPointAfter(computeOp);
       rewriter.eraseOp(computeOp);
-      return mlir::FailureOr<spatial::SpatWeightedCompute>(mlir::failure());
+      return mlir::FailureOr<spatial::SpatCompute>(mlir::failure());
     }
     rewriter.setInsertionPointAfter(computeOp);
-    return mlir::FailureOr<spatial::SpatWeightedCompute>(computeOp);
+    return mlir::FailureOr<spatial::SpatCompute>(computeOp);
   }
   else {
     static_assert(std::is_same_v<BodyResult, void>, "createSpatCompute body must return void or mlir::LogicalResult");

src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp

@@ -133,7 +133,7 @@ void ONNXToSpatialPass::runOnOperation() {
   if (coresCount != -1) {
     int computeOpsCount = 0;
     for (auto& op : entryFunc->getFunctionBody().front().getOperations())
-      if (isa<spatial::SpatWeightedCompute>(op))
+      if (isa<spatial::SpatCompute>(op))
         computeOpsCount++;
 
     if (computeOpsCount > coresCount) {
@@ -167,16 +167,16 @@ bool encapsulator(IRRewriter& rewriter, Location loc, Operation* inst, std::func
   if (T toRemoveOp = llvm::dyn_cast_if_present<T>(inst)) {
     Value source = funcSource(toRemoveOp);
     rewriter.setInsertionPointAfter(toRemoveOp);
-    if (isa_and_present<spatial::SpatWeightedCompute>(source.getDefiningOp())) {
-      auto newCompute = spatial::SpatWeightedCompute::create(rewriter, loc, inst->getResultTypes().front(), source);
+    if (isa_and_present<spatial::SpatCompute>(source.getDefiningOp())) {
+      auto newCompute = spatial::SpatCompute::create(rewriter, loc, inst->getResultTypes(), source);
       auto BB = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), {source.getType()}, {loc});
       newCompute.getProperties().setOperandSegmentSizes({(int) 0, (int) 1});
       rewriter.setInsertionPointToEnd(BB);
       IRMapping mapper;
       mapper.map(source, BB->getArgument(0));
       auto newInst = rewriter.clone(*inst, mapper);
-      spatial::SpatYieldOp::create(rewriter, loc, newInst->getResult(0));
-      inst->replaceAllUsesWith(newCompute);
+      spatial::SpatYieldOp::create(rewriter, loc, newInst->getResults());
+      inst->replaceAllUsesWith(newCompute->getResults());
       inst->erase();
       return true;
     }
@@ -189,8 +189,8 @@ bool encapsulateConcat(IRRewriter& rewriter, Location loc, Operation* inst) {
     auto sources = toRemoveOp.getInputs();
     rewriter.setInsertionPointAfter(toRemoveOp);
     if (llvm::any_of(
-          sources, [](auto source) { return isa_and_present<spatial::SpatWeightedCompute>(source.getDefiningOp()); })) {
-      auto newCompute = spatial::SpatWeightedCompute::create(rewriter, loc, inst->getResultTypes().front(), sources);
+          sources, [](auto source) { return isa_and_present<spatial::SpatCompute>(source.getDefiningOp()); })) {
+      auto newCompute = spatial::SpatCompute::create(rewriter, loc, inst->getResultTypes(), sources);
       SmallVector<Type> sourceTypes;
       SmallVector<Location> sourceLoc;
       for (auto source : sources) {
@@ -204,8 +204,8 @@ bool encapsulateConcat(IRRewriter& rewriter, Location loc, Operation* inst) {
       for (auto [source, bbArg] : llvm::zip(sources, BB->getArguments()))
         mapper.map(source, bbArg);
       auto newConcat = rewriter.clone(*inst, mapper);
-      spatial::SpatYieldOp::create(rewriter, loc, newConcat->getResult(0));
-      inst->replaceAllUsesWith(newCompute);
+      spatial::SpatYieldOp::create(rewriter, loc, newConcat->getResults());
+      inst->replaceAllUsesWith(newCompute->getResults());
       inst->erase();
       return true;
     }
@@ -298,14 +298,15 @@ void ONNXToSpatialPass::encapsulateGlobalInstruction(func::FuncOp funcOp) {
 void ONNXToSpatialPass::mergeTriviallyConnectedComputes(func::FuncOp funcOp) {
   Location loc = funcOp.getLoc();
   IRRewriter rewriter(&getContext());
-  SmallVector<spatial::SpatWeightedCompute> trivialComputes;
-  llvm::SmallSet<spatial::SpatWeightedCompute, 8> toErase;
+  SmallVector<spatial::SpatCompute> trivialComputes;
+  llvm::SmallSet<spatial::SpatCompute, 8> toErase;
 
-  for (auto compute : funcOp.getOps<spatial::SpatWeightedCompute>())
+  for (auto compute : funcOp.getOps<spatial::SpatCompute>())
     if (compute->hasOneUse()) {
-      auto user = dyn_cast<spatial::SpatWeightedCompute>(*compute->getUsers().begin());
+      auto& use = *compute->getUses().begin();
+      auto user = dyn_cast<spatial::SpatCompute>(use.getOwner());
 
-      if (user && user.getInputs().size() == 1)
+      if (user && user.getInputs().size() == 1 && use.getOperandNumber() >= user.getWeights().size())
         trivialComputes.push_back(compute);
     }
 
@@ -317,12 +318,15 @@ void ONNXToSpatialPass::mergeTriviallyConnectedComputes(func::FuncOp funcOp) {
       trivialComputes.pop_back();
       continue;
     }
-    auto child = cast<spatial::SpatWeightedCompute>(*compute->getUsers().begin());
+    auto& computeUse = *compute->getUses().begin();
+    auto child = cast<spatial::SpatCompute>(computeUse.getOwner());
+    auto usedResult = cast<OpResult>(computeUse.get()).getResultNumber();
+    auto childArgIndex = computeUse.getOperandNumber() - child.getWeights().size();
 
     rewriter.setInsertionPointAfter(compute.getOperation());
 
     auto newCompute =
-      spatial::SpatWeightedCompute::create(rewriter, loc, child.getResultTypes(), compute.getOperands());
+      spatial::SpatCompute::create(rewriter, loc, child.getResultTypes(), compute.getOperands());
     newCompute.getProperties().setOperandSegmentSizes(
       {static_cast<int>(compute.getWeights().size()), static_cast<int>(compute.getInputs().size())});
 
@@ -343,7 +347,7 @@ void ONNXToSpatialPass::mergeTriviallyConnectedComputes(func::FuncOp funcOp) {
 
     compute.getBodyRegion().cloneInto(&newCompute.getBodyRegion(), mapper);
     auto newTerminator = newCompute.getBody().front().getTerminator();
-    mapper.map(*child.getBody().front().getArguments().begin(), newTerminator->getOperand(0));
+    mapper.map(child.getBody().front().getArgument(childArgIndex), newTerminator->getOperand(usedResult));
     newTerminator->erase();
     rewriter.setInsertionPoint(&newCompute.getBody().front(), newCompute.getBody().front().end());
     for (auto& op : child.getBody().front()) {
@@ -371,14 +375,16 @@ void ONNXToSpatialPass::mergeTriviallyConnectedComputes(func::FuncOp funcOp) {
     toErase.insert(compute);
 
     if (newCompute->hasOneUse()) {
-      auto user = dyn_cast<spatial::SpatWeightedCompute>(*newCompute->getUsers().begin());
-      if (user && user.getInputs().size() == 1)
+      auto& use = *newCompute->getUses().begin();
+      auto user = dyn_cast<spatial::SpatCompute>(use.getOwner());
+      if (user && user.getInputs().size() == 1 && use.getOperandNumber() >= user.getWeights().size())
         trivialComputes.push_back(newCompute);
     }
   }
 
   for (auto compute : toErase) {
-    compute.getResult(0).dropAllUses();
+    for (Value result : compute->getResults())
+      result.dropAllUses();
     compute.erase();
   }
 }
@@ -386,7 +392,7 @@ void ONNXToSpatialPass::mergeTriviallyConnectedComputes(func::FuncOp funcOp) {
 void ONNXToSpatialPass::annotateWeightsConstants(func::FuncOp funcOp) const {
   funcOp.walk([&](arith::ConstantOp constantOp) {
     bool isAlwaysWeight =
-      llvm::all_of(constantOp->getUsers(), [](auto user) -> bool { return isa<spatial::SpatWeightedCompute>(user); });
+      llvm::all_of(constantOp->getUsers(), [](auto user) -> bool { return isa<spatial::SpatCompute>(user); });
     if (isAlwaysWeight)
       markWeightAlways(constantOp);
   });
@@ -394,7 +400,7 @@ void ONNXToSpatialPass::annotateWeightsConstants(func::FuncOp funcOp) const {
 
 LogicalResult ONNXToSpatialPass::promoteConstantInputsToWeights(func::FuncOp funcOp) {
   IRRewriter rewriter(&getContext());
-  SmallVector<spatial::SpatWeightedCompute> computes(funcOp.getOps<spatial::SpatWeightedCompute>());
+  SmallVector<spatial::SpatCompute> computes(funcOp.getOps<spatial::SpatCompute>());
 
   for (auto compute : computes) {
     SmallVector<bool> promoteInput(compute.getInputs().size(), false);
@@ -430,7 +436,7 @@ LogicalResult ONNXToSpatialPass::promoteConstantInputsToWeights(func::FuncOp fun
     }
 
     auto newCompute =
-      spatial::SpatWeightedCompute::create(rewriter, compute.getLoc(), compute.getResultTypes(), newWeights, newInputs);
+      spatial::SpatCompute::create(rewriter, compute.getLoc(), compute.getResultTypes(), newWeights, newInputs);
     auto* newBlock =
       rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), newInputTypes, newInputLocs);
     newCompute.getProperties().setOperandSegmentSizes(

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

@@ -147,33 +147,37 @@ static Value buildPackedBias(bool hasBias,
   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) {
+static SmallVector<Value> createIm2colRowComputes(Value x,
+                                                  RankedTensorType xType,
+                                                  RankedTensorType im2colType,
+                                                  RankedTensorType im2colRowType,
+                                                  RankedTensorType gemmInputRowType,
+                                                  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,
+                                                  int64_t packFactor,
+                                                  ConversionPatternRewriter& rewriter,
+                                                  Location loc) {
   auto elemType = xType.getElementType();
   constexpr size_t numInputs = 1;
-  auto im2colComputeOp = createSpatCompute<numInputs>(rewriter, loc, im2colType, {}, x, [&](Value xArg) {
+  const int64_t packedNumRows = ceilIntegerDivide(numPatches, packFactor);
+  SmallVector<Type> resultTypes(packedNumRows, gemmInputRowType);
+  auto im2colComputeOp = createSpatCompute<numInputs>(rewriter, loc, resultTypes, {}, x, [&](Value xArg) {
     Value paddedInput = xArg;
 
     // Pad input with zeros if needed:
@@ -240,7 +244,7 @@ static Value createIm2colCompute(Value x,
 
     Value row = tensor::CollapseShapeOp::create(rewriter,
                                                 loc,
-                                                rowType,
+                                                im2colRowType,
                                                 patch,
                                                 SmallVector<ReassociationIndices> {
                                                   {0},
@@ -256,121 +260,117 @@ static Value createIm2colCompute(Value x,
 
     rewriter.setInsertionPointAfter(im2colLoop);
     Value im2col = im2colLoop.getResult(0);
-    spatial::SpatYieldOp::create(rewriter, loc, im2col);
-  });
-  return im2colComputeOp.getResult(0);
-}
 
-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);
+    Value gemmInputRows = im2col;
+    if (packFactor != 1) {
+      const int64_t paddedNumPatches = packedNumRows * packFactor;
+      auto groupedType = RankedTensorType::get({packedNumRows, packFactor, patchSize}, elemType);
+      auto packedType = RankedTensorType::get({packedNumRows, packFactor * patchSize}, elemType);
+      Value paddedIm2col = createPaddedRows(im2col, im2colType, paddedNumPatches, rewriter, loc);
+      Value groupedIm2col = tensor::ExpandShapeOp::create(rewriter,
+                                                          loc,
+                                                          groupedType,
+                                                          paddedIm2col,
+                                                          SmallVector<ReassociationIndices> {
+                                                            {0, 1},
+                                                            {2}
+      });
+      gemmInputRows = tensor::CollapseShapeOp::create(rewriter,
+                                                      loc,
+                                                      packedType,
+                                                      groupedIm2col,
+                                                      SmallVector<ReassociationIndices> {
+                                                        {0},
+                                                        {1, 2}
+      });
     }
 
-    spatial::SpatYieldOp::create(rewriter, loc, unpackedOutput);
+    SmallVector<Value> rowResults;
+    rowResults.reserve(packedNumRows);
+    for (int64_t rowIdx = 0; rowIdx < packedNumRows; rowIdx++) {
+      SmallVector<OpFoldResult> offsets = {rewriter.getIndexAttr(rowIdx), rewriter.getIndexAttr(0)};
+      SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
+                                         rewriter.getIndexAttr(packFactor * patchSize)};
+      SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+      rowResults.push_back(
+        tensor::ExtractSliceOp::create(rewriter, loc, gemmInputRowType, gemmInputRows, offsets, sizes, strides));
+    }
+    spatial::SpatYieldOp::create(rewriter, loc, rowResults);
   });
-  return unpackComputeOp.getResult(0);
+
+  SmallVector<Value> rows;
+  rows.reserve(im2colComputeOp.getNumResults());
+  for (Value result : im2colComputeOp.getResults())
+    rows.push_back(result);
+  return rows;
 }
 
-static Value createCollectedConvOutput(Value gemmOut,
+static Value createCollectedConvOutput(ValueRange gemmRows,
                                        Type convType,
+                                       RankedTensorType gemmOutType,
                                        RankedTensorType nhwcType,
                                        RankedTensorType outType,
+                                       int64_t numPatches,
+                                       int64_t numChannelsOut,
+                                       int64_t packFactor,
                                        ConversionPatternRewriter& rewriter,
                                        Location loc) {
-  auto collectComputeOp =
-    createSpatCompute(rewriter, loc, convType, {}, ValueRange {gemmOut}, [&](ValueRange gemmOutArgs) {
-      Value gemmOutArg = gemmOutArgs.front();
-
-      // Restore to NCHW layout:
-      // [numPatches, numChannelsOut]
-      // -> [1, outHeight, outWidth, numChannelsOut]
-      // -> [1, numChannelsOut, outHeight, outWidth]
-      Value nhwcOut = tensor::ExpandShapeOp::create(rewriter,
-                                                    loc,
-                                                    nhwcType,
-                                                    gemmOutArg,
-                                                    SmallVector<ReassociationIndices> {
-                                                      {0, 1, 2},
-                                                      {3}
+  const int64_t packedNumRows = ceilIntegerDivide(numPatches, packFactor);
+  const int64_t paddedNumPatches = packedNumRows * packFactor;
+  auto collectComputeOp = createSpatCompute(rewriter, loc, convType, {}, gemmRows, [&](ValueRange gemmRowArgs) {
+    Value gemmOut;
+    if (packFactor == 1) {
+      gemmOut = gemmRowArgs.size() == 1 ? gemmRowArgs.front()
+                                        : tensor::ConcatOp::create(rewriter, loc, /*axis=*/0, gemmRowArgs).getResult();
+    }
+    else {
+      auto expandedType = RankedTensorType::get({packedNumRows, packFactor, numChannelsOut}, outType.getElementType());
+      auto paddedType = RankedTensorType::get({paddedNumPatches, numChannelsOut}, outType.getElementType());
+      Value packedOutput =
+        gemmRowArgs.size() == 1
+          ? gemmRowArgs.front()
+          : tensor::ConcatOp::create(rewriter, loc, /*axis=*/0, gemmRowArgs).getResult();
+      Value expandedOutput = tensor::ExpandShapeOp::create(rewriter,
+                                                           loc,
+                                                           expandedType,
+                                                           packedOutput,
+                                                           SmallVector<ReassociationIndices> {
+                                                             {0},
+                                                             {1, 2}
       });
-      Value nchwOut = ONNXTransposeOp::create(rewriter, loc, outType, nhwcOut, rewriter.getI64ArrayAttr({0, 3, 1, 2}));
-      spatial::SpatYieldOp::create(rewriter, loc, nchwOut);
+      Value paddedOutput = tensor::CollapseShapeOp::create(rewriter,
+                                                           loc,
+                                                           paddedType,
+                                                           expandedOutput,
+                                                           SmallVector<ReassociationIndices> {
+                                                             {0, 1},
+                                                             {2}
+      });
+
+      gemmOut = 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)};
+        gemmOut = tensor::ExtractSliceOp::create(rewriter, loc, gemmOutType, paddedOutput, offsets, sizes, strides);
+      }
+    }
+
+    // Restore to NCHW layout:
+    // [numPatches, numChannelsOut]
+    // -> [1, outHeight, outWidth, numChannelsOut]
+    // -> [1, numChannelsOut, outHeight, outWidth]
+    Value nhwcOut = tensor::ExpandShapeOp::create(rewriter,
+                                                  loc,
+                                                  nhwcType,
+                                                  gemmOut,
+                                                  SmallVector<ReassociationIndices> {
+                                                    {0, 1, 2},
+                                                    {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);
 }
 
@@ -487,11 +487,11 @@ LogicalResult ConvToGemm::matchAndRewrite(ONNXConvOp convOp,
 
   // 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 gemmBias = ONNXNoneOp::create(rewriter, loc, rewriter.getNoneType());
   Value biasMatrix;
   DenseElementsAttr biasDenseAttr;
   if (hasB) {
-    gemmC = b;
+    gemmBias = b;
     biasDenseAttr = getDenseConstantAttr(b);
     biasMatrix = expandBiasIfNeeded(b, rewriter, loc);
   }
@@ -500,94 +500,86 @@ LogicalResult ConvToGemm::matchAndRewrite(ONNXConvOp convOp,
   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);
+  // 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
+  // and optionally repack several old rows into one GEMM row to use the available crossbar size better.
+  //
+  // The im2col compute yields each GEMM input row as a separate result so every GEMM consumes only
+  // the row it needs instead of receiving a full packed tensor and slicing it locally.
+  auto gemmInputRowType =
+    RankedTensorType::get({1, effectiveMaxParallelPixels * patchSize}, elemType);
+  auto gemmOutputRowType =
+    RankedTensorType::get({1, effectiveMaxParallelPixels * numChannelsOut}, outType.getElementType());
+  SmallVector<Value> gemmInputRows = createIm2colRowComputes(x,
+                                                            xType,
+                                                            im2colType,
+                                                            rowType,
+                                                            gemmInputRowType,
+                                                            batchSize,
+                                                            numChannelsIn,
+                                                            xHeight,
+                                                            xWidth,
+                                                            wHeight,
+                                                            wWidth,
+                                                            padHeightBegin,
+                                                            padHeightEnd,
+                                                            padWidthBegin,
+                                                            padWidthEnd,
+                                                            strideHeight,
+                                                            strideWidth,
+                                                            dilationHeight,
+                                                            dilationWidth,
+                                                            outWidth,
+                                                            patchSize,
+                                                            numPatches,
+                                                            numPatchesPerBatch,
+                                                            effectiveMaxParallelPixels,
+                                                            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 gemmB = buildPackedWeight(wDenseAttr,
+                                  wTrans,
+                                  wType,
+                                  numChannelsIn,
+                                  numChannelsOut,
+                                  wHeight,
+                                  wWidth,
+                                  patchSize,
+                                  effectiveMaxParallelPixels,
+                                  rewriter,
+                                  loc);
+  Value gemmC = buildPackedBias(
+    hasB, gemmBias, biasMatrix, biasDenseAttr, outType, numChannelsOut, effectiveMaxParallelPixels, rewriter, loc);
 
-    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);
+  SmallVector<Value> gemmRows;
+  gemmRows.reserve(gemmInputRows.size());
+  for (Value gemmInputRow : gemmInputRows) {
+    Value gemmRow = ONNXGemmOp::create(rewriter,
+                                       loc,
+                                       gemmOutputRowType,
+                                       gemmInputRow,
+                                       gemmB,
+                                       gemmC,
+                                       rewriter.getF32FloatAttr(1.0f),
+                                       rewriter.getF32FloatAttr(1.0f),
+                                       rewriter.getBoolAttr(false),
+                                       rewriter.getBoolAttr(false))
+                      .getY();
+    gemmRows.push_back(gemmRow);
   }
 
-  rewriter.replaceOp(convOp, createCollectedConvOutput(gemmOut, convOp.getType(), nhwcType, outType, rewriter, loc));
+  rewriter.replaceOp(convOp,
+                     createCollectedConvOutput(gemmRows,
+                                               convOp.getType(),
+                                               gemmOutType,
+                                               nhwcType,
+                                               outType,
+                                               numPatches,
+                                               numChannelsOut,
+                                               effectiveMaxParallelPixels,
+                                               rewriter,
+                                               loc));
   return success();
 }
 

src/PIM/Conversion/SpatialToGraphviz/SpatialToGraphviz.cpp

@@ -42,15 +42,15 @@ private:
   raw_ostream& os;
 
   /**
-   * Draws the subgraph for a given spatial::SpatWeightedCompute, including:
+   * Draws the subgraph for a given spatial::SpatCompute, including:
    * 1. Input nodes (block arguments)
    * 2. Operations
    * 3. Edges between yield (output) and its users
    *
-   * @param op The spatial::SpatWeightedCompute to draw the subgraph for.
+   * @param op The spatial::SpatCompute to draw the subgraph for.
    * @param computeNum The number of the compute operation.
    */
-  void drawComputeOpSubgraph(spatial::SpatWeightedCompute op, size_t computeNum) {
+  void drawComputeOpSubgraph(spatial::SpatCompute op, size_t computeNum) {
     os << "\tsubgraph cluster" << computeNum << " {\n\t\tlabel=\"Compute" << computeNum << "\";\n"
        << "\t\tstyle=filled;\n"
        << "\t\tcolor=lightblue;\n";
@@ -217,7 +217,7 @@ void SpatialToGraphvizPass::runOnOperation() {
   // 1. Print their subgraph
   // 2. Print the edges from its inputs to its outputs
   for (Operation& op : func.getOps()) {
-    if (auto computeOp = dyn_cast<spatial::SpatWeightedCompute>(op)) {
+    if (auto computeOp = dyn_cast<spatial::SpatCompute>(op)) {
       drawComputeOpSubgraph(computeOp, computeNum++);
     }
     else if (auto concatOp = dyn_cast<tensor::ConcatOp>(op)) {

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.cpp

@@ -62,7 +62,7 @@ private:
   void runOnReceiveOp(spatial::SpatChannelReceiveOp receiveOp, IRRewriter& rewriter);
   void
   addReceiveOps(Value channelSourceOp, spatial::SpatChannelNewOp& channel, bool useBroadcastOp, IRRewriter& rewriter);
-  void replaceBlockArgumentWithRecvOp(spatial::SpatWeightedCompute& computeOp,
+  void replaceBlockArgumentWithRecvOp(spatial::SpatCompute& computeOp,
                                       unsigned int argIndex,
                                       Value channelSourceOp,
                                       Value consumerValue,
@@ -73,7 +73,7 @@ private:
   void annotateChannelCoreIds(func::FuncOp funcOp);
   void lowerBroadcastChannelOps(func::FuncOp funcOp, IRRewriter& rewriter);
 
-  void runOnComputeOp(spatial::SpatWeightedCompute computeOp, IRRewriter& rewriter);
+  void runOnComputeOp(spatial::SpatCompute computeOp, IRRewriter& rewriter);
 
   void enlargeVMMOutTensorsToCrossbarSize(func::FuncOp funcOp, IRRewriter& rewriter);
 
@@ -116,7 +116,7 @@ static size_t countComputeLeafUsers(Value value) {
   auto walkUses = [&](Value currentValue, auto& self) -> void {
     for (OpOperand& use : currentValue.getUses()) {
       Operation* owner = use.getOwner();
-      if (isa<spatial::SpatWeightedCompute>(owner)) {
+      if (isa<spatial::SpatCompute>(owner)) {
         leafUserCount++;
         continue;
       }
@@ -174,7 +174,7 @@ void SpatialToPimPass::runOnOperation() {
     markOpToRemove(receiveOp);
     runOnReceiveOp(receiveOp, rewriter);
   }
-  for (auto computeOp : funcOp.getOps<spatial::SpatWeightedCompute>()) {
+  for (auto computeOp : funcOp.getOps<spatial::SpatCompute>()) {
     markOpToRemove(computeOp);
     runOnComputeOp(computeOp, rewriter);
   }
@@ -222,7 +222,7 @@ void SpatialToPimPass::runOnOperation() {
   dumpModule(moduleOp, "pim0");
 }
 
-void SpatialToPimPass::runOnComputeOp(spatial::SpatWeightedCompute computeOp, IRRewriter& rewriter) {
+void SpatialToPimPass::runOnComputeOp(spatial::SpatCompute computeOp, IRRewriter& rewriter) {
   Location loc = computeOp->getLoc();
 
   auto& block = computeOp.getRegion().front();
@@ -504,7 +504,7 @@ LogicalResult SpatialToPimPass::allocateAndInitializeCoreLocalVariables(func::Fu
 
   llvm::SmallSet<tensor::ExtractSliceOp, 8> sliceOpsToRemove;
   for (auto& op : funcOp.getBody().getOps())
-    if (auto computeOp = dyn_cast<spatial::SpatWeightedCompute>(op)) {
+    if (auto computeOp = dyn_cast<spatial::SpatCompute>(op)) {
       unsigned numComputeWeights = computeOp.getWeights().size();
       for (auto [computeInputIdx, computeOpInput] : llvm::enumerate(computeOp.getInputs())) {
         TypedValue<TensorType> tensorSource;
@@ -513,7 +513,7 @@ LogicalResult SpatialToPimPass::allocateAndInitializeCoreLocalVariables(func::Fu
         if (auto sliceOp = dyn_cast<tensor::ExtractSliceOp>(computeOpInput.getDefiningOp())) {
           tensorSource = cast<TypedValue<TensorType>>(sliceOp.getSource());
 
-          if (isa<spatial::SpatWeightedCompute>(tensorSource.getDefiningOp()))
+          if (isa<spatial::SpatCompute>(tensorSource.getDefiningOp()))
             continue;
 
           ArrayRef<int64_t> sourceShape = tensorSource.getType().getShape();
@@ -538,7 +538,7 @@ LogicalResult SpatialToPimPass::allocateAndInitializeCoreLocalVariables(func::Fu
           tensorSource = cast<TypedValue<TensorType>>(computeOpInput);
 
         // Compute results must be transferred through channels via send/receive
-        if (isa<spatial::SpatWeightedCompute>(tensorSource.getDefiningOp()))
+        if (isa<spatial::SpatCompute>(tensorSource.getDefiningOp()))
           continue;
 
         BlockArgument computeBlockArgToReplace = computeOp.getBody().front().getArgument(computeInputIdx);
@@ -553,7 +553,7 @@ LogicalResult SpatialToPimPass::allocateAndInitializeCoreLocalVariables(func::Fu
   return success();
 }
 
-void SpatialToPimPass::replaceBlockArgumentWithRecvOp(spatial::SpatWeightedCompute& computeOp,
+void SpatialToPimPass::replaceBlockArgumentWithRecvOp(spatial::SpatCompute& computeOp,
                                                       unsigned int argIndex,
                                                       Value channelSourceOp,
                                                       Value consumerValue,
@@ -614,7 +614,7 @@ void SpatialToPimPass::addReceiveOps(Value channelSourceOp,
   auto replayUsesIntoConsumers = [&](Value currentValue, auto& self) -> void {
     for (OpOperand& use : currentValue.getUses()) {
       Operation* owner = use.getOwner();
-      if (auto computeUser = dyn_cast<spatial::SpatWeightedCompute>(owner)) {
+      if (auto computeUser = dyn_cast<spatial::SpatCompute>(owner)) {
         replaceBlockArgumentWithRecvOp(
           computeUser, use.getOperandNumber(), channelSourceOp, currentValue, channel, useBroadcastOp, rewriter);
         continue;

src/PIM/Dialect/Spatial/Spatial.td

@@ -32,7 +32,7 @@ def SpatChannelType : SpatType<"SpatChannel", "ch"> {
 // Execution
 //===----------------------------------------------------------------------===//
 
-def SpatWeightedCompute : SpatOp<"compute", [SingleBlock, AttrSizedOperandSegments]> {
+def SpatCompute : SpatOp<"compute", [SingleBlock, AttrSizedOperandSegments]> {
   let summary = "Compute region with attached constant weights";
 
   let arguments = (ins

src/PIM/Dialect/Spatial/SpatialOps.cpp

@@ -119,7 +119,7 @@ inline LogicalResult mvmOpVerifySize4(SpatWeightedMVMOp* emitter,
 }
 
 llvm::FailureOr<ArrayRef<int64_t>> getWeightShapeForWeightedOp(Operation* weigthedOp, size_t weightIndex) {
-  auto wcomputeOp = dyn_cast<SpatWeightedCompute>(weigthedOp->getParentOp());
+  auto wcomputeOp = dyn_cast<SpatCompute>(weigthedOp->getParentOp());
   if (wcomputeOp)
     return cast<ShapedType>(wcomputeOp.getWeights()[weightIndex].getType()).getShape();
 
@@ -134,7 +134,7 @@ llvm::FailureOr<ArrayRef<int64_t>> getWeightShapeForWeightedOp(Operation* weigth
 LogicalResult SpatWeightedMVMOp::verify() {
   auto matrixShapeOpt = getWeightShapeForWeightedOp(this->getOperation(), this->getWeightIndex());
   if (failed(matrixShapeOpt))
-    return emitError("SpatWeightedMVMOp was not within a SpatWeightedCompute or Core op");
+    return emitError("SpatWeightedMVMOp was not within a SpatCompute or Core op");
   auto matrixShape = *matrixShapeOpt;
   auto vectorShape = getInput().getType().getShape();
   auto outputShape = getOutput().getType().getShape();
@@ -155,7 +155,7 @@ LogicalResult SpatWeightedMVMOp::verify() {
 LogicalResult SpatWeightedVMMOp::verify() {
   auto matrixShapeOpt = getWeightShapeForWeightedOp(this->getOperation(), this->getWeightIndex());
   if (failed(matrixShapeOpt))
-    return emitError("SpatWeightedVMMOp was not within a SpatWeightedCompute or Core op");
+    return emitError("SpatWeightedVMMOp was not within a SpatCompute or Core op");
   auto matrixShape = *matrixShapeOpt;
   auto vectorShape = getInput().getType().getShape();
   auto outputShape = getOutput().getType().getShape();
@@ -200,9 +200,8 @@ LogicalResult SpatVMaxOp::verify() {
   return OpTrait::impl::verifySameOperandsAndResultType(*this);
 }
 
-LogicalResult SpatWeightedCompute::verify() {
-  // Check that it has a terminator, it is a yieldOp, and it has a single
-  // operand with the same type as the result
+LogicalResult SpatCompute::verify() {
+  // Check that the terminator yields the same number and types as the compute results.
   auto& block = getBody().front();
   if (block.mightHaveTerminator()) {
     auto yieldOp = dyn_cast_or_null<SpatYieldOp>(block.getTerminator());
@@ -257,7 +256,7 @@ LogicalResult SpatWeightedCompute::verify() {
   return success();
 }
 
-LogicalResult SpatWeightedCompute::fold(FoldAdaptor adaptor, ::llvm::SmallVectorImpl<::mlir::OpFoldResult>& results) {
+LogicalResult SpatCompute::fold(FoldAdaptor adaptor, ::llvm::SmallVectorImpl<::mlir::OpFoldResult>& results) {
   Block& block = getBody().front();
   if (!llvm::hasSingleElement(block))
     return failure();

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

@@ -74,15 +74,15 @@ std::vector<IndexedEdge> aggregateEdges(llvm::ArrayRef<IndexedEdge> edges) {
   return aggregatedEdges;
 }
 
-VirtualGraph buildInitialVirtualGraph(llvm::ArrayRef<SpatWeightedCompute> spatWeightedComputes,
+VirtualGraph buildInitialVirtualGraph(llvm::ArrayRef<SpatCompute> spatComputes,
                                       llvm::ArrayRef<IndexedEdge> edges) {
   VirtualGraph graph;
-  graph.nodes.reserve(spatWeightedComputes.size());
-  for (auto [index, spatWeightedCompute] : llvm::enumerate(spatWeightedComputes)) {
+  graph.nodes.reserve(spatComputes.size());
+  for (auto [index, spatCompute] : llvm::enumerate(spatComputes)) {
     VirtualNode node;
     node.originalComputeIndices.push_back(index);
-    node.weight = getSpatComputeWeight(spatWeightedCompute);
-    node.crossbarUsage = getSpatComputeCrossbarUsage(spatWeightedCompute);
+    node.weight = getSpatComputeWeight(spatCompute);
+    node.crossbarUsage = getSpatComputeCrossbarUsage(spatCompute);
     graph.nodes.push_back(std::move(node));
   }
   graph.edges = aggregateEdges(edges);
@@ -344,22 +344,22 @@ std::vector<size_t> computeOriginalTopologicalOrder(size_t computeCount, llvm::A
 }
 
 DCPAnalysisResult buildResultFromVirtualGraph(const VirtualGraph& graph,
-                                              llvm::ArrayRef<SpatWeightedCompute> spatWeightedComputes,
+                                              llvm::ArrayRef<SpatCompute> spatComputes,
                                               llvm::ArrayRef<IndexedEdge> originalEdges) {
   DCPAnalysisResult result;
-  std::vector<size_t> originalToVirtualNode(spatWeightedComputes.size(), 0);
+  std::vector<size_t> originalToVirtualNode(spatComputes.size(), 0);
   for (auto [virtualNodeIndex, virtualNode] : llvm::enumerate(graph.nodes))
     for (size_t originalIndex : virtualNode.originalComputeIndices)
       originalToVirtualNode[originalIndex] = virtualNodeIndex;
 
-  auto dominanceOrder = computeOriginalTopologicalOrder(spatWeightedComputes.size(), originalEdges);
+  auto dominanceOrder = computeOriginalTopologicalOrder(spatComputes.size(), originalEdges);
   result.dominanceOrderCompute.reserve(dominanceOrder.size());
   for (size_t originalIndex : dominanceOrder) {
-    SpatWeightedCompute spatWeightedCompute = spatWeightedComputes[originalIndex];
+    SpatCompute spatCompute = spatComputes[originalIndex];
     size_t cpu = originalToVirtualNode[originalIndex];
-    result.dominanceOrderCompute.push_back(spatWeightedCompute);
-    result.computeToCpuMap[spatWeightedCompute] = cpu;
-    result.cpuToLastComputeMap[cpu] = spatWeightedCompute;
+    result.dominanceOrderCompute.push_back(spatCompute);
+    result.computeToCpuMap[spatCompute] = cpu;
+    result.cpuToLastComputeMap[cpu] = spatCompute;
   }
 
   for (auto [cpu, lastCompute] : result.cpuToLastComputeMap)
@@ -367,10 +367,10 @@ DCPAnalysisResult buildResultFromVirtualGraph(const VirtualGraph& graph,
   return result;
 }
 
-DCPAnalysisResult runLegacyDcp(llvm::ArrayRef<SpatWeightedCompute> spatWeightedComputes,
+DCPAnalysisResult runLegacyDcp(llvm::ArrayRef<SpatCompute> spatComputes,
                                llvm::ArrayRef<IndexedEdge> edges,
                                MLIRContext* context) {
-  GraphDCP graphDCP(spatWeightedComputes, edges);
+  GraphDCP graphDCP(spatComputes, edges);
   if (coresCount.getValue() > 0)
     graphDCP.setMaxCpuCount(static_cast<int>(coresCount.getValue()));
   graphDCP.setContext(context);
@@ -380,7 +380,7 @@ DCPAnalysisResult runLegacyDcp(llvm::ArrayRef<SpatWeightedCompute> spatWeightedC
 
 } // namespace
 
-SpatWeightedCompute getOriginalSpatWeightedCompute(Operation* op) {
+SpatCompute getOriginalSpatCompute(Operation* op) {
   if (!op)
     return {};
   while (auto extract = llvm::dyn_cast<tensor::ExtractSliceOp>(op)) {
@@ -388,39 +388,33 @@ SpatWeightedCompute getOriginalSpatWeightedCompute(Operation* op) {
     if (!op)
       return {};
   }
-  if (auto res = llvm::dyn_cast<SpatWeightedCompute>(op))
+  if (auto res = llvm::dyn_cast<SpatCompute>(op))
     return res;
   return {};
 }
 
 DCPAnalysisResult DCPAnalysis::run() {
-  SmallVector<SpatWeightedCompute, 10> spatWeightedComputes;
+  SmallVector<SpatCompute, 10> spatComputes;
   SmallVector<IndexedEdge, 10> edges;
   for (auto& region : entryOp->getRegions())
-    for (SpatWeightedCompute spatWeightedCompute : region.getOps<SpatWeightedCompute>())
-      spatWeightedComputes.push_back(spatWeightedCompute);
+    for (SpatCompute spatCompute : region.getOps<SpatCompute>())
+      spatComputes.push_back(spatCompute);
 
-  for (auto [indexEndEdge, spatWeightedCompute] : llvm::enumerate(spatWeightedComputes)) {
-    for (Value input : spatWeightedCompute.getInputs()) {
-      if (auto producerCompute = getOriginalSpatWeightedCompute(input.getDefiningOp())) {
-        auto producerIt = llvm::find(spatWeightedComputes, producerCompute);
-        assert(producerIt != spatWeightedComputes.end());
-        auto indexStartEdge = std::distance(spatWeightedComputes.begin(), producerIt);
-        ResultRange outputs = producerCompute.getResults();
-        int64_t totalSize = 0;
-        for (auto output : outputs) {
-          ShapedType resultType = cast<ShapedType>(output.getType());
-          totalSize += getSizeInBytes(resultType);
-        }
-        edges.push_back({indexStartEdge, indexEndEdge, totalSize});
+  for (auto [indexEndEdge, spatCompute] : llvm::enumerate(spatComputes)) {
+    for (Value input : spatCompute.getInputs()) {
+      if (auto producerCompute = getOriginalSpatCompute(input.getDefiningOp())) {
+        auto producerIt = llvm::find(spatComputes, producerCompute);
+        assert(producerIt != spatComputes.end());
+        auto indexStartEdge = std::distance(spatComputes.begin(), producerIt);
+        edges.push_back({indexStartEdge, indexEndEdge, getSizeInBytes(cast<ShapedType>(input.getType()))});
       }
     }
   }
 
   if (dcpCriticalWindowSize.getValue() == 0)
-    return runLegacyDcp(spatWeightedComputes, edges, entryOp->getContext());
+    return runLegacyDcp(spatComputes, edges, entryOp->getContext());
 
-  VirtualGraph virtualGraph = buildInitialVirtualGraph(spatWeightedComputes, edges);
+  VirtualGraph virtualGraph = buildInitialVirtualGraph(spatComputes, edges);
   std::set<std::vector<size_t>> seenCriticalWindows;
   while (virtualGraph.nodes.size() > 1) {
     TimingInfo timing = computeTiming(virtualGraph);
@@ -446,7 +440,7 @@ DCPAnalysisResult DCPAnalysis::run() {
       break;
   }
 
-  return buildResultFromVirtualGraph(virtualGraph, spatWeightedComputes, edges);
+  return buildResultFromVirtualGraph(virtualGraph, spatComputes, edges);
 }
 
 } // namespace spatial

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

@@ -10,10 +10,10 @@
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
 struct DCPAnalysisResult {
-  std::vector<onnx_mlir::spatial::SpatWeightedCompute> dominanceOrderCompute;
-  llvm::DenseMap<onnx_mlir::spatial::SpatWeightedCompute, size_t> computeToCpuMap;
-  llvm::DenseSet<onnx_mlir::spatial::SpatWeightedCompute> isLastComputeOfCpu;
-  llvm::DenseMap<size_t, onnx_mlir::spatial::SpatWeightedCompute> cpuToLastComputeMap;
+  std::vector<onnx_mlir::spatial::SpatCompute> dominanceOrderCompute;
+  llvm::DenseMap<onnx_mlir::spatial::SpatCompute, size_t> computeToCpuMap;
+  llvm::DenseSet<onnx_mlir::spatial::SpatCompute> isLastComputeOfCpu;
+  llvm::DenseMap<size_t, onnx_mlir::spatial::SpatCompute> cpuToLastComputeMap;
 };
 
 namespace onnx_mlir {

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

@@ -1260,7 +1260,7 @@ DCPAnalysisResult GraphDCP::getResult() {
   auto dominanceOrder = dcp_graph::collectDominanceOrder(getRoots(), nodes.size());
   ret.dominanceOrderCompute.reserve(dominanceOrder.size());
   for (auto elem : dominanceOrder)
-    ret.dominanceOrderCompute.push_back(elem->getSpatWeightedCompute());
+    ret.dominanceOrderCompute.push_back(elem->getSpatCompute());
 
   for (CPU cpu = 0; cpu < getLastCpu(); ++cpu) {
     const CpuTaskList* tasks = findCpuTasks(cpu);
@@ -1268,10 +1268,10 @@ DCPAnalysisResult GraphDCP::getResult() {
       continue;
     size_t i = 0;
     for (auto node : *tasks) {
-      ret.computeToCpuMap[node->getSpatWeightedCompute()] = cpu;
+      ret.computeToCpuMap[node->getSpatCompute()] = cpu;
       if (i++ == tasks->size() - 1) {
-        ret.isLastComputeOfCpu.insert(node->getSpatWeightedCompute());
-        ret.cpuToLastComputeMap[cpu] = node->getSpatWeightedCompute();
+        ret.isLastComputeOfCpu.insert(node->getSpatCompute());
+        ret.cpuToLastComputeMap[cpu] = node->getSpatCompute();
       }
     }
   }

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

@@ -115,11 +115,11 @@ private:
 
 public:
   void runDcp();
-  GraphDCP(llvm::ArrayRef<onnx_mlir::spatial::SpatWeightedCompute> spatWeightedComputes,
+  GraphDCP(llvm::ArrayRef<onnx_mlir::spatial::SpatCompute> spatComputes,
            llvm::ArrayRef<IndexedEdge> edges)
   : nodes(), cpuTasks(), cpuCrossbarUsage() {
-    for (auto spatWeightedCompute : spatWeightedComputes)
-      nodes.emplace_back(spatWeightedCompute);
+    for (auto spatCompute : spatComputes)
+      nodes.emplace_back(spatCompute);
     for (auto [start, end, weight] : edges)
       makeEdge(start, end, weight);
   }

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

@@ -8,7 +8,7 @@
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
 class TaskDCP : public onnx_mlir::LabeledListNode<TaskDCP> {
-  onnx_mlir::spatial::SpatWeightedCompute spatWeightedCompute;
+  onnx_mlir::spatial::SpatCompute spatCompute;
   Time aest;
   Time alst;
   std::optional<CPU> scheduledCpu;
@@ -38,22 +38,22 @@ public:
   std::vector<Edge> parents;
   std::vector<Edge> children;
   TaskDCP() = default;
-  TaskDCP(onnx_mlir::spatial::SpatWeightedCompute spatWeightedCompute)
+  TaskDCP(onnx_mlir::spatial::SpatCompute spatCompute)
   : onnx_mlir::LabeledListNode<TaskDCP>(),
-    spatWeightedCompute(spatWeightedCompute),
+    spatCompute(spatCompute),
     aest(0),
     alst(0),
     scheduledCpu(),
-    weight(getSpatComputeWeight(spatWeightedCompute)),
+    weight(getSpatComputeWeight(spatCompute)),
     baseWeight(weight),
-    crossbarUsage(getSpatComputeCrossbarUsage(spatWeightedCompute)),
+    crossbarUsage(getSpatComputeCrossbarUsage(spatCompute)),
     syntheticId(-1),
     parents(),
     children() {}
 
   TaskDCP(int64_t id, Weight weight, CrossbarUsage crossbarUsage = 0)
   : onnx_mlir::LabeledListNode<TaskDCP>(),
-    spatWeightedCompute(),
+    spatCompute(),
     aest(0),
     alst(0),
     scheduledCpu(),
@@ -90,14 +90,14 @@ public:
   void setAlst(Time value) { alst = value; }
   bool hasDescendant(TaskDCP* child);
   int64_t Id() const {
-    if (spatWeightedCompute)
-      return reinterpret_cast<int64_t>(spatWeightedCompute.getAsOpaquePointer());
+    if (spatCompute)
+      return reinterpret_cast<int64_t>(spatCompute.getAsOpaquePointer());
     return syntheticId;
   }
 
   bool isCriticalPath() const { return alst == aest; }
   bool isScheduled() const { return scheduledCpu.has_value(); }
-  onnx_mlir::spatial::SpatWeightedCompute getSpatWeightedCompute() const { return spatWeightedCompute; }
+  onnx_mlir::spatial::SpatCompute getSpatCompute() const { return spatCompute; }
 
   void setFlag(long long val) { flag = val; }
   long long getFlag() const { return flag; }

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

@@ -92,18 +92,18 @@ inline T subtractOrZero(T lhs, T rhs) {
 
 inline Time slackOrZero(Time earliestStart, Time latestStart) { return subtractOrZero(latestStart, earliestStart); }
 
-inline Weight getSpatComputeWeight(onnx_mlir::spatial::SpatWeightedCompute spatWeightedCompute) {
+inline Weight getSpatComputeWeight(onnx_mlir::spatial::SpatCompute spatCompute) {
   constexpr Weight kOperationWeight = 100;
   Weight numOperations = 0;
-  for (auto& block : spatWeightedCompute.getBody())
+  for (auto& block : spatCompute.getBody())
     for ([[maybe_unused]] auto& op : block)
       numOperations = checkedAdd(numOperations, static_cast<Weight>(1));
   return checkedMultiply(numOperations, kOperationWeight);
 }
 
-inline CrossbarUsage getSpatComputeCrossbarUsage(onnx_mlir::spatial::SpatWeightedCompute spatWeightedCompute) {
+inline CrossbarUsage getSpatComputeCrossbarUsage(onnx_mlir::spatial::SpatCompute spatCompute) {
   CrossbarUsage crossbarUsage = 0;
-  for (auto& region : spatWeightedCompute.getBody())
+  for (auto& region : spatCompute.getBody())
     for (auto& inst : region)
       if (llvm::isa<onnx_mlir::spatial::SpatWeightedVMMOp>(inst))
         crossbarUsage = checkedAdd(crossbarUsage, static_cast<CrossbarUsage>(1));

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

@@ -24,30 +24,29 @@ using namespace mlir;
 
 namespace onnx_mlir {
 namespace {
-using SpatWeightedCompute = spatial::SpatWeightedCompute;
+using SpatCompute = spatial::SpatCompute;
 
 struct ComputeValueResults {
-  // Value yielded by the yieldOp
-  Value innerValue;
+  SmallVector<Value> innerValues;
+
+  Value get(size_t resultIndex) const {
+    assert(resultIndex < innerValues.size() && "compute result index out of range");
+    return innerValues[resultIndex];
+  }
 };
 
 class LazyInsertComputeResult {
   using InsertPoint = mlir::IRRewriter::InsertPoint;
   ComputeValueResults computeResults;
-  Value channelValue;
   bool onlyChannel;
-  std::function<void(InsertPoint insertPoint)> channelSendInserter;
-  InsertPoint sendInsertPoint;
-  std::function<std::pair<Value, std::function<void(InsertPoint)>>()> channelNewInserter;
+  std::function<std::pair<Value, std::function<void(InsertPoint)>>(size_t)> channelNewInserter;
 
 public:
   LazyInsertComputeResult(ComputeValueResults computeValueResults,
-                          std::function<std::pair<Value, std::function<void(InsertPoint)>>()> channelNewInserter,
+                          std::function<std::pair<Value, std::function<void(InsertPoint)>>(size_t)> channelNewInserter,
                           bool isOnlyChannel)
   : computeResults(computeValueResults),
     onlyChannel(isOnlyChannel),
-    channelSendInserter(nullptr),
-    sendInsertPoint({}),
     channelNewInserter(channelNewInserter) {}
 
   struct ChannelOrLocalOp {
@@ -57,12 +56,12 @@ public:
 
   bool onlyChanneled() const { return onlyChannel; }
 
-  ChannelOrLocalOp getAsChannelValueAndInsertSender(SpatWeightedCompute currentCompute) {
+  ChannelOrLocalOp getAsChannelValueAndInsertSender(SpatCompute currentCompute, size_t resultIndex) {
+    Value innerValue = computeResults.get(resultIndex);
 
-    auto [newChannelValue, senderInserter] = channelNewInserter();
-    channelValue = newChannelValue;
-    channelSendInserter = senderInserter;
-    auto* block = computeResults.innerValue.getParentBlock();
+    auto [channelValue, channelSendInserter] = channelNewInserter(resultIndex);
+    InsertPoint sendInsertPoint;
+    auto* block = innerValue.getParentBlock();
     if (!block->empty() && isa<spatial::SpatYieldOp>(block->back()))
       sendInsertPoint = InsertPoint(block, --block->end());
     else
@@ -70,28 +69,30 @@ public:
     if (currentCompute) {
       for (auto& block : currentCompute.getBody())
         if (&block == sendInsertPoint.getBlock())
-          return {computeResults.innerValue, false};
+          return {innerValue, false};
     }
     channelSendInserter(sendInsertPoint);
     return {channelValue, true};
   }
 
-  ChannelOrLocalOp getAsChannelValueAndInsertSender() { return getAsChannelValueAndInsertSender({}); }
+  ChannelOrLocalOp getAsChannelValueAndInsertSender(size_t resultIndex) {
+    return getAsChannelValueAndInsertSender({}, resultIndex);
+  }
 };
 
 struct MergeComputeNodesPass : PassWrapper<MergeComputeNodesPass, OperationPass<func::FuncOp>> {
 
 private:
-  DenseMap<SpatWeightedCompute, LazyInsertComputeResult> newComputeNodeResults;
-  DenseMap<SpatWeightedCompute, SpatWeightedCompute> oldToNewComputeMap;
-  DenseMap<int64_t, SpatWeightedCompute> cpuToNewComputeMap;
+  DenseMap<SpatCompute, LazyInsertComputeResult> newComputeNodeResults;
+  DenseMap<SpatCompute, SpatCompute> oldToNewComputeMap;
+  DenseMap<int64_t, SpatCompute> cpuToNewComputeMap;
 
 public:
   MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(MergeComputeNodesPass)
 
   StringRef getArgument() const override { return "pim-merge-compute-nodes-pass"; }
   StringRef getDescription() const override {
-    return "Merge Spatial-Weighted-Compute-Nodes in order to reduce the total "
+    return "Merge Spatial-Compute-Nodes in order to reduce the total "
            "execution time";
   }
 
@@ -105,22 +106,22 @@ public:
     for (auto currentComputeNode : analysisResult.dominanceOrderCompute) {
       size_t cpu = analysisResult.computeToCpuMap.at(currentComputeNode);
       if (!cpuToNewComputeMap.contains(cpu)) {
-        ValueTypeRange<ResultRange> newWeightedComputeType = cpuToLastComputeMap.at(cpu).getResultTypes();
-        auto [newWeightedCompute, computeValueResult] = createNewComputeNode(
-          currentComputeNode, newWeightedComputeType, lastComputeOfCpu.contains(currentComputeNode));
-        cpuToNewComputeMap[cpu] = newWeightedCompute;
+        ValueTypeRange<ResultRange> newComputeType = cpuToLastComputeMap.at(cpu).getResultTypes();
+        auto [newCompute, computeValueResult] = createNewComputeNode(
+          currentComputeNode, newComputeType, lastComputeOfCpu.contains(currentComputeNode));
+        cpuToNewComputeMap[cpu] = newCompute;
         newComputeNodeResults.insert(
           std::make_pair(currentComputeNode,
                          createLazyComputeResult(
-                           newWeightedCompute, computeValueResult, lastComputeOfCpu.contains(currentComputeNode))));
+                           newCompute, computeValueResult, lastComputeOfCpu.contains(currentComputeNode))));
       }
       else {
-        auto [newWeightedCompute, computeValueResult] = mergeIntoComputeNode(
+        auto [newCompute, computeValueResult] = mergeIntoComputeNode(
           cpuToNewComputeMap[cpu], currentComputeNode, lastComputeOfCpu.contains(currentComputeNode));
         newComputeNodeResults.insert(
           std::make_pair(currentComputeNode,
                          createLazyComputeResult(
-                           newWeightedCompute, computeValueResult, lastComputeOfCpu.contains(currentComputeNode))));
+                           newCompute, computeValueResult, lastComputeOfCpu.contains(currentComputeNode))));
       }
     }
 
@@ -134,8 +135,8 @@ public:
   }
 
 private:
-  std::pair<SpatWeightedCompute, ComputeValueResults> createNewComputeNode(
-    SpatWeightedCompute oldWeightedCompute, ValueTypeRange<ResultRange> newWeightedComputeType, bool lastCompute) {
+  std::pair<SpatCompute, ComputeValueResults> createNewComputeNode(
+    SpatCompute oldCompute, ValueTypeRange<ResultRange> newComputeType, bool lastCompute) {
     func::FuncOp func = getOperation();
     auto loc = func.getLoc();
     IRRewriter rewriter(&getContext());
@@ -148,50 +149,53 @@ private:
     llvm::SmallVector<Type> newBBOperandType;
     llvm::SmallVector<Location> newBBLocations;
 
-    for (auto arg : oldWeightedCompute.getWeights())
+    for (auto arg : oldCompute.getWeights())
       newComputeOperand.push_back(arg);
 
-    for (auto arg : oldWeightedCompute.getInputs())
-      if (!llvm::isa_and_present<SpatWeightedCompute>(arg.getDefiningOp())) {
+    for (auto arg : oldCompute.getInputs())
+      if (!llvm::isa_and_present<SpatCompute>(arg.getDefiningOp())) {
         newComputeOperand.push_back(arg);
         newBBOperandType.push_back(arg.getType());
         newBBLocations.push_back(loc);
       }
 
-    auto newWeightedCompute = SpatWeightedCompute::create(rewriter, loc, newWeightedComputeType, newComputeOperand);
+    auto newCompute = SpatCompute::create(rewriter, loc, newComputeType, newComputeOperand);
 
     rewriter.createBlock(
-      &newWeightedCompute.getBody(), newWeightedCompute.getBody().end(), newBBOperandType, newBBLocations);
-    newWeightedCompute.getProperties().setOperandSegmentSizes(
-      {(int) oldWeightedCompute.getWeights().size(), (int) newBBOperandType.size()});
+      &newCompute.getBody(), newCompute.getBody().end(), newBBOperandType, newBBLocations);
+    newCompute.getProperties().setOperandSegmentSizes(
+      {(int) oldCompute.getWeights().size(), (int) newBBOperandType.size()});
 
-    auto& newBB = newWeightedCompute.getBody().front();
-    auto& oldBB = oldWeightedCompute.getBody().front();
+    auto& newBB = newCompute.getBody().front();
+    auto& oldBB = oldCompute.getBody().front();
     rewriter.setInsertionPointToEnd(&newBB);
 
     int indexNew = 0;
-    size_t indexOld = oldWeightedCompute.getWeights().size();
-    size_t indexOldStart = oldWeightedCompute.getWeights().size();
-    for (; indexOld < oldWeightedCompute.getNumOperands(); ++indexOld) {
-      if (!llvm::isa_and_present<SpatWeightedCompute>(oldWeightedCompute.getOperand(indexOld).getDefiningOp())) {
+    size_t indexOld = oldCompute.getWeights().size();
+    size_t indexOldStart = oldCompute.getWeights().size();
+    for (; indexOld < oldCompute.getNumOperands(); ++indexOld) {
+      if (!llvm::isa_and_present<SpatCompute>(oldCompute.getOperand(indexOld).getDefiningOp())) {
         mapper.map(oldBB.getArgument(indexOld - indexOldStart), newBB.getArgument(indexNew++));
       }
       else {
         auto argWeightCompute =
-          llvm::dyn_cast_if_present<SpatWeightedCompute>(oldWeightedCompute.getOperand(indexOld).getDefiningOp());
+          llvm::dyn_cast_if_present<SpatCompute>(oldCompute.getOperand(indexOld).getDefiningOp());
+        auto argResultIndex = cast<OpResult>(oldCompute.getOperand(indexOld)).getResultNumber();
 
         LazyInsertComputeResult& lazyArgWeight = newComputeNodeResults.at(argWeightCompute);
-        auto [channelVal, isChannel] = lazyArgWeight.getAsChannelValueAndInsertSender();
+        auto [channelVal, isChannel] = lazyArgWeight.getAsChannelValueAndInsertSender(argResultIndex);
         assert(isChannel == true);
-        spatial::SpatChannelReceiveOp receiveOp =
-          spatial::SpatChannelReceiveOp::create(rewriter, loc, argWeightCompute.getType(0), channelVal);
+        spatial::SpatChannelReceiveOp receiveOp = spatial::SpatChannelReceiveOp::create(
+          rewriter, loc, oldCompute.getOperand(indexOld).getType(), channelVal);
         mapper.map(oldBB.getArgument(indexOld - indexOldStart), receiveOp);
       }
     }
 
-    for (auto& op : oldWeightedCompute.getOps()) {
+    for (auto& op : oldCompute.getOps()) {
       if (auto yield = dyn_cast<spatial::SpatYieldOp>(&op)) {
-        computeValueResults.innerValue = mapper.lookup(yield.getOperand(0));
+        computeValueResults.innerValues.reserve(yield.getNumOperands());
+        for (Value yieldOperand : yield.getOperands())
+          computeValueResults.innerValues.push_back(mapper.lookup(yieldOperand));
         if (lastCompute)
           rewriter.clone(op, mapper);
       }
@@ -199,16 +203,18 @@ private:
         rewriter.clone(op, mapper);
     }
 
-    for (auto& use : llvm::make_early_inc_range(oldWeightedCompute->getUses()))
-      if (isa<func::ReturnOp>(use.getOwner()))
-        use.assign(newWeightedCompute.getResult(0));
+    for (auto& use : llvm::make_early_inc_range(oldCompute->getUses()))
+      if (isa<func::ReturnOp>(use.getOwner())) {
+        auto resultIndex = cast<OpResult>(use.get()).getResultNumber();
+        use.assign(newCompute.getResult(resultIndex));
+      }
 
-    oldToNewComputeMap.insert({oldWeightedCompute, newWeightedCompute});
-    return {cast<SpatWeightedCompute>(newWeightedCompute), computeValueResults};
+    oldToNewComputeMap.insert({oldCompute, newCompute});
+    return {cast<SpatCompute>(newCompute), computeValueResults};
   }
 
-  std::pair<SpatWeightedCompute, ComputeValueResults>
-  mergeIntoComputeNode(SpatWeightedCompute toCompute, SpatWeightedCompute fromCompute, bool lastCompute) {
+  std::pair<SpatCompute, ComputeValueResults>
+  mergeIntoComputeNode(SpatCompute toCompute, SpatCompute fromCompute, bool lastCompute) {
     func::FuncOp func = getOperation();
     auto loc = func.getLoc();
     IRRewriter rewriter(&getContext());
@@ -239,14 +245,15 @@ private:
     // Insert receiveOp
     rewriter.setInsertionPointToEnd(&toBB);
     for (auto [bbIndex, arg] : llvm::enumerate(fromCompute.getInputs())) {
-      if (auto argWeightCompute = llvm::dyn_cast_if_present<SpatWeightedCompute>(arg.getDefiningOp())) {
+      if (auto argWeightCompute = llvm::dyn_cast_if_present<SpatCompute>(arg.getDefiningOp())) {
         LazyInsertComputeResult& lazyArgWeight = newComputeNodeResults.at(argWeightCompute);
+        auto argResultIndex = cast<OpResult>(arg).getResultNumber();
 
         LazyInsertComputeResult::ChannelOrLocalOp channelOrLocal =
-          lazyArgWeight.getAsChannelValueAndInsertSender(toCompute);
+          lazyArgWeight.getAsChannelValueAndInsertSender(toCompute, argResultIndex);
         if (channelOrLocal.isChannel) {
           spatial::SpatChannelReceiveOp receiveOp =
-            spatial::SpatChannelReceiveOp::create(rewriter, loc, argWeightCompute.getType(0), channelOrLocal.data);
+            spatial::SpatChannelReceiveOp::create(rewriter, loc, arg.getType(), channelOrLocal.data);
           mapper.map(fromBB.getArgument(bbIndex), receiveOp.getResult());
         }
         else {
@@ -286,7 +293,9 @@ private:
     };
     for (auto& op : fromCompute.getOps()) {
       if (auto yield = dyn_cast<spatial::SpatYieldOp>(&op)) {
-        computeValueResults.innerValue = mapper.lookup(yield.getOperand(0));
+        computeValueResults.innerValues.reserve(yield.getNumOperands());
+        for (Value yieldOperand : yield.getOperands())
+          computeValueResults.innerValues.push_back(mapper.lookup(yieldOperand));
         if (lastCompute)
           rewriter.clone(op, mapper);
       }
@@ -299,33 +308,36 @@ private:
       }
     }
 
-    for (auto users : fromCompute->getUsers())
-      if (auto funcRet = dyn_cast<func::ReturnOp>(users))
-        funcRet.setOperand(0, toCompute.getResult(0));
+    for (auto& use : llvm::make_early_inc_range(fromCompute->getUses()))
+      if (isa<func::ReturnOp>(use.getOwner())) {
+        auto resultIndex = cast<OpResult>(use.get()).getResultNumber();
+        use.assign(toCompute.getResult(resultIndex));
+      }
 
     oldToNewComputeMap.insert({fromCompute, toCompute});
-    return {cast<SpatWeightedCompute>(toCompute), computeValueResults};
+    return {cast<SpatCompute>(toCompute), computeValueResults};
   }
 
-  LazyInsertComputeResult createLazyComputeResult(SpatWeightedCompute weightedCompute,
+  LazyInsertComputeResult createLazyComputeResult(SpatCompute compute,
                                                   ComputeValueResults computeValueResults,
                                                   bool lastCompute) {
-    func::FuncOp funcOp = cast<func::FuncOp>(weightedCompute->getParentOp());
+    func::FuncOp funcOp = cast<func::FuncOp>(compute->getParentOp());
     auto* context = &getContext();
     auto loc = funcOp.getLoc();
     IRRewriter rewriter(context);
 
     rewriter.setInsertionPointToStart(&funcOp.front());
     auto savedChannelInsertPoint = rewriter.saveInsertionPoint();
-    auto insertNew = [savedChannelInsertPoint, context, loc, computeValueResults]() {
+    auto insertNew = [savedChannelInsertPoint, context, loc, computeValueResults](size_t resultIndex) {
       IRRewriter rewriter(context);
       rewriter.restoreInsertionPoint(savedChannelInsertPoint);
       auto channelOp = spatial::SpatChannelNewOp::create(rewriter, loc, spatial::SpatChannelType::get(context));
       auto channelVal = channelOp.getResult();
-      auto insertVal = [&context, loc, computeValueResults, channelVal](mlir::IRRewriter::InsertPoint sendInsertPoint) {
+      auto insertVal =
+        [&context, loc, computeValueResults, channelVal, resultIndex](mlir::IRRewriter::InsertPoint sendInsertPoint) {
         IRRewriter rewriter(context);
         rewriter.restoreInsertionPoint(sendInsertPoint);
-        auto spatSend = spatial::SpatChannelSendOp::create(rewriter, loc, channelVal, computeValueResults.innerValue);
+        auto spatSend = spatial::SpatChannelSendOp::create(rewriter, loc, channelVal, computeValueResults.get(resultIndex));
         return spatSend;
       };
       std::pair<Value, std::function<void(mlir::IRRewriter::InsertPoint)>> ret {channelVal, insertVal};

src/PIM/Pass/CountInstructionPass.cpp

@@ -31,7 +31,7 @@ struct CountInstructionPass : public PassWrapper<CountInstructionPass, Operation
     unsigned totalInstructionCount = 0;
 
     unsigned computeId = 0;
-    for (auto computeOp : func.getOps<spatial::SpatWeightedCompute>()) {
+    for (auto computeOp : func.getOps<spatial::SpatCompute>()) {
       unsigned instructionCount = 0;
       instructionCount += computeOp.getBody().front().getOperations().size();
       llvm::outs() << "Compute " << computeId << ": " << instructionCount << " instructions\n";

validation/validate_one.py

@@ -26,6 +26,10 @@ STAGE_COUNT = len(STAGE_TITLES)
 GENERATED_DIR_NAMES = ("inputs", "outputs", "raptor", "runner", "simulation")
 
 
+def sanitize_output_name(name):
+    return "".join(ch if ch.isalnum() or ch in "_.-" else "_" for ch in name[:255])
+
+
 @dataclass
 class ValidationResult:
     passed: bool
@@ -205,7 +209,7 @@ def build_dump_ranges(config_path, outputs_descriptor):
 
 def run_pim_simulator(simulator_dir, pim_dir, output_bin_path, dump_ranges, reporter=None):
     run_command(
-        ["cargo", "run", "--release", "--package", "pim-simulator", "--bin", "pim-simulator", "--",
+        ["cargo", "run", "--no-default-features", "--release", "--package", "pim-simulator", "--bin", "pim-simulator", "--",
          "-f", str(pim_dir), "-o", str(output_bin_path), "-d", dump_ranges],
         cwd=simulator_dir,
         reporter=reporter,
@@ -229,7 +233,7 @@ def validate_outputs(sim_arrays, runner_out_dir, outputs_descriptor, threshold=1
     all_passed = True
     rows = []
     for sim_array, (oi, name, _, shape) in zip(sim_arrays, outputs_descriptor):
-        csv_name = f"output{oi}_{name}.csv"
+        csv_name = f"output{oi}_{sanitize_output_name(name)}.csv"
         runner_array = np.loadtxt(runner_out_dir / csv_name, delimiter=',', dtype=np.float32).reshape(shape)
         max_diff = float(np.max(np.abs(sim_array.astype(np.float64) - runner_array.astype(np.float64))))
         passed = max_diff <= threshold