standardize spatial and pim dialects

remove old unused stuff

src/PIM/Common/PimCommon.hpp

@@ -6,13 +6,11 @@
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/IR/Value.h"
 
-#include "llvm/ADT/ArrayRef.h"
 #include "llvm/ADT/SmallVector.h"
 #include "llvm/ADT/StringRef.h"
 
 #include "src/Compiler/CompilerOptions.hpp"
 
-const llvm::StringRef PimConstantShouldAllocateAttrName = "pim.constant.should_allocate";
 inline constexpr llvm::StringRef PimWeightAlwaysAttrName = "weightAlways";
 
 namespace onnx_mlir {

src/PIM/Compiler/PimCodeGen.cpp

@@ -194,45 +194,45 @@ void PimCodeGen::emitMvmOp(size_t groupId, size_t rdAddr, size_t rdOffset, size_
 
 void PimCodeGen::codeGenLoadOp(pim::PimMemCopyHostToDevOp loadOp) const {
   emitMemCopyOp("ld",
-                memory.getValueAddress(loadOp.getDeviceDst()),
+                memory.getValueAddress(loadOp.getDeviceTarget()),
-                loadOp.getDeviceDstOffset(),
+                loadOp.getDeviceTargetOffset(),
-                memory.getValueAddress(loadOp.getHostSrc()),
+                memory.getValueAddress(loadOp.getHostSource()),
-                loadOp.getHostSrcOffset(),
+                loadOp.getHostSourceOffset(),
                 loadOp.getSize());
 }
 
 void PimCodeGen::codeGenStoreOp(pim::PimMemCopyDevToHostOp storeOp) const {
   emitMemCopyOp("st",
-                memory.getValueAddress(storeOp.getHostDst()),
+                memory.getValueAddress(storeOp.getHostTarget()),
-                storeOp.getHostDstOffset(),
+                storeOp.getHostTargetOffset(),
-                memory.getValueAddress(storeOp.getDeviceSrc()),
+                memory.getValueAddress(storeOp.getDeviceSource()),
-                storeOp.getDeviceSrcOffset(),
+                storeOp.getDeviceSourceOffset(),
                 storeOp.getSize());
 }
 
 void PimCodeGen::codeGenLmvOp(pim::PimMemCopyOp lmvOp) const {
   emitMemCopyOp("lmv",
-                memory.getValueAddress(lmvOp.getDst()),
+                memory.getValueAddress(lmvOp.getTarget()),
-                lmvOp.getDstOffset(),
+                lmvOp.getTargetOffset(),
-                memory.getValueAddress(lmvOp.getSrc()),
+                memory.getValueAddress(lmvOp.getSource()),
-                lmvOp.getSrcOffset(),
+                lmvOp.getSourceOffset(),
                 lmvOp.getSize(),
                 "len");
 }
 
 void PimCodeGen::codeGenReceiveOp(pim::PimReceiveOp receiveOp) const {
   emitCommunicationOp(
-    "recv", memory.getValueAddress(receiveOp.getDst()), receiveOp.getSrcCoreId(), receiveOp.getSize());
+    "recv", memory.getValueAddress(receiveOp.getOutputBuffer()), receiveOp.getSourceCoreId(), receiveOp.getSize());
 }
 
 void PimCodeGen::codeGenSendOp(pim::PimSendOp sendOp) const {
-  emitCommunicationOp("send", memory.getValueAddress(sendOp.getSrc()), sendOp.getTargetCoreId(), sendOp.getSize());
+  emitCommunicationOp("send", memory.getValueAddress(sendOp.getInput()), sendOp.getTargetCoreId(), sendOp.getSize());
 }
 
 template <typename MVMTy>
 void PimCodeGen::codeGenMVMLikeOp(size_t mvmId, MVMTy mvmLikeOp, bool transposeMatrix) {
   emitMvmOp(
-    mvmId, memory.getValueAddress(mvmLikeOp.getOutBuf()), 0, memory.getValueAddress(mvmLikeOp.getVectorInput()), 0);
+    mvmId, memory.getValueAddress(mvmLikeOp.getOutputBuffer()), 0, memory.getValueAddress(mvmLikeOp.getInput()), 0);
 
   // TODO: save weights somewhere (if transposeMatrix=true, transpose the weight matrix)
 }
@@ -243,10 +243,10 @@ static size_t getValueSizeInBytes(mlir::Value value) {
 }
 
 void PimCodeGen::codeGenVVAddOp(pim::PimVVAddOp vvaddOp) const {
-  auto outBufAddr = memory.getValueAddress(vvaddOp.getOutBuf());
+  auto outputBufferAddr = memory.getValueAddress(vvaddOp.getOutputBuffer());
-  auto aAddr = memory.getValueAddress(vvaddOp.getA());
+  auto lhsAddr = memory.getValueAddress(vvaddOp.getLhs());
-  auto bAddr = memory.getValueAddress(vvaddOp.getB());
+  auto rhsAddr = memory.getValueAddress(vvaddOp.getRhs());
-  setupRdRs1Rs2(outBufAddr, 0, aAddr, 0, bAddr, 0);
+  setupRdRs1Rs2(outputBufferAddr, 0, lhsAddr, 0, rhsAddr, 0);
 
   json::Object json;
   json["op"] = "vvadd";
@@ -254,15 +254,15 @@ void PimCodeGen::codeGenVVAddOp(pim::PimVVAddOp vvaddOp) const {
   json["rs1"] = 1;
   json["rs2"] = 2;
   json["offset"] = createEmptyOffset();
-  json["len"] = getValueSizeInBytes(vvaddOp.getA());
+  json["len"] = getValueSizeInBytes(vvaddOp.getLhs());
   emitInstruction(std::move(json));
 }
 
 void PimCodeGen::codeGenVVSubOp(pim::PimVVSubOp vvsubOp) const {
-  auto outBufAddr = memory.getValueAddress(vvsubOp.getOutBuf());
+  auto outputBufferAddr = memory.getValueAddress(vvsubOp.getOutputBuffer());
-  auto aAddr = memory.getValueAddress(vvsubOp.getA());
+  auto lhsAddr = memory.getValueAddress(vvsubOp.getLhs());
-  auto bAddr = memory.getValueAddress(vvsubOp.getB());
+  auto rhsAddr = memory.getValueAddress(vvsubOp.getRhs());
-  setupRdRs1Rs2(outBufAddr, 0, aAddr, 0, bAddr, 0);
+  setupRdRs1Rs2(outputBufferAddr, 0, lhsAddr, 0, rhsAddr, 0);
 
   json::Object json;
   json["op"] = "vvsub";
@@ -270,15 +270,15 @@ void PimCodeGen::codeGenVVSubOp(pim::PimVVSubOp vvsubOp) const {
   json["rs1"] = 1;
   json["rs2"] = 2;
   json["offset"] = createEmptyOffset();
-  json["len"] = getValueSizeInBytes(vvsubOp.getA());
+  json["len"] = getValueSizeInBytes(vvsubOp.getLhs());
   emitInstruction(std::move(json));
 }
 
 void PimCodeGen::codeGenVVMulOp(pim::PimVVMulOp vvmulOp) const {
-  auto outBufAddr = memory.getValueAddress(vvmulOp.getOutBuf());
+  auto outputBufferAddr = memory.getValueAddress(vvmulOp.getOutputBuffer());
-  auto aAddr = memory.getValueAddress(vvmulOp.getA());
+  auto lhsAddr = memory.getValueAddress(vvmulOp.getLhs());
-  auto bAddr = memory.getValueAddress(vvmulOp.getB());
+  auto rhsAddr = memory.getValueAddress(vvmulOp.getRhs());
-  setupRdRs1Rs2(outBufAddr, 0, aAddr, 0, bAddr, 0);
+  setupRdRs1Rs2(outputBufferAddr, 0, lhsAddr, 0, rhsAddr, 0);
 
   json::Object json;
   json["op"] = "vvmul";
@@ -286,15 +286,15 @@ void PimCodeGen::codeGenVVMulOp(pim::PimVVMulOp vvmulOp) const {
   json["rs1"] = 1;
   json["rs2"] = 2;
   json["offset"] = createEmptyOffset();
-  json["len"] = getValueSizeInBytes(vvmulOp.getA());
+  json["len"] = getValueSizeInBytes(vvmulOp.getLhs());
   emitInstruction(std::move(json));
 }
 
 void PimCodeGen::codeGenVVMaxOp(pim::PimVVMaxOp vvmaxOp) const {
-  auto outBufAddr = memory.getValueAddress(vvmaxOp.getOutBuf());
+  auto outputBufferAddr = memory.getValueAddress(vvmaxOp.getOutputBuffer());
-  auto aAddr = memory.getValueAddress(vvmaxOp.getA());
+  auto lhsAddr = memory.getValueAddress(vvmaxOp.getLhs());
-  auto bAddr = memory.getValueAddress(vvmaxOp.getB());
+  auto rhsAddr = memory.getValueAddress(vvmaxOp.getRhs());
-  setupRdRs1Rs2(outBufAddr, 0, aAddr, 0, bAddr, 0);
+  setupRdRs1Rs2(outputBufferAddr, 0, lhsAddr, 0, rhsAddr, 0);
 
   json::Object json;
   json["op"] = "vvmax";
@@ -302,15 +302,15 @@ void PimCodeGen::codeGenVVMaxOp(pim::PimVVMaxOp vvmaxOp) const {
   json["rs1"] = 1;
   json["rs2"] = 2;
   json["offset"] = createEmptyOffset();
-  json["len"] = getValueSizeInBytes(vvmaxOp.getA());
+  json["len"] = getValueSizeInBytes(vvmaxOp.getLhs());
   emitInstruction(std::move(json));
 }
 
 void PimCodeGen::codeGenVVDMulOp(pim::PimVVDMulOp vvdmulOp) const {
-  auto outBufAddr = memory.getValueAddress(vvdmulOp.getOutBuf());
+  auto outputBufferAddr = memory.getValueAddress(vvdmulOp.getOutputBuffer());
-  auto aAddr = memory.getValueAddress(vvdmulOp.getA());
+  auto lhsAddr = memory.getValueAddress(vvdmulOp.getLhs());
-  auto bAddr = memory.getValueAddress(vvdmulOp.getB());
+  auto rhsAddr = memory.getValueAddress(vvdmulOp.getRhs());
-  setupRdRs1Rs2(outBufAddr, 0, aAddr, 0, bAddr, 0);
+  setupRdRs1Rs2(outputBufferAddr, 0, lhsAddr, 0, rhsAddr, 0);
 
   json::Object json;
   json["op"] = "vvdmul";
@@ -318,132 +318,71 @@ void PimCodeGen::codeGenVVDMulOp(pim::PimVVDMulOp vvdmulOp) const {
   json["rs1"] = 1;
   json["rs2"] = 2;
   json["offset"] = createEmptyOffset();
-  json["len"] = getValueSizeInBytes(vvdmulOp.getA());
+  json["len"] = getValueSizeInBytes(vvdmulOp.getLhs());
   emitInstruction(std::move(json));
 }
 
 void PimCodeGen::codeGenVAvgOp(pim::PimVAvgOp vavgOp) const {
-  auto outBufAddr = memory.getValueAddress(vavgOp.getOutBuf());
+  auto outputBufferAddr = memory.getValueAddress(vavgOp.getOutputBuffer());
-  auto aAddr = memory.getValueAddress(vavgOp.getA());
+  auto inputAddr = memory.getValueAddress(vavgOp.getInput());
-  setupRdRs1(outBufAddr, 0, aAddr, 0);
+  setupRdRs1(outputBufferAddr, 0, inputAddr, 0);
 
   json::Object json;
   json["op"] = "vavg";
   json["rd"] = 0;
   json["rs1"] = 1;
   json["offset"] = createEmptyOffset();
-  json["len"] = getValueSizeInBytes(vavgOp.getA());
+  json["len"] = getValueSizeInBytes(vavgOp.getInput());
   emitInstruction(std::move(json));
 }
 
 void PimCodeGen::codeGenVReluOp(pim::PimVReluOp vreluOp) const {
-  auto outBufAddr = memory.getValueAddress(vreluOp.getOutBuf());
+  auto outputBufferAddr = memory.getValueAddress(vreluOp.getOutputBuffer());
-  auto aAddr = memory.getValueAddress(vreluOp.getA());
+  auto inputAddr = memory.getValueAddress(vreluOp.getInput());
-  setupRdRs1(outBufAddr, 0, aAddr, 0);
+  setupRdRs1(outputBufferAddr, 0, inputAddr, 0);
 
   json::Object json;
   json["op"] = "vrelu";
   json["rd"] = 0;
   json["rs1"] = 1;
   json["offset"] = createEmptyOffset();
-  json["len"] = getValueSizeInBytes(vreluOp.getA());
+  json["len"] = getValueSizeInBytes(vreluOp.getInput());
   emitInstruction(std::move(json));
 }
 
 void PimCodeGen::codeGenVTanhOp(pim::PimVTanhOp vtanhOp) const {
-  auto outBufAddr = memory.getValueAddress(vtanhOp.getOutBuf());
+  auto outputBufferAddr = memory.getValueAddress(vtanhOp.getOutputBuffer());
-  auto aAddr = memory.getValueAddress(vtanhOp.getA());
+  auto inputAddr = memory.getValueAddress(vtanhOp.getInput());
-  setupRdRs1(outBufAddr, 0, aAddr, 0);
+  setupRdRs1(outputBufferAddr, 0, inputAddr, 0);
 
   json::Object json;
   json["op"] = "vtanh";
   json["rd"] = 0;
   json["rs1"] = 1;
   json["offset"] = createEmptyOffset();
-  json["len"] = getValueSizeInBytes(vtanhOp.getA());
+  json["len"] = getValueSizeInBytes(vtanhOp.getInput());
   emitInstruction(std::move(json));
 }
 
 void PimCodeGen::codeGenVSigmOp(pim::PimVSigmOp vsigmOp) const {
-  auto outBufAddr = memory.getValueAddress(vsigmOp.getOutBuf());
+  auto outputBufferAddr = memory.getValueAddress(vsigmOp.getOutputBuffer());
-  auto aAddr = memory.getValueAddress(vsigmOp.getA());
+  auto inputAddr = memory.getValueAddress(vsigmOp.getInput());
-  setupRdRs1(outBufAddr, 0, aAddr, 0);
+  setupRdRs1(outputBufferAddr, 0, inputAddr, 0);
 
   json::Object json;
   json["op"] = "vsigm";
   json["rd"] = 0;
   json["rs1"] = 1;
   json["offset"] = createEmptyOffset();
-  json["len"] = getValueSizeInBytes(vsigmOp.getA());
+  json["len"] = getValueSizeInBytes(vsigmOp.getInput());
   emitInstruction(std::move(json));
 }
 
-void PimCodeGen::codeGenApplyFiltersOp(pim::PimApplyFiltersOp applyFiltersOp) const {
-  auto outBufAddr = memory.getValueAddress(applyFiltersOp.getOutBuf());
-  auto inBufAddr = memory.getValueAddress(applyFiltersOp.getInput());
-  auto accumBufAddr = memory.getValueAddress(applyFiltersOp.getAccumBuf());
-
-  auto weightIndices = applyFiltersOp.getWeightIndices();
-
-  auto inputType = cast<MemRefType>(applyFiltersOp.getInput().getType());
-  auto outputType = cast<MemRefType>(applyFiltersOp.getOutBuf().getType());
-  auto inShape = inputType.getShape();
-  auto outShape = outputType.getShape();
-
-  size_t inChannels = inShape[1];
-  size_t outChannels = outShape[1];
-  size_t dimX = inShape.size() > 2 ? inShape[2] : 1;
-  size_t dimY = inShape.size() > 3 ? inShape[3] : 1;
-
-  for (size_t outY = 0; outY < dimY; outY++) {
-    for (size_t outX = 0; outX < dimX; outX++) {
-
-      size_t weightIndex = 0;
-      for (Attribute weight : weightIndices) {
-        // --- STEP 1: Perform MVMUL operation ---
-        auto weightId = cast<IntegerAttr>(weight).getInt();
-        size_t xKer = cast<IntegerAttr>(applyFiltersOp.getXKernelPositions()[weightIndex]).getInt();
-        size_t yKer = cast<IntegerAttr>(applyFiltersOp.getYKernelPositions()[weightIndex]).getInt();
-        weightIndex++;
-
-        if (outX + xKer >= dimX || outY + yKer >= dimY)
-          continue;
-
-        size_t outputOffset = (outY * dimX + outX) * 32 * outChannels;
-        size_t inputOffset = ((outY + yKer) * dimX + (outX + xKer)) * 32 * inChannels;
-
-        bool isFirstWeight = (weightIndices[0] == weight);
-
-        // For the first weight, store directly in output buffer; otherwise use accumulator.
-        size_t rdAddr = isFirstWeight ? outBufAddr : accumBufAddr;
-        size_t rdOffset = isFirstWeight ? outputOffset : 0;
-        emitMvmOp(weightId, rdAddr, rdOffset, inBufAddr, inputOffset);
-
-        // --- STEP 2: Perform VADD operation (skip for first weight) ---
-        if (isFirstWeight)
-          continue;
-
-        // Sum accumulator with output buffer, store result in output buffer.
-        setupRdRs1Rs2(outBufAddr, outputOffset, accumBufAddr, 0, outBufAddr, outputOffset);
-
-        json::Object vaddJson;
-        vaddJson["op"] = "vvadd";
-        vaddJson["rd"] = 0;
-        vaddJson["rs1"] = 1;
-        vaddJson["rs2"] = 2;
-        vaddJson["offset"] = createEmptyOffset();
-        vaddJson["len"] = 32 * outChannels;
-        emitInstruction(std::move(vaddJson));
-      }
-    }
-  }
-}
-
 void PimCodeGen::codeGenTransposeOp(pim::PimTransposeOp transposeOp) const {
-  auto srcAddr = memory.getValueAddress(transposeOp.getData());
+  auto srcAddr = memory.getValueAddress(transposeOp.getInput());
-  auto dstAddr = memory.getValueAddress(transposeOp.getOutBuf());
+  auto dstAddr = memory.getValueAddress(transposeOp.getOutputBuffer());
 
-  auto srcType = cast<ShapedType>(transposeOp.getData().getType());
+  auto srcType = cast<ShapedType>(transposeOp.getInput().getType());
   auto srcShape = srcType.getShape();
   size_t rank = srcShape.size();
   size_t elementSize = srcType.getElementTypeBitWidth() / 8;
@@ -451,7 +390,7 @@ void PimCodeGen::codeGenTransposeOp(pim::PimTransposeOp transposeOp) const {
 
   // Read permutation. Destination dim i corresponds to source dim perm[i].
   SmallVector<int64_t> perm =
-    map_to_vector(transposeOp.getPerms().getAsRange<IntegerAttr>(), [](auto attr) -> int64_t { return attr.getInt(); });
+    map_to_vector(transposeOp.getPermutation().getAsRange<IntegerAttr>(), [](auto attr) -> int64_t { return attr.getInt(); });
 
   // Destination shape: dstShape[i] = srcShape[perm[i]]
   SmallVector<int64_t> dstShape(rank);
@@ -570,8 +509,6 @@ static int64_t codeGenCoreOps(pim::PimCoreOp coreOp, PimCodeGen& coreCodeGen) {
       coreCodeGen.codeGenMVMLikeOp<pim::PimVMMOp>(vmmOp.getWeightIndex(), vmmOp, true);
     else if (auto mvmOp = dyn_cast<pim::PimMVMOp>(op))
       coreCodeGen.codeGenMVMLikeOp<pim::PimMVMOp>(mvmOp.getWeightIndex(), mvmOp, false);
-    else if (auto applyFiltersOp = dyn_cast<pim::PimApplyFiltersOp>(op))
-      coreCodeGen.codeGenApplyFiltersOp(applyFiltersOp);
     else if (auto transposeOp = dyn_cast<pim::PimTransposeOp>(op))
       coreCodeGen.codeGenTransposeOp(transposeOp);
     else if (auto vvaddOp = dyn_cast<pim::PimVVAddOp>(op))
@@ -592,11 +529,6 @@ static int64_t codeGenCoreOps(pim::PimCoreOp coreOp, PimCodeGen& coreCodeGen) {
       coreCodeGen.codeGenVTanhOp(vtanhOp);
     else if (auto vsigmOp = dyn_cast<pim::PimVSigmOp>(op))
       coreCodeGen.codeGenVSigmOp(vsigmOp);
-    else if (isa<pim::PimSumOp>(op)) {
-      // TODO: Implement somehow?
-      op.emitWarning("Operation is not yet supported in code generation");
-      continue;
-    }
     else {
       op.emitError("Unsupported codegen for this operation");
       op.dump();

src/PIM/Compiler/PimCodeGen.hpp

@@ -99,7 +99,6 @@ public:
   void codeGenVReluOp(pim::PimVReluOp vreluOp) const;
   void codeGenVTanhOp(pim::PimVTanhOp vtanhOp) const;
   void codeGenVSigmOp(pim::PimVSigmOp vsigmOp) const;
-  void codeGenApplyFiltersOp(pim::PimApplyFiltersOp applyFiltersOp) const;
   void codeGenTransposeOp(pim::PimTransposeOp transposeOp) const;
 };
 

src/PIM/Conversion/ONNXToSpatial/Common.cpp

@@ -134,366 +134,4 @@ Value sumTensors(ArrayRef<Value> tensors, ConversionPatternRewriter& rewriter) {
   return (*currTensors)[0];
 }
 
-Value createMapOperation(PatternRewriter& rewriter, MapOperations mapOp, const Value& input) {
-  switch (mapOp) {
-  case MapOperations::None:            assert(false && "Invalid map operation during map operation creation.");
-  case MapOperations::ONNXSoftmaxOp:   return ONNXSoftmaxOp::create(rewriter, input.getLoc(), input.getType(), input);
-  case MapOperations::ONNXReluOp:      return ONNXReluOp::create(rewriter, input.getLoc(), input.getType(), input);
-  case MapOperations::ONNXLeakyReluOp: return ONNXLeakyReluOp::create(rewriter, input.getLoc(), input.getType(), input);
-  case MapOperations::ONNXExpOp:       return ONNXExpOp::create(rewriter, input.getLoc(), input.getType(), input);
-  }
-}
-
-void unpackOptionalPairVector(std::optional<mlir::ArrayAttr> valuesArray, size_t& value1, size_t& value2) {
-  if (auto unpackedStrides = valuesArray) {
-    value1 = mlir::cast<IntegerAttr>(unpackedStrides->getValue()[0]).getInt();
-    value2 = mlir::cast<IntegerAttr>(unpackedStrides->getValue()[1]).getInt();
-  }
-  else {
-    value1 = 1;
-    value2 = 1;
-  }
-}
-
-std::optional<llvm::Twine>
-unpackOptionalPadsVector(std::optional<mlir::ArrayAttr> valuesArray, size_t& pad_x, size_t& pad_y) {
-  if (valuesArray.has_value()) {
-    auto pads = mlir::ArrayAttr(*valuesArray);
-    if (pads.size() != 4)
-      return "pads must have 4 elements.";
-
-    pad_x = cast<IntegerAttr>(pads[2]).getInt();
-    pad_y = cast<IntegerAttr>(pads[3]).getInt();
-  }
-  else {
-    // Default padding is 0 unless specified otherwise.
-    // https://onnx.ai/onnx/operators/onnx__Conv.html
-    pad_x = pad_y = 0;
-  }
-
-  return std::nullopt;
-}
-
-void tileImageTensorByChannel(Value imageTensor,
-                              SmallVector<SmallVector<SmallVector<Value>>>& tiles,
-                              size_t tileSize,
-                              ConversionPatternRewriter& rewriter) {
-  ShapedType imageShape = mlir::cast<ShapedType>(imageTensor.getType());
-
-  size_t input_h = getImageHeight(imageShape);
-  size_t input_w = getImageWidth(imageShape);
-  size_t tileCount = ceilIntegerDivide(getImageChannel(imageShape), tileSize);
-  size_t tileRest = getImageChannel(imageShape) % tileSize;
-
-  SmallVector<OpFoldResult> strides(4, rewriter.getIndexAttr(1));
-  SmallVector<OpFoldResult> offsets(4, rewriter.getIndexAttr(0));
-  SmallVector<OpFoldResult> sizes = {
-    rewriter.getIndexAttr(1), rewriter.getIndexAttr(tileSize), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-
-  Location loc = imageTensor.getLoc();
-
-  for (size_t i = 0; i < tileCount; i++) {
-    if (i == tileCount - 1 && tileRest != 0)
-      sizes[1] = rewriter.getIndexAttr(tileRest);
-    for (size_t x = 0; x < input_w; x++) {
-      for (size_t y = 0; y < input_h; y++) {
-        offsets[1] = rewriter.getIndexAttr(i * tileSize);
-        offsets[2] = rewriter.getIndexAttr(x);
-        offsets[3] = rewriter.getIndexAttr(y);
-
-        tiles[i][x][y] = tensor::ExtractSliceOp::create(rewriter, loc, imageTensor, offsets, sizes, strides);
-      }
-    }
-  }
-}
-
-Value createImgConcatOp(SmallVector<SmallVector<SmallVector<Value>>>& outputTiles,
-                        ConversionPatternRewriter& rewriter,
-                        Location& loc,
-                        Type outputType) {
-  // Populate the outputTiles for the concat in the given order:
-  // 1. Start top left pixel
-  // 2. Continue on its right pixel till the end of the row
-  // 3. Restart on the next row
-  size_t outputTileCount = outputTiles.size();
-  size_t output_w = outputTiles[0].size();
-  size_t output_h = outputTiles[0][0].size();
-  SmallVector<Value> tilesToConcat;
-  tilesToConcat.reserve(output_h * output_w * outputTileCount * crossbarSize);
-  for (size_t outX = 0; outX < output_h; outX++)
-    for (size_t outY = 0; outY < output_w; outY++)
-      for (size_t outTile = 0; outTile < outputTileCount; outTile++)
-        tilesToConcat.push_back(outputTiles[outTile][outX][outY]);
-
-  return spatial::SpatImgConcatOp::create(rewriter, loc, outputType, tilesToConcat);
-}
-
-LogicalResult
-verifyWithinBoundsAndPaddings(size_t input_w, size_t input_h, int inX, int inY, size_t pad_x, size_t pad_y) {
-
-  if (inX < 0) {
-    assert((size_t) (-inX) <= pad_x && "verifyWithinBoundsAndPaddings: Negative x value out of padding");
-    return failure();
-  }
-
-  if (inY < 0) {
-    assert((size_t) (-inY) <= pad_y && "verifyWithinBoundsAndPaddings: Negative y value out of padding");
-    return failure();
-  }
-
-  if ((size_t) inX >= input_w || (size_t) inY >= input_h) {
-    assert((size_t) inX < input_w + pad_x && "verifyWithinBoundsAndPaddings: Positive x out of bounds");
-    assert((size_t) inY < input_h + pad_y && "verifyWithinBoundsAndPaddings: Positive y out of bounds");
-    return failure();
-  }
-
-  return success();
-}
-
-Value createExtractSliceImg(Value valToSlice,
-                            size_t x,
-                            size_t y,
-                            size_t t,
-                            size_t channelTileCount,
-                            size_t channelTileRest,
-                            size_t input_w,
-                            size_t input_h,
-                            PatternRewriter& rewriter) {
-  SmallVector<OpFoldResult> strides(4, rewriter.getIndexAttr(1));
-  SmallVector<OpFoldResult> offsets(4, rewriter.getIndexAttr(0));
-  SmallVector<OpFoldResult> sizes = {
-    rewriter.getIndexAttr(1), rewriter.getIndexAttr(crossbarSize), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-
-  if (t == channelTileCount - 1 && channelTileRest != 0)
-    sizes[1] = rewriter.getIndexAttr(channelTileRest);
-
-  offsets[1] = rewriter.getIndexAttr(t * crossbarSize);
-  offsets[2] = rewriter.getIndexAttr(x);
-  offsets[3] = rewriter.getIndexAttr(y);
-
-  return tensor::ExtractSliceOp::create(rewriter, valToSlice.getLoc(), valToSlice, offsets, sizes, strides);
-}
-
-Value indexImgValue(Value v,
-                    size_t x,
-                    size_t y,
-                    size_t t,
-                    size_t channelTileCount,
-                    size_t channelTileRest,
-                    size_t input_w,
-                    size_t input_h,
-                    ConversionPatternRewriter& rewriter) {
-
-  auto newV = rewriter.getRemappedValue(v);
-  if (newV)
-    v = newV;
-
-  if (!v.getDefiningOp())
-    return createExtractSliceImg(v, x, y, t, channelTileCount, channelTileRest, input_w, input_h, rewriter);
-
-  if (auto computeOp = v.getDefiningOp<spatial::SpatWeightedCompute>()) {
-    // We found the computeOp that produces the tile we want, just return this
-    // value.
-    // TODO: Should we assert that x,y,t are zero?
-    assert(x == 0 && y == 0 && t == 0 && "indexImgValue: WeightedComputeOp tile indeces should be zero");
-    return v;
-  }
-
-  if (auto receiveOp = v.getDefiningOp<spatial::SpatChannelReceiveOp>()) {
-    // This is a receiveOp, just return its value which will be resolved later
-    assert(x == 0 && y == 0 && t == 0 && "indexImgValue: receiveOp tile indeces should be zero");
-    return v;
-  }
-
-  if (auto imgConcatOp = v.getDefiningOp<spatial::SpatImgConcatOp>()) {
-    auto imgConcatInput = imgConcatOp.getInputTile(x, y, t);
-    // TODO: Is this correct?
-    // Above we already index exactly the tile we want, so `x=y=t=0` in
-    // recursive call
-
-    return indexImgValue(imgConcatInput, 0, 0, 0, channelTileCount, channelTileRest, input_w, input_h, rewriter);
-  }
-
-  if (auto tensorConcatOp = v.getDefiningOp<tensor::ConcatOp>()) {
-    // This can be recursive.
-    // First, get the input tensors of the tensor.concatOp
-    // Then, find the input tensor that contains the tile we want
-    // Finally, recursive call asking for the tile
-    auto concatAxis = tensorConcatOp.getDim();
-    assert(concatAxis != 0 && "Expecting to concat on channel/x/y axis");
-    assert(concatAxis == 1 && "TODO: Make sure this works and makes sense for other axis.");
-    SmallVector<size_t, 4> indexDims = {1, t * crossbarSize, x, y};
-
-    // Find the input tensor that contains the tile we want
-    size_t currentTile = 0;
-    for (auto concatInput : tensorConcatOp.getInputs()) {
-      auto concatInputShape = cast<ShapedType>(concatInput.getType());
-      assert(concatInputShape.getRank() == 4 && "Expecting an image tensor");
-      auto concatInputSizeOnAxis = concatInputShape.getDimSize(concatAxis);
-
-      if (currentTile + concatInputSizeOnAxis > indexDims[concatAxis]) {
-        // This input tensor contains the tile we want
-        indexDims[concatAxis] -= currentTile;
-        if (indexDims[1] % crossbarSize != 0) {
-          assert(ignoreConcatError
-                 && "TODO: Handle non-tile aligned tensor, or set "
-                    "--ignore-concat-error=true");
-        }
-        return indexImgValue(concatInput,
-                             indexDims[2],
-                             indexDims[3],
-                             indexDims[1] / crossbarSize,
-                             channelTileCount,
-                             channelTileRest,
-                             input_w,
-                             input_h,
-                             rewriter);
-      }
-      currentTile += concatInputSizeOnAxis;
-    }
-
-    assert(false
-           && "Could not find the input tensor that contains the tile "
-              "within tensor.ConcatOp");
-  }
-
-  v.dump();
-
-  assert(false && "indexImgValue: unsupported operation");
-}
-
-void resolveInputTensorTilesBlockArg(Value wholeInputTensor,
-                                     SmallVector<SmallVector<SmallVector<Value>>>& inputTiles,
-                                     size_t channelTileCount,
-                                     size_t channelTileRest,
-                                     size_t input_w,
-                                     size_t input_h,
-                                     PatternRewriter& rewriter) {
-  SmallVector<OpFoldResult> strides(4, rewriter.getIndexAttr(1));
-  SmallVector<OpFoldResult> offsets(4, rewriter.getIndexAttr(0));
-  SmallVector<OpFoldResult> sizes = {
-    rewriter.getIndexAttr(1), rewriter.getIndexAttr(crossbarSize), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-  Location loc = wholeInputTensor.getLoc();
-
-  for (size_t t = 0; t < channelTileCount; t++) {
-    if (t == channelTileCount - 1 && channelTileRest != 0)
-      sizes[1] = rewriter.getIndexAttr(channelTileRest);
-    for (size_t x = 0; x < input_w; x++) {
-      for (size_t y = 0; y < input_h; y++) {
-        offsets[1] = rewriter.getIndexAttr(t * crossbarSize);
-        offsets[2] = rewriter.getIndexAttr(x);
-        offsets[3] = rewriter.getIndexAttr(y);
-
-        inputTiles[t][x][y] = tensor::ExtractSliceOp::create(rewriter, loc, wholeInputTensor, offsets, sizes, strides);
-      }
-    }
-  }
-}
-
-std::optional<Twine> resolveImgInputTiles(Value wholeInputTensor,
-                                          SmallVector<SmallVector<SmallVector<Value>>>& inputTiles,
-                                          size_t channelTileCount,
-                                          size_t channelTileRest,
-                                          size_t input_w,
-                                          size_t input_h,
-                                          ConversionPatternRewriter& rewriter) {
-
-  for (size_t t = 0; t < channelTileCount; t++) {
-    for (size_t x = 0; x < input_w; x++) {
-      for (size_t y = 0; y < input_h; y++) {
-        inputTiles[t][x][y] =
-          indexImgValue(wholeInputTensor, x, y, t, channelTileCount, channelTileRest, input_w, input_h, rewriter);
-      }
-    }
-  }
-
-  return std::nullopt;
-}
-
-LogicalResult handleFlattenLikeOp(SmallVector<SmallVector<Value>>& inputTiles,
-                                  const size_t inputTilesCount,
-                                  const size_t lastInputTileDimension,
-                                  TensorType inputShape,
-                                  TensorType outputShape,
-                                  Value reshapeInput,
-                                  ConversionPatternRewriter& rewriter) {
-  // Only support reshape between an image and a vector (i.e. flatten)
-  if (inputShape.getRank() != 4 || outputShape.getRank() != 2) {
-    return rewriter.notifyMatchFailure(reshapeInput.getDefiningOp(),
-                                       "resolveVecInputTiles only supports reshapes from 4D to 2D tensors");
-  }
-
-  /*
-   * From a 4D tensor <N, C, W, H> to a 2D tensor <N, C*H*W>
-   */
-  auto N = inputShape.getDimSize(0);
-  auto C = inputShape.getDimSize(1);
-  auto H = inputShape.getDimSize(2);
-  auto W = inputShape.getDimSize(3);
-  assert(N == 1 && "Only support N = 1 for image tensors");
-
-  for (size_t i = 0; i < inputTilesCount; i++) {
-    auto c = (i / (H * W)) % C;
-    // TODO: Is this correct? Or should I invert h and w?
-    auto w = (i / H) % W;
-    auto h = i % H;
-
-    Value curTile = indexImgValue(reshapeInput, w, h, c, inputTilesCount, lastInputTileDimension, W, H, rewriter);
-
-    // Assert the shape of the tile, and reshape it
-    auto curTileShape = cast<TensorType>(curTile.getType());
-    assert(curTileShape.getRank() == 4 && "We just reshaped an image tensor, why rank != 4?");
-    assert(curTileShape.getDimSize(0) == 1 && "We just reshaped an image tensor with N = 1, why is it now != 1?");
-    assert(curTileShape.getDimSize(2) == 1 && "We should have just looked up a single pixel why W != 1?");
-    assert(curTileShape.getDimSize(3) == 1 && "We should have just looked up a single pixel why H != 1?");
-
-    // Reshape this pixel tensor into a vector, for compatibility with the
-    // rest
-    SmallVector<int64_t> newShapeVals = {curTileShape.getDimSize(0), curTileShape.getDimSize(1)};
-    auto shapeType = RankedTensorType::get({static_cast<int64_t>(newShapeVals.size())}, rewriter.getI64Type());
-    Value shapeTensor =
-      arith::ConstantOp::create(rewriter, reshapeInput.getLoc(), DenseIntElementsAttr::get(shapeType, newShapeVals));
-    auto reshapedType = RankedTensorType::get(newShapeVals, curTileShape.getElementType());
-    auto reshapedCurTile = tosa::ReshapeOp::create(rewriter, reshapeInput.getLoc(), reshapedType, curTile, shapeTensor);
-
-    size_t coreIndex = i / crossbarCountInCore;
-    inputTiles[coreIndex].push_back(reshapedCurTile);
-  }
-
-  return success();
-}
-
-std::pair<size_t, size_t> kernel_get_start_and_end(
-  int64_t out_pos, int64_t input_width, int64_t krn_width, int64_t stride, int64_t dilation, int64_t pad) {
-  int64_t firstValid = std::ceil(static_cast<float>(pad) / dilation) * dilation - pad;
-  int64_t start = std::max(firstValid, out_pos * stride - pad);
-  int64_t end = std::min(input_width, out_pos * stride + (krn_width - 1) * dilation + 1 - pad);
-
-  assert(start >= 0 && "Start position must be non-negative.");
-  assert(end >= 0 && "End position must be non-negative.");
-  return std::make_pair(start, end);
-}
-
-void incrementWeightedComputeInputsSegmentSize(spatial::SpatWeightedCompute wcomputeOp, int increment) {
-  auto oldSegmentSizes = wcomputeOp->getAttrOfType<DenseI32ArrayAttr>(wcomputeOp.getOperandSegmentSizesAttrName());
-
-  auto newSegmentSizes =
-    DenseI32ArrayAttr::get(wcomputeOp->getContext(), {oldSegmentSizes[0], oldSegmentSizes[1] + increment});
-
-  wcomputeOp->setAttr(wcomputeOp.getOperandSegmentSizesAttrName(), newSegmentSizes);
-}
-
-int getResultIndex(Operation* op, Value v) {
-  int resultNumber = -1;
-  for (auto result : op->getResults()) {
-    if (result == v) {
-      resultNumber = result.getResultNumber();
-      break;
-    }
-  }
-  assert(resultNumber >= 0 && "Value not found in given operation's results.");
-
-  return resultNumber;
-}
-
 }; // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/Common.hpp

@@ -2,7 +2,6 @@
 
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/BuiltinTypes.h"
-#include "mlir/IR/Operation.h"
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/Support/LLVM.h"
 #include "mlir/Transforms/DialectConversion.h"
@@ -10,7 +9,6 @@
 #include "llvm/Support/LogicalResult.h"
 
 #include <cassert>
-#include <cstddef>
 #include <optional>
 #include <type_traits>
 #include <utility>
@@ -144,164 +142,4 @@ mlir::tensor::SplatOp broadcastToVector(mlir::Value scalarToBroadcast,
 
 mlir::Value sumTensors(mlir::ArrayRef<mlir::Value> tensors, mlir::ConversionPatternRewriter& rewriter);
 
-mlir::Value createMapOperation(mlir::PatternRewriter& rewriter, MapOperations mapOp, const mlir::Value& input);
-
-/**
- * Unpacks an optional pair vector into two size_t values.
- *
- * @param valuesArray The optional `mlir::ArrayAttr` containing the pair of
- * values.
- * @param value1      The reference to the first `size_t` variable to store the
- * unpacked value.
- * @param value2      The reference to the second `size_t` variable to store the
- * unpacked value.
- */
-void unpackOptionalPairVector(std::optional<mlir::ArrayAttr> valuesArray, size_t& value1, size_t& value2);
-
-/**
- * Unpacks the optional pads vector.
- *
- * @param valuesArray The optional array attribute containing the values.
- * @param pad_x The output variable to store the value of pad_x.
- * @param pad_y The output variable to store the value of pad_y.
- * @param rewriter The rewriter to notify failure
- *
- * @return llvm::Optional<llvm::Twine> The error message if the pads are invalid
- */
-std::optional<llvm::Twine>
-unpackOptionalPadsVector(std::optional<mlir::ArrayAttr> valuesArray, size_t& pad_x, size_t& pad_y);
-
-/**
- * Tiles the image tensor by channel.
- *
- * This function takes an image tensor and tiles it into smaller tiles based on
- * the channel dimension. The size of each tile is specified by the tileSize
- * parameter.
- *
- * @param imageTensor The input image tensor (NxCxWxH) to be tiled.
- * @param tiles The output tiles vector to store the tiled image tensors.
- * @param tileSize The size of each tile.
- * @param rewriter The ConversionPatternRewriter used for creating operations.
- */
-void tileImageTensorByChannel(mlir::Value imageTensor,
-                              llvm::SmallVector<llvm::SmallVector<llvm::SmallVector<mlir::Value>>>& tiles,
-                              size_t tileSize,
-                              mlir::ConversionPatternRewriter& rewriter);
-
-/**
- * Creates an ImgConcatOp based on the given tiles.
- *
- * This function takes a 3-dimensional vector `outputTiles` representing the
- * tiles to concatenate. The tiles are indexed by [tile][x][y].
- *
- * @param outputTiles The tiles to concatenate.
- * @param rewriter The ConversionPatternRewriter used for creating the
- * ImgConcatOp.
- * @param loc The location of the operation.
- * @param outputType The type of the output tensor.
- *
- * @return The created ImgConcatOp.
- */
-mlir::Value createImgConcatOp(llvm::SmallVector<llvm::SmallVector<llvm::SmallVector<mlir::Value>>>& outputTiles,
-                              mlir::ConversionPatternRewriter& rewriter,
-                              mlir::Location& loc,
-                              mlir::Type outputType);
-
-/**
- * @brief Verifies if the given input coordinates and padding values are within
- * the bounds of the input tensor.
- *
- * @param input_w The width of the input tensor.
- * @param input_h The height of the input tensor.
- * @param inX The X-coordinate of the input.
- * @param inY The Y-coordinate of the input.
- * @param pad_x The padding value in the X-direction.
- * @param pad_y The padding value in the Y-direction.
- * @return LogicalResult Returns success if the coordinates and padding are
- * within bounds, failure otherwise.
- */
-mlir::LogicalResult
-verifyWithinBoundsAndPaddings(size_t input_w, size_t input_h, int inX, int inY, size_t pad_x, size_t pad_y);
-
-/**
- * Resolves the tiling of the input tensor into smaller tiles.
- *
- * This function takes a whole input tensor and tiles it into smaller tiles
- * using the provided parameters. The resulting tiles are stored in the
- * `inputTiles` vector.
- * Input tiles need to be indexed by:
- *    a. Channel Tile
- *    b. Pixel `x` position
- *    c. Pixel `y` position
- * For example: inputTiles[channelTile][x][y]
- *
- * @param wholeInputTensor The whole input tensor to be tiled.
- * @param inputTiles A vector of vectors of vectors of Values representing the
- *                   tiles of the input tensor. The outermost vector represents
- *                   the channels, the middle vector represents the rows, and
- *                   the innermost vector represents the columns of the tiles.
- * @param channelTileCount The number of tiles for the `channel` axis.
- * @param channelTileRest The size of the last channelTile. Set as 0 if tiles
- * fit exactly
- * @param input_w The width of the input tensor.
- * @param input_h The height of the input tensor.
- * @param rewriter The ConversionPatternRewriter used for creating operations.
- *
- * @return std::optional<llvm::Twine> An error message if the input tensor could
- * not be resolved into tiles.
- */
-std::optional<llvm::Twine>
-resolveImgInputTiles(mlir::Value wholeInputTensor,
-                     llvm::SmallVector<llvm::SmallVector<llvm::SmallVector<mlir::Value>>>& inputTiles,
-                     size_t channelTileCount,
-                     size_t channelTileRest,
-                     size_t input_w,
-                     size_t input_h,
-                     mlir::ConversionPatternRewriter& rewriter);
-
-/**
- * Computes the boundaries of an image kernel application.
- *
- * @param out_pos The position of the output element.
- * @param input_width The width of the input image.
- * @param krn_width The width of the kernel.
- * @param stride The stride value.
- * @param dilation The dilation value.
- * @param pad The padding value.
- * @return A pair of size_t values representing the start and end positions of
- * the kernel application.
- */
-std::pair<size_t, size_t> kernel_get_start_and_end(
-  int64_t out_pos, int64_t input_width, int64_t krn_width, int64_t stride, int64_t dilation, int64_t pad);
-
-/**
- * @brief Increment the `operandSegmentSizes` in the WeightedCompute operation
- * for the `inputs` operand.
- *
- * This function increments the size of the `inputs` operand segment in the
- * `operandSegmentSizes` of the given WeightedCompute operation by the specified
- * increment. This is necessary when new operands are programmatically added to
- * the WeightedCompute operation.
- *
- * @param wcomputeOp The WeightedCompute operation whose `operandSegmentSizes`
- * is to be incremented.
- * @param increment The value by which to increment the `inputs` operand segment
- * size.
- */
-void incrementWeightedComputeInputsSegmentSize(spatial::SpatWeightedCompute wcomputeOp, int increment);
-
-/**
- * @brief Finds the result index of the given operation that produces the
- * specified value.
- *
- * This function takes an operation and a value, and returns the index of the
- * result of the operation that corresponds to the given value.
- *
- * @param op Operation whose result index is to be found.
- * @param v The value for which the result index is to be determined.
- * @return The index of the result of the operation that produces the specified
- * value.
- */
-int getResultIndex(mlir::Operation* op, mlir::Value v);
-
 }; // namespace onnx_mlir

src/PIM/Conversion/SpatialToGraphviz/SpatialToGraphviz.cpp

@@ -223,9 +223,6 @@ void SpatialToGraphvizPass::runOnOperation() {
     else if (auto concatOp = dyn_cast<tensor::ConcatOp>(op)) {
       drawConcatOpSubgraph(concatOp, concatNum++);
     }
-    else if (auto imgConcatOp = dyn_cast<spatial::SpatImgConcatOp>(op)) {
-      drawConcatOpSubgraph(imgConcatOp, concatNum++);
-    }
     else if (auto extractSliceOp = dyn_cast<tensor::ExtractSliceOp>(op)) {
       auto producerOp = extractSliceOp->getOperand(0).getDefiningOp();
       if (producerOp) {

src/PIM/Conversion/SpatialToPim/Common.hpp

@@ -45,8 +45,4 @@ createEmptyTensorFromShaped(mlir::IRRewriter& rewriter, mlir::Location loc, mlir
   return mlir::tensor::EmptyOp::create(rewriter, loc, shapedType.getShape(), shapedType.getElementType());
 }
 
-inline bool isAConcatOp(mlir::Operation* op) {
-  return llvm::isa<mlir::tensor::ConcatOp>(op) || llvm::isa<spatial::SpatImgConcatOp>(op);
-}
-
 } // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/SpatialToPim.td

@@ -45,4 +45,5 @@ def spatToPimVVMaxOp : Pat<
     (NativeCodeCall<"onnx_mlir::getBestOutputTensorFromOperandsOrAllocate($_builder, $0.getDefiningOp())"> $srcOpRes))
 >;
 
+
 #endif // SPATIAL_TO_PIM

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.cpp

@@ -278,7 +278,7 @@ void SpatialToPimPass::runOnComputeOp(spatial::SpatWeightedCompute computeOp, IR
         continue;
       }
 
-      if (isa<tensor::ConcatOp>(resultUser) || isa<spatial::SpatImgConcatOp>(resultUser)) {
+      if (isa<tensor::ConcatOp>(resultUser)) {
         auto concatOp = resultUser;
         auto concatValue = concatOp->getResult(0);
         auto concatUses = concatValue.getUses();
@@ -368,8 +368,8 @@ void SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp funcOp, I
   };
 
   funcOp.walk([&](PimVMMOp vmmOp) {
-    auto outTensorOperand = vmmOp.getOutBuf();
+    auto outTensorOperand = vmmOp.getOutputBuffer();
-    auto resultTensor = vmmOp.getOutRes();
+    auto resultTensor = vmmOp.getOutput();
     auto outShape = getTensorShape(outTensorOperand);
     assert(isHVectorShape(outShape));
     if (outShape[1] != static_cast<int64_t>(crossbarSize)) {
@@ -602,9 +602,7 @@ void SpatialToPimPass::replaceReturnOpOperands(func::ReturnOp& returnOp, IRRewri
     rewriter.modifyOpInPlace(returnOp,
                              [&] { returnOp.setOperand(orderWithinReturn, outputTensors[orderWithinReturn]); });
 
-    // If the operand is a concatenation operation and the returnOp was the only
+    if (isa<tensor::ConcatOp>(returnOperand)) {
-    // user of the returnOperand, we can safely remove it
-    if (isAConcatOp(returnOperand)) {
       auto returnOperandUses = it.value().getUses();
       if (rangeLength(returnOperandUses) == 0)
         rewriter.eraseOp(returnOperand);
@@ -632,7 +630,7 @@ void SpatialToPimPass::runOnReceiveOp(spatial::SpatChannelReceiveOp receiveOp, I
     // user. This means that we need to get the replace the original SendOp with
     // a BroadcastSendOp
     rewriter.setInsertionPoint(sendOp);
-    rewriter.replaceOpWithNewOp<spatial::SpatChannelBroadcastSendOp>(sendOp, sendOp.getChannel(), sendOp.getData());
+    rewriter.replaceOpWithNewOp<spatial::SpatChannelBroadcastSendOp>(sendOp, sendOp.getChannel(), sendOp.getInput());
   }
 }
 

src/PIM/Dialect/Pim/Pim.td

@@ -20,45 +20,12 @@ class PimOp<string mnemonic, list<Trait> traits = []> :
 def PimTensor :
   AnyTypeOf<[AnyMemRef, AnyRankedTensor], "", "::mlir::ShapedType">;
 
-// Communication
+//===----------------------------------------------------------------------===//
-
+// Execution
-def PimSendOp: PimOp<"send", []> {
+//===----------------------------------------------------------------------===//
-  let arguments = (ins
-    PimTensor: $src,
-    I32Attr: $size,
-    I32Attr: $targetCoreId
-  );
-
-  let assemblyFormat = [{
-    `(` $src `)` attr-dict `:` type($src) `->` `(` `)`
-  }];
-}
-
-def PimReceiveOp: PimOp<"receive", [DestinationStyleOpInterface]> {
-  let arguments = (ins
-    PimTensor: $dst,
-    I32Attr: $size,
-    I32Attr: $srcCoreId
-  );
-
-  let results = (outs
-    PimTensor: $out
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getDstMutable();
-    }
-  }];
-
-  let assemblyFormat = [{
-    `(` $dst `)` attr-dict `:` type($dst) `->` type($out)
-  }];
-}
-
-// Core
 
 def PimCoreOp : PimOp<"core", [SingleBlock]> {
+  let summary = "Execute a block on a PIM core";
 
   let regions = (region SizedRegion<1>:$body);
 
@@ -72,412 +39,443 @@ def PimCoreOp: PimOp<"core", [SingleBlock]> {
   }];
 }
 
-// Memory
-
-def PimConstantOp: PimOp<"constant", []> {
-  let description = [{
-    Allocate a constant value in global memory
-  }];
-
-  let arguments = (ins
-    AnyAttr: $value,
-    BoolAttr: $shouldAllocate
-  );
-
-  let results = (outs
-    PimTensor: $out
-  );
-}
-
-def PimMemCopyHostToDevOp: PimOp<"memcp_hd", [DestinationStyleOpInterface]> {
-  let description = [{
-    Copy a memory region from host memory into device memory
-  }];
-
-  let arguments = (ins
-    PimTensor: $deviceDst,
-    PimTensor: $hostSrc,
-    I32Attr: $deviceDstOffset,
-    I32Attr: $hostSrcOffset,
-    I32Attr: $size
-  );
-
-  let results = (outs
-    PimTensor: $deviceDstOut
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getDeviceDstMutable();
-    }
-  }];
-
-  let assemblyFormat = [{
-    `(` $deviceDst `,` $hostSrc `)` attr-dict `:` `(` type($deviceDst) `,` type($hostSrc) `)` `->` type($deviceDstOut)
-  }];
-}
-
-def PimMemCopyDevToHostOp: PimOp<"memcp_dh", [DestinationStyleOpInterface]> {
-  let description = [{
-    Copy a memory region from device memory into host memory
-  }];
-
-  let arguments = (ins
-    PimTensor: $hostDst,
-    PimTensor: $deviceSrc,
-    I32Attr: $hostDstOffset,
-    I32Attr: $deviceSrcOffset,
-    I32Attr: $size
-  );
-
-  let results = (outs
-    PimTensor: $hostDstOut
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getHostDstMutable();
-    }
-  }];
-
-
-  let assemblyFormat = [{
-    `(` $hostDst `,` $deviceSrc `)` attr-dict `:` `(` type($hostDst) `,` type($deviceSrc) `)` `->` type($hostDstOut)
-  }];
-}
-
-def PimMemCopyOp: PimOp<"memcp", [DestinationStyleOpInterface]> {
-  let description = [{
-    Copy a memory region from and to the same memory
-  }];
-
-  let arguments = (ins
-    PimTensor: $dst,
-    PimTensor: $src,
-    I32Attr: $dstOffset,
-    I32Attr: $srcOffset,
-    I32Attr: $size
-  );
-
-  let results = (outs
-    PimTensor: $dstOut
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getDstMutable();
-    }
-  }];
-
-
-  let assemblyFormat = [{
-    `(` $dst `,` $src `)` attr-dict `:` `(` type($dst) `,` type($src) `)` `->` type($dstOut)
-  }];
-}
-
-// Algebra
-
-def PimTransposeOp: PimOp<"transpose", [DestinationStyleOpInterface]> {
-  let description = [{
-    Matrix transpose
-  }];
-
-  let arguments = (ins
-    PimTensor: $data,
-    I64ArrayAttr: $perms,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getOutBufMutable();
-    }
-  }];
-
-  let assemblyFormat = [{
-    `(` $data `,` $outBuf `)` attr-dict `:` `(` type($data) `,` type($outBuf) `)` `->` type($outRes)
-  }];
-}
-
-def PimVMMOp: PimOp<"vmm", [DestinationStyleOpInterface]> {
-  let description = [{
-    Vector-matrix multiplication: c = a * b
-  }];
-
-  let arguments = (ins
-    I32Attr: $weightIndex,
-    PimTensor: $vectorInput,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getOutBufMutable();
-    }
-  }];
-
-  let assemblyFormat = [{
-    `(` $vectorInput `,` $outBuf `)` attr-dict `:` `(` type($vectorInput) `,` type($outBuf) `)` `->` type($outRes)
-  }];
-}
-
-def PimMVMOp: PimOp<"mvm", [DestinationStyleOpInterface]> {
-  let description = [{
-    Matrix-vector multiplication: c = a * b
-  }];
-
-  let arguments = (ins
-    I32Attr: $weightIndex,
-    PimTensor: $vectorInput,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getOutBufMutable();
-    }
-  }];
-}
-
-def PimVVAddOp: PimOp<"vvadd", [DestinationStyleOpInterface]> {
-  let description = [{
-    Element-wise addition: c = a + b
-  }];
-
-  let arguments = (ins
-    PimTensor: $a,
-    PimTensor: $b,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getOutBufMutable();
-    }
-  }];
-
-  let assemblyFormat = [{
-    `(` $a `,` $b `,` $outBuf `)` attr-dict `:` `(` type($a) `,` type($b) `,` type($outBuf) `)` `->` type($outRes)
-  }];
-}
-
-def PimVVSubOp: PimOp<"vvsub", [DestinationStyleOpInterface]> {
-  let description = [{
-    Element-wise subtraction: c = a - b
-  }];
-
-  let arguments = (ins
-    PimTensor: $a,
-    PimTensor: $b,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getOutBufMutable();
-    }
-  }];
-
-  let assemblyFormat = [{
-    `(` $a `,` $b `,` $outBuf `)` attr-dict `:` `(` type($a) `,` type($b) `,` type($outBuf) `)` `->` type($outRes)
-  }];
-}
-
-def PimVVMulOp: PimOp<"vvmul", [DestinationStyleOpInterface]> {
-  let description = [{
-    Element-wise multiplication: c = a * b
-  }];
-
-  let arguments = (ins
-    PimTensor: $a,
-    PimTensor: $b,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getOutBufMutable();
-    }
-  }];
-
-  let assemblyFormat = [{
-    `(` $a `,` $b `,` $outBuf `)` attr-dict `:` `(` type($a) `,` type($b) `,` type($outBuf) `)` `->` type($outRes)
-  }];
-}
-
-def PimVVMaxOp: PimOp<"vvmax", [DestinationStyleOpInterface]> {
-  let description = [{
-    Element-wise max: c = max(a, b)
-  }];
-
-  let arguments = (ins
-    PimTensor: $a,
-    PimTensor: $b,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-
-  let extraClassDeclaration = [{
-    mlir::MutableOperandRange getDpsInitsMutable() {
-      return getOutBufMutable();
-    }
-  }];
-
-  let assemblyFormat = [{
-    `(` $a `,` $b `,` $outBuf `)` attr-dict `:` `(` type($a) `,` type($b) `,` type($outBuf) `)` `->` type($outRes)
-  }];
-}
-
-def PimVVDMulOp: PimOp<"vvdmul", [DeclareOpInterfaceMethods<BufferViewFlowOpInterface>]> {
-  let description = [{
-    Dot product: c = dot(a, b)
-  }];
-
-  let arguments = (ins
-    PimTensor: $a,
-    PimTensor: $b,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-}
-
-def PimApplyFiltersOp: PimOp<"apply_filters", [DeclareOpInterfaceMethods<BufferViewFlowOpInterface>]> {
-  let description = [{
-    Apply filters to a tensor
-  }];
-
-  let arguments = (ins
-    I64ArrayAttr: $weightIndices,
-    I64ArrayAttr: $xKernelPositions,
-    I64ArrayAttr: $yKernelPositions,
-    PimTensor: $input,
-    PimTensor: $outBuf,
-    PimTensor: $accumBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-
-  let assemblyFormat = [{
-    `(` `input` `=` $input `,` `outBuf` `=` $outBuf `,` `accumBuf` `=` $accumBuf `)` attr-dict `:` 
-    type($input) `,` type($outBuf) `,` type($accumBuf) `->` type($outRes)
-  }];
-}
-
-def PimSumOp: PimOp<"sum", [DeclareOpInterfaceMethods<BufferViewFlowOpInterface>]> {
-  let description = [{
-    Sum all elements into a single one
-  }];
-
-  let arguments = (ins
-    PimTensor: $a,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-}
-
-def PimVAvgOp: PimOp<"vavg", [DeclareOpInterfaceMethods<BufferViewFlowOpInterface>]> {
-  let description = [{
-    Average all elements into a single one
-  }];
-
-  let arguments = (ins
-    PimTensor: $a,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-}
-
-def PimVReluOp: PimOp<"vrelu", [DeclareOpInterfaceMethods<BufferViewFlowOpInterface>]> {
-  let description = [{
-    Element-wise ReLU: c = max(a, 0)
-  }];
-
-  let arguments = (ins
-    PimTensor: $a,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-}
-
-def PimVTanhOp: PimOp<"vtanh", [DeclareOpInterfaceMethods<BufferViewFlowOpInterface>]> {
-  let description = [{
-    Element-wise tanh activation
-  }];
-
-  let arguments = (ins
-    PimTensor: $a,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-}
-
-def PimVSigmOp: PimOp<"vsigm", [DeclareOpInterfaceMethods<BufferViewFlowOpInterface>]> {
-  let description = [{
-    Element-wise sigmoid activation
-  }];
-
-  let arguments = (ins
-    PimTensor: $a,
-    PimTensor: $outBuf
-  );
-
-  let results = (outs
-    PimTensor: $outRes
-  );
-}
-
 def PimHaltOp : PimOp<"halt", [Terminator]> {
-  let description = [{
+  let summary = "Halt execution of the core";
-    Halts the execution of the core
-  }];
 
   let assemblyFormat = [{
     attr-dict
   }];
 }
 
+//===----------------------------------------------------------------------===//
+// Communication
+//===----------------------------------------------------------------------===//
+
+def PimSendOp : PimOp<"send", []> {
+  let summary = "Send a tensor to another core";
+
+  let arguments = (ins
+    PimTensor:$input,
+    I32Attr:$size,
+    I32Attr:$targetCoreId
+  );
+
+  let assemblyFormat = [{
+    `(` $input `)` attr-dict `:` type($input) `->` `(` `)`
+  }];
+}
+
+def PimReceiveOp : PimOp<"receive", [DestinationStyleOpInterface]> {
+  let summary = "Receive a tensor from another core";
+
+  let arguments = (ins
+    PimTensor:$outputBuffer,
+    I32Attr:$size,
+    I32Attr:$sourceCoreId
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $outputBuffer `)` attr-dict `:` type($outputBuffer) `->` type($output)
+  }];
+}
+
+def PimMemCopyHostToDevOp : PimOp<"memcp_hd", [DestinationStyleOpInterface]> {
+  let summary = "Copy a memory region from host memory into device memory";
+
+  let arguments = (ins
+    PimTensor:$deviceTarget,
+    PimTensor:$hostSource,
+    I32Attr:$deviceTargetOffset,
+    I32Attr:$hostSourceOffset,
+    I32Attr:$size
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getDeviceTargetMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $deviceTarget `,` $hostSource `)` attr-dict `:` `(` type($deviceTarget) `,` type($hostSource) `)` `->` type($output)
+  }];
+}
+
+def PimMemCopyDevToHostOp : PimOp<"memcp_dh", [DestinationStyleOpInterface]> {
+  let summary = "Copy a memory region from device memory into host memory";
+
+  let arguments = (ins
+    PimTensor:$hostTarget,
+    PimTensor:$deviceSource,
+    I32Attr:$hostTargetOffset,
+    I32Attr:$deviceSourceOffset,
+    I32Attr:$size
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getHostTargetMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $hostTarget `,` $deviceSource `)` attr-dict `:` `(` type($hostTarget) `,` type($deviceSource) `)` `->` type($output)
+  }];
+}
+
+def PimMemCopyOp : PimOp<"memcp", [DestinationStyleOpInterface]> {
+  let summary = "Copy a memory region within the same memory space";
+
+  let arguments = (ins
+    PimTensor:$target,
+    PimTensor:$source,
+    I32Attr:$targetOffset,
+    I32Attr:$sourceOffset,
+    I32Attr:$size
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getTargetMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $target `,` $source `)` attr-dict `:` `(` type($target) `,` type($source) `)` `->` type($output)
+  }];
+}
+
+//===----------------------------------------------------------------------===//
+// Math
+//===----------------------------------------------------------------------===//
+
+def PimTransposeOp : PimOp<"transpose", [DestinationStyleOpInterface]> {
+  let summary = "Transpose a matrix";
+
+  let arguments = (ins
+    PimTensor:$input,
+    I64ArrayAttr:$permutation,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $input `,` $outputBuffer `)` attr-dict `:` `(` type($input) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimVMMOp : PimOp<"vmm", [DestinationStyleOpInterface]> {
+  let summary = "Vector-matrix multiplication: c = a * b";
+
+  let arguments = (ins
+    I32Attr:$weightIndex,
+    PimTensor:$input,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $input `,` $outputBuffer `)` attr-dict `:` `(` type($input) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimMVMOp : PimOp<"mvm", [DestinationStyleOpInterface]> {
+  let summary = "Matrix-vector multiplication: c = a * b";
+
+  let arguments = (ins
+    I32Attr:$weightIndex,
+    PimTensor:$input,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $input `,` $outputBuffer `)` attr-dict `:` `(` type($input) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimVVAddOp : PimOp<"vvadd", [DestinationStyleOpInterface]> {
+  let summary = "Element-wise addition: c = a + b";
+
+  let arguments = (ins
+    PimTensor:$lhs,
+    PimTensor:$rhs,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $lhs `,` $rhs `,` $outputBuffer `)` attr-dict `:` `(` type($lhs) `,` type($rhs) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimVVSubOp : PimOp<"vvsub", [DestinationStyleOpInterface]> {
+  let summary = "Element-wise subtraction: c = a - b";
+
+  let arguments = (ins
+    PimTensor:$lhs,
+    PimTensor:$rhs,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $lhs `,` $rhs `,` $outputBuffer `)` attr-dict `:` `(` type($lhs) `,` type($rhs) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimVVMulOp : PimOp<"vvmul", [DestinationStyleOpInterface]> {
+  let summary = "Element-wise multiplication: c = a * b";
+
+  let arguments = (ins
+    PimTensor:$lhs,
+    PimTensor:$rhs,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $lhs `,` $rhs `,` $outputBuffer `)` attr-dict `:` `(` type($lhs) `,` type($rhs) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimVVMaxOp : PimOp<"vvmax", [DestinationStyleOpInterface]> {
+  let summary = "Element-wise max: c = max(a, b)";
+
+  let arguments = (ins
+    PimTensor:$lhs,
+    PimTensor:$rhs,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $lhs `,` $rhs `,` $outputBuffer `)` attr-dict `:` `(` type($lhs) `,` type($rhs) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimVVDMulOp : PimOp<"vvdmul", [DestinationStyleOpInterface]> {
+  let summary = "Dot product: c = dot(a, b)";
+
+  let arguments = (ins
+    PimTensor:$lhs,
+    PimTensor:$rhs,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $lhs `,` $rhs `,` $outputBuffer `)` attr-dict `:` `(` type($lhs) `,` type($rhs) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimSumOp : PimOp<"sum", [DestinationStyleOpInterface]> {
+  let summary = "Reduce all elements to a single value";
+
+  let arguments = (ins
+    PimTensor:$input,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $input `,` $outputBuffer `)` attr-dict `:` `(` type($input) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimVAvgOp : PimOp<"vavg", [DestinationStyleOpInterface]> {
+  let summary = "Average all elements into a single value";
+
+  let arguments = (ins
+    PimTensor:$input,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $input `,` $outputBuffer `)` attr-dict `:` `(` type($input) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimVReluOp : PimOp<"vrelu", [DestinationStyleOpInterface]> {
+  let summary = "Element-wise ReLU: c = max(a, 0)";
+
+  let arguments = (ins
+    PimTensor:$input,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $input `,` $outputBuffer `)` attr-dict `:` `(` type($input) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimVTanhOp : PimOp<"vtanh", [DestinationStyleOpInterface]> {
+  let summary = "Element-wise tanh activation";
+
+  let arguments = (ins
+    PimTensor:$input,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $input `,` $outputBuffer `)` attr-dict `:` `(` type($input) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
+def PimVSigmOp : PimOp<"vsigm", [DestinationStyleOpInterface]> {
+  let summary = "Element-wise sigmoid activation";
+
+  let arguments = (ins
+    PimTensor:$input,
+    PimTensor:$outputBuffer
+  );
+
+  let results = (outs
+    PimTensor:$output
+  );
+
+  let extraClassDeclaration = [{
+    mlir::MutableOperandRange getDpsInitsMutable() {
+      return getOutputBufferMutable();
+    }
+  }];
+
+  let assemblyFormat = [{
+    `(` $input `,` $outputBuffer `)` attr-dict `:` `(` type($input) `,` type($outputBuffer) `)` `->` type($output)
+  }];
+}
+
 #endif // PIM_DIALECT_H

src/PIM/Dialect/Pim/PimOps.cpp

@@ -25,19 +25,6 @@ void PimDialect::initialize() {
     >();
 }
 
-#define POPULATE_DEPENDENCIES(OP_NAME)                                                               \
-  void OP_NAME::populateDependencies(bufferization::RegisterDependenciesFn registerDependenciesFn) { \
-    registerDependenciesFn(this->getOutBuf(), this->getResult());                                    \
-  }
-
-POPULATE_DEPENDENCIES(PimVVDMulOp)
-POPULATE_DEPENDENCIES(PimApplyFiltersOp)
-POPULATE_DEPENDENCIES(PimSumOp)
-POPULATE_DEPENDENCIES(PimVAvgOp)
-POPULATE_DEPENDENCIES(PimVReluOp)
-POPULATE_DEPENDENCIES(PimVTanhOp)
-POPULATE_DEPENDENCIES(PimVSigmOp)
-
 } // namespace pim
 } // namespace onnx_mlir
 

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

@@ -30,7 +30,7 @@ static Value materializeContiguousMemRef(Value memrefValue, Location loc, Rewrit
                               rewriter.getI32IntegerAttr(0),
                               rewriter.getI32IntegerAttr(0),
                               rewriter.getI32IntegerAttr(sizeInBytes))
-    .getDstOut();
+    .getOutput();
 }
 
 struct MemCopyHostToDevOpInterface
@@ -40,26 +40,26 @@ struct MemCopyHostToDevOpInterface
                           const BufferizationOptions& options,
                           BufferizationState& state) const {
     auto memCopyHostToDevOp = cast<PimMemCopyHostToDevOp>(op);
-    auto deviceDst = memCopyHostToDevOp.getDeviceDst();
+    auto deviceTarget = memCopyHostToDevOp.getDeviceTarget();
-    auto hostSrc = memCopyHostToDevOp.getHostSrc();
+    auto hostSource = memCopyHostToDevOp.getHostSource();
 
-    auto deviceDstOpt = getBuffer(rewriter, deviceDst, options, state);
+    auto deviceTargetOpt = getBuffer(rewriter, deviceTarget, options, state);
-    if (failed(deviceDstOpt))
+    if (failed(deviceTargetOpt))
       return failure();
-    auto deviceDstMemRef = *deviceDstOpt;
+    auto deviceTargetMemRef = *deviceTargetOpt;
 
-    auto hostSrcOpt = getBuffer(rewriter, hostSrc, options, state);
+    auto hostSourceOpt = getBuffer(rewriter, hostSource, options, state);
-    if (failed(hostSrcOpt))
+    if (failed(hostSourceOpt))
       return failure();
-    auto hostSrcMemRef = *hostSrcOpt;
+    auto hostSourceMemRef = *hostSourceOpt;
 
     replaceOpWithNewBufferizedOp<PimMemCopyHostToDevOp>(rewriter,
                                                         memCopyHostToDevOp,
-                                                        deviceDstMemRef.getType(),
+                                                        deviceTargetMemRef.getType(),
-                                                        deviceDstMemRef,
+                                                        deviceTargetMemRef,
-                                                        hostSrcMemRef,
+                                                        hostSourceMemRef,
-                                                        memCopyHostToDevOp.getDeviceDstOffsetAttr(),
+                                                        memCopyHostToDevOp.getDeviceTargetOffsetAttr(),
-                                                        memCopyHostToDevOp.getHostSrcOffsetAttr(),
+                                                        memCopyHostToDevOp.getHostSourceOffsetAttr(),
                                                         memCopyHostToDevOp.getSizeAttr());
     return success();
   }
@@ -73,25 +73,25 @@ struct MemCopyDevToHostOpInterface
                           BufferizationState& state) const {
     auto memCopyDevToHostOp = cast<PimMemCopyDevToHostOp>(op);
 
-    auto globalDst = memCopyDevToHostOp.getHostDst();
+    auto hostTarget = memCopyDevToHostOp.getHostTarget();
-    auto globalDstOpt = getBuffer(rewriter, globalDst, options, state);
+    auto hostTargetOpt = getBuffer(rewriter, hostTarget, options, state);
-    if (failed(globalDstOpt))
+    if (failed(hostTargetOpt))
       return failure();
-    auto globalDstMemRef = *globalDstOpt;
+    auto hostTargetMemRef = *hostTargetOpt;
 
-    auto localSrc = memCopyDevToHostOp.getDeviceSrc();
+    auto deviceSource = memCopyDevToHostOp.getDeviceSource();
-    auto localSrcOpt = getBuffer(rewriter, localSrc, options, state);
+    auto deviceSourceOpt = getBuffer(rewriter, deviceSource, options, state);
-    if (failed(localSrcOpt))
+    if (failed(deviceSourceOpt))
       return failure();
-    auto localSrcMemRef = *localSrcOpt;
+    auto deviceSourceMemRef = *deviceSourceOpt;
 
     replaceOpWithNewBufferizedOp<PimMemCopyDevToHostOp>(rewriter,
                                                         memCopyDevToHostOp,
-                                                        globalDstMemRef.getType(),
+                                                        hostTargetMemRef.getType(),
-                                                        globalDstMemRef,
+                                                        hostTargetMemRef,
-                                                        localSrcMemRef,
+                                                        deviceSourceMemRef,
-                                                        memCopyDevToHostOp.getHostDstOffsetAttr(),
+                                                        memCopyDevToHostOp.getHostTargetOffsetAttr(),
-                                                        memCopyDevToHostOp.getDeviceSrcOffsetAttr(),
+                                                        memCopyDevToHostOp.getDeviceSourceOffsetAttr(),
                                                         memCopyDevToHostOp.getSizeAttr());
     return success();
   }
@@ -109,16 +109,16 @@ struct TransposeOpBufferizeInterface
                           BufferizationState& state) const {
     auto transposeOp = cast<PimTransposeOp>(op);
 
-    auto dataOpt = getBuffer(rewriter, transposeOp.getData(), options, state);
+    auto inputOpt = getBuffer(rewriter, transposeOp.getInput(), options, state);
-    if (failed(dataOpt))
+    if (failed(inputOpt))
       return failure();
 
-    auto outBufOpt = getBuffer(rewriter, transposeOp.getOutBuf(), options, state);
+    auto outputBufferOpt = getBuffer(rewriter, transposeOp.getOutputBuffer(), options, state);
-    if (failed(outBufOpt))
+    if (failed(outputBufferOpt))
       return failure();
 
     replaceOpWithNewBufferizedOp<PimTransposeOp>(
-      rewriter, op, outBufOpt->getType(), *dataOpt, transposeOp.getPerms(), *outBufOpt);
+      rewriter, op, outputBufferOpt->getType(), *inputOpt, transposeOp.getPermutation(), *outputBufferOpt);
     return success();
   }
 };
@@ -132,9 +132,9 @@ struct VMMOpBufferizeInterface : DstBufferizableOpInterfaceExternalModel<VMMOpBu
     auto vmmOp = cast<PimVMMOp>(op);
     Value readVal = uRead->get();
     Value writeVal = uWrite->get();
-    if (writeVal != vmmOp.getOutBuf())
+    if (writeVal != vmmOp.getOutputBuffer())
       return false;
-    if (readVal == vmmOp.getVectorInput())
+    if (readVal == vmmOp.getInput())
       if (state.areEquivalentBufferizedValues(readVal, writeVal))
         return true;
     return false;
@@ -146,16 +146,16 @@ struct VMMOpBufferizeInterface : DstBufferizableOpInterfaceExternalModel<VMMOpBu
                           BufferizationState& state) const {
     auto vmmOp = cast<PimVMMOp>(op);
 
-    auto vectorInputOpt = getBuffer(rewriter, vmmOp.getVectorInput(), options, state);
+    auto inputOpt = getBuffer(rewriter, vmmOp.getInput(), options, state);
-    if (failed(vectorInputOpt))
+    if (failed(inputOpt))
       return failure();
 
-    auto outBufOpt = getBuffer(rewriter, vmmOp.getOutBuf(), options, state);
+    auto outputBufferOpt = getBuffer(rewriter, vmmOp.getOutputBuffer(), options, state);
-    if (failed(outBufOpt))
+    if (failed(outputBufferOpt))
       return failure();
 
     replaceOpWithNewBufferizedOp<PimVMMOp>(
-      rewriter, op, outBufOpt->getType(), vmmOp.getWeightIndexAttr(), *vectorInputOpt, *outBufOpt);
+      rewriter, op, outputBufferOpt->getType(), vmmOp.getWeightIndexAttr(), *inputOpt, *outputBufferOpt);
     return success();
   }
 };
@@ -171,16 +171,16 @@ struct MVMOpBufferizeInterface : DstBufferizableOpInterfaceExternalModel<MVMOpBu
                           BufferizationState& state) const {
     auto mvmOp = cast<PimMVMOp>(op);
 
-    auto vectorInputOpt = getBuffer(rewriter, mvmOp.getVectorInput(), options, state);
+    auto inputOpt = getBuffer(rewriter, mvmOp.getInput(), options, state);
-    if (failed(vectorInputOpt))
+    if (failed(inputOpt))
       return failure();
 
-    auto outBufOpt = getBuffer(rewriter, mvmOp.getOutBuf(), options, state);
+    auto outputBufferOpt = getBuffer(rewriter, mvmOp.getOutputBuffer(), options, state);
-    if (failed(outBufOpt))
+    if (failed(outputBufferOpt))
       return failure();
 
     replaceOpWithNewBufferizedOp<PimMVMOp>(
-      rewriter, op, outBufOpt->getType(), mvmOp.getWeightIndexAttr(), *vectorInputOpt, *outBufOpt);
+      rewriter, op, outputBufferOpt->getType(), mvmOp.getWeightIndexAttr(), *inputOpt, *outputBufferOpt);
     return success();
   }
 };
@@ -203,22 +203,23 @@ struct BinaryDstOpBufferizeInterface : DstBufferizableOpInterfaceExternalModel<B
                           BufferizationState& state) const {
     auto binaryOp = cast<OpTy>(op);
 
-    auto aOpt = getBuffer(rewriter, binaryOp.getA(), options, state);
+    auto lhsOpt = getBuffer(rewriter, binaryOp.getLhs(), options, state);
-    if (failed(aOpt))
+    if (failed(lhsOpt))
       return failure();
 
-    auto bOpt = getBuffer(rewriter, binaryOp.getB(), options, state);
+    auto rhsOpt = getBuffer(rewriter, binaryOp.getRhs(), options, state);
-    if (failed(bOpt))
+    if (failed(rhsOpt))
       return failure();
 
-    auto outBufOpt = getBuffer(rewriter, binaryOp.getOutBuf(), options, state);
+    auto outputBufferOpt = getBuffer(rewriter, binaryOp.getOutputBuffer(), options, state);
-    if (failed(outBufOpt))
+    if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousA = materializeContiguousMemRef(*aOpt, op->getLoc(), rewriter);
+    Value contiguousLhs = materializeContiguousMemRef(*lhsOpt, op->getLoc(), rewriter);
-    Value contiguousB = materializeContiguousMemRef(*bOpt, op->getLoc(), rewriter);
+    Value contiguousRhs = materializeContiguousMemRef(*rhsOpt, op->getLoc(), rewriter);
 
-    replaceOpWithNewBufferizedOp<OpTy>(rewriter, op, outBufOpt->getType(), contiguousA, contiguousB, *outBufOpt);
+    replaceOpWithNewBufferizedOp<OpTy>(
+      rewriter, op, outputBufferOpt->getType(), contiguousLhs, contiguousRhs, *outputBufferOpt);
     return success();
   }
 };

src/PIM/Dialect/Pim/Transforms/Bufferization/PimBufferization.td

@@ -16,4 +16,5 @@ def memrefCopyToPimMemCopyOp : Pat<
     (returnType $dst))
 >;
 
+
 #endif // PIM_BUFFERIZATION

src/PIM/Dialect/Spatial/Spatial.td

@@ -28,8 +28,12 @@ def SpatChannelType : SpatType<"SpatChannel", "ch"> {
   let summary = "Virtual channel type";
 }
 
+//===----------------------------------------------------------------------===//
+// Execution
+//===----------------------------------------------------------------------===//
+
 def SpatWeightedCompute : SpatOp<"compute", [SingleBlock, AttrSizedOperandSegments]> {
-  let summary = "Compute operation, with constant weights already attached";
+  let summary = "Compute region with attached constant weights";
 
   let arguments = (ins
     Variadic<SpatTensor>:$weights,
@@ -50,6 +54,8 @@ def SpatWeightedCompute: SpatOp<"compute", [SingleBlock, AttrSizedOperandSegment
 }
 
 def SpatYieldOp : SpatOp<"yield", [Terminator]> {
+  let summary = "Yield results from a compute region";
+
   let arguments = (ins
     Variadic<SpatTensor>:$outputs
   );
@@ -60,12 +66,14 @@ def SpatYieldOp: SpatOp<"yield", [Terminator]> {
 }
 
 //===----------------------------------------------------------------------===//
-// Data movement operations
+// Communication
 //===----------------------------------------------------------------------===//
 
 def SpatChannelNewOp : SpatOp<"channel_new", []> {
+  let summary = "Create a new virtual channel";
+
   let results = (outs
-    SpatChannelType:$new_channel
+    SpatChannelType:$channel
   );
 
   let builders = [
@@ -80,107 +88,73 @@ def SpatChannelNewOp: SpatOp<"channel_new", []> {
 }
 
 def SpatChannelSendOp : SpatOp<"channel_send", []> {
+  let summary = "Send a tensor through a channel";
+
   let arguments = (ins
     SpatChannelType:$channel,
-    SpatTensor: $data
+    SpatTensor:$input
   );
 
   let assemblyFormat = [{
-    $data `to` $channel attr-dict `:` `(` type($data) `->` type($channel) `)`
+    $input `to` $channel attr-dict `:` `(` type($input) `->` type($channel) `)`
   }];
 }
 
 def SpatChannelReceiveOp : SpatOp<"channel_receive", []> {
+  let summary = "Receive a tensor from a channel";
+
   let arguments = (ins
     SpatChannelType:$channel
   );
 
   let results = (outs
-    SpatTensor: $data
+    SpatTensor:$output
   );
 
   let assemblyFormat = [{
-    $channel attr-dict `:` `(` type($channel) `->` type($data) `)`
+    $channel attr-dict `:` `(` type($channel) `->` type($output) `)`
   }];
 }
 
 def SpatChannelBroadcastSendOp : SpatOp<"channel_broadcast_send", []> {
+  let summary = "Broadcast a tensor through a shared channel buffer";
+
   let arguments = (ins
     SpatChannelType:$channel,
-    SpatTensor: $data
+    SpatTensor:$input
   );
+
+  let assemblyFormat = [{
+    $input `to` $channel attr-dict `:` `(` type($input) `->` type($channel) `)`
+  }];
 }
 
 def SpatChannelBroadcastReceiveOp : SpatOp<"channel_broadcast_receive", []> {
+  let summary = "Receive a tensor from a shared channel buffer";
+
   let arguments = (ins
     SpatChannelType:$channel
   );
 
   let results = (outs
-    SpatTensor: $data
+    SpatTensor:$output
   );
-}
 
-//===----------------------------------------------------------------------===//
+  let assemblyFormat = [{
-// Math operations
+    $channel attr-dict `:` `(` type($channel) `->` type($output) `)`
-//===----------------------------------------------------------------------===//
-
-def SpatConstantOp: SpatOp<"constant", []> {
-  let description = [{
-    "Constant value, should be used for weights and biases"
   }];
-
-  let arguments = (ins
-    AnyAttr: $value,
-    BoolAttr: $shouldAllocate
-  );
-
-  let results = (outs
-    SpatTensor: $out
-  );
 }
 
+//===----------------------------------------------------------------------===//
+// Math
+//===----------------------------------------------------------------------===//
+
 def SpatWeightedVMMOp : SpatOp<"Wvmm", []> {
-  let summary = "Vector-matrix-Multiplication within a WeightedCompute operation. The matrix is found in the weights of the WeightedCompute operation, indexed by the weightIndex attribute.";
+  let summary = "Vector-matrix multiplication within a weighted compute operation";
 
   let arguments = (ins
     I32Attr:$weightIndex,
-    SpatTensor:$vector
+    SpatTensor:$input
-  );
-
-  let results = (outs
-    SpatTensor:$output
-  );
-
-  // TODO: Verifier that checks it is within a WeightedCompute operation,
-  // that the weightIndex is valid, and that the matrix is of the right size.
-  let hasVerifier = 1;
-}
-
-def SpatWeightedMVMOp: SpatOp<"Wmvm", []> {
-  let summary = "Matrix-vector multiplication within a WeightedCompute operation. The matrix is found in the weights of the WeightedCompute operation, indexed by the weightIndex attribute.";
-
-  let arguments = (ins
-    I32Attr: $weightIndex,
-    SpatTensor:$vector
-  );
-
-  let results = (outs
-    SpatTensor:$output
-  );
-
-  // TODO: Verifier that checks it is within a WeightedCompute operation,
-  // that the weightIndex is valid, and that the matrix is of the right size.
-  let hasVerifier = 1;
-}
-
-
-def SpatVAddOp: SpatOp<"vadd", []> {
-  let summary = "Element-wise add between tensors a and b. Tensor b must have the same size of tensor b or be a 1x1";
-
-  let arguments = (ins
-    SpatTensor: $a,
-    SpatTensor: $b
   );
 
   let results = (outs
@@ -190,65 +164,67 @@ def SpatVAddOp: SpatOp<"vadd", []> {
   let hasVerifier = 1;
 
   let assemblyFormat = [{
-    $a `,` $b attr-dict `:` `(` type($a) `,` type($b) `)` `->` type($output)
+    `(` $input `)` attr-dict `:` type($input) `->` type($output)
+  }];
+}
+
+def SpatWeightedMVMOp : SpatOp<"Wmvm", []> {
+  let summary = "Matrix-vector multiplication within a weighted compute operation";
+
+  let arguments = (ins
+    I32Attr:$weightIndex,
+    SpatTensor:$input
+  );
+
+  let results = (outs
+    SpatTensor:$output
+  );
+
+  let hasVerifier = 1;
+
+  let assemblyFormat = [{
+    `(` $input `)` attr-dict `:` type($input) `->` type($output)
+  }];
+}
+
+def SpatVAddOp : SpatOp<"vadd", []> {
+  let summary = "Element-wise addition between two tensors; rhs must match lhs or be 1x1";
+
+  let arguments = (ins
+    SpatTensor:$lhs,
+    SpatTensor:$rhs
+  );
+
+  let results = (outs
+    SpatTensor:$output
+  );
+
+  let hasVerifier = 1;
+
+  let assemblyFormat = [{
+    $lhs `,` $rhs attr-dict `:` `(` type($lhs) `,` type($rhs) `)` `->` type($output)
   }];
 }
 
 def SpatVMulOp : SpatOp<"vmul", []> {
-  let summary = "Element-wise multiplication between tensors a and b. Tensor b must have the same size of tensor b or be a 1x1";
+  let summary = "Element-wise multiplication between two tensors; rhs must match lhs or be 1x1";
 
   let arguments = (ins
-      SpatTensor: $a,
+    SpatTensor:$lhs,
-      SpatTensor: $b
+    SpatTensor:$rhs
   );
 
   let results = (outs
     SpatTensor:$output
   );
 
-    //let hasVerifier = 1;
-
   let assemblyFormat = [{
-      $a `,` $b attr-dict `:` `(` type($a) `,` type($b) `)` `->` type($output)
+    $lhs `,` $rhs attr-dict `:` `(` type($lhs) `,` type($rhs) `)` `->` type($output)
   }];
 }
 
-def SpatVDivOp: SpatOp<"vdiv", []> {
-  let summary = "Element-wise division between tensors a and b. Tensor b must have the same size of tensor b or be a 1x1";
-
-  let arguments = (ins
-    SpatTensor:$a,
-    SpatTensor:$b
-  );
-
-  let results = (outs
-    SpatTensor:$output
-  );
-
-  //let hasVerifier = 1;
-
-  let assemblyFormat = [{
-    $a `,` $b attr-dict `:` `(` type($a) `,` type($b) `)` `->` type($output)
-  }];
-}
-
-//TODO: remove
-def SpatVSDivOp: SpatOp<"vsdiv", []> {
-
-  let summary = "Element-wise division between each element of a vector, and a scalar (wrapped in a tensor for convenience)";
-
-  let arguments = (ins
-    SpatTensor:$dividend,
-    SpatTensor:$divisor
-  );
-
-  let results = (outs
-    SpatTensor:$output
-  );
-}
-
 def SpatSumOp : SpatOp<"sum", []> {
-  let summary = "Sum all the elements in the input tensors into a single scalar wrapped in tensor for convenience";
+  let summary = "Reduce all elements of the input tensor to a single scalar wrapped in a tensor";
 
   let arguments = (ins
     SpatTensor:$input
@@ -257,9 +233,15 @@ def SpatSumOp: SpatOp<"sum", []> {
   let results = (outs
     SpatTensor:$output
   );
+
+  let assemblyFormat = [{
+    `(` $input `)` attr-dict `:` type($input) `->` type($output)
+  }];
 }
 
 def SpatSigmoidOp : SpatOp<"sigmoid", []> {
+  let summary = "Element-wise sigmoid activation";
+
   let arguments = (ins
     SpatTensor:$input
   );
@@ -267,9 +249,15 @@ def SpatSigmoidOp: SpatOp<"sigmoid", []> {
   let results = (outs
     SpatTensor:$output
   );
+
+  let assemblyFormat = [{
+    `(` $input `)` attr-dict `:` type($input) `->` type($output)
+  }];
 }
 
 def SpatReluOp : SpatOp<"relu", []> {
+  let summary = "Element-wise ReLU activation";
+
   let arguments = (ins
     SpatTensor:$input
   );
@@ -277,15 +265,18 @@ def SpatReluOp: SpatOp<"relu", []> {
   let results = (outs
     SpatTensor:$output
   );
+
+  let assemblyFormat = [{
+    `(` $input `)` attr-dict `:` type($input) `->` type($output)
+  }];
 }
 
 def SpatVMaxOp : SpatOp<"vmax", []> {
-
+  let summary = "Element-wise max between two tensors";
-  let summary = "Element-wise max function";
 
   let arguments = (ins
-    SpatTensor: $a,
+    SpatTensor:$lhs,
-    SpatTensor: $b
+    SpatTensor:$rhs
   );
 
   let results = (outs
@@ -293,62 +284,9 @@ def SpatVMaxOp: SpatOp<"vmax", []> {
   );
 
   let hasVerifier = 1;
-}
-
-def SpatApplyFiltersOp : SpatOp<"apply_filters", []> {
-  let summary = "Apply multiple crossbar weights to a convolutional input tile.";
-  let description = [{
-    Applies a variable number of crossbar weights to a single large image tensor tile,
-    producing a corresponding output tile. This essentially encapsulates a big for loop
-    over all pixels in the input tile, where each pixel is multiplied by all the weights
-    in the operation.
-  }];
-
-  let arguments = (ins
-    I64ArrayAttr: $weightIndices,
-    I64ArrayAttr: $xKernelPositions,
-    I64ArrayAttr: $yKernelPositions,
-    SpatTensor: $input
-  );
-  let results = (outs SpatTensor);
 
   let assemblyFormat = [{
-    $input attr-dict `:` type($input) `->` type(results)
+    $lhs `,` $rhs attr-dict `:` `(` type($lhs) `,` type($rhs) `)` `->` type($output)
-  }];
-}
-
-//===----------------------------------------------------------------------===//
-// Other operations
-//===----------------------------------------------------------------------===//
-
-def SpatImgConcatOp: SpatOp<"img_concat", []> {
-
-  let summary = "Concatenate pixel tiles into a single image";
-
-  let description = [{
-    Concatenate pixel tiles into a single image:
-    1. First, concatenate the pixel tiles along the "channel" axis (axis 1).
-    2. Next, concatenate the pixel tiles along the "width" axis (axis 2).
-    3. Finally, concatenate the pixel tiles along the "height" axis (axis 3).
-
-    The input tiles should be provided in a specific order:
-      start from the top left pixel,
-      then continue with the pixel on its right,
-      and once you finish the first row of pixels, go to the next row.
-  }];
-
-  let arguments = (ins
-    Variadic<SpatTensor>:$inputs
-  );
-
-  let results = (outs
-    SpatTensor:$output
-  );
-
-  let hasVerifier = 1;
-
-  let extraClassDeclaration = [{
-      mlir::Value getInputTile(size_t x, size_t y, size_t tile);
   }];
 }
 

src/PIM/Dialect/Spatial/SpatialOps.cpp

@@ -136,7 +136,7 @@ LogicalResult SpatWeightedMVMOp::verify() {
   if (failed(matrixShapeOpt))
     return emitError("SpatWeightedMVMOp was not within a SpatWeightedCompute or Core op");
   auto matrixShape = *matrixShapeOpt;
-  auto vectorShape = getVector().getType().getShape();
+  auto vectorShape = getInput().getType().getShape();
   auto outputShape = getOutput().getType().getShape();
 
   /* Two possible accepted shapes:
@@ -157,7 +157,7 @@ LogicalResult SpatWeightedVMMOp::verify() {
   if (failed(matrixShapeOpt))
     return emitError("SpatWeightedVMMOp was not within a SpatWeightedCompute or Core op");
   auto matrixShape = *matrixShapeOpt;
-  auto vectorShape = getVector().getType().getShape();
+  auto vectorShape = getInput().getType().getShape();
   auto outputShape = getOutput().getType().getShape();
 
   /* Accepted shape:
@@ -200,59 +200,6 @@ LogicalResult SpatVMaxOp::verify() {
   return OpTrait::impl::verifySameOperandsAndResultType(*this);
 }
 
-LogicalResult SpatImgConcatOp::verify() {
-  auto imgShape = mlir::cast<ShapedType>(getType());
-  size_t img_w = getImageWidth(imgShape);
-  size_t img_h = getImageHeight(imgShape);
-  size_t img_c = getImageChannel(imgShape);
-
-  size_t channelTiles = ceilIntegerDivide(img_c, crossbarSize.getValue());
-  size_t channelTileRest = img_c % crossbarSize;
-
-  auto operands = getOperands();
-
-  // Check number of operands
-  if (img_w * img_h * channelTiles != operands.size())
-    return emitError("Number of operands does not match output image size");
-
-  // For each output pixel, check that the inputTiles have a correct shape
-  for (size_t x = 0; x < img_w; x++) {
-    for (size_t y = 0; y < img_h; y++) {
-      size_t channel_counts = 0;
-      for (size_t t = 0; t < channelTiles; t++) {
-        auto inputShape = mlir::cast<ShapedType>(getInputTile(x, y, t).getType());
-        if (!inputShape)
-          return emitError("Invalid input type, must be ShapedType");
-
-        // N == W == H == 1
-        if (getImageN(inputShape) != 1 || getImageWidth(inputShape) != 1 || getImageHeight(inputShape) != 1)
-          return emitError("Invalid input shape: N,W,H must all be 1");
-
-        size_t inputChannels = getImageChannel(inputShape);
-
-        // Check the number of channels in this tile are correct:
-        // - CASE1: last tile of pixel, if there is some rest it must match that
-        // - CASE2: common case, the channel count is exactly the crossbarSize
-        if (t == channelTiles - 1 && channelTileRest != 0) {
-          if (inputChannels != channelTileRest)
-            return emitError("Invalid channel count for last tile of pixel");
-        }
-        else {
-          if (inputChannels != crossbarSize)
-            return emitError("Invalid channel count for some pixel tile");
-        }
-
-        channel_counts += inputChannels;
-      }
-
-      if (channel_counts != img_c)
-        emitError("Invalid number of channels for some pixel");
-    }
-  }
-
-  return success();
-}
-
 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
@@ -308,22 +255,6 @@ LogicalResult SpatWeightedCompute::verify() {
   return success();
 }
 
-Value SpatImgConcatOp::getInputTile(size_t x, size_t y, size_t tile) {
-  auto operands = getOperands();
-  auto imgShape = mlir::cast<ShapedType>(getType());
-  size_t img_w = getImageWidth(imgShape);
-  size_t img_h = getImageHeight(imgShape);
-  size_t img_c = getImageChannel(imgShape);
-
-  size_t channelTiles = ceilIntegerDivide(img_c, crossbarSize.getValue());
-
-  assert(tile < channelTiles);
-  assert(x < img_w);
-  assert(y < img_h);
-
-  return operands[tile + x * channelTiles + y * img_w * channelTiles];
-}
-
 } // namespace spatial
 } // namespace onnx_mlir
 

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

@@ -53,7 +53,7 @@ Value materializeContiguousMemRef(Value memrefValue, Location loc, RewriterBase&
                                    rewriter.getI32IntegerAttr(0),
                                    rewriter.getI32IntegerAttr(0),
                                    rewriter.getI32IntegerAttr(sizeInBytes))
-    .getDstOut();
+    .getOutput();
 }
 
 const llvm::StringRef PRECOMPUTED_OTHER_CORE_ID_ATTR_NAME("precomp_other_core_id");
@@ -202,7 +202,7 @@ struct VariadicArgumentElementWiseOpInterface : BufferizableOpInterface::Externa
 
     memrefOperands.push_back(outputTensor);
 
-    Value newValue = ToTy::create(rewriter, op->getLoc(), outputTensor.getType(), memrefOperands).getOutRes();
+    Value newValue = ToTy::create(rewriter, op->getLoc(), outputTensor.getType(), memrefOperands).getOutput();
 
     replaceOpWithBufferizedValues(rewriter, op, newValue);
 
@@ -243,7 +243,7 @@ struct WeightedMultiplicationsOpInterface : BufferizableOpInterface::ExternalMod
                                   cast<OpTy>(op).getWeightIndexAttr(),
                                   memrefOperand,
                                   outputTensor)
-                       .getOutRes();
+                       .getOutput();
 
     replaceOpWithBufferizedValues(rewriter, op, newValue);
 
@@ -289,7 +289,7 @@ struct ChannelReceiveOpInterface
                                                outputTensor,
                                                rewriter.getI32IntegerAttr(numElements * elementSize),
                                                rewriter.getI32IntegerAttr(srcCoreId.value()))
-                       .getOut();
+                       .getOutput();
 
     replaceOpWithBufferizedValues(rewriter, op, newValue);
 
@@ -403,7 +403,7 @@ struct ChannelBroadcastReceiveOpInterface
                                                                  rewriter.getI32IntegerAttr(0),
                                                                  rewriter.getI32IntegerAttr(outputSize));
 
-    replaceOpWithBufferizedValues(rewriter, op, memCopyHostToDevOp.getDeviceDst());
+    replaceOpWithBufferizedValues(rewriter, op, memCopyHostToDevOp.getOutput());
 
     return success();
   }
@@ -490,84 +490,6 @@ struct SumOpInterface : VariadicArgumentElementWiseOpInterface<SumOpInterface, S
 
 struct VMaxOpInterface : VariadicArgumentElementWiseOpInterface<VMaxOpInterface, SpatVMaxOp, pim::PimVVMaxOp> {};
 
-// Create a new bufferizable op interface for the apply filters operation.
-struct ApplyFiltersOpInterface : BufferizableOpInterface::ExternalModel<ApplyFiltersOpInterface, SpatApplyFiltersOp> {
-
-  // One operand ($input) is read from. All other inputs are only written to.
-  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-
-    // Operand 0: $input
-    // Operand 1: $outBuf
-    // Operand 2: $accumBuf
-    return opOperand.getOperandNumber() == 0;
-  }
-
-  // One input ($accumBuf) is written to. All other inputs are only read.
-  bool bufferizesToMemoryWrite(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-
-    // Operand 0: $input
-    // Operand 1: $outBuf
-    // Operand 2: $accumBuf
-    return opOperand.getOperandNumber() == 2;
-  }
-
-  // No operands are aliased with any other operands.
-  AliasingValueList getAliasingValues(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
-    return {};
-  }
-
-  // Bufferize the operation.
-  LogicalResult bufferize(Operation* op,
-                          RewriterBase& rewriter,
-                          const BufferizationOptions& options,
-                          BufferizationState& state) const {
-
-    // Get the input tensor buffer.
-    auto inputBuffer = getBuffer(rewriter, op->getOperand(0), options, state);
-
-    if (failed(inputBuffer))
-      return failure();
-
-    // Create a new buffer for the output tensor.
-    auto outputTensor = createEmptyFromType(op->getResult(0).getType(), op->getLoc(), rewriter);
-
-    // Create a new buffer for the accumulation buffer.
-    // To do this, create a new allocation operation. Size must be axbx1x1,
-    // where axbxcxd is the size of the output tensor. Since the shape is
-    // different, we can't immediately use createEmptyFromType, we first need to
-    // create the shape of the accumulation buffer.
-    auto accumShape = llvm::to_vector<4>(cast<ShapedType>(op->getResult(0).getType()).getShape());
-
-    // Set the last two dimensions to 1.
-    accumShape[accumShape.size() - 1] = 1;
-    accumShape[accumShape.size() - 2] = 1;
-
-    auto accumType = MemRefType::get(accumShape, cast<ShapedType>(op->getResult(0).getType()).getElementType());
-
-    auto accumBuffer = createEmptyFromType(accumType, op->getLoc(), rewriter);
-
-    // Bufferize the operation.
-    auto weightIndices = cast<SpatApplyFiltersOp>(op).getWeightIndicesAttr();
-    auto xKernelPositions = cast<SpatApplyFiltersOp>(op).getXKernelPositionsAttr();
-    auto yKernelPositions = cast<SpatApplyFiltersOp>(op).getYKernelPositionsAttr();
-
-    Value bufferized = pim::PimApplyFiltersOp::create(rewriter,
-                                                      op->getLoc(),
-                                                      outputTensor.getType(),
-                                                      weightIndices,
-                                                      xKernelPositions,
-                                                      yKernelPositions,
-                                                      *inputBuffer,
-                                                      outputTensor,
-                                                      accumBuffer);
-
-    // Replace the operation with the bufferized value.
-    replaceOpWithBufferizedValues(rewriter, op, bufferized);
-
-    return success();
-  }
-};
-
 void registerBufferizableOpInterfaceExternalModels(DialectRegistry& registry) {
   registry.addExtension(+[](MLIRContext* ctx, SpatialDialect* dialect) {
     SpatWeightedCompute::attachInterface<WComputeOpInterface>(*ctx);
@@ -580,7 +502,6 @@ void registerBufferizableOpInterfaceExternalModels(DialectRegistry& registry) {
     SpatChannelSendOp::attachInterface<ChannelSendOpInterface>(*ctx);
     SpatChannelBroadcastReceiveOp::attachInterface<ChannelBroadcastReceiveOpInterface>(*ctx);
     SpatChannelBroadcastSendOp::attachInterface<ChannelBroadcastSendOpInterface>(*ctx);
-    SpatApplyFiltersOp::attachInterface<ApplyFiltersOpInterface>(*ctx);
   });
 }
 

src/PIM/Pass/Pim/ConstantFolding/Patterns/Constant.cpp

@@ -247,11 +247,11 @@ struct FoldConstantTransposePattern final : OpRewritePattern<pim::PimTransposeOp
   using OpRewritePattern::OpRewritePattern;
 
   LogicalResult matchAndRewrite(pim::PimTransposeOp transposeOp, PatternRewriter& rewriter) const override {
-    auto resultType = dyn_cast<MemRefType>(transposeOp.getOutRes().getType());
+    auto resultType = dyn_cast<MemRefType>(transposeOp.getOutput().getType());
     if (!resultType || !resultType.hasStaticShape())
       return failure();
 
-    auto sourceGetGlobal = transposeOp.getData().getDefiningOp<memref::GetGlobalOp>();
+    auto sourceGetGlobal = transposeOp.getInput().getDefiningOp<memref::GetGlobalOp>();
     if (!sourceGetGlobal)
       return failure();
 
@@ -268,8 +268,8 @@ struct FoldConstantTransposePattern final : OpRewritePattern<pim::PimTransposeOp
       return failure();
 
     SmallVector<int64_t> perms;
-    perms.reserve(transposeOp.getPerms().size());
+    perms.reserve(transposeOp.getPermutation().size());
-    for (IntegerAttr attr : transposeOp.getPerms().getAsRange<IntegerAttr>())
+    for (IntegerAttr attr : transposeOp.getPermutation().getAsRange<IntegerAttr>())
       perms.push_back(attr.getInt());
     FailureOr<DenseElementsAttr> transposedAttr = transposeDenseElements(denseAttr, perms);
     if (failed(transposedAttr))
@@ -389,18 +389,18 @@ struct FoldConstantMemCpPattern final : OpRewritePattern<pim::PimMemCopyOp> {
     if (copyOp->getParentOfType<pim::PimCoreOp>())
       return failure();
 
-    auto allocOp = copyOp.getDst().getDefiningOp<memref::AllocOp>();
+    auto allocOp = copyOp.getTarget().getDefiningOp<memref::AllocOp>();
     if (!allocOp)
       return failure();
     auto allocType = dyn_cast<MemRefType>(allocOp.getType());
     if (!allocType || !allocType.hasStaticShape())
       return failure();
 
-    if (copyOp.getDstOffset() != 0 || copyOp.getSrcOffset() != 0)
+    if (copyOp.getTargetOffset() != 0 || copyOp.getSourceOffset() != 0)
       return failure();
 
-    auto srcSubview = getStaticSubviewInfo(copyOp.getSrc());
+    auto srcSubview = getStaticSubviewInfo(copyOp.getSource());
-    Value globalSource = succeeded(srcSubview) ? srcSubview->source : stripMemRefCasts(copyOp.getSrc());
+    Value globalSource = succeeded(srcSubview) ? srcSubview->source : stripMemRefCasts(copyOp.getSource());
 
     auto moduleOp = copyOp->getParentOfType<ModuleOp>();
     if (!moduleOp)

src/PIM/Pass/Pim/ConstantFolding/Patterns/Subview.cpp

@@ -89,10 +89,10 @@ struct RewriteCoreSubviewCopyPattern final : OpRewritePattern<pim::PimMemCopyOp>
 
     auto status =
       rewriteSubviewCopyLikeOp(copyOp,
-                               copyOp.getDst(),
+                               copyOp.getTarget(),
-                               copyOp.getSrc(),
+                               copyOp.getSource(),
-                               copyOp.getDstOffset(),
+                               copyOp.getTargetOffset(),
-                               copyOp.getSrcOffset(),
+                               copyOp.getSourceOffset(),
                                copyOp.getSize(),
                                rewriter,
                                [&](MemRefType resultType,
@@ -114,7 +114,7 @@ struct RewriteCoreSubviewCopyPattern final : OpRewritePattern<pim::PimMemCopyOp>
     if (failed(status))
       return failure();
 
-    rewriter.replaceOp(copyOp, copyOp.getDst());
+    rewriter.replaceOp(copyOp, copyOp.getTarget());
     return success();
   }
 };
@@ -125,10 +125,10 @@ struct RewriteHostSubviewLoadPattern final : OpRewritePattern<pim::PimMemCopyHos
   LogicalResult matchAndRewrite(pim::PimMemCopyHostToDevOp copyOp, PatternRewriter& rewriter) const override {
     auto status =
       rewriteSubviewCopyLikeOp(copyOp,
-                               copyOp.getDeviceDst(),
+                               copyOp.getDeviceTarget(),
-                               copyOp.getHostSrc(),
+                               copyOp.getHostSource(),
-                               copyOp.getDeviceDstOffset(),
+                               copyOp.getDeviceTargetOffset(),
-                               copyOp.getHostSrcOffset(),
+                               copyOp.getHostSourceOffset(),
                                copyOp.getSize(),
                                rewriter,
                                [&](MemRefType resultType,
@@ -150,7 +150,7 @@ struct RewriteHostSubviewLoadPattern final : OpRewritePattern<pim::PimMemCopyHos
     if (failed(status))
       return failure();
 
-    rewriter.replaceOp(copyOp, copyOp.getDeviceDst());
+    rewriter.replaceOp(copyOp, copyOp.getDeviceTarget());
     return success();
   }
 };