Merge remote changes

fast spatial node merging with batch operations

.gitignore

@@ -1,5 +1,15 @@
+.zed
 .idea
 **/.vscode
+
 .claude
+.codex
 AGENTS.md
+
+CMakeUserPresets.json
+
 build
+cmake-build-debug
+cmake-build-release
+
+**/__pycache__

README.md

@@ -135,7 +135,7 @@ validate.py \
     --raptor-path ../cmake-build-release/Release/bin/onnx-mlir \
     --onnx-include-dir ../onnx-mlir/include \
     --operations-dir ./networks/yolo11n/depth_04 \
-    --crossbar-size 2048
+    --crossbar-size 2048 --crossbar-count 256
 ```
 
 Available networks under `validation/networks/`: `vgg16`, `yolo11n`.

src/PIM/Common/PimCommon.cpp

@@ -1,4 +1,5 @@
 #include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/Dialect/SCF/IR/SCF.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
@@ -48,7 +49,9 @@ void dumpModule(ModuleOp moduleOp, const std::string& name) {
 
   std::fstream file(dialectsDir + "/" + name + ".mlir", std::ios::out);
   llvm::raw_os_ostream os(file);
-  os << *moduleOp;
+  OpPrintingFlags flags;
+  flags.elideLargeElementsAttrs();
+  moduleOp.print(os, flags);
   os.flush();
   file.close();
 }
@@ -173,6 +176,13 @@ void walkPimMvmVmmWeightUses(Operation* root, function_ref<void(OpOperand&)> cal
   root->walk([&](pim::PimCoreOp coreOp) {
     walkMvmVmmWeightUses<pim::PimMVMOp, pim::PimVMMOp>(coreOp, callback);
   });
+  root->walk([&](pim::PimCoreBatchOp coreBatchOp) {
+    auto weights = coreBatchOp.getWeights();
+    for (auto weight : weights)
+      for (OpOperand& use : weight.getUses())
+        if (use.getOwner() == coreBatchOp.getOperation())
+          callback(use);
+  });
 }
 
 memref::GlobalOp lookupGlobalForGetGlobal(ModuleOp moduleOp, memref::GetGlobalOp getGlobalOp) {
@@ -181,66 +191,6 @@ memref::GlobalOp lookupGlobalForGetGlobal(ModuleOp moduleOp, memref::GetGlobalOp
   return moduleOp.lookupSymbol<memref::GlobalOp>(getGlobalOp.getName());
 }
 
-FailureOr<Operation*> getOtherEndOfChannel(Operation* op, bool opIsReceive, RewriterBase& rewriter) {
-
-  auto channelNewOp = op->getOperand(0).getDefiningOp<spatial::SpatChannelNewOp>();
-  if (!channelNewOp) {
-    op->emitError("User of Channel must have the first operand created by ChannelNewOp.");
-    return failure();
-  }
-  // channelNewOp should have two users: `op` and a
-  // `ChannelSendOp`/`ChannelReceiveOp`
-  auto channelUsers = channelNewOp->getUsers();
-  auto usersIterator = channelUsers.begin();
-  auto firstUser = *usersIterator;
-  usersIterator++;
-  if (usersIterator == channelUsers.end()) {
-    op->emitError("Operand generated by ChannelNewOp must have two users, "
-                  "only one found.");
-    channelNewOp->dump();
-    op->dump();
-    channelNewOp->getParentOp()->dump();
-    return failure();
-  }
-  auto secondUser = *usersIterator;
-  usersIterator++;
-  if (usersIterator != channelUsers.end()) {
-    op->emitError("Operand generated by ChannelNewOp must have two users, "
-                  "more than two found.");
-    return failure();
-  }
-  Operation* notOpUser;
-  if (firstUser == op) {
-    notOpUser = secondUser;
-  }
-  else if (secondUser == op) {
-    notOpUser = firstUser;
-  }
-  else {
-    op->emitError("Operand generated by ChannelNewOp must have two users, "
-                  "and one of them must be me, but"
-                  "none of them is actually me.");
-    return failure();
-  }
-
-  if (opIsReceive) {
-    if (!isa<spatial::SpatChannelSendOp>(notOpUser)) {
-      op->emitError("Operand generated by ChannelNewOp has two user, one is "
-                    "me, the other is not a ChannelSendOp.");
-      return failure();
-    }
-    return notOpUser;
-  }
-  else {
-    if (!isa<spatial::SpatChannelReceiveOp>(notOpUser)) {
-      op->emitError("Operand generated by ChannelNewOp has two user, one is "
-                    "me, the other is not a ChannelReceiveOp.");
-      return failure();
-    }
-    return notOpUser;
-  }
-}
-
 SmallVector<int64_t> computeRowMajorStrides(ArrayRef<int64_t> shape) {
   SmallVector<int64_t> strides(shape.size(), 1);
   for (int64_t dim = static_cast<int64_t>(shape.size()) - 2; dim >= 0; --dim)

src/PIM/Common/PimCommon.hpp

@@ -17,6 +17,8 @@ inline constexpr llvm::StringRef PimWeightAlwaysAttrName = "weightAlways";
 
 namespace onnx_mlir {
 
+inline constexpr llvm::StringLiteral kCoreIdAttrName = "core_id";
+
 struct ResolvedContiguousAddress {
   mlir::Value base;
   int64_t byteOffset = 0;
@@ -48,9 +50,6 @@ void walkPimMvmVmmWeightUses(mlir::Operation* root, llvm::function_ref<void(mlir
 
 mlir::memref::GlobalOp lookupGlobalForGetGlobal(mlir::ModuleOp moduleOp, mlir::memref::GetGlobalOp getGlobalOp);
 
-llvm::FailureOr<mlir::Operation*>
-getOtherEndOfChannel(mlir::Operation* op, bool opIsReceive, mlir::RewriterBase& rewriter);
-
 llvm::SmallVector<int64_t> computeRowMajorStrides(llvm::ArrayRef<int64_t> shape);
 
 llvm::SmallVector<int64_t>

src/PIM/Compiler/PimCodeGen.cpp

@@ -1,11 +1,15 @@
+#include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/IR/Attributes.h"
 #include "mlir/IR/BuiltinAttributes.h"
 #include "mlir/IR/BuiltinTypes.h"
+#include "mlir/IR/IRMapping.h"
+#include "mlir/IR/Value.h"
 
 #include "llvm/ADT/DenseMap.h"
 #include "llvm/ADT/SmallPtrSet.h"
+#include "llvm/ADT/StringExtras.h"
 #include "llvm/Support/FileSystem.h"
 #include "llvm/Support/JSON.h"
 #include "llvm/Support/raw_ostream.h"
@@ -53,9 +57,23 @@ void PimMemory::allocateMemoryForValue(mlir::Value value, MemEntry& memEntry) {
 void PimMemory::allocateHost(ModuleOp moduleOp, func::FuncOp funcOp) {
   SmallDenseMap<memref::GlobalOp, mlir::Value, 8> globalConstants;
   SmallVector<std::pair<mlir::Value, mlir::Value>, 16> globalAliases;
+  SmallVector<mlir::Value> args;
+
+
+  for (mlir::Value arg : funcOp.getArguments()){
+    gatherMemEntry(arg);
+    args.push_back(arg);
+  }
+
   funcOp.walk([&](memref::GetGlobalOp getGlobalOp) {
     if (!hasWeightAlways(getGlobalOp)) {
       auto globalMemrefOp = lookupGlobalForGetGlobal(moduleOp, getGlobalOp);
+      if (globalMemrefOp.getName().starts_with("arg")){
+        StringRef indexStr = globalMemrefOp.getName().substr(4);
+        int  index = 0;
+        llvm::to_integer(indexStr,index, 10);
+        globalAliases.push_back({getGlobalOp.getResult(),  args[index]});
+      }
       auto [iter, inserted] = globalConstants.try_emplace(globalMemrefOp, getGlobalOp.getResult());
       if (inserted)
         gatherMemEntry(getGlobalOp.getResult());
@@ -64,8 +82,6 @@ void PimMemory::allocateHost(ModuleOp moduleOp, func::FuncOp funcOp) {
     }
   });
 
-  for (mlir::Value arg : funcOp.getArguments())
-    gatherMemEntry(arg);
 
   funcOp.walk([&](memref::AllocOp allocOp) {
     if (!allocOp->getParentOfType<pim::PimCoreOp>())
@@ -131,6 +147,12 @@ json::Object PimCodeGen::createEmptyOffset() {
   return offset;
 }
 
+size_t PimCodeGen::remapCoreId(size_t coreId) const {
+  auto it = emittedCoreIds.find(coreId);
+  assert(it != emittedCoreIds.end() && "Missing emitted core id remapping");
+  return it->second;
+}
+
 static json::Object createRs1OnlyOffset() {
   json::Object offset;
   offset["offset_select"] = 1;
@@ -190,7 +212,7 @@ void PimCodeGen::emitCommunicationOp(StringRef opName, size_t bufferAddr, size_t
   json::Object json;
   json["op"] = opName;
   json["rd"] = 0;
-  json["core"] = coreId;
+  json["core"] = remapCoreId(coreId);
   json["size"] = size;
   json["offset"] = createEmptyOffset();
   emitInstruction(std::move(json));
@@ -412,6 +434,9 @@ void PimCodeGen::codeGenVSoftmaxOp(pim::PimVSoftmaxOp vsoftmaxOp, const StaticVa
   emitInstruction(std::move(json));
 }
 
+void PimCodeGen::codeGetGlobalOp(memref::GetGlobalOp getGlobalOp, const StaticValueKnowledge& knowledge) const {
+}
+
 void PimCodeGen::codeGenTransposeOp(pim::PimTransposeOp transposeOp, const StaticValueKnowledge& knowledge) const {
   auto srcAddr = addressOf(transposeOp.getInput(), knowledge);
   auto dstAddr = addressOf(transposeOp.getOutputBuffer(), knowledge);
@@ -474,19 +499,136 @@ std::string getMemorySizeAsString(size_t size) {
   return std::to_string(size) + " Bytes";
 }
 
-static SmallVector<unsigned, 8> getUsedWeightIndices(pim::PimCoreOp coreOp) {
+static SmallVector<unsigned, 8> getUsedWeightIndices(Block& block) {
   SmallVector<unsigned, 8> indices;
   auto addIndex = [&](unsigned weightIndex) {
     if (!llvm::is_contained(indices, weightIndex))
       indices.push_back(weightIndex);
   };
 
-  coreOp.walk([&](pim::PimMVMOp mvmOp) { addIndex(mvmOp.getWeightIndex()); });
-  coreOp.walk([&](pim::PimVMMOp vmmOp) { addIndex(vmmOp.getWeightIndex()); });
+  block.walk([&](pim::PimMVMOp mvmOp) { addIndex(mvmOp.getWeightIndex()); });
+  block.walk([&](pim::PimVMMOp vmmOp) { addIndex(vmmOp.getWeightIndex()); });
   llvm::sort(indices);
   return indices;
 }
 
+static SmallVector<unsigned, 8> getUsedWeightIndices(pim::PimCoreOp coreOp) {
+  return getUsedWeightIndices(coreOp.getBody().front());
+}
+
+static SmallVector<int32_t> getBatchCoreIds(pim::PimCoreBatchOp coreBatchOp) {
+  auto coreIdsAttr = coreBatchOp->getAttrOfType<DenseI32ArrayAttr>(onnx_mlir::kCoreIdAttrName);
+  assert(coreIdsAttr && "pim.core_batch requires core_id array attribute");
+  return SmallVector<int32_t>(coreIdsAttr.asArrayRef().begin(), coreIdsAttr.asArrayRef().end());
+}
+
+static SmallVector<Operation*> collectTopLevelCoreLikeOps(func::FuncOp funcOp) {
+  SmallVector<Operation*> coreLikeOps;
+  for (Operation& op : funcOp.getBody().front()) {
+    if (dyn_cast<pim::PimCoreOp>(&op) || dyn_cast<pim::PimCoreBatchOp>(&op))
+      coreLikeOps.push_back(&op);
+  }
+  return coreLikeOps;
+}
+
+static pim::PimCoreOp materializeScalarCoreFromBatchLane(pim::PimCoreBatchOp coreBatchOp, unsigned lane) {
+  OpBuilder builder(coreBatchOp);
+  builder.setInsertionPointAfter(coreBatchOp);
+
+  size_t laneCount = static_cast<size_t>(coreBatchOp.getLaneCount());
+  size_t weightsPerLane = coreBatchOp.getWeights().size() / laneCount;
+  SmallVector<mlir::Value> laneWeights;
+  laneWeights.reserve(weightsPerLane);
+  for (size_t weightIndex = 0; weightIndex < weightsPerLane; ++weightIndex)
+    laneWeights.push_back(coreBatchOp.getWeights()[lane * weightsPerLane + weightIndex]);
+
+  auto coreIds = getBatchCoreIds(coreBatchOp);
+  auto scalarCore = pim::PimCoreOp::create(builder,
+                                           coreBatchOp.getLoc(),
+                                           ValueRange(laneWeights),
+                                           builder.getI32IntegerAttr(coreIds[lane]));
+  Block* block = builder.createBlock(&scalarCore.getBody(), scalarCore.getBody().end());
+  IRMapping mapper;
+  if (coreBatchOp.getBody().front().getNumArguments() == 1)
+    mapper.map(coreBatchOp.getBody().front().getArgument(0), coreBatchOp.getInputs()[lane]);
+
+  builder.setInsertionPointToEnd(block);
+  for (Operation& op : coreBatchOp.getBody().front()) {
+    if (isa<pim::PimHaltOp>(op)) {
+      pim::PimHaltOp::create(builder, op.getLoc());
+      continue;
+    }
+
+    if (auto sendBatchOp = dyn_cast<pim::PimSendBatchOp>(op)) {
+      pim::PimSendOp::create(builder,
+                             sendBatchOp.getLoc(),
+                             mapper.lookup(sendBatchOp.getInput()),
+                             sendBatchOp.getSizeAttr(),
+                             builder.getI32IntegerAttr(sendBatchOp.getTargetCoreIds()[lane]));
+      continue;
+    }
+
+    if (auto receiveBatchOp = dyn_cast<pim::PimReceiveBatchOp>(op)) {
+      auto scalarReceive = pim::PimReceiveOp::create(builder,
+                                                     receiveBatchOp.getLoc(),
+                                                     receiveBatchOp.getOutput().getType(),
+                                                     mapper.lookup(receiveBatchOp.getOutputBuffer()),
+                                                     receiveBatchOp.getSizeAttr(),
+                                                     builder.getI32IntegerAttr(receiveBatchOp.getSourceCoreIds()[lane]));
+      mapper.map(receiveBatchOp.getOutput(), scalarReceive.getOutput());
+      continue;
+    }
+
+    if (auto memcpBatchOp = dyn_cast<pim::PimMemCopyHostToDevBatchOp>(op)) {
+      mlir::Value hostSource = mapper.lookupOrNull(memcpBatchOp.getHostSource());
+      if (!hostSource)
+        hostSource = memcpBatchOp.getHostSource();
+
+      auto scalarCopy = pim::PimMemCopyHostToDevOp::create(builder,
+                                                           memcpBatchOp.getLoc(),
+                                                           memcpBatchOp.getOutput().getType(),
+                                                           mapper.lookup(memcpBatchOp.getDeviceTarget()),
+                                                           hostSource,
+                                                           memcpBatchOp.getDeviceTargetOffsetAttr(),
+                                                           memcpBatchOp.getHostSourceOffsetAttr(),
+                                                           memcpBatchOp.getSizeAttr());
+      mapper.map(memcpBatchOp.getOutput(), scalarCopy.getOutput());
+      continue;
+    }
+
+    Operation* cloned = builder.clone(op, mapper);
+    for (auto [originalResult, clonedResult] : llvm::zip(op.getResults(), cloned->getResults()))
+      mapper.map(originalResult, clonedResult);
+  }
+
+  if (block->empty() || !isa<pim::PimHaltOp>(block->back()))
+    pim::PimHaltOp::create(builder, coreBatchOp.getLoc());
+  return scalarCore;
+}
+
+static void aliasMaterializedHostGlobals(
+  ModuleOp moduleOp, func::FuncOp funcOp, pim::PimCoreOp coreOp, PimAcceleratorMemory& memory) {
+  coreOp.walk([&](memref::GetGlobalOp getGlobalOp) {
+    if (hasWeightAlways(getGlobalOp) || memory.memEntriesMap.contains(getGlobalOp.getResult()))
+      return;
+
+    auto targetGlobal = lookupGlobalForGetGlobal(moduleOp, getGlobalOp);
+    if (!targetGlobal)
+      return;
+
+    mlir::Value aliasedValue;
+    funcOp.walk([&](memref::GetGlobalOp candidate) {
+      if (aliasedValue || candidate == getGlobalOp || !memory.memEntriesMap.contains(candidate.getResult()))
+        return;
+      if (lookupGlobalForGetGlobal(moduleOp, candidate) == targetGlobal)
+        aliasedValue = candidate.getResult();
+    });
+
+    if (aliasedValue)
+      memory.memEntriesMap[getGlobalOp.getResult()] = memory.memEntriesMap[aliasedValue];
+  });
+}
+
 /// Write global constant data into a binary memory image at their allocated addresses.
 static OnnxMlirCompilerErrorCodes
 writeMemoryBinary(ModuleOp moduleOp, func::FuncOp funcOp, PimAcceleratorMemory& memory, StringRef outputDirPath) {
@@ -581,6 +723,8 @@ static int64_t codeGenCoreOps(Block& block, PimCodeGen& coreCodeGen) {
         coreCodeGen.codeGenVSigmOp(vsigmOp, knowledge);
       else if (auto vsoftmaxOp = dyn_cast<pim::PimVSoftmaxOp>(op))
         coreCodeGen.codeGenVSoftmaxOp(vsoftmaxOp, knowledge);
+      else if (auto getGlobalOp = dyn_cast<memref::GetGlobalOp>(op))
+        coreCodeGen.codeGetGlobalOp(getGlobalOp, knowledge);
       else {
         op.emitError("Unsupported codegen for this operation");
         op.dump();
@@ -670,7 +814,7 @@ static OnnxMlirCompilerErrorCodes writeCrossbarWeights(ModuleOp moduleOp,
   return CompilerSuccess;
 }
 
-llvm::DenseMap<pim::PimCoreOp, llvm::DenseMap<mlir::Value, std::string>>
+llvm::DenseMap<size_t, llvm::DenseMap<mlir::Value, std::string>>
 createAndPopulateWeightFolder(func::FuncOp funcOp, StringRef outputDirPath) {
   ModuleOp moduleOp = funcOp->getParentOfType<ModuleOp>();
   auto coreWeightsDirPath = outputDirPath + "/weights";
@@ -679,85 +823,104 @@ createAndPopulateWeightFolder(func::FuncOp funcOp, StringRef outputDirPath) {
   size_t indexFileName = 0;
 
   int64_t xbarSize = crossbarSize.getValue();
-  llvm::DenseMap<pim::PimCoreOp, llvm::DenseMap<mlir::Value, std::string>> mapCoreWeightToFileName;
+  llvm::DenseMap<size_t, llvm::DenseMap<mlir::Value, std::string>> mapCoreWeightToFileName;
   llvm::DenseMap<memref::GlobalOp, std::string> mapGlobalOpToFileName;
 
-  for (pim::PimCoreOp coreOp : funcOp.getOps<pim::PimCoreOp>()) {
-    for (unsigned index : getUsedWeightIndices(coreOp)) {
-      if (index >= coreOp.getWeights().size()) {
-        coreOp.emitWarning("Weight index " + std::to_string(index) + " is out of range");
-        assert(index < coreOp.getWeights().size() && "Weight index is out of range");
-      }
-      mlir::Value weight = coreOp.getWeights()[index];
+  SmallVector<Operation*> coreLikeOps = collectTopLevelCoreLikeOps(funcOp);
 
-      auto getGlobalOp = weight.getDefiningOp<memref::GetGlobalOp>();
-      if (!getGlobalOp) {
-        coreOp.emitWarning("Weight is not from a memref.get_global at index " + std::to_string(index));
-        assert(!getGlobalOp && "Weight is not from a memref.get_global");
-      }
+  for (Operation* op : coreLikeOps) {
+    SmallVector<pim::PimCoreOp> scalarCores;
+    if (auto coreOp = dyn_cast<pim::PimCoreOp>(op)) {
+      scalarCores.push_back(coreOp);
+    }
+    else {
+      auto coreBatchOp = cast<pim::PimCoreBatchOp>(op);
+      for (unsigned lane = 0; lane < static_cast<unsigned>(coreBatchOp.getLaneCount()); ++lane)
+        scalarCores.push_back(materializeScalarCoreFromBatchLane(coreBatchOp, lane));
+    }
 
-      auto globalOp = lookupGlobalForGetGlobal(moduleOp, getGlobalOp);
-      if (!globalOp) {
-        coreOp.emitWarning("Could not find memref.global for weight at index " + std::to_string(index));
-        assert(!globalOp && "Could not find memref.global");
-      }
+    for (pim::PimCoreOp coreOp : scalarCores) {
+      size_t coreId = static_cast<size_t>(coreOp.getCoreId());
+      for (unsigned index : getUsedWeightIndices(coreOp)) {
+        if (index >= coreOp.getWeights().size()) {
+          coreOp.emitWarning("Weight index " + std::to_string(index) + " is out of range");
+          assert(index < coreOp.getWeights().size() && "Weight index is out of range");
+        }
+        mlir::Value weight = coreOp.getWeights()[index];
 
-      auto initialValue = globalOp.getInitialValue();
-      if (!initialValue) {
-        coreOp.emitWarning("memref.global has no initial value at index " + std::to_string(index));
-        assert(!initialValue && "memref.global has no initial value");
-      }
+        auto getGlobalOp = weight.getDefiningOp<memref::GetGlobalOp>();
+        if (!getGlobalOp) {
+          coreOp.emitWarning("Weight is not from a memref.get_global at index " + std::to_string(index));
+          assert(!getGlobalOp && "Weight is not from a memref.get_global");
+        }
 
-      auto denseAttr = dyn_cast<DenseElementsAttr>(*initialValue);
-      if (!denseAttr) {
-        coreOp.emitWarning("memref.global initial value is not dense at index " + std::to_string(index));
-        assert(!denseAttr && "memref.global initial value is not dense");
-      }
+        auto globalOp = lookupGlobalForGetGlobal(moduleOp, getGlobalOp);
+        if (!globalOp) {
+          coreOp.emitWarning("Could not find memref.global for weight at index " + std::to_string(index));
+          assert(!globalOp && "Could not find memref.global");
+        }
 
-      if (mapGlobalOpToFileName.contains(globalOp)) {
-        auto& fileName = mapGlobalOpToFileName[globalOp];
-        std::pair<mlir::Value, std::string> weightToFile = {weight, fileName};
-        mapCoreWeightToFileName[coreOp].insert(weightToFile);
-        continue;
-      }
+        auto initialValue = globalOp.getInitialValue();
+        if (!initialValue) {
+          coreOp.emitWarning("memref.global has no initial value at index " + std::to_string(index));
+          assert(!initialValue && "memref.global has no initial value");
+        }
 
-      auto type = denseAttr.getType();
-      auto shape = type.getShape();
-      assert(isMatrixShape(shape) && "Weight matrix must be 2-dimensional");
-      int64_t numRows = shape[0];
-      int64_t numCols = shape[1];
-      assert(numRows <= xbarSize && numCols <= xbarSize && "Weight dimensions must not exceed crossbar size");
+        auto denseAttr = dyn_cast<DenseElementsAttr>(*initialValue);
+        if (!denseAttr) {
+          coreOp.emitWarning("memref.global initial value is not dense at index " + std::to_string(index));
+          assert(!denseAttr && "memref.global initial value is not dense");
+        }
 
-      size_t elementByteWidth = type.getElementType().getIntOrFloatBitWidth() / 8;
+        if (mapGlobalOpToFileName.contains(globalOp)) {
+          auto& fileName = mapGlobalOpToFileName[globalOp];
+          std::pair<mlir::Value, std::string> weightToFile = {weight, fileName};
+          mapCoreWeightToFileName[coreId].insert(weightToFile);
+          continue;
+        }
 
-      std::string newFileName = "crossbar_" + std::to_string(indexFileName++) + ".bin";
-      auto weightFilePath = (coreWeightsDirPath + "/" + newFileName).str();
-      std::error_code errorCode;
-      raw_fd_ostream weightFileStream(weightFilePath, errorCode, sys::fs::OF_None);
-      if (errorCode) {
-        errs() << "Error while opening weight file `" << weightFilePath << "`: " << errorCode.message() << '\n';
-        assert(errorCode);
-      }
+        auto type = denseAttr.getType();
+        auto shape = type.getShape();
+        assert(isMatrixShape(shape) && "Weight matrix must be 2-dimensional");
+        int64_t numRows = shape[0];
+        int64_t numCols = shape[1];
+        assert(numRows <= xbarSize && numCols <= xbarSize && "Weight dimensions must not exceed crossbar size");
 
-      uint64_t zero = 0;
-      for (int64_t row = 0; row < xbarSize; row++) {
-        for (int64_t col = 0; col < xbarSize; col++) {
-          if (row < numRows && col < numCols) {
-            int64_t index = row * numCols + col;
-            APInt bits = denseAttr.getValues<APFloat>()[index].bitcastToAPInt();
-            uint64_t word = bits.getZExtValue();
-            weightFileStream.write(reinterpret_cast<const char*>(&word), elementByteWidth);
-          }
-          else {
-            weightFileStream.write(reinterpret_cast<const char*>(&zero), elementByteWidth);
+        size_t elementByteWidth = type.getElementType().getIntOrFloatBitWidth() / 8;
+
+        std::string newFileName = "crossbar_" + std::to_string(indexFileName++) + ".bin";
+        auto weightFilePath = (coreWeightsDirPath + "/" + newFileName).str();
+        std::error_code errorCode;
+        raw_fd_ostream weightFileStream(weightFilePath, errorCode, sys::fs::OF_None);
+        if (errorCode) {
+          errs() << "Error while opening weight file `" << weightFilePath << "`: " << errorCode.message() << '\n';
+          assert(errorCode);
+        }
+
+        uint64_t zero = 0;
+        for (int64_t row = 0; row < xbarSize; row++) {
+          for (int64_t col = 0; col < xbarSize; col++) {
+            if (row < numRows && col < numCols) {
+              int64_t index = row * numCols + col;
+              APInt bits = denseAttr.getValues<APFloat>()[index].bitcastToAPInt();
+              uint64_t word = bits.getZExtValue();
+              weightFileStream.write(reinterpret_cast<const char*>(&word), elementByteWidth);
+            }
+            else {
+              weightFileStream.write(reinterpret_cast<const char*>(&zero), elementByteWidth);
+            }
           }
         }
-      }
 
-      weightFileStream.close();
-      mapGlobalOpToFileName.insert({globalOp, newFileName});
-      mapCoreWeightToFileName[coreOp].insert({weight, newFileName});
+        weightFileStream.close();
+        mapGlobalOpToFileName.insert({globalOp, newFileName});
+        mapCoreWeightToFileName[coreId].insert({weight, newFileName});
+      }
     }
+
+    for (pim::PimCoreOp coreOp : scalarCores)
+      if (coreOp.getOperation() != op)
+        coreOp.erase();
   }
   return mapCoreWeightToFileName;
 }
@@ -765,13 +928,14 @@ createAndPopulateWeightFolder(func::FuncOp funcOp, StringRef outputDirPath) {
 /// Write the top-level PIM configuration JSON (core count, crossbar config, I/O addresses).
 static OnnxMlirCompilerErrorCodes writeConfigJson(func::FuncOp funcOp,
                                                   PimAcceleratorMemory& memory,
-                                                  size_t coreCount,
+                                                  size_t maxCoreId,
                                                   json::Object xbarsPerArrayGroup,
                                                   StringRef outputDirPath) {
   json::Object configJson;
 
-  // +1 because pimsim-nn also considers the host as a core
-  configJson["core_cnt"] = coreCount + 1;
+  // pimsim-nn indexes cores directly by their numeric core ID, with the host
+  // occupying core 0.
+  configJson["core_cnt"] = maxCoreId + 1;
 
   // TODO: Should this be based on the floating point type used in the model?
   // The 2 following values determine the bitwidth of the vectors' elements: bitwidth = adc_count * cell_precision
@@ -845,66 +1009,103 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimJson(ModuleOp& moduleOp, std::
   // For each core, specify the number of crossbar per array group.
   // This implementation always assigns one crossbar per group.
   json::Object xbarsPerArrayGroup;
-  size_t coreCount = 0;
+  size_t maxCoreId = 0;
 
   // Create Weight Folder
   auto mapCoreWeightToFileName = createAndPopulateWeightFolder(funcOp, outputDirPath);
 
-  for (auto coreOp : funcOp.getOps<pim::PimCoreOp>()) {
-    auto coreId = coreOp.getCoreId();
-    coreCount++;
+  SmallVector<Operation*> coreLikeOps = collectTopLevelCoreLikeOps(funcOp);
+  llvm::DenseMap<size_t, size_t> emittedCoreIds;
+  size_t nextEmittedCoreId = 1;
 
-    std::error_code errorCode;
-    auto outputCorePath = outputDirPath + "/core_" + std::to_string(coreId) + ".json";
-    raw_fd_ostream coreFileStream(outputCorePath, errorCode);
-    if (errorCode) {
-      errs() << "Error while opening core file `" << outputCorePath << "`: " << errorCode.message() << '\n';
-      return InvalidOutputFileAccess;
-    }
-    coreFileStream << '[';
-
-    PimCodeGen coreCodeGen(memory, coreFileStream);
-    memory.getOrCreateDeviceMem(coreId).allocateCore(coreOp);
-
-    int64_t processedOperations = codeGenCoreOps(coreOp.getBody().front(), coreCodeGen);
-    if (processedOperations < 0)
-      return CompilerFailure;
-    assert(processedOperations > 0);
-
-    // Remove trailing comma, close JSON array
-    coreFileStream.seek(coreFileStream.tell() - 1);
-    coreFileStream << ']';
-    coreFileStream.close();
-
-    // Write crossbar weights for this core
-    auto coreWeightsDirPath = outputDirPath + "/core_" + std::to_string(coreId);
-    if (auto error = sys::fs::create_directory(coreWeightsDirPath)) {
-      errs() << "Error creating core directory: " << coreWeightsDirPath << ": " << error.message() << '\n';
-      return InvalidOutputFileAccess;
+  for (Operation* op : coreLikeOps) {
+    if (auto coreOp = dyn_cast<pim::PimCoreOp>(op)) {
+      size_t originalCoreId = static_cast<size_t>(coreOp.getCoreId());
+      if (!emittedCoreIds.contains(originalCoreId))
+        emittedCoreIds[originalCoreId] = nextEmittedCoreId++;
+      continue;
     }
 
-    auto& mapWeightToFile = mapCoreWeightToFileName[coreOp];
-    json::Array xbarsPerGroup;
-    for (unsigned index : getUsedWeightIndices(coreOp)) {
-      if (index >= coreOp.getWeights().size()) {
-        coreOp.emitWarning("Weight index " + std::to_string(index) + " is out of range");
-        assert(index < coreOp.getWeights().size() && "Weight index is out of range");
-      }
-      mlir::Value weight = coreOp.getWeights()[index];
-      xbarsPerGroup.push_back(index);
-      assert(mapWeightToFile.contains(weight) && "Weight was not materialized into a file!!");
-      auto& fileName = mapWeightToFile[weight];
-      if (auto error = sys::fs::create_link(outputDirPath + "/weights/" + fileName,
-                                            coreWeightsDirPath + "/crossbar_" + std::to_string(index) + ".bin")) {
-        errs() << "Error creating link file: " << (outputDirPath + "/weights/" + fileName) << " to "
-               << (coreWeightsDirPath + "/crossbar_" + std::to_string(index) + ".bin") << "\nError:" << error.message()
-               << '\n';
-        return InvalidOutputFileAccess;
-      }
+    auto coreBatchOp = cast<pim::PimCoreBatchOp>(op);
+    auto batchCoreIds = getBatchCoreIds(coreBatchOp);
+    for (unsigned lane = 0; lane < static_cast<unsigned>(coreBatchOp.getLaneCount()); ++lane) {
+      size_t originalCoreId = static_cast<size_t>(batchCoreIds[lane]);
+      if (!emittedCoreIds.contains(originalCoreId))
+        emittedCoreIds[originalCoreId] = nextEmittedCoreId++;
     }
-
-    xbarsPerArrayGroup["core" + std::to_string(coreId)] = std::move(xbarsPerGroup);
   }
 
-  return writeConfigJson(funcOp, memory, coreCount, std::move(xbarsPerArrayGroup), outputDirPath);
+  for (Operation* op : coreLikeOps) {
+    SmallVector<pim::PimCoreOp> scalarCores;
+    if (auto coreOp = dyn_cast<pim::PimCoreOp>(op)) {
+      scalarCores.push_back(coreOp);
+    }
+    else {
+      auto coreBatchOp = cast<pim::PimCoreBatchOp>(op);
+      for (unsigned lane = 0; lane < static_cast<unsigned>(coreBatchOp.getLaneCount()); ++lane)
+        scalarCores.push_back(materializeScalarCoreFromBatchLane(coreBatchOp, lane));
+    }
+
+    for (pim::PimCoreOp coreOp : scalarCores) {
+      size_t originalCoreId = static_cast<size_t>(coreOp.getCoreId());
+      size_t coreId = emittedCoreIds.lookup(originalCoreId);
+      maxCoreId = std::max(maxCoreId, coreId);
+
+      std::error_code errorCode;
+      auto outputCorePath = outputDirPath + "/core_" + std::to_string(coreId) + ".json";
+      raw_fd_ostream coreFileStream(outputCorePath, errorCode);
+      if (errorCode) {
+        errs() << "Error while opening core file `" << outputCorePath << "`: " << errorCode.message() << '\n';
+        return InvalidOutputFileAccess;
+      }
+      coreFileStream << '[';
+
+      PimCodeGen coreCodeGen(memory, coreFileStream, emittedCoreIds);
+      aliasMaterializedHostGlobals(moduleOp, funcOp, coreOp, memory);
+      memory.getOrCreateDeviceMem(coreId).allocateCore(coreOp);
+
+      int64_t processedOperations = codeGenCoreOps(coreOp.getBody().front(), coreCodeGen);
+      if (processedOperations < 0)
+        return CompilerFailure;
+      assert(processedOperations > 0);
+
+      coreFileStream.seek(coreFileStream.tell() - 1);
+      coreFileStream << ']';
+      coreFileStream.close();
+
+      auto coreWeightsDirPath = outputDirPath + "/core_" + std::to_string(coreId);
+      if (auto error = sys::fs::create_directory(coreWeightsDirPath)) {
+        errs() << "Error creating core directory: " << coreWeightsDirPath << ": " << error.message() << '\n';
+        return InvalidOutputFileAccess;
+      }
+
+      auto& mapWeightToFile = mapCoreWeightToFileName[originalCoreId];
+      json::Array xbarsPerGroup;
+      for (unsigned index : getUsedWeightIndices(coreOp)) {
+        if (index >= coreOp.getWeights().size()) {
+          coreOp.emitWarning("Weight index " + std::to_string(index) + " is out of range");
+          assert(index < coreOp.getWeights().size() && "Weight index is out of range");
+        }
+        mlir::Value weight = coreOp.getWeights()[index];
+        xbarsPerGroup.push_back(index);
+        assert(mapWeightToFile.contains(weight) && "Weight was not materialized into a file!!");
+        auto& fileName = mapWeightToFile[weight];
+        if (auto error = sys::fs::create_link(outputDirPath + "/weights/" + fileName,
+                                              coreWeightsDirPath + "/crossbar_" + std::to_string(index) + ".bin")) {
+          errs() << "Error creating link file: " << (outputDirPath + "/weights/" + fileName) << " to "
+                 << (coreWeightsDirPath + "/crossbar_" + std::to_string(index) + ".bin") << "\nError:"
+                 << error.message() << '\n';
+          return InvalidOutputFileAccess;
+        }
+      }
+
+      xbarsPerArrayGroup["core" + std::to_string(coreId)] = std::move(xbarsPerGroup);
+    }
+
+    for (pim::PimCoreOp coreOp : scalarCores)
+      if (coreOp.getOperation() != op)
+        coreOp.erase();
+  }
+
+  return writeConfigJson(funcOp, memory, maxCoreId, std::move(xbarsPerArrayGroup), outputDirPath);
 }

src/PIM/Compiler/PimCodeGen.hpp

@@ -1,5 +1,6 @@
 #pragma once
 
+#include "llvm/ADT/DenseMap.h"
 #include "llvm-project/clang/include/clang/Basic/LLVM.h"
 #include "llvm/Support/JSON.h"
 
@@ -58,10 +59,12 @@ public:
 class PimCodeGen {
   PimAcceleratorMemory& memory;
   llvm::raw_fd_ostream& coreFileStream;
+  const llvm::DenseMap<size_t, size_t>& emittedCoreIds;
 
   size_t addressOf(mlir::Value value, const StaticValueKnowledge& knowledge) const {
     return memory.getValueAddress(value, knowledge);
   }
+  size_t remapCoreId(size_t coreId) const;
 
   static llvm::json::Object createEmptyOffset();
   void emitInstruction(llvm::json::Object instruction) const;
@@ -83,8 +86,10 @@ class PimCodeGen {
   void emitMvmOp(size_t groupId, size_t rdAddr, size_t rdOffset, size_t rs1Addr, size_t rs1Offset) const;
 
 public:
-  PimCodeGen(PimAcceleratorMemory& memory, llvm::raw_fd_ostream& coreJson)
-  : memory(memory), coreFileStream(coreJson) {}
+  PimCodeGen(PimAcceleratorMemory& memory,
+             llvm::raw_fd_ostream& coreJson,
+             const llvm::DenseMap<size_t, size_t>& emittedCoreIds)
+  : memory(memory), coreFileStream(coreJson), emittedCoreIds(emittedCoreIds) {}
 
   void codeGenLoadOp(pim::PimMemCopyHostToDevOp loadOp, const StaticValueKnowledge& knowledge) const;
   void codeGenStoreOp(pim::PimMemCopyDevToHostOp storeOp, const StaticValueKnowledge& knowledge) const;
@@ -106,6 +111,7 @@ public:
   void codeGenVTanhOp(pim::PimVTanhOp vtanhOp, const StaticValueKnowledge& knowledge) const;
   void codeGenVSigmOp(pim::PimVSigmOp vsigmOp, const StaticValueKnowledge& knowledge) const;
   void codeGenVSoftmaxOp(pim::PimVSoftmaxOp vsoftmaxOp, const StaticValueKnowledge& knowledge) const;
+  void codeGetGlobalOp(mlir::memref::GetGlobalOp getGlobalOp, const StaticValueKnowledge& knowledge) const;
   void codeGenTransposeOp(pim::PimTransposeOp transposeOp, const StaticValueKnowledge& knowledge) const;
 };
 

src/PIM/Conversion/ONNXToSpatial/Common.hpp

@@ -12,6 +12,7 @@
 #include <utility>
 
 #include "llvm/ADT/SmallPtrSet.h"
+#include "llvm/ADT/STLExtras.h"
 
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -174,6 +175,31 @@ using InvokeWithValueRangeResultT = std::invoke_result_t<Fn, mlir::ValueRange>;
 
 } // namespace detail
 
+template <typename RewriterT>
+inline mlir::Value createSpatConcat(RewriterT& rewriter, mlir::Location loc, int64_t axis, mlir::ValueRange inputs) {
+  assert(!inputs.empty() && "spat.concat requires at least one input");
+  if (inputs.size() == 1)
+    return inputs.front();
+
+  auto firstType = mlir::cast<mlir::RankedTensorType>(inputs.front().getType());
+  auto outputShape = llvm::to_vector(firstType.getShape());
+  int64_t concatDimSize = 0;
+  bool concatDimDynamic = false;
+
+  for (mlir::Value input : inputs) {
+    auto inputType = mlir::cast<mlir::RankedTensorType>(input.getType());
+    assert(inputType.getRank() == firstType.getRank() && "spat.concat expects same-rank inputs");
+    if (mlir::ShapedType::isDynamic(inputType.getDimSize(axis)))
+      concatDimDynamic = true;
+    else
+      concatDimSize += inputType.getDimSize(axis);
+  }
+
+  outputShape[axis] = concatDimDynamic ? mlir::ShapedType::kDynamic : concatDimSize;
+  auto outputType = mlir::RankedTensorType::get(outputShape, firstType.getElementType(), firstType.getEncoding());
+  return spatial::SpatConcatOp::create(rewriter, loc, outputType, rewriter.getI64IntegerAttr(axis), inputs).getOutput();
+}
+
 template <size_t NumInputs, typename RewriterT, typename BodyFn>
 auto createSpatCompute(RewriterT& rewriter,
                        mlir::Location loc,

src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp

@@ -1,3 +1,4 @@
+#include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/SCF/IR/SCF.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
@@ -11,6 +12,7 @@
 #include "llvm/ADT/SmallVector.h"
 #include "llvm/Support/Casting.h"
 #include "llvm/Support/Debug.h"
+#include "llvm/Support/ErrorHandling.h"
 #include "llvm/Support/raw_os_ostream.h"
 
 #include <fstream>
@@ -54,6 +56,43 @@ private:
 
 } // namespace
 
+static void foldSingleLaneComputeBatches(func::FuncOp funcOp) {
+  IRRewriter rewriter(funcOp.getContext());
+  SmallVector<spatial::SpatComputeBatch> batchOps;
+  funcOp.walk([&](spatial::SpatComputeBatch batchOp) { batchOps.push_back(batchOp); });
+
+  for (auto batchOp : batchOps) {
+    if (batchOp.getLaneCount() != 1)
+      continue;
+
+    auto loc = batchOp.getLoc();
+    rewriter.setInsertionPoint(batchOp);
+    auto computeOp = spatial::SpatCompute::create(rewriter, loc, batchOp.getResultTypes(), batchOp.getWeights(), batchOp.getInputs());
+    computeOp.getProperties().setOperandSegmentSizes(
+      {static_cast<int>(batchOp.getWeights().size()), static_cast<int>(batchOp.getInputs().size())});
+
+    Block& templateBlock = batchOp.getBody().front();
+    SmallVector<Type> blockArgTypes;
+    SmallVector<Location> blockArgLocs;
+    for (BlockArgument arg : templateBlock.getArguments()) {
+      blockArgTypes.push_back(arg.getType());
+      blockArgLocs.push_back(loc);
+    }
+    auto* newBlock = rewriter.createBlock(
+      &computeOp.getBody(), computeOp.getBody().end(), TypeRange(blockArgTypes), blockArgLocs);
+
+    IRMapping mapper;
+    for (auto [oldArg, newArg] : llvm::zip(templateBlock.getArguments(), newBlock->getArguments()))
+      mapper.map(oldArg, newArg);
+    rewriter.setInsertionPointToEnd(newBlock);
+    for (Operation& op : templateBlock)
+      rewriter.clone(op, mapper);
+
+    batchOp.replaceAllUsesWith(computeOp.getResults());
+    rewriter.eraseOp(batchOp);
+  }
+}
+
 void ONNXToSpatialPass::runOnOperation() {
   ModuleOp moduleOp = getOperation();
   MLIRContext* ctx = &getContext();
@@ -124,6 +163,8 @@ void ONNXToSpatialPass::runOnOperation() {
     return;
   }
 
+  foldSingleLaneComputeBatches(*entryFunc);
+
   // Count the number of compute ops and check they do not exceed the core count
   if (coresCount != -1) {
     int computeOpsCount = 0;
@@ -144,6 +185,7 @@ void ONNXToSpatialPass::runOnOperation() {
     llvm::dbgs() << "Failed to run canonicalization cleanup, continuing...\n";
 
   annotateWeightsConstants(*entryFunc);
+
   encapsulateGlobalInstruction(*entryFunc);
 
   if (failed(promoteConstantInputsToWeights(*entryFunc))) {
@@ -160,19 +202,36 @@ bool encapsulator(IRRewriter& rewriter, Location loc, Operation* inst, std::func
   if (T toRemoveOp = llvm::dyn_cast_if_present<T>(inst)) {
     Value source = funcSource(toRemoveOp);
     rewriter.setInsertionPointAfter(toRemoveOp);
-    if (isa_and_present<spatial::SpatCompute>(source.getDefiningOp())) {
-      auto newCompute = spatial::SpatCompute::create(rewriter, loc, inst->getResultTypes(), source);
-      auto BB = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), {source.getType()}, {loc});
-      newCompute.getProperties().setOperandSegmentSizes({(int) 0, (int) 1});
-      rewriter.setInsertionPointToEnd(BB);
-      IRMapping mapper;
-      mapper.map(source, BB->getArgument(0));
-      auto newInst = rewriter.clone(*inst, mapper);
-      spatial::SpatYieldOp::create(rewriter, loc, newInst->getResults());
-      inst->replaceAllUsesWith(newCompute->getResults());
-      inst->erase();
-      return true;
-    }
+    auto newCompute = spatial::SpatCompute::create(rewriter, loc, inst->getResultTypes(), source);
+    auto BB = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), {source.getType()}, {loc});
+    newCompute.getProperties().setOperandSegmentSizes({(int) 0, (int) 1});
+    rewriter.setInsertionPointToEnd(BB);
+    IRMapping mapper;
+    mapper.map(source, BB->getArgument(0));
+    auto newInst = rewriter.clone(*inst, mapper);
+    spatial::SpatYieldOp::create(rewriter, loc, newInst->getResults());
+    inst->replaceAllUsesWith(newCompute->getResults());
+    inst->erase();
+    return true;
+  }
+  return false;
+}
+
+bool encapsulateSlice(IRRewriter& rewriter, Location loc, Operation* inst) {
+  if (tensor::ExtractSliceOp toRemoveOp = llvm::dyn_cast_if_present<tensor::ExtractSliceOp>(inst)) {
+    auto source = toRemoveOp.getSource();
+    rewriter.setInsertionPointAfter(toRemoveOp);
+    auto newCompute = spatial::SpatCompute::create(rewriter, loc, inst->getResultTypes(), source);
+    auto BB = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), {source.getType()}, {loc});
+    newCompute.getProperties().setOperandSegmentSizes({(int) 0, (int) 1});
+    rewriter.setInsertionPointToEnd(BB);
+    IRMapping mapper;
+    mapper.map(source, BB->getArgument(0));
+    auto newInst = rewriter.clone(*inst, mapper);
+    spatial::SpatYieldOp::create(rewriter, loc, newInst->getResults());
+    inst->replaceAllUsesWith(newCompute->getResults());
+    inst->erase();
+    return true;
   }
   return false;
 }
@@ -196,12 +255,34 @@ bool encapsulateConcat(IRRewriter& rewriter, Location loc, Operation* inst) {
       IRMapping mapper;
       for (auto [source, bbArg] : llvm::zip(sources, BB->getArguments()))
         mapper.map(source, bbArg);
-      auto newConcat = rewriter.clone(*inst, mapper);
-      spatial::SpatYieldOp::create(rewriter, loc, newConcat->getResults());
+      auto newConcat = spatial::SpatConcatOp::create(rewriter,
+                                                     loc,
+                                                     toRemoveOp.getType(),
+                                                     rewriter.getI64IntegerAttr(toRemoveOp.getDim()),
+                                                     ValueRange(BB->getArguments()));
+      spatial::SpatYieldOp::create(rewriter, loc, newConcat.getOutput());
       inst->replaceAllUsesWith(newCompute->getResults());
       inst->erase();
       return true;
     }
+    auto newCompute = spatial::SpatCompute::create(rewriter, loc, inst->getResultTypes(), sources);
+    SmallVector<Type> sourceTypes;
+    SmallVector<Location> sourceLoc;
+    for (auto source : sources) {
+      sourceTypes.push_back(source.getType());
+      sourceLoc.push_back(loc);
+    }
+    auto BB = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), sourceTypes, sourceLoc);
+    newCompute.getProperties().setOperandSegmentSizes({(int) 0, (int) sources.size()});
+    rewriter.setInsertionPointToEnd(BB);
+    IRMapping mapper;
+    for (auto [source, bbArg] : llvm::zip(sources, BB->getArguments()))
+      mapper.map(source, bbArg);
+    auto newConcat = rewriter.clone(*inst, mapper);
+    spatial::SpatYieldOp::create(rewriter, loc, newConcat->getResults());
+    inst->replaceAllUsesWith(newCompute->getResults());
+    inst->erase();
+    return true;
   }
   return false;
 }
@@ -263,6 +344,89 @@ static FailureOr<Value> materializeWeightLikeValueInBlock(Value value, IRRewrite
   return cast<Value>(mapped);
 }
 
+bool sourceOpernadHasWeightAlways(Operation* op) {
+  if (op == nullptr)
+    return false;
+
+  Operation* source = nullptr;
+  do {
+
+    if (isa<spatial::SpatCompute, spatial::SpatComputeBatch>(*op)) {
+      return false;
+    }
+    else if (auto extractSliceOp = dyn_cast<tensor::ExtractSliceOp>(*op)) {
+      auto tmpSource = extractSliceOp.getSource();
+      auto definingOp = tmpSource.getDefiningOp();
+      if (definingOp)
+        op = definingOp;
+      else
+        return false;
+    }
+    else if (auto extractRowsOp = dyn_cast<spatial::SpatExtractRowsOp>(*op)) {
+      auto tmpSource = extractRowsOp.getInput();
+      auto definingOp = tmpSource.getDefiningOp();
+      if (definingOp)
+        op = definingOp;
+      else
+        return false;
+    }
+    else if (auto expandShapeOp = dyn_cast<tensor::ExpandShapeOp>(*op)) {
+      auto tmpSource = expandShapeOp.getSrc();
+      auto definingOp = tmpSource.getDefiningOp();
+      if (definingOp)
+        op = definingOp;
+      else
+        return false;
+    }
+    else if (auto transposeOp = dyn_cast<ONNXTransposeOp>(*op)) {
+      auto tmpSource = transposeOp.getData();
+      auto definingOp = tmpSource.getDefiningOp();
+      if (definingOp)
+        op = definingOp;
+      else
+        return false;
+    }
+    else if (auto collapseShapeOp = dyn_cast<tensor::CollapseShapeOp>(*op)) {
+      auto tmpSource = collapseShapeOp.getSrc();
+      auto definingOp = tmpSource.getDefiningOp();
+      if (definingOp)
+        op = definingOp;
+      else
+        return false;
+    }
+    else if (auto constantOp = dyn_cast<arith::ConstantOp>(*op)) {
+      source = constantOp;
+    }
+    else if (auto concatOp = dyn_cast<tensor::ConcatOp>(*op)) {
+      bool res = false;
+      for (auto operand : concatOp.getOperands()) {
+        res |= hasWeightAlways(operand.getDefiningOp());
+        if (res)
+          return res;
+      }
+      return res;
+    }
+    else if (auto concatOp = dyn_cast<spatial::SpatConcatOp>(*op)) {
+      bool res = false;
+      for (auto operand : concatOp.getOperands()) {
+        res |= hasWeightAlways(operand.getDefiningOp());
+        if (res)
+          return res;
+      }
+      return res;
+    }
+    else {
+      op->dump();
+      llvm_unreachable("Global instruction not handle in func");
+    }
+  }
+  while (source == nullptr);
+
+  if (hasWeightAlways(source))
+    return true;
+  return false;
+}
+
 // TODO what we want to keep in global?
 void ONNXToSpatialPass::encapsulateGlobalInstruction(func::FuncOp funcOp) {
   Location loc = funcOp.getLoc();
@@ -271,8 +435,14 @@ void ONNXToSpatialPass::encapsulateGlobalInstruction(func::FuncOp funcOp) {
   while (keep) {
     keep = false;
     for (auto& instruction : llvm::make_early_inc_range(funcOp.getOps())) {
-      keep |= encapsulator<tensor::ExtractSliceOp>(
-        rewriter, loc, &instruction, [](tensor::ExtractSliceOp extract) { return extract.getSource(); });
+
+      if (isa<spatial::SpatCompute, spatial::SpatComputeBatch, spatial::SpatConcatOp, spatial::SpatExtractRowsOp>(
+            instruction)
+          || isa<func::ReturnOp>(instruction)
+          || sourceOpernadHasWeightAlways(&instruction))
+        continue;
+
+      keep |= encapsulateSlice(rewriter, loc, &instruction);
 
       keep |= encapsulator<tensor::ExpandShapeOp>(
         rewriter, loc, &instruction, [](tensor::ExpandShapeOp expand) { return expand.getSrc(); });

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

@@ -147,161 +147,148 @@ static Value buildPackedBias(bool hasBias,
   return arith::ConstantOp::create(rewriter, loc, packedBiasType, packedBiasAttr).getResult();
 }
 
-static SmallVector<Value> createIm2colRowComputes(Value x,
-                                                  RankedTensorType xType,
-                                                  RankedTensorType im2colType,
-                                                  RankedTensorType im2colRowType,
-                                                  RankedTensorType gemmInputRowType,
-                                                  int64_t batchSize,
-                                                  int64_t numChannelsIn,
-                                                  int64_t xHeight,
-                                                  int64_t xWidth,
-                                                  int64_t wHeight,
-                                                  int64_t wWidth,
-                                                  int64_t padHeightBegin,
-                                                  int64_t padHeightEnd,
-                                                  int64_t padWidthBegin,
-                                                  int64_t padWidthEnd,
-                                                  int64_t strideHeight,
-                                                  int64_t strideWidth,
-                                                  int64_t dilationHeight,
-                                                  int64_t dilationWidth,
-                                                  int64_t outWidth,
-                                                  int64_t patchSize,
-                                                  int64_t numPatches,
-                                                  int64_t numPatchesPerBatch,
-                                                  int64_t packFactor,
-                                                  ConversionPatternRewriter& rewriter,
-                                                  Location loc) {
+static Value createIm2colRowComputes(Value x,
+                                     RankedTensorType xType,
+                                     RankedTensorType im2colType,
+                                     RankedTensorType im2colRowType,
+                                     RankedTensorType gemmInputRowsType,
+                                     int64_t batchSize,
+                                     int64_t numChannelsIn,
+                                     int64_t xHeight,
+                                     int64_t xWidth,
+                                     int64_t wHeight,
+                                     int64_t wWidth,
+                                     int64_t padHeightBegin,
+                                     int64_t padHeightEnd,
+                                     int64_t padWidthBegin,
+                                     int64_t padWidthEnd,
+                                     int64_t strideHeight,
+                                     int64_t strideWidth,
+                                     int64_t dilationHeight,
+                                     int64_t dilationWidth,
+                                     int64_t outWidth,
+                                     int64_t patchSize,
+                                     int64_t numPatches,
+                                     int64_t numPatchesPerBatch,
+                                     int64_t packFactor,
+                                     ConversionPatternRewriter& rewriter,
+                                     Location loc) {
   auto elemType = xType.getElementType();
   constexpr size_t numInputs = 1;
   const int64_t packedNumRows = ceilIntegerDivide(numPatches, packFactor);
-  SmallVector<Type> resultTypes(packedNumRows, gemmInputRowType);
-  auto im2colComputeOp = createSpatCompute<numInputs>(rewriter, loc, resultTypes, {}, x, [&](Value xArg) {
-    Value paddedInput = xArg;
+  auto im2colComputeOp =
+    createSpatCompute<numInputs>(rewriter, loc, TypeRange {gemmInputRowsType}, {}, x, [&](Value xArg) {
+      Value paddedInput = xArg;
 
-    // Pad input with zeros if needed:
-    // [1, numChannelsIn, xHeight, xWidth] -> [1, numChannelsIn, xHeight+padHeight, xWidth+padWidth]
-    if (padHeightBegin || padHeightEnd || padWidthBegin || padWidthEnd) {
-      const int64_t paddedHeight = xHeight + padHeightBegin + padHeightEnd;
-      const int64_t paddedWidth = xWidth + padWidthBegin + padWidthEnd;
-      auto paddedType = RankedTensorType::get({batchSize, numChannelsIn, paddedHeight, paddedWidth}, elemType);
-      SmallVector<OpFoldResult> lowPads = {rewriter.getIndexAttr(0),
-                                           rewriter.getIndexAttr(0),
-                                           rewriter.getIndexAttr(padHeightBegin),
-                                           rewriter.getIndexAttr(padWidthBegin)};
-      SmallVector<OpFoldResult> highPads = {rewriter.getIndexAttr(0),
-                                            rewriter.getIndexAttr(0),
-                                            rewriter.getIndexAttr(padHeightEnd),
-                                            rewriter.getIndexAttr(padWidthEnd)};
-      auto padOp = tensor::PadOp::create(rewriter, loc, paddedType, paddedInput, lowPads, highPads);
-      auto* padBlock = new Block();
-      for (int i = 0; i < 4; i++)
-        padBlock->addArgument(rewriter.getIndexType(), loc);
-      padOp.getRegion().push_back(padBlock);
-      rewriter.setInsertionPointToStart(padBlock);
-      auto zero = arith::ConstantOp::create(rewriter, loc, elemType, rewriter.getFloatAttr(elemType, 0.0));
-      tensor::YieldOp::create(rewriter, loc, zero.getResult());
-      rewriter.setInsertionPointAfter(padOp);
-      paddedInput = padOp.getResult();
-    }
+      // Pad input with zeros if needed:
+      // [1, numChannelsIn, xHeight, xWidth] -> [1, numChannelsIn, xHeight+padHeight, xWidth+padWidth]
+      if (padHeightBegin || padHeightEnd || padWidthBegin || padWidthEnd) {
+        const int64_t paddedHeight = xHeight + padHeightBegin + padHeightEnd;
+        const int64_t paddedWidth = xWidth + padWidthBegin + padWidthEnd;
+        auto paddedType = RankedTensorType::get({batchSize, numChannelsIn, paddedHeight, paddedWidth}, elemType);
+        SmallVector<OpFoldResult> lowPads = {rewriter.getIndexAttr(0),
+                                             rewriter.getIndexAttr(0),
+                                             rewriter.getIndexAttr(padHeightBegin),
+                                             rewriter.getIndexAttr(padWidthBegin)};
+        SmallVector<OpFoldResult> highPads = {rewriter.getIndexAttr(0),
+                                              rewriter.getIndexAttr(0),
+                                              rewriter.getIndexAttr(padHeightEnd),
+                                              rewriter.getIndexAttr(padWidthEnd)};
+        auto padOp = tensor::PadOp::create(rewriter, loc, paddedType, paddedInput, lowPads, highPads);
+        auto* padBlock = new Block();
+        for (int i = 0; i < 4; i++)
+          padBlock->addArgument(rewriter.getIndexType(), loc);
+        padOp.getRegion().push_back(padBlock);
+        rewriter.setInsertionPointToStart(padBlock);
+        auto zero = arith::ConstantOp::create(rewriter, loc, elemType, rewriter.getFloatAttr(elemType, 0.0));
+        tensor::YieldOp::create(rewriter, loc, zero.getResult());
+        rewriter.setInsertionPointAfter(padOp);
+        paddedInput = padOp.getResult();
+      }
 
-    // Build im2col [numPatches, patchSize] incrementally to keep the IR small
-    // until the late PIM unrolling step.
-    Value im2colInit = tensor::EmptyOp::create(rewriter, loc, im2colType.getShape(), elemType);
-    auto c0 = arith::ConstantIndexOp::create(rewriter, loc, 0);
-    auto c1 = arith::ConstantIndexOp::create(rewriter, loc, 1);
-    auto cNumPatches = arith::ConstantIndexOp::create(rewriter, loc, numPatches);
-    auto cNumPatchesPerBatch = arith::ConstantIndexOp::create(rewriter, loc, numPatchesPerBatch);
-    auto cOutWidth = arith::ConstantIndexOp::create(rewriter, loc, outWidth);
-    auto cStrideHeight = arith::ConstantIndexOp::create(rewriter, loc, strideHeight);
-    auto cStrideWidth = arith::ConstantIndexOp::create(rewriter, loc, strideWidth);
+      // Build im2col [numPatches, patchSize] incrementally to keep the IR small
+      // until the late PIM unrolling step.
+      Value im2colInit = tensor::EmptyOp::create(rewriter, loc, im2colType.getShape(), elemType);
+      auto c0 = arith::ConstantIndexOp::create(rewriter, loc, 0);
+      auto c1 = arith::ConstantIndexOp::create(rewriter, loc, 1);
+      auto cNumPatches = arith::ConstantIndexOp::create(rewriter, loc, numPatches);
+      auto cNumPatchesPerBatch = arith::ConstantIndexOp::create(rewriter, loc, numPatchesPerBatch);
+      auto cOutWidth = arith::ConstantIndexOp::create(rewriter, loc, outWidth);
+      auto cStrideHeight = arith::ConstantIndexOp::create(rewriter, loc, strideHeight);
+      auto cStrideWidth = arith::ConstantIndexOp::create(rewriter, loc, strideWidth);
 
-    auto im2colLoop = scf::ForOp::create(rewriter, loc, c0, cNumPatches, c1, ValueRange {im2colInit});
-    rewriter.setInsertionPointToStart(im2colLoop.getBody());
+      auto im2colLoop = scf::ForOp::create(rewriter, loc, c0, cNumPatches, c1, ValueRange {im2colInit});
+      rewriter.setInsertionPointToStart(im2colLoop.getBody());
 
-    Value patchIndex = im2colLoop.getInductionVar();
-    Value im2colAcc = im2colLoop.getRegionIterArgs().front();
+      Value patchIndex = im2colLoop.getInductionVar();
+      Value im2colAcc = im2colLoop.getRegionIterArgs().front();
 
-    Value batchIndex = arith::DivUIOp::create(rewriter, loc, patchIndex, cNumPatchesPerBatch);
-    Value batchPatchIndex = arith::RemUIOp::create(rewriter, loc, patchIndex, cNumPatchesPerBatch);
-    Value outHeightIndex = arith::DivUIOp::create(rewriter, loc, batchPatchIndex, cOutWidth);
-    Value outWidthIndex = arith::RemUIOp::create(rewriter, loc, batchPatchIndex, cOutWidth);
-    Value inputHeightOffset = arith::MulIOp::create(rewriter, loc, outHeightIndex, cStrideHeight);
-    Value inputWidthOffset = arith::MulIOp::create(rewriter, loc, outWidthIndex, cStrideWidth);
+      Value batchIndex = arith::DivUIOp::create(rewriter, loc, patchIndex, cNumPatchesPerBatch);
+      Value batchPatchIndex = arith::RemUIOp::create(rewriter, loc, patchIndex, cNumPatchesPerBatch);
+      Value outHeightIndex = arith::DivUIOp::create(rewriter, loc, batchPatchIndex, cOutWidth);
+      Value outWidthIndex = arith::RemUIOp::create(rewriter, loc, batchPatchIndex, cOutWidth);
+      Value inputHeightOffset = arith::MulIOp::create(rewriter, loc, outHeightIndex, cStrideHeight);
+      Value inputWidthOffset = arith::MulIOp::create(rewriter, loc, outWidthIndex, cStrideWidth);
 
-    SmallVector<OpFoldResult> offsets = {batchIndex, rewriter.getIndexAttr(0), inputHeightOffset, inputWidthOffset};
-    SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
-                                       rewriter.getIndexAttr(numChannelsIn),
-                                       rewriter.getIndexAttr(wHeight),
-                                       rewriter.getIndexAttr(wWidth)};
-    SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1),
-                                         rewriter.getIndexAttr(1),
-                                         rewriter.getIndexAttr(dilationHeight),
-                                         rewriter.getIndexAttr(dilationWidth)};
-    auto patchType = RankedTensorType::get({1, numChannelsIn, wHeight, wWidth}, elemType);
-    Value patch = tensor::ExtractSliceOp::create(rewriter, loc, patchType, paddedInput, offsets, sizes, strides);
+      SmallVector<OpFoldResult> offsets = {batchIndex, rewriter.getIndexAttr(0), inputHeightOffset, inputWidthOffset};
+      SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
+                                         rewriter.getIndexAttr(numChannelsIn),
+                                         rewriter.getIndexAttr(wHeight),
+                                         rewriter.getIndexAttr(wWidth)};
+      SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1),
+                                           rewriter.getIndexAttr(1),
+                                           rewriter.getIndexAttr(dilationHeight),
+                                           rewriter.getIndexAttr(dilationWidth)};
+      auto patchType = RankedTensorType::get({1, numChannelsIn, wHeight, wWidth}, elemType);
+      Value patch = tensor::ExtractSliceOp::create(rewriter, loc, patchType, paddedInput, offsets, sizes, strides);
 
-    Value row = tensor::CollapseShapeOp::create(rewriter,
-                                                loc,
-                                                im2colRowType,
-                                                patch,
-                                                SmallVector<ReassociationIndices> {
-                                                  {0},
-                                                  {1, 2, 3}
+      Value row = tensor::CollapseShapeOp::create(rewriter,
+                                                  loc,
+                                                  im2colRowType,
+                                                  patch,
+                                                  SmallVector<ReassociationIndices> {
+                                                    {0},
+                                                    {1, 2, 3}
+      });
+
+      SmallVector<OpFoldResult> rowOffsets = {patchIndex, rewriter.getIndexAttr(0)};
+      SmallVector<OpFoldResult> rowSizes = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(patchSize)};
+      SmallVector<OpFoldResult> rowStrides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+      Value updatedIm2col =
+        tensor::InsertSliceOp::create(rewriter, loc, row, im2colAcc, rowOffsets, rowSizes, rowStrides);
+      scf::YieldOp::create(rewriter, loc, updatedIm2col);
+
+      rewriter.setInsertionPointAfter(im2colLoop);
+      Value im2col = im2colLoop.getResult(0);
+
+      Value gemmInputRows = im2col;
+      if (packFactor != 1) {
+        const int64_t paddedNumPatches = packedNumRows * packFactor;
+        auto groupedType = RankedTensorType::get({packedNumRows, packFactor, patchSize}, elemType);
+        auto packedType = RankedTensorType::get({packedNumRows, packFactor * patchSize}, elemType);
+        Value paddedIm2col = createPaddedRows(im2col, im2colType, paddedNumPatches, rewriter, loc);
+        Value groupedIm2col = tensor::ExpandShapeOp::create(rewriter,
+                                                            loc,
+                                                            groupedType,
+                                                            paddedIm2col,
+                                                            SmallVector<ReassociationIndices> {
+                                                              {0, 1},
+                                                              {2}
+        });
+        gemmInputRows = tensor::CollapseShapeOp::create(rewriter,
+                                                        loc,
+                                                        packedType,
+                                                        groupedIm2col,
+                                                        SmallVector<ReassociationIndices> {
+                                                          {0},
+                                                          {1, 2}
+        });
+      }
+
+      spatial::SpatYieldOp::create(rewriter, loc, gemmInputRows);
     });
 
-    SmallVector<OpFoldResult> rowOffsets = {patchIndex, rewriter.getIndexAttr(0)};
-    SmallVector<OpFoldResult> rowSizes = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(patchSize)};
-    SmallVector<OpFoldResult> rowStrides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    Value updatedIm2col =
-      tensor::InsertSliceOp::create(rewriter, loc, row, im2colAcc, rowOffsets, rowSizes, rowStrides);
-    scf::YieldOp::create(rewriter, loc, updatedIm2col);
-
-    rewriter.setInsertionPointAfter(im2colLoop);
-    Value im2col = im2colLoop.getResult(0);
-
-    Value gemmInputRows = im2col;
-    if (packFactor != 1) {
-      const int64_t paddedNumPatches = packedNumRows * packFactor;
-      auto groupedType = RankedTensorType::get({packedNumRows, packFactor, patchSize}, elemType);
-      auto packedType = RankedTensorType::get({packedNumRows, packFactor * patchSize}, elemType);
-      Value paddedIm2col = createPaddedRows(im2col, im2colType, paddedNumPatches, rewriter, loc);
-      Value groupedIm2col = tensor::ExpandShapeOp::create(rewriter,
-                                                          loc,
-                                                          groupedType,
-                                                          paddedIm2col,
-                                                          SmallVector<ReassociationIndices> {
-                                                            {0, 1},
-                                                            {2}
-      });
-      gemmInputRows = tensor::CollapseShapeOp::create(rewriter,
-                                                      loc,
-                                                      packedType,
-                                                      groupedIm2col,
-                                                      SmallVector<ReassociationIndices> {
-                                                        {0},
-                                                        {1, 2}
-      });
-    }
-
-    SmallVector<Value> rowResults;
-    rowResults.reserve(packedNumRows);
-    for (int64_t rowIdx = 0; rowIdx < packedNumRows; rowIdx++) {
-      SmallVector<OpFoldResult> offsets = {rewriter.getIndexAttr(rowIdx), rewriter.getIndexAttr(0)};
-      SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(packFactor * patchSize)};
-      SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-      rowResults.push_back(
-        tensor::ExtractSliceOp::create(rewriter, loc, gemmInputRowType, gemmInputRows, offsets, sizes, strides));
-    }
-    spatial::SpatYieldOp::create(rewriter, loc, rowResults);
-  });
-
-  SmallVector<Value> rows;
-  rows.reserve(im2colComputeOp.getNumResults());
-  for (Value result : im2colComputeOp.getResults())
-    rows.push_back(result);
-  return rows;
+  return im2colComputeOp.getResult(0);
 }
 
 static Value createCollectedConvOutput(ValueRange gemmRows,
@@ -319,15 +306,12 @@ static Value createCollectedConvOutput(ValueRange gemmRows,
   auto collectComputeOp = createSpatCompute(rewriter, loc, convType, {}, gemmRows, [&](ValueRange gemmRowArgs) {
     Value gemmOut;
     if (packFactor == 1) {
-      gemmOut = gemmRowArgs.size() == 1 ? gemmRowArgs.front()
-                                        : tensor::ConcatOp::create(rewriter, loc, /*axis=*/0, gemmRowArgs).getResult();
+      gemmOut = createSpatConcat(rewriter, loc, /*axis=*/0, gemmRowArgs);
     }
     else {
       auto expandedType = RankedTensorType::get({packedNumRows, packFactor, numChannelsOut}, outType.getElementType());
       auto paddedType = RankedTensorType::get({paddedNumPatches, numChannelsOut}, outType.getElementType());
-      Value packedOutput = gemmRowArgs.size() == 1
-                           ? gemmRowArgs.front()
-                           : tensor::ConcatOp::create(rewriter, loc, /*axis=*/0, gemmRowArgs).getResult();
+      Value packedOutput = createSpatConcat(rewriter, loc, /*axis=*/0, gemmRowArgs);
       Value expandedOutput = tensor::ExpandShapeOp::create(rewriter,
                                                            loc,
                                                            expandedType,
@@ -509,35 +493,36 @@ LogicalResult ConvToGemm::matchAndRewrite(ONNXConvOp convOp,
   //   A_packed: [ceil(numPatches / N), N * patchSize]
   //   B_packed: [N * patchSize, N * cOut]
   //   Y_packed: [ceil(numPatches / N), N * cOut]
-  auto gemmInputRowType = RankedTensorType::get({1, effectiveMaxParallelPixels * patchSize}, elemType);
-  auto gemmOutputRowType =
-    RankedTensorType::get({1, effectiveMaxParallelPixels * numChannelsOut}, outType.getElementType());
-  SmallVector<Value> gemmInputRows = createIm2colRowComputes(x,
-                                                             xType,
-                                                             im2colType,
-                                                             rowType,
-                                                             gemmInputRowType,
-                                                             batchSize,
-                                                             numChannelsIn,
-                                                             xHeight,
-                                                             xWidth,
-                                                             wHeight,
-                                                             wWidth,
-                                                             padHeightBegin,
-                                                             padHeightEnd,
-                                                             padWidthBegin,
-                                                             padWidthEnd,
-                                                             strideHeight,
-                                                             strideWidth,
-                                                             dilationHeight,
-                                                             dilationWidth,
-                                                             outWidth,
-                                                             patchSize,
-                                                             numPatches,
-                                                             numPatchesPerBatch,
-                                                             effectiveMaxParallelPixels,
-                                                             rewriter,
-                                                             loc);
+  const int64_t packedNumRows = ceilIntegerDivide(numPatches, effectiveMaxParallelPixels);
+  auto gemmInputRowsType = RankedTensorType::get({packedNumRows, effectiveMaxParallelPixels * patchSize}, elemType);
+  auto gemmOutputRowsType =
+    RankedTensorType::get({packedNumRows, effectiveMaxParallelPixels * numChannelsOut}, outType.getElementType());
+  Value gemmInputRows = createIm2colRowComputes(x,
+                                                xType,
+                                                im2colType,
+                                                rowType,
+                                                gemmInputRowsType,
+                                                batchSize,
+                                                numChannelsIn,
+                                                xHeight,
+                                                xWidth,
+                                                wHeight,
+                                                wWidth,
+                                                padHeightBegin,
+                                                padHeightEnd,
+                                                padWidthBegin,
+                                                padWidthEnd,
+                                                strideHeight,
+                                                strideWidth,
+                                                dilationHeight,
+                                                dilationWidth,
+                                                outWidth,
+                                                patchSize,
+                                                numPatches,
+                                                numPatchesPerBatch,
+                                                effectiveMaxParallelPixels,
+                                                rewriter,
+                                                loc);
 
   Value gemmB = buildPackedWeight(wDenseAttr,
                                   wTrans,
@@ -553,25 +538,20 @@ LogicalResult ConvToGemm::matchAndRewrite(ONNXConvOp convOp,
   Value gemmC = buildPackedBias(
     hasB, gemmBias, biasMatrix, biasDenseAttr, outType, numChannelsOut, effectiveMaxParallelPixels, rewriter, loc);
 
-  SmallVector<Value> gemmRows;
-  gemmRows.reserve(gemmInputRows.size());
-  for (Value gemmInputRow : gemmInputRows) {
-    Value gemmRow = ONNXGemmOp::create(rewriter,
-                                       loc,
-                                       gemmOutputRowType,
-                                       gemmInputRow,
-                                       gemmB,
-                                       gemmC,
-                                       rewriter.getF32FloatAttr(1.0f),
-                                       rewriter.getF32FloatAttr(1.0f),
-                                       rewriter.getBoolAttr(false),
-                                       rewriter.getBoolAttr(false))
-                      .getY();
-    gemmRows.push_back(gemmRow);
-  }
+  Value gemmRows = ONNXGemmOp::create(rewriter,
+                                      loc,
+                                      gemmOutputRowsType,
+                                      gemmInputRows,
+                                      gemmB,
+                                      gemmC,
+                                      rewriter.getF32FloatAttr(1.0f),
+                                      rewriter.getF32FloatAttr(1.0f),
+                                      rewriter.getBoolAttr(false),
+                                      rewriter.getBoolAttr(false))
+                     .getY();
 
   rewriter.replaceOp(convOp,
-                     createCollectedConvOutput(gemmRows,
+                     createCollectedConvOutput(ValueRange {gemmRows},
                                                convOp.getType(),
                                                gemmOutType,
                                                nhwcType,

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

@@ -1,6 +1,7 @@
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Tosa/IR/TosaOps.h"
 #include "mlir/IR/BuiltinTypes.h"
+#include "mlir/IR/IRMapping.h"
 #include "mlir/IR/Location.h"
 #include "mlir/Support/LogicalResult.h"
 #include "mlir/Transforms/DialectConversion.h"
@@ -65,6 +66,66 @@ struct GemvToSpatialCompute : OpConversionPattern<ONNXGemmOp> {
                                 ConversionPatternRewriter& rewriter) const override;
 };
 
+struct GemmToSpatialComputeBatch : OpConversionPattern<ONNXGemmOp> {
+  using OpConversionPattern::OpConversionPattern;
+
+  LogicalResult matchAndRewrite(ONNXGemmOp gemmOp,
+                                ONNXGemmOpAdaptor gemmOpAdaptor,
+                                ConversionPatternRewriter& rewriter) const override;
+};
+
+static SmallVector<Value> materializeBatchRowSlices(Value matrix,
+                                                    RankedTensorType matrixType,
+                                                    ConversionPatternRewriter& rewriter,
+                                                    Location loc) {
+  const int64_t numRows = matrixType.getDimSize(0);
+  auto rowType = RankedTensorType::get({1, matrixType.getDimSize(1)}, matrixType.getElementType());
+  SmallVector<Type> resultTypes(static_cast<size_t>(numRows), rowType);
+
+  auto buildRowSlices = [&](Value matrixArg) {
+    auto extractRowsOp = spatial::SpatExtractRowsOp::create(rewriter, loc, TypeRange(resultTypes), matrixArg);
+    return SmallVector<Value>(extractRowsOp->result_begin(), extractRowsOp->result_end());
+  };
+
+  auto cloneBatchInputChainIntoSliceCompute =
+    [&](Value rootInput, SmallVector<Operation*> chainOps, Value rootValue) -> SmallVector<Value> {
+    auto sliceCompute =
+      createSpatCompute<1>(rewriter, loc, TypeRange(resultTypes), {}, ValueRange {rootInput}, [&](Value input) {
+        Value transformedMatrix = input;
+        if (!chainOps.empty()) {
+          IRMapping mapper;
+          mapper.map(rootValue, input);
+          for (Operation* chainOp : chainOps)
+            rewriter.clone(*chainOp, mapper);
+          transformedMatrix = cast<Value>(mapper.lookup(matrix));
+        }
+        spatial::SpatYieldOp::create(rewriter, loc, buildRowSlices(transformedMatrix));
+      });
+    SmallVector<Value> rowSlices(sliceCompute->result_begin(), sliceCompute->result_end());
+    return rowSlices;
+  };
+
+  SmallVector<Operation*> chainOps;
+  Value rootValue = matrix;
+  while (Operation* definingOp = rootValue.getDefiningOp()) {
+    if (auto rootCompute = dyn_cast<spatial::SpatCompute>(definingOp)) {
+      SmallVector<Operation*> reversedChainOps(chainOps.rbegin(), chainOps.rend());
+      return cloneBatchInputChainIntoSliceCompute(
+        rootCompute.getResult(cast<OpResult>(rootValue).getResultNumber()), reversedChainOps, rootValue);
+    }
+
+    if (definingOp->getNumOperands() != 1)
+      break;
+    if (!isa<tensor::ExtractSliceOp, tensor::ExpandShapeOp, tensor::CollapseShapeOp, ONNXTransposeOp>(definingOp))
+      break;
+
+    chainOps.push_back(definingOp);
+    rootValue = definingOp->getOperand(0);
+  }
+
+  return buildRowSlices(matrix);
+}
+
 } // namespace
 
 LogicalResult GemmToManyGemv::matchAndRewrite(ONNXGemmOp gemmOp,
@@ -156,8 +217,7 @@ LogicalResult GemmToManyGemv::matchAndRewrite(ONNXGemmOp gemmOp,
   }
 
   auto concatComputeOp = createSpatCompute(rewriter, loc, gemmOp.getType(), {}, gemvOps, [&](ValueRange gemvOpsArgs) {
-    auto concatOp = tensor::ConcatOp::create(rewriter, loc, /*axis=*/0, gemvOpsArgs);
-    spatial::SpatYieldOp::create(rewriter, loc, concatOp.getResult());
+    spatial::SpatYieldOp::create(rewriter, loc, createSpatConcat(rewriter, loc, /*axis=*/0, gemvOpsArgs));
   });
 
   rewriter.replaceOp(gemmOp, concatComputeOp);
@@ -313,15 +373,116 @@ LogicalResult GemvToSpatialCompute::matchAndRewrite(ONNXGemmOp gemmOp,
 
   auto concatComputeOp =
     createSpatCompute(rewriter, gemmLoc, gemmOp.getType(), {}, outHSlices, [&](ValueRange blockArgs) {
-      auto concatOp = tensor::ConcatOp::create(rewriter, gemmLoc, /*axis=*/1, blockArgs);
-      spatial::SpatYieldOp::create(rewriter, gemmLoc, concatOp.getResult());
+      spatial::SpatYieldOp::create(rewriter, gemmLoc, createSpatConcat(rewriter, gemmLoc, /*axis=*/1, blockArgs));
     });
 
   rewriter.replaceOp(gemmOp, concatComputeOp);
   return success();
 }
 
+LogicalResult GemmToSpatialComputeBatch::matchAndRewrite(ONNXGemmOp gemmOp,
+                                                         ONNXGemmOpAdaptor gemmOpAdaptor,
+                                                         ConversionPatternRewriter& rewriter) const {
+  Location loc = gemmOp.getLoc();
+  Value a = gemmOpAdaptor.getA();
+  Value b = gemmOpAdaptor.getB();
+  Value c = gemmOpAdaptor.getC();
+
+  assert("A should have been transposed already" && !gemmOpAdaptor.getTransA());
+
+  bool hasC = !isa<ONNXNoneOp>(c.getDefiningOp());
+
+  auto aType = cast<RankedTensorType>(a.getType());
+  auto bType = cast<RankedTensorType>(b.getType());
+  auto outType = cast<RankedTensorType>(gemmOp.getY().getType());
+  assert("Only support static shapes" && aType.hasStaticShape() && bType.hasStaticShape() && outType.hasStaticShape());
+
+  const int64_t numOutRows = aType.getDimSize(0);
+  if (numOutRows <= 1)
+    return failure();
+
+  // Only handle the single-tile case: K <= crossbarSize and N <= crossbarSize
+  if (aType.getDimSize(1) > static_cast<int64_t>(crossbarSize.getValue())
+      || outType.getDimSize(1) > static_cast<int64_t>(crossbarSize.getValue()))
+    return failure();
+
+  auto scaledB = materializeScaledConstantTensor(b, gemmOpAdaptor.getAlpha().convertToFloat(), rewriter, loc);
+  if (failed(scaledB))
+    return failure();
+  b = *scaledB;
+  bType = cast<RankedTensorType>(b.getType());
+
+  if (gemmOpAdaptor.getTransB()) {
+    auto bShape = bType.getShape();
+    auto transposedType = bType.cloneWith(ArrayRef({bShape[1], bShape[0]}), bType.getElementType());
+    b = ONNXTransposeOp::create(rewriter, loc, transposedType, b, rewriter.getI64ArrayAttr({1, 0}));
+    bType = cast<RankedTensorType>(b.getType());
+  }
+  (void) bType;
+
+  Value sharedBias;
+  if (hasC) {
+    auto scaledC = materializeScaledConstantTensor(c, gemmOpAdaptor.getBeta().convertToFloat(), rewriter, loc);
+    if (failed(scaledC))
+      return failure();
+    c = *scaledC;
+    auto cType = cast<RankedTensorType>(c.getType());
+    if (cType.getRank() == 1) {
+      auto expandedType = RankedTensorType::get({1, cType.getDimSize(0)}, cType.getElementType());
+      c = tensor::ExpandShapeOp::create(rewriter,
+                                        loc,
+                                        expandedType,
+                                        c,
+                                        SmallVector<ReassociationIndices> {
+                                          {0, 1}
+      });
+      cType = cast<RankedTensorType>(c.getType());
+    }
+    assert("Only support rank 2 tensor for C" && cType.getRank() == 2);
+    // Row-specific bias can't share a single template body; fall through to GemmToManyGemv
+    if (cType.getDimSize(0) == numOutRows && numOutRows > 1)
+      return failure();
+    if (cType.getDimSize(0) == 1 && cType.getDimSize(1) == 1)
+      c = broadcastToVector(c, outType.getDimSize(1), rewriter, loc);
+    sharedBias = c;
+  }
+
+  SmallVector<Value> aSlices = materializeBatchRowSlices(a, aType, rewriter, loc);
+  auto aSliceType = cast<RankedTensorType>(aSlices.front().getType());
+
+  auto outRowType = RankedTensorType::get({1, outType.getDimSize(1)}, outType.getElementType());
+  SmallVector<Type> resultTypes(static_cast<size_t>(numOutRows), outRowType);
+  SmallVector<Value> weights(static_cast<size_t>(numOutRows), b);
+
+  auto batchOp = spatial::SpatComputeBatch::create(rewriter,
+                                                   loc,
+                                                   TypeRange(resultTypes),
+                                                   rewriter.getI32IntegerAttr(static_cast<int32_t>(numOutRows)),
+                                                   ValueRange(weights),
+                                                   ValueRange(aSlices));
+
+  Block* body = rewriter.createBlock(
+    &batchOp.getBody(), batchOp.getBody().end(), TypeRange {aSliceType}, SmallVector<Location>(1, loc));
+  rewriter.setInsertionPointToEnd(body);
+
+  Value vmmResult = spatial::SpatWeightedVMMOp::create(rewriter, loc, outRowType, 0, body->getArgument(0)).getResult();
+  Value laneResult = vmmResult;
+  if (sharedBias)
+    laneResult = spatial::SpatVAddOp::create(rewriter, loc, outRowType, vmmResult, sharedBias).getResult();
+  spatial::SpatYieldOp::create(rewriter, loc, laneResult);
+
+  rewriter.setInsertionPointAfter(batchOp);
+  SmallVector<Value> laneResults(batchOp->result_begin(), batchOp->result_end());
+  auto concatComputeOp = createSpatCompute(rewriter, loc, gemmOp.getType(), {}, laneResults, [&](ValueRange args) {
+    spatial::SpatYieldOp::create(rewriter, loc, createSpatConcat(rewriter, loc, /*axis=*/0, args));
+  });
+
+  rewriter.replaceOp(gemmOp, concatComputeOp);
+  return success();
+}
+
 void populateGemmPatterns(RewritePatternSet& patterns, MLIRContext* ctx) {
+  patterns.insert<GemmToSpatialComputeBatch>(ctx, PatternBenefit(2));
   patterns.insert<GemmToManyGemv>(ctx);
   patterns.insert<GemvToSpatialCompute>(ctx);
 }

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

@@ -232,9 +232,7 @@ struct MatMulToGemm : OpRewritePattern<ONNXMatMulOp> {
       }));
     }
 
-    Value result = batchResults.size() == 1
-                   ? batchResults.front()
-                   : tensor::ConcatOp::create(rewriter, loc, /*axis=*/0, batchResults).getResult();
+    Value result = createSpatConcat(rewriter, loc, /*axis=*/0, batchResults);
     rewriter.replaceOp(matmulOp, result);
     return success();
   }

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

@@ -100,8 +100,7 @@ static Value buildReduceMeanKeepdims(Value input,
   for (Value slice : slices)
     reducedSlices.push_back(buildReduceMeanKeepdims(slice, reducedAxes, axis + 1, leafType, rewriter, loc));
 
-  return reducedSlices.size() == 1 ? reducedSlices.front()
-                                   : tensor::ConcatOp::create(rewriter, loc, axis, reducedSlices).getResult();
+  return createSpatConcat(rewriter, loc, axis, reducedSlices);
 }
 
 static Value squeezeReducedAxes(Value keepdimsValue,

src/PIM/Conversion/ONNXToSpatial/Patterns/NN/Pool.cpp

@@ -33,9 +33,7 @@ static int64_t getOptionalI64(std::optional<ArrayAttrT> arrayAttr, size_t index,
 
 static Value concatAlongAxis(ConversionPatternRewriter& rewriter, Location loc, int64_t axis, ArrayRef<Value> values) {
   assert(!values.empty() && "Expected at least one value to concatenate.");
-  if (values.size() == 1)
-    return values.front();
-  return tensor::ConcatOp::create(rewriter, loc, axis, values);
+  return createSpatConcat(rewriter, loc, axis, values);
 }
 
 static Value materializeContiguousTile(ConversionPatternRewriter& rewriter, Location loc, Value tile) {

src/PIM/Conversion/ONNXToSpatial/Patterns/NN/Softmax.cpp

@@ -47,8 +47,7 @@ buildSoftmax(Value input, int64_t softmaxAxis, int64_t axis, ConversionPatternRe
   for (Value slice : slices)
     rebuiltSlices.push_back(buildSoftmax(slice, softmaxAxis, axis + 1, rewriter, loc));
 
-  return rebuiltSlices.size() == 1 ? rebuiltSlices.front()
-                                   : tensor::ConcatOp::create(rewriter, loc, axis, rebuiltSlices).getResult();
+  return createSpatConcat(rewriter, loc, axis, rebuiltSlices);
 }
 
 struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Concat.cpp

@@ -2,6 +2,7 @@
 #include "mlir/IR/PatternMatch.h"
 
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
 using namespace mlir;
@@ -17,7 +18,7 @@ struct Concat : public OpConversionPattern<ONNXConcatOp> {
     auto inputs = adaptor.getInputs();
     int64_t axis = adaptor.getAxis();
 
-    rewriter.replaceOpWithNewOp<tensor::ConcatOp>(maxpoolOp, axis, inputs);
+    rewriter.replaceOp(maxpoolOp, createSpatConcat(rewriter, maxpoolOp.getLoc(), axis, inputs));
 
     return success();
   }

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Gather.cpp

@@ -49,7 +49,7 @@ static Value concatGatherSlices(Value data,
   }
   if (slices.empty())
     return {};
-  return slices.size() == 1 ? slices.front() : tensor::ConcatOp::create(rewriter, loc, axis, slices).getResult();
+  return createSpatConcat(rewriter, loc, axis, slices);
 }
 
 static Value addLeadingGatherDim(Value value, int64_t axis, ConversionPatternRewriter& rewriter, Location loc) {
@@ -130,9 +130,7 @@ struct Gather : OpConversionPattern<ONNXGatherOp> {
                                    return failure();
                                  rows.push_back(addLeadingGatherDim(gatheredRow, axis, rewriter, loc));
                                }
-                               result = rows.size() == 1
-                                        ? rows.front()
-                                        : tensor::ConcatOp::create(rewriter, loc, /*axis=*/axis, rows).getResult();
+                               result = createSpatConcat(rewriter, loc, /*axis=*/axis, rows);
                              }
                              else {
                                return failure();

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Resize.cpp

@@ -50,7 +50,7 @@ static Value buildNearestResize(Value input,
     slices.push_back(buildNearestResize(slice, inputShape, outputShape, axis + 1, rewriter, loc));
   }
 
-  return slices.size() == 1 ? slices.front() : tensor::ConcatOp::create(rewriter, loc, axis, slices).getResult();
+  return createSpatConcat(rewriter, loc, axis, slices);
 }
 
 struct Resize : OpConversionPattern<ONNXResizeOp> {

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Split.cpp

@@ -23,7 +23,10 @@ static Value extractSliceAt(
     sizes.push_back(rewriter.getIndexAttr(dim));
   offsets[axis] = rewriter.getIndexAttr(offset);
   sizes[axis] = rewriter.getIndexAttr(size);
-  return tensor::ExtractSliceOp::create(rewriter, loc, input, offsets, sizes, strides);
+  SmallVector<int64_t> resultShape(inputType.getShape());
+  resultShape[axis] = size;
+  auto resultType = RankedTensorType::get(resultShape, inputType.getElementType());
+  return tensor::ExtractSliceOp::create(rewriter, loc, resultType, input, offsets, sizes, strides);
 }
 
 struct Split : OpConversionPattern<ONNXSplitOp> {
@@ -49,12 +52,7 @@ struct Split : OpConversionPattern<ONNXSplitOp> {
       if (!resultType || !resultType.hasStaticShape())
         return failure();
       int64_t sliceSize = resultType.getShape()[axis];
-      auto computeOp =
-        createSpatCompute<1>(rewriter, splitOp.getLoc(), TypeRange {resultType}, {}, adaptor.getInput(), [&](Value x) {
-          Value output = extractSliceAt(x, axis, offset, sliceSize, rewriter, splitOp.getLoc());
-          spatial::SpatYieldOp::create(rewriter, splitOp.getLoc(), output);
-        });
-      outputs.push_back(computeOp.getResult(0));
+      outputs.push_back(extractSliceAt(adaptor.getInput(), axis, offset, sliceSize, rewriter, splitOp.getLoc()));
       offset += sliceSize;
     }
 

src/PIM/Conversion/SpatialToPim/CMakeLists.txt

@@ -5,6 +5,7 @@ add_public_tablegen_target(SpatialToPimIncGen)
 add_pim_library(OMSpatialToPim
   SpatialToPimPass.cpp
   Common.cpp
+  Patterns.cpp
 
   EXCLUDE_FROM_OM_LIBS
 

src/PIM/Conversion/SpatialToPim/Common.cpp

@@ -7,23 +7,12 @@
 #include <cstddef>
 
 #include "Common.hpp"
-#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
 using namespace llvm;
 using namespace mlir;
 
 namespace onnx_mlir {
 
-namespace {
-
-IntegerAttr getRequiredI32Attr(Builder& builder, Operation* op, llvm::StringRef attrName) {
-  auto attr = op->getAttrOfType<IntegerAttr>(attrName);
-  assert(attr && "required precomputed channel attr is missing");
-  return IntegerAttr::get(builder.getI32Type(), attr.getInt());
-}
-
-} // namespace
-
 size_t getSliceActualOffset(tensor::ExtractSliceOp& sliceOp, ShapedType& inputShape) {
   /*
     EXAMPLE RUN:
@@ -74,37 +63,6 @@ IntegerAttr getTensorSizeInBytesAttr(Builder& builder, mlir::Value value) {
   return builder.getI32IntegerAttr(static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(value.getType()))));
 }
 
-IntegerAttr getSpatialChannelSourceCoreIdAttr(Builder& builder, mlir::Value channel) {
-  auto channelNewOp = channel.getDefiningOp<spatial::SpatChannelNewOp>();
-  assert(channelNewOp && "spatial channel value must come from spat.channel_new");
-  return getRequiredI32Attr(builder, channelNewOp, kChannelSourceCoreIdAttrName);
-}
-
-IntegerAttr getSpatialChannelTargetCoreIdAttr(Builder& builder, mlir::Value channel) {
-  auto channelNewOp = channel.getDefiningOp<spatial::SpatChannelNewOp>();
-  assert(channelNewOp && "spatial channel value must come from spat.channel_new");
-  return getRequiredI32Attr(builder, channelNewOp, kChannelTargetCoreIdAttrName);
-}
-
-bool hasSpatialChannelSourceCoreIdAttr(mlir::Value channel) {
-  auto channelNewOp = channel.getDefiningOp<spatial::SpatChannelNewOp>();
-  return channelNewOp && channelNewOp->hasAttr(kChannelSourceCoreIdAttrName);
-}
-
-bool hasSpatialChannelTargetCoreIdAttr(mlir::Value channel) {
-  auto channelNewOp = channel.getDefiningOp<spatial::SpatChannelNewOp>();
-  return channelNewOp && channelNewOp->hasAttr(kChannelTargetCoreIdAttrName);
-}
-
-mlir::Value
-createPimReceiveFromSpatialChannel(PatternRewriter& rewriter, Location loc, mlir::Value output, mlir::Value channel) {
-  mlir::Value outputBuffer = getBestOutputTensorFromOperandsOrAllocate(rewriter, output.getDefiningOp());
-  auto sizeAttr = getTensorSizeInBytesAttr(rewriter, output);
-  auto sourceCoreIdAttr = getSpatialChannelSourceCoreIdAttr(rewriter, channel);
-  return pim::PimReceiveOp::create(rewriter, loc, outputBuffer.getType(), outputBuffer, sizeAttr, sourceCoreIdAttr)
-    .getOutput();
-}
-
 Operation* getEarliestUserWithinBlock(mlir::Value value) {
   auto users = value.getUsers();
 

src/PIM/Conversion/SpatialToPim/Common.hpp

@@ -2,16 +2,10 @@
 
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 
-#include "llvm/ADT/StringRef.h"
-
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
-#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
 namespace onnx_mlir {
 
-inline constexpr llvm::StringLiteral kChannelSourceCoreIdAttrName = "precomp_source_core_id";
-inline constexpr llvm::StringLiteral kChannelTargetCoreIdAttrName = "precomp_target_core_id";
-
 /**
  * \brief Get the offset of the ExtractSliceOp based on its static offsets and
  * its static tensor input.
@@ -30,17 +24,6 @@ size_t getShapedTypeSizeInBytes(mlir::ShapedType shapedType);
 
 mlir::IntegerAttr getTensorSizeInBytesAttr(mlir::Builder& builder, mlir::Value value);
 
-mlir::IntegerAttr getSpatialChannelSourceCoreIdAttr(mlir::Builder& builder, mlir::Value channel);
-
-mlir::IntegerAttr getSpatialChannelTargetCoreIdAttr(mlir::Builder& builder, mlir::Value channel);
-
-bool hasSpatialChannelSourceCoreIdAttr(mlir::Value channel);
-
-bool hasSpatialChannelTargetCoreIdAttr(mlir::Value channel);
-
-mlir::Value createPimReceiveFromSpatialChannel(
-  mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value output, mlir::Value channel);
-
 template <class T>
 size_t rangeLength(const mlir::iterator_range<T> range) {
   return std::distance(range.begin(), range.end());

src/PIM/Conversion/SpatialToPim/Patterns.cpp

@@ -0,0 +1,385 @@
+#include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/IR/BuiltinOps.h"
+#include "mlir/IR/BuiltinTypes.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/IR/Value.h"
+#include "mlir/Support/LLVM.h"
+
+#include "llvm/ADT/DenseMap.h"
+#include "llvm/ADT/STLExtras.h"
+#include "llvm/Support/Casting.h"
+#include "llvm/Support/ErrorHandling.h"
+#include "llvm/Support/LogicalResult.h"
+
+#include "Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+namespace {
+
+struct MoveExtractSliceIntoCompute final : OpRewritePattern<mlir::tensor::ExtractSliceOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(mlir::tensor::ExtractSliceOp extractSliceOp, PatternRewriter& rewriter) const override {
+    Location loc = extractSliceOp.getLoc();
+
+    if (!isa<func::FuncOp>(extractSliceOp->getParentOp()))
+      return failure();
+
+    for (auto& uses : extractSliceOp->getUses()) {
+      if (isa<spatial::SpatCompute>(uses.getOwner())) {
+        auto spatCompute = cast<spatial::SpatCompute>(uses.getOwner());
+        if (spatCompute.getInputs().empty())
+          return failure();
+        if (uses.getOperandNumber() < spatCompute.getInputs().getBeginOperandIndex())
+          return failure();
+      }
+      else if (isa_and_present<func::FuncOp>(uses.getOwner()->getParentOp())) {
+        return failure();
+      }
+    }
+
+    llvm::DenseMap<Operation*, Value> mapSpatToExtract;
+
+    for (auto& uses : llvm::make_early_inc_range(extractSliceOp->getUses())) {
+
+      if (auto spatCompute = dyn_cast<spatial::SpatCompute>(uses.getOwner())) {
+        auto BBArgIndex = uses.getOperandNumber() - spatCompute.getInputs().getBeginOperandIndex();
+        auto BBArgValue = spatCompute.getBody().front().getArgument(BBArgIndex);
+
+        if (BBArgValue.use_empty())
+          continue;
+
+        rewriter.setInsertionPoint(&spatCompute.getBody().front().front());
+        if (!mapSpatToExtract.contains(spatCompute.getOperation())) {
+          auto newExtractSlice = rewriter.clone(*extractSliceOp.getOperation());
+          mapSpatToExtract.insert({spatCompute.getOperation(), newExtractSlice->getResult(0)});
+        }
+
+        rewriter.startOpModification(spatCompute.getOperation());
+        BBArgValue.replaceAllUsesWith(mapSpatToExtract[spatCompute.getOperation()]);
+        spatCompute.getInputsMutable().erase(BBArgIndex);
+        spatCompute.getBody().front().eraseArgument(BBArgIndex);
+        rewriter.finalizeOpModification(spatCompute.getOperation());
+      }
+      else if (auto spatComputeBatch = dyn_cast<spatial::SpatComputeBatch>(uses.getOwner())) {
+        auto BBArgIndex = uses.getOperandNumber() - spatComputeBatch.getInputs().getBeginOperandIndex();
+        auto BBArgValue = spatComputeBatch.getBody().front().getArgument(BBArgIndex);
+
+        if (BBArgValue.use_empty())
+          continue;
+
+        rewriter.setInsertionPoint(&spatComputeBatch.getBody().front().front());
+        if (!mapSpatToExtract.contains(spatComputeBatch.getOperation())) {
+          auto newExtractSlice = rewriter.clone(*extractSliceOp.getOperation());
+          mapSpatToExtract.insert({spatComputeBatch.getOperation(), newExtractSlice->getResult(0)});
+        }
+
+        rewriter.startOpModification(spatComputeBatch.getOperation());
+        BBArgValue.replaceAllUsesWith(mapSpatToExtract[spatComputeBatch.getOperation()]);
+        spatComputeBatch.getInputsMutable().erase(BBArgIndex);
+        spatComputeBatch.getBody().front().eraseArgument(BBArgIndex);
+        rewriter.finalizeOpModification(spatComputeBatch.getOperation());
+      }
+      else {
+        {
+          if (auto spatCompute = uses.getOwner()->getParentOfType<spatial::SpatCompute>()) {
+            rewriter.setInsertionPoint(&spatCompute.getBody().front().front());
+            if (!mapSpatToExtract.contains(spatCompute.getOperation())) {
+              auto newExtractSlice = rewriter.clone(*extractSliceOp.getOperation());
+              mapSpatToExtract.insert({spatCompute.getOperation(), newExtractSlice->getResult(0)});
+            }
+
+            rewriter.startOpModification(spatCompute.getOperation());
+            uses.set(mapSpatToExtract[spatCompute.getOperation()]);
+            rewriter.finalizeOpModification(spatCompute.getOperation());
+          }
+          else if (auto spatComputeBatch = uses.getOwner()->getParentOfType<spatial::SpatComputeBatch>()) {
+            rewriter.setInsertionPoint(&spatComputeBatch.getBody().front().front());
+            if (!mapSpatToExtract.contains(spatComputeBatch.getOperation())) {
+              auto newExtractSlice = rewriter.clone(*extractSliceOp.getOperation());
+              mapSpatToExtract.insert({spatComputeBatch.getOperation(), newExtractSlice->getResult(0)});
+            }
+
+            rewriter.startOpModification(spatComputeBatch.getOperation());
+            uses.set(mapSpatToExtract[spatComputeBatch.getOperation()]);
+            rewriter.finalizeOpModification(spatComputeBatch.getOperation());
+          }
+        }
+      }
+    }
+
+    rewriter.eraseOp(extractSliceOp);
+    return success();
+  }
+};
+
+struct ArithConstToGlobalMemoryPattern final : OpRewritePattern<mlir::arith::ConstantOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(mlir::arith::ConstantOp constantOp, PatternRewriter& rewriter) const override {
+    static int i = 0;
+    Location loc = constantOp.getLoc();
+
+    if (hasWeightAlways(constantOp))
+      return failure();
+
+    if (!isa<func::FuncOp>(constantOp->getParentOp()))
+      return failure();
+
+    if (llvm::all_of(constantOp->getUsers(), [](Operation* op) {
+          if (isa<spatial::SpatCompute>(op))
+            return false;
+          if (isa<func::FuncOp>(op->getParentOp()))
+            return true;
+          return false;
+        }))
+      return failure();
+
+    rewriter.setInsertionPoint(constantOp->getParentOfType<func::FuncOp>());
+
+    auto constRankedTensorType = llvm::dyn_cast<mlir::RankedTensorType>(constantOp.getType());
+
+    if (constRankedTensorType) {
+      mlir::MemRefType memRefType =
+        mlir::MemRefType::get(constRankedTensorType.getShape(), constRankedTensorType.getElementType());
+      std::string argName = "const_" + std::to_string(i++);
+      memref::GlobalOp::create(rewriter,
+                               loc,
+                               rewriter.getStringAttr(argName),
+                               rewriter.getStringAttr("private"),
+                               TypeAttr::get(memRefType),
+                               constantOp.getValueAttr(),
+                               rewriter.getUnitAttr(),
+                               {});
+
+      llvm::DenseMap<Operation*, Value> mapSpatComputeToConst;
+
+      for (auto& constUses : llvm::make_early_inc_range(constantOp->getUses())) {
+        auto constUsers = constUses.getOwner();
+
+        if (auto spatCompute = llvm::dyn_cast<spatial::SpatCompute>(constUsers)) {
+
+          auto BBArgIndex = constUses.getOperandNumber() - spatCompute.getInputs().getBeginOperandIndex();
+          auto BBArgValue = spatCompute.getBody().front().getArgument(BBArgIndex);
+          rewriter.setInsertionPoint(&spatCompute.getBody().front().front());
+          if (!mapSpatComputeToConst.contains(spatCompute.getOperation())) {
+            auto getGlobalOp = memref::GetGlobalOp::create(rewriter, loc, memRefType, argName);
+            auto toTensor = bufferization::ToTensorOp::create(
+              rewriter, loc, constRankedTensorType, getGlobalOp, rewriter.getUnitAttr(), rewriter.getUnitAttr());
+            mapSpatComputeToConst.insert({spatCompute.getOperation(), toTensor.getResult()});
+          }
+
+          rewriter.startOpModification(spatCompute.getOperation());
+          BBArgValue.replaceAllUsesWith(mapSpatComputeToConst[spatCompute.getOperation()]);
+          spatCompute.getInputsMutable().erase(BBArgIndex);
+          spatCompute.getBody().front().eraseArgument(BBArgIndex);
+          rewriter.finalizeOpModification(spatCompute.getOperation());
+        }
+        else if (auto spatComputeBatch = llvm::dyn_cast<spatial::SpatComputeBatch>(constUsers)) {
+
+          auto BBArgIndex = constUses.getOperandNumber() - spatComputeBatch.getInputs().getBeginOperandIndex();
+          auto BBArgValue = spatComputeBatch.getBody().front().getArgument(BBArgIndex);
+          rewriter.setInsertionPoint(&spatComputeBatch.getBody().front().front());
+          if (!mapSpatComputeToConst.contains(spatComputeBatch.getOperation())) {
+            auto getGlobalOp = memref::GetGlobalOp::create(rewriter, loc, memRefType, argName);
+            auto toTensor = bufferization::ToTensorOp::create(
+              rewriter, loc, constRankedTensorType, getGlobalOp, rewriter.getUnitAttr(), rewriter.getUnitAttr());
+            mapSpatComputeToConst.insert({spatComputeBatch.getOperation(), toTensor.getResult()});
+          }
+
+          rewriter.startOpModification(spatComputeBatch.getOperation());
+          BBArgValue.replaceAllUsesWith(mapSpatComputeToConst[spatComputeBatch.getOperation()]);
+          spatComputeBatch.getInputsMutable().erase(BBArgIndex);
+          spatComputeBatch.getBody().front().eraseArgument(BBArgIndex);
+          rewriter.finalizeOpModification(spatComputeBatch.getOperation());
+        }
+        else {
+        {
+
+            if (auto spatCompute = constUses.getOwner()->getParentOfType<spatial::SpatCompute>()) {
+              rewriter.setInsertionPoint(&spatCompute.getBody().front().front());
+              if (!mapSpatComputeToConst.contains(spatCompute.getOperation())) {
+                auto getGlobalOp = memref::GetGlobalOp::create(rewriter, loc, memRefType, argName);
+                auto toTensor = bufferization::ToTensorOp::create(
+                  rewriter, loc, constRankedTensorType, getGlobalOp, rewriter.getUnitAttr(), rewriter.getUnitAttr());
+                mapSpatComputeToConst.insert({spatCompute.getOperation(), toTensor.getResult()});
+              }
+
+              rewriter.startOpModification(spatCompute.getOperation());
+              constUses.set(mapSpatComputeToConst[spatCompute.getOperation()]);
+              rewriter.finalizeOpModification(spatCompute.getOperation());
+            }
+            else if (auto spatComputeBatch = constUses.getOwner()->getParentOfType<spatial::SpatComputeBatch>()) {
+              rewriter.setInsertionPoint(&spatComputeBatch.getBody().front().front());
+              if (!mapSpatComputeToConst.contains(spatComputeBatch.getOperation())) {
+                auto getGlobalOp = memref::GetGlobalOp::create(rewriter, loc, memRefType, argName);
+                auto toTensor = bufferization::ToTensorOp::create(
+                  rewriter, loc, constRankedTensorType, getGlobalOp, rewriter.getUnitAttr(), rewriter.getUnitAttr());
+                mapSpatComputeToConst.insert({spatComputeBatch.getOperation(), toTensor.getResult()});
+              }
+
+              rewriter.startOpModification(spatComputeBatch.getOperation());
+              constUses.set(mapSpatComputeToConst[spatComputeBatch.getOperation()]);
+              rewriter.finalizeOpModification(spatComputeBatch.getOperation());
+            }
+          }
+        }
+      }
+    }
+    else if (constantOp.getType().isIntOrIndexOrFloat()) {
+      llvm::DenseMap<Operation*, Value> mapSpatComputeToConst;
+
+      for (auto& constUses : llvm::make_early_inc_range(constantOp->getUses())) {
+        auto constUsers = constUses.getOwner();
+
+        if (auto spatCompute = llvm::dyn_cast<spatial::SpatCompute>(constUsers)) {
+
+          auto BBArgIndex = constUses.getOperandNumber() - spatCompute.getInputs().getBeginOperandIndex();
+          auto BBArgValue = spatCompute.getBody().front().getArgument(BBArgIndex);
+          rewriter.setInsertionPoint(&spatCompute.getBody().front().front());
+          auto newConst = rewriter.clone(*constantOp);
+
+          rewriter.startOpModification(spatCompute.getOperation());
+          BBArgValue.replaceAllUsesWith(newConst->getResult(0));
+          spatCompute.getInputsMutable().erase(BBArgIndex);
+          spatCompute.getBody().front().eraseArgument(BBArgIndex);
+          rewriter.finalizeOpModification(spatCompute.getOperation());
+        }
+        else if (auto spatComputeBatch = llvm::dyn_cast<spatial::SpatComputeBatch>(constUsers)) {
+
+          auto BBArgIndex = constUses.getOperandNumber() - spatComputeBatch.getInputs().getBeginOperandIndex();
+          auto BBArgValue = spatComputeBatch.getBody().front().getArgument(BBArgIndex);
+          rewriter.setInsertionPoint(&spatComputeBatch.getBody().front().front());
+          auto newConst = rewriter.clone(*constantOp);
+
+          rewriter.startOpModification(spatComputeBatch.getOperation());
+          BBArgValue.replaceAllUsesWith(newConst->getResult(0));
+          spatComputeBatch.getInputsMutable().erase(BBArgIndex);
+          spatComputeBatch.getBody().front().eraseArgument(BBArgIndex);
+          rewriter.finalizeOpModification(spatComputeBatch.getOperation());
+        }
+        else {
+          if (auto parent = constUsers->getParentOfType<spatial::SpatCompute>()) {
+            if (!mapSpatComputeToConst.contains(parent)) {
+              rewriter.setInsertionPoint(&parent.getBody().front().front());
+              auto newConst = rewriter.clone(*constantOp);
+            mapSpatComputeToConst.insert({parent.getOperation(), newConst->getResult(0)});
+          }
+          constUses.set(mapSpatComputeToConst[parent.getOperation()]);
+        }
+        else {
+          auto batchParent = constUsers->getParentOfType<spatial::SpatComputeBatch>();
+          assert(batchParent && "Global Constant used direcly not within a compute");
+          if (!mapSpatComputeToConst.contains(batchParent.getOperation())) {
+            rewriter.setInsertionPoint(&batchParent.getBody().front().front());
+            auto newConst = rewriter.clone(*constantOp);
+            mapSpatComputeToConst.insert({batchParent.getOperation(), newConst->getResult(0)});
+          }
+          constUses.set(mapSpatComputeToConst[batchParent.getOperation()]);
+        }
+      }
+      }
+    }
+    auto parent = constantOp->getParentOp();
+    rewriter.eraseOp(constantOp);
+    return success();
+  }
+};
+
+struct FuncOpArgToGlobalMemoryPattern final : OpRewritePattern<mlir::func::FuncOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(mlir::func::FuncOp funcOp, PatternRewriter& rewriter) const override {
+
+    if (funcOp.getArguments().empty())
+      return failure();
+
+    if (llvm::all_of(funcOp.getArguments(),
+                     [](mlir::BlockArgument blockArgument) { return blockArgument.use_empty(); }))
+      return failure();
+
+    Location loc = funcOp.getLoc();
+
+    for (auto [index, arg] : llvm::enumerate(funcOp.getArguments())) {
+      if (arg.getUses().empty())
+        continue;
+
+      rewriter.setInsertionPoint(funcOp.getOperation());
+
+      assert(isa<mlir::RankedTensorType>(arg.getType()));
+
+      auto argRankedTensorType = llvm::dyn_cast<mlir::RankedTensorType>(arg.getType());
+      mlir::MemRefType memRefType =
+        mlir::MemRefType::get(argRankedTensorType.getShape(), argRankedTensorType.getElementType());
+
+      std::string argName = "arg_" + std::to_string(index);
+
+      memref::GlobalOp::create(rewriter,
+                               loc,
+                               rewriter.getStringAttr(argName),
+                               rewriter.getStringAttr("private"),
+                               TypeAttr::get(memRefType),
+                               {},
+                               {},
+                               {});
+
+      for (auto& argUses : llvm::make_early_inc_range(arg.getUses())) {
+        auto argUser = argUses.getOwner();
+        if (auto spatCompute = dyn_cast<spatial::SpatCompute>(argUser)) {
+          auto BBArgIndex = argUses.getOperandNumber() - spatCompute.getInputs().getBeginOperandIndex();
+          auto BBArgValue = spatCompute.getBody().front().getArgument(BBArgIndex);
+          rewriter.setInsertionPoint(&spatCompute.getBody().front().front());
+          auto getGlobalOp = memref::GetGlobalOp::create(rewriter, loc, memRefType, argName);
+          auto toTensor = bufferization::ToTensorOp::create(
+            rewriter, loc, argRankedTensorType, getGlobalOp, rewriter.getUnitAttr(), rewriter.getUnitAttr());
+
+          rewriter.startOpModification(spatCompute.getOperation());
+          BBArgValue.replaceAllUsesWith(toTensor);
+          spatCompute.getInputsMutable().erase(BBArgIndex);
+          spatCompute.getBody().front().eraseArgument(BBArgIndex);
+          rewriter.finalizeOpModification(spatCompute.getOperation());
+        }
+        else if (auto spatComputeBatch = dyn_cast<spatial::SpatComputeBatch>(argUser)) {
+          auto BBArgIndex = argUses.getOperandNumber() - spatComputeBatch.getInputs().getBeginOperandIndex();
+          auto BBArgValue = spatComputeBatch.getBody().front().getArgument(BBArgIndex);
+          rewriter.setInsertionPoint(&spatComputeBatch.getBody().front().front());
+          auto getGlobalOp = memref::GetGlobalOp::create(rewriter, loc, memRefType, argName);
+          auto toTensor = bufferization::ToTensorOp::create(
+            rewriter, loc, argRankedTensorType, getGlobalOp, rewriter.getUnitAttr(), rewriter.getUnitAttr());
+
+          rewriter.startOpModification(spatComputeBatch.getOperation());
+          BBArgValue.replaceAllUsesWith(toTensor);
+          spatComputeBatch.getInputsMutable().erase(BBArgIndex);
+          spatComputeBatch.getBody().front().eraseArgument(BBArgIndex);
+          rewriter.finalizeOpModification(spatComputeBatch.getOperation());
+        }
+        else {
+          rewriter.setInsertionPoint(argUser);
+          auto getGlobalOp = memref::GetGlobalOp::create(rewriter, loc, memRefType, argName);
+          auto toTensor = bufferization::ToTensorOp::create(
+            rewriter, loc, argRankedTensorType, getGlobalOp, rewriter.getUnitAttr(), rewriter.getUnitAttr());
+          rewriter.startOpModification(argUser);
+          argUses.set(toTensor);
+          rewriter.finalizeOpModification(argUser);
+        }
+      }
+    }
+
+    return success();
+  }
+};
+
+} // namespace
+void populateGlobalTensorToMemrefPatterns(RewritePatternSet& patterns) {
+  patterns.add<MoveExtractSliceIntoCompute, FuncOpArgToGlobalMemoryPattern, ArithConstToGlobalMemoryPattern>(
+    patterns.getContext());
+}
+
+} // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/Patterns.hpp

@@ -0,0 +1,10 @@
+#pragma once
+
+#include "mlir/IR/PatternMatch.h"
+
+
+namespace onnx_mlir {
+
+void populateGlobalTensorToMemrefPatterns(mlir::RewritePatternSet& patterns); 
+
+}

src/PIM/Conversion/SpatialToPim/SpatialToPim.td

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

src/PIM/Dialect/Pim/Pim.td

@@ -24,7 +24,7 @@ def PimTensor :
 // Execution
 //===----------------------------------------------------------------------===//
 
-def PimCoreOp : PimOp<"core", [SingleBlock]> {
+def PimCoreOp : PimOp<"core", [SingleBlock, IsolatedFromAbove]> {
   let summary = "Execute a block on a PIM core";
 
   let regions = (region SizedRegion<1>:$body);
@@ -39,6 +39,22 @@ def PimCoreOp : PimOp<"core", [SingleBlock]> {
   }];
 }
 
+def PimCoreBatchOp : PimOp<"core_batch", [SingleBlock, AttrSizedOperandSegments]> {
+  let summary = "Execute equivalent batched core bodies";
+
+  let regions = (region SizedRegion<1>:$body);
+
+  let arguments = (ins
+    I32Attr:$laneCount,
+    Variadic<PimTensor>:$weights,
+    Variadic<PimTensor>:$inputs
+  );
+
+  let assemblyFormat = [{
+    `lanes` $laneCount `(` $weights `)` `[` $inputs `]` attr-dict regions `:` type($weights) `[` type($inputs) `]` `->` `(` `)`
+  }];
+}
+
 def PimHaltOp : PimOp<"halt", [Terminator]> {
   let summary = "Halt execution of the core";
 
@@ -65,6 +81,20 @@ def PimSendOp : PimOp<"send", []> {
   }];
 }
 
+def PimSendBatchOp : PimOp<"send_batch", []> {
+  let summary = "Send a per-lane tensor to target cores from a batched core";
+
+  let arguments = (ins
+    PimTensor:$input,
+    I32Attr:$size,
+    DenseI32ArrayAttr:$targetCoreIds
+  );
+
+  let assemblyFormat = [{
+    `(` $input `)` attr-dict `:` type($input) `->` `(` `)`
+  }];
+}
+
 def PimReceiveOp : PimOp<"receive", [DestinationStyleOpInterface]> {
   let summary = "Receive a tensor from another core";
 
@@ -89,6 +119,30 @@ def PimReceiveOp : PimOp<"receive", [DestinationStyleOpInterface]> {
   }];
 }
 
+def PimReceiveBatchOp : PimOp<"receive_batch", [DestinationStyleOpInterface]> {
+  let summary = "Receive per-lane tensors from source cores into a batched core";
+
+  let arguments = (ins
+    PimTensor:$outputBuffer,
+    I32Attr:$size,
+    DenseI32ArrayAttr:$sourceCoreIds
+  );
+
+  let results = (outs
+    PimTensor:$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";
 
@@ -115,6 +169,32 @@ def PimMemCopyHostToDevOp : PimOp<"memcp_hd", [DestinationStyleOpInterface]> {
   }];
 }
 
+def PimMemCopyHostToDevBatchOp : PimOp<"memcp_hd_batch", [DestinationStyleOpInterface]> {
+  let summary = "Copy a per-lane tensor from host memory into device memory inside a batched core";
+
+  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";
 

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

@@ -1,6 +1,7 @@
 #include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
 #include "mlir/Dialect/Bufferization/IR/Bufferization.h"
 #include "mlir/Dialect/Bufferization/IR/DstBufferizableOpInterfaceImpl.h"
+#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 
 #include "OpBufferizationInterfaces.hpp"
@@ -65,6 +66,32 @@ struct MemCopyHostToDevOpInterface
   }
 };
 
+struct MemCopyHostToDevBatchOpInterface
+: DstBufferizableOpInterfaceExternalModel<MemCopyHostToDevBatchOpInterface, PimMemCopyHostToDevBatchOp> {
+  LogicalResult bufferize(Operation* op,
+                          RewriterBase& rewriter,
+                          const BufferizationOptions& options,
+                          BufferizationState& state) const {
+    auto memCopyHostToDevOp = cast<PimMemCopyHostToDevBatchOp>(op);
+    auto deviceTargetOpt = getBuffer(rewriter, memCopyHostToDevOp.getDeviceTarget(), options, state);
+    if (failed(deviceTargetOpt))
+      return failure();
+    auto hostSourceOpt = getBuffer(rewriter, memCopyHostToDevOp.getHostSource(), options, state);
+    if (failed(hostSourceOpt))
+      return failure();
+
+    replaceOpWithNewBufferizedOp<PimMemCopyHostToDevBatchOp>(rewriter,
+                                                             memCopyHostToDevOp,
+                                                             deviceTargetOpt->getType(),
+                                                             *deviceTargetOpt,
+                                                             *hostSourceOpt,
+                                                             memCopyHostToDevOp.getDeviceTargetOffsetAttr(),
+                                                             memCopyHostToDevOp.getHostSourceOffsetAttr(),
+                                                             memCopyHostToDevOp.getSizeAttr());
+    return success();
+  }
+};
+
 struct MemCopyDevToHostOpInterface
 : DstBufferizableOpInterfaceExternalModel<MemCopyDevToHostOpInterface, PimMemCopyDevToHostOp> {
   LogicalResult bufferize(Operation* op,
@@ -122,6 +149,127 @@ struct ReceiveOpInterface : DstBufferizableOpInterfaceExternalModel<ReceiveOpInt
   }
 };
 
+struct ReceiveBatchOpInterface : DstBufferizableOpInterfaceExternalModel<ReceiveBatchOpInterface, PimReceiveBatchOp> {
+  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
+    return !cast<DestinationStyleOpInterface>(op).isDpsInit(&opOperand);
+  }
+
+  LogicalResult bufferize(Operation* op,
+                          RewriterBase& rewriter,
+                          const BufferizationOptions& options,
+                          BufferizationState& state) const {
+    auto receiveOp = cast<PimReceiveBatchOp>(op);
+    auto outputBufferOpt = getBuffer(rewriter, receiveOp.getOutputBuffer(), options, state);
+    if (failed(outputBufferOpt))
+      return failure();
+
+    replaceOpWithNewBufferizedOp<PimReceiveBatchOp>(rewriter,
+                                                    op,
+                                                    outputBufferOpt->getType(),
+                                                    *outputBufferOpt,
+                                                    receiveOp.getSizeAttr(),
+                                                    receiveOp.getSourceCoreIdsAttr());
+    return success();
+  }
+};
+
+struct CoreBatchOpInterface : BufferizableOpInterface::ExternalModel<CoreBatchOpInterface, PimCoreBatchOp> {
+  bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
+    return true;
+  }
+
+  bool bufferizesToMemoryWrite(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
+    return false;
+  }
+
+  AliasingValueList getAliasingValues(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
+    return {};
+  }
+
+  AliasingOpOperandList getAliasingOpOperands(Operation* op, Value value, const AnalysisState& state) const {
+    auto coreBatchOp = cast<PimCoreBatchOp>(op);
+    auto bbArg = dyn_cast<BlockArgument>(value);
+    if (!bbArg || bbArg.getOwner() != &coreBatchOp.getBody().front())
+      return {};
+
+    unsigned inputOperandIndex = coreBatchOp.getWeights().size() + bbArg.getArgNumber();
+    return {{&coreBatchOp->getOpOperand(inputOperandIndex), BufferRelation::Equivalent}};
+  }
+
+  bool isWritable(Operation* op, Value value, const AnalysisState& state) const {
+    return false;
+  }
+
+  FailureOr<BufferLikeType>
+  getBufferType(Operation* op,
+                Value value,
+                const BufferizationOptions& options,
+                const BufferizationState& state,
+                SmallVector<Value>& invocationStack) const {
+    auto coreBatchOp = cast<PimCoreBatchOp>(op);
+    auto bbArg = dyn_cast<BlockArgument>(value);
+    if (!bbArg || bbArg.getOwner() != &coreBatchOp.getBody().front())
+      return failure();
+
+    Value tiedInput = coreBatchOp.getInputs()[bbArg.getArgNumber()];
+    if (auto memRefType = dyn_cast<BufferLikeType>(tiedInput.getType()))
+      return memRefType;
+
+    return bufferization::getBufferType(tiedInput, options, state, invocationStack);
+  }
+
+  LogicalResult bufferize(Operation* op,
+                          RewriterBase& rewriter,
+                          const BufferizationOptions& options,
+                          BufferizationState& state) const {
+    auto coreBatchOp = cast<PimCoreBatchOp>(op);
+
+    SmallVector<Value> weights;
+    SmallVector<Value> inputs;
+    weights.reserve(coreBatchOp.getWeights().size());
+    inputs.reserve(coreBatchOp.getInputs().size());
+
+    for (Value weight : coreBatchOp.getWeights()) {
+      if (isa<TensorType>(weight.getType())) {
+        auto weightOpt = getBuffer(rewriter, weight, options, state);
+        if (failed(weightOpt))
+          return failure();
+        weights.push_back(*weightOpt);
+      }
+      else {
+        weights.push_back(weight);
+      }
+    }
+
+    for (Value input : coreBatchOp.getInputs()) {
+      if (isa<TensorType>(input.getType())) {
+        auto inputOpt = getBuffer(rewriter, input, options, state);
+        if (failed(inputOpt))
+          return failure();
+        inputs.push_back(*inputOpt);
+      }
+      else {
+        inputs.push_back(input);
+      }
+    }
+
+    rewriter.setInsertionPoint(coreBatchOp);
+    auto newOp = PimCoreBatchOp::create(
+      rewriter, coreBatchOp.getLoc(), coreBatchOp.getLaneCountAttr(), ValueRange(weights), ValueRange(inputs));
+    newOp.getProperties().setOperandSegmentSizes({static_cast<int>(weights.size()), static_cast<int>(inputs.size())});
+    if (auto coreIdsAttr = coreBatchOp->getAttr(onnx_mlir::kCoreIdAttrName))
+      newOp->setAttr(onnx_mlir::kCoreIdAttrName, coreIdsAttr);
+
+    rewriter.inlineRegionBefore(coreBatchOp.getBody(), newOp.getBody(), newOp.getBody().begin());
+    for (Block& block : newOp.getBody())
+      if (failed(bufferization::bufferizeBlockSignature(&block, rewriter, options, state)))
+        return failure();
+
+    rewriter.eraseOp(coreBatchOp);
+    return success();
+  }
+};
+
 struct TransposeOpInterface : DstBufferizableOpInterfaceExternalModel<TransposeOpInterface, PimTransposeOp> {
   bool bufferizesToMemoryRead(Operation* op, OpOperand& opOperand, const AnalysisState& state) const {
     return !cast<DestinationStyleOpInterface>(op).isDpsInit(&opOperand);
@@ -287,8 +435,11 @@ struct UnaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<UnaryDstOpI
 
 void registerOpBufferizationInterfaces(DialectRegistry& registry) {
   registry.addExtension(+[](MLIRContext* ctx, PimDialect* dialect) {
+    PimCoreBatchOp::attachInterface<CoreBatchOpInterface>(*ctx);
     PimReceiveOp::attachInterface<ReceiveOpInterface>(*ctx);
+    PimReceiveBatchOp::attachInterface<ReceiveBatchOpInterface>(*ctx);
     PimMemCopyHostToDevOp::attachInterface<MemCopyHostToDevOpInterface>(*ctx);
+    PimMemCopyHostToDevBatchOp::attachInterface<MemCopyHostToDevBatchOpInterface>(*ctx);
     PimMemCopyDevToHostOp::attachInterface<MemCopyDevToHostOpInterface>(*ctx);
     PimTransposeOp::attachInterface<TransposeOpInterface>(*ctx);
     PimVMMOp::attachInterface<VMMOpInterface>(*ctx);

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

@@ -3,12 +3,17 @@
 #include "mlir/Dialect/Bufferization/Transforms/OneShotAnalysis.h"
 #include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/IR/Threading.h"
 #include "mlir/Pass/Pass.h"
 
+#include "llvm/Support/Casting.h"
+#include "llvm/Support/Debug.h"
+
 #include "Common/PimCommon.hpp"
 #include "Compiler/PimCodeGen.hpp"
 #include "Dialect/Pim/PimOps.hpp"
 #include "Dialect/Pim/Transforms/Bufferization/Common.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Accelerators/PIM/Pass/PIMPasses.h"
 #include "src/Compiler/CompilerOptions.hpp"
 
@@ -40,14 +45,44 @@ private:
 
 void PimBufferizationPass::runOnOperation() {
   auto moduleOp = getOperation();
+  // Refactor this into a function
+  {
+    auto funcOp = getPimEntryFunc(moduleOp);
 
-  // One-Shot-Bufferization
-  bufferization::OneShotBufferizationOptions options;
-  options.allowUnknownOps = true;
-  bufferization::BufferizationState state;
-  if (failed(bufferization::runOneShotBufferize(moduleOp, options, state))) {
-    moduleOp.emitError("Failed to bufferize PIM and Spatial ops");
-    signalPassFailure();
+    auto coreOps = llvm::to_vector(funcOp->getOps<pim::PimCoreOp>());
+    MLIRContext* ctx = moduleOp.getContext();
+    // failableParallelForEach will run the lambda in parallel and stop if any thread fails
+    LogicalResult result = mlir::failableParallelForEach(ctx, coreOps, [&](pim::PimCoreOp coreOp) {
+      // Again, allocate state LOCALLY per thread/function
+      bufferization::OneShotBufferizationOptions options;
+      options.allowUnknownOps = true;
+      bufferization::BufferizationState state;
+      if (failed(bufferization::runOneShotBufferize(coreOp, options, state))) {
+        coreOp.emitError("Failed to bufferize PIM and Spatial ops");
+        return failure();
+      }
+      return success();
+    });
+
+    if (failed(result)) {
+      moduleOp.emitError("Failed to bufferize-parallel PIM and Spatial ops");
+      signalPassFailure();
+    }
+
+    funcOp->walk([&](bufferization::ToTensorOp toTensorOp) {
+      if (llvm::isa_and_present<pim::PimCoreOp>(toTensorOp->getParentOp()))
+        toTensorOp->setAttr("restrict", UnitAttr::get(ctx));
+    });
+
+    // One-Shot-Bufferization
+    bufferization::OneShotBufferizationOptions options;
+    options.allowUnknownOps = true;
+    bufferization::BufferizationState state;
+
+    if (failed(bufferization::runOneShotBufferize(moduleOp, options, state))) {
+      moduleOp.emitError("Failed to bufferize PIM and Spatial ops");
+      signalPassFailure();
+    }
   }
 
   MLIRContext* ctx = moduleOp.getContext();
@@ -57,7 +92,18 @@ void PimBufferizationPass::runOnOperation() {
   RewritePatternSet patterns(ctx);
   populateWithGenerated(patterns);
 
-  if (failed(applyPartialConversion(moduleOp, target, std::move(patterns)))) {
+  // Only convert memref.copy → pim.memcp inside pim.core / pim.core_batch bodies.
+  // Host-level copies (e.g. from split/slice ops) must remain as memref.copy for CPU lowering.
+  FrozenRewritePatternSet frozenPatterns(std::move(patterns));
+  bool hasFailed = false;
+  moduleOp.walk<WalkOrder::PreOrder>([&](Operation* op) {
+    if (!isa<pim::PimCoreOp, pim::PimCoreBatchOp>(op))
+      return WalkResult::advance();
+    if (failed(applyPartialConversion(op, target, frozenPatterns)))
+      hasFailed = true;
+    return WalkResult::skip();
+  });
+  if (hasFailed) {
     signalPassFailure();
     return;
   }
@@ -93,8 +139,8 @@ void PimBufferizationPass::runOnOperation() {
 }
 
 void PimBufferizationPass::annotateWeightsMemrefs(ModuleOp moduleOp, func::FuncOp funcOp) const {
-  funcOp.walk([&](PimCoreOp coreOp) {
-    walkPimMvmVmmWeightUses(coreOp, [&](OpOperand& weightUse) {
+  auto markWeights = [&](Operation* op) {
+    walkPimMvmVmmWeightUses(op, [&](OpOperand& weightUse) {
       Value weight = weightUse.get();
       auto getGlobalOp = weight.getDefiningOp<memref::GetGlobalOp>();
       if (!getGlobalOp)
@@ -104,7 +150,10 @@ void PimBufferizationPass::annotateWeightsMemrefs(ModuleOp moduleOp, func::FuncO
       markWeightAlways(getGlobalOp);
       markWeightAlways(globalMemrefOp);
     });
-  });
+  };
+
+  funcOp.walk([&](PimCoreOp coreOp) { markWeights(coreOp); });
+  funcOp.walk([&](PimCoreBatchOp coreBatchOp) { markWeights(coreBatchOp); });
 }
 
 std::unique_ptr<Pass> createPimBufferizationPass() { return std::make_unique<PimBufferizationPass>(); }

src/PIM/Dialect/Spatial/CMakeLists.txt

@@ -2,6 +2,7 @@ add_onnx_mlir_dialect(Spatial spat)
 add_onnx_mlir_dialect_doc(spat Spatial.td)
 
 add_pim_library(SpatialOps
+  Channels.cpp
   SpatialOps.cpp
   Transforms/MergeComputeNodes/MergeComputeNodesPass.cpp
   Transforms/MergeComputeNodes/DCPGraph/Graph.cpp

src/PIM/Dialect/Spatial/Channels.cpp

@@ -0,0 +1,120 @@
+#include "src/Accelerators/PIM/Dialect/Spatial/Channels.hpp"
+
+#include "mlir/IR/BuiltinAttributes.h"
+#include "mlir/IR/Diagnostics.h"
+
+using namespace mlir;
+
+namespace onnx_mlir::spatial {
+
+namespace {
+
+static Channels::ChannelId getChannelId(SpatChannelSendOp sendOp) { return sendOp.getChannelId(); }
+
+static Channels::ChannelId getChannelId(SpatChannelReceiveOp receiveOp) { return receiveOp.getChannelId(); }
+
+static LogicalResult verifyEndpointPair(ChannelEndpoints endpoints) {
+  if (!endpoints.send || !endpoints.receive)
+    return failure();
+
+  if (endpoints.send.getSourceCoreId() != endpoints.receive.getSourceCoreId()) {
+    endpoints.send.emitOpError("sourceCoreId does not match paired spat.channel_receive");
+    return failure();
+  }
+  if (endpoints.send.getTargetCoreId() != endpoints.receive.getTargetCoreId()) {
+    endpoints.send.emitOpError("targetCoreId does not match paired spat.channel_receive");
+    return failure();
+  }
+  if (endpoints.send.getInput().getType() != endpoints.receive.getOutput().getType()) {
+    endpoints.send.emitOpError("input type does not match paired spat.channel_receive result type");
+    return failure();
+  }
+
+  return success();
+}
+
+} // namespace
+
+Channels::Channels(func::FuncOp funcOp) {
+  if (!funcOp)
+    return;
+
+  funcOp.walk([&](SpatChannelSendOp sendOp) { insertSend(sendOp); });
+  funcOp.walk([&](SpatChannelReceiveOp receiveOp) { insertReceive(receiveOp); });
+}
+
+Channels::ChannelId Channels::allocate() { return nextChannelId++; }
+
+void Channels::insertSend(SpatChannelSendOp sendOp) {
+  ChannelId channelId = getChannelId(sendOp);
+  nextChannelId = std::max(nextChannelId, channelId + 1);
+  endpoints[channelId].send = sendOp;
+}
+
+void Channels::insertReceive(SpatChannelReceiveOp receiveOp) {
+  ChannelId channelId = getChannelId(receiveOp);
+  nextChannelId = std::max(nextChannelId, channelId + 1);
+  endpoints[channelId].receive = receiveOp;
+}
+
+void Channels::eraseSend(SpatChannelSendOp sendOp) {
+  ChannelId channelId = getChannelId(sendOp);
+  auto it = endpoints.find(channelId);
+  if (it == endpoints.end())
+    return;
+  it->second.send = {};
+  if (!it->second.receive)
+    endpoints.erase(it);
+}
+
+void Channels::eraseReceive(SpatChannelReceiveOp receiveOp) {
+  ChannelId channelId = getChannelId(receiveOp);
+  auto it = endpoints.find(channelId);
+  if (it == endpoints.end())
+    return;
+  it->second.receive = {};
+  if (!it->second.send)
+    endpoints.erase(it);
+}
+
+FailureOr<ChannelEndpoints> Channels::lookup(ChannelId id) const {
+  auto it = endpoints.find(id);
+  if (it == endpoints.end())
+    return failure();
+  return it->second;
+}
+
+FailureOr<SpatChannelReceiveOp> Channels::getReceiveFor(SpatChannelSendOp sendOp) const {
+  auto endpointsOr = lookup(getChannelId(sendOp));
+  if (failed(endpointsOr) || !endpointsOr->receive)
+    return failure();
+  return endpointsOr->receive;
+}
+
+FailureOr<SpatChannelSendOp> Channels::getSendFor(SpatChannelReceiveOp receiveOp) const {
+  auto endpointsOr = lookup(getChannelId(receiveOp));
+  if (failed(endpointsOr) || !endpointsOr->send)
+    return failure();
+  return endpointsOr->send;
+}
+
+LogicalResult Channels::verify() const {
+  for (const auto& [channelId, pair] : endpoints) {
+    if (!pair.send || !pair.receive) {
+      if (pair.send) {
+        auto sendOp = pair.send;
+        sendOp.emitOpError("channel_id ") << channelId << " is missing a paired spat.channel_receive";
+      }
+      else if (pair.receive) {
+        auto receiveOp = pair.receive;
+        receiveOp.emitOpError("channel_id ") << channelId << " is missing a paired spat.channel_send";
+      }
+      return failure();
+    }
+    if (failed(verifyEndpointPair(pair)))
+      return failure();
+  }
+  return success();
+}
+
+} // namespace onnx_mlir::spatial

src/PIM/Dialect/Spatial/Channels.hpp

@@ -0,0 +1,43 @@
+#pragma once
+
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/Support/LogicalResult.h"
+
+#include "llvm/ADT/DenseMap.h"
+#include "llvm/ADT/StringRef.h"
+
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+
+namespace onnx_mlir::spatial {
+
+struct ChannelEndpoints {
+  SpatChannelSendOp send;
+  SpatChannelReceiveOp receive;
+};
+
+class Channels {
+public:
+  using ChannelId = int64_t;
+
+  explicit Channels(mlir::func::FuncOp funcOp);
+
+  ChannelId allocate();
+
+  void insertSend(SpatChannelSendOp sendOp);
+  void insertReceive(SpatChannelReceiveOp receiveOp);
+  void eraseSend(SpatChannelSendOp sendOp);
+  void eraseReceive(SpatChannelReceiveOp receiveOp);
+
+  llvm::FailureOr<ChannelEndpoints> lookup(ChannelId id) const;
+  llvm::FailureOr<SpatChannelReceiveOp> getReceiveFor(SpatChannelSendOp sendOp) const;
+  llvm::FailureOr<SpatChannelSendOp> getSendFor(SpatChannelReceiveOp receiveOp) const;
+
+  mlir::LogicalResult verify() const;
+
+private:
+  ChannelId nextChannelId = 0;
+  llvm::DenseMap<ChannelId, ChannelEndpoints> endpoints;
+};
+
+} // namespace onnx_mlir::spatial

src/PIM/Dialect/Spatial/Spatial.td

@@ -9,7 +9,6 @@ def SpatialDialect : Dialect {
   let name = "spat";
   let summary = "Dialect designed for deep learning computation in a spatial architecture";
   let cppNamespace = "::onnx_mlir::spatial";
-  let useDefaultTypePrinterParser = 1;
 }
 
 class SpatOp<string mnemonic, list<Trait> traits = []> :
@@ -19,15 +18,6 @@ class SpatOp<string mnemonic, list<Trait> traits = []> :
 def SpatTensor :
   AnyTypeOf<[AnyMemRef, AnyRankedTensor], "", "::mlir::ShapedType">;
 
-class SpatType<string name, string typeMnemonic, list<Trait> traits = []>
-    : TypeDef<SpatialDialect, name, traits> {
-  let mnemonic = typeMnemonic;
-}
-
-def SpatChannelType : SpatType<"SpatChannel", "ch"> {
-  let summary = "Virtual channel type";
-}
-
 //===----------------------------------------------------------------------===//
 // Execution
 //===----------------------------------------------------------------------===//
@@ -48,10 +38,27 @@ def SpatCompute : SpatOp<"compute", [SingleBlock, AttrSizedOperandSegments]> {
 
   let hasVerifier = 1;
   let hasFolder = 1;
+  let hasCustomAssemblyFormat = 1;
+}
 
-  let assemblyFormat = [{
-    `[` $weights `]` `(` $inputs `)` attr-dict `:` `[` type($weights) `]` `(` type($inputs) `)` `->` type($outputs) $body
-  }];
+def SpatComputeBatch : SpatOp<"compute_batch",
+    [SingleBlock, AttrSizedOperandSegments]> {
+  let summary = "Compressed batch of independent equivalent compute lanes";
+
+  let arguments = (ins
+    I32Attr:$laneCount,
+    Variadic<SpatTensor>:$weights,
+    Variadic<SpatTensor>:$inputs
+  );
+
+  let results = (outs
+    Variadic<SpatTensor>:$outputs
+  );
+
+  let regions = (region SizedRegion<1>:$body);
+
+  let hasVerifier = 1;
+  let hasCustomAssemblyFormat = 1;
 }
 
 def SpatYieldOp : SpatOp<"yield", [Terminator]> {
@@ -61,51 +68,66 @@ def SpatYieldOp : SpatOp<"yield", [Terminator]> {
     Variadic<SpatTensor>:$outputs
   );
 
-  let assemblyFormat = [{
-    $outputs attr-dict `:` type($outputs)
-  }];
+  let hasCustomAssemblyFormat = 1;
+}
+
+def SpatExtractRowsOp : SpatOp<"extract_rows", []> {
+  let summary = "Extract every row of a rank-2 tensor as separate rank-2 row tensors";
+
+  let arguments = (ins
+    SpatTensor:$input
+  );
+
+  let results = (outs
+    Variadic<SpatTensor>:$outputs
+  );
+
+  let hasVerifier = 1;
+  let hasCustomAssemblyFormat = 1;
+}
+
+def SpatConcatOp : SpatOp<"concat", []> {
+  let summary = "Concatenate tensors with compact Spatial operand syntax";
+
+  let arguments = (ins
+    I64Attr:$axis,
+    Variadic<SpatTensor>:$inputs
+  );
+
+  let results = (outs
+    SpatTensor:$output
+  );
+
+  let hasVerifier = 1;
+  let hasCustomAssemblyFormat = 1;
 }
 
 //===----------------------------------------------------------------------===//
 // Communication
 //===----------------------------------------------------------------------===//
 
-def SpatChannelNewOp : SpatOp<"channel_new", []> {
-  let summary = "Create a new virtual channel";
-
-  let results = (outs
-    SpatChannelType:$channel
-  );
-
-  let builders = [
-    OpBuilder<(ins ), [{
-      $_state.addTypes(SpatChannelType());
-    }]>
-  ];
-
-  let assemblyFormat = [{
-    attr-dict
-  }];
-}
-
 def SpatChannelSendOp : SpatOp<"channel_send", []> {
-  let summary = "Send a tensor through a channel";
+  let summary = "Send a tensor through a logical channel";
 
   let arguments = (ins
-    SpatChannelType:$channel,
+    I64Attr:$channelId,
+    I32Attr:$sourceCoreId,
+    I32Attr:$targetCoreId,
     SpatTensor:$input
   );
 
   let assemblyFormat = [{
-    $input `to` $channel attr-dict `:` `(` type($input) `->` type($channel) `)`
+    $input attr-dict `:` type($input)
   }];
 }
 
 def SpatChannelReceiveOp : SpatOp<"channel_receive", []> {
-  let summary = "Receive a tensor from a channel";
+  let summary = "Receive a tensor from a logical channel";
 
   let arguments = (ins
-    SpatChannelType:$channel
+    I64Attr:$channelId,
+    I32Attr:$sourceCoreId,
+    I32Attr:$targetCoreId
   );
 
   let results = (outs
@@ -113,37 +135,70 @@ def SpatChannelReceiveOp : SpatOp<"channel_receive", []> {
   );
 
   let assemblyFormat = [{
-    $channel attr-dict `:` `(` type($channel) `->` type($output) `)`
+    attr-dict `:` type($output)
   }];
 }
 
-def SpatChannelBroadcastSendOp : SpatOp<"channel_broadcast_send", []> {
-  let summary = "Broadcast a tensor through a shared channel buffer";
+def SpatChannelSendManyOp : SpatOp<"channel_send_many", []> {
+  let summary = "Send multiple tensors through logical channels";
 
   let arguments = (ins
-    SpatChannelType:$channel,
+    DenseI64ArrayAttr:$channelIds,
+    DenseI32ArrayAttr:$sourceCoreIds,
+    DenseI32ArrayAttr:$targetCoreIds,
+    Variadic<SpatTensor>:$inputs
+  );
+
+  let hasVerifier = 1;
+  let hasCustomAssemblyFormat = 1;
+}
+
+def SpatChannelReceiveManyOp : SpatOp<"channel_receive_many", []> {
+  let summary = "Receive multiple tensors from logical channels";
+
+  let arguments = (ins
+    DenseI64ArrayAttr:$channelIds,
+    DenseI32ArrayAttr:$sourceCoreIds,
+    DenseI32ArrayAttr:$targetCoreIds
+  );
+
+  let results = (outs
+    Variadic<SpatTensor>:$outputs
+  );
+
+  let hasVerifier = 1;
+  let hasCustomAssemblyFormat = 1;
+}
+
+def SpatChannelSendBatchOp : SpatOp<"channel_send_batch", []> {
+  let summary = "Send per-lane tensors through logical channels in a batch body";
+
+  let arguments = (ins
+    DenseI64ArrayAttr:$channelIds,
+    DenseI32ArrayAttr:$sourceCoreIds,
+    DenseI32ArrayAttr:$targetCoreIds,
     SpatTensor:$input
   );
 
-  let assemblyFormat = [{
-    $input `to` $channel attr-dict `:` `(` type($input) `->` type($channel) `)`
-  }];
+  let hasVerifier = 1;
+  let hasCustomAssemblyFormat = 1;
 }
 
-def SpatChannelBroadcastReceiveOp : SpatOp<"channel_broadcast_receive", []> {
-  let summary = "Receive a tensor from a shared channel buffer";
+def SpatChannelReceiveBatchOp : SpatOp<"channel_receive_batch", []> {
+  let summary = "Receive a per-lane tensor through logical channels in a batch body";
 
   let arguments = (ins
-    SpatChannelType:$channel
+    DenseI64ArrayAttr:$channelIds,
+    DenseI32ArrayAttr:$sourceCoreIds,
+    DenseI32ArrayAttr:$targetCoreIds
   );
 
   let results = (outs
     SpatTensor:$output
   );
 
-  let assemblyFormat = [{
-    $channel attr-dict `:` `(` type($channel) `->` type($output) `)`
-  }];
+  let hasVerifier = 1;
+  let hasCustomAssemblyFormat = 1;
 }
 
 //===----------------------------------------------------------------------===//

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

@@ -28,6 +28,8 @@ namespace spatial {
 using namespace mlir;
 
 namespace {
+using SpatCompute = onnx_mlir::spatial::SpatCompute;
+using SpatComputeBatch = onnx_mlir::spatial::SpatComputeBatch;
 
 struct VirtualNode {
   SmallVector<size_t, 4> originalComputeIndices;
@@ -54,6 +56,45 @@ struct WindowScheduleResult {
   size_t maxMergeGroupSize = 0;
 };
 
+constexpr CPU kDefaultMaxCpuCount = 1000;
+
+size_t getSchedulingCpuBudget() {
+  if (coresCount.getValue() > 0)
+    return static_cast<size_t>(coresCount.getValue());
+  return static_cast<size_t>(kDefaultMaxCpuCount);
+}
+
+size_t getBatchChunkTargetCount(int32_t laneCount) {
+  assert(laneCount > 0 && "laneCount must be positive");
+  return std::min(static_cast<size_t>(laneCount), std::max<size_t>(1, getSchedulingCpuBudget()));
+}
+
+ComputeInstance getBatchChunkForIndex(SpatComputeBatch batch, size_t chunkIndex) {
+  size_t totalLanes = static_cast<size_t>(batch.getLaneCount());
+  size_t chunkCount = getBatchChunkTargetCount(batch.getLaneCount());
+  size_t baseChunkSize = totalLanes / chunkCount;
+  size_t largeChunkCount = totalLanes % chunkCount;
+
+  size_t laneStart = chunkIndex * baseChunkSize + std::min(chunkIndex, largeChunkCount);
+  size_t laneCount = baseChunkSize + (chunkIndex < largeChunkCount ? 1 : 0);
+  return {batch.getOperation(), static_cast<uint32_t>(laneStart), static_cast<uint32_t>(laneCount)};
+}
+
+ComputeInstance getBatchChunkForLane(SpatComputeBatch batch, uint32_t lane) {
+  size_t totalLanes = static_cast<size_t>(batch.getLaneCount());
+  size_t chunkCount = getBatchChunkTargetCount(batch.getLaneCount());
+  size_t baseChunkSize = totalLanes / chunkCount;
+  size_t largeChunkCount = totalLanes % chunkCount;
+  size_t largeChunkSpan = largeChunkCount * (baseChunkSize + 1);
+
+  size_t chunkIndex = 0;
+  if (static_cast<size_t>(lane) < largeChunkSpan)
+    chunkIndex = static_cast<size_t>(lane) / (baseChunkSize + 1);
+  else
+    chunkIndex = largeChunkCount + (static_cast<size_t>(lane) - largeChunkSpan) / baseChunkSize;
+  return getBatchChunkForIndex(batch, chunkIndex);
+}
+
 std::vector<IndexedEdge> aggregateEdges(ArrayRef<IndexedEdge> edges) {
   llvm::DenseMap<std::pair<size_t, size_t>, Weight> edgeWeights;
   for (auto [start, end, weight] : edges) {
@@ -81,14 +122,96 @@ std::vector<IndexedEdge> aggregateEdges(ArrayRef<IndexedEdge> edges) {
   return aggregatedEdges;
 }
 
-VirtualGraph buildInitialVirtualGraph(ArrayRef<SpatCompute> spatComputes, ArrayRef<IndexedEdge> edges) {
+Weight getComputeBodyWeight(Region& body) {
+  constexpr Weight kOperationWeight = 100;
+  Weight numOperations = 0;
+  for (auto& block : body)
+    for ([[maybe_unused]] auto& op : block)
+      numOperations = checkedAdd(numOperations, static_cast<Weight>(1));
+  return checkedMultiply(numOperations, kOperationWeight);
+}
+
+CrossbarUsage getComputeBodyCrossbarUsage(Region& body) {
+  CrossbarUsage crossbarUsage = 0;
+  for (auto& block : body)
+    for (auto& op : block)
+      if (isa<SpatWeightedVMMOp>(op))
+        crossbarUsage = checkedAdd(crossbarUsage, static_cast<CrossbarUsage>(1));
+  return crossbarUsage;
+}
+
+Weight getComputeInstanceWeight(const ComputeInstance& instance) {
+  if (auto spatCompute = dyn_cast<SpatCompute>(instance.op))
+    return getSpatComputeWeight(spatCompute);
+  auto batch = cast<SpatComputeBatch>(instance.op);
+  return checkedMultiply(getComputeBodyWeight(batch.getBody()), static_cast<Weight>(instance.laneCount));
+}
+
+CrossbarUsage getComputeInstanceCrossbarUsage(const ComputeInstance& instance) {
+  if (auto spatCompute = dyn_cast<SpatCompute>(instance.op))
+    return getSpatComputeCrossbarUsage(spatCompute);
+  auto batch = cast<SpatComputeBatch>(instance.op);
+  return checkedMultiply(getComputeBodyCrossbarUsage(batch.getBody()), static_cast<CrossbarUsage>(instance.laneCount));
+}
+
+SmallVector<Value, 4> getComputeInstanceInputs(const ComputeInstance& instance) {
+  if (auto spatCompute = dyn_cast<SpatCompute>(instance.op))
+    return SmallVector<Value, 4>(spatCompute.getInputs().begin(), spatCompute.getInputs().end());
+  auto batch = cast<SpatComputeBatch>(instance.op);
+  SmallVector<Value, 4> inputs;
+  inputs.reserve(instance.laneCount);
+  for (uint32_t lane = instance.laneStart; lane < instance.laneStart + instance.laneCount; ++lane)
+    inputs.push_back(batch.getInputs()[lane]);
+  return inputs;
+}
+
+std::optional<ComputeInstance> getOriginalComputeInstance(Value value) {
+  Operation* op = value.getDefiningOp();
+  if (!op)
+    return std::nullopt;
+
+  while (auto extract = dyn_cast<tensor::ExtractSliceOp>(op)) {
+    value = extract.getSource();
+    op = value.getDefiningOp();
+    if (!op)
+      return std::nullopt;
+  }
+
+  if (auto spatCompute = dyn_cast<SpatCompute>(op))
+    return ComputeInstance {spatCompute.getOperation(), 0, 1};
+  if (auto batch = dyn_cast<SpatComputeBatch>(op))
+    return getBatchChunkForLane(batch, static_cast<uint32_t>(cast<OpResult>(value).getResultNumber()));
+  return std::nullopt;
+}
+
+SmallVector<ComputeInstance> collectComputeInstances(Operation* entryOp) {
+  SmallVector<ComputeInstance> instances;
+  for (Region& region : entryOp->getRegions()) {
+    for (Block& block : region) {
+      for (Operation& op : block) {
+        if (auto spatCompute = dyn_cast<SpatCompute>(&op)) {
+          instances.push_back({spatCompute.getOperation(), 0, 1});
+          continue;
+        }
+        if (auto batch = dyn_cast<SpatComputeBatch>(&op)) {
+          size_t chunkCount = getBatchChunkTargetCount(batch.getLaneCount());
+          for (size_t chunkIndex = 0; chunkIndex < chunkCount; ++chunkIndex)
+            instances.push_back(getBatchChunkForIndex(batch, chunkIndex));
+        }
+      }
+    }
+  }
+  return instances;
+}
+
+VirtualGraph buildInitialVirtualGraph(ArrayRef<ComputeInstance> computeInstances, ArrayRef<IndexedEdge> edges) {
   VirtualGraph graph;
-  graph.nodes.reserve(spatComputes.size());
-  for (auto [index, spatCompute] : llvm::enumerate(spatComputes)) {
+  graph.nodes.reserve(computeInstances.size());
+  for (auto [index, computeInstance] : llvm::enumerate(computeInstances)) {
     VirtualNode node;
     node.originalComputeIndices.push_back(index);
-    node.weight = getSpatComputeWeight(spatCompute);
-    node.crossbarUsage = getSpatComputeCrossbarUsage(spatCompute);
+    node.weight = getComputeInstanceWeight(computeInstance);
+    node.crossbarUsage = getComputeInstanceCrossbarUsage(computeInstance);
     graph.nodes.push_back(std::move(node));
   }
   graph.edges = aggregateEdges(edges);
@@ -116,22 +239,34 @@ TimingInfo computeTiming(const VirtualGraph& graph) {
     incomingEdgeCount[endIndex]++;
   }
 
-  std::vector<size_t> readyNodes;
-  readyNodes.reserve(nodeCount);
+  auto getVirtualNodeOrderKey = [&](size_t nodeIndex) {
+    const VirtualNode& node = graph.nodes[nodeIndex];
+    if (!node.originalComputeIndices.empty())
+      return node.originalComputeIndices.front();
+    return nodeIndex;
+  };
+  auto readyNodeGreater = [&](size_t lhs, size_t rhs) {
+    size_t lhsKey = getVirtualNodeOrderKey(lhs);
+    size_t rhsKey = getVirtualNodeOrderKey(rhs);
+    if (lhsKey != rhsKey)
+      return lhsKey > rhsKey;
+    return lhs > rhs;
+  };
+  std::priority_queue<size_t, std::vector<size_t>, decltype(readyNodeGreater)> readyNodes(readyNodeGreater);
   for (size_t i = 0; i < nodeCount; ++i)
     if (incomingEdgeCount[i] == 0)
-      readyNodes.push_back(i);
+      readyNodes.push(i);
 
-  size_t readyIndex = 0;
-  while (readyIndex != readyNodes.size()) {
-    size_t current = readyNodes[readyIndex++];
+  while (!readyNodes.empty()) {
+    size_t current = readyNodes.top();
+    readyNodes.pop();
     timing.topologicalOrder.push_back(current);
     for (auto [child, weight] : children[current]) {
       (void) weight;
       assert(incomingEdgeCount[child] > 0 && "incoming edge count underflow");
       incomingEdgeCount[child]--;
       if (incomingEdgeCount[child] == 0)
-        readyNodes.push_back(child);
+        readyNodes.push(child);
     }
   }
 
@@ -287,17 +422,21 @@ std::vector<IndexedEdge> buildWindowEdges(const VirtualGraph& graph, const std::
 WindowScheduleResult scheduleWindow(const VirtualGraph& graph, ArrayRef<size_t> selectedNodes, MLIRContext* context) {
   std::vector<Weight> windowWeights;
   std::vector<CrossbarUsage> windowCrossbarUsage;
+  std::vector<int64_t> windowNodeOrderKeys;
   std::vector<int64_t> nodeToWindowIndex(graph.nodes.size(), -1);
   windowWeights.reserve(selectedNodes.size());
   windowCrossbarUsage.reserve(selectedNodes.size());
+  windowNodeOrderKeys.reserve(selectedNodes.size());
 
   for (auto [windowIndex, nodeIndex] : llvm::enumerate(selectedNodes)) {
     nodeToWindowIndex[nodeIndex] = static_cast<int64_t>(windowIndex);
     windowWeights.push_back(graph.nodes[nodeIndex].weight);
     windowCrossbarUsage.push_back(graph.nodes[nodeIndex].crossbarUsage);
+    windowNodeOrderKeys.push_back(static_cast<int64_t>(nodeIndex));
   }
 
-  GraphDCP windowGraph(windowWeights, buildWindowEdges(graph, nodeToWindowIndex), windowCrossbarUsage);
+  GraphDCP windowGraph(
+    windowWeights, buildWindowEdges(graph, nodeToWindowIndex), windowNodeOrderKeys, windowCrossbarUsage);
   if (coresCount.getValue() > 0)
     windowGraph.setMaxCpuCount(static_cast<int>(coresCount.getValue()));
   windowGraph.setContext(context);
@@ -414,13 +553,7 @@ bool coarsenGraph(const VirtualGraph& graph,
   return true;
 }
 
-constexpr CPU kDefaultMaxCpuCount = 1000;
-
-CPU getVirtualGraphMaxCpuCount() {
-  if (coresCount.getValue() > 0)
-    return static_cast<CPU>(coresCount.getValue());
-  return kDefaultMaxCpuCount;
-}
+CPU getVirtualGraphMaxCpuCount() { return static_cast<CPU>(getSchedulingCpuBudget()); }
 
 size_t getDcpCoarseningWindowSize(size_t nodeCount) {
   size_t windowSize = std::min(dcpCriticalWindowSize.getValue(), nodeCount);
@@ -430,7 +563,7 @@ size_t getDcpCoarseningWindowSize(size_t nodeCount) {
   return windowSize;
 }
 
-DCPAnalysisResult buildResultFromVirtualGraph(const VirtualGraph& graph, ArrayRef<SpatCompute> spatComputes) {
+DCPAnalysisResult buildResultFromVirtualGraph(const VirtualGraph& graph, ArrayRef<ComputeInstance> computeInstances) {
   DCPAnalysisResult result;
 
   TimingInfo timing = computeTiming(graph);
@@ -443,19 +576,19 @@ DCPAnalysisResult buildResultFromVirtualGraph(const VirtualGraph& graph, ArrayRe
     std::iota(virtualNodeOrder.begin(), virtualNodeOrder.end(), 0);
   }
 
-  std::vector<size_t> originalComputeToCpu(spatComputes.size(), 0);
+  std::vector<size_t> originalComputeToCpu(computeInstances.size(), 0);
   for (auto [cpu, virtualNodeIndex] : llvm::enumerate(virtualNodeOrder)) {
     const VirtualNode& virtualNode = graph.nodes[virtualNodeIndex];
     for (size_t originalIndex : virtualNode.originalComputeIndices)
       originalComputeToCpu[originalIndex] = cpu;
   }
 
-  result.dominanceOrderCompute.reserve(spatComputes.size());
-  for (auto [originalIndex, spatCompute] : llvm::enumerate(spatComputes)) {
+  result.dominanceOrderCompute.reserve(computeInstances.size());
+  for (auto [originalIndex, computeInstance] : llvm::enumerate(computeInstances)) {
     size_t cpu = originalComputeToCpu[originalIndex];
-    result.dominanceOrderCompute.push_back(spatCompute);
-    result.computeToCpuMap[spatCompute] = cpu;
-    result.cpuToLastComputeMap[cpu] = spatCompute;
+    result.dominanceOrderCompute.push_back(computeInstance);
+    result.computeToCpuMap[computeInstance] = cpu;
+    result.cpuToLastComputeMap[cpu] = computeInstance;
   }
   for (const auto& [cpu, lastCompute] : result.cpuToLastComputeMap)
     result.isLastComputeOfCpu.insert(lastCompute);
@@ -463,13 +596,44 @@ DCPAnalysisResult buildResultFromVirtualGraph(const VirtualGraph& graph, ArrayRe
   return result;
 }
 
-DCPAnalysisResult runLegacyDcp(ArrayRef<SpatCompute> spatComputes, ArrayRef<IndexedEdge> edges, MLIRContext* context) {
-  GraphDCP graphDCP(spatComputes, edges);
+DCPAnalysisResult buildResultFromScheduledGraph(GraphDCP& graphDCP, ArrayRef<ComputeInstance> computeInstances) {
+  DCPAnalysisResult result;
+  result.dominanceOrderCompute.assign(computeInstances.begin(), computeInstances.end());
+
+  for (CPU cpu = 0; cpu < graphDCP.cpuCount(); ++cpu) {
+    auto scheduledTasks = graphDCP.getScheduledTasks(cpu);
+    if (scheduledTasks.empty())
+      continue;
+
+    for (const auto& task : scheduledTasks)
+      result.computeToCpuMap[computeInstances[task.nodeIndex]] = cpu;
+    result.cpuToLastComputeMap[cpu] = computeInstances[scheduledTasks.back().nodeIndex];
+    result.isLastComputeOfCpu.insert(computeInstances[scheduledTasks.back().nodeIndex]);
+  }
+
+  return result;
+}
+
+DCPAnalysisResult
+runLegacyDcp(ArrayRef<ComputeInstance> computeInstances, ArrayRef<IndexedEdge> edges, MLIRContext* context) {
+  SmallVector<Weight> nodeWeights;
+  SmallVector<CrossbarUsage> nodeCrossbarUsage;
+  SmallVector<int64_t> nodeOrderKeys;
+  nodeWeights.reserve(computeInstances.size());
+  nodeCrossbarUsage.reserve(computeInstances.size());
+  nodeOrderKeys.reserve(computeInstances.size());
+  for (auto [index, instance] : llvm::enumerate(computeInstances)) {
+    nodeWeights.push_back(getComputeInstanceWeight(instance));
+    nodeCrossbarUsage.push_back(getComputeInstanceCrossbarUsage(instance));
+    nodeOrderKeys.push_back(static_cast<int64_t>(index));
+  }
+
+  GraphDCP graphDCP(nodeWeights, edges, nodeOrderKeys, nodeCrossbarUsage);
   if (coresCount.getValue() > 0)
     graphDCP.setMaxCpuCount(static_cast<int>(coresCount.getValue()));
   graphDCP.setContext(context);
   graphDCP.runDcp();
-  return graphDCP.getResult();
+  return buildResultFromScheduledGraph(graphDCP, computeInstances);
 }
 
 } // namespace
@@ -488,27 +652,31 @@ SpatCompute getOriginalSpatCompute(Operation* op) {
 }
 
 DCPAnalysisResult DCPAnalysis::run() {
-  SmallVector<SpatCompute, 10> spatComputes;
+  SmallVector<ComputeInstance> computeInstances = collectComputeInstances(entryOp);
   SmallVector<IndexedEdge, 10> edges;
-  for (auto& region : entryOp->getRegions())
-    for (SpatCompute spatCompute : region.getOps<SpatCompute>())
-      spatComputes.push_back(spatCompute);
 
-  for (auto [indexEndEdge, spatCompute] : llvm::enumerate(spatComputes)) {
-    for (Value input : spatCompute.getInputs()) {
-      if (auto producerCompute = getOriginalSpatCompute(input.getDefiningOp())) {
-        auto producerIt = llvm::find(spatComputes, producerCompute);
-        assert(producerIt != spatComputes.end());
-        auto indexStartEdge = std::distance(spatComputes.begin(), producerIt);
-        edges.push_back({indexStartEdge, indexEndEdge, getSizeInBytes(cast<ShapedType>(input.getType()))});
+  llvm::DenseMap<ComputeInstance, size_t> instanceToIndex;
+  instanceToIndex.reserve(computeInstances.size());
+  for (auto [index, instance] : llvm::enumerate(computeInstances))
+    instanceToIndex[instance] = index;
+
+  for (auto [indexEndEdge, computeInstance] : llvm::enumerate(computeInstances)) {
+    for (Value input : getComputeInstanceInputs(computeInstance)) {
+      if (auto producerInstance = getOriginalComputeInstance(input)) {
+        auto producerIt = instanceToIndex.find(*producerInstance);
+        assert(producerIt != instanceToIndex.end());
+        auto indexStartEdge = producerIt->second;
+        edges.push_back({static_cast<int64_t>(indexStartEdge),
+                         static_cast<int64_t>(indexEndEdge),
+                         static_cast<int64_t>(getSizeInBytes(cast<ShapedType>(input.getType())))});
       }
     }
   }
 
   if (dcpCriticalWindowSize.getValue() == 0)
-    return runLegacyDcp(spatComputes, edges, entryOp->getContext());
+    return runLegacyDcp(computeInstances, edges, entryOp->getContext());
 
-  VirtualGraph virtualGraph = buildInitialVirtualGraph(spatComputes, edges);
+  VirtualGraph virtualGraph = buildInitialVirtualGraph(computeInstances, edges);
   size_t iteration = 0;
   auto tryCoarsenSelectedNodes = [&](ArrayRef<size_t> selectedNodes) {
     size_t oldNodeCount = virtualGraph.nodes.size();
@@ -545,6 +713,13 @@ DCPAnalysisResult DCPAnalysis::run() {
   };
 
   while (virtualGraph.nodes.size() > 1) {
+    if (virtualGraph.nodes.size() <= getSchedulingCpuBudget()) {
+      if (virtualGraph.nodes.size() >= 200)
+        llvm::errs() << llvm::formatv(
+          "[DCP-COARSEN] iter={0} old={1} stop=cpu-budget\n", iteration, virtualGraph.nodes.size());
+      break;
+    }
+
     iteration++;
     TimingInfo timing = computeTiming(virtualGraph);
     if (!timing.valid) {
@@ -576,7 +751,7 @@ DCPAnalysisResult DCPAnalysis::run() {
     break;
   }
 
-  return buildResultFromVirtualGraph(virtualGraph, spatComputes);
+  return buildResultFromVirtualGraph(virtualGraph, computeInstances);
 }
 
 } // namespace spatial

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

@@ -5,15 +5,28 @@
 #include "llvm/ADT/DenseMap.h"
 #include "llvm/ADT/DenseSet.h"
 
+#include <cstdint>
 #include <vector>
 
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
+// A scheduling identity that covers both spat.compute and scheduled shards of
+// spat.compute_batch.
+struct ComputeInstance {
+  mlir::Operation* op = nullptr;
+  uint32_t laneStart = 0;
+  uint32_t laneCount = 1;
+
+  bool operator==(const ComputeInstance& other) const {
+    return op == other.op && laneStart == other.laneStart && laneCount == other.laneCount;
+  }
+};
+
 struct DCPAnalysisResult {
-  std::vector<onnx_mlir::spatial::SpatCompute> dominanceOrderCompute;
-  llvm::DenseMap<onnx_mlir::spatial::SpatCompute, size_t> computeToCpuMap;
-  llvm::DenseSet<onnx_mlir::spatial::SpatCompute> isLastComputeOfCpu;
-  llvm::DenseMap<size_t, onnx_mlir::spatial::SpatCompute> cpuToLastComputeMap;
+  std::vector<ComputeInstance> dominanceOrderCompute;
+  llvm::DenseMap<ComputeInstance, size_t> computeToCpuMap;
+  llvm::DenseSet<ComputeInstance> isLastComputeOfCpu;
+  llvm::DenseMap<size_t, ComputeInstance> cpuToLastComputeMap;
 };
 
 namespace onnx_mlir {
@@ -34,3 +47,21 @@ public:
 
 } // namespace spatial
 } // namespace onnx_mlir
+
+namespace llvm {
+template <>
+struct DenseMapInfo<ComputeInstance> {
+  static ComputeInstance getEmptyKey() {
+    return {DenseMapInfo<mlir::Operation*>::getEmptyKey(), UINT32_MAX, UINT32_MAX};
+  }
+  static ComputeInstance getTombstoneKey() {
+    return {DenseMapInfo<mlir::Operation*>::getTombstoneKey(), UINT32_MAX, UINT32_MAX};
+  }
+  static unsigned getHashValue(const ComputeInstance& v) {
+    return llvm::hash_combine(v.op, v.laneStart, v.laneCount);
+  }
+  static bool isEqual(const ComputeInstance& a, const ComputeInstance& b) {
+    return a == b;
+  }
+};
+} // namespace llvm

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

@@ -1491,18 +1491,21 @@ void GraphDCP::runDcp() {
   struct ReadyEntry {
     Time slack;
     Time aest;
+    int64_t orderKey;
     TaskDCP* task;
     bool operator>(const ReadyEntry& other) const {
       if (slack != other.slack)
         return slack > other.slack;
-      return aest > other.aest;
+      if (aest != other.aest)
+        return aest > other.aest;
+      return orderKey > other.orderKey;
     }
   };
   std::priority_queue<ReadyEntry, std::vector<ReadyEntry>, std::greater<ReadyEntry>> readyQueue;
   size_t readyCount = 0;
 
   auto pushReady = [&](TaskDCP* node) {
-    readyQueue.push({slackOrZero(node->getAest(), node->getAlst()), node->getAest(), node});
+    readyQueue.push({slackOrZero(node->getAest(), node->getAlst()), node->getAest(), node->Id(), node});
   };
 
   for (auto& node : nodes) {
@@ -1528,7 +1531,7 @@ void GraphDCP::runDcp() {
         candidate = entry.task;
         break;
       }
-      readyQueue.push({curSlack, curAest, entry.task});
+      readyQueue.push({curSlack, curAest, entry.orderKey, entry.task});
     }
     assert(candidate != nullptr && "readyCount > 0 but heap exhausted");
     --readyCount;
@@ -1579,8 +1582,11 @@ DCPAnalysisResult GraphDCP::getResult() {
 
   auto dominanceOrder = dcp_graph::collectDominanceOrder(getRoots(), nodes.size());
   ret.dominanceOrderCompute.reserve(dominanceOrder.size());
-  for (auto elem : dominanceOrder)
-    ret.dominanceOrderCompute.push_back(elem->getSpatCompute());
+  for (auto elem : dominanceOrder) {
+    auto spatCompute = elem->getSpatCompute();
+    if (spatCompute)
+      ret.dominanceOrderCompute.push_back({spatCompute.getOperation(), 0});
+  }
 
   for (CPU cpu = 0; cpu < getLastCpu(); ++cpu) {
     const CpuTaskList* tasks = findCpuTasks(cpu);
@@ -1588,10 +1594,14 @@ DCPAnalysisResult GraphDCP::getResult() {
       continue;
     size_t i = 0;
     for (auto node : *tasks) {
-      ret.computeToCpuMap[node->getSpatCompute()] = cpu;
+      auto spatCompute = node->getSpatCompute();
+      if (!spatCompute)
+        continue;
+      ComputeInstance instance {spatCompute.getOperation(), 0};
+      ret.computeToCpuMap[instance] = cpu;
       if (i++ == tasks->size() - 1) {
-        ret.isLastComputeOfCpu.insert(node->getSpatCompute());
-        ret.cpuToLastComputeMap[cpu] = node->getSpatCompute();
+        ret.isLastComputeOfCpu.insert(instance);
+        ret.cpuToLastComputeMap[cpu] = instance;
       }
     }
   }

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

@@ -138,13 +138,18 @@ public:
 
   GraphDCP(llvm::ArrayRef<Weight> nodeWeights,
            llvm::ArrayRef<IndexedEdge> edges,
+           llvm::ArrayRef<int64_t> nodeOrderKeys = {},
            llvm::ArrayRef<CrossbarUsage> nodeCrossbarUsage = {})
   : nodes(), cpuTasks(), cpuCrossbarUsage() {
     assert((nodeCrossbarUsage.empty() || nodeCrossbarUsage.size() == nodeWeights.size())
            && "synthetic crossbar usage must match synthetic node weights");
+    assert((nodeOrderKeys.empty() || nodeOrderKeys.size() == nodeWeights.size())
+           && "synthetic node order keys must match synthetic node weights");
     nodes.reserve(nodeWeights.size());
     for (auto [index, weight] : llvm::enumerate(nodeWeights))
-      nodes.emplace_back(index, weight, nodeCrossbarUsage.empty() ? 0 : nodeCrossbarUsage[index]);
+      nodes.emplace_back(nodeOrderKeys.empty() ? static_cast<int64_t>(index) : nodeOrderKeys[index],
+                         weight,
+                         nodeCrossbarUsage.empty() ? 0 : nodeCrossbarUsage[index]);
     for (auto [start, end, weight] : edges)
       makeEdge(start, end, weight);
   }

src/PIM/Pass/PimCodegen/HostConstantFolding/Patterns/Constant.cpp

@@ -116,10 +116,9 @@ struct FoldConstantCoreMapPattern final : OpRewritePattern<linalg::MapOp> {
     auto globalOp = createFoldedGlobal(moduleOp, mapOp.getLoc(), initType, splatAttr, "pim_core_fill");
 
     OpBuilder::InsertionGuard guard(rewriter);
-    rewriter.setInsertionPoint(coreOp);
-    auto getGlobalOp = memref::GetGlobalOp::create(rewriter, mapOp.getLoc(), initType, globalOp.getName());
 
     rewriter.setInsertionPoint(mapOp);
+    auto getGlobalOp = memref::GetGlobalOp::create(rewriter, mapOp.getLoc(), initType, globalOp.getName());
     auto sizeInBytes = initType.getNumElements() * initType.getElementTypeBitWidth() / 8;
     pim::PimMemCopyOp::create(rewriter,
                               mapOp.getLoc(),
@@ -258,9 +257,18 @@ struct FoldConstantTransposePattern final : OpRewritePattern<pim::PimTransposeOp
     if (!resultType || !resultType.hasStaticShape())
       return failure();
 
+    // Look through an optional pim.memcp_hd to find the source get_global.
+    // This occurs when the constant was staged into device memory before transposing.
+    pim::PimMemCopyHostToDevOp memcpHd;
     auto sourceGetGlobal = transposeOp.getInput().getDefiningOp<memref::GetGlobalOp>();
-    if (!sourceGetGlobal)
-      return failure();
+    if (!sourceGetGlobal) {
+      memcpHd = transposeOp.getInput().getDefiningOp<pim::PimMemCopyHostToDevOp>();
+      if (!memcpHd)
+        return failure();
+      sourceGetGlobal = memcpHd.getHostSource().getDefiningOp<memref::GetGlobalOp>();
+      if (!sourceGetGlobal)
+        return failure();
+    }
 
     auto moduleOp = transposeOp->getParentOfType<ModuleOp>();
     if (!moduleOp)
@@ -298,13 +306,26 @@ struct FoldConstantTransposePattern final : OpRewritePattern<pim::PimTransposeOp
 
     bool isAlwaysWeight =
       !transposeOp->getUsers().empty()
-      && llvm::all_of(transposeOp->getUsers(), [](Operation* user) { return isa<pim::PimCoreOp>(user); });
+      && llvm::all_of(transposeOp->getUsers(), [](Operation* user) {
+           return isa<pim::PimCoreOp, pim::PimCoreBatchOp>(user);
+         });
     if (isAlwaysWeight) {
       markWeightAlways(newGlobal);
       markWeightAlways(newGetGlobal);
     }
 
+    auto outputAllocOp = transposeOp.getOutputBuffer().getDefiningOp<memref::AllocOp>();
     rewriter.replaceOp(transposeOp, newGetGlobal.getResult());
+
+    if (memcpHd && memcpHd.use_empty()) {
+      auto deviceAllocOp = memcpHd.getDeviceTarget().getDefiningOp<memref::AllocOp>();
+      rewriter.eraseOp(memcpHd);
+      if (deviceAllocOp && deviceAllocOp->use_empty())
+        rewriter.eraseOp(deviceAllocOp);
+    }
+    if (outputAllocOp && outputAllocOp->use_empty())
+      rewriter.eraseOp(outputAllocOp);
+
     return success();
   }
 };
@@ -341,18 +362,25 @@ struct FoldConstantAllocPattern final : OpRewritePattern<memref::AllocOp> {
         continue;
       }
 
-      if (!isa<pim::PimCoreOp>(user))
+      if (!isa<pim::PimCoreOp, pim::PimCoreBatchOp>(user))
         return failure();
     }
 
     if (!llvm::all_of(castsToReplace, [](memref::CastOp castOp) {
-          return llvm::all_of(castOp->getUsers(), [](Operation* user) { return isa<pim::PimCoreOp>(user); });
+          return llvm::all_of(castOp->getUsers(), [](Operation* user) {
+            return isa<pim::PimCoreOp, pim::PimCoreBatchOp>(user);
+          });
         })) {
       allLiveUsersAreCoreOps = false;
     }
 
     if (!llvm::all_of(allocOp->getUsers(), [](Operation* user) {
-          return isa<linalg::MapOp, memref::SubViewOp, memref::DeallocOp, memref::CastOp, pim::PimCoreOp>(user);
+          return isa<linalg::MapOp,
+                     memref::SubViewOp,
+                     memref::DeallocOp,
+                     memref::CastOp,
+                     pim::PimCoreOp,
+                     pim::PimCoreBatchOp>(user);
         })) {
       return failure();
     }
@@ -389,6 +417,83 @@ struct FoldConstantAllocPattern final : OpRewritePattern<memref::AllocOp> {
   }
 };
 
+struct FoldConstantHostCopyPattern final : OpRewritePattern<memref::CopyOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(memref::CopyOp copyOp, PatternRewriter& rewriter) const override {
+    if (copyOp->getParentOfType<pim::PimCoreOp>())
+      return failure();
+
+    auto allocOp = copyOp.getTarget().getDefiningOp<memref::AllocOp>();
+    if (!allocOp)
+      return failure();
+    auto allocType = dyn_cast<MemRefType>(allocOp.getType());
+    if (!allocType || !allocType.hasStaticShape())
+      return failure();
+
+    auto srcSubview = getStaticSubviewInfo(copyOp.getSource());
+    Value globalSource = succeeded(srcSubview) ? srcSubview->source : stripMemRefCasts(copyOp.getSource());
+
+    auto moduleOp = copyOp->getParentOfType<ModuleOp>();
+    if (!moduleOp)
+      return failure();
+
+    auto denseAttr = getDenseGlobalValue(moduleOp, globalSource);
+    if (failed(denseAttr))
+      return failure();
+
+    DenseElementsAttr foldedAttr;
+    if (succeeded(srcSubview)) {
+      if (llvm::any_of(srcSubview->strides, [](int64_t stride) { return stride != 1; }))
+        return failure();
+      auto staticOffsets = getStaticSubviewOffsets(*srcSubview);
+      if (failed(staticOffsets))
+        return failure();
+
+      auto maybeFoldedAttr = foldDenseSubview(*denseAttr, *staticOffsets, allocType.getShape());
+      if (failed(maybeFoldedAttr))
+        return failure();
+      foldedAttr = *maybeFoldedAttr;
+    }
+    else {
+      auto resultTensorType = RankedTensorType::get(allocType.getShape(), allocType.getElementType());
+      if (resultTensorType != denseAttr->getType())
+        return failure();
+      foldedAttr = *denseAttr;
+    }
+
+    bool allLiveUsersAreCores = true;
+    for (Operation* user : allocOp->getUsers()) {
+      if (user == copyOp)
+        continue;
+      if (isa<memref::DeallocOp>(user))
+        continue;
+      if (isa<pim::PimCoreOp, pim::PimCoreBatchOp>(user))
+        continue;
+      if (isa<memref::SubViewOp>(user)) {
+        allLiveUsersAreCores = false;
+        continue;
+      }
+      return failure();
+    }
+
+    auto newGlobal = createFoldedGlobal(moduleOp, allocOp.getLoc(), allocType, foldedAttr, "pim_folded_host_copy");
+    if (allLiveUsersAreCores)
+      markWeightAlways(newGlobal);
+
+    rewriter.setInsertionPoint(allocOp);
+    auto newGetGlobal = memref::GetGlobalOp::create(rewriter, allocOp.getLoc(), allocType, newGlobal.getName());
+    if (allLiveUsersAreCores)
+      markWeightAlways(newGetGlobal);
+
+    rewriter.replaceAllUsesWith(allocOp.getResult(), newGetGlobal.getResult());
+    rewriter.eraseOp(copyOp);
+    if (allocOp.use_empty())
+      rewriter.eraseOp(allocOp);
+    return success();
+  }
+};
+
 struct FoldConstantMemCpPattern final : OpRewritePattern<pim::PimMemCopyOp> {
   using OpRewritePattern::OpRewritePattern;
 
@@ -443,7 +548,7 @@ struct FoldConstantMemCpPattern final : OpRewritePattern<pim::PimMemCopyOp> {
         continue;
       if (isa<memref::DeallocOp>(user))
         continue;
-      if (isa<pim::PimCoreOp>(user))
+      if (isa<pim::PimCoreOp, pim::PimCoreBatchOp>(user))
         continue;
       if (isa<memref::SubViewOp>(user)) {
         allLiveUsersAreCores = false;
@@ -473,7 +578,11 @@ struct FoldConstantMemCpPattern final : OpRewritePattern<pim::PimMemCopyOp> {
 
 void populateConstantFoldingConstantPatterns(RewritePatternSet& patterns) {
   patterns
-    .add<FoldConstantTransposePattern, FoldConstantAllocPattern, FoldConstantCoreMapPattern, FoldConstantMemCpPattern>(
+    .add<FoldConstantTransposePattern,
+         FoldConstantAllocPattern,
+         FoldConstantCoreMapPattern,
+         FoldConstantHostCopyPattern,
+         FoldConstantMemCpPattern>(
       patterns.getContext());
 }
 

src/PIM/Pass/PimCodegen/VerificationPass.cpp

@@ -24,7 +24,26 @@ static bool isAddressOnlyHostOp(Operation* op) {
              memref::CastOp,
              memref::CollapseShapeOp,
              memref::ExpandShapeOp,
-             spatial::SpatChannelNewOp>(op);
+             memref::CopyOp>(op);
+}
+
+// Looser than isCodegenAddressableValue: follows view ops without requiring contiguity.
+// Used for memref.copy operands which may be non-contiguous subviews.
+static bool isBaseAddressableValue(Value value) {
+  while (true) {
+    if (isa<BlockArgument>(value))
+      return true;
+    Operation* defOp = value.getDefiningOp();
+    if (!defOp)
+      return false;
+    if (isa<memref::AllocOp, memref::GetGlobalOp>(defOp))
+      return true;
+    if (auto subview = dyn_cast<memref::SubViewOp>(defOp)) { value = subview.getSource(); continue; }
+    if (auto cast = dyn_cast<memref::CastOp>(defOp)) { value = cast.getSource(); continue; }
+    if (auto collapse = dyn_cast<memref::CollapseShapeOp>(defOp)) { value = collapse.getSrc(); continue; }
+    if (auto expand = dyn_cast<memref::ExpandShapeOp>(defOp)) { value = expand.getSrc(); continue; }
+    return false;
+  }
 }
 
 static bool isCodegenAddressableValue(Value value) {
@@ -38,6 +57,8 @@ static bool isCodegenAddressableValue(Value value) {
 static bool isExplicitHostOperand(Operation* op, unsigned operandIndex) {
   if (isa<pim::PimMemCopyHostToDevOp>(op))
     return operandIndex == 1;
+  if (isa<pim::PimMemCopyHostToDevBatchOp>(op))
+    return operandIndex == 1;
   if (isa<pim::PimMemCopyDevToHostOp>(op))
     return operandIndex == 0;
   return false;
@@ -69,6 +90,12 @@ struct VerificationPass : PassWrapper<VerificationPass, OperationPass<ModuleOp>>
           continue;
         }
 
+        if (auto coreBatchOp = dyn_cast<pim::PimCoreBatchOp>(&op)) {
+          if (failed(verifyCoreWeights(moduleOp, coreBatchOp)) || failed(verifyCoreOperands(coreBatchOp)))
+            hasFailure = true;
+          continue;
+        }
+
         if (auto returnOp = dyn_cast<func::ReturnOp>(&op)) {
           if (failed(verifyReturnOp(returnOp)))
             hasFailure = true;
@@ -92,10 +119,11 @@ struct VerificationPass : PassWrapper<VerificationPass, OperationPass<ModuleOp>>
   }
 
 private:
-  static LogicalResult verifyCoreWeights(ModuleOp moduleOp, pim::PimCoreOp coreOp) {
+  template <typename CoreOpTy>
+  static LogicalResult verifyCoreWeights(ModuleOp moduleOp, CoreOpTy coreOp) {
     bool hasFailure = false;
     for (auto [weightIndex, weight] : llvm::enumerate(coreOp.getWeights())) {
-      auto getGlobalOp = weight.getDefiningOp<memref::GetGlobalOp>();
+      auto getGlobalOp = weight.template getDefiningOp<memref::GetGlobalOp>();
       if (!getGlobalOp) {
         coreOp.emitOpError() << "weight #" << weightIndex
                              << " must be materialized as memref.get_global before JSON codegen";
@@ -131,7 +159,8 @@ private:
     return success(!hasFailure);
   }
 
-  static LogicalResult verifyCoreOperands(pim::PimCoreOp coreOp) {
+  template <typename CoreOpTy>
+  static LogicalResult verifyCoreOperands(CoreOpTy coreOp) {
     return walkPimCoreBlock(
       coreOp.getBody().front(), StaticValueKnowledge {}, [](Operation& op, const StaticValueKnowledge& knowledge) {
         bool hasFailure = false;
@@ -174,6 +203,13 @@ private:
       return verifyAddressOnlySource(op, collapseOp.getSrc());
     if (auto expandOp = dyn_cast<memref::ExpandShapeOp>(op))
       return verifyAddressOnlySource(op, expandOp.getSrc());
+    if (auto copyOp = dyn_cast<memref::CopyOp>(op)) {
+      if (!isBaseAddressableValue(copyOp.getSource()) || !isBaseAddressableValue(copyOp.getTarget())) {
+        op->emitOpError("depends on a value that is not backed by addressable storage");
+        return failure();
+      }
+      return success();
+    }
     return success();
   }
 

validation/validate_one.py

@@ -37,7 +37,7 @@ class ValidationResult:
 
 
 class ProgressReporter:
-    def __init__(self, total_models, stages_per_model=STAGE_COUNT):
+    def __init__(self, total_models, stages_per_model=STAGE_COUNT, enabled=None):
         self.total_models = total_models
         self.stages_per_model = stages_per_model
         self.total_steps = max(1, total_models * stages_per_model)
@@ -45,7 +45,7 @@ class ProgressReporter:
         self.passed_models = 0
         self.failed_models = 0
         self.current_label = ""
-        self.enabled = True
+        self.enabled = sys.stdout.isatty() if enabled is None else enabled
         self.columns = shutil.get_terminal_size((100, 20)).columns
         self.suspended = False