update cost model of batch lanes to consider only a slice of the shared batch input

support for tensors of index values

CMakeLists.txt

@@ -3,31 +3,99 @@ cmake_minimum_required(VERSION 3.20.0)
 
 project(raptor)
 
-# Add symlink to PIM as accelerator in onnx-mlir
+# Materialize a CMake shim directory
-function(raptor_ensure_symlink link_path target_path)
+function(raptor_write_external_cmake_shim shim_dir external_source_dir description)
-  get_filename_component(link_parent "${link_path}" DIRECTORY)
+  get_filename_component(real_external_source_dir "${external_source_dir}" REALPATH)
+  file(RELATIVE_PATH relative_external_source_dir "${shim_dir}" "${real_external_source_dir}")
 
-  if(NOT EXISTS "${link_parent}")
+  if (NOT EXISTS "${real_external_source_dir}/CMakeLists.txt")
-    message(FATAL_ERROR "Directory not found: ${link_parent}")
+    message(FATAL_ERROR
-  endif()
+      "External CMake source directory not found or missing CMakeLists.txt:\n"
-
+      "  ${real_external_source_dir}"
-  if(NOT EXISTS "${link_path}")
-    message(STATUS "Creating symlink ${link_path} -> ${target_path}")
-    file(CREATE_LINK
-        "${target_path}"
-        "${link_path}"
-        SYMBOLIC
     )
-  endif()
+  endif ()
+
+  if (IS_SYMLINK "${shim_dir}")
+    message(STATUS "Removing old full-directory symlink: ${shim_dir}")
+    file(REMOVE "${shim_dir}")
+  endif ()
+
+  if (EXISTS "${shim_dir}" AND NOT IS_DIRECTORY "${shim_dir}")
+    message(FATAL_ERROR "Expected directory or absent path, got file: ${shim_dir}")
+  endif ()
+
+  file(MAKE_DIRECTORY "${shim_dir}")
+
+  set(shim_file "${shim_dir}/CMakeLists.txt")
+  set(shim_contents
+    "get_filename_component(raptor_external_source_dir
+    \"\${CMAKE_CURRENT_LIST_DIR}/${relative_external_source_dir}\"
+    REALPATH
+)
+add_subdirectory(
+    \"\${raptor_external_source_dir}\"
+    \"\${CMAKE_CURRENT_BINARY_DIR}/raptor-external\"
+)
+if (DEFINED PIM_ENABLED)
+    set(PIM_ENABLED \"\${PIM_ENABLED}\" PARENT_SCOPE)
+endif ()
+"
+  )
+
+  if (EXISTS "${shim_file}")
+    file(READ "${shim_file}" old_contents)
+  else ()
+    set(old_contents "")
+  endif ()
+
+  if (NOT old_contents STREQUAL shim_contents)
+    file(WRITE "${shim_file}" "${shim_contents}")
+    message(STATUS "Wrote CMake shim for ${description}: ${shim_file}")
+  else ()
+    message(STATUS "CMake shim already up to date for ${description}")
+  endif ()
+
+  # Mirror the external tree's first-level entries into the shim directory
+  # so legacy includes like src/Accelerators/PIM/Compiler/... keep working.
+  file(GLOB children RELATIVE "${real_external_source_dir}" "${real_external_source_dir}/*")
+
+  foreach (child IN LISTS children)
+    if (child STREQUAL "CMakeLists.txt")
+      continue()
+    endif ()
+
+    set(real_child "${real_external_source_dir}/${child}")
+    set(shim_child "${shim_dir}/${child}")
+
+    if (IS_SYMLINK "${shim_child}")
+      file(READ_SYMLINK "${shim_child}" existing_link_target)
+      if (existing_link_target STREQUAL real_child)
+        continue()
+      endif ()
+      file(REMOVE_RECURSE "${shim_child}")
+    elseif (EXISTS "${shim_child}")
+      # Do not delete real files/directories. This protects the generated shim.
+      continue()
+    endif ()
+
+    file(CREATE_LINK
+      "${real_child}"
+      "${shim_child}"
+      SYMBOLIC
+    )
+  endforeach ()
 endfunction()
 
-raptor_ensure_symlink(
+raptor_write_external_cmake_shim(
-    "${CMAKE_CURRENT_SOURCE_DIR}/onnx-mlir/src/Accelerators/PIM"
+  "${CMAKE_CURRENT_SOURCE_DIR}/onnx-mlir/src/Accelerators/PIM"
-    "${CMAKE_CURRENT_SOURCE_DIR}/src/PIM"
+  "${CMAKE_CURRENT_SOURCE_DIR}/src/PIM"
+  "PIM accelerator"
 )
-raptor_ensure_symlink(
+
-    "${CMAKE_CURRENT_SOURCE_DIR}/onnx-mlir/test/accelerators/PIM"
+raptor_write_external_cmake_shim(
-    "${CMAKE_CURRENT_SOURCE_DIR}/test/PIM"
+  "${CMAKE_CURRENT_SOURCE_DIR}/onnx-mlir/test/accelerators/PIM"
+  "${CMAKE_CURRENT_SOURCE_DIR}/test/PIM"
+  "PIM accelerator tests"
 )
 
 # Patch onnx-mlir sources for PIM accelerator support.
@@ -38,21 +106,21 @@ function(raptor_apply_patch file_path anchor replacement description)
 
   # Already applied – replacement text is present
   string(FIND "${contents}" "${replacement}" already_applied_pos)
-  if(NOT already_applied_pos EQUAL -1)
+  if (NOT already_applied_pos EQUAL -1)
     message(STATUS "Patch already applied: ${description}")
     return()
-  endif()
+  endif ()
 
   # Anchor must exist for the patch to be applicable
   string(FIND "${contents}" "${anchor}" anchor_pos)
-  if(anchor_pos EQUAL -1)
+  if (anchor_pos EQUAL -1)
     message(FATAL_ERROR
       "Patch anchor not found – onnx-mlir may have changed.\n"
       "  Patch : ${description}\n"
       "  File  : ${file_path}\n"
       "  Anchor: ${anchor}"
     )
-  endif()
+  endif ()
 
   string(REPLACE "${anchor}" "${replacement}" patched "${contents}")
   file(WRITE "${file_path}" "${patched}")

backend-simulators/pim/pim-simulator/src/lib/pimcore.rs

@@ -299,10 +299,11 @@ fn detect_deadlock(cores_instructions: &[CoreInstructions]) -> Option<DeadlockIn
             if in_path.contains(&waiting_for) {
                 let cycle_start = path.iter().position(|&c| c == waiting_for).unwrap();
                 let cycle = &path[cycle_start..];
+                let format_core = |core: &i32| (core - 1).to_string();
 
                 let cycle_str = cycle
                     .iter()
-                    .map(|c| c.to_string())
+                    .map(format_core)
                     .collect::<Vec<_>>()
                     .join(" -> ");
 
@@ -311,19 +312,19 @@ fn detect_deadlock(cores_instructions: &[CoreInstructions]) -> Option<DeadlockIn
                     .copied()
                     .chain(std::iter::once(waiting_for))
                     .collect::<Vec<_>>();
-                let cycle_msg = format!("{} -> {}", cycle_str, waiting_for);
+                let cycle_msg = format!("{} -> {}", cycle_str, waiting_for - 1);
                 let states_msg = cycle
                     .iter()
                     .filter_map(|core| {
                         states.get(core).map(|state| match state {
                             CoreState::SendingTo(target, size) => {
-                                format!("core {} send {}B -> {}", core, size, target)
+                                format!("core {} send {}B -> {}", core - 1, size, target - 1)
                             }
                             CoreState::ReceivingFrom(source, size) => {
-                                format!("core {} recv {}B <- {}", core, size, source)
+                                format!("core {} recv {}B <- {}", core - 1, size, source - 1)
                             }
-                            CoreState::Working => format!("core {} working", core),
+                            CoreState::Working => format!("core {} working", core - 1),
-                            CoreState::Halted => format!("core {} halted", core),
+                            CoreState::Halted => format!("core {} halted", core - 1),
                         })
                     })
                     .collect::<Vec<_>>()

src/PIM/CMakeLists.txt

@@ -10,6 +10,56 @@ set(PIM_INCLUDE_PATH ${CMAKE_INCLUDE_OUTPUT_DIRECTORY})
 set(PIM_ONNX_MLIR_SRC_ROOT ${ONNX_MLIR_SRC_ROOT})
 set(PIM_ONNX_MLIR_BIN_ROOT ${ONNX_MLIR_BIN_ROOT})
 
+set(PIM_GENERATED_PATH_SHIM_TARGET "")
+get_filename_component(PIM_BIN_ROOT_NAME "${PIM_BIN_ROOT}" NAME)
+if (PIM_BIN_ROOT_NAME STREQUAL "raptor-external")
+  get_filename_component(PIM_GENERATED_PATH_SHIM_ROOT "${PIM_BIN_ROOT}" DIRECTORY)
+  set(PIM_GENERATED_PATH_SHIM_OUTPUTS)
+
+  function(add_pim_generated_path_shim relative_path)
+    set(real_file "${PIM_BIN_ROOT}/${relative_path}")
+    set(shim_file "${PIM_GENERATED_PATH_SHIM_ROOT}/${relative_path}")
+    get_filename_component(shim_dir "${shim_file}" DIRECTORY)
+
+    add_custom_command(
+      OUTPUT "${shim_file}"
+      DEPENDS "${real_file}"
+      COMMAND "${CMAKE_COMMAND}" -E make_directory "${shim_dir}"
+      COMMAND "${CMAKE_COMMAND}" -E rm -f "${shim_file}"
+      COMMAND "${CMAKE_COMMAND}" -E create_symlink "${real_file}" "${shim_file}"
+      VERBATIM
+    )
+
+    list(APPEND PIM_GENERATED_PATH_SHIM_OUTPUTS "${shim_file}")
+    set(PIM_GENERATED_PATH_SHIM_OUTPUTS "${PIM_GENERATED_PATH_SHIM_OUTPUTS}" PARENT_SCOPE)
+  endfunction()
+
+  file(GLOB_RECURSE pim_generated_path_scan_sources
+    CONFIGURE_DEPENDS
+    "${PIM_SRC_ROOT}/*.cpp"
+    "${PIM_SRC_ROOT}/*.hpp"
+  )
+
+  set(pim_generated_path_shims)
+  foreach (source_file IN LISTS pim_generated_path_scan_sources)
+    file(READ "${source_file}" source_contents)
+    string(REGEX MATCHALL "#include \"src/Accelerators/PIM/[^\"]+\\.inc\"" source_inc_matches "${source_contents}")
+
+    foreach (inc_match IN LISTS source_inc_matches)
+      string(REGEX REPLACE "^#include \"src/Accelerators/PIM/(.+)\"$" "\\1" relative_inc_path "${inc_match}")
+      list(APPEND pim_generated_path_shims "${relative_inc_path}")
+    endforeach ()
+  endforeach ()
+
+  list(REMOVE_DUPLICATES pim_generated_path_shims)
+  foreach (relative_inc_path IN LISTS pim_generated_path_shims)
+    add_pim_generated_path_shim("${relative_inc_path}")
+  endforeach ()
+
+  add_custom_target(OMPimGeneratedPathShims DEPENDS ${PIM_GENERATED_PATH_SHIM_OUTPUTS})
+  set(PIM_GENERATED_PATH_SHIM_TARGET OMPimGeneratedPathShims)
+endif ()
+
 set(PIM_PUBLIC_INCLUDE_DIRS
   ${ONNX_MLIR_SRC_ROOT}/include
   ${ONNX_MLIR_SRC_ROOT}
@@ -37,6 +87,9 @@ set(PIM_GENERATED_INCLUDE_DIRS
 
 function(add_pim_library name)
   add_onnx_mlir_library(${name} STATIC ${ARGN})
+  if (PIM_GENERATED_PATH_SHIM_TARGET)
+    add_dependencies(${name} ${PIM_GENERATED_PATH_SHIM_TARGET})
+  endif ()
 endfunction()
 
 add_subdirectory(Dialect)

src/PIM/Common/IR/AddressAnalysis.cpp

@@ -264,7 +264,7 @@ llvm::FailureOr<ResolvedContiguousAddress> resolveContiguousAddressImpl(mlir::Va
         return mlir::failure();
 
       auto sourceStrides = computeRowMajorStrides(sourceType.getShape());
-      byteOffset += linearizeIndex(offsets, sourceStrides) * subviewType.getElementTypeBitWidth() / 8;
+      byteOffset += linearizeIndex(offsets, sourceStrides) * getElementTypeSizeInBytes(subviewType.getElementType());
       value = resolveAlias(subviewOp.getSource(), knowledge);
       continue;
     }

src/PIM/Common/IR/ShapeUtils.cpp

@@ -1,4 +1,5 @@
 #include "llvm/ADT/STLExtras.h"
+#include "llvm/Support/ErrorHandling.h"
 
 #include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
 
@@ -35,6 +36,30 @@ int64_t getNumElements(llvm::ArrayRef<int64_t> shape) {
   return numElements;
 }
 
+bool hasByteSizedElementType(mlir::Type elementType) {
+  if (mlir::isa<mlir::IndexType>(elementType))
+    return true;
+  if (auto intType = mlir::dyn_cast<mlir::IntegerType>(elementType))
+    return intType.getWidth() > 0 && intType.getWidth() % 8 == 0;
+  if (auto floatType = mlir::dyn_cast<mlir::FloatType>(elementType))
+    return floatType.getWidth() > 0 && floatType.getWidth() % 8 == 0;
+  return false;
+}
+
+size_t getElementTypeSizeInBytes(mlir::Type elementType) {
+  if (mlir::isa<mlir::IndexType>(elementType))
+    return mlir::IndexType::kInternalStorageBitWidth / 8;
+  if (auto intType = mlir::dyn_cast<mlir::IntegerType>(elementType))
+    return static_cast<size_t>(intType.getWidth() / 8);
+  if (auto floatType = mlir::dyn_cast<mlir::FloatType>(elementType))
+    return static_cast<size_t>(floatType.getWidth() / 8);
+  llvm_unreachable("expected byte-sized integer, float, or index element type");
+}
+
+size_t getShapedTypeSizeInBytes(mlir::ShapedType shapedType) {
+  return static_cast<size_t>(shapedType.getNumElements()) * getElementTypeSizeInBytes(shapedType.getElementType());
+}
+
 bool isMemoryContiguous(llvm::ArrayRef<int64_t> srcShape,
                         llvm::ArrayRef<int64_t> offsets,
                         llvm::ArrayRef<int64_t> sizes,

src/PIM/Common/IR/ShapeUtils.hpp

@@ -1,8 +1,13 @@
 #pragma once
 
+#include "mlir/IR/BuiltinTypes.h"
+#include "mlir/IR/Value.h"
+
 #include "llvm/ADT/ArrayRef.h"
 #include "llvm/ADT/SmallVector.h"
 
+#include <cstddef>
+
 namespace onnx_mlir {
 
 llvm::SmallVector<int64_t> computeRowMajorStrides(llvm::ArrayRef<int64_t> shape);
@@ -14,6 +19,12 @@ int64_t linearizeIndex(llvm::ArrayRef<int64_t> indices, llvm::ArrayRef<int64_t>
 
 int64_t getNumElements(llvm::ArrayRef<int64_t> shape);
 
+bool hasByteSizedElementType(mlir::Type elementType);
+
+size_t getElementTypeSizeInBytes(mlir::Type elementType);
+
+size_t getShapedTypeSizeInBytes(mlir::ShapedType shapedType);
+
 bool isMemoryContiguous(llvm::ArrayRef<int64_t> srcShape,
                         llvm::ArrayRef<int64_t> offsets,
                         llvm::ArrayRef<int64_t> sizes,

src/PIM/Common/IR/WeightUtils.cpp

@@ -21,13 +21,15 @@ namespace {
 
 template <typename MVMOpTy, typename VMMOpTy, typename ParentOpTy>
 bool hasMvmVmmWeightUse(ParentOpTy parentOp, unsigned weightIndex) {
-  mlir::Value weightArg = parentOp.getWeightArgument(weightIndex);
+  auto weightArg = parentOp.getWeightArgument(weightIndex);
+  if (!weightArg)
+    return false;
   bool found = false;
   parentOp.walk([&](mlir::Operation* op) {
     if (auto mvmOp = mlir::dyn_cast<MVMOpTy>(op))
-      found |= mvmOp.getWeight() == weightArg;
+      found |= mvmOp.getWeight() == *weightArg;
     else if (auto vmmOp = mlir::dyn_cast<VMMOpTy>(op))
-      found |= vmmOp.getWeight() == weightArg;
+      found |= vmmOp.getWeight() == *weightArg;
   });
   return found;
 }
@@ -38,7 +40,8 @@ void walkMvmVmmWeightUses(ParentOpTy parentOp, llvm::function_ref<void(mlir::OpO
   llvm::SmallSet<unsigned, 8> visited;
   auto walkWeight = [&](mlir::Value weight) {
     for (unsigned weightIndex = 0; weightIndex < weights.size(); ++weightIndex) {
-      if (parentOp.getWeightArgument(weightIndex) != weight)
+      auto weightArg = parentOp.getWeightArgument(weightIndex);
+      if (!weightArg || *weightArg != weight)
         continue;
       if (visited.insert(weightIndex).second)
         callback(parentOp->getOpOperand(weightIndex));

src/PIM/Common/Support/Diagnostics.hpp

@@ -13,7 +13,8 @@
 namespace onnx_mlir::pim {
 
 struct CappedDiagnosticReporter {
-  explicit CappedDiagnosticReporter(int64_t maxReportedFailures = 8) : maxReportedFailures(maxReportedFailures) {}
+  explicit CappedDiagnosticReporter(int64_t maxReportedFailures = 8)
+  : maxReportedFailures(maxReportedFailures) {}
 
   template <typename EmitFn>
   void report(mlir::Operation* op, EmitFn&& emit) {
@@ -24,8 +25,7 @@ struct CappedDiagnosticReporter {
 
   void emitSuppressedSummary(mlir::Operation* op, llvm::StringRef failureDescription) const {
     if (numFailures > maxReportedFailures)
-      op->emitError() << "suppressed " << (numFailures - maxReportedFailures) << " additional "
+      op->emitError() << "suppressed " << (numFailures - maxReportedFailures) << " additional " << failureDescription;
-                      << failureDescription;
   }
 
   bool hasFailure() const { return numFailures != 0; }

src/PIM/Compiler/PimBatchEmission.cpp

@@ -28,23 +28,47 @@ static SmallVector<int32_t> getLaneChunkCoreIds(ArrayRef<int32_t> coreIds, size_
   return laneCoreIds;
 }
 
+static Value getOrCloneCapturedValue(OpBuilder& builder, Block& oldBlock, Value value, IRMapping& mapper) {
+  if (Value mapped = mapper.lookupOrNull(value))
+    return mapped;
+
+  if (auto blockArgument = dyn_cast<BlockArgument>(value)) {
+    assert(blockArgument.getOwner() != &oldBlock && "expected block argument to be mapped before cloning");
+    assert(false && "unexpected captured block argument while scalarizing pim.core_batch");
+  }
+
+  Operation* definingOp = value.getDefiningOp();
+  assert(definingOp && "expected captured value to be defined by an operation");
+  assert(definingOp->getBlock() != &oldBlock && "expected in-block value to be mapped before cloning");
+
+  for (Value operand : definingOp->getOperands())
+    (void) getOrCloneCapturedValue(builder, oldBlock, operand, mapper);
+
+  Operation* cloned = builder.clone(*definingOp, mapper);
+  for (auto [originalResult, clonedResult] : llvm::zip(definingOp->getResults(), cloned->getResults()))
+    mapper.map(originalResult, clonedResult);
+  return mapper.lookup(value);
+}
+
 static void cloneScalarizedLaneBody(OpBuilder& builder,
                                     pim::PimCoreBatchOp coreBatchOp,
                                     unsigned lane,
                                     OperationFolder& constantFolder) {
   Block& oldBlock = coreBatchOp.getBody().front();
+  Operation* anchorOp = builder.getInsertionBlock()->getParentOp();
   size_t laneCount = static_cast<size_t>(coreBatchOp.getLaneCount());
   size_t weightCount = coreBatchOp.getWeights().size();
 
   IRMapping mapper;
   for (auto [argIndex, blockArg] : llvm::enumerate(oldBlock.getArguments())) {
     if (blockArg.getType().isIndex()) {
-      mapper.map(blockArg, getOrCreateHostIndexConstant(coreBatchOp, static_cast<int64_t>(lane), constantFolder));
+      mapper.map(blockArg, getOrCreateHostIndexConstant(anchorOp, static_cast<int64_t>(lane), constantFolder));
       continue;
     }
 
     if (argIndex <= weightCount) {
-      mapper.map(blockArg, coreBatchOp.getWeights()[argIndex - 1]);
+      auto scalarCoreOp = cast<pim::PimCoreOp>(anchorOp);
+      mapper.map(blockArg, scalarCoreOp.getWeightArgument(argIndex - 1));
       continue;
     }
 
@@ -57,8 +81,10 @@ static void cloneScalarizedLaneBody(OpBuilder& builder,
     if (isa<pim::PimHaltOp>(op))
       continue;
 
+    for (Value operand : op.getOperands())
+      (void) getOrCloneCapturedValue(builder, oldBlock, operand, mapper);
+
     if (auto sendBatchOp = dyn_cast<pim::PimSendBatchOp>(op)) {
-      Operation* anchorOp = builder.getInsertionBlock()->getParentOp();
       pim::PimSendOp::create(
         builder,
         sendBatchOp.getLoc(),
@@ -78,7 +104,6 @@ static void cloneScalarizedLaneBody(OpBuilder& builder,
     }
 
     if (auto receiveBatchOp = dyn_cast<pim::PimReceiveBatchOp>(op)) {
-      Operation* anchorOp = builder.getInsertionBlock()->getParentOp();
       auto scalarReceive = pim::PimReceiveOp::create(
         builder,
         receiveBatchOp.getLoc(),
@@ -106,8 +131,8 @@ static void cloneScalarizedLaneBody(OpBuilder& builder,
         builder,
         memcpBatchOp.getLoc(),
         memcpBatchOp.getOutput().getType(),
-        getOrCreateHostIndexConstant(coreBatchOp, memcpBatchOp.getDeviceTargetOffset(), constantFolder),
+        getOrCreateHostIndexConstant(anchorOp, memcpBatchOp.getDeviceTargetOffset(), constantFolder),
-        getOrCreateHostIndexConstant(coreBatchOp, memcpBatchOp.getHostSourceOffset(), constantFolder),
+        getOrCreateHostIndexConstant(anchorOp, memcpBatchOp.getHostSourceOffset(), constantFolder),
         mapper.lookup(memcpBatchOp.getDeviceTarget()),
         mapper.lookup(memcpBatchOp.getHostSource()),
         memcpBatchOp.getSizeAttr());
@@ -141,7 +166,16 @@ LogicalResult withScalarCoreFromBatchLanes(pim::PimCoreBatchOp coreBatchOp,
 
   auto scalarCore =
     pim::PimCoreOp::create(builder, coreBatchOp.getLoc(), ValueRange(weights), builder.getI32IntegerAttr(coreId));
-  Block* block = builder.createBlock(&scalarCore.getBody(), scalarCore.getBody().end());
+  SmallVector<Type> weightTypes;
+  SmallVector<Location> weightLocs;
+  weightTypes.reserve(weights.size());
+  weightLocs.reserve(weights.size());
+  for (Value weight : weights) {
+    weightTypes.push_back(weight.getType());
+    weightLocs.push_back(weight.getLoc());
+  }
+  Block* block =
+    builder.createBlock(&scalarCore.getBody(), scalarCore.getBody().end(), TypeRange(weightTypes), weightLocs);
   builder.setInsertionPointToEnd(block);
   for (unsigned lane : lanes)
     cloneScalarizedLaneBody(builder, coreBatchOp, lane, constantFolder);

src/PIM/Compiler/PimCodeGen.cpp

@@ -41,23 +41,10 @@ using namespace mlir;
 using namespace onnx_mlir;
 using namespace onnx_mlir::compact_asm;
 
-static size_t getElementTypeSizeInBytes(mlir::Type elementType) {
-  if (elementType.isIndex())
-    return sizeof(int64_t);
-  if (elementType.isIntOrFloat())
-    return elementType.getIntOrFloatBitWidth() / 8;
-  llvm_unreachable("unsupported shaped element type");
-}
-
-static size_t getValueSizeInBytes(mlir::Value value) {
-  auto type = cast<ShapedType>(value.getType());
-  return type.getNumElements() * getElementTypeSizeInBytes(type.getElementType());
-}
-
 MemEntry* PimMemory::gatherMemEntry(mlir::Value value) {
   auto type = cast<ShapedType>(value.getType());
   assert("Only static shape is supported" && type.hasStaticShape());
-  size_t allocSize = type.getNumElements() * getElementTypeSizeInBytes(type.getElementType());
+  size_t allocSize = getShapedTypeSizeInBytes(type);
   MemEntry memEntry = {0, allocSize};
   return &memEntries.emplace_back(memEntry, value).first;
 }
@@ -450,7 +437,8 @@ void PimCodeGen::codeGenReceiveOp(pim::PimReceiveOp receiveOp, const StaticValue
 void PimCodeGen::codeGenReceiveTensorOp(pim::PimReceiveTensorOp receiveTensorOp,
                                         const StaticValueKnowledge& knowledge) const {
   size_t outputAddr = addressOf(receiveTensorOp.getOutputBuffer(), knowledge);
-  size_t chunkSize = getValueSizeInBytes(receiveTensorOp.getOutputBuffer()) / receiveTensorOp.getSourceCoreIds().size();
+  size_t chunkSize = getShapedTypeSizeInBytes(cast<ShapedType>(receiveTensorOp.getOutputBuffer().getType()))
+                   / receiveTensorOp.getSourceCoreIds().size();
   for (auto [chunkIndex, sourceCoreId] : llvm::enumerate(receiveTensorOp.getSourceCoreIds()))
     emitCommunicationOp("recv", outputAddr + chunkIndex * chunkSize, sourceCoreId, chunkSize);
 }
@@ -463,7 +451,8 @@ void PimCodeGen::codeGenSendOp(pim::PimSendOp sendOp, const StaticValueKnowledge
 
 void PimCodeGen::codeGenSendTensorOp(pim::PimSendTensorOp sendTensorOp, const StaticValueKnowledge& knowledge) const {
   size_t inputAddr = addressOf(sendTensorOp.getInput(), knowledge);
-  size_t chunkSize = getValueSizeInBytes(sendTensorOp.getInput()) / sendTensorOp.getTargetCoreIds().size();
+  size_t chunkSize = getShapedTypeSizeInBytes(cast<ShapedType>(sendTensorOp.getInput().getType()))
+                   / sendTensorOp.getTargetCoreIds().size();
   for (auto [chunkIndex, targetCoreId] : llvm::enumerate(sendTensorOp.getTargetCoreIds()))
     emitCommunicationOp("send", inputAddr + chunkIndex * chunkSize, targetCoreId, chunkSize);
 }
@@ -474,7 +463,7 @@ void PimCodeGen::codeGenConcatOp(pim::PimConcatOp concatOp, const StaticValueKno
 
   int64_t axis = concatOp.getAxis();
   ArrayRef<int64_t> outputShape = outputType.getShape();
-  size_t elementSize = outputType.getElementTypeBitWidth() / 8;
+  size_t elementSize = getElementTypeSizeInBytes(outputType.getElementType());
   size_t outputAddr = addressOf(concatOp.getOutputBuffer(), knowledge);
 
   size_t outerCount = 1;
@@ -526,7 +515,7 @@ void PimCodeGen::codeGenVVAddOp(pim::PimVVAddOp vvaddOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getValueSizeInBytes(vvaddOp.getLhs()));
+  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvaddOp.getLhs().getType())));
   emitInstruction(instruction);
 }
 
@@ -541,7 +530,7 @@ void PimCodeGen::codeGenVVSubOp(pim::PimVVSubOp vvsubOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getValueSizeInBytes(vvsubOp.getLhs()));
+  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvsubOp.getLhs().getType())));
   emitInstruction(instruction);
 }
 
@@ -556,7 +545,7 @@ void PimCodeGen::codeGenVVMulOp(pim::PimVVMulOp vvmulOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getValueSizeInBytes(vvmulOp.getLhs()));
+  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvmulOp.getLhs().getType())));
   emitInstruction(instruction);
 }
 
@@ -571,7 +560,7 @@ void PimCodeGen::codeGenVVMaxOp(pim::PimVVMaxOp vvmaxOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getValueSizeInBytes(vvmaxOp.getLhs()));
+  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvmaxOp.getLhs().getType())));
   emitInstruction(instruction);
 }
 
@@ -586,7 +575,7 @@ void PimCodeGen::codeGenVVDMulOp(pim::PimVVDMulOp vvdmulOp, const StaticValueKno
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getValueSizeInBytes(vvdmulOp.getLhs()));
+  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvdmulOp.getLhs().getType())));
   emitInstruction(instruction);
 }
 
@@ -601,7 +590,7 @@ void PimCodeGen::codeGenVAvgOp(pim::PimVAvgOp vavgOp, const StaticValueKnowledge
   instruction.r1 = 1;
   instruction.r2OrImm = 1;
   instruction.generic1 = 1;
-  instruction.generic3 = static_cast<int32_t>(getValueSizeInBytes(vavgOp.getInput()));
+  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vavgOp.getInput().getType())));
   emitInstruction(instruction);
 }
 
@@ -614,7 +603,7 @@ void PimCodeGen::codeGenVReluOp(pim::PimVReluOp vreluOp, const StaticValueKnowle
   instruction.opcode = pim_binary::Opcode::vrelu;
   instruction.rd = 0;
   instruction.r1 = 1;
-  instruction.generic3 = static_cast<int32_t>(getValueSizeInBytes(vreluOp.getInput()));
+  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vreluOp.getInput().getType())));
   emitInstruction(instruction);
 }
 
@@ -627,7 +616,7 @@ void PimCodeGen::codeGenVTanhOp(pim::PimVTanhOp vtanhOp, const StaticValueKnowle
   instruction.opcode = pim_binary::Opcode::vtanh;
   instruction.rd = 0;
   instruction.r1 = 1;
-  instruction.generic3 = static_cast<int32_t>(getValueSizeInBytes(vtanhOp.getInput()));
+  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vtanhOp.getInput().getType())));
   emitInstruction(instruction);
 }
 
@@ -640,7 +629,7 @@ void PimCodeGen::codeGenVSigmOp(pim::PimVSigmOp vsigmOp, const StaticValueKnowle
   instruction.opcode = pim_binary::Opcode::vsigm;
   instruction.rd = 0;
   instruction.r1 = 1;
-  instruction.generic3 = static_cast<int32_t>(getValueSizeInBytes(vsigmOp.getInput()));
+  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vsigmOp.getInput().getType())));
   emitInstruction(instruction);
 }
 
@@ -653,7 +642,8 @@ void PimCodeGen::codeGenVSoftmaxOp(pim::PimVSoftmaxOp vsoftmaxOp, const StaticVa
   instruction.opcode = pim_binary::Opcode::vsoftmax;
   instruction.rd = 0;
   instruction.r1 = 1;
-  instruction.generic3 = static_cast<int32_t>(getValueSizeInBytes(vsoftmaxOp.getInput()));
+  instruction.generic3 =
+    static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vsoftmaxOp.getInput().getType())));
   emitInstruction(instruction);
 }
 
@@ -666,7 +656,7 @@ void PimCodeGen::codeGenTransposeOp(pim::PimTransposeOp transposeOp, const Stati
   auto srcType = cast<ShapedType>(transposeOp.getInput().getType());
   auto srcShape = srcType.getShape();
   size_t rank = srcShape.size();
-  size_t elementSize = srcType.getElementTypeBitWidth() / 8;
+  size_t elementSize = getElementTypeSizeInBytes(srcType.getElementType());
   size_t totalElements = srcType.getNumElements();
 
   // Read permutation. Destination dim i corresponds to source dim perm[i].

src/PIM/Compiler/PimCompilerOptions.cpp

@@ -1,7 +1,7 @@
-#include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
-
 #include "llvm/Support/ErrorHandling.h"
 
+#include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
+
 #define DEBUG_TYPE "PimCompilerOptions"
 
 namespace onnx_mlir {
@@ -15,13 +15,13 @@ llvm::cl::opt<PimEmissionTargetType> pimEmissionTarget(
   llvm::cl::init(EmitPimCodegen),
   llvm::cl::cat(OnnxMlirOptions));
 
-llvm::cl::opt<PimMergeSchedulerType> pimMergeScheduler(
+llvm::cl::opt<PimMergeSchedulerType>
-  "pim-merge-scheduler",
+  pimMergeScheduler("pim-merge-scheduler",
-  llvm::cl::desc("Scheduler used by the Spatial merge-compute-nodes pass"),
+                    llvm::cl::desc("Scheduler used by the Spatial merge-compute-nodes pass"),
-  llvm::cl::values(clEnumValN(MergeSchedulerPeft, "peft", "Use PEFT scheduling")),
+                    llvm::cl::values(clEnumValN(MergeSchedulerPeft, "peft", "Use PEFT scheduling")),
-  llvm::cl::values(clEnumValN(MergeSchedulerDcp, "dcp", "Use the legacy DCP-inspired scheduler")),
+                    llvm::cl::values(clEnumValN(MergeSchedulerDcp, "dcp", "Use the legacy DCP-inspired scheduler")),
-  llvm::cl::init(MergeSchedulerPeft),
+                    llvm::cl::init(MergeSchedulerPeft),
-  llvm::cl::cat(OnnxMlirOptions));
+                    llvm::cl::cat(OnnxMlirOptions));
 
 llvm::cl::opt<bool>
   pimOnlyCodegen("pim-only-codegen",

src/PIM/Compiler/PimWeightEmitter.cpp

@@ -208,7 +208,7 @@ createAndPopulateWeightFolder(func::FuncOp funcOp, StringRef outputDirPath) {
         int64_t numCols = shape[1];
         assert(numRows <= xbarSize && numCols <= xbarSize && "Weight dimensions must not exceed crossbar size");
 
-        size_t elementByteWidth = denseAttr.getElementType().getIntOrFloatBitWidth() / 8;
+        size_t elementByteWidth = getElementTypeSizeInBytes(denseAttr.getElementType());
 
         std::string newFileName = "crossbar_" + std::to_string(indexFileName++) + ".bin";
         auto weightFilePath = (coreWeightsDirPath + "/" + newFileName).str();

src/PIM/Conversion/ONNXToSpatial/Common/ComputeRegionBuilder.hpp

@@ -99,15 +99,17 @@ auto createSpatCompute(RewriterT& rewriter,
 
   using BodyResult = detail::InvokeWithBlockArgsResultT<std::decay_t<BodyFn>, std::make_index_sequence<NumInputs>>;
   if constexpr (std::is_same_v<BodyResult, void>) {
-    detail::invokeWithValues(
+    detail::invokeWithValues(std::forward<BodyFn>(body),
-      std::forward<BodyFn>(body), detail::getInputBlockArgs(block, weights.size()), std::make_index_sequence<NumInputs> {});
+                             detail::getInputBlockArgs(block, weights.size()),
+                             std::make_index_sequence<NumInputs> {});
 
     rewriter.setInsertionPointAfter(computeOp);
     return computeOp;
   }
   else {
-    auto bodyResult = detail::invokeWithValues(
+    auto bodyResult = detail::invokeWithValues(std::forward<BodyFn>(body),
-      std::forward<BodyFn>(body), detail::getInputBlockArgs(block, weights.size()), std::make_index_sequence<NumInputs> {});
+                                               detail::getInputBlockArgs(block, weights.size()),
+                                               std::make_index_sequence<NumInputs> {});
     if (mlir::failed(bodyResult)) {
       rewriter.setInsertionPointAfter(computeOp);
       rewriter.eraseOp(computeOp);

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

@@ -422,9 +422,13 @@ LogicalResult GemvToSpatialCompute::matchAndRewrite(ONNXGemmOp gemmOp,
 
       SmallVector<Value> vmmOutputs;
       vmmOutputs.reserve(aHSlices[coreId].size());
-      for (auto aHSliceId : llvm::seq<size_t>(0, aHSlices[coreId].size()))
+      for (auto aHSliceId : llvm::seq<size_t>(0, aHSlices[coreId].size())) {
-        vmmOutputs.push_back(spatial::SpatVMMOp::create(
+        auto weightArg = computeOp.getWeightArgument(aHSliceId);
-          rewriter, gemmLoc, currOutHSliceType, computeOp.getWeightArgument(aHSliceId), computeOp.getInputArgument(aHSliceId)));
+        auto inputArg = computeOp.getInputArgument(aHSliceId);
+        if (!weightArg || !inputArg)
+          return failure();
+        vmmOutputs.push_back(spatial::SpatVMMOp::create(rewriter, gemmLoc, currOutHSliceType, *weightArg, *inputArg));
+      }
       if (vmmOutputs.empty()) {
         gemmOp.emitOpError("requires at least one non-empty slice when lowering tiled Gemm to Spatial VMMs");
         return failure();
@@ -558,29 +562,31 @@ LogicalResult GemmToSpatialComputeBatch::matchAndRewrite(ONNXGemmOp gemmOp,
     rewriter.createBlock(&batchOp.getBody(), batchOp.getBody().end(), TypeRange(blockArgTypes), blockArgLocs);
   rewriter.setInsertionPointToEnd(body);
 
-  Value lane = batchOp.getLaneArgument();
+  auto lane = batchOp.getLaneArgument();
-  Value weight = batchOp.getWeightArgument(0);
+  auto weight = batchOp.getWeightArgument(0);
-  Value packedInput = batchOp.getInputArgument(0);
+  auto packedInput = batchOp.getInputArgument(0);
-  Value packedOutput = batchOp.getOutputArgument(0);
+  auto packedOutput = batchOp.getOutputArgument(0);
+  if (!lane || !weight || !packedInput || !packedOutput)
+    return failure();
 
-  SmallVector<OpFoldResult> inputOffsets {lane, rewriter.getIndexAttr(0)};
+  SmallVector<OpFoldResult> inputOffsets {*lane, rewriter.getIndexAttr(0)};
   SmallVector<OpFoldResult> inputSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(aType.getDimSize(1))};
   SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
   Value row =
-    tensor::ExtractSliceOp::create(rewriter, loc, aRowType, packedInput, inputOffsets, inputSizes, unitStrides)
+    tensor::ExtractSliceOp::create(rewriter, loc, aRowType, *packedInput, inputOffsets, inputSizes, unitStrides)
       .getResult();
 
-  Value vmmResult = spatial::SpatVMMOp::create(rewriter, loc, outRowType, weight, row).getResult();
+  Value vmmResult = spatial::SpatVMMOp::create(rewriter, loc, outRowType, *weight, row).getResult();
   Value laneResult = vmmResult;
   if (sharedBias)
     laneResult = spatial::SpatVAddOp::create(rewriter, loc, outRowType, vmmResult, sharedBias).getResult();
 
   auto inParallelOp = spatial::SpatInParallelOp::create(rewriter, loc);
   rewriter.setInsertionPointToStart(&inParallelOp.getRegion().front());
-  SmallVector<OpFoldResult> outputOffsets {lane, rewriter.getIndexAttr(0)};
+  SmallVector<OpFoldResult> outputOffsets {*lane, rewriter.getIndexAttr(0)};
   SmallVector<OpFoldResult> outputSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(outType.getDimSize(1))};
-  tensor::ParallelInsertSliceOp::create(rewriter, loc, laneResult, packedOutput, outputOffsets, outputSizes,
+  tensor::ParallelInsertSliceOp::create(
-                                        unitStrides);
+    rewriter, loc, laneResult, *packedOutput, outputOffsets, outputSizes, unitStrides);
   rewriter.setInsertionPointAfter(batchOp);
 
   rewriter.replaceOp(gemmOp, batchOp.getResults());

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

@@ -38,23 +38,16 @@ static FailureOr<SmallVector<int64_t>> inferSupportedBatchShape(ArrayRef<int64_t
   return SmallVector<int64_t>(lhsBatchShape.begin(), lhsBatchShape.end());
 }
 
-static Value collapseBatchDims(Value value,
+static Value
-                               int64_t batchSize,
+collapseBatchDims(Value value, int64_t batchSize, int64_t rows, int64_t cols, PatternRewriter& rewriter, Location loc) {
-                               int64_t rows,
-                               int64_t cols,
-                               PatternRewriter& rewriter,
-                               Location loc) {
   auto type = cast<RankedTensorType>(value.getType());
   if (type.getRank() == 2 || type.getRank() == 3)
     return value;
 
-  auto collapsedType =
+  auto collapsedType = RankedTensorType::get({batchSize, rows, cols}, type.getElementType(), type.getEncoding());
-    RankedTensorType::get({batchSize, rows, cols}, type.getElementType(), type.getEncoding());
+  SmallVector<ReassociationIndices> reassociation = {ReassociationIndices {},
-  SmallVector<ReassociationIndices> reassociation = {
+                                                     ReassociationIndices {static_cast<int64_t>(type.getRank() - 2)},
-    ReassociationIndices {},
+                                                     ReassociationIndices {static_cast<int64_t>(type.getRank() - 1)}};
-    ReassociationIndices {static_cast<int64_t>(type.getRank() - 2)},
-    ReassociationIndices {static_cast<int64_t>(type.getRank() - 1)}
-  };
   for (int64_t dim = 0; dim < type.getRank() - 2; ++dim)
     reassociation.front().push_back(dim);
 
@@ -72,19 +65,14 @@ static Value collapseBatchDims(Value value,
   return collapseCompute.getResult(0);
 }
 
-static Value expandBatchDims(Value value,
+static Value
-                             RankedTensorType outputType,
+expandBatchDims(Value value, RankedTensorType outputType, size_t batchRank, PatternRewriter& rewriter, Location loc) {
-                             size_t batchRank,
-                             PatternRewriter& rewriter,
-                             Location loc) {
   if (cast<RankedTensorType>(value.getType()) == outputType)
     return value;
 
-  SmallVector<ReassociationIndices> reassociation = {
+  SmallVector<ReassociationIndices> reassociation = {ReassociationIndices {},
-    ReassociationIndices {},
+                                                     ReassociationIndices {static_cast<int64_t>(batchRank)},
-    ReassociationIndices {static_cast<int64_t>(batchRank)},
+                                                     ReassociationIndices {static_cast<int64_t>(batchRank + 1)}};
-    ReassociationIndices {static_cast<int64_t>(batchRank + 1)}
-  };
   for (size_t dim = 0; dim < batchRank; ++dim)
     reassociation.front().push_back(static_cast<int64_t>(dim));
 

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

@@ -58,24 +58,21 @@ static Value buildNearestResizeLoop(Value input,
 
   Value outputC = channelLoop.getInductionVar();
   Value outputChannelAcc = channelLoop.getRegionIterArgs().front();
-  Value inputC =
+  Value inputC = buildNearestAsymmetricIndex(outputC, inputType.getDimSize(1), resultType.getDimSize(1), rewriter, loc);
-    buildNearestAsymmetricIndex(outputC, inputType.getDimSize(1), resultType.getDimSize(1), rewriter, loc);
 
   auto heightLoop = scf::ForOp::create(rewriter, loc, c0, cOutputH, c1, ValueRange {outputChannelAcc});
   rewriter.setInsertionPointToStart(heightLoop.getBody());
 
   Value outputH = heightLoop.getInductionVar();
   Value outputHeightAcc = heightLoop.getRegionIterArgs().front();
-  Value inputH =
+  Value inputH = buildNearestAsymmetricIndex(outputH, inputType.getDimSize(2), resultType.getDimSize(2), rewriter, loc);
-    buildNearestAsymmetricIndex(outputH, inputType.getDimSize(2), resultType.getDimSize(2), rewriter, loc);
 
   auto widthLoop = scf::ForOp::create(rewriter, loc, c0, cOutputW, c1, ValueRange {outputHeightAcc});
   rewriter.setInsertionPointToStart(widthLoop.getBody());
 
   Value outputW = widthLoop.getInductionVar();
   Value outputWidthAcc = widthLoop.getRegionIterArgs().front();
-  Value inputW =
+  Value inputW = buildNearestAsymmetricIndex(outputW, inputType.getDimSize(3), resultType.getDimSize(3), rewriter, loc);
-    buildNearestAsymmetricIndex(outputW, inputType.getDimSize(3), resultType.getDimSize(3), rewriter, loc);
 
   SmallVector<OpFoldResult> inputOffsets = {inputN, inputC, inputH, inputW};
   Value inputSlice =
@@ -114,8 +111,8 @@ struct Resize : OpConversionPattern<ONNXResizeOp> {
 
     if (resizeOp.getMode() != "nearest" || resizeOp.getCoordinateTransformationMode() != "asymmetric"
         || resizeOp.getNearestMode() != "floor")
-      return rewriter.notifyMatchFailure(
+      return rewriter.notifyMatchFailure(resizeOp,
-        resizeOp, "resize lowering currently supports only nearest + asymmetric + floor.");
+                                         "resize lowering currently supports only nearest + asymmetric + floor.");
 
     if (llvm::any_of(inputType.getShape(), [](int64_t dim) { return dim <= 0; })
         || llvm::any_of(resultType.getShape(), [](int64_t dim) { return dim <= 0; }))

src/PIM/Conversion/ONNXToSpatial/PostPatterns.cpp

@@ -27,13 +27,16 @@ static bool canPromoteInputBlockArgument(BlockArgument arg) {
   return !arg.use_empty() && llvm::all_of(arg.getUsers(), isWeightMaterializationHelperUser);
 }
 
+static bool canPromoteInputBlockArgument(std::optional<BlockArgument> arg) {
+  return arg && canPromoteInputBlockArgument(*arg);
+}
+
 static bool isDirectConstantValue(Value value) {
   return isa_and_nonnull<arith::ConstantOp, ONNXConstantOp>(value.getDefiningOp());
 }
 
 template <typename ComputeOpTy>
 static bool hasPromotableWeightLikeInputs(ComputeOpTy compute) {
-  Block& block = compute.getBody().front();
   for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
     if (!isWeightLikeComputeOperand(input))
       continue;
@@ -94,8 +97,8 @@ struct PromoteWeightLikeComputeInputsPattern : OpRewritePattern<spatial::SpatCom
     }
     llvm::append_range(newBlockArgTypes, newInputTypes);
     llvm::append_range(newBlockArgLocs, newInputLocs);
-    auto* newBlock =
+    auto* newBlock = rewriter.createBlock(
-      rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), TypeRange(newBlockArgTypes), newBlockArgLocs);
+      &newCompute.getBody(), newCompute.getBody().end(), TypeRange(newBlockArgTypes), newBlockArgLocs);
     newCompute.getProperties().setOperandSegmentSizes(
       {static_cast<int>(newWeights.size()), static_cast<int>(newInputs.size())});
     rewriter.setInsertionPointToStart(newBlock);
@@ -104,20 +107,30 @@ struct PromoteWeightLikeComputeInputsPattern : OpRewritePattern<spatial::SpatCom
     bodyRewriter.setInsertionPointToStart(newBlock);
 
     IRMapping mapper;
-    for (auto [weightIndex, weight] : llvm::enumerate(compute.getWeights()))
+    for (auto [weightIndex, weight] : llvm::enumerate(compute.getWeights())) {
-      mapper.map(compute.getWeightArgument(weightIndex), newCompute.getWeightArgument(weightIndex));
+      auto oldWeightArg = compute.getWeightArgument(weightIndex);
+      auto newWeightArg = newCompute.getWeightArgument(weightIndex);
+      if (!oldWeightArg || !newWeightArg)
+        return rewriter.notifyMatchFailure(compute, "missing compute weight block argument during rewrite");
+      mapper.map(*oldWeightArg, *newWeightArg);
+    }
     size_t newInputIdx = 0;
     for (auto [oldInputIdx, input] : llvm::enumerate(compute.getInputs())) {
-      BlockArgument oldArg = compute.getInputArgument(oldInputIdx);
+      auto oldArg = compute.getInputArgument(oldInputIdx);
+      if (!oldArg)
+        return rewriter.notifyMatchFailure(compute, "missing compute input block argument during rewrite");
       if (!promoteInput[oldInputIdx]) {
-        mapper.map(oldArg, newCompute.getInputArgument(newInputIdx++));
+        auto newInputArg = newCompute.getInputArgument(newInputIdx++);
+        if (!newInputArg)
+          return rewriter.notifyMatchFailure(compute, "missing rewritten compute input block argument");
+        mapper.map(*oldArg, *newInputArg);
         continue;
       }
 
       auto clonedValue = materializeWeightLikeValueInBlock(input, bodyRewriter, mapper);
       if (failed(clonedValue))
         return rewriter.notifyMatchFailure(compute, "failed to materialize promoted weight-like operand");
-      mapper.map(oldArg, *clonedValue);
+      mapper.map(*oldArg, *clonedValue);
     }
 
     for (Operation& op : oldBlock.without_terminator())
@@ -184,12 +197,15 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
                                         rewriter.getI32IntegerAttr(static_cast<int32_t>(compute.getLaneCount())),
                                         newWeights,
                                         newInputs);
+    auto laneArg = compute.getLaneArgument();
+    if (!laneArg)
+      return rewriter.notifyMatchFailure(compute, "missing compute_batch lane block argument");
     SmallVector<Type> newBlockArgTypes;
     SmallVector<Location> newBlockArgLocs;
     newBlockArgTypes.reserve(1 + newWeights.size() + newInputTypes.size() + compute.getNumResults());
     newBlockArgLocs.reserve(1 + newWeights.size() + newInputLocs.size() + compute.getNumResults());
-    newBlockArgTypes.push_back(compute.getLaneArgument().getType());
+    newBlockArgTypes.push_back(laneArg->getType());
-    newBlockArgLocs.push_back(compute.getLaneArgument().getLoc());
+    newBlockArgLocs.push_back(laneArg->getLoc());
     for (Value weight : newWeights) {
       newBlockArgTypes.push_back(weight.getType());
       newBlockArgLocs.push_back(weight.getLoc());
@@ -197,8 +213,11 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
     llvm::append_range(newBlockArgTypes, newInputTypes);
     llvm::append_range(newBlockArgLocs, newInputLocs);
     for (auto [resultIndex, resultType] : llvm::enumerate(compute.getResultTypes())) {
+      auto outputArg = compute.getOutputArgument(resultIndex);
+      if (!outputArg)
+        return rewriter.notifyMatchFailure(compute, "missing compute_batch output block argument");
       newBlockArgTypes.push_back(resultType);
-      newBlockArgLocs.push_back(compute.getOutputArgument(resultIndex).getLoc());
+      newBlockArgLocs.push_back(outputArg->getLoc());
     }
 
     auto* newBlock = rewriter.createBlock(
@@ -211,24 +230,41 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
     bodyRewriter.setInsertionPointToStart(newBlock);
 
     IRMapping mapper;
-    mapper.map(compute.getLaneArgument(), newCompute.getLaneArgument());
+    auto newLaneArg = newCompute.getLaneArgument();
-    for (auto [weightIndex, weight] : llvm::enumerate(compute.getWeights()))
+    if (!newLaneArg)
-      mapper.map(compute.getWeightArgument(weightIndex), newCompute.getWeightArgument(weightIndex));
+      return rewriter.notifyMatchFailure(compute, "missing rewritten compute_batch lane block argument");
+    mapper.map(*laneArg, *newLaneArg);
+    for (auto [weightIndex, weight] : llvm::enumerate(compute.getWeights())) {
+      auto oldWeightArg = compute.getWeightArgument(weightIndex);
+      auto newWeightArg = newCompute.getWeightArgument(weightIndex);
+      if (!oldWeightArg || !newWeightArg)
+        return rewriter.notifyMatchFailure(compute, "missing compute_batch weight block argument during rewrite");
+      mapper.map(*oldWeightArg, *newWeightArg);
+    }
     size_t newInputIdx = 0;
     for (auto [oldInputIdx, input] : llvm::enumerate(compute.getInputs())) {
-      BlockArgument oldArg = compute.getInputArgument(oldInputIdx);
+      auto oldArg = compute.getInputArgument(oldInputIdx);
+      if (!oldArg)
+        return rewriter.notifyMatchFailure(compute, "missing compute_batch input block argument during rewrite");
       if (!promoteInput[oldInputIdx]) {
-        mapper.map(oldArg, newCompute.getInputArgument(newInputIdx++));
+        auto newInputArg = newCompute.getInputArgument(newInputIdx++);
+        if (!newInputArg)
+          return rewriter.notifyMatchFailure(compute, "missing rewritten compute_batch input block argument");
+        mapper.map(*oldArg, *newInputArg);
         continue;
       }
 
       auto clonedValue = materializeWeightLikeValueInBlock(input, bodyRewriter, mapper);
       if (failed(clonedValue))
         return rewriter.notifyMatchFailure(compute, "failed to materialize promoted batch weight-like operand");
-      mapper.map(oldArg, *clonedValue);
+      mapper.map(*oldArg, *clonedValue);
+    }
+    for (auto resultIndex : llvm::seq<size_t>(0, compute.getNumResults())) {
+      auto outputArg = compute.getOutputArgument(resultIndex);
+      if (!outputArg)
+        return rewriter.notifyMatchFailure(compute, "missing compute_batch output block argument during rewrite");
+      mapper.map(*outputArg, newBlock->getArgument(1 + newWeights.size() + newInputs.size() + resultIndex));
     }
-    for (auto resultIndex : llvm::seq<size_t>(0, compute.getNumResults()))
-      mapper.map(compute.getOutputArgument(resultIndex), newBlock->getArgument(1 + newWeights.size() + newInputs.size() + resultIndex));
 
     for (Operation& op : oldBlock)
       rewriter.clone(op, mapper);

src/PIM/Conversion/SpatialToPim/BatchCoreLoweringPatterns.cpp

@@ -1,12 +1,14 @@
+#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/IRMapping.h"
 #include "mlir/IR/Matchers.h"
 
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/BatchCoreLoweringPatterns.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
@@ -97,22 +99,75 @@ static LogicalResult lowerChannelReceiveTensorBatch(spatial::SpatChannelReceiveT
   return success();
 }
 
+static FailureOr<unsigned> getDirectReturnOperandIndex(OpResult result) {
+  if (!result.hasOneUse())
+    return failure();
+
+  auto returnOp = dyn_cast<func::ReturnOp>(*result.getUsers().begin());
+  if (!returnOp)
+    return failure();
+  return result.getUses().begin()->getOperandNumber();
+}
+
+static Value createScaledIndexValue(IRRewriter& rewriter, Location loc, Value base, int64_t scale) {
+  if (scale == 1)
+    return base;
+
+  auto scaleValue = arith::ConstantIndexOp::create(rewriter, loc, scale).getResult();
+  return arith::MulIOp::create(rewriter, loc, base, scaleValue).getResult();
+}
+
+static Value createHostTargetOffset(IRRewriter& rewriter,
+                                    tensor::ParallelInsertSliceOp insertSlice,
+                                    ShapedType destinationType,
+                                    IRMapping& mapper) {
+  int64_t elementBytes = static_cast<int64_t>(getElementTypeSizeInBytes(destinationType.getElementType()));
+  SmallVector<int64_t> strides(destinationType.getRank(), 1);
+  ArrayRef<int64_t> shape = destinationType.getShape();
+  for (int64_t dim = destinationType.getRank() - 2; dim >= 0; --dim)
+    strides[dim] = strides[dim + 1] * shape[dim + 1];
+
+  Value totalOffset;
+  Location loc = insertSlice.getLoc();
+  for (auto [dim, offset] : llvm::enumerate(insertSlice.getMixedOffsets())) {
+    int64_t scale = strides[dim] * elementBytes;
+    Value scaledOffset;
+    if (auto attr = dyn_cast<Attribute>(offset)) {
+      auto intAttr = dyn_cast<IntegerAttr>(attr);
+      assert(intAttr && "expected integer offset attribute");
+      scaledOffset = arith::ConstantIndexOp::create(rewriter, loc, intAttr.getInt() * scale).getResult();
+    }
+    else {
+      scaledOffset = createScaledIndexValue(rewriter, loc, mapper.lookupOrDefault(cast<Value>(offset)), scale);
+    }
+
+    totalOffset =
+      totalOffset ? arith::AddIOp::create(rewriter, loc, totalOffset, scaledOffset).getResult() : scaledOffset;
+  }
+
+  if (!totalOffset)
+    totalOffset = arith::ConstantIndexOp::create(rewriter, loc, 0).getResult();
+  return totalOffset;
+}
+
 } // namespace
 
-LogicalResult
+LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatComputeBatch computeBatchOp,
-lowerComputeBatchOp(spatial::SpatComputeBatch computeBatchOp, CoreLoweringState& state, IRRewriter& rewriter) {
+                                                            IRRewriter& rewriter) {
   Location loc = computeBatchOp.getLoc();
   Block& oldBlock = computeBatchOp.getBody().front();
-  if (computeBatchOp.getNumResults() != 0)
-    return computeBatchOp.emitOpError(
-      "batched Spatial-to-PIM lowering currently requires channelized compute_batch with no results; "
-      "materialize explicit communication before lowering to PIM");
-
   auto oldYield = dyn_cast<spatial::SpatYieldOp>(oldBlock.getTerminator());
-  if (!oldYield || oldYield.getNumOperands() != 0)
+  auto inParallelOp = dyn_cast<spatial::SpatInParallelOp>(oldBlock.getTerminator());
-    return computeBatchOp.emitOpError("resultless compute_batch lowering requires empty spat.yield");
+  if (computeBatchOp.getNumResults() == 0) {
+    if (!oldYield || oldYield.getNumOperands() != 0)
+      return computeBatchOp.emitOpError("resultless compute_batch lowering requires empty spat.yield");
+  }
+  else if (!inParallelOp) {
+    return computeBatchOp.emitOpError(
+      "resultful compute_batch lowering currently requires a spat.in_parallel terminator");
+  }
 
-  SmallVector<int32_t> coreIds = getPimCoreIdsForBatchOp(computeBatchOp, state.nextCoreId);
+  SmallVector<int32_t> coreIds = getPimCoreIdsForBatchOp(computeBatchOp, coreId);
   SmallVector<Value> batchWeights(computeBatchOp.getWeights().begin(), computeBatchOp.getWeights().end());
   SmallVector<Value> batchInputs;
   if (!computeBatchOp.getInputs().empty())
@@ -128,9 +183,22 @@ lowerComputeBatchOp(spatial::SpatComputeBatch computeBatchOp, CoreLoweringState&
     {static_cast<int>(batchWeights.size()), static_cast<int>(batchInputs.size())});
   coreBatchOp->setAttr(onnx_mlir::kCoreIdsAttrName, rewriter.getDenseI32ArrayAttr(coreIds));
 
+  SmallVector<unsigned> returnOperandIndices;
+  if (computeBatchOp.getNumResults() != 0) {
+    returnOperandIndices.resize(computeBatchOp.getNumResults());
+    for (auto [resultIndex, result] : llvm::enumerate(computeBatchOp.getResults())) {
+      FailureOr<unsigned> returnOperandIndex = getDirectReturnOperandIndex(cast<OpResult>(result));
+      if (failed(returnOperandIndex))
+        return computeBatchOp.emitOpError(
+          "resultful compute_batch lowering currently requires each result to be used directly by func.return");
+      returnOperandIndices[resultIndex] = *returnOperandIndex;
+    }
+  }
+
   SmallVector<Type> blockArgTypes;
   SmallVector<Location> blockArgLocs;
-  for (BlockArgument arg : oldBlock.getArguments()) {
+  unsigned inputArgLimit = 1 + computeBatchOp.getWeights().size() + computeBatchOp.getInputs().size();
+  for (BlockArgument arg : oldBlock.getArguments().take_front(inputArgLimit)) {
     blockArgTypes.push_back(arg.getType());
     blockArgLocs.push_back(arg.getLoc());
   }
@@ -139,11 +207,20 @@ lowerComputeBatchOp(spatial::SpatComputeBatch computeBatchOp, CoreLoweringState&
 
   IRMapping mapper;
   rewriter.setInsertionPointToStart(newBlock);
-  mapper.map(computeBatchOp.getLaneArgument(), coreBatchOp.getLaneArgument());
+  auto oldLaneArg = computeBatchOp.getLaneArgument();
-  for (unsigned weightIndex = 0; weightIndex < computeBatchOp.getWeights().size(); ++weightIndex)
+  if (!oldLaneArg)
-    mapper.map(computeBatchOp.getWeightArgument(weightIndex), coreBatchOp.getWeightArgument(weightIndex));
+    return computeBatchOp.emitOpError("expected compute_batch lane block argument before lowering");
+  mapper.map(*oldLaneArg, coreBatchOp.getLaneArgument());
+  for (unsigned weightIndex = 0; weightIndex < computeBatchOp.getWeights().size(); ++weightIndex) {
+    auto oldWeightArg = computeBatchOp.getWeightArgument(weightIndex);
+    if (!oldWeightArg)
+      return computeBatchOp.emitOpError("expected compute_batch weight block arguments before lowering");
+    mapper.map(*oldWeightArg, coreBatchOp.getWeightArgument(weightIndex));
+  }
   for (unsigned inputIndex = 0; inputIndex < computeBatchOp.getInputs().size(); ++inputIndex) {
-    BlockArgument oldArg = computeBatchOp.getInputArgument(inputIndex);
+    auto oldArg = computeBatchOp.getInputArgument(inputIndex);
+    if (!oldArg)
+      return computeBatchOp.emitOpError("expected compute_batch input block arguments before lowering");
     BlockArgument newArg = coreBatchOp.getInputArgument(inputIndex);
     auto newArgType = cast<ShapedType>(newArg.getType());
     auto outputBuffer = createEmptyTensorFromShaped(rewriter, loc, newArgType);
@@ -156,7 +233,7 @@ lowerComputeBatchOp(spatial::SpatComputeBatch computeBatchOp, CoreLoweringState&
                                                           rewriter.getI32IntegerAttr(0),
                                                           getTensorSizeInBytesAttr(rewriter, newArg))
                     .getOutput();
-    mapper.map(oldArg, copied);
+    mapper.map(*oldArg, copied);
   }
 
   auto materializeCapturedTensor = [&](Value capturedTensor) -> Value {
@@ -178,11 +255,55 @@ lowerComputeBatchOp(spatial::SpatComputeBatch computeBatchOp, CoreLoweringState&
     return copied;
   };
 
+  SmallVector<Value> hostOutputTensors(returnOperandIndices.size());
+  auto getOrCreateHostOutputTensor = [&](unsigned resultIndex, Location resultLoc) -> Value {
+    Value& hostOutputTensor = hostOutputTensors[resultIndex];
+    if (hostOutputTensor)
+      return hostOutputTensor;
+
+    hostOutputTensor = outputTensors[returnOperandIndices[resultIndex]](rewriter, resultLoc);
+    return hostOutputTensor;
+  };
+
   rewriter.setInsertionPointToEnd(newBlock);
   for (Operation& op : oldBlock) {
     if (isa<spatial::SpatYieldOp>(op))
       continue;
 
+    if (auto parallelOp = dyn_cast<spatial::SpatInParallelOp>(op)) {
+      auto firstOutputArg = computeBatchOp.getOutputArgument(0);
+      if (!firstOutputArg)
+        return computeBatchOp.emitOpError("expected compute_batch output block arguments before lowering");
+      for (Operation& nestedOp : parallelOp.getRegion().front()) {
+        auto insertSlice = dyn_cast<tensor::ParallelInsertSliceOp>(&nestedOp);
+        if (!insertSlice)
+          return parallelOp.emitOpError("expected only tensor.parallel_insert_slice in spat.in_parallel");
+
+        auto outputArg = dyn_cast<BlockArgument>(insertSlice.getDest());
+        if (!outputArg || outputArg.getOwner() != &oldBlock)
+          return insertSlice.emitOpError("expected compute_batch output block argument destination");
+
+        unsigned resultIndex = outputArg.getArgNumber() - firstOutputArg->getArgNumber();
+        if (resultIndex >= returnOperandIndices.size())
+          return insertSlice.emitOpError("result index out of range while lowering host batch output");
+
+        Value mappedSource = mapper.lookup(insertSlice.getSource());
+        Value hostTarget = getOrCreateHostOutputTensor(resultIndex, insertSlice.getLoc());
+        auto hostTargetType = cast<ShapedType>(hostTarget.getType());
+        Value hostTargetOffset = createHostTargetOffset(rewriter, insertSlice, hostTargetType, mapper);
+        Value zeroOffset = arith::ConstantIndexOp::create(rewriter, insertSlice.getLoc(), 0).getResult();
+        pim::PimMemCopyDevToHostOp::create(rewriter,
+                                           insertSlice.getLoc(),
+                                           hostTarget.getType(),
+                                           hostTargetOffset,
+                                           zeroOffset,
+                                           hostTarget,
+                                           mappedSource,
+                                           getTensorSizeInBytesAttr(rewriter, mappedSource));
+      }
+      continue;
+    }
+
     if (auto sendBatchOp = dyn_cast<spatial::SpatChannelSendBatchOp>(op)) {
       FailureOr<SmallVector<int32_t>> targetCoreIds = getConstantI32Values(sendBatchOp.getTargetCoreIds());
       if (failed(targetCoreIds))

src/PIM/Conversion/SpatialToPim/BatchCoreLoweringPatterns.hpp

@@ -1,10 +0,0 @@
-#pragma once
-
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/CoreLoweringPatterns.hpp"
-
-namespace onnx_mlir {
-
-mlir::LogicalResult
-lowerComputeBatchOp(spatial::SpatComputeBatch computeBatchOp, CoreLoweringState& state, mlir::IRRewriter& rewriter);
-
-} // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/CMakeLists.txt

@@ -6,7 +6,6 @@ add_pim_library(OMSpatialToPim
   SpatialToPimPass.cpp
   BatchCoreLoweringPatterns.cpp
   ChannelLoweringPatterns.cpp
-  Cleanup.cpp
   Common.cpp
   ComputeLikeRegionUtils.cpp
   CoreLoweringPatterns.cpp

src/PIM/Conversion/SpatialToPim/Cleanup.cpp

@@ -1,42 +0,0 @@
-#include "llvm/ADT/STLExtras.h"
-
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/Cleanup.hpp"
-
-using namespace mlir;
-
-namespace onnx_mlir {
-
-LogicalResult erasePendingOps(SmallVectorImpl<Operation*>& pendingOps, IRRewriter& rewriter) {
-  while (!pendingOps.empty()) {
-    bool erasedAnyOp = false;
-    for (auto it = pendingOps.begin(); it != pendingOps.end();) {
-      Operation* opToRemove = *it;
-      if (!opToRemove->use_empty()) {
-        ++it;
-        continue;
-      }
-
-      rewriter.eraseOp(opToRemove);
-      it = pendingOps.erase(it);
-      erasedAnyOp = true;
-    }
-
-    if (erasedAnyOp)
-      continue;
-
-    for (Operation* opToRemove : pendingOps) {
-      InFlightDiagnostic diag = opToRemove->emitError("pending Spatial-to-PIM cleanup could not erase operation");
-      diag << "; op has " << llvm::range_size(opToRemove->getUsers()) << " remaining user(s)";
-      for (Operation* user : opToRemove->getUsers()) {
-        bool userPendingRemoval = llvm::is_contained(pendingOps, user);
-        opToRemove->emitRemark() << "remaining user `" << user->getName() << "`"
-                                 << (userPendingRemoval ? " is also pending removal" : " is not pending removal");
-      }
-    }
-    return failure();
-  }
-
-  return success();
-}
-
-} // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/Cleanup.hpp

@@ -1,11 +0,0 @@
-#pragma once
-
-#include "mlir/IR/Operation.h"
-#include "mlir/IR/PatternMatch.h"
-#include "mlir/Support/LLVM.h"
-
-namespace onnx_mlir {
-
-mlir::LogicalResult erasePendingOps(llvm::SmallVectorImpl<mlir::Operation*>& pendingOps, mlir::IRRewriter& rewriter);
-
-} // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/Common.cpp

@@ -55,10 +55,6 @@ size_t getSliceActualOffset(tensor::ExtractSliceOp& sliceOp, ShapedType& inputSh
   return returnValue;
 }
 
-size_t getShapedTypeSizeInBytes(ShapedType shapedType) {
-  return shapedType.getNumElements() * shapedType.getElementTypeBitWidth() / 8;
-}
-
 IntegerAttr getTensorSizeInBytesAttr(Builder& builder, mlir::Value value) {
   return builder.getI32IntegerAttr(static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(value.getType()))));
 }

src/PIM/Conversion/SpatialToPim/Common.hpp

@@ -20,8 +20,6 @@ namespace onnx_mlir {
  */
 size_t getSliceActualOffset(mlir::tensor::ExtractSliceOp& sliceOp, mlir::ShapedType& inputShape);
 
-size_t getShapedTypeSizeInBytes(mlir::ShapedType shapedType);
-
 mlir::IntegerAttr getTensorSizeInBytesAttr(mlir::Builder& builder, mlir::Value value);
 
 template <class T>

src/PIM/Conversion/SpatialToPim/ComputeLikeRegionUtils.cpp

@@ -1,3 +1,5 @@
+#include <cassert>
+
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/ComputeLikeRegionUtils.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
@@ -29,9 +31,17 @@ void replaceAndEraseDirectComputeLikeInput(PatternRewriter& rewriter,
                                            unsigned inputIndex,
                                            Value replacement) {
   Block& body = owner->getRegion(0).front();
-  BlockArgument bodyArgument = isa<spatial::SpatCompute>(owner)
+  BlockArgument bodyArgument;
-                                 ? cast<spatial::SpatCompute>(owner).getInputArgument(inputIndex)
+  if (auto compute = dyn_cast<spatial::SpatCompute>(owner)) {
-                                 : cast<spatial::SpatComputeBatch>(owner).getInputArgument(inputIndex);
+    auto computeArg = compute.getInputArgument(inputIndex);
+    assert(computeArg && "expected compute input block argument");
+    bodyArgument = *computeArg;
+  }
+  else {
+    auto batchArg = cast<spatial::SpatComputeBatch>(owner).getInputArgument(inputIndex);
+    assert(batchArg && "expected compute_batch input block argument");
+    bodyArgument = *batchArg;
+  }
   unsigned bodyArgIndex = bodyArgument.getArgNumber();
 
   rewriter.startOpModification(owner);

src/PIM/Conversion/SpatialToPim/CoreLoweringPatterns.cpp

@@ -6,9 +6,9 @@
 #include "mlir/IR/Matchers.h"
 
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/CoreLoweringPatterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
@@ -131,8 +131,12 @@ static bool inlineInputlessHelperComputeForWeightLikeUsers(spatial::SpatCompute
 
   rewriter.setInsertionPoint(computeOp);
   IRMapping mapping;
-  for (auto [weightIndex, weight] : llvm::enumerate(computeOp.getWeights()))
+  for (auto [weightIndex, weight] : llvm::enumerate(computeOp.getWeights())) {
-    mapping.map(computeOp.getWeightArgument(weightIndex), weight);
+    auto weightArg = computeOp.getWeightArgument(weightIndex);
+    if (!weightArg)
+      return false;
+    mapping.map(*weightArg, weight);
+  }
   for (Operation& op : block.without_terminator()) {
     cloneMappedHelperOperands(&op, mapping, rewriter, constantFolder);
     Operation* clonedOp = rewriter.clone(op, mapping);
@@ -148,15 +152,12 @@ static bool inlineInputlessHelperComputeForWeightLikeUsers(spatial::SpatCompute
 
 } // namespace
 
-void markOpToRemove(CoreLoweringState& state, Operation* op) {
+LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute computeOp,
-  if (!llvm::is_contained(state.operationsToRemove, op))
+                                                       IRRewriter& rewriter,
-    state.operationsToRemove.push_back(op);
+                                                       OperationFolder& constantFolder) {
-}
-
-LogicalResult lowerComputeOp(spatial::SpatCompute computeOp, CoreLoweringState& state, IRRewriter& rewriter) {
   Location loc = computeOp->getLoc();
 
-  if (inlineInputlessHelperComputeForWeightLikeUsers(computeOp, rewriter, state.constantFolder))
+  if (inlineInputlessHelperComputeForWeightLikeUsers(computeOp, rewriter, constantFolder))
     return success();
 
   SmallVector<Operation*> helperChain;
@@ -167,31 +168,33 @@ LogicalResult lowerComputeOp(spatial::SpatCompute computeOp, CoreLoweringState&
   auto yieldOp = cast<spatial::SpatYieldOp>(block.getTerminator());
 
   for (auto [inputIndex, input] : llvm::enumerate(computeOp.getInputs())) {
-    BlockArgument blockArg = computeOp.getInputArgument(inputIndex);
+    auto blockArg = computeOp.getInputArgument(inputIndex);
+    if (!blockArg)
+      return computeOp.emitOpError("expected compute input block arguments during lowering");
     auto receiveOp = dyn_cast_or_null<spatial::SpatChannelReceiveOp>(input.getDefiningOp());
-    if (receiveOp && !blockArg.use_empty()) {
+    if (receiveOp && !blockArg->use_empty()) {
-      rewriter.setInsertionPoint(getEarliestUserWithinBlock(blockArg));
+      rewriter.setInsertionPoint(getEarliestUserWithinBlock(*blockArg));
-      auto outputType = cast<ShapedType>(blockArg.getType());
+      auto outputType = cast<ShapedType>(blockArg->getType());
       auto outputBuffer = createEmptyTensorFromShaped(rewriter, receiveOp.getLoc(), outputType);
-      auto sizeAttr = getTensorSizeInBytesAttr(rewriter, blockArg);
+      auto sizeAttr = getTensorSizeInBytesAttr(rewriter, *blockArg);
       Value received =
         PimReceiveOp::create(
           rewriter, receiveOp.getLoc(), outputBuffer.getType(), outputBuffer, sizeAttr, receiveOp.getSourceCoreId())
           .getOutput();
-      blockArg.replaceAllUsesWith(received);
+      blockArg->replaceAllUsesWith(received);
-      markOpToRemove(state, receiveOp);
+      markOpToRemove(receiveOp);
       continue;
     }
 
     auto receiveTensorOp = dyn_cast_or_null<spatial::SpatChannelReceiveTensorOp>(input.getDefiningOp());
-    if (receiveTensorOp && !blockArg.use_empty()) {
+    if (receiveTensorOp && !blockArg->use_empty()) {
       FailureOr<SmallVector<int32_t>> sourceCoreIds = getConstantI32Values(receiveTensorOp.getSourceCoreIds());
       if (failed(sourceCoreIds))
         return receiveTensorOp.emitOpError("expected constant sourceCoreIds");
       for (int32_t& sourceCoreId : *sourceCoreIds)
         sourceCoreId = translateSpatialCoreIdToPimCoreId(sourceCoreId);
-      rewriter.setInsertionPoint(getEarliestUserWithinBlock(blockArg));
+      rewriter.setInsertionPoint(getEarliestUserWithinBlock(*blockArg));
-      auto outputType = cast<ShapedType>(blockArg.getType());
+      auto outputType = cast<ShapedType>(blockArg->getType());
       auto outputBuffer = createEmptyTensorFromShaped(rewriter, receiveTensorOp.getLoc(), outputType);
       Value received = PimReceiveTensorOp::create(rewriter,
                                                   receiveTensorOp.getLoc(),
@@ -199,8 +202,8 @@ LogicalResult lowerComputeOp(spatial::SpatCompute computeOp, CoreLoweringState&
                                                   outputBuffer,
                                                   rewriter.getDenseI32ArrayAttr(*sourceCoreIds))
                          .getOutput();
-      blockArg.replaceAllUsesWith(received);
+      blockArg->replaceAllUsesWith(received);
-      markOpToRemove(state, receiveTensorOp);
+      markOpToRemove(receiveTensorOp);
     }
   }
 
@@ -211,9 +214,8 @@ LogicalResult lowerComputeOp(spatial::SpatCompute computeOp, CoreLoweringState&
     if (result.use_empty())
       continue;
 
-    ReturnPathState returnPathState {state.outputTensors, state.operationsToRemove};
     ReturnPathLoweringResult returnPathResult =
-      lowerComputeResultReturnPath(computeOp, cast<OpResult>(result), yieldValue, returnPathState, rewriter);
+      lowerComputeResultReturnPath(computeOp, cast<OpResult>(result), yieldValue, rewriter);
     if (returnPathResult == ReturnPathLoweringResult::Failure)
       return failure();
     if (returnPathResult == ReturnPathLoweringResult::Handled)
@@ -237,19 +239,19 @@ LogicalResult lowerComputeOp(spatial::SpatCompute computeOp, CoreLoweringState&
   if (!computeOp.getWeights().empty())
     computeWeights.append(computeOp.getWeights().begin(), computeOp.getWeights().end());
   rewriter.setInsertionPointAfter(computeOp);
-  auto coreOp = PimCoreOp::create(rewriter,
+  auto coreOp = PimCoreOp::create(
-                                  loc,
+    rewriter, loc, ValueRange(computeWeights), rewriter.getI32IntegerAttr(getPimCoreIdForComputeOp(computeOp, coreId)));
-                                  ValueRange(computeWeights),
-                                  rewriter.getI32IntegerAttr(getPimCoreIdForComputeOp(computeOp, state.nextCoreId)));
   rewriter.setInsertionPointToStart(&block);
   auto& coreOpBlocks = coreOp.getBody().getBlocks();
   for (auto [inputIndex, input] : llvm::enumerate(computeOp.getInputs())) {
-    BlockArgument blockArg = computeOp.getInputArgument(inputIndex);
+    auto blockArg = computeOp.getInputArgument(inputIndex);
-    if (blockArg.use_empty())
+    if (!blockArg)
+      return computeOp.emitOpError("expected compute input block arguments during input materialization");
+    if (blockArg->use_empty())
       continue;
 
     if (auto constantOp = input.getDefiningOp<arith::ConstantOp>()) {
-      blockArg.replaceAllUsesWith(getOrCreateHostConstantLike(constantOp, state.constantFolder));
+      blockArg->replaceAllUsesWith(getOrCreateHostConstantLike(constantOp, constantFolder));
       continue;
     }
 
@@ -261,13 +263,13 @@ LogicalResult lowerComputeOp(spatial::SpatCompute computeOp, CoreLoweringState&
       PimMemCopyHostToDevOp::create(rewriter,
                                     loc,
                                     outputBuffer.getType(),
-                                    getOrCreateHostIndexConstant(outputBuffer.getOperation(), 0, state.constantFolder),
+                                    getOrCreateHostIndexConstant(outputBuffer.getOperation(), 0, constantFolder),
-                                    getOrCreateHostIndexConstant(outputBuffer.getOperation(), 0, state.constantFolder),
+                                    getOrCreateHostIndexConstant(outputBuffer.getOperation(), 0, constantFolder),
                                     outputBuffer,
                                     input,
                                     getTensorSizeInBytesAttr(rewriter, input))
         .getOutput();
-    blockArg.replaceAllUsesWith(copied);
+    blockArg->replaceAllUsesWith(copied);
   }
   if (!computeOp.getInputs().empty())
     block.eraseArguments(computeOp.getWeights().size(), computeOp.getInputs().size());

src/PIM/Conversion/SpatialToPim/CoreLoweringPatterns.hpp

@@ -1,23 +0,0 @@
-#pragma once
-
-#include "mlir/IR/PatternMatch.h"
-#include "mlir/Transforms/FoldUtils.h"
-
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/ReturnPathNormalization.hpp"
-#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
-
-namespace onnx_mlir {
-
-struct CoreLoweringState {
-  size_t& nextCoreId;
-  llvm::SmallVectorImpl<OutputTensorFactory>& outputTensors;
-  llvm::SmallVectorImpl<mlir::Operation*>& operationsToRemove;
-  mlir::OperationFolder& constantFolder;
-};
-
-void markOpToRemove(CoreLoweringState& state, mlir::Operation* op);
-
-mlir::LogicalResult
-lowerComputeOp(spatial::SpatCompute computeOp, CoreLoweringState& state, mlir::IRRewriter& rewriter);
-
-} // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/GlobalTensorMaterialization.cpp

@@ -77,8 +77,10 @@ struct MoveExtractSliceIntoCompute final : OpRewritePattern<mlir::tensor::Extrac
         if (!inputIndex)
           return failure();
         auto BBArgValue = spatCompute.getInputArgument(*inputIndex);
+        if (!BBArgValue)
+          return failure();
 
-        if (BBArgValue.use_empty())
+        if (BBArgValue->use_empty())
           continue;
 
         rewriter.setInsertionPoint(&spatCompute.getBody().front().front());
@@ -95,8 +97,10 @@ struct MoveExtractSliceIntoCompute final : OpRewritePattern<mlir::tensor::Extrac
         if (!inputIndex)
           return failure();
         auto BBArgValue = spatComputeBatch.getInputArgument(*inputIndex);
+        if (!BBArgValue)
+          return failure();
 
-        if (BBArgValue.use_empty())
+        if (BBArgValue->use_empty())
           continue;
 
         rewriter.setInsertionPoint(&spatComputeBatch.getBody().front().front());
@@ -141,152 +145,6 @@ struct MoveExtractSliceIntoCompute final : OpRewritePattern<mlir::tensor::Extrac
   }
 };
 
-// Turns runtime constants consumed by compute regions into private globals and local loads.
-struct ArithConstToGlobalMemoryPattern final : OpRewritePattern<mlir::arith::ConstantOp> {
-  using OpRewritePattern::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(mlir::arith::ConstantOp constantOp, PatternRewriter& rewriter) const override {
-    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());
-      auto globalOp = createPrivateMemrefGlobalWithUniqueName(rewriter,
-                                                              loc,
-                                                              constantOp->getParentOfType<ModuleOp>(),
-                                                              "const",
-                                                              memRefType,
-                                                              constantOp.getValueAttr(),
-                                                              rewriter.getUnitAttr());
-      std::string argName = globalOp.getSymName().str();
-
-      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 inputIndex = getDirectComputeLikeInputIndex(spatCompute, constUses.getOperandNumber());
-          if (!inputIndex)
-            return failure();
-          auto BBArgIndex = *inputIndex;
-          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()});
-          }
-
-          replaceAndEraseDirectComputeLikeInput(
-            rewriter, spatCompute.getOperation(), BBArgIndex, mapSpatComputeToConst[spatCompute.getOperation()]);
-        }
-        else if (auto spatComputeBatch = llvm::dyn_cast<spatial::SpatComputeBatch>(constUsers)) {
-          auto inputIndex = getDirectComputeLikeInputIndex(spatComputeBatch, constUses.getOperandNumber());
-          if (!inputIndex)
-            return failure();
-          auto BBArgIndex = *inputIndex;
-          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()});
-          }
-
-          replaceAndEraseDirectComputeLikeInput(rewriter,
-                                                spatComputeBatch.getOperation(),
-                                                BBArgIndex,
-                                                mapSpatComputeToConst[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()) {
-      Value hostConstant = constantOp.getResult();
-
-      for (auto& constUses : llvm::make_early_inc_range(constantOp->getUses())) {
-        auto constUsers = constUses.getOwner();
-
-        if (auto spatCompute = llvm::dyn_cast<spatial::SpatCompute>(constUsers)) {
-          auto inputIndex = getDirectComputeLikeInputIndex(spatCompute, constUses.getOperandNumber());
-          if (!inputIndex)
-            return failure();
-          auto BBArgIndex = *inputIndex;
-          replaceAndEraseDirectComputeLikeInput(rewriter, spatCompute.getOperation(), BBArgIndex, hostConstant);
-        }
-        else if (auto spatComputeBatch = llvm::dyn_cast<spatial::SpatComputeBatch>(constUsers)) {
-          auto inputIndex = getDirectComputeLikeInputIndex(spatComputeBatch, constUses.getOperandNumber());
-          if (!inputIndex)
-            return failure();
-          auto BBArgIndex = *inputIndex;
-          replaceAndEraseDirectComputeLikeInput(rewriter, spatComputeBatch.getOperation(), BBArgIndex, hostConstant);
-        }
-        else if (constUsers->getParentOfType<spatial::SpatCompute>()) {
-          constUses.set(hostConstant);
-        }
-        else {
-          auto batchParent = constUsers->getParentOfType<spatial::SpatComputeBatch>();
-          assert(batchParent && "Global Constant used direcly not within a compute");
-          constUses.set(hostConstant);
-        }
-      }
-    }
-    if (constantOp->use_empty())
-      rewriter.eraseOp(constantOp);
-    return success();
-  }
-};
-
 // Materializes public function tensor inputs as globals so compute bodies can load them uniformly.
 struct FuncOpArgToGlobalMemoryPattern final : OpRewritePattern<mlir::func::FuncOp> {
   using OpRewritePattern::OpRewritePattern;
@@ -363,8 +221,7 @@ struct FuncOpArgToGlobalMemoryPattern final : OpRewritePattern<mlir::func::FuncO
 
 } // namespace
 void populateGlobalTensorMaterializationPatterns(RewritePatternSet& patterns) {
-  patterns.add<MoveExtractSliceIntoCompute, FuncOpArgToGlobalMemoryPattern, ArithConstToGlobalMemoryPattern>(
+  patterns.add<MoveExtractSliceIntoCompute, FuncOpArgToGlobalMemoryPattern>(patterns.getContext());
-    patterns.getContext());
 }
 
 } // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/ReturnPathNormalization.cpp

@@ -9,9 +9,9 @@
 #include "mlir/Transforms/FoldUtils.h"
 
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/ReturnPathNormalization.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
@@ -44,11 +44,6 @@ static bool isReturnHelperChainOp(Operation* op) {
              pim::PimTransposeOp>(op);
 }
 
-static void markOpToRemove(ReturnPathState& state, Operation* op) {
-  if (!llvm::is_contained(state.operationsToRemove, op))
-    state.operationsToRemove.push_back(op);
-}
-
 static std::string makeUniqueSymbolName(Operation* symbolTableOp, StringRef baseName) {
   std::string name = baseName.str();
   unsigned suffix = 0;
@@ -390,9 +385,7 @@ static Value emitHostCopy(IRRewriter& rewriter,
 
 } // namespace
 
-void addReturnOutputBuffers(func::ReturnOp returnOp,
+void raptor::SpatialToPimPass::addReturnOutputBuffers(func::ReturnOp returnOp, IRRewriter& rewriter) {
-                            IRRewriter& rewriter,
-                            SmallVectorImpl<OutputTensorFactory>& outputTensors) {
   outputTensors.reserve(returnOp->getNumOperands());
   for (auto [index, returnValue] : llvm::enumerate(returnOp->getOperands())) {
     Value currentReturnValue = returnValue;
@@ -427,8 +420,8 @@ void addReturnOutputBuffers(func::ReturnOp returnOp,
   }
 }
 
-ReturnPathLoweringResult lowerProducedValueReturnPath(
+raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::lowerProducedValueReturnPath(
-  Operation* producerOp, Value producedValue, Value storedValue, ReturnPathState& state, IRRewriter& rewriter) {
+  Operation* producerOp, Value producedValue, Value storedValue, IRRewriter& rewriter) {
   Location loc = producerOp->getLoc();
   OperationFolder constantFolder(producerOp->getContext());
   auto storedTensorType = cast<TensorType>(storedValue.getType());
@@ -437,13 +430,13 @@ ReturnPathLoweringResult lowerProducedValueReturnPath(
     Value currentStoredValue = storedValue;
     cloneHelperChain(storedValue, returnUse->helperChain, rewriter, constantFolder, currentStoredValue);
     for (Operation* op : returnUse->helperChain)
-      markOpToRemove(state, op);
+      markOpToRemove(op);
 
     auto storedType = cast<ShapedType>(currentStoredValue.getType());
-    size_t elementSize = storedType.getElementTypeBitWidth() / 8;
+    size_t elementSize = getElementTypeSizeInBytes(storedType.getElementType());
     if (auto storedOp = currentStoredValue.getDefiningOp())
       rewriter.setInsertionPointAfter(storedOp);
-    Value outputTensor = state.outputTensors[returnUse->returnIndex](rewriter, loc);
+    Value outputTensor = outputTensors[returnUse->returnIndex](rewriter, loc);
     emitHostCopy(rewriter,
                  loc,
                  outputTensor,
@@ -462,9 +455,9 @@ ReturnPathLoweringResult lowerProducedValueReturnPath(
 
     if (isa<func::ReturnOp>(resultUser)) {
       size_t resultIndexInReturn = resultUse.getOperandNumber();
-      size_t elementSize = storedTensorType.getElementType().getIntOrFloatBitWidth() / 8;
+      size_t elementSize = getElementTypeSizeInBytes(storedTensorType.getElementType());
       rewriter.setInsertionPointAfterValue(storedValue);
-      Value outputTensor = state.outputTensors[resultIndexInReturn](rewriter, loc);
+      Value outputTensor = outputTensors[resultIndexInReturn](rewriter, loc);
       emitHostCopy(rewriter,
                    loc,
                    outputTensor,
@@ -478,13 +471,13 @@ ReturnPathLoweringResult lowerProducedValueReturnPath(
   }
 
   if (auto concatReturnUse = analyzeConcatReturnUse(producedValue)) {
-    size_t elementSize = storedTensorType.getElementTypeBitWidth() / 8;
+    size_t elementSize = getElementTypeSizeInBytes(storedTensorType.getElementType());
     for (Operation* concatOp : concatReturnUse->concatChain)
-      markOpToRemove(state, concatOp);
+      markOpToRemove(concatOp);
 
     if (concatReturnUse->helperChain.empty()) {
       rewriter.setInsertionPointAfterValue(storedValue);
-      Value outputTensor = state.outputTensors[concatReturnUse->returnIndex](rewriter, loc);
+      Value outputTensor = outputTensors[concatReturnUse->returnIndex](rewriter, loc);
       auto outputType = cast<ShapedType>(outputTensor.getType());
       int64_t flatOffset = computeFlatElementIndex(concatReturnUse->sliceOffsets, outputType.getShape());
       emitHostCopy(rewriter,
@@ -505,7 +498,7 @@ ReturnPathLoweringResult lowerProducedValueReturnPath(
       return ReturnPathLoweringResult::Failure;
     }
     rewriter.setInsertionPointAfterValue(storedValue);
-    Value outputTensor = state.outputTensors[concatReturnUse->returnIndex](rewriter, loc);
+    Value outputTensor = outputTensors[concatReturnUse->returnIndex](rewriter, loc);
     auto outputType = cast<ShapedType>(outputTensor.getType());
     for (int64_t linearIndex = 0; linearIndex < storedType.getNumElements(); ++linearIndex) {
       SmallVector<int64_t> sourceIndices = expandFlatElementIndex(linearIndex, storedType.getShape());
@@ -553,12 +546,12 @@ ReturnPathLoweringResult lowerProducedValueReturnPath(
   return ReturnPathLoweringResult::NotReturnPath;
 }
 
-ReturnPathLoweringResult lowerComputeResultReturnPath(
+raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::lowerComputeResultReturnPath(
-  spatial::SpatCompute computeOp, OpResult result, Value yieldValue, ReturnPathState& state, IRRewriter& rewriter) {
+  spatial::SpatCompute computeOp, OpResult result, Value yieldValue, IRRewriter& rewriter) {
-  return lowerProducedValueReturnPath(computeOp.getOperation(), result, yieldValue, state, rewriter);
+  return lowerProducedValueReturnPath(computeOp.getOperation(), result, yieldValue, rewriter);
 }
 
-void replaceReturnWithOutputBuffers(func::ReturnOp returnOp, IRRewriter& rewriter, ReturnPathState& state) {
+void raptor::SpatialToPimPass::replaceReturnWithOutputBuffers(func::ReturnOp returnOp, IRRewriter& rewriter) {
   auto markOwnedReturnChain = [&](Operation* op, auto&& markOwnedReturnChain) -> void {
     if (!op)
       return;
@@ -575,13 +568,13 @@ void replaceReturnWithOutputBuffers(func::ReturnOp returnOp, IRRewriter& rewrite
 
     if (isReturnHelperChainOp(op)) {
       Value source = op->getOperand(0);
-      markOpToRemove(state, op);
+      markOpToRemove(op);
       markOwnedReturnChain(source.getDefiningOp(), markOwnedReturnChain);
       return;
     }
 
     if (auto computeOp = dyn_cast<spatial::SpatCompute>(op)) {
-      markOpToRemove(state, computeOp);
+      markOpToRemove(computeOp);
       if (!computeOp.getInputs().empty())
         for (Value input : computeOp.getInputs())
           markOwnedReturnChain(input.getDefiningOp(), markOwnedReturnChain);
@@ -589,33 +582,33 @@ void replaceReturnWithOutputBuffers(func::ReturnOp returnOp, IRRewriter& rewrite
     }
 
     if (auto concatOp = dyn_cast<tensor::ConcatOp>(op)) {
-      markOpToRemove(state, concatOp);
+      markOpToRemove(concatOp);
       for (Value operand : concatOp.getOperands())
         markOwnedReturnChain(operand.getDefiningOp(), markOwnedReturnChain);
       return;
     }
 
     if (auto concatOp = dyn_cast<spatial::SpatConcatOp>(op)) {
-      markOpToRemove(state, concatOp);
+      markOpToRemove(concatOp);
       for (Value operand : concatOp.getInputs())
         markOwnedReturnChain(operand.getDefiningOp(), markOwnedReturnChain);
       return;
     }
 
     if (auto concatOp = dyn_cast<pim::PimConcatOp>(op)) {
-      markOpToRemove(state, concatOp);
+      markOpToRemove(concatOp);
       for (Value operand : concatOp.getInputs())
         markOwnedReturnChain(operand.getDefiningOp(), markOwnedReturnChain);
       return;
     }
 
     if (auto receiveOp = dyn_cast<spatial::SpatChannelReceiveOp>(op)) {
-      markOpToRemove(state, receiveOp);
+      markOpToRemove(receiveOp);
       return;
     }
 
     if (auto receiveTensorOp = dyn_cast<spatial::SpatChannelReceiveTensorOp>(op))
-      markOpToRemove(state, receiveTensorOp);
+      markOpToRemove(receiveTensorOp);
   };
 
   SmallVector<Value> originalOperands(returnOp.getOperands().begin(), returnOp.getOperands().end());
@@ -624,7 +617,7 @@ void replaceReturnWithOutputBuffers(func::ReturnOp returnOp, IRRewriter& rewrite
     size_t orderWithinReturn = it.index();
     Operation* returnOperand = it.value().getDefiningOp();
     rewriter.setInsertionPoint(returnOp);
-    Value outputTensor = state.outputTensors[orderWithinReturn](rewriter, loc);
+    Value outputTensor = outputTensors[orderWithinReturn](rewriter, loc);
     rewriter.modifyOpInPlace(returnOp, [&] { returnOp.setOperand(orderWithinReturn, outputTensor); });
     markOwnedReturnChain(returnOperand, markOwnedReturnChain);
   }

src/PIM/Conversion/SpatialToPim/ReturnPathNormalization.hpp

@@ -1,43 +0,0 @@
-#pragma once
-
-#include "mlir/Dialect/Func/IR/FuncOps.h"
-#include "mlir/IR/PatternMatch.h"
-
-#include <functional>
-
-#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
-
-namespace onnx_mlir {
-
-using OutputTensorFactory = std::function<mlir::Value(mlir::IRRewriter& rewriter, mlir::Location loc)>;
-
-struct ReturnPathState {
-  llvm::SmallVectorImpl<OutputTensorFactory>& outputTensors;
-  llvm::SmallVectorImpl<mlir::Operation*>& operationsToRemove;
-};
-
-enum class ReturnPathLoweringResult {
-  Handled,
-  NotReturnPath,
-  Failure
-};
-
-void addReturnOutputBuffers(mlir::func::ReturnOp returnOp,
-                            mlir::IRRewriter& rewriter,
-                            llvm::SmallVectorImpl<OutputTensorFactory>& outputTensors);
-
-ReturnPathLoweringResult lowerComputeResultReturnPath(spatial::SpatCompute computeOp,
-                                                      mlir::OpResult result,
-                                                      mlir::Value yieldValue,
-                                                      ReturnPathState& state,
-                                                      mlir::IRRewriter& rewriter);
-
-ReturnPathLoweringResult lowerProducedValueReturnPath(mlir::Operation* producerOp,
-                                                      mlir::Value producedValue,
-                                                      mlir::Value storedValue,
-                                                      ReturnPathState& state,
-                                                      mlir::IRRewriter& rewriter);
-
-void replaceReturnWithOutputBuffers(mlir::func::ReturnOp returnOp, mlir::IRRewriter& rewriter, ReturnPathState& state);
-
-} // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.cpp

@@ -14,7 +14,6 @@
 #include "mlir/IR/Value.h"
 #include "mlir/Pass/Pass.h"
 #include "mlir/Transforms/FoldUtils.h"
-#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "mlir/Transforms/WalkPatternRewriteDriver.h"
 
 #include "llvm/ADT/StringRef.h"
@@ -24,54 +23,28 @@
 #include <cassert>
 #include <utility>
 
+#include "Common/PimCommon.hpp"
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
-#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "Conversion/SpatialToPim/ChannelLoweringPatterns.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/BatchCoreLoweringPatterns.hpp"
+#include "Conversion/SpatialToPim/Common.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/ChannelLoweringPatterns.hpp"
+#include "Conversion/SpatialToPim/GlobalTensorMaterialization.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/Cleanup.hpp"
+#include "Conversion/SpatialToPim/PhaseVerification.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
+#include "Conversion/SpatialToPim/TensorPackingPatterns.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/CoreLoweringPatterns.hpp"
+#include "Dialect/Pim/PimOps.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/GlobalTensorMaterialization.hpp"
+#include "Dialect/Spatial/SpatialOps.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/PhaseVerification.hpp"
+#include "Pass/PIMPasses.h"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/ReturnPathNormalization.hpp"
+#include "SpatialToPimPass.hpp"
-#include "src/Accelerators/PIM/Conversion/SpatialToPim/TensorPackingPatterns.hpp"
-#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
-#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
-#include "src/Accelerators/PIM/Pass/PIMPasses.h"
 
 using namespace mlir;
 using namespace onnx_mlir;
 using namespace pim;
 
 namespace onnx_mlir {
-
+namespace raptor {
-namespace {
 
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/SpatialToPim.hpp.inc"
 
-struct SpatialToPimPass : PassWrapper<SpatialToPimPass, OperationPass<ModuleOp>> {
+} // namespace raptor
-  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(SpatialToPimPass)
-  StringRef getArgument() const override { return "convert-spatial-to-pim"; }
-  StringRef getDescription() const override { return "Lower Spatial ops to PIM-ready format"; }
-
-  SpatialToPimPass() = default;
-  SpatialToPimPass(const SpatialToPimPass& pass) {}
-
-  void runOnOperation() final;
-
-private:
-  SmallVector<OutputTensorFactory> outputTensors;
-  size_t coreId = 0;
-  SmallVector<Operation*> operationsToRemove;
-
-  LogicalResult allocateAndInitializeCoreLocalVariables(func::FuncOp funcOp, IRRewriter& rewriter);
-
-  void markOpToRemove(Operation* op);
-
-  void enlargeVMMOutTensorsToCrossbarSize(func::FuncOp funcOp, IRRewriter& rewriter);
-};
-
-} // namespace
 
 static memref::GlobalOp getOrCreateZeroGlobal(IRRewriter& rewriter, Location loc, RankedTensorType tensorType) {
   auto moduleOp = rewriter.getBlock()->getParentOp()->getParentOfType<ModuleOp>();
@@ -151,8 +124,10 @@ padHVectorInputToCrossbarSize(IRRewriter& rewriter, Location loc, Value vector,
   return PimMemCopyOp::create(rewriter, loc, paddedType, zeroed, vector, zeroAttr, zeroAttr, sizeAttr).getOutput();
 }
 
-void SpatialToPimPass::runOnOperation() {
+void onnx_mlir::raptor::SpatialToPimPass::runOnOperation() {
   coreId = 0;
+  outputTensors.clear();
+  operationsToRemove.clear();
   ModuleOp moduleOp = getOperation();
   MLIRContext* ctx = moduleOp.getContext();
 
@@ -198,18 +173,16 @@ void SpatialToPimPass::runOnOperation() {
   walkAndApplyPatterns(moduleOp, std::move(globalTensorPatterns));
 
   auto returnOp = cast<func::ReturnOp>(funcOp.front().getTerminator());
-  addReturnOutputBuffers(returnOp, rewriter, outputTensors);
+  addReturnOutputBuffers(returnOp, rewriter);
-  ReturnPathState returnPathState {outputTensors, operationsToRemove};
   if (failed(allocateAndInitializeCoreLocalVariables(funcOp, rewriter))) {
     funcOp.emitOpError("failed to allocate or initialize core-local tensors during Spatial-to-PIM lowering");
     signalPassFailure();
     return;
   }
 
-  CoreLoweringState coreLoweringState {coreId, outputTensors, operationsToRemove, constantFolder};
   for (auto computeOp : funcOp.getOps<spatial::SpatCompute>()) {
     markOpToRemove(computeOp);
-    if (failed(lowerComputeOp(computeOp, coreLoweringState, rewriter))) {
+    if (failed(lowerComputeOp(computeOp, rewriter, constantFolder))) {
       computeOp.emitOpError("failed to lower spat.compute to pim.core");
       signalPassFailure();
       return;
@@ -218,7 +191,7 @@ void SpatialToPimPass::runOnOperation() {
 
   for (auto computeBatchOp : funcOp.getOps<spatial::SpatComputeBatch>()) {
     markOpToRemove(computeBatchOp);
-    if (failed(lowerComputeBatchOp(computeBatchOp, coreLoweringState, rewriter))) {
+    if (failed(lowerComputeBatchOp(computeBatchOp, rewriter))) {
       computeBatchOp.emitOpError("failed to lower spat.compute_batch to pim.core_batch");
       signalPassFailure();
       return;
@@ -267,14 +240,8 @@ void SpatialToPimPass::runOnOperation() {
   }
 
   enlargeVMMOutTensorsToCrossbarSize(funcOp, rewriter);
-  replaceReturnWithOutputBuffers(returnOp, rewriter, returnPathState);
+  replaceReturnWithOutputBuffers(returnOp, rewriter);
-
+  eraseOpsToRemove();
-  SmallVector<Operation*> pendingRemovals(operationsToRemove.begin(), operationsToRemove.end());
-  if (failed(erasePendingOps(pendingRemovals, rewriter))) {
-    funcOp.emitOpError("failed to erase obsolete Spatial ops after lowering to PIM");
-    signalPassFailure();
-    return;
-  }
 
   RewritePatternSet finalTensorPackingPatterns(ctx);
   populateTensorPackingPatterns(finalTensorPackingPatterns);
@@ -316,7 +283,7 @@ void SpatialToPimPass::runOnOperation() {
   dumpModule(moduleOp, "pim0");
 }
 
-void SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp funcOp, IRRewriter& rewriter) {
+void raptor::SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp funcOp, IRRewriter& rewriter) {
   OperationFolder constantFolder(funcOp.getContext());
   funcOp.walk([&](PimVMMOp vmmOp) {
     auto outputType = cast<RankedTensorType>(vmmOp.getOutput().getType());
@@ -350,16 +317,17 @@ void SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp funcOp, I
   });
 }
 
-LogicalResult SpatialToPimPass::allocateAndInitializeCoreLocalVariables(func::FuncOp funcOp, IRRewriter& rewriter) {
+LogicalResult raptor::SpatialToPimPass::allocateAndInitializeCoreLocalVariables(func::FuncOp funcOp,
+                                                                                IRRewriter& rewriter) {
   Location loc = funcOp.getLoc();
   OperationFolder constantFolder(funcOp.getContext());
 
   auto insertMemCopyHostToDev = [&](Value inputTensor, int64_t elementsOffset) {
     auto tensorType = cast<ShapedType>(inputTensor.getType());
     Type elementType = tensorType.getElementType();
-    if (!elementType.isIntOrFloat())
+    if (!hasByteSizedElementType(elementType))
       return;
-    size_t elementByteSize = elementType.getIntOrFloatBitWidth() / 8;
+    size_t elementByteSize = getElementTypeSizeInBytes(elementType);
     rewriter.setInsertionPointAfter(inputTensor.getDefiningOp());
 
     auto deviceTensor = tensor::EmptyOp::create(rewriter, loc, tensorType.getShape(), elementType);
@@ -394,11 +362,18 @@ LogicalResult SpatialToPimPass::allocateAndInitializeCoreLocalVariables(func::Fu
   return success();
 }
 
-void SpatialToPimPass::markOpToRemove(Operation* op) {
+void raptor::SpatialToPimPass::markOpToRemove(Operation* op) {
   if (!llvm::is_contained(operationsToRemove, op))
     operationsToRemove.push_back(op);
 }
 
-std::unique_ptr<Pass> createSpatialToPimPass() { return std::make_unique<SpatialToPimPass>(); }
+void raptor::SpatialToPimPass::eraseOpsToRemove() {
+  for (Operation* op : operationsToRemove) {
+    op->dropAllUses();
+    op->erase();
+  }
+}
+
+std::unique_ptr<Pass> createSpatialToPimPass() { return std::make_unique<raptor::SpatialToPimPass>(); }
 
 } // namespace onnx_mlir

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.hpp

@@ -0,0 +1,72 @@
+#pragma once
+
+#include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/Dialect/SCF/Utils/Utils.h"
+#include "mlir/IR/BuiltinOps.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/IR/Value.h"
+#include "mlir/Pass/Pass.h"
+#include "mlir/Transforms/FoldUtils.h"
+
+#include "llvm/ADT/StringRef.h"
+
+#include <functional>
+
+#include "Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "Conversion/SpatialToPim/Common.hpp"
+#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+
+namespace onnx_mlir {
+namespace raptor {
+
+struct SpatialToPimPass : mlir::PassWrapper<SpatialToPimPass, mlir::OperationPass<mlir::ModuleOp>> {
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(SpatialToPimPass)
+  llvm::StringRef getArgument() const override { return "convert-spatial-to-pim"; }
+  llvm::StringRef getDescription() const override { return "Lower Spatial ops to PIM-ready format"; }
+
+  SpatialToPimPass() = default;
+  SpatialToPimPass(const SpatialToPimPass& pass) {}
+
+  void runOnOperation() final;
+
+private:
+  using OutputTensorFactory = std::function<mlir::Value(mlir::IRRewriter& rewriter, mlir::Location loc)>;
+
+  llvm::SmallVector<OutputTensorFactory> outputTensors;
+  size_t coreId = 0;
+  llvm::SmallVector<mlir::Operation*> operationsToRemove;
+
+  mlir::LogicalResult allocateAndInitializeCoreLocalVariables(mlir::func::FuncOp funcOp, mlir::IRRewriter& rewriter);
+  mlir::LogicalResult
+  lowerComputeOp(spatial::SpatCompute computeOp, mlir::IRRewriter& rewriter, mlir::OperationFolder& constantFolder);
+  mlir::LogicalResult lowerComputeBatchOp(spatial::SpatComputeBatch computeBatchOp, mlir::IRRewriter& rewriter);
+
+  enum class ReturnPathLoweringResult {
+    Handled,
+    NotReturnPath,
+    Failure
+  };
+
+  void addReturnOutputBuffers(mlir::func::ReturnOp returnOp, mlir::IRRewriter& rewriter);
+  ReturnPathLoweringResult lowerComputeResultReturnPath(spatial::SpatCompute computeOp,
+                                                        mlir::OpResult result,
+                                                        mlir::Value yieldValue,
+                                                        mlir::IRRewriter& rewriter);
+  ReturnPathLoweringResult lowerProducedValueReturnPath(mlir::Operation* producerOp,
+                                                        mlir::Value producedValue,
+                                                        mlir::Value storedValue,
+                                                        mlir::IRRewriter& rewriter);
+  void replaceReturnWithOutputBuffers(mlir::func::ReturnOp returnOp, mlir::IRRewriter& rewriter);
+
+  void markOpToRemove(mlir::Operation* op);
+  void eraseOpsToRemove();
+
+  void enlargeVMMOutTensorsToCrossbarSize(mlir::func::FuncOp funcOp, mlir::IRRewriter& rewriter);
+};
+
+} // namespace raptor
+
+} // namespace onnx_mlir

src/PIM/Dialect/Pim/PimOps.cpp

@@ -1,7 +1,7 @@
-#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
-
 #include <string>
 
+#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
+
 using namespace mlir;
 
 namespace onnx_mlir {

src/PIM/Dialect/Pim/PimOpsAsm.cpp

@@ -56,7 +56,8 @@ static ParseResult parseBlockArgumentList(OpAsmParser& parser, SmallVectorImpl<O
   return parser.parseRParen();
 }
 
-static void printBoundValueList(OpAsmPrinter& printer, ValueRange arguments, ValueRange operands, ListDelimiter delimiter) {
+static void
+printBoundValueList(OpAsmPrinter& printer, ValueRange arguments, ValueRange operands, ListDelimiter delimiter) {
   printCompressedValueList(printer, arguments, delimiter);
   printer << " = ";
   printCompressedValueList(printer, operands, delimiter);
@@ -82,10 +83,8 @@ static ParseResult parseBoundValueList(OpAsmParser& parser,
 
   auto parseCloseDelimiter = [&](ListDelimiter currentDelimiter) -> ParseResult {
     switch (currentDelimiter) {
-    case ListDelimiter::Paren:
+    case ListDelimiter::Paren:  return parser.parseRParen();
-      return parser.parseRParen();
+    case ListDelimiter::Square: return parser.parseRSquare();
-    case ListDelimiter::Square:
-      return parser.parseRSquare();
     }
     llvm_unreachable("unsupported delimiter");
   };

src/PIM/Dialect/Pim/PimOpsVerify.cpp

@@ -1,11 +1,14 @@
-#include "mlir/IR/BuiltinTypeInterfaces.h"
+#include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/IR/Block.h"
+#include "mlir/IR/BuiltinOps.h"
+#include "mlir/IR/BuiltinTypeInterfaces.h"
 #include "mlir/IR/Diagnostics.h"
 #include "mlir/IR/OpDefinition.h"
 #include "mlir/IR/TypeUtilities.h"
 
 #include "llvm/Support/LogicalResult.h"
 
+#include "src/Accelerators/PIM/Common/IR/AddressAnalysis.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
@@ -40,7 +43,18 @@ static bool isDefinedInsideRegion(Value value, Region& region) {
 
 static bool isConstantExternalValue(Value value) {
   Operation* definingOp = value.getDefiningOp();
-  return definingOp && definingOp->hasTrait<OpTrait::ConstantLike>();
+  if (!definingOp)
+    return false;
+  if (definingOp->hasTrait<OpTrait::ConstantLike>())
+    return true;
+
+  auto getGlobalOp = dyn_cast<memref::GetGlobalOp>(definingOp);
+  if (!getGlobalOp)
+    return false;
+
+  auto moduleOp = definingOp->getParentOfType<ModuleOp>();
+  auto globalOp = lookupGlobalForGetGlobal(moduleOp, getGlobalOp);
+  return globalOp && globalOp.getConstant();
 }
 
 static LogicalResult verifyOnlyConstantExternalValues(Operation* ownerOp, Region& region, StringRef kind) {
@@ -52,8 +66,8 @@ static LogicalResult verifyOnlyConstantExternalValues(Operation* ownerOp, Region
           || isExplicitHostOperand(op, operand.getOperandNumber()))
         continue;
 
-      InFlightDiagnostic diagnostic =
+      InFlightDiagnostic diagnostic = ownerOp->emitOpError()
-        ownerOp->emitOpError() << kind << " body may only directly reference external constants";
+                                   << kind << " body may only directly reference external constants";
       diagnostic.attachNote(op->getLoc())
         << "non-constant external operand #" << operand.getOperandNumber() << " is used by " << op->getName();
       hasFailure = true;
@@ -139,10 +153,9 @@ LogicalResult PimCoreOp::verify() {
   Block& block = getBody().front();
   if (block.getNumArguments() != getWeights().size())
     return emitError("core body must have one block argument per weight");
-  for (auto [weightIndex, weight] : llvm::enumerate(getWeights())) {
+  for (auto [weightIndex, weight] : llvm::enumerate(getWeights()))
     if (getWeightArgument(weightIndex).getType() != weight.getType())
       return emitError("core weight block argument types must match weight operand types exactly");
-  }
   return verifyOnlyConstantExternalValues(getOperation(), getBody(), "pim.core");
 }
 
@@ -155,14 +168,12 @@ LogicalResult PimCoreBatchOp::verify() {
     return emitError("core_batch body must have lane, weight, and input block arguments");
   if (!getLaneArgument().getType().isIndex())
     return emitError("core_batch first block argument must have index type");
-  for (auto [weightIndex, weight] : llvm::enumerate(getWeights())) {
+  for (auto [weightIndex, weight] : llvm::enumerate(getWeights()))
     if (getWeightArgument(weightIndex).getType() != weight.getType())
       return emitError("core_batch weight block argument types must match weight operand types exactly");
-  }
+  for (auto [inputIndex, input] : llvm::enumerate(getInputs()))
-  for (auto [inputIndex, input] : llvm::enumerate(getInputs())) {
     if (getInputArgument(inputIndex).getType() != input.getType())
       return emitError("core_batch input block argument types must match input operand types exactly");
-  }
   return verifyOnlyConstantExternalValues(getOperation(), getBody(), "pim.core_batch");
 }
 

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

@@ -17,7 +17,7 @@ Value materializeContiguousMemRef(Value memrefValue, Location loc, RewriterBase&
   auto shapedType = cast<ShapedType>(memrefValue.getType());
   auto contiguousType = MemRefType::get(shapedType.getShape(), shapedType.getElementType());
   Value contiguousBuffer = memref::AllocOp::create(rewriter, loc, contiguousType);
-  auto sizeInBytes = shapedType.getNumElements() * shapedType.getElementTypeBitWidth() / 8;
+  auto sizeInBytes = getShapedTypeSizeInBytes(shapedType);
 
   return PimMemCopyOp::create(rewriter,
                               loc,

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

@@ -1,9 +1,10 @@
 #include "Dialect/Pim/Transforms/Bufferization/Common.hpp"
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
 
 using namespace mlir;
 
 IntegerAttr onnx_mlir::pim::getMemRefSizeInBytesAttr(OpBuilder& builder, Value memref) {
   auto type = mlir::cast<MemRefType>(memref.getType());
-  int32_t sizeInBytes = static_cast<int32_t>(type.getNumElements() * type.getElementTypeBitWidth() / 8);
+  int32_t sizeInBytes = static_cast<int32_t>(getShapedTypeSizeInBytes(type));
   return builder.getI32IntegerAttr(sizeInBytes);
 }

src/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.cpp

@@ -9,6 +9,7 @@
 
 #include <limits>
 
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.hpp"
 
 using namespace mlir;
@@ -23,11 +24,12 @@ static bool isSupportedAliasOp(Operation* op) {
 }
 
 static bool isCandidateAllocType(MemRefType type) {
-  return type && type.hasStaticShape() && type.getLayout().isIdentity() && type.getElementTypeBitWidth() > 0;
+  return type && type.hasStaticShape() && type.getLayout().isIdentity()
+      && hasByteSizedElementType(type.getElementType());
 }
 
 static uint64_t getTypeSizeBytes(MemRefType type) {
-  return static_cast<uint64_t>(type.getNumElements() * type.getElementTypeBitWidth() / 8);
+  return static_cast<uint64_t>(type.getNumElements() * getElementTypeSizeInBytes(type.getElementType()));
 }
 
 static FailureOr<uint64_t>
@@ -50,10 +52,9 @@ getLastUseInstruction(memref::AllocOp allocOp, Block& body, const DenseMap<Opera
           pendingValues.push_back(result);
 
       if (auto forOp = dyn_cast<scf::ForOp>(user)) {
-        for (auto [index, initArg] : llvm::enumerate(forOp.getInitArgs())) {
+        for (auto [index, initArg] : llvm::enumerate(forOp.getInitArgs()))
           if (initArg == value)
             pendingValues.push_back(forOp.getResult(index));
-        }
       }
 
       if (auto dpsOp = dyn_cast<DestinationStyleOpInterface>(user)) {

src/PIM/Dialect/Spatial/Spatial.td

@@ -43,8 +43,14 @@ def SpatCompute : SpatOp<"compute",
   let regions = (region SizedRegion<1>:$body);
 
   let extraClassDeclaration = [{
-    ::mlir::BlockArgument getWeightArgument(unsigned idx);
+    std::optional<::mlir::BlockArgument> getWeightArgument(unsigned idx);
-    ::mlir::BlockArgument getInputArgument(unsigned idx);
+    std::optional<::mlir::BlockArgument> getInputArgument(unsigned idx);
+    std::optional<std::tuple<::mlir::Value, ::mlir::BlockArgument>>
+      insertWeight(unsigned idx, ::mlir::Value weight, ::mlir::Location loc);
+    std::optional<std::tuple<::mlir::Value, ::mlir::BlockArgument>>
+      insertInput(unsigned idx, ::mlir::Value input, ::mlir::Location loc);
+    ::mlir::FailureOr<std::tuple<::mlir::OpResult, SpatCompute>>
+      insertOutput(::mlir::RewriterBase &rewriter, unsigned idx, ::mlir::Type type, ::mlir::Location loc);
   }];
 
   let hasVerifier = 1;
@@ -70,10 +76,16 @@ def SpatComputeBatch : SpatOp<"compute_batch",
   let regions = (region SizedRegion<1>:$body);
 
   let extraClassDeclaration = [{
-    ::mlir::BlockArgument getLaneArgument();
+    std::optional<::mlir::BlockArgument> getLaneArgument();
-    ::mlir::BlockArgument getWeightArgument(unsigned idx);
+    std::optional<::mlir::BlockArgument> getWeightArgument(unsigned idx);
-    ::mlir::BlockArgument getInputArgument(unsigned idx);
+    std::optional<::mlir::BlockArgument> getInputArgument(unsigned idx);
-    ::mlir::BlockArgument getOutputArgument(unsigned idx);
+    std::optional<::mlir::BlockArgument> getOutputArgument(unsigned idx);
+    std::optional<std::tuple<::mlir::Value, ::mlir::BlockArgument>>
+      insertWeight(unsigned idx, ::mlir::Value weight, ::mlir::Location loc);
+    std::optional<std::tuple<::mlir::Value, ::mlir::BlockArgument>>
+      insertInput(unsigned idx, ::mlir::Value input, ::mlir::Location loc);
+    ::mlir::FailureOr<std::tuple<::mlir::OpResult, ::mlir::BlockArgument, SpatComputeBatch>>
+      insertOutput(::mlir::RewriterBase &rewriter, unsigned idx, ::mlir::Type type, ::mlir::Location loc);
   }];
 
   let hasVerifier = 1;

src/PIM/Dialect/Spatial/SpatialOps.cpp

@@ -1,16 +1,90 @@
-#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
-
 #include <string>
 
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+
 using namespace mlir;
 
 namespace onnx_mlir {
 namespace spatial {
+namespace {
 
-BlockArgument SpatCompute::getWeightArgument(unsigned idx) { return getBody().front().getArgument(idx); }
+std::optional<BlockArgument> getBatchBodyArgument(Region& body, unsigned argIdx) {
+  if (body.empty())
+    return std::nullopt;
 
-BlockArgument SpatCompute::getInputArgument(unsigned idx) {
+  Block& block = body.front();
-  return getBody().front().getArgument(getWeights().size() + idx);
+  if (argIdx >= block.getNumArguments())
+    return std::nullopt;
+  return block.getArgument(argIdx);
+}
+
+std::optional<BlockArgument> insertBatchBodyArgument(Region& body, unsigned argIdx, Type type, Location loc) {
+  if (body.empty())
+    return std::nullopt;
+  return body.insertArgument(argIdx, type, loc);
+}
+
+void setComputeOperandSegmentSizes(Operation* op, int32_t weightCount, int32_t inputCount) {
+  if (auto compute = dyn_cast<SpatCompute>(op)) {
+    compute.getProperties().setOperandSegmentSizes({weightCount, inputCount});
+    return;
+  }
+  cast<SpatComputeBatch>(op).getProperties().setOperandSegmentSizes({weightCount, inputCount});
+}
+
+} // namespace
+
+std::optional<BlockArgument> SpatCompute::getWeightArgument(unsigned idx) {
+  return getBatchBodyArgument(getBody(), idx);
+}
+
+std::optional<BlockArgument> SpatCompute::getInputArgument(unsigned idx) {
+  return getBatchBodyArgument(getBody(), getWeights().size() + idx);
+}
+
+std::optional<std::tuple<Value, BlockArgument>> SpatCompute::insertWeight(unsigned idx, Value weight, Location loc) {
+  unsigned weightCount = getWeights().size();
+  unsigned inputCount = getInputs().size();
+  getOperation()->insertOperands(idx, ValueRange {weight});
+  setComputeOperandSegmentSizes(
+    getOperation(), static_cast<int32_t>(weightCount + 1), static_cast<int32_t>(inputCount));
+  auto blockArg = insertBatchBodyArgument(getBody(), idx, weight.getType(), loc);
+  if (!blockArg)
+    return std::nullopt;
+  return std::make_tuple(getOperation()->getOperand(idx), *blockArg);
+}
+
+std::optional<std::tuple<Value, BlockArgument>> SpatCompute::insertInput(unsigned idx, Value input, Location loc) {
+  unsigned weightCount = getWeights().size();
+  unsigned inputCount = getInputs().size();
+  getOperation()->insertOperands(weightCount + idx, ValueRange {input});
+  setComputeOperandSegmentSizes(
+    getOperation(), static_cast<int32_t>(weightCount), static_cast<int32_t>(inputCount + 1));
+  auto blockArg = insertBatchBodyArgument(getBody(), weightCount + idx, input.getType(), loc);
+  if (!blockArg)
+    return std::nullopt;
+  return std::make_tuple(getOperation()->getOperand(weightCount + idx), *blockArg);
+}
+
+FailureOr<std::tuple<OpResult, SpatCompute>>
+SpatCompute::insertOutput(RewriterBase& rewriter, unsigned idx, Type type, Location loc) {
+  if (idx > getNumResults())
+    return failure();
+
+  rewriter.setInsertionPoint(getOperation());
+  SmallVector<Type> resultTypes(getResultTypes().begin(), getResultTypes().end());
+  resultTypes.insert(resultTypes.begin() + idx, type);
+  auto newCompute = SpatCompute::create(rewriter, getLoc(), TypeRange(resultTypes), getWeights(), getInputs());
+  newCompute->setAttrs((*this)->getAttrs());
+  setComputeOperandSegmentSizes(newCompute.getOperation(),
+                                static_cast<int32_t>(newCompute.getWeights().size()),
+                                static_cast<int32_t>(newCompute.getInputs().size()));
+  rewriter.inlineRegionBefore(getBody(), newCompute.getBody(), newCompute.getBody().end());
+  for (unsigned oldResultIdx = 0; oldResultIdx < getNumResults(); ++oldResultIdx)
+    getResult(oldResultIdx)
+      .replaceAllUsesWith(newCompute.getResult(oldResultIdx < idx ? oldResultIdx : oldResultIdx + 1));
+  rewriter.eraseOp(getOperation());
+  return std::make_tuple(cast<OpResult>(newCompute.getResult(idx)), newCompute);
 }
 
 void SpatCompute::getAsmBlockArgumentNames(Region& region, OpAsmSetValueNameFn setNameFn) {
@@ -18,42 +92,105 @@ void SpatCompute::getAsmBlockArgumentNames(Region& region, OpAsmSetValueNameFn s
     return;
 
   for (unsigned index = 0; index < getWeights().size(); ++index)
-    setNameFn(getWeightArgument(index), ("w" + std::to_string(index)).c_str());
+    if (auto weightArg = getWeightArgument(index))
+      setNameFn(*weightArg, ("w" + std::to_string(index)).c_str());
 
   for (unsigned index = 0; index < getInputs().size(); ++index)
-    setNameFn(getInputArgument(index), ("in" + std::to_string(index)).c_str());
+    if (auto inputArg = getInputArgument(index))
+      setNameFn(*inputArg, ("in" + std::to_string(index)).c_str());
 }
 
-BlockArgument SpatComputeBatch::getLaneArgument() { return getBody().front().getArgument(0); }
+std::optional<BlockArgument> SpatComputeBatch::getLaneArgument() { return getBatchBodyArgument(getBody(), 0); }
 
-BlockArgument SpatComputeBatch::getWeightArgument(unsigned idx) { return getBody().front().getArgument(1 + idx); }
+std::optional<BlockArgument> SpatComputeBatch::getWeightArgument(unsigned idx) {
-
+  return getBatchBodyArgument(getBody(), 1 + idx);
-BlockArgument SpatComputeBatch::getInputArgument(unsigned idx) {
-  return getBody().front().getArgument(1 + getWeights().size() + idx);
 }
 
-BlockArgument SpatComputeBatch::getOutputArgument(unsigned idx) {
+std::optional<BlockArgument> SpatComputeBatch::getInputArgument(unsigned idx) {
-  return getBody().front().getArgument(1 + getWeights().size() + getInputs().size() + idx);
+  return getBatchBodyArgument(getBody(), 1 + getWeights().size() + idx);
+}
+
+std::optional<BlockArgument> SpatComputeBatch::getOutputArgument(unsigned idx) {
+  return getBatchBodyArgument(getBody(), 1 + getWeights().size() + getInputs().size() + idx);
+}
+
+std::optional<std::tuple<Value, BlockArgument>>
+SpatComputeBatch::insertWeight(unsigned idx, Value weight, Location loc) {
+  unsigned weightCount = getWeights().size();
+  unsigned inputCount = getInputs().size();
+  getOperation()->insertOperands(idx, ValueRange {weight});
+  setComputeOperandSegmentSizes(
+    getOperation(), static_cast<int32_t>(weightCount + 1), static_cast<int32_t>(inputCount));
+  auto blockArg = insertBatchBodyArgument(getBody(), 1 + idx, weight.getType(), loc);
+  if (!blockArg)
+    return std::nullopt;
+  return std::make_tuple(getOperation()->getOperand(idx), *blockArg);
+}
+
+std::optional<std::tuple<Value, BlockArgument>> SpatComputeBatch::insertInput(unsigned idx, Value input, Location loc) {
+  unsigned weightCount = getWeights().size();
+  unsigned inputCount = getInputs().size();
+  getOperation()->insertOperands(weightCount + idx, ValueRange {input});
+  setComputeOperandSegmentSizes(
+    getOperation(), static_cast<int32_t>(weightCount), static_cast<int32_t>(inputCount + 1));
+  auto blockArg = insertBatchBodyArgument(getBody(), 1 + weightCount + idx, input.getType(), loc);
+  if (!blockArg)
+    return std::nullopt;
+  return std::make_tuple(getOperation()->getOperand(weightCount + idx), *blockArg);
+}
+
+FailureOr<std::tuple<OpResult, BlockArgument, SpatComputeBatch>>
+SpatComputeBatch::insertOutput(RewriterBase& rewriter, unsigned idx, Type type, Location loc) {
+  if (idx > getNumResults())
+    return failure();
+
+  rewriter.setInsertionPoint(getOperation());
+  SmallVector<Type> resultTypes(getResultTypes().begin(), getResultTypes().end());
+  resultTypes.insert(resultTypes.begin() + idx, type);
+  auto newBatch =
+    SpatComputeBatch::create(rewriter, getLoc(), TypeRange(resultTypes), getLaneCountAttr(), getWeights(), getInputs());
+  newBatch->setAttrs((*this)->getAttrs());
+  setComputeOperandSegmentSizes(newBatch.getOperation(),
+                                static_cast<int32_t>(newBatch.getWeights().size()),
+                                static_cast<int32_t>(newBatch.getInputs().size()));
+  rewriter.inlineRegionBefore(getBody(), newBatch.getBody(), newBatch.getBody().end());
+  if (newBatch.getBody().empty()) {
+    rewriter.eraseOp(newBatch);
+    return failure();
+  }
+  auto blockArg = newBatch.getBody().front().insertArgument(
+    1 + newBatch.getWeights().size() + newBatch.getInputs().size() + idx, type, loc);
+  for (unsigned oldResultIdx = 0; oldResultIdx < getNumResults(); ++oldResultIdx)
+    getResult(oldResultIdx)
+      .replaceAllUsesWith(newBatch.getResult(oldResultIdx < idx ? oldResultIdx : oldResultIdx + 1));
+  rewriter.eraseOp(getOperation());
+  return std::make_tuple(cast<OpResult>(newBatch.getResult(idx)), blockArg, newBatch);
 }
 
 void SpatComputeBatch::getAsmBlockArgumentNames(Region& region, OpAsmSetValueNameFn setNameFn) {
   if (region.empty())
     return;
 
-  setNameFn(getLaneArgument(), "lane");
+  if (auto laneArg = getLaneArgument())
+    setNameFn(*laneArg, "lane");
 
   for (unsigned index = 0; index < getWeights().size(); ++index)
-    setNameFn(getWeightArgument(index), ("w" + std::to_string(index)).c_str());
+    if (auto weightArg = getWeightArgument(index))
+      setNameFn(*weightArg, ("w" + std::to_string(index)).c_str());
 
   for (unsigned index = 0; index < getInputs().size(); ++index)
-    setNameFn(getInputArgument(index), ("in" + std::to_string(index)).c_str());
+    if (auto inputArg = getInputArgument(index))
+      setNameFn(*inputArg, ("in" + std::to_string(index)).c_str());
 
   for (unsigned index = 0; index < getNumResults(); ++index) {
+    auto outputArg = getOutputArgument(index);
+    if (!outputArg)
+      continue;
     if (index == 0) {
-      setNameFn(getOutputArgument(index), "out");
+      setNameFn(*outputArg, "out");
       continue;
     }
-    setNameFn(getOutputArgument(index), ("out" + std::to_string(index)).c_str());
+    setNameFn(*outputArg, ("out" + std::to_string(index)).c_str());
   }
 }
 
@@ -65,9 +202,7 @@ void SpatInParallelOp::build(OpBuilder& builder, OperationState& result) {
 
 OpResult SpatInParallelOp::getParentResult(int64_t idx) { return getOperation()->getParentOp()->getResult(idx); }
 
-llvm::iterator_range<Block::iterator> SpatInParallelOp::getYieldingOps() {
+llvm::iterator_range<Block::iterator> SpatInParallelOp::getYieldingOps() { return getRegion().front().getOperations(); }
-  return getRegion().front().getOperations();
-}
 
 void SpatialDialect::initialize() {
   addTypes<

src/PIM/Dialect/Spatial/SpatialOps.hpp

@@ -5,12 +5,15 @@
 #include "mlir/IR/BuiltinTypes.h"
 #include "mlir/IR/Dialect.h"
 #include "mlir/IR/OpDefinition.h"
+#include "mlir/IR/PatternMatch.h"
 #include "mlir/IR/RegionKindInterface.h"
 #include "mlir/IR/Types.h"
 #include "mlir/Interfaces/ParallelCombiningOpInterface.h"
 
 #include <map>
+#include <optional>
 #include <string>
+#include <tuple>
 
 /// Include the auto-generated header files containing the declarations
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialDialect.hpp.inc"

src/PIM/Dialect/Spatial/SpatialOpsAsm.cpp

@@ -104,16 +104,13 @@ static ParseResult parseBoundValueList(OpAsmParser& parser,
       return failure();
   auto parseCloseDelimiter = [&](ListDelimiter currentDelimiter) -> ParseResult {
     switch (currentDelimiter) {
-    case ListDelimiter::Paren:
+    case ListDelimiter::Paren:  return parser.parseRParen();
-      return parser.parseRParen();
+    case ListDelimiter::Square: return parser.parseRSquare();
-    case ListDelimiter::Square:
-      return parser.parseRSquare();
     }
     llvm_unreachable("unsupported delimiter");
   };
-  if (parseCloseDelimiter(delimiter) || parser.parseEqual() || parseCompressedOperandList(parser, delimiter, operands)) {
+  if (parseCloseDelimiter(delimiter) || parser.parseEqual() || parseCompressedOperandList(parser, delimiter, operands))
     return failure();
-  }
   return success();
 }
 
@@ -221,17 +218,26 @@ ParseResult SpatConcatOp::parse(OpAsmParser& parser, OperationState& result) {
 }
 
 void SpatCompute::print(OpAsmPrinter& printer) {
-  printer << " ";
   SmallVector<Value> weightArgs;
   weightArgs.reserve(getWeights().size());
-  for (unsigned index = 0; index < getWeights().size(); ++index)
+  for (unsigned index = 0; index < getWeights().size(); ++index) {
-    weightArgs.push_back(getWeightArgument(index));
+    auto weightArg = getWeightArgument(index);
-  printBoundValueList(printer, weightArgs, getWeights(), ListDelimiter::Square);
+    if (!weightArg)
-  printer << " ";
+      return printer.printGenericOp(getOperation(), /*printOpName=*/false);
+    weightArgs.push_back(*weightArg);
+  }
   SmallVector<Value> inputArgs;
   inputArgs.reserve(getInputs().size());
-  for (unsigned index = 0; index < getInputs().size(); ++index)
+  for (unsigned index = 0; index < getInputs().size(); ++index) {
-    inputArgs.push_back(getInputArgument(index));
+    auto inputArg = getInputArgument(index);
+    if (!inputArg)
+      return printer.printGenericOp(getOperation(), /*printOpName=*/false);
+    inputArgs.push_back(*inputArg);
+  }
+
+  printer << " ";
+  printBoundValueList(printer, weightArgs, getWeights(), ListDelimiter::Square);
+  printer << " ";
   printBoundValueList(printer, inputArgs, getInputs(), ListDelimiter::Paren);
 
   if (auto coreIdAttr = (*this)->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName))
@@ -312,29 +318,48 @@ ParseResult SpatCompute::parse(OpAsmParser& parser, OperationState& result) {
 }
 
 void SpatComputeBatch::print(OpAsmPrinter& printer) {
+  auto laneArg = getLaneArgument();
+  SmallVector<Value> weightArgs;
+  weightArgs.reserve(getWeights().size());
+  for (unsigned index = 0; index < getWeights().size(); ++index) {
+    auto weightArg = getWeightArgument(index);
+    if (!weightArg)
+      return printer.printGenericOp(getOperation(), /*printOpName=*/false);
+    weightArgs.push_back(*weightArg);
+  }
+  SmallVector<Value> inputArgs;
+  inputArgs.reserve(getInputs().size());
+  for (unsigned index = 0; index < getInputs().size(); ++index) {
+    auto inputArg = getInputArgument(index);
+    if (!inputArg)
+      return printer.printGenericOp(getOperation(), /*printOpName=*/false);
+    inputArgs.push_back(*inputArg);
+  }
+
+  SmallVector<BlockArgument> outputArgs;
+  if (!laneArg)
+    return printer.printGenericOp(getOperation(), /*printOpName=*/false);
+  if (getNumResults() != 0) {
+    outputArgs.reserve(getNumResults());
+    for (unsigned index = 0; index < getNumResults(); ++index) {
+      auto outputArg = getOutputArgument(index);
+      if (!outputArg)
+        return printer.printGenericOp(getOperation(), /*printOpName=*/false);
+      outputArgs.push_back(*outputArg);
+    }
+  }
+
   printer << " ";
-  printer.printOperand(getLaneArgument());
+  printer.printOperand(*laneArg);
   printer << " = 0 to " << getLaneCount();
 
   printer << " ";
-  SmallVector<Value> weightArgs;
-  weightArgs.reserve(getWeights().size());
-  for (unsigned index = 0; index < getWeights().size(); ++index)
-    weightArgs.push_back(getWeightArgument(index));
   printBoundValueList(printer, weightArgs, getWeights(), ListDelimiter::Square);
   printer << " ";
-  SmallVector<Value> inputArgs;
-  inputArgs.reserve(getInputs().size());
-  for (unsigned index = 0; index < getInputs().size(); ++index)
-    inputArgs.push_back(getInputArgument(index));
   printBoundValueList(printer, inputArgs, getInputs(), ListDelimiter::Paren);
 
   if (getNumResults() != 0) {
     printer << " shared_outs";
-    SmallVector<BlockArgument> outputArgs;
-    outputArgs.reserve(getNumResults());
-    for (unsigned index = 0; index < getNumResults(); ++index)
-      outputArgs.push_back(getOutputArgument(index));
     printBlockArgumentList(printer, outputArgs);
   }
 

src/PIM/Dialect/Spatial/SpatialOpsVerify.cpp

@@ -107,8 +107,11 @@ static bool isBatchOutputArgument(SpatComputeBatch batchOp, Value value) {
     return false;
 
   unsigned argNumber = blockArg.getArgNumber();
-  unsigned firstOutputArg = batchOp.getOutputArgument(0).getArgNumber();
+  auto firstOutputArg = batchOp.getOutputArgument(0);
-  return argNumber >= firstOutputArg && argNumber < firstOutputArg + batchOp.getNumResults();
+  if (!firstOutputArg)
+    return false;
+  unsigned firstOutputArgNumber = firstOutputArg->getArgNumber();
+  return argNumber >= firstOutputArgNumber && argNumber < firstOutputArgNumber + batchOp.getNumResults();
 }
 
 static bool isConstantIndexLike(Value value) {
@@ -120,6 +123,15 @@ static bool isSupportedLaneOffsetExpr(Value value, BlockArgument laneArg) {
   if (value == laneArg || isConstantIndexLike(value))
     return true;
 
+  auto extractOp = value.getDefiningOp<tensor::ExtractOp>();
+  if (extractOp) {
+    auto constantTensor = extractOp.getTensor().getDefiningOp<arith::ConstantOp>();
+    auto denseAttr = constantTensor ? dyn_cast<DenseIntElementsAttr>(constantTensor.getValue()) : nullptr;
+    if (!denseAttr || denseAttr.getType().getRank() != 1 || extractOp.getIndices().size() != 1)
+      return false;
+    return isSupportedLaneOffsetExpr(extractOp.getIndices().front(), laneArg);
+  }
+
   auto addOp = value.getDefiningOp<arith::AddIOp>();
   if (!addOp)
     return false;
@@ -263,9 +275,9 @@ static LogicalResult verifyOnlyConstantExternalValues(Operation* ownerOp, Region
         continue;
 
       InFlightDiagnostic diagnostic = ownerOp->emitOpError()
-                                    << kind << " body may only directly reference external constants";
+                                   << kind << " body may only directly reference external constants";
-      diagnostic.attachNote(op->getLoc()) << "non-constant external operand #" << operand.getOperandNumber()
+      diagnostic.attachNote(op->getLoc())
-                                          << " is used by " << op->getName();
+        << "non-constant external operand #" << operand.getOperandNumber() << " is used by " << op->getName();
       hasFailure = true;
     }
   });
@@ -284,10 +296,12 @@ static LogicalResult verifyBatchBody(SpatComputeBatch batchOp, Block& block) {
     return batchOp.emitError("resultful compute_batch body must terminate with spat.in_parallel");
   }
 
-  BlockArgument laneArg = batchOp.getLaneArgument();
+  auto laneArg = batchOp.getLaneArgument();
+  if (!laneArg)
+    return batchOp.emitError("compute_batch body must have a lane block argument");
   for (auto& bodyOp : block) {
     if (auto extractSlice = dyn_cast<tensor::ExtractSliceOp>(&bodyOp))
-      if (failed(verifyStaticUnitStrideExtractSliceOp(extractSlice, laneArg, "tensor.extract_slice")))
+      if (failed(verifyStaticUnitStrideExtractSliceOp(extractSlice, *laneArg, "tensor.extract_slice")))
         return failure();
   }
   return success();
@@ -449,11 +463,13 @@ LogicalResult SpatCompute::verify() {
     return emitError("compute body must have weight and input block arguments");
 
   for (auto [weightIndex, weight] : llvm::enumerate(getWeights())) {
-    if (getWeightArgument(weightIndex).getType() != weight.getType())
+    auto blockArg = getWeightArgument(weightIndex);
+    if (!blockArg || blockArg->getType() != weight.getType())
       return emitError("compute weight block argument types must match weight operand types exactly");
   }
   for (auto [inputIndex, input] : llvm::enumerate(getInputs())) {
-    if (getInputArgument(inputIndex).getType() != input.getType())
+    auto blockArg = getInputArgument(inputIndex);
+    if (!blockArg || blockArg->getType() != input.getType())
       return emitError("compute input block argument types must match input operand types exactly");
   }
 
@@ -490,7 +506,7 @@ LogicalResult SpatCompute::verify() {
   }
 
   for (unsigned inputIndex = 0; inputIndex < getInputs().size(); ++inputIndex)
-    if (getInputArgument(inputIndex).use_empty())
+    if (auto inputArg = getInputArgument(inputIndex); !inputArg || inputArg->use_empty())
       return emitError("ComputeOp block argument is not used");
   if (failed(verifyOnlyConstantExternalValues(this->getOperation(), getBody(), "spat.compute")))
     return failure();
@@ -567,24 +583,28 @@ LogicalResult SpatComputeBatch::verify() {
   }
 
   Block& block = getBody().front();
+  if (block.getNumArguments() == 0)
+    return emitError("compute_batch body must have exactly one lane block argument");
   unsigned expectedArgCount = 1 + getWeights().size() + getInputs().size() + getNumResults();
   if (block.getNumArguments() != expectedArgCount)
-    return emitError("compute_batch body must have lane, weight, input, and output block arguments");
+    return emitError("compute_batch body block arguments must match lane, weight, input, and output operands/results");
-  if (!getLaneArgument().getType().isIndex())
+  auto laneArg = getLaneArgument();
+  if (!laneArg || !laneArg->getType().isIndex())
     return emitError("compute_batch first block argument must have index type");
 
   for (auto [weightIndex, weight] : llvm::enumerate(getWeights())) {
-    if (getWeightArgument(weightIndex).getType() != weight.getType())
+    auto blockArg = getWeightArgument(weightIndex);
+    if (!blockArg || blockArg->getType() != weight.getType())
       return emitError("compute_batch weight block argument types must match weight operand types exactly");
   }
   for (auto [inputIndex, input] : llvm::enumerate(getInputs())) {
-    BlockArgument blockArg = getInputArgument(inputIndex);
+    auto blockArg = getInputArgument(inputIndex);
-    if (blockArg.getType() != input.getType())
+    if (!blockArg || blockArg->getType() != input.getType())
       return emitError("compute_batch input block argument types must match input operand types exactly");
   }
   for (auto [resultIndex, resultType] : llvm::enumerate(getResultTypes())) {
-    BlockArgument blockArg = getOutputArgument(resultIndex);
+    auto blockArg = getOutputArgument(resultIndex);
-    if (blockArg.getType() != resultType)
+    if (!blockArg || blockArg->getType() != resultType)
       return emitError("compute_batch output block argument types must match result types exactly");
   }
 
@@ -602,13 +622,15 @@ LogicalResult SpatInParallelOp::verify() {
   if (batchOp.getNumResults() == 0)
     return emitOpError("requires a resultful spat.compute_batch parent");
 
-  BlockArgument laneArg = batchOp.getLaneArgument();
+  auto laneArg = batchOp.getLaneArgument();
+  if (!laneArg)
+    return emitOpError("expected compute_batch lane block argument");
   for (Operation& op : getRegion().front().getOperations()) {
     auto insertSliceOp = dyn_cast<tensor::ParallelInsertSliceOp>(&op);
     if (!insertSliceOp)
       return emitOpError("expected only tensor.parallel_insert_slice ops");
 
-    if (failed(verifyStaticUnitStrideParallelInsertSliceOp(insertSliceOp, laneArg, "tensor.parallel_insert_slice")))
+    if (failed(verifyStaticUnitStrideParallelInsertSliceOp(insertSliceOp, *laneArg, "tensor.parallel_insert_slice")))
       return failure();
 
     MutableOperandRange destinations = insertSliceOp.getUpdatedDestinations();

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

@@ -1,6 +1,6 @@
-#include "DCPAnalysis.hpp"
 #include "../Scheduling/ComputeGraph.hpp"
 #include "../Scheduling/DcpScheduler.hpp"
+#include "DCPAnalysis.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 
 namespace onnx_mlir {

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

@@ -11,15 +11,15 @@ using DCPAnalysisResult = MergeScheduleResult;
 struct DCPAnalysis {
 private:
   DCPAnalysisResult result;
-  mlir::Operation *entryOp;
+  mlir::Operation* entryOp;
   DCPAnalysisResult run();
 
 public:
-  DCPAnalysis(mlir::Operation *op)
+  DCPAnalysis(mlir::Operation* op)
   : entryOp(op) {
     result = run();
   }
-  DCPAnalysisResult &getResult() { return result; }
+  DCPAnalysisResult& getResult() { return result; }
 };
 
 } // namespace spatial

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/MaterializeMergeSchedule.hpp

@@ -10,8 +10,7 @@ namespace spatial {
 
 class MergeScheduleMaterializer {
 public:
-  mlir::LogicalResult
+  mlir::LogicalResult run(mlir::func::FuncOp func, const MergeScheduleResult& schedule, int64_t& nextChannelId);
-  run(mlir::func::FuncOp func, const MergeScheduleResult &schedule, int64_t &nextChannelId);
 };
 
 } // namespace spatial

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

@@ -57,8 +57,7 @@ bool isMergeProfilingEnabled() { return std::getenv("RAPTOR_PROFILE_MERGE") != n
 class ScopedMergePhaseTimer {
 public:
   explicit ScopedMergePhaseTimer(StringRef phaseName)
-    : enabled(isMergeProfilingEnabled()),
+  : enabled(isMergeProfilingEnabled()), phase(phaseName.str()) {
-      phase(phaseName.str()) {
     if (enabled)
       start = std::chrono::steady_clock::now();
   }
@@ -130,15 +129,12 @@ void emitMergeIrCounts(StringRef phaseName, func::FuncOp funcOp) {
 
   MergeIrCounts counts = collectMergeIrCounts(funcOp);
   llvm::errs() << "[merge-profile] " << phaseName << " counts:"
-               << " compute=" << counts.topLevelComputeCount
+               << " compute=" << counts.topLevelComputeCount << " compute_batch=" << counts.topLevelComputeBatchCount
-               << " compute_batch=" << counts.topLevelComputeBatchCount
                << " scalar_send=" << counts.scalarChannelSendCount
                << " scalar_recv=" << counts.scalarChannelReceiveCount
                << " tensor_send=" << counts.tensorChannelSendCount
-               << " tensor_recv=" << counts.tensorChannelReceiveCount
+               << " tensor_recv=" << counts.tensorChannelReceiveCount << " wvmm=" << counts.wvmmCount
-               << " wvmm=" << counts.wvmmCount
+               << " vadd=" << counts.vaddCount << " scf_for=" << counts.scfForCount << "\n";
-               << " vadd=" << counts.vaddCount
-               << " scf_for=" << counts.scfForCount << "\n";
 }
 
 static std::optional<int32_t> getComputeCoreId(SpatCompute compute) {
@@ -167,7 +163,8 @@ bool isTrivialSerialMergeCandidate(SpatCompute compute) {
   return user && user.getInputs().size() == 1 && use.getOperandNumber() >= user.getWeights().size();
 }
 
-SmallVector<size_t> appendMissingWeightsAndBuildIndexMap(SmallVectorImpl<Value>& targetWeights, ValueRange sourceWeights) {
+SmallVector<size_t> appendMissingWeightsAndBuildIndexMap(SmallVectorImpl<Value>& targetWeights,
+                                                         ValueRange sourceWeights) {
   DenseMap<Value, SmallVector<size_t, 4>> targetWeightIndices;
   for (auto [weightIndex, weight] : llvm::enumerate(targetWeights))
     targetWeightIndices[weight].push_back(weightIndex);
@@ -226,18 +223,32 @@ void mergeTriviallyConnectedComputes(func::FuncOp funcOp) {
       newBody->addArgument(input.getType(), loc);
 
     IRMapping mapper;
-    for (auto [weightIndex, _] : llvm::enumerate(compute.getWeights()))
+    for (auto [weightIndex, _] : llvm::enumerate(compute.getWeights())) {
-      mapper.map(compute.getWeightArgument(weightIndex), newCompute.getWeightArgument(weightIndex));
+      auto oldWeightArg = compute.getWeightArgument(weightIndex);
-    for (auto [inputIndex, _] : llvm::enumerate(compute.getInputs()))
+      auto newWeightArg = newCompute.getWeightArgument(weightIndex);
-      mapper.map(compute.getInputArgument(inputIndex), newCompute.getInputArgument(inputIndex));
+      assert(oldWeightArg && newWeightArg && "expected compute weight block arguments");
-    for (auto [oldIndex, weight] : llvm::enumerate(child.getWeights()))
+      mapper.map(*oldWeightArg, *newWeightArg);
-      mapper.map(child.getWeightArgument(oldIndex), newCompute.getWeightArgument(childWeightToNewIndex[oldIndex]));
+    }
+    for (auto [inputIndex, _] : llvm::enumerate(compute.getInputs())) {
+      auto oldInputArg = compute.getInputArgument(inputIndex);
+      auto newInputArg = newCompute.getInputArgument(inputIndex);
+      assert(oldInputArg && newInputArg && "expected compute input block arguments");
+      mapper.map(*oldInputArg, *newInputArg);
+    }
+    for (auto [oldIndex, weight] : llvm::enumerate(child.getWeights())) {
+      auto oldWeightArg = child.getWeightArgument(oldIndex);
+      auto newWeightArg = newCompute.getWeightArgument(childWeightToNewIndex[oldIndex]);
+      assert(oldWeightArg && newWeightArg && "expected child compute weight block arguments");
+      mapper.map(*oldWeightArg, *newWeightArg);
+    }
 
     rewriter.setInsertionPointToEnd(newBody);
     auto computeYield = cast<spatial::SpatYieldOp>(compute.getBody().front().getTerminator());
     for (Operation& op : compute.getBody().front().without_terminator())
       rewriter.clone(op, mapper);
-    mapper.map(child.getInputArgument(childInputIndex), mapper.lookupOrDefault(computeYield.getOperand(usedResult)));
+    auto childInputArg = child.getInputArgument(childInputIndex);
+    assert(childInputArg && "expected child compute input block argument");
+    mapper.map(*childInputArg, mapper.lookupOrDefault(computeYield.getOperand(usedResult)));
 
     rewriter.setInsertionPointToEnd(newBody);
     for (auto& op : child.getBody().front())
@@ -649,12 +660,12 @@ public:
 
     emitMergeIrCounts("after-materialization", func);
 
-    if (failed(runPostMergeCompactionPipeline(func, nextChannelId))) {
+    /*if (failed(runPostMergeCompactionPipeline(func, nextChannelId))) {
       signalPassFailure();
       return;
     }
 
-    emitMergeIrCounts("after-post-merge-compaction", func);
+    emitMergeIrCounts("after-post-merge-compaction", func);*/
 
     {
       ScopedMergePhaseTimer timer("cleanup-topological-sort-report");

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

@@ -267,212 +267,6 @@ bool areEquivalentForRebatch(SpatCompute lhs, SpatCompute rhs) {
   return lhsIt == lhsBlock.end() && rhsIt == rhsBlock.end();
 }
 
-struct BatchYieldInfo {
-  Value yieldedValue;
-  tensor::ParallelInsertSliceOp insertSlice;
-};
-
-static bool isHostOnlyBatchResultUser(Operation* user) {
-  return isa<func::ReturnOp,
-             spatial::SpatConcatOp,
-             tensor::ExtractSliceOp,
-             tensor::CastOp,
-             tensor::CollapseShapeOp,
-             tensor::ExpandShapeOp>(user);
-}
-
-static FailureOr<DenseMap<BlockArgument, BatchYieldInfo>> collectBatchYieldInfo(SpatComputeBatch batchOp) {
-  Block& block = batchOp.getBody().front();
-  auto inParallel = dyn_cast<spatial::SpatInParallelOp>(block.getTerminator());
-  if (!inParallel)
-    return failure();
-
-  DenseMap<BlockArgument, BatchYieldInfo> batchYieldByOutputArg;
-  for (Operation& op : inParallel.getRegion().front()) {
-    auto insertSlice = dyn_cast<tensor::ParallelInsertSliceOp>(&op);
-    if (!insertSlice)
-      return failure();
-    auto outputArg = dyn_cast<BlockArgument>(insertSlice.getDest());
-    if (!outputArg || outputArg.getOwner() != &block)
-      return failure();
-    batchYieldByOutputArg[outputArg] = {insertSlice.getSource(), insertSlice};
-  }
-  return batchYieldByOutputArg;
-}
-
-static FailureOr<SpatComputeBatch> cloneBatchAsResultless(SpatComputeBatch batchOp, IRRewriter& rewriter) {
-  auto coreIdsAttr = batchOp->getAttrOfType<DenseI32ArrayAttr>(onnx_mlir::kCoreIdsAttrName);
-  if (!coreIdsAttr)
-    return failure();
-
-  Block& oldBlock = batchOp.getBody().front();
-  rewriter.setInsertionPoint(batchOp);
-  auto newBatch = SpatComputeBatch::create(rewriter,
-                                           batchOp.getLoc(),
-                                           TypeRange {},
-                                           rewriter.getI32IntegerAttr(batchOp.getLaneCount()),
-                                           batchOp.getWeights(),
-                                           batchOp.getInputs());
-  newBatch.getProperties().setOperandSegmentSizes(
-    {static_cast<int>(batchOp.getWeights().size()), static_cast<int>(batchOp.getInputs().size())});
-  newBatch->setAttr(onnx_mlir::kCoreIdsAttrName, coreIdsAttr);
-
-  SmallVector<Type> blockArgTypes;
-  SmallVector<Location> blockArgLocs;
-  blockArgTypes.reserve(1 + batchOp.getWeights().size() + batchOp.getInputs().size());
-  blockArgLocs.reserve(1 + batchOp.getWeights().size() + batchOp.getInputs().size());
-  blockArgTypes.push_back(batchOp.getLaneArgument().getType());
-  blockArgLocs.push_back(batchOp.getLaneArgument().getLoc());
-  for (unsigned weightIndex = 0; weightIndex < batchOp.getWeights().size(); ++weightIndex) {
-    blockArgTypes.push_back(batchOp.getWeightArgument(weightIndex).getType());
-    blockArgLocs.push_back(batchOp.getWeightArgument(weightIndex).getLoc());
-  }
-  for (unsigned inputIndex = 0; inputIndex < batchOp.getInputs().size(); ++inputIndex) {
-    blockArgTypes.push_back(batchOp.getInputArgument(inputIndex).getType());
-    blockArgLocs.push_back(batchOp.getInputArgument(inputIndex).getLoc());
-  }
-
-  Block* newBlock =
-    rewriter.createBlock(&newBatch.getBody(), newBatch.getBody().end(), TypeRange(blockArgTypes), blockArgLocs);
-  rewriter.setInsertionPointToStart(newBlock);
-
-  IRMapping mapper;
-  mapper.map(batchOp.getLaneArgument(), newBatch.getLaneArgument());
-  for (unsigned weightIndex = 0; weightIndex < batchOp.getWeights().size(); ++weightIndex)
-    mapper.map(batchOp.getWeightArgument(weightIndex), newBatch.getWeightArgument(weightIndex));
-  for (unsigned inputIndex = 0; inputIndex < batchOp.getInputs().size(); ++inputIndex)
-    mapper.map(batchOp.getInputArgument(inputIndex), newBatch.getInputArgument(inputIndex));
-
-  for (Operation& op : oldBlock.without_terminator()) {
-    Operation* cloned = rewriter.clone(op, mapper);
-    for (auto [oldResult, newResult] : llvm::zip(op.getResults(), cloned->getResults()))
-      mapper.map(oldResult, newResult);
-  }
-
-  return newBatch;
-}
-
-static LogicalResult materializeBatchResultCommunication(func::FuncOp funcOp, int64_t& nextChannelId) {
-  IRRewriter rewriter(funcOp.getContext());
-  OperationFolder constantFolder(funcOp.getContext());
-  SmallVector<SpatComputeBatch> batches(funcOp.getOps<SpatComputeBatch>());
-
-  for (auto batchOp : batches) {
-    if (batchOp.getNumResults() == 0)
-      continue;
-
-    auto coreIdsAttr = batchOp->getAttrOfType<DenseI32ArrayAttr>(onnx_mlir::kCoreIdsAttrName);
-    if (!coreIdsAttr)
-      return batchOp.emitOpError("missing coreIds while materializing batch result communication");
-
-    FailureOr<DenseMap<BlockArgument, BatchYieldInfo>> batchYieldInfo = collectBatchYieldInfo(batchOp);
-    if (failed(batchYieldInfo))
-      return batchOp.emitOpError("failed to collect per-result yielded values from compute_batch body");
-
-    FailureOr<SpatComputeBatch> newBatch = cloneBatchAsResultless(batchOp, rewriter);
-    if (failed(newBatch))
-      return batchOp.emitOpError("failed to clone resultful compute_batch as resultless");
-
-    Block& oldBlock = batchOp.getBody().front();
-    Block& newBlock = newBatch->getBody().front();
-    IRMapping mapper;
-    mapper.map(batchOp.getLaneArgument(), newBatch->getLaneArgument());
-    for (unsigned weightIndex = 0; weightIndex < batchOp.getWeights().size(); ++weightIndex)
-      mapper.map(batchOp.getWeightArgument(weightIndex), newBatch->getWeightArgument(weightIndex));
-    for (unsigned inputIndex = 0; inputIndex < batchOp.getInputs().size(); ++inputIndex)
-      mapper.map(batchOp.getInputArgument(inputIndex), newBatch->getInputArgument(inputIndex));
-    auto oldIt = oldBlock.begin();
-    auto newIt = newBlock.begin();
-    for (; oldIt != oldBlock.end() && newIt != newBlock.end(); ++oldIt, ++newIt)
-      for (auto [oldResult, newResult] : llvm::zip(oldIt->getResults(), newIt->getResults()))
-        mapper.map(oldResult, newResult);
-
-    SmallVector<int32_t> sourceCoreIds(coreIdsAttr.asArrayRef().begin(), coreIdsAttr.asArrayRef().end());
-    rewriter.setInsertionPointToEnd(&newBlock);
-
-    for (unsigned resultIndex = 0; resultIndex < batchOp.getNumResults(); ++resultIndex) {
-      BlockArgument outputArg = batchOp.getOutputArgument(resultIndex);
-      auto yieldInfoIt = batchYieldInfo->find(outputArg);
-      if (yieldInfoIt == batchYieldInfo->end())
-        return batchOp.emitOpError(
-          "missing yielded value for compute_batch result during communication materialization");
-      Value mappedYieldedValue = mapper.lookup(yieldInfoIt->second.yieldedValue);
-
-      DenseMap<int32_t, SmallVector<OpOperand*>> computeUsesByTargetCore;
-      SmallVector<OpOperand*> hostUses;
-      for (OpOperand& use : batchOp.getResult(resultIndex).getUses()) {
-        if (auto computeOp = dyn_cast<SpatCompute>(use.getOwner())) {
-          auto coreIdAttr = computeOp->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName);
-          if (!coreIdAttr)
-            return batchOp.emitOpError("compute user of compute_batch result is missing coreId");
-          computeUsesByTargetCore[static_cast<int32_t>(coreIdAttr.getInt())].push_back(&use);
-          continue;
-        }
-        if (isHostOnlyBatchResultUser(use.getOwner())) {
-          hostUses.push_back(&use);
-          continue;
-        }
-        return batchOp.emitOpError("unsupported user of compute_batch result during communication materialization")
-            << ": " << use.getOwner()->getName();
-      }
-
-      auto createReceiveForUses = [&](ArrayRef<OpOperand*> uses, ArrayRef<int32_t> targetCoreIds) -> LogicalResult {
-        if (uses.empty())
-          return success();
-
-        SmallVector<int64_t> channelIds;
-        channelIds.reserve(sourceCoreIds.size());
-        for ([[maybe_unused]] int32_t sourceCoreId : sourceCoreIds)
-          channelIds.push_back(nextChannelId++);
-        SmallVector<Value> sendChannelIdValues = createIndexConstants(batchOp, channelIds, constantFolder);
-        SmallVector<Value> sendSourceCoreIdValues = createIndexConstants(batchOp, sourceCoreIds, constantFolder);
-        SmallVector<Value> sendTargetCoreIdValues = createIndexConstants(batchOp, targetCoreIds, constantFolder);
-
-        spatial::SpatChannelSendBatchOp::create(rewriter,
-                                                batchOp.getLoc(),
-                                                sendChannelIdValues,
-                                                sendSourceCoreIdValues,
-                                                sendTargetCoreIdValues,
-                                                mappedYieldedValue);
-
-        OpBuilder::InsertionGuard guard(rewriter);
-        rewriter.setInsertionPointAfter(newBatch->getOperation());
-        SmallVector<Value> receiveChannelIdValues = createIndexConstants(batchOp, channelIds, constantFolder);
-        SmallVector<Value> receiveSourceCoreIdValues = createIndexConstants(batchOp, sourceCoreIds, constantFolder);
-        SmallVector<Value> receiveTargetCoreIdValues = createIndexConstants(batchOp, targetCoreIds, constantFolder);
-        auto received = spatial::SpatChannelReceiveTensorOp::create(rewriter,
-                                                                    batchOp.getLoc(),
-                                                                    batchOp.getResult(resultIndex).getType(),
-                                                                    receiveChannelIdValues,
-                                                                    receiveSourceCoreIdValues,
-                                                                    receiveTargetCoreIdValues);
-        for (OpOperand* use : uses)
-          use->set(received.getOutput());
-        rewriter.setInsertionPointToEnd(&newBlock);
-        return success();
-      };
-
-      for (auto& [targetCoreId, uses] : computeUsesByTargetCore) {
-        SmallVector<int32_t> targetCoreIds(static_cast<size_t>(batchOp.getLaneCount()), targetCoreId);
-        if (failed(createReceiveForUses(uses, targetCoreIds)))
-          return failure();
-      }
-
-      if (!hostUses.empty()) {
-        SmallVector<int32_t> hostTargetCoreIds(static_cast<size_t>(batchOp.getLaneCount()), 0);
-        if (failed(createReceiveForUses(hostUses, hostTargetCoreIds)))
-          return failure();
-      }
-    }
-
-    rewriter.setInsertionPointToEnd(&newBlock);
-    spatial::SpatYieldOp::create(rewriter, batchOp.getLoc(), ValueRange {});
-    rewriter.eraseOp(batchOp);
-  }
-
-  return success();
-}
-
 void rebatchEquivalentComputes(func::FuncOp funcOp) {
   IRRewriter rewriter(funcOp.getContext());
   OperationFolder constantFolder(funcOp.getContext());
@@ -731,11 +525,6 @@ LogicalResult runPostMergeCompactionPipeline(func::FuncOp funcOp, int64_t& nextC
     ScopedMergePhaseTimer timer("cleanup-dead-packing-ops");
     cleanupDeadPackingOps(funcOp);
   }
-  {
-    ScopedMergePhaseTimer timer("materialize-batch-result-communication");
-    if (failed(materializeBatchResultCommunication(funcOp, nextChannelId)))
-      return failure();
-  }
 
   return success();
 }

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/PostMergeCompaction.hpp

@@ -7,6 +7,6 @@
 
 namespace onnx_mlir {
 
-mlir::LogicalResult runPostMergeCompactionPipeline(mlir::func::FuncOp funcOp, int64_t &nextChannelId);
+mlir::LogicalResult runPostMergeCompactionPipeline(mlir::func::FuncOp funcOp, int64_t& nextChannelId);
 
 } // namespace onnx_mlir

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

@@ -707,8 +707,10 @@ void compactScalarChannelRuns(func::FuncOp funcOp, int64_t& nextChannelId) {
           Value packedInput = createPackedTensorForValues(ValueRange(inputs), rewriter, run.ops.front().getLoc());
           if (packedInput) {
             SmallVector<Value> channelIdValues = createIndexConstants(run.ops.front(), channelIds, constantFolder);
-            SmallVector<Value> sourceCoreIdValues = createIndexConstants(run.ops.front(), sourceCoreIds, constantFolder);
+            SmallVector<Value> sourceCoreIdValues =
-            SmallVector<Value> targetCoreIdValues = createIndexConstants(run.ops.front(), targetCoreIds, constantFolder);
+              createIndexConstants(run.ops.front(), sourceCoreIds, constantFolder);
+            SmallVector<Value> targetCoreIdValues =
+              createIndexConstants(run.ops.front(), targetCoreIds, constantFolder);
             spatial::SpatChannelSendTensorOp::create(
               rewriter, run.ops.front().getLoc(), channelIdValues, sourceCoreIdValues, targetCoreIdValues, packedInput);
             for (auto op : run.ops)

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/ComputeGraph.cpp

@@ -7,7 +7,7 @@
 #include "llvm/Support/Casting.h"
 
 #include <algorithm>
-#include <limits>
+#include <iterator>
 #include <optional>
 #include <queue>
 #include <utility>
@@ -64,6 +64,49 @@ bool isUsedAsWeightOnly(Operation* producerOp) {
   return true;
 }
 
+bool isLaneOffset(OpFoldResult offset, Value laneArg) {
+  auto offsetValue = llvm::dyn_cast<Value>(offset);
+  return offsetValue == laneArg;
+}
+
+std::optional<Weight> getBatchProjectedInputTransferCost(SpatComputeBatch batch, Value input) {
+  auto inputIt = llvm::find(batch.getInputs(), input);
+  if (inputIt == batch.getInputs().end())
+    return std::nullopt;
+
+  size_t inputIndex = std::distance(batch.getInputs().begin(), inputIt);
+  std::optional<BlockArgument> inputArg = batch.getInputArgument(inputIndex);
+  std::optional<BlockArgument> laneArg = batch.getLaneArgument();
+  if (!inputArg || !laneArg)
+    return std::nullopt;
+
+  Weight projectedCost = 0;
+  for (Operation* user : inputArg->getUsers()) {
+    auto extract = dyn_cast<tensor::ExtractSliceOp>(user);
+    if (!extract || extract.getSource() != *inputArg)
+      return std::nullopt;
+    if (extract.getMixedOffsets().empty() || !isLaneOffset(extract.getMixedOffsets().front(), *laneArg))
+      return std::nullopt;
+
+    auto resultType = dyn_cast<ShapedType>(extract.getResult().getType());
+    if (!resultType || !resultType.hasStaticShape())
+      return std::nullopt;
+    projectedCost = checkedAdd(projectedCost, static_cast<Weight>(getSizeInBytes(resultType)));
+  }
+
+  if (projectedCost == 0)
+    return std::nullopt;
+  return projectedCost;
+}
+
+Weight getInputTransferCost(const ComputeInstance& consumerInstance, Value input) {
+  auto inputType = cast<ShapedType>(input.getType());
+  if (auto batch = dyn_cast<SpatComputeBatch>(consumerInstance.op))
+    if (std::optional<Weight> projectedCost = getBatchProjectedInputTransferCost(batch, input))
+      return *projectedCost;
+  return static_cast<Weight>(getSizeInBytes(inputType));
+}
+
 std::vector<ComputeGraphEdge> aggregateEdges(llvm::ArrayRef<ComputeGraphEdge> edges) {
   llvm::DenseMap<std::pair<size_t, size_t>, Weight> edgeWeights;
   for (const ComputeGraphEdge& edge : edges) {
@@ -136,15 +179,16 @@ ComputeGraph buildComputeGraph(Operation* entryOp) {
 
   llvm::SmallVector<ComputeGraphEdge, 16> rawEdges;
   for (const auto& [targetIndex, node] : llvm::enumerate(graph.nodes)) {
-    for (Value input : getComputeInstanceInputs(node.instance)) {
+    llvm::SmallVector<Value, 4> inputs = getComputeInstanceInputs(node.instance);
+    for (Value input : inputs) {
+      Weight transferCost = getInputTransferCost(node.instance, input);
       if (auto producerBatch = dyn_cast_or_null<SpatComputeBatch>(input.getDefiningOp());
           producerBatch && producerBatch.getNumResults() != 0 && !isa<SpatComputeBatch>(node.instance.op)) {
         for (uint32_t lane = 0; lane < static_cast<uint32_t>(producerBatch.getLaneCount()); ++lane) {
           auto producerIt = graph.instanceToIndex.find(getBatchChunkForLane(producerBatch, lane));
           if (producerIt == graph.instanceToIndex.end())
             continue;
-          rawEdges.push_back(
+          rawEdges.push_back({producerIt->second, targetIndex, transferCost});
-            {producerIt->second, targetIndex, static_cast<Weight>(getSizeInBytes(cast<ShapedType>(input.getType())))});
         }
         continue;
       }
@@ -155,8 +199,7 @@ ComputeGraph buildComputeGraph(Operation* entryOp) {
       auto producerIt = graph.instanceToIndex.find(*producerInstance);
       if (producerIt == graph.instanceToIndex.end())
         continue;
-      rawEdges.push_back(
+      rawEdges.push_back({producerIt->second, targetIndex, transferCost});
-        {producerIt->second, targetIndex, static_cast<Weight>(getSizeInBytes(cast<ShapedType>(input.getType())))});
     }
   }
 

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/ComputeGraph.hpp

@@ -39,11 +39,11 @@ struct ComputeGraph {
   llvm::DenseMap<ComputeInstance, size_t> instanceToIndex;
 };
 
-ComputeGraph buildComputeGraph(mlir::Operation *entryOp);
+ComputeGraph buildComputeGraph(mlir::Operation* entryOp);
-bool verifyAcyclic(const ComputeGraph &graph);
+bool verifyAcyclic(const ComputeGraph& graph);
 
-Weight getComputeInstanceWeight(const ComputeInstance &instance);
+Weight getComputeInstanceWeight(const ComputeInstance& instance);
-CrossbarUsage getComputeInstanceCrossbarUsage(const ComputeInstance &instance);
+CrossbarUsage getComputeInstanceCrossbarUsage(const ComputeInstance& instance);
 
 } // namespace spatial
 } // namespace onnx_mlir

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/ComputeInstance.hpp

@@ -11,11 +11,11 @@ namespace onnx_mlir {
 namespace spatial {
 
 struct ComputeInstance {
-  mlir::Operation *op = nullptr;
+  mlir::Operation* op = nullptr;
   uint32_t laneStart = 0;
   uint32_t laneCount = 1;
 
-  bool operator==(const ComputeInstance &other) const {
+  bool operator==(const ComputeInstance& other) const {
     return op == other.op && laneStart == other.laneStart && laneCount == other.laneCount;
   }
 };
@@ -29,16 +29,15 @@ namespace llvm {
 template <>
 struct DenseMapInfo<onnx_mlir::spatial::ComputeInstance> {
   static onnx_mlir::spatial::ComputeInstance getEmptyKey() {
-    return {DenseMapInfo<mlir::Operation *>::getEmptyKey(), UINT32_MAX, UINT32_MAX};
+    return {DenseMapInfo<mlir::Operation*>::getEmptyKey(), UINT32_MAX, UINT32_MAX};
   }
   static onnx_mlir::spatial::ComputeInstance getTombstoneKey() {
-    return {DenseMapInfo<mlir::Operation *>::getTombstoneKey(), UINT32_MAX, UINT32_MAX};
+    return {DenseMapInfo<mlir::Operation*>::getTombstoneKey(), UINT32_MAX, UINT32_MAX};
   }
-  static unsigned getHashValue(const onnx_mlir::spatial::ComputeInstance &value) {
+  static unsigned getHashValue(const onnx_mlir::spatial::ComputeInstance& value) {
     return llvm::hash_combine(value.op, value.laneStart, value.laneCount);
   }
-  static bool isEqual(const onnx_mlir::spatial::ComputeInstance &lhs,
+  static bool isEqual(const onnx_mlir::spatial::ComputeInstance& lhs, const onnx_mlir::spatial::ComputeInstance& rhs) {
-                      const onnx_mlir::spatial::ComputeInstance &rhs) {
     return lhs == rhs;
   }
 };

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/ComputeInstanceUtils.hpp

@@ -27,15 +27,15 @@ ComputeInstance getBatchChunkForIndex(SpatComputeBatch batch, size_t chunkIndex)
 ComputeInstance getBatchChunkForLane(SpatComputeBatch batch, uint32_t lane);
 
 std::optional<ProducerValueRef> getProducerValueRef(mlir::Value value,
-                                                    const ComputeInstance *consumerInstance = nullptr);
+                                                    const ComputeInstance* consumerInstance = nullptr);
 std::optional<ComputeInstance> getComputeProducerInstance(mlir::Value value,
-                                                          const ComputeInstance *consumerInstance = nullptr);
+                                                          const ComputeInstance* consumerInstance = nullptr);
 
-llvm::SmallVector<mlir::Value, 4> getComputeInstanceInputs(const ComputeInstance &instance);
+llvm::SmallVector<mlir::Value, 4> getComputeInstanceInputs(const ComputeInstance& instance);
-llvm::SmallVector<mlir::Value, 4> getComputeInstanceWeights(const ComputeInstance &instance);
+llvm::SmallVector<mlir::Value, 4> getComputeInstanceWeights(const ComputeInstance& instance);
-llvm::SmallVector<mlir::Value, 4> getComputeInstanceOutputValues(const ComputeInstance &instance);
+llvm::SmallVector<mlir::Value, 4> getComputeInstanceOutputValues(const ComputeInstance& instance);
-llvm::SmallVector<mlir::Type, 4> getComputeInstanceOutputTypes(const ComputeInstance &instance);
+llvm::SmallVector<mlir::Type, 4> getComputeInstanceOutputTypes(const ComputeInstance& instance);
-mlir::Block &getComputeInstanceTemplateBlock(const ComputeInstance &instance);
+mlir::Block& getComputeInstanceTemplateBlock(const ComputeInstance& instance);
 
 } // namespace spatial
 } // namespace onnx_mlir

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/DcpScheduler.cpp

@@ -10,8 +10,8 @@
 #include <queue>
 #include <vector>
 
-#include "DcpScheduler.hpp"
 #include "../DCPGraph/Graph.hpp"
+#include "DcpScheduler.hpp"
 
 namespace onnx_mlir {
 namespace spatial {
@@ -47,7 +47,7 @@ struct WindowScheduleResult {
   size_t maxMergeGroupSize = 0;
 };
 
-size_t getSchedulingCpuBudget(const DcpScheduleOptions &options) {
+size_t getSchedulingCpuBudget(const DcpScheduleOptions& options) {
   if (options.processorCount > 0)
     return options.processorCount;
   return std::numeric_limits<size_t>::max();
@@ -72,7 +72,7 @@ std::vector<IndexedEdge> aggregateEdges(llvm::ArrayRef<IndexedEdge> edges) {
   for (auto [key, weight] : edgeWeights)
     aggregatedEdges.push_back(
       {static_cast<int64_t>(key.first), static_cast<int64_t>(key.second), static_cast<int64_t>(weight)});
-  llvm::sort(aggregatedEdges, [](const IndexedEdge &lhs, const IndexedEdge &rhs) {
+  llvm::sort(aggregatedEdges, [](const IndexedEdge& lhs, const IndexedEdge& rhs) {
     if (std::get<0>(lhs) != std::get<0>(rhs))
       return std::get<0>(lhs) < std::get<0>(rhs);
     return std::get<1>(lhs) < std::get<1>(rhs);
@@ -80,7 +80,7 @@ std::vector<IndexedEdge> aggregateEdges(llvm::ArrayRef<IndexedEdge> edges) {
   return aggregatedEdges;
 }
 
-VirtualGraph buildInitialVirtualGraph(const ComputeGraph &graph) {
+VirtualGraph buildInitialVirtualGraph(const ComputeGraph& graph) {
   VirtualGraph virtualGraph;
   virtualGraph.nodes.reserve(graph.nodes.size());
   for (auto [index, node] : llvm::enumerate(graph.nodes)) {
@@ -93,14 +93,14 @@ VirtualGraph buildInitialVirtualGraph(const ComputeGraph &graph) {
 
   std::vector<IndexedEdge> edges;
   edges.reserve(graph.edges.size());
-  for (const ComputeGraphEdge &edge : graph.edges)
+  for (const ComputeGraphEdge& edge : graph.edges)
     edges.push_back(
       {static_cast<int64_t>(edge.source), static_cast<int64_t>(edge.target), static_cast<int64_t>(edge.transferCost)});
   virtualGraph.edges = aggregateEdges(edges);
   return virtualGraph;
 }
 
-TimingInfo computeTiming(const VirtualGraph &graph) {
+TimingInfo computeTiming(const VirtualGraph& graph) {
   TimingInfo timing;
   size_t nodeCount = graph.nodes.size();
   timing.aest.assign(nodeCount, 0);
@@ -122,7 +122,7 @@ TimingInfo computeTiming(const VirtualGraph &graph) {
   }
 
   auto getVirtualNodeOrderKey = [&](size_t nodeIndex) {
-    const VirtualNode &node = graph.nodes[nodeIndex];
+    const VirtualNode& node = graph.nodes[nodeIndex];
     if (!node.originalNodeIndices.empty())
       return node.originalNodeIndices.front();
     return nodeIndex;
@@ -181,7 +181,7 @@ TimingInfo computeTiming(const VirtualGraph &graph) {
   return timing;
 }
 
-std::vector<std::vector<size_t>> buildUndirectedAdjacency(const VirtualGraph &graph) {
+std::vector<std::vector<size_t>> buildUndirectedAdjacency(const VirtualGraph& graph) {
   std::vector<std::vector<size_t>> adjacency(graph.nodes.size());
   for (auto [start, end, weight] : graph.edges) {
     (void) weight;
@@ -191,14 +191,14 @@ std::vector<std::vector<size_t>> buildUndirectedAdjacency(const VirtualGraph &gr
     adjacency[startIndex].push_back(endIndex);
     adjacency[endIndex].push_back(startIndex);
   }
-  for (auto &neighbours : adjacency) {
+  for (auto& neighbours : adjacency) {
     llvm::sort(neighbours);
     neighbours.erase(std::unique(neighbours.begin(), neighbours.end()), neighbours.end());
   }
   return adjacency;
 }
 
-std::vector<size_t> selectCriticalWindow(const VirtualGraph &graph, const TimingInfo &timing, size_t windowSize) {
+std::vector<size_t> selectCriticalWindow(const VirtualGraph& graph, const TimingInfo& timing, size_t windowSize) {
   std::vector<size_t> ranked(timing.aest.size());
   std::iota(ranked.begin(), ranked.end(), 0);
   auto isHigherPriority = [&](size_t lhs, size_t rhs) {
@@ -240,7 +240,7 @@ std::vector<size_t> selectCriticalWindow(const VirtualGraph &graph, const Timing
   auto frontierCompare = [&](FrontierEntry lhs, FrontierEntry rhs) { return isHigherPriority(rhs.node, lhs.node); };
   std::priority_queue<FrontierEntry, std::vector<FrontierEntry>, decltype(frontierCompare)> frontier(frontierCompare);
 
-  auto addToWindow = [&](size_t node, const std::vector<char> &eligible) {
+  auto addToWindow = [&](size_t node, const std::vector<char>& eligible) {
     if (inWindow[node])
       return;
     inWindow[node] = true;
@@ -288,7 +288,7 @@ std::vector<size_t> selectCriticalWindow(const VirtualGraph &graph, const Timing
   return selected;
 }
 
-std::vector<IndexedEdge> buildWindowEdges(const VirtualGraph &graph, const std::vector<int64_t> &nodeToWindowIndex) {
+std::vector<IndexedEdge> buildWindowEdges(const VirtualGraph& graph, const std::vector<int64_t>& nodeToWindowIndex) {
   std::vector<IndexedEdge> windowEdges;
   windowEdges.reserve(graph.edges.size());
   for (auto [start, end, weight] : graph.edges) {
@@ -301,10 +301,10 @@ std::vector<IndexedEdge> buildWindowEdges(const VirtualGraph &graph, const std::
   return aggregateEdges(windowEdges);
 }
 
-WindowScheduleResult scheduleWindow(const VirtualGraph &graph,
+WindowScheduleResult scheduleWindow(const VirtualGraph& graph,
                                     llvm::ArrayRef<size_t> selectedNodes,
-                                    const DcpScheduleOptions &options,
+                                    const DcpScheduleOptions& options,
-                                    mlir::MLIRContext *context) {
+                                    mlir::MLIRContext* context) {
   std::vector<Weight> windowWeights;
   std::vector<CrossbarUsage> windowCrossbarUsage;
   std::vector<int64_t> windowNodeOrderKeys;
@@ -338,17 +338,17 @@ WindowScheduleResult scheduleWindow(const VirtualGraph &graph,
     result.maxMergeGroupSize = std::max(result.maxMergeGroupSize, scheduledTasks.size());
     std::vector<size_t> mergeGroup;
     mergeGroup.reserve(scheduledTasks.size());
-    for (const auto &task : scheduledTasks)
+    for (const auto& task : scheduledTasks)
       mergeGroup.push_back(selectedNodes[task.nodeIndex]);
     result.mergeGroups.push_back(std::move(mergeGroup));
   }
   return result;
 }
 
-bool coarsenGraph(const VirtualGraph &graph,
+bool coarsenGraph(const VirtualGraph& graph,
                   llvm::ArrayRef<std::vector<size_t>> mergeGroups,
-                  VirtualGraph &coarsenedGraph,
+                  VirtualGraph& coarsenedGraph,
-                  std::vector<size_t> &oldToNewNode) {
+                  std::vector<size_t>& oldToNewNode) {
   TimingInfo timing = computeTiming(graph);
   std::vector<size_t> topologicalRank(graph.nodes.size());
   std::iota(topologicalRank.begin(), topologicalRank.end(), 0);
@@ -358,7 +358,7 @@ bool coarsenGraph(const VirtualGraph &graph,
 
   std::vector<std::vector<size_t>> orderedMergeGroups;
   orderedMergeGroups.reserve(mergeGroups.size());
-  for (const auto &mergeGroup : mergeGroups) {
+  for (const auto& mergeGroup : mergeGroups) {
     orderedMergeGroups.emplace_back(mergeGroup.begin(), mergeGroup.end());
     std::stable_sort(orderedMergeGroups.back().begin(), orderedMergeGroups.back().end(), [&](size_t lhs, size_t rhs) {
       if (topologicalRank[lhs] != topologicalRank[rhs])
@@ -395,7 +395,7 @@ bool coarsenGraph(const VirtualGraph &graph,
       continue;
     }
 
-    auto &newNodeIndex = mergeGroupToNewNode[static_cast<size_t>(mergeGroupIndex)];
+    auto& newNodeIndex = mergeGroupToNewNode[static_cast<size_t>(mergeGroupIndex)];
     if (newNodeIndex.has_value()) {
       oldToNewNode[nodeIndex] = *newNodeIndex;
       continue;
@@ -403,8 +403,9 @@ bool coarsenGraph(const VirtualGraph &graph,
 
     VirtualNode mergedNode;
     for (size_t memberIndex : orderedMergeGroups[static_cast<size_t>(mergeGroupIndex)]) {
-      const VirtualNode &memberNode = graph.nodes[memberIndex];
+      const VirtualNode& memberNode = graph.nodes[memberIndex];
-      mergedNode.originalNodeIndices.append(memberNode.originalNodeIndices.begin(), memberNode.originalNodeIndices.end());
+      mergedNode.originalNodeIndices.append(memberNode.originalNodeIndices.begin(),
+                                            memberNode.originalNodeIndices.end());
       mergedNode.weight = addOrMax(mergedNode.weight, memberNode.weight);
       mergedNode.crossbarUsage = addOrMax(mergedNode.crossbarUsage, memberNode.crossbarUsage);
     }
@@ -437,7 +438,7 @@ bool coarsenGraph(const VirtualGraph &graph,
   return true;
 }
 
-size_t getDcpCoarseningWindowSize(size_t nodeCount, const DcpScheduleOptions &options) {
+size_t getDcpCoarseningWindowSize(size_t nodeCount, const DcpScheduleOptions& options) {
   size_t windowSize = std::min(options.criticalWindowSize, nodeCount);
   CPU maxCpuCount = std::max<CPU>(1, static_cast<CPU>(getSchedulingCpuBudget(options)));
   if (nodeCount > static_cast<size_t>(maxCpuCount))
@@ -445,7 +446,7 @@ size_t getDcpCoarseningWindowSize(size_t nodeCount, const DcpScheduleOptions &op
   return windowSize;
 }
 
-void assignFeasibleAest(const ComputeGraph &graph, MergeScheduleResult &result) {
+void assignFeasibleAest(const ComputeGraph& graph, MergeScheduleResult& result) {
   llvm::DenseMap<ComputeInstance, size_t> nodeIndexByInstance;
   nodeIndexByInstance.reserve(graph.nodes.size());
   for (auto [nodeIndex, node] : llvm::enumerate(graph.nodes))
@@ -458,7 +459,7 @@ void assignFeasibleAest(const ComputeGraph &graph, MergeScheduleResult &result)
 
   std::vector<std::vector<ScheduledEdge>> scheduledChildren(graph.nodes.size());
   std::vector<size_t> incomingEdgeCount(graph.nodes.size(), 0);
-  for (const ComputeGraphEdge &edge : graph.edges) {
+  for (const ComputeGraphEdge& edge : graph.edges) {
     const ComputeInstance sourceInstance = graph.nodes[edge.source].instance;
     const ComputeInstance targetInstance = graph.nodes[edge.target].instance;
     const size_t sourceCpu = result.computeToCpuMap.lookup(sourceInstance);
@@ -473,15 +474,15 @@ void assignFeasibleAest(const ComputeGraph &graph, MergeScheduleResult &result)
   }
 
   llvm::DenseMap<size_t, std::vector<std::pair<size_t, size_t>>> tasksByCpu;
-  for (const ComputeGraphNode &node : graph.nodes) {
+  for (const ComputeGraphNode& node : graph.nodes) {
     size_t cpu = result.computeToCpuMap.lookup(node.instance);
     size_t slot = result.computeToCpuSlotMap.lookup(node.instance);
     tasksByCpu[cpu].push_back({slot, nodeIndexByInstance.lookup(node.instance)});
   }
 
-  for (auto &entry : tasksByCpu) {
+  for (auto& entry : tasksByCpu) {
-    auto &scheduledTasks = entry.second;
+    auto& scheduledTasks = entry.second;
-    llvm::sort(scheduledTasks, [](const auto &lhs, const auto &rhs) {
+    llvm::sort(scheduledTasks, [](const auto& lhs, const auto& rhs) {
       if (lhs.first != rhs.first)
         return lhs.first < rhs.first;
       return lhs.second < rhs.second;
@@ -512,7 +513,7 @@ void assignFeasibleAest(const ComputeGraph &graph, MergeScheduleResult &result)
     readyNodes.pop();
     processedNodeCount++;
 
-    for (const ScheduledEdge &edge : scheduledChildren[sourceIndex]) {
+    for (const ScheduledEdge& edge : scheduledChildren[sourceIndex]) {
       startTimes[edge.target] = std::max(startTimes[edge.target], addOrMax(startTimes[sourceIndex], edge.delay));
       assert(incomingEdgeCount[edge.target] > 0 && "scheduled incoming edge count underflow");
       incomingEdgeCount[edge.target]--;
@@ -528,7 +529,7 @@ void assignFeasibleAest(const ComputeGraph &graph, MergeScheduleResult &result)
     result.computeToAestMap[node.instance] = startTimes[nodeIndex];
 }
 
-MergeScheduleResult buildResultFromVirtualGraph(const VirtualGraph &graph, const ComputeGraph &originalGraph) {
+MergeScheduleResult buildResultFromVirtualGraph(const VirtualGraph& graph, const ComputeGraph& originalGraph) {
   MergeScheduleResult result;
 
   TimingInfo timing = computeTiming(graph);
@@ -542,7 +543,7 @@ MergeScheduleResult buildResultFromVirtualGraph(const VirtualGraph &graph, const
 
   std::vector<size_t> originalNodeToCpu(originalGraph.nodes.size(), 0);
   for (auto [cpu, virtualNodeIndex] : llvm::enumerate(virtualNodeOrder)) {
-    const VirtualNode &virtualNode = graph.nodes[virtualNodeIndex];
+    const VirtualNode& virtualNode = graph.nodes[virtualNodeIndex];
     for (size_t originalIndex : virtualNode.originalNodeIndices)
       originalNodeToCpu[originalIndex] = cpu;
   }
@@ -556,17 +557,17 @@ MergeScheduleResult buildResultFromVirtualGraph(const VirtualGraph &graph, const
     result.computeToCpuSlotMap[node.instance] = nextCpuSlot[cpu]++;
     result.cpuToLastComputeMap[cpu] = node.instance;
   }
-  for (const auto &[cpu, lastCompute] : result.cpuToLastComputeMap)
+  for (const auto& [cpu, lastCompute] : result.cpuToLastComputeMap)
     result.isLastComputeOfCpu.insert(lastCompute);
   assignFeasibleAest(originalGraph, result);
 
   return result;
 }
 
-MergeScheduleResult buildResultFromScheduledGraph(GraphDCP &graphDCP, const ComputeGraph &graph) {
+MergeScheduleResult buildResultFromScheduledGraph(GraphDCP& graphDCP, const ComputeGraph& graph) {
   MergeScheduleResult result;
   result.dominanceOrderCompute.reserve(graph.nodes.size());
-  for (const ComputeGraphNode &node : graph.nodes)
+  for (const ComputeGraphNode& node : graph.nodes)
     result.dominanceOrderCompute.push_back(node.instance);
 
   for (CPU cpu = 0; cpu < graphDCP.cpuCount(); ++cpu) {
@@ -589,7 +590,8 @@ MergeScheduleResult buildResultFromScheduledGraph(GraphDCP &graphDCP, const Comp
   return result;
 }
 
-MergeScheduleResult runLegacyDcp(const ComputeGraph &graph, const DcpScheduleOptions &options, mlir::MLIRContext *context) {
+MergeScheduleResult
+runLegacyDcp(const ComputeGraph& graph, const DcpScheduleOptions& options, mlir::MLIRContext* context) {
   llvm::SmallVector<Weight> nodeWeights;
   llvm::SmallVector<CrossbarUsage> nodeCrossbarUsage;
   llvm::SmallVector<int64_t> nodeOrderKeys;
@@ -599,12 +601,12 @@ MergeScheduleResult runLegacyDcp(const ComputeGraph &graph, const DcpScheduleOpt
   nodeOrderKeys.reserve(graph.nodes.size());
   edges.reserve(graph.edges.size());
 
-  for (const ComputeGraphNode &node : graph.nodes) {
+  for (const ComputeGraphNode& node : graph.nodes) {
     nodeWeights.push_back(node.weight);
     nodeCrossbarUsage.push_back(node.crossbarUsage);
     nodeOrderKeys.push_back(static_cast<int64_t>(node.originalOrder));
   }
-  for (const ComputeGraphEdge &edge : graph.edges) {
+  for (const ComputeGraphEdge& edge : graph.edges) {
     edges.push_back(
       {static_cast<int64_t>(edge.source), static_cast<int64_t>(edge.target), static_cast<int64_t>(edge.transferCost)});
   }
@@ -617,11 +619,11 @@ MergeScheduleResult runLegacyDcp(const ComputeGraph &graph, const DcpScheduleOpt
   return buildResultFromScheduledGraph(graphDCP, graph);
 }
 
-bool needsExactScheduledBatches(const ComputeGraph &graph, const DcpScheduleOptions &options) {
+bool needsExactScheduledBatches(const ComputeGraph& graph, const DcpScheduleOptions& options) {
   if (options.processorCount == 0 || !options.allowFallbackForAutoCoreCount)
     return false;
   size_t schedulingCpuBudget = getSchedulingCpuBudget(options);
-  return llvm::any_of(graph.nodes, [&](const ComputeGraphNode &node) {
+  return llvm::any_of(graph.nodes, [&](const ComputeGraphNode& node) {
     auto batch = dyn_cast<SpatComputeBatch>(node.instance.op);
     return batch && static_cast<size_t>(batch.getLaneCount()) > schedulingCpuBudget;
   });
@@ -630,7 +632,7 @@ bool needsExactScheduledBatches(const ComputeGraph &graph, const DcpScheduleOpti
 } // namespace
 
 MergeScheduleResult
-runDcpScheduler(const ComputeGraph &graph, const DcpScheduleOptions &options, mlir::MLIRContext *context) {
+runDcpScheduler(const ComputeGraph& graph, const DcpScheduleOptions& options, mlir::MLIRContext* context) {
   if (needsExactScheduledBatches(graph, options))
     return runLegacyDcp(graph, options, context);
 

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/DcpScheduler.hpp

@@ -15,7 +15,7 @@ struct DcpScheduleOptions {
 };
 
 MergeScheduleResult
-runDcpScheduler(const ComputeGraph &graph, const DcpScheduleOptions &options, mlir::MLIRContext *context);
+runDcpScheduler(const ComputeGraph& graph, const DcpScheduleOptions& options, mlir::MLIRContext* context);
 
 } // namespace spatial
 } // namespace onnx_mlir

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/MergeSchedule.hpp

@@ -19,6 +19,7 @@ struct MergeScheduleResult {
   llvm::DenseMap<ComputeInstance, uint64_t> computeToAestMap;
   llvm::DenseSet<ComputeInstance> isLastComputeOfCpu;
   llvm::DenseMap<size_t, ComputeInstance> cpuToLastComputeMap;
+  llvm::DenseMap<size_t, mlir::SmallVector<size_t, 5>> equivalentClass;
 };
 
 } // namespace spatial

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/MergeSchedulingAnalysis.cpp

@@ -1,13 +1,13 @@
-#include "llvm/ADT/STLExtras.h"
 #include "llvm/ADT/DenseMap.h"
+#include "llvm/ADT/STLExtras.h"
 #include "llvm/Support/ErrorHandling.h"
 #include "llvm/Support/FormatVariadic.h"
 
 #include <limits>
 #include <vector>
 
-#include "ComputeGraph.hpp"
 #include "../DCPGraph/DCPAnalysis.hpp"
+#include "ComputeGraph.hpp"
 #include "DcpScheduler.hpp"
 #include "MergeSchedulingAnalysis.hpp"
 #include "PeftScheduler.hpp"
@@ -20,15 +20,13 @@ namespace {
 
 MergeSchedulerKind getSchedulerKind() {
   switch (pimMergeScheduler.getValue()) {
-  case MergeSchedulerPeft:
+  case MergeSchedulerPeft: return MergeSchedulerKind::Peft;
-    return MergeSchedulerKind::Peft;
+  case MergeSchedulerDcp:  return MergeSchedulerKind::Dcp;
-  case MergeSchedulerDcp:
-    return MergeSchedulerKind::Dcp;
   }
   llvm_unreachable("unknown merge scheduler kind");
 }
 
-void verifySchedule(const ComputeGraph &graph, const MergeScheduleResult &result, CrossbarUsage crossbarCapacity) {
+void verifySchedule(const ComputeGraph& graph, const MergeScheduleResult& result, CrossbarUsage crossbarCapacity) {
   llvm::DenseMap<size_t, std::vector<std::pair<size_t, size_t>>> tasksByCpu;
   tasksByCpu.reserve(result.cpuToLastComputeMap.size());
 
@@ -45,9 +43,9 @@ void verifySchedule(const ComputeGraph &graph, const MergeScheduleResult &result
       {result.computeToCpuSlotMap.lookup(instance), nodeIndex});
   }
 
-  for (auto &entry : tasksByCpu) {
+  for (auto& entry : tasksByCpu) {
-    auto &scheduledTasks = entry.second;
+    auto& scheduledTasks = entry.second;
-    llvm::sort(scheduledTasks, [](const auto &lhs, const auto &rhs) {
+    llvm::sort(scheduledTasks, [](const auto& lhs, const auto& rhs) {
       if (lhs.first != rhs.first)
         return lhs.first < rhs.first;
       return lhs.second < rhs.second;
@@ -70,7 +68,7 @@ void verifySchedule(const ComputeGraph &graph, const MergeScheduleResult &result
       llvm::report_fatal_error("merge scheduling: missing last-compute marker");
   }
 
-  for (const ComputeGraphEdge &edge : graph.edges) {
+  for (const ComputeGraphEdge& edge : graph.edges) {
     const ComputeInstance source = graph.nodes[edge.source].instance;
     const ComputeInstance target = graph.nodes[edge.target].instance;
     const size_t sourceCpu = result.computeToCpuMap.lookup(source);
@@ -97,8 +95,8 @@ void verifySchedule(const ComputeGraph &graph, const MergeScheduleResult &result
 
 } // namespace
 
-MergeSchedulingAnalysis::MergeSchedulingAnalysis(mlir::Operation *op)
+MergeSchedulingAnalysis::MergeSchedulingAnalysis(mlir::Operation* op)
-  : entryOp(op) {
+: entryOp(op) {
   result = run();
 }
 
@@ -115,20 +113,17 @@ MergeScheduleResult MergeSchedulingAnalysis::run() {
 
   MergeScheduleResult schedule;
   if (options.kind == MergeSchedulerKind::Peft) {
-    schedule = runPeftScheduler(
+    schedule = runPeftScheduler(graph,
-      graph,
+                                PeftScheduleOptions {options.processorCount,
-      PeftScheduleOptions {options.processorCount, static_cast<CrossbarUsage>(crossbarCountInCore.getValue()),
+                                                     static_cast<CrossbarUsage>(crossbarCountInCore.getValue()),
-                           entryOp->getContext()});
+                                                     entryOp->getContext()});
   }
   else {
-    schedule = runDcpScheduler(
+    schedule = runDcpScheduler(graph,
-      graph,
+                               DcpScheduleOptions {options.processorCount,
-      DcpScheduleOptions {
+                                                   dcpCriticalWindowSize.getValue(),
-        options.processorCount,
+                                                   options.allowDcpFallbackForAutoCoreCount},
-        dcpCriticalWindowSize.getValue(),
+                               entryOp->getContext());
-        options.allowDcpFallbackForAutoCoreCount
-      },
-      entryOp->getContext());
   }
 
   verifySchedule(graph, schedule, static_cast<CrossbarUsage>(crossbarCountInCore.getValue()));

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/MergeSchedulingAnalysis.hpp

@@ -22,11 +22,11 @@ struct MergeSchedulingOptions {
 
 class MergeSchedulingAnalysis {
 public:
-  explicit MergeSchedulingAnalysis(mlir::Operation *op);
+  explicit MergeSchedulingAnalysis(mlir::Operation* op);
-  MergeScheduleResult &getResult() { return result; }
+  MergeScheduleResult& getResult() { return result; }
 
 private:
-  mlir::Operation *entryOp = nullptr;
+  mlir::Operation* entryOp = nullptr;
   MergeScheduleResult result;
 
   MergeScheduleResult run();

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/PeftScheduler.cpp

@@ -19,7 +19,6 @@ struct ScheduledTask {
   size_t processor = std::numeric_limits<size_t>::max();
   Time startTime = 0;
   Time endTime = 0;
-  size_t slot = 0;
 };
 
 std::vector<std::vector<size_t>> buildReverseLevels(const ComputeGraph& graph) {
@@ -243,7 +242,7 @@ MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftSchedu
       llvm::report_fatal_error(llvm::StringRef(message));
     }
 
-    schedules[task] = {bestProcessor, bestEst, bestEft, 0};
+    schedules[task] = {bestProcessor, bestEst, bestEft};
     scheduled[task] = true;
     ++scheduledCount;
     processorCrossbars[bestProcessor] = addOrMax(processorCrossbars[bestProcessor], graph.nodes[task].crossbarUsage);
@@ -274,7 +273,65 @@ MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftSchedu
     return graph.nodes[a].originalOrder < graph.nodes[b].originalOrder;
   });
 
-  // 5. Populate Final Result
+  // 5. Check if equal schedule in two level
+  llvm::DenseMap<size_t, mlir::SmallVector<size_t, 5>> equivalentClass;
+  for (size_t currentProcessor = 0; currentProcessor < processorCount - 1; ++currentProcessor) {
+    for (size_t controlProcessor = currentProcessor; controlProcessor < processorCount; ++controlProcessor) {
+      if (tasksByProcessor[currentProcessor].size() != tasksByProcessor[controlProcessor].size())
+        continue;
+      auto& currentTasks = tasksByProcessor[currentProcessor];
+      auto& controlTasks = tasksByProcessor[controlProcessor];
+      bool equalSchedule = true;
+
+      for (auto [currentTask, controlTask] : llvm::zip(currentTasks, controlTasks)) {
+        const ComputeInstance currentComputeInstance = graph.nodes[currentTask].instance;
+        const ComputeInstance controlComputeInstance = graph.nodes[controlTask].instance;
+        if (currentComputeInstance.op != controlComputeInstance.op
+            || currentComputeInstance.laneCount != controlComputeInstance.laneCount) {
+          equalSchedule = false;
+          break;
+        }
+      }
+
+      if (equalSchedule) {
+        equivalentClass[currentProcessor].push_back(controlProcessor);
+        equivalentClass[controlProcessor].push_back(currentProcessor);
+      }
+    }
+  }
+  /*{
+    llvm::dbgs() << "--- Scheduling Equivalence Classes ---\n";
+    std::vector<bool> visited(processorCount, false);
+    size_t uniqueClassCount = 0;
+
+    for (size_t i = 0; i < processorCount; ++i) {
+      if (visited[i])
+        continue;
+
+      // We found a new unique schedule (equivalence class)
+      ++uniqueClassCount;
+      visited[i] = true;
+
+      llvm::dbgs() << "Class " << uniqueClassCount << ": CPUs { " << i;
+
+      // Find and mark all identical companions
+      auto it = equivalentClass.find(i);
+      if (it != equivalentClass.end()) {
+        for (size_t eqCpu : it->second) {
+          if (!visited[eqCpu]) {
+            llvm::dbgs() << ", " << eqCpu;
+            visited[eqCpu] = true;
+          }
+        }
+      }
+      llvm::dbgs() << " }\n";
+    }
+
+    llvm::dbgs() << "Total unique CPU nodes to emit: " << uniqueClassCount << "\n";
+    llvm::dbgs() << "--------------------------------------\n";
+  }*/
+
+  // 6. Populate Final Result
   MergeScheduleResult result;
   result.dominanceOrderCompute.reserve(nodeCount);
 
@@ -296,8 +353,9 @@ MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftSchedu
     }
   }
 
+  result.equivalentClass = equivalentClass;
+
   return result;
 }
 } // namespace spatial
 } // namespace onnx_mlir
-

src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes/Scheduling/PeftScheduler.hpp

@@ -11,10 +11,10 @@ namespace spatial {
 struct PeftScheduleOptions {
   size_t processorCount = 0;
   CrossbarUsage crossbarCapacity = 0;
-  mlir::MLIRContext *context = nullptr;
+  mlir::MLIRContext* context = nullptr;
 };
 
-MergeScheduleResult runPeftScheduler(const ComputeGraph &graph, const PeftScheduleOptions &options);
+MergeScheduleResult runPeftScheduler(const ComputeGraph& graph, const PeftScheduleOptions& options);
 
 } // namespace spatial
 } // namespace onnx_mlir

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

@@ -120,7 +120,7 @@ struct FoldConstantCoreMapPattern final : OpRewritePattern<linalg::MapOp> {
 
     rewriter.setInsertionPoint(mapOp);
     auto getGlobalOp = memref::GetGlobalOp::create(rewriter, mapOp.getLoc(), initType, globalOp.getName());
-    auto sizeInBytes = initType.getNumElements() * initType.getElementTypeBitWidth() / 8;
+    auto sizeInBytes = getShapedTypeSizeInBytes(initType);
     pim::PimMemCopyOp::create(rewriter,
                               mapOp.getLoc(),
                               initType,

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

@@ -176,9 +176,9 @@ static LogicalResult rewriteSubviewCopyLikeOp(CopyOp copyOp,
   if (splitSrc && splitDst && copyShape != ArrayRef<int64_t>(dstSubview->sizes))
     return failure();
 
-  const int64_t elementByteWidth = sourceType.getElementTypeBitWidth() / 8;
+  if (!hasByteSizedElementType(sourceType.getElementType()))
-  if (elementByteWidth <= 0)
     return failure();
+  const int64_t elementByteWidth = static_cast<int64_t>(getElementTypeSizeInBytes(sourceType.getElementType()));
 
   const int64_t totalBytes = getNumElements(copyShape) * elementByteWidth;
   if (size != totalBytes)

src/PIM/Pass/PimCodegen/MaterializeHostConstantsPass.cpp

@@ -31,13 +31,6 @@ static bool isExplicitHostOperand(Operation* op, unsigned operandIndex) {
   return false;
 }
 
-static int64_t getValueSizeInBytes(Value value) {
-  auto type = dyn_cast<ShapedType>(value.getType());
-  if (!type || !type.hasStaticShape())
-    return -1;
-  return type.getNumElements() * type.getElementTypeBitWidth() / 8;
-}
-
 template <typename CoreOpTy>
 static void materializeHostConstantsInCore(CoreOpTy coreOp,
                                            IRRewriter& rewriter,
@@ -82,7 +75,9 @@ static void materializeHostConstantsInCore(CoreOpTy coreOp,
         continue;
       }
 
-      int64_t totalBytes = getValueSizeInBytes(originalValue);
+      int64_t totalBytes = -1;
+      if (auto type = dyn_cast<ShapedType>(originalValue.getType()); type && type.hasStaticShape())
+        totalBytes = static_cast<int64_t>(getShapedTypeSizeInBytes(type));
       if (totalBytes < 0 || !llvm::isInt<32>(totalBytes) || !llvm::isInt<32>(resolvedAddress->byteOffset)) {
         op->emitOpError("host constant materialization requires 32-bit copy sizes and offsets");
         hasFailure = true;

src/PIM/Pass/PimCodegen/VerificationPass.cpp

@@ -8,8 +8,8 @@
 
 #include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
-#include "src/Accelerators/PIM/Compiler/PimBatchEmission.hpp"
 #include "src/Accelerators/PIM/Common/Support/Diagnostics.hpp"
+#include "src/Accelerators/PIM/Compiler/PimBatchEmission.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
@@ -211,8 +211,9 @@ struct VerificationPass : PassWrapper<VerificationPass, OperationPass<ModuleOp>>
         if (auto coreBatchOp = dyn_cast<pim::PimCoreBatchOp>(&op)) {
           (void) verifyCoreWeights(moduleOp, coreBatchOp, diagnostics);
           for (unsigned lane = 0; lane < static_cast<unsigned>(coreBatchOp.getLaneCount()); ++lane)
-            (void) withScalarCoreFromBatchLane(
+            (void) withScalarCoreFromBatchLane(coreBatchOp, lane, [&](pim::PimCoreOp scalarCore) {
-              coreBatchOp, lane, [&](pim::PimCoreOp scalarCore) { return verifyCoreOperands(scalarCore, diagnostics); });
+              return verifyCoreOperands(scalarCore, diagnostics);
+            });
           continue;
         }
 

validation/operations/README.md

@@ -30,7 +30,7 @@ python3 validation/operations/gen_tests.py
 
 | Test          | Directory               | A (input) | W (weight) | Output   | transB | alpha | beta | Bias  | Notes                        |
 |---------------|-------------------------|-----------|------------|----------|--------|-------|------|-------|------------------------------|
-| Default       | `gemm/`                 | [10,132]  | [132,132]  | [10,132] | no     | 1     | 1    | no    | Hand-crafted, square weights |
+| Simple        | `gemm/simple`           | [10,132]  | [132,132]  | [10,132] | no     | 1     | 1    | no    | Square weights               |
 | Non-square    | `gemm/non_square`       | [4,128]   | [128,64]   | [4,64]   | no     | 1     | 1    | no    | K != N                       |
 | With bias     | `gemm/with_bias`        | [4,128]   | [128,128]  | [4,128]  | no     | 1     | 1    | [128] | Bias vector                  |
 | transB        | `gemm/transB`           | [4,128]   | [64,128]   | [4,64]   | yes    | 1     | 1    | no    | Transposed weight            |

validation/operations/gen_tests.py

@@ -185,6 +185,18 @@ def conv_depthwise_grouped():
 # GEMM tests
 # ---------------------------------------------------------------------------
 
+def gemm_simple():
+    """Simple GEMM with square weights: [10, 132] @ [132, 132]."""
+    B, K, N = 10, 132, 132
+    W = numpy_helper.from_array(np.random.default_rng(41).uniform(-1, 1, (K, N)).astype(np.float32), name="W")
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
+    node = helper.make_node("Gemm", ["A", "W"], ["Y"])
+    graph = helper.make_graph([node], "gemm_simple", [A], [Y], initializer=[W])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "gemm/simple", "gemm_simple.onnx")
+
+
 def gemm_non_square():
     """GEMM with non-square weight matrix: [B, K] @ [K, N], K != N."""
     B, K, N = 4, 128, 64
@@ -823,6 +835,7 @@ def div_after_gemm():
 
 if __name__ == "__main__":
     print("Generating GEMM tests:")
+    gemm_simple()
     gemm_non_square()
     gemm_with_bias()
     gemm_transB()