add shared loop creation helpers

add shared checked arithmetic helpers
refactor pim passes into Pim/Transforms
more robust memory coalescing pass

src/PIM/CMakeLists.txt

@@ -121,6 +121,9 @@ add_pim_library(OMPIMAccel
   OMSpatialToPim
   OMPimCommon
   OMPimBufferization
-  OMPimStaticMemoryCoalescing
+  OMPimMemoryCoalescing
+  OMPimHostConstantFolding
+  OMPimHostConstantMaterialization
+  OMPimVerification
   MLIRTensorInferTypeOpInterfaceImpl
 )

src/PIM/Common/CMakeLists.txt

@@ -5,9 +5,11 @@ add_pim_library(OMPimCommon
   IR/ConstantUtils.cpp
   IR/CoreBlockUtils.cpp
   IR/EntryPointUtils.cpp
+  IR/LoopUtils.cpp
   IR/ShapeUtils.cpp
   IR/SubviewUtils.cpp
   IR/WeightUtils.cpp
+  Support/CheckedArithmetic.cpp
   Support/DebugDump.cpp
   Support/Diagnostics.cpp
   Support/FileSystemUtils.cpp
@@ -20,6 +22,7 @@ add_pim_library(OMPimCommon
 
   LINK_LIBS PUBLIC
   MLIRLinalgDialect
+  MLIRSCFDialect
   onnx
   SpatialOps
   PimOps

src/PIM/Common/IR/LoopUtils.cpp

@@ -0,0 +1,96 @@
+#include "mlir/Dialect/SCF/IR/SCF.h"
+
+#include "llvm/Support/MathExtras.h"
+
+#include <optional>
+
+#include "ConstantUtils.hpp"
+#include "LoopUtils.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+
+namespace {
+
+static std::optional<int64_t> getStaticTripCount(Value lowerBound, Value upperBound, Value step) {
+  auto lower = matchConstantIndexValue(lowerBound);
+  auto upper = matchConstantIndexValue(upperBound);
+  auto stepValue = matchConstantIndexValue(step);
+  if (!lower || !upper || !stepValue)
+    return std::nullopt;
+  if (*stepValue <= 0)
+    return std::nullopt;
+  if (*upper <= *lower)
+    return int64_t {0};
+  return llvm::divideCeil(*upper - *lower, *stepValue);
+}
+
+} // namespace
+
+static LogicalResult validateNormalizedLoopYields(Location loc, ValueRange initArgs, ArrayRef<Value> yieldedValues) {
+  if (yieldedValues.size() == initArgs.size())
+    return success();
+
+  emitError(loc) << "normalized loop body yielded " << yieldedValues.size() << " values for " << initArgs.size()
+                 << " iter args";
+  return failure();
+}
+
+FailureOr<NormalizedLoopResult> buildNormalizedScfFor(OpBuilder& builder,
+                                                      Location loc,
+                                                      Value lowerBound,
+                                                      Value upperBound,
+                                                      Value step,
+                                                      ValueRange initArgs,
+                                                      NormalizedLoopBodyBuilder bodyBuilder) {
+  NormalizedLoopResult result;
+
+  if (auto stepValue = matchConstantIndexValue(step); stepValue && *stepValue <= 0) {
+    emitError(loc) << "normalized scf.for requires a positive step, got " << *stepValue;
+    return failure();
+  }
+
+  if (auto tripCount = getStaticTripCount(lowerBound, upperBound, step)) {
+    if (*tripCount == 0) {
+      llvm::append_range(result.results, initArgs);
+      return result;
+    }
+
+    if (*tripCount == 1) {
+      result.inductionVar = lowerBound;
+      if (failed(bodyBuilder(builder, loc, lowerBound, initArgs, result.results)))
+        return failure();
+      if (failed(validateNormalizedLoopYields(loc, initArgs, result.results)))
+        return failure();
+      return result;
+    }
+  }
+
+  result.loop = scf::ForOp::create(builder, loc, lowerBound, upperBound, step, initArgs);
+  result.inductionVar = result.loop.getInductionVar();
+
+  {
+    OpBuilder::InsertionGuard guard(builder);
+    Block* body = result.loop.getBody();
+    if (!body->empty())
+      if (auto yieldOp = dyn_cast<scf::YieldOp>(body->back()))
+        yieldOp->erase();
+    builder.setInsertionPointToEnd(body);
+    ValueRange iterArgs = result.loop.getRegionIterArgs();
+    if (failed(bodyBuilder(builder, loc, result.inductionVar, iterArgs, result.results))) {
+      result.loop.erase();
+      return failure();
+    }
+    if (failed(validateNormalizedLoopYields(loc, initArgs, result.results))) {
+      result.loop.erase();
+      return failure();
+    }
+    scf::YieldOp::create(builder, loc, result.results);
+  }
+  builder.setInsertionPointAfter(result.loop);
+  result.results.assign(result.loop.getResults().begin(), result.loop.getResults().end());
+  return result;
+}
+
+} // namespace onnx_mlir

src/PIM/Common/IR/LoopUtils.hpp

@@ -0,0 +1,30 @@
+#pragma once
+
+#include "mlir/Dialect/SCF/IR/SCF.h"
+#include "mlir/IR/Builders.h"
+
+#include "llvm/ADT/STLFunctionalExtras.h"
+#include "llvm/ADT/SmallVector.h"
+
+namespace onnx_mlir {
+
+struct NormalizedLoopResult {
+  mlir::Value inductionVar;
+  llvm::SmallVector<mlir::Value, 4> results;
+  mlir::scf::ForOp loop;
+
+  bool wasInlined() const { return !loop; }
+};
+
+using NormalizedLoopBodyBuilder = llvm::function_ref<mlir::LogicalResult(
+  mlir::OpBuilder&, mlir::Location, mlir::Value, mlir::ValueRange, llvm::SmallVectorImpl<mlir::Value>&)>;
+
+mlir::FailureOr<NormalizedLoopResult> buildNormalizedScfFor(mlir::OpBuilder& builder,
+                                                            mlir::Location loc,
+                                                            mlir::Value lowerBound,
+                                                            mlir::Value upperBound,
+                                                            mlir::Value step,
+                                                            mlir::ValueRange initArgs,
+                                                            NormalizedLoopBodyBuilder bodyBuilder);
+
+} // namespace onnx_mlir

src/PIM/Common/Support/CheckedArithmetic.cpp

@@ -0,0 +1,222 @@
+#include "llvm/Support/ErrorHandling.h"
+#include "llvm/Support/raw_ostream.h"
+
+#include "CheckedArithmetic.hpp"
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir::pim {
+
+namespace {
+
+static void emitCrashMessage(llvm::StringRef fieldName, llvm::StringRef message) {
+  llvm::errs() << "PIM " << fieldName << " " << message << "\n";
+}
+
+template <typename To, typename From>
+static FailureOr<To> checkedCastAtLocation(From value, Location loc, llvm::StringRef fieldName) {
+  static_assert(std::is_integral_v<To> && std::is_integral_v<From>, "checkedCastAtLocation requires integral types");
+
+  using ToLimits = std::numeric_limits<To>;
+
+  if constexpr (std::is_signed_v<From> == std::is_signed_v<To>) {
+    if (value < static_cast<From>(ToLimits::min()) || value > static_cast<From>(ToLimits::max())) {
+      emitCheckedArithmeticError(loc, fieldName, "is outside representable range");
+      return failure();
+    }
+  }
+  else if constexpr (std::is_signed_v<From>) {
+    using UnsignedFrom = std::make_unsigned_t<From>;
+    using UnsignedTo = std::make_unsigned_t<To>;
+    if (value < 0 || static_cast<UnsignedFrom>(value) > static_cast<UnsignedTo>(ToLimits::max())) {
+      emitCheckedArithmeticError(loc, fieldName, "is outside representable range");
+      return failure();
+    }
+  }
+  else {
+    using UnsignedFrom = std::make_unsigned_t<From>;
+    using UnsignedTo = std::conditional_t<std::is_signed_v<To>, std::make_unsigned_t<To>, To>;
+    if (static_cast<UnsignedFrom>(value) > static_cast<UnsignedTo>(ToLimits::max())) {
+      emitCheckedArithmeticError(loc, fieldName, "is outside representable range");
+      return failure();
+    }
+  }
+
+  return static_cast<To>(value);
+}
+
+template <typename UInt>
+FailureOr<UInt> checkedMulAtLocation(UInt lhs, UInt rhs, Location loc, llvm::StringRef fieldName) {
+  static_assert(std::is_integral_v<UInt> && std::is_unsigned_v<UInt>,
+                "checkedMulAtLocation requires unsigned integral types");
+  if (lhs != 0 && rhs > std::numeric_limits<UInt>::max() / lhs) {
+    emitCheckedArithmeticError(loc, fieldName, "multiplication overflow");
+    return failure();
+  }
+  return lhs * rhs;
+}
+
+} // namespace
+
+InFlightDiagnostic emitCheckedArithmeticError(Operation* anchor, llvm::StringRef fieldName, llvm::StringRef message) {
+  assert(anchor && "expected arithmetic diagnostics to have an anchor op");
+  return anchor->emitOpError() << fieldName << " " << message;
+}
+
+InFlightDiagnostic emitCheckedArithmeticError(Location loc, llvm::StringRef fieldName, llvm::StringRef message) {
+  return emitError(loc) << "PIM " << fieldName << " " << message;
+}
+
+FailureOr<int32_t> checkedI32(int64_t value, Operation* anchor, llvm::StringRef fieldName) {
+  return checkedCast<int32_t>(value, anchor, fieldName);
+}
+
+FailureOr<int32_t> checkedI32(uint64_t value, Operation* anchor, llvm::StringRef fieldName) {
+  return checkedCast<int32_t>(value, anchor, fieldName);
+}
+
+FailureOr<uint8_t> checkedU8(uint64_t value, Operation* anchor, llvm::StringRef fieldName) {
+  return checkedCast<uint8_t>(value, anchor, fieldName);
+}
+
+FailureOr<size_t> checkedSize(int64_t value, Operation* anchor, llvm::StringRef fieldName) {
+  return checkedCast<size_t>(value, anchor, fieldName);
+}
+
+FailureOr<IntegerAttr>
+getCheckedI32Attr(Builder& builder, Operation* anchor, int64_t value, llvm::StringRef fieldName) {
+  assert(anchor && "checked op-based attrs require a non-null diagnostic anchor");
+  auto checkedValue = checkedI32(value, anchor, fieldName);
+  if (failed(checkedValue))
+    return failure();
+  return builder.getI32IntegerAttr(*checkedValue);
+}
+
+FailureOr<IntegerAttr>
+getCheckedI32Attr(Builder& builder, Operation* anchor, uint64_t value, llvm::StringRef fieldName) {
+  assert(anchor && "checked op-based attrs require a non-null diagnostic anchor");
+  auto checkedValue = checkedI32(value, anchor, fieldName);
+  if (failed(checkedValue))
+    return failure();
+  return builder.getI32IntegerAttr(*checkedValue);
+}
+
+FailureOr<IntegerAttr> getCheckedI32Attr(Builder& builder, Location loc, int64_t value, llvm::StringRef fieldName) {
+  auto checkedValue = checkedCastAtLocation<int32_t>(value, loc, fieldName);
+  if (failed(checkedValue))
+    return failure();
+  return builder.getI32IntegerAttr(*checkedValue);
+}
+
+FailureOr<IntegerAttr> getCheckedI32Attr(Builder& builder, Location loc, uint64_t value, llvm::StringRef fieldName) {
+  auto checkedValue = checkedCastAtLocation<int32_t>(value, loc, fieldName);
+  if (failed(checkedValue))
+    return failure();
+  return builder.getI32IntegerAttr(*checkedValue);
+}
+
+FailureOr<uint64_t> getCheckedShapedTypeSizeInBytes(ShapedType type, Operation* anchor, llvm::StringRef fieldName) {
+  assert(anchor && "checked op-based size helpers require a non-null diagnostic anchor");
+  if (!type.hasStaticShape()) {
+    emitCheckedArithmeticError(anchor, fieldName, "requires static shaped type");
+    return failure();
+  }
+  if (!hasByteSizedElementType(type.getElementType())) {
+    emitCheckedArithmeticError(anchor, fieldName, "requires byte-sized element type");
+    return failure();
+  }
+
+  uint64_t elements = 1;
+  for (int64_t dim : type.getShape()) {
+    if (dim < 0) {
+      emitCheckedArithmeticError(anchor, fieldName, "requires nonnegative dimensions");
+      return failure();
+    }
+
+    auto nextElements = checkedMul(elements, static_cast<uint64_t>(dim), anchor, fieldName);
+    if (failed(nextElements))
+      return failure();
+    elements = *nextElements;
+  }
+
+  return checkedMul(
+    elements, static_cast<uint64_t>(getElementTypeSizeInBytes(type.getElementType())), anchor, fieldName);
+}
+
+FailureOr<uint64_t> getCheckedShapedTypeSizeInBytes(ShapedType type, Location loc, llvm::StringRef fieldName) {
+  if (!type.hasStaticShape()) {
+    emitCheckedArithmeticError(loc, fieldName, "requires static shaped type");
+    return failure();
+  }
+  if (!hasByteSizedElementType(type.getElementType())) {
+    emitCheckedArithmeticError(loc, fieldName, "requires byte-sized element type");
+    return failure();
+  }
+
+  uint64_t elements = 1;
+  for (int64_t dim : type.getShape()) {
+    if (dim < 0) {
+      emitCheckedArithmeticError(loc, fieldName, "requires nonnegative dimensions");
+      return failure();
+    }
+
+    auto nextElements = checkedMulAtLocation(elements, static_cast<uint64_t>(dim), loc, fieldName);
+    if (failed(nextElements))
+      return failure();
+    elements = *nextElements;
+  }
+
+  return checkedMulAtLocation(
+    elements, static_cast<uint64_t>(getElementTypeSizeInBytes(type.getElementType())), loc, fieldName);
+}
+
+int32_t checkedI32OrCrash(int64_t value, llvm::StringRef fieldName) {
+  if (value < std::numeric_limits<int32_t>::min() || value > std::numeric_limits<int32_t>::max()) {
+    emitCrashMessage(fieldName, "is outside representable range");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return static_cast<int32_t>(value);
+}
+
+int32_t checkedI32OrCrash(uint64_t value, llvm::StringRef fieldName) {
+  if (value > static_cast<uint64_t>(std::numeric_limits<int32_t>::max())) {
+    emitCrashMessage(fieldName, "is outside representable range");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return static_cast<int32_t>(value);
+}
+
+uint8_t checkedU8OrCrash(uint64_t value, llvm::StringRef fieldName) {
+  if (value > static_cast<uint64_t>(std::numeric_limits<uint8_t>::max())) {
+    emitCrashMessage(fieldName, "is outside representable range");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return static_cast<uint8_t>(value);
+}
+
+size_t checkedSizeOrCrash(int64_t value, llvm::StringRef fieldName) {
+  if (value < 0) {
+    emitCrashMessage(fieldName, "is outside representable range");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return static_cast<size_t>(value);
+}
+
+size_t checkedAddOrCrash(size_t lhs, size_t rhs, llvm::StringRef fieldName) {
+  if (rhs > std::numeric_limits<size_t>::max() - lhs) {
+    emitCrashMessage(fieldName, "addition overflow");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return lhs + rhs;
+}
+
+size_t checkedMulOrCrash(size_t lhs, size_t rhs, llvm::StringRef fieldName) {
+  if (lhs != 0 && rhs > std::numeric_limits<size_t>::max() / lhs) {
+    emitCrashMessage(fieldName, "multiplication overflow");
+    llvm_unreachable("PIM checked arithmetic failure");
+  }
+  return lhs * rhs;
+}
+
+} // namespace onnx_mlir::pim

src/PIM/Common/Support/CheckedArithmetic.hpp

@@ -0,0 +1,107 @@
+#pragma once
+
+#include "mlir/IR/Builders.h"
+#include "mlir/IR/BuiltinTypeInterfaces.h"
+#include "mlir/IR/Operation.h"
+#include "mlir/Support/LogicalResult.h"
+
+#include "llvm/ADT/StringRef.h"
+
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+#include <type_traits>
+
+namespace onnx_mlir::pim {
+
+mlir::InFlightDiagnostic
+emitCheckedArithmeticError(mlir::Operation* anchor, llvm::StringRef fieldName, llvm::StringRef message);
+
+mlir::InFlightDiagnostic
+emitCheckedArithmeticError(mlir::Location loc, llvm::StringRef fieldName, llvm::StringRef message);
+
+template <typename To, typename From>
+mlir::FailureOr<To> checkedCast(From value, mlir::Operation* anchor, llvm::StringRef fieldName) {
+  static_assert(std::is_integral_v<To> && std::is_integral_v<From>, "checkedCast requires integral types");
+
+  using ToLimits = std::numeric_limits<To>;
+
+  if constexpr (std::is_signed_v<From> == std::is_signed_v<To>) {
+    if (value < static_cast<From>(ToLimits::min()) || value > static_cast<From>(ToLimits::max())) {
+      emitCheckedArithmeticError(anchor, fieldName, "is outside representable range");
+      return mlir::failure();
+    }
+  }
+  else if constexpr (std::is_signed_v<From>) {
+    using UnsignedFrom = std::make_unsigned_t<From>;
+    using UnsignedTo = std::make_unsigned_t<To>;
+    if (value < 0 || static_cast<UnsignedFrom>(value) > static_cast<UnsignedTo>(ToLimits::max())) {
+      emitCheckedArithmeticError(anchor, fieldName, "is outside representable range");
+      return mlir::failure();
+    }
+  }
+  else {
+    using UnsignedFrom = std::make_unsigned_t<From>;
+    using UnsignedTo = std::conditional_t<std::is_signed_v<To>, std::make_unsigned_t<To>, To>;
+    if (static_cast<UnsignedFrom>(value) > static_cast<UnsignedTo>(ToLimits::max())) {
+      emitCheckedArithmeticError(anchor, fieldName, "is outside representable range");
+      return mlir::failure();
+    }
+  }
+
+  return static_cast<To>(value);
+}
+
+template <typename UInt>
+mlir::FailureOr<UInt> checkedAdd(UInt lhs, UInt rhs, mlir::Operation* anchor, llvm::StringRef fieldName) {
+  static_assert(std::is_integral_v<UInt> && std::is_unsigned_v<UInt>, "checkedAdd requires unsigned integral types");
+  if (rhs > std::numeric_limits<UInt>::max() - lhs) {
+    emitCheckedArithmeticError(anchor, fieldName, "addition overflow");
+    return mlir::failure();
+  }
+  return lhs + rhs;
+}
+
+template <typename UInt>
+mlir::FailureOr<UInt> checkedMul(UInt lhs, UInt rhs, mlir::Operation* anchor, llvm::StringRef fieldName) {
+  static_assert(std::is_integral_v<UInt> && std::is_unsigned_v<UInt>, "checkedMul requires unsigned integral types");
+  if (lhs != 0 && rhs > std::numeric_limits<UInt>::max() / lhs) {
+    emitCheckedArithmeticError(anchor, fieldName, "multiplication overflow");
+    return mlir::failure();
+  }
+  return lhs * rhs;
+}
+
+mlir::FailureOr<int32_t> checkedI32(int64_t value, mlir::Operation* anchor, llvm::StringRef fieldName);
+mlir::FailureOr<int32_t> checkedI32(uint64_t value, mlir::Operation* anchor, llvm::StringRef fieldName);
+
+mlir::FailureOr<uint8_t> checkedU8(uint64_t value, mlir::Operation* anchor, llvm::StringRef fieldName);
+
+mlir::FailureOr<size_t> checkedSize(int64_t value, mlir::Operation* anchor, llvm::StringRef fieldName);
+
+mlir::FailureOr<mlir::IntegerAttr>
+getCheckedI32Attr(mlir::Builder& builder, mlir::Operation* anchor, int64_t value, llvm::StringRef fieldName);
+
+mlir::FailureOr<mlir::IntegerAttr>
+getCheckedI32Attr(mlir::Builder& builder, mlir::Operation* anchor, uint64_t value, llvm::StringRef fieldName);
+
+mlir::FailureOr<mlir::IntegerAttr>
+getCheckedI32Attr(mlir::Builder& builder, mlir::Location loc, int64_t value, llvm::StringRef fieldName);
+
+mlir::FailureOr<mlir::IntegerAttr>
+getCheckedI32Attr(mlir::Builder& builder, mlir::Location loc, uint64_t value, llvm::StringRef fieldName);
+
+mlir::FailureOr<uint64_t>
+getCheckedShapedTypeSizeInBytes(mlir::ShapedType type, mlir::Operation* anchor, llvm::StringRef fieldName);
+
+mlir::FailureOr<uint64_t>
+getCheckedShapedTypeSizeInBytes(mlir::ShapedType type, mlir::Location loc, llvm::StringRef fieldName);
+
+int32_t checkedI32OrCrash(int64_t value, llvm::StringRef fieldName);
+int32_t checkedI32OrCrash(uint64_t value, llvm::StringRef fieldName);
+uint8_t checkedU8OrCrash(uint64_t value, llvm::StringRef fieldName);
+size_t checkedSizeOrCrash(int64_t value, llvm::StringRef fieldName);
+size_t checkedAddOrCrash(size_t lhs, size_t rhs, llvm::StringRef fieldName);
+size_t checkedMulOrCrash(size_t lhs, size_t rhs, llvm::StringRef fieldName);
+
+} // namespace onnx_mlir::pim

src/PIM/Compiler/CMakeLists.txt

@@ -28,7 +28,10 @@ add_pim_library(OMPimCompilerUtils
   OMPimCompilerOptions
   OMPimCommon
   OMPimBufferization
-  OMPimStaticMemoryCoalescing
+  OMPimMemoryCoalescing
+  OMPimHostConstantFolding
+  OMPimHostConstantMaterialization
+  OMPimVerification
   OMPimPasses
   OMONNXToSpatial
   OMSpatialToPim

src/PIM/Compiler/PimBinaryFormat.hpp

@@ -6,8 +6,8 @@
 #include "llvm/Support/raw_ostream.h"
 
 #include <array>
-#include <cassert>
-#include <limits>
+
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 
 namespace onnx_mlir::pim_binary {
 
@@ -95,15 +95,10 @@ inline void writeInstructionRecord(llvm::raw_ostream& os, const InstructionRecor
   writeInt32LE(os, record.generic3);
 }
 
-inline int32_t toI32(int64_t value) {
-  assert(value >= std::numeric_limits<int32_t>::min() && value <= std::numeric_limits<int32_t>::max()
-         && "PIM binary field out of int32 range");
-  return static_cast<int32_t>(value);
-}
+inline int32_t toI32(int64_t value) { return onnx_mlir::pim::checkedI32OrCrash(value, "binary field"); }
 
 inline uint8_t toU8(int64_t value) {
-  assert(value >= 0 && value <= std::numeric_limits<uint8_t>::max() && "PIM binary field out of uint8 range");
-  return static_cast<uint8_t>(value);
+  return onnx_mlir::pim::checkedU8OrCrash(static_cast<uint64_t>(value), "binary field");
 }
 
 inline int32_t getOptionalInt(const llvm::json::Object& object, llvm::StringRef key, int32_t defaultValue = 0) {

src/PIM/Compiler/PimCodeGen.cpp

@@ -25,12 +25,14 @@
 #include <cassert>
 #include <cstdint>
 #include <fstream>
+#include <limits>
 #include <memory>
 #include <string>
 #include <utility>
 
 #include "Common/IR/CompactAsmUtils.hpp"
 #include "Common/PimCommon.hpp"
+#include "Common/Support/CheckedArithmetic.hpp"
 #include "Common/Support/ReportUtils.hpp"
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
@@ -71,12 +73,23 @@ static MemoryValueKey getMemoryValueKey(mlir::Value value, std::optional<unsigne
   return {value, getLaneForMemoryValue(value, lane)};
 }
 
+static int32_t getVectorByteSizeOrCrash(ShapedType type) {
+  auto byteSize = pim::getCheckedShapedTypeSizeInBytes(type, UnknownLoc::get(type.getContext()), "vector byte size");
+  if (failed(byteSize))
+    llvm_unreachable("Failed to compute checked vector byte size");
+  return pim::checkedI32OrCrash(*byteSize, "vector byte size");
+}
+
 } // namespace
 
 MemEntry* PimMemory::gatherMemEntry(mlir::Value value, std::optional<unsigned> lane) {
   auto type = cast<ShapedType>(value.getType());
   assert("Only static shape is supported" && type.hasStaticShape());
-  size_t allocSize = getShapedTypeSizeInBytes(type);
+  auto checkedAllocSize =
+    pim::getCheckedShapedTypeSizeInBytes(type, UnknownLoc::get(type.getContext()), "memory allocation byte size");
+  if (failed(checkedAllocSize))
+    llvm_unreachable("Failed to compute checked allocation byte size");
+  size_t allocSize = static_cast<size_t>(*checkedAllocSize);
   MemEntry memEntry = {0, allocSize};
   return &memEntries.emplace_back(memEntry, getMemoryValueKey(value, lane)).first;
 }
@@ -272,7 +285,8 @@ size_t PimAcceleratorMemory::getValueAddress(mlir::Value value,
     llvm_unreachable("Missing mem entry");
   }
 
-  return iter->second.address + resolvedAddress->byteOffset;
+  size_t byteOffset = pim::checkedSizeOrCrash(resolvedAddress->byteOffset, "resolved PIM byte offset");
+  return pim::checkedAddOrCrash(iter->second.address, byteOffset, "resolved PIM address");
 }
 
 llvm::FailureOr<int64_t> PimAcceleratorMemory::getIndexValue(mlir::Value value,
@@ -291,8 +305,12 @@ llvm::FailureOr<int64_t> PimAcceleratorMemory::getIndexValue(mlir::Value value,
 void PimAcceleratorMemory::reportHost() { hostReportRow = hostMem.getReportRow(); }
 
 void PimAcceleratorMemory::recordCoreReport(size_t coreId, const MemoryReportRow& row) {
-  reportEntries.push_back(
-    {MemoryReportEntry::Kind::Core, coreId, {static_cast<int32_t>(coreId)}, row, row.numAlloca, row.sizeAlloca});
+  reportEntries.push_back({MemoryReportEntry::Kind::Core,
+                           coreId,
+                           {pim::checkedI32OrCrash(coreId, "memory report core id")},
+                           row,
+                           row.numAlloca,
+                           row.sizeAlloca});
 }
 
 void PimAcceleratorMemory::recordBatchReport(uint64_t batchId,
@@ -402,24 +420,24 @@ void PimCodeGen::genSetRegisterImmediateUnsigned(size_t registerNumber, size_t i
   pim_binary::InstructionRecord instruction;
   instruction.opcode = pim_binary::Opcode::sldi;
   instruction.rd = static_cast<uint8_t>(registerNumber);
-  instruction.r2OrImm = static_cast<int32_t>(immediate);
+  instruction.r2OrImm = pim::checkedI32OrCrash(immediate, "register immediate");
   emitInstruction(instruction);
 }
 
 void PimCodeGen::setupRd(size_t rdAddress, size_t rdOffset) const {
-  genSetRegisterImmediateUnsigned(0, rdAddress + rdOffset);
+  genSetRegisterImmediateUnsigned(0, pim::checkedAddOrCrash(rdAddress, rdOffset, "rd address"));
 }
 
 void PimCodeGen::setupRdRs1(size_t rdAddress, size_t rdOffset, size_t rs1Address, size_t rs1Offset) const {
-  genSetRegisterImmediateUnsigned(0, rdAddress + rdOffset);
-  genSetRegisterImmediateUnsigned(1, rs1Address + rs1Offset);
+  genSetRegisterImmediateUnsigned(0, pim::checkedAddOrCrash(rdAddress, rdOffset, "rd address"));
+  genSetRegisterImmediateUnsigned(1, pim::checkedAddOrCrash(rs1Address, rs1Offset, "rs1 address"));
 }
 
 void PimCodeGen::setupRdRs1Rs2(
   size_t rdAddress, size_t rdOffset, size_t rs1Address, size_t rs1Offset, size_t rs2Address, size_t rs2Offset) const {
-  genSetRegisterImmediateUnsigned(0, rdAddress + rdOffset);
-  genSetRegisterImmediateUnsigned(1, rs1Address + rs1Offset);
-  genSetRegisterImmediateUnsigned(2, rs2Address + rs2Offset);
+  genSetRegisterImmediateUnsigned(0, pim::checkedAddOrCrash(rdAddress, rdOffset, "rd address"));
+  genSetRegisterImmediateUnsigned(1, pim::checkedAddOrCrash(rs1Address, rs1Offset, "rs1 address"));
+  genSetRegisterImmediateUnsigned(2, pim::checkedAddOrCrash(rs2Address, rs2Offset, "rs2 address"));
 }
 
 void PimCodeGen::emitMemCopyOp(StringRef opName,
@@ -437,8 +455,7 @@ void PimCodeGen::emitMemCopyOp(StringRef opName,
   instruction.r1 = 1;
   instruction.generic1 = 0;
   instruction.generic2 = 0;
-  instruction.generic3 = static_cast<int32_t>(size);
-  (void) sizeFieldName;
+  instruction.generic3 = pim::checkedI32OrCrash(size, sizeFieldName);
   emitInstruction(instruction);
 }
 
@@ -448,10 +465,10 @@ void PimCodeGen::emitCommunicationOp(StringRef opName, size_t bufferAddr, size_t
   pim_binary::InstructionRecord instruction;
   instruction.opcode = pim_binary::opcodeFromString(opName);
   instruction.rd = 0;
-  instruction.r2OrImm = static_cast<int32_t>(remapCoreId(coreId));
+  instruction.r2OrImm = pim::checkedI32OrCrash(remapCoreId(coreId), "communication core id");
   instruction.generic1 = 0;
   instruction.generic2 = 0;
-  instruction.generic3 = static_cast<int32_t>(size);
+  instruction.generic3 = pim::checkedI32OrCrash(size, "communication byte size");
   emitInstruction(instruction);
 }
 
@@ -464,7 +481,7 @@ void PimCodeGen::emitMvmOp(size_t groupId, size_t rdAddr, size_t rdOffset, size_
   instruction.r1 = 1;
   instruction.r2OrImm = 8;
   instruction.generic1 = 0;
-  instruction.generic2 = static_cast<int32_t>(groupId);
+  instruction.generic2 = pim::checkedI32OrCrash(groupId, "mvm group id");
   emitInstruction(instruction);
 }
 
@@ -578,7 +595,7 @@ void PimCodeGen::codeGenVVAddOp(pim::PimVVAddOp vvaddOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvaddOp.getLhs().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vvaddOp.getLhs().getType()));
   emitInstruction(instruction);
 }
 
@@ -593,7 +610,7 @@ void PimCodeGen::codeGenVVSubOp(pim::PimVVSubOp vvsubOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvsubOp.getLhs().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vvsubOp.getLhs().getType()));
   emitInstruction(instruction);
 }
 
@@ -608,7 +625,7 @@ void PimCodeGen::codeGenVVMulOp(pim::PimVVMulOp vvmulOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvmulOp.getLhs().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vvmulOp.getLhs().getType()));
   emitInstruction(instruction);
 }
 
@@ -623,7 +640,7 @@ void PimCodeGen::codeGenVVMaxOp(pim::PimVVMaxOp vvmaxOp, const StaticValueKnowle
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvmaxOp.getLhs().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vvmaxOp.getLhs().getType()));
   emitInstruction(instruction);
 }
 
@@ -638,7 +655,7 @@ void PimCodeGen::codeGenVVDMulOp(pim::PimVVDMulOp vvdmulOp, const StaticValueKno
   instruction.rd = 0;
   instruction.r1 = 1;
   instruction.r2OrImm = 2;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vvdmulOp.getLhs().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vvdmulOp.getLhs().getType()));
   emitInstruction(instruction);
 }
 
@@ -653,7 +670,7 @@ void PimCodeGen::codeGenVAvgOp(pim::PimVAvgOp vavgOp, const StaticValueKnowledge
   instruction.r1 = 1;
   instruction.r2OrImm = 1;
   instruction.generic1 = 1;
-  instruction.generic3 = static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(vavgOp.getInput().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vavgOp.getInput().getType()));
   emitInstruction(instruction);
 }
 
@@ -666,7 +683,7 @@ 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>(getShapedTypeSizeInBytes(cast<ShapedType>(vreluOp.getInput().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vreluOp.getInput().getType()));
   emitInstruction(instruction);
 }
 
@@ -679,7 +696,7 @@ 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>(getShapedTypeSizeInBytes(cast<ShapedType>(vtanhOp.getInput().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vtanhOp.getInput().getType()));
   emitInstruction(instruction);
 }
 
@@ -692,7 +709,7 @@ 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>(getShapedTypeSizeInBytes(cast<ShapedType>(vsigmOp.getInput().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vsigmOp.getInput().getType()));
   emitInstruction(instruction);
 }
 
@@ -705,8 +722,7 @@ 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>(getShapedTypeSizeInBytes(cast<ShapedType>(vsoftmaxOp.getInput().getType())));
+  instruction.generic3 = getVectorByteSizeOrCrash(cast<ShapedType>(vsoftmaxOp.getInput().getType()));
   emitInstruction(instruction);
 }
 
@@ -1370,7 +1386,7 @@ OnnxMlirCompilerErrorCodes onnx_mlir::compileToPimCode(ModuleOp& moduleOp, std::
       if (auto err = linkCoreWeights(job.emittedCoreId, mapCoreWeightToFileName[job.emittedCoreId], xbarsPerGroup))
         return err;
       xbarsPerArrayGroup["core" + std::to_string(job.emittedCoreId)] = std::move(xbarsPerGroup);
-      reportedCoreIds.push_back(static_cast<int32_t>(job.emittedCoreId));
+      reportedCoreIds.push_back(pim::checkedI32OrCrash(job.emittedCoreId, "batch report core id"));
       if (!batchPerCoreRow)
         batchPerCoreRow = result.reportRow;
       batchRow = addMemoryReportRows(batchRow, result.reportRow);

src/PIM/Compiler/PimCompilerUtils.cpp

@@ -40,7 +40,6 @@ void addPassesPim(OwningOpRef<ModuleOp>& module,
 
   if (pimEmissionTarget >= EmitPimBufferized) {
     pm.addPass(createPimBufferizationPass());
-    pm.addPass(createPimStaticMemoryCoalescingPass());
     pm.addPass(createMessagePass("Pim bufferized"));
   }
 
@@ -48,6 +47,7 @@ void addPassesPim(OwningOpRef<ModuleOp>& module,
     pm.addPass(createPimHostConstantFoldingPass());
     pm.addPass(createMessagePass("Pim host constants folded"));
     pm.addPass(createPimMaterializeHostConstantsPass());
+    pm.addPass(createPimMemoryCoalescingPass());
     pm.addPass(createPimVerificationPass());
     pm.addPass(createMessagePass("Pim verified"));
     pm.addPass(createEmitPimCodePass());

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

@@ -12,6 +12,7 @@
 #include <type_traits>
 #include <utility>
 
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
@@ -180,8 +181,11 @@ auto createSpatComputeBatch(RewriterT& rewriter,
   if (laneCount <= 0 || laneCount > std::numeric_limits<int32_t>::max())
     return mlir::FailureOr<spatial::SpatComputeBatch>(mlir::failure());
 
-  auto batchOp = spatial::SpatComputeBatch::create(
-    rewriter, loc, resultTypes, rewriter.getI32IntegerAttr(static_cast<int32_t>(laneCount)), weights, inputs);
+  auto laneCountAttr = pim::getCheckedI32Attr(rewriter, loc, laneCount, "spatial compute_batch lane count");
+  if (mlir::failed(laneCountAttr))
+    return mlir::FailureOr<spatial::SpatComputeBatch>(mlir::failure());
+
+  auto batchOp = spatial::SpatComputeBatch::create(rewriter, loc, resultTypes, *laneCountAttr, weights, inputs);
 
   mlir::SmallVector<mlir::Type> blockArgTypes {rewriter.getIndexType()};
   mlir::SmallVector<mlir::Location> blockArgLocs {loc};

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

@@ -8,6 +8,7 @@
 
 #include <algorithm>
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Common/Support/Diagnostics.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
@@ -305,58 +306,67 @@ static Value createIm2colRowComputes(Value x,
       auto cStrideHeight = getOrCreateIndexConstant(rewriter, anchorOp, strideHeight);
       auto cStrideWidth = getOrCreateIndexConstant(rewriter, anchorOp, strideWidth);
 
-      auto im2colLoop = scf::ForOp::create(rewriter, loc, c0, cNumPatches, c1, ValueRange {im2colInit});
-      rewriter.setInsertionPointToStart(im2colLoop.getBody());
+      auto im2colLoop = buildNormalizedScfFor(
+        rewriter,
+        loc,
+        c0,
+        cNumPatches,
+        c1,
+        ValueRange {im2colInit},
+        [&](OpBuilder&, Location nestedLoc, Value patchIndex, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+          Value im2colAcc = iterArgs.front();
+          Value batchIndex = arith::DivUIOp::create(rewriter, nestedLoc, patchIndex, cNumPatchesPerBatch);
+          Value batchPatchIndex = arith::RemUIOp::create(rewriter, nestedLoc, patchIndex, cNumPatchesPerBatch);
+          Value outHeightIndex = arith::DivUIOp::create(rewriter, nestedLoc, batchPatchIndex, cOutWidth);
+          Value outWidthIndex = arith::RemUIOp::create(rewriter, nestedLoc, batchPatchIndex, cOutWidth);
+          Value inputHeightOffset = arith::MulIOp::create(rewriter, nestedLoc, outHeightIndex, cStrideHeight);
+          Value inputWidthOffset = arith::MulIOp::create(rewriter, nestedLoc, outWidthIndex, cStrideWidth);
 
-      Value patchIndex = im2colLoop.getInductionVar();
-      Value im2colAcc = im2colLoop.getRegionIterArgs().front();
+          SmallVector<OpFoldResult> offsets = {
+            batchIndex, rewriter.getIndexAttr(0), inputHeightOffset, inputWidthOffset};
+          SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
+                                             rewriter.getIndexAttr(numChannelsIn),
+                                             rewriter.getIndexAttr(wHeight),
+                                             rewriter.getIndexAttr(wWidth)};
+          SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1),
+                                               rewriter.getIndexAttr(1),
+                                               rewriter.getIndexAttr(dilationHeight),
+                                               rewriter.getIndexAttr(dilationWidth)};
+          auto patchType = RankedTensorType::get({1, numChannelsIn, wHeight, wWidth}, elemType);
+          Value patch =
+            tensor::ExtractSliceOp::create(rewriter, nestedLoc, patchType, paddedInput, offsets, sizes, strides);
 
-      Value batchIndex = arith::DivUIOp::create(rewriter, loc, patchIndex, cNumPatchesPerBatch);
-      Value batchPatchIndex = arith::RemUIOp::create(rewriter, loc, patchIndex, cNumPatchesPerBatch);
-      Value outHeightIndex = arith::DivUIOp::create(rewriter, loc, batchPatchIndex, cOutWidth);
-      Value outWidthIndex = arith::RemUIOp::create(rewriter, loc, batchPatchIndex, cOutWidth);
-      Value inputHeightOffset = arith::MulIOp::create(rewriter, loc, outHeightIndex, cStrideHeight);
-      Value inputWidthOffset = arith::MulIOp::create(rewriter, loc, outWidthIndex, cStrideWidth);
+          Value row = tensor::CollapseShapeOp::create(rewriter,
+                                                      nestedLoc,
+                                                      im2colRowType,
+                                                      patch,
+                                                      SmallVector<ReassociationIndices> {
+                                                        {0},
+                                                        {1, 2, 3}
+          });
 
-      SmallVector<OpFoldResult> offsets = {batchIndex, rewriter.getIndexAttr(0), inputHeightOffset, inputWidthOffset};
-      SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
-                                         rewriter.getIndexAttr(numChannelsIn),
-                                         rewriter.getIndexAttr(wHeight),
-                                         rewriter.getIndexAttr(wWidth)};
-      SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1),
-                                           rewriter.getIndexAttr(1),
-                                           rewriter.getIndexAttr(dilationHeight),
-                                           rewriter.getIndexAttr(dilationWidth)};
-      auto patchType = RankedTensorType::get({1, numChannelsIn, wHeight, wWidth}, elemType);
-      Value patch = tensor::ExtractSliceOp::create(rewriter, loc, patchType, paddedInput, offsets, sizes, strides);
-
-      Value row = tensor::CollapseShapeOp::create(rewriter,
-                                                  loc,
-                                                  im2colRowType,
-                                                  patch,
-                                                  SmallVector<ReassociationIndices> {
-                                                    {0},
-                                                    {1, 2, 3}
-      });
-
-      SmallVector<OpFoldResult> rowOffsets = {patchIndex, rewriter.getIndexAttr(0)};
-      SmallVector<OpFoldResult> rowSizes = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(patchSize)};
-      SmallVector<OpFoldResult> rowStrides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-      Value updatedIm2col =
-        tensor::InsertSliceOp::create(rewriter, loc, row, im2colAcc, rowOffsets, rowSizes, rowStrides);
-      scf::YieldOp::create(rewriter, loc, updatedIm2col);
-
-      rewriter.setInsertionPointAfter(im2colLoop);
-      Value im2col = im2colLoop.getResult(0);
+          SmallVector<OpFoldResult> rowOffsets = {patchIndex, rewriter.getIndexAttr(0)};
+          SmallVector<OpFoldResult> rowSizes = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(patchSize)};
+          SmallVector<OpFoldResult> rowStrides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+          Value updatedIm2col =
+            tensor::InsertSliceOp::create(rewriter, nestedLoc, row, im2colAcc, rowOffsets, rowSizes, rowStrides);
+          yielded.push_back(updatedIm2col);
+          return success();
+        });
+      if (failed(im2colLoop))
+        return failure();
+      Value im2col = im2colLoop->results.front();
 
       Value gemmInputRows = im2col;
       if (packFactor != 1)
         gemmInputRows = packRowsForParallelGemm(im2col, im2colType, packFactor, rewriter, loc);
 
       spatial::SpatYieldOp::create(rewriter, loc, gemmInputRows);
+      return success();
     });
 
-  return im2colComputeOp.getResult(0);
+  assert(succeeded(im2colComputeOp) && "Conv im2col compute construction must succeed");
+  return im2colComputeOp->getResult(0);
 }
 
 static Value createCollectedConvOutput(ValueRange gemmRows,

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

@@ -15,6 +15,7 @@
 
 #include "Common/IR/ConstantUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/AffineUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Common/Support/Diagnostics.hpp"
@@ -247,16 +248,16 @@ static Value createPaddedInputCompute(Value input,
   return computeOp.getResult(0);
 }
 
-static spatial::SpatComputeBatch createVmmBatch(Value a,
-                                                Value b,
-                                                RankedTensorType aType,
-                                                RankedTensorType paddedBType,
-                                                RankedTensorType partialPiecesType,
-                                                int64_t numOutRows,
-                                                int64_t numKSlices,
-                                                int64_t numOutHSlices,
-                                                ConversionPatternRewriter& rewriter,
-                                                Location loc) {
+static FailureOr<spatial::SpatComputeBatch> createVmmBatch(Value a,
+                                                           Value b,
+                                                           RankedTensorType aType,
+                                                           RankedTensorType paddedBType,
+                                                           RankedTensorType partialPiecesType,
+                                                           int64_t numOutRows,
+                                                           int64_t numKSlices,
+                                                           int64_t numOutHSlices,
+                                                           ConversionPatternRewriter& rewriter,
+                                                           Location loc) {
   const int64_t laneCount = partialPiecesType.getDimSize(0);
   auto batchOp = createSpatComputeBatch(
     rewriter,
@@ -294,7 +295,8 @@ static spatial::SpatComputeBatch createVmmBatch(Value a,
       createParallelInsertSliceIntoBatchOutput(
         rewriter, loc, piece, args.outputs.front(), pieceOffsets, pieceSizes, unitStrides);
     });
-  assert(succeeded(batchOp) && "expected Gemm VMM batch construction to succeed");
+  if (failed(batchOp))
+    return failure();
   return *batchOp;
 }
 
@@ -416,15 +418,15 @@ static Value createBroadcastedBiasScalar(Value bias,
   return tensor::SplatOp::create(rewriter, loc, scalarType, scalar).getResult();
 }
 
-static spatial::SpatComputeBatch createVvdmulBatch(Value a,
-                                                   Value b,
-                                                   RankedTensorType aType,
-                                                   RankedTensorType bType,
-                                                   RankedTensorType scalarPiecesType,
-                                                   RankedTensorType outType,
-                                                   bool bAlreadyTransposed,
-                                                   ConversionPatternRewriter& rewriter,
-                                                   Location loc) {
+static FailureOr<spatial::SpatComputeBatch> createVvdmulBatch(Value a,
+                                                              Value b,
+                                                              RankedTensorType aType,
+                                                              RankedTensorType bType,
+                                                              RankedTensorType scalarPiecesType,
+                                                              RankedTensorType outType,
+                                                              bool bAlreadyTransposed,
+                                                              ConversionPatternRewriter& rewriter,
+                                                              Location loc) {
   const int64_t numOutRows = outType.getDimSize(0);
   const int64_t numOutCols = outType.getDimSize(1);
   const int64_t reductionSize = aType.getDimSize(1);
@@ -454,26 +456,27 @@ static spatial::SpatComputeBatch createVvdmulBatch(Value a,
       createParallelInsertSliceIntoBatchOutput(
         rewriter, loc, scalar, args.outputs.front(), outputOffsets, scalarSizes, unitStrides);
     });
-  assert(succeeded(batchOp) && "expected Gemm VVDMul batch construction to succeed");
+  if (failed(batchOp))
+    return failure();
   return *batchOp;
 }
 
-static spatial::SpatCompute createDynamicGemmOutputCompute(Value scalarPieces,
-                                                           Value bias,
-                                                           RankedTensorType scalarPiecesType,
-                                                           RankedTensorType biasType,
-                                                           RankedTensorType outType,
-                                                           float alpha,
-                                                           float beta,
-                                                           ConversionPatternRewriter& rewriter,
-                                                           Location loc) {
+static FailureOr<spatial::SpatCompute> createDynamicGemmOutputCompute(Value scalarPieces,
+                                                                      Value bias,
+                                                                      RankedTensorType scalarPiecesType,
+                                                                      RankedTensorType biasType,
+                                                                      RankedTensorType outType,
+                                                                      float alpha,
+                                                                      float beta,
+                                                                      ConversionPatternRewriter& rewriter,
+                                                                      Location loc) {
   const int64_t laneCount = scalarPiecesType.getDimSize(0);
   const int64_t numOutCols = outType.getDimSize(1);
   SmallVector<Value> inputs {scalarPieces};
   if (bias)
     inputs.push_back(bias);
 
-  return createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) {
+  return createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) -> LogicalResult {
     Value pieces = blockArgs[0];
     Value biasArg = bias ? blockArgs[1] : Value();
     auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
@@ -481,40 +484,50 @@ static spatial::SpatCompute createDynamicGemmOutputCompute(Value scalarPieces,
     Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
     Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
     Value cLaneCount = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), laneCount);
-    auto loop = scf::ForOp::create(rewriter, loc, c0, cLaneCount, c1, ValueRange {outputInit});
-    rewriter.setInsertionPointToStart(loop.getBody());
+    auto loop = buildNormalizedScfFor(
+      rewriter,
+      loc,
+      c0,
+      cLaneCount,
+      c1,
+      ValueRange {outputInit},
+      [&](OpBuilder&, Location nestedLoc, Value lane, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+        Value outputAcc = iterArgs.front();
+        Value row = createDynamicGemmBatchRow(lane, numOutCols, rewriter, nestedLoc);
+        Value column =
+          onnx_mlir::affineModConst(rewriter, nestedLoc, lane, numOutCols, rewriter.getInsertionBlock()->getParentOp());
+        SmallVector<OpFoldResult> scalarOffsets {lane, rewriter.getIndexAttr(0)};
+        SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+        SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+        Value scalar = tensor::ExtractSliceOp::create(
+                         rewriter, nestedLoc, scalarType, pieces, scalarOffsets, scalarSizes, unitStrides)
+                         .getResult();
+        if (alpha != 1.0f) {
+          Value alphaTensor = createScalarTensorConstant(scalarType, alpha, rewriter, nestedLoc);
+          scalar = spatial::SpatVMulOp::create(rewriter, nestedLoc, scalarType, scalar, alphaTensor).getResult();
+        }
+        if (biasArg) {
+          Value biasScalar =
+            createBroadcastedBiasScalar(biasArg, biasType, row, column, scalarType, rewriter, nestedLoc);
+          if (beta != 1.0f) {
+            Value betaTensor = createScalarTensorConstant(scalarType, beta, rewriter, nestedLoc);
+            biasScalar =
+              spatial::SpatVMulOp::create(rewriter, nestedLoc, scalarType, biasScalar, betaTensor).getResult();
+          }
+          scalar = spatial::SpatVAddOp::create(rewriter, nestedLoc, scalarType, scalar, biasScalar).getResult();
+        }
+        SmallVector<OpFoldResult> outputOffsets {row, column};
+        Value outputNext =
+          tensor::InsertSliceOp::create(rewriter, nestedLoc, scalar, outputAcc, outputOffsets, scalarSizes, unitStrides)
+            .getResult();
+        yielded.push_back(outputNext);
+        return success();
+      });
+    if (failed(loop))
+      return failure();
 
-    Value lane = loop.getInductionVar();
-    Value outputAcc = loop.getRegionIterArgs().front();
-    Value row = createDynamicGemmBatchRow(lane, numOutCols, rewriter, loc);
-    Value column =
-      onnx_mlir::affineModConst(rewriter, loc, lane, numOutCols, rewriter.getInsertionBlock()->getParentOp());
-    SmallVector<OpFoldResult> scalarOffsets {lane, rewriter.getIndexAttr(0)};
-    SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    Value scalar =
-      tensor::ExtractSliceOp::create(rewriter, loc, scalarType, pieces, scalarOffsets, scalarSizes, unitStrides)
-        .getResult();
-    if (alpha != 1.0f) {
-      Value alphaTensor = createScalarTensorConstant(scalarType, alpha, rewriter, loc);
-      scalar = spatial::SpatVMulOp::create(rewriter, loc, scalarType, scalar, alphaTensor).getResult();
-    }
-    if (biasArg) {
-      Value biasScalar = createBroadcastedBiasScalar(biasArg, biasType, row, column, scalarType, rewriter, loc);
-      if (beta != 1.0f) {
-        Value betaTensor = createScalarTensorConstant(scalarType, beta, rewriter, loc);
-        biasScalar = spatial::SpatVMulOp::create(rewriter, loc, scalarType, biasScalar, betaTensor).getResult();
-      }
-      scalar = spatial::SpatVAddOp::create(rewriter, loc, scalarType, scalar, biasScalar).getResult();
-    }
-    SmallVector<OpFoldResult> outputOffsets {row, column};
-    Value outputNext =
-      tensor::InsertSliceOp::create(rewriter, loc, scalar, outputAcc, outputOffsets, scalarSizes, unitStrides)
-        .getResult();
-    scf::YieldOp::create(rewriter, loc, outputNext);
-
-    rewriter.setInsertionPointAfter(loop);
-    spatial::SpatYieldOp::create(rewriter, loc, loop.getResult(0));
+    spatial::SpatYieldOp::create(rewriter, loc, loop->results.front());
+    return success();
   });
 }
 
@@ -579,85 +592,92 @@ static Value reducePartialPiecesForHSlice(Value partialPiecesArg,
   return activePieces.front();
 }
 
-static spatial::SpatCompute createReductionCompute(Value partialPieces,
-                                                   Value bias,
-                                                   RankedTensorType partialPiecesType,
-                                                   RankedTensorType outType,
-                                                   RankedTensorType paddedOutType,
-                                                   int64_t numKSlices,
-                                                   ConversionPatternRewriter& rewriter,
-                                                   Location loc) {
+static FailureOr<spatial::SpatCompute> createReductionCompute(Value partialPieces,
+                                                              Value bias,
+                                                              RankedTensorType partialPiecesType,
+                                                              RankedTensorType outType,
+                                                              RankedTensorType paddedOutType,
+                                                              int64_t numKSlices,
+                                                              ConversionPatternRewriter& rewriter,
+                                                              Location loc) {
   SmallVector<Value> inputs {partialPieces};
   if (bias)
     inputs.push_back(bias);
 
-  auto computeOp = createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) {
-    Value partialPiecesArg = blockArgs[0];
-    Value biasArg = bias ? blockArgs[1] : Value();
-    if (biasArg && cast<RankedTensorType>(biasArg.getType()) != paddedOutType)
-      biasArg = createZeroPaddedTensor(biasArg, paddedOutType, rewriter, loc);
+  auto computeOp =
+    createSpatCompute(rewriter, loc, TypeRange {outType}, {}, inputs, [&](ValueRange blockArgs) -> LogicalResult {
+      Value partialPiecesArg = blockArgs[0];
+      Value biasArg = bias ? blockArgs[1] : Value();
+      if (biasArg && cast<RankedTensorType>(biasArg.getType()) != paddedOutType)
+        biasArg = createZeroPaddedTensor(biasArg, paddedOutType, rewriter, loc);
 
-    const int64_t numOutRows = outType.getDimSize(0);
-    const int64_t numOutHSlices = ceilIntegerDivide(outType.getDimSize(1), crossbarSize.getValue());
-    auto pieceType = RankedTensorType::get({numOutRows, static_cast<int64_t>(crossbarSize.getValue())},
-                                           partialPiecesType.getElementType());
+      const int64_t numOutRows = outType.getDimSize(0);
+      const int64_t numOutHSlices = ceilIntegerDivide(outType.getDimSize(1), crossbarSize.getValue());
+      auto pieceType = RankedTensorType::get({numOutRows, static_cast<int64_t>(crossbarSize.getValue())},
+                                             partialPiecesType.getElementType());
 
-    Value outputInit =
-      tensor::EmptyOp::create(rewriter, loc, paddedOutType.getShape(), paddedOutType.getElementType()).getResult();
-    SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    SmallVector<OpFoldResult> pieceSizes {rewriter.getIndexAttr(numOutRows),
-                                          rewriter.getIndexAttr(crossbarSize.getValue())};
+      Value outputInit =
+        tensor::EmptyOp::create(rewriter, loc, paddedOutType.getShape(), paddedOutType.getElementType()).getResult();
+      SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+      SmallVector<OpFoldResult> pieceSizes {rewriter.getIndexAttr(numOutRows),
+                                            rewriter.getIndexAttr(crossbarSize.getValue())};
 
-    auto buildOutputSlice = [&](Value outputAcc, Value hSlice) -> Value {
-      Value reduced =
-        reducePartialPiecesForHSlice(partialPiecesArg, hSlice, pieceType, numKSlices, numOutRows, rewriter, loc);
-      Value hOffset = onnx_mlir::affineMulConst(
-        rewriter, loc, hSlice, crossbarSize.getValue(), rewriter.getInsertionBlock()->getParentOp());
-      if (biasArg) {
-        SmallVector<OpFoldResult> biasOffsets {rewriter.getIndexAttr(0), hOffset};
-        Value biasSlice =
-          tensor::ExtractSliceOp::create(rewriter, loc, pieceType, biasArg, biasOffsets, pieceSizes, unitStrides)
-            .getResult();
-        reduced = spatial::SpatVAddOp::create(rewriter, loc, pieceType, reduced, biasSlice).getResult();
+      auto buildOutputSlice = [&](Value outputAcc, Value hSlice) -> Value {
+        Value reduced =
+          reducePartialPiecesForHSlice(partialPiecesArg, hSlice, pieceType, numKSlices, numOutRows, rewriter, loc);
+        Value hOffset = onnx_mlir::affineMulConst(
+          rewriter, loc, hSlice, crossbarSize.getValue(), rewriter.getInsertionBlock()->getParentOp());
+        if (biasArg) {
+          SmallVector<OpFoldResult> biasOffsets {rewriter.getIndexAttr(0), hOffset};
+          Value biasSlice =
+            tensor::ExtractSliceOp::create(rewriter, loc, pieceType, biasArg, biasOffsets, pieceSizes, unitStrides)
+              .getResult();
+          reduced = spatial::SpatVAddOp::create(rewriter, loc, pieceType, reduced, biasSlice).getResult();
+        }
+
+        SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), hOffset};
+        return tensor::InsertSliceOp::create(rewriter, loc, reduced, outputAcc, outputOffsets, pieceSizes, unitStrides)
+          .getResult();
+      };
+
+      Value paddedOutput = outputInit;
+      if (numOutHSlices == 1) {
+        Value hSlice = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
+        paddedOutput = buildOutputSlice(outputInit, hSlice);
+      }
+      else {
+        Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
+        Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
+        Value cOutHSlices =
+          getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), numOutHSlices);
+        auto hLoop = buildNormalizedScfFor(
+          rewriter,
+          loc,
+          c0,
+          cOutHSlices,
+          c1,
+          ValueRange {outputInit},
+          [&](OpBuilder&, Location, Value hSlice, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+            yielded.push_back(buildOutputSlice(iterArgs.front(), hSlice));
+            return success();
+          });
+        if (failed(hLoop))
+          return failure();
+        paddedOutput = hLoop->results.front();
       }
 
-      SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), hOffset};
-      return tensor::InsertSliceOp::create(rewriter, loc, reduced, outputAcc, outputOffsets, pieceSizes, unitStrides)
-        .getResult();
-    };
-
-    Value paddedOutput = outputInit;
-    if (numOutHSlices == 1) {
-      Value hSlice = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
-      paddedOutput = buildOutputSlice(outputInit, hSlice);
-    }
-    else {
-      Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
-      Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
-      Value cOutHSlices =
-        getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), numOutHSlices);
-      auto hLoop = scf::ForOp::create(rewriter, loc, c0, cOutHSlices, c1, ValueRange {outputInit});
-      rewriter.setInsertionPointToStart(hLoop.getBody());
-
-      Value hSlice = hLoop.getInductionVar();
-      Value outputAcc = hLoop.getRegionIterArgs().front();
-      scf::YieldOp::create(rewriter, loc, buildOutputSlice(outputAcc, hSlice));
-
-      rewriter.setInsertionPointAfter(hLoop);
-      paddedOutput = hLoop.getResult(0);
-    }
-
-    Value result = paddedOutput;
-    if (paddedOutType != outType) {
-      SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
-      SmallVector<OpFoldResult> outputSizes {rewriter.getIndexAttr(outType.getDimSize(0)),
-                                             rewriter.getIndexAttr(outType.getDimSize(1))};
-      result =
-        tensor::ExtractSliceOp::create(rewriter, loc, outType, paddedOutput, outputOffsets, outputSizes, unitStrides)
-          .getResult();
-    }
-    spatial::SpatYieldOp::create(rewriter, loc, result);
-  });
+      Value result = paddedOutput;
+      if (paddedOutType != outType) {
+        SmallVector<OpFoldResult> outputOffsets {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
+        SmallVector<OpFoldResult> outputSizes {rewriter.getIndexAttr(outType.getDimSize(0)),
+                                               rewriter.getIndexAttr(outType.getDimSize(1))};
+        result =
+          tensor::ExtractSliceOp::create(rewriter, loc, outType, paddedOutput, outputOffsets, outputSizes, unitStrides)
+            .getResult();
+      }
+      spatial::SpatYieldOp::create(rewriter, loc, result);
+      return success();
+    });
 
   return computeOp;
 }
@@ -755,9 +775,13 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
     auto scalarPiecesType = RankedTensorType::get({laneCount64, 1}, outType.getElementType());
     auto batchOp =
       createVvdmulBatch(a, b, aType, bType, scalarPiecesType, outType, gemmOpAdaptor.getTransB(), rewriter, loc);
+    if (failed(batchOp))
+      return failure();
     auto outputCompute = createDynamicGemmOutputCompute(
-      batchOp.getResult(0), hasC ? c : Value(), scalarPiecesType, biasType, outType, alpha, beta, rewriter, loc);
-    rewriter.replaceOp(gemmOp, outputCompute.getResults());
+      batchOp->getResult(0), hasC ? c : Value(), scalarPiecesType, biasType, outType, alpha, beta, rewriter, loc);
+    if (failed(outputCompute))
+      return failure();
+    rewriter.replaceOp(gemmOp, outputCompute->getResults());
     return success();
   }
 
@@ -832,10 +856,14 @@ LogicalResult GemmToSpatialComputes::matchAndRewrite(ONNXGemmOp gemmOp,
     RankedTensorType::get({laneCount64, static_cast<int64_t>(crossbarSize.getValue())}, outType.getElementType());
   auto batchOp =
     createVmmBatch(a, b, aType, paddedBType, partialPiecesType, numOutRows, numKSlices, numOutHSlices, rewriter, loc);
+  if (failed(batchOp))
+    return failure();
   auto reductionCompute = createReductionCompute(
-    batchOp.getResult(0), bias, partialPiecesType, outType, paddedOutType, numKSlices, rewriter, loc);
+    batchOp->getResult(0), bias, partialPiecesType, outType, paddedOutType, numKSlices, rewriter, loc);
+  if (failed(reductionCompute))
+    return failure();
 
-  rewriter.replaceOp(gemmOp, reductionCompute.getResults());
+  rewriter.replaceOp(gemmOp, reductionCompute->getResults());
   return success();
 }
 

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

@@ -8,6 +8,7 @@
 #include "llvm/ADT/SmallVector.h"
 
 #include "src/Accelerators/PIM/Common/IR/AffineUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
@@ -281,18 +282,18 @@ static Value getBatchLaneIndex(
     rewriter, loc, lane, numOutRows * numKSlices * numOutHSlices, rewriter.getInsertionBlock()->getParentOp());
 }
 
-static spatial::SpatComputeBatch createBatchedVmmBatch(Value a,
-                                                       Value b,
-                                                       RankedTensorType aType,
-                                                       int64_t aBatchCount,
-                                                       RankedTensorType bType,
-                                                       int64_t bBatchCount,
-                                                       RankedTensorType partialPiecesType,
-                                                       int64_t numOutRows,
-                                                       int64_t numKSlices,
-                                                       int64_t numOutHSlices,
-                                                       PatternRewriter& rewriter,
-                                                       Location loc) {
+static FailureOr<spatial::SpatComputeBatch> createBatchedVmmBatch(Value a,
+                                                                  Value b,
+                                                                  RankedTensorType aType,
+                                                                  int64_t aBatchCount,
+                                                                  RankedTensorType bType,
+                                                                  int64_t bBatchCount,
+                                                                  RankedTensorType partialPiecesType,
+                                                                  int64_t numOutRows,
+                                                                  int64_t numKSlices,
+                                                                  int64_t numOutHSlices,
+                                                                  PatternRewriter& rewriter,
+                                                                  Location loc) {
   const int64_t laneCount = partialPiecesType.getDimSize(0);
   auto batchOp = createSpatComputeBatch(
     rewriter,
@@ -331,7 +332,8 @@ static spatial::SpatComputeBatch createBatchedVmmBatch(Value a,
       createParallelInsertSliceIntoBatchOutput(
         rewriter, loc, piece, args.outputs.front(), pieceOffsets, pieceSizes, getUnitStrides(rewriter, 2));
     });
-  assert(succeeded(batchOp) && "expected batched MatMul VMM construction to succeed");
+  if (failed(batchOp))
+    return failure();
   return *batchOp;
 }
 
@@ -422,17 +424,17 @@ static Value extractDynamicBatchedRowVector(Value matrix,
   });
 }
 
-static spatial::SpatComputeBatch createBatchedVvdmulBatch(Value a,
-                                                          int64_t aBatchCount,
-                                                          Value b,
-                                                          int64_t bBatchCount,
-                                                          RankedTensorType aType,
-                                                          RankedTensorType bType,
-                                                          RankedTensorType scalarPiecesType,
-                                                          RankedTensorType outType,
-                                                          bool bAlreadyTransposed,
-                                                          PatternRewriter& rewriter,
-                                                          Location loc) {
+static FailureOr<spatial::SpatComputeBatch> createBatchedVvdmulBatch(Value a,
+                                                                     int64_t aBatchCount,
+                                                                     Value b,
+                                                                     int64_t bBatchCount,
+                                                                     RankedTensorType aType,
+                                                                     RankedTensorType bType,
+                                                                     RankedTensorType scalarPiecesType,
+                                                                     RankedTensorType outType,
+                                                                     bool bAlreadyTransposed,
+                                                                     PatternRewriter& rewriter,
+                                                                     Location loc) {
   const int64_t numBatches = outType.getDimSize(0);
   const int64_t numOutRows = outType.getDimSize(1);
   const int64_t numOutCols = outType.getDimSize(2);
@@ -466,64 +468,73 @@ static spatial::SpatComputeBatch createBatchedVvdmulBatch(Value a,
       createParallelInsertSliceIntoBatchOutput(
         rewriter, loc, scalar, args.outputs.front(), outputOffsets, scalarSizes, getUnitStrides(rewriter, 2));
     });
-  assert(succeeded(batchOp) && "expected batched MatMul VVDMul construction to succeed");
+  if (failed(batchOp))
+    return failure();
   return *batchOp;
 }
 
-static Value createBatchedDynamicOutputCompute(Value scalarPieces,
-                                               RankedTensorType scalarPiecesType,
-                                               RankedTensorType outType,
-                                               PatternRewriter& rewriter,
-                                               Location loc) {
+static FailureOr<Value> createBatchedDynamicOutputCompute(Value scalarPieces,
+                                                          RankedTensorType scalarPiecesType,
+                                                          RankedTensorType outType,
+                                                          PatternRewriter& rewriter,
+                                                          Location loc) {
   const int64_t laneCount = scalarPiecesType.getDimSize(0);
   const int64_t numOutRows = outType.getDimSize(1);
   const int64_t numOutCols = outType.getDimSize(2);
   auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
   auto outputScalarType = RankedTensorType::get({1, 1, 1}, outType.getElementType());
 
-  auto computeOp =
-    createSpatCompute<1>(rewriter, loc, TypeRange {outType}, {}, ValueRange {scalarPieces}, [&](Value pieces) {
+  auto computeOp = createSpatCompute<1>(
+    rewriter, loc, TypeRange {outType}, {}, ValueRange {scalarPieces}, [&](Value pieces) -> LogicalResult {
       Value outputInit =
         tensor::EmptyOp::create(rewriter, loc, outType.getShape(), outType.getElementType()).getResult();
       Value c0 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
       Value c1 = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
       Value cLaneCount = getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), laneCount);
-      auto loop = scf::ForOp::create(rewriter, loc, c0, cLaneCount, c1, ValueRange {outputInit});
-      rewriter.setInsertionPointToStart(loop.getBody());
-
-      Value lane = loop.getInductionVar();
-      Value outputAcc = loop.getRegionIterArgs().front();
-      Operation* anchorOp = rewriter.getInsertionBlock()->getParentOp();
-      Value batch = affineFloorDivConst(rewriter, loc, lane, numOutRows * numOutCols, anchorOp);
-      Value batchLane = affineModConst(rewriter, loc, lane, numOutRows * numOutCols, anchorOp);
-      Value row = affineFloorDivConst(rewriter, loc, batchLane, numOutCols, anchorOp);
-      Value column = affineModConst(rewriter, loc, batchLane, numOutCols, anchorOp);
-      SmallVector<OpFoldResult> scalarOffsets {lane, rewriter.getIndexAttr(0)};
-      SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-      Value scalar = tensor::ExtractSliceOp::create(
-        rewriter, loc, scalarType, pieces, scalarOffsets, scalarSizes, getUnitStrides(rewriter, 2));
-      Value expanded = tensor::ExpandShapeOp::create(rewriter,
-                                                     loc,
-                                                     outputScalarType,
-                                                     scalar,
-                                                     SmallVector<ReassociationIndices> {
-                                                       {0},
-                                                       {1, 2}
-      });
-      SmallVector<OpFoldResult> outputOffsets {batch, row, column};
-      SmallVector<OpFoldResult> outputSizes {
-        rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-      scf::YieldOp::create(
+      auto loop = buildNormalizedScfFor(
         rewriter,
         loc,
-        tensor::InsertSliceOp::create(
-          rewriter, loc, expanded, outputAcc, outputOffsets, outputSizes, getUnitStrides(rewriter, 3))
-          .getResult());
-
-      rewriter.setInsertionPointAfter(loop);
-      spatial::SpatYieldOp::create(rewriter, loc, loop.getResult(0));
+        c0,
+        cLaneCount,
+        c1,
+        ValueRange {outputInit},
+        [&](OpBuilder&, Location nestedLoc, Value lane, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+          Value outputAcc = iterArgs.front();
+          Operation* anchorOp = rewriter.getInsertionBlock()->getParentOp();
+          Value batch = affineFloorDivConst(rewriter, nestedLoc, lane, numOutRows * numOutCols, anchorOp);
+          Value batchLane = affineModConst(rewriter, nestedLoc, lane, numOutRows * numOutCols, anchorOp);
+          Value row = affineFloorDivConst(rewriter, nestedLoc, batchLane, numOutCols, anchorOp);
+          Value column = affineModConst(rewriter, nestedLoc, batchLane, numOutCols, anchorOp);
+          SmallVector<OpFoldResult> scalarOffsets {lane, rewriter.getIndexAttr(0)};
+          SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+          Value scalar = tensor::ExtractSliceOp::create(
+            rewriter, nestedLoc, scalarType, pieces, scalarOffsets, scalarSizes, getUnitStrides(rewriter, 2));
+          Value expanded = tensor::ExpandShapeOp::create(rewriter,
+                                                         nestedLoc,
+                                                         outputScalarType,
+                                                         scalar,
+                                                         SmallVector<ReassociationIndices> {
+                                                           {0},
+                                                           {1, 2}
+          });
+          SmallVector<OpFoldResult> outputOffsets {batch, row, column};
+          SmallVector<OpFoldResult> outputSizes = {
+            rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+          Value next =
+            tensor::InsertSliceOp::create(
+              rewriter, nestedLoc, expanded, outputAcc, outputOffsets, outputSizes, getUnitStrides(rewriter, 3))
+              .getResult();
+          yielded.push_back(next);
+          return success();
+        });
+      if (failed(loop))
+        return failure();
+      spatial::SpatYieldOp::create(rewriter, loc, loop->results.front());
+      return success();
     });
-  return computeOp.getResult(0);
+  if (failed(computeOp))
+    return failure();
+  return computeOp->getResult(0);
 }
 
 static Value transposeBatchedOutput(Value value, RankedTensorType outputType, PatternRewriter& rewriter, Location loc) {
@@ -587,16 +598,16 @@ static Value reduceBatchedPartialPiecesForHSlice(Value partialPiecesArg,
   return activePieces.front();
 }
 
-static Value createBatchedReductionCompute(Value partialPieces,
-                                           RankedTensorType partialPiecesType,
-                                           RankedTensorType outType,
-                                           RankedTensorType paddedOutType,
-                                           int64_t numBatches,
-                                           int64_t numKSlices,
-                                           PatternRewriter& rewriter,
-                                           Location loc) {
+static FailureOr<Value> createBatchedReductionCompute(Value partialPieces,
+                                                      RankedTensorType partialPiecesType,
+                                                      RankedTensorType outType,
+                                                      RankedTensorType paddedOutType,
+                                                      int64_t numBatches,
+                                                      int64_t numKSlices,
+                                                      PatternRewriter& rewriter,
+                                                      Location loc) {
   auto computeOp = createSpatCompute<1>(
-    rewriter, loc, TypeRange {outType}, {}, ValueRange {partialPieces}, [&](Value partialPiecesArg) {
+    rewriter, loc, TypeRange {outType}, {}, ValueRange {partialPieces}, [&](Value partialPiecesArg) -> LogicalResult {
       const int64_t numOutRows = outType.getDimSize(1);
       const int64_t numOutHSlices = ceilIntegerDivide(outType.getDimSize(2), crossbarSize.getValue());
       auto pieceType = RankedTensorType::get({numOutRows, static_cast<int64_t>(crossbarSize.getValue())},
@@ -612,43 +623,55 @@ static Value createBatchedReductionCompute(Value partialPieces,
       Value cNumOutHSlices =
         getOrCreateIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), numOutHSlices);
 
-      auto batchLoop = scf::ForOp::create(rewriter, loc, c0, cNumBatches, c1, ValueRange {outputInit});
-      rewriter.setInsertionPointToStart(batchLoop.getBody());
-      Value batch = batchLoop.getInductionVar();
-      Value batchAcc = batchLoop.getRegionIterArgs().front();
-
-      auto hLoop = scf::ForOp::create(rewriter, loc, c0, cNumOutHSlices, c1, ValueRange {batchAcc});
-      rewriter.setInsertionPointToStart(hLoop.getBody());
-      Value hSlice = hLoop.getInductionVar();
-      Value outputAcc = hLoop.getRegionIterArgs().front();
-
-      Value reduced = reduceBatchedPartialPiecesForHSlice(
-        partialPiecesArg, batch, hSlice, pieceType, numKSlices, numOutHSlices, numOutRows, rewriter, loc);
-      Value expandedReduced = tensor::ExpandShapeOp::create(rewriter,
-                                                            loc,
-                                                            outputSliceType,
-                                                            reduced,
-                                                            SmallVector<ReassociationIndices> {
-                                                              {0, 1},
-                                                              {2}
-      });
-      Value hOffset =
-        affineMulConst(rewriter, loc, hSlice, crossbarSize.getValue(), rewriter.getInsertionBlock()->getParentOp());
-      SmallVector<OpFoldResult> outputOffsets {batch, rewriter.getIndexAttr(0), hOffset};
-      SmallVector<OpFoldResult> outputSizes {
-        rewriter.getIndexAttr(1), rewriter.getIndexAttr(numOutRows), rewriter.getIndexAttr(crossbarSize.getValue())};
-      scf::YieldOp::create(
+      auto batchLoop = buildNormalizedScfFor(
         rewriter,
         loc,
-        tensor::InsertSliceOp::create(
-          rewriter, loc, expandedReduced, outputAcc, outputOffsets, outputSizes, getUnitStrides(rewriter, 3))
-          .getResult());
-
-      rewriter.setInsertionPointAfter(hLoop);
-      scf::YieldOp::create(rewriter, loc, hLoop.getResult(0));
-
-      rewriter.setInsertionPointAfter(batchLoop);
-      Value paddedOutput = batchLoop.getResult(0);
+        c0,
+        cNumBatches,
+        c1,
+        ValueRange {outputInit},
+        [&](
+          OpBuilder&, Location batchLoc, Value batch, ValueRange batchIterArgs, SmallVectorImpl<Value>& batchYielded) {
+          auto hLoop = buildNormalizedScfFor(
+            rewriter,
+            batchLoc,
+            c0,
+            cNumOutHSlices,
+            c1,
+            ValueRange {batchIterArgs.front()},
+            [&](OpBuilder&, Location hLoc, Value hSlice, ValueRange hIterArgs, SmallVectorImpl<Value>& hYielded) {
+              Value outputAcc = hIterArgs.front();
+              Value reduced = reduceBatchedPartialPiecesForHSlice(
+                partialPiecesArg, batch, hSlice, pieceType, numKSlices, numOutHSlices, numOutRows, rewriter, hLoc);
+              Value expandedReduced = tensor::ExpandShapeOp::create(rewriter,
+                                                                    hLoc,
+                                                                    outputSliceType,
+                                                                    reduced,
+                                                                    SmallVector<ReassociationIndices> {
+                                                                      {0, 1},
+                                                                      {2}
+              });
+              Value hOffset = affineMulConst(
+                rewriter, hLoc, hSlice, crossbarSize.getValue(), rewriter.getInsertionBlock()->getParentOp());
+              SmallVector<OpFoldResult> outputOffsets {batch, rewriter.getIndexAttr(0), hOffset};
+              SmallVector<OpFoldResult> outputSizes {rewriter.getIndexAttr(1),
+                                                     rewriter.getIndexAttr(numOutRows),
+                                                     rewriter.getIndexAttr(crossbarSize.getValue())};
+              Value next =
+                tensor::InsertSliceOp::create(
+                  rewriter, hLoc, expandedReduced, outputAcc, outputOffsets, outputSizes, getUnitStrides(rewriter, 3))
+                  .getResult();
+              hYielded.push_back(next);
+              return success();
+            });
+          if (failed(hLoop))
+            return failure();
+          batchYielded.push_back(hLoop->results.front());
+          return success();
+        });
+      if (failed(batchLoop))
+        return failure();
+      Value paddedOutput = batchLoop->results.front();
       Value result = paddedOutput;
       if (paddedOutType != outType) {
         SmallVector<OpFoldResult> outputOffsets {
@@ -660,8 +683,11 @@ static Value createBatchedReductionCompute(Value partialPieces,
           rewriter, loc, outType, paddedOutput, outputOffsets, outputSizes, getUnitStrides(rewriter, 3));
       }
       spatial::SpatYieldOp::create(rewriter, loc, result);
+      return success();
     });
-  return computeOp.getResult(0);
+  if (failed(computeOp))
+    return failure();
+  return computeOp->getResult(0);
 }
 
 struct MatMulShapeInfo {
@@ -841,22 +867,27 @@ struct MatMulBatchedToSpatialComputes : OpRewritePattern<ONNXMatMulOp> {
                                              numOutHSlices,
                                              rewriter,
                                              loc);
-        Value result = createBatchedReductionCompute(batchOp.getResult(0),
-                                                     partialPiecesType,
-                                                     directOutType,
-                                                     paddedOutType,
-                                                     shapeInfo->batch,
-                                                     numKSlices,
-                                                     rewriter,
-                                                     loc);
+        if (failed(batchOp))
+          return failure();
+        auto result = createBatchedReductionCompute(batchOp->getResult(0),
+                                                    partialPiecesType,
+                                                    directOutType,
+                                                    paddedOutType,
+                                                    shapeInfo->batch,
+                                                    numKSlices,
+                                                    rewriter,
+                                                    loc);
+        if (failed(result))
+          return failure();
+        Value finalResult = *result;
         if (useTransposedForm)
-          result = transposeBatchedOutput(
-            result,
+          finalResult = transposeBatchedOutput(
+            finalResult,
             RankedTensorType::get({shapeInfo->batch, shapeInfo->m, shapeInfo->n}, shapeInfo->outType.getElementType()),
             rewriter,
             loc);
-        result = expandBatchDims(result, shapeInfo->outType, shapeInfo->batchShape.size(), rewriter, loc);
-        rewriter.replaceOp(matmulOp, result);
+        finalResult = expandBatchDims(finalResult, shapeInfo->outType, shapeInfo->batchShape.size(), rewriter, loc);
+        rewriter.replaceOp(matmulOp, finalResult);
         return success();
       }
     }
@@ -873,16 +904,21 @@ struct MatMulBatchedToSpatialComputes : OpRewritePattern<ONNXMatMulOp> {
                                             false,
                                             rewriter,
                                             loc);
-    Value result =
-      createBatchedDynamicOutputCompute(batchOp.getResult(0), scalarPiecesType, directOutType, rewriter, loc);
+    if (failed(batchOp))
+      return failure();
+    auto result =
+      createBatchedDynamicOutputCompute(batchOp->getResult(0), scalarPiecesType, directOutType, rewriter, loc);
+    if (failed(result))
+      return failure();
+    Value finalResult = *result;
     if (useTransposedForm)
-      result = transposeBatchedOutput(
-        result,
+      finalResult = transposeBatchedOutput(
+        finalResult,
         RankedTensorType::get({shapeInfo->batch, shapeInfo->m, shapeInfo->n}, shapeInfo->outType.getElementType()),
         rewriter,
         loc);
-    result = expandBatchDims(result, shapeInfo->outType, shapeInfo->batchShape.size(), rewriter, loc);
-    rewriter.replaceOp(matmulOp, result);
+    finalResult = expandBatchDims(finalResult, shapeInfo->outType, shapeInfo->batchShape.size(), rewriter, loc);
+    rewriter.replaceOp(matmulOp, finalResult);
     return success();
   }
 };

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

@@ -12,6 +12,7 @@
 #include <optional>
 #include <type_traits>
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
@@ -275,86 +276,102 @@ struct PoolToSpatialComputeBase : public OpConversionPattern<PoolOp> {
         Value cStrideHeight = getOrCreateIndexConstant(rewriter, anchorOp, strideHeight);
         Value cStrideWidth = getOrCreateIndexConstant(rewriter, anchorOp, strideWidth);
 
-        auto outputLoop = scf::ForOp::create(rewriter, loc, c0, cOutputPatchCount, c1, ValueRange {pooledOutputInit});
-        rewriter.setInsertionPointToStart(outputLoop.getBody());
+        auto outputLoop = buildNormalizedScfFor(
+          rewriter,
+          loc,
+          c0,
+          cOutputPatchCount,
+          c1,
+          ValueRange {pooledOutputInit},
+          [&](OpBuilder&,
+              Location nestedLoc,
+              Value outputPatchIndex,
+              ValueRange iterArgs,
+              SmallVectorImpl<Value>& yielded) {
+            Value pooledOutputAcc = iterArgs.front();
+            Value batchIndex = arith::DivUIOp::create(rewriter, nestedLoc, outputPatchIndex, cOutputPixelsPerBatch);
+            Value batchPatchIndex =
+              arith::RemUIOp::create(rewriter, nestedLoc, outputPatchIndex, cOutputPixelsPerBatch);
+            Value outHeightIndex = arith::DivUIOp::create(rewriter, nestedLoc, batchPatchIndex, cOutputWidth);
+            Value outWidthIndex = arith::RemUIOp::create(rewriter, nestedLoc, batchPatchIndex, cOutputWidth);
+            Value windowBaseH = arith::MulIOp::create(rewriter, nestedLoc, outHeightIndex, cStrideHeight);
+            Value windowBaseW = arith::MulIOp::create(rewriter, nestedLoc, outWidthIndex, cStrideWidth);
 
-        Value outputPatchIndex = outputLoop.getInductionVar();
-        Value pooledOutputAcc = outputLoop.getRegionIterArgs().front();
+            Value updatedOutput = pooledOutputAcc;
+            for (int64_t channelTile = 0; channelTile < channelTileCount; ++channelTile) {
+              const int64_t tileChannels = std::min<int64_t>(xbarSize, channels - channelTile * xbarSize);
+              auto tileType = RankedTensorType::get({1, tileChannels, 1, 1}, outType.getElementType());
+              Value reducedWindow =
+                createPoolFillTensor(rewriter, nestedLoc, tileType, std::is_same_v<PoolOp, ONNXMaxPoolSingleOutOp>);
 
-        Value batchIndex = arith::DivUIOp::create(rewriter, loc, outputPatchIndex, cOutputPixelsPerBatch);
-        Value batchPatchIndex = arith::RemUIOp::create(rewriter, loc, outputPatchIndex, cOutputPixelsPerBatch);
-        Value outHeightIndex = arith::DivUIOp::create(rewriter, loc, batchPatchIndex, cOutputWidth);
-        Value outWidthIndex = arith::RemUIOp::create(rewriter, loc, batchPatchIndex, cOutputWidth);
-        Value windowBaseH = arith::MulIOp::create(rewriter, loc, outHeightIndex, cStrideHeight);
-        Value windowBaseW = arith::MulIOp::create(rewriter, loc, outWidthIndex, cStrideWidth);
+              for (int64_t kernelH = 0; kernelH < kernelHeight; ++kernelH) {
+                Value paddedInH = windowBaseH;
+                if (kernelH * dilationHeight != 0) {
+                  Value kernelHOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelH * dilationHeight);
+                  paddedInH = arith::AddIOp::create(rewriter, nestedLoc, paddedInH, kernelHOffset);
+                }
 
-        Value updatedOutput = pooledOutputAcc;
-        for (int64_t channelTile = 0; channelTile < channelTileCount; ++channelTile) {
-          const int64_t tileChannels = std::min<int64_t>(xbarSize, channels - channelTile * xbarSize);
-          auto tileType = RankedTensorType::get({1, tileChannels, 1, 1}, outType.getElementType());
-          Value reducedWindow =
-            createPoolFillTensor(rewriter, loc, tileType, std::is_same_v<PoolOp, ONNXMaxPoolSingleOutOp>);
+                for (int64_t kernelW = 0; kernelW < kernelWidth; ++kernelW) {
+                  Value paddedInW = windowBaseW;
+                  if (kernelW * dilationWidth != 0) {
+                    Value kernelWOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelW * dilationWidth);
+                    paddedInW = arith::AddIOp::create(rewriter, nestedLoc, paddedInW, kernelWOffset);
+                  }
 
-          for (int64_t kernelH = 0; kernelH < kernelHeight; ++kernelH) {
-            Value paddedInH = windowBaseH;
-            if (kernelH * dilationHeight != 0) {
-              Value kernelHOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelH * dilationHeight);
-              paddedInH = arith::AddIOp::create(rewriter, loc, paddedInH, kernelHOffset);
-            }
-
-            for (int64_t kernelW = 0; kernelW < kernelWidth; ++kernelW) {
-              Value paddedInW = windowBaseW;
-              if (kernelW * dilationWidth != 0) {
-                Value kernelWOffset = getOrCreateIndexConstant(rewriter, anchorOp, kernelW * dilationWidth);
-                paddedInW = arith::AddIOp::create(rewriter, loc, paddedInW, kernelWOffset);
+                  SmallVector<OpFoldResult> offsets = {
+                    batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), paddedInH, paddedInW};
+                  SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
+                                                     rewriter.getIndexAttr(tileChannels),
+                                                     rewriter.getIndexAttr(1),
+                                                     rewriter.getIndexAttr(1)};
+                  SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1)};
+                  Value windowValue =
+                    tensor::ExtractSliceOp::create(rewriter, nestedLoc, tileType, paddedInput, offsets, sizes, strides);
+                  windowValue = materializeTileTensor(rewriter, nestedLoc, windowValue);
+                  reducedWindow = ReduceOp::create(rewriter, nestedLoc, tileType, reducedWindow, windowValue);
+                }
               }
 
-              SmallVector<OpFoldResult> offsets = {
-                batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), paddedInH, paddedInW};
-              SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1),
-                                                 rewriter.getIndexAttr(tileChannels),
-                                                 rewriter.getIndexAttr(1),
-                                                 rewriter.getIndexAttr(1)};
-              SmallVector<OpFoldResult> strides = {
+              if constexpr (std::is_same_v<PoolOp, ONNXAveragePoolOp>) {
+                SmallVector<OpFoldResult> scaleOffsets = {rewriter.getIndexAttr(0),
+                                                          rewriter.getIndexAttr(channelTile * xbarSize),
+                                                          outHeightIndex,
+                                                          outWidthIndex};
+                SmallVector<OpFoldResult> scaleSizes = {rewriter.getIndexAttr(1),
+                                                        rewriter.getIndexAttr(tileChannels),
+                                                        rewriter.getIndexAttr(1),
+                                                        rewriter.getIndexAttr(1)};
+                SmallVector<OpFoldResult> scaleStrides = {rewriter.getIndexAttr(1),
+                                                          rewriter.getIndexAttr(1),
+                                                          rewriter.getIndexAttr(1),
+                                                          rewriter.getIndexAttr(1)};
+                Value scaleSlice = tensor::ExtractSliceOp::create(
+                  rewriter, nestedLoc, tileType, averageScaleTensor, scaleOffsets, scaleSizes, scaleStrides);
+                scaleSlice = materializeTileTensor(rewriter, nestedLoc, scaleSlice);
+                reducedWindow = spatial::SpatVMulOp::create(rewriter, nestedLoc, tileType, reducedWindow, scaleSlice);
+              }
+
+              SmallVector<OpFoldResult> outputOffsets = {
+                batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), outHeightIndex, outWidthIndex};
+              SmallVector<OpFoldResult> outputSizes = {rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(tileChannels),
+                                                       rewriter.getIndexAttr(1),
+                                                       rewriter.getIndexAttr(1)};
+              SmallVector<OpFoldResult> outputStrides = {
                 rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-              Value windowValue =
-                tensor::ExtractSliceOp::create(rewriter, loc, tileType, paddedInput, offsets, sizes, strides);
-              windowValue = materializeTileTensor(rewriter, loc, windowValue);
-              reducedWindow = ReduceOp::create(rewriter, loc, tileType, reducedWindow, windowValue);
+              updatedOutput = tensor::InsertSliceOp::create(
+                rewriter, nestedLoc, reducedWindow, updatedOutput, outputOffsets, outputSizes, outputStrides);
             }
-          }
+            yielded.push_back(updatedOutput);
+            return success();
+          });
+        if (failed(outputLoop))
+          return failure();
 
-          if constexpr (std::is_same_v<PoolOp, ONNXAveragePoolOp>) {
-            SmallVector<OpFoldResult> scaleOffsets = {
-              rewriter.getIndexAttr(0), rewriter.getIndexAttr(channelTile * xbarSize), outHeightIndex, outWidthIndex};
-            SmallVector<OpFoldResult> scaleSizes = {rewriter.getIndexAttr(1),
-                                                    rewriter.getIndexAttr(tileChannels),
-                                                    rewriter.getIndexAttr(1),
-                                                    rewriter.getIndexAttr(1)};
-            SmallVector<OpFoldResult> scaleStrides = {
-              rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-            Value scaleSlice = tensor::ExtractSliceOp::create(
-              rewriter, loc, tileType, averageScaleTensor, scaleOffsets, scaleSizes, scaleStrides);
-            scaleSlice = materializeTileTensor(rewriter, loc, scaleSlice);
-            reducedWindow = spatial::SpatVMulOp::create(rewriter, loc, tileType, reducedWindow, scaleSlice);
-          }
-
-          SmallVector<OpFoldResult> outputOffsets = {
-            batchIndex, rewriter.getIndexAttr(channelTile * xbarSize), outHeightIndex, outWidthIndex};
-          SmallVector<OpFoldResult> outputSizes = {rewriter.getIndexAttr(1),
-                                                   rewriter.getIndexAttr(tileChannels),
-                                                   rewriter.getIndexAttr(1),
-                                                   rewriter.getIndexAttr(1)};
-          SmallVector<OpFoldResult> outputStrides = {
-            rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-          updatedOutput = tensor::InsertSliceOp::create(
-            rewriter, loc, reducedWindow, updatedOutput, outputOffsets, outputSizes, outputStrides);
-        }
-
-        scf::YieldOp::create(rewriter, loc, updatedOutput);
-
-        rewriter.setInsertionPointAfter(outputLoop);
-        spatial::SpatYieldOp::create(rewriter, loc, outputLoop.getResult(0));
+        spatial::SpatYieldOp::create(rewriter, loc, outputLoop->results.front());
         return success();
       });
     if (failed(computeOp))

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

@@ -3,6 +3,7 @@
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Transforms/DialectConversion.h"
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -42,13 +43,13 @@ static Value buildLoopSoftmaxSlice(Value input,
   return tensor::InsertSliceOp::create(rewriter, loc, softmaxSlice, accumulator, offsets, sizes, strides);
 }
 
-static Value buildLoopSoftmaxNest(Value input,
-                                  Value accumulator,
-                                  RankedTensorType inputType,
-                                  int64_t axis,
-                                  SmallVectorImpl<Value>& outerIndices,
-                                  ConversionPatternRewriter& rewriter,
-                                  Location loc) {
+static FailureOr<Value> buildLoopSoftmaxNest(Value input,
+                                             Value accumulator,
+                                             RankedTensorType inputType,
+                                             int64_t axis,
+                                             SmallVectorImpl<Value>& outerIndices,
+                                             ConversionPatternRewriter& rewriter,
+                                             Location loc) {
   if (axis == inputType.getRank() - 1)
     return buildLoopSoftmaxSlice(input, accumulator, inputType, outerIndices, rewriter, loc);
 
@@ -57,38 +58,50 @@ static Value buildLoopSoftmaxNest(Value input,
   Value c1 = getOrCreateIndexConstant(rewriter, anchorOp, 1);
   Value cUpper = getOrCreateIndexConstant(rewriter, anchorOp, inputType.getDimSize(axis));
 
-  auto loop = scf::ForOp::create(rewriter, loc, c0, cUpper, c1, ValueRange {accumulator});
-  rewriter.setInsertionPointToStart(loop.getBody());
-
-  Value loopIndex = loop.getInductionVar();
-  Value loopAccumulator = loop.getRegionIterArgs().front();
-  outerIndices.push_back(loopIndex);
-  Value updatedAccumulator =
-    buildLoopSoftmaxNest(input, loopAccumulator, inputType, axis + 1, outerIndices, rewriter, loc);
-  outerIndices.pop_back();
-
-  scf::YieldOp::create(rewriter, loc, updatedAccumulator);
-  rewriter.setInsertionPointAfter(loop);
-  return loop.getResult(0);
+  auto loop = buildNormalizedScfFor(
+    rewriter,
+    loc,
+    c0,
+    cUpper,
+    c1,
+    ValueRange {accumulator},
+    [&](OpBuilder& builder, Location nestedLoc, Value loopIndex, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+      outerIndices.push_back(loopIndex);
+      auto updatedAccumulator =
+        buildLoopSoftmaxNest(input, iterArgs.front(), inputType, axis + 1, outerIndices, rewriter, nestedLoc);
+      outerIndices.pop_back();
+      if (failed(updatedAccumulator))
+        return failure();
+      yielded.push_back(*updatedAccumulator);
+      return success();
+    });
+  if (failed(loop))
+    return failure();
+  return loop->results.front();
 }
 
-static Value createLoopSoftmaxCompute(Value input, ConversionPatternRewriter& rewriter, Location loc) {
+static FailureOr<Value> createLoopSoftmaxCompute(Value input, ConversionPatternRewriter& rewriter, Location loc) {
   auto inputType = cast<RankedTensorType>(input.getType());
   constexpr size_t numInputs = 1;
-  auto computeOp =
-    createSpatCompute<numInputs>(rewriter, loc, TypeRange {inputType}, {}, ValueRange {input}, [&](Value x) {
+  auto computeOp = createSpatCompute<numInputs>(
+    rewriter, loc, TypeRange {inputType}, {}, ValueRange {input}, [&](Value x) -> LogicalResult {
       if (inputType.getRank() == 1) {
         Value softmax = spatial::SpatSoftmaxOp::create(rewriter, loc, inputType, x).getResult();
         spatial::SpatYieldOp::create(rewriter, loc, softmax);
-        return;
+        return success();
       }
 
       Value outputInit = tensor::EmptyOp::create(rewriter, loc, inputType.getShape(), inputType.getElementType());
       SmallVector<Value> outerIndices;
-      Value result = buildLoopSoftmaxNest(x, outputInit, inputType, /*axis=*/0, outerIndices, rewriter, loc);
-      spatial::SpatYieldOp::create(rewriter, loc, result);
+      auto result = buildLoopSoftmaxNest(x, outputInit, inputType, /*axis=*/0, outerIndices, rewriter, loc);
+      if (failed(result))
+        return failure();
+      spatial::SpatYieldOp::create(rewriter, loc, *result);
+      return success();
     });
-  return computeOp.getResult(0);
+  if (failed(computeOp))
+    return failure();
+  return computeOp->getResult(0);
 }
 
 struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
@@ -108,7 +121,10 @@ struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
     Value input = adaptor.getInput();
     Value result;
     if (*axis == inputType.getRank() - 1) {
-      result = createLoopSoftmaxCompute(input, rewriter, softmaxOp.getLoc());
+      auto computed = createLoopSoftmaxCompute(input, rewriter, softmaxOp.getLoc());
+      if (failed(computed))
+        return failure();
+      result = *computed;
     }
     else {
       SmallVector<int64_t> permutation;
@@ -122,8 +138,10 @@ struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
       auto transposedType = RankedTensorType::get(
         permuteShape(inputType.getShape(), permutation), inputType.getElementType(), inputType.getEncoding());
       Value transposedInput = transposeMaybeInCompute(input, transposedType, permutation, rewriter, softmaxOp.getLoc());
-      Value transposedResult = createLoopSoftmaxCompute(transposedInput, rewriter, softmaxOp.getLoc());
-      result = transposeMaybeInCompute(transposedResult, inputType, inversePermutation, rewriter, softmaxOp.getLoc());
+      auto transposedResult = createLoopSoftmaxCompute(transposedInput, rewriter, softmaxOp.getLoc());
+      if (failed(transposedResult))
+        return failure();
+      result = transposeMaybeInCompute(*transposedResult, inputType, inversePermutation, rewriter, softmaxOp.getLoc());
     }
 
     rewriter.replaceOp(softmaxOp, result);

src/PIM/Conversion/ONNXToSpatial/Patterns/Post.cpp

@@ -9,6 +9,7 @@
 #include "llvm/ADT/SmallVector.h"
 
 #include "src/Accelerators/PIM/Common/IR/WeightUtils.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/WeightMaterialization.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -192,13 +193,12 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
 
     rewriter.setInsertionPointAfter(compute);
 
-    auto newCompute =
-      spatial::SpatComputeBatch::create(rewriter,
-                                        compute.getLoc(),
-                                        compute.getResultTypes(),
-                                        rewriter.getI32IntegerAttr(static_cast<int32_t>(compute.getLaneCount())),
-                                        promoted->newWeights,
-                                        promoted->newInputs);
+    auto laneCountAttr = pim::getCheckedI32Attr(
+      rewriter, compute, static_cast<uint64_t>(compute.getLaneCount()), "promoted compute_batch lane count");
+    if (failed(laneCountAttr))
+      return failure();
+    auto newCompute = spatial::SpatComputeBatch::create(
+      rewriter, compute.getLoc(), compute.getResultTypes(), *laneCountAttr, promoted->newWeights, promoted->newInputs);
     auto laneArg = compute.getLaneArgument();
     if (!laneArg)
       return rewriter.notifyMatchFailure(compute, "missing compute_batch lane block argument");

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

@@ -5,6 +5,7 @@
 
 #include "llvm/ADT/STLExtras.h"
 
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -26,11 +27,11 @@ static Value buildNearestAsymmetricIndex(
   return arith::MinUIOp::create(rewriter, loc, inputIndex, cInputDimLast);
 }
 
-static Value buildNearestResizeLoop(Value input,
-                                    RankedTensorType inputType,
-                                    RankedTensorType resultType,
-                                    ConversionPatternRewriter& rewriter,
-                                    Location loc) {
+static FailureOr<Value> buildNearestResizeLoop(Value input,
+                                               RankedTensorType inputType,
+                                               RankedTensorType resultType,
+                                               ConversionPatternRewriter& rewriter,
+                                               Location loc) {
   auto elemType = resultType.getElementType();
   SmallVector<int64_t> unitShape(resultType.getRank(), 1);
   auto unitTensorType = RankedTensorType::get(unitShape, elemType);
@@ -48,54 +49,94 @@ static Value buildNearestResizeLoop(Value input,
 
   Value outputInit = tensor::EmptyOp::create(rewriter, loc, resultType.getShape(), elemType);
 
-  auto batchLoop = scf::ForOp::create(rewriter, loc, c0, cOutputN, c1, ValueRange {outputInit});
-  rewriter.setInsertionPointToStart(batchLoop.getBody());
+  auto batchLoop = buildNormalizedScfFor(
+    rewriter,
+    loc,
+    c0,
+    cOutputN,
+    c1,
+    ValueRange {outputInit},
+    [&](OpBuilder&, Location nestedLoc, Value outputN, ValueRange batchIterArgs, SmallVectorImpl<Value>& batchYielded) {
+      Value outputBatchAcc = batchIterArgs.front();
+      Value inputN =
+        buildNearestAsymmetricIndex(outputN, inputType.getDimSize(0), resultType.getDimSize(0), rewriter, nestedLoc);
 
-  Value outputN = batchLoop.getInductionVar();
-  Value outputBatchAcc = batchLoop.getRegionIterArgs().front();
-  Value inputN = buildNearestAsymmetricIndex(outputN, inputType.getDimSize(0), resultType.getDimSize(0), rewriter, loc);
+      auto channelLoop = buildNormalizedScfFor(
+        rewriter,
+        nestedLoc,
+        c0,
+        cOutputC,
+        c1,
+        ValueRange {outputBatchAcc},
+        [&](OpBuilder&,
+            Location channelLoc,
+            Value outputC,
+            ValueRange channelIterArgs,
+            SmallVectorImpl<Value>& channelYielded) {
+          Value outputChannelAcc = channelIterArgs.front();
+          Value inputC = buildNearestAsymmetricIndex(
+            outputC, inputType.getDimSize(1), resultType.getDimSize(1), rewriter, channelLoc);
 
-  auto channelLoop = scf::ForOp::create(rewriter, loc, c0, cOutputC, c1, ValueRange {outputBatchAcc});
-  rewriter.setInsertionPointToStart(channelLoop.getBody());
+          auto heightLoop = buildNormalizedScfFor(
+            rewriter,
+            channelLoc,
+            c0,
+            cOutputH,
+            c1,
+            ValueRange {outputChannelAcc},
+            [&](OpBuilder&,
+                Location heightLoc,
+                Value outputH,
+                ValueRange heightIterArgs,
+                SmallVectorImpl<Value>& heightYielded) {
+              Value outputHeightAcc = heightIterArgs.front();
+              Value inputH = buildNearestAsymmetricIndex(
+                outputH, inputType.getDimSize(2), resultType.getDimSize(2), rewriter, heightLoc);
 
-  Value outputC = channelLoop.getInductionVar();
-  Value outputChannelAcc = channelLoop.getRegionIterArgs().front();
-  Value inputC = buildNearestAsymmetricIndex(outputC, inputType.getDimSize(1), resultType.getDimSize(1), rewriter, loc);
+              auto widthLoop = buildNormalizedScfFor(
+                rewriter,
+                heightLoc,
+                c0,
+                cOutputW,
+                c1,
+                ValueRange {outputHeightAcc},
+                [&](OpBuilder&,
+                    Location widthLoc,
+                    Value outputW,
+                    ValueRange widthIterArgs,
+                    SmallVectorImpl<Value>& widthYielded) {
+                  Value outputWidthAcc = widthIterArgs.front();
+                  Value inputW = buildNearestAsymmetricIndex(
+                    outputW, inputType.getDimSize(3), resultType.getDimSize(3), rewriter, widthLoc);
 
-  auto heightLoop = scf::ForOp::create(rewriter, loc, c0, cOutputH, c1, ValueRange {outputChannelAcc});
-  rewriter.setInsertionPointToStart(heightLoop.getBody());
+                  SmallVector<OpFoldResult> inputOffsets = {inputN, inputC, inputH, inputW};
+                  Value inputSlice = tensor::ExtractSliceOp::create(
+                    rewriter, widthLoc, unitTensorType, input, inputOffsets, unitSizes, unitStrides);
 
-  Value outputH = heightLoop.getInductionVar();
-  Value outputHeightAcc = heightLoop.getRegionIterArgs().front();
-  Value inputH = 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 = buildNearestAsymmetricIndex(outputW, inputType.getDimSize(3), resultType.getDimSize(3), rewriter, loc);
-
-  SmallVector<OpFoldResult> inputOffsets = {inputN, inputC, inputH, inputW};
-  Value inputSlice =
-    tensor::ExtractSliceOp::create(rewriter, loc, unitTensorType, input, inputOffsets, unitSizes, unitStrides);
-
-  SmallVector<OpFoldResult> outputOffsets = {outputN, outputC, outputH, outputW};
-  Value updatedOutput =
-    tensor::InsertSliceOp::create(rewriter, loc, inputSlice, outputWidthAcc, outputOffsets, unitSizes, unitStrides);
-  scf::YieldOp::create(rewriter, loc, updatedOutput);
-
-  rewriter.setInsertionPointAfter(widthLoop);
-  scf::YieldOp::create(rewriter, loc, widthLoop.getResult(0));
-
-  rewriter.setInsertionPointAfter(heightLoop);
-  scf::YieldOp::create(rewriter, loc, heightLoop.getResult(0));
-
-  rewriter.setInsertionPointAfter(channelLoop);
-  scf::YieldOp::create(rewriter, loc, channelLoop.getResult(0));
-
-  rewriter.setInsertionPointAfter(batchLoop);
-  return batchLoop.getResult(0);
+                  SmallVector<OpFoldResult> outputOffsets = {outputN, outputC, outputH, outputW};
+                  Value updatedOutput = tensor::InsertSliceOp::create(
+                    rewriter, widthLoc, inputSlice, outputWidthAcc, outputOffsets, unitSizes, unitStrides);
+                  widthYielded.push_back(updatedOutput);
+                  return success();
+                });
+              if (failed(widthLoop))
+                return failure();
+              heightYielded.push_back(widthLoop->results.front());
+              return success();
+            });
+          if (failed(heightLoop))
+            return failure();
+          channelYielded.push_back(heightLoop->results.front());
+          return success();
+        });
+      if (failed(channelLoop))
+        return failure();
+      batchYielded.push_back(channelLoop->results.front());
+      return success();
+    });
+  if (failed(batchLoop))
+    return failure();
+  return batchLoop->results.front();
 }
 
 struct Resize : OpConversionPattern<ONNXResizeOp> {
@@ -120,12 +161,17 @@ struct Resize : OpConversionPattern<ONNXResizeOp> {
         || llvm::any_of(resultType.getShape(), [](int64_t dim) { return dim <= 0; }))
       return rewriter.notifyMatchFailure(resizeOp, "resize lowering requires positive static dimensions.");
 
-    auto computeOp =
-      createSpatCompute<1>(rewriter, resizeOp.getLoc(), TypeRange {resultType}, {}, adaptor.getX(), [&](Value x) {
-        Value result = buildNearestResizeLoop(x, inputType, resultType, rewriter, resizeOp.getLoc());
-        spatial::SpatYieldOp::create(rewriter, resizeOp.getLoc(), result);
+    auto computeOp = createSpatCompute<1>(
+      rewriter, resizeOp.getLoc(), TypeRange {resultType}, {}, adaptor.getX(), [&](Value x) -> LogicalResult {
+        auto result = buildNearestResizeLoop(x, inputType, resultType, rewriter, resizeOp.getLoc());
+        if (failed(result))
+          return failure();
+        spatial::SpatYieldOp::create(rewriter, resizeOp.getLoc(), *result);
+        return success();
       });
-    rewriter.replaceOp(resizeOp, computeOp.getResults());
+    if (failed(computeOp))
+      return failure();
+    rewriter.replaceOp(resizeOp, computeOp->getResults());
     return success();
   }
 };

src/PIM/Conversion/SpatialToPim/BatchCoreLoweringPatterns.cpp

@@ -10,6 +10,7 @@
 #include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
 #include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
@@ -25,14 +26,21 @@ static bool isUsedOnlyAsExplicitHostOperand(Value value) {
   });
 }
 
-static SmallVector<int32_t> getPimCoreIdsForBatchOp(spatial::SpatComputeBatch computeBatchOp, size_t& fallbackCoreId) {
+static FailureOr<SmallVector<int32_t>> getPimCoreIdsForBatchOp(spatial::SpatComputeBatch computeBatchOp,
+                                                               size_t& fallbackCoreId) {
   if (auto coreIdsAttr = computeBatchOp->getAttrOfType<DenseI32ArrayAttr>(onnx_mlir::kCoreIdsAttrName))
     return SmallVector<int32_t>(coreIdsAttr.asArrayRef().begin(), coreIdsAttr.asArrayRef().end());
 
   SmallVector<int32_t> coreIds;
   coreIds.reserve(static_cast<size_t>(computeBatchOp.getLaneCount()));
-  for (uint32_t lane = 0; lane < computeBatchOp.getLaneCount(); ++lane)
-    coreIds.push_back(static_cast<int32_t>(fallbackCoreId++));
+  for (uint32_t lane = 0; lane < computeBatchOp.getLaneCount(); ++lane) {
+    auto checkedCoreId =
+      pim::checkedI32(static_cast<uint64_t>(fallbackCoreId), computeBatchOp, "fallback spatial compute_batch core id");
+    if (failed(checkedCoreId))
+      return failure();
+    coreIds.push_back(*checkedCoreId);
+    ++fallbackCoreId;
+  }
   return coreIds;
 }
 
@@ -102,21 +110,24 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
       "resultful compute_batch lowering currently requires a spat.in_parallel terminator");
   }
 
-  SmallVector<int32_t> coreIds = getPimCoreIdsForBatchOp(computeBatchOp, coreId);
+  auto coreIds = getPimCoreIdsForBatchOp(computeBatchOp, coreId);
+  if (failed(coreIds))
+    return failure();
   SmallVector<Value> batchWeights(computeBatchOp.getWeights().begin(), computeBatchOp.getWeights().end());
   SmallVector<Value> batchInputs;
   if (!computeBatchOp.getInputs().empty())
     batchInputs.append(computeBatchOp.getInputs().begin(), computeBatchOp.getInputs().end());
 
   rewriter.setInsertionPointAfter(computeBatchOp);
-  auto coreBatchOp = pim::PimCoreBatchOp::create(rewriter,
-                                                 loc,
-                                                 rewriter.getI32IntegerAttr(computeBatchOp.getLaneCount()),
-                                                 ValueRange(batchWeights),
-                                                 ValueRange(batchInputs));
+  auto laneCountAttr = pim::getCheckedI32Attr(
+    rewriter, computeBatchOp, static_cast<uint64_t>(computeBatchOp.getLaneCount()), "pim core_batch lane count");
+  if (failed(laneCountAttr))
+    return failure();
+  auto coreBatchOp =
+    pim::PimCoreBatchOp::create(rewriter, loc, *laneCountAttr, ValueRange(batchWeights), ValueRange(batchInputs));
   coreBatchOp.getProperties().setOperandSegmentSizes(
     {static_cast<int>(batchWeights.size()), static_cast<int>(batchInputs.size())});
-  coreBatchOp->setAttr(onnx_mlir::kCoreIdsAttrName, rewriter.getDenseI32ArrayAttr(coreIds));
+  coreBatchOp->setAttr(onnx_mlir::kCoreIdsAttrName, rewriter.getDenseI32ArrayAttr(*coreIds));
 
   SmallVector<unsigned> returnOperandIndices;
   if (computeBatchOp.getNumResults() != 0) {
@@ -160,14 +171,11 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
     auto newArgType = cast<ShapedType>(newArg.getType());
     auto outputBuffer = createEmptyTensorFromShaped(rewriter, loc, newArgType);
     Value zeroOffset = getOrCreateIndexConstant(rewriter, coreBatchOp.getOperation(), 0);
-    auto copied = pim::PimMemCopyHostToDevOp::create(rewriter,
-                                                     loc,
-                                                     outputBuffer.getType(),
-                                                     zeroOffset,
-                                                     zeroOffset,
-                                                     outputBuffer,
-                                                     newArg,
-                                                     getTensorSizeInBytesAttr(rewriter, newArg))
+    auto sizeAttr = getTensorSizeInBytesAttr(rewriter, coreBatchOp.getOperation(), newArg);
+    if (failed(sizeAttr))
+      return failure();
+    auto copied = pim::PimMemCopyHostToDevOp::create(
+                    rewriter, loc, outputBuffer.getType(), zeroOffset, zeroOffset, outputBuffer, newArg, *sizeAttr)
                     .getOutput();
     mapper.map(*oldArg, copied);
   }
@@ -209,6 +217,9 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
         auto hostTargetType = cast<ShapedType>(hostTarget.getType());
         Value hostTargetOffset = createHostTargetOffset(rewriter, insertSlice, hostTargetType, mapper);
         Value zeroOffset = getOrCreateIndexConstant(rewriter, coreBatchOp.getOperation(), 0);
+        auto sizeAttr = getTensorSizeInBytesAttr(rewriter, coreBatchOp.getOperation(), mappedSource);
+        if (failed(sizeAttr))
+          return failure();
         pim::PimMemCopyDevToHostOp::create(rewriter,
                                            insertSlice.getLoc(),
                                            hostTarget.getType(),
@@ -216,7 +227,7 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
                                            zeroOffset,
                                            hostTarget,
                                            mappedSource,
-                                           getTensorSizeInBytesAttr(rewriter, mappedSource));
+                                           *sizeAttr);
       }
       continue;
     }
@@ -232,15 +243,13 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeBatchOp(spatial::SpatCompute
         auto clonedType = cast<ShapedType>(clonedTensor.getType());
         auto outputBuffer = createEmptyTensorFromShaped(rewriter, loc, clonedType);
         Value zeroOffset = getOrCreateIndexConstant(rewriter, coreBatchOp.getOperation(), 0);
-        auto copied = pim::PimMemCopyHostToDevOp::create(rewriter,
-                                                         loc,
-                                                         outputBuffer.getType(),
-                                                         zeroOffset,
-                                                         zeroOffset,
-                                                         outputBuffer,
-                                                         clonedTensor,
-                                                         getTensorSizeInBytesAttr(rewriter, clonedTensor))
-                        .getOutput();
+        auto sizeAttr = getTensorSizeInBytesAttr(rewriter, coreBatchOp.getOperation(), clonedTensor);
+        if (failed(sizeAttr))
+          return failure();
+        auto copied =
+          pim::PimMemCopyHostToDevOp::create(
+            rewriter, loc, outputBuffer.getType(), zeroOffset, zeroOffset, outputBuffer, clonedTensor, *sizeAttr)
+            .getOutput();
         mapper.map(toTensorOp.getResult(), copied);
         continue;
       }

src/PIM/Conversion/SpatialToPim/Common.cpp

@@ -5,14 +5,18 @@
 #include <cassert>
 
 #include "Common.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 
 using namespace llvm;
 using namespace mlir;
 
 namespace onnx_mlir {
 
-IntegerAttr getTensorSizeInBytesAttr(Builder& builder, mlir::Value value) {
-  return builder.getI32IntegerAttr(static_cast<int32_t>(getShapedTypeSizeInBytes(cast<ShapedType>(value.getType()))));
+FailureOr<IntegerAttr> getTensorSizeInBytesAttr(Builder& builder, Operation* anchor, mlir::Value value) {
+  auto byteSize = pim::getCheckedShapedTypeSizeInBytes(cast<ShapedType>(value.getType()), anchor, "tensor byte size");
+  if (failed(byteSize))
+    return failure();
+  return pim::getCheckedI32Attr(builder, anchor, *byteSize, "tensor byte size");
 }
 
 Operation* getEarliestUserWithinBlock(mlir::Value value) {

src/PIM/Conversion/SpatialToPim/Common.hpp

@@ -1,12 +1,14 @@
 #pragma once
 
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/Support/LogicalResult.h"
 
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 
 namespace onnx_mlir {
 
-mlir::IntegerAttr getTensorSizeInBytesAttr(mlir::Builder& builder, mlir::Value value);
+mlir::FailureOr<mlir::IntegerAttr>
+getTensorSizeInBytesAttr(mlir::Builder& builder, mlir::Operation* anchor, mlir::Value value);
 
 template <class T>
 size_t rangeLength(const mlir::iterator_range<T> range) {

src/PIM/Conversion/SpatialToPim/CoreLoweringPatterns.cpp

@@ -9,6 +9,7 @@
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
@@ -54,10 +55,15 @@ cloneMappedHelperOperands(Operation* op, IRMapping& mapping, IRRewriter& rewrite
   }
 }
 
-static int32_t getPimCoreIdForComputeOp(spatial::SpatCompute computeOp, size_t& fallbackCoreId) {
+static FailureOr<int32_t> getPimCoreIdForComputeOp(spatial::SpatCompute computeOp, size_t& fallbackCoreId) {
   if (auto spatialCoreIdAttr = computeOp->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName))
-    return static_cast<int32_t>(spatialCoreIdAttr.getInt());
-  return static_cast<int32_t>(fallbackCoreId++);
+    return pim::checkedI32(spatialCoreIdAttr.getInt(), computeOp, "spatial compute core id");
+  auto checkedCoreId =
+    pim::checkedI32(static_cast<uint64_t>(fallbackCoreId), computeOp, "fallback spatial compute core id");
+  if (failed(checkedCoreId))
+    return failure();
+  ++fallbackCoreId;
+  return *checkedCoreId;
 }
 
 static LogicalResult collectHelperComputeChain(spatial::SpatCompute computeOp,
@@ -163,10 +169,12 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute comp
       rewriter.setInsertionPoint(getEarliestUserWithinBlock(*blockArg));
       auto outputType = cast<ShapedType>(blockArg->getType());
       auto outputBuffer = createEmptyTensorFromShaped(rewriter, receiveOp.getLoc(), outputType);
-      auto sizeAttr = getTensorSizeInBytesAttr(rewriter, *blockArg);
+      auto sizeAttr = getTensorSizeInBytesAttr(rewriter, computeOp.getOperation(), *blockArg);
+      if (failed(sizeAttr))
+        return failure();
       Value received =
         PimReceiveOp::create(
-          rewriter, receiveOp.getLoc(), outputBuffer.getType(), outputBuffer, sizeAttr, receiveOp.getSourceCoreId())
+          rewriter, receiveOp.getLoc(), outputBuffer.getType(), outputBuffer, *sizeAttr, receiveOp.getSourceCoreId())
           .getOutput();
       blockArg->replaceAllUsesWith(received);
       markOpToRemove(receiveOp);
@@ -206,8 +214,13 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute comp
   if (!computeOp.getWeights().empty())
     computeWeights.append(computeOp.getWeights().begin(), computeOp.getWeights().end());
   rewriter.setInsertionPointAfter(computeOp);
-  auto coreOp = PimCoreOp::create(
-    rewriter, loc, ValueRange(computeWeights), rewriter.getI32IntegerAttr(getPimCoreIdForComputeOp(computeOp, coreId)));
+  auto checkedCoreId = getPimCoreIdForComputeOp(computeOp, coreId);
+  if (failed(checkedCoreId))
+    return failure();
+  auto coreIdAttr = pim::getCheckedI32Attr(rewriter, computeOp, static_cast<int64_t>(*checkedCoreId), "pim core id");
+  if (failed(coreIdAttr))
+    return failure();
+  auto coreOp = PimCoreOp::create(rewriter, loc, ValueRange(computeWeights), *coreIdAttr);
   rewriter.setInsertionPointToStart(&block);
   auto& coreOpBlocks = coreOp.getBody().getBlocks();
   for (auto [inputIndex, input] : llvm::enumerate(computeOp.getInputs())) {
@@ -226,6 +239,9 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute comp
     if (!inputType)
       return computeOp.emitOpError("expected shaped compute input during pim.core lowering");
     auto outputBuffer = createEmptyTensorFromShaped(rewriter, loc, inputType);
+    auto sizeAttr = getTensorSizeInBytesAttr(rewriter, computeOp.getOperation(), input);
+    if (failed(sizeAttr))
+      return failure();
     auto copied =
       PimMemCopyHostToDevOp::create(rewriter,
                                     loc,
@@ -234,7 +250,7 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute comp
                                     getOrCreateIndexConstant(constantFolder, outputBuffer.getOperation(), 0),
                                     outputBuffer,
                                     input,
-                                    getTensorSizeInBytesAttr(rewriter, input))
+                                    *sizeAttr)
         .getOutput();
     blockArg->replaceAllUsesWith(copied);
   }

src/PIM/Conversion/SpatialToPim/Patterns/ChannelLowering.cpp

@@ -14,8 +14,10 @@ struct ChannelSendLowering : OpRewritePattern<spatial::SpatChannelSendOp> {
   using OpRewritePattern::OpRewritePattern;
 
   LogicalResult matchAndRewrite(spatial::SpatChannelSendOp op, PatternRewriter& rewriter) const override {
-    pim::PimSendOp::create(
-      rewriter, op.getLoc(), op.getInput(), getTensorSizeInBytesAttr(rewriter, op.getInput()), op.getTargetCoreId());
+    auto sizeAttr = getTensorSizeInBytesAttr(rewriter, op.getOperation(), op.getInput());
+    if (failed(sizeAttr))
+      return failure();
+    pim::PimSendOp::create(rewriter, op.getLoc(), op.getInput(), *sizeAttr, op.getTargetCoreId());
     rewriter.eraseOp(op);
     return success();
   }
@@ -32,12 +34,11 @@ struct ChannelReceiveLowering : OpRewritePattern<spatial::SpatChannelReceiveOp>
     auto outputType = cast<ShapedType>(op.getResult().getType());
     Value outputBuffer =
       tensor::EmptyOp::create(rewriter, op.getLoc(), outputType.getShape(), outputType.getElementType()).getResult();
-    Value received = pim::PimReceiveOp::create(rewriter,
-                                               op.getLoc(),
-                                               op.getResult().getType(),
-                                               outputBuffer,
-                                               getTensorSizeInBytesAttr(rewriter, op.getResult()),
-                                               op.getSourceCoreId())
+    auto sizeAttr = getTensorSizeInBytesAttr(rewriter, op.getOperation(), op.getResult());
+    if (failed(sizeAttr))
+      return failure();
+    Value received = pim::PimReceiveOp::create(
+                       rewriter, op.getLoc(), op.getResult().getType(), outputBuffer, *sizeAttr, op.getSourceCoreId())
                        .getOutput();
     rewriter.replaceOp(op, received);
     return success();

src/PIM/Conversion/SpatialToPim/ReturnPathNormalization.cpp

@@ -12,6 +12,7 @@
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "Conversion/SpatialToPim/SpatialToPimPass.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
@@ -71,6 +72,20 @@ static SmallVector<int64_t> expandFlatElementIndex(int64_t flatIndex, ArrayRef<i
   return indices;
 }
 
+static FailureOr<int64_t>
+getCheckedByteOffset(int64_t elementOffset, size_t elementSize, Operation* anchor, StringRef fieldName) {
+  if (elementOffset < 0) {
+    anchor->emitOpError() << fieldName << " requires a nonnegative element offset";
+    return failure();
+  }
+
+  auto byteOffset =
+    pim::checkedMul(static_cast<uint64_t>(elementOffset), static_cast<uint64_t>(elementSize), anchor, fieldName);
+  if (failed(byteOffset))
+    return failure();
+  return pim::checkedCast<int64_t>(*byteOffset, anchor, fieldName);
+}
+
 static LogicalResult collectHelperComputeChain(spatial::SpatCompute computeOp,
                                                SmallVectorImpl<Operation*>& helperChain) {
   if (computeOp.getInputs().size() != 1 || computeOp.getNumResults() != 1)
@@ -360,18 +375,21 @@ static void cloneHelperChain(Value sourceValue,
   }
 }
 
-static Value emitHostCopy(IRRewriter& rewriter,
-                          Location loc,
-                          Value outputTensor,
-                          Value sourceValue,
-                          int32_t hostTargetOffset,
-                          int32_t deviceSourceOffset,
-                          int32_t sizeInBytes,
-                          OperationFolder& constantFolder) {
+static FailureOr<Value> emitHostCopy(IRRewriter& rewriter,
+                                     Location loc,
+                                     Value outputTensor,
+                                     Value sourceValue,
+                                     int64_t hostTargetOffset,
+                                     int64_t deviceSourceOffset,
+                                     uint64_t sizeInBytes,
+                                     OperationFolder& constantFolder) {
   Operation* anchorOp = sourceValue.getDefiningOp() ? sourceValue.getDefiningOp() : outputTensor.getDefiningOp();
   assert(anchorOp && "expected a concrete op anchor for return-path host copy constants");
   Value hostTargetOffsetValue = getOrCreateIndexConstant(constantFolder, anchorOp, hostTargetOffset);
   Value deviceSourceOffsetValue = getOrCreateIndexConstant(constantFolder, anchorOp, deviceSourceOffset);
+  auto sizeAttr = pim::getCheckedI32Attr(rewriter, anchorOp, sizeInBytes, "return-path host copy byte size");
+  if (failed(sizeAttr))
+    return failure();
   return PimMemCopyDevToHostOp::create(rewriter,
                                        loc,
                                        outputTensor.getType(),
@@ -379,7 +397,7 @@ static Value emitHostCopy(IRRewriter& rewriter,
                                        deviceSourceOffsetValue,
                                        outputTensor,
                                        sourceValue,
-                                       rewriter.getI32IntegerAttr(sizeInBytes))
+                                       *sizeAttr)
     .getOutput();
 }
 
@@ -433,18 +451,15 @@ raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::low
       markOpToRemove(op);
 
     auto storedType = cast<ShapedType>(currentStoredValue.getType());
-    size_t elementSize = getElementTypeSizeInBytes(storedType.getElementType());
+    auto byteSize = pim::getCheckedShapedTypeSizeInBytes(storedType, producerOp, "return-path host copy byte size");
+    if (failed(byteSize))
+      return ReturnPathLoweringResult::Failure;
     if (auto storedOp = currentStoredValue.getDefiningOp())
       rewriter.setInsertionPointAfter(storedOp);
     Value outputTensor = outputTensors[returnUse->returnIndex](rewriter, loc);
-    emitHostCopy(rewriter,
-                 loc,
-                 outputTensor,
-                 currentStoredValue,
-                 0,
-                 0,
-                 static_cast<int32_t>(storedType.getNumElements() * elementSize),
-                 constantFolder);
+    auto copied = emitHostCopy(rewriter, loc, outputTensor, currentStoredValue, 0, 0, *byteSize, constantFolder);
+    if (failed(copied))
+      return ReturnPathLoweringResult::Failure;
     return ReturnPathLoweringResult::Handled;
   }
 
@@ -455,23 +470,25 @@ raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::low
 
     if (isa<func::ReturnOp>(resultUser)) {
       size_t resultIndexInReturn = resultUse.getOperandNumber();
-      size_t elementSize = getElementTypeSizeInBytes(storedTensorType.getElementType());
+      auto byteSize =
+        pim::getCheckedShapedTypeSizeInBytes(storedTensorType, producerOp, "return-path host copy byte size");
+      if (failed(byteSize))
+        return ReturnPathLoweringResult::Failure;
       rewriter.setInsertionPointAfterValue(storedValue);
       Value outputTensor = outputTensors[resultIndexInReturn](rewriter, loc);
-      emitHostCopy(rewriter,
-                   loc,
-                   outputTensor,
-                   storedValue,
-                   0,
-                   0,
-                   static_cast<int32_t>(storedTensorType.getNumElements() * elementSize),
-                   constantFolder);
+      auto copied = emitHostCopy(rewriter, loc, outputTensor, storedValue, 0, 0, *byteSize, constantFolder);
+      if (failed(copied))
+        return ReturnPathLoweringResult::Failure;
       return ReturnPathLoweringResult::Handled;
     }
   }
 
   if (auto concatReturnUse = analyzeConcatReturnUse(producedValue)) {
     size_t elementSize = getElementTypeSizeInBytes(storedTensorType.getElementType());
+    auto storedByteSize =
+      pim::getCheckedShapedTypeSizeInBytes(storedTensorType, producerOp, "concat return-path copy byte size");
+    if (failed(storedByteSize))
+      return ReturnPathLoweringResult::Failure;
     for (Operation* concatOp : concatReturnUse->concatChain)
       markOpToRemove(concatOp);
 
@@ -480,14 +497,13 @@ raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::low
       Value outputTensor = outputTensors[concatReturnUse->returnIndex](rewriter, loc);
       auto outputType = cast<ShapedType>(outputTensor.getType());
       int64_t flatOffset = computeFlatElementIndex(concatReturnUse->sliceOffsets, outputType.getShape());
-      emitHostCopy(rewriter,
-                   loc,
-                   outputTensor,
-                   storedValue,
-                   static_cast<int32_t>(flatOffset * elementSize),
-                   0,
-                   static_cast<int32_t>(storedTensorType.getNumElements() * elementSize),
-                   constantFolder);
+      auto hostOffset = getCheckedByteOffset(flatOffset, elementSize, producerOp, "concat return-path host offset");
+      if (failed(hostOffset))
+        return ReturnPathLoweringResult::Failure;
+      auto copied =
+        emitHostCopy(rewriter, loc, outputTensor, storedValue, *hostOffset, 0, *storedByteSize, constantFolder);
+      if (failed(copied))
+        return ReturnPathLoweringResult::Failure;
       return ReturnPathLoweringResult::Handled;
     }
 
@@ -531,14 +547,18 @@ raptor::SpatialToPimPass::ReturnPathLoweringResult raptor::SpatialToPimPass::low
       rewriter.setInsertionPointAfter(elementSlice);
 
       int64_t destinationFlatOffset = computeFlatElementIndex(destinationIndices, outputType.getShape());
-      outputTensor = emitHostCopy(rewriter,
-                                  loc,
-                                  outputTensor,
-                                  elementSlice.getResult(),
-                                  static_cast<int32_t>(destinationFlatOffset * elementSize),
-                                  0,
-                                  static_cast<int32_t>(elementSize),
-                                  constantFolder);
+      auto hostOffset =
+        getCheckedByteOffset(destinationFlatOffset, elementSize, producerOp, "concat helper return-path host offset");
+      if (failed(hostOffset))
+        return ReturnPathLoweringResult::Failure;
+      auto elementByteSize = pim::checkedCast<uint64_t>(elementSize, producerOp, "return-path scalar copy byte size");
+      if (failed(elementByteSize))
+        return ReturnPathLoweringResult::Failure;
+      auto copied = emitHostCopy(
+        rewriter, loc, outputTensor, elementSlice.getResult(), *hostOffset, 0, *elementByteSize, constantFolder);
+      if (failed(copied))
+        return ReturnPathLoweringResult::Failure;
+      outputTensor = *copied;
     }
     return ReturnPathLoweringResult::Handled;
   }

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.cpp

@@ -25,8 +25,9 @@
 #include <cassert>
 #include <utility>
 
-#include "Common/PimCommon.hpp"
 #include "Common/IR/ConstantUtils.hpp"
+#include "Common/PimCommon.hpp"
+#include "Common/Support/CheckedArithmetic.hpp"
 #include "Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "Conversion/SpatialToPim/Common.hpp"
 #include "Conversion/SpatialToPim/Patterns.hpp"
@@ -75,21 +76,28 @@ static memref::GlobalOp getOrCreateZeroGlobal(IRRewriter& rewriter, Location loc
                                   IntegerAttr {});
 }
 
-static Value createZeroedDeviceHVector(IRRewriter& rewriter,
-                                       Location loc,
-                                       RankedTensorType tensorType,
-                                       OperationFolder& constantFolder) {
+static FailureOr<Value> createZeroedDeviceHVector(IRRewriter& rewriter,
+                                                  Location loc,
+                                                  RankedTensorType tensorType,
+                                                  OperationFolder& constantFolder) {
   auto outputBuffer = createEmptyTensorFromShaped(rewriter, loc, tensorType);
   auto zeroGlobal = getOrCreateZeroGlobal(rewriter, loc, tensorType);
   auto zeroValue = memref::GetGlobalOp::create(rewriter, loc, zeroGlobal.getType(), zeroGlobal.getName());
   auto zeroIndex = getOrCreateIndexConstant(constantFolder, outputBuffer.getOperation(), 0);
-  auto sizeAttr = rewriter.getI32IntegerAttr(static_cast<int32_t>(getShapedTypeSizeInBytes(tensorType)));
+  auto byteSize =
+    pim::getCheckedShapedTypeSizeInBytes(tensorType, outputBuffer.getOperation(), "host-to-device zero copy byte size");
+  if (failed(byteSize))
+    return failure();
+  auto sizeAttr =
+    pim::getCheckedI32Attr(rewriter, outputBuffer.getOperation(), *byteSize, "host-to-device zero copy byte size");
+  if (failed(sizeAttr))
+    return failure();
   return PimMemCopyHostToDevOp::create(
-           rewriter, loc, tensorType, zeroIndex, zeroIndex, outputBuffer, zeroValue, sizeAttr)
+           rewriter, loc, tensorType, zeroIndex, zeroIndex, outputBuffer, zeroValue, *sizeAttr)
     .getOutput();
 }
 
-static Value
+static FailureOr<Value>
 padHVectorInputToCrossbarSize(IRRewriter& rewriter, Location loc, Value vector, OperationFolder& constantFolder) {
   auto vectorType = cast<RankedTensorType>(vector.getType());
   ArrayRef<int64_t> shape = vectorType.getShape();
@@ -101,10 +109,18 @@ padHVectorInputToCrossbarSize(IRRewriter& rewriter, Location loc, Value vector,
 
   auto paddedType = RankedTensorType::get(
     {shape[0], static_cast<int64_t>(crossbarSize)}, vectorType.getElementType(), vectorType.getEncoding());
-  Value zeroed = createZeroedDeviceHVector(rewriter, loc, paddedType, constantFolder);
-  Value zeroIndex = getOrCreateIndexConstant(constantFolder, zeroed.getDefiningOp(), 0);
-  auto sizeAttr = rewriter.getI32IntegerAttr(static_cast<int32_t>(getShapedTypeSizeInBytes(vectorType)));
-  return PimMemCopyOp::create(rewriter, loc, paddedType, zeroIndex, zeroIndex, zeroed, vector, sizeAttr).getOutput();
+  auto zeroed = createZeroedDeviceHVector(rewriter, loc, paddedType, constantFolder);
+  if (failed(zeroed))
+    return failure();
+  Value zeroIndex = getOrCreateIndexConstant(constantFolder, zeroed->getDefiningOp(), 0);
+  auto byteSize =
+    pim::getCheckedShapedTypeSizeInBytes(vectorType, zeroed->getDefiningOp(), "device padding copy byte size");
+  if (failed(byteSize))
+    return failure();
+  auto sizeAttr = pim::getCheckedI32Attr(rewriter, zeroed->getDefiningOp(), *byteSize, "device padding copy byte size");
+  if (failed(sizeAttr))
+    return failure();
+  return PimMemCopyOp::create(rewriter, loc, paddedType, zeroIndex, zeroIndex, *zeroed, vector, *sizeAttr).getOutput();
 }
 
 void onnx_mlir::raptor::SpatialToPimPass::runOnOperation() {
@@ -234,7 +250,11 @@ void onnx_mlir::raptor::SpatialToPimPass::runOnOperation() {
     }
   }
 
-  enlargeVMMOutTensorsToCrossbarSize(funcOp, rewriter);
+  if (failed(enlargeVMMOutTensorsToCrossbarSize(funcOp, rewriter))) {
+    funcOp.emitOpError("failed to enlarge VMM output tensors to crossbar size");
+    signalPassFailure();
+    return;
+  }
   replaceReturnWithOutputBuffers(returnOp, rewriter);
   eraseOpsToRemove();
 
@@ -271,8 +291,9 @@ void onnx_mlir::raptor::SpatialToPimPass::runOnOperation() {
   dumpModule(moduleOp, "pim0");
 }
 
-void raptor::SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp funcOp, IRRewriter& rewriter) {
+LogicalResult raptor::SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp funcOp, IRRewriter& rewriter) {
   OperationFolder constantFolder(funcOp.getContext());
+  bool hasFailure = false;
   funcOp.walk([&](PimVMMOp vmmOp) {
     auto outputType = cast<RankedTensorType>(vmmOp.getOutput().getType());
     ArrayRef<int64_t> outputShape = outputType.getShape();
@@ -280,19 +301,23 @@ void raptor::SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp f
     assert(outputShape[1] <= static_cast<int64_t>(crossbarSize) && "output width must fit in one crossbar");
 
     rewriter.setInsertionPoint(vmmOp);
-    Value paddedInput = padHVectorInputToCrossbarSize(rewriter, vmmOp.getLoc(), vmmOp.getInput(), constantFolder);
+    auto paddedInput = padHVectorInputToCrossbarSize(rewriter, vmmOp.getLoc(), vmmOp.getInput(), constantFolder);
+    if (failed(paddedInput)) {
+      hasFailure = true;
+      return WalkResult::interrupt();
+    }
     auto paddedOutputType = RankedTensorType::get(
       {outputShape[0], static_cast<int64_t>(crossbarSize)}, outputType.getElementType(), outputType.getEncoding());
     Value paddedOutputBuffer = outputShape[1] == static_cast<int64_t>(crossbarSize)
                                ? vmmOp.getOutputBuffer()
                                : createEmptyTensorFromShaped(rewriter, vmmOp.getLoc(), paddedOutputType).getResult();
-    vmmOp.getInputMutable().assign(paddedInput);
+    vmmOp.getInputMutable().assign(*paddedInput);
     vmmOp.getOutputBufferMutable().assign(paddedOutputBuffer);
 
     vmmOp.getOutput().setType(paddedOutputType);
 
     if (outputShape[1] == static_cast<int64_t>(crossbarSize))
-      return;
+      return WalkResult::advance();
 
     SmallVector<OpFoldResult> offsets = {rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
     SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(outputShape[0]), rewriter.getIndexAttr(outputShape[1])};
@@ -302,13 +327,16 @@ void raptor::SpatialToPimPass::enlargeVMMOutTensorsToCrossbarSize(func::FuncOp f
       tensor::ExtractSliceOp::create(rewriter, vmmOp.getLoc(), outputType, vmmOp.getOutput(), offsets, sizes, strides);
     SmallPtrSet<Operation*, 2> exceptions = {vmmOp, sliceOp};
     vmmOp.getOutput().replaceAllUsesExcept(sliceOp.getResult(), exceptions);
+    return WalkResult::advance();
   });
+  return success(!hasFailure);
 }
 
 LogicalResult raptor::SpatialToPimPass::allocateAndInitializeCoreLocalVariables(func::FuncOp funcOp,
                                                                                 IRRewriter& rewriter) {
   Location loc = funcOp.getLoc();
   OperationFolder constantFolder(funcOp.getContext());
+  bool hasFailure = false;
 
   auto insertMemCopyHostToDev = [&](Value inputTensor, int64_t elementsOffset) {
     auto tensorType = cast<ShapedType>(inputTensor.getType());
@@ -319,17 +347,28 @@ LogicalResult raptor::SpatialToPimPass::allocateAndInitializeCoreLocalVariables(
     rewriter.setInsertionPointAfter(inputTensor.getDefiningOp());
 
     auto deviceTensor = tensor::EmptyOp::create(rewriter, loc, tensorType.getShape(), elementType);
+    auto offsetBytes = pim::checkedMul(
+      static_cast<size_t>(elementsOffset), elementByteSize, deviceTensor.getOperation(), "host input byte offset");
+    auto byteSize =
+      pim::getCheckedShapedTypeSizeInBytes(tensorType, deviceTensor.getOperation(), "host input copy byte size");
+    auto sizeAttr =
+      succeeded(byteSize)
+        ? pim::getCheckedI32Attr(rewriter, deviceTensor.getOperation(), *byteSize, "host input copy byte size")
+        : FailureOr<IntegerAttr>(failure());
+    if (failed(offsetBytes) || failed(sizeAttr)) {
+      hasFailure = true;
+      return;
+    }
 
     auto memCopyHostToDevOp = PimMemCopyHostToDevOp::create(
       rewriter,
       loc,
       tensorType,
       getOrCreateIndexConstant(constantFolder, deviceTensor.getOperation(), 0),
-      getOrCreateIndexConstant(
-        constantFolder, deviceTensor.getOperation(), static_cast<int64_t>(elementsOffset * elementByteSize)),
+      getOrCreateIndexConstant(constantFolder, deviceTensor.getOperation(), static_cast<int64_t>(*offsetBytes)),
       deviceTensor,
       inputTensor,
-      rewriter.getI32IntegerAttr(static_cast<int32_t>(tensorType.getNumElements() * elementByteSize)));
+      *sizeAttr);
 
     rewriter.replaceAllUsesExcept(inputTensor, memCopyHostToDevOp.getResult(), {memCopyHostToDevOp});
   };
@@ -347,7 +386,7 @@ LogicalResult raptor::SpatialToPimPass::allocateAndInitializeCoreLocalVariables(
       }
     }
 
-  return success();
+  return success(!hasFailure);
 }
 
 void raptor::SpatialToPimPass::markOpToRemove(Operation* op) {

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.hpp

@@ -64,7 +64,7 @@ private:
   void markOpToRemove(mlir::Operation* op);
   void eraseOpsToRemove();
 
-  void enlargeVMMOutTensorsToCrossbarSize(mlir::func::FuncOp funcOp, mlir::IRRewriter& rewriter);
+  mlir::LogicalResult enlargeVMMOutTensorsToCrossbarSize(mlir::func::FuncOp funcOp, mlir::IRRewriter& rewriter);
 };
 
 } // namespace raptor

src/PIM/Dialect/Pim/CMakeLists.txt

@@ -2,7 +2,10 @@ add_onnx_mlir_dialect(Pim pim)
 add_onnx_mlir_dialect_doc(pim Pim.td)
 
 add_subdirectory(Transforms/Bufferization)
-add_subdirectory(Transforms/StaticMemoryCoalescing)
+add_subdirectory(Transforms/MemoryCoalescing)
+add_subdirectory(Transforms/HostConstantFolding)
+add_subdirectory(Transforms/HostConstantMaterialization)
+add_subdirectory(Transforms/Verification)
 
 add_pim_library(PimOps
   PimOps.hpp

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

@@ -3,6 +3,7 @@
 
 #include "src/Accelerators/PIM/Common/IR/AddressAnalysis.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/Transforms/Bufferization/BufferizationUtils.hpp"
 
@@ -11,24 +12,25 @@ using namespace bufferization;
 
 namespace onnx_mlir::pim {
 
-Value materializeContiguousInputMemRef(Value memrefValue, Location loc, RewriterBase& rewriter) {
+FailureOr<Value> materializeContiguousInputMemRef(Value memrefValue, Location loc, RewriterBase& rewriter) {
   if (succeeded(resolveContiguousAddress(memrefValue)) || succeeded(compileContiguousAddressExpr(memrefValue)))
     return memrefValue;
 
   auto shapedType = cast<ShapedType>(memrefValue.getType());
   auto contiguousType = MemRefType::get(shapedType.getShape(), shapedType.getElementType());
   Value contiguousBuffer = memref::AllocOp::create(rewriter, loc, contiguousType);
-  auto sizeInBytes = getShapedTypeSizeInBytes(shapedType);
+  auto sizeInBytes =
+    getCheckedShapedTypeSizeInBytes(shapedType, contiguousBuffer.getDefiningOp(), "contiguous copy byte size");
+  if (failed(sizeInBytes))
+    return failure();
   Value zeroOffset = getOrCreateIndexConstant(rewriter, contiguousBuffer.getDefiningOp(), 0);
+  auto sizeAttr =
+    getCheckedI32Attr(rewriter, contiguousBuffer.getDefiningOp(), *sizeInBytes, "contiguous copy byte size");
+  if (failed(sizeAttr))
+    return failure();
 
-  return PimMemCopyOp::create(rewriter,
-                              loc,
-                              contiguousType,
-                              zeroOffset,
-                              zeroOffset,
-                              contiguousBuffer,
-                              memrefValue,
-                              rewriter.getI32IntegerAttr(sizeInBytes))
+  return PimMemCopyOp::create(
+           rewriter, loc, contiguousType, zeroOffset, zeroOffset, contiguousBuffer, memrefValue, *sizeAttr)
     .getOutput();
 }
 

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

@@ -5,7 +5,8 @@
 
 namespace onnx_mlir::pim {
 
-mlir::Value materializeContiguousInputMemRef(mlir::Value memrefValue, mlir::Location loc, mlir::RewriterBase& rewriter);
+llvm::FailureOr<mlir::Value>
+materializeContiguousInputMemRef(mlir::Value memrefValue, mlir::Location loc, mlir::RewriterBase& rewriter);
 mlir::Value
 allocateContiguousResultMemRefLike(mlir::Value memrefValue, mlir::Location loc, mlir::RewriterBase& rewriter);
 

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

@@ -1,10 +1,13 @@
 #include "Dialect/Pim/Transforms/Bufferization/Common.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 
 using namespace mlir;
 
-IntegerAttr onnx_mlir::pim::getMemRefSizeInBytesAttr(OpBuilder& builder, Value memref) {
+FailureOr<IntegerAttr> onnx_mlir::pim::getMemRefSizeInBytesAttr(OpBuilder& builder, Operation* anchor, Value memref) {
   auto type = mlir::cast<MemRefType>(memref.getType());
-  int32_t sizeInBytes = static_cast<int32_t>(getShapedTypeSizeInBytes(type));
-  return builder.getI32IntegerAttr(sizeInBytes);
+  auto byteSize = getCheckedShapedTypeSizeInBytes(type, anchor, "memref byte size");
+  if (failed(byteSize))
+    return failure();
+  return getCheckedI32Attr(builder, anchor, *byteSize, "memref byte size");
 }

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

@@ -5,7 +5,8 @@
 namespace onnx_mlir {
 namespace pim {
 
-mlir::IntegerAttr getMemRefSizeInBytesAttr(mlir::OpBuilder& builder, mlir::Value memref);
+mlir::FailureOr<mlir::IntegerAttr>
+getMemRefSizeInBytesAttr(mlir::OpBuilder& builder, mlir::Operation* anchor, mlir::Value memref);
 
 } // namespace pim
 } // namespace onnx_mlir

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

@@ -4,8 +4,10 @@
 
 #include "ContiguityPatterns.hpp"
 #include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
+#include "src/Accelerators/PIM/Common/IR/LoopUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 
 using namespace mlir;
 
@@ -85,7 +87,13 @@ static FailureOr<SmallVector<int64_t>> getStaticMemRefStrides(MemRefType type) {
 static FailureOr<int64_t> getShapedByteSize(MemRefType type) {
   if (!type.hasStaticShape() || !hasByteSizedElementType(type.getElementType()))
     return failure();
-  return static_cast<int64_t>(getShapedTypeSizeInBytes(type));
+  auto byteSize =
+    pim::getCheckedShapedTypeSizeInBytes(type, UnknownLoc::get(type.getContext()), "normalized copy byte size");
+  if (failed(byteSize))
+    return failure();
+  if (*byteSize > static_cast<uint64_t>(std::numeric_limits<int64_t>::max()))
+    return failure();
+  return static_cast<int64_t>(*byteSize);
 }
 
 static FailureOr<SmallVector<int64_t>>
@@ -325,12 +333,11 @@ static LogicalResult rewriteCopyLikeOp(CopyOp copyOp,
   if (plan->kind == CopyRewritePlan::Kind::Direct) {
     Value newTargetOffset = materializeByteOffset(rewriter, loc, anchorOp, plan->target.offset);
     Value newSourceOffset = materializeByteOffset(rewriter, loc, anchorOp, plan->source.offset);
-    auto newCopyOp = createCopyOp(loc,
-                                  plan->target.base,
-                                  plan->source.base,
-                                  newTargetOffset,
-                                  newSourceOffset,
-                                  static_cast<int32_t>(plan->directBytes));
+    auto checkedDirectBytes = pim::checkedI32(plan->directBytes, anchorOp, "normalized direct copy byte size");
+    if (failed(checkedDirectBytes))
+      return failure();
+    auto newCopyOp =
+      createCopyOp(loc, plan->target.base, plan->source.base, newTargetOffset, newSourceOffset, *checkedDirectBytes);
     assert(isNormalizedCopyOp(newCopyOp) && "copy normalization emitted a non-normalized copy");
     rewriter.replaceOp(copyOp, replacementValue);
     return success();
@@ -339,23 +346,30 @@ static LogicalResult rewriteCopyLikeOp(CopyOp copyOp,
   Value c0 = createIndexConstant(rewriter, anchorOp, 0);
   Value cUpper = createIndexConstant(rewriter, anchorOp, getNumElements(plan->loop.outerShape));
   Value cStep = createIndexConstant(rewriter, anchorOp, 1);
-  auto loop = scf::ForOp::create(rewriter, loc, c0, cUpper, cStep, ValueRange {});
-  rewriter.setInsertionPointToStart(loop.getBody());
-
-  SmallVector<Value> outerIndices =
-    materializeDelinearizedIndices(rewriter, loc, anchorOp, loop.getInductionVar(), plan->loop.outerShape);
-  Value loopTargetOffset = materializeOuterByteOffset(
-    rewriter, loc, anchorOp, plan->loop.targetBaseOffset, outerIndices, plan->loop.targetOuterByteStrides);
-  Value loopSourceOffset = materializeOuterByteOffset(
-    rewriter, loc, anchorOp, plan->loop.sourceBaseOffset, outerIndices, plan->loop.sourceOuterByteStrides);
-  auto newCopyOp = createCopyOp(loc,
-                                plan->target.base,
-                                plan->source.base,
-                                loopTargetOffset,
-                                loopSourceOffset,
-                                static_cast<int32_t>(plan->loop.chunkBytes));
-  assert(isNormalizedCopyOp(newCopyOp) && "copy normalization emitted a non-normalized copy");
-  rewriter.setInsertionPointAfter(loop);
+  auto loop = buildNormalizedScfFor(
+    rewriter,
+    loc,
+    c0,
+    cUpper,
+    cStep,
+    ValueRange {},
+    [&](OpBuilder&, Location nestedLoc, Value inductionVar, ValueRange iterArgs, SmallVectorImpl<Value>& yielded) {
+      SmallVector<Value> outerIndices =
+        materializeDelinearizedIndices(rewriter, nestedLoc, anchorOp, inductionVar, plan->loop.outerShape);
+      Value loopTargetOffset = materializeOuterByteOffset(
+        rewriter, nestedLoc, anchorOp, plan->loop.targetBaseOffset, outerIndices, plan->loop.targetOuterByteStrides);
+      Value loopSourceOffset = materializeOuterByteOffset(
+        rewriter, nestedLoc, anchorOp, plan->loop.sourceBaseOffset, outerIndices, plan->loop.sourceOuterByteStrides);
+      auto checkedChunkBytes = pim::checkedI32(plan->loop.chunkBytes, anchorOp, "normalized loop copy byte size");
+      if (failed(checkedChunkBytes))
+        return failure();
+      auto newCopyOp = createCopyOp(
+        nestedLoc, plan->target.base, plan->source.base, loopTargetOffset, loopSourceOffset, *checkedChunkBytes);
+      assert(isNormalizedCopyOp(newCopyOp) && "copy normalization emitted a non-normalized copy");
+      return success();
+    });
+  if (failed(loop))
+    return failure();
   rewriter.replaceOp(copyOp, replacementValue);
   return success();
 }

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

@@ -148,7 +148,10 @@ struct ConcatOpInterface : DstBufferizableOpInterfaceExternalModel<ConcatOpInter
       auto inputOpt = getBufferOrValue(rewriter, input, options, state);
       if (failed(inputOpt))
         return failure();
-      inputs.push_back(materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter));
+      auto contiguous = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+      if (failed(contiguous))
+        return failure();
+      inputs.push_back(*contiguous);
     }
 
     auto outputBufferOpt = getBufferOrValue(rewriter, concatOp.getOutputBuffer(), options, state);
@@ -179,12 +182,12 @@ struct SendOpInterface : BufferizableOpInterface::ExternalModel<SendOpInterface,
     auto inputOpt = getBufferOrValue(rewriter, sendOp.getInput(), options, state);
     if (failed(inputOpt))
       return failure();
+    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    if (failed(contiguousInput))
+      return failure();
 
-    replaceOpWithNewBufferizedOp<PimSendOp>(rewriter,
-                                            op,
-                                            materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter),
-                                            sendOp.getSizeAttr(),
-                                            sendOp.getTargetCoreId());
+    replaceOpWithNewBufferizedOp<PimSendOp>(
+      rewriter, op, *contiguousInput, sendOp.getSizeAttr(), sendOp.getTargetCoreId());
     return success();
   }
 };
@@ -407,11 +410,13 @@ struct TransposeOpInterface : DstBufferizableOpInterfaceExternalModel<TransposeO
     if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    if (failed(contiguousInput))
+      return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
 
     replaceOpWithNewBufferizedOp<PimTransposeOp>(
-      rewriter, op, contiguousOutput.getType(), contiguousInput, transposeOp.getPermutation(), contiguousOutput);
+      rewriter, op, contiguousOutput.getType(), *contiguousInput, transposeOp.getPermutation(), contiguousOutput);
     return success();
   }
 };
@@ -451,11 +456,13 @@ struct VMMOpInterface : DstBufferizableOpInterfaceExternalModel<VMMOpInterface,
     if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    if (failed(contiguousInput))
+      return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
 
     replaceOpWithNewBufferizedOp<PimVMMOp>(
-      rewriter, op, contiguousOutput.getType(), *weightOpt, contiguousInput, contiguousOutput);
+      rewriter, op, contiguousOutput.getType(), *weightOpt, *contiguousInput, contiguousOutput);
     return success();
   }
 };
@@ -490,12 +497,16 @@ struct BinaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<BinaryDstO
     if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousLhs = materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter);
-    Value contiguousRhs = materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter);
+    auto contiguousLhs = materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter);
+    if (failed(contiguousLhs))
+      return failure();
+    auto contiguousRhs = materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter);
+    if (failed(contiguousRhs))
+      return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
 
     replaceOpWithNewBufferizedOp<OpTy>(
-      rewriter, op, contiguousOutput.getType(), contiguousLhs, contiguousRhs, contiguousOutput);
+      rewriter, op, contiguousOutput.getType(), *contiguousLhs, *contiguousRhs, contiguousOutput);
     return success();
   }
 };
@@ -523,12 +534,16 @@ struct VVDMulOpInterface : DstBufferizableOpInterfaceExternalModel<VVDMulOpInter
     if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousLhs = materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter);
-    Value contiguousRhs = materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter);
+    auto contiguousLhs = materializeContiguousInputMemRef(*lhsOpt, op->getLoc(), rewriter);
+    if (failed(contiguousLhs))
+      return failure();
+    auto contiguousRhs = materializeContiguousInputMemRef(*rhsOpt, op->getLoc(), rewriter);
+    if (failed(contiguousRhs))
+      return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
 
     replaceOpWithNewBufferizedOp<PimVVDMulOp>(
-      rewriter, op, contiguousOutput.getType(), contiguousLhs, contiguousRhs, contiguousOutput);
+      rewriter, op, contiguousOutput.getType(), *contiguousLhs, *contiguousRhs, contiguousOutput);
     return success();
   }
 };
@@ -559,10 +574,12 @@ struct UnaryDstOpInterface : DstBufferizableOpInterfaceExternalModel<UnaryDstOpI
     if (failed(outputBufferOpt))
       return failure();
 
-    Value contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    auto contiguousInput = materializeContiguousInputMemRef(*inputOpt, op->getLoc(), rewriter);
+    if (failed(contiguousInput))
+      return failure();
     Value contiguousOutput = allocateContiguousResultMemRefLike(*outputBufferOpt, op->getLoc(), rewriter);
 
-    replaceOpWithNewBufferizedOp<OpTy>(rewriter, op, contiguousOutput.getType(), contiguousInput, contiguousOutput);
+    replaceOpWithNewBufferizedOp<OpTy>(rewriter, op, contiguousOutput.getType(), *contiguousInput, contiguousOutput);
     return success();
   }
 };

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

@@ -42,7 +42,9 @@ struct MemRefCopyToPimMemCopyPattern final : OpRewritePattern<memref::CopyOp> {
       return failure();
 
     Value zeroOffset = getOrCreateIndexConstant(rewriter, copyOp, 0);
-    IntegerAttr sizeAttr = getMemRefSizeInBytesAttr(rewriter, copyOp.getSource());
+    auto sizeAttr = getMemRefSizeInBytesAttr(rewriter, copyOp.getOperation(), copyOp.getSource());
+    if (failed(sizeAttr))
+      return failure();
     pim::PimMemCopyOp::create(rewriter,
                               copyOp.getLoc(),
                               copyOp.getTarget().getType(),
@@ -50,7 +52,7 @@ struct MemRefCopyToPimMemCopyPattern final : OpRewritePattern<memref::CopyOp> {
                               zeroOffset,
                               copyOp.getTarget(),
                               copyOp.getSource(),
-                              sizeAttr);
+                              *sizeAttr);
     rewriter.eraseOp(copyOp);
     return success();
   }

src/PIM/Dialect/Pim/Transforms/HostConstantFolding/CMakeLists.txt

@@ -0,0 +1,12 @@
+add_pim_library(OMPimHostConstantFolding
+  Common.cpp
+  Patterns/Constant.cpp
+  HostConstantFoldingPass.cpp
+  Patterns/Subview.cpp
+
+  EXCLUDE_FROM_OM_LIBS
+
+  LINK_LIBS PUBLIC
+  MLIRLinalgDialect
+  OMPimCommon
+)

src/PIM/Dialect/Pim/Transforms/HostConstantFolding/Patterns/Constant.cpp

@@ -7,6 +7,7 @@
 #include "../Common.hpp"
 #include "../Patterns.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
 using namespace mlir;
@@ -120,16 +121,15 @@ struct FoldConstantCoreMapPattern final : OpRewritePattern<linalg::MapOp> {
 
     rewriter.setInsertionPoint(mapOp);
     auto getGlobalOp = memref::GetGlobalOp::create(rewriter, mapOp.getLoc(), initType, globalOp.getName());
-    auto sizeInBytes = getShapedTypeSizeInBytes(initType);
+    auto sizeInBytes = pim::getCheckedShapedTypeSizeInBytes(initType, mapOp, "host constant folding byte size");
+    if (failed(sizeInBytes))
+      return failure();
     Value zeroOffset = getOrCreateIndexConstant(rewriter, mapOp, 0);
-    pim::PimMemCopyOp::create(rewriter,
-                              mapOp.getLoc(),
-                              initType,
-                              zeroOffset,
-                              zeroOffset,
-                              mapOp.getInit(),
-                              getGlobalOp.getResult(),
-                              rewriter.getI32IntegerAttr(sizeInBytes));
+    auto sizeAttr = pim::getCheckedI32Attr(rewriter, mapOp, *sizeInBytes, "host constant folding byte size");
+    if (failed(sizeAttr))
+      return failure();
+    pim::PimMemCopyOp::create(
+      rewriter, mapOp.getLoc(), initType, zeroOffset, zeroOffset, mapOp.getInit(), getGlobalOp.getResult(), *sizeAttr);
     rewriter.eraseOp(mapOp);
     return success();
   }

src/PIM/Dialect/Pim/Transforms/HostConstantMaterialization/CMakeLists.txt

@@ -0,0 +1,9 @@
+add_pim_library(OMPimHostConstantMaterialization
+  MaterializeHostConstantsPass.cpp
+
+  EXCLUDE_FROM_OM_LIBS
+
+  LINK_LIBS PUBLIC
+  OMPimCommon
+  PimOps
+)

src/PIM/Dialect/Pim/Transforms/HostConstantMaterialization/MaterializeHostConstantsPass.cpp

@@ -12,6 +12,7 @@
 
 #include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
 
 using namespace mlir;
@@ -65,11 +66,15 @@ static void materializeHostConstantsInCore(CoreOpTy coreOp,
         continue;
       }
 
-      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");
+      auto type = dyn_cast<ShapedType>(originalValue.getType());
+      auto totalBytes = type ? pim::getCheckedShapedTypeSizeInBytes(type, op, "host constant materialization byte size")
+                             : FailureOr<uint64_t>(failure());
+      auto totalBytesAttr =
+        succeeded(totalBytes)
+          ? pim::getCheckedI32Attr(rewriter, op, *totalBytes, "host constant materialization byte size")
+          : FailureOr<IntegerAttr>(failure());
+      if (failed(totalBytesAttr)
+          || failed(pim::checkedSize(resolvedAddress->byteOffset, op, "host constant materialization byte offset"))) {
         hasFailure = true;
         continue;
       }
@@ -84,16 +89,15 @@ static void materializeHostConstantsInCore(CoreOpTy coreOp,
 
       Value zeroOffset = getOrCreateIndexConstant(constantFolder, op, 0);
       Value hostOffset = getOrCreateIndexConstant(constantFolder, op, resolvedAddress->byteOffset);
-      Value copiedValue =
-        pim::PimMemCopyHostToDevOp::create(rewriter,
-                                           op->getLoc(),
-                                           originalType,
-                                           zeroOffset,
-                                           hostOffset,
-                                           deviceDst,
-                                           getGlobalOp.getResult(),
-                                           rewriter.getI32IntegerAttr(static_cast<int32_t>(totalBytes)))
-          .getOutput();
+      Value copiedValue = pim::PimMemCopyHostToDevOp::create(rewriter,
+                                                             op->getLoc(),
+                                                             originalType,
+                                                             zeroOffset,
+                                                             hostOffset,
+                                                             deviceDst,
+                                                             getGlobalOp.getResult(),
+                                                             *totalBytesAttr)
+                            .getOutput();
 
       cachedByType[originalType] = copiedValue;
       operand.set(copiedValue);

src/PIM/Dialect/Pim/Transforms/MemoryCoalescing/CMakeLists.txt

@@ -0,0 +1,14 @@
+add_pim_library(OMPimMemoryCoalescing
+  MemoryCoalescing.cpp
+  MemoryCoalescing.hpp
+  MemoryCoalescingPass.cpp
+
+  EXCLUDE_FROM_OM_LIBS
+
+  INCLUDE_DIRS PUBLIC
+  ${PIM_PUBLIC_INCLUDE_DIRS}
+
+  LINK_LIBS PUBLIC
+  OMPimCommon
+  PimOps
+)

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

@@ -10,7 +10,7 @@
 #include <limits>
 
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
-#include "src/Accelerators/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.hpp"
+#include "src/Accelerators/PIM/Dialect/Pim/Transforms/MemoryCoalescing/MemoryCoalescing.hpp"
 
 using namespace mlir;
 
@@ -32,6 +32,13 @@ static uint64_t getTypeSizeBytes(MemRefType type) {
   return static_cast<uint64_t>(type.getNumElements() * getElementTypeSizeInBytes(type.getElementType()));
 }
 
+static Operation* getTopLevelAncestorInBody(Operation* op, Block& body) {
+  Operation* current = op;
+  while (current && current->getBlock() != &body)
+    current = current->getParentOp();
+  return current;
+}
+
 static FailureOr<uint64_t>
 getLastUseInstruction(memref::AllocOp allocOp, Block& body, const DenseMap<Operation*, uint64_t>& opOrder) {
   uint64_t endInstruction = opOrder.lookup(allocOp);
@@ -42,7 +49,8 @@ getLastUseInstruction(memref::AllocOp allocOp, Block& body, const DenseMap<Opera
   while (!pendingValues.empty()) {
     Value value = pendingValues.pop_back_val();
     for (Operation* user : value.getUsers()) {
-      if (user->getBlock() != &body)
+      Operation* orderedUser = getTopLevelAncestorInBody(user, body);
+      if (!orderedUser)
         return failure();
       if (!visited.insert(user).second)
         continue;
@@ -51,6 +59,15 @@ getLastUseInstruction(memref::AllocOp allocOp, Block& body, const DenseMap<Opera
         for (Value result : user->getResults())
           pendingValues.push_back(result);
 
+      if (auto yieldOp = dyn_cast<scf::YieldOp>(user)) {
+        auto forOp = dyn_cast<scf::ForOp>(yieldOp->getParentOp());
+        if (!forOp)
+          return failure();
+        for (auto [index, operand] : llvm::enumerate(yieldOp.getOperands()))
+          if (operand == value)
+            pendingValues.push_back(forOp.getResult(index));
+      }
+
       if (auto forOp = dyn_cast<scf::ForOp>(user)) {
         for (auto [index, initArg] : llvm::enumerate(forOp.getInitArgs()))
           if (initArg == value)
@@ -66,7 +83,7 @@ getLastUseInstruction(memref::AllocOp allocOp, Block& body, const DenseMap<Opera
         }
       }
 
-      auto order = opOrder.find(user);
+      auto order = opOrder.find(orderedUser);
       if (order == opOrder.end())
         return failure();
       endInstruction = std::max(endInstruction, order->second);
@@ -78,8 +95,8 @@ getLastUseInstruction(memref::AllocOp allocOp, Block& body, const DenseMap<Opera
 
 } // namespace
 
-StaticMemoryCoalescingAnalysis analyzeStaticMemoryCoalescingCandidates(Operation* coreLikeOp) {
-  StaticMemoryCoalescingAnalysis analysis;
+MemoryCoalescingAnalysis analyzeMemoryCoalescingCandidates(Operation* coreLikeOp) {
+  MemoryCoalescingAnalysis analysis;
   if (!coreLikeOp || coreLikeOp->getNumRegions() != 1 || coreLikeOp->getRegion(0).empty())
     return analysis;
 
@@ -107,18 +124,19 @@ StaticMemoryCoalescingAnalysis analyzeStaticMemoryCoalescingCandidates(Operation
     }
 
     analysis.candidates.push_back(
-      StaticAllocationCandidate {allocOp, opOrder.lookup(allocOp), *endInstruction, getTypeSizeBytes(allocType)});
+      AllocationCandidate {allocOp, opOrder.lookup(allocOp), *endInstruction, getTypeSizeBytes(allocType)});
   }
 
   return analysis;
 }
 
-StaticMemoryCoalescingStats coalesceStaticMemory(Operation* coreLikeOp, RewriterBase& rewriter) {
-  StaticMemoryCoalescingStats stats;
-  auto analysis = analyzeStaticMemoryCoalescingCandidates(coreLikeOp);
+MemoryCoalescingStats
+coalesceMemory(Operation* coreLikeOp, const MemoryCoalescingAnalysis& analysis, RewriterBase& rewriter) {
+  MemoryCoalescingStats stats;
   stats.skippedAllocations = analysis.skippedAllocations;
 
-  llvm::sort(analysis.candidates, [](const StaticAllocationCandidate& lhs, const StaticAllocationCandidate& rhs) {
+  auto candidates = analysis.candidates;
+  llvm::sort(candidates, [](const AllocationCandidate& lhs, const AllocationCandidate& rhs) {
     if (lhs.startInstruction != rhs.startInstruction)
       return lhs.startInstruction < rhs.startInstruction;
     return lhs.endInstruction < rhs.endInstruction;
@@ -132,7 +150,7 @@ StaticMemoryCoalescingStats coalesceStaticMemory(Operation* coreLikeOp, Rewriter
   SmallVector<ActiveStorage> active;
   SmallVector<memref::AllocOp> freeList;
 
-  for (StaticAllocationCandidate& candidate : analysis.candidates) {
+  for (AllocationCandidate& candidate : candidates) {
     for (auto it = active.begin(); it != active.end();) {
       if (it->endInstruction < candidate.startInstruction) {
         freeList.push_back(it->root);

src/PIM/Dialect/Pim/Transforms/MemoryCoalescing/MemoryCoalescing.hpp

@@ -8,27 +8,28 @@
 namespace onnx_mlir {
 namespace pim {
 
-struct StaticAllocationCandidate {
+struct AllocationCandidate {
   mlir::memref::AllocOp alloc;
   uint64_t startInstruction = 0;
   uint64_t endInstruction = 0;
   uint64_t sizeBytes = 0;
 };
 
-struct StaticMemoryCoalescingAnalysis {
-  llvm::SmallVector<StaticAllocationCandidate> candidates;
+struct MemoryCoalescingAnalysis {
+  llvm::SmallVector<AllocationCandidate> candidates;
   uint64_t skippedAllocations = 0;
 };
 
-struct StaticMemoryCoalescingStats {
+struct MemoryCoalescingStats {
   uint64_t removedAllocs = 0;
   uint64_t savedBytes = 0;
   uint64_t skippedAllocations = 0;
 };
 
-StaticMemoryCoalescingAnalysis analyzeStaticMemoryCoalescingCandidates(mlir::Operation* coreLikeOp);
+MemoryCoalescingAnalysis analyzeMemoryCoalescingCandidates(mlir::Operation* coreLikeOp);
 
-StaticMemoryCoalescingStats coalesceStaticMemory(mlir::Operation* coreLikeOp, mlir::RewriterBase& rewriter);
+MemoryCoalescingStats
+coalesceMemory(mlir::Operation* coreLikeOp, const MemoryCoalescingAnalysis& analysis, mlir::RewriterBase& rewriter);
 
 } // namespace pim
 } // namespace onnx_mlir

src/PIM/Dialect/Pim/Transforms/MemoryCoalescing/MemoryCoalescingPass.cpp

@@ -10,10 +10,11 @@
 #include "Common/IR/CompactAsmUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/BatchCoreUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Common/Support/DebugDump.hpp"
 #include "src/Accelerators/PIM/Common/Support/ReportUtils.hpp"
 #include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
-#include "src/Accelerators/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.hpp"
+#include "src/Accelerators/PIM/Dialect/Pim/Transforms/MemoryCoalescing/MemoryCoalescing.hpp"
 #include "src/Accelerators/PIM/Pass/PIMPasses.h"
 
 using namespace mlir;
@@ -151,34 +152,39 @@ static void emitReport(ArrayRef<CoalescingReportEntry> entries) {
   file.close();
 }
 
-struct StaticMemoryCoalescingPass : PassWrapper<StaticMemoryCoalescingPass, OperationPass<ModuleOp>> {
-  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(StaticMemoryCoalescingPass)
+struct PimMemoryCoalescingPass : PassWrapper<PimMemoryCoalescingPass, OperationPass<ModuleOp>> {
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(PimMemoryCoalescingPass)
 
-  StringRef getArgument() const override { return "pim-static-memory-coalescing"; }
-  StringRef getDescription() const override { return "Analyze static local PIM memory reuse opportunities"; }
+  StringRef getArgument() const override { return "pim-memory-coalescing"; }
+  StringRef getDescription() const override { return "Analyze local PIM memory reuse opportunities"; }
 
-  StaticMemoryCoalescingPass() = default;
-  StaticMemoryCoalescingPass(const StaticMemoryCoalescingPass& pass) {}
+  PimMemoryCoalescingPass() = default;
+  PimMemoryCoalescingPass(const PimMemoryCoalescingPass& pass) {}
 
   void runOnOperation() override {
     IRRewriter rewriter(&getContext());
     SmallVector<CoalescingReportEntry, 32> reportEntries;
     uint64_t nextBatchId = 0;
+    bool hasFailure = false;
 
     getOperation().walk([&](Operation* op) {
-      if (!isa<pim::PimCoreOp, pim::PimCoreBatchOp>(op))
+      if (hasFailure || !isa<pim::PimCoreOp, pim::PimCoreBatchOp>(op))
         return;
 
-      auto analysis = pim::analyzeStaticMemoryCoalescingCandidates(op);
-      auto stats = pim::coalesceStaticMemory(op, rewriter);
+      auto analysis = pim::analyzeMemoryCoalescingCandidates(op);
+      auto stats = pim::coalesceMemory(op, analysis, rewriter);
       CoalescingReportRow row {
         analysis.candidates.size(), stats.skippedAllocations, stats.removedAllocs, stats.savedBytes};
 
       if (auto coreOp = dyn_cast<pim::PimCoreOp>(op)) {
-        reportEntries.push_back({CoalescingReportEntry::Kind::Core,
-                                 static_cast<uint64_t>(coreOp.getCoreId()),
-                                 {static_cast<int32_t>(coreOp.getCoreId())},
-                                 row});
+        auto checkedCoreId =
+          pim::checkedI32(static_cast<uint64_t>(coreOp.getCoreId()), coreOp, "memory coalescing core id");
+        if (failed(checkedCoreId)) {
+          hasFailure = true;
+          return;
+        }
+        reportEntries.push_back(
+          {CoalescingReportEntry::Kind::Core, static_cast<uint64_t>(coreOp.getCoreId()), {*checkedCoreId}, row});
         return;
       }
 
@@ -191,6 +197,11 @@ struct StaticMemoryCoalescingPass : PassWrapper<StaticMemoryCoalescingPass, Oper
       reportEntries.push_back(std::move(entry));
     });
 
+    if (hasFailure) {
+      signalPassFailure();
+      return;
+    }
+
     emitReport(reportEntries);
     dumpModule(getOperation(), "pim2_coalesced");
   }
@@ -198,6 +209,6 @@ struct StaticMemoryCoalescingPass : PassWrapper<StaticMemoryCoalescingPass, Oper
 
 } // namespace
 
-std::unique_ptr<Pass> createPimStaticMemoryCoalescingPass() { return std::make_unique<StaticMemoryCoalescingPass>(); }
+std::unique_ptr<Pass> createPimMemoryCoalescingPass() { return std::make_unique<PimMemoryCoalescingPass>(); }
 
 } // namespace onnx_mlir

src/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/CMakeLists.txt

@@ -1,14 +0,0 @@
-add_pim_library(OMPimStaticMemoryCoalescing
-  StaticMemoryCoalescing.cpp
-  StaticMemoryCoalescing.hpp
-  StaticMemoryCoalescingPass.cpp
-
-  EXCLUDE_FROM_OM_LIBS
-
-  INCLUDE_DIRS PUBLIC
-  ${PIM_PUBLIC_INCLUDE_DIRS}
-
-  LINK_LIBS PUBLIC
-  OMPimCommon
-  PimOps
-)

src/PIM/Dialect/Pim/Transforms/Verification/CMakeLists.txt

@@ -0,0 +1,11 @@
+add_pim_library(OMPimVerification
+  VerificationPass.cpp
+
+  EXCLUDE_FROM_OM_LIBS
+
+  LINK_LIBS PUBLIC
+  OMPimCommon
+  OMPimBufferization
+  PimOps
+  SpatialOps
+)

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

@@ -37,6 +37,7 @@
 #include "Scheduling/MergeSchedulingAnalysis.hpp"
 #include "src/Accelerators/PIM/Common/IR/CompactAsmUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
 #include "src/Accelerators/PIM/Common/Support/ReportUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ONNXToSpatialVerifier.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -128,8 +129,12 @@ void emitMergeIrCounts(StringRef phaseName, func::FuncOp funcOp) {
 }
 
 static std::optional<int32_t> getComputeCoreId(SpatCompute compute) {
-  if (auto coreIdAttr = compute->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName))
-    return static_cast<int32_t>(coreIdAttr.getInt());
+  if (auto coreIdAttr = compute->getAttrOfType<IntegerAttr>(onnx_mlir::kCoreIdAttrName)) {
+    auto checkedCoreId = pim::checkedI32(coreIdAttr.getInt(), compute, "merge compute core id");
+    if (failed(checkedCoreId))
+      return std::nullopt;
+    return *checkedCoreId;
+  }
   return std::nullopt;
 }
 

src/PIM/Pass/CMakeLists.txt

@@ -1,11 +1,5 @@
 add_pim_library(OMPimPasses
   MessagePass.cpp
-  PimCodegen/HostConstantFolding/Common.cpp
-  PimCodegen/HostConstantFolding/Patterns/Constant.cpp
-  PimCodegen/HostConstantFolding/HostConstantFoldingPass.cpp
-  PimCodegen/HostConstantFolding/Patterns/Subview.cpp
-  PimCodegen/MaterializeHostConstantsPass.cpp
-  PimCodegen/VerificationPass.cpp
   PimCodegen/EmitPimCodePass.cpp
 
   EXCLUDE_FROM_OM_LIBS

src/PIM/Pass/PIMPasses.h

@@ -15,7 +15,7 @@ std::unique_ptr<mlir::Pass> createSpatialToPimPass();
 
 std::unique_ptr<mlir::Pass> createPimBufferizationPass();
 
-std::unique_ptr<mlir::Pass> createPimStaticMemoryCoalescingPass();
+std::unique_ptr<mlir::Pass> createPimMemoryCoalescingPass();
 
 std::unique_ptr<mlir::Pass> createMergeComputeNodesPass();
 

src/PIM/PimAccelerator.cpp

@@ -75,7 +75,7 @@ void PimAccelerator::registerPasses(int optLevel) const {
   registerPass(createSpatialToGraphvizPass);
   registerPass(createSpatialToPimPass);
   registerPass(createPimBufferizationPass);
-  registerPass(createPimStaticMemoryCoalescingPass);
+  registerPass(createPimMemoryCoalescingPass);
   registerPass(createMergeComputeNodesPass);
   registerPass(createPimHostConstantFoldingPass);
   registerPass(createPimMaterializeHostConstantsPass);