Refactor + ReduceMean batched

src/PIM/Common/IR/ConstantUtils.cpp

@@ -28,7 +28,7 @@ Block* getHostConstantBlock(Operation* anchorOp) {
   return anchorOp->getBlock();
 }
 
-Value getOrCreateHostConstant(Operation* anchorOp, Attribute value, Type type, OperationFolder& folder) {
+Value getOrCreateHostConstant(OperationFolder& folder, Operation* anchorOp, Attribute value, Type type) {
   assert(anchorOp && "expected a valid anchor operation");
   Block* hostBlock = getHostConstantBlock(anchorOp);
   for (Operation& op : *hostBlock) {
@@ -42,7 +42,7 @@ Value getOrCreateHostConstant(Operation* anchorOp, Attribute value, Type type, O
   return folder.getOrCreateConstant(hostBlock, arithDialect, value, type);
 }
 
-Value getOrCreateHostConstant(Operation* anchorOp, Attribute value, Type type, RewriterBase& rewriter) {
+Value getOrCreateHostConstant(RewriterBase& rewriter, Operation* anchorOp, Attribute value, Type type) {
   assert(anchorOp && "expected a valid anchor operation");
   Block* hostBlock = getHostConstantBlock(anchorOp);
   for (Operation& op : *hostBlock) {
@@ -57,28 +57,28 @@ Value getOrCreateHostConstant(Operation* anchorOp, Attribute value, Type type, R
   return arith::ConstantOp::create(rewriter, anchorOp->getLoc(), type, cast<TypedAttr>(value)).getResult();
 }
 
-Value getOrCreateHostConstantLike(arith::ConstantOp constantOp, OperationFolder& folder) {
+Value getOrCreateHostConstantLike(OperationFolder& folder, arith::ConstantOp constantOp) {
-  return getOrCreateHostConstant(constantOp.getOperation(), constantOp.getValue(), constantOp.getType(), folder);
+  return getOrCreateHostConstant(folder, constantOp.getOperation(), constantOp.getValue(), constantOp.getType());
 }
 
-Value getOrCreateHostIndexConstant(Operation* anchorOp, int64_t value, OperationFolder& folder) {
+Value getOrCreateHostIndexConstant(OperationFolder& folder, Operation* anchorOp, int64_t value) {
   Builder builder(anchorOp->getContext());
-  return getOrCreateHostConstant(anchorOp, builder.getIndexAttr(value), builder.getIndexType(), folder);
+  return getOrCreateHostConstant(folder, anchorOp, builder.getIndexAttr(value), builder.getIndexType() );
 }
 
-Value getOrCreateHostIndexConstant(Operation* anchorOp, int64_t value, RewriterBase& rewriter) {
+Value getOrCreateHostIndexConstant(RewriterBase& rewriter, Operation* anchorOp, int64_t value) {
   Builder builder(anchorOp->getContext());
-  return getOrCreateHostConstant(anchorOp, builder.getIndexAttr(value), builder.getIndexType(), rewriter);
+  return getOrCreateHostConstant(rewriter, anchorOp, builder.getIndexAttr(value), builder.getIndexType());
 }
 
 Value getOrCreateHostI32Constant(Operation* anchorOp, int32_t value, OperationFolder& folder) {
   Builder builder(anchorOp->getContext());
-  return getOrCreateHostConstant(anchorOp, builder.getI32IntegerAttr(value), builder.getI32Type(), folder);
+  return getOrCreateHostConstant(folder, anchorOp, builder.getI32IntegerAttr(value), builder.getI32Type() );
 }
 
 Value getOrCreateHostI64Constant(Operation* anchorOp, int64_t value, OperationFolder& folder) {
   Builder builder(anchorOp->getContext());
-  return getOrCreateHostConstant(anchorOp, builder.getI64IntegerAttr(value), builder.getI64Type(), folder);
+  return getOrCreateHostConstant(folder, anchorOp, builder.getI64IntegerAttr(value), builder.getI64Type() );
 }
 
 Value createAffineApplyOrFoldedConstant(
@@ -95,7 +95,7 @@ Value createAffineApplyOrFoldedConstant(
   SmallVector<Attribute> foldedResults;
   if (succeeded(map.constantFold(operandConstants, foldedResults))) {
     if (auto constantResult = dyn_cast<IntegerAttr>(foldedResults.front()))
-      return getOrCreateHostIndexConstant(anchorOp, constantResult.getInt(), rewriter);
+      return getOrCreateHostIndexConstant(rewriter, anchorOp, constantResult.getInt());
   }
 
   return affine::AffineApplyOp::create(rewriter, loc, map, operands).getResult();

src/PIM/Common/IR/ConstantUtils.hpp

@@ -10,25 +10,25 @@ namespace onnx_mlir {
 
 mlir::Block* getHostConstantBlock(mlir::Operation* anchorOp);
 
-mlir::Value getOrCreateHostConstant(mlir::Operation* anchorOp,
+mlir::Value getOrCreateHostConstant(mlir::OperationFolder& folder,
+                                    mlir::Operation* anchorOp,
                                     mlir::Attribute value,
-                                    mlir::Type type,
+                                    mlir::Type type);
-                                    mlir::OperationFolder& folder);
 
-mlir::Value getOrCreateHostConstant(mlir::Operation* anchorOp,
+mlir::Value getOrCreateHostConstant(mlir::RewriterBase& rewriter,
+                                    mlir::Operation* anchorOp,
                                     mlir::Attribute value,
-                                    mlir::Type type,
+                                    mlir::Type type);
-                                    mlir::RewriterBase& rewriter);
 
-mlir::Value getOrCreateHostConstantLike(mlir::arith::ConstantOp constantOp, mlir::OperationFolder& folder);
+mlir::Value getOrCreateHostConstantLike(mlir::OperationFolder& folder, mlir::arith::ConstantOp constantOp);
 
-mlir::Value getOrCreateHostIndexConstant(mlir::Operation* anchorOp, int64_t value, mlir::OperationFolder& folder);
+mlir::Value getOrCreateHostIndexConstant(mlir::OperationFolder& folder, mlir::Operation* anchorOp, int64_t value);
 
-mlir::Value getOrCreateHostIndexConstant(mlir::Operation* anchorOp, int64_t value, mlir::RewriterBase& rewriter);
+mlir::Value getOrCreateHostIndexConstant(mlir::RewriterBase& rewriter, mlir::Operation* anchorOp, int64_t value);
 
-mlir::Value getOrCreateHostI32Constant(mlir::Operation* anchorOp, int32_t value, mlir::OperationFolder& folder);
+mlir::Value getOrCreateHostI32Constant(mlir::OperationFolder& folder, mlir::Operation* anchorOp, int32_t value);
 
-mlir::Value getOrCreateHostI64Constant(mlir::Operation* anchorOp, int64_t value, mlir::OperationFolder& folder);
+mlir::Value getOrCreateHostI64Constant(mlir::OperationFolder& folder, mlir::Operation* anchorOp, int64_t value);
 
 mlir::Value createAffineApplyOrFoldedConstant(mlir::RewriterBase& rewriter,
                                               mlir::Location loc,

src/PIM/Conversion/ONNXToSpatial/CMakeLists.txt

@@ -25,7 +25,9 @@ add_pim_library(OMONNXToSpatial
   Patterns/Tensor/Split.cpp
   Patterns/Tensor/Transpose.cpp
   ONNXToSpatialPass.cpp
+  Common/AttributeUtils.cpp
   Common/ComputeRegionBuilder.cpp
+  Common/IndexingUtils.cpp
   Common/ShapeTilingUtils.cpp
   Common/WeightMaterialization.cpp
 

src/PIM/Conversion/ONNXToSpatial/Common/AttributeUtils.cpp

@@ -0,0 +1,23 @@
+#include "AttributeUtils.hpp"
+
+#include "mlir/IR/BuiltinAttributes.h"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+
+int64_t getI64Attr(ArrayAttr attr, size_t index) { return cast<IntegerAttr>(attr[index]).getInt(); }
+
+int64_t getOptionalI64Attr(std::optional<ArrayAttr> attr, size_t index, int64_t defaultValue) {
+  return attr ? getI64Attr(*attr, index) : defaultValue;
+}
+
+llvm::SmallVector<int64_t> getI64ArrayAttrValues(ArrayAttr attr) {
+  llvm::SmallVector<int64_t> values;
+  values.reserve(attr.size());
+  for (Attribute value : attr)
+    values.push_back(cast<IntegerAttr>(value).getInt());
+  return values;
+}
+
+} // namespace onnx_mlir

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

@@ -0,0 +1,18 @@
+#pragma once
+
+#include "mlir/IR/BuiltinAttributes.h"
+
+#include "llvm/ADT/SmallVector.h"
+
+#include <cstddef>
+#include <optional>
+
+namespace onnx_mlir {
+
+int64_t getI64Attr(mlir::ArrayAttr attr, size_t index);
+
+int64_t getOptionalI64Attr(std::optional<mlir::ArrayAttr> attr, size_t index, int64_t defaultValue);
+
+llvm::SmallVector<int64_t> getI64ArrayAttrValues(mlir::ArrayAttr attr);
+
+} // namespace onnx_mlir

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

@@ -1,6 +1,8 @@
 #pragma once
 
+#include "AttributeUtils.hpp"
 #include "ComputeRegionBuilder.hpp"
+#include "IndexingUtils.hpp"
 #include "ShapeTilingUtils.hpp"
 #include "WeightMaterialization.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"

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

@@ -7,9 +7,13 @@
 
 #include <cassert>
 #include <cstddef>
+#include <limits>
 #include <type_traits>
 #include <utility>
 
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 
 namespace onnx_mlir {
@@ -49,6 +53,13 @@ using InvokeWithBlockArgsResultT = typename InvokeWithBlockArgsResult<Fn, Seq>::
 template <typename Fn>
 using InvokeWithValueRangeResultT = std::invoke_result_t<Fn, mlir::ValueRange>;
 
+struct SpatComputeBatchBodyArgs {
+  mlir::Value lane;
+  mlir::ValueRange weights;
+  mlir::ValueRange inputs;
+  mlir::ValueRange outputs;
+};
+
 } // namespace detail
 
 template <typename RewriterT>
@@ -159,6 +170,96 @@ auto createSpatCompute(RewriterT& rewriter,
   }
 }
 
+template <typename RewriterT, typename BodyFn>
+auto createSpatComputeBatch(RewriterT& rewriter,
+                            mlir::Location loc,
+                            mlir::TypeRange resultTypes,
+                            int64_t laneCount,
+                            mlir::ValueRange weights,
+                            mlir::ValueRange inputs,
+                            BodyFn&& body) {
+  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);
+
+  mlir::SmallVector<mlir::Type> blockArgTypes {rewriter.getIndexType()};
+  mlir::SmallVector<mlir::Location> blockArgLocs {loc};
+  blockArgTypes.reserve(1 + weights.size() + inputs.size() + resultTypes.size());
+  blockArgLocs.reserve(1 + weights.size() + inputs.size() + resultTypes.size());
+  for (mlir::Value weight : weights) {
+    blockArgTypes.push_back(weight.getType());
+    blockArgLocs.push_back(weight.getLoc());
+  }
+  for (mlir::Value input : inputs) {
+    blockArgTypes.push_back(input.getType());
+    blockArgLocs.push_back(input.getLoc());
+  }
+  for (mlir::Type resultType : resultTypes) {
+    blockArgTypes.push_back(resultType);
+    blockArgLocs.push_back(loc);
+  }
+
+  auto* block =
+    rewriter.createBlock(&batchOp.getBody(), batchOp.getBody().end(), mlir::TypeRange(blockArgTypes), blockArgLocs);
+  rewriter.setInsertionPointToStart(block);
+
+  detail::SpatComputeBatchBodyArgs args {
+    block->getArgument(0),
+    mlir::ValueRange(block->getArguments()).slice(1, weights.size()),
+    mlir::ValueRange(block->getArguments()).slice(1 + weights.size(), inputs.size()),
+    mlir::ValueRange(block->getArguments()).drop_front(1 + weights.size() + inputs.size())
+  };
+
+  using BodyResult = std::invoke_result_t<BodyFn, detail::SpatComputeBatchBodyArgs>;
+  if constexpr (std::is_same_v<BodyResult, void>) {
+    std::forward<BodyFn>(body)(args);
+    rewriter.setInsertionPointAfter(batchOp);
+    return mlir::FailureOr<spatial::SpatComputeBatch>(batchOp);
+  }
+  else {
+    auto bodyResult = std::forward<BodyFn>(body)(args);
+    if (mlir::failed(bodyResult)) {
+      rewriter.setInsertionPointAfter(batchOp);
+      rewriter.eraseOp(batchOp);
+      return mlir::FailureOr<spatial::SpatComputeBatch>(mlir::failure());
+    }
+    rewriter.setInsertionPointAfter(batchOp);
+    return mlir::FailureOr<spatial::SpatComputeBatch>(batchOp);
+  }
+}
+
+inline void createParallelInsertSliceIntoBatchOutput(mlir::PatternRewriter& rewriter,
+                                                     mlir::Location loc,
+                                                     mlir::Value source,
+                                                     mlir::Value dest,
+                                                     mlir::ArrayRef<mlir::OpFoldResult> offsets,
+                                                     mlir::ArrayRef<mlir::OpFoldResult> sizes,
+                                                     mlir::ArrayRef<mlir::OpFoldResult> strides) {
+  auto inParallelOp = spatial::SpatInParallelOp::create(rewriter, loc);
+  rewriter.setInsertionPointToStart(&inParallelOp.getRegion().front());
+  mlir::tensor::ParallelInsertSliceOp::create(rewriter, loc, source, dest, offsets, sizes, strides);
+}
+
+template <typename BodyFn>
+mlir::Value materializeOrComputeUnary(mlir::Value input,
+                                      mlir::RankedTensorType resultType,
+                                      mlir::PatternRewriter& rewriter,
+                                      mlir::Location loc,
+                                      BodyFn&& build) {
+  auto&& buildFn = build;
+  if (isCompileTimeComputable(input))
+    return buildFn(input);
+
+  auto computeOp =
+    createSpatCompute<1>(rewriter, loc, mlir::TypeRange {resultType}, {}, mlir::ValueRange {input}, [&](mlir::Value computeInput) {
+      mlir::Value result = buildFn(computeInput);
+      spatial::SpatYieldOp::create(rewriter, loc, result);
+    });
+  return computeOp.getResult(0);
+}
+
 mlir::Value sumTensors(mlir::ArrayRef<mlir::Value> tensors, mlir::ConversionPatternRewriter& rewriter);
 
 } // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/Common/IndexingUtils.cpp

@@ -0,0 +1,104 @@
+#include "IndexingUtils.hpp"
+
+#include "mlir/Dialect/Arith/IR/Arith.h"
+
+#include "llvm/ADT/APInt.h"
+
+#include <algorithm>
+
+#include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+
+int64_t normalizeAxis(int64_t axis, int64_t rank) { return axis >= 0 ? axis : rank + axis; }
+
+FailureOr<int64_t> normalizeAxisChecked(int64_t axis, int64_t rank) {
+  int64_t normalizedAxis = normalizeAxis(axis, rank);
+  if (normalizedAxis < 0 || normalizedAxis >= rank)
+    return failure();
+  return normalizedAxis;
+}
+
+int64_t normalizeIndex(int64_t index, int64_t dimSize) { return index >= 0 ? index : dimSize + index; }
+
+static SmallVector<int64_t> normalizeAxesImpl(std::optional<ArrayAttr> axesAttr, int64_t rank) {
+  SmallVector<int64_t> normalizedAxes;
+  if (!axesAttr) {
+    normalizedAxes.reserve(rank);
+    for (int64_t axis = 0; axis < rank; ++axis)
+      normalizedAxes.push_back(axis);
+  }
+  else {
+    normalizedAxes.reserve(axesAttr->size());
+    for (Attribute attr : *axesAttr)
+      normalizedAxes.push_back(normalizeAxis(cast<IntegerAttr>(attr).getInt(), rank));
+    llvm::sort(normalizedAxes);
+    normalizedAxes.erase(std::unique(normalizedAxes.begin(), normalizedAxes.end()), normalizedAxes.end());
+  }
+  return normalizedAxes;
+}
+
+SmallVector<int64_t> normalizeAxes(ArrayAttr axesAttr, int64_t rank) {
+  return normalizeAxesImpl(std::optional<ArrayAttr>(axesAttr), rank);
+}
+
+SmallVector<int64_t> normalizeAxes(std::optional<ArrayAttr> axesAttr, int64_t rank) {
+  return normalizeAxesImpl(axesAttr, rank);
+}
+
+FailureOr<SmallVector<int64_t>> normalizeAxesChecked(std::optional<ArrayAttr> axesAttr, int64_t rank) {
+  SmallVector<int64_t> normalizedAxes = normalizeAxesImpl(axesAttr, rank);
+  for (int64_t axis : normalizedAxes)
+    if (axis < 0 || axis >= rank)
+      return failure();
+  return normalizedAxes;
+}
+
+FailureOr<SmallVector<int64_t>> normalizeAxesChecked(ArrayAttr axesAttr, int64_t rank) {
+  return normalizeAxesChecked(std::optional<ArrayAttr>(axesAttr), rank);
+}
+
+Value createAffineApplyOrConstant(PatternRewriter& rewriter, Location loc, AffineExpr expr, ValueRange operands) {
+  AffineMap map = AffineMap::get(/*dimCount=*/operands.size(), /*symbolCount=*/0, expr);
+  Operation* anchorOp = rewriter.getInsertionBlock()->getParentOp();
+  return createAffineApplyOrFoldedConstant(rewriter, loc, map, operands, anchorOp);
+}
+
+Value multiplyIndexByConstant(PatternRewriter& rewriter, Operation* anchorOp, Value value, int64_t multiplier) {
+  if (multiplier == 0)
+    return getOrCreateHostIndexConstant(rewriter, anchorOp, 0);
+  if (multiplier == 1)
+    return value;
+
+  MLIRContext* context = rewriter.getContext();
+  AffineExpr d0 = getAffineDimExpr(0, context);
+  return createAffineApplyOrConstant(rewriter, anchorOp->getLoc(), d0 * multiplier, ValueRange {value});
+}
+
+Value modIndexByConstant(PatternRewriter& rewriter, Location loc, Value value, int64_t divisor) {
+  if (divisor == 1)
+    return getOrCreateHostIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
+
+  MLIRContext* context = rewriter.getContext();
+  AffineExpr d0 = getAffineDimExpr(0, context);
+  return createAffineApplyOrConstant(rewriter, loc, d0 % divisor, ValueRange {value});
+}
+
+Value floorDivIndexByConstant(PatternRewriter& rewriter, Location loc, Value value, int64_t divisor) {
+  if (divisor == 1)
+    return value;
+
+  MLIRContext* context = rewriter.getContext();
+  AffineExpr d0 = getAffineDimExpr(0, context);
+  return createAffineApplyOrConstant(rewriter, loc, d0.floorDiv(divisor), ValueRange {value});
+}
+
+Value getOrMaterializeIndexValue(PatternRewriter& rewriter, Location loc, OpFoldResult value) {
+  if (auto attr = dyn_cast<Attribute>(value))
+    return getOrCreateHostIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), cast<IntegerAttr>(attr).getInt());
+  return cast<Value>(value);
+}
+
+} // namespace onnx_mlir

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

@@ -0,0 +1,45 @@
+#pragma once
+
+#include "mlir/IR/AffineExpr.h"
+#include "mlir/IR/BuiltinAttributes.h"
+#include "mlir/IR/Value.h"
+#include "mlir/Interfaces/FoldInterfaces.h"
+#include "mlir/Support/LogicalResult.h"
+#include "mlir/Transforms/DialectConversion.h"
+
+#include "llvm/ADT/SmallVector.h"
+
+#include <optional>
+
+namespace onnx_mlir {
+
+int64_t normalizeAxis(int64_t axis, int64_t rank);
+
+mlir::FailureOr<int64_t> normalizeAxisChecked(int64_t axis, int64_t rank);
+
+int64_t normalizeIndex(int64_t index, int64_t dimSize);
+
+llvm::SmallVector<int64_t> normalizeAxes(mlir::ArrayAttr axesAttr, int64_t rank);
+
+llvm::SmallVector<int64_t> normalizeAxes(std::optional<mlir::ArrayAttr> axesAttr, int64_t rank);
+
+mlir::FailureOr<llvm::SmallVector<int64_t>> normalizeAxesChecked(mlir::ArrayAttr axesAttr, int64_t rank);
+
+mlir::FailureOr<llvm::SmallVector<int64_t>> normalizeAxesChecked(std::optional<mlir::ArrayAttr> axesAttr, int64_t rank);
+
+mlir::Value createAffineApplyOrConstant(mlir::PatternRewriter& rewriter,
+                                        mlir::Location loc,
+                                        mlir::AffineExpr expr,
+                                        mlir::ValueRange operands);
+
+mlir::Value
+multiplyIndexByConstant(mlir::PatternRewriter& rewriter, mlir::Operation* anchorOp, mlir::Value value, int64_t multiplier);
+
+mlir::Value modIndexByConstant(mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value value, int64_t divisor);
+
+mlir::Value
+floorDivIndexByConstant(mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value value, int64_t divisor);
+
+mlir::Value getOrMaterializeIndexValue(mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::OpFoldResult value);
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/Common/ShapeTilingUtils.cpp

@@ -6,20 +6,21 @@
 #include "llvm/ADT/SmallVector.h"
 
 #include <algorithm>
+#include <functional>
 
 #include "ShapeTilingUtils.hpp"
+#include "IndexingUtils.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
+#include "src/Dialect/ONNX/ONNXOps.hpp"
 
 using namespace mlir;
 
 namespace onnx_mlir {
 
 static Value getIndexValue(OpFoldResult result, ConversionPatternRewriter& rewriter, Location loc) {
-  if (auto attr = dyn_cast<Attribute>(result))
+  return getOrMaterializeIndexValue(rewriter, loc, result);
-    return arith::ConstantIndexOp::create(rewriter, loc, cast<IntegerAttr>(attr).getInt()).getResult();
-  return cast<Value>(result);
 }
 
 static Value addIndexValues(Value lhs, Value rhs, ConversionPatternRewriter& rewriter, Location loc) {
@@ -50,6 +51,84 @@ static Value multiplyIndexValue(Value value, OpFoldResult factor, ConversionPatt
   return arith::MulIOp::create(rewriter, loc, value, factorValue).getResult();
 }
 
+bool hasStaticPositiveShape(ArrayRef<int64_t> shape) {
+  return llvm::all_of(shape, [](int64_t dim) { return dim > 0; });
+}
+
+bool hasStaticPositiveShape(RankedTensorType type) { return type.hasStaticShape() && hasStaticPositiveShape(type.getShape()); }
+
+int64_t getStaticShapeElementCount(ArrayRef<int64_t> shape) {
+  return std::accumulate(shape.begin(), shape.end(), int64_t {1}, std::multiplies<int64_t> {});
+}
+
+int64_t getStaticShapeElementCount(RankedTensorType type) { return getStaticShapeElementCount(type.getShape()); }
+
+SmallVector<int64_t> permuteShape(ArrayRef<int64_t> shape, ArrayRef<int64_t> permutation) {
+  SmallVector<int64_t> permutedShape;
+  permutedShape.reserve(permutation.size());
+  for (int64_t axis : permutation)
+    permutedShape.push_back(shape[axis]);
+  return permutedShape;
+}
+
+SmallVector<int64_t> invertPermutation(ArrayRef<int64_t> permutation) {
+  SmallVector<int64_t> inversePermutation(permutation.size());
+  for (auto [newIndex, oldIndex] : llvm::enumerate(permutation))
+    inversePermutation[oldIndex] = static_cast<int64_t>(newIndex);
+  return inversePermutation;
+}
+
+FailureOr<SmallVector<int64_t>> getTransposePermutationChecked(std::optional<ArrayAttr> permAttr, int64_t rank) {
+  SmallVector<int64_t> permutation;
+  if (!permAttr) {
+    permutation.reserve(rank);
+    for (int64_t dim = rank - 1; dim >= 0; --dim)
+      permutation.push_back(dim);
+    return permutation;
+  }
+
+  if (static_cast<int64_t>(permAttr->size()) != rank)
+    return failure();
+
+  permutation.reserve(permAttr->size());
+  SmallVector<bool> seen(rank, false);
+  for (IntegerAttr attr : permAttr->getAsRange<IntegerAttr>()) {
+    int64_t axis = attr.getInt();
+    if (axis < 0 || axis >= rank || seen[axis])
+      return failure();
+    seen[axis] = true;
+    permutation.push_back(axis);
+  }
+  return permutation;
+}
+
+Value transposeMaybeInCompute(Value value,
+                              RankedTensorType resultType,
+                              ArrayRef<int64_t> permutation,
+                              PatternRewriter& rewriter,
+                              Location loc) {
+  auto buildTranspose = [&](Value input) -> Value {
+    return ONNXTransposeOp::create(rewriter, loc, resultType, input, rewriter.getI64ArrayAttr(permutation)).getResult();
+  };
+  return materializeOrComputeUnary(value, resultType, rewriter, loc, buildTranspose);
+}
+
+SmallVector<OpFoldResult> getUnitStrides(PatternRewriter& rewriter, int64_t rank) {
+  return SmallVector<OpFoldResult>(rank, rewriter.getIndexAttr(1));
+}
+
+SmallVector<OpFoldResult> getZeroOffsets(PatternRewriter& rewriter, int64_t rank) {
+  return SmallVector<OpFoldResult>(rank, rewriter.getIndexAttr(0));
+}
+
+SmallVector<OpFoldResult> getStaticSizes(PatternRewriter& rewriter, ArrayRef<int64_t> shape) {
+  SmallVector<OpFoldResult> sizes;
+  sizes.reserve(shape.size());
+  for (int64_t dim : shape)
+    sizes.push_back(rewriter.getIndexAttr(dim));
+  return sizes;
+}
+
 static bool isContiguousTensorSlice(Value source, RankedTensorType resultType, ArrayRef<OpFoldResult> strides) {
   auto sourceType = dyn_cast<RankedTensorType>(source.getType());
   if (!sourceType || !sourceType.hasStaticShape() || !resultType.hasStaticShape() || sourceType.getRank() != resultType.getRank())
@@ -88,11 +167,8 @@ SmallVector<Value> sliceTensor(
   assert("Invalid axis" && axis < shape.size());
 
   SmallVector<OpFoldResult> strides(shape.size(), rewriter.getIndexAttr(1));
-  SmallVector<OpFoldResult> offsets(shape.size(), rewriter.getIndexAttr(0));
+  SmallVector<OpFoldResult> offsets = getZeroOffsets(rewriter, shape.size());
-  SmallVector<OpFoldResult> sizes;
+  SmallVector<OpFoldResult> sizes = getStaticSizes(rewriter, shape);
-  sizes.reserve(shape.size());
-  for (const auto size : shape)
-    sizes.push_back(rewriter.getIndexAttr(size));
   sizes[axis] = rewriter.getIndexAttr(sliceSize);
 
   long length = shape[axis];
@@ -276,4 +352,43 @@ Value materializeContiguousTensorSlice(Value source,
   return buildLoopNest(buildLoopNest, 0, init);
 }
 
+Value extractStaticSlice(PatternRewriter& rewriter,
+                         Location loc,
+                         Value source,
+                         RankedTensorType resultType,
+                         ArrayRef<OpFoldResult> offsets) {
+  return tensor::ExtractSliceOp::create(
+           rewriter, loc, resultType, source, offsets, getStaticSizes(rewriter, resultType.getShape()),
+           getUnitStrides(rewriter, resultType.getRank()))
+    .getResult();
+}
+
+Value extractAxisSlice(
+  PatternRewriter& rewriter, Location loc, Value source, int64_t axis, int64_t offset, int64_t size) {
+  auto sourceType = cast<RankedTensorType>(source.getType());
+  SmallVector<int64_t> resultShape(sourceType.getShape());
+  resultShape[axis] = size;
+  auto resultType = RankedTensorType::get(resultShape, sourceType.getElementType(), sourceType.getEncoding());
+
+  SmallVector<OpFoldResult> offsets = getZeroOffsets(rewriter, sourceType.getRank());
+  SmallVector<OpFoldResult> sizes = getStaticSizes(rewriter, sourceType.getShape());
+  offsets[axis] = rewriter.getIndexAttr(offset);
+  sizes[axis] = rewriter.getIndexAttr(size);
+  return tensor::ExtractSliceOp::create(rewriter, loc, resultType, source, offsets, sizes, getUnitStrides(rewriter, sourceType.getRank()))
+    .getResult();
+}
+
+Value insertStaticSlice(
+  PatternRewriter& rewriter, Location loc, Value source, Value dest, ArrayRef<OpFoldResult> offsets) {
+  auto sourceType = cast<RankedTensorType>(source.getType());
+  return tensor::InsertSliceOp::create(rewriter,
+                                       loc,
+                                       source,
+                                       dest,
+                                       offsets,
+                                       getStaticSizes(rewriter, sourceType.getShape()),
+                                       getUnitStrides(rewriter, sourceType.getRank()))
+    .getResult();
+}
+
 } // namespace onnx_mlir

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

@@ -3,6 +3,7 @@
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/BuiltinTypes.h"
 #include "mlir/IR/Value.h"
+#include "mlir/IR/ValueRange.h"
 #include "mlir/Transforms/DialectConversion.h"
 
 #include "llvm/ADT/ArrayRef.h"
@@ -11,6 +12,7 @@
 
 #include <cassert>
 #include <cstddef>
+#include <optional>
 #include <type_traits>
 #include <utility>
 
@@ -109,6 +111,33 @@ inline bool haveSameStaticShape(mlir::Value lhs, mlir::Value rhs) {
       && lhsType.getShape() == rhsType.getShape();
 }
 
+bool hasStaticPositiveShape(mlir::ArrayRef<int64_t> shape);
+
+bool hasStaticPositiveShape(mlir::RankedTensorType type);
+
+int64_t getStaticShapeElementCount(mlir::ArrayRef<int64_t> shape);
+
+int64_t getStaticShapeElementCount(mlir::RankedTensorType type);
+
+llvm::SmallVector<int64_t> permuteShape(mlir::ArrayRef<int64_t> shape, mlir::ArrayRef<int64_t> permutation);
+
+llvm::SmallVector<int64_t> invertPermutation(mlir::ArrayRef<int64_t> permutation);
+
+mlir::FailureOr<llvm::SmallVector<int64_t>> getTransposePermutationChecked(std::optional<mlir::ArrayAttr> permAttr,
+                                                                           int64_t rank);
+
+mlir::Value transposeMaybeInCompute(mlir::Value value,
+                                    mlir::RankedTensorType resultType,
+                                    mlir::ArrayRef<int64_t> permutation,
+                                    mlir::PatternRewriter& rewriter,
+                                    mlir::Location loc);
+
+llvm::SmallVector<mlir::OpFoldResult> getUnitStrides(mlir::PatternRewriter& rewriter, int64_t rank);
+
+llvm::SmallVector<mlir::OpFoldResult> getZeroOffsets(mlir::PatternRewriter& rewriter, int64_t rank);
+
+llvm::SmallVector<mlir::OpFoldResult> getStaticSizes(mlir::PatternRewriter& rewriter, mlir::ArrayRef<int64_t> shape);
+
 /// Slices a statically shaped tensor along one axis into contiguous pieces of
 /// at most `sliceSize` elements.
 llvm::SmallVector<mlir::Value> sliceTensor(const mlir::Value& tensorToSlice,
@@ -148,4 +177,23 @@ mlir::Value materializeContiguousTensorSlice(mlir::Value source,
                                              mlir::ConversionPatternRewriter& rewriter,
                                              mlir::Location loc);
 
+mlir::Value extractStaticSlice(mlir::PatternRewriter& rewriter,
+                               mlir::Location loc,
+                               mlir::Value source,
+                               mlir::RankedTensorType resultType,
+                               llvm::ArrayRef<mlir::OpFoldResult> offsets);
+
+mlir::Value extractAxisSlice(mlir::PatternRewriter& rewriter,
+                             mlir::Location loc,
+                             mlir::Value source,
+                             int64_t axis,
+                             int64_t offset,
+                             int64_t size);
+
+mlir::Value insertStaticSlice(mlir::PatternRewriter& rewriter,
+                              mlir::Location loc,
+                              mlir::Value source,
+                              mlir::Value dest,
+                              llvm::ArrayRef<mlir::OpFoldResult> offsets);
+
 } // namespace onnx_mlir

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

@@ -28,8 +28,6 @@ struct ConvToGemm : OpConversionPattern<ONNXConvOp> {
                                 ConversionPatternRewriter& rewriter) const override;
 };
 
-static int64_t getI64FromArrayAttr(ArrayAttr arr, size_t idx) { return cast<IntegerAttr>(arr[idx]).getInt(); }
-
 static Value expandBiasIfNeeded(Value bias, ConversionPatternRewriter& rewriter, Location loc) {
   auto biasType = cast<RankedTensorType>(bias.getType());
   if (biasType.getRank() != 1)
@@ -615,10 +613,10 @@ LogicalResult ConvToGemm::matchAndRewrite(ONNXConvOp convOp,
     return failure();
   }
 
-  const int64_t strideHeight = stridesAttr ? getI64FromArrayAttr(*stridesAttr, 0) : 1;
+  const int64_t strideHeight = getOptionalI64Attr(stridesAttr, 0, 1);
-  const int64_t strideWidth = stridesAttr ? getI64FromArrayAttr(*stridesAttr, 1) : 1;
+  const int64_t strideWidth = getOptionalI64Attr(stridesAttr, 1, 1);
-  const int64_t dilationHeight = dilationsAttr ? getI64FromArrayAttr(*dilationsAttr, 0) : 1;
+  const int64_t dilationHeight = getOptionalI64Attr(dilationsAttr, 0, 1);
-  const int64_t dilationWidth = dilationsAttr ? getI64FromArrayAttr(*dilationsAttr, 1) : 1;
+  const int64_t dilationWidth = getOptionalI64Attr(dilationsAttr, 1, 1);
 
   int64_t padHeightBegin = 0;
   int64_t padHeightEnd = 0;
@@ -626,10 +624,10 @@ LogicalResult ConvToGemm::matchAndRewrite(ONNXConvOp convOp,
   int64_t padWidthEnd = 0;
 
   if (padsAttr) {
-    padHeightBegin = getI64FromArrayAttr(*padsAttr, 0);
+    padHeightBegin = getI64Attr(*padsAttr, 0);
-    padWidthBegin = getI64FromArrayAttr(*padsAttr, 1);
+    padWidthBegin = getI64Attr(*padsAttr, 1);
-    padHeightEnd = getI64FromArrayAttr(*padsAttr, 2);
+    padHeightEnd = getI64Attr(*padsAttr, 2);
-    padWidthEnd = getI64FromArrayAttr(*padsAttr, 3);
+    padWidthEnd = getI64Attr(*padsAttr, 3);
   }
   else {
     // Compute padding from auto_pad attribute

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

@@ -13,7 +13,7 @@
 #include <limits>
 #include <utility>
 
-#include "src/Accelerators/PIM/Common/IR/ConstantUtils.hpp"
+#include "Common/IR/ConstantUtils.hpp"
 #include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Common/Support/Diagnostics.hpp"
@@ -58,47 +58,16 @@ static Value transposeForSpatial(Value value,
                                  ArrayRef<int64_t> permutation,
                                  ConversionPatternRewriter& rewriter,
                                  Location loc) {
-  if (isCompileTimeComputable(value))
+  return transposeMaybeInCompute(value, resultType, permutation, rewriter, loc);
-    return ONNXTransposeOp::create(rewriter, loc, resultType, value, rewriter.getI64ArrayAttr(permutation));
-
-  auto computeOp = createSpatCompute<1>(rewriter, loc, TypeRange {resultType}, {}, value, [&](Value input) {
-    Value transposed = ONNXTransposeOp::create(rewriter, loc, resultType, input, rewriter.getI64ArrayAttr(permutation));
-    spatial::SpatYieldOp::create(rewriter, loc, transposed);
-  });
-  return computeOp.getResult(0);
-}
-
-static Value createIndexConstant(ConversionPatternRewriter& rewriter, int64_t value) {
-  Operation* anchorOp = rewriter.getInsertionBlock()->getParentOp();
-  return getOrCreateHostIndexConstant(anchorOp, value, rewriter);
-}
-
-static Value
-createAffineApply(ConversionPatternRewriter& rewriter, Location loc, AffineExpr expr, ValueRange operands) {
-  AffineMap map = AffineMap::get(/*dimCount=*/operands.size(), /*symbolCount=*/0, expr);
-  Operation* anchorOp = rewriter.getInsertionBlock()->getParentOp();
-  return createAffineApplyOrFoldedConstant(rewriter, loc, map, operands, anchorOp);
 }
 
 static Value
 multiplyIndexByConstant(Value value, int64_t multiplier, ConversionPatternRewriter& rewriter, Location loc) {
-  if (multiplier == 0)
+  return onnx_mlir::multiplyIndexByConstant(rewriter, value.getDefiningOp(), value, multiplier);
-    return createIndexConstant(rewriter, 0);
-  if (multiplier == 1)
-    return value;
-
-  MLIRContext* context = rewriter.getContext();
-  AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApply(rewriter, loc, d0 * multiplier, ValueRange {value});
 }
 
 static Value modIndexByConstant(Value value, int64_t divisor, ConversionPatternRewriter& rewriter, Location loc) {
-  if (divisor == 1)
+  return onnx_mlir::modIndexByConstant(rewriter, loc, value, divisor);
-    return createIndexConstant(rewriter, 0);
-
-  MLIRContext* context = rewriter.getContext();
-  AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApply(rewriter, loc, d0 % divisor, ValueRange {value});
 }
 
 static Value createGemmBatchRow(Value lane, int64_t numOutRows, ConversionPatternRewriter& rewriter, Location loc) {
@@ -108,11 +77,11 @@ static Value createGemmBatchRow(Value lane, int64_t numOutRows, ConversionPatter
 static Value createGemmBatchKOffset(
   Value lane, int64_t numOutRows, int64_t numKSlices, ConversionPatternRewriter& rewriter, Location loc) {
   if (numKSlices == 1)
-    return createIndexConstant(rewriter, 0);
+    return getOrCreateHostIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
 
   MLIRContext* context = rewriter.getContext();
   AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApply(
+  return createAffineApplyOrConstant(
     rewriter, loc, (d0.floorDiv(numOutRows) % numKSlices) * crossbarSize.getValue(), ValueRange {lane});
 }
 
@@ -123,11 +92,11 @@ static Value createGemmBatchHOffset(Value lane,
                                     ConversionPatternRewriter& rewriter,
                                     Location loc) {
   if (numOutHSlices == 1)
-    return createIndexConstant(rewriter, 0);
+    return getOrCreateHostIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
 
   MLIRContext* context = rewriter.getContext();
   AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApply(
+  return createAffineApplyOrConstant(
     rewriter, loc, d0.floorDiv(numOutRows * numKSlices) * crossbarSize.getValue(), ValueRange {lane});
 }
 
@@ -303,53 +272,37 @@ static spatial::SpatComputeBatch createVmmBatch(Value a,
                                                 ConversionPatternRewriter& rewriter,
                                                 Location loc) {
   const int64_t laneCount = partialPiecesType.getDimSize(0);
-  auto batchOp = spatial::SpatComputeBatch::create(rewriter,
+  auto batchOp = createSpatComputeBatch(
-                                                   loc,
+    rewriter, loc, TypeRange {partialPiecesType}, laneCount, ValueRange {b}, ValueRange {a}, [&](detail::SpatComputeBatchBodyArgs args) {
-                                                   TypeRange {partialPiecesType},
+      Value row = createGemmBatchRow(args.lane, numOutRows, rewriter, loc);
-                                                   rewriter.getI32IntegerAttr(static_cast<int32_t>(laneCount)),
+      Value kOffset = createGemmBatchKOffset(args.lane, numOutRows, numKSlices, rewriter, loc);
-                                                   ValueRange {b},
+      Value hOffset = createGemmBatchHOffset(args.lane, numOutRows, numKSlices, numOutHSlices, rewriter, loc);
-                                                   ValueRange {a});
 
-  SmallVector<Type> blockArgTypes {rewriter.getIndexType(), paddedBType, aType, partialPiecesType};
+      auto aTileType =
-  SmallVector<Location> blockArgLocs(blockArgTypes.size(), loc);
+        RankedTensorType::get({1, static_cast<int64_t>(crossbarSize.getValue())}, aType.getElementType());
-  Block* body =
-    rewriter.createBlock(&batchOp.getBody(), batchOp.getBody().end(), TypeRange(blockArgTypes), blockArgLocs);
-  rewriter.setInsertionPointToEnd(body);
-
-  auto lane = batchOp.getLaneArgument();
-  auto weight = batchOp.getWeightArgument(0);
-  auto input = batchOp.getInputArgument(0);
-  auto output = batchOp.getOutputArgument(0);
-  assert(lane && weight && input && output && "malformed Gemm compute_batch body");
-
-  Value row = createGemmBatchRow(*lane, numOutRows, rewriter, loc);
-  Value kOffset = createGemmBatchKOffset(*lane, numOutRows, numKSlices, rewriter, loc);
-  Value hOffset = createGemmBatchHOffset(*lane, numOutRows, numKSlices, numOutHSlices, rewriter, loc);
-
-  auto aTileType = RankedTensorType::get({1, static_cast<int64_t>(crossbarSize.getValue())}, aType.getElementType());
       auto bTileType = RankedTensorType::get(
         {static_cast<int64_t>(crossbarSize.getValue()), static_cast<int64_t>(crossbarSize.getValue())},
         paddedBType.getElementType());
       auto pieceType =
         RankedTensorType::get({1, static_cast<int64_t>(crossbarSize.getValue())}, partialPiecesType.getElementType());
-  Value aTile = extractATile(*input, row, kOffset, aTileType, rewriter, loc);
+      Value aTile = extractATile(args.inputs.front(), row, kOffset, aTileType, rewriter, loc);
 
       SmallVector<OpFoldResult> bOffsets {kOffset, hOffset};
       SmallVector<OpFoldResult> bSizes {rewriter.getIndexAttr(crossbarSize.getValue()),
                                         rewriter.getIndexAttr(crossbarSize.getValue())};
-  SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+      SmallVector<OpFoldResult> unitStrides = getUnitStrides(rewriter, 2);
       Value bTile =
-    tensor::ExtractSliceOp::create(rewriter, loc, bTileType, *weight, bOffsets, bSizes, unitStrides).getResult();
+        tensor::ExtractSliceOp::create(rewriter, loc, bTileType, args.weights.front(), bOffsets, bSizes, unitStrides)
+          .getResult();
       Value piece = spatial::SpatVMMOp::create(rewriter, loc, pieceType, bTile, aTile).getResult();
 
-  auto inParallelOp = spatial::SpatInParallelOp::create(rewriter, loc);
+      SmallVector<OpFoldResult> pieceOffsets {args.lane, rewriter.getIndexAttr(0)};
-  rewriter.setInsertionPointToStart(&inParallelOp.getRegion().front());
-  SmallVector<OpFoldResult> pieceOffsets {*lane, rewriter.getIndexAttr(0)};
       SmallVector<OpFoldResult> pieceSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(crossbarSize.getValue())};
-  tensor::ParallelInsertSliceOp::create(rewriter, loc, piece, *output, pieceOffsets, pieceSizes, unitStrides);
+      createParallelInsertSliceIntoBatchOutput(
-
+        rewriter, loc, piece, args.outputs.front(), pieceOffsets, pieceSizes, unitStrides);
-  rewriter.setInsertionPointAfter(batchOp);
+    });
-  return batchOp;
+  assert(succeeded(batchOp) && "expected Gemm VMM batch construction to succeed");
+  return *batchOp;
 }
 
 static Value createDynamicGemmBatchRow(
@@ -359,7 +312,7 @@ static Value createDynamicGemmBatchRow(
 
   MLIRContext* context = rewriter.getContext();
   AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApply(rewriter, loc, d0.floorDiv(numOutCols), ValueRange {lane});
+  return createAffineApplyOrConstant(rewriter, loc, d0.floorDiv(numOutCols), ValueRange {lane});
 }
 
 static Value createDynamicGemmBatchColumn(
@@ -479,45 +432,27 @@ static spatial::SpatComputeBatch createVvdmulBatch(Value a,
   const int64_t numOutCols = outType.getDimSize(1);
   const int64_t reductionSize = aType.getDimSize(1);
   const int64_t laneCount = numOutRows * numOutCols;
-  auto batchOp = spatial::SpatComputeBatch::create(rewriter,
+  auto batchOp = createSpatComputeBatch(
-                                                   loc,
+    rewriter, loc, TypeRange {scalarPiecesType}, laneCount, ValueRange {}, ValueRange {a, b}, [&](detail::SpatComputeBatchBodyArgs args) {
-                                                   TypeRange {scalarPiecesType},
+      Value row = createDynamicGemmBatchRow(args.lane, numOutCols, rewriter, loc);
-                                                   rewriter.getI32IntegerAttr(static_cast<int32_t>(laneCount)),
+      Value column = createDynamicGemmBatchColumn(args.lane, numOutCols, rewriter, loc);
-                                                   ValueRange {},
-                                                   ValueRange {a, b});
-
-  SmallVector<Type> blockArgTypes {rewriter.getIndexType(), aType, bType, scalarPiecesType};
-  SmallVector<Location> blockArgLocs(blockArgTypes.size(), loc);
-  Block* body =
-    rewriter.createBlock(&batchOp.getBody(), batchOp.getBody().end(), TypeRange(blockArgTypes), blockArgLocs);
-  rewriter.setInsertionPointToEnd(body);
-
-  auto lane = batchOp.getLaneArgument();
-  auto inputA = batchOp.getInputArgument(0);
-  auto inputB = batchOp.getInputArgument(1);
-  auto output = batchOp.getOutputArgument(0);
-  assert(lane && inputA && inputB && output && "malformed dynamic Gemm compute_batch body");
-
-  Value row = createDynamicGemmBatchRow(*lane, numOutCols, rewriter, loc);
-  Value column = createDynamicGemmBatchColumn(*lane, numOutCols, rewriter, loc);
 
       auto vectorType = RankedTensorType::get({1, reductionSize}, aType.getElementType());
       auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
-  Value aVector = extractDynamicGemmRowVector(*inputA, row, vectorType, rewriter, loc);
+      Value aVector = extractDynamicGemmRowVector(args.inputs[0], row, vectorType, rewriter, loc);
       Value bVector = bAlreadyTransposed
-                    ? extractTransposedBRow(*inputB, column, vectorType, rewriter, loc)
+                        ? extractTransposedBRow(args.inputs[1], column, vectorType, rewriter, loc)
-                    : extractDynamicGemmBColumn(*inputB, column, vectorType, rewriter, loc);
+                        : extractDynamicGemmBColumn(args.inputs[1], column, vectorType, rewriter, loc);
       Value scalar = spatial::SpatVVDMulOp::create(rewriter, loc, scalarType, aVector, bVector).getResult();
 
-  auto inParallelOp = spatial::SpatInParallelOp::create(rewriter, loc);
+      SmallVector<OpFoldResult> outputOffsets {args.lane, rewriter.getIndexAttr(0)};
-  rewriter.setInsertionPointToStart(&inParallelOp.getRegion().front());
-  SmallVector<OpFoldResult> outputOffsets {*lane, rewriter.getIndexAttr(0)};
       SmallVector<OpFoldResult> scalarSizes {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-  SmallVector<OpFoldResult> unitStrides {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+      SmallVector<OpFoldResult> unitStrides = getUnitStrides(rewriter, 2);
-  tensor::ParallelInsertSliceOp::create(rewriter, loc, scalar, *output, outputOffsets, scalarSizes, unitStrides);
+      createParallelInsertSliceIntoBatchOutput(
-
+        rewriter, loc, scalar, args.outputs.front(), outputOffsets, scalarSizes, unitStrides);
-  rewriter.setInsertionPointAfter(batchOp);
+    });
-  return batchOp;
+  assert(succeeded(batchOp) && "expected Gemm VVDMul batch construction to succeed");
+  return *batchOp;
 }
 
 static spatial::SpatCompute createDynamicGemmOutputCompute(Value scalarPieces,
@@ -540,9 +475,9 @@ static spatial::SpatCompute createDynamicGemmOutputCompute(Value scalarPieces,
     Value biasArg = bias ? blockArgs[1] : Value();
     auto scalarType = RankedTensorType::get({1, 1}, outType.getElementType());
     Value outputInit = tensor::EmptyOp::create(rewriter, loc, outType.getShape(), outType.getElementType()).getResult();
-    Value c0 = createIndexConstant(rewriter, 0);
+    Value c0 = getOrCreateHostIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
-    Value c1 = createIndexConstant(rewriter, 1);
+    Value c1 = getOrCreateHostIndexConstant(rewriter,  rewriter.getInsertionBlock()->getParentOp(), 1);
-    Value cLaneCount = createIndexConstant(rewriter, laneCount);
+    Value cLaneCount = getOrCreateHostIndexConstant(rewriter,  rewriter.getInsertionBlock()->getParentOp(), laneCount);
     auto loop = scf::ForOp::create(rewriter, loc, c0, cLaneCount, c1, ValueRange {outputInit});
     rewriter.setInsertionPointToStart(loop.getBody());
 
@@ -587,7 +522,8 @@ static Value createPartialGroupOffset(Value hSlice,
                                       Location loc) {
   MLIRContext* context = rewriter.getContext();
   AffineExpr d0 = getAffineDimExpr(0, context);
-  return createAffineApply(rewriter, loc, d0 * (numKSlices * numOutRows) + kSlice * numOutRows, ValueRange {hSlice});
+  return createAffineApplyOrConstant(
+    rewriter, loc, d0 * (numKSlices * numOutRows) + kSlice * numOutRows, ValueRange {hSlice});
 }
 
 static Value extractReductionPiece(Value partialPiecesArg,
@@ -684,13 +620,13 @@ static spatial::SpatCompute createReductionCompute(Value partialPieces,
 
     Value paddedOutput = outputInit;
     if (numOutHSlices == 1) {
-      Value hSlice = createIndexConstant(rewriter, 0);
+      Value hSlice = getOrCreateHostIndexConstant(rewriter,  rewriter.getInsertionBlock()->getParentOp(), 0);
       paddedOutput = buildOutputSlice(outputInit, hSlice);
     }
     else {
-      Value c0 = createIndexConstant(rewriter, 0);
+      Value c0 = getOrCreateHostIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 0);
-      Value c1 = createIndexConstant(rewriter, 1);
+      Value c1 = getOrCreateHostIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), 1);
-      Value cOutHSlices = createIndexConstant(rewriter, numOutHSlices);
+      Value cOutHSlices = getOrCreateHostIndexConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), numOutHSlices);
       auto hLoop = scf::ForOp::create(rewriter, loc, c0, cOutHSlices, c1, ValueRange {outputInit});
       rewriter.setInsertionPointToStart(hLoop.getBody());
 

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

@@ -19,14 +19,6 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
-static bool haveStaticPositiveShape(ArrayRef<int64_t> shape) {
-  return llvm::all_of(shape, [](int64_t dim) { return dim > 0; });
-}
-
-static int64_t getStaticShapeElementCount(ArrayRef<int64_t> shape) {
-  return std::accumulate(shape.begin(), shape.end(), int64_t {1}, std::multiplies<int64_t> {});
-}
-
 static FailureOr<SmallVector<int64_t>> inferSupportedBatchShape(ArrayRef<int64_t> lhsBatchShape,
                                                                 ArrayRef<int64_t> rhsBatchShape) {
   if (lhsBatchShape.empty())
@@ -54,15 +46,7 @@ collapseBatchDims(Value value, int64_t batchSize, int64_t rows, int64_t cols, Pa
   auto buildCollapsed = [&](Value input) -> Value {
     return tensor::CollapseShapeOp::create(rewriter, loc, collapsedType, input, reassociation);
   };
-
+  return materializeOrComputeUnary(value, collapsedType, rewriter, loc, buildCollapsed);
-  if (isCompileTimeComputable(value))
-    return buildCollapsed(value);
-
-  auto collapseCompute =
-    createSpatCompute<1>(rewriter, loc, TypeRange {collapsedType}, {}, ValueRange {value}, [&](Value input) {
-      spatial::SpatYieldOp::create(rewriter, loc, buildCollapsed(input));
-    });
-  return collapseCompute.getResult(0);
 }
 
 static Value
@@ -76,12 +60,10 @@ expandBatchDims(Value value, RankedTensorType outputType, size_t batchRank, Patt
   for (size_t dim = 0; dim < batchRank; ++dim)
     reassociation.front().push_back(static_cast<int64_t>(dim));
 
-  auto expandCompute =
+  auto buildExpanded = [&](Value input) -> Value {
-    createSpatCompute<1>(rewriter, loc, TypeRange {outputType}, {}, ValueRange {value}, [&](Value input) {
+    return tensor::ExpandShapeOp::create(rewriter, loc, outputType, input, reassociation).getResult();
-      Value expanded = tensor::ExpandShapeOp::create(rewriter, loc, outputType, input, reassociation);
+  };
-      spatial::SpatYieldOp::create(rewriter, loc, expanded);
+  return materializeOrComputeUnary(value, outputType, rewriter, loc, buildExpanded);
-    });
-  return expandCompute.getResult(0);
 }
 
 static Value extractBatchMatrix(Value value,
@@ -100,7 +82,7 @@ static Value extractBatchMatrix(Value value,
     rewriter.getIndexAttr(batchSize == 1 ? 0 : batchIndex), rewriter.getIndexAttr(0), rewriter.getIndexAttr(0)};
   SmallVector<OpFoldResult> sizes = {
     rewriter.getIndexAttr(1), rewriter.getIndexAttr(rows), rewriter.getIndexAttr(cols)};
-  SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
+  SmallVector<OpFoldResult> strides = getUnitStrides(rewriter, 3);
   auto matrixType = RankedTensorType::get({rows, cols}, type.getElementType());
   auto buildMatrix = [&](Value input) -> Value {
     Value slice = tensor::ExtractSliceOp::create(rewriter, loc, sliceType, input, offsets, sizes, strides);
@@ -114,14 +96,7 @@ static Value extractBatchMatrix(Value value,
     });
   };
 
-  if (isCompileTimeComputable(value))
+  return materializeOrComputeUnary(value, matrixType, rewriter, loc, buildMatrix);
-    return buildMatrix(value);
-
-  auto batchMatrixCompute =
-    createSpatCompute<1>(rewriter, loc, TypeRange {matrixType}, {}, ValueRange {value}, [&](Value input) {
-      spatial::SpatYieldOp::create(rewriter, loc, buildMatrix(input));
-    });
-  return batchMatrixCompute.getResult(0);
 }
 
 static Value transposeLastTwoDims(Value value, PatternRewriter& rewriter, Location loc) {
@@ -138,18 +113,7 @@ static Value transposeLastTwoDims(Value value, PatternRewriter& rewriter, Locati
     perm = {0, 2, 1};
   }
 
-  auto buildTranspose = [&](Value input) -> Value {
+  return transposeMaybeInCompute(value, transposedType, perm, rewriter, loc);
-    return ONNXTransposeOp::create(rewriter, loc, transposedType, input, rewriter.getI64ArrayAttr(perm));
-  };
-
-  if (isCompileTimeComputable(value))
-    return buildTranspose(value);
-
-  auto transposeCompute =
-    createSpatCompute<1>(rewriter, loc, TypeRange {transposedType}, {}, ValueRange {value}, [&](Value input) {
-      spatial::SpatYieldOp::create(rewriter, loc, buildTranspose(input));
-    });
-  return transposeCompute.getResult(0);
 }
 
 static Value transposeLastTwoDimsInCompute(Value value, PatternRewriter& rewriter, Location loc) {
@@ -166,7 +130,8 @@ static Value transposeLastTwoDimsInCompute(Value value, PatternRewriter& rewrite
     perm = {0, 2, 1};
   }
 
-  auto transposeCompute = createSpatCompute<1>(rewriter, loc, transposedType, {}, ValueRange {value}, [&](Value input) {
+  auto transposeCompute =
+    createSpatCompute<1>(rewriter, loc, transposedType, {}, ValueRange {value}, [&](Value input) {
       Value transposed = ONNXTransposeOp::create(rewriter, loc, transposedType, input, rewriter.getI64ArrayAttr(perm));
       spatial::SpatYieldOp::create(rewriter, loc, transposed);
     });
@@ -203,8 +168,7 @@ struct MatMulToGemm : OpRewritePattern<ONNXMatMulOp> {
       return failure();
     if (lhsType.getRank() < 2 || rhsType.getRank() < 2 || outType.getRank() < 2)
       return failure();
-    if (!haveStaticPositiveShape(lhsType.getShape()) || !haveStaticPositiveShape(rhsType.getShape())
+    if (!hasStaticPositiveShape(lhsType) || !hasStaticPositiveShape(rhsType) || !hasStaticPositiveShape(outType))
-        || !haveStaticPositiveShape(outType.getShape()))
       return failure();
 
     SmallVector<int64_t> lhsBatchShape(lhsType.getShape().begin(), lhsType.getShape().end() - 2);

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

@@ -1,9 +1,11 @@
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Transforms/DialectConversion.h"
 
 #include "llvm/ADT/SmallVector.h"
 
 #include <algorithm>
+#include <numeric>
 
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/CompileTime.hpp"
@@ -16,26 +18,6 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
-static SmallVector<int64_t> normalizeAxes(ArrayAttr axesAttr, int64_t rank) {
-  SmallVector<int64_t> normalizedAxes;
-  if (!axesAttr) {
-    normalizedAxes.reserve(rank);
-    for (int64_t axis = 0; axis < rank; axis++)
-      normalizedAxes.push_back(axis);
-    return normalizedAxes;
-  }
-
-  normalizedAxes.reserve(axesAttr.size());
-  for (Attribute attr : axesAttr) {
-    int64_t axis = cast<IntegerAttr>(attr).getInt();
-    normalizedAxes.push_back(axis >= 0 ? axis : rank + axis);
-  }
-
-  llvm::sort(normalizedAxes);
-  normalizedAxes.erase(std::unique(normalizedAxes.begin(), normalizedAxes.end()), normalizedAxes.end());
-  return normalizedAxes;
-}
-
 static SmallVector<bool> buildReducedAxesMask(ArrayRef<int64_t> axes, int64_t rank) {
   SmallVector<bool> reducedAxes(rank, false);
   for (int64_t axis : axes) {
@@ -50,6 +32,181 @@ static RankedTensorType getAllOnesType(RankedTensorType inputType, Type elementT
   return RankedTensorType::get(SmallVector<int64_t>(inputType.getRank(), 1), elementType);
 }
 
+static RankedTensorType getKeepdimsType(RankedTensorType inputType, Type elementType, ArrayRef<bool> reducedAxes) {
+  SmallVector<int64_t> shape;
+  shape.reserve(inputType.getRank());
+  for (auto [dim, isReduced] : llvm::zip_equal(inputType.getShape(), reducedAxes))
+    shape.push_back(isReduced ? 1 : dim);
+  return RankedTensorType::get(shape, elementType, inputType.getEncoding());
+}
+
+static RankedTensorType getCompactKeptType(RankedTensorType inputType, Type elementType, ArrayRef<bool> reducedAxes) {
+  SmallVector<int64_t> shape;
+  for (auto [dim, isReduced] : llvm::zip_equal(inputType.getShape(), reducedAxes))
+    if (!isReduced)
+      shape.push_back(dim);
+  return RankedTensorType::get(shape, elementType, inputType.getEncoding());
+}
+
+static RankedTensorType getReducedSliceType(RankedTensorType inputType, ArrayRef<bool> reducedAxes) {
+  SmallVector<int64_t> shape;
+  shape.reserve(inputType.getRank());
+  for (auto [dim, isReduced] : llvm::zip_equal(inputType.getShape(), reducedAxes))
+    shape.push_back(isReduced ? dim : 1);
+  return RankedTensorType::get(shape, inputType.getElementType(), inputType.getEncoding());
+}
+
+static RankedTensorType getLanePackedKeepdimsType(int64_t laneCount, RankedTensorType leafType) {
+  SmallVector<int64_t> shape(leafType.getShape().begin(), leafType.getShape().end());
+  shape.front() = laneCount;
+  return RankedTensorType::get(shape, leafType.getElementType(), leafType.getEncoding());
+}
+
+static SmallVector<int64_t> getKeptAxes(ArrayRef<bool> reducedAxes) {
+  SmallVector<int64_t> keptAxes;
+  for (auto [axis, isReduced] : llvm::enumerate(reducedAxes))
+    if (!isReduced)
+      keptAxes.push_back(static_cast<int64_t>(axis));
+  return keptAxes;
+}
+
+static Value computeLaneIndex(Value lane,
+                              int64_t stride,
+                              int64_t dimSize,
+                              ConversionPatternRewriter& rewriter,
+                              Location loc) {
+  if (dimSize == 1)
+    return arith::ConstantIndexOp::create(rewriter, loc, 0);
+
+  MLIRContext* context = rewriter.getContext();
+  AffineExpr d0 = getAffineDimExpr(0, context);
+  AffineExpr expr = d0;
+  if (stride != 1)
+    expr = expr.floorDiv(stride);
+  if (dimSize != 1)
+    expr = expr % dimSize;
+  return createAffineApplyOrConstant(rewriter, loc, expr, ValueRange {lane});
+}
+
+static FailureOr<Value> buildReduceMeanKeepdimsBatch(Value input,
+                                                     ArrayRef<bool> reducedAxes,
+                                                     RankedTensorType batchType,
+                                                     RankedTensorType leafType,
+                                                     ConversionPatternRewriter& rewriter,
+                                                     Location loc) {
+  auto inputType = cast<RankedTensorType>(input.getType());
+  auto sliceType = getReducedSliceType(inputType, reducedAxes);
+  SmallVector<int64_t> keptAxes = getKeptAxes(reducedAxes);
+
+  int64_t laneCount = 1;
+  SmallVector<int64_t> keptAxisStrides(keptAxes.size(), 1);
+  for (int64_t index = static_cast<int64_t>(keptAxes.size()) - 1; index >= 0; --index) {
+    keptAxisStrides[index] = laneCount;
+    int64_t dimSize = inputType.getDimSize(keptAxes[index]);
+    if (dimSize <= 0)
+      return failure();
+    if (laneCount > std::numeric_limits<int32_t>::max() / dimSize)
+      return failure();
+    laneCount *= dimSize;
+  }
+
+  SmallVector<OpFoldResult> sliceOffsets;
+  SmallVector<OpFoldResult> sliceSizes;
+  SmallVector<OpFoldResult> insertOffsets;
+  SmallVector<OpFoldResult> insertSizes(inputType.getRank(), rewriter.getIndexAttr(1));
+  SmallVector<OpFoldResult> unitStrides = getUnitStrides(rewriter, inputType.getRank());
+  sliceOffsets.reserve(inputType.getRank());
+  sliceSizes.reserve(inputType.getRank());
+  insertOffsets.reserve(inputType.getRank());
+
+  auto batchOp = createSpatComputeBatch(
+    rewriter, loc, TypeRange {batchType}, laneCount, {}, ValueRange {input}, [&](detail::SpatComputeBatchBodyArgs args) {
+      size_t keptAxisIndex = 0;
+      sliceOffsets.clear();
+      sliceSizes.clear();
+      insertOffsets.clear();
+      for (auto [axis, isReduced] : llvm::enumerate(reducedAxes)) {
+        if (isReduced) {
+          sliceOffsets.push_back(rewriter.getIndexAttr(0));
+          sliceSizes.push_back(rewriter.getIndexAttr(inputType.getDimSize(axis)));
+          continue;
+        }
+
+        Value axisIndex =
+          computeLaneIndex(args.lane, keptAxisStrides[keptAxisIndex], inputType.getDimSize(axis), rewriter, loc);
+        ++keptAxisIndex;
+        sliceOffsets.push_back(axisIndex);
+        sliceSizes.push_back(rewriter.getIndexAttr(1));
+      }
+
+      insertOffsets.push_back(args.lane);
+      insertOffsets.append(inputType.getRank() - 1, rewriter.getIndexAttr(0));
+
+      Value slice =
+        tensor::ExtractSliceOp::create(rewriter, loc, sliceType, args.inputs.front(), sliceOffsets, sliceSizes, unitStrides);
+      Value reduced = spatial::SpatVAvgOp::create(rewriter, loc, leafType, slice).getResult();
+      createParallelInsertSliceIntoBatchOutput(
+        rewriter, loc, reduced, args.outputs.front(), insertOffsets, insertSizes, unitStrides);
+    });
+  if (failed(batchOp))
+    return failure();
+  return (*batchOp).getResult(0);
+}
+
+static Value buildKeepdimsFromLanePackedBatch(Value batchValue,
+                                              RankedTensorType keepdimsType,
+                                              RankedTensorType compactKeptType,
+                                              ArrayRef<bool> reducedAxes,
+                                              ConversionPatternRewriter& rewriter,
+                                              Location loc) {
+  auto batchType = cast<RankedTensorType>(batchValue.getType());
+  if (batchType == keepdimsType)
+    return batchValue;
+
+  SmallVector<ReassociationIndices> collapseToFlat {{}};
+  for (int64_t axis = 0; axis < batchType.getRank(); ++axis)
+    collapseToFlat.front().push_back(axis);
+
+  SmallVector<ReassociationIndices> expandFlatToCompact(1);
+  for (int64_t axis = 0; axis < compactKeptType.getRank(); ++axis)
+    expandFlatToCompact.front().push_back(axis);
+
+  SmallVector<ReassociationIndices> expandCompactToKeepdims;
+  ReassociationIndices pendingLeadingReducedAxes;
+  for (auto [axis, isReduced] : llvm::enumerate(reducedAxes)) {
+    if (isReduced) {
+      if (expandCompactToKeepdims.empty())
+        pendingLeadingReducedAxes.push_back(axis);
+      else
+        expandCompactToKeepdims.back().push_back(axis);
+      continue;
+    }
+
+    expandCompactToKeepdims.emplace_back();
+    auto& group = expandCompactToKeepdims.back();
+    group.append(pendingLeadingReducedAxes.begin(), pendingLeadingReducedAxes.end());
+    pendingLeadingReducedAxes.clear();
+    group.push_back(axis);
+  }
+  if (!pendingLeadingReducedAxes.empty())
+    expandCompactToKeepdims.back().append(pendingLeadingReducedAxes.begin(), pendingLeadingReducedAxes.end());
+
+  auto reshapeCompute =
+    createSpatCompute<1>(rewriter, loc, TypeRange {keepdimsType}, {}, ValueRange {batchValue}, [&](Value input) {
+      auto flatType = RankedTensorType::get({batchType.getDimSize(0)}, batchType.getElementType(), batchType.getEncoding());
+      Value flat = tensor::CollapseShapeOp::create(rewriter, loc, flatType, input, collapseToFlat);
+      Value compact = flat;
+      if (compactKeptType != flatType)
+        compact = tensor::ExpandShapeOp::create(rewriter, loc, compactKeptType, flat, expandFlatToCompact);
+      Value keepdims = compact;
+      if (keepdimsType != compactKeptType)
+        keepdims =
+          tensor::ExpandShapeOp::create(rewriter, loc, keepdimsType, compact, expandCompactToKeepdims);
+      spatial::SpatYieldOp::create(rewriter, loc, keepdims);
+    });
+  return reshapeCompute.getResult(0);
+}
+
 static SmallVector<ReassociationIndices> buildCollapseReassociation(ArrayRef<bool> reducedAxes) {
   SmallVector<ReassociationIndices> reassociation;
   ReassociationIndices currentGroup;
@@ -72,56 +229,6 @@ static SmallVector<ReassociationIndices> buildCollapseReassociation(ArrayRef<boo
   return reassociation;
 }
 
-static Value
-createAverageCompute(Value input, RankedTensorType resultType, ConversionPatternRewriter& rewriter, Location loc) {
-  constexpr size_t numInputs = 1;
-  auto computeOp = createSpatCompute<numInputs>(rewriter, loc, resultType, {}, ValueRange {input}, [&](Value x) {
-    auto avgOp = spatial::SpatVAvgOp::create(rewriter, loc, resultType, x);
-    spatial::SpatYieldOp::create(rewriter, loc, avgOp.getResult());
-  });
-  return computeOp.getResult(0);
-}
-
-static Value concatValues(ValueRange inputs, int64_t axis, ConversionPatternRewriter& rewriter, Location loc) {
-  auto firstType = cast<RankedTensorType>(inputs.front().getType());
-  SmallVector<int64_t> outputShape(firstType.getShape().begin(), firstType.getShape().end());
-  int64_t concatDimSize = 0;
-  for (Value input : inputs)
-    concatDimSize += cast<RankedTensorType>(input.getType()).getDimSize(axis);
-  outputShape[axis] = concatDimSize;
-  auto resultType = RankedTensorType::get(outputShape, firstType.getElementType(), firstType.getEncoding());
-
-  if (llvm::all_of(inputs, isCompileTimeComputable))
-    return createSpatConcat(rewriter, loc, axis, inputs);
-
-  auto concatCompute = createSpatCompute(rewriter, loc, TypeRange {resultType}, {}, inputs, [&](ValueRange args) {
-    spatial::SpatYieldOp::create(rewriter, loc, createSpatConcat(rewriter, loc, axis, args));
-  });
-  return concatCompute.getResult(0);
-}
-
-static Value buildReduceMeanKeepdims(Value input,
-                                     ArrayRef<bool> reducedAxes,
-                                     int64_t axis,
-                                     RankedTensorType leafType,
-                                     ConversionPatternRewriter& rewriter,
-                                     Location loc) {
-  int64_t rank = cast<RankedTensorType>(input.getType()).getRank();
-  if (axis == rank)
-    return createAverageCompute(input, leafType, rewriter, loc);
-
-  if (reducedAxes[axis])
-    return buildReduceMeanKeepdims(input, reducedAxes, axis + 1, leafType, rewriter, loc);
-
-  SmallVector<Value> slices = sliceTensor(input, axis, /*sliceSize=*/1, rewriter, loc);
-  SmallVector<Value> reducedSlices;
-  reducedSlices.reserve(slices.size());
-  for (Value slice : slices)
-    reducedSlices.push_back(buildReduceMeanKeepdims(slice, reducedAxes, axis + 1, leafType, rewriter, loc));
-
-  return concatValues(reducedSlices, axis, rewriter, loc);
-}
-
 static Value squeezeReducedAxes(Value keepdimsValue,
                                 RankedTensorType resultType,
                                 ArrayRef<bool> reducedAxes,
@@ -156,16 +263,33 @@ struct ReduceMeanToSpatialCompute : OpConversionPattern<ONNXReduceMeanV13Op> {
     auto resultType = dyn_cast<RankedTensorType>(reduceMeanOp.getReduced().getType());
     if (!inputType || !resultType || !inputType.hasStaticShape() || !resultType.hasStaticShape())
       return failure();
+    if (inputType.getRank() == 0) {
+      rewriter.replaceOp(reduceMeanOp, adaptor.getData());
+      return success();
+    }
 
-    SmallVector<int64_t> axes = normalizeAxes(reduceMeanOp.getAxesAttr(), inputType.getRank());
+    auto axes = normalizeAxesChecked(reduceMeanOp.getAxesAttr(), inputType.getRank());
-    SmallVector<bool> reducedAxes = buildReducedAxesMask(axes, inputType.getRank());
+    if (failed(axes))
+      return failure();
+    SmallVector<bool> reducedAxes = buildReducedAxesMask(*axes, inputType.getRank());
     if (reducedAxes.empty() && inputType.getRank() != 0)
       return failure();
 
     Location loc = reduceMeanOp.getLoc();
     RankedTensorType leafType = getAllOnesType(inputType, resultType.getElementType());
+    RankedTensorType compactKeptType = getCompactKeptType(inputType, resultType.getElementType(), reducedAxes);
+    RankedTensorType keepdimsType = getKeepdimsType(inputType, resultType.getElementType(), reducedAxes);
+    int64_t laneCount = 1;
+    for (int64_t dim : compactKeptType.getShape())
+      laneCount *= dim;
+    RankedTensorType batchType = getLanePackedKeepdimsType(laneCount, leafType);
+
+    auto lanePackedKeepdims =
+      buildReduceMeanKeepdimsBatch(adaptor.getData(), reducedAxes, batchType, leafType, rewriter, loc);
+    if (failed(lanePackedKeepdims))
+      return failure();
     Value reducedKeepdims =
-      buildReduceMeanKeepdims(adaptor.getData(), reducedAxes, /*axis=*/0, leafType, rewriter, loc);
+      buildKeepdimsFromLanePackedBatch(*lanePackedKeepdims, keepdimsType, compactKeptType, reducedAxes, rewriter, loc);
 
     if (reduceMeanOp.getKeepdims() != 0) {
       rewriter.replaceOp(reduceMeanOp, reducedKeepdims);

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

@@ -23,28 +23,10 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
-template <typename ArrayAttrT>
-static int64_t getI64(ArrayAttrT arrayAttr, size_t index) {
-  return cast<IntegerAttr>(arrayAttr[index]).getInt();
-}
-
-template <typename ArrayAttrT>
-static int64_t getOptionalI64(std::optional<ArrayAttrT> arrayAttr, size_t index, int64_t defaultValue) {
-  return arrayAttr ? getI64(*arrayAttr, index) : defaultValue;
-}
-
 static Value materializeContiguousTile(ConversionPatternRewriter& rewriter, Location loc, Value tile) {
   auto tileType = cast<RankedTensorType>(tile.getType());
   Value empty = tensor::EmptyOp::create(rewriter, loc, tileType.getShape(), tileType.getElementType());
-
+  return insertStaticSlice(rewriter, loc, tile, empty, getZeroOffsets(rewriter, tileType.getRank()));
-  SmallVector<OpFoldResult> offsets(tileType.getRank(), rewriter.getIndexAttr(0));
-  SmallVector<OpFoldResult> sizes;
-  sizes.reserve(tileType.getRank());
-  for (int64_t dimSize : tileType.getShape())
-    sizes.push_back(rewriter.getIndexAttr(dimSize));
-  SmallVector<OpFoldResult> strides(tileType.getRank(), rewriter.getIndexAttr(1));
-
-  return tensor::InsertSliceOp::create(rewriter, loc, tile, empty, offsets, sizes, strides);
 }
 
 static Value
@@ -197,12 +179,12 @@ struct PoolToSpatialComputeBase : public OpConversionPattern<PoolOp> {
     const int64_t inputWidth = xType.getDimSize(3);
     const int64_t outputHeight = outType.getDimSize(2);
     const int64_t outputWidth = outType.getDimSize(3);
-    const int64_t kernelHeight = getI64(kernelAttr, 0);
+    const int64_t kernelHeight = getI64Attr(kernelAttr, 0);
-    const int64_t kernelWidth = getI64(kernelAttr, 1);
+    const int64_t kernelWidth = getI64Attr(kernelAttr, 1);
-    const int64_t strideHeight = getOptionalI64(poolOp.getStrides(), 0, 1);
+    const int64_t strideHeight = getOptionalI64Attr(poolOp.getStrides(), 0, 1);
-    const int64_t strideWidth = getOptionalI64(poolOp.getStrides(), 1, 1);
+    const int64_t strideWidth = getOptionalI64Attr(poolOp.getStrides(), 1, 1);
-    const int64_t dilationHeight = getOptionalI64(poolOp.getDilations(), 0, 1);
+    const int64_t dilationHeight = getOptionalI64Attr(poolOp.getDilations(), 0, 1);
-    const int64_t dilationWidth = getOptionalI64(poolOp.getDilations(), 1, 1);
+    const int64_t dilationWidth = getOptionalI64Attr(poolOp.getDilations(), 1, 1);
 
     int64_t padTop = 0;
     int64_t padLeft = 0;
@@ -212,10 +194,10 @@ struct PoolToSpatialComputeBase : public OpConversionPattern<PoolOp> {
     if (auto padsAttr = poolOp.getPads()) {
       if (padsAttr->size() != 4)
         return rewriter.notifyMatchFailure(poolOp, "pads must have four elements.");
-      padTop = getI64(*padsAttr, 0);
+      padTop = getI64Attr(*padsAttr, 0);
-      padLeft = getI64(*padsAttr, 1);
+      padLeft = getI64Attr(*padsAttr, 1);
-      padBottom = getI64(*padsAttr, 2);
+      padBottom = getI64Attr(*padsAttr, 2);
-      padRight = getI64(*padsAttr, 3);
+      padRight = getI64Attr(*padsAttr, 3);
     }
     else {
       StringRef autoPad = poolOp.getAutoPad();

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

@@ -13,16 +13,6 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
-static int64_t normalizeAxis(int64_t axis, int64_t rank) { return axis >= 0 ? axis : rank + axis; }
-
-static SmallVector<int64_t> permuteShape(ArrayRef<int64_t> shape, ArrayRef<int64_t> permutation) {
-  SmallVector<int64_t> permutedShape;
-  permutedShape.reserve(permutation.size());
-  for (int64_t axis : permutation)
-    permutedShape.push_back(shape[axis]);
-  return permutedShape;
-}
-
 static Value buildLoopSoftmaxSlice(Value input,
                                    Value accumulator,
                                    RankedTensorType inputType,
@@ -36,7 +26,7 @@ static Value buildLoopSoftmaxSlice(Value input,
 
   SmallVector<OpFoldResult> offsets;
   SmallVector<OpFoldResult> sizes;
-  SmallVector<OpFoldResult> strides(rank, rewriter.getIndexAttr(1));
+  SmallVector<OpFoldResult> strides = getUnitStrides(rewriter, rank);
   offsets.reserve(rank);
   sizes.reserve(rank);
 
@@ -110,44 +100,31 @@ struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
     if (!inputType || !inputType.hasStaticShape())
       return failure();
 
-    int64_t axis = normalizeAxis(softmaxOp.getAxis(), inputType.getRank());
+    auto axis = normalizeAxisChecked(softmaxOp.getAxis(), inputType.getRank());
-    if (axis < 0 || axis >= inputType.getRank())
+    if (failed(axis))
       return failure();
 
     Value input = adaptor.getInput();
     Value result;
-    if (axis == inputType.getRank() - 1) {
+    if (*axis == inputType.getRank() - 1) {
       result = createLoopSoftmaxCompute(input, rewriter, softmaxOp.getLoc());
     }
     else {
       SmallVector<int64_t> permutation;
       permutation.reserve(inputType.getRank());
       for (int64_t dim = 0; dim < inputType.getRank(); ++dim)
-        if (dim != axis)
+        if (dim != *axis)
           permutation.push_back(dim);
-      permutation.push_back(axis);
+      permutation.push_back(*axis);
-
+      SmallVector<int64_t> inversePermutation = invertPermutation(permutation);
-      SmallVector<int64_t> inversePermutation(inputType.getRank());
-      for (auto [newIndex, oldIndex] : llvm::enumerate(permutation))
-        inversePermutation[oldIndex] = static_cast<int64_t>(newIndex);
 
       auto transposedType = RankedTensorType::get(
         permuteShape(inputType.getShape(), permutation), inputType.getElementType(), inputType.getEncoding());
-      auto preTransposeCompute =
+      Value transposedInput =
-        createSpatCompute<1>(rewriter, softmaxOp.getLoc(), TypeRange {transposedType}, {}, input, [&](Value x) {
+        transposeMaybeInCompute(input, transposedType, permutation, rewriter, softmaxOp.getLoc());
-          Value transposed = ONNXTransposeOp::create(
-            rewriter, softmaxOp.getLoc(), transposedType, x, rewriter.getI64ArrayAttr(permutation));
-          spatial::SpatYieldOp::create(rewriter, softmaxOp.getLoc(), transposed);
-        });
-      Value transposedInput = preTransposeCompute.getResult(0);
       Value transposedResult = createLoopSoftmaxCompute(transposedInput, rewriter, softmaxOp.getLoc());
-      auto postTransposeCompute =
+      result = transposeMaybeInCompute(
-        createSpatCompute<1>(rewriter, softmaxOp.getLoc(), TypeRange {inputType}, {}, transposedResult, [&](Value x) {
+        transposedResult, inputType, inversePermutation, rewriter, softmaxOp.getLoc());
-          Value transposed = ONNXTransposeOp::create(
-            rewriter, softmaxOp.getLoc(), inputType, x, rewriter.getI64ArrayAttr(inversePermutation));
-          spatial::SpatYieldOp::create(rewriter, softmaxOp.getLoc(), transposed);
-        });
-      result = postTransposeCompute.getResult(0);
     }
 
     rewriter.replaceOp(softmaxOp, result);

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

@@ -36,6 +36,14 @@ static bool isDirectConstantValue(Value value) {
   return isa_and_nonnull<arith::ConstantOp, ONNXConstantOp>(value.getDefiningOp());
 }
 
+struct PromotedOperands {
+  SmallVector<bool> promoteInput;
+  SmallVector<Value> newWeights;
+  SmallVector<Value> newInputs;
+  SmallVector<Type> newInputTypes;
+  SmallVector<Location> newInputLocs;
+};
+
 template <typename ComputeOpTy>
 static bool hasPromotableWeightLikeInputs(ComputeOpTy compute) {
   for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
@@ -48,60 +56,91 @@ static bool hasPromotableWeightLikeInputs(ComputeOpTy compute) {
   return false;
 }
 
+template <typename ComputeOpTy>
+static FailureOr<PromotedOperands> computePromotedOperands(ComputeOpTy compute) {
+  PromotedOperands promoted;
+  promoted.promoteInput.assign(compute.getInputs().size(), false);
+  promoted.newWeights.append(compute.getWeights().begin(), compute.getWeights().end());
+  promoted.newWeights.reserve(compute.getWeights().size() + compute.getInputs().size());
+  promoted.newInputs.reserve(compute.getInputs().size());
+  promoted.newInputTypes.reserve(compute.getInputs().size());
+  promoted.newInputLocs.reserve(compute.getInputs().size());
+
+  bool needsRewrite = false;
+  for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
+    if (!isWeightLikeComputeOperand(input))
+      goto keep_input;
+    if (isDirectConstantValue(input) && !canPromoteInputBlockArgument(compute.getInputArgument(inputIdx)))
+      goto keep_input;
+    promoted.promoteInput[inputIdx] = true;
+    promoted.newWeights.push_back(input);
+    needsRewrite = true;
+    continue;
+
+  keep_input:
+    promoted.newInputs.push_back(input);
+    promoted.newInputTypes.push_back(input.getType());
+    promoted.newInputLocs.push_back(input.getLoc());
+  }
+
+  if (!needsRewrite)
+    return failure();
+  return promoted;
+}
+
+template <typename ComputeOpTy>
+static LogicalResult mapPromotedInputArguments(ComputeOpTy compute,
+                                               const PromotedOperands& promoted,
+                                               IRRewriter& bodyRewriter,
+                                               IRMapping& mapper,
+                                               std::function<std::optional<BlockArgument>(size_t)> getNewInputArg,
+                                               PatternRewriter& rewriter) {
+  size_t newInputIdx = 0;
+  for (auto [oldInputIdx, input] : llvm::enumerate(compute.getInputs())) {
+    auto oldArg = compute.getInputArgument(oldInputIdx);
+    if (!oldArg)
+      return rewriter.notifyMatchFailure(compute, "missing input block argument during rewrite");
+    if (!promoted.promoteInput[oldInputIdx]) {
+      auto newInputArg = getNewInputArg(newInputIdx++);
+      if (!newInputArg)
+        return rewriter.notifyMatchFailure(compute, "missing rewritten input block argument");
+      mapper.map(*oldArg, *newInputArg);
+      continue;
+    }
+
+    auto clonedValue = materializeWeightLikeValueInBlock(input, bodyRewriter, mapper);
+    if (failed(clonedValue))
+      return rewriter.notifyMatchFailure(compute, "failed to materialize promoted weight-like operand");
+    mapper.map(*oldArg, *clonedValue);
+  }
+  return success();
+}
+
 // Promotes foldable helper chains from runtime inputs to weights to avoid artificial compute inputs.
 struct PromoteWeightLikeComputeInputsPattern : OpRewritePattern<spatial::SpatCompute> {
   using OpRewritePattern<spatial::SpatCompute>::OpRewritePattern;
 
   LogicalResult matchAndRewrite(spatial::SpatCompute compute, PatternRewriter& rewriter) const override {
-    SmallVector<bool> promoteInput(compute.getInputs().size(), false);
+    auto promoted = computePromotedOperands(compute);
-    bool needsRewrite = false;
+    if (failed(promoted))
-    Block& oldBlock = compute.getBody().front();
-    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
-      if (!isWeightLikeComputeOperand(input))
-        continue;
-      if (isDirectConstantValue(input) && !canPromoteInputBlockArgument(compute.getInputArgument(inputIdx)))
-        continue;
-      promoteInput[inputIdx] = true;
-      needsRewrite = true;
-    }
-    if (!needsRewrite)
       return rewriter.notifyMatchFailure(compute, "no weight-like inputs to promote");
+    Block& oldBlock = compute.getBody().front();
 
     rewriter.setInsertionPointAfter(compute);
-
-    SmallVector<Value> newWeights(compute.getWeights().begin(), compute.getWeights().end());
-    SmallVector<Value> newInputs;
-    SmallVector<Type> newInputTypes;
-    SmallVector<Location> newInputLocs;
-    newWeights.reserve(compute.getWeights().size() + compute.getInputs().size());
-    newInputs.reserve(compute.getInputs().size());
-    newInputTypes.reserve(compute.getInputs().size());
-    newInputLocs.reserve(compute.getInputs().size());
-
-    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
-      if (promoteInput[inputIdx]) {
-        newWeights.push_back(input);
-        continue;
-      }
-      newInputs.push_back(input);
-      newInputTypes.push_back(input.getType());
-      newInputLocs.push_back(input.getLoc());
-    }
-
     auto newCompute =
-      spatial::SpatCompute::create(rewriter, compute.getLoc(), compute.getResultTypes(), newWeights, newInputs);
+      spatial::SpatCompute::create(rewriter, compute.getLoc(), compute.getResultTypes(), promoted->newWeights, promoted->newInputs);
     SmallVector<Type> newBlockArgTypes;
     SmallVector<Location> newBlockArgLocs;
-    for (Value weight : newWeights) {
+    for (Value weight : promoted->newWeights) {
       newBlockArgTypes.push_back(weight.getType());
       newBlockArgLocs.push_back(weight.getLoc());
     }
-    llvm::append_range(newBlockArgTypes, newInputTypes);
+    llvm::append_range(newBlockArgTypes, promoted->newInputTypes);
-    llvm::append_range(newBlockArgLocs, newInputLocs);
+    llvm::append_range(newBlockArgLocs, promoted->newInputLocs);
     auto* newBlock = rewriter.createBlock(
       &newCompute.getBody(), newCompute.getBody().end(), TypeRange(newBlockArgTypes), newBlockArgLocs);
     newCompute.getProperties().setOperandSegmentSizes(
-      {static_cast<int>(newWeights.size()), static_cast<int>(newInputs.size())});
+      {static_cast<int>(promoted->newWeights.size()), static_cast<int>(promoted->newInputs.size())});
     rewriter.setInsertionPointToStart(newBlock);
 
     IRRewriter bodyRewriter(rewriter.getContext());
@@ -115,24 +154,9 @@ struct PromoteWeightLikeComputeInputsPattern : OpRewritePattern<spatial::SpatCom
         return rewriter.notifyMatchFailure(compute, "missing compute weight block argument during rewrite");
       mapper.map(*oldWeightArg, *newWeightArg);
     }
-    size_t newInputIdx = 0;
+    if (failed(mapPromotedInputArguments(
-    for (auto [oldInputIdx, input] : llvm::enumerate(compute.getInputs())) {
+          compute, *promoted, bodyRewriter, mapper, [&](size_t index) { return newCompute.getInputArgument(index); }, rewriter)))
-      auto oldArg = compute.getInputArgument(oldInputIdx);
+      return failure();
-      if (!oldArg)
-        return rewriter.notifyMatchFailure(compute, "missing compute input block argument during rewrite");
-      if (!promoteInput[oldInputIdx]) {
-        auto newInputArg = newCompute.getInputArgument(newInputIdx++);
-        if (!newInputArg)
-          return rewriter.notifyMatchFailure(compute, "missing rewritten compute input block argument");
-        mapper.map(*oldArg, *newInputArg);
-        continue;
-      }
-
-      auto clonedValue = materializeWeightLikeValueInBlock(input, bodyRewriter, mapper);
-      if (failed(clonedValue))
-        return rewriter.notifyMatchFailure(compute, "failed to materialize promoted weight-like operand");
-      mapper.map(*oldArg, *clonedValue);
-    }
 
     for (Operation& op : oldBlock.without_terminator())
       rewriter.clone(op, mapper);
@@ -156,63 +180,35 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
   using OpRewritePattern<spatial::SpatComputeBatch>::OpRewritePattern;
 
   LogicalResult matchAndRewrite(spatial::SpatComputeBatch compute, PatternRewriter& rewriter) const override {
-    SmallVector<bool> promoteInput(compute.getInputs().size(), false);
+    auto promoted = computePromotedOperands(compute);
-    bool needsRewrite = false;
+    if (failed(promoted))
-    Block& oldBlock = compute.getBody().front();
-    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
-      if (!isWeightLikeComputeOperand(input))
-        continue;
-      if (isDirectConstantValue(input) && !canPromoteInputBlockArgument(compute.getInputArgument(inputIdx)))
-        continue;
-      promoteInput[inputIdx] = true;
-      needsRewrite = true;
-    }
-    if (!needsRewrite)
       return rewriter.notifyMatchFailure(compute, "no weight-like batch inputs to promote");
+    Block& oldBlock = compute.getBody().front();
 
     rewriter.setInsertionPointAfter(compute);
 
-    SmallVector<Value> newWeights(compute.getWeights().begin(), compute.getWeights().end());
-    SmallVector<Value> newInputs;
-    SmallVector<Type> newInputTypes;
-    SmallVector<Location> newInputLocs;
-    newWeights.reserve(compute.getWeights().size() + compute.getInputs().size());
-    newInputs.reserve(compute.getInputs().size());
-    newInputTypes.reserve(compute.getInputs().size());
-    newInputLocs.reserve(compute.getInputs().size());
-
-    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
-      if (promoteInput[inputIdx]) {
-        newWeights.push_back(input);
-        continue;
-      }
-      newInputs.push_back(input);
-      newInputTypes.push_back(input.getType());
-      newInputLocs.push_back(input.getLoc());
-    }
-
     auto newCompute =
       spatial::SpatComputeBatch::create(rewriter,
                                         compute.getLoc(),
                                         compute.getResultTypes(),
                                         rewriter.getI32IntegerAttr(static_cast<int32_t>(compute.getLaneCount())),
-                                        newWeights,
+                                        promoted->newWeights,
-                                        newInputs);
+                                        promoted->newInputs);
     auto laneArg = compute.getLaneArgument();
     if (!laneArg)
       return rewriter.notifyMatchFailure(compute, "missing compute_batch lane block argument");
     SmallVector<Type> newBlockArgTypes;
     SmallVector<Location> newBlockArgLocs;
-    newBlockArgTypes.reserve(1 + newWeights.size() + newInputTypes.size() + compute.getNumResults());
+    newBlockArgTypes.reserve(1 + promoted->newWeights.size() + promoted->newInputTypes.size() + compute.getNumResults());
-    newBlockArgLocs.reserve(1 + newWeights.size() + newInputLocs.size() + compute.getNumResults());
+    newBlockArgLocs.reserve(1 + promoted->newWeights.size() + promoted->newInputLocs.size() + compute.getNumResults());
     newBlockArgTypes.push_back(laneArg->getType());
     newBlockArgLocs.push_back(laneArg->getLoc());
-    for (Value weight : newWeights) {
+    for (Value weight : promoted->newWeights) {
       newBlockArgTypes.push_back(weight.getType());
       newBlockArgLocs.push_back(weight.getLoc());
     }
-    llvm::append_range(newBlockArgTypes, newInputTypes);
+    llvm::append_range(newBlockArgTypes, promoted->newInputTypes);
-    llvm::append_range(newBlockArgLocs, newInputLocs);
+    llvm::append_range(newBlockArgLocs, promoted->newInputLocs);
     for (auto [resultIndex, resultType] : llvm::enumerate(compute.getResultTypes())) {
       auto outputArg = compute.getOutputArgument(resultIndex);
       if (!outputArg)
@@ -224,7 +220,7 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
     auto* newBlock = rewriter.createBlock(
       &newCompute.getBody(), newCompute.getBody().end(), TypeRange(newBlockArgTypes), newBlockArgLocs);
     newCompute.getProperties().setOperandSegmentSizes(
-      {static_cast<int>(newWeights.size()), static_cast<int>(newInputs.size())});
+      {static_cast<int>(promoted->newWeights.size()), static_cast<int>(promoted->newInputs.size())});
     rewriter.setInsertionPointToStart(newBlock);
 
     IRRewriter bodyRewriter(rewriter.getContext());
@@ -242,29 +238,15 @@ struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::Sp
         return rewriter.notifyMatchFailure(compute, "missing compute_batch weight block argument during rewrite");
       mapper.map(*oldWeightArg, *newWeightArg);
     }
-    size_t newInputIdx = 0;
+    if (failed(mapPromotedInputArguments(
-    for (auto [oldInputIdx, input] : llvm::enumerate(compute.getInputs())) {
+          compute, *promoted, bodyRewriter, mapper, [&](size_t index) { return newCompute.getInputArgument(index); }, rewriter)))
-      auto oldArg = compute.getInputArgument(oldInputIdx);
+      return failure();
-      if (!oldArg)
-        return rewriter.notifyMatchFailure(compute, "missing compute_batch input block argument during rewrite");
-      if (!promoteInput[oldInputIdx]) {
-        auto newInputArg = newCompute.getInputArgument(newInputIdx++);
-        if (!newInputArg)
-          return rewriter.notifyMatchFailure(compute, "missing rewritten compute_batch input block argument");
-        mapper.map(*oldArg, *newInputArg);
-        continue;
-      }
-
-      auto clonedValue = materializeWeightLikeValueInBlock(input, bodyRewriter, mapper);
-      if (failed(clonedValue))
-        return rewriter.notifyMatchFailure(compute, "failed to materialize promoted batch weight-like operand");
-      mapper.map(*oldArg, *clonedValue);
-    }
     for (auto resultIndex : llvm::seq<size_t>(0, compute.getNumResults())) {
       auto outputArg = compute.getOutputArgument(resultIndex);
       if (!outputArg)
         return rewriter.notifyMatchFailure(compute, "missing compute_batch output block argument during rewrite");
-      mapper.map(*outputArg, newBlock->getArgument(1 + newWeights.size() + newInputs.size() + resultIndex));
+      mapper.map(*outputArg,
+                 newBlock->getArgument(1 + promoted->newWeights.size() + promoted->newInputs.size() + resultIndex));
     }
 
     for (Operation& op : oldBlock)

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

@@ -15,24 +15,6 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
-static int64_t normalizeAxis(int64_t axis, int64_t rank) { return axis >= 0 ? axis : rank + axis; }
-
-static int64_t normalizeIndex(int64_t index, int64_t dimSize) { return index >= 0 ? index : dimSize + index; }
-
-static Value
-extractSliceAt(Value input, int64_t axis, int64_t offset, ConversionPatternRewriter& rewriter, Location loc) {
-  auto inputType = cast<RankedTensorType>(input.getType());
-  SmallVector<OpFoldResult> offsets(inputType.getRank(), rewriter.getIndexAttr(0));
-  SmallVector<OpFoldResult> sizes;
-  SmallVector<OpFoldResult> strides(inputType.getRank(), rewriter.getIndexAttr(1));
-  sizes.reserve(inputType.getRank());
-  for (int64_t dim : inputType.getShape())
-    sizes.push_back(rewriter.getIndexAttr(dim));
-  offsets[axis] = rewriter.getIndexAttr(offset);
-  sizes[axis] = rewriter.getIndexAttr(1);
-  return tensor::ExtractSliceOp::create(rewriter, loc, input, offsets, sizes, strides);
-}
-
 static Value concatGatherSlices(Value data,
                                 int64_t axis,
                                 ArrayRef<int64_t> indices,
@@ -45,7 +27,7 @@ static Value concatGatherSlices(Value data,
     int64_t normalizedIndex = normalizeIndex(index, axisDim);
     if (normalizedIndex < 0 || normalizedIndex >= axisDim)
       return {};
-    slices.push_back(extractSliceAt(data, axis, normalizedIndex, rewriter, loc));
+    slices.push_back(extractAxisSlice(rewriter, loc, data, axis, normalizedIndex, /*size=*/1));
   }
   if (slices.empty())
     return {};
@@ -96,11 +78,11 @@ struct Gather : OpConversionPattern<ONNXGatherOp> {
       return failure();
 
     int64_t rank = dataType.getRank();
-    int64_t axis = normalizeAxis(gatherOp.getAxis(), rank);
+    auto axis = normalizeAxisChecked(gatherOp.getAxis(), rank);
-    if (axis < 0 || axis >= rank)
+    if (failed(axis))
       return failure();
 
-    int64_t axisDim = dataType.getShape()[axis];
+    int64_t axisDim = dataType.getShape()[*axis];
     if (axisDim <= 0)
       return failure();
 
@@ -116,7 +98,7 @@ struct Gather : OpConversionPattern<ONNXGatherOp> {
                            [&](Value data) -> LogicalResult {
                              Value result;
                              if (indicesType.getRank() == 1) {
-                               result = concatGatherSlices(data, axis, flatIndices, axisDim, rewriter, loc);
+                               result = concatGatherSlices(data, *axis, flatIndices, axisDim, rewriter, loc);
                              }
                              else if (indicesType.getRank() == 2) {
                                int64_t rowCount = indicesType.getShape()[0];
@@ -125,12 +107,13 @@ struct Gather : OpConversionPattern<ONNXGatherOp> {
                                rows.reserve(rowCount);
                                for (int64_t row = 0; row < rowCount; ++row) {
                                  ArrayRef<int64_t> rowIndices(flatIndices.data() + row * rowWidth, rowWidth);
-                                 Value gatheredRow = concatGatherSlices(data, axis, rowIndices, axisDim, rewriter, loc);
+                                 Value gatheredRow =
+                                   concatGatherSlices(data, *axis, rowIndices, axisDim, rewriter, loc);
                                  if (!gatheredRow)
                                    return failure();
-                                 rows.push_back(addLeadingGatherDim(gatheredRow, axis, rewriter, loc));
+                                 rows.push_back(addLeadingGatherDim(gatheredRow, *axis, rewriter, loc));
                                }
-                               result = createSpatConcat(rewriter, loc, /*axis=*/axis, rows);
+                               result = createSpatConcat(rewriter, loc, /*axis=*/*axis, rows);
                              }
                              else {
                                return failure();

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

@@ -14,10 +14,6 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
-static bool haveStaticPositiveShape(ArrayRef<int64_t> shape) {
-  return llvm::all_of(shape, [](int64_t dim) { return dim > 0; });
-}
-
 static bool inferCollapseReassociation(ArrayRef<int64_t> sourceShape,
                                        ArrayRef<int64_t> resultShape,
                                        SmallVector<ReassociationIndices>& reassociation) {
@@ -106,7 +102,7 @@ struct Reshape : OpConversionPattern<ONNXReshapeOp> {
     auto resultType = dyn_cast<RankedTensorType>(reshapeOp.getReshaped().getType());
     if (!sourceType || !resultType || !sourceType.hasStaticShape() || !resultType.hasStaticShape())
       return failure();
-    if (!haveStaticPositiveShape(sourceType.getShape()) || !haveStaticPositiveShape(resultType.getShape()))
+    if (!hasStaticPositiveShape(sourceType) || !hasStaticPositiveShape(resultType))
       return failure();
 
     if (sourceType == resultType) {
@@ -115,17 +111,8 @@ struct Reshape : OpConversionPattern<ONNXReshapeOp> {
     }
 
     auto replaceWithReshape = [&](auto buildReshape) -> LogicalResult {
-      if (isCompileTimeComputable(adaptor.getData())) {
+      Value reshaped = materializeOrComputeUnary(adaptor.getData(), resultType, rewriter, reshapeOp.getLoc(), buildReshape);
-        rewriter.replaceOp(reshapeOp, buildReshape(adaptor.getData()));
+      rewriter.replaceOp(reshapeOp, reshaped);
-        return success();
-      }
-
-      auto computeOp = createSpatCompute<1>(
-        rewriter, reshapeOp.getLoc(), TypeRange {resultType}, {}, adaptor.getData(), [&](Value data) {
-          Value reshaped = buildReshape(data);
-          spatial::SpatYieldOp::create(rewriter, reshapeOp.getLoc(), reshaped);
-        });
-      rewriter.replaceOp(reshapeOp, computeOp.getResults());
       return success();
     };
 

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

@@ -12,25 +12,6 @@ using namespace mlir;
 namespace onnx_mlir {
 namespace {
 
-static int64_t normalizeAxis(int64_t axis, int64_t rank) { return axis >= 0 ? axis : rank + axis; }
-
-static Value extractSliceAt(
-  Value input, int64_t axis, int64_t offset, int64_t size, ConversionPatternRewriter& rewriter, Location loc) {
-  auto inputType = cast<RankedTensorType>(input.getType());
-  SmallVector<OpFoldResult> offsets(inputType.getRank(), rewriter.getIndexAttr(0));
-  SmallVector<OpFoldResult> sizes;
-  SmallVector<OpFoldResult> strides(inputType.getRank(), rewriter.getIndexAttr(1));
-  sizes.reserve(inputType.getRank());
-  for (int64_t dim : inputType.getShape())
-    sizes.push_back(rewriter.getIndexAttr(dim));
-  offsets[axis] = rewriter.getIndexAttr(offset);
-  sizes[axis] = rewriter.getIndexAttr(size);
-  SmallVector<int64_t> resultShape(inputType.getShape());
-  resultShape[axis] = size;
-  auto resultType = RankedTensorType::get(resultShape, inputType.getElementType());
-  return tensor::ExtractSliceOp::create(rewriter, loc, resultType, input, offsets, sizes, strides);
-}
-
 struct Split : OpConversionPattern<ONNXSplitOp> {
   using OpConversionPattern::OpConversionPattern;
 
@@ -41,8 +22,8 @@ struct Split : OpConversionPattern<ONNXSplitOp> {
       return failure();
 
     int64_t rank = inputType.getRank();
-    int64_t axis = normalizeAxis(splitOp.getAxis(), rank);
+    auto axis = normalizeAxisChecked(splitOp.getAxis(), rank);
-    if (axis < 0 || axis >= rank)
+    if (failed(axis))
       return failure();
 
     SmallVector<Value> outputs;
@@ -58,12 +39,13 @@ struct Split : OpConversionPattern<ONNXSplitOp> {
       if (!resultType || !resultType.hasStaticShape())
         return failure();
       resultTypes.push_back(resultType);
-      sliceSizes.push_back(resultType.getShape()[axis]);
+      sliceSizes.push_back(resultType.getShape()[*axis]);
     }
 
     if (isCompileTimeComputable(adaptor.getInput())) {
       for (int64_t sliceSize : sliceSizes) {
-        outputs.push_back(extractSliceAt(adaptor.getInput(), axis, offset, sliceSize, rewriter, splitOp.getLoc()));
+        outputs.push_back(
+          extractAxisSlice(rewriter, splitOp.getLoc(), adaptor.getInput(), *axis, offset, sliceSize));
         offset += sliceSize;
       }
       rewriter.replaceOp(splitOp, outputs);
@@ -76,7 +58,8 @@ struct Split : OpConversionPattern<ONNXSplitOp> {
         runtimeOutputs.reserve(resultTypes.size());
         int64_t runtimeOffset = 0;
         for (int64_t sliceSize : sliceSizes) {
-          runtimeOutputs.push_back(extractSliceAt(input, axis, runtimeOffset, sliceSize, rewriter, splitOp.getLoc()));
+          runtimeOutputs.push_back(
+            extractAxisSlice(rewriter, splitOp.getLoc(), input, *axis, runtimeOffset, sliceSize));
           runtimeOffset += sliceSize;
         }
         spatial::SpatYieldOp::create(rewriter, splitOp.getLoc(), runtimeOutputs);

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

@@ -4,6 +4,7 @@
 
 #include "llvm/ADT/SmallVector.h"
 
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
@@ -29,22 +30,6 @@ static Value createTransposeInit(Value input,
   return tensor::EmptyOp::create(rewriter, loc, sizes, resultType.getElementType()).getResult();
 }
 
-static SmallVector<int64_t> getTransposePermutation(ONNXTransposeOp transposeOp) {
-  auto inputType = cast<RankedTensorType>(transposeOp.getData().getType());
-  SmallVector<int64_t> permutation;
-  if (auto permAttr = transposeOp.getPermAttr()) {
-    permutation.reserve(permAttr.size());
-    for (IntegerAttr attr : permAttr.getAsRange<IntegerAttr>())
-      permutation.push_back(attr.getInt());
-    return permutation;
-  }
-
-  permutation.reserve(inputType.getRank());
-  for (int64_t dim = inputType.getRank() - 1; dim >= 0; --dim)
-    permutation.push_back(dim);
-  return permutation;
-}
-
 struct TransposeToLinalgTranspose : OpConversionPattern<ONNXTransposeOp> {
   using OpConversionPattern::OpConversionPattern;
 
@@ -56,10 +41,12 @@ struct TransposeToLinalgTranspose : OpConversionPattern<ONNXTransposeOp> {
     if (!inputType || !resultType)
       return failure();
 
-    SmallVector<int64_t> permutation = getTransposePermutation(transposeOp);
+    auto permutation = getTransposePermutationChecked(transposeOp.getPermAttr(), inputType.getRank());
-    Value init = createTransposeInit(adaptor.getData(), resultType, permutation, rewriter, transposeOp.getLoc());
+    if (failed(permutation))
+      return failure();
+    Value init = createTransposeInit(adaptor.getData(), resultType, *permutation, rewriter, transposeOp.getLoc());
     Value transposed =
-      linalg::TransposeOp::create(rewriter, transposeOp.getLoc(), adaptor.getData(), init, permutation)
+      linalg::TransposeOp::create(rewriter, transposeOp.getLoc(), adaptor.getData(), init, *permutation)
         .getResult()[0];
     rewriter.replaceOp(transposeOp, transposed);
     return success();

src/PIM/Conversion/SpatialToPim/CoreLoweringPatterns.cpp

@@ -40,7 +40,7 @@ cloneMappedHelperOperands(Operation* op, IRMapping& mapping, IRRewriter& rewrite
       continue;
 
     if (auto constantOp = dyn_cast<arith::ConstantOp>(definingOp)) {
-      mapping.map(operand, getOrCreateHostConstantLike(constantOp, constantFolder));
+      mapping.map(operand, getOrCreateHostConstantLike(constantFolder, constantOp));
       continue;
     }
 
@@ -218,7 +218,7 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute comp
       continue;
 
     if (auto constantOp = input.getDefiningOp<arith::ConstantOp>()) {
-      blockArg->replaceAllUsesWith(getOrCreateHostConstantLike(constantOp, constantFolder));
+      blockArg->replaceAllUsesWith(getOrCreateHostConstantLike(constantFolder, constantOp));
       continue;
     }
 
@@ -230,8 +230,8 @@ LogicalResult raptor::SpatialToPimPass::lowerComputeOp(spatial::SpatCompute comp
       PimMemCopyHostToDevOp::create(rewriter,
                                     loc,
                                     outputBuffer.getType(),
-                                    getOrCreateHostIndexConstant(outputBuffer.getOperation(), 0, constantFolder),
+                                    getOrCreateHostIndexConstant(constantFolder, outputBuffer.getOperation(), 0),
-                                    getOrCreateHostIndexConstant(outputBuffer.getOperation(), 0, constantFolder),
+                                    getOrCreateHostIndexConstant(constantFolder, outputBuffer.getOperation(), 0),
                                     outputBuffer,
                                     input,
                                     getTensorSizeInBytesAttr(rewriter, input))

src/PIM/Conversion/SpatialToPim/ReturnPathNormalization.cpp

@@ -326,7 +326,7 @@ cloneMappedHelperOperands(Operation* op, IRMapping& mapping, IRRewriter& rewrite
       continue;
 
     if (auto constantOp = dyn_cast<arith::ConstantOp>(definingOp)) {
-      mapping.map(operand, getOrCreateHostConstantLike(constantOp, constantFolder));
+      mapping.map(operand, getOrCreateHostConstantLike(constantFolder, constantOp));
       continue;
     }
 
@@ -370,8 +370,8 @@ static Value emitHostCopy(IRRewriter& rewriter,
                           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 = getOrCreateHostIndexConstant(anchorOp, hostTargetOffset, constantFolder);
+  Value hostTargetOffsetValue = getOrCreateHostIndexConstant(constantFolder, anchorOp, hostTargetOffset);
-  Value deviceSourceOffsetValue = getOrCreateHostIndexConstant(anchorOp, deviceSourceOffset, constantFolder);
+  Value deviceSourceOffsetValue = getOrCreateHostIndexConstant(constantFolder, anchorOp, deviceSourceOffset);
   return PimMemCopyDevToHostOp::create(rewriter,
                                        loc,
                                        outputTensor.getType(),

src/PIM/Conversion/SpatialToPim/SpatialToPimPass.cpp

@@ -81,7 +81,7 @@ static Value createZeroedDeviceHVector(IRRewriter& rewriter,
   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 = getOrCreateHostIndexConstant(outputBuffer.getOperation(), 0, constantFolder);
+  auto zeroIndex = getOrCreateHostIndexConstant(constantFolder, outputBuffer.getOperation(), 0);
   auto sizeAttr = rewriter.getI32IntegerAttr(static_cast<int32_t>(getShapedTypeSizeInBytes(tensorType)));
 
   if (outputBuffer->getParentOfType<PimCoreBatchOp>())
@@ -333,9 +333,9 @@ LogicalResult raptor::SpatialToPimPass::allocateAndInitializeCoreLocalVariables(
       rewriter,
       loc,
       tensorType,
-      getOrCreateHostIndexConstant(deviceTensor.getOperation(), 0, constantFolder),
+      getOrCreateHostIndexConstant(constantFolder, deviceTensor.getOperation(), 0),
-      getOrCreateHostIndexConstant(
+      getOrCreateHostIndexConstant(constantFolder,
-        deviceTensor.getOperation(), static_cast<int64_t>(elementsOffset * elementByteSize), constantFolder),
+        deviceTensor.getOperation(), static_cast<int64_t>(elementsOffset * elementByteSize) ),
       deviceTensor,
       inputTensor,
       rewriter.getI32IntegerAttr(static_cast<int32_t>(tensorType.getNumElements() * elementByteSize)));

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

@@ -619,7 +619,7 @@ BlockArgument appendInput(MaterializerState& state, MaterializedClass& materiali
 }
 
 Value createIndexConstant(MaterializerState& state, Operation* anchor, int64_t value) {
-  return getOrCreateHostIndexConstant(anchor, value, state.constantFolder);
+  return getOrCreateHostIndexConstant(state.constantFolder, anchor, value);
 }
 
 // -----------------------------------------------------------------------------
@@ -939,7 +939,7 @@ Value createIndexTensorConstant(MaterializerState& state, Operation* anchor, Arr
 
   auto type = RankedTensorType::get({static_cast<int64_t>(values.size())}, state.rewriter.getIndexType());
   auto attr = DenseIntElementsAttr::get(type, elements);
-  return getOrCreateHostConstant(anchor, attr, type, state.constantFolder);
+  return getOrCreateHostConstant(state.constantFolder, anchor, attr, type);
 }
 
 bool allEqual(ArrayRef<int64_t> values) {

src/PIM/Pass/PimCodegen/MaterializeHostConstantsPass.cpp

@@ -113,8 +113,8 @@ static void materializeHostConstantsInCore(CoreOpTy coreOp,
             rewriter,
             op->getLoc(),
             originalType,
-            getOrCreateHostIndexConstant(op, 0, constantFolder),
+            getOrCreateHostIndexConstant(constantFolder, op, 0),
-            getOrCreateHostIndexConstant(op, static_cast<int64_t>(resolvedAddress->byteOffset), constantFolder),
+            getOrCreateHostIndexConstant(constantFolder, op, static_cast<int64_t>(resolvedAddress->byteOffset) ),
             deviceDst,
             getGlobalOp.getResult(),
             rewriter.getI32IntegerAttr(static_cast<int32_t>(totalBytes)))