nnicolosi/Raptor
1
0
Fork 0
Code Issues Pull Requests Actions 6 Packages Projects Releases Wiki Activity

finish helper refactoring
Validate Operations / validate-operations (push) Has been cancelled
Details

Browse Source 
use uniqued constant helpers everywhere
materialize transposed constants directly
This commit is contained in:
NiccoloN
2026-05-29 17:05:45 +02:00
parent 819d8af0f7
commit 8bb0babf1b
32 changed files with 300 additions and 467 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/PIM/Conversion/ONNXToSpatial/Common/ShapeTilingUtils.hpp
-68
View File
@@ -18,41 +18,6 @@
namespace onnx_mlir {
template <class ShapedType>
inline auto getImageWidth(const ShapedType& shapedType) {
return shapedType.getDimSize(2);
}
template <class ShapedType>
inline auto getImageHeight(const ShapedType& shapedType) {
return shapedType.getDimSize(3);
}
template <class ShapedType>
inline auto getImageChannel(const ShapedType& shapedType) {
return shapedType.getDimSize(1);
}
template <class ShapedType>
inline auto getImageN(const ShapedType& shapedType) {
return shapedType.getDimSize(0);
}
template <class ShapedType>
inline auto getKernelWidth(const ShapedType& shapedType) {
return shapedType.getDimSize(2);
}
template <class ShapedType>
inline auto getKernelHeight(const ShapedType& shapedType) {
return shapedType.getDimSize(3);
}
template <class ShapedType>
inline auto getFilterCount(const ShapedType& shapedType) {
return shapedType.getDimSize(0);
}
using HSliceId = size_t;
using CoreId = size_t;
@@ -89,17 +54,6 @@ bool isHVectorShape(mlir::ArrayRef<T> shape) {
return shape.size() == 2 && shape[0] == 1;
}
template <class T>
bool isVVectorShape(mlir::ArrayRef<T> shape) {
return shape.size() == 2 && shape[1] == 1;
}
template <class T>
T getVectorLength(mlir::ArrayRef<T> shape) {
assert(isVectorShape(shape));
return shape[0] != 1 ? shape[0] : shape[1];
}
inline auto getTensorShape(mlir::Value tensor) {
return mlir::cast<mlir::RankedTensorType>(tensor.getType()).getShape();
}
@@ -117,8 +71,6 @@ 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);
@@ -156,20 +108,6 @@ llvm::SmallVector<mlir::Value> sliceVector(const mlir::Value& vectorToSlice,
llvm::DenseMap<CoreId, llvm::SmallVector<mlir::Value>> sliceVectorPerCrossbarPerCore(
const mlir::Value& vectorToSlice, mlir::ConversionPatternRewriter& rewriter, mlir::Location loc);
/// Tiles a matrix first across output columns and then across input rows so it
/// can be assigned to crossbars grouped by core.
llvm::DenseMap<HSliceId, llvm::DenseMap<CoreId, llvm::SmallVector<mlir::Value>>>
tileMatrix(mlir::Value& matrixToTile,
int64_t hSliceSize,
int64_t vSliceSize,
mlir::ConversionPatternRewriter& rewriter,
mlir::Location& loc);
mlir::Value broadcastToVector(mlir::Value scalarToBroadcast,
int64_t length,
mlir::ConversionPatternRewriter& rewriter,
mlir::Location loc);
mlir::Value materializeContiguousTensorSlice(mlir::Value source,
mlir::RankedTensorType resultType,
llvm::ArrayRef<mlir::OpFoldResult> offsets,
@@ -177,12 +115,6 @@ 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,