meno diamantini

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

@@ -5,7 +5,7 @@
 
 #include "llvm/ADT/SmallVector.h"
 
-#include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/BiasAddUtils.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
@@ -47,38 +47,28 @@ static FailureOr<Value> materializeBroadcastedConstantTensor(Value value,
     return failure();
 
   const int64_t rankOffset = static_cast<int64_t>(resultShape.size() - sourceShape.size());
-  for (int64_t i = 0; i < static_cast<int64_t>(resultShape.size()); ++i) {
-    const int64_t sourceIndex = i - rankOffset;
-    const int64_t sourceDim = sourceIndex < 0 ? 1 : sourceShape[sourceIndex];
-    const int64_t resultDim = resultShape[i];
-    if (sourceDim != 1 && sourceDim != resultDim)
-      return failure();
-  }
-
-  SmallVector<Attribute> sourceValues(denseAttr.getValues<Attribute>());
   SmallVector<int64_t> sourceStrides = computeRowMajorStrides(sourceShape);
   SmallVector<int64_t> resultStrides = computeRowMajorStrides(resultShape);
-
+  SmallVector<Attribute> sourceValues(denseAttr.getValues<Attribute>());
   SmallVector<Attribute> resultValues;
   resultValues.reserve(resultType.getNumElements());
 
   for (int64_t flatIndex = 0; flatIndex < resultType.getNumElements(); ++flatIndex) {
     int64_t remaining = flatIndex;
     int64_t sourceFlatIndex = 0;
-
     for (int64_t i = 0; i < static_cast<int64_t>(resultShape.size()); ++i) {
       const int64_t resultIndex = resultStrides.empty() ? 0 : remaining / resultStrides[i];
       remaining = resultStrides.empty() ? 0 : remaining % resultStrides[i];
-
       const int64_t sourceIndex = i - rankOffset;
       if (sourceIndex < 0)
         continue;
-
       const int64_t sourceDim = sourceShape[sourceIndex];
+      const int64_t resultDim = resultShape[i];
+      if (sourceDim != 1 && sourceDim != resultDim)
+        return failure();
       const int64_t mappedIndex = sourceDim == 1 ? 0 : resultIndex;
       sourceFlatIndex += mappedIndex * sourceStrides[sourceIndex];
     }
-
     resultValues.push_back(sourceValues[sourceFlatIndex]);
   }
 
@@ -106,7 +96,7 @@ static FailureOr<Value> materializeReciprocalTensor(Value value,
   if (failed(broadcastedValue))
     return failure();
 
-  auto denseAttr = dyn_cast<DenseFPElementsAttr>(getDenseConstantAttr(*broadcastedValue));
+  auto denseAttr = dyn_cast<DenseFPElementsAttr>(getHostConstDenseElementsAttr(*broadcastedValue));
   if (!denseAttr)
     return failure();
 
@@ -185,10 +175,45 @@ struct DivToSpatialCompute : OpConversionPattern<ONNXDivOp> {
   }
 };
 
+struct AddToSpatialCompute : OpConversionPattern<ONNXAddOp> {
+  using OpConversionPattern::OpConversionPattern;
+
+  LogicalResult
+  matchAndRewrite(ONNXAddOp op, ONNXAddOpAdaptor adaptor, ConversionPatternRewriter& rewriter) const override {
+    auto resultType = dyn_cast<RankedTensorType>(op.getResult().getType());
+    if (!resultType || !resultType.hasStaticShape())
+      return failure();
+
+    FailureOr<BiasAddPlanCandidate> candidate =
+      classifyBiasAddPlanCandidate(adaptor.getA(), adaptor.getB(), resultType);
+    if (succeeded(candidate)) {
+      auto plan = spatial::SpatBiasAddPlanOp::create(
+        rewriter, op.getLoc(), resultType, candidate->data, candidate->bias, rewriter.getStringAttr("nchw"));
+      rewriter.replaceOp(op, plan.getResult());
+      return success();
+    }
+
+    auto lhs = prepareElementwiseOperand(adaptor.getA(), resultType, rewriter, op.getLoc());
+    if (failed(lhs))
+      return failure();
+    auto rhs = prepareElementwiseOperand(adaptor.getB(), resultType, rewriter, op.getLoc());
+    if (failed(rhs))
+      return failure();
+
+    auto computeOp =
+      createSpatCompute<2>(rewriter, op.getLoc(), resultType, {}, ValueRange {*lhs, *rhs}, [&](Value x, Value y) {
+        auto loweredOp = spatial::SpatVAddOp::create(rewriter, op.getLoc(), resultType, x, y);
+        spatial::SpatYieldOp::create(rewriter, op.getLoc(), loweredOp.getResult());
+      });
+    rewriter.replaceOp(op, computeOp);
+    return success();
+  }
+};
+
 } // namespace
 
 void populateElementwisePatterns(RewritePatternSet& patterns, MLIRContext* ctx) {
-  patterns.add<BinaryElementwiseToSpatialCompute<ONNXAddOp, spatial::SpatVAddOp>>(ctx);
+  patterns.add<AddToSpatialCompute>(ctx);
   patterns.add<BinaryElementwiseToSpatialCompute<ONNXSubOp, spatial::SpatVSubOp>>(ctx);
   patterns.add<BinaryElementwiseToSpatialCompute<ONNXMulOp, spatial::SpatVMulOp>>(ctx);
   patterns.add<DivToSpatialCompute>(ctx);