remove slice_scatter from TorchToLinalg/DataMovement.cpp

lib/Conversion/TorchToLinalg/DataMovement.cpp

@@ -1288,55 +1288,6 @@ public:
 };
 } // namespace
 
-namespace {
-class ConvertAtenSliceScatterOp
-    : public OpConversionPattern<AtenSliceScatterOp> {
-public:
-  using OpConversionPattern::OpConversionPattern;
-  LogicalResult
-  matchAndRewrite(AtenSliceScatterOp op, OpAdaptor adaptor,
-                  ConversionPatternRewriter &rewriter) const override {
-
-    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
-      return failure();
-
-    Location loc = op.getLoc();
-    TypeConverter *typeConverter = getTypeConverter();
-
-    auto input = adaptor.self();
-
-    RankedTensorType resultType =
-        typeConverter->convertType(op->getResult(0).getType())
-            .cast<RankedTensorType>();
-
-    SmallVector<Value> resultShape;
-    SmallVector<Value> offsets;
-    SmallVector<Value> strides;
-    if (failed(prepareArgumentsForSlicingOp<AtenSliceScatterOp,
-                                            AtenSliceScatterOpAdaptor>(
-            op, adaptor, rewriter, resultShape, offsets, strides))) {
-      return failure();
-    }
-
-    Value src = adaptor.src();
-    auto srcType = src.getType().cast<RankedTensorType>();
-    int64_t srcRank = srcType.getRank();
-    SmallVector<int64_t> srcAbstractSizes(srcRank, kUnknownSize);
-    auto abstractSrcType =
-        RankedTensorType::get(srcAbstractSizes, srcType.getElementType());
-    Value abstractSrc =
-        rewriter.create<tensor::CastOp>(loc, abstractSrcType, src);
-
-    Value result = rewriter.create<tensor::InsertSliceOp>(
-        loc, abstractSrc, input, offsets, resultShape, strides);
-
-    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, result);
-
-    return success();
-  }
-};
-} // namespace
-
 void mlir::torch::torch_to_linalg::populateDataMovementPatternsAndLegality(
     TypeConverter &typeConverter, RewritePatternSet &patterns,
     ConversionTarget &target) {
@@ -1365,6 +1316,4 @@ void mlir::torch::torch_to_linalg::populateDataMovementPatternsAndLegality(
   patterns.add<ConvertAtenContiguousOp>(typeConverter, context);
   target.addIllegalOp<AtenCopyOp>();
   patterns.add<ConvertAtenCopyOp>(typeConverter, context);
-  target.addIllegalOp<AtenSliceScatterOp>();
-  patterns.add<ConvertAtenSliceScatterOp>(typeConverter, context);
 }

python/torch_mlir/__init__.py

@@ -240,7 +240,7 @@ class ExampleArgs:
 # compiler where each backend can "own" its set of legal ops.
 BACKEND_LEGAL_OPS = {
     OutputType.TOSA: ['torch.aten.flatten.using_ints', 'torch.aten.native_layer_norm', 'torch.aten.linear'],
-    OutputType.LINALG_ON_TENSORS: ['torch.aten.flatten.using_ints', 'torch.aten.slice_scatter'],
+    OutputType.LINALG_ON_TENSORS: ['torch.aten.flatten.using_ints'],
     OutputType.MHLO: [],
 }