diff --git a/lib/Conversion/TorchToTMTensor/TorchToTMTensor.cpp b/lib/Conversion/TorchToTMTensor/TorchToTMTensor.cpp
index b0a8d5e90fc9..ea37606aefe9 100644
--- a/lib/Conversion/TorchToTMTensor/TorchToTMTensor.cpp
+++ b/lib/Conversion/TorchToTMTensor/TorchToTMTensor.cpp
@@ -675,12 +675,12 @@ static Value collapseAndMoveBatchDims(Location loc, Value values, int64_t batch,
   return b.create<AtenViewOp>(loc, valuesTy, values, outDimsList);
 }
 
-class ConvertAten_IndexPutImplOp
-    : public OpConversionPattern<Aten_IndexPutImplOp> {
+class ConvertAtenIndexPutHackedTwinOp
+    : public OpConversionPattern<AtenIndexPutHackedTwinOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
-  matchAndRewrite(Aten_IndexPutImplOp op, OpAdaptor adaptor,
+  matchAndRewrite(AtenIndexPutHackedTwinOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
       return failure();
@@ -699,17 +699,6 @@ class ConvertAten_IndexPutImplOp
       return rewriter.notifyMatchFailure(
           op, "unimplemented: the values tensor type must have sizes.");
 
-    // The unsafe should be either `False` or `none`.
-    if (!op.getUnsafe().getType().isa<Torch::NoneType>()) {
-      bool unsafe;
-      if (!matchPattern(op.getUnsafe(), m_TorchConstantBool(&unsafe)))
-        return rewriter.notifyMatchFailure(
-            op, "unimplemented: unsafe must be a constant");
-      else if (unsafe)
-        return rewriter.notifyMatchFailure(
-            op, "unimplemented: unsafe is expected to be false");
-    }
-
     // The accumulate should be a torch constant of boolean type.
     bool accumulate;
     if (!matchPattern(op.getAccumulate(), m_TorchConstantBool(&accumulate)))
@@ -1624,8 +1613,8 @@ class ConvertTorchToTMTensor
     RewritePatternSet patterns(context);
     target.addIllegalOp<AtenBincountOp>();
     patterns.add<ConvertAtenBincountOp>(typeConverter, context);
-    target.addIllegalOp<Aten_IndexPutImplOp>();
-    patterns.add<ConvertAten_IndexPutImplOp>(typeConverter, context);
+    target.addIllegalOp<AtenIndexPutHackedTwinOp>();
+    patterns.add<ConvertAtenIndexPutHackedTwinOp>(typeConverter, context);
     target.addIllegalOp<AtenMaxPool2dWithIndicesBackwardOp>();
     patterns.add<ConvertAtenMaxPool2dWithIndicesBackwardOp>(typeConverter,
                                                             context);
diff --git a/lib/Conversion/TorchToTosa/TorchToTosa.cpp b/lib/Conversion/TorchToTosa/TorchToTosa.cpp
index 467cd227ad92..a570d4a93e0e 100644
--- a/lib/Conversion/TorchToTosa/TorchToTosa.cpp
+++ b/lib/Conversion/TorchToTosa/TorchToTosa.cpp
@@ -3970,8 +3970,8 @@ class SimplifyAten_IndexPutImplOp
 
 // Handle Aten_IndexPutImplOp on 1d tensors
 template <>
-LogicalResult ConvertAtenOp<Aten_IndexPutImplOp>::matchAndRewrite(
-    Aten_IndexPutImplOp op, OpAdaptor adaptor,
+LogicalResult ConvertAtenOp<AtenIndexPutHackedTwinOp>::matchAndRewrite(
+    AtenIndexPutHackedTwinOp op, OpAdaptor adaptor,
     ConversionPatternRewriter &rewriter) const {
   // TOSA scatter:
   // // Copy the values_in tensor to the values_out tensor.
@@ -6227,7 +6227,7 @@ class ConvertTorchToTosa : public ConvertTorchToTosaBase<ConvertTorchToTosa> {
     INSERT_ATENOP_PATTERN(AtenSliceTensorOp);
     INSERT_ATENOP_PATTERN(AtenBroadcastToOp);
     INSERT_ATENOP_PATTERN(AtenGatherOp);
-    INSERT_ATENOP_PATTERN(Aten_IndexPutImplOp);
+    INSERT_ATENOP_PATTERN(AtenIndexPutHackedTwinOp);
     INSERT_ATENOP_PATTERN(AtenIndexTensorHackedTwinOp);
     INSERT_ATENOP_PATTERN(AtenAbsOp);
     INSERT_ATENOP_PATTERN(AtenWhereSelfOp);
diff --git a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
index 83ad93c5e879..315264333b3a 100644
--- a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
+++ b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
@@ -5523,23 +5523,6 @@ class DecomposeAtenNewFullOp : public OpRewritePattern<AtenNewFullOp> {
 };
 } // namespace
 
-namespace {
-// Decompose `aten.indexPut` op into `valsem.aten.indexPutImpl` op.
-class DecomposeAtenIndexPutOp : public OpRewritePattern<AtenIndexPutOp> {
-public:
-  using OpRewritePattern::OpRewritePattern;
-  LogicalResult matchAndRewrite(AtenIndexPutOp op,
-                                PatternRewriter &rewriter) const override {
-    Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
-    rewriter.replaceOpWithNewOp<Aten_IndexPutImplOp>(
-        op, op.getType(), op.getSelf(), op.getIndices(), op.getValues(),
-        op.getAccumulate(),
-        /*unsafe=*/cstFalse);
-    return success();
-  }
-};
-} // namespace
-
 namespace {
 class DecomposeAtenExpandAsOp : public OpRewritePattern<AtenExpandAsOp> {
   using OpRewritePattern::OpRewritePattern;
@@ -5635,61 +5618,6 @@ class DecomposeAtenNewEmptyOp : public OpRewritePattern<AtenNewEmptyOp> {
 };
 } // namespace
 
-namespace {
-// Decompose `aten.indexPut.hackedTwin` op into `valsem.aten.indexPutImpl`
-// op.
-class DecomposeAtenIndexPutHackedTwinOp
-    : public OpRewritePattern<AtenIndexPutHackedTwinOp> {
-public:
-  using OpRewritePattern::OpRewritePattern;
-  LogicalResult matchAndRewrite(AtenIndexPutHackedTwinOp op,
-                                PatternRewriter &rewriter) const override {
-    Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
-    rewriter.replaceOpWithNewOp<Aten_IndexPutImplOp>(
-        op, op.getType(), op.getSelf(), op.getIndices(), op.getValues(),
-        op.getAccumulate(),
-        /*unsafe=*/cstFalse);
-    return success();
-  }
-};
-} // namespace
-
-namespace {
-// Decompose `aten._index_put_impl_.hacked_twin` op into `aten._index_put_impl`
-// op.
-class DecomposeAten_IndexPutImpl_HackedTwinOp
-    : public OpRewritePattern<Aten_IndexPutImpl_HackedTwinOp> {
-public:
-  using OpRewritePattern::OpRewritePattern;
-  LogicalResult matchAndRewrite(Aten_IndexPutImpl_HackedTwinOp op,
-                                PatternRewriter &rewriter) const override {
-    rewriter.replaceOpWithNewOp<Aten_IndexPutImplOp>(
-        op, op.getType(), op.getSelf(), op.getIndices(), op.getValues(),
-        op.getAccumulate(), op.getUnsafe());
-    return success();
-  }
-};
-} // namespace
-
-namespace {
-// Decompose `aten._unsafe_indexPut.hackedTwin` op into `aten._index_put_impl`
-// op.
-class DecomposeAten_UnsafeIndexPutHackedTwinOp
-    : public OpRewritePattern<Aten_UnsafeIndexPutHackedTwinOp> {
-public:
-  using OpRewritePattern::OpRewritePattern;
-  LogicalResult matchAndRewrite(Aten_UnsafeIndexPutHackedTwinOp op,
-                                PatternRewriter &rewriter) const override {
-    Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
-    rewriter.replaceOpWithNewOp<Aten_IndexPutImplOp>(
-        op, op.getType(), op.getSelf(), op.getIndices(), op.getValues(),
-        op.getAccumulate(),
-        /*unsafe=*/cstFalse);
-    return success();
-  }
-};
-} // namespace
-
 namespace {
 // Decompose `aten.pad` op into `aten.constantPadNd` op.
 class DecomposeAtenPadOp : public OpRewritePattern<AtenPadOp> {
@@ -7514,65 +7442,138 @@ class DecomposeAtenMaxPool2dWithIndicesOp
 };
 } // namespace
 
-// AtenIndexTensorOp
+// Torch ops related to indexing tensors, e.g., AtenIndexTensor, AtenIndexPut.
 namespace {
-// The goal of this pattern is to eliminate none index in aten.Index.Tensor's
-// `indices` param for the ease of various backend. The detailed steps are:
-//    1. reorder input tensor so that the non-none index appears at adjacent
-//    positions.
-//    2. manually generate index tensor with some ops like iota, to replace the
-//    none index in `indices`
-//    3. replace the old aten.Index.Tensor with a new
-//    aten.Index.Tensor_hacked_twin.
-class DecomposeAtenIndexTensorOp : public OpRewritePattern<AtenIndexTensorOp> {
-public:
-  using OpRewritePattern::OpRewritePattern;
-
-  // TODO: It might be better to use aten.view op instead of mulitple
-  // aten.unsqueeze. But currently, torch-to-linalg pass has limited support for
-  // view on dynamic shapes, such as [?] -> [?,1,1,1]. Using aten.view op will
-  // cause relevant e2e tests fail.
-  static FailureOr<Value>
-  unsqueezeTensorAtTrailingDim(Operation *op, PatternRewriter &rewriter,
-                               Value input, int count) {
-    Location loc = op->getLoc();
-    Value constMinusOne = rewriter.create<Torch::ConstantIntOp>(
-        loc, rewriter.getI64IntegerAttr(-1));
-    Value result = input;
-    while (count--) {
-      auto unsqzTensorInfo =
-          unsqueezeTensor(rewriter, op, result, /*dim=*/constMinusOne);
-      if (failed(unsqzTensorInfo)) {
-        return failure();
-      }
 
-      result = *unsqzTensorInfo;
+// unsqueeze is more easily optimized than a generic view, and we prefer to
+// enjoy ops with more structure than less in compositions.
+static FailureOr<Value> unsqueezeTensorAtTrailingDim(Operation *op,
+                                                     PatternRewriter &rewriter,
+                                                     Value input, int count) {
+  Location loc = op->getLoc();
+  Value constMinusOne = rewriter.create<Torch::ConstantIntOp>(
+      loc, rewriter.getI64IntegerAttr(-1));
+  Value result = input;
+  while (count--) {
+    auto unsqzTensorInfo =
+        unsqueezeTensor(rewriter, op, result, /*dim=*/constMinusOne);
+    if (failed(unsqzTensorInfo)) {
+      return failure();
     }
-    return result;
+
+    result = *unsqzTensorInfo;
   }
+  return result;
+}
 
-  static Value createIndexToReplaceNone(Operation *op,
-                                        PatternRewriter &rewriter, Value input,
-                                        int dimInt, int64_t dimSize) {
-    Location loc = op->getLoc();
-    MLIRContext *context = op->getContext();
-    Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
-    auto int64Dtype = getDtypeIntValueForType(
-        rewriter, loc,
-        rewriter.getIntegerType(/*width=*/64, /*isSigned=*/true));
+static Value createIndexToReplaceNone(Operation *op, PatternRewriter &rewriter,
+                                      Value input, int dimInt,
+                                      int64_t dimSize) {
+  Location loc = op->getLoc();
+  MLIRContext *context = op->getContext();
+  Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
+  auto int64Dtype = getDtypeIntValueForType(
+      rewriter, loc, rewriter.getIntegerType(/*width=*/64, /*isSigned=*/true));
+
+  auto resultType = ValueTensorType::get(
+      context, {dimSize},
+      rewriter.getIntegerType(/*width=*/64, /*isSigned=*/true));
+  auto dim = rewriter.create<Torch::ConstantIntOp>(
+      loc, rewriter.getI64IntegerAttr(dimInt));
+  auto end = rewriter.create<Torch::AtenSizeIntOp>(loc, input, dim);
+  auto v = rewriter.create<Torch::AtenArangeOp>(
+      loc, resultType, /*end=*/end, /*dtype=*/int64Dtype, /*layout=*/none,
+      /*device=*/none, /*pin_memory=*/none);
+  return v;
+}
 
-    auto resultType = ValueTensorType::get(
-        context, {dimSize},
-        rewriter.getIntegerType(/*width=*/64, /*isSigned=*/true));
-    auto dim = rewriter.create<Torch::ConstantIntOp>(
-        loc, rewriter.getI64IntegerAttr(dimInt));
-    auto end = rewriter.create<Torch::AtenSizeIntOp>(loc, input, dim);
-    auto v = rewriter.create<Torch::AtenArangeOp>(
-        loc, resultType, /*end=*/end, /*dtype=*/int64Dtype, /*layout=*/none,
-        /*device=*/none, /*pin_memory=*/none);
-    return v;
+static FailureOr<Value> createNewIndices(Operation *op,
+                                         PatternRewriter &rewriter, Value input,
+                                         llvm::ArrayRef<Value> oldIndices,
+                                         llvm::ArrayRef<int64_t> newToOldDimMap,
+                                         llvm::ArrayRef<bool> oldIndexUsed) {
+  Location loc = op->getLoc();
+  MLIRContext *context = op->getContext();
+
+  auto inputType = input.getType().cast<BaseTensorType>();
+  if (!inputType.hasSizes()) {
+    return failure();
+  }
+  auto inputSizes = inputType.getSizes();
+  int64_t inputRank = inputSizes.size();
+
+  int64_t maxIndexRank = 0;
+  for (auto index : oldIndices) {
+    auto indexType = index.getType().dyn_cast<BaseTensorType>();
+    if (!indexType) // None index
+      continue;
+    if (!indexType.hasSizes())
+      return failure();
+    int64_t indexRank = indexType.getSizes().size();
+    maxIndexRank = maxIndexRank > indexRank ? maxIndexRank : indexRank;
+  }
+
+  // manually generate new indices.
+  SmallVector<Value> listElements(inputRank);
+
+  int64_t noneIndexCnt = 0;
+  int64_t i;
+  // handle trailing none indices.
+  for (i = inputRank - 1; i >= 0; --i) {
+    int64_t oldI = newToOldDimMap[i];
+    if (oldIndexUsed[oldI])
+      break;
+    Value v = createIndexToReplaceNone(op, rewriter, input, i, inputSizes[i]);
+    auto vInfo = unsqueezeTensorAtTrailingDim(op, rewriter, v, noneIndexCnt);
+    if (failed(vInfo)) {
+      return failure();
+    }
+    listElements[i] = *vInfo;
+    noneIndexCnt++;
+  }
+  // handle non-none index in between.
+  for (; i >= 0; --i) {
+    int64_t oldI = newToOldDimMap[i];
+    if (!oldIndexUsed[oldI])
+      break;
+    auto vInfo = unsqueezeTensorAtTrailingDim(op, rewriter, oldIndices[oldI],
+                                              noneIndexCnt);
+    if (failed(vInfo)) {
+      return failure();
+    }
+    listElements[i] = *vInfo;
+  }
+
+  // handle possible leading none indices.
+  for (; i >= 0; --i) {
+    int64_t oldI = newToOldDimMap[i];
+    if (oldIndexUsed[oldI]) {
+      return failure();
+    }
+    Value v = createIndexToReplaceNone(op, rewriter, input, i, inputSizes[i]);
+    auto vInfo = unsqueezeTensorAtTrailingDim(op, rewriter, v,
+                                              noneIndexCnt + maxIndexRank);
+    if (failed(vInfo)) {
+      return failure();
+    }
+    listElements[i] = *vInfo;
+    noneIndexCnt++;
   }
 
+  auto listElemType = ValueTensorType::get(context, std::nullopt, nullptr);
+  Value newIndexList = rewriter.create<Torch::PrimListConstructOp>(
+      loc, Torch::ListType::get(listElemType), listElements);
+
+  return newIndexList;
+}
+
+// The goal of this pattern is to eliminate `None` index in aten.Index.Tensor's
+// `indices` param and transform it to aten.index.Tensor_hacked_twin, for the
+// ease of various backend.
+class DecomposeAtenIndexTensorOp : public OpRewritePattern<AtenIndexTensorOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+
   LogicalResult matchAndRewrite(AtenIndexTensorOp op,
                                 PatternRewriter &rewriter) const override {
     Location loc = op.getLoc();
@@ -7590,12 +7591,6 @@ class DecomposeAtenIndexTensorOp : public OpRewritePattern<AtenIndexTensorOp> {
     }
     auto inputSizes = inputType.getSizes();
     int64_t inputRank = inputSizes.size();
-    auto outputType = cast<BaseTensorType>(op.getType());
-    if (!outputType.hasSizes()) {
-      return rewriter.notifyMatchFailure(
-          op, "only output with shape information is supported");
-    }
-    auto outputRank = outputType.getSizes().size();
 
     auto isTensor = [](Value v) {
       return v.getType().isa<Torch::BaseTensorType>();
@@ -7603,19 +7598,15 @@ class DecomposeAtenIndexTensorOp : public OpRewritePattern<AtenIndexTensorOp> {
 
     // directly replace aten.Index.Tensor with aten.index.Tensor_hacked_twin
     if (llvm::all_of(indices, isTensor)) {
-      if (indices.size() == 0) {
-        return rewriter.notifyMatchFailure(
-            op, "the indices is empty, it should be folded as a nop");
-      }
       // By default, we regard the first index type as the list element type.
       auto indexElemType = indices[0]
                                .getType()
                                .template cast<BaseTensorType>()
                                .getWithSizesAndDtype(std::nullopt, nullptr);
-      auto newIndex = rewriter.create<PrimListConstructOp>(
+      auto newIndices = rewriter.create<PrimListConstructOp>(
           loc, Torch::ListType::get(indexElemType), indices);
-      rewriter.replaceOpWithNewOp<AtenIndexTensorHackedTwinOp>(op, op.getType(),
-                                                               input, newIndex);
+      rewriter.replaceOpWithNewOp<AtenIndexTensorHackedTwinOp>(
+          op, op.getType(), input, newIndices);
       return success();
     }
 
@@ -7623,6 +7614,7 @@ class DecomposeAtenIndexTensorOp : public OpRewritePattern<AtenIndexTensorOp> {
         llvm::to_vector(llvm::map_range(indices, isTensor));
     for (int64_t i = indices.size(); i < inputRank; ++i)
       indexUsed.emplace_back(false);
+
     bool indexIsConsecutive = true;
     int64_t firstUsedIndex = -1;
     for (size_t i = 0; i < indices.size(); ++i) {
@@ -7634,17 +7626,15 @@ class DecomposeAtenIndexTensorOp : public OpRewritePattern<AtenIndexTensorOp> {
       }
     }
 
-    // use aten.permute to reorder the input
     Value newInput;
-    // `dims` stores the mapping from new index to the old index of input
-    // tensor.
-    SmallVector<int64_t> dims;
+    SmallVector<int64_t> newToOldDimMap;
+    // permute input to make the non-none indices consecutive.
     if (!indexIsConsecutive) {
       SmallVector<Value> dimValues;
       SmallVector<int64_t> permutedSizes;
       for (int i = 0; i < inputRank; i++) {
         if (indexUsed[i]) {
-          dims.emplace_back(i);
+          newToOldDimMap.emplace_back(i);
           dimValues.emplace_back(rewriter.create<Torch::ConstantIntOp>(
               loc, rewriter.getI64IntegerAttr(i)));
           permutedSizes.emplace_back(inputSizes[i]);
@@ -7652,7 +7642,7 @@ class DecomposeAtenIndexTensorOp : public OpRewritePattern<AtenIndexTensorOp> {
       }
       for (int i = 0; i < inputRank; i++) {
         if (!indexUsed[i]) {
-          dims.emplace_back(i);
+          newToOldDimMap.emplace_back(i);
           dimValues.emplace_back(rewriter.create<Torch::ConstantIntOp>(
               loc, rewriter.getI64IntegerAttr(i)));
           permutedSizes.emplace_back(inputSizes[i]);
@@ -7668,66 +7658,100 @@ class DecomposeAtenIndexTensorOp : public OpRewritePattern<AtenIndexTensorOp> {
     } else {
       newInput = input;
       for (int i = 0; i < inputRank; i++) {
-        dims.emplace_back(i);
+        newToOldDimMap.emplace_back(i);
       }
     }
 
-    // manually generate new indices.
-    SmallVector<Value> listElements(inputRank);
+    auto newIndeicesInfo = createNewIndices(op, rewriter, newInput, indices,
+                                            newToOldDimMap, indexUsed);
+    if (failed(newIndeicesInfo)) {
+      return rewriter.notifyMatchFailure(op, "failed to replcae `None` index");
+    }
+    rewriter.replaceOpWithNewOp<Torch::AtenIndexTensorHackedTwinOp>(
+        op, op.getType(), newInput, *newIndeicesInfo);
+    return success();
+  }
+};
 
-    int64_t trailingDimCnt = 0;
-    int64_t i;
-    // handle trailing none index.
-    for (i = inputRank - 1; i >= 0; --i) {
-      int64_t oldI = dims[i];
-      if (indexUsed[oldI])
-        break;
-      Value v =
-          createIndexToReplaceNone(op, rewriter, newInput, i, inputSizes[oldI]);
-      auto vInfo =
-          unsqueezeTensorAtTrailingDim(op, rewriter, v, trailingDimCnt);
-      if (failed(vInfo)) {
-        return rewriter.notifyMatchFailure(op, "failed to unsqueeze tensor");
-      }
-      listElements[i] = *vInfo;
-      trailingDimCnt++;
+// The goal of this pattern is to eliminate `None` index in aten.inde_put-like
+// ops' `indices` param and transform it to aten.index_put.hacked_twin, for the
+// ease of various backend.
+template <typename AtenIndexPutLikeOpT>
+class DecomposeAtenIndexPutLikeOp
+    : public OpRewritePattern<AtenIndexPutLikeOpT> {
+public:
+  using OpRewritePattern<AtenIndexPutLikeOpT>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(AtenIndexPutLikeOpT op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    SmallVector<Value> indices;
+    if (!getListConstructElements(op.getIndices(), indices))
+      return rewriter.notifyMatchFailure(op,
+                                         "failed to get elements of `indices`");
+
+    auto input = op.getSelf();
+    auto inputType = input.getType().template cast<BaseTensorType>();
+    if (!inputType.hasSizes()) {
+      return rewriter.notifyMatchFailure(
+          op, "only input with shape information is supported");
+    }
+    auto inputSizes = inputType.getSizes();
+    int64_t inputRank = inputSizes.size();
+
+    auto isTensor = [](Value v) {
+      return v.getType().isa<Torch::BaseTensorType>();
+    };
+
+    // directly replace current op with aten.index_put.hacked_twin
+    if (llvm::all_of(indices, isTensor)) {
+      // By default, we regard the first index type as the list element type.
+      auto indexElemType = indices[0]
+                               .getType()
+                               .template cast<BaseTensorType>()
+                               .getWithSizesAndDtype(std::nullopt, nullptr);
+      auto newIndex = rewriter.create<PrimListConstructOp>(
+          loc, Torch::ListType::get(indexElemType), indices);
+      rewriter.replaceOpWithNewOp<AtenIndexPutHackedTwinOp>(
+          op, op.getType(), input, newIndex, op.getValues(),
+          op.getAccumulate());
+      return success();
     }
-    // handle non-none index in between.
-    for (; i >= 0; --i) {
-      int64_t oldI = dims[i];
-      if (!indexUsed[oldI])
+
+    SmallVector<bool> indexUsed =
+        llvm::to_vector(llvm::map_range(indices, isTensor));
+    for (int64_t i = indices.size(); i < inputRank; ++i)
+      indexUsed.emplace_back(false);
+
+    // check if non-None index is consecutive
+    bool indexIsConsecutive = true;
+    int64_t firstUsedIndex = -1;
+    for (size_t i = 0; i < indices.size(); ++i) {
+      if (indexUsed[i] && firstUsedIndex == -1) {
+        firstUsedIndex = i;
+      } else if (indexUsed[i] && !indexUsed[i - 1]) {
+        indexIsConsecutive = false;
         break;
-      auto vInfo = unsqueezeTensorAtTrailingDim(op, rewriter, indices[oldI],
-                                                trailingDimCnt);
-      if (failed(vInfo)) {
-        return rewriter.notifyMatchFailure(op, "failed to unsqueeze tensor");
       }
-      listElements[i] = *vInfo;
+    }
+    if (!indexIsConsecutive) {
+      return rewriter.notifyMatchFailure(
+          op, "non consecutive indices is not supported");
     }
 
-    // handle possible leading none dimensions.
-    for (; i >= 0; --i) {
-      int64_t oldI = dims[i];
-      if (indexUsed[oldI]) {
-        return rewriter.notifyMatchFailure(
-            op, "the indices are still unconsecutive after reordering input "
-                "tensor");
-      }
-      Value v =
-          createIndexToReplaceNone(op, rewriter, newInput, i, inputSizes[oldI]);
-      auto vInfo =
-          unsqueezeTensorAtTrailingDim(op, rewriter, v, outputRank - 1 - i);
-      if (failed(vInfo)) {
-        return rewriter.notifyMatchFailure(op, "failed to unsqueeze tensor");
-      }
-      listElements[i] = *vInfo;
+    SmallVector<int64_t> newToOldDimMap;
+    for (int i = 0; i < inputRank; i++) {
+      newToOldDimMap.emplace_back(i);
     }
 
-    auto listElemType = ValueTensorType::get(context, std::nullopt, nullptr);
-    auto newIndexList = rewriter.create<Torch::PrimListConstructOp>(
-        loc, Torch::ListType::get(listElemType), listElements);
-    rewriter.replaceOpWithNewOp<Torch::AtenIndexTensorHackedTwinOp>(
-        op, op.getType(), newInput, newIndexList);
+    auto newIndicesInfo = createNewIndices(op, rewriter, input, indices,
+                                           newToOldDimMap, indexUsed);
+    if (failed(newIndicesInfo)) {
+      return rewriter.notifyMatchFailure(op, "failed to replace `None` index");
+    }
+    rewriter.replaceOpWithNewOp<AtenIndexPutHackedTwinOp>(
+        op, op.getType(), input, *newIndicesInfo, op.getValues(),
+        op.getAccumulate());
     return success();
   }
 };
@@ -8020,18 +8044,19 @@ class DecomposeComplexOpsPass
     addPatternIfTargetOpIsIllegal<DecomposeAtenMishOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenFullLikeOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenNewFullOp>(patterns);
-    addPatternIfTargetOpIsIllegal<DecomposeAtenIndexPutOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenExpandAsOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAten_ToCopyOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenCopyOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenDropoutOp>(patterns);
     addPatternIfTargetOpIsIllegal<DeomposeAtenNativeDropoutOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenNewEmptyOp>(patterns);
-    addPatternIfTargetOpIsIllegal<DecomposeAtenIndexPutHackedTwinOp>(patterns);
-    addPatternIfTargetOpIsIllegal<DecomposeAten_IndexPutImpl_HackedTwinOp>(
-        patterns);
-    addPatternIfTargetOpIsIllegal<DecomposeAten_UnsafeIndexPutHackedTwinOp>(
+    addPatternIfTargetOpIsIllegal<DecomposeAtenIndexTensorOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenIndexPutLikeOp<AtenIndexPutOp>>(
         patterns);
+    addPatternIfTargetOpIsIllegal<
+        DecomposeAtenIndexPutLikeOp<Aten_UnsafeIndexPutHackedTwinOp>>(patterns);
+    addPatternIfTargetOpIsIllegal<
+        DecomposeAtenIndexPutLikeOp<Aten_IndexPutImplOp>>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenPadOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenToDtypeLayoutOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenToDeviceOp>(patterns);
@@ -8106,7 +8131,6 @@ class DecomposeComplexOpsPass
     addPatternIfTargetOpIsIllegal<DecomposeAtenTypeAsOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenTileOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenReshapeAsOp>(patterns);
-    addPatternIfTargetOpIsIllegal<DecomposeAtenIndexTensorOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenTriuOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenLinalgNormOp>(patterns);
     // More specific conv ops
diff --git a/lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp b/lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp
index cceee7e82dd1..7eedb2c6053e 100644
--- a/lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp
+++ b/lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp
@@ -466,13 +466,14 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenMishOp>();
   target.addIllegalOp<AtenFullLikeOp>();
   target.addIllegalOp<AtenNewFullOp>();
-  target.addIllegalOp<AtenIndexPutOp>();
   target.addIllegalOp<AtenExpandAsOp>();
   target.addIllegalOp<Aten_ToCopyOp>();
   target.addIllegalOp<AtenDropoutOp>();
   target.addIllegalOp<AtenNativeDropoutOp>();
   target.addIllegalOp<AtenNewEmptyOp>();
-  target.addIllegalOp<AtenIndexPutHackedTwinOp>();
+  target.addIllegalOp<AtenIndexTensorOp>();
+  target.addIllegalOp<AtenIndexPutOp>();
+  target.addIllegalOp<Aten_IndexPutImplOp>();
   target.addIllegalOp<Aten_UnsafeIndexPutHackedTwinOp>();
   target.addIllegalOp<AtenPadOp>();
   target.addIllegalOp<AtenPreluOp>();
@@ -500,7 +501,6 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<Aten_EmbeddingBagOp>();
   target.addIllegalOp<AtenLiftFreshCopyOp>();
   target.addIllegalOp<AtenLerpScalarOp>();
-  target.addIllegalOp<AtenIndexTensorOp>();
   target.addIllegalOp<AtenMseLossOp>();
   target.addIllegalOp<AtenRandintLowOp>();
   target.addIllegalOp<AtenRandintOp>();
diff --git a/projects/pt1/e2e_testing/xfail_sets.py b/projects/pt1/e2e_testing/xfail_sets.py
index 70de9c28c200..dbf1b6959605 100644
--- a/projects/pt1/e2e_testing/xfail_sets.py
+++ b/projects/pt1/e2e_testing/xfail_sets.py
@@ -1831,8 +1831,6 @@
     "IndexPutHackedTwin1DIntNonAccumulateModule_basic",
     "IndexPutImpl1DFloatNonAccumulateModule_basic",
     "IndexPutImpl1DIntNonAccumulateModule_basic",
-    "IndexPutImpl2DNoneIndexBroadcastStaticModule_basic",
-    "IndexPutImpl2DNoneIndexStaticModule_basic",
     "IndexTensorModule3dInputStatic_basic",
     "IndexTensorMultiIndexStaticModule_basic",
     "IndexTensorStaticModule_basic",
@@ -2128,6 +2126,8 @@
     "ElementwisePreluModule_basic",
     "ElementwisePreluStaticModule_basic",
     "ElementwiseLogSigmoidModule_basic",
+    "IndexPutImpl1DFloatNonAccumulateModule_basic",
+    "IndexPutImpl1DIntNonAccumulateModule_basic",
     # It appears that you're trying to get value out of a tracing tensor
     "PrimListUnpackNumMismatchModule_basic",
     # RuntimeError: shape '[2, -1, 6]' is invalid for input of size 210