diff --git a/lib/Conversion/TorchToLinalg/DataMovement.cpp b/lib/Conversion/TorchToLinalg/DataMovement.cpp
index 452215b35..c075babf7 100644
--- a/lib/Conversion/TorchToLinalg/DataMovement.cpp
+++ b/lib/Conversion/TorchToLinalg/DataMovement.cpp
@@ -366,11 +366,7 @@ public:
 
     auto [inputShape, outputShape] =
         getInputAndOutputShape(op.getSelf(), outputSizeTorchInt, resultType.getShape());
-    llvm::errs() << "inputShape: ";
-    llvm::interleaveComma(inputShape, llvm::errs());
 
-    llvm::errs() << " \noutput shape: ";
-    llvm::interleaveComma(outputShape, llvm::errs());
 
     // Currently, we only handle the cases where each dimension is either
     // being expanded or collapsed. We do not handle cases where it's neither
@@ -395,10 +391,6 @@ public:
                        m_TorchTensorSizeInt(op.getSelf(), &inputDim))) {
         unchangedDims.push_back(std::make_pair(inputDim, outputDim));
 
-        llvm::errs() << "inputDim: ";
-	llvm::errs() << inputDim;
-        llvm::errs() << " \noutput Dim: ";
-        llvm::errs() << outputDim;
       }
     }
     // Mark the end of the input/output shapes
@@ -440,10 +432,10 @@ public:
       // Used for ensuring that we don't have an ambiguous expansion
       bool assumedDynamicDimNotSplit = false;
       while (inputDim < nextUnchangedInput && outputDim < nextUnchangedOutput) {
-        auto inputShapeSlice = // 1, ?, 4096
+        auto inputShapeSlice = //  ?, 4096
             MutableArrayRef<int64_t>(inputShape)
                 .slice(inputDim, nextUnchangedInput - inputDim);
-        auto outputShapeSlice = // ?, 4096
+        auto outputShapeSlice = //1, ?, 4096
             MutableArrayRef<int64_t>(outputShape)
                 .slice(outputDim, nextUnchangedOutput - outputDim);
         SmallVector<int64_t> inputSliceIndices;
@@ -483,13 +475,21 @@ public:
           /// known to have the same number of elements.
         } else if (inputShapeSlice[0] == kUnknownSize) {
           // If the input is dynamic, assume it is not split
-          checkDimEqualHelper(rewriter, loc, inputSize[inputDim],
-                              outputSizeInt[outputDim]);
+	  // Elide any unit dims in the output and checkDimEqual on dynamic dims
+
+	  int64_t idx = 0;
+          while (idx < outputShapeSlice.size() && outputShapeSlice[idx] == 1) {
+            outputSliceIndices.push_back(idx++);
+	  }
+
+
+//checkDimEqualHelper(rewriter, loc, inputSize[inputDim],
+  //                            outputSizeInt[idx]);
           // If output dimension is not dynamic, improve static information of
           // input
-          inputShape[inputDim] = outputShape[outputDim];
+          inputShape[inputDim] = outputShape[idx];
           inputSliceIndices.push_back(0);
-          outputSliceIndices.push_back(0);
+          outputSliceIndices.push_back(idx);
           assumedDynamicDimNotSplit = true;
         } else if (inputShapeSlice[0] == 1 && outputShapeSlice[0] == kUnknownSize) {
 	  int64_t idx = 0;
diff --git a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
index 6136db092..58776d0bb 100644
--- a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
+++ b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
@@ -1543,6 +1543,8 @@ public:
   using OpRewritePattern::OpRewritePattern;
   LogicalResult matchAndRewrite(AtenMaskedFillScalarOp op,
                                 PatternRewriter &rewriter) const override {
+    
+    llvm::errs() << "dbg decompose fill_scalar\n";
     Location loc = op.getLoc();
     auto resType = op.getType().cast<BaseTensorType>();
     if (!resType.hasDtype()) {
@@ -4429,13 +4431,18 @@ public:
   LogicalResult matchAndRewrite(AtenEmptyStridedOp op,
                                 PatternRewriter &rewriter) const override {
     SmallVector<int64_t> sizeListInts, strideListInts;
-    if (!matchPattern(op.getSize(), m_TorchListOfConstantInts(sizeListInts)))
-      return rewriter.notifyMatchFailure(
-          op, "all size list elements must be constant ints");
-    if (!matchPattern(op.getStride(),
-                      m_TorchListOfConstantInts(strideListInts)))
-      return rewriter.notifyMatchFailure(
-          op, "all stride list elements must be constant ints");
+    llvm::errs() << "dbg Try decompose empty strided\n";
+    //if (!matchPattern(op.getSize(), m_TorchListOfConstantInts(sizeListInts))){
+
+    //  llvm::errs() << "dbg all size list ele const ints\n";
+    //  return rewriter.notifyMatchFailure(
+    //      op, "all size list elements must be constant ints");
+    //}
+    //if (!matchPattern(op.getStride(),
+    //                  m_TorchListOfConstantInts(strideListInts))){
+    //  llvm::errs() << "dbg all stride list ele const ints\n";
+    //  return rewriter.notifyMatchFailure(
+    //      op, "all stride list elements must be constant ints");}
 
     // We only support the cases with default stride values.
     // For ex: aten.new_empty_strided(self, size=[2, 3, 4], stride=[12, 4, 1])
@@ -4451,16 +4458,18 @@ public:
         break;
       }
     }
-    if (!isDefaultStride)
+    if (!isDefaultStride){
+      llvm::errs() << "dbg non default strides\n";
       return rewriter.notifyMatchFailure(
           op, "only default strides supported for new_empty_strided op");
-
+    }
     Value noneVal = rewriter.create<ConstantNoneOp>(op.getLoc());
 
     rewriter.replaceOpWithNewOp<AtenEmptyMemoryFormatOp>(
         op, op.getType(), op.getSize(), op.getDtype(), op.getLayout(), op.getDevice(),
         op.getPinMemory(), /*memoryFormat=*/noneVal);
 
+    llvm::errs() << "dbg success\n";
     return success();
 
 
diff --git a/lib/Dialect/Torch/Transforms/ReduceOpVariants.cpp b/lib/Dialect/Torch/Transforms/ReduceOpVariants.cpp
index 3b30b8a82..e7db5f678 100644
--- a/lib/Dialect/Torch/Transforms/ReduceOpVariants.cpp
+++ b/lib/Dialect/Torch/Transforms/ReduceOpVariants.cpp
@@ -227,12 +227,14 @@ public:
     if (!op->hasTrait<Torch::OpTrait::IsTrailingUnderscoreInplaceVariant>())
       return rewriter.notifyMatchFailure(op, "is not trailing_ variant");
 
+    llvm::errs() << "dbg op name: " << op->getName().getStringRef() << "\n";
     SmallVector<StringRef> fragments;
     llvm::SplitString(op->getName().getStringRef(), fragments, ".");
     assert(fragments.size() >= 3 && fragments[2].endswith("_") &&
            "IsTrailingUnderscoreInplaceVariant incorrectly applied");
     fragments[2] = fragments[2].drop_back();
     std::string noUnderscoreName = llvm::join(fragments, ".");
+    llvm::errs() << "dbg fragments good\n";
 
     OperationState state(op->getLoc(), noUnderscoreName);
     state.addTypes(op->getResultTypes());
@@ -241,14 +243,19 @@ public:
     // Note: No successors or regions. Torch JIT operators don't have any.
     assert(op->getNumRegions() == 0 && op->getNumSuccessors() == 0 &&
            "Torch JIT operators shouldn't have regions or successors");
+    llvm::errs() << "dbg good regions, good successors\n";
 
     Operation *newOp = rewriter.create(state);
     auto tensor =
         rewriter.create<CopyToValueTensorOp>(op->getLoc(), newOp->getResult(0));
+    
+    llvm::errs() << "dbg create copy_to_value_tensor\n"; 
     createOverwriteTensorContents(rewriter, op->getLoc(), tensor,
                                   op->getOperand(0));
+
     rewriter.replaceOp(op, op->getOperand(0));
 
+    llvm::errs() << "dbg success\n"; 
     return success();
   }
 };