Addressed the problem with testing

Author: amemov

lib/Conversion/TorchToLinalg/Linear.cpp

@@ -1688,8 +1688,8 @@ class ConvertAtenOuterOp : public OpConversionPattern<AtenOuterOp> {
     if (failed(verifyLinalgCompatibleTypes(op, rewriter))) {
       return failure();
     }
-    auto lhsType = cast<RankedTensorType>(lhs.getType());
-    auto rhsType = cast<RankedTensorType>(rhs.getType());
+    auto lhsType = dyn_cast<RankedTensorType>(lhs.getType());
+    auto rhsType = dyn_cast<RankedTensorType>(rhs.getType());
 
     if (!lhsType || !rhsType)
       return rewriter.notifyMatchFailure(op,
@@ -1698,8 +1698,8 @@ class ConvertAtenOuterOp : public OpConversionPattern<AtenOuterOp> {
       return rewriter.notifyMatchFailure(
           op, "outer: expected 1D tensors for outer op lowering");
 
-    Value lhsDim = getDimOp(rewriter, loc, lhs, 1);
-    Value rhsDim = getDimOp(rewriter, loc, rhs, 1);
+    Value lhsDim = getDimOp(rewriter, loc, lhs, 0);
+    Value rhsDim = getDimOp(rewriter, loc, rhs, 0);
     Type elementType = lhsType.getElementType();
     Type newResultType = getTypeConverter()->convertType(op.getType());
 

projects/pt1/python/torch_mlir_e2e_test/test_suite/matmul.py

@@ -931,8 +931,8 @@ def __init__(self):
     @annotate_args(
         [
             None,
-            ([-1], torch.float32, True),
-            ([-1], torch.float32, True),
+            ([3], torch.float32, True),
+            ([3], torch.float32, True),
         ]
     )
     def forward(self, lhs, rhs):