diff --git a/include/tvm/topi/detail/ravel_unravel.h b/include/tvm/topi/detail/ravel_unravel.h
index dd7bcac09a041..e91d6afb666a5 100644
--- a/include/tvm/topi/detail/ravel_unravel.h
+++ b/include/tvm/topi/detail/ravel_unravel.h
@@ -44,7 +44,9 @@ using namespace tvm::te;
  */
 inline PrimExpr RavelIndex(Array<PrimExpr> indices, Array<PrimExpr> shape) {
   ICHECK_EQ(indices.size(), shape.size()) << "indices and shape must have equal size";
-  ICHECK_GT(indices.size(), 0) << "indices must not be empty";
+  if (indices.size() == 0U) {
+    return 0;
+  }
   PrimExpr idx;
   for (size_t i = 0; i < indices.size(); ++i) {
     if (i == 0) {
diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index acc33d73e826b..f56243d83e88e 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -1444,7 +1444,16 @@ def linear(self, inputs, input_types):
         # 0 - input
         # 1 - weight
         bias = inputs[2]
-        mm_out = self.matmul(inputs[:2], input_types[:2])
+        a_shape = self.infer_shape_with_prelude(inputs[0])
+        b_shape = self.infer_shape_with_prelude(inputs[1])
+        if len(a_shape) == 2 and len(b_shape) == 2:
+            mm_out = _op.nn.dense(inputs[0], inputs[1])
+        elif len(b_shape) == 1:
+            mm_out = self.matmul([inputs[0], inputs[1]], input_types[:2])
+        else:
+            mm_out = self.matmul(
+                [inputs[0], _op.transpose(inputs[1], axes=(1, 0))], input_types[:2]
+            )
         if isinstance(bias, _expr.Expr):
             bias_ndims = len(self.infer_shape_with_prelude(bias))
             if bias_ndims == 1:
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index be4d74ed205ac..ae2bedac0b29b 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -1569,8 +1569,10 @@ def forward(self, input, weight):
             return F.linear(input, weight)
 
     input2d = torch.rand([2, 2]).float()
+    input3d = torch.rand([4, 3, 2]).float()
     weight1d = torch.rand([2]).float()
     weight2d = torch.rand([2, 2]).float()
+    weight3x2 = torch.rand([3, 2]).float()
     bias1d = torch.rand([2]).float()
     bias2d = torch.rand([2, 2]).float()
     # 2D input, 2D weight, 1D bias
@@ -1579,9 +1581,12 @@ def forward(self, input, weight):
     verify_model(Linear(), input_data=[input2d, weight2d, bias2d])
     # 2D input, 2D weight, no bias
     verify_model(LinearNoBias(), input_data=[input2d, weight2d])
+    verify_model(LinearNoBias(), input_data=[input2d, weight3x2])
     # 2D input, 1D weight, 1D bias is not supported by torch.linear()
     # 2D input, 1D weight, no bias
     verify_model(LinearNoBias(), input_data=[input2d, weight1d])
+    # 3D input, 2D weight, no bias
+    verify_model(LinearNoBias(), input_data=[input3d, weight3x2])
     # TODO: Add the following cases when matmul(1D, _) is supported by TVM
     # 1D input, 2D weight, 1D bias
     # 1D input, 2D weight, no bias
@@ -3939,6 +3944,7 @@ def test_fn(is_sorted, return_inverse, return_counts):
     test_forward_logsoftmax()
     test_forward_sigmoid()
     test_forward_dense()
+    test_forward_linear()
     test_forward_avgpool1d()
     test_forward_avgpool2d()
     test_forward_avgpool3d()
diff --git a/tests/python/relay/test_op_level3.py b/tests/python/relay/test_op_level3.py
index 65e8b01fd2b3d..51dadb21b2a25 100644
--- a/tests/python/relay/test_op_level3.py
+++ b/tests/python/relay/test_op_level3.py
@@ -293,6 +293,7 @@ def verify_reshape(shape, newshape, oshape):
     verify_reshape((2, 3, 4), (-3, -2), (6, 4))
     verify_reshape((2, 3, 4), (-4, 1, 2, -2), (1, 2, 3, 4))
     verify_reshape((2, 3, 4), (2, -4, -1, 3, -2), (2, 1, 3, 4))
+    verify_reshape((1,), (), ())
 
 
 def test_reshape_fail():