diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index f0ba99291727..acc33d73e826 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -754,9 +754,13 @@ def relu(self, inputs, input_types):
         return _op.nn.relu(data)
 
     def prelu(self, inputs, input_types):
+        # Reference: https://pytorch.org/docs/stable/generated/torch.nn.PReLU.html#torch.nn.PReLU
         data = inputs[0]
-        alpha = inputs[1]
-        return _op.nn.prelu(data, alpha)
+        dim = self.get_dims(data)
+        ndims = len(dim)
+        axis = 0 if ndims == 1 else 1
+        alpha = _op.broadcast_to(inputs[1], (dim[axis]))
+        return _op.nn.prelu(data, alpha, axis)
 
     def leaky_relu(self, inputs, input_types):
         data = inputs[0]
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index 07f0d8e75c4d..be4d74ed205a 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -643,6 +643,10 @@ def test_forward_prelu():
     input_shape = [1, 3, 10, 10]
     input_data = torch.rand(input_shape).float()
     verify_model(torch.nn.PReLU(num_parameters=3).eval(), input_data=input_data)
+    # Test when input channel > 1 and num parameters = 1
+    verify_model(torch.nn.PReLU(num_parameters=1).eval(), input_data=input_data)
+    # Test when input dims < 2
+    verify_model(torch.nn.PReLU(num_parameters=1).eval(), input_data=torch.randn(2))
 
 
 @tvm.testing.uses_gpu