diff --git a/imperative/python/megengine/module/activation.py b/imperative/python/megengine/module/activation.py
index f2f7c9ee561b192d719174235077f57b82bae458..2a39c8b0dad275953235ca1c5c4eef98a752082f 100644
--- a/imperative/python/megengine/module/activation.py
+++ b/imperative/python/megengine/module/activation.py
@@ -239,12 +239,6 @@ class PReLU(Module):
             self.weight = Parameter(data=[init])
 
     def forward(self, inputs):
-        assert self.weight.shape == (1,) or self.weight.shape == (
-            1,
-            int(inputs.shape[1]),
-            1,
-            1,
-        ), "invalid weight's shape"
         return prelu(inputs, self.weight)
 
 
diff --git a/imperative/python/test/unit/module/test_activation.py b/imperative/python/test/unit/module/test_activation.py
index 993da4ea0315ce62dd5c035441704476a6b4bd12..84f8a9e15a5512cbd7f8a1e043b0146974b274e4 100644
--- a/imperative/python/test/unit/module/test_activation.py
+++ b/imperative/python/test/unit/module/test_activation.py
@@ -7,9 +7,11 @@
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import numpy as np
+import pytest
 
 import megengine as mge
-from megengine.module import LeakyReLU
+from megengine.jit.tracing import set_symbolic_shape
+from megengine.module import LeakyReLU, PReLU
 
 
 def test_leaky_relu():
@@ -21,3 +23,19 @@ def test_leaky_relu():
 
     np_output = np.maximum(0, data) + negative_slope * np.minimum(0, data)
     np.testing.assert_equal(output.numpy(), np_output)
+
+
+@pytest.mark.parametrize("shape", [(1, 64, 15, 15), (64,)])
+@pytest.mark.parametrize("use_symbolic", [False, True])
+def test_prelu(shape, use_symbolic):
+    old_flag = set_symbolic_shape(use_symbolic)
+    data = np.random.random(size=shape)
+
+    num_channel = 1 if len(shape) == 1 else shape[1]
+    prelu = PReLU(num_parameters=num_channel, init=0.25)
+    output = prelu(mge.Tensor(data))
+
+    np_output = np.maximum(data, 0) + prelu.weight.numpy() * np.minimum(data, 0)
+    set_symbolic_shape(old_flag)
+
+    np.testing.assert_allclose(output.numpy(), np_output, atol=1e-5)