[Bugfix][Relay][Frontend][Keras] Add a assertion to reject a invalid value for attribute units in RNN layers (#15334)

Author: jikechao

python/tvm/relay/frontend/keras.py

@@ -256,6 +256,8 @@ def _convert_dense(
     weightList = keras_layer.get_weights()
     weight = etab.new_const(weightList[0].transpose([1, 0]))
     params = {"weight": weight, "units": weightList[0].shape[1]}
+    units = list(weightList[0].shape)[1]
+    assert units > 0, "The value of units must be a positive integer"
     if input_shape is None:
         input_shape = keras_layer.input_shape
     input_dim = len(input_shape)
@@ -1010,6 +1012,7 @@ def _convert_lstm(
     if keras_layer.go_backwards:
         in_data = _op.reverse(in_data, axis=1)
     units = list(weightList[0].shape)[1]
+    assert units > 0, "The value of units must be a positive integer"
     time_steps = in_shape[1]
     in_data = _op.squeeze(in_data, axis=[0])
     in_data = _op.split(in_data, indices_or_sections=time_steps, axis=0)
@@ -1053,6 +1056,7 @@ def _convert_simple_rnn(
     if keras_layer.use_bias:
         in_bias = etab.new_const(weightList[2])
     units = list(weightList[0].shape)[1]
+    assert units > 0, "The value of units must be a positive integer"
     in_data = _op.nn.batch_flatten(in_data)
     ixh = _op.nn.dense(in_data, kernel_weight, units=units)
     if keras_layer.use_bias:
@@ -1082,6 +1086,7 @@ def _convert_gru(
     if keras_layer.use_bias:
         in_bias = etab.new_const(weightList[2])
     units = list(weightList[0].shape)[1]
+    assert units > 0, "The value of units must be a positive integer"
     in_data = _op.nn.batch_flatten(in_data)
     matrix_x = _op.nn.dense(in_data, kernel_weight, units=units)
     if keras_layer.use_bias:

tests/python/frontend/keras/test_forward.py

@@ -251,7 +251,7 @@ def test_forward_activations_except(self, keras_mod):
         ):
             act_funcs = [
                 keras_mod.layers.LeakyReLU(alpha=None),
-                keras_mod.layers.LEU(2, 3, 4),
+                keras_mod.layers.ELU(2, 3, 4),
                 keras_mod.layers.ReLU(threshold=None),
             ]
             data = keras_mod.layers.Input(shape=(2, 3, 4))