Keras-MXNet directly crash when the hidden layer size of LSTM is 1

[maybeLee]

When I am using Keras to construct a one LSTM layer DL model with mxnet backend, Keras-MXNet will directly crash with the following traceback:

MXNET:
Traceback (most recent call last):
  File "exp1/job1/scripts/generation/script_prediction.py", line 123, in <module>
    _get_prediction(bk=bk, x=x[:1500], model_path=flags.model_path, batch_size=batch_size)
  File "exp1/job1/scripts/generation/script_prediction.py", line 35, in _get_prediction
    model = keras.models.load_model(model_path, custom_objects=custom_objects())
  File "lib/python3.6/site-packages/keras/engine/saving.py", line 496, in load_model
    model = _deserialize_model(f, custom_objects, compile)
  File "lib/python3.6/site-packages/keras/engine/saving.py", line 302, in _deserialize_model
    model = model_from_config(model_config, custom_objects=custom_objects)
  File "lib/python3.6/site-packages/keras/engine/saving.py", line 535, in model_from_config
    return deserialize(config, custom_objects=custom_objects)
  File "lib/python3.6/site-packages/keras/layers/__init__.py", line 55, in deserialize
    printable_module_name='layer')
  File "lib/python3.6/site-packages/keras/utils/generic_utils.py", line 145, in deserialize_keras_object
    list(custom_objects.items())))
  File "lib/python3.6/site-packages/keras/engine/sequential.py", line 301, in from_config
    model.add(layer)
  File "lib/python3.6/site-packages/keras/engine/sequential.py", line 165, in add
    layer(x)
  File "lib/python3.6/site-packages/keras/layers/recurrent.py", line 532, in __call__
    return super(RNN, self).__call__(inputs, **kwargs)
  File "lib/python3.6/site-packages/keras/engine/base_layer.py", line 444, in __call__
    self.build(unpack_singleton(input_shapes))
  File "lib/python3.6/site-packages/keras/layers/recurrent.py", line 493, in build
    self.cell.build(step_input_shape)
  File "lib/python3.6/site-packages/keras/layers/recurrent.py", line 1901, in build
    constraint=self.bias_constraint)
  File "lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "lib/python3.6/site-packages/keras/engine/base_layer.py", line 255, in add_weight
    weight = K.variable(initializer(shape),
  File "lib/python3.6/site-packages/keras/layers/recurrent.py", line 1893, in bias_initializer
    self.bias_initializer((self.units * 2,), *args, **kwargs),
  File "lib/python3.6/site-packages/keras/backend/mxnet_backend.py", line 94, in func_wrapper
    train_symbol = func(*args, **kwargs)
  File "lib/python3.6/site-packages/keras/backend/mxnet_backend.py", line 2060, in concatenate
    symbols = [t.symbol for t in tensors]
  File "lib/python3.6/site-packages/keras/backend/mxnet_backend.py", line 2060, in <listcomp>
    symbols = [t.symbol for t in tensors]
AttributeError: 'NDArray' object has no attribute 'symbol'

This crash bug happens when I change the hidden size of LSTM layer to be 1. If the hidden size is set to a value other than 1, this bug will not appear.

you can reproduce this bug by simply running the following script:

import os
import argparse
import sys
import warnings
parse = argparse.ArgumentParser()
parse.add_argument("--backend", type=str,default="mxnet", help="the name of backend")
flags, _ = parse.parse_known_args(sys.argv[1:])
os.environ["KERAS_BACKEND"]=flags.backend
import keras
from keras import initializers, layers
import numpy as np
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)
model = keras.models.Sequential()
model.add(layers.LSTM(1))
model.build((None, 49,1))
x = np.random.rand(10,49,1)
pred = model.predict(x)

The tested version of MXNet is 1.5.1 and the version of keras-mxnet is 2.2.4.2