Theano crash when LSTM layer's hidden size is 1 using Keras backend

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 42, in _get_prediction
    pred = model.predict(x,batch_size=batch_size)
  File "lib/python3.6/site-packages/keras/engine/training.py", line 1164, in predict
    self._make_predict_function()
  File "lib/python3.6/site-packages/keras/engine/training.py", line 554, in _make_predict_function
    **kwargs)
  File "lib/python3.6/site-packages/keras/backend/theano_backend.py", line 1397, in function
    return Function(inputs, outputs, updates=updates, **kwargs)
  File "lib/python3.6/site-packages/keras/backend/theano_backend.py", line 1383, in __init__
    **kwargs)
  File "lib/python3.6/site-packages/theano/compile/function.py", line 317, in function
    output_keys=output_keys)
  File "lib/python3.6/site-packages/theano/compile/pfunc.py", line 486, in pfunc
    output_keys=output_keys)
  File "lib/python3.6/site-packages/theano/compile/function_module.py", line 1841, in orig_function
    fn = m.create(defaults)
  File "lib/python3.6/site-packages/theano/compile/function_module.py", line 1715, in create
    input_storage=input_storage_lists, storage_map=storage_map)
  File "lib/python3.6/site-packages/theano/gof/link.py", line 699, in make_thunk
    storage_map=storage_map)[:3]
  File "lib/python3.6/site-packages/theano/gof/vm.py", line 1091, in make_all
    impl=impl))
  File "lib/python3.6/site-packages/theano/scan_module/scan_op.py", line 741, in make_thunk
    self.validate_inner_graph()
  File "lib/python3.6/site-packages/theano/scan_module/scan_op.py", line 249, in validate_inner_graph
    (self.name, type_input, type_output))
TypeError: ('The following error happened while compiling the node', forall_inplace,gpu,scan_fn}(Shape_i{1}.0, GpuSubtensor{int64:int64:int8}.0, Rebroadcast{?,?,0}.0, GpuIncSubtensor{InplaceSet;:int64:}.0, Shape_i{1}.0, GpuSubtensor{::, int64:int64:}.0, InplaceGpuDimShuffle{x,0}.0, GpuSubtensor{::, int64:int64:}.0, GpuSubtensor{::, :int64:}.0, InplaceGpuDimShuffle{x,0}.0, GpuSubtensor{::, :int64:}.0, GpuSubtensor{::, int64:int64:}.0, InplaceGpuDimShuffle{x,0}.0, GpuSubtensor{::, int64:int64:}.0, GpuSubtensor{::, int64::}.0, InplaceGpuDimShuffle{x,0}.0, GpuSubtensor{::, int64::}.0), '\n', "Inconsistency in the inner graph of scan 'scan_fn' : an input and an output are associated with the same recurrent state and should have the same type but have type 'GpuArrayType<None>(float32, col)' and 'GpuArrayType<None>(float32, matrix)' respectively.")

import os
import argparse
import sys
import warnings
parse = argparse.ArgumentParser()
parse.add_argument("--backend", type=str,default="theano", 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)