Theano AdvancedSubtensor memory leak

gistfile1.py

import theano.tensor as T
import theano
import numpy as np
import gc

def freemem():
    gc.collect()
    gc.collect()
    gc.collect()
    return theano.sandbox.cuda.cuda_ndarray.cuda_ndarray.mem_info()[0] / 1024**2

def reproduce_problem():
    test_params=np.asarray(np.random.randn(5000 * 6000),'float32')
    
    some_vector = T.vector('some_vector') 
    some_matrix = some_vector.reshape((6000, 5000))
    
    lalala = T.constant(np.arange(6000))
    
    variables = theano.shared(np.ones((5000,),dtype='float32'))
    derp = T.sum(T.dot(some_matrix[lalala], variables))
    
    print("Avail. memory before compilation: %d MB" % freemem())
    obj = theano.function([some_vector], derp)
    print("Avail. memory after objective function compilation: %d MB" % freemem())
    grad_derp = T.grad(derp, some_vector)
    grad = theano.function([some_vector], grad_derp)
    print("Avail. memory after gradient function compilation: %d MB" % freemem())
    
    for i in range(3):
        obj(test_params)
        print("Avail. memory after objective evaluation: %d MB" % freemem())
        grad(test_params)
        print("Avail. memory after gradient evaluation: %d MB" % freemem())
       
reproduce_problem()