运行：model.train_and_evaluate（）时发生InvalidArgumentError

Question

InvalidArgumentError: No OpKernel was registered to support Op 'CudnnRNN' with these attrs.  Registered devices: [CPU,XLA_CPU], Registered kernels:
  <no registered kernels>

     [[{{node cu_dnnlstm/CudnnRNN}} = CudnnRNN[T=DT_FLOAT, direction="unidirectional", dropout=0, input_mode="linear_input", is_training=true, rnn_mode="lstm", seed=0, seed2=0](cu_dnnlstm/transpose, cu_dnnlstm/ExpandDims, cu_dnnlstm/ExpandDims_1, cu_dnnlstm/concat)]]

这是我的代码：

import tensorflow as tf
import logging
import numpy as np
import os
from collections import OrderedDict, defaultdict
from sogou_mrc.train.trainer import Trainer


class BaseModel(object):
    def __init__(self, vocab=None):
        self.vocab = vocab

        sess_conf = tf.ConfigProto()
        sess_conf.gpu_options.allow_growth = True
        self.session = tf.Session(config=sess_conf)
        self.initialized = False
        self.ema_decay = 0

    def __del__(self):
        self.session.close()

    def load(self, path, var_list=None):
        # var_list = None returns the list of all saveable variables
        logging.info('Loading model from %s' % path)
        saver = tf.train.Saver(var_list)
        checkpoint_path = tf.train.latest_checkpoint(path)
        saver.restore(self.session, save_path=checkpoint_path)
        self.initialized = True

    def save(self, path, global_step=None, var_list=None):
        saver = tf.train.Saver(var_list)
        saver.save(self.session, path, global_step=global_step)

    def _build_graph(self):
        raise NotImplementedError

    def compile(self, *input):
        raise NotImplementedError

    def get_best_answer(self, *input):
        raise NotImplementedError

    def train_and_evaluate(self, train_generator, eval_generator, evaluator, epochs=1, eposides=1,
                           save_dir=None, summary_dir=None, save_summary_steps=10):
        if not self.initialized:
            self.session.run(tf.global_variables_initializer())

        Trainer._train_and_evaluate(self, train_generator, eval_generator, evaluator, epochs=epochs,
                                    eposides=eposides,
                                    save_dir=save_dir, summary_dir=summary_dir, save_summary_steps=save_summary_steps)

    def evaluate(self, batch_generator, evaluator):
        Trainer._evaluate(self, batch_generator, evaluator)

    def inference(self, batch_generator):
        Trainer._inference(self, batch_generator)


model.train_and_evaluate(train_batch_generator, eval_batch_generator,evaluator, epochs=40, eposides=2)
model.evaluate(eval_batch_generator,eval_data,evaluator)