下面的tensorflow.python.keras.saving.hdf5_format中的私人帮助函数似乎可以解决问题。该函数执行更一般的任务，即在CuDNNGRU / GRU和CuDNNLSTM / LSTM格式之间转换权重，因此它不仅对我的用例有用。该功能似乎起源于独立的Keras中的this pull request。

import numpy as np


def _convert_rnn_weights(layer,weights):
  """Converts weights for RNN layers between native and CuDNN format.

  Input kernels for each gate are transposed and converted between Fortran
  and C layout,recurrent kernels are transposed. For LSTM biases are summed/
  split in half,for GRU biases are reshaped.

  Weights can be converted in both directions between `LSTM` and`CuDNNSLTM`
  and between `CuDNNGRU` and `GRU(reset_after=True)`. Default `GRU` is not
  compatible with `CuDNNGRU`.

  For missing biases in `LSTM`/`GRU` (`use_bias=False`) no conversion is made.

  Arguments:
      layer: Target layer instance.
      weights: List of source weights values (input kernels,recurrent
          kernels,[biases]) (Numpy arrays).

  Returns:
      A list of converted weights values (Numpy arrays).

  Raises:
      ValueError: for incompatible GRU layer/weights or incompatible biases
  """


  def transform_kernels(kernels,func,n_gates):
    """Transforms kernel for each gate separately using given function.

    Arguments:
        kernels: Stacked array of kernels for individual gates.
        func: Function applied to kernel of each gate.
        n_gates: Number of gates (4 for LSTM,3 for GRU).

    Returns:
        Stacked array of transformed kernels.
    """
    return np.hstack([func(k) for k in np.hsplit(kernels,n_gates)])


  def transpose_input(from_cudnn):
    """Makes a function that transforms input kernels from/to CuDNN format.

    It keeps the shape,but changes between the layout (Fortran/C). Eg.:

    ```
    Keras                 CuDNN
    [[0,1,2],<--->  [[0,2,4],[3,4,5]]          [1,3,5]]
    ```

    It can be passed to `transform_kernels()`.

    Arguments:
        from_cudnn: `True` if source weights are in CuDNN format,`False`
            if they're in plain Keras format.

    Returns:
        Function that converts input kernel to the other format.
    """
    order = 'F' if from_cudnn else 'C'


    def transform(kernel):
      return kernel.T.reshape(kernel.shape,order=order)


    return transform


  target_class = layer.__class__.__name__


  # convert the weights between CuDNNLSTM and LSTM
  if target_class in ['LSTM','CuDNNLSTM'] and len(weights) == 3:
    # determine if we're loading a CuDNNLSTM layer
    # from the number of bias weights:
    # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4)
    # if there's no bias weight in the file,skip this conversion
    units = weights[1].shape[0]
    bias_shape = weights[2].shape
    n_gates = 4


    if bias_shape == (2 * units * n_gates,):
      source = 'CuDNNLSTM'
    elif bias_shape == (units * n_gates,):
      source = 'LSTM'
    else:
      raise ValueError('Invalid bias shape: ' + str(bias_shape))


    def convert_lstm_weights(weights,from_cudnn=True):
      """Converts the weights between CuDNNLSTM and LSTM.

      Arguments:
        weights: Original weights.
        from_cudnn: Indicates whether original weights are from CuDNN layer.

      Returns:
        Updated weights compatible with LSTM.
      """


      # Transpose (and reshape) input and recurrent kernels
      kernels = transform_kernels(weights[0],transpose_input(from_cudnn),n_gates)
      recurrent_kernels = transform_kernels(weights[1],lambda k: k.T,n_gates)
      if from_cudnn:
        # merge input and recurrent biases into a single set
        biases = np.sum(np.split(weights[2],axis=0),axis=0)
      else:
        # Split single set of biases evenly to two sets. The way of
        # splitting doesn't matter as long as the two sets sum is kept.
        biases = np.tile(0.5 * weights[2],2)
      return [kernels,recurrent_kernels,biases]


    if source != target_class:
      weights = convert_lstm_weights(weights,from_cudnn=source == 'CuDNNLSTM')


  # convert the weights between CuDNNGRU and GRU(reset_after=True)
  if target_class in ['GRU','CuDNNGRU'] and len(weights) == 3:
    # We can determine the source of the weights from the shape of the bias.
    # If there is no bias we skip the conversion since
    # CuDNNGRU always has biases.


    units = weights[1].shape[0]
    bias_shape = weights[2].shape
    n_gates = 3


    def convert_gru_weights(weights,from_cudnn=True):
      """Converts the weights between CuDNNGRU and GRU.

      Arguments:
        weights: Original weights.
        from_cudnn: Indicates whether original weights are from CuDNN layer.

      Returns:
        Updated weights compatible with GRU.
      """


      kernels = transform_kernels(weights[0],n_gates)
      biases = np.array(weights[2]).reshape((2,-1) if from_cudnn else -1)
      return [kernels,biases]


    if bias_shape == (2 * units * n_gates,):
      source = 'CuDNNGRU'
    elif bias_shape == (2,units * n_gates):
      source = 'GRU(reset_after=True)'
    elif bias_shape == (units * n_gates,):
      source = 'GRU(reset_after=False)'
    else:
      raise ValueError('Invalid bias shape: ' + str(bias_shape))


    if target_class == 'CuDNNGRU':
      target = 'CuDNNGRU'
    elif layer.reset_after:
      target = 'GRU(reset_after=True)'
    else:
      target = 'GRU(reset_after=False)'


    # only convert between different types
    if source != target:
      types = (source,target)
      if 'GRU(reset_after=False)' in types:
        raise ValueError('%s is not compatible with %s' % types)
      if source == 'CuDNNGRU':
        weights = convert_gru_weights(weights,from_cudnn=True)
      elif source == 'GRU(reset_after=True)':
        weights = convert_gru_weights(weights,from_cudnn=False)


  return weights

对于我的用例（将CuDNNGRU权重放入GRU中），使用此函数的解决方案如下：

# cudnn_gru and gru are built CuDNNGRU and GRU layers,respectively
kernel,recurrent_kernel,bias = _convert_rnn_weights(
    layer=gru,weights=[
        cudnn_gru.kernel.numpy(),cudnn_gru.recurrent_kernel.numpy(),cudnn_gru.bias.numpy(),],)
gru.cell.kernel.assign(kernel)
gru.cell.recurrent_kernel.assign(recurrent_kernel)
gru.cell.bias.assign(bias)

请注意，要使用tf.keras.layers.GRU的cuDNN兼容实现，必须使用use a specific combination of parameters（尤其是use_bias=True）。