Tensorflow2 LSTM（cuDNN args）与tensorflow1 CuDNNLSTM实现差异

在tensorflow 1，还有的层tf.compat.v1.keras.layers.CuDNNLSTM，而在tensorflow 2这一层已经被赞成使用的不提倡使用的是使用cuDNN内置tf.keras.layers.LSTM有

  1. `activation` == `tanh`
  2. `recurrent_activation` == `sigmoid`
  3. `recurrent_dropout` == 0
  4. `unroll` is `False`
  5. `use_bias` is `True`
  6. Inputs are not masked or strictly right padded.

用于cuDNN实现。我不知道是否存在未实现的bug或某些差异，但是CuDNNLSTM使用输入偏差和循环偏差似乎有所不同，如LSTM上述tf2 cuDNN规则所述，该方法仅使用循环偏差。

相关代码

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM
from tensorflow.compat.v1.keras.layers import CuDNNLSTM

print(tf.__version__)

model1 = Sequential()
model1.add(LSTM(1, activation='tanh', recurrent_dropout=0, unroll=False, use_bias=True, return_sequences=0, input_shape=(1, 1)))
print(model1.summary())

model2 = Sequential()
model2.add(CuDNNLSTM(1, return_sequences=0, input_shape=(1, 1)))
print(model2.summary())

2.2.0
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)               (None, 1)                 12        
=================================================================
Total params: 12
Trainable params: 12
Non-trainable params: 0
_________________________________________________________________
None
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
cu_dnnlstm (CuDNNLSTM)    (None, 1)                 16        
=================================================================
Total params: 16
Trainable params: 16
Non-trainable params: 0
_________________________________________________________________

请注意，总参数之间的差异为N_units * 4，表示每个参数缺少一个额外的偏差向量。

请注意，LSTM的pytorch实现与tf1 CuDNNLSTM匹配，这是我偶然发现的方法。

我是否缺少一些修复程序，或者应该将其提升为github问题？