fix RNN LSTM GRU

Author: hanjr92

examples/basic_tutorials/imdb_LSTM_simple.py

@@ -5,8 +5,8 @@
 import os
 # os.environ['TL_BACKEND'] = 'tensorflow'
 # os.environ['TL_BACKEND'] = 'mindspore'
-os.environ['TL_BACKEND'] = 'paddle'
-# os.environ['TL_BACKEND'] = 'torch'
+# os.environ['TL_BACKEND'] = 'paddle'
+os.environ['TL_BACKEND'] = 'torch'
 import tensorlayerx as tlx
 from tensorlayerx.nn import Module
 from tensorlayerx.nn import Linear, LSTM, Embedding
@@ -42,7 +42,7 @@ class ImdbNet(Module):
     def __init__(self):
         super(ImdbNet, self).__init__()
         self.embedding = Embedding(num_embeddings=vocab_size, embedding_dim=64)
-        self.lstm = LSTM(input_size=64, hidden_size=64)
+        self.lstm = LSTM(input_size=64, hidden_size=64, num_layers=2)
         self.linear1 = Linear(in_features=64, out_features=64, act=tlx.nn.ReLU)
         self.linear2 = Linear(in_features=64, out_features=2)
 

tensorlayerx/nn/core/core_mindspore.py

@@ -12,9 +12,9 @@
 import numpy
 from mindspore.common.api import _pynative_executor
 from collections import OrderedDict, abc as container_abcs
-from mindspore.nn.cell import ParameterTuple
 
-__all__ = ['Module', 'Sequential', 'ModuleList', 'ModuleDict', 'Parameter', 'ParameterList', 'ParameterDict', 'ParameterTuple']
+
+__all__ = ['Module', 'Sequential', 'ModuleList', 'ModuleDict', 'Parameter', 'ParameterList', 'ParameterDict']
 
 _global_layer_name_dict = {}
 _global_layer_node = []

tensorlayerx/nn/core/core_paddle.py

@@ -14,12 +14,11 @@
 from paddle.fluid.dygraph import parallel_helper
 import paddle as pd
 from collections import OrderedDict, abc as container_abcs
-from paddle.nn import ParameterList as ParameterTuple
 
 _global_layer_name_dict = {}
 _global_layer_node = []
-# TODO Need to implement ParameterTuple
-__all__ = ['Module', 'Sequential', 'ModuleList', 'ModuleDict', 'Parameter', 'ParameterList', 'ParameterDict', 'ParameterTuple']
+
+__all__ = ['Module', 'Sequential', 'ModuleList', 'ModuleDict', 'Parameter', 'ParameterList', 'ParameterDict']
 
 
 class Module(Layer):

tensorlayerx/nn/core/core_tensorflow.py

@@ -10,7 +10,7 @@
 import tensorflow as tf
 from tensorlayerx.nn.layers.utils import (get_variable_with_initializer, random_normal)
 
-__all__ = ['Module', 'Sequential', 'ModuleList', 'ModuleDict', 'Parameter', 'ParameterList', 'ParameterDict', 'ParameterTuple']
+__all__ = ['Module', 'Sequential', 'ModuleList', 'ModuleDict', 'Parameter', 'ParameterList', 'ParameterDict']
 
 _global_layer_name_dict = {}
 _global_layer_node = []
@@ -1287,34 +1287,6 @@ def update(self, parameters):
     def __call__(self, input):
         raise RuntimeError('ParameterDict should not be called.')
 
-
-class ParameterTuple(tuple):
-    """
-    ParameterTuple for storing tuple of parameters.
-    """
-    def __new__(cls, iterable):
-        data = tuple(iterable)
-        ids = set()
-        orders = {}
-        for x in data:
-            if not isinstance(x, tf.Variable):
-                raise TypeError(f"ParameterTuple input should be `Parameter` collection."
-                                f"But got a {type(iterable)}, {iterable}")
-            if id(x) not in ids:
-                ids.add(id(x))
-                if x.name not in orders.keys():
-                    orders[x.name] = [0, x]
-                else:
-                    if isinstance(orders[x.name], list):
-                        name = x.name
-                        orders[name][1].name = name + "_" + str(0)
-                        x.name = x.name + "_" + str(1)
-                        orders[name] = 1
-                    else:
-                        orders[x.name] += 1
-                        x.name = x.name + "_" + str(orders[x.name])
-        return tuple.__new__(ParameterTuple, tuple(data))
-
 def _valid_index(layer_num, index):
     if not isinstance(index, int):
         raise TypeError("Index {} is not int type")

tensorlayerx/nn/core/core_torch.py

@@ -12,12 +12,11 @@
 from collections import OrderedDict, abc as container_abcs
 import warnings
 import tensorlayerx as tlx
-from torch.nn import ParameterList as ParameterTuple
 
 _global_layer_name_dict = {}
 _global_layer_node = []
 
-__all__ = ['Module', 'Sequential', 'ModuleList', 'ModuleDict', 'Parameter', 'ParameterList', 'ParameterDict', 'ParameterTuple']
+__all__ = ['Module', 'Sequential', 'ModuleList', 'ModuleDict', 'Parameter', 'ParameterList', 'ParameterDict']
 
 
 class Module(T_Module):

tensorlayerx/nn/layers/recurrent.py

@@ -4,7 +4,7 @@
 import numpy as np
 import tensorlayerx as tlx
 from tensorlayerx import logging
-from tensorlayerx.nn.core import Module, ParameterTuple
+from tensorlayerx.nn.core import Module, ParameterList
 
 __all__ = [
     'RNN',
@@ -488,10 +488,10 @@ def build(self, inputs_shape):
                             var_name='bias_hh_l{}{}'.format(layer, suffix), shape=(gate_size, ), init=_init
                         )
                     )
-        # self.weight_ih = ParameterTuple(self.w_ih)
-        # self.weight_hh = ParameterTuple(self.w_hh)
-        # self.bias_ih = ParameterTuple(self.b_ih)
-        # self.bias_hh = ParameterTuple(self.b_hh)
+        self.weight_ih = ParameterList(self.w_ih)
+        self.weight_hh = ParameterList(self.w_hh)
+        self.bias_ih = ParameterList(self.b_ih)
+        self.bias_hh =ParameterList(self.b_hh)
         self.rnn = tlx.ops.rnnbase(
             mode=self.mode, input_size=self.input_size, hidden_size=self.hidden_size, num_layers=self.num_layers,
             bias=self.bias, batch_first=self.batch_first, dropout=self.dropout, bidirectional=self.bidirectional,