TF refactor that we'll need later

Author: Rocketknight1

src/transformers/models/t5/modeling_tf_t5.py

@@ -75,16 +75,17 @@
 
 
 class TFT5LayerNorm(tf.keras.layers.Layer):
-    def __init__(self, epsilon=1e-6, **kwargs):
+    def __init__(self, hidden_size, epsilon=1e-6, **kwargs):
         """
         Construct a layernorm module in the T5 style No bias and no subtraction of mean.
         """
         super().__init__(**kwargs)
         self.variance_epsilon = epsilon
+        self.hidden_size = hidden_size
 
     def build(self, input_shape):
         """Build shared word embedding layer"""
-        self.weight = self.add_weight("weight", shape=(input_shape[-1],), initializer="ones")
+        self.weight = self.add_weight("weight", shape=(self.hidden_size,), initializer="ones")
         super().build(input_shape)
 
     def call(self, hidden_states):
@@ -157,7 +158,7 @@ def __init__(self, config, **kwargs):
         else:
             self.DenseReluDense = TFT5DenseActDense(config, name="DenseReluDense")
 
-        self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
+        self.layer_norm = TFT5LayerNorm(config.d_model, epsilon=config.layer_norm_epsilon, name="layer_norm")
         self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
 
     def call(self, hidden_states, training=False):
@@ -439,7 +440,7 @@ def __init__(self, config, has_relative_attention_bias=False, **kwargs):
             has_relative_attention_bias=has_relative_attention_bias,
             name="SelfAttention",
         )
-        self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
+        self.layer_norm = TFT5LayerNorm(config.d_model, epsilon=config.layer_norm_epsilon, name="layer_norm")
         self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
 
     def call(
@@ -477,7 +478,7 @@ def __init__(self, config, **kwargs):
             has_relative_attention_bias=False,
             name="EncDecAttention",
         )
-        self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
+        self.layer_norm = TFT5LayerNorm(config.d_model, epsilon=config.layer_norm_epsilon, name="layer_norm")
         self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
 
     def call(
@@ -640,7 +641,7 @@ def __init__(self, config, embed_tokens=None, **kwargs):
             TFT5Block(config, has_relative_attention_bias=bool(i == 0), name=f"block_._{i}")
             for i in range(config.num_layers)
         ]
-        self.final_layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="final_layer_norm")
+        self.final_layer_norm = TFT5LayerNorm(config.d_model, epsilon=config.layer_norm_epsilon, name="final_layer_norm")
         self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
 
     def _prune_heads(self, heads_to_prune):