Hardcode GELU as the intermediate activation for ESM (#22892)

* Hardcode GELU as the intermediate activation for ESM

* Sneak a quick fix to the weight tying in too

* Make the call to gelu explicit

src/transformers/models/esm/modeling_tf_esm.py

@@ -37,7 +37,6 @@ from ...modeling_tf_utils import (
     TFSequenceClassificationLoss,
     TFTokenClassificationLoss,
     get_initializer,
-    get_tf_activation,
     shape_list,
     unpack_inputs,
 )
@@ -476,24 +475,19 @@ class TFEsmAttention(Layer):
         return outputs
 
 
-# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Esm
 class TFEsmIntermediate(tf.keras.layers.Layer):
     def __init__(self, config: EsmConfig, **kwargs):
         super().__init__(**kwargs)
 
         self.dense = tf.keras.layers.Dense(
-            units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+            units=config.intermediate_size,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="dense",
         )
 
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = get_tf_activation(config.hidden_act)
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-
+        hidden_states = tf.nn.gelu(hidden_states)
         return hidden_states
 
 
@@ -1216,23 +1210,21 @@ class TFEsmLMHead(Layer):
         )
 
         self.layer_norm = LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
-
+        if config.tie_word_embeddings:
             self.decoder = None
+        else:
+            self.decoder = Dense(
+                config.vocab_size,
+                kernel_initializer=get_initializer(config.initializer_range),
+                name="decoder",
+                use_bias=False,
+            )
         self.config = config
 
     def build(self, input_shape):
         super().build(input_shape)
         # Separate bias to match the PT model and allow weight cross-loading to work
         # Put it in the build so it gets the right name when adding it as a weight
-        if not self.config.tie_word_embeddings:
-            if self.decoder is not None:
-                raise ValueError("Expected decoder not to be initialized before build when not tying weights!")
-            self.decoder = self.add_weight(
-                "decoder.weight",
-                shape=(self.config.hidden_size, self.config.vocab_size),
-                initializer=get_initializer(self.config.initializer_range),
-                trainable=True,
-            )
         self.bias = self.add_weight("bias", shape=(self.config.vocab_size,), initializer="zeros", trainable=True)
 
     def get_bias(self):
@@ -1244,7 +1236,10 @@ class TFEsmLMHead(Layer):
         x = self.layer_norm(x)
 
         # project back to size of vocabulary with bias
+        if self.config.tie_word_embeddings:
             x = tf.matmul(x, self.decoder, transpose_b=True) + self.bias
+        else:
+            x = self.decoder(x) + self.bias
         return x