Fix tf shared embedding (#17730)

* fix the naming

* from pt in test for now

* make style

* slow test and removed from_pt

src/transformers/models/opt/modeling_tf_opt.py

@@ -30,7 +30,6 @@
     TFModelInputType,
     TFPreTrainedModel,
     TFSharedEmbeddings,
-    TFWrappedEmbeddings,
     keras_serializable,
     unpack_inputs,
 )
@@ -495,31 +494,15 @@ def __init__(self, config: OPTConfig, load_weight_prefix=None, **kwargs):
         self.padding_idx = config.pad_token_id
         self.layerdrop = config.layerdrop
         num_embeddings = config.max_position_embeddings
-
-        self.shared = TFSharedEmbeddings(
-            config.vocab_size, config.word_embed_proj_dim, config.pad_token_id, name="model.decoder.embed_tokens"
+        self.embed_tokens = TFSharedEmbeddings(
+            config.vocab_size, config.word_embed_proj_dim, config.pad_token_id, name="embed_tokens"
         )
-
         self.embed_positions = TFOPTLearnedPositionalEmbedding(
             num_embeddings,
             config.hidden_size,
             name="embed_positions",
         )
 
-        # set tf scope correctly
-        if load_weight_prefix is None:
-            load_weight_prefix = "decoder.embed_tokens"
-
-        with tf.compat.v1.variable_scope(load_weight_prefix) as shared_abs_scope_name:
-            pass
-
-        # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
-        embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
-        embed_tokens.vocab_size = self.shared.vocab_size
-        embed_tokens.hidden_size = self.shared.hidden_size
-
-        self.embed_tokens = embed_tokens
-
         if config.word_embed_proj_dim != config.hidden_size:
             self.project_out = tf.keras.layers.Dense(config.word_embed_proj_dim, name="project_out", use_bias=False)
             self.project_in = tf.keras.layers.Dense(config.hidden_size, name="project_in", use_bias=False)
@@ -538,17 +521,11 @@ def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
     def set_input_embeddings(self, new_embeddings):
-        self.shared.weight = new_embeddings
-        self.shared.vocab_size = self.shared.weight.shape[0]
-        # retrieve correct absolute scope for embed token wrapper
-        with tf.compat.v1.variable_scope("decoder.embed_tokens") as shared_abs_scope_name:
-            pass
-        # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
-        embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
-        self.set_embed_tokens(embed_tokens)
+        self.embed_tokens.vocab_size = new_embeddings.shape[0]
+        self.embed_tokens.weight = new_embeddings
 
     def get_input_embeddings(self):
-        return self.shared
+        return self.embed_tokens
 
     def _prepare_decoder_attention_mask(self, attention_mask, input_shape, past_key_values_length):
         # create causal mask
@@ -731,7 +708,7 @@ def __init__(self, config: OPTConfig, **kwargs):
         self.decoder = TFOPTDecoder(config, name="decoder")
 
     def get_input_embeddings(self):
-        return self.decoder.shared
+        return self.decoder.embed_tokens
 
     def set_input_embeddings(self, new_embeddings):
         self.decoder.set_input_embeddings(new_embeddings)
@@ -797,7 +774,7 @@ def __init__(self, config: OPTConfig, **kwargs):
         self.model = TFOPTMainLayer(config, name="model")
 
     def get_input_embeddings(self):
-        return self.model.decoder.shared
+        return self.model.decoder.embed_tokens
 
     def set_input_embeddings(self, new_embeddings):
         self.model.set_input_embeddings(new_embeddings)
@@ -1013,8 +990,7 @@ def call(
             training=training,
         )
 
-        logits = self.model.decoder.shared(outputs[0], mode="linear")
-
+        logits = self.model.decoder.embed_tokens(outputs[0], mode="linear")
         loss = None
         if labels is not None:
             # shift labels to the left and cut last logit token