Esm checkpointing

src/transformers/models/esm/modeling_esm.py

@@ -214,7 +214,7 @@ def forward(
         # This is analogous to the way that dropout layers scale down outputs during evaluation when not
         # actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
         if self.token_dropout:
-            embeddings.masked_fill_((input_ids == self.mask_token_id).unsqueeze(-1), 0.0)
+            embeddings = embeddings.masked_fill((input_ids == self.mask_token_id).unsqueeze(-1), 0.0)
             mask_ratio_train = 0.15 * 0.8  # Hardcoded as the ratio used in all ESM model training runs
             src_lengths = attention_mask.sum(-1)
             mask_ratio_observed = (input_ids == self.mask_token_id).sum(-1).float() / src_lengths
@@ -224,7 +224,7 @@ def forward(
 
         if self.position_embedding_type == "absolute":
             position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
+            embeddings = embeddings + position_embeddings
 
         if self.layer_norm is not None:
             embeddings = self.layer_norm(embeddings)
@@ -399,7 +399,7 @@ def __init__(self, config):
     def forward(self, hidden_states, input_tensor):
         hidden_states = self.dense(hidden_states)
         hidden_states = self.dropout(hidden_states)
-        hidden_states += input_tensor
+        hidden_states = hidden_states + input_tensor
         return hidden_states
 
 
@@ -474,7 +474,7 @@ def __init__(self, config):
     def forward(self, hidden_states, input_tensor):
         hidden_states = self.dense(hidden_states)
         hidden_states = self.dropout(hidden_states)
-        hidden_states += input_tensor
+        hidden_states = hidden_states + input_tensor
         return hidden_states
 
 
@@ -633,7 +633,7 @@ def custom_forward(*inputs):
 
             hidden_states = layer_outputs[0]
             if use_cache:
-                next_decoder_cache += (layer_outputs[-1],)
+                next_decoder_cache = next_decoder_cache + (layer_outputs[-1],)
             if output_attentions:
                 all_self_attentions = all_self_attentions + (layer_outputs[1],)
                 if self.config.add_cross_attention: