Make attention_mask optional (default to all 1s)

src/transformers/models/esm/modeling_esmfold.py

@@ -2040,7 +2040,7 @@ def forward(self, seq_feats, pair_feats, true_aa, residx, mask, no_recycles):
         s_z_0 = pair_feats
 
         if no_recycles is None:
-            no_recycles = self.cfg.max_recycles
+            no_recycles = self.config.max_recycles
         else:
             if no_recycles < 0:
                 raise ValueError("Number of recycles must not be negative.")
@@ -2190,7 +2190,7 @@ def _af2_to_esm_from_vocab_list(vocab_list: List[str]) -> torch.Tensor:
     def forward(
         self,
         input_ids: torch.Tensor,
-        attention_mask: torch.Tensor,
+        attention_mask: torch.Tensor = None,
         position_ids: Optional[torch.Tensor] = None,
         masking_pattern: Optional[torch.Tensor] = None,
         num_recycles: Optional[int] = None,
@@ -2201,6 +2201,8 @@ def forward(
         B = aa.shape[0]
         L = aa.shape[1]
         device = input_ids.device
+        if attention_mask is None:
+            attention_mask = torch.ones_like(aa, device=device)
         if position_ids is None:
             position_ids = torch.arange(L, device=device).expand_as(input_ids)