update for transformers >= 4.29.1

modeling_lsg_albert.py

@@ -188,19 +188,25 @@ class CausalAttentionProduct(nn.Module):
         del key_layer
 
         if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in AlbertModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
             # Add causal mask
             causal_shape = (self.block_size, self.block_size) if causal_shape is None else causal_shape
             causal_mask = torch.tril(
                 torch.ones(*causal_shape, device=attention_mask.device, dtype=attention_scores.dtype), 
                 diagonal=-1
                 ) 
-            causal_mask = causal_mask.T * torch.finfo(attention_scores.dtype).min
-            attention_scores[..., -causal_shape[0]:, -causal_shape[1] + 1:] = causal_mask[:, 1:]
+            
+            # Min value
+            dtype_min = torch.tensor(
+                        torch.finfo(attention_scores.dtype).min, device=attention_scores.device, dtype=attention_scores.dtype
+                    )
+
+            # Build causal + attention_mask
+            causal_mask = torch.nn.functional.pad(causal_mask.T * dtype_min, (attention_mask.size()[-1] - self.block_size, 0), value=0)
+            attention_mask = torch.max(attention_mask + causal_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0), dtype_min)
 
+            attention_scores = attention_scores + attention_mask
             del attention_mask
+            del causal_mask
 
         # Normalize the attention scores to probabilities.
         attention_probs = nn.Softmax(dim=-1)(attention_scores)
@@ -838,7 +844,6 @@ class LSGAlbertPreTrainedModel(PreTrainedModel):
     config_class = LSGAlbertConfig
     load_tf_weights = load_tf_weights_in_albert
     base_model_prefix = "albert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """Initialize the weights."""
@@ -881,6 +886,8 @@ class LSGAlbertModel(LSGAlbertPreTrainedModel, AlbertModel):
     
 class LSGAlbertForPreTraining(LSGAlbertPreTrainedModel, AlbertForPreTraining):
 
+    _tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
+
     def __init__(self, config):
 
         LSGAlbertPreTrainedModel.__init__(self, config)
@@ -895,7 +902,7 @@ class LSGAlbertForPreTraining(LSGAlbertPreTrainedModel, AlbertForPreTraining):
 
 class LSGAlbertForMaskedLM(LSGAlbertPreTrainedModel, AlbertForMaskedLM):
 
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
 
     def __init__(self, config):
         LSGAlbertPreTrainedModel.__init__(self, config)
@@ -925,8 +932,6 @@ class LSGAlbertForSequenceClassification(LSGAlbertPreTrainedModel, AlbertForSequ
 
 class LSGAlbertForTokenClassification(LSGAlbertPreTrainedModel, AlbertForTokenClassification):
 
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
     def __init__(self, config):
 
         LSGAlbertPreTrainedModel.__init__(self, config)
@@ -947,8 +952,6 @@ class LSGAlbertForTokenClassification(LSGAlbertPreTrainedModel, AlbertForTokenCl
 
 class LSGAlbertForQuestionAnswering(LSGAlbertPreTrainedModel, AlbertForQuestionAnswering):
 
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
     def __init__(self, config):
 
         LSGAlbertPreTrainedModel.__init__(self, config)