use config attr

src/transformers/models/gpt2/modeling_gpt2.py

@@ -819,7 +819,6 @@ class GPT2Model(GPT2PreTrainedModel):
         self.model_parallel = False
         self.device_map = None
         self.gradient_checkpointing = False
-        self._attn_implementation = config._attn_implementation
 
         # Initialize weights and apply final processing
         self.post_init()
@@ -978,7 +977,7 @@ class GPT2Model(GPT2PreTrainedModel):
 
         # If a 2D or 3D attention mask is provided for the cross-attention
         # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        _use_sdpa = self._attn_implementation == "sdpa" and output_attentions is False and head_mask is None
+        _use_sdpa = self.config._attn_implementation == "sdpa" and output_attentions is False and head_mask is None
         if self.config.add_cross_attention and encoder_hidden_states is not None:
             encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
             encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
@@ -988,7 +987,7 @@ class GPT2Model(GPT2PreTrainedModel):
                 encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
                     mask=encoder_attention_mask, dtype=inputs_embeds.dtype, tgt_len=input_shape[-1]
                 )
-            elif not self._attn_implementation == "flash_attention_2":
+            elif not self.config._attn_implementation == "flash_attention_2":
                 encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
         else:
             encoder_attention_mask = None