Upload DogeForCausalLM

modeling_doge.py

@@ -875,16 +875,6 @@ class DogeModel(DogePreTrainedModel):
         past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
         using_static_cache = isinstance(past_key_values, StaticCache)
 
-        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
-        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
-            if AttentionMaskConverter._ignore_causal_mask_sdpa(
-                attention_mask,
-                inputs_embeds=input_tensor,
-                past_key_values_length=past_seen_tokens,
-                is_training=self.training,
-            ):
-                return None
-
         dtype, device = input_tensor.dtype, input_tensor.device
         sequence_length = input_tensor.shape[1]
         if using_static_cache: