[`Falcon`] Remove SDPA for falcon to support earlier versions of PyTorch (< 2.0) (#25947)

* remove SDPA for falcon

* revert previous behaviour and add warning

* nit

* Update src/transformers/models/falcon/modeling_falcon.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

* Update src/transformers/models/falcon/modeling_falcon.py

---------
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>

src/transformers/models/falcon/modeling_falcon.py

@@ -422,9 +422,19 @@ class FalconAttention(nn.Module):
         value_layer_ = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
 
         if alibi is None:
-            if output_attentions:
-                # F.scaled_dot_product_attention doesn't return the attention weights, so we have
-                # to do it by hand if we want them
+            if hasattr(F, "scaled_dot_product_attention") and not output_attentions:
+                # TODO: deprecate this once we add FA2 support in Falcon
+                logger.warning_once(
+                    "The current implementation of Falcon calls `torch.scaled_dot_product_attention` directly, this will be deprecated in the"
+                    " future in favor of the `BetterTransformer` API. Please install the latest optimum library with `pip install -U optimum` and call "
+                    "`model.to_bettertransformer()` to benefit from `torch.scaled_dot_product_attention` and future performance optimizations."
+                )
+
+                attn_output = F.scaled_dot_product_attention(
+                    query_layer_, key_layer_, value_layer_, attention_mask_float, 0.0, is_causal=False
+                )
+                attention_scores = None
+            else:
                 attention_scores = query_layer_ @ key_layer_.transpose(-1, -2)
                 attention_scores /= math.sqrt(self.head_dim)
 
@@ -432,11 +442,6 @@ class FalconAttention(nn.Module):
                     attention_scores + attention_mask_float, dim=-1, dtype=hidden_states.dtype
                 )
                 attn_output = attention_scores @ value_layer_
-            else:
-                attn_output = F.scaled_dot_product_attention(
-                    query_layer_, key_layer_, value_layer_, attention_mask_float, 0.0, is_causal=False
-                )
-                attention_scores = None
 
             attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
             attn_output = attn_output.permute(0, 2, 1, 3)