Fix Mistral DPO/preference training crash on non-xformers platforms (e.g. Intel XPU) (#4889)

* Fix Mistral training crash when xformers is unavailable

* Fix/adjust Mistral DPO training crash fix for PR #4889

- Clarify comment in MistralForCausalLM_fast_forward: the DPO embed-masking
  block runs BEFORE attention_mask is nulled out, and it is the consumer that
  requires a 2D mask.
- Add defensive attention_mask.ndim == 2 guard to the LlamaModel_fast_forward
  DPO embed-masking block so it self-protects if a 4D mask ever reaches it.

---------

Co-authored-by: Daniel Han <danielhanchen@gmail.com>

unsloth/models/llama.py

@@ -1030,6 +1030,7 @@ def LlamaModel_fast_forward(
     if (
         getattr(self, "_has_no_labels", False) is True
         and (attention_mask is not None)
+        and attention_mask.ndim == 2
         and (past_key_values is None)
         and (not train_embed_tokens)
         and self.training

unsloth/models/mistral.py

@@ -188,6 +188,16 @@ def MistralForCausalLM_fast_forward(
 
             # If attention_mask exists, it will be handled in the attention forward
 
+        elif self.training:
+            # During training, LlamaModel_fast_forward's DPO embed-masking
+            # block requires a 2D attention_mask (it does
+            # inputs_embeds *= attention_mask.unsqueeze(0).transpose(0, 1).transpose(1, 2)).
+            # Afterwards, LlamaModel_fast_forward sets attention_mask=None
+            # before the attention layers anyway, so leaving the 2D mask
+            # untouched here is safe and avoids converting to 4D (which would
+            # crash the DPO block).
+            pass
+
         else:
             # Not using xformers - need to create attention masks
             if (