fix: saving state dict and eval for Context Parallel (#3382) [skip ci]

* clone state_dict if none

* patch calculating  eval loss for cp

src/axolotl/core/trainers/base.py

@@ -719,6 +719,13 @@ class AxolotlTrainer(
         output_dir = output_dir if output_dir is not None else self.args.output_dir
         os.makedirs(output_dir, exist_ok=True)
         LOG.info(f"Saving model checkpoint to {output_dir}")
+        if state_dict is None:
+            state_dict = self.accelerator.get_state_dict(self.model)
+        if state_dict is not None:
+            state_dict = {
+                k: v.clone() if isinstance(v, torch.Tensor) else v
+                for k, v in state_dict.items()
+            }
         supported_classes = (
             (PreTrainedModel,)
             if not is_peft_available()

src/axolotl/utils/ctx_managers/sequence_parallel.py

@@ -218,6 +218,9 @@ class SequenceParallelContextManager:
         self.original_seq_len = 0
         self.pad_len = 0
 
+        # Track local valid token count for eval loss correction across CP ranks
+        self._local_valid_tokens: torch.Tensor | None = None
+
         # Create a partially applied version of the apply_sequence_parallelism function
         self.apply_sequence_parallelism = functools.partial(
             apply_sequence_parallelism,
@@ -270,6 +273,18 @@ class SequenceParallelContextManager:
                 self.apply_sequence_parallelism(updated_kwargs)
             )
 
+            # Track local valid tokens for eval loss correction
+            if "labels" in updated_kwargs and not self.models[0].training:
+                self._local_valid_tokens = (
+                    (updated_kwargs["labels"] != -100).sum().float()
+                )
+                # Strip num_items_in_batch during eval so the model uses
+                # reduction='mean', allowing the post-hook weighted all-reduce
+                # formula (loss * local_valid) to correctly recover the loss sum
+                updated_kwargs.pop("num_items_in_batch", None)
+            else:
+                self._local_valid_tokens = None
+
             return remaining_args, updated_kwargs
 
         # Forward post-hook to gather outputs
@@ -287,6 +302,44 @@ class SequenceParallelContextManager:
 
             return output
 
+        # Post-hook to correct eval loss via weighted all-reduce across CP ranks
+        def eval_loss_correction_post_hook(_, __, output: ModelOutput) -> ModelOutput:
+            if self._local_valid_tokens is None:
+                return output
+            if not hasattr(output, "loss") or output.loss is None:
+                return output
+
+            local_valid = self._local_valid_tokens.to(output.loss.device)
+            loss = output.loss.detach().clone()
+
+            # Handle rank with zero valid tokens (loss is NaN)
+            if local_valid.item() == 0:
+                weighted_loss = torch.zeros(1, device=loss.device, dtype=loss.dtype)
+            else:
+                weighted_loss = loss * local_valid
+
+            total_valid = local_valid.clone()
+            dist.all_reduce(
+                weighted_loss,
+                op=dist.ReduceOp.SUM,
+                group=self.process_group,
+            )
+            dist.all_reduce(
+                total_valid,
+                op=dist.ReduceOp.SUM,
+                group=self.process_group,
+            )
+
+            if total_valid.item() > 0:
+                output["loss"] = (weighted_loss / total_valid).squeeze()
+            else:
+                output["loss"] = torch.tensor(
+                    float("nan"), device=loss.device, dtype=loss.dtype
+                )
+
+            self._local_valid_tokens = None
+            return output
+
         # Register hooks
         for model in self.models:
             self.hook_handles.append(
@@ -298,6 +351,10 @@ class SequenceParallelContextManager:
                 self.hook_handles.append(
                     model.register_forward_hook(sequence_parallel_post_hook)
                 )
+            # Always register eval loss correction hook
+            self.hook_handles.append(
+                model.register_forward_hook(eval_loss_correction_post_hook)
+            )
 
     def _gather_outputs(self, output: CausalLMOutputWithPast) -> CausalLMOutputWithPast:
         """Gather sharded outputs from all ranks and reconstruct the full tensor."""