fixing final upload to hub

src/lerobot/policies/pretrained.py

@@ -303,6 +303,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         self,
         cfg: TrainPipelineConfig,
         peft_model=None,
+        state_dict: dict[str, Tensor] | None = None,
     ):
         api = HfApi()
         repo_id = api.create_repo(
@@ -320,7 +321,8 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
                 peft_model.save_pretrained(saved_path)
                 self.config.save_pretrained(saved_path)
             else:
-                self.save_pretrained(saved_path)  # Calls _save_pretrained and stores model tensors
+                # Calls _save_pretrained and stores model tensors
+                self.save_pretrained(saved_path, state_dict=state_dict)
 
             card = self.generate_model_card(
                 cfg.dataset.repo_id, self.config.type, self.config.license, self.config.tags, cfg=cfg

src/lerobot/scripts/lerobot_train.py

@@ -644,6 +644,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     if eval_env:
         close_envs(eval_env)
 
+    is_fsdp = accelerator.distributed_type == DistributedType.FSDP
+    model_state_dict = accelerator.get_state_dict(policy) if is_fsdp else None
     if is_main_process:
         logging.info("End of training")
 
@@ -653,7 +655,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             if not cfg.is_reward_model_training and cfg.policy.use_peft:
                 unwrapped_model.push_model_to_hub(cfg, peft_model=unwrapped_model)
             else:
-                unwrapped_model.push_model_to_hub(cfg)
+                unwrapped_model.push_model_to_hub(cfg, state_dict=model_state_dict)
             preprocessor.push_to_hub(active_cfg.repo_id)
             postprocessor.push_to_hub(active_cfg.repo_id)