fix(autocast): route inference autocasts through safe helper

Apply get_safe_autocast_context to the control_utils and sync inference
paths for uniformity with lerobot_eval. AMP is now enabled on any
AMP-capable device (cuda, xpu, cpu) when use_amp is set, and stays a
no-op on mps.

src/lerobot/common/control_utils.py

@@ -18,7 +18,6 @@ from __future__ import annotations
 # Utilities
 ########################################################################################
 import time
-from contextlib import nullcontext
 from copy import copy
 from typing import TYPE_CHECKING, Any
 
@@ -26,6 +25,7 @@ import numpy as np
 import torch
 
 from lerobot.policies import PreTrainedPolicy, prepare_observation_for_inference
+from lerobot.utils.device_utils import get_safe_autocast_context
 from lerobot.utils.import_utils import _deepdiff_available, require_package
 
 if TYPE_CHECKING or _deepdiff_available:
@@ -76,7 +76,7 @@ def predict_action(
     observation = copy(observation)
     with (
         torch.inference_mode(),
-        torch.autocast(device_type=device.type) if device.type == "cuda" and use_amp else nullcontext(),
+        get_safe_autocast_context(device, enabled=use_amp),
     ):
         # Convert to pytorch format: channel first and float32 in [0,1] with batch dimension
         observation = prepare_observation_for_inference(observation, device, task, robot_type)

src/lerobot/rollout/inference/sync.py

@@ -17,7 +17,6 @@
 from __future__ import annotations
 
 import logging
-from contextlib import nullcontext
 from copy import copy
 
 import torch
@@ -25,6 +24,7 @@ import torch
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.utils import make_robot_action, prepare_observation_for_inference
 from lerobot.processor import PolicyProcessorPipeline
+from lerobot.utils.device_utils import get_safe_autocast_context
 
 from .base import InferenceEngine
 
@@ -102,11 +102,7 @@ class SyncInferenceEngine(InferenceEngine):
         # ``obs_frame`` fresh per tick via ``build_dataset_frame``, so the
         # tensor/array values are not shared with any other reader.
         observation = copy(obs_frame)
-        autocast_ctx = (
-            torch.autocast(device_type=self._device.type)
-            if self._device.type == "cuda" and self._policy.config.use_amp
-            else nullcontext()
-        )
+        autocast_ctx = get_safe_autocast_context(self._device, enabled=self._policy.config.use_amp)
         with torch.inference_mode(), autocast_ctx:
             observation = prepare_observation_for_inference(
                 observation, self._device, self._task, self._robot_type