chore(deps): allow torch 2.11/2.12 and fix autocast deprecation (#3435)

* chore(deps): allow torch 2.11/2.12 and fix autocast deprecation

- Bump torch to >=2.7,<2.13 (was <2.11), torchvision to <0.28 (was <0.26),
  and torchcodec to <0.13 (was <0.11) to allow installs against the latest
  stable torch 2.11 and the upcoming 2.12 line.
- Replace removed torch.get_autocast_gpu_dtype() with torch.get_autocast_dtype("cuda")
  in Florence2 and Qwen2.5-VL-MoE FlashAttention paths (the former is removed in 2.11+).
- Refresh uv.lock for the new resolution (torch 2.11.0+cu130, torchvision 0.26.0+cu130,
  torchcodec 0.11.1, full CUDA 13 stack).

Verified locally with `uv sync --locked` from a clean .venv and the lerobot
test suite (pytest -n 8 --dist=loadfile --timeout=300). Failure set is
identical to the pre-bump baseline: 18 pre-existing failures
(test_sac_policy*, test_pi0_rtc*, test_pi05_rtc*, test_replay_buffer*),
0 new, 0 fixed.

AI assistance: this change was authored with Claude Code per AI_POLICY.md.

* fix(policies): use device-agnostic autocast dtype lookup

Pass query_states.device.type to torch.get_autocast_dtype() instead of
hardcoding 'cuda', so the cast matches the active autocast context when
running under CPU/MPS/XPU autocast.

---------

Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>

src/lerobot/policies/wall_x/qwen_model/qwen2_5_vl_moe.py

@@ -939,7 +939,7 @@ class Qwen2_5_VLFlashAttention2(Qwen2_5_VLAttention):
         input_dtype = query_states.dtype
         if input_dtype == torch.float32:
             if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
+                target_dtype = torch.get_autocast_dtype(query_states.device.type)
             # Handle the case where the model is quantized
             elif hasattr(self.config, "_pre_quantization_dtype"):
                 target_dtype = self.config._pre_quantization_dtype

src/lerobot/policies/xvla/modeling_florence2.py

@@ -985,7 +985,7 @@ class Florence2FlashAttention2(Florence2Attention):
         input_dtype = query_states.dtype
         if input_dtype == torch.float32:
             if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
+                target_dtype = torch.get_autocast_dtype(query_states.device.type)
             # Handle the case where the model is quantized
             elif hasattr(self.config, "_pre_quantization_dtype"):
                 target_dtype = self.config._pre_quantization_dtype