[Port HIl-Serl] Refactor gym-manipulator (#1034)

lerobot/scripts/server/utils.py

@@ -19,7 +19,9 @@ import logging
 import signal
 import sys
 from queue import Empty
+from typing import TypedDict
 
+import torch
 from torch.multiprocessing import Queue
 
 shutdown_event_counter = 0
@@ -71,3 +73,69 @@ def get_last_item_from_queue(queue: Queue):
     logging.debug(f"Drained {counter} items from queue")
 
     return item
+
+
+class Transition(TypedDict):
+    state: dict[str, torch.Tensor]
+    action: torch.Tensor
+    reward: float
+    next_state: dict[str, torch.Tensor]
+    done: bool
+    truncated: bool
+    complementary_info: dict[str, torch.Tensor | float | int] | None = None
+
+
+def move_transition_to_device(transition: Transition, device: str = "cpu") -> Transition:
+    device = torch.device(device)
+    non_blocking = device.type == "cuda"
+
+    # Move state tensors to device
+    transition["state"] = {
+        key: val.to(device, non_blocking=non_blocking) for key, val in transition["state"].items()
+    }
+
+    # Move action to device
+    transition["action"] = transition["action"].to(device, non_blocking=non_blocking)
+
+    # Move reward and done if they are tensors
+    if isinstance(transition["reward"], torch.Tensor):
+        transition["reward"] = transition["reward"].to(device, non_blocking=non_blocking)
+
+    if isinstance(transition["done"], torch.Tensor):
+        transition["done"] = transition["done"].to(device, non_blocking=non_blocking)
+
+    if isinstance(transition["truncated"], torch.Tensor):
+        transition["truncated"] = transition["truncated"].to(device, non_blocking=non_blocking)
+
+    # Move next_state tensors to device
+    transition["next_state"] = {
+        key: val.to(device, non_blocking=non_blocking) for key, val in transition["next_state"].items()
+    }
+
+    # Move complementary_info tensors if present
+    if transition.get("complementary_info") is not None:
+        for key, val in transition["complementary_info"].items():
+            if isinstance(val, torch.Tensor):
+                transition["complementary_info"][key] = val.to(device, non_blocking=non_blocking)
+            elif isinstance(val, (int, float, bool)):
+                transition["complementary_info"][key] = torch.tensor(val, device=device)
+            else:
+                raise ValueError(f"Unsupported type {type(val)} for complementary_info[{key}]")
+    return transition
+
+
+def move_state_dict_to_device(state_dict, device="cpu"):
+    """
+    Recursively move all tensors in a (potentially) nested
+    dict/list/tuple structure to the CPU.
+    """
+    if isinstance(state_dict, torch.Tensor):
+        return state_dict.to(device)
+    elif isinstance(state_dict, dict):
+        return {k: move_state_dict_to_device(v, device=device) for k, v in state_dict.items()}
+    elif isinstance(state_dict, list):
+        return [move_state_dict_to_device(v, device=device) for v in state_dict]
+    elif isinstance(state_dict, tuple):
+        return tuple(move_state_dict_to_device(v, device=device) for v in state_dict)
+    else:
+        return state_dict