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#!/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TypedDict
import torch
from lerobot.utils.constants import ACTION
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
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