mirror of
https://github.com/huggingface/lerobot.git
synced 2026-06-01 03:11:29 +00:00
142 lines
4.7 KiB
Python
142 lines
4.7 KiB
Python
#!/usr/bin/env python
|
|
|
|
# Copyright 2024 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.
|
|
|
|
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
|
|
|
|
|
|
def setup_process_handlers(use_threads: bool) -> any:
|
|
if use_threads:
|
|
from threading import Event
|
|
else:
|
|
from multiprocessing import Event
|
|
|
|
shutdown_event = Event()
|
|
|
|
# Define signal handler
|
|
def signal_handler(signum, frame):
|
|
logging.info("Shutdown signal received. Cleaning up...")
|
|
shutdown_event.set()
|
|
global shutdown_event_counter
|
|
shutdown_event_counter += 1
|
|
|
|
if shutdown_event_counter > 1:
|
|
logging.info("Force shutdown")
|
|
sys.exit(1)
|
|
|
|
signal.signal(signal.SIGINT, signal_handler) # Ctrl+C
|
|
signal.signal(signal.SIGTERM, signal_handler) # Termination request (kill)
|
|
signal.signal(signal.SIGHUP, signal_handler) # Terminal closed/Hangup
|
|
signal.signal(signal.SIGQUIT, signal_handler) # Ctrl+\
|
|
|
|
def signal_handler(signum, frame):
|
|
logging.info("Shutdown signal received. Cleaning up...")
|
|
shutdown_event.set()
|
|
|
|
return shutdown_event
|
|
|
|
|
|
def get_last_item_from_queue(queue: Queue):
|
|
item = queue.get()
|
|
counter = 1
|
|
|
|
# Drain queue and keep only the most recent parameters
|
|
try:
|
|
while True:
|
|
item = queue.get_nowait()
|
|
counter += 1
|
|
except Empty:
|
|
pass
|
|
|
|
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
|