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refactor(config): Move device & amp args to PreTrainedConfig (#812)

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Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
This commit is contained in:
Steven Palma
2025-03-06 17:59:28 +01:00
committed by GitHub
parent 10706ed753
commit 5e9473806c
19 changed files with 62 additions and 136 deletions
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9
lerobot/common/policies/factory.py
View File

@@ -16,7 +16,6 @@
import logging
import torch
from torch import nn
from lerobot.common.datasets.lerobot_dataset import LeRobotDatasetMetadata
@@ -76,7 +75,6 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
def make_policy(
cfg: PreTrainedConfig,
device: str | torch.device,
ds_meta: LeRobotDatasetMetadata | None = None,
env_cfg: EnvConfig | None = None,
) -> PreTrainedPolicy:
@@ -88,7 +86,6 @@ def make_policy(
Args:
cfg (PreTrainedConfig): The config of the policy to make. If `pretrained_path` is set, the policy will
be loaded with the weights from that path.
device (str): the device to load the policy onto.
ds_meta (LeRobotDatasetMetadata | None, optional): Dataset metadata to take input/output shapes and
statistics to use for (un)normalization of inputs/outputs in the policy. Defaults to None.
env_cfg (EnvConfig | None, optional): The config of a gym environment to parse features from. Must be
@@ -96,7 +93,7 @@ def make_policy(
Raises:
ValueError: Either ds_meta or env and env_cfg must be provided.
NotImplementedError: if the policy.type is 'vqbet' and the device 'mps' (due to an incompatibility)
NotImplementedError: if the policy.type is 'vqbet' and the policy device 'mps' (due to an incompatibility)
Returns:
PreTrainedPolicy: _description_
@@ -111,7 +108,7 @@ def make_policy(
# https://github.com/pytorch/pytorch/issues/77764. As a temporary fix, you can set the environment
# variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be
# slower than running natively on MPS.
if cfg.type == "vqbet" and str(device) == "mps":
if cfg.type == "vqbet" and cfg.device == "mps":
raise NotImplementedError(
"Current implementation of VQBeT does not support `mps` backend. "
"Please use `cpu` or `cuda` backend."
@@ -145,7 +142,7 @@ def make_policy(
# Make a fresh policy.
policy = policy_cls(**kwargs)
policy.to(device)
policy.to(cfg.device)
assert isinstance(policy, nn.Module)
# policy = torch.compile(policy, mode="reduce-overhead")

1
lerobot/common/policies/pi0/configuration_pi0.py
View File

@@ -90,6 +90,7 @@ class PI0Config(PreTrainedConfig):
def __post_init__(self):
super().__post_init__()
# TODO(Steven): Validate device and amp? in all policy configs?
"""Input validation (not exhaustive)."""
if self.n_action_steps > self.chunk_size:
raise ValueError(

2
lerobot/common/policies/pi0/conversion_scripts/benchmark.py
View File

@@ -45,7 +45,7 @@ def main():
cfg = PreTrainedConfig.from_pretrained(ckpt_torch_dir)
cfg.pretrained_path = ckpt_torch_dir
policy = make_policy(cfg, device, ds_meta=dataset.meta)
policy = make_policy(cfg, ds_meta=dataset.meta)
# policy = torch.compile(policy, mode="reduce-overhead")

2
lerobot/common/policies/pi0/conversion_scripts/compare_with_jax.py
View File

@@ -101,7 +101,7 @@ def main():
cfg = PreTrainedConfig.from_pretrained(ckpt_torch_dir)
cfg.pretrained_path = ckpt_torch_dir
policy = make_policy(cfg, device, dataset_meta)
policy = make_policy(cfg, dataset_meta)
# loss_dict = policy.forward(batch, noise=noise, time=time_beta)
# loss_dict["loss"].backward()

7
lerobot/common/policies/pretrained.py
View File

@@ -86,7 +86,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
cache_dir: str | Path | None = None,
local_files_only: bool = False,
revision: str | None = None,
map_location: str = "cpu",
strict: bool = False,
**kwargs,
) -> T:
@@ -111,7 +110,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
if os.path.isdir(model_id):
print("Loading weights from local directory")
model_file = os.path.join(model_id, SAFETENSORS_SINGLE_FILE)
policy = cls._load_as_safetensor(instance, model_file, map_location, strict)
policy = cls._load_as_safetensor(instance, model_file, config.device, strict)
else:
try:
model_file = hf_hub_download(
@@ -125,13 +124,13 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
token=token,
local_files_only=local_files_only,
)
policy = cls._load_as_safetensor(instance, model_file, map_location, strict)
policy = cls._load_as_safetensor(instance, model_file, config.device, strict)
except HfHubHTTPError as e:
raise FileNotFoundError(
f"{SAFETENSORS_SINGLE_FILE} not found on the HuggingFace Hub in {model_id}"
) from e
policy.to(map_location)
policy.to(config.device)
policy.eval()
return policy

29
lerobot/common/robot_devices/control_configs.py
View File

@@ -12,17 +12,14 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from dataclasses import dataclass
from pathlib import Path
import draccus
from lerobot.common.robot_devices.robots.configs import RobotConfig
from lerobot.common.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available
from lerobot.configs import parser
from lerobot.configs.policies import PreTrainedConfig
from lerobot.configs.train import TrainPipelineConfig
@dataclass
@@ -57,11 +54,6 @@ class RecordControlConfig(ControlConfig):
# Root directory where the dataset will be stored (e.g. 'dataset/path').
root: str | Path | None = None
policy: PreTrainedConfig | None = None
# TODO(rcadene, aliberts): By default, use device and use_amp values from policy checkpoint.
device: str | None = None # cuda | cpu | mps
# `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP,
# automatic gradient scaling is used.
use_amp: bool | None = None
# Limit the frames per second. By default, uses the policy fps.
fps: int | None = None
# Number of seconds before starting data collection. It allows the robot devices to warmup and synchronize.
@@ -104,27 +96,6 @@ class RecordControlConfig(ControlConfig):
self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
self.policy.pretrained_path = policy_path
# When no device or use_amp are given, use the one from training config.
if self.device is None or self.use_amp is None:
train_cfg = TrainPipelineConfig.from_pretrained(policy_path)
if self.device is None:
self.device = train_cfg.device
if self.use_amp is None:
self.use_amp = train_cfg.use_amp
# Automatically switch to available device if necessary
if not is_torch_device_available(self.device):
auto_device = auto_select_torch_device()
logging.warning(f"Device '{self.device}' is not available. Switching to '{auto_device}'.")
self.device = auto_device
# Automatically deactivate AMP if necessary
if self.use_amp and not is_amp_available(self.device):
logging.warning(
f"Automatic Mixed Precision (amp) is not available on device '{self.device}'. Deactivating AMP."
)
self.use_amp = False
@ControlConfig.register_subclass("replay")
@dataclass

16
lerobot/common/robot_devices/control_utils.py
View File

@@ -32,6 +32,7 @@ from termcolor import colored
from lerobot.common.datasets.image_writer import safe_stop_image_writer
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
from lerobot.common.datasets.utils import get_features_from_robot
from lerobot.common.policies.pretrained import PreTrainedPolicy
from lerobot.common.robot_devices.robots.utils import Robot
from lerobot.common.robot_devices.utils import busy_wait
from lerobot.common.utils.utils import get_safe_torch_device, has_method
@@ -193,8 +194,6 @@ def record_episode(
episode_time_s,
display_cameras,
policy,
device,
use_amp,
fps,
single_task,
):
@@ -205,8 +204,6 @@ def record_episode(
dataset=dataset,
events=events,
policy=policy,
device=device,
use_amp=use_amp,
fps=fps,
teleoperate=policy is None,
single_task=single_task,
@@ -221,9 +218,7 @@ def control_loop(
display_cameras=False,
dataset: LeRobotDataset | None = None,
events=None,
policy=None,
device: torch.device | str | None = None,
use_amp: bool | None = None,
policy: PreTrainedPolicy = None,
fps: int | None = None,
single_task: str | None = None,
):
@@ -246,9 +241,6 @@ def control_loop(
if dataset is not None and fps is not None and dataset.fps != fps:
raise ValueError(f"The dataset fps should be equal to requested fps ({dataset['fps']} != {fps}).")
if isinstance(device, str):
device = get_safe_torch_device(device)
timestamp = 0
start_episode_t = time.perf_counter()
while timestamp < control_time_s:
@@ -260,7 +252,9 @@ def control_loop(
observation = robot.capture_observation()
if policy is not None:
pred_action = predict_action(observation, policy, device, use_amp)
pred_action = predict_action(
observation, policy, get_safe_torch_device(policy.config.device), policy.config.use_amp
)
# Action can eventually be clipped using `max_relative_target`,
# so action actually sent is saved in the dataset.
action = robot.send_action(pred_action)

5
lerobot/common/utils/utils.py
View File

@@ -51,8 +51,10 @@ def auto_select_torch_device() -> torch.device:
return torch.device("cpu")
# TODO(Steven): Remove log. log shouldn't be an argument, this should be handled by the logger level
def get_safe_torch_device(try_device: str, log: bool = False) -> torch.device:
"""Given a string, return a torch.device with checks on whether the device is available."""
try_device = str(try_device)
match try_device:
case "cuda":
assert torch.cuda.is_available()
@@ -85,6 +87,7 @@ def get_safe_dtype(dtype: torch.dtype, device: str | torch.device):
def is_torch_device_available(try_device: str) -> bool:
try_device = str(try_device) # Ensure try_device is a string
if try_device == "cuda":
return torch.cuda.is_available()
elif try_device == "mps":
@@ -92,7 +95,7 @@ def is_torch_device_available(try_device: str) -> bool:
elif try_device == "cpu":
return True
else:
raise ValueError(f"Unknown device '{try_device}.")
raise ValueError(f"Unknown device {try_device}. Supported devices are: cuda, mps or cpu.")
def is_amp_available(device: str):