mirror of
https://github.com/huggingface/lerobot.git
synced 2026-06-01 11:21:27 +00:00
f125d5e3bf
- Changed the internal device variable from `_device` to `tensor_device` for improved readability and consistency. - Updated references throughout the class to reflect the new variable name.
135 lines
5.0 KiB
Python
135 lines
5.0 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from typing import Any
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import torch
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from lerobot.configs.types import PolicyFeature
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from lerobot.utils.utils import get_safe_torch_device
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from .core import EnvTransition, TransitionKey
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from .pipeline import ProcessorStep, ProcessorStepRegistry
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@ProcessorStepRegistry.register("device_processor")
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@dataclass
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class DeviceProcessorStep(ProcessorStep):
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"""Processes transitions by moving tensors to the specified device and optionally converting float dtypes.
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This processor ensures that all tensors in the transition are moved to the
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specified device (CPU or GPU) before they are returned. It can also convert
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floating-point tensors to a specified dtype while preserving non-float types
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(int, long, bool, etc.).
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"""
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device: str = "cpu"
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float_dtype: str | None = None
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DTYPE_MAPPING = {
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"float16": torch.float16,
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"float32": torch.float32,
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"float64": torch.float64,
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"bfloat16": torch.bfloat16,
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"half": torch.float16,
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"float": torch.float32,
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"double": torch.float64,
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}
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def __post_init__(self):
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self.tensor_device: torch.device = get_safe_torch_device(self.device)
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self.device = self.tensor_device.type # cuda might have changed to cuda:1
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self.non_blocking = "cuda" in str(self.device)
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# Validate and convert float_dtype string to torch dtype
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if self.float_dtype is not None:
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if self.float_dtype not in self.DTYPE_MAPPING:
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raise ValueError(
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f"Invalid float_dtype '{self.float_dtype}'. Available options: {list(self.DTYPE_MAPPING.keys())}"
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)
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self._target_float_dtype = self.DTYPE_MAPPING[self.float_dtype]
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else:
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self._target_float_dtype = None
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def _process_tensor(self, tensor: torch.Tensor) -> torch.Tensor:
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"""Process a tensor by moving to device and optionally converting float dtype.
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If the tensor is already on a GPU and we're configured for a GPU, it preserves
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that GPU placement (useful for multi-GPU training with Accelerate).
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Otherwise, it moves to the configured device.
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"""
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# Determine target device
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if tensor.is_cuda and self.tensor_device.type == "cuda":
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# Both tensor and target are on GPU - preserve tensor's GPU placement
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# This handles multi-GPU scenarios where Accelerate has already placed
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# tensors on the correct GPU for each process
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target_device = tensor.device
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else:
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# Either tensor is on CPU, or we're configured for CPU
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# In both cases, use the configured device
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target_device = self.tensor_device
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# Only move if necessary
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if tensor.device != target_device:
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tensor = tensor.to(target_device, non_blocking=self.non_blocking)
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# Convert float dtype if specified and tensor is floating point
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if self._target_float_dtype is not None and tensor.is_floating_point():
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tensor = tensor.to(dtype=self._target_float_dtype)
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return tensor
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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new_transition = transition.copy()
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simple_tensor_keys = [
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TransitionKey.ACTION,
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TransitionKey.REWARD,
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TransitionKey.DONE,
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TransitionKey.TRUNCATED,
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]
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dict_tensor_keys = [
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TransitionKey.OBSERVATION,
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TransitionKey.COMPLEMENTARY_DATA,
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]
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# Process simple tensors
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for key in simple_tensor_keys:
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value = transition.get(key)
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if isinstance(value, torch.Tensor):
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new_transition[key] = self._process_tensor(value)
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# Process dictionary-like tensors
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for key in dict_tensor_keys:
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data_dict = transition.get(key)
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if data_dict is not None:
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new_data_dict = {
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k: self._process_tensor(v) if isinstance(v, torch.Tensor) else v
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for k, v in data_dict.items()
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}
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new_transition[key] = new_data_dict
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return new_transition
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def get_config(self) -> dict[str, Any]:
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"""Return configuration for serialization."""
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return {"device": self.device, "float_dtype": self.float_dtype}
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def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
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return features
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