# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
# Copyright 2026 Tensor Auto Inc. 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.
"""
Configuration module for the PI0 Policy.
This module defines the `PI0Config` class, which handles the configuration parameters
for the PI0 Vision-Language-Action Flow Model. It includes settings for the model architecture,
optimization, scheduling, and data processing.
"""
from dataclasses import dataclass, field
from typing import Literal
from opentau.configs.policies import PreTrainedConfig
from opentau.configs.types import FeatureType, NormalizationMode, PolicyFeature
from opentau.optim.optimizers import AdamWConfig
from opentau.optim.schedulers import (
CosineDecayWithWarmupSchedulerConfig,
LRSchedulerConfig,
)
[docs]
@PreTrainedConfig.register_subclass("pi0")
@dataclass
class PI0Config(PreTrainedConfig):
"""Configuration class for the PI0 Policy.
This class defines the configuration parameters for the PI0 model, including
input/output structure, model architecture, training settings, and preprocessing.
Args:
n_obs_steps: Number of observation steps to use. Defaults to 1.
chunk_size: Trained action-chunk length, i.e. the prediction horizon the model
always decodes at inference. Upper bound for n_action_steps. Defaults to 50.
n_action_steps: Inference execution horizon -- how many actions from each
predicted chunk are executed before the policy re-queries with fresh
observations. Must be <= chunk_size. Defaults to 50.
safety_buffer: Safety buffer size. Defaults to 0.
normalization_mapping: Mapping of feature names to normalization modes.
Defaults to identity for visual features and mean-std for state and action.
max_state_dim: Maximum dimension for state vectors. Shorter vectors are padded. Defaults to 32.
max_action_dim: Maximum dimension for action vectors. Shorter vectors are padded. Defaults to 32.
resize_imgs_with_padding: Target size (height, width) for image resizing with padding.
Defaults to (224, 224).
empty_cameras: Number of empty camera inputs to add. Used for specific adaptations like
Aloha simulation. Defaults to 0.
tokenizer_max_length: Maximum length for tokenizer. Defaults to 48.
proj_width: Width of the projection layer. Defaults to 1024.
dropout: Dropout rate. Defaults to 0.1.
num_steps: Number of flow matching steps for decoding. Defaults to 10.
advantage_threshold: Advantage binarization threshold for AWR. Defaults to 0.0.
advantage: Advantage conditioning mode. One of "ignore", "on", "use".
"use" uses values from dataset, "ignore" disables conditioning,
"on" sets advantage to True (for expert demos). Defaults to "use".
use_cache: Whether to use KV cache during inference. Defaults to True.
attention_implementation: Attention implementation to use ("eager", "sdpa", or "fa2").
Defaults to "eager". "sdpa" dispatches to ``torch.nn.functional.scaled_dot_product_attention``
and is typically 2-3x faster on A100 + bf16 (FlashAttention-2 backend). "fa2" is accepted
for backward compatibility but logs a warning and falls back to "eager".
freeze_vision_encoder: Whether to freeze the vision encoder during fine-tuning. Defaults to True.
train_expert_only: Whether to train only the expert module. Defaults to False.
train_state_proj: Whether to train the state projection layer. Defaults to True.
optimizer_lr: Learning rate for the optimizer. Defaults to 2.5e-5.
optimizer_betas: Beta parameters for AdamW optimizer. Defaults to (0.9, 0.95).
optimizer_eps: Epsilon parameter for AdamW optimizer. Defaults to 1e-8.
optimizer_weight_decay: Weight decay for AdamW optimizer. Defaults to 1e-10.
scheduler_warmup_steps: Number of warmup steps for the scheduler. Defaults to 1_000.
scheduler_decay_steps: Number of decay steps for the scheduler. Defaults to 30_000.
scheduler_decay_lr: Target learning rate after decay. Defaults to 2.5e-6.
"""
# Input / output structure.
n_obs_steps: int = 1
chunk_size: int = 50
n_action_steps: int = 50
safety_buffer: int = 0
normalization_mapping: dict[str, NormalizationMode] = field(
default_factory=lambda: {
"VISUAL": NormalizationMode.IDENTITY,
"STATE": NormalizationMode.MEAN_STD,
"ACTION": NormalizationMode.MEAN_STD,
}
)
# Shorter state and action vectors will be padded
max_state_dim: int = 32
max_action_dim: int = 32
# Image preprocessing
resize_imgs_with_padding: tuple[int, int] = (224, 224)
# Add empty images. Used by pi0_aloha_sim which adds the empty
# left and right wrist cameras in addition to the top camera.
empty_cameras: int = 0
# Tokenizer
tokenizer_max_length: int = 48
# Projector
proj_width: int = 1024
# Dropout
dropout: float = 0.1
# Decoding
num_steps: int = 10
# advantage binarization threshold for AWR
advantage_threshold: float = 0.0
# When set to "use", the advantage values provided in the dataset will be used.
# When set to "ignore", no advantage conditioning will be applied.
# When set to "on", the advantage will always be True.
# This should only be "on" when training on expert demonstrations or interventions.
advantage: Literal["ignore", "on", "use"] = "use"
# Attention utils
use_cache: bool = True
attention_implementation: str = "eager" # or "sdpa" / "fa2"
# Finetuning settings
freeze_vision_encoder: bool = True
train_expert_only: bool = False
train_state_proj: bool = True
# Wrap each transformer-layer forward in torch.utils.checkpoint to trade
# ~25-33%% same-batch compute for ~30-40 GB of activation memory per rank,
# typically netting +10-25%% throughput once the freed memory is spent on
# a larger per-rank batch. Only supported with distributed_type=MULTI_GPU
# (DDP), NO (single process), or DeepSpeed ZeRO-1/2 — src/opentau/scripts/
# train.py raises if the accelerator's distributed_type is anything else
# (ZeRO-3, FSDP) because pi0's custom per-layer forward does not wire up
# the backend-specific activation-checkpointing hooks those strategies
# require. Defaults to False (no ckpt, lowest risk).
gradient_checkpointing: bool = False
# Training presets
optimizer_lr: float = 2.5e-5
optimizer_betas: tuple[float, float] = (0.9, 0.95)
optimizer_eps: float = 1e-8
optimizer_weight_decay: float = 1e-10
scheduler_warmup_steps: int = 1_000
scheduler_decay_steps: int = 30_000
scheduler_decay_lr: float = 2.5e-6
# AWR settings
use_awr: bool = False
awr_max_weight: float = 10.0
# TODO: Add EMA
def __post_init__(self):
"""Post-initialization validation."""
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(
f"The chunk size is the upper bound for the number of action steps per model invocation. Got "
f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`."
)
if self.n_obs_steps != 1:
raise ValueError(
f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`"
)
if self.attention_implementation == "flash_cuda":
raise ValueError(
"attention_implementation='flash_cuda' is only supported by pi07_paligemma, "
"which builds the per-token block-ids the kernel requires. This policy does "
"not build them, so the kernel would hard-error at the first forward. "
"Use 'eager' or 'sdpa' instead."
)
if self.n_action_steps < self.chunk_size and self.safety_buffer != 0:
raise ValueError(
"A shortened execution horizon (n_action_steps < chunk_size) is not yet "
"supported together with a non-zero safety_buffer; they would entangle the "
"action-queue refill logic. Got "
f"n_action_steps={self.n_action_steps}, chunk_size={self.chunk_size}, "
f"safety_buffer={self.safety_buffer}."
)
[docs]
def validate_features(self) -> None:
"""Validates the features and adds empty cameras if configured.
This method checks feature configurations and dynamically adds empty camera inputs
to `self.input_features` based on the `empty_cameras` parameter.
"""
# TODO: implement value error
# if not self.image_features and not self.env_state_feature:
# raise ValueError("You must provide at least one image or the environment state among the inputs.")
for i in range(self.empty_cameras):
key = f"observation.images.empty_camera_{i}"
empty_camera = PolicyFeature(
type=FeatureType.VISUAL,
shape=(3, 480, 640),
)
self.input_features[key] = empty_camera
[docs]
def get_optimizer_preset(self) -> AdamWConfig:
"""Returns the default optimizer configuration.
Returns:
AdamWConfig: The optimizer configuration with default parameters.
"""
return AdamWConfig(
lr=self.optimizer_lr,
betas=self.optimizer_betas,
eps=self.optimizer_eps,
weight_decay=self.optimizer_weight_decay,
)
[docs]
def get_scheduler_preset(self) -> LRSchedulerConfig:
"""Returns the default scheduler configuration.
Returns:
CosineDecayWithWarmupSchedulerConfig: The scheduler configuration with default parameters.
"""
return CosineDecayWithWarmupSchedulerConfig(
peak_lr=self.optimizer_lr,
decay_lr=self.scheduler_decay_lr,
num_warmup_steps=self.scheduler_warmup_steps,
num_decay_steps=self.scheduler_decay_steps,
)
@property
def observation_delta_indices(self) -> None:
"""Indices for observation deltas.
Returns:
None: As observation deltas are not used.
"""
return None
@property
def action_delta_indices(self) -> list[int]:
"""Indices for action deltas.
Returns:
list[int]: A list of indices corresponding to the chunk size.
"""
return list(range(self.chunk_size))
@property
def reward_delta_indices(self) -> None:
"""Indices for reward deltas.
Returns:
None: As reward deltas are not used.
"""
return None