# 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 PI05 Policy.
This module defines the `PI05Config` class, which handles the configuration parameters
for the PI05 Vision-Language-Action Flow Model. It includes settings for the model architecture,
optimization, scheduling, and data processing.
"""
import warnings
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("pi05")
@dataclass
class PI05Config(PreTrainedConfig):
"""Configuration class for the PI05 Policy.
This class defines the configuration parameters for the PI05 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.
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.
predict_response: Whether to predict the response. Defaults to False.
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.
prompt_max_length: Maximum length for tokenizer. Defaults to 256.
discrete_action_max_length: Maximum length for discrete action tokens. Defaults to 32.
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.
attention_implementation: Attention implementation to use ("eager", "sdpa", or "fa2").
Defaults to "eager". "sdpa" dispatches to ``torch.nn.functional.scaled_dot_product_attention``
(frees ~5.6 GiB on forward at the bs ceiling tested; see PR #182). "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.
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
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
predict_response: bool = False
# "discrete" encodes state as binned text tokens in the language prompt;
# "continuous" projects the raw state vector into VLM embedding space.
state_type: Literal["discrete", "continuous"] = "discrete"
# Image preprocessing
resize_imgs_with_padding: tuple[int, int] = (224, 224)
# Add empty images. Used by pi05_aloha_sim which adds the empty
# left and right wrist cameras in addition to the top camera.
empty_cameras: int = 0
# Language Tokenizer
prompt_max_length: int = 256
# Maximum length of the indicator tokens
response_indicator_max_length: int = 3
discrete_action_indicator_max_length: int = 3
# Response Tokenizer
response_max_length: int = 52
# Maximum length of the action tokens
discrete_action_max_length: int = 32
# HF repo id or local path for the FAST action tokenizer
# (``AutoProcessor.from_pretrained(..., trust_remote_code=True)``).
# Override to use a tokenizer specialized to your mixture (see
# ``opentau.scripts.fit_fast_tokenizer``).
discrete_action_tokenizer_path: str = "physical-intelligence/fast"
# Projector
proj_width: int = 1024
# Dropout
dropout: float = 0.1
# Decoding
num_steps: int = 10
# Real Time Inference
# maximum number of frozen actions
max_delay: int = 0
# Modality-specific learnable embedding.
# When True, a learnable embedding selected by input modality (vision,
# language, state, response, discrete action, action expert) is *added* on
# top of every token embedding before the transformer. This is an additive
# positional signal that tells the model which modality each token came
# from, layered over the existing RoPE positions (it does not replace them).
# The table is zero-initialized so enabling it on an existing checkpoint is
# a no-op at step 0 and the signal is learned from there. Defaults to False
# (original behavior, what existing checkpoints expect).
use_modality_embedding: bool = False
# Attention utils
attention_implementation: str = "eager"
# Finetuning settings
freeze_vision_encoder: bool = True
train_expert_only: bool = False
# Knowledge insulation (π0.5): when True (default), the prefix/VLM KV cache
# is detached before the action expert reads it, so the flow-matching action
# loss does NOT backpropagate into the VLM backbone. Set False to let the
# action gradient flow into the VLM (end-to-end action training). Default
# True preserves existing behavior and is what current checkpoints expect.
knowledge_insulation: 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 pi05'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
# torch.compile defaults ON for pi05: PI05Policy is wired for it
# (forward dispatches self.model via __call__, supports_torch_compile=True)
# and it gives a measured ~1.3-1.5x faster forward with 0 recompiles under
# DeepSpeed ZeRO-1/2 / DDP. Set False to train in eager (train.py also
# auto-falls back to eager under FSDP / ZeRO-3, which compile can't support).
use_torch_compile: bool = True
# 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
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.state_type not in ("discrete", "continuous"):
raise ValueError(f"state_type must be 'discrete' or 'continuous', got '{self.state_type}'")
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.max_delay > self.chunk_size:
raise ValueError(
f"The max delay must be less than or equal to the chunk size. Got {self.max_delay} for `max_delay` and {self.chunk_size} for `chunk_size`."
)
if self.n_action_steps < self.chunk_size and self.max_delay != 0:
raise ValueError(
"A shortened execution horizon (n_action_steps < chunk_size) is not yet "
"supported together with real-time inference delay (max_delay > 0); they "
"would entangle the action-queue prefix logic. Got "
f"n_action_steps={self.n_action_steps}, chunk_size={self.chunk_size}, "
f"max_delay={self.max_delay}."
)
[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.
"""
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
[docs]
@PreTrainedConfig.register_subclass("pi05_continuous_state")
@dataclass
class PI05ContinuousStateConfig(PI05Config):
"""Deprecated: use ``PI05Config(state_type="continuous")`` instead."""
state_type: Literal["discrete", "continuous"] = "continuous"
def __post_init__(self):
warnings.warn(
"PI05ContinuousStateConfig is deprecated. Use PI05Config with state_type='continuous' instead.",
DeprecationWarning,
stacklevel=2,
)
super().__post_init__()