lerobot-clone/src/lerobot/policies/pi0/processor_pi0.py

#!/usr/bin/env python

# Copyright 2025 Physical Intelligence and 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 torch

from lerobot.configs.types import PipelineFeatureType, PolicyFeature
from lerobot.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
from lerobot.policies.pi0.configuration_pi0 import PI0Config
from lerobot.processor import (
    AddBatchDimensionProcessorStep,
    ComplementaryDataProcessorStep,
    DeviceProcessorStep,
    NormalizerProcessorStep,
    PolicyProcessorPipeline,
    ProcessorKwargs,
    ProcessorStep,
    ProcessorStepRegistry,
    RenameObservationsProcessorStep,
    TokenizerProcessorStep,
    UnnormalizerProcessorStep,
)


@ProcessorStepRegistry.register(name="pi0_new_line_processor")
class Pi0NewLineProcessor(ComplementaryDataProcessorStep):
    """
    Ensures that the task description string ends with a newline character.

    This processing step is required for compatibility with the PaliGemma tokenizer,
    which expects a newline at the end of the text prompt. It handles both single
    strings and lists of strings for the 'task' key in complementary data.
    """

    def complementary_data(self, complementary_data):
        """
        Adds a newline to the 'task' field if it doesn't already have one.

        Args:
            complementary_data: A dictionary that may contain a 'task' key with a
                                string or list of strings.

        Returns:
            A new dictionary with the modified 'task' field.
        """
        if "task" not in complementary_data:
            return complementary_data

        task = complementary_data["task"]
        if task is None:
            return complementary_data

        new_complementary_data = dict(complementary_data)

        # Handle both string and list of strings
        if isinstance(task, str):
            # Single string: add newline if not present
            if not task.endswith("\n"):
                new_complementary_data["task"] = f"{task}\n"
        elif isinstance(task, list) and all(isinstance(t, str) for t in task):
            # List of strings: add newline to each if not present
            new_complementary_data["task"] = [t if t.endswith("\n") else f"{t}\n" for t in task]
        # If task is neither string nor list of strings, leave unchanged

        return new_complementary_data

    def transform_features(
        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
        """
        This step does not alter the feature definitions.

        Args:
            features: The input feature dictionary.

        Returns:
            The unchanged feature dictionary.
        """
        return features


def make_pi0_pre_post_processors(
    config: PI0Config,
    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
    preprocessor_kwargs: ProcessorKwargs | None = None,
    postprocessor_kwargs: ProcessorKwargs | None = None,
) -> tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]:
    """
    Constructs pre-processor and post-processor pipelines for the PI0 policy.

    The pre-processing pipeline prepares input data for the model by:
    1. Renaming features to match pretrained configurations.
    2. Normalizing input and output features based on dataset statistics.
    3. Adding a batch dimension.
    4. Appending a newline character to the task description for tokenizer compatibility.
    5. Tokenizing the text prompt using the PaliGemma tokenizer.
    6. Moving all data to the specified device.

    The post-processing pipeline handles the model's output by:
    1. Moving data to the CPU.
    2. Unnormalizing the output features to their original scale.

    Args:
        config: The configuration object for the PI0 policy.
        dataset_stats: A dictionary of statistics for normalization.
        preprocessor_kwargs: Additional arguments for the pre-processor pipeline.
        postprocessor_kwargs: Additional arguments for the post-processor pipeline.

    Returns:
        A tuple containing the configured pre-processor and post-processor pipelines.
    """
    if preprocessor_kwargs is None:
        preprocessor_kwargs = {}
    if postprocessor_kwargs is None:
        postprocessor_kwargs = {}

    # Add remaining processors
    input_steps: list[ProcessorStep] = [
        RenameObservationsProcessorStep(rename_map={}),  # To mimic the same processor as pretrained one
        AddBatchDimensionProcessorStep(),
        Pi0NewLineProcessor(),  # Add newlines before tokenization for PaliGemma
        TokenizerProcessorStep(
            tokenizer_name="google/paligemma-3b-pt-224",
            max_length=config.tokenizer_max_length,
            padding_side="right",
            padding="max_length",
        ),
        DeviceProcessorStep(device=config.device),
        NormalizerProcessorStep(
            features={**config.input_features, **config.output_features},
            norm_map=config.normalization_mapping,
            stats=dataset_stats,
        ),
    ]

    output_steps: list[ProcessorStep] = [
        DeviceProcessorStep(device="cpu"),
        UnnormalizerProcessorStep(
            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
        ),
    ]

    return (
        PolicyProcessorPipeline(
            steps=input_steps,
            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
            **preprocessor_kwargs,
        ),
        PolicyProcessorPipeline(
            steps=output_steps,
            name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
            **postprocessor_kwargs,
        ),
    )