mirror of
https://github.com/huggingface/lerobot.git
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refactor(pi05): rename PI0OpenPIPolicy to PI0Policy and update configuration
- Renamed `PI0OpenPIPolicy` to `PI0Policy` for consistency with naming conventions. - Updated the `PI05OpenPIConfig` to include a new `tokenizer_max_length` attribute and changed the normalization mode for state from `MEAN_STD` to `QUANTILES`. - Simplified model initialization in `PI05OpenPIPolicy` by removing unused `dataset_stats` parameter. - Added a new processor class for `Pi05PrepareStateTokenizerProcessorStep` with `@dataclass` for improved readability. - Introduced a test script to compare the integration of the PI0OpenPI policy with the original implementation, ensuring local testing compatibility.
This commit is contained in:
AdilZouitine
@@ -0,0 +1,437 @@
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"""Test script to verify PI0OpenPI policy integration with LeRobot vs the original implementation, only meant to be run locally!"""
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import os
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from copy import deepcopy
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from typing import Any
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import pytest
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import torch
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# Skip if openpi or transformers is not available
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pytest.importorskip("openpi")
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pytest.importorskip("transformers")
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# Skip this entire module in CI
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pytestmark = pytest.mark.skipif(
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os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
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reason="This test requires local OpenPI installation and is not meant for CI",
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)
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from openpi.models_pytorch import preprocessing_pytorch as openpi_preprocessing # noqa: E402
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# NOTE: Assumes PYTHONPATH is set to include OpenPI src as per instructions.
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from openpi.models_pytorch.pi0_pytorch import PI0Pytorch # noqa: E402
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from transformers import AutoTokenizer # noqa: E402
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from lerobot.policies.pi05 import PI05OpenPIConfig, PI05OpenPIPolicy # noqa: E402
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from lerobot.policies.pi05.processor_pi05openpi import make_pi05_openpi_pre_post_processors # noqa: E402
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from lerobot.processor import PolicyAction, PolicyProcessorPipeline # noqa: E402
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# TODO: ADDING DEFAULT IMAGES_FEATURES TO CONFIG
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DUMMY_ACTION_DIM = 32
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DUMMY_STATE_DIM = 32
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DUMMY_ACTION_HORIZON = 50
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DUMMY_MAX_TOKEN_LEN = 48 # Default for PI0 (non-pi05)
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DEVICE = "cpu" # Use CPU to avoid memory issues for testing
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DUMMY_DATASET_STATS = {
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"observation.state": {
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"mean": torch.zeros(DUMMY_STATE_DIM),
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"std": torch.ones(DUMMY_STATE_DIM),
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"q01": torch.zeros(DUMMY_STATE_DIM),
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"q99": torch.ones(DUMMY_STATE_DIM),
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},
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"action": {
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"mean": torch.zeros(DUMMY_ACTION_DIM),
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"std": torch.ones(DUMMY_ACTION_DIM),
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"q01": torch.zeros(DUMMY_ACTION_DIM),
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"q99": torch.ones(DUMMY_ACTION_DIM),
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},
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"images": {
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"base_0_rgb": {
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"mean": torch.zeros(3, 224, 224),
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"std": torch.ones(3, 224, 224),
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"q01": torch.zeros(3, 224, 224),
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"q99": torch.ones(3, 224, 224),
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},
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"left_wrist_0_rgb": {
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"mean": torch.zeros(3, 224, 224),
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"std": torch.ones(3, 224, 224),
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"q01": torch.zeros(3, 224, 224),
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"q99": torch.ones(3, 224, 224),
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},
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"right_wrist_0_rgb": {
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"mean": torch.zeros(3, 224, 224),
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"std": torch.ones(3, 224, 224),
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"q01": torch.zeros(3, 224, 224),
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"q99": torch.ones(3, 224, 224),
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},
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},
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}
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class PI0BaseOriginalConfig:
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action_dim: int = DUMMY_ACTION_DIM
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action_horizon: int = DUMMY_ACTION_HORIZON
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paligemma_variant: str = "gemma_2b"
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action_expert_variant: str = "gemma_300m"
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precision: str = "float32"
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pi05: bool = True
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dtype: str = "float32"
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def instantiate_lerobot_pi0(
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from_pretrained: bool = False,
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) -> tuple[
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PI05OpenPIPolicy,
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PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
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PolicyProcessorPipeline[PolicyAction, PolicyAction],
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]:
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if from_pretrained:
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# Load the policy first
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policy = PI05OpenPIPolicy.from_pretrained(
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pretrained_name_or_path="pepijn223/pi05_base_fp32", strict=True
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)
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else:
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config = PI05OpenPIConfig(
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max_action_dim=DUMMY_ACTION_DIM, max_state_dim=DUMMY_STATE_DIM, dtype="float32"
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)
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policy = PI05OpenPIPolicy(config)
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policy.to(DEVICE)
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policy.config.device = DEVICE
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preprocessor, postprocessor = make_pi05_openpi_pre_post_processors(
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config=policy.config, dataset_stats=DUMMY_DATASET_STATS
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)
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return (policy, preprocessor, postprocessor)
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def instantiate_original_pi0(from_pretrained: bool = False, model_path: str | None = None):
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config = PI0BaseOriginalConfig()
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policy = PI0Pytorch(config)
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if from_pretrained:
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try:
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print("Loading converted PyTorch weights from HuggingFace Hub (pepijn223/pi05_base_fp32)...")
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# Download the model from HuggingFace Hub
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import safetensors.torch
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from huggingface_hub import snapshot_download
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# Download the entire repository
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if model_path and os.path.exists(model_path):
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cache_dir = model_path
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print(f"Using cached model from: {cache_dir}")
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else:
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cache_dir = snapshot_download(repo_id="pepijn223/pi05_base_fp32", repo_type="model")
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print(f"Downloaded model to: {cache_dir}")
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# Try to load safetensors format first
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model_file = os.path.join(cache_dir, "model.safetensors")
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if os.path.exists(model_file):
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state_dict = safetensors.torch.load_file(model_file)
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print(f"Loaded {len(state_dict)} parameters from safetensors")
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else:
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raise FileNotFoundError(f"No safetensors file found in {cache_dir}")
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# Load the state dict into the model
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missing_keys, unexpected_keys = policy.load_state_dict(state_dict, strict=False)
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if missing_keys:
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print(f"Missing keys: {len(missing_keys)}")
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if len(missing_keys) <= 5:
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for key in missing_keys:
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print(f" - {key}")
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else:
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for key in missing_keys[:5]:
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print(f" - {key}")
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print(f" ... and {len(missing_keys) - 5} more")
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if unexpected_keys:
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print(f"Unexpected keys: {len(unexpected_keys)}")
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if len(unexpected_keys) <= 5:
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for key in unexpected_keys:
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print(f" - {key}")
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else:
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for key in unexpected_keys[:5]:
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print(f" - {key}")
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print(f" ... and {len(unexpected_keys) - 5} more")
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if not missing_keys and not unexpected_keys:
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print("All pretrained weights loaded successfully!")
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else:
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print("Pretrained weights loaded with some missing/unexpected keys (this may be normal)")
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except Exception as e:
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print(f"Failed to load pretrained weights: {e}")
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print(" Using randomly initialized weights...")
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import traceback
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traceback.print_exc()
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policy.to(DEVICE)
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return policy
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def create_dummy_data():
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batch_size = 2 # Reduce batch size for testing
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device = DEVICE
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# Use the exact same prompt for both implementations
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prompt = "Pick up the red block and place it in the bin"
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batch = {
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"observation.state": torch.randn(batch_size, DUMMY_STATE_DIM, dtype=torch.float32, device=device),
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"action": torch.randn(
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batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM, dtype=torch.float32, device=device
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),
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# Create images in [0, 1] range as expected by LeRobot (will be converted to [-1, 1] internally)
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"observation.images.base_0_rgb": torch.rand(
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batch_size, 3, 224, 224, dtype=torch.float32, device=device
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),
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"observation.images.left_wrist_0_rgb": torch.rand(
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batch_size, 3, 224, 224, dtype=torch.float32, device=device
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),
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"observation.images.right_wrist_0_rgb": torch.rand(
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batch_size, 3, 224, 224, dtype=torch.float32, device=device
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),
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# Add the task prompt for LeRobot - provide as list with single element to trigger expansion
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"task": [prompt for _ in range(batch_size)],
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}
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return batch
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def extract_lerobot_processed_inputs(lerobot_pi0, batch):
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"""Extract the exact same processed inputs that LeRobot uses internally."""
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# Get the tokenized language from LeRobot's internal method
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lang_tokens, lang_masks = lerobot_pi0._tokenize_language(batch)
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# Get the preprocessed images from LeRobot's internal method
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images, img_masks = lerobot_pi0._preprocess_images(batch, train=False)
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# Create dummy token_ar_mask and token_loss_mask for original implementation
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token_ar_mask = torch.zeros_like(lang_tokens, dtype=torch.int32)
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token_loss_mask = torch.ones_like(lang_masks, dtype=torch.bool)
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return images, img_masks, lang_tokens, lang_masks, token_ar_mask, token_loss_mask
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class PI0Observation:
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"""Observation class that matches the original OpenPI format."""
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def __init__(
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self,
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state,
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images,
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image_masks,
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tokenized_prompt,
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tokenized_prompt_mask,
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token_ar_mask,
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token_loss_mask,
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):
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self.state = state
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self.images = images
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self.image_masks = image_masks
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self.tokenized_prompt = tokenized_prompt
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self.tokenized_prompt_mask = tokenized_prompt_mask
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self.token_ar_mask = token_ar_mask
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self.token_loss_mask = token_loss_mask
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def create_original_observation_with_openpi_preprocessing(batch):
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"""Create observation object for OpenPI using OpenPI's own preprocessing."""
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batch_size = batch["observation.state"].shape[0]
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device = batch["observation.state"].device
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# Create tokenizer for OpenPI (same as LeRobot uses)
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tokenizer = AutoTokenizer.from_pretrained("google/paligemma-3b-pt-224")
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# Get task description
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if "task" in batch:
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tasks = batch["task"]
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if isinstance(tasks, str):
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# Single string: add newline if not present, then convert to list
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if not tasks.endswith("\n"):
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tasks = f"{tasks}\n"
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tasks = [tasks]
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elif isinstance(tasks, list) and all(isinstance(t, str) for t in tasks):
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# List of strings: add newline to each if not present
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tasks = [t if t.endswith("\n") else f"{t}\n" for t in tasks]
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if len(tasks) == 1:
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# Expand to batch size
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tasks = tasks * batch_size
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if len(tasks) != batch_size:
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raise ValueError(f"Expected batch size {batch_size}, got {len(tasks)}")
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# If task is neither string nor list of strings, leave unchanged
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else:
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# Default task if not provided
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tasks = ["Pick up the object\n"] * batch_size
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# Tokenize with max_length padding to match OpenPI's expected format
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tokenized = tokenizer(
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tasks,
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padding="max_length",
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padding_side="right",
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truncation=True,
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max_length=DUMMY_MAX_TOKEN_LEN,
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return_tensors="pt",
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)
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lang_tokens = tokenized["input_ids"].to(device)
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lang_masks = tokenized["attention_mask"].to(device, dtype=torch.bool)
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# Create dummy token_ar_mask and token_loss_mask for OpenPI
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token_ar_mask = torch.zeros_like(lang_tokens, dtype=torch.int32)
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token_loss_mask = torch.ones_like(lang_masks, dtype=torch.bool)
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# Convert LeRobot images format to OpenPI format (convert [0,1] to [-1,1] range)
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image_dict = {
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"base_0_rgb": batch["observation.images.base_0_rgb"] * 2.0 - 1.0,
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"left_wrist_0_rgb": batch["observation.images.left_wrist_0_rgb"] * 2.0 - 1.0,
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"right_wrist_0_rgb": batch["observation.images.right_wrist_0_rgb"] * 2.0 - 1.0,
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}
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# Create image masks (all ones for real images)
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image_masks_dict = {}
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for key in image_dict:
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image_masks_dict[key] = torch.ones(batch_size, dtype=torch.bool, device=device)
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# Create raw observation object (before preprocessing)
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raw_observation = PI0Observation(
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state=batch["observation.state"],
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images=image_dict,
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image_masks=image_masks_dict,
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tokenized_prompt=lang_tokens,
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tokenized_prompt_mask=lang_masks,
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token_ar_mask=token_ar_mask,
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token_loss_mask=token_loss_mask,
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)
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# Now use OpenPI's preprocessing
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processed_obs = openpi_preprocessing.preprocess_observation_pytorch(raw_observation, train=False)
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return processed_obs
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def create_original_observation_from_lerobot(lerobot_pi0, batch):
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"""Create observation object compatible with original OpenPI using the exact same inputs as LeRobot."""
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_batch_size = batch["observation.state"].shape[0]
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_device = batch["observation.state"].device
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# Extract the exact same processed inputs that LeRobot uses
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images, img_masks, lang_tokens, lang_masks, token_ar_mask, token_loss_mask = (
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extract_lerobot_processed_inputs(lerobot_pi0, batch)
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)
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# Convert images list to dict with original OpenPI keys
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image_dict = {
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"base_0_rgb": images[0],
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"left_wrist_0_rgb": images[1],
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"right_wrist_0_rgb": images[2],
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}
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# Convert image masks list to dict with original OpenPI keys
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image_masks_dict = {
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"base_0_rgb": img_masks[0],
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"left_wrist_0_rgb": img_masks[1],
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"right_wrist_0_rgb": img_masks[2],
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}
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return PI0Observation(
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state=batch["observation.state"],
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images=image_dict,
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image_masks=image_masks_dict,
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tokenized_prompt=lang_tokens,
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tokenized_prompt_mask=lang_masks,
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token_ar_mask=token_ar_mask,
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token_loss_mask=token_loss_mask,
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)
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def test_pi0_original_vs_lerobot():
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"""Test PI0 original implementation vs LeRobot implementation."""
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print("Initializing models...")
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lerobot_pi0, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_pi0(
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from_pretrained=True
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) # Load pretrained LeRobot model
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original_pi0 = instantiate_original_pi0(
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from_pretrained=True
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) # Load pretrained OpenPI model from HuggingFace Hub
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print("Creating dummy data...")
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batch = create_dummy_data()
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batch_lerobot = deepcopy(batch)
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# Test 1: Each model with its own preprocessing (more realistic end-to-end test)
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print("\nTEST 1: Each model with its own preprocessing")
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print("Creating observation for OpenPI using OpenPI's own preprocessing...")
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pi0_obs_openpi = create_original_observation_with_openpi_preprocessing(batch)
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print(f"Task prompt: '{batch['task'][0]}'")
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print(f"OpenPI tokenized prompt shape: {pi0_obs_openpi.tokenized_prompt.shape}")
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print(f"OpenPI image shapes: {[img.shape for img in pi0_obs_openpi.images.values()]}")
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print(f"OpenPI state shape: {pi0_obs_openpi.state.shape}")
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print("Testing OpenPI with own preprocessing...")
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original_pi0.eval()
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torch.manual_seed(42) # Set seed for reproducibility
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batch_size = batch["observation.state"].shape[0]
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noise_shape = (batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM)
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fixed_noise = torch.randn(noise_shape, dtype=torch.float32, device=DEVICE)
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with torch.no_grad():
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openpi_actions = original_pi0.sample_actions(
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device=DEVICE, observation=pi0_obs_openpi, noise=fixed_noise, num_steps=10
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)
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openpi_actions_unit = openpi_actions[:, 0, :]
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print(f"OpenPI (own preprocessing) Actions shape: {openpi_actions.shape}")
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print(f"OpenPI (own preprocessing) Actions unit shape: {openpi_actions_unit.shape}")
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print(f"OpenPI (own preprocessing) Actions mean: {openpi_actions.mean().item():.6f}")
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print(f"OpenPI (own preprocessing) Actions std: {openpi_actions.std().item():.6f}")
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print("Testing LeRobot with own preprocessing...")
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lerobot_pi0.eval()
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torch.manual_seed(42) # Set the same seed
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batch_lerobot_processed = lerobot_preprocessor(batch_lerobot)
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with torch.no_grad():
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lerobot_actions_own = lerobot_pi0.predict_action_chunk(
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batch_lerobot_processed
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) # batch_size, n_action_steps, action_dim
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lerobot_actions_unit = lerobot_actions_own[:, 0, :]
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print(f"LeRobot (own preprocessing) Actions shape: {lerobot_actions_own.shape}")
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print(f"LeRobot (own preprocessing) Actions unit shape: {lerobot_actions_unit.shape}")
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print(f"LeRobot (own preprocessing) Actions mean: {lerobot_actions_own.mean().item():.6f}")
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print(f"LeRobot (own preprocessing) Actions std: {lerobot_actions_own.std().item():.6f}")
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print("\nComparing end-to-end implementations:")
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print(f"Actions close (atol=1e-4): {torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-4)}")
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print(f"Actions close (atol=1e-2): {torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-2)}")
|
||||
print(f"Max absolute difference: {torch.abs(lerobot_actions_own - openpi_actions).max().item():.6f}")
|
||||
|
||||
# # Test 2: Both models with LeRobot preprocessing (isolates model differences)
|
||||
# print("\nTEST 2: Both models with LeRobot preprocessing (model comparison)")
|
||||
# print("Creating observation for OpenPI using LeRobot's preprocessing...")
|
||||
# pi0_obs_lerobot = create_original_observation_from_lerobot(lerobot_pi0, batch)
|
||||
|
||||
# print("Testing OpenPI with LeRobot preprocessing...")
|
||||
# torch.manual_seed(42) # Set seed for reproducibility
|
||||
# with torch.no_grad():
|
||||
# openpi_actions_lerobot_preproc = original_pi0.sample_actions(
|
||||
# device=DEVICE, observation=pi0_obs_lerobot, noise=fixed_noise, num_steps=10
|
||||
# )
|
||||
# print(f"OpenPI (LeRobot preprocessing) Actions shape: {openpi_actions_lerobot_preproc.shape}")
|
||||
# print(f"OpenPI (LeRobot preprocessing) Actions mean: {openpi_actions_lerobot_preproc.mean().item():.6f}")
|
||||
# print(f"OpenPI (LeRobot preprocessing) Actions std: {openpi_actions_lerobot_preproc.std().item():.6f}")
|
||||
|
||||
# print("\nComparing models with same preprocessing:")
|
||||
# is_close_1e4 = torch.allclose(lerobot_actions_own, openpi_actions_lerobot_preproc, atol=1e-4)
|
||||
# is_close_1e2 = torch.allclose(lerobot_actions_own, openpi_actions_lerobot_preproc, atol=1e-2)
|
||||
# max_diff = torch.abs(lerobot_actions_own - openpi_actions_lerobot_preproc).max().item()
|
||||
|
||||
# print(f"Actions close (atol=1e-4): {is_close_1e4}")
|
||||
# print(f"Actions close (atol=1e-2): {is_close_1e2}")
|
||||
# print(f"Max absolute difference: {max_diff:.6f}")
|
||||
|
||||
# # Add assertions for pytest
|
||||
# assert is_close_1e2, f"Models should produce similar results (atol=1e-2), max diff: {max_diff}"
|
||||
Reference in New Issue
Block a user