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https://github.com/huggingface/lerobot.git
synced 2026-05-15 16:49:55 +00:00
Remove previous pi0 and rename pi0_openpi and pi05_openpi
This commit is contained in:
Pepijn
@@ -1,424 +0,0 @@
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#!/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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"""Tests for PI0 policy processor."""
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from unittest.mock import patch
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import pytest
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import torch
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from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
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from lerobot.constants import ACTION, OBS_IMAGE, OBS_STATE
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from lerobot.policies.pi0.configuration_pi0 import PI0Config
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from lerobot.policies.pi0.processor_pi0 import Pi0NewLineProcessor, make_pi0_pre_post_processors
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from lerobot.processor import (
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AddBatchDimensionProcessorStep,
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DeviceProcessorStep,
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EnvTransition,
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NormalizerProcessorStep,
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ProcessorStep,
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RenameObservationsProcessorStep,
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TransitionKey,
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UnnormalizerProcessorStep,
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)
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from lerobot.processor.converters import create_transition, transition_to_batch
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class MockTokenizerProcessorStep(ProcessorStep):
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"""Mock tokenizer processor step for testing."""
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def __init__(self, *args, **kwargs):
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# Accept any arguments to mimic the real TokenizerProcessorStep interface
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pass
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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# Pass through transition unchanged
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return transition
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def transform_features(self, features):
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# Pass through features unchanged
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return features
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def create_default_config():
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"""Create a default PI0 configuration for testing."""
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config = PI0Config()
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config.input_features = {
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OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,)),
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OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
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}
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config.output_features = {
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ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(6,)),
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}
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config.normalization_mapping = {
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FeatureType.STATE: NormalizationMode.MEAN_STD,
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FeatureType.VISUAL: NormalizationMode.IDENTITY,
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FeatureType.ACTION: NormalizationMode.MIN_MAX,
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}
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config.device = "cpu"
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config.tokenizer_max_length = 128
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return config
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def create_default_stats():
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"""Create default dataset statistics for testing."""
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return {
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OBS_STATE: {"mean": torch.zeros(10), "std": torch.ones(10)},
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OBS_IMAGE: {}, # No normalization for images
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ACTION: {"min": torch.full((6,), -1.0), "max": torch.ones(6)},
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}
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def test_make_pi0_processor_basic():
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"""Test basic creation of PI0 processor."""
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config = create_default_config()
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stats = create_default_stats()
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with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
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preprocessor, postprocessor = make_pi0_pre_post_processors(
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config,
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stats,
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)
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# Check processor names
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assert preprocessor.name == "policy_preprocessor"
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assert postprocessor.name == "policy_postprocessor"
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# Check steps in preprocessor
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assert len(preprocessor.steps) == 6
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assert isinstance(preprocessor.steps[0], RenameObservationsProcessorStep)
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assert isinstance(preprocessor.steps[1], AddBatchDimensionProcessorStep)
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assert isinstance(preprocessor.steps[2], Pi0NewLineProcessor)
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# Step 3 would be TokenizerProcessorStep but it's mocked
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assert isinstance(preprocessor.steps[4], DeviceProcessorStep)
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assert isinstance(preprocessor.steps[5], NormalizerProcessorStep)
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# Check steps in postprocessor
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assert len(postprocessor.steps) == 2
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assert isinstance(postprocessor.steps[0], UnnormalizerProcessorStep)
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assert isinstance(postprocessor.steps[1], DeviceProcessorStep)
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def test_pi0_newline_processor_single_task():
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"""Test Pi0NewLineProcessor with single task string."""
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processor = Pi0NewLineProcessor()
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# Test with task that doesn't have newline
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transition = create_transition(complementary_data={"task": "test task"})
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result = processor(transition)
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assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == "test task\n"
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# Test with task that already has newline
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transition = create_transition(complementary_data={"task": "test task\n"})
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result = processor(transition)
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assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == "test task\n"
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def test_pi0_newline_processor_list_of_tasks():
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"""Test Pi0NewLineProcessor with list of task strings."""
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processor = Pi0NewLineProcessor()
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# Test with list of tasks
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tasks = ["task1", "task2\n", "task3"]
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transition = create_transition(complementary_data={"task": tasks})
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result = processor(transition)
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expected = ["task1\n", "task2\n", "task3\n"]
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assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == expected
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def test_pi0_newline_processor_empty_transition():
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"""Test Pi0NewLineProcessor with empty transition."""
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processor = Pi0NewLineProcessor()
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# Test with no complementary_data
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transition = create_transition()
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result = processor(transition)
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assert result == transition
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# Test with complementary_data but no task
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transition = create_transition(complementary_data={"other": "data"})
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result = processor(transition)
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assert result == transition
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# Test with None task
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transition = create_transition(complementary_data={"task": None})
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result = processor(transition)
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assert result == transition
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_pi0_processor_cuda():
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"""Test PI0 processor with CUDA device."""
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config = create_default_config()
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config.device = "cuda"
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stats = create_default_stats()
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# Mock the tokenizer processor to act as pass-through
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class MockTokenizerProcessorStep(ProcessorStep):
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def __init__(self, *args, **kwargs):
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pass
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def __call__(self, transition):
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return transition
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def state_dict(self):
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return {}
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def load_state_dict(self, state):
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pass
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def reset(self):
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pass
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def get_config(self):
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return {"tokenizer_name": "google/paligemma-3b-pt-224"}
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def transform_features(self, features):
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return features
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with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
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preprocessor, postprocessor = make_pi0_pre_post_processors(
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config,
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stats,
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)
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# Create CPU data
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observation = {
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OBS_STATE: torch.randn(10),
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OBS_IMAGE: torch.randn(3, 224, 224),
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}
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action = torch.randn(6)
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transition = create_transition(observation, action, complementary_data={"task": "test task"})
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batch = transition_to_batch(transition)
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# Process through preprocessor
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processed = preprocessor(batch)
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# Check that data is on CUDA
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assert processed[OBS_STATE].device.type == "cuda"
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assert processed[OBS_IMAGE].device.type == "cuda"
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assert processed[TransitionKey.ACTION.value].device.type == "cuda"
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_pi0_processor_accelerate_scenario():
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"""Test PI0 processor in simulated Accelerate scenario."""
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config = create_default_config()
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config.device = "cuda:0"
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stats = create_default_stats()
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# Mock the tokenizer processor to act as pass-through
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class MockTokenizerProcessorStep(ProcessorStep):
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def __init__(self, *args, **kwargs):
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pass
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def __call__(self, transition):
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return transition
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def state_dict(self):
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return {}
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def load_state_dict(self, state):
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pass
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def reset(self):
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pass
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def get_config(self):
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return {"tokenizer_name": "google/paligemma-3b-pt-224"}
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def transform_features(self, features):
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return features
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with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
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preprocessor, postprocessor = make_pi0_pre_post_processors(
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config,
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stats,
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)
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# Simulate Accelerate: data already on GPU and batched
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device = torch.device("cuda:0")
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observation = {
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OBS_STATE: torch.randn(1, 10).to(device),
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OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device),
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}
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action = torch.randn(1, 6).to(device)
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transition = create_transition(observation, action, complementary_data={"task": ["test task"]})
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batch = transition_to_batch(transition)
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# Process through preprocessor
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processed = preprocessor(batch)
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# Check that data stays on same GPU
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assert processed[OBS_STATE].device == device
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assert processed[OBS_IMAGE].device == device
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assert processed[TransitionKey.ACTION.value].device == device
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs")
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def test_pi0_processor_multi_gpu():
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"""Test PI0 processor with multi-GPU setup."""
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config = create_default_config()
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config.device = "cuda:0"
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stats = create_default_stats()
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# Mock the tokenizer processor to act as pass-through
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class MockTokenizerProcessorStep(ProcessorStep):
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def __init__(self, *args, **kwargs):
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pass
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def __call__(self, transition):
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return transition
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def state_dict(self):
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return {}
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def load_state_dict(self, state):
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pass
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def reset(self):
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pass
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def get_config(self):
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return {"tokenizer_name": "google/paligemma-3b-pt-224"}
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def transform_features(self, features):
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return features
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with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
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preprocessor, postprocessor = make_pi0_pre_post_processors(
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config,
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stats,
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)
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# Simulate data on different GPU
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device = torch.device("cuda:1")
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observation = {
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OBS_STATE: torch.randn(1, 10).to(device),
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OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device),
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}
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action = torch.randn(1, 6).to(device)
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transition = create_transition(observation, action, complementary_data={"task": ["test task"]})
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batch = transition_to_batch(transition)
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# Process through preprocessor
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processed = preprocessor(batch)
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# Check that data stays on cuda:1
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assert processed[OBS_STATE].device == device
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assert processed[OBS_IMAGE].device == device
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assert processed[TransitionKey.ACTION.value].device == device
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def test_pi0_processor_without_stats():
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"""Test PI0 processor creation without dataset statistics."""
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config = create_default_config()
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# Mock the tokenizer processor
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with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
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preprocessor, postprocessor = make_pi0_pre_post_processors(
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config,
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dataset_stats=None,
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)
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# Should still create processors
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assert preprocessor is not None
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assert postprocessor is not None
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def test_pi0_newline_processor_state_dict():
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"""Test Pi0NewLineProcessor state dict methods."""
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processor = Pi0NewLineProcessor()
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# Test state_dict (should be empty)
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state = processor.state_dict()
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assert state == {}
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# Test load_state_dict (should do nothing)
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processor.load_state_dict({})
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# Test reset (should do nothing)
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processor.reset()
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# Test get_config
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config = processor.get_config()
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assert config == {}
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_pi0_processor_bfloat16_device_float32_normalizer():
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"""Test: DeviceProcessor(bfloat16) + NormalizerProcessor(float32) → output bfloat16 via automatic adaptation"""
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config = create_default_config()
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stats = create_default_stats()
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config.device = "cuda"
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with patch("lerobot.policies.pi0.processor_pi0.TokenizerProcessorStep", MockTokenizerProcessorStep):
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preprocessor, _ = make_pi0_pre_post_processors(
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config,
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stats,
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)
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# Modify the pipeline to use bfloat16 device processor with float32 normalizer
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modified_steps = []
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for step in preprocessor.steps:
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if isinstance(step, DeviceProcessorStep):
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# Device processor converts to bfloat16
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modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="bfloat16"))
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elif isinstance(step, NormalizerProcessorStep):
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# Normalizer stays configured as float32 (will auto-adapt to bfloat16)
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norm_step = step # Now type checker knows this is NormalizerProcessorStep
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modified_steps.append(
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NormalizerProcessorStep(
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features=norm_step.features,
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norm_map=norm_step.norm_map,
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stats=norm_step.stats,
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device=config.device,
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dtype=torch.float32, # Deliberately configured as float32
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)
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)
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else:
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modified_steps.append(step)
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preprocessor.steps = modified_steps
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# Verify initial normalizer configuration (PI0 has NormalizerProcessorStep at index 5)
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normalizer_step = preprocessor.steps[5] # NormalizerProcessorStep
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assert normalizer_step.dtype == torch.float32
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# Create test data with both state and visual observations
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observation = {
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OBS_STATE: torch.randn(10, dtype=torch.float32), # PI0 expects size 10
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OBS_IMAGE: torch.randn(3, 224, 224, dtype=torch.float32),
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}
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action = torch.randn(6, dtype=torch.float32) # PI0 expects size 6
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transition = create_transition(
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observation, action, complementary_data={"task": "test bfloat16 adaptation"}
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)
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batch = transition_to_batch(transition)
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# Process through full pipeline
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processed = preprocessor(batch)
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# Verify: DeviceProcessor → bfloat16, NormalizerProcessor adapts → final output is bfloat16
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assert processed[OBS_STATE].dtype == torch.bfloat16
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assert processed[OBS_IMAGE].dtype == torch.bfloat16 # IDENTITY normalization still gets dtype conversion
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assert processed[TransitionKey.ACTION.value].dtype == torch.bfloat16
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# Verify normalizer automatically adapted its internal state
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assert normalizer_step.dtype == torch.bfloat16
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# Check state stats (has normalization)
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for stat_tensor in normalizer_step._tensor_stats[OBS_STATE].values():
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assert stat_tensor.dtype == torch.bfloat16
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# OBS_IMAGE uses IDENTITY normalization, so no stats to check
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