mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-15 16:49:55 +00:00
Steven Palma
e881fb6678
* refactor(processor): signature of transform_features * refactor(processor): remove prefixes + processor respect new transform_features signature + update test accordingly * refactor(processor): rename now is only for visual * refactor(processor): update normalize processor * refactor(processor): update vanilla processor features * refactor(processor): feature contract now uses its own enum * chore(processor): rename renameprocessor * chore(processor): minor changes * refactor(processor): add create & change aggregate * refactor(processor): update aggregate * refactor(processor): simplify to functions, fix features contracts and rename function * test(processor): remove to converter tests as now they are very simple * chore(docs): recover docs joint observations processor * fix(processor): update RKP * fix(tests): recv diff test_pipeline * chore(tests): add docs to test * chore(processor): leave obs language constant untouched * fix(processor): correct new shape of feature in crop image processor
492 lines
18 KiB
Python
492 lines
18 KiB
Python
#!/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 TDMPC policy processor."""
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import tempfile
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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.tdmpc.configuration_tdmpc import TDMPCConfig
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from lerobot.policies.tdmpc.processor_tdmpc import make_tdmpc_pre_post_processors
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from lerobot.processor import (
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AddBatchDimensionProcessorStep,
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DataProcessorPipeline,
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DeviceProcessorStep,
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NormalizerProcessorStep,
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RenameObservationsProcessorStep,
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TransitionKey,
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UnnormalizerProcessorStep,
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)
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def create_transition(observation=None, action=None, **kwargs):
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"""Helper function to create a transition dictionary."""
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transition = {}
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if observation is not None:
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transition[TransitionKey.OBSERVATION] = observation
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if action is not None:
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transition[TransitionKey.ACTION] = action
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for key, value in kwargs.items():
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if hasattr(TransitionKey, key.upper()):
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transition[getattr(TransitionKey, key.upper())] = value
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return transition
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def create_default_config():
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"""Create a default TDMPC configuration for testing."""
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config = TDMPCConfig()
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config.input_features = {
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OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(12,)),
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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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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(12), "std": torch.ones(12)},
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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_tdmpc_processor_basic():
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"""Test basic creation of TDMPC processor."""
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config = create_default_config()
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stats = create_default_stats()
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preprocessor, postprocessor = make_tdmpc_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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postprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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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) == 4
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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], DeviceProcessorStep)
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assert isinstance(preprocessor.steps[3], 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], DeviceProcessorStep)
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assert isinstance(postprocessor.steps[1], UnnormalizerProcessorStep)
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def test_tdmpc_processor_normalization():
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"""Test that TDMPC processor correctly normalizes and unnormalizes data."""
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config = create_default_config()
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stats = create_default_stats()
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preprocessor, postprocessor = make_tdmpc_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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postprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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)
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# Create test data
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observation = {
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OBS_STATE: torch.randn(12),
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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)
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# Process through preprocessor
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processed = preprocessor(transition)
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# Check that data is processed and batched
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 12)
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].shape == (1, 3, 224, 224)
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assert processed[TransitionKey.ACTION].shape == (1, 6)
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# Process action through postprocessor
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action_transition = create_transition(action=processed[TransitionKey.ACTION])
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postprocessed = postprocessor(action_transition)
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# Check that action is unnormalized (but still batched)
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assert postprocessed[TransitionKey.ACTION].shape == (1, 6)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_tdmpc_processor_cuda():
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"""Test TDMPC 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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preprocessor, postprocessor = make_tdmpc_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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postprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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)
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# Create CPU data
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observation = {
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OBS_STATE: torch.randn(12),
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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)
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# Process through preprocessor
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processed = preprocessor(transition)
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# Check that data is on CUDA
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cuda"
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].device.type == "cuda"
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assert processed[TransitionKey.ACTION].device.type == "cuda"
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# Process through postprocessor
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action_transition = create_transition(action=processed[TransitionKey.ACTION])
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postprocessed = postprocessor(action_transition)
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# Check that action is back on CPU
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assert postprocessed[TransitionKey.ACTION].device.type == "cpu"
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_tdmpc_processor_accelerate_scenario():
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"""Test TDMPC 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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preprocessor, postprocessor = make_tdmpc_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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postprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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)
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# Simulate Accelerate: data already on GPU
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device = torch.device("cuda:0")
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observation = {
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OBS_STATE: torch.randn(12).to(device),
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OBS_IMAGE: torch.randn(3, 224, 224).to(device),
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}
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action = torch.randn(6).to(device)
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transition = create_transition(observation, action)
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# Process through preprocessor
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processed = preprocessor(transition)
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# Check that data stays on same GPU
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].device == device
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].device == device
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assert processed[TransitionKey.ACTION].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_tdmpc_processor_multi_gpu():
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"""Test TDMPC 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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preprocessor, postprocessor = make_tdmpc_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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postprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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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(12).to(device),
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OBS_IMAGE: torch.randn(3, 224, 224).to(device),
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}
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action = torch.randn(6).to(device)
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transition = create_transition(observation, action)
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# Process through preprocessor
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processed = preprocessor(transition)
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# Check that data stays on cuda:1
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].device == device
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].device == device
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assert processed[TransitionKey.ACTION].device == device
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def test_tdmpc_processor_without_stats():
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"""Test TDMPC processor creation without dataset statistics."""
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config = create_default_config()
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# Get the steps from the factory function
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factory_preprocessor, factory_postprocessor = make_tdmpc_pre_post_processors(config, dataset_stats=None)
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# Create new processors with EnvTransition input/output
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preprocessor = DataProcessorPipeline(
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factory_preprocessor.steps,
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name=factory_preprocessor.name,
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to_transition=lambda x: x,
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to_output=lambda x: x,
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)
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postprocessor = DataProcessorPipeline(
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factory_postprocessor.steps,
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name=factory_postprocessor.name,
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to_transition=lambda x: x,
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to_output=lambda x: x,
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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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# Process should still work
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observation = {
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OBS_STATE: torch.randn(12),
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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)
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processed = preprocessor(transition)
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assert processed is not None
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def test_tdmpc_processor_save_and_load():
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"""Test saving and loading TDMPC processor."""
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config = create_default_config()
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stats = create_default_stats()
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preprocessor, postprocessor = make_tdmpc_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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postprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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)
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with tempfile.TemporaryDirectory() as tmpdir:
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# Save preprocessor
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preprocessor.save_pretrained(tmpdir)
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# Load preprocessor
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loaded_preprocessor = DataProcessorPipeline.from_pretrained(
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tmpdir, to_transition=lambda x: x, to_output=lambda x: x
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)
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# Test that loaded processor works
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observation = {
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OBS_STATE: torch.randn(12),
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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)
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processed = loaded_preprocessor(transition)
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 12)
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].shape == (1, 3, 224, 224)
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assert processed[TransitionKey.ACTION].shape == (1, 6)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_tdmpc_processor_mixed_precision():
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"""Test TDMPC processor with mixed precision."""
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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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# Create processor
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preprocessor, postprocessor = make_tdmpc_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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postprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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)
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# Replace DeviceProcessorStep with one that uses float16
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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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modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="float16"))
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elif isinstance(step, NormalizerProcessorStep):
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# Update normalizer to use the same device as the device processor
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modified_steps.append(
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NormalizerProcessorStep(
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features=step.features,
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norm_map=step.norm_map,
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stats=step.stats,
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device=config.device,
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dtype=torch.float16, # Match the float16 dtype
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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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# Create test data
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observation = {
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OBS_STATE: torch.randn(12, dtype=torch.float32),
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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)
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transition = create_transition(observation, action)
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# Process through preprocessor
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processed = preprocessor(transition)
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# Check that data is converted to float16
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].dtype == torch.float16
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].dtype == torch.float16
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assert processed[TransitionKey.ACTION].dtype == torch.float16
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def test_tdmpc_processor_batch_data():
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"""Test TDMPC processor with batched data."""
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config = create_default_config()
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stats = create_default_stats()
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preprocessor, postprocessor = make_tdmpc_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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postprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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)
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# Test with batched data
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batch_size = 64
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observation = {
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OBS_STATE: torch.randn(batch_size, 12),
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OBS_IMAGE: torch.randn(batch_size, 3, 224, 224),
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}
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action = torch.randn(batch_size, 6)
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transition = create_transition(observation, action)
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# Process through preprocessor
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processed = preprocessor(transition)
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# Check that batch dimension is preserved
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (batch_size, 12)
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].shape == (batch_size, 3, 224, 224)
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assert processed[TransitionKey.ACTION].shape == (batch_size, 6)
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def test_tdmpc_processor_edge_cases():
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"""Test TDMPC processor with edge cases."""
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config = create_default_config()
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stats = create_default_stats()
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preprocessor, postprocessor = make_tdmpc_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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postprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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)
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# Test with only state observation (no image)
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observation = {OBS_STATE: torch.randn(12)}
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action = torch.randn(6)
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transition = create_transition(observation, action)
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processed = preprocessor(transition)
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 12)
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assert OBS_IMAGE not in processed[TransitionKey.OBSERVATION]
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# Test with only image observation (no state)
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observation = {OBS_IMAGE: torch.randn(3, 224, 224)}
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transition = create_transition(observation, action)
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processed = preprocessor(transition)
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assert processed[TransitionKey.OBSERVATION][OBS_IMAGE].shape == (1, 3, 224, 224)
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assert OBS_STATE not in processed[TransitionKey.OBSERVATION]
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_tdmpc_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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config.device = "cuda"
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stats = create_default_stats()
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preprocessor, _ = make_tdmpc_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": lambda x: x, "to_output": lambda x: x},
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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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modified_steps.append(
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NormalizerProcessorStep(
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features=step.features,
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norm_map=step.norm_map,
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stats=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
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normalizer_step = preprocessor.steps[3] # 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(12, dtype=torch.float32),
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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)
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transition = create_transition(observation, action)
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# Process through full pipeline
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processed = preprocessor(transition)
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# Verify: DeviceProcessor → bfloat16, NormalizerProcessor adapts → final output is bfloat16
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].dtype == torch.bfloat16
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assert (
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processed[TransitionKey.OBSERVATION][OBS_IMAGE].dtype == torch.bfloat16
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) # IDENTITY normalization still gets dtype conversion
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assert processed[TransitionKey.ACTION].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
|