mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-15 16:49:55 +00:00
Steven Palma
6745958362
* fix(processor): specialized processor now raise * test(processor): fix tests for now raise specialized processors * test(processor): use identity in newly introduced pipeline
437 lines
16 KiB
Python
437 lines
16 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 SAC 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_STATE
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from lerobot.policies.sac.configuration_sac import SACConfig
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from lerobot.policies.sac.processor_sac import make_sac_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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from lerobot.processor.converters import create_transition, identity_transition
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def create_default_config():
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"""Create a default SAC configuration for testing."""
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config = SACConfig()
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config.input_features = {
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OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,)),
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}
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config.output_features = {
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ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(5,)),
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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.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(10), "std": torch.ones(10)},
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ACTION: {"min": torch.full((5,), -1.0), "max": torch.ones(5)},
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}
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def test_make_sac_processor_basic():
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"""Test basic creation of SAC processor."""
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config = create_default_config()
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stats = create_default_stats()
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preprocessor, postprocessor = make_sac_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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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_sac_processor_normalization_modes():
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"""Test that SAC processor correctly handles different normalization modes."""
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config = create_default_config()
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stats = create_default_stats()
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preprocessor, postprocessor = make_sac_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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)
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# Create test data
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observation = {OBS_STATE: torch.randn(10) * 2} # Larger values to test normalization
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action = torch.rand(5) * 2 - 1 # Range [-1, 1]
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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 normalized and batched
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# State should be mean-std normalized
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# Action should be min-max normalized to [-1, 1]
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 10)
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assert processed[TransitionKey.ACTION].shape == (1, 5)
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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, 5)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_sac_processor_cuda():
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"""Test SAC 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_sac_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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)
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# Create CPU data
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observation = {OBS_STATE: torch.randn(10)}
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action = torch.randn(5)
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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.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_sac_processor_accelerate_scenario():
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"""Test SAC 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_sac_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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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 = {OBS_STATE: torch.randn(10).to(device)}
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action = torch.randn(5).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.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_sac_processor_multi_gpu():
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"""Test SAC 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_sac_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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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 = {OBS_STATE: torch.randn(10).to(device)}
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action = torch.randn(5).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.ACTION].device == device
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def test_sac_processor_without_stats():
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"""Test SAC 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_sac_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=identity_transition,
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to_output=identity_transition,
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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=identity_transition,
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to_output=identity_transition,
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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 = {OBS_STATE: torch.randn(10)}
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action = torch.randn(5)
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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_sac_processor_save_and_load():
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"""Test saving and loading SAC processor."""
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config = create_default_config()
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stats = create_default_stats()
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preprocessor, postprocessor = make_sac_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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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=identity_transition, to_output=identity_transition
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)
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# Test that loaded processor works
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observation = {OBS_STATE: torch.randn(10)}
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action = torch.randn(5)
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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, 10)
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assert processed[TransitionKey.ACTION].shape == (1, 5)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_sac_processor_mixed_precision():
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"""Test SAC 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_sac_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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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 = {OBS_STATE: torch.randn(10, dtype=torch.float32)}
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action = torch.randn(5, 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.ACTION].dtype == torch.float16
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def test_sac_processor_batch_data():
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"""Test SAC 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_sac_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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)
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# Test with batched data
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batch_size = 32
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observation = {OBS_STATE: torch.randn(batch_size, 10)}
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action = torch.randn(batch_size, 5)
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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, 10)
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assert processed[TransitionKey.ACTION].shape == (batch_size, 5)
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def test_sac_processor_edge_cases():
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"""Test SAC 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_sac_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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)
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# Test with empty observation
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transition = create_transition(observation={}, action=torch.randn(5))
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processed = preprocessor(transition)
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assert processed[TransitionKey.OBSERVATION] == {}
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assert processed[TransitionKey.ACTION].shape == (1, 5)
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# Test with None action
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transition = create_transition(observation={OBS_STATE: torch.randn(10)}, action={})
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processed = preprocessor(transition)
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assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 10)
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# When action is None, it may still be present with None value
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assert TransitionKey.ACTION not in processed or processed[TransitionKey.ACTION] is None
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_sac_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_sac_pre_post_processors(
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config,
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stats,
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preprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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postprocessor_kwargs={"to_transition": identity_transition, "to_output": identity_transition},
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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
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observation = {OBS_STATE: torch.randn(10, dtype=torch.float32)} # Start with float32
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action = torch.randn(5, 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 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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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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