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https://github.com/huggingface/lerobot.git
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test(rewards): restore missing CUDA and mixed precision classifier processor tests
This commit is contained in:
Khalil Meftah
@@ -1,5 +1,3 @@
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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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@@ -17,6 +15,7 @@
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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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@@ -46,7 +45,7 @@ def create_default_config():
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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.IDENTITY,
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FeatureType.ACTION: NormalizationMode.IDENTITY, # No normalization for classifier output
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}
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config.device = "cpu"
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return config
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@@ -56,8 +55,8 @@ 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: {},
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"reward": {},
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OBS_IMAGE: {}, # No normalization for images
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"reward": {}, # No normalization for classifier output
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}
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@@ -68,14 +67,17 @@ def test_make_classifier_processor_basic():
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preprocessor, postprocessor = make_classifier_processor(config, stats)
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# Check processor names
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assert preprocessor.name == "classifier_preprocessor"
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assert postprocessor.name == "classifier_postprocessor"
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# Check steps in preprocessor
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assert len(preprocessor.steps) == 3
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assert isinstance(preprocessor.steps[0], NormalizerProcessorStep)
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assert isinstance(preprocessor.steps[1], NormalizerProcessorStep)
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assert isinstance(preprocessor.steps[0], NormalizerProcessorStep) # For input features
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assert isinstance(preprocessor.steps[1], NormalizerProcessorStep) # For output features
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assert isinstance(preprocessor.steps[2], DeviceProcessorStep)
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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], IdentityProcessorStep)
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@@ -88,6 +90,7 @@ def test_classifier_processor_normalization():
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preprocessor, postprocessor = make_classifier_processor(config, stats)
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# Create test 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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@@ -96,22 +99,118 @@ def test_classifier_processor_normalization():
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transition = create_transition(observation, action)
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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 processed
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assert processed[OBS_STATE].shape == (10,)
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assert processed[OBS_IMAGE].shape == (3, 224, 224)
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assert processed[TransitionKey.ACTION.value].shape == (1,)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_classifier_processor_cuda():
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"""Test Classifier 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_classifier_processor(config, stats)
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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(1)
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transition = create_transition(observation, action)
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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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# Process through postprocessor
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postprocessed = postprocessor(processed[TransitionKey.ACTION.value])
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# Check that output is back on CPU
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assert postprocessed.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_classifier_processor_accelerate_scenario():
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"""Test Classifier 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_classifier_processor(config, stats)
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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(10).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(1).to(device)
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transition = create_transition(observation, action)
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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_classifier_processor_multi_gpu():
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"""Test Classifier 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_classifier_processor(config, stats)
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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(10).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(1).to(device)
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transition = create_transition(observation, action)
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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_classifier_processor_without_stats():
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"""Test Classifier processor creation without dataset statistics."""
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config = create_default_config()
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preprocessor, postprocessor = make_classifier_processor(config, dataset_stats=None)
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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(10),
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OBS_IMAGE: torch.randn(3, 224, 224),
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@@ -132,12 +231,15 @@ def test_classifier_processor_save_and_load():
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preprocessor, postprocessor = make_classifier_processor(config, stats)
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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, config_filename="classifier_preprocessor.json"
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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(10),
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OBS_IMAGE: torch.randn(3, 224, 224),
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@@ -152,6 +254,43 @@ def test_classifier_processor_save_and_load():
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assert processed[TransitionKey.ACTION.value].shape == (1,)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_classifier_processor_mixed_precision():
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"""Test Classifier 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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preprocessor, postprocessor = make_classifier_processor(config, stats)
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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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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(10, 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(1, dtype=torch.float32)
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transition = create_transition(observation, action)
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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 converted to float16
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assert processed[OBS_STATE].dtype == torch.float16
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assert processed[OBS_IMAGE].dtype == torch.float16
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assert processed[TransitionKey.ACTION.value].dtype == torch.float16
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def test_classifier_processor_batch_data():
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"""Test Classifier processor with batched data."""
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config = create_default_config()
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@@ -159,6 +298,7 @@ def test_classifier_processor_batch_data():
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preprocessor, postprocessor = make_classifier_processor(config, stats)
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# Test with batched data
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batch_size = 16
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observation = {
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OBS_STATE: torch.randn(batch_size, 10),
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@@ -168,8 +308,11 @@ def test_classifier_processor_batch_data():
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transition = create_transition(observation, action)
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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 batch dimension is preserved
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assert processed[OBS_STATE].shape == (batch_size, 10)
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assert processed[OBS_IMAGE].shape == (batch_size, 3, 224, 224)
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assert processed[TransitionKey.ACTION.value].shape == (batch_size, 1)
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@@ -182,11 +325,14 @@ def test_classifier_processor_postprocessor_identity():
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preprocessor, postprocessor = make_classifier_processor(config, stats)
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# Create test data for postprocessor
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reward = torch.tensor([[0.8], [0.3], [0.9]])
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transition = create_transition(action=reward)
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_ = transition_to_batch(transition)
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# Process through postprocessor
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processed = postprocessor(reward)
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# IdentityProcessor should leave values unchanged (except device)
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assert torch.allclose(processed.cpu(), reward.cpu())
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assert processed.device.type == "cpu"
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