feature(pipeline): port tokenizer pipeline for VLA (#1645)

* feat(tokenizer): Introduce TokenizerProcessor for text tokenization - Added TokenizerProcessor class to handle tokenization of task strings using Hugging Face's AutoTokenizer. - Supports both string and list inputs, with customizable parameters for task key, output key, and tokenization settings. - Implemented comprehensive unit tests to validate functionality, including handling of various input scenarios and integration with RobotProcessor. - Updated types.py to include LANGUAGE feature type and modified __init__.py to register the new processor. * feat(language): Enhance language processing in TokenizerProcessor - Added OBS_LANGUAGE constant to define the observation language key. - Updated TokenizerProcessor to store tokenized task data in the observation dictionary, ensuring compatibility with the new language feature. - Introduced Pi0NewLineProcessor to append newlines to tasks for proper tokenization. - Modified tests to validate the integration of language tokens and attention masks in the observation structure. * feat(tokenizer): Add padding configuration to TokenizerProcessor - Introduced `padding_side` parameter to the TokenizerProcessor for customizable padding direction. - Updated the `make_pi0_processor` function to include the new padding configuration. - Enhanced unit tests to validate the functionality of the `padding_side` parameter in various scenarios. * feat(processor): Add state management methods to Pi0NewLineProcessor * feat(normalization): Track normalization and unnormalization info in complementary data - Updated NormalizerProcessor and UnnormalizerProcessor to accept additional parameters for tracking normalization modes. - Enhanced the __call__ methods to store normalization and unnormalization information in the complementary data of transitions. - Added unit tests to verify the correct tracking of normalization info, including scenarios with missing stats and selective normalization keys. * feat(factory): Add preprocessor and postprocessor overrides to ProcessorConfigKwargs - Updated ProcessorConfigKwargs to include optional overrides for preprocessor and postprocessor configurations. - Enhanced the make_processor function to utilize the new overrides, allowing for more flexible processor initialization. * feat(processors): Integrate RenameProcessor into various processor configurations - Added RenameProcessor to the input steps of multiple processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor. - Consolidated normalization features from input and output into a single NormalizerProcessor for improved efficiency. - Updated the input steps to ensure compatibility with the new RenameProcessor integration. * feat(smolvla): Refactor language processing and introduce new line processor (#1658) - Removed the prepare_language method and directly accessed language tokens and masks from the batch using the OBS_LANGUAGE constant. - Added SmolVLANewLineProcessor to ensure tasks end with a newline, enhancing tokenization compatibility. - Updated the make_smolvla_processor function to include the new line processor and tokenizer processor for improved input handling. * feture(policies): add device processor (#1659) * feat(processors): Integrate DeviceProcessor into multiple processor configurations - Added DeviceProcessor to the input and output steps of various processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_pi0fast_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor. - Enhanced the DeviceProcessor class with state management methods and ensured compatibility with existing processor pipelines. - Introduced unit tests for DeviceProcessor to validate functionality across different scenarios, including CPU and CUDA operations. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * refactor(pipeline): Remove to() method for device management - Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices. - Removed associated unit tests that validated the functionality of the to() method across various scenarios. - Streamlined the pipeline code by focusing on other device management strategies. * feat(processor): Enhance DeviceProcessor with float dtype conversion - Added support for optional float dtype conversion in DeviceProcessor, allowing tensors to be converted to specified floating-point types while preserving non-float types. - Implemented validation for float dtype input and updated the processor's configuration methods to include float dtype. - Refactored tensor processing logic to streamline device movement and dtype conversion. - Introduced comprehensive unit tests to validate the new float dtype functionality across various scenarios. * feat(policies): Add new line processors and update module exports * feat(processor): Enhance batch and device processors to handle index and task_index fields - Added logic to ToBatchProcessor for unsqueezing 0D tensors for index and task_index fields, ensuring they are processed as 1D tensors. - Updated DeviceProcessor to process index and task_index fields in complementary data, preserving their tensor types and ensuring non-tensor fields remain unchanged. - Enhanced unit tests to validate the correct handling of index and task_index fields across various scenarios, including device compatibility and dtype preservation.
2026-05-15 08:39:49 +00:00 · 2025-08-05 10:53:08 +02:00
parent a1734cf575
commit 5326ffe77e
26 changed files with 2776 additions and 232 deletions
@@ -0,0 +1,874 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import tempfile
+
+import pytest
+import torch
+
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.processor import DeviceProcessor, RobotProcessor
+from lerobot.processor.pipeline import TransitionKey
+
+
+def create_transition(
+    observation=None, action=None, reward=None, done=None, truncated=None, info=None, complementary_data=None
+):
+    """Helper function to create a transition dictionary."""
+    transition = {}
+    if observation is not None:
+        transition[TransitionKey.OBSERVATION] = observation
+    if action is not None:
+        transition[TransitionKey.ACTION] = action
+    if reward is not None:
+        transition[TransitionKey.REWARD] = reward
+    if done is not None:
+        transition[TransitionKey.DONE] = done
+    if truncated is not None:
+        transition[TransitionKey.TRUNCATED] = truncated
+    if info is not None:
+        transition[TransitionKey.INFO] = info
+    if complementary_data is not None:
+        transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
+    return transition
+
+
+def test_basic_functionality():
+    """Test basic device processor functionality on CPU."""
+    processor = DeviceProcessor(device="cpu")
+
+    # Create a transition with CPU tensors
+    observation = {"observation.state": torch.randn(10), "observation.image": torch.randn(3, 224, 224)}
+    action = torch.randn(5)
+    reward = torch.tensor(1.0)
+    done = torch.tensor(False)
+    truncated = torch.tensor(False)
+
+    transition = create_transition(
+        observation=observation, action=action, reward=reward, done=done, truncated=truncated
+    )
+
+    result = processor(transition)
+
+    # Check that all tensors are on CPU
+    assert result[TransitionKey.OBSERVATION]["observation.state"].device.type == "cpu"
+    assert result[TransitionKey.OBSERVATION]["observation.image"].device.type == "cpu"
+    assert result[TransitionKey.ACTION].device.type == "cpu"
+    assert result[TransitionKey.REWARD].device.type == "cpu"
+    assert result[TransitionKey.DONE].device.type == "cpu"
+    assert result[TransitionKey.TRUNCATED].device.type == "cpu"
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_cuda_functionality():
+    """Test device processor functionality on CUDA."""
+    processor = DeviceProcessor(device="cuda")
+
+    # Create a transition with CPU tensors
+    observation = {"observation.state": torch.randn(10), "observation.image": torch.randn(3, 224, 224)}
+    action = torch.randn(5)
+    reward = torch.tensor(1.0)
+    done = torch.tensor(False)
+    truncated = torch.tensor(False)
+
+    transition = create_transition(
+        observation=observation, action=action, reward=reward, done=done, truncated=truncated
+    )
+
+    result = processor(transition)
+
+    # Check that all tensors are on CUDA
+    assert result[TransitionKey.OBSERVATION]["observation.state"].device.type == "cuda"
+    assert result[TransitionKey.OBSERVATION]["observation.image"].device.type == "cuda"
+    assert result[TransitionKey.ACTION].device.type == "cuda"
+    assert result[TransitionKey.REWARD].device.type == "cuda"
+    assert result[TransitionKey.DONE].device.type == "cuda"
+    assert result[TransitionKey.TRUNCATED].device.type == "cuda"
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_specific_cuda_device():
+    """Test device processor with specific CUDA device."""
+    processor = DeviceProcessor(device="cuda:0")
+
+    observation = {"observation.state": torch.randn(10)}
+    action = torch.randn(5)
+
+    transition = create_transition(observation=observation, action=action)
+    result = processor(transition)
+
+    assert result[TransitionKey.OBSERVATION]["observation.state"].device.type == "cuda"
+    assert result[TransitionKey.OBSERVATION]["observation.state"].device.index == 0
+    assert result[TransitionKey.ACTION].device.type == "cuda"
+    assert result[TransitionKey.ACTION].device.index == 0
+
+
+def test_non_tensor_values():
+    """Test that non-tensor values are preserved."""
+    processor = DeviceProcessor(device="cpu")
+
+    observation = {
+        "observation.state": torch.randn(10),
+        "observation.metadata": {"key": "value"},  # Non-tensor data
+        "observation.list": [1, 2, 3],  # Non-tensor data
+    }
+    action = torch.randn(5)
+    info = {"episode": 1, "step": 42}
+
+    transition = create_transition(observation=observation, action=action, info=info)
+
+    result = processor(transition)
+
+    # Check tensors are processed
+    assert isinstance(result[TransitionKey.OBSERVATION]["observation.state"], torch.Tensor)
+    assert isinstance(result[TransitionKey.ACTION], torch.Tensor)
+
+    # Check non-tensor values are preserved
+    assert result[TransitionKey.OBSERVATION]["observation.metadata"] == {"key": "value"}
+    assert result[TransitionKey.OBSERVATION]["observation.list"] == [1, 2, 3]
+    assert result[TransitionKey.INFO] == {"episode": 1, "step": 42}
+
+
+def test_none_values():
+    """Test handling of None values."""
+    processor = DeviceProcessor(device="cpu")
+
+    # Test with None observation
+    transition = create_transition(observation=None, action=torch.randn(5))
+    result = processor(transition)
+    assert TransitionKey.OBSERVATION not in result
+    assert result[TransitionKey.ACTION].device.type == "cpu"
+
+    # Test with None action
+    transition = create_transition(observation={"observation.state": torch.randn(10)}, action=None)
+    result = processor(transition)
+    assert result[TransitionKey.OBSERVATION]["observation.state"].device.type == "cpu"
+    assert TransitionKey.ACTION not in result
+
+
+def test_empty_observation():
+    """Test handling of empty observation dictionary."""
+    processor = DeviceProcessor(device="cpu")
+
+    transition = create_transition(observation={}, action=torch.randn(5))
+    result = processor(transition)
+
+    assert result[TransitionKey.OBSERVATION] == {}
+    assert result[TransitionKey.ACTION].device.type == "cpu"
+
+
+def test_scalar_tensors():
+    """Test handling of scalar tensors."""
+    processor = DeviceProcessor(device="cpu")
+
+    observation = {"observation.scalar": torch.tensor(1.5)}
+    action = torch.tensor(2.0)
+    reward = torch.tensor(0.5)
+
+    transition = create_transition(observation=observation, action=action, reward=reward)
+
+    result = processor(transition)
+
+    assert result[TransitionKey.OBSERVATION]["observation.scalar"].item() == 1.5
+    assert result[TransitionKey.ACTION].item() == 2.0
+    assert result[TransitionKey.REWARD].item() == 0.5
+
+
+def test_dtype_preservation():
+    """Test that tensor dtypes are preserved."""
+    processor = DeviceProcessor(device="cpu")
+
+    observation = {
+        "observation.float32": torch.randn(5, dtype=torch.float32),
+        "observation.float64": torch.randn(5, dtype=torch.float64),
+        "observation.int32": torch.randint(0, 10, (5,), dtype=torch.int32),
+        "observation.bool": torch.tensor([True, False, True], dtype=torch.bool),
+    }
+    action = torch.randn(3, dtype=torch.float16)
+
+    transition = create_transition(observation=observation, action=action)
+    result = processor(transition)
+
+    assert result[TransitionKey.OBSERVATION]["observation.float32"].dtype == torch.float32
+    assert result[TransitionKey.OBSERVATION]["observation.float64"].dtype == torch.float64
+    assert result[TransitionKey.OBSERVATION]["observation.int32"].dtype == torch.int32
+    assert result[TransitionKey.OBSERVATION]["observation.bool"].dtype == torch.bool
+    assert result[TransitionKey.ACTION].dtype == torch.float16
+
+
+def test_shape_preservation():
+    """Test that tensor shapes are preserved."""
+    processor = DeviceProcessor(device="cpu")
+
+    observation = {
+        "observation.1d": torch.randn(10),
+        "observation.2d": torch.randn(5, 10),
+        "observation.3d": torch.randn(3, 224, 224),
+        "observation.4d": torch.randn(2, 3, 224, 224),
+    }
+    action = torch.randn(2, 5, 3)
+
+    transition = create_transition(observation=observation, action=action)
+    result = processor(transition)
+
+    assert result[TransitionKey.OBSERVATION]["observation.1d"].shape == (10,)
+    assert result[TransitionKey.OBSERVATION]["observation.2d"].shape == (5, 10)
+    assert result[TransitionKey.OBSERVATION]["observation.3d"].shape == (3, 224, 224)
+    assert result[TransitionKey.OBSERVATION]["observation.4d"].shape == (2, 3, 224, 224)
+    assert result[TransitionKey.ACTION].shape == (2, 5, 3)
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_mixed_devices():
+    """Test handling of tensors already on different devices."""
+    processor = DeviceProcessor(device="cuda")
+
+    # Create tensors on different devices
+    observation = {
+        "observation.cpu": torch.randn(5),  # CPU
+        "observation.cuda": torch.randn(5).cuda(),  # Already on CUDA
+    }
+    action = torch.randn(3).cuda()  # Already on CUDA
+
+    transition = create_transition(observation=observation, action=action)
+    result = processor(transition)
+
+    # All should be on CUDA
+    assert result[TransitionKey.OBSERVATION]["observation.cpu"].device.type == "cuda"
+    assert result[TransitionKey.OBSERVATION]["observation.cuda"].device.type == "cuda"
+    assert result[TransitionKey.ACTION].device.type == "cuda"
+
+
+def test_non_blocking_flag():
+    """Test that non_blocking flag is set correctly."""
+    # CPU processor should have non_blocking=False
+    cpu_processor = DeviceProcessor(device="cpu")
+    assert cpu_processor.non_blocking is False
+
+    # CUDA processor should have non_blocking=True
+    cuda_processor = DeviceProcessor(device="cuda")
+    assert cuda_processor.non_blocking is True
+
+    cuda_0_processor = DeviceProcessor(device="cuda:0")
+    assert cuda_0_processor.non_blocking is True
+
+
+def test_serialization_methods():
+    """Test get_config, state_dict, and load_state_dict methods."""
+    processor = DeviceProcessor(device="cuda")
+
+    # Test get_config
+    config = processor.get_config()
+    assert config == {"device": "cuda", "float_dtype": None}
+
+    # Test state_dict (should be empty)
+    state = processor.state_dict()
+    assert state == {}
+
+    # Test load_state_dict (should be no-op)
+    processor.load_state_dict({})
+    assert processor.device == "cuda"
+
+    # Test reset (should be no-op)
+    processor.reset()
+    assert processor.device == "cuda"
+
+
+def test_feature_contract():
+    """Test that feature_contract returns features unchanged."""
+    processor = DeviceProcessor(device="cpu")
+
+    features = {
+        "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(10,)),
+        "action": PolicyFeature(type=FeatureType.ACTION, shape=(5,)),
+    }
+
+    result = processor.feature_contract(features)
+    assert result == features
+    assert result is features  # Should return the same object
+
+
+def test_integration_with_robot_processor():
+    """Test integration with RobotProcessor."""
+    from lerobot.processor import ToBatchProcessor
+
+    # Create a pipeline with DeviceProcessor
+    device_processor = DeviceProcessor(device="cpu")
+    batch_processor = ToBatchProcessor()
+
+    processor = RobotProcessor(steps=[batch_processor, device_processor], name="test_pipeline")
+
+    # Create test data
+    observation = {"observation.state": torch.randn(10)}
+    action = torch.randn(5)
+
+    transition = create_transition(observation=observation, action=action)
+    result = processor(transition)
+
+    # Check that tensors are batched and on correct device
+    assert result[TransitionKey.OBSERVATION]["observation.state"].shape[0] == 1  # Batched
+    assert result[TransitionKey.OBSERVATION]["observation.state"].device.type == "cpu"
+    assert result[TransitionKey.ACTION].shape[0] == 1  # Batched
+    assert result[TransitionKey.ACTION].device.type == "cpu"
+
+
+def test_save_and_load_pretrained():
+    """Test saving and loading processor with DeviceProcessor."""
+    processor = DeviceProcessor(device="cuda:0", float_dtype="float16")
+    robot_processor = RobotProcessor(steps=[processor], name="device_test_processor")
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Save
+        robot_processor.save_pretrained(tmpdir)
+
+        # Load
+        loaded_processor = RobotProcessor.from_pretrained(tmpdir)
+
+        assert len(loaded_processor.steps) == 1
+        loaded_device_processor = loaded_processor.steps[0]
+        assert isinstance(loaded_device_processor, DeviceProcessor)
+        assert loaded_device_processor.device == "cuda:0"
+        assert loaded_device_processor.float_dtype == "float16"
+
+
+def test_registry_functionality():
+    """Test that DeviceProcessor is properly registered."""
+    from lerobot.processor.pipeline import ProcessorStepRegistry
+
+    # Check that DeviceProcessor is registered
+    registered_class = ProcessorStepRegistry.get("device_processor")
+    assert registered_class is DeviceProcessor
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_performance_with_large_tensors():
+    """Test performance with large tensors and non_blocking flag."""
+    processor = DeviceProcessor(device="cuda")
+
+    # Create large tensors
+    observation = {
+        "observation.large_image": torch.randn(10, 3, 512, 512),  # Large image batch
+        "observation.features": torch.randn(10, 2048),  # Large feature vector
+    }
+    action = torch.randn(10, 100)  # Large action space
+
+    transition = create_transition(observation=observation, action=action)
+
+    # Process should not raise any errors
+    result = processor(transition)
+
+    # Verify all tensors are on CUDA
+    assert result[TransitionKey.OBSERVATION]["observation.large_image"].device.type == "cuda"
+    assert result[TransitionKey.OBSERVATION]["observation.features"].device.type == "cuda"
+    assert result[TransitionKey.ACTION].device.type == "cuda"
+
+
+def test_reward_done_truncated_types():
+    """Test handling of different types for reward, done, and truncated."""
+    processor = DeviceProcessor(device="cpu")
+
+    # Test with scalar values (not tensors)
+    transition = create_transition(
+        observation={"observation.state": torch.randn(5)},
+        action=torch.randn(3),
+        reward=1.0,  # float
+        done=False,  # bool
+        truncated=True,  # bool
+    )
+
+    result = processor(transition)
+
+    # Non-tensor values should be preserved as-is
+    assert result[TransitionKey.REWARD] == 1.0
+    assert result[TransitionKey.DONE] is False
+    assert result[TransitionKey.TRUNCATED] is True
+
+    # Test with tensor values
+    transition = create_transition(
+        observation={"observation.state": torch.randn(5)},
+        action=torch.randn(3),
+        reward=torch.tensor(1.0),
+        done=torch.tensor(False),
+        truncated=torch.tensor(True),
+    )
+
+    result = processor(transition)
+
+    # Tensor values should be moved to device
+    assert isinstance(result[TransitionKey.REWARD], torch.Tensor)
+    assert isinstance(result[TransitionKey.DONE], torch.Tensor)
+    assert isinstance(result[TransitionKey.TRUNCATED], torch.Tensor)
+    assert result[TransitionKey.REWARD].device.type == "cpu"
+    assert result[TransitionKey.DONE].device.type == "cpu"
+    assert result[TransitionKey.TRUNCATED].device.type == "cpu"
+
+
+def test_complementary_data_preserved():
+    """Test that complementary_data is preserved unchanged."""
+    processor = DeviceProcessor(device="cpu")
+
+    complementary_data = {
+        "task": "pick_object",
+        "episode_id": 42,
+        "metadata": {"sensor": "camera_1"},
+        "observation_is_pad": torch.tensor([False, False, True]),  # This should be moved to device
+    }
+
+    transition = create_transition(
+        observation={"observation.state": torch.randn(5)}, complementary_data=complementary_data
+    )
+
+    result = processor(transition)
+
+    # Check that complementary_data is preserved
+    assert TransitionKey.COMPLEMENTARY_DATA in result
+    assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == "pick_object"
+    assert result[TransitionKey.COMPLEMENTARY_DATA]["episode_id"] == 42
+    assert result[TransitionKey.COMPLEMENTARY_DATA]["metadata"] == {"sensor": "camera_1"}
+    # Note: Currently DeviceProcessor doesn't process tensors in complementary_data
+    # This is intentional as complementary_data is typically metadata
+
+
+def test_float_dtype_conversion():
+    """Test float dtype conversion functionality."""
+    processor = DeviceProcessor(device="cpu", float_dtype="float16")
+
+    # Create tensors of different types
+    observation = {
+        "observation.float32": torch.randn(5, dtype=torch.float32),
+        "observation.float64": torch.randn(5, dtype=torch.float64),
+        "observation.int32": torch.randint(0, 10, (5,), dtype=torch.int32),
+        "observation.int64": torch.randint(0, 10, (5,), dtype=torch.int64),
+        "observation.bool": torch.tensor([True, False, True], dtype=torch.bool),
+    }
+    action = torch.randn(3, dtype=torch.float32)
+    reward = torch.tensor(1.0, dtype=torch.float32)
+
+    transition = create_transition(observation=observation, action=action, reward=reward)
+    result = processor(transition)
+
+    # Check that float tensors are converted to float16
+    assert result[TransitionKey.OBSERVATION]["observation.float32"].dtype == torch.float16
+    assert result[TransitionKey.OBSERVATION]["observation.float64"].dtype == torch.float16
+    assert result[TransitionKey.ACTION].dtype == torch.float16
+    assert result[TransitionKey.REWARD].dtype == torch.float16
+
+    # Check that non-float tensors are preserved
+    assert result[TransitionKey.OBSERVATION]["observation.int32"].dtype == torch.int32
+    assert result[TransitionKey.OBSERVATION]["observation.int64"].dtype == torch.int64
+    assert result[TransitionKey.OBSERVATION]["observation.bool"].dtype == torch.bool
+
+
+def test_float_dtype_none():
+    """Test that when float_dtype is None, no dtype conversion occurs."""
+    processor = DeviceProcessor(device="cpu", float_dtype=None)
+
+    observation = {
+        "observation.float32": torch.randn(5, dtype=torch.float32),
+        "observation.float64": torch.randn(5, dtype=torch.float64),
+        "observation.int32": torch.randint(0, 10, (5,), dtype=torch.int32),
+    }
+    action = torch.randn(3, dtype=torch.float64)
+
+    transition = create_transition(observation=observation, action=action)
+    result = processor(transition)
+
+    # Check that dtypes are preserved when float_dtype is None
+    assert result[TransitionKey.OBSERVATION]["observation.float32"].dtype == torch.float32
+    assert result[TransitionKey.OBSERVATION]["observation.float64"].dtype == torch.float64
+    assert result[TransitionKey.OBSERVATION]["observation.int32"].dtype == torch.int32
+    assert result[TransitionKey.ACTION].dtype == torch.float64
+
+
+def test_float_dtype_bfloat16():
+    """Test conversion to bfloat16."""
+    processor = DeviceProcessor(device="cpu", float_dtype="bfloat16")
+
+    observation = {"observation.state": torch.randn(5, dtype=torch.float32)}
+    action = torch.randn(3, dtype=torch.float64)
+
+    transition = create_transition(observation=observation, action=action)
+    result = processor(transition)
+
+    assert result[TransitionKey.OBSERVATION]["observation.state"].dtype == torch.bfloat16
+    assert result[TransitionKey.ACTION].dtype == torch.bfloat16
+
+
+def test_float_dtype_float64():
+    """Test conversion to float64."""
+    processor = DeviceProcessor(device="cpu", float_dtype="float64")
+
+    observation = {"observation.state": torch.randn(5, dtype=torch.float16)}
+    action = torch.randn(3, dtype=torch.float32)
+
+    transition = create_transition(observation=observation, action=action)
+    result = processor(transition)
+
+    assert result[TransitionKey.OBSERVATION]["observation.state"].dtype == torch.float64
+    assert result[TransitionKey.ACTION].dtype == torch.float64
+
+
+def test_float_dtype_invalid():
+    """Test that invalid float_dtype raises ValueError."""
+    with pytest.raises(ValueError, match="Invalid float_dtype 'invalid_dtype'"):
+        DeviceProcessor(device="cpu", float_dtype="invalid_dtype")
+
+
+def test_float_dtype_aliases():
+    """Test that dtype aliases work correctly."""
+    # Test 'half' alias for float16
+    processor_half = DeviceProcessor(device="cpu", float_dtype="half")
+    assert processor_half._target_float_dtype == torch.float16
+
+    # Test 'float' alias for float32
+    processor_float = DeviceProcessor(device="cpu", float_dtype="float")
+    assert processor_float._target_float_dtype == torch.float32
+
+    # Test 'double' alias for float64
+    processor_double = DeviceProcessor(device="cpu", float_dtype="double")
+    assert processor_double._target_float_dtype == torch.float64
+
+
+def test_float_dtype_with_mixed_tensors():
+    """Test float dtype conversion with mixed tensor types."""
+    processor = DeviceProcessor(device="cpu", float_dtype="float32")
+
+    observation = {
+        "observation.image": torch.randint(0, 255, (3, 64, 64), dtype=torch.uint8),  # Should not convert
+        "observation.state": torch.randn(10, dtype=torch.float64),  # Should convert
+        "observation.mask": torch.tensor([True, False, True], dtype=torch.bool),  # Should not convert
+        "observation.indices": torch.tensor([1, 2, 3], dtype=torch.long),  # Should not convert
+    }
+    action = torch.randn(5, dtype=torch.float16)  # Should convert
+
+    transition = create_transition(observation=observation, action=action)
+    result = processor(transition)
+
+    # Check conversions
+    assert result[TransitionKey.OBSERVATION]["observation.image"].dtype == torch.uint8  # Unchanged
+    assert result[TransitionKey.OBSERVATION]["observation.state"].dtype == torch.float32  # Converted
+    assert result[TransitionKey.OBSERVATION]["observation.mask"].dtype == torch.bool  # Unchanged
+    assert result[TransitionKey.OBSERVATION]["observation.indices"].dtype == torch.long  # Unchanged
+    assert result[TransitionKey.ACTION].dtype == torch.float32  # Converted
+
+
+def test_float_dtype_serialization():
+    """Test that float_dtype is properly serialized in get_config."""
+    processor = DeviceProcessor(device="cuda", float_dtype="float16")
+    config = processor.get_config()
+
+    assert config == {"device": "cuda", "float_dtype": "float16"}
+
+    # Test with None float_dtype
+    processor_none = DeviceProcessor(device="cpu", float_dtype=None)
+    config_none = processor_none.get_config()
+
+    assert config_none == {"device": "cpu", "float_dtype": None}
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_float_dtype_with_cuda():
+    """Test float dtype conversion combined with CUDA device."""
+    processor = DeviceProcessor(device="cuda", float_dtype="float16")
+
+    # Create tensors on CPU with different dtypes
+    observation = {
+        "observation.float32": torch.randn(5, dtype=torch.float32),
+        "observation.int64": torch.tensor([1, 2, 3], dtype=torch.int64),
+    }
+    action = torch.randn(3, dtype=torch.float64)
+
+    transition = create_transition(observation=observation, action=action)
+    result = processor(transition)
+
+    # Check that tensors are on CUDA and float types are converted
+    assert result[TransitionKey.OBSERVATION]["observation.float32"].device.type == "cuda"
+    assert result[TransitionKey.OBSERVATION]["observation.float32"].dtype == torch.float16
+
+    assert result[TransitionKey.OBSERVATION]["observation.int64"].device.type == "cuda"
+    assert result[TransitionKey.OBSERVATION]["observation.int64"].dtype == torch.int64  # Unchanged
+
+    assert result[TransitionKey.ACTION].device.type == "cuda"
+    assert result[TransitionKey.ACTION].dtype == torch.float16
+
+
+def test_complementary_data_index_fields():
+    """Test processing of index and task_index fields in complementary_data."""
+    processor = DeviceProcessor(device="cpu")
+
+    # Create transition with index and task_index in complementary_data
+    complementary_data = {
+        "task": ["pick_cube"],
+        "index": torch.tensor([42], dtype=torch.int64),
+        "task_index": torch.tensor([3], dtype=torch.int64),
+        "episode_id": 123,  # Non-tensor field
+    }
+    transition = create_transition(
+        observation={"observation.state": torch.randn(1, 7)},
+        action=torch.randn(1, 4),
+        complementary_data=complementary_data,
+    )
+
+    result = processor(transition)
+
+    # Check that tensors in complementary_data are processed
+    processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
+
+    # Check index tensor
+    assert isinstance(processed_comp_data["index"], torch.Tensor)
+    assert processed_comp_data["index"].device.type == "cpu"
+    assert torch.equal(processed_comp_data["index"], complementary_data["index"])
+
+    # Check task_index tensor
+    assert isinstance(processed_comp_data["task_index"], torch.Tensor)
+    assert processed_comp_data["task_index"].device.type == "cpu"
+    assert torch.equal(processed_comp_data["task_index"], complementary_data["task_index"])
+
+    # Check non-tensor fields remain unchanged
+    assert processed_comp_data["task"] == ["pick_cube"]
+    assert processed_comp_data["episode_id"] == 123
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_complementary_data_index_fields_cuda():
+    """Test moving index and task_index fields to CUDA."""
+    processor = DeviceProcessor(device="cuda:0")
+
+    # Create CPU tensors
+    complementary_data = {
+        "index": torch.tensor([100, 101], dtype=torch.int64),
+        "task_index": torch.tensor([5], dtype=torch.int64),
+    }
+    transition = create_transition(complementary_data=complementary_data)
+
+    result = processor(transition)
+
+    processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
+
+    # Check tensors moved to CUDA
+    assert processed_comp_data["index"].device.type == "cuda"
+    assert processed_comp_data["index"].device.index == 0
+    assert processed_comp_data["task_index"].device.type == "cuda"
+    assert processed_comp_data["task_index"].device.index == 0
+
+
+def test_complementary_data_without_index_fields():
+    """Test that complementary_data without index/task_index fields works correctly."""
+    processor = DeviceProcessor(device="cpu")
+
+    complementary_data = {
+        "task": ["navigate"],
+        "episode_id": 456,
+    }
+    transition = create_transition(complementary_data=complementary_data)
+
+    result = processor(transition)
+
+    # Should process without errors and preserve non-tensor fields
+    processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
+    assert processed_comp_data["task"] == ["navigate"]
+    assert processed_comp_data["episode_id"] == 456
+
+
+def test_complementary_data_mixed_tensors():
+    """Test complementary_data with mix of tensors and non-tensors."""
+    processor = DeviceProcessor(device="cpu")
+
+    complementary_data = {
+        "task": ["pick_and_place"],
+        "index": torch.tensor([42], dtype=torch.int64),
+        "task_index": torch.tensor([3], dtype=torch.int64),
+        "metrics": [1.0, 2.0, 3.0],  # List, not tensor
+        "config": {"speed": "fast"},  # Dict
+        "episode_id": 789,  # Int
+    }
+    transition = create_transition(complementary_data=complementary_data)
+
+    result = processor(transition)
+
+    processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
+
+    # Check tensors are processed
+    assert isinstance(processed_comp_data["index"], torch.Tensor)
+    assert isinstance(processed_comp_data["task_index"], torch.Tensor)
+
+    # Check non-tensors remain unchanged
+    assert processed_comp_data["task"] == ["pick_and_place"]
+    assert processed_comp_data["metrics"] == [1.0, 2.0, 3.0]
+    assert processed_comp_data["config"] == {"speed": "fast"}
+    assert processed_comp_data["episode_id"] == 789
+
+
+def test_complementary_data_float_dtype_conversion():
+    """Test that float dtype conversion doesn't affect int tensors in complementary_data."""
+    processor = DeviceProcessor(device="cpu", float_dtype="float16")
+
+    complementary_data = {
+        "index": torch.tensor([42], dtype=torch.int64),
+        "task_index": torch.tensor([3], dtype=torch.int64),
+        "float_tensor": torch.tensor([1.5, 2.5], dtype=torch.float32),  # Should be converted
+    }
+    transition = create_transition(complementary_data=complementary_data)
+
+    result = processor(transition)
+
+    processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
+
+    # Int tensors should keep their dtype
+    assert processed_comp_data["index"].dtype == torch.int64
+    assert processed_comp_data["task_index"].dtype == torch.int64
+
+    # Float tensor should be converted
+    assert processed_comp_data["float_tensor"].dtype == torch.float16
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_complementary_data_full_pipeline_cuda():
+    """Test full transition with complementary_data on CUDA."""
+    processor = DeviceProcessor(device="cuda:0", float_dtype="float16")
+
+    # Create full transition with mixed CPU tensors
+    observation = {"observation.state": torch.randn(1, 7, dtype=torch.float32)}
+    action = torch.randn(1, 4, dtype=torch.float32)
+    reward = torch.tensor(1.5, dtype=torch.float32)
+    done = torch.tensor(False)
+    complementary_data = {
+        "task": ["reach_target"],
+        "index": torch.tensor([1000], dtype=torch.int64),
+        "task_index": torch.tensor([10], dtype=torch.int64),
+    }
+
+    transition = create_transition(
+        observation=observation,
+        action=action,
+        reward=reward,
+        done=done,
+        complementary_data=complementary_data,
+    )
+
+    result = processor(transition)
+
+    # Check all components moved to CUDA
+    assert result[TransitionKey.OBSERVATION]["observation.state"].device.type == "cuda"
+    assert result[TransitionKey.ACTION].device.type == "cuda"
+    assert result[TransitionKey.REWARD].device.type == "cuda"
+    assert result[TransitionKey.DONE].device.type == "cuda"
+
+    # Check complementary_data tensors
+    processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
+    assert processed_comp_data["index"].device.type == "cuda"
+    assert processed_comp_data["task_index"].device.type == "cuda"
+
+    # Check float conversion happened for float tensors
+    assert result[TransitionKey.OBSERVATION]["observation.state"].dtype == torch.float16
+    assert result[TransitionKey.ACTION].dtype == torch.float16
+    assert result[TransitionKey.REWARD].dtype == torch.float16
+
+    # Check int tensors kept their dtype
+    assert processed_comp_data["index"].dtype == torch.int64
+    assert processed_comp_data["task_index"].dtype == torch.int64
+
+
+def test_complementary_data_empty():
+    """Test empty complementary_data handling."""
+    processor = DeviceProcessor(device="cpu")
+
+    transition = create_transition(
+        observation={"observation.state": torch.randn(1, 7)},
+        complementary_data={},
+    )
+
+    result = processor(transition)
+
+    # Should have empty dict
+    assert result[TransitionKey.COMPLEMENTARY_DATA] == {}
+
+
+def test_complementary_data_none():
+    """Test None complementary_data handling."""
+    processor = DeviceProcessor(device="cpu")
+
+    transition = create_transition(
+        observation={"observation.state": torch.randn(1, 7)},
+        complementary_data=None,
+    )
+
+    result = processor(transition)
+
+    # Complementary data should not be in the result (same as input)
+    assert TransitionKey.COMPLEMENTARY_DATA not in result
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_policy_processor_integration():
+    """Test integration with policy processors - input on GPU, output on CPU."""
+    from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+    from lerobot.processor import NormalizerProcessor, ToBatchProcessor, UnnormalizerProcessor
+
+    # Create features and stats
+    features = {
+        "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(10,)),
+        "action": PolicyFeature(type=FeatureType.ACTION, shape=(5,)),
+    }
+
+    stats = {
+        "observation.state": {"mean": torch.zeros(10), "std": torch.ones(10)},
+        "action": {"mean": torch.zeros(5), "std": torch.ones(5)},
+    }
+
+    norm_map = {FeatureType.STATE: NormalizationMode.MEAN_STD, FeatureType.ACTION: NormalizationMode.MEAN_STD}
+
+    # Create input processor (preprocessor) that moves to GPU
+    input_processor = RobotProcessor(
+        steps=[
+            NormalizerProcessor(features=features, norm_map=norm_map, stats=stats),
+            ToBatchProcessor(),
+            DeviceProcessor(device="cuda"),
+        ],
+        name="test_preprocessor",
+    )
+
+    # Create output processor (postprocessor) that moves to CPU
+    output_processor = RobotProcessor(
+        steps=[
+            DeviceProcessor(device="cpu"),
+            UnnormalizerProcessor(features={"action": features["action"]}, norm_map=norm_map, stats=stats),
+        ],
+        name="test_postprocessor",
+    )
+
+    # Test data on CPU
+    observation = {"observation.state": torch.randn(10)}
+    action = torch.randn(5)
+    transition = create_transition(observation=observation, action=action)
+
+    # Process through input processor
+    input_result = input_processor(transition)
+
+    # Verify tensors are on GPU and batched
+    assert input_result[TransitionKey.OBSERVATION]["observation.state"].device.type == "cuda"
+    assert input_result[TransitionKey.OBSERVATION]["observation.state"].shape[0] == 1
+    assert input_result[TransitionKey.ACTION].device.type == "cuda"
+    assert input_result[TransitionKey.ACTION].shape[0] == 1
+
+    # Simulate model output on GPU
+    model_output = create_transition(action=torch.randn(1, 5).cuda())
+
+    # Process through output processor
+    output_result = output_processor(model_output)
+
+    # Verify action is back on CPU and unnormalized
+    assert output_result[TransitionKey.ACTION].device.type == "cpu"
+    assert output_result[TransitionKey.ACTION].shape == (1, 5)