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Merge branch 'feat/language-annotation-pipeline' into feat/smolvla-on-steerable
Resolves conflicts from 66 commits on the base branch: * pyproject.toml — keep base's transformers>=5.4.0,<5.6.0; add the sentencepiece-dep entry pi052 (FAST action tokenizer) needs. * policies/__init__.py — keep pi052 export; drop the RewardClassifierConfig export that base removed. * policies/factory.py — docstring list resolution (keep pi052; drop reward_classifier, removed by base). * annotations/steerable_pipeline/executor.py — adopt base's renamed _ensure_annotation_metadata_in_info (it already advertises the say tool); drop pi052's older _ensure_tools_in_info call. * configs/train.py — keep pi052's vqa_target_fraction; adopt base's SampleWeightingConfig (legacy RA-BC inline params already covered by the migration shim base added). * scripts/lerobot_train.py — merge pi052's per-policy processor rebuild + dataset_repo_id pass-through with base's active_cfg / is_reward_model_training tightening, and re-route vqa-weighted sampler to active_cfg.drop_n_last_frames. * datasets/language_render.py — adopt base's _select_one + timestamp tolerance (drops pi052's stale _select_latest / per-style sort_key). * tests — adopt base's parametrized per-camera blend + tolerance test; drop pi052 tests that overlap with base's tighter rewrites; keep pi052's flow-only / VQA-blend coverage; add a test_canonical_recipe_loads check on subtask_mem_vqa_speech.yaml. * policies/pi052/processor_pi052.py — import RenderMessagesStep directly from render_messages_processor (base intentionally dropped it from lerobot.processor's re-exports). * uv.lock — regenerated cleanly from base + pi052's pocket-tts / beartype. All 67 touched tests pass (30 pi052 + 37 recipe / language-render / pipeline / render-messages). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Pepijn
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#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for Reward Classifier 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.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
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from lerobot.policies.sac.reward_model.processor_classifier import make_classifier_processor
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from lerobot.processor import (
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DataProcessorPipeline,
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DeviceProcessorStep,
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IdentityProcessorStep,
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NormalizerProcessorStep,
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TransitionKey,
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)
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from lerobot.processor.converters import create_transition, transition_to_batch
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from lerobot.utils.constants import OBS_IMAGE, OBS_STATE
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def create_default_config():
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"""Create a default Reward Classifier configuration for testing."""
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config = RewardClassifierConfig()
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config.input_features = {
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OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,)),
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OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
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}
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config.output_features = {
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"reward": PolicyFeature(type=FeatureType.ACTION, shape=(1,)), # Classifier output
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}
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config.normalization_mapping = {
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FeatureType.STATE: NormalizationMode.MEAN_STD,
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FeatureType.VISUAL: NormalizationMode.IDENTITY,
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FeatureType.ACTION: NormalizationMode.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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def create_default_stats():
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"""Create default dataset statistics for testing."""
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return {
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OBS_STATE: {"mean": torch.zeros(10), "std": torch.ones(10)},
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OBS_IMAGE: {}, # No normalization for images
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"reward": {}, # No normalization for classifier output
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}
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def test_make_classifier_processor_basic():
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"""Test basic creation of Classifier processor."""
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config = create_default_config()
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stats = create_default_stats()
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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) # 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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def test_classifier_processor_normalization():
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"""Test that Classifier processor correctly normalizes data."""
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config = create_default_config()
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stats = create_default_stats()
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preprocessor, postprocessor = make_classifier_processor(
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config,
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stats,
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)
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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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}
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action = torch.randn(1) # Dummy action/reward
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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(
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config,
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stats,
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)
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# Create CPU data
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observation = {
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OBS_STATE: torch.randn(10),
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OBS_IMAGE: torch.randn(3, 224, 224),
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}
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action = torch.randn(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(
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config,
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stats,
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)
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# Simulate Accelerate: data already on GPU
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device = torch.device("cuda:0")
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observation = {
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OBS_STATE: torch.randn(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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}
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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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processed = preprocessor(batch)
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assert processed is not None
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def test_classifier_processor_save_and_load():
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"""Test saving and loading Classifier processor."""
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config = create_default_config()
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stats = create_default_stats()
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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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}
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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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processed = loaded_preprocessor(batch)
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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_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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stats = create_default_stats()
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preprocessor, postprocessor = make_classifier_processor(
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config,
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stats,
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)
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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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OBS_IMAGE: torch.randn(batch_size, 3, 224, 224),
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}
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action = torch.randn(batch_size, 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 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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def test_classifier_processor_postprocessor_identity():
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"""Test that Classifier postprocessor uses IdentityProcessor correctly."""
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config = create_default_config()
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stats = create_default_stats()
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preprocessor, postprocessor = make_classifier_processor(
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config,
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stats,
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)
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# Create test data for postprocessor
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reward = torch.tensor([[0.8], [0.3], [0.9]]) # Batch of rewards/predictions
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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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@@ -1,11 +1,15 @@
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#!/usr/bin/env python
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import torch
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import pytest
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from lerobot.configs.recipe import MessageTurn, TrainingRecipe
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from lerobot.processor.converters import create_transition
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from lerobot.processor.render_messages_processor import RenderMessagesStep
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from lerobot.types import TransitionKey
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pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
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import torch # noqa: E402
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from lerobot.configs.recipe import MessageTurn, TrainingRecipe # noqa: E402
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from lerobot.processor.converters import create_transition # noqa: E402
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from lerobot.processor.render_messages_processor import RenderMessagesStep # noqa: E402
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from lerobot.types import TransitionKey # noqa: E402
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def test_render_messages_step_noops_without_language_columns():
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