mirror of
https://github.com/huggingface/lerobot.git
synced 2026-08-08 17:39:44 +00:00
Pepijn
573 lines
21 KiB
Python
573 lines
21 KiB
Python
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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from __future__ import annotations
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import os
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from pathlib import Path
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import pytest
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import torch
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from torch import nn
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from lerobot.configs.policies import PreTrainedConfig
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from lerobot.configs.types import FeatureType, PolicyFeature
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from lerobot.policies.factory import get_policy_class, make_policy_config, make_pre_post_processors
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from lerobot.policies.g05.configuration_g05 import G05_EMBODIMENT_MAPPINGS, G05Config
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from lerobot.policies.g05.modeling_g05 import G05Policy
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from lerobot.processor import PolicyProcessorPipeline
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from lerobot.utils.constants import ACTION, OBS_STATE, POLICY_PREPROCESSOR_DEFAULT_NAME
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class TinyG05Backend(nn.Module):
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def __init__(self):
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super().__init__()
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self.proj = nn.Linear(20, 20)
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self.last_samples = None
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def predict_action(self, batch):
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self.last_samples = batch["samples"]
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state = batch[OBS_STATE]
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if state.ndim == 2:
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state = state.unsqueeze(1)
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step = self.proj(state[:, -1])
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return {
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ACTION: step.unsqueeze(1).expand(-1, 4, -1),
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"ar_action": (step + 1).unsqueeze(1).expand(-1, 4, -1),
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"cot_text": ["Subtask: move carefully"] * step.shape[0],
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}
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def forward(self, batch):
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prediction = self.proj(batch[OBS_STATE][:, -1])
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target = batch[ACTION][:, 0]
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loss = torch.nn.functional.mse_loss(prediction, target)
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return loss, {"fm_loss": loss.detach()}
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def _features():
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return {
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OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(7,)),
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"observation.images.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 8, 8)),
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"observation.images.wrist_image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 8, 8)),
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}
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def _config(**kwargs):
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normalization_mode = kwargs.pop("normalization_mode", "identity")
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return G05Config(
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checkpoint_profile="custom",
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normalization_mode=normalization_mode,
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input_features=_features(),
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output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,))},
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chunk_size=4,
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n_action_steps=kwargs.pop("n_action_steps", 4),
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device="cpu",
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**kwargs,
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)
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def _policy_batch(task: str = " Pick café cup\nverbatim "):
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return {
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OBS_STATE: torch.zeros(1, 1, 20),
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ACTION: torch.zeros(1, 4, 20),
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"observation.images.image": torch.zeros(1, 3, 8, 8),
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"observation.images.wrist_image": torch.zeros(1, 3, 8, 8),
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"task": [task],
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"proprio_dim_is_pad": torch.zeros(20, dtype=torch.bool),
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}
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def test_factory_wiring_is_lazy():
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assert make_policy_config("g05", checkpoint_profile="custom").type == "g05"
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assert get_policy_class("g05") is G05Policy
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def test_system2_fm_only_builder_uses_exact_cot_template_without_action_tokens():
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config = _config(
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action_head="flow",
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runtime_system="system2",
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predict_cot=True,
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discrete_action=False,
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continuous_action=True,
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return_continuous_action=True,
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processor_metadata={
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"samples_builder": {
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"_target_": ("g05.data_processor.processor.samples_builder.SubtaskCoTBuilderFMOnly")
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}
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},
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)
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assert "<prompt_text_!>\n<EOC><atomic_task_text>|Action: <EOV><eos>" in config.prompt_template
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assert "<action_action" not in config.prompt_template
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def test_so101_runtime_pads_optional_left_wrist():
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config = G05Config(
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checkpoint_profile="g05-so101",
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embodiment="so100",
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action_head="flow",
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runtime_system="system2",
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predict_cot=True,
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discrete_action=True,
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continuous_action=True,
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return_continuous_action=True,
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policy_action_dim=20,
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policy_state_dim=20,
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raw_action_dim=6,
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raw_state_dim=6,
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chunk_size=32,
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n_action_steps=16,
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normalization_mode="identity",
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camera_order=(
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"observation.images.exterior",
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"observation.images.wrist_left",
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"observation.images.wrist_right",
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),
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camera_sizes={
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"observation.images.exterior": (8, 8),
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"observation.images.wrist_left": (8, 8),
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"observation.images.wrist_right": (8, 8),
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},
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optional_camera_keys=("observation.images.wrist_left",),
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input_features={
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OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(6,)),
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"observation.images.exterior": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 8, 8)),
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"observation.images.wrist_right": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 8, 8)),
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},
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output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(6,))},
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device="cpu",
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)
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preprocessor, _ = make_pre_post_processors(config)
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processed = preprocessor(
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{
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OBS_STATE: torch.zeros(6),
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"observation.images.exterior": torch.zeros(3, 8, 8, dtype=torch.uint8),
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"observation.images.wrist_right": torch.zeros(3, 8, 8, dtype=torch.uint8),
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"task": "pick up the cube",
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}
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)
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assert processed["observation.images.wrist_left"].shape == (1, 3, 8, 8)
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assert torch.all(processed["observation.images.wrist_left"] == -1)
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assert not processed["action_dim_is_pad"][0, 10:16].any()
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def test_libero_runtime_executes_ten_step_window_and_binarizes_gripper():
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config = G05Config(
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checkpoint_profile="custom",
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embodiment="libero",
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action_head="flow",
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discrete_action=False,
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continuous_action=True,
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return_continuous_action=True,
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chunk_size=32,
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n_action_steps=10,
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normalization_mode="identity",
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libero_gripper_binarize=True,
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)
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_, postprocessor = make_pre_post_processors(config)
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policy_action = torch.zeros(5, 20)
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policy_action[:, 19] = torch.tensor([0.0, 0.5, 1.0, -0.2, 1.2])
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env_action = postprocessor(policy_action)
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torch.testing.assert_close(env_action[:, -1], torch.tensor([1.0, 1.0, -1.0, 1.0, -1.0]))
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def test_select_action_discards_tail_beyond_execution_window():
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config = _config(n_action_steps=2)
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policy = G05Policy(config, backend=TinyG05Backend())
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calls = 0
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def predict_action_chunk(batch, **kwargs):
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nonlocal calls
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calls += 1
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return torch.full((1, 4, 20), float(calls))
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policy.predict_action_chunk = predict_action_chunk
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batch = _policy_batch()
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assert policy.select_action(batch)[0, 0].item() == 1
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assert policy.select_action(batch)[0, 0].item() == 1
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assert policy.select_action(batch)[0, 0].item() == 2
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assert calls == 2
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def test_libero_and_atomic4_are_distinct_validated_mappings():
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with pytest.raises(ValueError, match="27D"):
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G05Config(
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checkpoint_profile="custom",
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embodiment="atomic_4",
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raw_state_dim=16,
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raw_action_dim=12,
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camera_order=(
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"observation.images.robot0_agentview_left",
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"observation.images.robot0_eye_in_hand",
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"observation.images.robot0_agentview_right",
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),
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)
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cfg = G05Config(
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checkpoint_profile="custom",
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embodiment="atomic_4",
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raw_state_dim=16,
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raw_action_dim=12,
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policy_state_dim=27,
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policy_action_dim=27,
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camera_order=(
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"observation.images.robot0_agentview_left",
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"observation.images.robot0_eye_in_hand",
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"observation.images.robot0_agentview_right",
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),
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)
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assert cfg.embodiment == "atomic_4"
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def test_libero_projection_mask_and_inverse_roundtrip():
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config = _config()
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preprocessor, postprocessor = make_pre_post_processors(config)
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raw_action = torch.arange(7, dtype=torch.float32).repeat(4, 1)
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batch = {
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OBS_STATE: torch.arange(7, dtype=torch.float32),
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ACTION: raw_action,
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"observation.images.image": torch.zeros(3, 8, 8),
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"observation.images.wrist_image": torch.zeros(3, 8, 8),
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"task": "test",
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}
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processed = preprocessor(batch)
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assert processed[OBS_STATE].shape == (1, 20)
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assert processed[ACTION].shape == (4, 20)
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assert processed["action_dim_is_pad"].shape == (1, 20)
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assert processed["action_dim_is_pad"].sum() == 13
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assert torch.equal(processed["action_op_mask"], ~processed["action_dim_is_pad"])
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assert processed["action_parts_meta"] == {
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"left_control": 9,
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"left_gripper": 1,
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"right_control": 9,
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"right_gripper": 1,
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}
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assert torch.equal(processed[ACTION][:, [10, 11, 12, 13, 14, 15, 19]], raw_action)
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restored = postprocessor(processed[ACTION])
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assert torch.equal(restored, raw_action)
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def test_inference_without_ground_truth_action_still_emits_action_dimension_mask():
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config = _config()
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preprocessor, _ = make_pre_post_processors(config)
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processed = preprocessor(
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{
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OBS_STATE: torch.arange(7, dtype=torch.float32),
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"observation.images.image": torch.zeros(3, 8, 8),
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"observation.images.wrist_image": torch.zeros(3, 8, 8),
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"task": "inference",
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}
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)
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assert processed["action_dim_is_pad"].shape == (1, 20)
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assert processed["action_dim_is_pad"].sum() == 13
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def test_lerobot_libero_two_finger_state_matches_author_first_qpos_contract():
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config = _config()
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preprocessor, _ = make_pre_post_processors(config)
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env_state = torch.arange(8, dtype=torch.float32)
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processed = preprocessor(
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{
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OBS_STATE: env_state,
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"observation.images.image": torch.zeros(3, 8, 8),
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"observation.images.wrist_image": torch.zeros(3, 8, 8),
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"task": "libero env",
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}
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)
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checkpoint_slots = G05_EMBODIMENT_MAPPINGS["libero"]["state"]
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assert torch.equal(processed[OBS_STATE][0, list(checkpoint_slots)], env_state[:7])
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def test_atomic4_projection_has_mobile_base_control_mode_and_exact_inverse():
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config = G05Config(
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checkpoint_profile="custom",
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embodiment="atomic_4",
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raw_state_dim=16,
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raw_action_dim=12,
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policy_state_dim=27,
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policy_action_dim=27,
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normalization_mode="identity",
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camera_order=(
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"observation.images.robot0_agentview_left",
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"observation.images.robot0_eye_in_hand",
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"observation.images.robot0_agentview_right",
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),
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input_features={
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OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(16,)),
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"observation.images.robot0_agentview_left": PolicyFeature(
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type=FeatureType.VISUAL, shape=(3, 8, 8)
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),
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"observation.images.robot0_eye_in_hand": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 8, 8)),
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"observation.images.robot0_agentview_right": PolicyFeature(
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type=FeatureType.VISUAL, shape=(3, 8, 8)
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),
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},
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output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(12,))},
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device="cpu",
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)
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preprocessor, postprocessor = make_pre_post_processors(config)
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raw_action = torch.arange(12, dtype=torch.float32).repeat(3, 1)
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batch = {
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OBS_STATE: torch.arange(16, dtype=torch.float32),
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ACTION: raw_action,
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**{camera: torch.zeros(3, 8, 8) for camera in config.camera_order},
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"task": "atomic",
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}
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processed = preprocessor(batch)
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indices = G05_EMBODIMENT_MAPPINGS["atomic_4"]["action"]
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assert torch.equal(processed[ACTION][..., list(indices)], raw_action)
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assert torch.equal(postprocessor(processed[ACTION]), raw_action)
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# Last five raw dimensions are base motion[4] and control mode.
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assert indices[-5:] == (20, 21, 22, 23, 24)
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def test_quantile_mode_refuses_minmax_substitution():
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config = _config(normalization_mode="q01_q99")
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stats = {
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OBS_STATE: {"min": torch.zeros(7), "max": torch.ones(7)},
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ACTION: {"min": torch.zeros(7), "max": torch.ones(7)},
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}
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with pytest.raises(ValueError, match="real q01/q99"):
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make_pre_post_processors(config, dataset_stats=stats)
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def test_checkpoint_normalization_clips_to_author_finite_range():
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config = _config(normalization_mode="q01_q99", normalization_clip=(-5.0, 5.0))
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stats = {
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OBS_STATE: {"q01": torch.zeros(7), "q99": torch.ones(7)},
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ACTION: {"q01": torch.zeros(4, 7), "q99": torch.ones(4, 7)},
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}
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preprocessor, _ = make_pre_post_processors(config, dataset_stats=stats)
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processed = preprocessor(
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{
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OBS_STATE: torch.full((7,), -100.0),
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ACTION: torch.full((4, 7), 100.0),
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"observation.images.image": torch.zeros(3, 8, 8),
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"observation.images.wrist_image": torch.zeros(3, 8, 8),
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"task": "clip",
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}
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)
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assert processed[OBS_STATE].min() == -5
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assert processed[ACTION].max() == 5
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def test_stepwise_quantiles_constant_dimension_are_finite_and_serializable(tmp_path: Path):
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config = _config(
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normalization_mode="q01_q99",
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use_stepwise_action_norm=True,
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n_action_steps=2,
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)
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q01_action = torch.zeros(4, 7)
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q99_action = torch.ones(4, 7)
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q99_action[:, 2] = 0
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stats = {
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OBS_STATE: {"q01": torch.zeros(7), "q99": torch.ones(7)},
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ACTION: {"q01": q01_action, "q99": q99_action},
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}
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preprocessor, postprocessor = make_pre_post_processors(config, dataset_stats=stats)
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processed = preprocessor(
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{
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OBS_STATE: torch.zeros(7),
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ACTION: torch.zeros(4, 7),
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"observation.images.image": torch.zeros(3, 8, 8),
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"observation.images.wrist_image": torch.zeros(3, 8, 8),
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"task": "constant",
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}
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)
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assert torch.isfinite(processed[ACTION]).all()
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torch.testing.assert_close(postprocessor(processed[ACTION]), torch.zeros(4, 7))
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step_q01 = torch.arange(4, dtype=torch.float32).view(4, 1).expand(4, 7)
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step_stats = {
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OBS_STATE: {"q01": torch.zeros(7), "q99": torch.ones(7)},
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ACTION: {"q01": step_q01, "q99": step_q01 + 2},
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}
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_, stepwise_postprocessor = make_pre_post_processors(config, dataset_stats=step_stats)
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normalized_action = torch.zeros(1, config.policy_action_dim)
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torch.testing.assert_close(stepwise_postprocessor(normalized_action), torch.ones(1, 7))
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torch.testing.assert_close(stepwise_postprocessor(normalized_action), torch.full((1, 7), 2.0))
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torch.testing.assert_close(stepwise_postprocessor(normalized_action), torch.ones(1, 7))
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stepwise_postprocessor.reset()
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torch.testing.assert_close(stepwise_postprocessor(normalized_action), torch.ones(1, 7))
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preprocessor.save_pretrained(tmp_path)
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loaded = PolicyProcessorPipeline.from_pretrained(
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tmp_path, config_filename=f"{POLICY_PREPROCESSOR_DEFAULT_NAME}.json"
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)
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assert [step.__class__.__name__ for step in loaded.steps] == [
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step.__class__.__name__ for step in preprocessor.steps
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]
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def test_exact_raw_task_reaches_author_command_and_head_selection():
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backend = TinyG05Backend()
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policy = G05Policy(_config(), backend=backend)
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raw_task = " 把 red cup 放到左边\nexactly as written "
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action, metadata = policy.predict_action_chunk_with_runtime(_policy_batch(), task=raw_task)
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assert backend.last_samples[0]["command"] == raw_task
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assert action.shape == (1, 4, 20)
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assert metadata["cot_text"] == ["Subtask: move carefully"]
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def test_author_action_payload_fills_required_tokenizer_metadata():
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policy = G05Policy(_config(), backend=TinyG05Backend())
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prepared = policy._prepare_author_batch(_policy_batch())
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assert set(prepared["samples"][0]["action"]) == {
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"value",
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"action_dim_is_pad",
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"action_op_mask",
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"parts_meta",
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}
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def test_author_inference_payload_synthesizes_required_dummy_action():
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policy = G05Policy(_config(), backend=TinyG05Backend())
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batch = _policy_batch()
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del batch[ACTION]
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prepared = policy._prepare_author_batch(batch)
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assert prepared["samples"][0]["action"]["value"].shape == (4, 20)
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def test_policy_to_moves_non_module_action_tokenizer_sidecar():
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class TrackingTokenizer:
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device = None
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def to(self, device):
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self.device = device
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backend = TinyG05Backend()
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backend.action_tokenizer = TrackingTokenizer()
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policy = G05Policy(_config(), backend=backend).to("cpu")
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|
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assert backend.action_tokenizer.device == next(policy.parameters()).device
|
|
|
|
|
|
def test_author_inference_precision_preserves_declared_fp32_parameters():
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|
class MixedPrecisionBackend(TinyG05Backend):
|
|
def __init__(self):
|
|
super().__init__()
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|
self.bulk_weight = nn.Parameter(torch.ones(2))
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|
self.precision_weight = nn.Parameter(torch.ones(2))
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|
|
|
def apply_fp32_params(self):
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|
self.precision_weight.data = self.precision_weight.data.float()
|
|
|
|
backend = MixedPrecisionBackend()
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|
policy = G05Policy(_config(), backend=backend)
|
|
|
|
policy._apply_author_inference_precision()
|
|
|
|
assert backend.bulk_weight.dtype is torch.bfloat16
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|
assert backend.precision_weight.dtype is torch.float32
|
|
|
|
|
|
def test_batch_two_preserves_each_raw_task_and_every_camera_slot():
|
|
backend = TinyG05Backend()
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|
policy = G05Policy(_config(), backend=backend)
|
|
batch = _policy_batch()
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|
batch[OBS_STATE] = batch[OBS_STATE].expand(2, -1, -1)
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|
batch[ACTION] = batch[ACTION].expand(2, -1, -1)
|
|
batch["observation.images.image"] = batch["observation.images.image"].expand(2, -1, -1, -1)
|
|
batch["observation.images.wrist_image"] = batch["observation.images.wrist_image"].expand(2, -1, -1, -1)
|
|
batch["proprio_dim_is_pad"] = torch.zeros(2, 20, dtype=torch.bool)
|
|
batch["task"] = [" first\n", "第二个 task"]
|
|
|
|
action = policy.predict_action_chunk(batch)
|
|
|
|
assert action.shape == (2, 4, 20)
|
|
assert [sample["command"] for sample in backend.last_samples] == batch["task"]
|
|
assert all(sample["image0"] == (224, 224) for sample in backend.last_samples)
|
|
assert all(sample["image1"] == (224, 224) for sample in backend.last_samples)
|
|
|
|
|
|
def test_forward_backward_update_and_save_reload(tmp_path: Path):
|
|
policy = G05Policy(_config(), backend=TinyG05Backend())
|
|
optimizer = torch.optim.AdamW(policy.get_optim_params()["params"], lr=1e-3)
|
|
loss, metrics = policy(_policy_batch("train"))
|
|
loss.backward()
|
|
grad_norm = torch.stack(
|
|
[parameter.grad.norm() for parameter in policy.parameters() if parameter.grad is not None]
|
|
).sum()
|
|
assert torch.isfinite(loss)
|
|
assert grad_norm > 0 and torch.isfinite(grad_norm)
|
|
optimizer.step()
|
|
assert metrics is not None and metrics["fm_loss"] >= 0
|
|
|
|
policy.save_pretrained(tmp_path)
|
|
reloaded = G05Policy.from_pretrained(
|
|
tmp_path, backend=TinyG05Backend(), local_files_only=True, strict=True
|
|
)
|
|
expected = policy.predict_action_chunk(_policy_batch("save"))
|
|
actual = reloaded.predict_action_chunk(_policy_batch("save"))
|
|
torch.testing.assert_close(actual, expected)
|
|
|
|
|
|
def test_save_pretrained_copies_required_gated_sidecars_portably(tmp_path: Path):
|
|
source = tmp_path / "checkpoint"
|
|
processor = source / "hf_processor"
|
|
processor.mkdir(parents=True)
|
|
(processor / "tokenizer.json").write_text("{}")
|
|
tokenizer = source / "action_tokenizer.pt"
|
|
torch.save({"codec": "ActionCodec"}, tokenizer)
|
|
for name in ("LICENSE-G0.5", "NOTICE"):
|
|
(source / name).write_text("{}")
|
|
config = _config(
|
|
author_model_config={
|
|
"hf_processor_path": str(processor),
|
|
"AT_CONFIG": {"ckpt_dir": str(tokenizer)},
|
|
}
|
|
)
|
|
output = tmp_path / "saved"
|
|
|
|
G05Policy(config, backend=TinyG05Backend()).save_pretrained(output)
|
|
|
|
assert (output / "hf_processor" / "tokenizer.json").is_file()
|
|
assert (output / "action_tokenizer.pt").is_file()
|
|
assert (output / "LICENSE-G0.5").is_file()
|
|
loaded_config = PreTrainedConfig.from_pretrained(output)
|
|
assert isinstance(loaded_config, G05Config)
|
|
assert loaded_config.author_model_config["hf_processor_path"] == "hf_processor"
|
|
assert loaded_config.author_model_config["AT_CONFIG"]["ckpt_dir"] == "action_tokenizer.pt"
|
|
|
|
|
|
def test_tiny_fixed_batch_overfit_reduces_loss():
|
|
policy = G05Policy(_config(), backend=TinyG05Backend())
|
|
optimizer = torch.optim.AdamW(policy.get_optim_params()["params"], lr=5e-2)
|
|
batch = _policy_batch("overfit")
|
|
initial = policy(batch)[0].item()
|
|
for _ in range(20):
|
|
optimizer.zero_grad()
|
|
loss, _ = policy(batch)
|
|
loss.backward()
|
|
optimizer.step()
|
|
final = policy(batch)[0].item()
|
|
assert final < initial * 0.25
|
|
|
|
|
|
@pytest.mark.skipif(
|
|
not os.environ.get("LEROBOT_G05_CHECKPOINT"),
|
|
reason="requires an accepted gated OpenGalaxea/G05 checkpoint and author CUDA environment",
|
|
)
|
|
def test_gated_checkpoint_loads_strictly():
|
|
checkpoint = Path(os.environ["LEROBOT_G05_CHECKPOINT"])
|
|
policy = G05Policy.from_pretrained(checkpoint, local_files_only=True, strict=True)
|
|
assert policy.config.source_checkpoint_revision
|