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Organize test folders (#856)

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Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
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Simon Alibert
2025-03-13 14:05:55 +01:00
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#!/usr/bin/env python
# Copyright 2024 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 inspect
from copy import deepcopy
from pathlib import Path
import einops
import pytest
import torch
from safetensors.torch import load_file
from lerobot import available_policies
from lerobot.common.datasets.factory import make_dataset
from lerobot.common.datasets.utils import cycle, dataset_to_policy_features
from lerobot.common.envs.factory import make_env, make_env_config
from lerobot.common.envs.utils import preprocess_observation
from lerobot.common.optim.factory import make_optimizer_and_scheduler
from lerobot.common.policies.act.modeling_act import ACTTemporalEnsembler
from lerobot.common.policies.factory import (
get_policy_class,
make_policy,
make_policy_config,
)
from lerobot.common.policies.normalize import Normalize, Unnormalize
from lerobot.common.policies.pretrained import PreTrainedPolicy
from lerobot.common.utils.random_utils import seeded_context
from lerobot.configs.default import DatasetConfig
from lerobot.configs.train import TrainPipelineConfig
from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
from tests.artifacts.policies.save_policy_to_safetensors import get_policy_stats
from tests.utils import DEVICE, require_cpu, require_env, require_x86_64_kernel
@pytest.fixture
def dummy_dataset_metadata(lerobot_dataset_metadata_factory, info_factory, tmp_path):
# Create only one camera input which is squared to fit all current policy constraints
# e.g. vqbet and tdmpc works with one camera only, and tdmpc requires it to be squared
camera_features = {
"observation.images.laptop": {
"shape": (84, 84, 3),
"names": ["height", "width", "channels"],
"info": None,
},
}
motor_features = {
"action": {
"dtype": "float32",
"shape": (6,),
"names": ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"],
},
"observation.state": {
"dtype": "float32",
"shape": (6,),
"names": ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"],
},
}
info = info_factory(
total_episodes=1, total_frames=1, camera_features=camera_features, motor_features=motor_features
)
ds_meta = lerobot_dataset_metadata_factory(root=tmp_path / "init", info=info)
return ds_meta
@pytest.mark.parametrize("policy_name", available_policies)
def test_get_policy_and_config_classes(policy_name: str):
"""Check that the correct policy and config classes are returned."""
policy_cls = get_policy_class(policy_name)
policy_cfg = make_policy_config(policy_name)
assert policy_cls.name == policy_name
assert issubclass(
policy_cfg.__class__, inspect.signature(policy_cls.__init__).parameters["config"].annotation
)
@pytest.mark.parametrize(
"ds_repo_id,env_name,env_kwargs,policy_name,policy_kwargs",
[
("lerobot/xarm_lift_medium", "xarm", {}, "tdmpc", {"use_mpc": True}),
("lerobot/pusht", "pusht", {}, "diffusion", {}),
("lerobot/pusht", "pusht", {}, "vqbet", {}),
("lerobot/pusht", "pusht", {}, "act", {}),
("lerobot/aloha_sim_insertion_human", "aloha", {"task": "AlohaInsertion-v0"}, "act", {}),
(
"lerobot/aloha_sim_insertion_scripted",
"aloha",
{"task": "AlohaInsertion-v0"},
"act",
{},
),
(
"lerobot/aloha_sim_insertion_human",
"aloha",
{"task": "AlohaInsertion-v0"},
"diffusion",
{},
),
(
"lerobot/aloha_sim_transfer_cube_human",
"aloha",
{"task": "AlohaTransferCube-v0"},
"act",
{},
),
(
"lerobot/aloha_sim_transfer_cube_scripted",
"aloha",
{"task": "AlohaTransferCube-v0"},
"act",
{},
),
],
)
@require_env
def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
"""
Tests:
- Making the policy object.
- Checking that the policy follows the correct protocol and subclasses nn.Module
and PyTorchModelHubMixin.
- Updating the policy.
- Using the policy to select actions at inference time.
- Test the action can be applied to the policy
Note: We test various combinations of policy and dataset. The combinations are by no means exhaustive,
and for now we add tests as we see fit.
"""
train_cfg = TrainPipelineConfig(
# TODO(rcadene, aliberts): remove dataset download
dataset=DatasetConfig(repo_id=ds_repo_id, episodes=[0]),
policy=make_policy_config(policy_name, **policy_kwargs),
env=make_env_config(env_name, **env_kwargs),
)
# Check that we can make the policy object.
dataset = make_dataset(train_cfg)
policy = make_policy(train_cfg.policy, ds_meta=dataset.meta)
assert isinstance(policy, PreTrainedPolicy)
# Check that we run select_actions and get the appropriate output.
env = make_env(train_cfg.env, n_envs=2)
dataloader = torch.utils.data.DataLoader(
dataset,
num_workers=0,
batch_size=2,
shuffle=True,
pin_memory=DEVICE != "cpu",
drop_last=True,
)
dl_iter = cycle(dataloader)
batch = next(dl_iter)
for key in batch:
if isinstance(batch[key], torch.Tensor):
batch[key] = batch[key].to(DEVICE, non_blocking=True)
# Test updating the policy (and test that it does not mutate the batch)
batch_ = deepcopy(batch)
policy.forward(batch)
assert set(batch) == set(batch_), "Batch keys are not the same after a forward pass."
assert all(
torch.equal(batch[k], batch_[k]) if isinstance(batch[k], torch.Tensor) else batch[k] == batch_[k]
for k in batch
), "Batch values are not the same after a forward pass."
# reset the policy and environment
policy.reset()
observation, _ = env.reset(seed=train_cfg.seed)
# apply transform to normalize the observations
observation = preprocess_observation(observation)
# send observation to device/gpu
observation = {key: observation[key].to(DEVICE, non_blocking=True) for key in observation}
# get the next action for the environment (also check that the observation batch is not modified)
observation_ = deepcopy(observation)
with torch.inference_mode():
action = policy.select_action(observation).cpu().numpy()
assert set(observation) == set(observation_), (
"Observation batch keys are not the same after a forward pass."
)
assert all(torch.equal(observation[k], observation_[k]) for k in observation), (
"Observation batch values are not the same after a forward pass."
)
# Test step through policy
env.step(action)
# TODO(rcadene, aliberts): This test is quite end-to-end. Move this test in test_optimizer?
def test_act_backbone_lr():
"""
Test that the ACT policy can be instantiated with a different learning rate for the backbone.
"""
cfg = TrainPipelineConfig(
# TODO(rcadene, aliberts): remove dataset download
dataset=DatasetConfig(repo_id="lerobot/aloha_sim_insertion_scripted", episodes=[0]),
policy=make_policy_config("act", optimizer_lr=0.01, optimizer_lr_backbone=0.001),
)
cfg.validate() # Needed for auto-setting some parameters
assert cfg.policy.optimizer_lr == 0.01
assert cfg.policy.optimizer_lr_backbone == 0.001
dataset = make_dataset(cfg)
policy = make_policy(cfg.policy, ds_meta=dataset.meta)
optimizer, _ = make_optimizer_and_scheduler(cfg, policy)
assert len(optimizer.param_groups) == 2
assert optimizer.param_groups[0]["lr"] == cfg.policy.optimizer_lr
assert optimizer.param_groups[1]["lr"] == cfg.policy.optimizer_lr_backbone
assert len(optimizer.param_groups[0]["params"]) == 133
assert len(optimizer.param_groups[1]["params"]) == 20
@pytest.mark.parametrize("policy_name", available_policies)
def test_policy_defaults(dummy_dataset_metadata, policy_name: str):
"""Check that the policy can be instantiated with defaults."""
policy_cls = get_policy_class(policy_name)
policy_cfg = make_policy_config(policy_name)
features = dataset_to_policy_features(dummy_dataset_metadata.features)
policy_cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
policy_cfg.input_features = {
key: ft for key, ft in features.items() if key not in policy_cfg.output_features
}
policy_cls(policy_cfg)
@pytest.mark.parametrize("policy_name", available_policies)
def test_save_and_load_pretrained(dummy_dataset_metadata, tmp_path, policy_name: str):
policy_cls = get_policy_class(policy_name)
policy_cfg = make_policy_config(policy_name)
features = dataset_to_policy_features(dummy_dataset_metadata.features)
policy_cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
policy_cfg.input_features = {
key: ft for key, ft in features.items() if key not in policy_cfg.output_features
}
policy = policy_cls(policy_cfg)
policy.to(policy_cfg.device)
save_dir = tmp_path / f"test_save_and_load_pretrained_{policy_cls.__name__}"
policy.save_pretrained(save_dir)
loaded_policy = policy_cls.from_pretrained(save_dir, config=policy_cfg)
torch.testing.assert_close(list(policy.parameters()), list(loaded_policy.parameters()), rtol=0, atol=0)
@pytest.mark.parametrize("insert_temporal_dim", [False, True])
def test_normalize(insert_temporal_dim):
"""
Test that normalize/unnormalize can run without exceptions when properly set up, and that they raise
an exception when the forward pass is called without the stats having been provided.
TODO(rcadene, alexander-soare): This should also test that the normalization / unnormalization works as
expected.
"""
input_features = {
"observation.image": PolicyFeature(
type=FeatureType.VISUAL,
shape=(3, 96, 96),
),
"observation.state": PolicyFeature(
type=FeatureType.STATE,
shape=(10,),
),
}
output_features = {
"action": PolicyFeature(
type=FeatureType.ACTION,
shape=(5,),
),
}
norm_map = {
"VISUAL": NormalizationMode.MEAN_STD,
"STATE": NormalizationMode.MIN_MAX,
"ACTION": NormalizationMode.MIN_MAX,
}
dataset_stats = {
"observation.image": {
"mean": torch.randn(3, 1, 1),
"std": torch.randn(3, 1, 1),
"min": torch.randn(3, 1, 1),
"max": torch.randn(3, 1, 1),
},
"observation.state": {
"mean": torch.randn(10),
"std": torch.randn(10),
"min": torch.randn(10),
"max": torch.randn(10),
},
"action": {
"mean": torch.randn(5),
"std": torch.randn(5),
"min": torch.randn(5),
"max": torch.randn(5),
},
}
bsize = 2
input_batch = {
"observation.image": torch.randn(bsize, 3, 96, 96),
"observation.state": torch.randn(bsize, 10),
}
output_batch = {
"action": torch.randn(bsize, 5),
}
if insert_temporal_dim:
tdim = 4
for key in input_batch:
# [2,3,96,96] -> [2,tdim,3,96,96]
input_batch[key] = torch.stack([input_batch[key]] * tdim, dim=1)
for key in output_batch:
output_batch[key] = torch.stack([output_batch[key]] * tdim, dim=1)
# test without stats
normalize = Normalize(input_features, norm_map, stats=None)
with pytest.raises(AssertionError):
normalize(input_batch)
# test with stats
normalize = Normalize(input_features, norm_map, stats=dataset_stats)
normalize(input_batch)
# test loading pretrained models
new_normalize = Normalize(input_features, norm_map, stats=None)
new_normalize.load_state_dict(normalize.state_dict())
new_normalize(input_batch)
# test without stats
unnormalize = Unnormalize(output_features, norm_map, stats=None)
with pytest.raises(AssertionError):
unnormalize(output_batch)
# test with stats
unnormalize = Unnormalize(output_features, norm_map, stats=dataset_stats)
unnormalize(output_batch)
# test loading pretrained models
new_unnormalize = Unnormalize(output_features, norm_map, stats=None)
new_unnormalize.load_state_dict(unnormalize.state_dict())
unnormalize(output_batch)
@pytest.mark.parametrize(
"ds_repo_id, policy_name, policy_kwargs, file_name_extra",
[
# TODO(alexander-soare): `policy.use_mpc=false` was previously the default in the config yaml but it
# was changed to true. For some reason, tests would pass locally, but not in CI. So here we override
# to test with `policy.use_mpc=false`.
("lerobot/xarm_lift_medium", "tdmpc", {"use_mpc": False}, "use_policy"),
# ("lerobot/xarm_lift_medium", "tdmpc", {"use_mpc": True}, "use_mpc"),
# TODO(rcadene): the diffusion model was normalizing the image in mean=0.5 std=0.5 which is a hack supposed to
# to normalize the image at all. In our current codebase we dont normalize at all. But there is still a minor difference
# that fails the test. However, by testing to normalize the image with 0.5 0.5 in the current codebase, the test pass.
# Thus, we deactivate this test for now.
(
"lerobot/pusht",
"diffusion",
{
"n_action_steps": 8,
"num_inference_steps": 10,
"down_dims": [128, 256, 512],
},
"",
),
("lerobot/aloha_sim_insertion_human", "act", {"n_action_steps": 10}, ""),
(
"lerobot/aloha_sim_insertion_human",
"act",
{"n_action_steps": 1000, "chunk_size": 1000},
"1000_steps",
),
],
)
# As artifacts have been generated on an x86_64 kernel, this test won't
# pass if it's run on another platform due to floating point errors
@require_x86_64_kernel
@require_cpu
def test_backward_compatibility(ds_repo_id: str, policy_name: str, policy_kwargs: dict, file_name_extra: str):
"""
NOTE: If this test does not pass, and you have intentionally changed something in the policy:
1. Inspect the differences in policy outputs and make sure you can account for them. Your PR should
include a report on what changed and how that affected the outputs.
2. Go to the `if __name__ == "__main__"` block of `tests/scripts/save_policy_to_safetensors.py` and
add the policies you want to update the test artifacts for.
3. Run `python tests/scripts/save_policy_to_safetensors.py`. The test artifact
should be updated.
4. Check that this test now passes.
5. Remember to restore `tests/scripts/save_policy_to_safetensors.py` to its original state.
6. Remember to stage and commit the resulting changes to `tests/artifacts`.
"""
ds_name = ds_repo_id.split("/")[-1]
artifact_dir = Path("tests/artifacts/policies") / f"{ds_name}_{policy_name}_{file_name_extra}"
saved_output_dict = load_file(artifact_dir / "output_dict.safetensors")
saved_grad_stats = load_file(artifact_dir / "grad_stats.safetensors")
saved_param_stats = load_file(artifact_dir / "param_stats.safetensors")
saved_actions = load_file(artifact_dir / "actions.safetensors")
output_dict, grad_stats, param_stats, actions = get_policy_stats(ds_repo_id, policy_name, policy_kwargs)
for key in saved_output_dict:
torch.testing.assert_close(output_dict[key], saved_output_dict[key])
for key in saved_grad_stats:
torch.testing.assert_close(grad_stats[key], saved_grad_stats[key])
for key in saved_param_stats:
torch.testing.assert_close(param_stats[key], saved_param_stats[key])
for key in saved_actions:
rtol, atol = (2e-3, 5e-6) if policy_name == "diffusion" else (None, None) # HACK
torch.testing.assert_close(actions[key], saved_actions[key], rtol=rtol, atol=atol)
def test_act_temporal_ensembler():
"""Check that the online method in ACTTemporalEnsembler matches a simple offline calculation."""
temporal_ensemble_coeff = 0.01
chunk_size = 100
episode_length = 101
ensembler = ACTTemporalEnsembler(temporal_ensemble_coeff, chunk_size)
# An batch of arbitrary sequences of 1D actions we wish to compute the average over. We'll keep the
# "action space" in [-1, 1]. Apart from that, there is no real reason for the numbers chosen.
with seeded_context(0):
# Dimension is (batch, episode_length, chunk_size, action_dim(=1))
# Stepping through the episode_length dim is like running inference at each rollout step and getting
# a different action chunk.
batch_seq = torch.stack(
[
torch.rand(episode_length, chunk_size) * 0.05 - 0.6,
torch.rand(episode_length, chunk_size) * 0.02 - 0.01,
torch.rand(episode_length, chunk_size) * 0.2 + 0.3,
],
dim=0,
).unsqueeze(-1) # unsqueeze for action dim
batch_size = batch_seq.shape[0]
# Exponential weighting (normalized). Unsqueeze once to match the position of the `episode_length`
# dimension of `batch_seq`.
weights = torch.exp(-temporal_ensemble_coeff * torch.arange(chunk_size)).unsqueeze(-1)
# Simulate stepping through a rollout and computing a batch of actions with model on each step.
for i in range(episode_length):
# Mock a batch of actions.
actions = torch.zeros(size=(batch_size, chunk_size, 1)) + batch_seq[:, i]
online_avg = ensembler.update(actions)
# Simple offline calculation: avg = Σ(aᵢ*wᵢ) / Σ(wᵢ).
# Note: The complicated bit here is the slicing. Think about the (episode_length, chunk_size) grid.
# What we want to do is take diagonal slices across it starting from the left.
# eg: chunk_size=4, episode_length=6
# ┌───────┐
# │0 1 2 3│
# │1 2 3 4│
# │2 3 4 5│
# │3 4 5 6│
# │4 5 6 7│
# │5 6 7 8│
# └───────┘
chunk_indices = torch.arange(min(i, chunk_size - 1), -1, -1)
episode_step_indices = torch.arange(i + 1)[-len(chunk_indices) :]
seq_slice = batch_seq[:, episode_step_indices, chunk_indices]
offline_avg = (
einops.reduce(seq_slice * weights[: i + 1], "b s 1 -> b 1", "sum") / weights[: i + 1].sum()
)
# Sanity check. The average should be between the extrema.
assert torch.all(einops.reduce(seq_slice, "b s 1 -> b 1", "min") <= offline_avg)
assert torch.all(offline_avg <= einops.reduce(seq_slice, "b s 1 -> b 1", "max"))
# Selected atol=1e-4 keeping in mind actions in [-1, 1] and excepting 0.01% error.
torch.testing.assert_close(online_avg, offline_avg, rtol=1e-4, atol=1e-4)