src/openpi/models/model_test.py

from flax import nnx
import jax
import pytest

from openpi.models import model as _model
from openpi.models import pi0
from openpi.models import pi0_fast
from openpi.shared import download
from openpi.shared import nnx_utils


def test_pi0_model():
    key = jax.random.key(0)
    config = pi0.Pi0Config()
    model = config.create(key)

    batch_size = 2
    obs, act = config.fake_obs(batch_size), config.fake_act(batch_size)

    loss = nnx_utils.module_jit(model.compute_loss)(key, obs, act)
    assert loss.shape == (batch_size, config.action_horizon)

    actions = nnx_utils.module_jit(model.sample_actions)(key, obs, num_steps=10)
    assert actions.shape == (batch_size, model.action_horizon, model.action_dim)


def test_pi0_lora_model():
    key = jax.random.key(0)
    config = pi0.Pi0Config(paligemma_variant="gemma_2b_lora")
    model = config.create(key)

    batch_size = 2
    obs, act = config.fake_obs(batch_size), config.fake_act(batch_size)

    loss = nnx_utils.module_jit(model.compute_loss)(key, obs, act)
    assert loss.shape == (batch_size, config.action_horizon)

    actions = nnx_utils.module_jit(model.sample_actions)(key, obs, num_steps=10)
    assert actions.shape == (batch_size, model.action_horizon, model.action_dim)


def test_pi0_fast_model():
    key = jax.random.key(0)
    config = pi0_fast.Pi0FASTConfig()
    model = config.create(key)

    batch_size = 2
    obs, act = config.fake_obs(batch_size), config.fake_act(batch_size)

    loss = nnx_utils.module_jit(model.compute_loss)(key, obs, act)
    assert loss.shape == (batch_size,)

    actions = nnx_utils.module_jit(model.sample_actions)(key, obs)
    assert actions.shape == (batch_size, 256)


def test_pi0_fast_lora_model():
    key = jax.random.key(0)
    config = pi0_fast.Pi0FASTConfig(paligemma_variant="gemma_2b_lora")
    model = config.create(key)

    batch_size = 2
    obs, act = config.fake_obs(batch_size), config.fake_act(batch_size)

    loss = nnx_utils.module_jit(model.compute_loss)(key, obs, act)
    assert loss.shape == (batch_size,)

    actions = nnx_utils.module_jit(model.sample_actions)(key, obs)
    assert actions.shape == (batch_size, 256)

    lora_filter = nnx_utils.PathRegex(".*lora.*")
    model_state = nnx.state(model)

    lora_state_elems = list(model_state.filter(lora_filter))
    assert len(lora_state_elems) > 0


@pytest.mark.manual
def test_model_restore():
    key = jax.random.key(0)
    config = pi0.Pi0Config()

    batch_size = 2
    obs, act = config.fake_obs(batch_size), config.fake_act(batch_size)

    model = config.load(
        _model.restore_params(download.maybe_download("s3://openpi-assets/checkpoints/pi0_base/params"))
    )

    loss = model.compute_loss(key, obs, act)
    assert loss.shape == (batch_size, config.action_horizon)

    actions = model.sample_actions(key, obs, num_steps=10)
    assert actions.shape == (batch_size, model.action_horizon, model.action_dim)