Baruch Tabanpour
d07f39b4a0
PiperOrigin-RevId: 865380826 Change-Id: I840b48027e1142f87eb745d0d19b0cd3e87904b2
299 lines
9.5 KiB
Python
299 lines
9.5 KiB
Python
# Copyright 2025 DeepMind Technologies Limited
|
|
#
|
|
# 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.
|
|
# ==============================================================================
|
|
"""Tests for forward functions."""
|
|
|
|
import functools
|
|
import os
|
|
import tempfile
|
|
|
|
from absl.testing import absltest
|
|
from absl.testing import parameterized
|
|
import jax
|
|
import jax.numpy as jp
|
|
import mujoco
|
|
from mujoco import mjx
|
|
from mujoco.mjx._src import io
|
|
from mujoco.mjx._src import test_util
|
|
import mujoco.mjx.warp as mjxw
|
|
from mujoco.mjx.warp import test_util as tu
|
|
from mujoco.mjx.warp import warp as wp # pylint: disable=g-importing-member
|
|
import numpy as np
|
|
|
|
try:
|
|
from mujoco.mjx.warp import forward # pylint: disable=g-import-not-at-top
|
|
except ImportError:
|
|
forward = None
|
|
|
|
_FORCE_TEST = os.environ.get('MJX_WARP_FORCE_TEST', '0') == '1'
|
|
|
|
|
|
class ForwardTest(parameterized.TestCase):
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
if mjxw.WARP_INSTALLED:
|
|
self.tempdir = tempfile.TemporaryDirectory()
|
|
wp.config.kernel_cache_dir = self.tempdir.name
|
|
np.random.seed(0)
|
|
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
if hasattr(self, 'tempdir'):
|
|
self.tempdir.cleanup()
|
|
|
|
@parameterized.parameters(
|
|
'pendula.xml',
|
|
'humanoid/humanoid.xml',
|
|
)
|
|
def test_jit_caching(self, xml):
|
|
"""Tests jit caching on the full step function."""
|
|
if not _FORCE_TEST:
|
|
if not mjxw.WARP_INSTALLED:
|
|
self.skipTest('Warp not installed.')
|
|
if not io.has_cuda_gpu_device():
|
|
self.skipTest('No CUDA GPU device available.')
|
|
|
|
batch_size = 7
|
|
m = test_util.load_test_file(xml)
|
|
mx = mjx.put_model(m, impl='warp')
|
|
|
|
keys = jp.arange(batch_size)
|
|
dx_batch = jax.vmap(functools.partial(tu.make_data, m))(keys)
|
|
|
|
step_fn = jax.jit(jax.vmap(forward.step, in_axes=(None, 0)))
|
|
dx_batch1 = step_fn(mx, dx_batch)
|
|
jax.tree_util.tree_map(lambda x: x.block_until_ready(), dx_batch1)
|
|
self.assertEqual(step_fn._cache_size(), 1)
|
|
|
|
# Re-generate data and run step_fn again to test jit caching.
|
|
keys = jp.arange(batch_size, batch_size * 2)
|
|
dx_batch = jax.vmap(functools.partial(tu.make_data, m))(keys)
|
|
dx_batch2 = step_fn(mx, dx_batch)
|
|
jax.tree_util.tree_map(lambda x: x.block_until_ready(), dx_batch2)
|
|
self.assertEqual(step_fn._cache_size(), 1)
|
|
|
|
@parameterized.product(
|
|
xml=(
|
|
'humanoid/humanoid.xml',
|
|
'pendula.xml',
|
|
),
|
|
batch_size=(1, 7),
|
|
)
|
|
def test_forward(self, xml: str, batch_size: int):
|
|
if not _FORCE_TEST:
|
|
if not mjxw.WARP_INSTALLED:
|
|
self.skipTest('Warp not installed.')
|
|
if not io.has_cuda_gpu_device():
|
|
self.skipTest('No CUDA GPU device available.')
|
|
|
|
m = test_util.load_test_file(xml)
|
|
m.opt.iterations = 10
|
|
m.opt.ls_iterations = 10
|
|
m.opt.jacobian = mujoco.mjtJacobian.mjJAC_DENSE
|
|
mx = mjx.put_model(m, impl='warp')
|
|
|
|
d = mujoco.MjData(m)
|
|
worldids = jp.arange(batch_size)
|
|
dx_batch = jax.vmap(functools.partial(tu.make_data, m))(worldids)
|
|
|
|
dx_batch = jax.jit(jax.vmap(forward.forward, in_axes=(None, 0)))(
|
|
mx, dx_batch
|
|
)
|
|
|
|
for i in range(batch_size):
|
|
dx = dx_batch[i]
|
|
|
|
d.qpos[:] = dx.qpos
|
|
d.qvel[:] = dx.qvel
|
|
d.ctrl[:] = dx.ctrl
|
|
d.mocap_pos[:] = dx.mocap_pos
|
|
d.mocap_quat[:] = dx.mocap_quat
|
|
mujoco.mj_forward(m, d)
|
|
|
|
# fwd_position
|
|
tu.assert_attr_eq(dx, d, 'xpos')
|
|
tu.assert_attr_eq(dx, d, 'xquat')
|
|
tu.assert_attr_eq(dx, d, 'xipos')
|
|
tu.assert_eq(d.ximat.reshape((-1, 3, 3)), dx.ximat, 'ximat')
|
|
tu.assert_attr_eq(dx, d, 'xanchor')
|
|
tu.assert_attr_eq(dx, d, 'xaxis')
|
|
tu.assert_attr_eq(dx, d, 'geom_xpos')
|
|
tu.assert_eq(dx.geom_xmat, d.geom_xmat.reshape((-1, 3, 3)), 'geom_xmat')
|
|
if m.nsite:
|
|
tu.assert_attr_eq(dx, d, 'site_xpos')
|
|
tu.assert_eq(dx.site_xmat, d.site_xmat.reshape((-1, 3, 3)), 'site_xmat')
|
|
tu.assert_attr_eq(dx, d, 'cdof')
|
|
tu.assert_attr_eq(dx._impl, d, 'cinert')
|
|
tu.assert_attr_eq(dx, d, 'subtree_com')
|
|
if m.nlight:
|
|
tu.assert_attr_eq(dx._impl, d, 'light_xpos')
|
|
tu.assert_attr_eq(dx._impl, d, 'light_xdir')
|
|
if m.ncam:
|
|
tu.assert_attr_eq(dx, d, 'cam_xpos')
|
|
tu.assert_eq(dx.cam_xmat, d.cam_xmat.reshape((-1, 3, 3)), 'cam_xmat')
|
|
tu.assert_attr_eq(dx, d, 'ten_length')
|
|
tu.assert_attr_eq(dx._impl, d, 'ten_J')
|
|
tu.assert_attr_eq(dx._impl, d, 'ten_wrapadr')
|
|
tu.assert_attr_eq(dx._impl, d, 'ten_wrapnum')
|
|
tu.assert_attr_eq(dx._impl, d, 'wrap_xpos')
|
|
tu.assert_attr_eq(dx._impl, d, 'wrap_obj')
|
|
tu.assert_attr_eq(dx._impl, d, 'crb')
|
|
|
|
qm = np.zeros((m.nv, m.nv))
|
|
mujoco.mj_fullM(m, qm, d.qM)
|
|
# mjwarp adds padding to qM
|
|
tu.assert_eq(qm, dx._impl.qM[: m.nv, : m.nv], 'qM')
|
|
# qLD is fused in a cholesky factorize and solve, and not written to.
|
|
|
|
tu.assert_contact_eq(d, dx, worldid=i)
|
|
|
|
tu.assert_attr_eq(dx, d, 'actuator_length')
|
|
actuator_moment = np.zeros((m.nu, m.nv))
|
|
mujoco.mju_sparse2dense(
|
|
actuator_moment,
|
|
d.actuator_moment,
|
|
d.moment_rownnz,
|
|
d.moment_rowadr,
|
|
d.moment_colind,
|
|
)
|
|
tu.assert_eq(dx._impl.actuator_moment, actuator_moment, 'actuator_moment')
|
|
|
|
# fwd_velocity
|
|
tu.assert_attr_eq(dx._impl, d, 'actuator_velocity')
|
|
tu.assert_attr_eq(dx, d, 'cvel')
|
|
tu.assert_attr_eq(dx, d, 'cdof_dot')
|
|
tu.assert_attr_eq(dx._impl, d, 'qfrc_spring')
|
|
tu.assert_attr_eq(dx._impl, d, 'qfrc_damper')
|
|
tu.assert_attr_eq(dx, d, 'qfrc_gravcomp')
|
|
tu.assert_attr_eq(dx, d, 'qfrc_fluid')
|
|
tu.assert_attr_eq(dx, d, 'qfrc_passive')
|
|
tu.assert_attr_eq(dx, d, 'qfrc_bias')
|
|
tu.assert_efc_eq(d, dx, worldid=i)
|
|
|
|
# fwd_actuation
|
|
tu.assert_attr_eq(dx, d, 'act_dot')
|
|
tu.assert_attr_eq(dx, d, 'actuator_force')
|
|
tu.assert_attr_eq(dx, d, 'qfrc_actuator')
|
|
|
|
# fwd_acceleration
|
|
tu.assert_attr_eq(dx, d, 'qfrc_smooth')
|
|
tu.assert_attr_eq(dx, d, 'qacc_smooth')
|
|
|
|
np.testing.assert_allclose(
|
|
dx.qacc, d.qacc, err_msg='qacc', rtol=1e-5, atol=1.0
|
|
)
|
|
|
|
|
|
class StepTest(parameterized.TestCase):
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
if mjxw.WARP_INSTALLED:
|
|
self.tempdir = tempfile.TemporaryDirectory()
|
|
wp.config.kernel_cache_dir = self.tempdir.name
|
|
np.random.seed(0)
|
|
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
if hasattr(self, 'tempdir'):
|
|
self.tempdir.cleanup()
|
|
|
|
@parameterized.product(
|
|
xml=(
|
|
'humanoid/humanoid.xml',
|
|
'pendula.xml',
|
|
),
|
|
batch_size=(1, 7),
|
|
# NOTE: GraphMode.JAX is incompatible with MuJoCo Warp at the moment,
|
|
# even when setting graph_conditional=False.
|
|
graph_mode=('WARP',),
|
|
)
|
|
def test_step(self, xml: str, batch_size: int, graph_mode: str):
|
|
if not _FORCE_TEST:
|
|
if not mjxw.WARP_INSTALLED:
|
|
self.skipTest('Warp not installed.')
|
|
if not io.has_cuda_gpu_device():
|
|
self.skipTest('No CUDA GPU device available.')
|
|
|
|
m = test_util.load_test_file(xml)
|
|
m.opt.iterations = 10
|
|
m.opt.ls_iterations = 10
|
|
mx = mjx.put_model(
|
|
m, impl='warp', graph_mode=getattr(mjxw.types.GraphMode, graph_mode)
|
|
)
|
|
|
|
d = mujoco.MjData(m)
|
|
worldids = jp.arange(batch_size)
|
|
dx_batch = jax.vmap(functools.partial(tu.make_data, m))(worldids)
|
|
dx_batch_orig = dx_batch
|
|
|
|
for _ in range(10):
|
|
dx_batch = jax.jit(jax.vmap(forward.step, in_axes=(None, 0)))(
|
|
mx, dx_batch
|
|
)
|
|
|
|
for i in range(batch_size):
|
|
dx = dx_batch[i]
|
|
dx_orig = dx_batch_orig[i]
|
|
|
|
d.qpos[:] = dx_orig.qpos
|
|
d.qvel[:] = dx_orig.qvel
|
|
d.ctrl[:] = dx_orig.ctrl
|
|
d.mocap_pos[:] = dx_orig.mocap_pos
|
|
d.mocap_quat[:] = dx_orig.mocap_quat
|
|
d.time = dx_orig.time
|
|
mujoco.mj_step(m, d, 10)
|
|
|
|
tu.assert_attr_eq(dx, d, 'qpos')
|
|
tu.assert_attr_eq(dx, d, 'qvel')
|
|
tu.assert_attr_eq(dx, d, 'time')
|
|
tu.assert_attr_eq(dx, d, 'ctrl')
|
|
tu.assert_attr_eq(dx, d, 'act')
|
|
tu.assert_attr_eq(dx, d, 'mocap_pos')
|
|
tu.assert_attr_eq(dx, d, 'mocap_quat')
|
|
tu.assert_attr_eq(dx, d, 'sensordata')
|
|
|
|
def test_step_leading_dim_mismatch(self):
|
|
if not _FORCE_TEST:
|
|
if not mjxw.WARP_INSTALLED:
|
|
self.skipTest('Warp not installed.')
|
|
if not io.has_cuda_gpu_device():
|
|
self.skipTest('No CUDA GPU device available.')
|
|
|
|
xml = 'humanoid/humanoid.xml'
|
|
batch_size = 7
|
|
|
|
m = test_util.load_test_file(xml)
|
|
mx = mjx.put_model(m, impl='warp')
|
|
|
|
worldids = jp.arange(batch_size)
|
|
dx_batch = jax.vmap(functools.partial(tu.make_data, m))(worldids)
|
|
dx_batch_orig = dx_batch
|
|
|
|
with self.assertRaises(ValueError):
|
|
dx_batch = dx_batch.replace(qpos=dx_batch.qpos[1:])
|
|
_ = jax.jit(jax.vmap(forward.step, in_axes=(None, 0)))(mx, dx_batch)
|
|
|
|
dx_batch = dx_batch_orig
|
|
with self.assertRaises(ValueError):
|
|
dx_batch = dx_batch.tree_replace(
|
|
{'_impl.contact__pos': dx_batch._impl.contact__pos[1:]}
|
|
)
|
|
_ = jax.jit(jax.vmap(forward.step, in_axes=(None, 0)))(mx, dx_batch)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
absltest.main()
|