Make benchmarks and viewer more accessible.

- Drop google_benchmark module dependency, wasn't really using it.
- Move benchmark models into test_data
- Rename benchmark => testspeed, make default output more like MJ testspeed.
- Update viewer to use new io functions and add get_data_into.
- Add "mjx-testspeed" and "mjx-viewer" bin scripts

PiperOrigin-RevId: 601277135
Change-Id: I782f666c58a96ac1a0a1c5ffc8ecf29d9fcd810f

mjx/MANIFEST.in

@@ -1,2 +1 @@
 recursive-include mujoco/mjx/test_data *
-recursive-include mujoco/mjx/benchmark *.obj *.stl *.xml

mjx/mujoco/mjx/__init__.py

@@ -29,6 +29,7 @@ from mujoco.mjx._src.forward import fwd_velocity
 from mujoco.mjx._src.forward import rungekutta4
 from mujoco.mjx._src.forward import step
 from mujoco.mjx._src.io import get_data
+from mujoco.mjx._src.io import get_data_into
 from mujoco.mjx._src.io import make_data
 from mujoco.mjx._src.io import put_data
 from mujoco.mjx._src.io import put_model

mjx/mujoco/mjx/_src/io.py

@@ -233,8 +233,33 @@ def get_data(
     m: mujoco.MjModel, d: types.Data
 ) -> Union[mujoco.MjData, List[mujoco.MjData]]:
   """Gets mjx.Data from a device, resulting in mujoco.MjData or List[MjData]."""
-  dx = jax.device_get(d)
   batched = len(d.qpos.shape) > 1
+  batch_size = d.qpos.shape[0] if batched else 1
+
+  if batched:
+    result = [mujoco.MjData(m) for _ in range(batch_size)]
+  else:
+    result = mujoco.MjData(m)
+
+  get_data_into(result, m, d)
+
+  return result
+
+
+def get_data_into(
+    result: Union[mujoco.MjData, List[mujoco.MjData]],
+    m: mujoco.MjModel,
+    d: types.Data,
+):
+  """Gets mjx.Data from a device into an existing mujoco.MjData or list."""
+  batched = isinstance(result, list)
+  if batched and len(d.qpos.shape) < 2:
+    raise ValueError('dst is a list, but d is not batched.')
+  if not batched and len(d.qpos.shape) >= 2:
+    raise ValueError('dst is a an MjData, but d is batched.')
+
+  d = jax.device_get(d)
+
   batch_size = d.qpos.shape[0] if batched else 1
   ne, nf, nl, nc = constraint.count_constraints(m)
   efc_type = np.array([
@@ -252,26 +277,25 @@ def get_data(
       dof_j.append(j)
       j = m.dof_parentid[j]
 
-  ds = []
   for i in range(batch_size):
-    dx_i = jax.tree_map(lambda x, i=i: x[i], dx) if batched else d
-    ncon = (dx_i.contact.dist <= 0).sum()
-    efc_active = (dx_i.efc_J != 0).any(axis=1)
+    d_i = jax.tree_map(lambda x, i=i: x[i], d) if batched else d
+    result_i = result[i] if batched else result
+    ncon = (d_i.contact.dist <= 0).sum()
+    efc_active = (d_i.efc_J != 0).any(axis=1)
     efc_con = efc_type == mujoco.mjtConstraint.mjCNSTR_CONTACT_PYRAMIDAL
     nefc, nc = efc_active.sum(), (efc_active & efc_con).sum()
-    d_i = mujoco.MjData(m)
-    d_i.nnzJ = nefc * m.nv
-    mujoco._functions._realloc_con_efc(d_i, ncon=ncon, nefc=nefc)  # pylint: disable=protected-access
-    d_i.efc_J_rownnz[:] = np.repeat(m.nv, nefc)
-    d_i.efc_J_rowadr[:] = np.arange(0, nefc * m.nv, m.nv)
-    d_i.efc_J_colind[:] = np.tile(np.arange(m.nv), nefc)
+    result_i.nnzJ = nefc * m.nv
+    mujoco._functions._realloc_con_efc(result_i, ncon=ncon, nefc=nefc)  # pylint: disable=protected-access
+    result_i.efc_J_rownnz[:] = np.repeat(m.nv, nefc)
+    result_i.efc_J_rowadr[:] = np.arange(0, nefc * m.nv, m.nv)
+    result_i.efc_J_colind[:] = np.tile(np.arange(m.nv), nefc)
 
     for field in types.Data.fields():
       if field.name == 'contact':
-        _get_contact(d_i.contact, dx_i.contact, nefc - nc)
+        _get_contact(result_i.contact, d_i.contact, nefc - nc)
         continue
 
-      value = getattr(dx_i, field.name)
+      value = getattr(d_i, field.name)
 
       if field.name in ('xmat', 'ximat', 'geom_xmat', 'site_xmat'):
         value = value.reshape((-1, 9))
@@ -292,14 +316,11 @@ def get_data(
         value = np.ones(m.nv)
 
       if value.shape:
-        getattr(d_i, field.name)[:] = value
+        getattr(result_i, field.name)[:] = value
       else:
-        setattr(d_i, field.name, value)
+        setattr(result_i, field.name, value)
 
-    d_i.efc_type[:] = efc_type[efc_active]
-    ds.append(d_i)
-
-  return ds if batched else ds[0]
+    result_i.efc_type[:] = efc_type[efc_active]
 
 
 def _put_contact(

mjx/mujoco/mjx/_src/io_test.py

@@ -412,6 +412,27 @@ class DataIOTest(parameterized.TestCase):
     self.assertEqual(ds[0].ncon, 1)
     self.assertEqual(ds[1].ncon, 0)
 
+  def test_get_data_into(self):
+    """Test that get_data_into correctly populates an MjData."""
+
+    m = mujoco.MjModel.from_xml_string(_MULTIPLE_CONSTRAINTS)
+    d = mujoco.MjData(m)
+    mujoco.mj_step(m, d, 2)
+    dx = mjx.put_data(m, d)
+    d_2 = mujoco.MjData(m)
+    mjx.get_data_into(d_2, m, dx)
+
+    # check a few fields
+    np.testing.assert_allclose(d_2.qpos, d.qpos)
+    np.testing.assert_allclose(d_2.xpos, d.xpos)
+    np.testing.assert_allclose(d_2.qM, d.qM)
+
+    # only 1 contact active
+    self.assertEqual(d_2.contact.dist.shape, (1,))
+    self.assertEqual(d_2.ncon, 1)
+    np.testing.assert_allclose(d_2.contact.dist, d.contact.dist)
+    self.assertEqual(d_2.contact.frame.shape, (1, 9))
+    np.testing.assert_allclose(d_2.contact.frame, d.contact.frame)
 
 if __name__ == '__main__':
   absltest.main()

mjx/mujoco/mjx/benchmark/__init__.py

@@ -1,14 +0,0 @@
-# Copyright 2023 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.
-# ==============================================================================

mjx/mujoco/mjx/benchmark/benchmark.py

@@ -1,109 +0,0 @@
-# Copyright 2023 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.
-# ==============================================================================
-"""Run benchmarks on various devices."""
-
-import sys
-import time
-
-from absl import flags
-from etils import epath
-import google_benchmark as benchmark
-import jax
-from jax import numpy as jp
-import mujoco
-from mujoco import mjx
-
-FLAGS = flags.FLAGS
-
-flags.DEFINE_string('mjcf', None, 'path to model', required=True)
-flags.DEFINE_integer('step_count', 1000, 'number of steps per rollout')
-flags.DEFINE_integer('batch_size', 1024, 'number of parallel rollouts')
-flags.DEFINE_integer('unroll', 1, 'loop unroll length')
-flags.DEFINE_enum('solver', 'cg', ['cg', 'newton'], 'constraint solver')
-flags.DEFINE_integer('iterations', 1, 'number of solver iterations')
-flags.DEFINE_integer('ls_iterations', 4, 'number of linesearch iterations')
-
-
-def _measure(state, init_fn, step_fn) -> float:
-  """Reports jit time and op time for a function."""
-
-  @jax.pmap
-  def run_batch(seed: jp.ndarray):
-    batch_size = FLAGS.batch_size // jax.device_count()
-    rngs = jax.random.split(jax.random.PRNGKey(seed), batch_size)
-    state = jax.vmap(init_fn)(rngs)
-
-    @jax.vmap
-    def step(state, _):
-      state = step_fn(state)
-      return state, None
-
-    state, _ = jax.lax.scan(
-        step, state, None, length=FLAGS.step_count, unroll=FLAGS.unroll
-    )
-    return state
-
-  # run once to jit
-  beg = time.perf_counter()
-  seed = 0
-  seeds = jp.arange(seed, seed + jax.device_count(), dtype=int)
-  jax.tree_util.tree_map(lambda x: x.block_until_ready(), run_batch(seeds))
-  first_t = time.perf_counter() - beg
-
-  times = []
-  while state:
-    seed += jax.device_count()
-    seeds = jp.arange(seed, seed + jax.device_count(), dtype=int)
-    beg = time.perf_counter()
-    jax.tree_util.tree_map(lambda x: x.block_until_ready(), run_batch(seeds))
-    times.append(time.perf_counter() - beg)
-
-  op_time = jp.mean(jp.array(times))
-  batch_sps = FLAGS.batch_size * FLAGS.step_count / op_time
-
-  state.counters['jit_time'] = first_t - op_time
-  state.counters['batch_sps'] = batch_sps
-
-
-@benchmark.option.unit(benchmark.kSecond)
-def _run(state: benchmark.State):
-  """Benchmark a model."""
-
-  f = epath.resource_path('mujoco.mjx') / 'benchmark/model' / FLAGS.mjcf
-  m = mujoco.MjModel.from_xml_path(f.as_posix())
-  m.opt.solver = {
-      'cg': mujoco.mjtSolver.mjSOL_CG,
-      'newton': mujoco.mjtSolver.mjSOL_NEWTON,
-  }[FLAGS.solver.lower()]
-  m.opt.iterations = FLAGS.iterations
-  m.opt.ls_iterations = FLAGS.ls_iterations
-  m = mjx.device_put(m)
-
-  def init(rng):
-    d = mjx.make_data(m)
-    qvel = 0.01 * jax.random.normal(rng, shape=(m.nv,))
-    d = d.replace(qvel=qvel)
-    return d
-
-  def step(d):
-    return mjx.step(m, d)
-
-  _measure(state, init, step)
-
-
-if __name__ == '__main__':
-  FLAGS(sys.argv)
-  benchmark.register(_run, name=sys.argv[0].split('/')[-1])
-  benchmark.main()

mjx/mujoco/mjx/testspeed.py

@@ -0,0 +1,123 @@
+# Copyright 2023 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.
+# ==============================================================================
+"""Run benchmarks on various devices."""
+
+import time
+from typing import Sequence, Tuple
+
+from absl import app
+from absl import flags
+from etils import epath
+import jax
+import mujoco
+from mujoco import mjx
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('mjcf', None, 'path to model', required=True)
+flags.DEFINE_integer('nstep', 1000, 'number of steps per rollout')
+flags.DEFINE_integer('batch_size', 1024, 'number of parallel rollouts')
+flags.DEFINE_integer('unroll', 1, 'loop unroll length')
+flags.DEFINE_enum('solver', 'cg', ['cg', 'newton'], 'constraint solver')
+flags.DEFINE_integer('iterations', 1, 'number of solver iterations')
+flags.DEFINE_integer('ls_iterations', 4, 'number of linesearch iterations')
+flags.DEFINE_enum('output', 'text', ['text', 'tsv'], 'format to print results')
+
+
+def _measure(fn, *args) -> Tuple[float, float]:
+  """Reports jit time and op time for a function."""
+
+  beg = time.perf_counter()
+  compiled_fn = fn.lower(*args).compile()
+  end = time.perf_counter()
+  jit_time = end - beg
+
+  beg = time.perf_counter()
+  result = compiled_fn(*args)
+  jax.block_until_ready(result)
+  end = time.perf_counter()
+  run_time = end - beg
+
+  return jit_time, run_time
+
+
+def _main(argv: Sequence[str]):
+  """Benchmark a model."""
+
+  f = epath.resource_path('mujoco.mjx') / 'test_data' / FLAGS.mjcf
+  m = mujoco.MjModel.from_xml_path(f.as_posix())
+  m.opt.solver = {
+      'cg': mujoco.mjtSolver.mjSOL_CG,
+      'newton': mujoco.mjtSolver.mjSOL_NEWTON,
+  }[FLAGS.solver.lower()]
+  m.opt.iterations = FLAGS.iterations
+  m.opt.ls_iterations = FLAGS.ls_iterations
+  m = mjx.put_model(m)
+
+  if FLAGS.output == 'text':
+    print(f"Rolling out {FLAGS.nstep} steps at dt = {m.opt.timestep:.3f}...")
+
+  @jax.pmap
+  def init(key):
+    key = jax.random.split(key, FLAGS.batch_size // jax.device_count())
+
+    @jax.vmap
+    def random_init(key):
+      d = mjx.make_data(m)
+      qvel = 0.01 * jax.random.normal(key, shape=(m.nv,))
+      d = d.replace(qvel=qvel)
+      return d
+
+    return random_init(key)
+
+  key = jax.random.split(jax.random.key(0), jax.device_count())
+  d = init(key)
+  jax.block_until_ready(d)
+
+  @jax.pmap
+  def unroll(d):
+
+    @jax.vmap
+    def step(d, _):
+      d = mjx.step(m, d)
+      return d, None
+
+    d, _ = jax.lax.scan(step, d, None, length=FLAGS.nstep, unroll=FLAGS.unroll)
+
+    return d
+
+  jit_time, run_time = _measure(unroll, d)
+  steps = FLAGS.nstep * FLAGS.batch_size
+
+  if FLAGS.output == 'text':
+    print(f"""
+Summary for {FLAGS.batch_size} parallel rollouts
+
+ Total JIT time: {jit_time:.2f} s
+ Total simulation time: {run_time:.2f} s
+ Total steps per second: { steps / run_time:.0f}
+ Total realtime factor: { steps * m.opt.timestep / run_time:.2f} x
+ Total time per step: { 1e6 * run_time / steps:.2f} µs""")
+  elif FLAGS.output == 'tsv':
+    name = argv[0].split('/')[-1].replace('testspeed_', '')
+    print(f"{name}\tjit: {jit_time:.2f}s\tsteps/second: {steps / run_time:.0f}")
+
+
+def main():
+  app.run(_main)
+
+
+if __name__ == '__main__':
+  main()

mjx/mujoco/mjx/viewer.py

@@ -28,7 +28,8 @@ _MODEL_PATH = flags.DEFINE_string('mjcf', None, 'Path to a MuJoCo MJCF file.',
                                   required=True)
 
 
-def main(argv: Sequence[str]) -> None:
+def _main(argv: Sequence[str]) -> None:
+  """Launches MuJoCo passive viewer fed by MJX."""
   if len(argv) > 1:
     raise app.UsageError('Too many command-line arguments.')
 
@@ -37,20 +38,15 @@ def main(argv: Sequence[str]) -> None:
   print(f'Loading model from: {_MODEL_PATH.value}.')
   m = mujoco.MjModel.from_xml_path(_MODEL_PATH.value)
   d = mujoco.MjData(m)
+  mx = mjx.put_model(m)
+  dx = mjx.put_data(m, d)
 
-  mx = mjx.device_put(m)
-  dx = mjx.make_data(mx)
-
-  dt = jax.device_get(mx.opt.timestep)
-  step_fn = jax.jit(mjx.step)
-
-  print(f'JAX default backend: {jax.default_backend()}')
-  print('JIT-compiling the MJX step (this may take a while)...')
+  print(f'Default backend: {jax.default_backend()}')
+  print('JIT-compiling the model physics step...')
   start = time.time()
-  dx = step_fn(mx, dx)
-  mjx.device_get_into(d, dx)
+  step_fn = jax.jit(mjx.step).lower(dx).compile()
   elapsed = time.time() - start
-  print(f'JIT compilation took {elapsed}s.')
+  print(f'Compilation took {elapsed}s.')
 
   with mujoco.viewer.launch_passive(m, d) as v:
     while True:
@@ -72,9 +68,13 @@ def main(argv: Sequence[str]) -> None:
       v.sync()
 
       elapsed = time.time() - start
-      if elapsed < dt:
-        time.sleep(dt - elapsed)
+      if elapsed < m.opt.timestep:
+        time.sleep(m.opt.timestep - elapsed)
+
+
+def main():
+  app.run(_main)
 
 
 if __name__ == '__main__':
-  app.run(main)
+  main()

mjx/pyproject.toml

@@ -36,6 +36,10 @@ dependencies = [
     "trimesh",
 ]
 
+[project.scripts]
+mjx-testspeed = "mujoco.mjx.testspeed:main"
+mjx-viewer = "mujoco.mjx.viewer:main"
+
 [project.urls]
 Homepage = "https://github.com/google-deepmind/mujoco/tree/main/mjx"
 Documentation = "https://mujoco.readthedocs.io/en/3.1.2"