Sam Haves
dade2d8c3e
Previously mjx dataclasses would return np.ndarray as bytes for jax tracing to hash since they are not inherently hashable. This meant that any tracing that was cached would hold on to a copy of the numpy arrays in the dataclass. Children such as mjx.Model that store large numpy arrays would end up duplicating that data 3+ times in some cases. This eliminates O(N * array_size) memory duplication across N cached pytree traces, saving a lot of memory on models with heavy mesh/texture data. PiperOrigin-RevId: 880985898 Change-Id: I58d4e91fda4112e4c633818f92907d20138e962a
118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
# Copyright 2025 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for custom PyTreeNode object."""
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from absl.testing import absltest
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import jax
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from jax import numpy as jp
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from mujoco.mjx._src import dataclasses
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import numpy as np
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class Obj(dataclasses.PyTreeNode):
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a: int
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b: np.ndarray
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c: tuple[int, ...]
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d: tuple[np.ndarray, ...]
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e: jax.Array
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f: tuple[jax.Array, ...]
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class LargeArrayNode(dataclasses.PyTreeNode):
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array_a: np.ndarray
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array_b: np.ndarray
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array_c: np.ndarray
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class DataclassesTest(absltest.TestCase):
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def test_pytree_structure(self):
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obj = Obj(
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a=1,
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b=np.array([1, 2, 3]),
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c=(4, 5, 6),
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d=(np.array([7, 8]), np.array([9, 10])),
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e=jax.numpy.array([11, 12]),
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f=(jax.numpy.array([13, 14]), jax.numpy.array([15, 16])),
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)
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data, meta = jax.tree_util.tree_flatten_with_path(obj)
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# data fields
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self.assertLen(data, 3)
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self.assertEqual(data[0][0][0].name, 'e')
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np.testing.assert_array_equal(data[0][1], jp.array([11, 12]))
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self.assertEqual(data[1][0][0].name, 'f')
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np.testing.assert_array_equal(data[1][1], jp.array([13, 14]))
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self.assertEqual(data[2][0][0].name, 'f')
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np.testing.assert_array_equal(data[2][1], jp.array([15, 16]))
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# meta fields
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unflattened_meta = meta.unflatten([x[1] for x in data])
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self.assertEqual(unflattened_meta.a, 1)
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np.testing.assert_array_equal(unflattened_meta.b, np.array([1, 2, 3]))
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self.assertEqual(unflattened_meta.c, (4, 5, 6))
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np.testing.assert_array_equal(unflattened_meta.d[0], np.array([7, 8]))
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np.testing.assert_array_equal(unflattened_meta.d[1], np.array([9, 10]))
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# ensure hashable meta
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hash(meta)
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def test_metadata_equality(self):
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array_size = 1_000
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data = np.random.rand(array_size, 3).astype(np.float32)
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tex = np.random.randint(0, 255, (1024, 1024, 3), dtype=np.uint8)
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obj1 = LargeArrayNode(
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array_a=data.copy(),
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array_b=data.copy(),
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array_c=tex.copy(),
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)
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obj2 = LargeArrayNode(
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array_a=data.copy(),
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array_b=data.copy(),
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array_c=tex.copy(),
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)
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_, meta1 = jax.tree_util.tree_flatten(obj1)
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_, meta2 = jax.tree_util.tree_flatten(obj2)
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self.assertEqual(
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meta1,
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meta2,
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'Two objects with identical numpy data should produce equal pytree'
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' metadata (same trace cache key).',
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)
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def test_metadata_does_not_copy_arrays(self):
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obj = LargeArrayNode(
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array_a=np.random.rand(100, 3).astype(np.float32),
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array_b=np.random.rand(100, 3).astype(np.float32),
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array_c=np.random.randint(0, 255, (16, 16, 3), dtype=np.uint8),
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)
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leaves, meta = jax.tree_util.tree_flatten(obj)
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reconstructed = meta.unflatten(leaves)
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self.assertIs(
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reconstructed.array_a,
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obj.array_a,
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'Metadata should reference the original array.',
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)
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self.assertIs(reconstructed.array_c, obj.array_c)
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if __name__ == '__main__':
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absltest.main()
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