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7210d6f5d0
This allows executing collectives over the gmapped axes. This requires some extra manipulation of the gmapped jaxpr, since gmap exposes a single logical axis name, but evaluates the program using multiple "physical" axes. This also fixes some bugs around handling `multiple_returns` in vmap collective implementation.
129 lines
4.2 KiB
Python
129 lines
4.2 KiB
Python
# Copyright 2020 Google LLC
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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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# https://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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# flake8: noqa
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import functools
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import itertools
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import os
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import unittest
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from unittest import SkipTest, skip, skipIf
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import numpy as np
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from absl.testing import absltest
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from absl.testing import parameterized
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import jax.numpy as jnp
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from jax import test_util as jtu
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from jax import vmap
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from jax import lax
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from jax.experimental.general_map import gmap
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from jax.lib import xla_bridge
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from jax.util import curry
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from jax.config import config
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config.parse_flags_with_absl()
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ignore_gmap_warning = functools.partial(
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jtu.ignore_warning, message="gmap is an experimental.*")
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# TODO(mattjj): de-duplicate setUpModule and tearDownModule with pmap_test.py
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# Run all tests with 8 CPU devices.
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def setUpModule():
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global prev_xla_flags
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prev_xla_flags = os.getenv("XLA_FLAGS")
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flags_str = prev_xla_flags or ""
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# Don't override user-specified device count, or other XLA flags.
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if "xla_force_host_platform_device_count" not in flags_str:
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os.environ["XLA_FLAGS"] = (flags_str +
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" --xla_force_host_platform_device_count=8")
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# Clear any cached backends so new CPU backend will pick up the env var.
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xla_bridge.get_backend.cache_clear()
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# Reset to previous configuration in case other test modules will be run.
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def tearDownModule():
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if prev_xla_flags is None:
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del os.environ["XLA_FLAGS"]
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else:
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os.environ["XLA_FLAGS"] = prev_xla_flags
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xla_bridge.get_backend.cache_clear()
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@curry
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def skip_insufficient_devices(axis_size, fun):
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@functools.wraps(fun)
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def wrapper(*args, schedule, **kwargs):
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for loop, n in schedule:
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approx_n = axis_size if n is None else n
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if loop == 'parallel' and approx_n > xla_bridge.device_count():
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raise SkipTest("this test requires more XLA devices")
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return fun(*args, schedule=schedule, **kwargs)
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return wrapper
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@curry
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def check_default_schedules(cond, fun):
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schedules = [
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('seq', [('sequential', None)]),
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('vec', [('vectorized', None)]),
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('par', [('parallel', None)]),
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('lim_vmap', [('sequential', None), ('vectorized', 2)]),
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('soft_pmap', [('parallel', 2), ('vectorized', None)])
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]
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schedules = [s for s in schedules if cond(s[1])]
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return parameterized.named_parameters(
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{"testcase_name": "_" + name, "schedule": schedule}
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for name, schedule in schedules)(fun)
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class GmapTest(jtu.JaxTestCase):
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@check_default_schedules(lambda _: True)
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@skip_insufficient_devices(8)
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@ignore_gmap_warning()
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@skipIf(not config.omnistaging_enabled,
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"vmap collectives only supported when omnistaging is enabled")
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def testBasicSchedules(self, schedule):
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def f(x):
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return jnp.dot(jnp.sin(x), x.T) * 4 + x
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x = jnp.arange(800).reshape((8, 10, 10))
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self.assertAllClose(gmap(f, schedule)(x), vmap(f)(x))
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@check_default_schedules(lambda s: not any(c[0] == 'sequential' for c in s))
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@skip_insufficient_devices(8)
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@ignore_gmap_warning()
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@skipIf(not config.omnistaging_enabled,
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"vmap collectives only supported when omnistaging is enabled")
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def testAxisName(self, schedule):
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def f(x):
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return x - lax.psum(x, 'i')
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x = jnp.arange(8)
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self.assertAllClose(gmap(f, schedule, axis_name='i')(x),
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vmap(f, axis_name='i')(x))
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@ignore_gmap_warning()
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@skipIf(not config.omnistaging_enabled,
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"vmap collectives only supported when omnistaging is enabled")
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def testAxisName2d(self):
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def f(x):
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return x - lax.psum(x, 'i') + lax.pmax(x, 'j')
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x = jnp.arange(8 * 8).reshape((8, 8))
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s = [('vectorized', None)]
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self.assertAllClose(gmap(gmap(f, s, axis_name='i'), s, axis_name='j')(x),
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vmap(vmap(f, axis_name='i'), axis_name='j')(x))
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if __name__ == '__main__':
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absltest.main(testLoader=jtu.JaxTestLoader())
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