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4236eb2b59
This change, when enabled, stages out all primitive calls in the dynamic scope of a jitted, pmapped, or control flow function, rather than only staging out based on data dependence. One improvement is that jitted functions can consume less memory, by avoiding instantiating large constants at trace time, and cause less memory fragmentation as well. It also simplifies several internals. See https://github.com/google/jax/pull/3370 fo more information.
102 lines
3.2 KiB
Python
102 lines
3.2 KiB
Python
# Copyright 2019 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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import threading
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from absl.testing import absltest
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import jax
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from jax import lax, numpy as jnp
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from jax import config
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from jax.experimental import host_callback as hcb
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from jax.lib import xla_client
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import jax.test_util as jtu
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import numpy as np
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config.parse_flags_with_absl()
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FLAGS = config.FLAGS
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class InfeedTest(jtu.JaxTestCase):
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def testInfeed(self):
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@jax.jit
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def f(x):
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token = lax.create_token(x)
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(y,), token = lax.infeed(
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token, shape=(jax.ShapedArray((3, 4), jnp.float32),))
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(z,), _ = lax.infeed(
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token, shape=(jax.ShapedArray((3, 1, 1), jnp.float32),))
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return x + y + z
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x = np.float32(1.5)
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y = np.reshape(np.arange(12, dtype=np.float32), (3, 4)) # np.random.randn(3, 4).astype(np.float32)
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z = np.random.randn(3, 1, 1).astype(np.float32)
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device = jax.local_devices()[0]
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device.transfer_to_infeed((y,))
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device.transfer_to_infeed((z,))
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self.assertAllClose(f(x), x + y + z)
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def testInfeedThenOutfeed(self):
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hcb.stop_outfeed_receiver()
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@jax.jit
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def f(x):
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token = lax.create_token(x)
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y, token = lax.infeed(
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token, shape=jax.ShapedArray((3, 4), jnp.float32))
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token = lax.outfeed(token, y + np.float32(1))
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return x - 1 if config.omnistaging_enabled else lax.tie_in(token, x - 1)
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x = np.float32(7.5)
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y = np.random.randn(3, 4).astype(np.float32)
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execution = threading.Thread(target=lambda: f(x))
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execution.start()
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device = jax.local_devices()[0]
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device.transfer_to_infeed((y,))
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out, = device.transfer_from_outfeed(
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xla_client.shape_from_pyval((y,)).with_major_to_minor_layout_if_absent())
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execution.join()
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self.assertAllClose(out, y + np.float32(1))
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def testInfeedThenOutfeedInALoop(self):
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hcb.stop_outfeed_receiver()
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def doubler(_, token):
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y, token = lax.infeed(
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token, shape=jax.ShapedArray((3, 4), jnp.float32))
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return lax.outfeed(token, y * np.float32(2))
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@jax.jit
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def f(n):
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token = lax.create_token(n)
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token = lax.fori_loop(0, n, doubler, token)
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return n if config.omnistaging_enabled else lax.tie_in(token, n)
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device = jax.local_devices()[0]
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n = 10
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execution = threading.Thread(target=lambda: f(n))
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execution.start()
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for _ in range(n):
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x = np.random.randn(3, 4).astype(np.float32)
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device.transfer_to_infeed((x,))
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y, = device.transfer_from_outfeed(xla_client.shape_from_pyval((x,))
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.with_major_to_minor_layout_if_absent())
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self.assertAllClose(y, x * np.float32(2))
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execution.join()
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if __name__ == '__main__':
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absltest.main(testLoader=jtu.JaxTestLoader())
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