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https://github.com/ROCm/jax.git
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fffdb2daa8
* Make check_dtypes, atol, and rtol keyword-only arguments in jax.test_util APIs. Default to check_dtypes=True. Remove explicit usages of check_dtypes=True from tests. This mostly just removes visual noise from tests. Testing for exact type equality is the sensible default, although there are cases where opting out makes sense. No functional changes intended. * Fix a number of lax reference implementations to preserve types.
96 lines
3.0 KiB
Python
96 lines
3.0 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 np
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from jax.config import config
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from jax.lib import xla_client
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import jax.test_util
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import numpy as onp
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config.parse_flags_with_absl()
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FLAGS = config.FLAGS
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class InfeedTest(jax.test_util.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), np.float32),))
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(z,), _ = lax.infeed(
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token, shape=(jax.ShapedArray((3, 1, 1), np.float32),))
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return x + y + z
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x = onp.float32(1.5)
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y = onp.reshape(onp.arange(12, dtype=onp.float32), (3, 4)) # onp.random.randn(3, 4).astype(onp.float32)
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z = onp.random.randn(3, 1, 1).astype(onp.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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@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), np.float32))
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token = lax.outfeed(token, y + onp.float32(1))
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return lax.tie_in(token, x - 1)
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x = onp.float32(7.5)
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y = onp.random.randn(3, 4).astype(onp.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 + onp.float32(1))
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def testInfeedThenOutfeedInALoop(self):
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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), np.float32))
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return lax.outfeed(token, y * onp.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 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 = onp.random.randn(3, 4).astype(onp.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 * onp.float32(2))
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execution.join()
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if __name__ == '__main__':
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absltest.main()
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