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24b47318bd
This test started failing when we changed our CI to use L4 GPUs. Using highest precision resolves the problem.
70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
# Copyright 2018 The JAX Authors.
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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 os
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import sys
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import zlib
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from absl.testing import absltest
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from absl.testing import parameterized
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import numpy as np
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import jax
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from jax import random
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import jax.numpy as jnp
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from jax._src import test_util as jtu
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del jtu # Needed for flags
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from examples import kernel_lsq
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sys.path.pop()
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jax.config.parse_flags_with_absl()
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def _CheckShapeAgreement(test_case, init_fun, apply_fun, input_shape):
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jax_rng = random.key(0)
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result_shape, params = init_fun(jax_rng, input_shape)
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result = apply_fun(params, test_case.rng.normal(size=input_shape).astype("float32"))
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test_case.assertEqual(result.shape, result_shape)
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class ExamplesTest(parameterized.TestCase):
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def setUp(self):
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self.rng = np.random.default_rng(zlib.adler32(self.__class__.__name__.encode()))
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def testKernelRegressionGram(self):
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n, d = 100, 20
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xs = self.rng.normal(size=(n, d))
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kernel = lambda x, y: jnp.dot(x, y)
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np.testing.assert_allclose(kernel_lsq.gram(kernel, xs), jnp.dot(xs, xs.T), atol=1E-5)
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@jax.default_matmul_precision("float32")
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def testKernelRegressionTrainAndPredict(self):
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n, d = 100, 20
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truth = self.rng.normal(size=d)
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xs = self.rng.normal(size=(n, d))
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ys = jnp.dot(xs, truth)
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kernel = lambda x, y: jnp.dot(x, y)
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predict = kernel_lsq.train(kernel, xs, ys)
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np.testing.assert_allclose(predict(xs), ys, atol=1e-3, rtol=1e-3)
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if __name__ == "__main__":
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absltest.main()
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