66c48046f1
This is mostly the result of an internal cleanup and formatting pass. PiperOrigin-RevId: 286318018 Change-Id: I8f9e2f7519070035da73f9f24d2fc90864abc51b
95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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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 tensorflow.ops.svd."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import itertools
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_linalg_ops
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from tensorflow.python.ops import linalg_ops
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from tensorflow.python.platform import test
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class SvdOpTest(xla_test.XLATestCase, parameterized.TestCase):
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def _compute_usvt(self, s, u, v):
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m = u.shape[-1]
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n = v.shape[-1]
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if m <= n:
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v = v[..., :m]
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else:
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u = u[..., :n]
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return np.matmul(u * s[..., None, :], np.swapaxes(v, -1, -2))
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def _testSvdCorrectness(self, dtype, shape):
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np.random.seed(1)
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x_np = np.random.uniform(low=-1.0, high=1.0, size=shape).astype(dtype)
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m, n = shape[-2], shape[-1]
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_, s_np, _ = np.linalg.svd(x_np)
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with self.session() as sess:
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x_tf = array_ops.placeholder(dtype)
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with self.test_scope():
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s, u, v = linalg_ops.svd(x_tf, full_matrices=True)
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s_val, u_val, v_val = sess.run([s, u, v], feed_dict={x_tf: x_np})
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u_diff = np.matmul(u_val, np.swapaxes(u_val, -1, -2)) - np.eye(m)
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v_diff = np.matmul(v_val, np.swapaxes(v_val, -1, -2)) - np.eye(n)
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# Check u_val and v_val are orthogonal matrices.
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self.assertLess(np.linalg.norm(u_diff), 1e-2)
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self.assertLess(np.linalg.norm(v_diff), 1e-2)
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# Check that the singular values are correct, i.e., close to the ones from
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# numpy.lingal.svd.
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self.assertLess(np.linalg.norm(s_val - s_np), 1e-2)
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# The tolerance is set based on our tests on numpy's svd. As our tests
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# have batch dimensions and all our operations are on float32, we set the
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# tolerance a bit larger. Numpy's svd calls LAPACK's svd, which operates
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# on double precision.
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self.assertLess(
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np.linalg.norm(self._compute_usvt(s_val, u_val, v_val) - x_np), 2e-2)
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# Check behavior with compute_uv=False. We expect to still see 3 outputs,
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# with a sentinel scalar 0 in the last two outputs.
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with self.test_scope():
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no_uv_s, no_uv_u, no_uv_v = gen_linalg_ops.svd(
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x_tf, full_matrices=True, compute_uv=False)
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no_uv_s_val, no_uv_u_val, no_uv_v_val = sess.run(
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[no_uv_s, no_uv_u, no_uv_v], feed_dict={x_tf: x_np})
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self.assertAllClose(no_uv_s_val, s_val, atol=1e-4, rtol=1e-4)
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self.assertEqual(no_uv_u_val, 0.0)
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self.assertEqual(no_uv_v_val, 0.0)
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SIZES = [1, 2, 5, 10, 32, 64]
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DTYPES = [np.float32]
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PARAMS = itertools.product(SIZES, DTYPES)
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@parameterized.parameters(*PARAMS)
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def testSvd(self, n, dtype):
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for batch_dims in [(), (3,)] + [(3, 2)] * (n < 10):
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self._testSvdCorrectness(dtype, batch_dims + (n, n))
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self._testSvdCorrectness(dtype, batch_dims + (2 * n, n))
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self._testSvdCorrectness(dtype, batch_dims + (n, 2 * n))
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if __name__ == "__main__":
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test.main()
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