6762ca15c4
The session returned by cached_session uses soft placement, something we don't want for XLA_* devices. With soft placement ops lacking XLA kernels silently fall back and run on the CPU, misleading us into thinking we have more test coverage than we actually do. With this test some tests (rightly) start failing because they were testing ops with dtypes the XLA kernels do not support. I've removed these dtypes from the tests. This CL partially addresses b/132430685. It stubs out "cached_session" and "test_session" to raise errors, so we have more confidence that the compiler is being exercised. However, we still use XLA_* devices to exercise XLA, which has a different code path than xla.compile and tpu.rewrite. This needs to be incrementally fixed. PiperOrigin-RevId: 248437673
116 lines
4.4 KiB
Python
116 lines
4.4 KiB
Python
# Copyright 2018 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.math_ops.matrix_inverse."""
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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 linalg_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import test
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class QrOpTest(xla_test.XLATestCase, parameterized.TestCase):
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def AdjustedNorm(self, x):
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"""Computes the norm of matrices in 'x', adjusted for dimension and type."""
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norm = np.linalg.norm(x, axis=(-2, -1))
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return norm / (max(x.shape[-2:]) * np.finfo(x.dtype).eps)
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def CompareOrthogonal(self, x, y, rank):
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# We only compare the first 'rank' orthogonal vectors since the
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# remainder form an arbitrary orthonormal basis for the
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# (row- or column-) null space, whose exact value depends on
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# implementation details. Notice that since we check that the
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# matrices of singular vectors are unitary elsewhere, we do
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# implicitly test that the trailing vectors of x and y span the
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# same space.
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x = x[..., 0:rank]
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y = y[..., 0:rank]
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# Q is only unique up to sign (complex phase factor for complex matrices),
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# so we normalize the sign first.
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sum_of_ratios = np.sum(np.divide(y, x), -2, keepdims=True)
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phases = np.divide(sum_of_ratios, np.abs(sum_of_ratios))
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x *= phases
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self.assertTrue(np.all(self.AdjustedNorm(x - y) < 30.0))
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def CheckApproximation(self, a, q, r):
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# Tests that a ~= q*r.
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precision = self.AdjustedNorm(a - np.matmul(q, r))
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self.assertTrue(np.all(precision < 10.0))
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def CheckUnitary(self, x):
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# Tests that x[...,:,:]^H * x[...,:,:] is close to the identity.
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xx = math_ops.matmul(x, x, adjoint_a=True)
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identity = array_ops.matrix_band_part(array_ops.ones_like(xx), 0, 0)
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precision = self.AdjustedNorm(xx.eval() - self.evaluate(identity))
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self.assertTrue(np.all(precision < 5.0))
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def _test(self, dtype, shape, full_matrices):
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np.random.seed(1)
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x_np = np.random.uniform(
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low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype)
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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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q_tf, r_tf = linalg_ops.qr(x_tf, full_matrices=full_matrices)
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q_tf_val, r_tf_val = sess.run([q_tf, r_tf], feed_dict={x_tf: x_np})
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q_dims = q_tf_val.shape
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np_q = np.ndarray(q_dims, dtype)
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np_q_reshape = np.reshape(np_q, (-1, q_dims[-2], q_dims[-1]))
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new_first_dim = np_q_reshape.shape[0]
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x_reshape = np.reshape(x_np, (-1, x_np.shape[-2], x_np.shape[-1]))
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for i in range(new_first_dim):
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if full_matrices:
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np_q_reshape[i, :, :], _ = np.linalg.qr(
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x_reshape[i, :, :], mode="complete")
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else:
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np_q_reshape[i, :, :], _ = np.linalg.qr(
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x_reshape[i, :, :], mode="reduced")
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np_q = np.reshape(np_q_reshape, q_dims)
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self.CompareOrthogonal(np_q, q_tf_val, min(shape[-2:]))
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self.CheckApproximation(x_np, q_tf_val, r_tf_val)
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self.CheckUnitary(q_tf_val)
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SIZES = [1, 2, 5, 10, 32, 100, 300]
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DTYPES = [np.float32]
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PARAMS = itertools.product(SIZES, SIZES, DTYPES)
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@parameterized.parameters(*PARAMS)
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def testQR(self, rows, cols, dtype):
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# TODO(b/111317468): Test other types.
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for full_matrices in [True, False]:
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# Only tests the (3, 2) case for small numbers of rows/columns.
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for batch_dims in [(), (3,)] + [(3, 2)] * (max(rows, cols) < 10):
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self._test(dtype, batch_dims + (rows, cols), full_matrices)
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def testLarge2000x2000(self):
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self._test(np.float32, (2000, 2000), full_matrices=True)
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if __name__ == "__main__":
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test.main()
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