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Enable tests for tf.linalg.matrix_logarithm in eager mode.

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PiperOrigin-RevId: 312757336
Change-Id: I0323132c43830f37bbb2480be700d6c2bc65f175
This commit is contained in:
A. Unique TensorFlower 2020-05-21 15:54:05 -07:00 committed by TensorFlower Gardener
parent e312350702
commit 7221ad6eda
1 changed files with 25 additions and 20 deletions
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45
tensorflow/python/kernel_tests/matrix_logarithm_op_test.py
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@ -23,12 +23,13 @@ import numpy as np
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import stateless_random_ops
from tensorflow.python.ops import variables
from tensorflow.python.ops.linalg import linalg_impl
from tensorflow.python.platform import benchmark
@ -57,7 +58,7 @@ class LogarithmOpTest(test.TestCase):
matrix_batch = np.tile(matrix_batch, [2, 3, 1, 1])
return matrix_batch
@test_util.run_v1_only("b/120545219")
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
def testNonsymmetric(self):
# 2x2 matrices
matrix1 = np.array([[1., 2.], [3., 4.]])
@ -71,7 +72,7 @@ class LogarithmOpTest(test.TestCase):
# Complex batch
self._verifyLogarithmComplex(self._makeBatch(matrix1, matrix2))
@test_util.run_v1_only("b/120545219")
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
def testSymmetricPositiveDefinite(self):
# 2x2 matrices
matrix1 = np.array([[2., 1.], [1., 2.]])
@ -85,27 +86,27 @@ class LogarithmOpTest(test.TestCase):
# Complex batch
self._verifyLogarithmComplex(self._makeBatch(matrix1, matrix2))
@test_util.run_v1_only("b/120545219")
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
def testNonSquareMatrix(self):
# When the logarithm of a non-square matrix is attempted we should return
# an error
with self.assertRaises(ValueError):
with self.assertRaises((ValueError, errors_impl.InvalidArgumentError)):
gen_linalg_ops.matrix_logarithm(
np.array([[1., 2., 3.], [3., 4., 5.]], dtype=np.complex64))
@test_util.run_v1_only("b/120545219")
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
def testWrongDimensions(self):
# The input to the logarithm should be at least a 2-dimensional tensor.
tensor3 = constant_op.constant([1., 2.], dtype=dtypes.complex64)
with self.assertRaises(ValueError):
with self.assertRaises((ValueError, errors_impl.InvalidArgumentError)):
gen_linalg_ops.matrix_logarithm(tensor3)
@test_util.run_v1_only("b/120545219")
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
def testEmpty(self):
self._verifyLogarithmComplex(np.empty([0, 2, 2], dtype=np.complex64))
self._verifyLogarithmComplex(np.empty([2, 0, 0], dtype=np.complex64))
@test_util.run_v1_only("b/120545219")
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
def testRandomSmallAndLargeComplex64(self):
np.random.seed(42)
for batch_dims in [(), (1,), (3,), (2, 2)]:
@ -116,7 +117,7 @@ class LogarithmOpTest(test.TestCase):
size=np.prod(shape)).reshape(shape).astype(np.complex64)
self._verifyLogarithmComplex(matrix)
@test_util.run_v1_only("b/120545219")
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
def testRandomSmallAndLargeComplex128(self):
np.random.seed(42)
for batch_dims in [(), (1,), (3,), (2, 2)]:
@ -127,17 +128,21 @@ class LogarithmOpTest(test.TestCase):
size=np.prod(shape)).reshape(shape).astype(np.complex128)
self._verifyLogarithmComplex(matrix)
@test_util.run_v1_only("b/120545219")
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
def testConcurrentExecutesWithoutError(self):
with self.session(use_gpu=True) as sess:
matrix1 = math_ops.cast(
random_ops.random_normal([5, 5], seed=42), dtypes.complex64)
matrix2 = math_ops.cast(
random_ops.random_normal([5, 5], seed=42), dtypes.complex64)
logm1 = gen_linalg_ops.matrix_logarithm(matrix1)
logm2 = gen_linalg_ops.matrix_logarithm(matrix2)
logm = self.evaluate([logm1, logm2])
self.assertAllEqual(logm[0], logm[1])
matrix_shape = [5, 5]
seed = [42, 24]
matrix1 = math_ops.cast(
stateless_random_ops.stateless_random_normal(matrix_shape, seed=seed),
dtypes.complex64)
matrix2 = math_ops.cast(
stateless_random_ops.stateless_random_normal(matrix_shape, seed=seed),
dtypes.complex64)
self.assertAllEqual(matrix1, matrix2)
logm1 = gen_linalg_ops.matrix_logarithm(matrix1)
logm2 = gen_linalg_ops.matrix_logarithm(matrix2)
logm = self.evaluate([logm1, logm2])
self.assertAllEqual(logm[0], logm[1])
class MatrixLogarithmBenchmark(test.Benchmark):