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[TF:XLA] Disable or fix assertions for various tests on XLA.

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PiperOrigin-RevId: 269520699
This commit is contained in:
Tres Popp 2019-09-17 01:59:47 -07:00 committed by TensorFlower Gardener
parent 137de6aad1
commit db3b4c518e
8 changed files with 46 additions and 25 deletions
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2
tensorflow/contrib/distribute/python/BUILD
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@ -87,6 +87,8 @@ cuda_py_test(
"multi_and_single_gpu",
"no_oss", # TODO(b/133330625)
],
# TODO(b/141096229): Find problem with AssignAddVariableOps
xla_enable_strict_auto_jit = False,
)
py_library(

28
tensorflow/contrib/distributions/python/kernel_tests/independent_test.py
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@ -67,8 +67,7 @@ class ProductDistributionTest(test.TestCase):
self.assertEqual([4, 5], log_prob_x.shape)
expected_log_prob_x = stats.norm(loc, scale).logpdf(x_).sum(-1)
self.assertAllClose(expected_log_prob_x, actual_log_prob_x,
rtol=1e-5, atol=0.)
self.assertAllCloseAccordingToType(expected_log_prob_x, actual_log_prob_x)
def testSampleAndLogProbMultivariate(self):
loc = np.float32([[-1., 1], [1, -1]])
@ -91,8 +90,7 @@ class ProductDistributionTest(test.TestCase):
expected_log_prob_x = stats.norm(loc, scale[:, None]).logpdf(
x_).sum(-1).sum(-1)
self.assertAllClose(expected_log_prob_x, actual_log_prob_x,
rtol=1e-6, atol=0.)
self.assertAllCloseAccordingToType(expected_log_prob_x, actual_log_prob_x)
def testSampleConsistentStats(self):
loc = np.float32([[-1., 1], [1, -1]])
@ -121,11 +119,12 @@ class ProductDistributionTest(test.TestCase):
ind.mean(), ind.variance(), ind.stddev(), ind.entropy(), ind.mode(),
])
self.assertAllClose(sample_mean_, actual_mean_, rtol=0.02, atol=0.)
self.assertAllClose(sample_var_, actual_var_, rtol=0.04, atol=0.)
self.assertAllClose(sample_std_, actual_std_, rtol=0.02, atol=0.)
self.assertAllClose(sample_entropy_, actual_entropy_, rtol=0.01, atol=0.)
self.assertAllClose(loc, actual_mode_, rtol=1e-6, atol=0.)
self.assertAllCloseAccordingToType(sample_mean_, actual_mean_, rtol=0.02)
self.assertAllCloseAccordingToType(sample_var_, actual_var_, rtol=0.04)
self.assertAllCloseAccordingToType(sample_std_, actual_std_, rtol=0.02)
self.assertAllCloseAccordingToType(
sample_entropy_, actual_entropy_, rtol=0.01)
self.assertAllCloseAccordingToType(loc, actual_mode_, rtol=1e-6)
def testKLRaises(self):
ind1 = independent_lib.Independent(
@ -173,7 +172,7 @@ class ProductDistributionTest(test.TestCase):
normal_kl = kullback_leibler.kl_divergence(normal1, normal2)
ind_kl = kullback_leibler.kl_divergence(ind1, ind2)
self.assertAllClose(
self.assertAllCloseAccordingToType(
self.evaluate(math_ops.reduce_sum(normal_kl, axis=-1)),
self.evaluate(ind_kl))
@ -196,7 +195,7 @@ class ProductDistributionTest(test.TestCase):
normal_kl = kullback_leibler.kl_divergence(normal1, normal2)
ind_kl = kullback_leibler.kl_divergence(ind1, ind2)
self.assertAllClose(
self.assertAllCloseAccordingToType(
self.evaluate(normal_kl), self.evaluate(ind_kl))
def testKLMultivariateToMultivariate(self):
@ -217,7 +216,7 @@ class ProductDistributionTest(test.TestCase):
mvn_kl = kullback_leibler.kl_divergence(mvn1, mvn2)
ind_kl = kullback_leibler.kl_divergence(ind1, ind2)
self.assertAllClose(
self.assertAllCloseAccordingToType(
self.evaluate(math_ops.reduce_sum(mvn_kl, axis=[-1, -2])),
self.evaluate(ind_kl))
@ -264,9 +263,8 @@ class ProductDistributionTest(test.TestCase):
self.assertAllEqual(image_shape, ind_event_shape)
self.assertAllEqual(sample_shape + batch_shape + image_shape, x_shape)
self.assertAllEqual(sample_shape + batch_shape, log_prob_x_shape)
self.assertAllClose(expected_log_prob(x_, logits),
actual_log_prob_x,
rtol=1e-6, atol=0.)
self.assertAllCloseAccordingToType(
expected_log_prob(x_, logits), actual_log_prob_x)
def testMnistLikeStaticShape(self):
self._testMnistLike(static_shape=True)

3
tensorflow/contrib/distributions/python/kernel_tests/relaxed_bernoulli_test.py
View File

@ -24,6 +24,7 @@ from tensorflow.contrib.distributions.python.ops import relaxed_bernoulli
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 test_util
from tensorflow.python.platform import test
@ -136,6 +137,8 @@ class RelaxedBernoulliTest(test.TestCase):
self.assertAllClose(np.nan, dist.log_prob(0.0).eval())
self.assertAllClose([np.nan], [dist.log_prob(1.0).eval()])
@test_util.disable_xla(
"TODO(b/141092326): prevent negative values in Sigmoid on XLA:CPU")
def testSampleN(self):
"""mean of quantized samples still approximates the Bernoulli mean."""
with self.cached_session():

10
tensorflow/contrib/distributions/python/kernel_tests/wishart_test.py
View File

@ -24,6 +24,7 @@ from tensorflow.contrib import distributions as distributions_lib
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import random_seed
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
@ -98,14 +99,18 @@ class WishartCholeskyTest(test.TestCase):
scale = make_pd(1., 2)
df = 4
w = distributions.WishartCholesky(df, chol(scale))
self.assertAllEqual(chol(wishart_var(df, scale)), w.stddev().eval())
self.assertAllCloseAccordingToType(
chol(wishart_var(df, scale)),
w.stddev().eval())
def testVariance(self):
with self.cached_session():
scale = make_pd(1., 2)
df = 4
w = distributions.WishartCholesky(df, chol(scale))
self.assertAllEqual(wishart_var(df, scale), w.variance().eval())
self.assertAllCloseAccordingToType(
wishart_var(df, scale),
w.variance().eval())
def testSample(self):
with self.cached_session():
@ -318,6 +323,7 @@ class WishartCholeskyTest(test.TestCase):
sess.run(w.event_shape_tensor(),
feed_dict={scale_deferred: [chol_scale, chol_scale]}))
@test_util.disable_xla("XLA cannot assert inside of an op.")
def testValidateArgs(self):
with self.cached_session() as sess:
df_deferred = array_ops.placeholder(dtypes.float32)

14
tensorflow/python/kernel_tests/batch_matmul_op_test.py
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@ -24,6 +24,7 @@ from tensorflow.python import tf2
from tensorflow.python.client import session
from tensorflow.python.compat import compat
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker_v2
from tensorflow.python.ops import math_ops
@ -278,14 +279,19 @@ if __name__ == "__main__":
setattr(
BatchMatmulOpTest,
"testBatchMatmulOp_" + name + "_{}".format(use_static_shape_),
_GetBatchMatmulOpTest(dtype_, adjoint_a_, adjoint_b_,
use_static_shape_))
test_util.xla_allow_fallback(
"TODO(b/134526360): XLA:CPU hasn't implemented int32 dot.")(
_GetBatchMatmulOpTest(dtype_, adjoint_a_, adjoint_b_,
use_static_shape_)))
# Broadcasting is supported only in v2.
setattr(
BatchMatmulOpTest, "testBatchMatmulBroadcasting_" + name +
("_%s" % use_static_shape_),
_GetBatchMatmulOpBroadcastingTest(dtype_, adjoint_a_, adjoint_b_,
use_static_shape_))
test_util.xla_allow_fallback(
"TODO(b/134526360): XLA:CPU hasn't implemented int32 dot.")(
_GetBatchMatmulOpBroadcastingTest(dtype_, adjoint_a_,
adjoint_b_,
use_static_shape_)))
if dtype_ == np.int32:
continue
setattr(BatchMatmulGradientTest, "testBatchMatmulGradient_" + name,

2
tensorflow/python/kernel_tests/conv_ops_test.py
View File

@ -858,7 +858,7 @@ class Conv2DTest(test.TestCase):
self.assertShapeEqual(value, conv)
tf_logging.debug("expected = %s", expected)
tf_logging.debug("actual = %s", value)
self.assertArrayNear(expected, value.flatten(), err)
self.assertAllCloseAccordingToType(expected, value.flatten())
def _CompareBackpropInput(self, input_sizes, filter_sizes, output_sizes,
conv_strides, padding):

4
tensorflow/python/kernel_tests/gather_op_test.py
View File

@ -205,6 +205,10 @@ class GatherTest(test.TestCase, parameterized.TestCase):
gather_t = array_ops.gather(params, indices, axis=axis)
self.assertEqual(None, gather_t.shape)
@test_util.disable_xla(
"Assertion inside an op is not supported in XLA. Instead XLA clamps the "
"index to be in bounds and returns the indexed value there (Don't rely "
"on this behavior).")
def testBadIndicesCPU(self):
with test_util.force_cpu():
params = [[0, 1, 2], [3, 4, 5]]

8
tensorflow/python/kernel_tests/sparse_ops_test.py
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@ -778,12 +778,14 @@ class SparseMathOpsTest(test_util.TensorFlowTestCase):
def _check(self, result_tensor, result_np, input_sp_t):
self.assertTrue(isinstance(result_tensor, sparse_tensor.SparseTensor))
self.assertTrue(isinstance(input_sp_t, sparse_tensor.SparseTensor))
self.assertAllEqual(input_sp_t.indices, result_tensor.indices)
self.assertAllEqual(input_sp_t.dense_shape, result_tensor.dense_shape)
self.assertAllCloseAccordingToType(input_sp_t.indices,
result_tensor.indices)
self.assertAllCloseAccordingToType(input_sp_t.dense_shape,
result_tensor.dense_shape)
res_densified = sparse_ops.sparse_to_dense(
result_tensor.indices, result_tensor.dense_shape, result_tensor.values)
self.assertAllEqual(result_np, res_densified)
self.assertAllCloseAccordingToType(result_np, res_densified)
@test_util.run_deprecated_v1
def testCwiseShapeValidation(self):