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Merge pull request #42350 from reedwm:tf32_test_fix

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PiperOrigin-RevId: 328562236
Change-Id: I7c5e7d608e6c5eb3e347e6b928f8968724478555
This commit is contained in:
TensorFlower Gardener 2020-08-26 10:50:55 -07:00
commit 977fa3ed96
43 changed files with 257 additions and 27 deletions
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3
tensorflow/compiler/tests/cholesky_op_test.py
View File

@ -28,7 +28,6 @@ from tensorflow.python.framework import errors
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
@ -61,7 +60,7 @@ class CholeskyOpTest(xla_test.XLATestCase):
dtypes.as_dtype(x.dtype), shape=x.shape)
with self.test_scope():
chol = linalg_ops.cholesky(placeholder)
verification = math_ops.matmul(chol, chol, adjoint_b=True)
verification = test_util.matmul_without_tf32(chol, chol, adjoint_b=True)
self._verifyCholeskyBase(sess, placeholder, x, chol, verification, atol)
def testBasic(self):

4
tensorflow/compiler/tests/matrix_triangular_solve_op_test.py
View File

@ -29,7 +29,6 @@ from tensorflow.python.framework import errors
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
@ -65,7 +64,8 @@ class MatrixTriangularSolveOpTest(xla_test.XLATestCase):
with self.test_scope():
x = linalg_ops.matrix_triangular_solve(
placeholder_a, placeholder_b, lower=lower, adjoint=adjoint)
verification = math_ops.matmul(placeholder_ca, x, adjoint_a=adjoint)
verification = test_util.matmul_without_tf32(
placeholder_ca, x, adjoint_a=adjoint)
self._VerifyTriangularSolveBase(sess, placeholder_a, placeholder_ca,
placeholder_b, a, clean_a, b,
verification, atol)

7
tensorflow/compiler/tests/qr_op_test.py
View File

@ -24,12 +24,17 @@ from absl.testing import parameterized
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
@test_util.run_all_without_tensor_float_32(
"XLA QR op calls matmul. Also, matmul used for verification. Also with "
'TF32, mysterious "Unable to launch cuBLAS gemm" error occasionally occurs')
# TODO(b/165435566): Fix "Unable to launch cuBLAS gemm" error
class QrOpTest(xla_test.XLATestCase, parameterized.TestCase):
def AdjustedNorm(self, x):
@ -73,7 +78,7 @@ class QrOpTest(xla_test.XLATestCase, parameterized.TestCase):
with self.session() as sess:
x_tf = array_ops.placeholder(dtype)
with self.test_scope():
with self.device_scope():
q_tf, r_tf = linalg_ops.qr(x_tf, full_matrices=full_matrices)
q_tf_val, r_tf_val = sess.run([q_tf, r_tf], feed_dict={x_tf: x_np})

13
tensorflow/compiler/tests/xla_test.py
View File

@ -237,8 +237,8 @@ class XLATestCase(test.TestCase):
'test_session not supported on XLATestCase, please use session')
@contextlib.contextmanager
def test_scope(self):
"""Test scope that runs tests on `self.device`.
def device_scope(self):
"""Scope that runs tests on `self.device`.
Yields:
A scope to apply to the operators under test.
@ -246,6 +246,15 @@ class XLATestCase(test.TestCase):
with ops.device('device:{}:0'.format(self.device)):
yield
def test_scope(self):
"""Deprecated alias of `device_scope`.
This should be avoided as the name starts with `test`, so test runners
treat it as a test. This interferes with class decorators that operate on
each test method.
"""
return self.device_scope()
def Benchmark(tf_bench,
builder_fn,

1
tensorflow/compiler/xla/client/lib/BUILD
View File

@ -305,6 +305,7 @@ xla_test(
"//tensorflow/compiler/xla/tests:test_macros_header",
"//tensorflow/compiler/xla/tests:xla_internal_test_main",
"//tensorflow/core:test",
"//tensorflow/core/platform:tf32_utils",
],
)

4
tensorflow/compiler/xla/client/lib/qr_test.cc
View File

@ -27,12 +27,14 @@ limitations under the License.
#include "tensorflow/compiler/xla/tests/test_macros.h"
#include "tensorflow/compiler/xla/xla_data.pb.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/tf32_utils.h"
namespace {
using QrTest = xla::ClientLibraryTestBase;
XLA_TEST_F(QrTest, Simple) {
tensorflow::allow_tf32_execution(false); // Test fails with tf32 allowed
xla::XlaBuilder builder(TestName());
xla::Array2D<float> a_vals({
@ -61,6 +63,7 @@ XLA_TEST_F(QrTest, Simple) {
}
XLA_TEST_F(QrTest, ZeroDiagonal) {
tensorflow::allow_tf32_execution(false); // Test fails with tf32 allowed
xla::XlaBuilder builder(TestName());
xla::Array2D<float> a_vals({
@ -88,6 +91,7 @@ XLA_TEST_F(QrTest, ZeroDiagonal) {
}
XLA_TEST_F(QrTest, SimpleBatched) {
tensorflow::allow_tf32_execution(false); // Test fails with tf32 allowed
xla::XlaBuilder builder(TestName());
xla::Array3D<float> a_vals({

1
tensorflow/compiler/xla/tests/BUILD
View File

@ -2699,5 +2699,6 @@ xla_test(
"//tensorflow/compiler/xla/tests:literal_test_util",
"//tensorflow/compiler/xla/tests:xla_internal_test_main",
"//tensorflow/core:test",
"//tensorflow/core/platform:tf32_utils",
],
)

2
tensorflow/compiler/xla/tests/cholesky_test.cc
View File

@ -30,6 +30,7 @@ limitations under the License.
#include "tensorflow/compiler/xla/tests/test_macros.h"
#include "tensorflow/compiler/xla/types.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/tf32_utils.h"
namespace xla {
namespace {
@ -181,6 +182,7 @@ class RandomCholeskyTest
public ::testing::WithParamInterface<CholeskyTestCase> {};
XLA_TEST_P(RandomCholeskyTest, Random) {
tensorflow::allow_tf32_execution(false); // Test fails with tf32 allowed
XlaBuilder builder(TestName());
auto test_params = GetParam();

1
tensorflow/core/kernels/BUILD
View File

@ -1671,6 +1671,7 @@ tf_cuda_cc_test(
"//tensorflow/core:test_main",
"//tensorflow/core:testlib",
"//tensorflow/core/kernels/image",
"//tensorflow/core/platform:tf32_utils",
"@com_google_absl//absl/algorithm:container",
],
)

2
tensorflow/core/kernels/conv_ops_test.cc
View File

@ -32,6 +32,7 @@ limitations under the License.
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"
#include "tensorflow/core/platform/tf32_utils.h"
#include "tensorflow/core/protobuf/rewriter_config.pb.h"
#include "tensorflow/core/public/session.h"
@ -1038,6 +1039,7 @@ TYPED_TEST_P(FusedConv2DWithBiasOpTest, ExplicitPaddingConvolution) {
#endif
TYPED_TEST_P(FusedConv2DWithBiasOpTest, OneByOneConvolutionAndActivation) {
tensorflow::allow_tf32_execution(false); // Requires full precision Conv2D op
const int filter_size = 1;
const int filter_count = 12;
for (const string& activation : {"Relu", "Relu6", "Elu"}) {

1
tensorflow/python/framework/function_test.py
View File

@ -1592,6 +1592,7 @@ class UnrollLSTMTest(test.TestCase):
self.assertAllClose(mv0, mv2, rtol=1e-4)
self.assertAllClose(mv0, mv3, rtol=1e-4)
@test_util.run_without_tensor_float_32("Calls matmul in custom LSTM function")
def testUnrollLSTMGrad(self):
# Run one step of the unrolled lstm graph.
def RunForwardBackward(mode, cfg=None):

64
tensorflow/python/framework/test_util.py
View File

@ -54,6 +54,7 @@ from tensorflow.python.eager import backprop
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
from tensorflow.python.eager import tape
from tensorflow.python.framework import config
from tensorflow.python.framework import device as pydev
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
@ -70,6 +71,7 @@ from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_util
from tensorflow.python.ops import control_flow_util_v2
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import script_ops
from tensorflow.python.ops import summary_ops_v2
from tensorflow.python.ops import variables
@ -1908,6 +1910,68 @@ def xla_allow_fallback(description): # pylint: disable=unused-argument
return xla_allow_fallback_impl
# The description is just for documentation purposes.
def run_without_tensor_float_32(description): # pylint: disable=unused-argument
"""Execute test without TensorFloat-32 being allowed.
While almost every real-world deep learning model runs fine with
TensorFloat-32 (TF32), many tests use assertAllClose or similar methods. TF32
matmuls typically will cause such methods to fail with the default tolerances.
Args:
description: A description used for documentation purposes, describing why
the test requires TensorFloat-32 to be disallowed.
"""
def decorator(f):
@functools.wraps(f)
def decorated(self, *args, **kwargs):
allowed = config.tensor_float_32_execution_allowed()
try:
config.allow_tensor_float_32_execution(False)
f(self, *args, **kwargs)
finally:
config.allow_tensor_float_32_execution(allowed)
return decorated
return decorator
# The description is just for documentation purposes.
def run_all_without_tensor_float_32(description): # pylint: disable=unused-argument
"""Execute all tests in a class without TensorFloat-32 being allowed."""
return for_all_test_methods(run_without_tensor_float_32, description)
def matmul_without_tf32(a, b, *args, **kwargs):
"""Run matmul, but cast float32 inputs to float64 if TF32 is allowed.
This effectively runs matmul without TensorFloat-32 (TF32). It should only be
used in tests when verifying some other op or functions works correctly, e.g.
to test `tf.linalg.sqrtm` by matrix multiplying the output of the op
by itself. In such cases, the matmul itself is not being tested so it's OK to
run it with higher precision.
If a matmul itself is being tested, or some other op which uses matmul, use
`run_without_tensor_float_32` instead.
Args:
a: First input to tf.linalg.matmul
b: Second input to tf.linalg.matmul
args: Other positional arguments to tf.linalg.matmul
**kwargs: Other keyword arguments to tf.linalg.matmul
"""
if config.tensor_float_32_execution_allowed() and a.dtype == "float32":
a = math_ops.cast(a, "float64")
b = math_ops.cast(b, "float64")
ret = math_ops.matmul(a, b, *args, **kwargs)
return math_ops.cast(ret, a.dtype)
else:
return math_ops.matmul(a, b, *args, **kwargs)
class EagerSessionWarner(object):
def __getattr__(self, attr):

1
tensorflow/python/keras/BUILD
View File

@ -307,6 +307,7 @@ py_library(
deps = [
":backend",
":models",
"//tensorflow/python:config",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:tensor_shape",
"//tensorflow/python:tensor_spec",

2
tensorflow/python/keras/distribute/distribute_strategy_test.py
View File

@ -1605,6 +1605,8 @@ class TestRegularizerLoss(test.TestCase, parameterized.TestCase):
self.assertEqual(-1.0, v)
@testing_utils.run_all_without_tensor_float_32(
'Uses Dense layers, which call matmul')
class TestDistributionStrategyWithKerasModels(test.TestCase,
parameterized.TestCase):

5
tensorflow/python/keras/distribute/keras_dnn_correctness_test.py
View File

@ -24,6 +24,7 @@ from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import distribution_strategy_context
from tensorflow.python.eager import context
from tensorflow.python.keras import backend as K
from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.distribute import keras_correctness_test_base
from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_descent_keras
from tensorflow.python.platform import test
@ -47,6 +48,8 @@ def is_default_strategy(strategy):
return not distribution_strategy_context.has_strategy()
@testing_utils.run_all_without_tensor_float_32(
'Uses Dense layers, which call matmul')
class TestDistributionStrategyDnnCorrectness(
keras_correctness_test_base.TestDistributionStrategyCorrectnessBase):
@ -240,6 +243,8 @@ class SubclassedModel(keras.Model):
return self.dense4(x)
@testing_utils.run_all_without_tensor_float_32(
'Uses Dense layers, which call matmul')
class TestDistributionStrategyDnnCorrectnessWithSubclassedModel(
TestDistributionStrategyDnnCorrectness):

4
tensorflow/python/keras/distribute/keras_image_model_correctness_test.py
View File

@ -21,11 +21,15 @@ import numpy as np
from tensorflow.python import keras
from tensorflow.python.distribute import combinations
from tensorflow.python.eager import context
from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.distribute import keras_correctness_test_base
from tensorflow.python.keras.optimizer_v2 import gradient_descent
from tensorflow.python.platform import test
@testing_utils.run_all_without_tensor_float_32(
'Uses Dense layers, which call matmul. Even if Dense layers run in '
'float64, the test sometimes fails with tf32 enabled for unknown reasons')
class DistributionStrategyCnnCorrectnessTest(
keras_correctness_test_base.TestDistributionStrategyCorrectnessBase):

4
tensorflow/python/keras/distribute/keras_rnn_model_correctness_test.py
View File

@ -69,6 +69,8 @@ class _DistributionStrategyRnnModelCorrectnessTest(
return model
@testing_utils.run_all_without_tensor_float_32(
'Uses Dense layers, which call matmul')
class DistributionStrategyGruModelCorrectnessTest(
_DistributionStrategyRnnModelCorrectnessTest):
@ -88,6 +90,8 @@ class DistributionStrategyGruModelCorrectnessTest(
self.run_correctness_test(distribution, use_numpy, use_validation_data)
@testing_utils.run_all_without_tensor_float_32(
'Uses Dense layers, which call matmul')
class DistributionStrategyLstmModelCorrectnessTest(
_DistributionStrategyRnnModelCorrectnessTest):

3
tensorflow/python/keras/distribute/keras_save_load_test.py
View File

@ -20,10 +20,13 @@ from __future__ import print_function
from tensorflow.python.distribute import combinations
from tensorflow.python.eager import test
from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.distribute import saved_model_test_base as test_base
from tensorflow.python.keras.saving import save
@testing_utils.run_all_without_tensor_float_32(
'Uses Dense layers, which call matmul')
class KerasSaveLoadTest(test_base.TestSavedModelBase):
def setUp(self):

3
tensorflow/python/keras/distribute/saved_model_mixed_api_test.py
View File

@ -26,12 +26,15 @@ from __future__ import print_function
from tensorflow.python.distribute import combinations
from tensorflow.python.eager import test
from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.distribute import saved_model_test_base as test_base
from tensorflow.python.keras.saving import save
_DEFAULT_FUNCTION_KEY = 'serving_default'
@testing_utils.run_all_without_tensor_float_32(
'Uses Dense layers, which call matmul')
class SavedModelSaveAndLoadTest(test_base.TestSavedModelBase):
def setUp(self):

3
tensorflow/python/keras/distribute/saved_model_save_load_test.py
View File

@ -24,6 +24,7 @@ from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import strategy_combinations
from tensorflow.python.eager import test
from tensorflow.python.framework import tensor_spec
from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.distribute import model_combinations
from tensorflow.python.keras.distribute import saved_model_test_base as test_base
from tensorflow.python.ops import array_ops
@ -32,6 +33,8 @@ from tensorflow.python.saved_model import save_options as save_options_lib
from tensorflow.python.saved_model import saved_model
@testing_utils.run_all_without_tensor_float_32(
'Uses Dense layers, which call matmul')
class SavedModelKerasModelTest(test_base.TestSavedModelBase):
def setUp(self):

63
tensorflow/python/keras/testing_utils.py
View File

@ -26,6 +26,7 @@ import numpy as np
from tensorflow.python import tf2
from tensorflow.python.eager import context
from tensorflow.python.framework import config
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
@ -937,3 +938,65 @@ def use_gpu():
"""Uses gpu when requested and available."""
with device(should_use_gpu=True):
yield
def for_all_test_methods(decorator, *args, **kwargs):
"""Generate class-level decorator from given method-level decorator.
It is expected for the given decorator to take some arguments and return
a method that is then called on the test method to produce a decorated
method.
Args:
decorator: The decorator to apply.
*args: Positional arguments
**kwargs: Keyword arguments
Returns: Function that will decorate a given classes test methods with the
decorator.
"""
def all_test_methods_impl(cls):
"""Apply decorator to all test methods in class."""
for name in dir(cls):
value = getattr(cls, name)
if callable(value) and name.startswith('test') and (name !=
'test_session'):
setattr(cls, name, decorator(*args, **kwargs)(value))
return cls
return all_test_methods_impl
# The description is just for documentation purposes.
def run_without_tensor_float_32(description): # pylint: disable=unused-argument
"""Execute test without TensorFloat-32 being allowed.
While almost every real-world deep learning model runs fine with
TensorFloat-32 (TF32), many tests use assertAllClose or similar methods. TF32
matmuls typically will cause such methods to fail with the default tolerances.
Args:
description: A description used for documentation purposes, describing why
the test requires TensorFloat-32 to be disallowed.
"""
def decorator(f):
@functools.wraps(f)
def decorated(self, *args, **kwargs):
allowed = config.tensor_float_32_execution_allowed()
try:
config.allow_tensor_float_32_execution(False)
f(self, *args, **kwargs)
finally:
config.allow_tensor_float_32_execution(allowed)
return decorated
return decorator
# The description is just for documentation purposes.
def run_all_without_tensor_float_32(description): # pylint: disable=unused-argument
"""Execute all tests in a class without TensorFloat-32 being allowed."""
return for_all_test_methods(run_without_tensor_float_32, description)

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

@ -130,6 +130,7 @@ class BatchMatmulOpTest(test.TestCase):
def _GetBatchMatmulOpTest(dtype, adjoint_a, adjoint_b, use_static_shape):
@test_util.run_without_tensor_float_32("Tests batch matmul")
def Test(self):
np.random.seed(42)
self._testNonEmpty(dtype, adjoint_a, adjoint_b, use_static_shape)
@ -141,6 +142,7 @@ def _GetBatchMatmulOpTest(dtype, adjoint_a, adjoint_b, use_static_shape):
def _GetBatchMatmulOpBroadcastingTest(dtype, adjoint_a, adjoint_b,
use_static_shape):
@test_util.run_without_tensor_float_32("Tests batch matmul")
def Test(self):
np.random.seed(42)
self._testBroadcasting(dtype, adjoint_a, adjoint_b, use_static_shape)

6
tensorflow/python/kernel_tests/cholesky_op_test.py
View File

@ -106,7 +106,7 @@ class CholeskyOpTest(test.TestCase):
def _verifyCholesky(self, x):
# Verify that LL^T == x.
chol = linalg_ops.cholesky(x)
verification = math_ops.matmul(chol, chol, adjoint_b=True)
verification = test_util.matmul_without_tf32(chol, chol, adjoint_b=True)
self._verifyCholeskyBase(x, chol, verification)
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
@ -271,8 +271,8 @@ class CholeskyGradTest(test.TestCase):
def Compute(x):
# Turn the random matrix x into a Hermitian matrix by
# computing the quadratic form x * x^H.
a = math_ops.matmul(x, math_ops.conj(
array_ops.matrix_transpose(x))) / shape[0]
a = test_util.matmul_without_tf32(
x, math_ops.conj(array_ops.matrix_transpose(x))) / shape[0]
if batch:
a = array_ops.tile(array_ops.expand_dims(a, 0), [2, 1, 1])
# Finally take the cholesky decomposition of the Hermitian matrix.

4
tensorflow/python/kernel_tests/conv_ops_3d_test.py
View File

@ -48,6 +48,10 @@ def GetTestConfigs():
return test_configs
@test_util.run_all_without_tensor_float_32(
"Tests Conv3d, which in some cases is implemented with a matmul. With "
"tf32, tests fail in some of those cases (and as of August 13 2020, only "
"those cases)")
class Conv3DTest(test.TestCase):
def _DtypesToTest(self, use_gpu):

2
tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py
View File

@ -268,6 +268,8 @@ class DirichletMultinomialTest(test.TestCase):
self.assertAllClose(sample_var_, analytic_var, atol=0.05, rtol=0.)
self.assertAllClose(sample_stddev_, analytic_stddev, atol=0.02, rtol=0.)
@test_util.run_without_tensor_float_32(
"Tests DirichletMultinomial.covariance, which calls matmul")
def testCovariance(self):
# Shape [2]
alpha = [1., 2]

2
tensorflow/python/kernel_tests/distributions/dirichlet_test.py
View File

@ -200,6 +200,8 @@ class DirichletTest(test.TestCase):
self.assertAllClose(sample_var_, analytic_var, atol=0.03, rtol=0.)
self.assertAllClose(sample_stddev_, analytic_stddev, atol=0.02, rtol=0.)
@test_util.run_without_tensor_float_32(
"Calls Dirichlet.covariance, which calls matmul")
def testVariance(self):
alpha = [1., 2, 3]
denominator = np.sum(alpha)**2 * (np.sum(alpha) + 1)

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

@ -35,6 +35,8 @@ from tensorflow.python.platform import benchmark
from tensorflow.python.platform import test
@test_util.run_all_without_tensor_float_32(
'Tests einsum, which sometimes does a matmul with cuBLAS')
class EinsumOpTest(test.TestCase):
def _check(self, s, *input_shapes, **kwargs):
@ -285,6 +287,8 @@ class EinsumOpTest(test.TestCase):
@test_util.run_all_in_graph_and_eager_modes
@test_util.run_all_without_tensor_float_32(
"Tests einsum's gradient, which sometimes does a matmul with cuBLAS")
class EinsumGradTest(test.TestCase):
def _check_gradient(self, s, *input_shapes):

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

@ -945,6 +945,8 @@ class ConvolutionDeltaOrthogonalInitializerTest(test.TestCase):
self.assertAllClose(abs_value, count, rtol=tol, atol=tol)
@test_util.run_all_without_tensor_float_32(
"Tests convolutional_orthogonal_1d, which calls matmul")
class ConvolutionOrthogonal1dInitializerTest(test.TestCase):
@test_util.run_deprecated_v1
@ -1174,6 +1176,8 @@ class ConvolutionOrthogonal2dInitializerTest(test.TestCase):
self.assertAllClose(self.evaluate(ratio), gain, rtol=tol, atol=tol)
@test_util.run_all_without_tensor_float_32(
"Tests convolutional_orthogonal_3d, which calls matmul")
class ConvolutionOrthogonal3dInitializerTest(test.TestCase):
@test_util.run_deprecated_v1

16
tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_ops_test.py
View File

@ -534,7 +534,7 @@ class CSRSparseMatrixOpsTest(test.TestCase):
c_value = self.evaluate(c)
expected_c_value = self.evaluate(
math_ops.conj(math_ops.matmul(a_dense, b)))
math_ops.conj(test_util.matmul_without_tf32(a_dense, b)))
self.assertAllClose(expected_c_value, c_value)
@test_util.run_in_graph_and_eager_modes
@ -576,7 +576,7 @@ class CSRSparseMatrixOpsTest(test.TestCase):
transpose_b=transpose_b,
adjoint_a=adjoint_a,
adjoint_b=adjoint_b)
c_dense_t = math_ops.matmul(
c_dense_t = test_util.matmul_without_tf32(
a_mats,
b_mats,
transpose_a=transpose_a,
@ -640,7 +640,7 @@ class CSRSparseMatrixOpsTest(test.TestCase):
adjoint_b=adjoint_b)
# Example: t(adj(a) . b) = t(b) . conj(a)
c_dense_t = math_ops.matmul(
c_dense_t = test_util.matmul_without_tf32(
math_ops.conj(b_mats) if adjoint_b else b_mats,
math_ops.conj(a_mats) if adjoint_a else a_mats,
transpose_a=not (transpose_b or adjoint_b),
@ -670,7 +670,7 @@ class CSRSparseMatrixOpsTest(test.TestCase):
c_t = sparse_csr_matrix_ops.sparse_matrix_mat_mul(
a_sm, b_mats, conjugate_output=True)
c_dense_t = math_ops.conj(math_ops.matmul(a_mats, b_mats))
c_dense_t = math_ops.conj(test_util.matmul_without_tf32(a_mats, b_mats))
self.assertAllEqual(c_t.shape, c_dense_t.shape)
c_t_value, c_dense_t_value = self.evaluate((c_t, c_dense_t))
@ -772,7 +772,7 @@ class CSRSparseMatrixOpsTest(test.TestCase):
adjoint_b=adjoint_b)
c_sm_dense = sparse_csr_matrix_ops.csr_sparse_matrix_to_dense(
c_sm, dtypes.float32)
c_dense_t = math_ops.matmul(
c_dense_t = test_util.matmul_without_tf32(
a_mats,
b_mats,
transpose_a=transpose_a,
@ -1143,7 +1143,7 @@ class CSRSparseMatrixOpsTest(test.TestCase):
dense_cholesky = sparse_csr_matrix_ops.csr_sparse_matrix_to_dense(
cholesky_sparse_matrices, dtype)
# Compute L * Lh where L is the Sparse Cholesky factor.
verification = math_ops.matmul(
verification = test_util.matmul_without_tf32(
dense_cholesky, array_ops.transpose(dense_cholesky, conjugate=True))
verification = twist_matrix(verification, ordering_amd)
# Assert that input matrix A satisfies A = L * Lh.
@ -1197,7 +1197,7 @@ class CSRSparseMatrixOpsTest(test.TestCase):
cholesky_sparse_matrix, dtype)
# Compute L * Lh.
verification = math_ops.matmul(
verification = test_util.matmul_without_tf32(
dense_cholesky,
array_ops.transpose(dense_cholesky, perm=[0, 2, 1], conjugate=True))
verification = twist_matrix(verification, ordering_amd)
@ -1238,7 +1238,7 @@ class CSRSparseMatrixOpsTest(test.TestCase):
cholesky_sparse_matrix, dtypes.float32)
# Compute L * Lh.
verification = math_ops.matmul(
verification = test_util.matmul_without_tf32(
dense_cholesky, array_ops.transpose(dense_cholesky, perm=[0, 2, 1]))
verification = twist_matrix(verification, ordering_amd)
verification_values = self.evaluate(verification)

6
tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_test.py
View File

@ -162,7 +162,7 @@ class SparseMatrixMatmulTest(test.TestCase):
1.j * np.random.randn(*dense_shape_b))).astype(dtype)
a_sm = sparse_csr_matrix_ops.CSRSparseMatrix(a_mats)
b_sm = sparse_csr_matrix_ops.CSRSparseMatrix(b_mats)
c_dense = math_ops.matmul(
c_dense = test_util.matmul_without_tf32(
a_mats,
b_mats,
transpose_a=transpose_a,
@ -202,7 +202,7 @@ class SparseMatrixMatmulTest(test.TestCase):
b_mats = (np.random.randn(*dense_shape_b) +
1.j * np.random.randn(*dense_shape_b)).astype(dtype)
a_sm = sparse_csr_matrix_ops.CSRSparseMatrix(a_mats)
c_dense = math_ops.matmul(
c_dense = test_util.matmul_without_tf32(
a_mats,
b_mats,
transpose_a=transpose_a,
@ -240,7 +240,7 @@ class SparseMatrixMatmulTest(test.TestCase):
b_mats = sparsify((np.random.randn(*dense_shape_b) +
1.j * np.random.randn(*dense_shape_b))).astype(dtype)
b_sm = sparse_csr_matrix_ops.CSRSparseMatrix(b_mats)
c_dense = math_ops.matmul(
c_dense = test_util.matmul_without_tf32(
a_mats,
b_mats,
transpose_a=transpose_a,

13
tensorflow/python/kernel_tests/linalg_grad_test.py
View File

@ -63,6 +63,9 @@ def _GetMatrixUnaryFunctorGradientTest(functor_, dtype_, shape_, **kwargs_):
@test_util.enable_control_flow_v2
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
@test_util.run_without_tensor_float_32(
'Tests `tf.linalg.expm`, which call matmul. Additionally, calls ops '
'which do matmul in their gradient, such as MatrixSolve.')
def Test(self):
def RandomInput():
@ -102,6 +105,16 @@ def _GetMatrixBinaryFunctorGradientTest(functor_,
**kwargs_):
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
@test_util.run_without_tensor_float_32(
'Tests `tf.linalg.lstsq`, which call matmul. Additionally, calls ops '
'which do matmul in their gradient, such as MatrixSolveLs.')
# TODO(b/164254522): With tf32, some tests fails with extremely high absolute
# and relative differences when calling assertAllClose. For example, the test
# test_MatrixSolveLsGradient_float32_10_10_1e-06 of class
# MatrixBinaryFunctorGradientTest fails with a max absolute difference of
# 0.883 and a max relative difference of 736892. We should consider disabling
# tf32 within `tf.linalg.lstsq and perhaps other linear algebra functions,
# even if tf32 is allowed globally.
def Test(self):
def RandomInput():

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

@ -91,7 +91,7 @@ class LuOpTest(test.TestCase):
# Prepare the upper factor.
upper = array_ops.matrix_band_part(lu, 0, -1)
verification = math_ops.matmul(lower, upper)
verification = test_util.matmul_without_tf32(lower, upper)
# Permute the rows of product of the Cholesky factors.
if num_rows > 0:

1
tensorflow/python/kernel_tests/matmul_op_test.py
View File

@ -70,6 +70,7 @@ class MatMulTest(test_lib.TestCase):
def _GetMatMulTest(a_np_, b_np_, use_static_shape_, **kwargs_):
@test_util.run_without_tensor_float_32("Tests matmul")
def Test(self):
np_val = np.matrix(a_np_) * np.matrix(b_np_)

3
tensorflow/python/kernel_tests/matrix_inverse_op_test.py
View File

@ -26,7 +26,6 @@ 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 linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import benchmark
@ -41,7 +40,7 @@ class InverseOpTest(test.TestCase):
with self.cached_session(use_gpu=True):
# Verify that x^{-1} * x == Identity matrix.
inv = linalg_ops.matrix_inverse(y, adjoint=adjoint)
tf_ans = math_ops.matmul(inv, y, adjoint_b=adjoint)
tf_ans = test_util.matmul_without_tf32(inv, y, adjoint_b=adjoint)
np_ans = np.identity(y.shape[-1])
if x.ndim > 2:
tiling = list(y.shape)

3
tensorflow/python/kernel_tests/matrix_square_root_op_test.py
View File

@ -29,6 +29,7 @@ from tensorflow.python.ops import stateless_random_ops
from tensorflow.python.platform import test
@test_util.run_all_without_tensor_float_32
class SquareRootOpTest(test.TestCase):
def _verifySquareRoot(self, matrix, np_type):
@ -36,7 +37,7 @@ class SquareRootOpTest(test.TestCase):
# Verify that matmul(sqrtm(A), sqrtm(A)) = A
sqrt = gen_linalg_ops.matrix_square_root(matrix)
square = math_ops.matmul(sqrt, sqrt)
square = test_util.matmul_without_tf32(sqrt, sqrt)
self.assertShapeEqual(matrix, square)
self.assertAllClose(matrix, square, rtol=1e-4, atol=1e-3)

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

@ -200,6 +200,8 @@ def _GetQrGradOpTest(dtype_, shape_, full_matrices_):
return a
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
@test_util.run_without_tensor_float_32("Tests Qr gradient, which calls matmul"
)
def Test(self):
np.random.seed(42)
# Optimal stepsize for central difference is O(epsilon^{1/3}).

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

@ -3062,6 +3062,8 @@ class RNNCellTest(test.TestCase, parameterized.TestCase):
@test_util.run_all_in_graph_and_eager_modes
@test_util.run_all_without_tensor_float_32(
"Uses an LSTMCell, which calls matmul")
class DropoutWrapperTest(test.TestCase, parameterized.TestCase):
def _testDropoutWrapper(self,

5
tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py
View File

@ -38,6 +38,7 @@ def _AddTest(test_class, op_name, testcase_name, fn):
setattr(test_class, test_name, fn)
@test_util.run_all_without_tensor_float_32
class SelfAdjointEigTest(test.TestCase):
@test_util.run_deprecated_v1
@ -160,8 +161,8 @@ def _GetSelfAdjointEigTest(dtype_, shape_, compute_v_):
tf_e, tf_v = linalg_ops.self_adjoint_eig(constant_op.constant(a))
# Check that V*diag(E)*V^T is close to A.
a_ev = math_ops.matmul(
math_ops.matmul(tf_v, array_ops.matrix_diag(tf_e)),
a_ev = test_util.matmul_without_tf32(
test_util.matmul_without_tf32(tf_v, array_ops.matrix_diag(tf_e)),
tf_v,
adjoint_b=True)
self.assertAllClose(self.evaluate(a_ev), a, atol=atol)

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

@ -165,6 +165,7 @@ def _get_tensordot_tests(dtype_, rank_a_, rank_b_, num_dims_, dynamic_shape_):
return a, b, a_dims, b_dims
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
@test_util.run_without_tensor_float_32("Tests tensordot, which calls matmul")
def test_tensordot(self):
if dynamic_shape_ and context.executing_eagerly():
self.skipTest("Placeholders not support in eager mode")
@ -196,6 +197,7 @@ def _get_tensordot_tests(dtype_, rank_a_, rank_b_, num_dims_, dynamic_shape_):
self.assertAllEqual(tf_ans.shape, np_ans.shape)
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
@test_util.run_without_tensor_float_32("Tests tensordot, which calls matmul")
def test_tensordot_scalar_axes(self):
if dynamic_shape_ and context.executing_eagerly():
self.skipTest("Placeholders not support in eager mode")

4
tensorflow/python/ops/image_ops_test.py
View File

@ -97,6 +97,8 @@ class RGBToHSVTest(test_util.TensorFlowTestCase):
class RGBToYIQTest(test_util.TensorFlowTestCase):
@test_util.run_without_tensor_float_32(
"Calls rgb_to_yiq and yiq_to_rgb, which use matmul")
def testBatch(self):
# Build an arbitrary RGB image
np.random.seed(7)
@ -127,6 +129,8 @@ class RGBToYIQTest(test_util.TensorFlowTestCase):
class RGBToYUVTest(test_util.TensorFlowTestCase):
@test_util.run_without_tensor_float_32(
"Calls rgb_to_yuv and yuv_to_rgb, which use matmul")
def testBatch(self):
# Build an arbitrary RGB image
np.random.seed(7)

2
tensorflow/python/ops/nn_test.py
View File

@ -541,6 +541,8 @@ class DropoutTest(test_lib.TestCase):
_ = nn_ops.dropout(x, 0.5)
@test_util.run_all_without_tensor_float_32(
"Tests _compute_sampled_logits and related functions, which call matmul")
class ComputeSampledLogitsTest(test_lib.TestCase):
def setUp(self):

6
tensorflow/python/ops/parallel_for/math_test.py
View File

@ -261,6 +261,9 @@ class MathTest(PForTestCase, parameterized.TestCase):
self._test_loop_fn(loop_fn, 4)
@test_util.run_without_tensor_float_32(
"Calls matmul in parallel for-loop and compares result to calling matmul "
"in sequential for-loop")
def test_matmul(self):
for tr_a in (True, False):
for tr_b in (True, False):
@ -745,6 +748,9 @@ class LinalgTest(PForTestCase):
self._test_loop_fn(loop_fn, 2)
@test_util.run_without_tensor_float_32(
"Calls einsum in parallel for-loop and compares result to calling einsum "
"in sequential for-loop")
def test_einsum(self):
b = 10
x_series = random_ops.random_uniform([b, 9, 9])

2
tensorflow/python/ops/special_math_ops_test.py
View File

@ -635,6 +635,8 @@ class BesselTest(test.TestCase, parameterized.TestCase):
@test_util.run_all_in_graph_and_eager_modes
@test_util.run_all_without_tensor_float_32(
'Tests einsum, which sometimes does a matmul with cuBLAS')
class EinsumTest(test.TestCase):
def _check(self, s, *input_shapes, **kwargs):