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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tensorflow.ops.linalg_grad."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.linalg import linalg_impl
from tensorflow.python.platform import test as test_lib
def _AddTest(test, op_name, testcase_name, fn):
test_name = '_'.join(['test', op_name, testcase_name])
if hasattr(test, test_name):
raise RuntimeError('Test %s defined more than once' % test_name)
setattr(test, test_name, fn)
class ShapeTest(test_lib.TestCase):
def testBatchGradientUnknownSize(self):
with self.cached_session():
batch_size = constant_op.constant(3)
matrix_size = constant_op.constant(4)
batch_identity = array_ops.tile(
array_ops.expand_dims(
array_ops.diag(array_ops.ones([matrix_size])), 0),
[batch_size, 1, 1])
determinants = linalg_ops.matrix_determinant(batch_identity)
reduced = math_ops.reduce_sum(determinants)
sum_grad = gradients_impl.gradients(reduced, batch_identity)[0]
self.assertAllClose(batch_identity.eval(), sum_grad.eval())
class MatrixUnaryFunctorGradientTest(test_lib.TestCase):
pass # Filled in below
def _GetMatrixUnaryFunctorGradientTest(functor_, dtype_, shape_, **kwargs_):
def Test(self):
with self.test_session(use_gpu=True):
np.random.seed(1)
a_np = np.random.uniform(
low=-1.0, high=1.0,
size=np.prod(shape_)).reshape(shape_).astype(dtype_)
a = constant_op.constant(a_np)
b = functor_(a, **kwargs_)
# Optimal stepsize for central difference is O(epsilon^{1/3}).
epsilon = np.finfo(dtype_).eps
delta = epsilon**(1.0 / 3.0)
# tolerance obtained by looking at actual differences using
# np.linalg.norm(theoretical-numerical, np.inf) on -mavx build
tol = 1e-6 if dtype_ == np.float64 else 0.05
theoretical, numerical = gradient_checker.compute_gradient(
a,
a.get_shape().as_list(),
b,
b.get_shape().as_list(),
x_init_value=a_np,
delta=delta)
self.assertAllClose(theoretical, numerical, atol=tol, rtol=tol)
return Test
class MatrixBinaryFunctorGradientTest(test_lib.TestCase):
pass # Filled in below
def _GetMatrixBinaryFunctorGradientTest(functor_,
dtype_,
shape_,
float32_tol_fudge=1.0,
**kwargs_):
def Test(self):
# TODO(rmlarsen): Debug illegal address bug on CUDA and re-enable
# GPU test for matrix_solve.
use_gpu = False if functor_ == linalg_ops.matrix_solve else True
with self.test_session(use_gpu=use_gpu):
np.random.seed(1)
a_np = np.random.uniform(
low=-1.0, high=1.0,
size=np.prod(shape_)).reshape(shape_).astype(dtype_)
a = constant_op.constant(a_np)
b_np = np.random.uniform(
low=-1.0, high=1.0,
size=np.prod(shape_)).reshape(shape_).astype(dtype_)
b = constant_op.constant(b_np)
c = functor_(a, b, **kwargs_)
# Optimal stepsize for central difference is O(epsilon^{1/3}).
epsilon = np.finfo(dtype_).eps
delta = epsilon**(1.0 / 3.0)
# tolerance obtained by looking at actual differences using
# np.linalg.norm(theoretical-numerical, np.inf) on -mavx build
tol = 1e-6 if dtype_ == np.float64 else float32_tol_fudge * 0.05
# The gradients for a and b may be of very different magnitudes,
# so to not get spurious failures we test them separately.
for factor, factor_init in [a, a_np], [b, b_np]:
theoretical, numerical = gradient_checker.compute_gradient(
factor,
factor.get_shape().as_list(),
c,
c.get_shape().as_list(),
x_init_value=factor_init,
delta=delta)
self.assertAllClose(theoretical, numerical, atol=tol, rtol=tol)
return Test
if __name__ == '__main__':
# Tests for gradients of binary matrix operations.
for dtype in np.float32, np.float64:
for size in 2, 5, 10:
# We skip the rank 4, size 10 case: it is slow and conceptually covered
# by the other cases.
for extra in [(), (2,), (3,)] + [(3, 2)] * (size < 10):
for adjoint in False, True:
shape = extra + (size, size)
name = '%s_%s_adj_%s' % (dtype.__name__, '_'.join(map(str, shape)),
str(adjoint))
_AddTest(MatrixBinaryFunctorGradientTest, 'MatrixSolveGradient', name,
_GetMatrixBinaryFunctorGradientTest(
linalg_ops.matrix_solve, dtype, shape, adjoint=adjoint))
for lower in True, False:
name = '%s_low_%s' % (name, lower)
_AddTest(MatrixBinaryFunctorGradientTest,
'MatrixTriangularSolveGradient', name,
_GetMatrixBinaryFunctorGradientTest(
linalg_ops.matrix_triangular_solve,
dtype,
shape,
float32_tol_fudge=4.0,
adjoint=adjoint,
lower=lower))
# Tests for gradients of unary matrix operations.
for dtype in np.float32, np.float64:
for size in 2, 5, 10:
# We skip the rank 4, size 10 case: it is slow and conceptually covered
# by the other cases.
for extra in [(), (2,), (3,)] + [(3, 2)] * (size < 10):
shape = extra + (size, size)
name = '%s_%s' % (dtype.__name__, '_'.join(map(str, shape)))
_AddTest(MatrixUnaryFunctorGradientTest, 'MatrixInverseGradient', name,
_GetMatrixUnaryFunctorGradientTest(linalg_ops.matrix_inverse,
dtype, shape))
_AddTest(MatrixUnaryFunctorGradientTest, 'MatrixExponentialGradient',
name,
_GetMatrixUnaryFunctorGradientTest(
linalg_impl.matrix_exponential, dtype, shape))
_AddTest(
MatrixUnaryFunctorGradientTest, 'MatrixDeterminantGradient', name,
_GetMatrixUnaryFunctorGradientTest(linalg_ops.matrix_determinant,
dtype, shape))
_AddTest(
MatrixUnaryFunctorGradientTest, 'LogMatrixDeterminantGradient',
name,
_GetMatrixUnaryFunctorGradientTest(
lambda x: linalg_ops.log_matrix_determinant(x)[1],
dtype, shape))
# Tests for gradients of matrix_solve_ls
for dtype in np.float32, np.float64:
for rows in 2, 5, 10:
for cols in 2, 5, 10:
for l2_regularization in 1e-6, 0.001, 1.0:
shape = (rows, cols)
name = '%s_%s_%s' % (dtype.__name__, '_'.join(map(str, shape)),
l2_regularization)
_AddTest(
MatrixBinaryFunctorGradientTest,
'MatrixSolveLsGradient',
name,
# pylint: disable=long-lambda,g-long-lambda
_GetMatrixBinaryFunctorGradientTest(
(lambda a, b, l=l2_regularization:
linalg_ops.matrix_solve_ls(a, b, l)),
dtype,
shape,
float32_tol_fudge=4.0))
test_lib.main()
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