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Enable gradient tests for tf.linalg.qr in eager mode. 

PiperOrigin-RevId: 312572186
Change-Id: I4d1e62478fa41b277bd5191210f2da5e5c090653
This commit is contained in:
A. Unique TensorFlower 2020-05-20 15:57:24 -07:00 committed by TensorFlower Gardener
parent 99350ea1f0
commit e28b37be96
1 changed files with 17 additions and 21 deletions
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38
tensorflow/python/kernel_tests/qr_op_test.py
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@ -28,7 +28,7 @@ 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 control_flow_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import gradient_checker_v2
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import stateless_random_ops
@ -175,13 +175,16 @@ class QrGradOpTest(test.TestCase):
def _GetQrGradOpTest(dtype_, shape_, full_matrices_):
@test_util.run_v1_only("b/120545219")
def Test(self):
np.random.seed(42)
def RandomInput():
a = np.random.uniform(low=-1.0, high=1.0, size=shape_).astype(dtype_)
if dtype_ in [np.complex64, np.complex128]:
a += 1j * np.random.uniform(
low=-1.0, high=1.0, size=shape_).astype(dtype_)
return a
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
def Test(self):
np.random.seed(42)
# Optimal stepsize for central difference is O(epsilon^{1/3}).
epsilon = np.finfo(dtype_).eps
delta = 0.1 * epsilon**(1.0 / 3.0)
@ -189,23 +192,16 @@ def _GetQrGradOpTest(dtype_, shape_, full_matrices_):
tol = 3e-2
else:
tol = 1e-6
with self.session(use_gpu=True):
tf_a = constant_op.constant(a)
tf_b = linalg_ops.qr(tf_a, full_matrices=full_matrices_)
for b in tf_b:
x_init = np.random.uniform(
low=-1.0, high=1.0, size=shape_).astype(dtype_)
if dtype_ in [np.complex64, np.complex128]:
x_init += 1j * np.random.uniform(
low=-1.0, high=1.0, size=shape_).astype(dtype_)
theoretical, numerical = gradient_checker.compute_gradient(
tf_a,
tf_a.get_shape().as_list(),
b,
b.get_shape().as_list(),
x_init_value=x_init,
delta=delta)
self.assertAllClose(theoretical, numerical, atol=tol, rtol=tol)
# TODO(b/157171666): Sadly we have to double the computation because
# gradient_checker_v2.compute_gradient expects a list of functions.
funcs = [
lambda a: linalg_ops.qr(a, full_matrices=full_matrices_)[0],
lambda a: linalg_ops.qr(a, full_matrices=full_matrices_)[1]
]
for f in funcs:
theoretical, numerical = gradient_checker_v2.compute_gradient(
f, [RandomInput()], delta=delta)
self.assertAllClose(theoretical, numerical, atol=tol, rtol=tol)
return Test