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Update (non-gradient) tests for tf.linalg.qr to also run in eager mode.

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PiperOrigin-RevId: 311818375
Change-Id: I70d721522f4060f1a4997c271837fbd6f3629e9f
This commit is contained in:
A. Unique TensorFlower 2020-05-15 16:05:39 -07:00 committed by TensorFlower Gardener
parent eb07fd848a
commit 763710df31
1 changed files with 33 additions and 28 deletions
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45
tensorflow/python/kernel_tests/qr_op_test.py
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@ -20,9 +20,10 @@ from __future__ import print_function
import numpy as np import numpy as np
from tensorflow.python import tf2
from tensorflow.python.client import session from tensorflow.python.client import session
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op from tensorflow.python.framework import constant_op
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import ops from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops from tensorflow.python.ops import array_ops
@ -30,7 +31,7 @@ from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_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 import variables
from tensorflow.python.platform import benchmark from tensorflow.python.platform import benchmark
from tensorflow.python.platform import test from tensorflow.python.platform import test
@ -45,35 +46,37 @@ def _AddTest(test_class, op_name, testcase_name, fn):
class QrOpTest(test.TestCase): class QrOpTest(test.TestCase):
@test_util.run_v1_only("b/120545219") @test_util.run_in_graph_and_eager_modes(use_gpu=True)
def testWrongDimensions(self): def testWrongDimensions(self):
# The input to qr should be a tensor of at least rank 2. # The input to svd should be a tensor of at least rank 2.
scalar = constant_op.constant(1.) scalar = constant_op.constant(1.)
with self.assertRaisesRegexp(ValueError, with self.assertRaisesRegexp((ValueError, errors_impl.InvalidArgumentError),
"Shape must be at least rank 2 but is rank 0"): "rank.* 2.*0"):
linalg_ops.qr(scalar) linalg_ops.qr(scalar)
vector = constant_op.constant([1., 2.]) vector = constant_op.constant([1., 2.])
with self.assertRaisesRegexp(ValueError, with self.assertRaisesRegexp((ValueError, errors_impl.InvalidArgumentError),
"Shape must be at least rank 2 but is rank 1"): "rank.* 2.*1"):
linalg_ops.qr(vector) linalg_ops.qr(vector)
@test_util.run_deprecated_v1 @test_util.run_in_graph_and_eager_modes(use_gpu=True)
def testConcurrentExecutesWithoutError(self): def testConcurrentExecutesWithoutError(self):
with self.session(use_gpu=True) as sess: seed = [42, 24]
all_ops = [] all_ops = []
for full_matrices_ in True, False: for full_matrices_ in True, False:
for rows_ in 4, 5: for rows_ in 4, 5:
for cols_ in 4, 5: for cols_ in 4, 5:
matrix1 = random_ops.random_normal([rows_, cols_], seed=42) matrix_shape = [rows_, cols_]
matrix2 = random_ops.random_normal([rows_, cols_], seed=42) matrix1 = stateless_random_ops.stateless_random_normal(
matrix_shape, seed)
matrix2 = stateless_random_ops.stateless_random_normal(
matrix_shape, seed)
self.assertAllEqual(matrix1, matrix2)
q1, r1 = linalg_ops.qr(matrix1, full_matrices=full_matrices_) q1, r1 = linalg_ops.qr(matrix1, full_matrices=full_matrices_)
q2, r2 = linalg_ops.qr(matrix2, full_matrices=full_matrices_) q2, r2 = linalg_ops.qr(matrix2, full_matrices=full_matrices_)
all_ops += [q1, r1, q2, r2] all_ops += [q1, q2, r1, r2]
val = self.evaluate(all_ops) val = self.evaluate(all_ops)
for i in range(8): for i in range(0, len(val), 2):
q = 4 * i self.assertAllClose(val[i], val[i + 1])
self.assertAllClose(val[q], val[q + 2]) # q1 == q2
self.assertAllClose(val[q + 1], val[q + 3]) # r1 == r2
def _GetQrOpTest(dtype_, shape_, full_matrices_, use_static_shape_): def _GetQrOpTest(dtype_, shape_, full_matrices_, use_static_shape_):
@ -121,8 +124,10 @@ def _GetQrOpTest(dtype_, shape_, full_matrices_, use_static_shape_):
tol = 1e-14 tol = 1e-14
self.assertAllClose(identity, xx, atol=tol) self.assertAllClose(identity, xx, atol=tol)
@test_util.run_v1_only("b/120545219") @test_util.run_in_graph_and_eager_modes(use_gpu=True)
def Test(self): def Test(self):
if not use_static_shape_ and context.executing_eagerly():
return
np.random.seed(1) np.random.seed(1)
x_np = np.random.uniform( x_np = np.random.uniform(
low=-1.0, high=1.0, size=np.prod(shape_)).reshape(shape_).astype(dtype_) low=-1.0, high=1.0, size=np.prod(shape_)).reshape(shape_).astype(dtype_)
@ -131,7 +136,6 @@ def _GetQrOpTest(dtype_, shape_, full_matrices_, use_static_shape_):
low=-1.0, high=1.0, low=-1.0, high=1.0,
size=np.prod(shape_)).reshape(shape_).astype(dtype_) size=np.prod(shape_)).reshape(shape_).astype(dtype_)
with self.session(use_gpu=True) as sess:
if use_static_shape_: if use_static_shape_:
x_tf = constant_op.constant(x_np) x_tf = constant_op.constant(x_np)
else: else:
@ -141,6 +145,7 @@ def _GetQrOpTest(dtype_, shape_, full_matrices_, use_static_shape_):
if use_static_shape_: if use_static_shape_:
q_tf_val, r_tf_val = self.evaluate([q_tf, r_tf]) q_tf_val, r_tf_val = self.evaluate([q_tf, r_tf])
else: else:
with self.session(use_gpu=True) as sess:
q_tf_val, r_tf_val = sess.run([q_tf, r_tf], feed_dict={x_tf: x_np}) q_tf_val, r_tf_val = sess.run([q_tf, r_tf], feed_dict={x_tf: x_np})
q_dims = q_tf_val.shape q_dims = q_tf_val.shape
@ -266,7 +271,7 @@ if __name__ == "__main__":
for full_matrices in False, True: for full_matrices in False, True:
for batch_dims in [(), (3,)] + [(3, 2)] * (max(rows, cols) < 10): for batch_dims in [(), (3,)] + [(3, 2)] * (max(rows, cols) < 10):
# TF2 does not support placeholders under eager so we skip it # TF2 does not support placeholders under eager so we skip it
for use_static_shape in set([True, tf2.enabled()]): for use_static_shape in [True, False]:
shape = batch_dims + (rows, cols) shape = batch_dims + (rows, cols)
name = "%s_%s_full_%s_static_%s" % (dtype.__name__, name = "%s_%s_full_%s_static_%s" % (dtype.__name__,
"_".join(map(str, shape)), "_".join(map(str, shape)),