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Fix @test_util.run_deprecated_v1 in third_party/tensorflow/python/kernel_tests/basic_gpu_test.py

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PiperOrigin-RevId: 323784664
Change-Id: I286870945ddcee9073fc65ef54aa3fcf173f97bb
This commit is contained in:
A. Unique TensorFlower 2020-07-29 07:40:48 -07:00 committed by TensorFlower Gardener
parent a3fc5b10c0
commit 9f1aadb48f
1 changed files with 53 additions and 17 deletions
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70
tensorflow/python/kernel_tests/basic_gpu_test.py
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@ -29,7 +29,7 @@ from tensorflow.python.framework import ops
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 gradient_checker_v2
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
@ -156,8 +156,10 @@ class MathBuiltinUnaryTest(test.TestCase):
class BroadcastSimpleTest(test.TestCase):
def _GetGradientArgs(self, xs, ys):
return self.evaluate(broadcast_gradient_args(xs, ys))
with self.cached_session(use_gpu=True) as sess:
return sess.run(broadcast_gradient_args(xs, ys))
@test_util.run_deprecated_v1
def testBroadcast(self):
r0, r1 = self._GetGradientArgs([2, 3, 5], [1])
self.assertAllEqual(r0, [])
@ -165,6 +167,48 @@ class BroadcastSimpleTest(test.TestCase):
_GRAD_TOL = {dtypes.float32: 1e-3}
def _compareGradientX(self,
x,
y,
np_func,
tf_func,
numeric_gradient_type=None):
z = np_func(x, y)
zs = list(z.shape)
with self.cached_session():
inx = ops.convert_to_tensor(x)
iny = ops.convert_to_tensor(y)
if x.dtype in (np.float32, np.float64):
out = 1.1 * tf_func(inx, iny)
else:
out = tf_func(inx, iny)
xs = list(x.shape)
jacob_t, jacob_n = gradient_checker.compute_gradient(
inx, xs, out, zs, x_init_value=x)
tol = self._GRAD_TOL[dtypes.as_dtype(x.dtype)]
self.assertAllClose(jacob_t, jacob_n, rtol=tol, atol=tol)
def _compareGradientY(self,
x,
y,
np_func,
tf_func,
numeric_gradient_type=None):
z = np_func(x, y)
zs = list(z.shape)
with self.cached_session():
inx = ops.convert_to_tensor(x)
iny = ops.convert_to_tensor(y)
if x.dtype in (np.float32, np.float64):
out = 1.1 * tf_func(inx, iny)
else:
out = tf_func(inx, iny)
ys = list(np.shape(y))
jacob_t, jacob_n = gradient_checker.compute_gradient(
iny, ys, out, zs, x_init_value=y)
tol = self._GRAD_TOL[dtypes.as_dtype(x.dtype)]
self.assertAllClose(jacob_t, jacob_n, rtol=tol, atol=tol)
def _compareGpu(self, x, y, np_func, tf_func):
np_ans = np_func(x, y)
with self.cached_session(use_gpu=True):
@ -176,25 +220,17 @@ class BroadcastSimpleTest(test.TestCase):
self.assertShapeEqual(np_ans, out)
# TODO(zhifengc/ke): make gradient checker work on GPU.
@test_util.run_deprecated_v1
def testGradient(self):
x1 = (1 + np.linspace(0, 5, np.prod([1, 3, 2]))).astype(np.float32).reshape(
x = (1 + np.linspace(0, 5, np.prod([1, 3, 2]))).astype(np.float32).reshape(
[1, 3, 2])
x2 = (1 + np.linspace(0, 5, np.prod([1, 3, 2]))).astype(np.float32).reshape(
y = (1 + np.linspace(0, 5, np.prod([1, 3, 2]))).astype(np.float32).reshape(
[1, 3, 2])
def div_x1(x1):
return math_ops.truediv(x1, x2) * math_ops.cast(1.1, dtype=x1.dtype)
def div_x2(x2):
return math_ops.truediv(x1, x2) * math_ops.cast(1.1, dtype=x2.dtype)
gradient_checker_v2.compute_gradient(
div_x1, [x1], self._GRAD_TOL[dtypes.as_dtype(x1.dtype)])
gradient_checker_v2.compute_gradient(
div_x2, [x2], self._GRAD_TOL[dtypes.as_dtype(x2.dtype)])
self._compareGpu(x1, x2, np.true_divide, math_ops.truediv)
self._compareGpu(x1, x2 + 0.1, np.floor_divide, math_ops.floordiv)
self._compareGradientX(x, y, np.true_divide, math_ops.truediv)
self._compareGradientY(x, y, np.true_divide, math_ops.truediv)
self._compareGpu(x, y, np.true_divide, math_ops.truediv)
self._compareGpu(x, y + 0.1, np.floor_divide, math_ops.floordiv)
class GpuMultiSessionMemoryTest(test_util.TensorFlowTestCase):