From e323183bf62993da23eb52c18944a4670aa5ce53 Mon Sep 17 00:00:00 2001
From: Reed Wanderman-Milne <reedwm@google.com>
Date: Mon, 30 Sep 2019 14:35:03 -0700
Subject: [PATCH] Rename LossScalingGradientTape to LossScaleGradientTape.
This makes it more consistent with LossScale and LossScaleOptimizer.
Since LossScalingGradientTape is not yet in a stable release, no need to worry about breaking anyone.
PiperOrigin-RevId: 272067559
---
.../loss_scaling_gradient_tape.py | 16 +++++-----
.../loss_scaling_gradient_tape_test.py | 32 +++++++++----------
...erimental.-loss-scale-gradient-tape.pbtxt} | 4 +--
...sorflow.mixed_precision.experimental.pbtxt | 2 +-
4 files changed, 27 insertions(+), 27 deletions(-)
rename tensorflow/tools/api/golden/v2/{tensorflow.mixed_precision.experimental.-loss-scaling-gradient-tape.pbtxt => tensorflow.mixed_precision.experimental.-loss-scale-gradient-tape.pbtxt} (92%)
diff --git a/tensorflow/python/training/experimental/loss_scaling_gradient_tape.py b/tensorflow/python/training/experimental/loss_scaling_gradient_tape.py
index aea2c1f61f5..53e4926e407 100644
--- a/tensorflow/python/training/experimental/loss_scaling_gradient_tape.py
+++ b/tensorflow/python/training/experimental/loss_scaling_gradient_tape.py
@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
-"""Contains Loss Scaling Gradient Tape."""
+"""Contains Loss Scale Gradient Tape."""
from __future__ import absolute_import
from __future__ import division
@@ -26,8 +26,8 @@ from tensorflow.python.util import nest
from tensorflow.python.util.tf_export import tf_export
-@tf_export("mixed_precision.experimental.LossScalingGradientTape", v1=[])
-class LossScalingGradientTape(backprop.GradientTape):
+@tf_export("mixed_precision.experimental.LossScaleGradientTape", v1=[])
+class LossScaleGradientTape(backprop.GradientTape):
"""A gradient tape that scales losses and unscales resulting gradients.
Operates as a normal gradient tape, but takes in a
@@ -51,7 +51,7 @@ class LossScalingGradientTape(backprop.GradientTape):
model_loss_scale = tf.train.experimental.DynamicLossScale()
for step in training_steps:
- with LossScalingGradientTape(model_loss_scale) as tape:
+ with LossScaleGradientTape(model_loss_scale) as tape:
logits = ... # Run model and get logits
loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits,
labels=labels)
@@ -66,7 +66,7 @@ class LossScalingGradientTape(backprop.GradientTape):
loss_scale,
persistent=False,
watch_accessed_variables=True):
- """Creates a new LossScalingGradientTape.
+ """Creates a new LossScaleGradientTape.
Args:
loss_scale: `tf.train.experimental.LossScale` object that
@@ -89,8 +89,8 @@ class LossScalingGradientTape(backprop.GradientTape):
raise ValueError("`loss_scale` must be an instance of LossScale.")
# always make a persistent tape to loop over loss scaling
- super(LossScalingGradientTape, self).__init__(True,
- watch_accessed_variables)
+ super(LossScaleGradientTape, self).__init__(True,
+ watch_accessed_variables)
self._outer_persistent = persistent
self._loss_scale = loss_scale
@@ -142,7 +142,7 @@ class LossScalingGradientTape(backprop.GradientTape):
loss_scale = self._loss_scale()
scaled_target = nest.map_structure(lambda t: t * loss_scale, target)
- old_grads = super(LossScalingGradientTape, self).gradient(
+ old_grads = super(LossScaleGradientTape, self).gradient(
scaled_target, sources, output_gradients, unconnected_gradients)
inv_loss_scale = 1.0 / self._loss_scale()
grads = nest.map_structure(lambda g: inv_loss_scale * g, old_grads)
diff --git a/tensorflow/python/training/experimental/loss_scaling_gradient_tape_test.py b/tensorflow/python/training/experimental/loss_scaling_gradient_tape_test.py
index 25aa9cbcb13..b8c85a929da 100644
--- a/tensorflow/python/training/experimental/loss_scaling_gradient_tape_test.py
+++ b/tensorflow/python/training/experimental/loss_scaling_gradient_tape_test.py
@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
-"""Tests for lsgt.LossScalingGradientTape."""
+"""Tests for lsgt.LossScaleGradientTape."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
@@ -27,13 +27,13 @@ from tensorflow.python.training.experimental import loss_scale as loss_scale_mod
from tensorflow.python.training.experimental import loss_scaling_gradient_tape as lsgt
-class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
+class LossScaleGradientTapeTest(test.TestCase, parameterized.TestCase):
@parameterized.parameters(loss_scale_module.FixedLossScale,
loss_scale_module.DynamicLossScale)
def test_basic_tapes_eager_mode(self, loss_scale):
x = constant_op.constant(3.0)
- with lsgt.LossScalingGradientTape(loss_scale(32)) as g:
+ with lsgt.LossScaleGradientTape(loss_scale(32)) as g:
g.watch(x)
y = x * x
dy_dx = g.gradient(y, x)
@@ -47,7 +47,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
@def_function.function
def _inner_test():
x = constant_op.constant(3.0)
- with lsgt.LossScalingGradientTape(loss_scale) as g:
+ with lsgt.LossScaleGradientTape(loss_scale) as g:
g.watch(x)
y = x * x
return g.gradient(y, x)
@@ -57,9 +57,9 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
loss_scale_module.DynamicLossScale)
def test_nested_tapes(self, loss_scale):
x = constant_op.constant(3.0)
- with lsgt.LossScalingGradientTape(loss_scale(32)) as g:
+ with lsgt.LossScaleGradientTape(loss_scale(32)) as g:
g.watch(x)
- with lsgt.LossScalingGradientTape(loss_scale(32)) as gg:
+ with lsgt.LossScaleGradientTape(loss_scale(32)) as gg:
gg.watch(x)
y = x * x
dy_dx = gg.gradient(y, x)
@@ -71,7 +71,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
loss_scale_module.DynamicLossScale)
def test_non_persistent_tapes_error(self, loss_scale):
x = constant_op.constant(3.0)
- with lsgt.LossScalingGradientTape(loss_scale(32), persistent=False) as g:
+ with lsgt.LossScaleGradientTape(loss_scale(32), persistent=False) as g:
g.watch(x)
y = x * x
z = y * y
@@ -83,7 +83,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
loss_scale_module.DynamicLossScale)
def test_persistent_tapes(self, loss_scale):
x = constant_op.constant(3.0)
- with lsgt.LossScalingGradientTape(loss_scale(32), persistent=True) as g:
+ with lsgt.LossScaleGradientTape(loss_scale(32), persistent=True) as g:
g.watch(x)
y = x * x
z = y * y
@@ -97,7 +97,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
def test_nested_sources(self, loss_scale):
x = (constant_op.constant(19.0), (constant_op.constant(8.),
constant_op.constant(9.)))
- with lsgt.LossScalingGradientTape(loss_scale(32)) as g:
+ with lsgt.LossScaleGradientTape(loss_scale(32)) as g:
g.watch(x)
y = x * 13
dy_dx = g.gradient(y, x)
@@ -107,7 +107,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
loss_scale_module.DynamicLossScale)
def test_nested_targets(self, loss_scale):
w = constant_op.constant(3.0)
- with lsgt.LossScalingGradientTape(loss_scale(32)) as g:
+ with lsgt.LossScaleGradientTape(loss_scale(32)) as g:
g.watch(w)
x = w * 5
y = w * 7
@@ -119,7 +119,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
loss_scale_module.DynamicLossScale)
def test_scaling_inf_gradient(self, loss_scale):
x = constant_op.constant(1.0)
- with lsgt.LossScalingGradientTape(loss_scale(32)) as g:
+ with lsgt.LossScaleGradientTape(loss_scale(32)) as g:
g.watch(x)
y = x * np.inf
dy_dx = g.gradient(y, x)
@@ -129,7 +129,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
loss_scale_module.DynamicLossScale)
def test_scaling_nan_gradient(self, loss_scale):
x = constant_op.constant(1.0)
- with lsgt.LossScalingGradientTape(loss_scale(32)) as g:
+ with lsgt.LossScaleGradientTape(loss_scale(32)) as g:
g.watch(x)
y = x * np.nan
dy_dx = g.gradient(y, x)
@@ -139,7 +139,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
def test_dynamic_scale_to_one_on_non_finite_gradient(self, non_finite_term):
loss_scale = loss_scale_module.DynamicLossScale(initial_loss_scale=32)
x = constant_op.constant(1.0)
- with lsgt.LossScalingGradientTape(loss_scale) as g:
+ with lsgt.LossScaleGradientTape(loss_scale) as g:
g.watch(x)
y = x * non_finite_term
g.gradient(y, x)
@@ -150,7 +150,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
is_non_finite):
loss_scale = loss_scale_module.FixedLossScale(32)
x = constant_op.constant(1.0)
- with lsgt.LossScalingGradientTape(loss_scale) as g:
+ with lsgt.LossScaleGradientTape(loss_scale) as g:
g.watch(x)
y = x * non_finite_term
dy_dx = g.gradient(y, x)
@@ -160,7 +160,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
def test_dynamic_loss_scaling_down_loop(self):
loss_scale = loss_scale_module.DynamicLossScale(initial_loss_scale=32)
x = constant_op.constant(1.0)
- with lsgt.LossScalingGradientTape(loss_scale) as g:
+ with lsgt.LossScaleGradientTape(loss_scale) as g:
g.watch(x)
y = x * (3.0 * (10**37)) # grad will be inf after scaling
dy_dx = g.gradient(y, x)
@@ -170,7 +170,7 @@ class LossScalingGradientTapeTest(test.TestCase, parameterized.TestCase):
def test_dynamic_loss_scaling_inf_target_post_scale(self):
loss_scale = loss_scale_module.DynamicLossScale(initial_loss_scale=32.0)
x = constant_op.constant(3.0 * (10**37))
- with lsgt.LossScalingGradientTape(loss_scale) as g:
+ with lsgt.LossScaleGradientTape(loss_scale) as g:
g.watch(x)
y = x * 3.0 # target will be inf after scaling
dy_dx = g.gradient(y, x)
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.mixed_precision.experimental.-loss-scaling-gradient-tape.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.mixed_precision.experimental.-loss-scale-gradient-tape.pbtxt
similarity index 92%
rename from tensorflow/tools/api/golden/v2/tensorflow.mixed_precision.experimental.-loss-scaling-gradient-tape.pbtxt
rename to tensorflow/tools/api/golden/v2/tensorflow.mixed_precision.experimental.-loss-scale-gradient-tape.pbtxt
index 95c09bfb16c..7f4715832e2 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.mixed_precision.experimental.-loss-scaling-gradient-tape.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.mixed_precision.experimental.-loss-scale-gradient-tape.pbtxt
@@ -1,6 +1,6 @@
-path: "tensorflow.mixed_precision.experimental.LossScalingGradientTape"
+path: "tensorflow.mixed_precision.experimental.LossScaleGradientTape"
tf_class {
- is_instance: "<class \'tensorflow.python.training.experimental.loss_scaling_gradient_tape.LossScalingGradientTape\'>"
+ is_instance: "<class \'tensorflow.python.training.experimental.loss_scaling_gradient_tape.LossScaleGradientTape\'>"
is_instance: "<class \'tensorflow.python.eager.backprop.GradientTape\'>"
is_instance: "<type \'object\'>"
member_method {
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.mixed_precision.experimental.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.mixed_precision.experimental.pbtxt
index 43615a11b55..30414f7f9ea 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.mixed_precision.experimental.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.mixed_precision.experimental.pbtxt
@@ -1,7 +1,7 @@
path: "tensorflow.mixed_precision.experimental"
tf_module {
member {
- name: "LossScalingGradientTape"
+ name: "LossScaleGradientTape"
mtype: "<type \'type\'>"
}
}