# Copyright 2017 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.

# ==============================================================================

"""Basic tests for gradients."""

from tensorflow.python.eager import backprop

from tensorflow.python.eager import context

from tensorflow.python.eager import record

from tensorflow.python.eager import test

from tensorflow.python.framework import constant_op

from tensorflow.python.framework import dtypes

from tensorflow.python.ops import array_ops

from tensorflow.python.ops import custom_gradient

from tensorflow.python.ops import gradients_impl

from tensorflow.python.ops import math_ops

# Importing nn_grad for the registration functions.

from tensorflow.python.ops import nn_grad # pylint: disable=unused-import

from tensorflow.python.ops import nn_ops

from tensorflow.python.ops import variables

@custom_gradient.custom_gradient

def two_outputs(a, b):

mm = math_ops.matmul(a, b)

r = math_ops.reduce_sum(mm)

def grad(dmm, dr):

return [

math_ops.matmul(dmm, b, transpose_b=True) +

math_ops.matmul(array_ops.ones_like(b * dr), b, transpose_b=True),

math_ops.matmul(a, dmm, transpose_b=True) +

math_ops.matmul(a, array_ops.ones_like(a) * dr, transpose_b=True)

]

return [mm, r], grad

@custom_gradient.custom_gradient

def gradient_is_constant(x):

result = x * x

def grad(dr):

return [dr]

return result, grad

class TapeTest(test.TestCase):

def testMultiOutput(self):

def fn(x, y):

c = x + y

# Multiple outputs from split.

d, f = array_ops.split(c, 2)

return d + f

a = constant_op.constant([[1., 0.], [0., 1.]])

b = constant_op.constant([[1., 2.], [3., 4.]])

da, db = backprop.gradients_function(fn, [0, 1])(a, b)

with context.graph_mode(), self.cached_session():

tf_a = constant_op.constant([[1, 0], [0, 1]], dtype=dtypes.float32)

tf_b = constant_op.constant([[1, 2], [3, 4]], dtype=dtypes.float32)

tf_c = tf_a + tf_b

tf_d, tf_f = array_ops.split(tf_c, 2, axis=1)

tf_e = tf_d + tf_f

tf_da, tf_db = gradients_impl.gradients(tf_e, [tf_a, tf_b])

self.assertAllEqual(da, self.evaluate(tf_da))

self.assertAllEqual(db, self.evaluate(tf_db))

def testBasicFunctional(self):

def forward(a, b):

mm = math_ops.matmul(a, b)

return math_ops.reduce_sum(mm)

aa = constant_op.constant([[1., 0.], [0., 1.]])

bb = constant_op.constant([[1., 2.], [3., 4.]])

da, = backprop.gradients_function(forward, ['a'])(aa, bb)

self.assertAllEqual(da,

math_ops.matmul(

array_ops.ones_like(aa),

array_ops.transpose(bb)).numpy())

def testBasicFunctionalPositionalArg(self):

def forward(a, b):

mm = math_ops.matmul(a, b)

return math_ops.reduce_sum(mm)

aa = constant_op.constant([[1., 0.], [0., 1.]])

bb = constant_op.constant([[1., 2.], [3., 4.]])

da, = backprop.gradients_function(forward, [0])(aa, bb)

self.assertAllEqual(da,

math_ops.matmul(

array_ops.ones_like(aa),

array_ops.transpose(bb)).numpy())

def testBasicFunctionalWithValue(self):

def forward(a, b):

mm = math_ops.matmul(a, b)

return math_ops.reduce_sum(mm)

aa = constant_op.constant([[1., 0.], [0., 1.]])

bb = constant_op.constant([[1., 2.], [3., 4.]])

val, (da,) = backprop.val_and_grad_function(forward, ['a'])(aa, bb)

self.assertAllEqual(da,

math_ops.matmul(

array_ops.ones_like(aa),

array_ops.transpose(bb)))

self.assertAllEqual(val, forward(aa, bb))

def testTwoOutputs(self):

def fn(x, y):

mm, r = two_outputs(x, y)

return r + math_ops.reduce_sum(mm)

a = constant_op.constant([[1., 0.], [0., 1.]])

b = constant_op.constant([[1., 2.], [3., 4.]])

da, db = backprop.gradients_function(fn, [0, 1])(a, b)

with context.graph_mode(), self.cached_session():

tf_a = constant_op.constant([[1, 0], [0, 1]], dtype=dtypes.float32)

tf_b = constant_op.constant([[1, 2], [3, 4]], dtype=dtypes.float32)

tf_mm = math_ops.matmul(tf_a, tf_b)

tf_rr = 2 * math_ops.reduce_sum(tf_mm)

tf_da, tf_db = gradients_impl.gradients(tf_rr, [tf_a, tf_b])

self.assertAllEqual(da, self.evaluate(tf_da))

self.assertAllEqual(db, self.evaluate(tf_db))

def testGcTwoOutputs(self):

def fn(x, y):

return nn_ops.sparse_softmax_cross_entropy_with_logits(logits=x,

labels=y)[0]

labels = constant_op.constant([0])

logits = constant_op.constant([[0.0]])

grad, = backprop.gradients_function(fn, [0])(logits, labels)

self.assertAllEqual(grad, [[0.0]])

def testTfTensor(self):

def fn(x):

return x

t = constant_op.constant(1.0)

g, = backprop.gradients_function(fn, [0])(t)

self.assertAllEqual(g, 1.0)

class VariableWatcherTest(test.TestCase):

def testBasic(self):

var1 = variables.Variable(0.0)

var2 = variables.Variable(1.0)

with record.VariableWatcher() as variable_watcher:

var1.assign_add(1.0)

var2.assign_add(2.0)

self.assertAllEqual(variable_watcher.watched_variables(), (var1, var2))

def testNonTrainableVariables(self):

var1 = variables.Variable(0.0)

var2 = variables.Variable(1.0, trainable=False)

with record.VariableWatcher() as variable_watcher:

var1.assign_add(1.0)

var2.assign_add(2.0)

self.assertAllEqual(variable_watcher.watched_variables(), (var1,))

def testMultipleScopes(self):

var1 = variables.Variable(0.0)

var2 = variables.Variable(1.0)

with record.VariableWatcher() as variable_watcher1:

var1.assign_add(1.0)

with record.VariableWatcher() as variable_watcher2:

var2.assign_add(2.0)

# variable_watcher1 should see both vars and variable_watcher2 only sees

# var2

self.assertAllEqual(variable_watcher1.watched_variables(), (var1, var2))

self.assertAllEqual(variable_watcher2.watched_variables(), (var2,))

def testCreateVariables(self):

with record.VariableWatcher() as variable_watcher:

var1 = variables.Variable(0.0)

var2 = variables.Variable(1.0)

var1.assign_add(1.0)

var2.assign_add(2.0)

self.assertAllEqual(variable_watcher.watched_variables(), (var1, var2))

if __name__ == '__main__':

test.main()