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-rwxr-xr-xconfigure4
-rw-r--r--tensorflow/contrib/rnn/BUILD2
-rw-r--r--tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py165
-rw-r--r--tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py103
-rw-r--r--tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py201
-rw-r--r--tensorflow/contrib/seq2seq/python/ops/sampling_decoder.py14
-rw-r--r--tensorflow/core/platform/default/build_config.bzl13
-rw-r--r--tensorflow/core/platform/default/build_config_root.bzl17
-rw-r--r--tensorflow/python/BUILD2
-rw-r--r--tensorflow/tensorflow.bzl29
-rw-r--r--tensorflow/tools/pip_package/BUILD2
11 files changed, 161 insertions, 391 deletions
diff --git a/configure b/configure
index 372ec2cee8..a8e7bb7738 100755
--- a/configure
+++ b/configure
@@ -168,10 +168,10 @@ done
if [ "$TF_ENABLE_XLA" == "1" ]; then
# Update Bazel build configuration.
- sed -i -e "s/WITH_XLA_SUPPORT = (False|True)/WITH_XLA_SUPPORT = True/" tensorflow/core/platform/default/build_config_root.bzl
+ perl -pi -e "s,WITH_XLA_SUPPORT = (False|True),WITH_XLA_SUPPORT = True,s" tensorflow/core/platform/default/build_config.bzl
else
# Update Bazel build configuration.
- sed -i -e "s/WITH_XLA_SUPPORT = (False|True)/WITH_XLA_SUPPORT = False/" tensorflow/core/platform/default/build_config_root.bzl
+ perl -pi -e "s,WITH_XLA_SUPPORT = (False|True),WITH_XLA_SUPPORT = False,s" tensorflow/core/platform/default/build_config.bzl
fi
diff --git a/tensorflow/contrib/rnn/BUILD b/tensorflow/contrib/rnn/BUILD
index c02423f7a3..bed23625d3 100644
--- a/tensorflow/contrib/rnn/BUILD
+++ b/tensorflow/contrib/rnn/BUILD
@@ -71,7 +71,6 @@ cuda_py_tests(
"//tensorflow/python:variable_scope",
"//tensorflow/python:variables",
],
- xla_enabled = True,
)
cuda_py_tests(
@@ -92,7 +91,6 @@ cuda_py_tests(
"//tensorflow/python:variable_scope",
"//tensorflow/python:variables",
],
- xla_enabled = True,
)
cuda_py_tests(
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
index 8090743e6c..0d9285ccb8 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
@@ -19,7 +19,6 @@ from __future__ import division
from __future__ import print_function
import functools
-import itertools
import sys
# TODO: #6568 Remove this hack that makes dlopen() not crash.
@@ -34,14 +33,9 @@ import numpy as np
from tensorflow.contrib.rnn.python.ops import core_rnn_cell_impl
from tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl import _linear as linear
-from tensorflow.core.protobuf import config_pb2
-from tensorflow.python.client import session
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
-from tensorflow.python.framework import random_seed
from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
@@ -49,41 +43,10 @@ from tensorflow.python.ops import rnn
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables as variables_lib
from tensorflow.python.platform import test
-from tensorflow.python.util import nest
# pylint: enable=protected-access
-def _CreateMultiLSTMCellOps(batch_size, num_units, input_depth,
- num_layers, max_time, compiled):
- with variable_scope.variable_scope(
- "root",
- initializer=init_ops.random_uniform_initializer(-0.1, 0.1, seed=2)):
- inputs = random_ops.random_uniform(
- (max_time, batch_size, input_depth), seed=1)
- rnn_cell = core_rnn_cell_impl.MultiRNNCell(
- [core_rnn_cell_impl.LSTMCell(num_units, compiled=compiled)
- for _ in range(num_layers)])
- initial_state = rnn_cell.zero_state(
- batch_size=batch_size, dtype=dtypes.float32)
- outputs, final_state = rnn.dynamic_rnn(
- cell=rnn_cell, inputs=inputs, initial_state=initial_state,
- time_major=True)
- flat_final_state = nest.flatten(final_state)
- trainable_variables = variables_lib.trainable_variables()
- outputs_grad = gradients_impl.gradients(
- [outputs],
- trainable_variables + [inputs] + nest.flatten(initial_state))
- final_state_grad = gradients_impl.gradients(
- flat_final_state,
- trainable_variables + [inputs] + nest.flatten(initial_state))
-
- return {"outputs": outputs,
- "final_state": flat_final_state,
- "outputs_grad": outputs_grad,
- "final_state_grad": final_state_grad}
-
-
class RNNCellTest(test.TestCase):
def testLinear(self):
@@ -154,8 +117,8 @@ class RNNCellTest(test.TestCase):
x = array_ops.zeros([1, 2])
m = array_ops.zeros([1, 8])
g, out_m = core_rnn_cell_impl.MultiRNNCell(
- [core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)
- for _ in range(2)],
+ [core_rnn_cell_impl.BasicLSTMCell(
+ 2, state_is_tuple=False)] * 2,
state_is_tuple=False)(x, m)
sess.run([variables_lib.global_variables_initializer()])
res = sess.run(
@@ -202,8 +165,7 @@ class RNNCellTest(test.TestCase):
m0 = (array_ops.zeros([1, 2]),) * 2
m1 = (array_ops.zeros([1, 2]),) * 2
cell = core_rnn_cell_impl.MultiRNNCell(
- [core_rnn_cell_impl.BasicLSTMCell(2) for _ in range(2)],
- state_is_tuple=True)
+ [core_rnn_cell_impl.BasicLSTMCell(2)] * 2, state_is_tuple=True)
self.assertTrue(isinstance(cell.state_size, tuple))
self.assertTrue(
isinstance(cell.state_size[0], core_rnn_cell_impl.LSTMStateTuple))
@@ -235,8 +197,8 @@ class RNNCellTest(test.TestCase):
m0 = array_ops.zeros([1, 4])
m1 = array_ops.zeros([1, 4])
cell = core_rnn_cell_impl.MultiRNNCell(
- [core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)
- for _ in range(2)],
+ [core_rnn_cell_impl.BasicLSTMCell(
+ 2, state_is_tuple=False)] * 2,
state_is_tuple=True)
g, (out_m0, out_m1) = cell(x, (m0, m1))
sess.run([variables_lib.global_variables_initializer()])
@@ -445,8 +407,7 @@ class RNNCellTest(test.TestCase):
x = array_ops.zeros([1, 2])
m = array_ops.zeros([1, 4])
_, ml = core_rnn_cell_impl.MultiRNNCell(
- [core_rnn_cell_impl.GRUCell(2) for _ in range(2)],
- state_is_tuple=False)(x, m)
+ [core_rnn_cell_impl.GRUCell(2)] * 2, state_is_tuple=False)(x, m)
sess.run([variables_lib.global_variables_initializer()])
res = sess.run(ml, {
x.name: np.array([[1., 1.]]),
@@ -455,48 +416,6 @@ class RNNCellTest(test.TestCase):
# The numbers in results were not calculated, this is just a smoke test.
self.assertAllClose(res, [[0.175991, 0.175991, 0.13248, 0.13248]])
- def testMultiRNNCellWithLSTMCellAndXLA(self):
- # TODO(b/34735319): Don't run this test if XLA is not available.
- batch_size = 16
- num_units = 32
- input_depth = 12
- num_layers = 2
- max_time = 20
-
- random_seed.set_random_seed(1234)
- with self.test_session(graph=ops.Graph()) as sess:
- xla_ops = _CreateMultiLSTMCellOps(
- batch_size=batch_size, num_units=num_units,
- input_depth=input_depth, num_layers=num_layers,
- max_time=max_time,
- compiled=True)
- sess.run([variables_lib.global_variables_initializer()])
- xla_results = sess.run(xla_ops)
-
- random_seed.set_random_seed(1234)
- with self.test_session(graph=ops.Graph()) as sess:
- non_xla_ops = _CreateMultiLSTMCellOps(
- batch_size=batch_size, num_units=num_units,
- input_depth=input_depth, num_layers=num_layers,
- max_time=max_time,
- compiled=False)
- sess.run([variables_lib.global_variables_initializer()])
- non_xla_results = sess.run(non_xla_ops)
-
- self.assertAllClose(non_xla_results["outputs"], xla_results["outputs"])
-
- for xla_value, non_xla_value in zip(
- xla_results["final_state"], non_xla_results["final_state"]):
- self.assertAllClose(xla_value, non_xla_value)
-
- for xla_g, non_xla_g in zip(
- xla_results["outputs_grad"], non_xla_results["outputs_grad"]):
- self.assertAllClose(xla_g, non_xla_g)
-
- for xla_g, non_xla_g in zip(
- xla_results["final_state_grad"], non_xla_results["final_state_grad"]):
- self.assertAllClose(xla_g, non_xla_g)
-
def testMultiRNNCellWithStateTuple(self):
with self.test_session() as sess:
with variable_scope.variable_scope(
@@ -508,12 +427,11 @@ class RNNCellTest(test.TestCase):
# Test incorrectness of state
with self.assertRaisesRegexp(ValueError, "Expected state .* a tuple"):
core_rnn_cell_impl.MultiRNNCell(
- [core_rnn_cell_impl.GRUCell(2) for _ in range(2)],
+ [core_rnn_cell_impl.GRUCell(2)] * 2,
state_is_tuple=True)(x, m_bad)
_, ml = core_rnn_cell_impl.MultiRNNCell(
- [core_rnn_cell_impl.GRUCell(2) for _ in range(2)],
- state_is_tuple=True)(x, m_good)
+ [core_rnn_cell_impl.GRUCell(2)] * 2, state_is_tuple=True)(x, m_good)
sess.run([variables_lib.global_variables_initializer()])
res = sess.run(ml, {
@@ -572,7 +490,7 @@ class SlimRNNCellTest(test.TestCase):
self.assertAllClose(res[1], res[3])
-def basic_rnn_cell(inputs, state, num_units, scope=None): # pylint: disable=invalid-name
+def basic_rnn_cell(inputs, state, num_units, scope=None):
if state is None:
if inputs is not None:
batch_size = inputs.get_shape()[0]
@@ -594,70 +512,5 @@ def basic_rnn_cell(inputs, state, num_units, scope=None): # pylint: disable=inv
return output, output
-class BenchmarkLSTMCellXLA(test.Benchmark):
-
- def benchmarkDynamicRNNWithMultiLSTMCell(self):
- num_layers = 3
- max_time = 50
- print("benchmarkDynamicRNNWithMultiLSTMCell")
- print("\t" +
- "\t".join(["inter_th", "intra_th",
- "batch_size", "num_units", "input_depth", "device",
- "compiled", "wall_time"]))
-
- warmup_run = True
- for (threads,
- device,
- num_units,
- batch_size,
- input_depth,
- compiled) in itertools.product(
- [{"inter": 0, "intra": 0}, {"inter": 1, "intra": 4}],
- ["cpu", "gpu"],
- [32, 512],
- [1, 32, 256],
- [32, 512],
- [False, True]):
- if threads["inter"] != 0:
- # We only care about testing inter/intra op limitations on
- # CPU with small batch size, to mimic embedded devices.
- if device != "cpu" or batch_size != 1:
- continue
- if device == "cpu" and batch_size > 32:
- continue
- random_seed.set_random_seed(1234)
- config = config_pb2.ConfigProto(
- inter_op_parallelism_threads=threads["inter"],
- intra_op_parallelism_threads=threads["intra"],
- allow_soft_placement=False)
- with session.Session(config=config, graph=ops.Graph()) as sess:
- with ops.device("/%s:0" % device):
- ops_dict = _CreateMultiLSTMCellOps(
- batch_size=batch_size, num_units=num_units,
- input_depth=input_depth, num_layers=num_layers,
- max_time=max_time,
- compiled=compiled)
- sess.run([variables_lib.global_variables_initializer()])
- all_ops = nest.flatten(ops_dict.values())
- all_ops_group = control_flow_ops.group(*all_ops)
- name_suffix = (
- "inter_th_%d_intra_th_%d_bs_%d_units_%d_inputdepth_%d"
- "_device_%s_xla_%s" % (
- threads["inter"], threads["intra"],
- batch_size, num_units, input_depth, device, compiled))
- if warmup_run:
- self.run_op_benchmark(
- sess, all_ops_group, min_iters=30, name="ignore_warmup")
- warmup_run = False
- benchmark_results = self.run_op_benchmark(
- sess, all_ops_group, min_iters=30,
- name="benchmarkDynamicRNNWithMultiLSTMCell_%s" % name_suffix)
- print("\t" +
- "\t".join(["%s" % x for x in [
- threads["inter"], threads["intra"],
- batch_size, num_units, input_depth, device, compiled,
- benchmark_results["wall_time"]]]))
-
-
if __name__ == "__main__":
test.main()
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
index 67e026dabf..3c84c34726 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
@@ -154,7 +154,6 @@ class RNNTest(test.TestCase):
def setUp(self):
self._seed = 23489
np.random.seed(self._seed)
- ops_lib.reset_default_graph()
def testInvalidSequenceLengthShape(self):
cell = Plus1RNNCell()
@@ -584,7 +583,7 @@ class LSTMTest(test.TestCase):
(state_notuple_v,) = sess.run((state_notuple,),
feed_dict={inputs[0]: input_value})
state_tuple_v = sess.run(state_tuple, feed_dict={inputs[0]: input_value})
- self.assertAllClose(state_notuple_v, np.hstack(state_tuple_v))
+ self.assertAllEqual(state_notuple_v, np.hstack(state_tuple_v))
def _testProjSharding(self, use_gpu):
num_units = 3
@@ -807,7 +806,7 @@ class LSTMTest(test.TestCase):
self.assertEqual(len(outputs0_values), len(outputs2_values))
for o1, o2, o3 in zip(outputs0_values, outputs1_values, outputs2_values):
# Same weights used by both RNNs so outputs should be the same.
- self.assertAllClose(o1, o2)
+ self.assertAllEqual(o1, o2)
# Different weights used so outputs should be different.
self.assertTrue(np.linalg.norm(o1 - o3) > 1e-6)
@@ -845,7 +844,7 @@ class LSTMTest(test.TestCase):
outputs1_values = output_values[max_length:]
self.assertEqual(len(outputs0_values), len(outputs1_values))
for out0, out1 in zip(outputs0_values, outputs1_values):
- self.assertAllClose(out0, out1)
+ self.assertAllEqual(out0, out1)
def testNoProjNoShardingSimpleStateSaver(self):
self._testNoProjNoShardingSimpleStateSaver(use_gpu=False)
@@ -935,13 +934,13 @@ class LSTMTest(test.TestCase):
feed_dict={inputs[0]: input_value})
outputs_dynamic_v = sess.run(outputs_dynamic,
feed_dict={inputs[0]: input_value})
- self.assertAllClose(outputs_static_v, outputs_dynamic_v)
+ self.assertAllEqual(outputs_static_v, outputs_dynamic_v)
state_static_v = sess.run(state_static,
feed_dict={inputs[0]: input_value})
state_dynamic_v = sess.run(state_dynamic,
feed_dict={inputs[0]: input_value})
- self.assertAllClose(np.hstack(state_static_v), np.hstack(state_dynamic_v))
+ self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v))
def testDynamicRNNWithNestedTupleStates(self):
num_units = 3
@@ -1004,13 +1003,13 @@ class LSTMTest(test.TestCase):
feed_dict={inputs[0]: input_value})
outputs_dynamic_v = sess.run(outputs_dynamic,
feed_dict={inputs[0]: input_value})
- self.assertAllClose(outputs_static_v, outputs_dynamic_v)
+ self.assertAllEqual(outputs_static_v, outputs_dynamic_v)
state_static_v = sess.run(nest.flatten(state_static),
feed_dict={inputs[0]: input_value})
state_dynamic_v = sess.run(nest.flatten(state_dynamic),
feed_dict={inputs[0]: input_value})
- self.assertAllClose(np.hstack(state_static_v), np.hstack(state_dynamic_v))
+ self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v))
def _testDynamicEquivalentToStaticRNN(self, use_gpu, use_sequence_length):
time_steps = 8
@@ -1039,9 +1038,7 @@ class LSTMTest(test.TestCase):
use_peepholes=True,
initializer=initializer,
num_proj=num_proj,
- state_is_tuple=False,
- # TODO(b/XXX): Defun name aliasing causes errors
- compiled=False)
+ state_is_tuple=False)
with variable_scope.variable_scope("dynamic_scope"):
outputs_static, state_static = core_rnn.static_rnn(
@@ -1099,9 +1096,7 @@ class LSTMTest(test.TestCase):
use_peepholes=True,
initializer=initializer,
num_proj=num_proj,
- state_is_tuple=False,
- # TODO(b/XXX): Defun name aliasing causes errors
- compiled=False)
+ state_is_tuple=False)
with variable_scope.variable_scope("dynamic_scope"):
outputs_dynamic, state_dynamic = rnn.dynamic_rnn(
@@ -1155,10 +1150,10 @@ class LSTMTest(test.TestCase):
######### Step 3: Comparisons
self.assertEqual(len(values_static), len(values_dynamic))
for (value_static, value_dynamic) in zip(values_static, values_dynamic):
- self.assertAllClose(value_static, value_dynamic)
- self.assertAllClose(state_value_static, state_value_dynamic)
+ self.assertAllEqual(value_static, value_dynamic)
+ self.assertAllEqual(state_value_static, state_value_dynamic)
- self.assertAllClose(static_grad_values, dynamic_grad_values)
+ self.assertAllEqual(static_grad_values, dynamic_grad_values)
self.assertEqual(
len(static_individual_grad_values), len(dynamic_individual_grad_values))
@@ -1169,14 +1164,14 @@ class LSTMTest(test.TestCase):
for i, (a, b) in enumerate(
zip(static_individual_grad_values, dynamic_individual_grad_values)):
tf_logging.info("Comparing individual gradients iteration %d" % i)
- self.assertAllClose(a, b)
+ self.assertAllEqual(a, b)
for i, (a, b) in enumerate(
zip(static_individual_var_grad_values,
dynamic_individual_var_grad_values)):
tf_logging.info("Comparing individual variable gradients iteration %d" %
i)
- self.assertAllClose(a, b)
+ self.assertAllEqual(a, b)
def testDynamicEquivalentToStaticRNN(self):
self._testDynamicEquivalentToStaticRNN(
@@ -1298,13 +1293,13 @@ class BidirectionalRNNTest(test.TestCase):
# Both sequences in batch are length=8. Check that the time=i
# forward output is equal to time=8-1-i backward output
for i in xrange(8):
- self.assertAllClose(out[i][0][0], out[8 - 1 - i][0][3])
- self.assertAllClose(out[i][0][1], out[8 - 1 - i][0][4])
- self.assertAllClose(out[i][0][2], out[8 - 1 - i][0][5])
+ self.assertEqual(out[i][0][0], out[8 - 1 - i][0][3])
+ self.assertEqual(out[i][0][1], out[8 - 1 - i][0][4])
+ self.assertEqual(out[i][0][2], out[8 - 1 - i][0][5])
for i in xrange(8):
- self.assertAllClose(out[i][1][0], out[8 - 1 - i][1][3])
- self.assertAllClose(out[i][1][1], out[8 - 1 - i][1][4])
- self.assertAllClose(out[i][1][2], out[8 - 1 - i][1][5])
+ self.assertEqual(out[i][1][0], out[8 - 1 - i][1][3])
+ self.assertEqual(out[i][1][1], out[8 - 1 - i][1][4])
+ self.assertEqual(out[i][1][2], out[8 - 1 - i][1][5])
# Via the reasoning above, the forward and backward final state should be
# exactly the same
self.assertAllClose(s_fw, s_bw)
@@ -1404,27 +1399,27 @@ class BidirectionalRNNTest(test.TestCase):
# Check that the time=0 forward output is equal to time=1 backward output
if not use_time_major:
out = np.swapaxes(out, 0, 1)
- self.assertAllClose(out[0][0][0], out[1][0][3])
- self.assertAllClose(out[0][0][1], out[1][0][4])
- self.assertAllClose(out[0][0][2], out[1][0][5])
+ self.assertEqual(out[0][0][0], out[1][0][3])
+ self.assertEqual(out[0][0][1], out[1][0][4])
+ self.assertEqual(out[0][0][2], out[1][0][5])
# Check that the time=1 forward output is equal to time=0 backward output
- self.assertAllClose(out[1][0][0], out[0][0][3])
- self.assertAllClose(out[1][0][1], out[0][0][4])
- self.assertAllClose(out[1][0][2], out[0][0][5])
+ self.assertEqual(out[1][0][0], out[0][0][3])
+ self.assertEqual(out[1][0][1], out[0][0][4])
+ self.assertEqual(out[1][0][2], out[0][0][5])
# Second sequence in batch is length=3
# Check that the time=0 forward output is equal to time=2 backward output
- self.assertAllClose(out[0][1][0], out[2][1][3])
- self.assertAllClose(out[0][1][1], out[2][1][4])
- self.assertAllClose(out[0][1][2], out[2][1][5])
+ self.assertEqual(out[0][1][0], out[2][1][3])
+ self.assertEqual(out[0][1][1], out[2][1][4])
+ self.assertEqual(out[0][1][2], out[2][1][5])
# Check that the time=1 forward output is equal to time=1 backward output
- self.assertAllClose(out[1][1][0], out[1][1][3])
- self.assertAllClose(out[1][1][1], out[1][1][4])
- self.assertAllClose(out[1][1][2], out[1][1][5])
+ self.assertEqual(out[1][1][0], out[1][1][3])
+ self.assertEqual(out[1][1][1], out[1][1][4])
+ self.assertEqual(out[1][1][2], out[1][1][5])
# Check that the time=2 forward output is equal to time=0 backward output
- self.assertAllClose(out[2][1][0], out[0][1][3])
- self.assertAllClose(out[2][1][1], out[0][1][4])
- self.assertAllClose(out[2][1][2], out[0][1][5])
+ self.assertEqual(out[2][1][0], out[0][1][3])
+ self.assertEqual(out[2][1][1], out[0][1][4])
+ self.assertEqual(out[2][1][2], out[0][1][5])
# Via the reasoning above, the forward and backward final state should be
# exactly the same
self.assertAllClose(s_fw, s_bw)
@@ -1565,13 +1560,13 @@ class MultiDimensionalLSTMTest(test.TestCase):
outputs_sav_v = sess.run(outputs_sav,
feed_dict={inputs_using_dim[0]: input_value})
- self.assertAllClose(outputs_static_v, outputs_dynamic_v)
- self.assertAllClose(outputs_static_v, outputs_sav_v)
+ self.assertAllEqual(outputs_static_v, outputs_dynamic_v)
+ self.assertAllEqual(outputs_static_v, outputs_sav_v)
outputs_static_array = np.array(outputs_static_v)
outputs_static_array_double = np.concatenate(
(outputs_static_array, outputs_static_array), axis=2)
outputs_bid_array = np.array(outputs_bid_v)
- self.assertAllClose(outputs_static_array_double, outputs_bid_array)
+ self.assertAllEqual(outputs_static_array_double, outputs_bid_array)
state_static_v = sess.run(state_static,
feed_dict={inputs[0]: input_value})
@@ -1583,10 +1578,10 @@ class MultiDimensionalLSTMTest(test.TestCase):
feed_dict={inputs_using_dim[0]: input_value})
state_sav_v = sess.run(state_sav,
feed_dict={inputs_using_dim[0]: input_value})
- self.assertAllClose(np.hstack(state_static_v), np.hstack(state_dynamic_v))
- self.assertAllClose(np.hstack(state_static_v), np.hstack(state_sav_v))
- self.assertAllClose(np.hstack(state_static_v), np.hstack(state_bid_fw_v))
- self.assertAllClose(np.hstack(state_static_v), np.hstack(state_bid_bw_v))
+ self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v))
+ self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_sav_v))
+ self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_fw_v))
+ self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_bw_v))
class NestedLSTMTest(test.TestCase):
@@ -1668,14 +1663,14 @@ class NestedLSTMTest(test.TestCase):
outputs_bid_v = sess.run(outputs_bid,
feed_dict={single_input_using_dim: input_value})
- self.assertAllClose(outputs_static_v,
+ self.assertAllEqual(outputs_static_v,
np.transpose(outputs_dynamic_v, (1, 0, 2, 3)))
- self.assertAllClose(outputs_static_v, outputs_sav_v)
+ self.assertAllEqual(outputs_static_v, outputs_sav_v)
outputs_static_array = np.array(outputs_static_v)
outputs_static_array_double = np.concatenate(
(outputs_static_array, outputs_static_array), axis=3)
outputs_bid_array = np.array(outputs_bid_v)
- self.assertAllClose(outputs_static_array_double, outputs_bid_array)
+ self.assertAllEqual(outputs_static_array_double, outputs_bid_array)
state_dynamic_v = sess.run(state_dynamic,
feed_dict={single_input: input_value})
@@ -1687,10 +1682,10 @@ class NestedLSTMTest(test.TestCase):
feed_dict={single_input_using_dim: input_value})
state_sav_v = sess.run(state_sav,
feed_dict={single_input_using_dim: input_value})
- self.assertAllClose(np.hstack(state_static_v), np.hstack(state_dynamic_v))
- self.assertAllClose(np.hstack(state_static_v), np.hstack(state_sav_v))
- self.assertAllClose(np.hstack(state_static_v), np.hstack(state_bid_fw_v))
- self.assertAllClose(np.hstack(state_static_v), np.hstack(state_bid_bw_v))
+ self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v))
+ self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_sav_v))
+ self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_fw_v))
+ self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_bw_v))
class StateSaverRNNTest(test.TestCase):
diff --git a/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py b/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py
index c2843edaf2..2d65d956a8 100644
--- a/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py
+++ b/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py
@@ -22,7 +22,6 @@ from __future__ import print_function
import collections
import math
-from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import clip_ops
@@ -62,7 +61,7 @@ class BasicRNNCell(RNNCell):
"""Most basic RNN: output = new_state = act(W * input + U * state + B)."""
with vs.variable_scope(scope or "basic_rnn_cell"):
output = self._activation(
- _linear([inputs, state], self._num_units, True))
+ _linear([inputs, state], self._num_units, True, scope=scope))
return output, output
@@ -90,13 +89,14 @@ class GRUCell(RNNCell):
# We start with bias of 1.0 to not reset and not update.
r, u = array_ops.split(
value=_linear(
- [inputs, state], 2 * self._num_units, True, 1.0),
+ [inputs, state], 2 * self._num_units, True, 1.0, scope=scope),
num_or_size_splits=2,
axis=1)
r, u = sigmoid(r), sigmoid(u)
with vs.variable_scope("candidate"):
c = self._activation(_linear([inputs, r * state],
- self._num_units, True))
+ self._num_units, True,
+ scope=scope))
new_h = u * state + (1 - u) * c
return new_h, new_h
@@ -176,7 +176,7 @@ class BasicLSTMCell(RNNCell):
c, h = state
else:
c, h = array_ops.split(value=state, num_or_size_splits=2, axis=1)
- concat = _linear([inputs, h], 4 * self._num_units, True)
+ concat = _linear([inputs, h], 4 * self._num_units, True, scope=scope)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=1)
@@ -192,13 +192,6 @@ class BasicLSTMCell(RNNCell):
return new_h, new_state
-def _maybe_compile(fun, compiled):
- if not compiled:
- return fun
- else:
- return function.Defun(noinline=True, compiled=True)(fun)
-
-
class LSTMCell(RNNCell):
"""Long short-term memory unit (LSTM) recurrent network cell.
@@ -226,7 +219,7 @@ class LSTMCell(RNNCell):
initializer=None, num_proj=None, proj_clip=None,
num_unit_shards=None, num_proj_shards=None,
forget_bias=1.0, state_is_tuple=True,
- activation=tanh, compiled=False):
+ activation=tanh):
"""Initialize the parameters for an LSTM cell.
Args:
@@ -253,12 +246,6 @@ class LSTMCell(RNNCell):
the `c_state` and `m_state`. If False, they are concatenated
along the column axis. This latter behavior will soon be deprecated.
activation: Activation function of the inner states.
- compiled: Python boolean. If `True`, the core computation of the LSTM
- cell is compiled via XLA. As of now, this provides speedups for
- most GPU calculations, and on small batch CPU and embedded calculations.
-
- Raises:
- ValueError: if compiled=True and state_is_tuple=False (not supported).
"""
if not state_is_tuple:
logging.warn("%s: Using a concatenated state is slower and will soon be "
@@ -270,9 +257,6 @@ class LSTMCell(RNNCell):
"%s: The num_unit_shards and proj_unit_shards parameters are "
"deprecated and will be removed in Jan 2017. "
"Use a variable scope with a partitioner instead.", self)
- if not state_is_tuple and compiled:
- raise ValueError(
- "Combining state_is_tuple=False and compiled=True is not supported.")
self._num_units = num_units
self._use_peepholes = use_peepholes
@@ -285,7 +269,6 @@ class LSTMCell(RNNCell):
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = activation
- self._compiled = compiled
if num_proj:
self._state_size = (
@@ -334,111 +317,73 @@ class LSTMCell(RNNCell):
"""
num_proj = self._num_units if self._num_proj is None else self._num_proj
- def _kernel(k_inputs, state_p0, state_p1):
- """Internal kernel for the single step of LSTM.
-
- Args:
- k_inputs: Input Tensor.
- state_p0: Either the state or the c component of the state.
- state_p1: Either the state or the m component of the state.
-
- Returns:
- (m, c) or (m, concat([c, m])) depending on state_is_tuple.
-
- Raises:
- ValueError: see above docstring.
- """
- k_inputs.set_shape(inputs.get_shape())
- if self._state_is_tuple:
- (c_prev, m_prev) = state_p0, state_p1
- c_prev.set_shape(state[0].get_shape())
- m_prev.set_shape(state[1].get_shape())
+ if self._state_is_tuple:
+ (c_prev, m_prev) = state
+ else:
+ c_prev = array_ops.slice(state, [0, 0], [-1, self._num_units])
+ m_prev = array_ops.slice(state, [0, self._num_units], [-1, num_proj])
+
+ dtype = inputs.dtype
+ input_size = inputs.get_shape().with_rank(2)[1]
+ if input_size.value is None:
+ raise ValueError("Could not infer input size from inputs.get_shape()[-1]")
+ with vs.variable_scope(scope or "lstm_cell",
+ initializer=self._initializer) as unit_scope:
+ if self._num_unit_shards is not None:
+ unit_scope.set_partitioner(
+ partitioned_variables.fixed_size_partitioner(
+ self._num_unit_shards))
+ # i = input_gate, j = new_input, f = forget_gate, o = output_gate
+ lstm_matrix = _linear([inputs, m_prev], 4 * self._num_units, bias=True,
+ scope=scope)
+ i, j, f, o = array_ops.split(
+ value=lstm_matrix, num_or_size_splits=4, axis=1)
+
+ # Diagonal connections
+ if self._use_peepholes:
+ with vs.variable_scope(unit_scope) as projection_scope:
+ if self._num_unit_shards is not None:
+ projection_scope.set_partitioner(None)
+ w_f_diag = vs.get_variable(
+ "w_f_diag", shape=[self._num_units], dtype=dtype)
+ w_i_diag = vs.get_variable(
+ "w_i_diag", shape=[self._num_units], dtype=dtype)
+ w_o_diag = vs.get_variable(
+ "w_o_diag", shape=[self._num_units], dtype=dtype)
+
+ if self._use_peepholes:
+ c = (sigmoid(f + self._forget_bias + w_f_diag * c_prev) * c_prev +
+ sigmoid(i + w_i_diag * c_prev) * self._activation(j))
else:
- k_state = state_p0
- c_prev = array_ops.slice(k_state, [0, 0], [-1, self._num_units])
- m_prev = array_ops.slice(k_state, [0, self._num_units], [-1, num_proj])
-
- dtype = k_inputs.dtype
- input_size = k_inputs.get_shape().with_rank(2)[1]
- if input_size.value is None:
- raise ValueError(
- "Could not infer input size from inputs.get_shape()[-1]")
- with vs.variable_scope(scope or "lstm_cell",
- initializer=self._initializer) as unit_scope:
- if self._num_unit_shards is not None:
- unit_scope.set_partitioner(
- partitioned_variables.fixed_size_partitioner(
- self._num_unit_shards))
- # i = input_gate, j = new_input, f = forget_gate, o = output_gate
- lstm_matrix = _linear(
- [k_inputs, m_prev], 4 * self._num_units, bias=True,
- compiled=self._compiled)
- i, j, f, o = array_ops.split(
- value=lstm_matrix, num_or_size_splits=4, axis=1)
-
- # Diagonal connections
- if self._use_peepholes:
- with vs.variable_scope(unit_scope) as projection_scope:
- if self._num_unit_shards is not None:
- projection_scope.set_partitioner(None)
- w_f_diag = vs.get_variable(
- "w_f_diag", shape=[self._num_units], dtype=dtype)
- w_i_diag = vs.get_variable(
- "w_i_diag", shape=[self._num_units], dtype=dtype)
- w_o_diag = vs.get_variable(
- "w_o_diag", shape=[self._num_units], dtype=dtype)
- c = (sigmoid(f + self._forget_bias + w_f_diag * c_prev) * c_prev +
- sigmoid(i + w_i_diag * c_prev) * self._activation(j))
- else:
- c = (sigmoid(f + self._forget_bias) * c_prev + sigmoid(i) *
- self._activation(j))
-
- if self._cell_clip is not None:
- # pylint: disable=invalid-unary-operand-type
- c = clip_ops.clip_by_value(c, -self._cell_clip, self._cell_clip)
- # pylint: enable=invalid-unary-operand-type
-
- if self._use_peepholes:
- m = sigmoid(o + w_o_diag * c) * self._activation(c)
- else:
- m = sigmoid(o) * self._activation(c)
+ c = (sigmoid(f + self._forget_bias) * c_prev + sigmoid(i) *
+ self._activation(j))
- if self._num_proj is not None:
- with vs.variable_scope("projection") as proj_scope:
- if self._num_proj_shards is not None:
- proj_scope.set_partitioner(
- partitioned_variables.fixed_size_partitioner(
- self._num_proj_shards))
- m = _linear(m, self._num_proj, bias=False, compiled=self._compiled)
+ if self._cell_clip is not None:
+ # pylint: disable=invalid-unary-operand-type
+ c = clip_ops.clip_by_value(c, -self._cell_clip, self._cell_clip)
+ # pylint: enable=invalid-unary-operand-type
- if self._proj_clip is not None:
- # pylint: disable=invalid-unary-operand-type
- m = clip_ops.clip_by_value(m, -self._proj_clip, self._proj_clip)
- # pylint: enable=invalid-unary-operand-type
-
- if self._state_is_tuple:
- return m, c
+ if self._use_peepholes:
+ m = sigmoid(o + w_o_diag * c) * self._activation(c)
else:
- return m, array_ops.concat([c, m], 1)
+ m = sigmoid(o) * self._activation(c)
- compiled_kernel = _maybe_compile(_kernel, self._compiled)
+ if self._num_proj is not None:
+ with vs.variable_scope("projection") as proj_scope:
+ if self._num_proj_shards is not None:
+ proj_scope.set_partitioner(
+ partitioned_variables.fixed_size_partitioner(
+ self._num_proj_shards))
+ m = _linear(m, self._num_proj, bias=False, scope=scope)
- if self._state_is_tuple:
- batch_shape = (
- inputs.get_shape()[:1].merge_with(
- state[0].get_shape()[:1]).merge_with(
- state[1].get_shape()[:1]))
- emit_m, emit_c = compiled_kernel(inputs, state[0], state[1])
- emit_c.set_shape(batch_shape.concatenate([state[0].get_shape()[1]]))
- emit_m.set_shape(batch_shape.concatenate([state[1].get_shape()[1]]))
- emit_state = LSTMStateTuple(emit_c, emit_m)
- else:
- batch_shape = inputs.get_shape()[:1].merge_with(state.get_shape()[:1])
- emit_m, emit_state = compiled_kernel(inputs, state, state)
- emit_m.set_shape(batch_shape.concatenate([num_proj]))
- emit_state.set_shape(batch_shape.concatenate([state.get_shape()[1]]))
+ if self._proj_clip is not None:
+ # pylint: disable=invalid-unary-operand-type
+ m = clip_ops.clip_by_value(m, -self._proj_clip, self._proj_clip)
+ # pylint: enable=invalid-unary-operand-type
- return emit_m, emit_state
+ new_state = (LSTMStateTuple(c, m) if self._state_is_tuple else
+ array_ops.concat([c, m], 1))
+ return m, new_state
class OutputProjectionWrapper(RNNCell):
@@ -481,7 +426,7 @@ class OutputProjectionWrapper(RNNCell):
output, res_state = self._cell(inputs, state)
# Default scope: "OutputProjectionWrapper"
with vs.variable_scope(scope or "output_projection_wrapper"):
- projected = _linear(output, self._output_size, True)
+ projected = _linear(output, self._output_size, True, scope=scope)
return projected, res_state
@@ -523,7 +468,7 @@ class InputProjectionWrapper(RNNCell):
"""Run the input projection and then the cell."""
# Default scope: "InputProjectionWrapper"
with vs.variable_scope(scope or "input_projection_wrapper"):
- projected = _linear(inputs, self._num_proj, True)
+ projected = _linear(inputs, self._num_proj, True, scope=scope)
return self._cell(projected, state)
@@ -817,7 +762,7 @@ class _SlimRNNCell(RNNCell):
return output, state
-def _linear(args, output_size, bias, bias_start=0.0, compiled=False):
+def _linear(args, output_size, bias, bias_start=0.0, scope=None):
"""Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
Args:
@@ -825,7 +770,7 @@ def _linear(args, output_size, bias, bias_start=0.0, compiled=False):
output_size: int, second dimension of W[i].
bias: boolean, whether to add a bias term or not.
bias_start: starting value to initialize the bias; 0 by default.
- compiled: boolean, _linear plays nicely with XLA if it is enabled.
+ scope: (optional) Variable scope to create parameters in.
Returns:
A 2D Tensor with shape [batch x output_size] equal to
@@ -870,8 +815,4 @@ def _linear(args, output_size, bias, bias_start=0.0, compiled=False):
"biases", [output_size],
dtype=dtype,
initializer=init_ops.constant_initializer(bias_start, dtype=dtype))
- if compiled:
- # TODO(b/34505635): Defuns don't play well with bias_add
- return res + biases
- else:
- return nn_ops.bias_add(res, biases)
+ return nn_ops.bias_add(res, biases)
diff --git a/tensorflow/contrib/seq2seq/python/ops/sampling_decoder.py b/tensorflow/contrib/seq2seq/python/ops/sampling_decoder.py
index 2e754b7f22..cfb964d885 100644
--- a/tensorflow/contrib/seq2seq/python/ops/sampling_decoder.py
+++ b/tensorflow/contrib/seq2seq/python/ops/sampling_decoder.py
@@ -113,8 +113,7 @@ class BasicSamplingDecoder(decoder.Decoder):
dtypes.int32)
def initialize(self, name=None):
- with ops.name_scope("basic_sampling_decoder_initialize"):
- return self._sampler.initialize() + (self._initial_state,)
+ return self._sampler.initialize() + (self._initial_state,)
def step(self, time, inputs, state):
"""Perform a decoding step.
@@ -127,12 +126,11 @@ class BasicSamplingDecoder(decoder.Decoder):
Returns:
`(outputs, next_state, next_inputs, finished)`.
"""
- with ops.name_scope("basic_sampling_decoder_step"):
- cell_outputs, next_state = self._cell(inputs, state)
- (sample_id, finished, next_inputs) = self._sampler.sample(
- time=time, outputs=cell_outputs, state=next_state)
- outputs = SamplingDecoderOutput(cell_outputs, sample_id)
- return (outputs, next_state, next_inputs, finished)
+ cell_outputs, next_state = self._cell(inputs, state)
+ (sample_id, finished, next_inputs) = self._sampler.sample(
+ time=time, outputs=cell_outputs, state=next_state)
+ outputs = SamplingDecoderOutput(cell_outputs, sample_id)
+ return (outputs, next_state, next_inputs, finished)
class BasicTrainingSampler(Sampler):
diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index 56d4f6ff58..ebf835d110 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -7,6 +7,7 @@ load("//tensorflow:tensorflow.bzl", "if_not_mobile")
# configure may change the following lines
WITH_GCP_SUPPORT = False
WITH_HDFS_SUPPORT = False
+WITH_XLA_SUPPORT = False
WITH_JEMALLOC = True
# Appends a suffix to a list of deps.
@@ -241,3 +242,15 @@ def tf_additional_cloud_kernel_deps():
#if WITH_GCP_SUPPORT:
# deps = if_not_mobile(["//tensorflow/core:cloud_ops_op_lib"])
return deps
+
+def tf_additional_plugin_deps():
+ deps = []
+ if WITH_XLA_SUPPORT:
+ deps.append("//tensorflow/compiler/jit")
+ return deps
+
+def tf_additional_license_deps():
+ licenses = []
+ if WITH_XLA_SUPPORT:
+ licenses.append("@llvm//:LICENSE.TXT")
+ return licenses
diff --git a/tensorflow/core/platform/default/build_config_root.bzl b/tensorflow/core/platform/default/build_config_root.bzl
index 23a7b9065a..2fa2726bde 100644
--- a/tensorflow/core/platform/default/build_config_root.bzl
+++ b/tensorflow/core/platform/default/build_config_root.bzl
@@ -2,25 +2,8 @@
# The functions in this file might be referred by tensorflow.bzl. They have to
# be separate to avoid cyclic references.
-WITH_XLA_SUPPORT = False
-
def tf_cuda_tests_tags():
return ["local"]
def tf_sycl_tests_tags():
return ["local"]
-
-def tf_additional_plugin_deps():
- deps = []
- if WITH_XLA_SUPPORT:
- deps.append("//tensorflow/compiler/jit")
- return deps
-
-def tf_additional_xla_deps_py():
- return []
-
-def tf_additional_license_deps():
- licenses = []
- if WITH_XLA_SUPPORT:
- licenses.append("@llvm//:LICENSE.TXT")
- return licenses
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 2befe43be6..1834ce570e 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -23,7 +23,7 @@ load("//tensorflow:tensorflow.bzl", "cuda_py_tests")
load("//tensorflow/core:platform/default/build_config.bzl", "tf_proto_library")
load("//tensorflow/core:platform/default/build_config.bzl", "tf_proto_library_py")
load("//tensorflow/core:platform/default/build_config.bzl", "tf_additional_lib_deps")
-load("//tensorflow/core:platform/default/build_config_root.bzl", "tf_additional_plugin_deps")
+load("//tensorflow/core:platform/default/build_config.bzl", "tf_additional_plugin_deps")
load("//tensorflow/python:build_defs.bzl", "tf_gen_op_wrapper_private_py")
py_library(
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 0e5b39af10..7fa7e4a91d 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -12,7 +12,6 @@ load(
"//tensorflow/core:platform/default/build_config_root.bzl",
"tf_cuda_tests_tags",
"tf_sycl_tests_tags",
- "tf_additional_xla_deps_py",
)
load(
"@local_config_cuda//cuda:build_defs.bzl",
@@ -790,10 +789,7 @@ def py_test(deps=[], **kwargs):
**kwargs)
def tf_py_test(name, srcs, size="medium", data=[], main=None, args=[],
- tags=[], shard_count=1, additional_deps=[], flaky=0,
- xla_enabled=False):
- if xla_enabled:
- additional_deps += tf_additional_xla_deps_py()
+ tags=[], shard_count=1, additional_deps=[], flaky=0):
native.py_test(
name=name,
size=size,
@@ -815,8 +811,7 @@ def tf_py_test(name, srcs, size="medium", data=[], main=None, args=[],
srcs_version="PY2AND3")
def cuda_py_test(name, srcs, size="medium", data=[], main=None, args=[],
- shard_count=1, additional_deps=[], tags=[], flaky=0,
- xla_enabled=False):
+ shard_count=1, additional_deps=[], tags=[], flaky=0):
test_tags = tags + tf_cuda_tests_tags()
tf_py_test(name=name,
size=size,
@@ -827,12 +822,10 @@ def cuda_py_test(name, srcs, size="medium", data=[], main=None, args=[],
tags=test_tags,
shard_count=shard_count,
additional_deps=additional_deps,
- flaky=flaky,
- xla_enabled=xla_enabled)
+ flaky=flaky)
def sycl_py_test(name, srcs, size="medium", data=[], main=None, args=[],
- shard_count=1, additional_deps=[], tags=[], flaky=0,
- xla_enabled=False):
+ shard_count=1, additional_deps=[], tags=[], flaky=0):
test_tags = tags + tf_sycl_tests_tags()
tf_py_test(name=name,
size=size,
@@ -843,8 +836,7 @@ def sycl_py_test(name, srcs, size="medium", data=[], main=None, args=[],
tags=test_tags,
shard_count=shard_count,
additional_deps=additional_deps,
- flaky=flaky,
- xla_enabled=xla_enabled)
+ flaky=flaky)
def py_tests(name,
srcs,
@@ -853,8 +845,7 @@ def py_tests(name,
data=[],
tags=[],
shard_count=1,
- prefix="",
- xla_enabled=False):
+ prefix=""):
for src in srcs:
test_name = src.split("/")[-1].split(".")[0]
if prefix:
@@ -866,15 +857,13 @@ def py_tests(name,
tags=tags,
shard_count=shard_count,
data=data,
- additional_deps=additional_deps,
- xla_enabled=xla_enabled)
+ additional_deps=additional_deps)
def cuda_py_tests(name, srcs, size="medium", additional_deps=[], data=[],
- shard_count=1, tags=[], prefix="", xla_enabled=False):
+ shard_count=1, tags=[], prefix=""):
test_tags = tags + tf_cuda_tests_tags()
py_tests(name=name, size=size, srcs=srcs, additional_deps=additional_deps,
- data=data, tags=test_tags, shard_count=shard_count,prefix=prefix,
- xla_enabled=xla_enabled)
+ data=data, tags=test_tags, shard_count=shard_count,prefix=prefix)
# Creates a genrule named <name> for running tools/proto_text's generator to
# make the proto_text functions, for the protos passed in <srcs>.
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 85a8b79f85..0ffbec8b3c 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -4,7 +4,7 @@
package(default_visibility = ["//visibility:private"])
load("//tensorflow:tensorflow.bzl", "transitive_hdrs")
-load("//tensorflow/core:platform/default/build_config_root.bzl", "tf_additional_license_deps")
+load("//tensorflow/core:platform/default/build_config.bzl", "tf_additional_license_deps")
# This returns a list of headers of all public header libraries (e.g.,
# framework, lib), and all of the transitive dependencies of those
generated by cgit v1.2.3 (git 2.39.1) at 2024-05-18 14:23:17 +0000