experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Add tf.math.sobol_sample for generating Sobol sequences (CPU implementation only).

Browse Source 
PiperOrigin-RevId: 286365254
Change-Id: Ia0c2482f4f264f36fe61db5f9c72f24db35faf65
This commit is contained in:
A. Unique TensorFlower 2019-12-19 03:46:07 -08:00 committed by TensorFlower Gardener
parent ecce7990b9
commit 50a1c3be8b
19 changed files with 447 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
tensorflow/core/BUILD
View File

@ -1254,7 +1254,9 @@ cc_library(
cc_library(
name = "dynamic_kernels_impl",
visibility = [":__subpackages__"],
deps = [],
deps = [
"//tensorflow/core/kernels:sobol_op",
],
)
cc_library(

41
tensorflow/core/api_def/base_api/api_def_SobolSample.pbtxt Normal file
View File

@ -0,0 +1,41 @@
op {
graph_op_name: "SobolSample"
visibility: HIDDEN
in_arg {
name: "dim"
description: <<END
Positive scalar `Tensor` representing each sample's dimension.
END
}
in_arg {
name: "num_results"
description: <<END
Positive scalar `Tensor` of dtype int32. The number of Sobol points to return
in the output.
END
}
in_arg {
name: "skip"
description: <<END
Positive scalar `Tensor` of dtype int32. The number of initial points of the
Sobol sequence to skip.
END
}
attr {
name: "dtype"
description: <<END
The type of the sample. One of: `float32` or `float64`.
END
}
out_arg {
name: "samples"
description: <<END
`Tensor` of samples from Sobol sequence with `shape` [num_results, dim].
END
}
summary: "Generates points from the Sobol sequence."
description: <<END
Creates a Sobol sequence with `num_results` samples. Each sample has dimension
`dim`. Skips the first `skip` samples.
END
}

14
tensorflow/core/kernels/BUILD
View File

@ -6705,6 +6705,7 @@ filegroup(
"decode_proto_op.cc",
"encode_proto_op.cc",
"rpc_op.cc",
"sobol_op.cc",
# Excluded due to experimental status:
"debug_ops.*",
"mutex_ops.*",
@ -8278,3 +8279,16 @@ exports_files([
"sparse_reshape_op.cc",
"unary_ops_composition.cc",
])
tf_kernel_library(
name = "sobol_op",
srcs = [
"sobol_op.cc",
],
deps = [
"//tensorflow/core:framework",
"//tensorflow/core:lib",
"//third_party/eigen3",
"@sobol_data",
],
)

182
tensorflow/core/kernels/sobol_op.cc Normal file
View File

@ -0,0 +1,182 @@
/* Copyright 2019 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.
==============================================================================*/
// Based on "Notes on generating Sobol sequences. August 2008" by Joe and Kuo.
// [1] https://web.maths.unsw.edu.au/~fkuo/sobol/joe-kuo-notes.pdf
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <limits>
#include "third_party/eigen3/Eigen/Core"
#include "sobol_data.h"
#include "tensorflow/core/framework/device_base.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/lib/core/threadpool.h"
#include "tensorflow/core/platform/platform_strings.h"
namespace tensorflow {
// Embed the platform strings in this binary.
TF_PLATFORM_STRINGS()
typedef Eigen::ThreadPoolDevice CPUDevice;
namespace {
// Each thread will calculate at least kMinBlockSize points in the sequence.
constexpr int kMinBlockSize = 512;
// Returns number of digits in binary representation of n.
// Example: n=13. Binary representation is 1101. NumBinaryDigits(13) -> 4.
int NumBinaryDigits(int n) {
return static_cast<int>(std::log2(n) + 1);
}
// Returns position of rightmost zero digit in binary representation of n.
// Example: n=13. Binary representation is 1101. RightmostZeroBit(13) -> 1.
int RightmostZeroBit(int n) {
int k = 0;
while (n & 1) {
n >>= 1;
++k;
}
return k;
}
// Returns an integer representation of point `i` in the Sobol sequence of
// dimension `dim` using the given direction numbers.
Eigen::VectorXi GetFirstPoint(int i, int dim,
const Eigen::MatrixXi& direction_numbers) {
// Index variables used in this function, consistent with notation in [1].
// i - point in the Sobol sequence
// j - dimension
// k - binary digit
Eigen::VectorXi integer_sequence = Eigen::VectorXi::Zero(dim);
// go/wiki/Sobol_sequence#A_fast_algorithm_for_the_construction_of_Sobol_sequences
int gray_code = i ^ (i >> 1);
int num_digits = NumBinaryDigits(i);
for (int j = 0; j < dim; ++j) {
for (int k = 0; k < num_digits; ++k) {
if ((gray_code >> k) & 1) integer_sequence(j) ^= direction_numbers(j, k);
}
}
return integer_sequence;
}
// Calculates `num_results` Sobol points of dimension `dim` starting at the
// point `start_point + skip` and writes them into `output` starting at point
// `start_point`.
template <typename T>
void CalculateSobolSample(int32_t dim, int32_t num_results, int32_t skip,
int32_t start_point,
typename TTypes<T>::Flat output) {
// Index variables used in this function, consistent with notation in [1].
// i - point in the Sobol sequence
// j - dimension
// k - binary digit
const int num_digits =
NumBinaryDigits(skip + start_point + num_results + 1);
Eigen::MatrixXi direction_numbers(dim, num_digits);
// Shift things so we can use integers everywhere. Before we write to output,
// divide by constant to convert back to floats.
const T normalizing_constant = 1./(1 << num_digits);
for (int j = 0; j < dim; ++j) {
for (int k = 0; k < num_digits; ++k) {
direction_numbers(j, k) = sobol_data::kDirectionNumbers[j][k]
<< (num_digits - k - 1);
}
}
// If needed, skip ahead to the appropriate point in the sequence. Otherwise
// we start with the first column of direction numbers.
Eigen::VectorXi integer_sequence =
(skip + start_point > 0)
? GetFirstPoint(skip + start_point + 1, dim, direction_numbers)
: direction_numbers.col(0);
for (int j = 0; j < dim; ++j) {
output(start_point * dim + j) = integer_sequence(j) * normalizing_constant;
}
// go/wiki/Sobol_sequence#A_fast_algorithm_for_the_construction_of_Sobol_sequences
for (int i = start_point + 1; i < num_results + start_point; ++i) {
// The Gray code for the current point differs from the preceding one by
// just a single bit -- the rightmost bit.
int k = RightmostZeroBit(i + skip);
// Update the current point from the preceding one with a single XOR
// operation per dimension.
for (int j = 0; j < dim; ++j) {
integer_sequence(j) ^= direction_numbers(j, k);
output(i * dim + j) = integer_sequence(j) * normalizing_constant;
}
}
}
} // namespace
template <typename Device, typename T>
class SobolSampleOp : public OpKernel {
public:
explicit SobolSampleOp(OpKernelConstruction* context)
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
int32_t dim = context->input(0).scalar<int32_t>()();
int32_t num_results = context->input(1).scalar<int32_t>()();
int32_t skip = context->input(2).scalar<int32_t>()();
OP_REQUIRES(context, dim >= 1,
errors::InvalidArgument("dim must be at least one"));
OP_REQUIRES(context, dim <= sobol_data::kMaxSobolDim,
errors::InvalidArgument("dim must be at most ",
sobol_data::kMaxSobolDim));
OP_REQUIRES(context, num_results >= 1,
errors::InvalidArgument("num_results must be at least one"));
OP_REQUIRES(context, skip >= 0,
errors::InvalidArgument("skip must be non-negative"));
OP_REQUIRES(context,
num_results < std::numeric_limits<int32_t>::max() - skip,
errors::InvalidArgument("num_results+skip must be less than ",
std::numeric_limits<int32_t>::max()));
Tensor* output = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(
0, TensorShape({num_results, dim}), &output));
auto output_flat = output->flat<T>();
const DeviceBase::CpuWorkerThreads& worker_threads =
*(context->device()->tensorflow_cpu_worker_threads());
int num_threads = worker_threads.num_threads;
int block_size = std::max(
kMinBlockSize, static_cast<int>(std::ceil(
static_cast<float>(num_results) / num_threads)));
worker_threads.workers->TransformRangeConcurrently(
block_size, num_results /* total */,
[&dim, &skip, &output_flat](const int start, const int end) {
CalculateSobolSample<T>(dim, end - start /* num_results */, skip,
start, output_flat);
});
}
};
REGISTER_KERNEL_BUILDER(
Name("SobolSample").Device(DEVICE_CPU).TypeConstraint<double>("dtype"),
SobolSampleOp<CPUDevice, double>);
REGISTER_KERNEL_BUILDER(
Name("SobolSample").Device(DEVICE_CPU).TypeConstraint<float>("dtype"),
SobolSampleOp<CPUDevice, float>);
} // namespace tensorflow

24
tensorflow/core/ops/math_ops.cc
View File

@ -1907,4 +1907,28 @@ REGISTER_OP("NextAfter")
.Output("output: T")
.SetShapeFn(shape_inference::BroadcastBinaryOpShapeFn);
REGISTER_OP("SobolSample")
.Input("dim: int32")
.Input("num_results: int32")
.Input("skip: int32")
.Attr("dtype: {float, double} = DT_DOUBLE")
.Output("samples: dtype")
.SetShapeFn([](shape_inference::InferenceContext* c) {
ShapeHandle unused;
// inputs must be scalars
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
const Tensor* dim_t = c->input_tensor(0);
const Tensor* num_results_t = c->input_tensor(1);
if (dim_t == nullptr || num_results_t == nullptr) {
c->set_output(0, c->Vector(InferenceContext::kUnknownDim));
return Status::OK();
}
const int32 output_size =
dim_t->scalar<int32>()() * num_results_t->scalar<int32>()();
c->set_output(0, c->Vector(output_size));
return Status::OK();
});
} // namespace tensorflow

11
tensorflow/core/ops/math_ops_test.cc
View File

@ -593,4 +593,15 @@ TEST(MathOpsTest, Bincount_ShapeFn) {
INFER_OK(op, "[?];[];?", "[?]");
INFER_OK(op, "[?];[];[?]", "[?]");
}
TEST(MathOpsTest, SobolSample) {
ShapeInferenceTestOp op("SobolSample");
// All inputs should be scalar.
INFER_ERROR("must be rank 0", op, "[1];?;?");
INFER_ERROR("must be rank 0", op, "?;[1];?");
INFER_ERROR("must be rank 0", op, "?;?;[1]");
INFER_OK(op, "[];[];[]", "[?]");
}
} // end namespace tensorflow

17
tensorflow/python/BUILD
View File

@ -4828,6 +4828,23 @@ py_test(
],
)
cuda_py_test(
name = "sobol_ops_test",
size = "small",
srcs = ["ops/sobol_ops_test.py"],
additional_deps = [
":framework_for_generated_wrappers",
":framework_test_lib",
":math_ops",
":platform_test",
"//third_party/py/numpy",
],
kernels = [
"//tensorflow/core/kernels:libtfkernel_sobol_op.so",
],
tags = ["no_windows_gpu"],
)
cuda_py_test(
name = "special_math_ops_test",
size = "medium",

24
tensorflow/python/ops/math_ops.py
View File

@ -4450,3 +4450,27 @@ def exp(x, name=None):
# pylint: enable=g-docstring-has-escape
@tf_export("math.sobol_sample")
def sobol_sample(dim, num_results, skip=0, dtype=None, name=None):
"""Generates points from the Sobol sequence.
Creates a Sobol sequence with `num_results` samples. Each sample has dimension
`dim`. Skips the first `skip` samples.
Args:
dim: Positive scalar `Tensor` representing each sample's dimension.
num_results: Positive scalar `Tensor` of dtype int32. The number of Sobol
points to return in the output.
skip: (Optional) Positive scalar `Tensor` of dtype int32. The number of
initial points of the Sobol sequence to skip. Default value is 0.
dtype: (Optional) The dtype of the sample. One of: `float32` or `float64`.
Default value is determined by the C++ kernel.
name: (Optional) Python `str` name prefixed to ops created by this function.
Returns:
`Tensor` of samples from Sobol sequence with `shape` [num_results, dim].
"""
with ops.name_scope(name, "sobol", [dim, num_results, skip]):
return gen_math_ops.sobol_sample(dim, num_results, skip, dtype=dtype)

83
tensorflow/python/ops/sobol_ops_test.py Normal file
View File

@ -0,0 +1,83 @@
# Copyright 2019 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.
# ==============================================================================
"""Tests Sobol sequence generator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import test_util
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import googletest
class SobolSampleOpTest(test_util.TensorFlowTestCase):
def test_basic(self):
for dtype in [np.float64, np.float32]:
expected = np.array([[.5, .5], [.75, .25], [.25, .75], [.375, .375]])
sample = self.evaluate(math_ops.sobol_sample(2, 4, dtype=dtype))
self.assertAllClose(expected, sample, 0.001)
def test_more_known_values(self):
for dtype in [np.float64, np.float32]:
sample = math_ops.sobol_sample(5, 31, dtype=dtype)
expected = [[0.50, 0.50, 0.50, 0.50, 0.50],
[0.75, 0.25, 0.25, 0.25, 0.75],
[0.25, 0.75, 0.75, 0.75, 0.25],
[0.375, 0.375, 0.625, 0.875, 0.375],
[0.875, 0.875, 0.125, 0.375, 0.875],
[0.625, 0.125, 0.875, 0.625, 0.625],
[0.125, 0.625, 0.375, 0.125, 0.125],
[0.1875, 0.3125, 0.9375, 0.4375, 0.5625],
[0.6875, 0.8125, 0.4375, 0.9375, 0.0625],
[0.9375, 0.0625, 0.6875, 0.1875, 0.3125],
[0.4375, 0.5625, 0.1875, 0.6875, 0.8125],
[0.3125, 0.1875, 0.3125, 0.5625, 0.9375],
[0.8125, 0.6875, 0.8125, 0.0625, 0.4375],
[0.5625, 0.4375, 0.0625, 0.8125, 0.1875],
[0.0625, 0.9375, 0.5625, 0.3125, 0.6875],
[0.09375, 0.46875, 0.46875, 0.65625, 0.28125],
[0.59375, 0.96875, 0.96875, 0.15625, 0.78125],
[0.84375, 0.21875, 0.21875, 0.90625, 0.53125],
[0.34375, 0.71875, 0.71875, 0.40625, 0.03125],
[0.46875, 0.09375, 0.84375, 0.28125, 0.15625],
[0.96875, 0.59375, 0.34375, 0.78125, 0.65625],
[0.71875, 0.34375, 0.59375, 0.03125, 0.90625],
[0.21875, 0.84375, 0.09375, 0.53125, 0.40625],
[0.15625, 0.15625, 0.53125, 0.84375, 0.84375],
[0.65625, 0.65625, 0.03125, 0.34375, 0.34375],
[0.90625, 0.40625, 0.78125, 0.59375, 0.09375],
[0.40625, 0.90625, 0.28125, 0.09375, 0.59375],
[0.28125, 0.28125, 0.15625, 0.21875, 0.71875],
[0.78125, 0.78125, 0.65625, 0.71875, 0.21875],
[0.53125, 0.03125, 0.40625, 0.46875, 0.46875],
[0.03125, 0.53125, 0.90625, 0.96875, 0.96875]]
self.assertAllClose(expected, self.evaluate(sample), .001)
def test_skip(self):
dim = 10
n = 50
skip = 17
for dtype in [np.float64, np.float32]:
sample_noskip = math_ops.sobol_sample(dim, n + skip, dtype=dtype)
sample_skip = math_ops.sobol_sample(dim, n, skip, dtype=dtype)
self.assertAllClose(
self.evaluate(sample_noskip)[skip:, :], self.evaluate(sample_skip))
if __name__ == '__main__':
googletest.main()

4
tensorflow/tools/api/golden/v1/tensorflow.math.pbtxt
View File

@ -420,6 +420,10 @@ tf_module {
name: "sinh"
argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "sobol_sample"
argspec: "args=[\'dim\', \'num_results\', \'skip\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\'], "
}
member_method {
name: "softmax"
argspec: "args=[\'logits\', \'axis\', \'name\', \'dim\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "

4
tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt
View File

@ -3836,6 +3836,10 @@ tf_module {
name: "SnapshotDataset"
argspec: "args=[\'input_dataset\', \'path\', \'output_types\', \'output_shapes\', \'compression\', \'reader_path_prefix\', \'writer_path_prefix\', \'shard_size_bytes\', \'pending_snapshot_expiry_seconds\', \'num_reader_threads\', \'reader_buffer_size\', \'num_writer_threads\', \'writer_buffer_size\', \'shuffle_on_read\', \'seed\', \'seed2\', \'mode\', \'snapshot_name\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'\', \'\', \'10737418240\', \'86400\', \'1\', \'1\', \'1\', \'1\', \'False\', \'0\', \'0\', \'auto\', \'\', \'None\'], "
}
member_method {
name: "SobolSample"
argspec: "args=[\'dim\', \'num_results\', \'skip\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\"<dtype: \'float64\'>\", \'None\'], "
}
member_method {
name: "Softmax"
argspec: "args=[\'logits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "

4
tensorflow/tools/api/golden/v2/tensorflow.math.pbtxt
View File

@ -420,6 +420,10 @@ tf_module {
name: "sinh"
argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "sobol_sample"
argspec: "args=[\'dim\', \'num_results\', \'skip\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\'], "
}
member_method {
name: "softmax"
argspec: "args=[\'logits\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "

4
tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt
View File

@ -3836,6 +3836,10 @@ tf_module {
name: "SnapshotDataset"
argspec: "args=[\'input_dataset\', \'path\', \'output_types\', \'output_shapes\', \'compression\', \'reader_path_prefix\', \'writer_path_prefix\', \'shard_size_bytes\', \'pending_snapshot_expiry_seconds\', \'num_reader_threads\', \'reader_buffer_size\', \'num_writer_threads\', \'writer_buffer_size\', \'shuffle_on_read\', \'seed\', \'seed2\', \'mode\', \'snapshot_name\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'\', \'\', \'10737418240\', \'86400\', \'1\', \'1\', \'1\', \'1\', \'False\', \'0\', \'0\', \'auto\', \'\', \'None\'], "
}
member_method {
name: "SobolSample"
argspec: "args=[\'dim\', \'num_results\', \'skip\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\"<dtype: \'float64\'>\", \'None\'], "
}
member_method {
name: "Softmax"
argspec: "args=[\'logits\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "

2
tensorflow/tools/lib_package/BUILD
View File

@ -161,6 +161,7 @@ genrule(
"@png//:LICENSE",
"@com_google_protobuf//:LICENSE",
"@snappy//:COPYING",
"@sobol_data//:LICENSE",
"@zlib_archive//:zlib.h",
"@six_archive//:LICENSE",
] + select({
@ -232,6 +233,7 @@ genrule(
"@png//:LICENSE",
"@com_google_protobuf//:LICENSE",
"@snappy//:COPYING",
"@sobol_data//:LICENSE",
"@zlib_archive//:zlib.h",
"@grpc//:LICENSE",
"@grpc//third_party/address_sorting:LICENSE",

2
tensorflow/tools/pip_package/BUILD
View File

@ -47,6 +47,7 @@ py_binary(
# Add dynamic kernel dso files here.
DYNAMIC_LOADED_KERNELS = [
"//tensorflow/core/kernels:libtfkernel_sobol_op.so",
]
COMMON_PIP_DEPS = [
@ -160,6 +161,7 @@ filegroup(
"@com_google_protobuf//:LICENSE",
"@six_archive//:LICENSE",
"@snappy//:COPYING",
"@sobol_data//:LICENSE",
"@swig//:LICENSE",
"@termcolor_archive//:COPYING.txt",
"@zlib_archive//:zlib.h",

2
tensorflow/workspace.bzl
View File

@ -38,6 +38,7 @@ load("//third_party/kissfft:workspace.bzl", kissfft = "repo")
load("//third_party/pasta:workspace.bzl", pasta = "repo")
load("//third_party/psimd:workspace.bzl", psimd = "repo")
load("//third_party/pthreadpool:workspace.bzl", pthreadpool = "repo")
load("//third_party/sobol_data:workspace.bzl", sobol_data = "repo")
def initialize_third_party():
""" Load third party repositories. See above load() statements. """
@ -58,6 +59,7 @@ def initialize_third_party():
pasta()
psimd()
pthreadpool()
sobol_data()
# Sanitize a dependency so that it works correctly from code that includes
# TensorFlow as a submodule.

1
third_party/sobol_data/BUILD vendored Normal file
View File

@ -0,0 +1 @@
# This empty BUILD file is required to make Bazel treat this directory as a package.

10
third_party/sobol_data/BUILD.bazel vendored Normal file
View File

@ -0,0 +1,10 @@
licenses(["notice"])
package(default_visibility = ["//visibility:public"])
exports_files(["LICENSE"])
cc_library(
name = "sobol_data",
hdrs = ["sobol_data.h"],
)

15
third_party/sobol_data/workspace.bzl vendored Normal file
View File

@ -0,0 +1,15 @@
"""Loads the sobol_data library, used by TF."""
load("//third_party:repo.bzl", "third_party_http_archive")
def repo():
third_party_http_archive(
name = "sobol_data",
urls = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/sobol_data/archive/835a7d7b1ee3bc83e575e302a985c66ec4b65249.tar.gz",
"https://github.com/joe-kuo/sobol_data/archive/835a7d7b1ee3bc83e575e302a985c66ec4b65249.tar.gz",
],
sha256 = "583d7b975e506c076fc579d9139530596906b9195b203d42361417e9aad79b73",
strip_prefix = "sobol_data-835a7d7b1ee3bc83e575e302a985c66ec4b65249",
build_file = "//third_party/sobol_data:BUILD.bazel",
)