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// Copyright 2016 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.
// ==============================================================================
#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/lib/random/simple_philox.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"
namespace tensorflow {
namespace {
constexpr int k100Dim = 100;
// Number of points for tests.
constexpr int k10Points = 10;
constexpr int k100Points = 100;
constexpr int k1kPoints = 1000;
constexpr int k10kPoints = 10000;
constexpr int k1MPoints = 1000000;
// Number of centers for tests.
constexpr int k2Centers = 2;
constexpr int k5Centers = 5;
constexpr int k10Centers = 10;
constexpr int k20Centers = 20;
constexpr int k50Centers = 50;
constexpr int k100Centers = 100;
constexpr int k200Centers = 200;
constexpr int k500Centers = 500;
constexpr int k1kCenters = 1000;
constexpr int k10kCenters = 10000;
// Number of retries for tests.
constexpr int k0RetriesPerSample = 0;
constexpr int k3RetriesPerSample = 3;
Graph* SetUpKmeansPlusPlusInitialization(int num_dims, int num_points,
int num_to_sample,
int retries_per_sample) {
Graph* g = new Graph(OpRegistry::Global());
Tensor points(DT_FLOAT, TensorShape({num_points, num_dims}));
Tensor sample_size(DT_INT64, TensorShape({}));
Tensor seed(DT_INT64, TensorShape({}));
Tensor num_retries_per_sample(DT_INT64, TensorShape({}));
points.flat<float>().setRandom();
sample_size.flat<int64>().setConstant(num_to_sample);
seed.flat<int64>().setConstant(12345);
num_retries_per_sample.flat<int64>().setConstant(retries_per_sample);
TF_CHECK_OK(NodeBuilder("kmeans_plus_plus_initialization_op",
"KmeansPlusPlusInitialization")
.Input(test::graph::Constant(g, points))
.Input(test::graph::Constant(g, sample_size))
.Input(test::graph::Constant(g, seed))
.Input(test::graph::Constant(g, num_retries_per_sample))
.Finalize(g, nullptr /* node */));
return g;
}
template <int num_points, int num_to_sample, int num_dims,
int retries_per_sample>
void BM_KmeansPlusPlusInitialization(int iters) {
testing::StopTiming();
testing::ItemsProcessed(static_cast<int64>(iters) * num_points * num_dims *
num_to_sample);
testing::UseRealTime();
Graph* g = SetUpKmeansPlusPlusInitialization(
num_dims, num_points, num_to_sample, retries_per_sample);
testing::StartTiming();
test::Benchmark("cpu", g).Run(iters);
}
#define BENCHMARK_KMEANS_PLUS_PLUS(p, c, d, r) \
void BM_KmeansPlusPlusInitialization_##p##_##c##_##d##_##r(int iters) { \
BM_KmeansPlusPlusInitialization<p, c, d, r>(iters); \
} \
BENCHMARK(BM_KmeansPlusPlusInitialization_##p##_##c##_##d##_##r);
#define RUN_BM_KmeansPlusPlusInitialization(retries) \
BENCHMARK_KMEANS_PLUS_PLUS(k10Points, k2Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k10Points, k5Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k10Points, k10Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k100Points, k10Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k100Points, k20Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k100Points, k50Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k100Points, k100Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k1kPoints, k100Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k1kPoints, k200Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k1kPoints, k500Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k1kPoints, k1kCenters, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k10kPoints, k100Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k10kPoints, k200Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k10kPoints, k500Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k10kPoints, k1kCenters, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k1MPoints, k100Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k1MPoints, k200Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k1MPoints, k500Centers, k100Dim, retries); \
BENCHMARK_KMEANS_PLUS_PLUS(k1MPoints, k1kCenters, k100Dim, retries)
RUN_BM_KmeansPlusPlusInitialization(k0RetriesPerSample);
RUN_BM_KmeansPlusPlusInitialization(k3RetriesPerSample);
#undef RUN_BM_KmeansPlusPlusInitialization
#undef BENCHMARK_KMEANS_PLUS_PLUS
Graph* SetUpNearestNeighbors(int num_dims, int num_points, int num_centers,
int k) {
Graph* g = new Graph(OpRegistry::Global());
Tensor points(DT_FLOAT, TensorShape({num_points, num_dims}));
Tensor centers(DT_FLOAT, TensorShape({num_centers, num_dims}));
Tensor top(DT_INT64, TensorShape({}));
points.flat<float>().setRandom();
centers.flat<float>().setRandom();
top.flat<int64>().setConstant(k);
TF_CHECK_OK(NodeBuilder("nearest_centers_op", "NearestNeighbors")
.Input(test::graph::Constant(g, points))
.Input(test::graph::Constant(g, centers))
.Input(test::graph::Constant(g, top))
.Finalize(g, nullptr /* node */));
return g;
}
template <int num_dims, int num_points, int num_centers, int k>
void BM_NearestNeighbors(int iters) {
testing::StopTiming();
testing::ItemsProcessed(static_cast<int64>(iters) * num_points * num_dims *
num_centers);
testing::UseRealTime();
Graph* g = SetUpNearestNeighbors(num_dims, num_points, num_centers, k);
testing::StartTiming();
test::Benchmark("cpu", g).Run(iters);
}
constexpr int kTop1 = 1;
constexpr int kTop2 = 2;
constexpr int kTop5 = 5;
constexpr int kTop10 = 10;
#define BENCHMARK_NEAREST_NEIGHBORS(d, p, c, k) \
void BM_NearestNeighbors##d##_##p##_##c##_##k(int iters) { \
BM_NearestNeighbors<d, p, c, k>(iters); \
} \
BENCHMARK(BM_NearestNeighbors##d##_##p##_##c##_##k);
#define RUN_BM_NearestNeighbors(k) \
BENCHMARK_NEAREST_NEIGHBORS(k100Dim, k1kPoints, k100Centers, k); \
BENCHMARK_NEAREST_NEIGHBORS(k100Dim, k1kPoints, k1kCenters, k); \
BENCHMARK_NEAREST_NEIGHBORS(k100Dim, k1kPoints, k10kCenters, k); \
BENCHMARK_NEAREST_NEIGHBORS(k100Dim, k1MPoints, k100Centers, k); \
BENCHMARK_NEAREST_NEIGHBORS(k100Dim, k1MPoints, k1kCenters, k); \
BENCHMARK_NEAREST_NEIGHBORS(k100Dim, k1MPoints, k10kCenters, k)
RUN_BM_NearestNeighbors(kTop1);
// k > 1
RUN_BM_NearestNeighbors(kTop2);
RUN_BM_NearestNeighbors(kTop5);
RUN_BM_NearestNeighbors(kTop10);
#undef RUN_BM_NearestNeighbors
#undef BENCHMARK_NEAREST_NEIGHBORS
} // namespace
} // namespace tensorflow
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