From 4af6daae77dc13c6ea5164269a13f89177c26973 Mon Sep 17 00:00:00 2001
From: Russell Power <power@google.com>
Date: Fri, 26 Feb 2021 15:43:25 -0800
Subject: [PATCH] Remove stale trainer example.
PiperOrigin-RevId: 359854240
Change-Id: Ic18e62c79b42c25aeb6069711e4c5d39cee22a78
---
tensorflow/cc/BUILD | 31 ---
tensorflow/cc/tutorials/example_trainer.cc | 234 ---------------------
2 files changed, 265 deletions(-)
delete mode 100644 tensorflow/cc/tutorials/example_trainer.cc
diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index 3ff127952f3..61399f33292 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -6,7 +6,6 @@ load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")
load(
"//tensorflow:tensorflow.bzl",
"cc_library_with_android_deps",
- "tf_cc_binary",
"tf_cc_test",
"tf_copts",
"transitive_hdrs",
@@ -748,36 +747,6 @@ tf_gen_op_wrappers_cc(
],
)
-tf_cc_binary(
- name = "tutorials_example_trainer",
- srcs = ["tutorials/example_trainer.cc"],
- copts = tf_copts(),
- linkopts = select({
- "//tensorflow:windows": [],
- "//tensorflow:macos": [
- "-lm",
- "-lpthread",
- ],
- "//tensorflow:ios": [
- "-lm",
- "-lpthread",
- ],
- "//conditions:default": [
- "-lm",
- "-lpthread",
- "-lrt",
- ],
- }),
- deps = [
- ":cc_ops",
- "//tensorflow/core:core_cpu",
- "//tensorflow/core:framework",
- "//tensorflow/core:lib",
- "//tensorflow/core:protos_all_cc",
- "//tensorflow/core:tensorflow",
- ],
-)
-
cc_library(
name = "queue_runner",
srcs = ["training/queue_runner.cc"],
diff --git a/tensorflow/cc/tutorials/example_trainer.cc b/tensorflow/cc/tutorials/example_trainer.cc
deleted file mode 100644
index 789662f84d0..00000000000
--- a/tensorflow/cc/tutorials/example_trainer.cc
+++ /dev/null
@@ -1,234 +0,0 @@
-/* Copyright 2015 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 <cstdio>
-#include <functional>
-#include <string>
-#include <vector>
-
-#include "tensorflow/cc/ops/standard_ops.h"
-#include "tensorflow/core/framework/graph.pb.h"
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/graph/default_device.h"
-#include "tensorflow/core/graph/graph_def_builder.h"
-#include "tensorflow/core/lib/core/threadpool.h"
-#include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/core/lib/strings/stringprintf.h"
-#include "tensorflow/core/platform/init_main.h"
-#include "tensorflow/core/platform/logging.h"
-#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/public/session.h"
-
-using tensorflow::string;
-using tensorflow::int32;
-
-namespace tensorflow {
-namespace example {
-
-struct Options {
- int num_concurrent_sessions = 1; // The number of concurrent sessions
- int num_concurrent_steps = 10; // The number of concurrent steps
- int num_iterations = 100; // Each step repeats this many times
- bool use_gpu = false; // Whether to use gpu in the training
-};
-
-// A = [3 2; -1 0]; x = rand(2, 1);
-// We want to compute the largest eigenvalue for A.
-// repeat x = y / y.norm(); y = A * x; end
-GraphDef CreateGraphDef() {
- // TODO(jeff,opensource): This should really be a more interesting
- // computation. Maybe turn this into an mnist model instead?
- Scope root = Scope::NewRootScope();
- using namespace ::tensorflow::ops; // NOLINT(build/namespaces)
-
- // A = [3 2; -1 0]. Using Const<float> means the result will be a
- // float tensor even though the initializer has integers.
- auto a = Const<float>(root, {{3, 2}, {-1, 0}});
-
- // x = [1.0; 1.0]
- auto x = Const(root.WithOpName("x"), {{1.f}, {1.f}});
-
- // y = A * x
- auto y = MatMul(root.WithOpName("y"), a, x);
-
- // y2 = y.^2
- auto y2 = Square(root, y);
-
- // y2_sum = sum(y2). Note that you can pass constants directly as
- // inputs. Sum() will automatically create a Const node to hold the
- // 0 value.
- auto y2_sum = Sum(root, y2, 0);
-
- // y_norm = sqrt(y2_sum)
- auto y_norm = Sqrt(root, y2_sum);
-
- // y_normalized = y ./ y_norm
- Div(root.WithOpName("y_normalized"), y, y_norm);
-
- GraphDef def;
- TF_CHECK_OK(root.ToGraphDef(&def));
-
- return def;
-}
-
-string DebugString(const Tensor& x, const Tensor& y) {
- CHECK_EQ(x.NumElements(), 2);
- CHECK_EQ(y.NumElements(), 2);
- auto x_flat = x.flat<float>();
- auto y_flat = y.flat<float>();
- // Compute an estimate of the eigenvalue via
- // (x' A x) / (x' x) = (x' y) / (x' x)
- // and exploit the fact that x' x = 1 by assumption
- Eigen::Tensor<float, 0, Eigen::RowMajor> lambda = (x_flat * y_flat).sum();
- return strings::Printf("lambda = %8.6f x = [%8.6f %8.6f] y = [%8.6f %8.6f]",
- lambda(), x_flat(0), x_flat(1), y_flat(0), y_flat(1));
-}
-
-void ConcurrentSteps(const Options* opts, int session_index) {
- // Creates a session.
- SessionOptions options;
- std::unique_ptr<Session> session(NewSession(options));
- GraphDef def = CreateGraphDef();
- if (options.target.empty()) {
- graph::SetDefaultDevice(opts->use_gpu ? "/device:GPU:0" : "/cpu:0", &def);
- }
-
- TF_CHECK_OK(session->Create(def));
-
- // Spawn M threads for M concurrent steps.
- const int M = opts->num_concurrent_steps;
- std::unique_ptr<thread::ThreadPool> step_threads(
- new thread::ThreadPool(Env::Default(), "trainer", M));
-
- for (int step = 0; step < M; ++step) {
- step_threads->Schedule([&session, opts, session_index, step]() {
- // Randomly initialize the input.
- Tensor x(DT_FLOAT, TensorShape({2, 1}));
- auto x_flat = x.flat<float>();
- x_flat.setRandom();
- Eigen::Tensor<float, 0, Eigen::RowMajor> inv_norm =
- x_flat.square().sum().sqrt().inverse();
- x_flat = x_flat * inv_norm();
-
- // Iterations.
- std::vector<Tensor> outputs;
- for (int iter = 0; iter < opts->num_iterations; ++iter) {
- outputs.clear();
- TF_CHECK_OK(
- session->Run({{"x", x}}, {"y:0", "y_normalized:0"}, {}, &outputs));
- CHECK_EQ(size_t{2}, outputs.size());
-
- const Tensor& y = outputs[0];
- const Tensor& y_norm = outputs[1];
- // Print out lambda, x, and y.
- std::printf("%06d/%06d %s\n", session_index, step,
- DebugString(x, y).c_str());
- // Copies y_normalized to x.
- x = y_norm;
- }
- });
- }
-
- // Delete the threadpool, thus waiting for all threads to complete.
- step_threads.reset(nullptr);
- TF_CHECK_OK(session->Close());
-}
-
-void ConcurrentSessions(const Options& opts) {
- // Spawn N threads for N concurrent sessions.
- const int N = opts.num_concurrent_sessions;
-
- // At the moment our Session implementation only allows
- // one concurrently computing Session on GPU.
- CHECK_EQ(1, N) << "Currently can only have one concurrent session.";
-
- thread::ThreadPool session_threads(Env::Default(), "trainer", N);
- for (int i = 0; i < N; ++i) {
- session_threads.Schedule(std::bind(&ConcurrentSteps, &opts, i));
- }
-}
-
-} // end namespace example
-} // end namespace tensorflow
-
-namespace {
-
-bool ParseInt32Flag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
- int32* dst) {
- if (absl::ConsumePrefix(&arg, flag) && absl::ConsumePrefix(&arg, "=")) {
- char extra;
- return (sscanf(arg.data(), "%d%c", dst, &extra) == 1);
- }
-
- return false;
-}
-
-bool ParseBoolFlag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
- bool* dst) {
- if (absl::ConsumePrefix(&arg, flag)) {
- if (arg.empty()) {
- *dst = true;
- return true;
- }
-
- if (arg == "=true") {
- *dst = true;
- return true;
- } else if (arg == "=false") {
- *dst = false;
- return true;
- }
- }
-
- return false;
-}
-
-} // namespace
-
-int main(int argc, char* argv[]) {
- tensorflow::example::Options opts;
- std::vector<char*> unknown_flags;
- for (int i = 1; i < argc; ++i) {
- if (string(argv[i]) == "--") {
- while (i < argc) {
- unknown_flags.push_back(argv[i]);
- ++i;
- }
- break;
- }
-
- if (ParseInt32Flag(argv[i], "--num_concurrent_sessions",
- &opts.num_concurrent_sessions) ||
- ParseInt32Flag(argv[i], "--num_concurrent_steps",
- &opts.num_concurrent_steps) ||
- ParseInt32Flag(argv[i], "--num_iterations", &opts.num_iterations) ||
- ParseBoolFlag(argv[i], "--use_gpu", &opts.use_gpu)) {
- continue;
- }
-
- fprintf(stderr, "Unknown flag: %s\n", argv[i]);
- return -1;
- }
-
- // Passthrough any unknown flags.
- int dst = 1; // Skip argv[0]
- for (char* f : unknown_flags) {
- argv[dst++] = f;
- }
- argv[dst++] = nullptr;
- argc = static_cast<int>(unknown_flags.size() + 1);
- tensorflow::port::InitMain(argv[0], &argc, &argv);
- tensorflow::example::ConcurrentSessions(opts);
-}