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// Copyright 2018 Google LLC
//
// 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.
//
// Standalone test code that runs the Minigo model on a Cloud TPU.
// Helpful for debugging issues.
//
// Example usage (you will need to supply your own values for tpu_name, model_a
// and model_b):
// bazel build --define=tf=1 --define=tpu=1 cc:tpu_test
// ./bazel-bin/cc/tpu_test \
// --tpu_name=grpc://10.240.2.10:8470 \
// --model_a=gs://tmadams-sandbox/tpu_cpp/000674-neptune.pb \
// --model_b=gs://tmadams-sandbox/tpu_cpp/000001-bootstrap.pb
#include <algorithm>
#include <iostream>
#include <string>
#include <utility>
#include <vector>
#include "absl/strings/str_cat.h"
#include "absl/strings/str_format.h"
#include "gflags/gflags.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/public/session.h"
DEFINE_string(
tpu_name, "",
"Cloud TPU name to run inference on, e.g. \"grpc://10.240.2.10:8470\"");
DEFINE_string(model_a, "", "Path to first model to load");
DEFINE_string(model_b, "", "Path to second model to load");
using tensorflow::DT_FLOAT;
using tensorflow::Env;
using tensorflow::GraphDef;
using tensorflow::NewSession;
using tensorflow::ReadBinaryProto;
using tensorflow::Session;
using tensorflow::SessionOptions;
using tensorflow::Tensor;
using tensorflow::TensorShape;
constexpr int kNumReplicas = 8;
constexpr int kN = 19;
constexpr int kNumMoves = kN * kN + 1;
constexpr int kNumStoneFeatures = 17;
constexpr int kNumBoardFeatures = kN * kN * kNumStoneFeatures;
using Features = std::array<float, kNumBoardFeatures>;
using Policy = std::array<float, kNumMoves>;
// Simple wrapper around the Minigo model.
class Model {
public:
explicit Model(const std::string& path) : path_(path) {
// Load model.
auto* env = Env::Default();
GraphDef graph_def;
TF_CHECK_OK(ReadBinaryProto(env, path, &graph_def));
// Create a session.
SessionOptions options;
options.target = FLAGS_tpu_name;
options.config.set_allow_soft_placement(true);
options.config.set_log_device_placement(true);
session_.reset(NewSession(options));
TF_CHECK_OK(session_->Create(graph_def));
// Initialize model inputs & outputs.
for (int i = 0; i < kNumReplicas; ++i) {
inputs_.emplace_back(
absl::StrCat("pos_tensor_", i),
Tensor(DT_FLOAT, TensorShape({1, kN, kN, kNumStoneFeatures})));
output_names_.push_back(absl::StrCat("policy_output_", i));
output_names_.push_back(absl::StrCat("value_output_", i));
}
}
~Model() {
Log() << "Closing session" << std::endl;
TF_CHECK_OK(session_->Close());
}
void InitializeTpu() {
Log() << "Initializing TPU" << std::endl;
TF_CHECK_OK(session_->Run({}, {}, {"ConfigureDistributedTPU"}, nullptr));
}
void ShutdownTpu() {
Log() << "Shutting down TPU" << std::endl;
TF_CHECK_OK(session_->Run({}, {}, {"ShutdownDistributedTPU"}, nullptr));
}
void Run(const Features& features) {
Log() << "Running inference" << std::endl;
// Copy features into all input tensors.
for (int replica = 0; replica < kNumReplicas; ++replica) {
auto* data = inputs_[replica].second.flat<float>().data();
memcpy(data, features.data(), sizeof(features));
}
// Run inference.
TF_CHECK_OK(session_->Run(inputs_, output_names_, {}, &outputs_));
// Copy results out of the output tensors.
Policy policy[kNumReplicas];
float value[kNumReplicas];
for (int replica = 0; replica < kNumReplicas; ++replica) {
const auto* policy_data = outputs_[replica * 2].flat<float>().data();
const auto* value_data = outputs_[replica * 2 + 1].flat<float>().data();
memcpy(policy[replica].data(), policy_data, sizeof(Policy));
value[replica] = *value_data;
}
// Check the outputs from all replicas are the same.
for (int replica = 1; replica < kNumReplicas; ++replica) {
for (int i = 0; i < kNumMoves; ++i) {
if (policy[0][i] != policy[replica][i]) {
Log() << absl::StreamFormat("policy[0][%d] == %f\n", i, policy[0][i]);
Log() << absl::StreamFormat("policy[%d][%d] == %f\n", replica, i,
policy[replica][i]);
LOG(FATAL) << ":(";
}
if (value[0] != value[replica]) {
Log() << absl::StreamFormat("value[0] == %f\n", value[0]);
Log() << absl::StreamFormat("value[%d] == %f\n", replica,
value[replica]);
LOG(FATAL) << ":(";
}
}
}
// Log the output of the first replica (since the other replica outputs
// all match the first).
for (int i = 0; i < kNumMoves; ++i) {
if (i != 0 && (i % kN) == 0) {
std::cerr << "\n";
}
std::cerr << absl::StrFormat(" %0.2f", policy[0][i]);
}
std::cerr << "\n" << value[0] << "\n";
}
private:
std::ostream& Log() { return std::cerr << "(" << path_ << ") "; }
std::string path_;
std::unique_ptr<Session> session_;
std::vector<std::pair<std::string, tensorflow::Tensor>> inputs_;
std::vector<tensorflow::Tensor> outputs_;
std::vector<std::string> output_names_;
};
void SimpleTest() {
// Initialize some features that represent an empty board.
Features features;
int i = 0;
for (int pos = 0; pos < kN * kN; ++pos) {
for (int f = 0; f < kNumStoneFeatures; ++f) {
features[i++] = (f == kNumStoneFeatures - 1) ? 1 : 0;
}
}
Model model_a(FLAGS_model_a);
Model model_b(FLAGS_model_b);
// -----------------------------------
// This works as expected: the outputs of model_a.Run() and model_b.Run()
// are different.
// model_a.InitializeTpu();
// model_a.Run(features);
// model_a.ShutdownTpu();
//
// model_b.InitializeTpu();
// model_b.Run(features);
// model_b.ShutdownTpu();
// -----------------------------------
// This does not work.
model_a.InitializeTpu();
model_a.Run(features);
model_b.InitializeTpu();
model_b.Run(features);
// This call produces the output from model_b.
model_a.Run(features);
model_a.ShutdownTpu();
// Calling model_b.ShutdownTpu() here crashes because the TPU is already
// shutdown.
// -----------------------------------
}
int main(int argc, char* argv[]) {
gflags::SetUsageMessage(argv[0]);
gflags::ParseCommandLineFlags(&argc, &argv, true);
SimpleTest();
return 0;
}