Repository navigation
Expand file tree
/
Copy pathtpu_test.cc
More file actions
224 lines (191 loc) · 6.97 KB
/
Copy pathtpu_test.cc
File metadata and controls
224 lines (191 loc) · 6.97 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
// 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;
}