forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
net_parallel.cc
199 lines (170 loc) · 5.72 KB
/
net_parallel.cc
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
#include "caffe2/core/net_parallel.h"
#include "caffe2/core/operator.h"
#include <sstream>
C10_DEFINE_string(
caffe2_task_graph_engine,
"futures",
"Task graph engine type used by net executor");
namespace caffe2 {
ParallelNet::ParallelNet(
const std::shared_ptr<const NetDef>& net_def,
Workspace* ws)
: NetBase(net_def, ws), options_(net_def), run_future_(nullptr) {
num_workers_ = net_def->num_workers();
CAFFE_ENFORCE_GT(
num_workers_, 0, "Expected positive number of worker threads");
helper_ = std::make_unique<ParallelNetExecutorHelper>(this);
// initialize operators
operator_nodes_ = dag_utils::prepareOperatorNodes(net_def, ws);
operators_.reserve(operator_nodes_.size());
for (const auto& node : operator_nodes_) {
auto op = node.operator_.get();
op->SetExecutorHelper(helper_.get());
operators_.push_back(op);
}
task_graph_ = TaskGraphRegistry()->Create(
FLAGS_caffe2_task_graph_engine, helper_.get(), options_);
CAFFE_ENFORCE(task_graph_, "Couldn't initialize task graph");
// compute chains
// TODO: inference mode for chaining
auto execution_chains = dag_utils::computeChains(operator_nodes_);
std::vector<std::vector<int>> chains;
chains.reserve(execution_chains.size());
for (const auto& kv : execution_chains) {
chains.push_back(kv.second);
}
auto chain_nodes = dag_utils::prepareChainGraphNodes(operator_nodes_, chains);
CAFFE_ENFORCE_EQ(chains.size(), chain_nodes.size());
// disable unused events
for (const auto& chain : chains) {
for (const auto& op_id : chain) {
if (op_id == chain.back() || op_id == chain.front()) {
continue;
}
auto op = operators_[op_id];
if (IsCPUDeviceType(op->device_option().device_type()) &&
op->HasAsyncPart()) {
continue;
}
op->DisableEvent();
}
}
// initialize task graph
for (auto chain_id = 0; chain_id < chains.size(); ++chain_id) {
std::vector<OperatorBase*> ops;
ops.reserve(chains[chain_id].size());
for (auto op_id : chains[chain_id]) {
ops.push_back(operators_[op_id]);
}
CAFFE_ENFORCE(task_graph_->CreateNode(chain_id, ops));
}
for (auto chain_id = 0; chain_id < chain_nodes.size(); ++chain_id) {
if (!chain_nodes[chain_id].parents_.empty()) {
CAFFE_ENFORCE(
task_graph_->AddDependency(chain_id, chain_nodes[chain_id].parents_));
}
}
// Freeze graph and initialize graph execution future
task_graph_->FreezeGraph();
run_future_ = task_graph_->GetFuture();
run_future_->SetCallback([this](const AsyncTaskFuture* /* unused */) {
StopAllObservers();
finishRun();
});
LOG(INFO) << "Initialized parallel net: '" << Name()
<< "', #ops: " << net_def->op_size()
<< ", #chains: " << chains.size() << ", #workers: " << num_workers_
<< ", dfs scheduling: " << options_.use_dfs_scheduling_
<< ", task graph engine: " << FLAGS_caffe2_task_graph_engine;
}
bool ParallelNet::RunAsync() {
reset();
StartAllObservers();
try {
task_graph_->ExecuteGraph();
} catch (const std::exception&) {
StopAllObservers();
return false;
}
return true;
}
void ParallelNet::Wait() {
CAFFE_ENFORCE(run_future_);
run_future_->Wait();
}
void ParallelNet::reset() {
task_graph_->Reset();
}
bool ParallelNet::handleRunError() {
CAFFE_ENFORCE(run_future_ && run_future_->IsCompleted());
// TODO: throw saved exceptions
if (run_future_->IsFailed()) {
LOG(ERROR) << "Failed parallel run (" << Name()
<< "): " << run_future_->ErrorMessage();
}
return !run_future_->IsFailed();
}
TaskThreadPoolBase* ParallelNet::poolGetter(
PoolsMap& pools,
int device_type,
int device_id,
int pool_size) {
std::unique_lock<std::mutex> pools_lock(pools_mutex_);
auto pool = pools[device_id][pool_size];
if (!pool) {
pool = c10::ThreadPoolRegistry()->Create(
DeviceTypeName(device_type),
device_id,
pool_size,
options_.use_per_net_pools_);
pools[device_id][pool_size] = pool;
}
return pool.get();
}
TaskThreadPoolBase* ParallelNet::Pool(const DeviceOption& device_option) {
if (options_.use_single_pool_) {
return poolGetter(cpu_pools_, PROTO_CPU, -1, num_workers_);
}
const auto device_type = device_option.device_type();
if (IsCPUDeviceType(device_type)) {
auto numa_node_id = -1;
if (device_option.has_numa_node_id()) {
numa_node_id = device_option.numa_node_id();
CAFFE_ENFORCE_GE(numa_node_id, 0, "Invalid NUMA node id: ", numa_node_id);
}
CAFFE_ENFORCE_LT(
numa_node_id,
FLAGS_caffe2_net_async_max_numa_nodes,
"Invalid NUMA node id: ",
numa_node_id);
return poolGetter(cpu_pools_, device_type, numa_node_id, num_workers_);
} else if (IsGPUDeviceType(device_type)) {
auto gpu_id = device_option.device_id();
CAFFE_ENFORCE(
gpu_id >= 0 && gpu_id < FLAGS_caffe2_net_async_max_gpus,
"Invalid GPU id: " + caffe2::to_string(gpu_id));
return poolGetter(gpu_pools_, device_type, gpu_id, num_workers_);
} else {
CAFFE_THROW("Unsupported device type " + caffe2::to_string(device_type));
}
}
bool ParallelNet::SupportsAsync() {
return true;
}
void ParallelNet::finishRun() {}
std::vector<OperatorBase*> ParallelNet::GetOperators() const {
return operators_;
}
std::shared_ptr<AsyncTaskGraphBase> GetAsyncTaskGraph(
ExecutorHelper* helper,
const ExecutionOptions& options) {
return std::make_shared<AsyncTaskGraph>(helper, options);
}
C10_DEFINE_SHARED_REGISTRY(
TaskGraphRegistry,
AsyncTaskGraphBase,
ExecutorHelper*,
const ExecutionOptions&);
C10_REGISTER_CREATOR(TaskGraphRegistry, futures, GetAsyncTaskGraph);
REGISTER_NET(parallel, ParallelNet);
} // namespace caffe2