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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/distributed_runtime/remote_device.h"
#include <vector>
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/process_util.h"
#include "tensorflow/core/distributed_runtime/worker_cache.h"
#include "tensorflow/core/distributed_runtime/worker_interface.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/gtl/cleanup.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/protobuf/worker.pb.h"
namespace tensorflow {
// TODO(zhifengc): We need to consolidate (full/partial) device name
// parsing into one place.
//
// Parses and returns the local device part (e.g., cpu:0, gpu:4).
string GetLocalDeviceName(StringPiece fullname) {
auto pos = fullname.rfind('/');
CHECK_NE(pos, StringPiece::npos);
fullname.remove_prefix(pos + 1);
return string(fullname);
}
class RemoteDevice : public Device {
public:
RemoteDevice(Env* env, const DeviceAttributes& da)
: Device(env, da), local_dev_name_(GetLocalDeviceName(da.name())) {}
Status Sync() override { return Status::OK(); }
Allocator* GetAllocator(AllocatorAttributes attr) override { return nullptr; }
private:
const string local_dev_name_;
TF_DISALLOW_COPY_AND_ASSIGN(RemoteDevice);
};
void NewRemoteDevices(Env* env, WorkerCacheInterface* worker_cache,
const string& worker_name, NewRemoteDevicesDone done) {
WorkerInterface* wi = worker_cache->CreateWorker(worker_name);
if (wi == nullptr) {
std::vector<Device*> empty;
done(errors::NotFound("Device ", worker_name, " is not found."), &empty);
return;
}
struct Call {
GetStatusRequest req;
GetStatusResponse resp;
};
Call* call = new Call;
auto cb = [env, worker_cache, worker_name, done, wi,
call](const Status& status) {
Status s = status;
std::vector<Device*> remote_devices;
auto cleanup = gtl::MakeCleanup(
[&worker_cache, &worker_name, &wi, &done, &remote_devices, &s, call] {
worker_cache->ReleaseWorker(worker_name, wi);
done(s, &remote_devices);
delete call;
});
if (s.ok()) {
DeviceNameUtils::ParsedName worker_name_parsed;
if (!DeviceNameUtils::ParseFullName(worker_name, &worker_name_parsed) ||
!worker_name_parsed.has_job || !worker_name_parsed.has_replica ||
!worker_name_parsed.has_task) {
s = errors::InvalidArgument("Could not parse worker name: ",
worker_name);
LOG(WARNING) << s;
return;
}
remote_devices.reserve(call->resp.device_attributes_size());
for (const DeviceAttributes& da : call->resp.device_attributes()) {
DeviceNameUtils::ParsedName device_name_parsed;
CHECK(DeviceNameUtils::ParseFullName(da.name(), &device_name_parsed))
<< "Device attribute name '" << da.name() << "' could not be "
<< "parsed. Device Attribute: " << da.DebugString();
// Preserve the exact name, if possible.
// TODO(b/37868888): Simplify when legacy device name formats removed.
if (device_name_parsed.job == worker_name_parsed.job &&
device_name_parsed.replica == worker_name_parsed.replica &&
device_name_parsed.task == worker_name_parsed.task) {
auto d = new RemoteDevice(env, da);
remote_devices.push_back(d);
} else {
DeviceAttributes da_rewritten = da;
da_rewritten.set_name(DeviceNameUtils::FullName(
worker_name_parsed.job, worker_name_parsed.replica,
worker_name_parsed.task, device_name_parsed.type,
device_name_parsed.id));
auto d = new RemoteDevice(env, da_rewritten);
remote_devices.push_back(d);
}
}
}
};
wi->GetStatusAsync(&call->req, &call->resp, cb);
}
} // namespace tensorflow
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