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// This is a registration-oriented interface for multiple platforms. It will
// replace the MachineManager singleton interface, as MachineManager does not
// currently support simultaneous use of multiple platforms.
//
// Usage:
//
// In your BUILD rule, add a dependency on a platform plugin that you'd like
// to use, such as:
//
// //perftools/gputools/executor/cuda:cuda_platform
// //perftools/gputools/executor/opencl:opencl_platform
//
// This will register platform plugins that can be discovered via this
// interface. Sample API usage:
//
// port::StatusOr<Platform*> platform_status =
// gpu::MultiPlatformManager::PlatformWithName("OpenCL");
// if (!platform_status.ok()) { ... }
// Platform* platform = platform_status.ValueOrDie();
// LOG(INFO) << platform->VisibleDeviceCount() << " devices visible";
// if (platform->VisibleDeviceCount() <= 0) { return; }
//
// for (int i = 0; i < platform->VisibleDeviceCount(); ++i) {
// port::StatusOr<StreamExecutor*> executor_status =
// platform->ExecutorForDevice(i);
// if (!executor_status.ok()) {
// LOG(INFO) << "could not retrieve executor for device ordinal " << i
// << ": " << executor_status.status();
// continue;
// }
// LOG(INFO) << "found usable executor: " << executor_status.ValueOrDie();
// }
//
// A few things to note:
// - There is no standard formatting/practice for identifying the name of a
// platform. Ideally, a platform will list its registered name in its header
// or in other associated documentation.
// - Platform name lookup is case-insensitive. "OpenCL" or "opencl" (or even
// ("OpEnCl") would work correctly in the above example.
//
// And similarly, for standard interfaces (BLAS, RNG, etc.) you can add
// dependencies on support libraries, e.g.:
//
// //perftools/gputools/executor/cuda:pluton_blas_plugin
// //perftools/gputools/executor/cuda:cudnn_plugin
// //perftools/gputools/executor/cuda:cublas_plugin
// //perftools/gputools/executor/cuda:curand_plugin
#ifndef TENSORFLOW_STREAM_EXECUTOR_MULTI_PLATFORM_MANAGER_H_
#define TENSORFLOW_STREAM_EXECUTOR_MULTI_PLATFORM_MANAGER_H_
#include <functional>
#include <map>
#include <memory>
#include "tensorflow/stream_executor/platform/port.h"
#include "tensorflow/stream_executor/lib/status.h"
#include "tensorflow/stream_executor/lib/statusor.h"
#include "tensorflow/stream_executor/platform.h"
#include "tensorflow/stream_executor/platform/mutex.h"
#include "tensorflow/stream_executor/platform/port.h"
namespace perftools {
namespace gputools {
// Manages multiple platforms that may be present on the current machine.
class MultiPlatformManager {
public:
// Registers a platform object, returns an error status if the platform is
// already registered. The associated listener, if not null, will be used to
// trace events for ALL executors for that platform.
// Takes ownership of listener.
static port::Status RegisterPlatform(std::unique_ptr<Platform> platform);
// Retrieves the platform registered with the given platform name; e.g.
// "CUDA", "OpenCL", ...
//
// If the requested platform is not registered, an error status is returned.
// Ownership of the platform is NOT transferred to the caller --
// the MultiPlatformManager owns the platforms in a singleton-like fashion.
static port::StatusOr<Platform*> PlatformWithName(const string& target);
// Retrieves the platform registered with the given platform ID, which
// is an opaque (but comparable) value.
//
// If the requested platform is not registered, an error status is returned.
// Ownership of the platform is NOT transferred to the caller --
// the MultiPlatformManager owns the platforms in a singleton-like fashion.
static port::StatusOr<Platform*> PlatformWithId(const Platform::Id& id);
// Clears the set of registered platforms, primarily used for testing.
static void ClearPlatformRegistry();
// Although the MultiPlatformManager "owns" its platforms, it holds them as
// undecorated pointers to prevent races during program exit (between this
// object's data and the underlying platforms (e.g., CUDA, OpenCL).
// Because certain platforms have unpredictable deinitialization
// times/sequences, it is not possible to strucure a safe deinitialization
// sequence. Thus, we intentionally "leak" allocated platforms to defer
// cleanup to the OS. This should be acceptable, as these are one-time
// allocations per program invocation.
// The MultiPlatformManager should be considered the owner
// of any platforms registered with it, and leak checking should be disabled
// during allocation of such Platforms, to avoid spurious reporting at program
// exit.
using PlatformMap = std::map<string, Platform*>;
// Provides access to the available set of platforms under a lock.
static port::Status WithPlatforms(
std::function<port::Status(PlatformMap*)> callback) {
mutex_lock lock(platforms_mutex_);
return callback(GetPlatformMap());
}
private:
// mutex that guards the platform map.
static mutex platforms_mutex_;
// TODO(b/22689637): Clean up these two maps; make sure they coexist nicely.
// TODO(b/22689637): Move this (whatever the final/"official" map is) to
// plugin_regstry.h, along with the associated functionality.
// Platform-name-to-object mapping. These platforms are registered via module
// initializers, and linkage determines which platforms are available to a
// given target.
static PlatformMap* GetPlatformMap() {
static PlatformMap* instance = new PlatformMap;
return instance;
}
// Holds a Platform::Id-to-object mapping.
// Unlike platforms_ above, this map does not own its contents.
static std::map<Platform::Id, Platform*>* GetPlatformByIdMap() {
using PlatformIdMap = std::map<Platform::Id, Platform*>;
static PlatformIdMap* instance = new PlatformIdMap;
return instance;
}
SE_DISALLOW_COPY_AND_ASSIGN(MultiPlatformManager);
};
} // namespace gputools
} // namespace perftools
#endif // TENSORFLOW_STREAM_EXECUTOR_MULTI_PLATFORM_MANAGER_H_
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