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#pragma once
#include <stdint.h>
#include <atomic>
#include <deque>
#include <mutex>
#include <typeinfo>
#include <utility>
#include <c10/core/Device.h>
#include <c10/core/DispatchKeySet.h>
#include <c10/core/TensorImpl.h>
#include <c10/util/C++17.h>
#include <c10/util/Exception.h>
#include <c10/util/intrusive_ptr.h>
#include <c10/util/python_stub.h>
/**
* Note [Generator]
* ~~~~~~~~~~~~~~~~
* A Pseudo Random Number Generator (PRNG) is an engine that uses an algorithm
* to generate a seemingly random sequence of numbers, that may be later be used
* in creating a random distribution. Such an engine almost always maintains a
* state and requires a seed to start off the creation of random numbers. Often
* times, users have found it beneficial to be able to explicitly create,
* retain, and destroy PRNG states and also be able to have control over the
* seed value.
*
* A Generator in ATen gives users the ability to read, write and modify a PRNG
* engine. For instance, it does so by letting users seed a PRNG engine, fork
* the state of the engine, etc.
*
* By default, there is one generator per device, and a device's generator is
* lazily created. A user can use the torch.Generator() api to create their own
* generator. Currently torch.Generator() can only create a CPUGeneratorImpl.
*/
/**
* Note [Acquire lock when using random generators]
* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
* Generator and its derived classes are NOT thread-safe. Please note that most
* of the places where we have inserted locking for generators are historically
* based, and we haven't actually checked that everything is truly thread safe
* (and it probably isn't). Please use the public mutex_ when using any methods
* from these classes, except for the read-only methods. You can learn about the
* usage by looking into the unittests (aten/src/ATen/cpu_generator_test.cpp)
* and other places where we have used lock_guard.
*
* TODO: Look into changing the threading semantics of Generators in ATen (e.g.,
* making them non-thread safe and instead making the generator state
* splittable, to accommodate forks into other threads).
*/
namespace c10 {
// The default seed is selected to be a large number
// with good distribution of 0s and 1s in bit representation
constexpr uint64_t default_rng_seed_val = 67280421310721;
struct C10_API GeneratorImpl : public c10::intrusive_ptr_target {
// Constructors
GeneratorImpl(Device device_in, DispatchKeySet key_set);
// Delete all copy and move assignment in favor of clone()
// method
GeneratorImpl(const GeneratorImpl& other) = delete;
GeneratorImpl(GeneratorImpl&& other) = delete;
GeneratorImpl& operator=(const GeneratorImpl& other) = delete;
virtual ~GeneratorImpl() = default;
c10::intrusive_ptr<GeneratorImpl> clone() const;
// Common methods for all generators
virtual void set_current_seed(uint64_t seed) = 0;
virtual uint64_t current_seed() const = 0;
virtual uint64_t seed() = 0;
virtual void set_state(const c10::TensorImpl& new_state) = 0;
virtual c10::intrusive_ptr<c10::TensorImpl> get_state() const = 0;
Device device() const;
// See Note [Acquire lock when using random generators]
std::mutex mutex_;
DispatchKeySet key_set() const {
return key_set_;
}
inline void set_pyobj(PyObject* pyobj) noexcept {
pyobj_ = pyobj;
}
inline PyObject* pyobj() const noexcept {
return pyobj_;
}
protected:
Device device_;
DispatchKeySet key_set_;
PyObject* pyobj_ = nullptr;
virtual GeneratorImpl* clone_impl() const = 0;
};
namespace detail {
TORCH_API uint64_t getNonDeterministicRandom(bool is_cuda = false);
} // namespace detail
} // namespace c10
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