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44cc873fba
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/56830 Opt into formatting on GitHub and format everything. This is a trial run before turning on formatting for more and eventually all of the codebase. Test Plan: CI Reviewed By: zertosh Differential Revision: D27979080 fbshipit-source-id: a80f0c48691c08ae8ca0af06377b87e6a2351151
111 lines
3.6 KiB
C++
111 lines
3.6 KiB
C++
#pragma once
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#include <stdint.h>
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#include <atomic>
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#include <deque>
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#include <mutex>
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#include <typeinfo>
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#include <utility>
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#include <c10/core/Device.h>
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#include <c10/core/DispatchKeySet.h>
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#include <c10/core/TensorImpl.h>
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#include <c10/util/C++17.h>
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#include <c10/util/Exception.h>
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#include <c10/util/intrusive_ptr.h>
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#include <c10/util/python_stub.h>
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/**
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* Note [Generator]
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* ~~~~~~~~~~~~~~~~
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* A Pseudo Random Number Generator (PRNG) is an engine that uses an algorithm
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* to generate a seemingly random sequence of numbers, that may be later be used
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* in creating a random distribution. Such an engine almost always maintains a
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* state and requires a seed to start off the creation of random numbers. Often
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* times, users have found it beneficial to be able to explicitly create,
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* retain, and destroy PRNG states and also be able to have control over the
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* seed value.
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*
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* A Generator in ATen gives users the ability to read, write and modify a PRNG
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* engine. For instance, it does so by letting users seed a PRNG engine, fork
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* the state of the engine, etc.
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*
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* By default, there is one generator per device, and a device's generator is
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* lazily created. A user can use the torch.Generator() api to create their own
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* generator. Currently torch.Generator() can only create a CPUGeneratorImpl.
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*/
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/**
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* Note [Acquire lock when using random generators]
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* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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* Generator and its derived classes are NOT thread-safe. Please note that most
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* of the places where we have inserted locking for generators are historically
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* based, and we haven't actually checked that everything is truly thread safe
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* (and it probably isn't). Please use the public mutex_ when using any methods
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* from these classes, except for the read-only methods. You can learn about the
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* usage by looking into the unittests (aten/src/ATen/cpu_generator_test.cpp)
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* and other places where we have used lock_guard.
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*
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* TODO: Look into changing the threading semantics of Generators in ATen (e.g.,
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* making them non-thread safe and instead making the generator state
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* splittable, to accommodate forks into other threads).
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*/
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namespace c10 {
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// The default seed is selected to be a large number
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// with good distribution of 0s and 1s in bit representation
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constexpr uint64_t default_rng_seed_val = 67280421310721;
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struct C10_API GeneratorImpl : public c10::intrusive_ptr_target {
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// Constructors
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GeneratorImpl(Device device_in, DispatchKeySet key_set);
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// Delete all copy and move assignment in favor of clone()
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// method
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GeneratorImpl(const GeneratorImpl& other) = delete;
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GeneratorImpl(GeneratorImpl&& other) = delete;
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GeneratorImpl& operator=(const GeneratorImpl& other) = delete;
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virtual ~GeneratorImpl() = default;
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c10::intrusive_ptr<GeneratorImpl> clone() const;
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// Common methods for all generators
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virtual void set_current_seed(uint64_t seed) = 0;
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virtual uint64_t current_seed() const = 0;
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virtual uint64_t seed() = 0;
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virtual void set_state(const c10::TensorImpl& new_state) = 0;
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virtual c10::intrusive_ptr<c10::TensorImpl> get_state() const = 0;
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Device device() const;
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// See Note [Acquire lock when using random generators]
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std::mutex mutex_;
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DispatchKeySet key_set() const {
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return key_set_;
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}
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inline void set_pyobj(PyObject* pyobj) noexcept {
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pyobj_ = pyobj;
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}
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inline PyObject* pyobj() const noexcept {
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return pyobj_;
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}
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protected:
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Device device_;
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DispatchKeySet key_set_;
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PyObject* pyobj_ = nullptr;
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virtual GeneratorImpl* clone_impl() const = 0;
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};
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namespace detail {
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TORCH_API uint64_t getNonDeterministicRandom(bool is_cuda = false);
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} // namespace detail
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} // namespace c10
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