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11a40410e7
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/34774 This PR provides pybind11's `type_caster<at::Generator>` that allows mapping `at::Generator` instance returned from user-defined method to python `torch::Generator`, defined as `THPGenerator ` c++ class. This allows 1) defining custom RNG in c++ extension 2) using custom RNG in python code. `TestRNGExtension.test_rng` shows how to use custom RNG defined in `rng_extension.cpp` Test Plan: Imported from OSS Differential Revision: D20549451 Pulled By: pbelevich fbshipit-source-id: 312a6deccf8228f7f60695bbf95834620d52f5eb
72 lines
2.8 KiB
C++
72 lines
2.8 KiB
C++
#include <torch/extension.h>
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#include <ATen/Generator.h>
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#include <ATen/Tensor.h>
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#include <ATen/native/DistributionTemplates.h>
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#include <ATen/native/cpu/DistributionTemplates.h>
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#include <ATen/core/op_registration/op_registration.h>
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#include <memory>
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using namespace at;
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static size_t instance_count = 0;
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struct TestCPUGenerator : public c10::GeneratorImpl {
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TestCPUGenerator(uint64_t value) : c10::GeneratorImpl{Device(DeviceType::CPU), DispatchKeySet(DispatchKey::CustomRNGKeyId)}, value_(value) {
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++instance_count;
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}
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~TestCPUGenerator() {
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--instance_count;
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}
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uint32_t random() { return static_cast<uint32_t>(value_); }
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uint64_t random64() { return value_; }
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void set_current_seed(uint64_t seed) override { throw std::runtime_error("not implemented"); }
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uint64_t current_seed() const override { throw std::runtime_error("not implemented"); }
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uint64_t seed() override { throw std::runtime_error("not implemented"); }
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TestCPUGenerator* clone_impl() const override { throw std::runtime_error("not implemented"); }
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static DeviceType device_type() { return DeviceType::CPU; }
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uint64_t value_;
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};
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Tensor& random_(Tensor& self, Generator generator) {
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return at::native::templates::random_impl<native::templates::cpu::RandomKernel, TestCPUGenerator>(self, generator);
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}
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Tensor& random_from_to(Tensor& self, int64_t from, optional<int64_t> to, Generator generator) {
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return at::native::templates::random_from_to_impl<native::templates::cpu::RandomFromToKernel, TestCPUGenerator>(self, from, to, generator);
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}
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Tensor& random_to(Tensor& self, int64_t to, Generator generator) {
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return random_from_to(self, 0, to, generator);
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}
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Generator createTestCPUGenerator(uint64_t value) {
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return at::make_generator<TestCPUGenerator>(value);
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}
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Generator identity(Generator g) {
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return g;
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}
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size_t getInstanceCount() {
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return instance_count;
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}
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static auto registry = torch::RegisterOperators()
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.op(torch::RegisterOperators::options()
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.schema("aten::random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!)")
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.impl_unboxedOnlyKernel<decltype(random_from_to), &random_from_to>(DispatchKey::CustomRNGKeyId))
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.op(torch::RegisterOperators::options()
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.schema("aten::random_.to(Tensor(a!) self, int to, *, Generator? generator=None) -> Tensor(a!)")
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.impl_unboxedOnlyKernel<decltype(random_to), &random_to>(DispatchKey::CustomRNGKeyId))
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.op(torch::RegisterOperators::options()
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.schema("aten::random_(Tensor(a!) self, *, Generator? generator=None) -> Tensor(a!)")
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.impl_unboxedOnlyKernel<decltype(random_), &random_>(DispatchKey::CustomRNGKeyId));
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("createTestCPUGenerator", &createTestCPUGenerator);
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m.def("getInstanceCount", &getInstanceCount);
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m.def("identity", &identity);
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}
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