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ecbe82b9ce
This PR proposes to use std::optional<Generator>& for underlying functions to avoid unnecessary copy and move operations. The torchgen code was changed to generate the new type. Pull Request resolved: https://github.com/pytorch/pytorch/pull/120076 Approved by: https://github.com/malfet
71 lines
2.7 KiB
C++
71 lines
2.7 KiB
C++
#include <torch/extension.h>
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#include <torch/library.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 <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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void set_offset(uint64_t offset) override { throw std::runtime_error("not implemented"); }
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uint64_t get_offset() const 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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void set_state(const c10::TensorImpl& new_state) override { throw std::runtime_error("not implemented"); }
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c10::intrusive_ptr<c10::TensorImpl> get_state() const 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, const std::optional<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, const std::optional<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, const std::optional<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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TORCH_LIBRARY_IMPL(aten, CustomRNGKeyId, m) {
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m.impl("aten::random_.from", random_from_to);
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m.impl("aten::random_.to", random_to);
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m.impl("aten::random_", random_);
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}
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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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