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# Motivation According to [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842) and [[RFC] Intel GPU Runtime Upstreaming for Allocator](https://github.com/pytorch/pytorch/issues/116322), we will upstream the key functionality of device `Allocator` dedicated for XPU to PyTorch. And following our design prepare to generalize `Allocator` in parallel. # Design In the current design, XPU uses an `XPUAllocator` class, inherited from `c10::Allocator`. `XPUAllocator` is a manager to handle `DeviceCachingAllocator`, which is a per-device implementation of the caching mechanism to manage the already cached or newly allocated memory. The caching mechanism is similar to other backends, like CUDA. We can visualize the design as below. <p align="center"> <img width="162" alt="image" src="https://github.com/pytorch/pytorch/assets/106960996/6b17b8cf-e7d1-48b4-b684-f830c409d218"> </p> # Additional Context We're going to implement our design gradually. This PR covers the device `Allocator` dedicated to XPU. The second PR covers the host `Allocator`. Besides these PRs, we plan to generalize the device `Allocator` device-agnostic through another PR. In this PR, our device `Allocator` has the same memory management mechanism as CUDA, but lacks features such as expendable segments and statistics. We will add these features back in the subsequent PR which intend to generalize `Allocator`. The differences with CUDA: only key functionality, and lack of AsyncAllocator, gpu_trace, history_record, graph functionality, memory snapshot, memory statistics, expandable segment... Pull Request resolved: https://github.com/pytorch/pytorch/pull/118091 Approved by: https://github.com/EikanWang, https://github.com/gujinghui, https://github.com/jgong5, https://github.com/albanD ghstack dependencies: #117611, #117619, #117734
325 lines
10 KiB
C++
325 lines
10 KiB
C++
#include <ATen/ATen.h>
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#include <ATen/xpu/XPUContext.h>
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#include <c10/util/CallOnce.h>
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#include <c10/xpu/XPUCachingAllocator.h>
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#include <c10/xpu/XPUFunctions.h>
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#include <torch/csrc/Module.h>
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#include <torch/csrc/THP.h>
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#include <torch/csrc/utils/device_lazy_init.h>
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#include <torch/csrc/utils/pycfunction_helpers.h>
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#include <torch/csrc/utils/python_numbers.h>
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#include <torch/csrc/utils/python_strings.h>
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#include <pthread.h>
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using namespace torch;
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static bool in_bad_fork = false; // True for children forked after xpu init
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// Called in the forked child if xpu has already been initialized
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static void forked_child() {
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in_bad_fork = true;
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torch::utils::set_requires_device_init(at::kXPU, true);
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}
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// Should be called before the first xpu call. It is mainly called in lazy_init.
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// Note: This is distinct from initExtension because a stub xpu implementation
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// has some working functions (e.g. device_count) but cannot fully initialize.
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static void poison_fork() {
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static c10::once_flag flag;
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c10::call_once(flag, [] { pthread_atfork(nullptr, nullptr, forked_child); });
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}
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// XPU management methods
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static PyObject* THXPModule_isInBadFork_wrap(PyObject* self, PyObject* noargs) {
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HANDLE_TH_ERRORS
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return PyBool_FromLong(in_bad_fork);
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END_HANDLE_TH_ERRORS
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}
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PyObject* THXPModule_setDevice_wrap(PyObject* self, PyObject* arg) {
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HANDLE_TH_ERRORS
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TORCH_CHECK(THPUtils_checkLong(arg), "invalid argument to set_device");
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auto device_index = THPUtils_unpackDeviceIndex(arg);
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c10::xpu::set_device(device_index);
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Py_RETURN_NONE;
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END_HANDLE_TH_ERRORS
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}
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PyObject* THXPModule_exchangeDevice_wrap(PyObject* self, PyObject* arg) {
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HANDLE_TH_ERRORS
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TORCH_CHECK(THPUtils_checkLong(arg), "invalid argument to exchange_device");
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auto device_index = THPUtils_unpackDeviceIndex(arg);
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if (device_index < 0) {
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return THPUtils_packInt32(-1);
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}
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torch::utils::device_lazy_init(at::kXPU);
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auto current_device = c10::xpu::exchange_device(device_index);
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return THPUtils_packDeviceIndex(current_device);
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END_HANDLE_TH_ERRORS
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}
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PyObject* THXPModule_maybeExchangeDevice_wrap(PyObject* self, PyObject* arg) {
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HANDLE_TH_ERRORS
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TORCH_CHECK(
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THPUtils_checkLong(arg), "invalid argument to maybe_exchange_device");
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auto device_index = THPUtils_unpackDeviceIndex(arg);
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if (device_index < 0) {
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return THPUtils_packInt32(-1);
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}
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torch::utils::device_lazy_init(at::kXPU);
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auto current_device = c10::xpu::maybe_exchange_device(device_index);
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return THPUtils_packDeviceIndex(current_device);
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END_HANDLE_TH_ERRORS
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}
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PyObject* THXPModule_getDevice_wrap(PyObject* self, PyObject* noargs) {
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HANDLE_TH_ERRORS
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auto device_index = c10::xpu::current_device();
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return THPUtils_packDeviceIndex(device_index);
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END_HANDLE_TH_ERRORS
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}
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PyObject* THXPModule_getDeviceCount_wrap(PyObject* self, PyObject* noargs) {
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HANDLE_TH_ERRORS
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poison_fork();
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return THPUtils_packUInt64(at::xpu::device_count());
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END_HANDLE_TH_ERRORS
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}
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PyObject* THXPModule_getCurrentStream_wrap(
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PyObject* self,
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PyObject* device_index) {
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HANDLE_TH_ERRORS
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TORCH_CHECK(
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THPUtils_checkLong(device_index), "invalid argument to current_stream");
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auto c10_device_index = THPUtils_unpackDeviceIndex(device_index);
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auto stream = at::xpu::getCurrentXPUStream(c10_device_index);
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PyObject* output_tuple = PyTuple_New(3);
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PyTuple_SetItem(
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output_tuple, 0, THPUtils_packInt64(static_cast<int64_t>(stream.id())));
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PyTuple_SetItem(
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output_tuple, 1, THPUtils_packDeviceIndex(stream.device_index()));
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PyTuple_SetItem(
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output_tuple,
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2,
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THPUtils_packInt64(static_cast<int64_t>(stream.device_type())));
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return output_tuple;
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END_HANDLE_TH_ERRORS
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}
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PyObject* THXPModule_getCurrentStream_raw(
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PyObject* self,
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PyObject* device_index) {
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HANDLE_TH_ERRORS
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TORCH_CHECK(
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THPUtils_checkLong(device_index),
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"invalid argument to getCurrentRawStream");
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auto c10_device_index = THPUtils_unpackDeviceIndex(device_index);
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return PyLong_FromVoidPtr(
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&at::xpu::getCurrentXPUStream(c10_device_index).queue());
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END_HANDLE_TH_ERRORS
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}
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PyObject* THXPModule_setStream_wrap(
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PyObject* self,
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PyObject* args,
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PyObject* kwargs) {
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HANDLE_TH_ERRORS
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int64_t stream_id = 0;
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int64_t device_index = 0;
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int64_t device_type = 0;
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// NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays)
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constexpr const char* kwlist[] = {
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"stream_id", "device_index", "device_type", nullptr};
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if (!PyArg_ParseTupleAndKeywords(
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args,
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kwargs,
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"|LLL",
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const_cast<char**>(kwlist),
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&stream_id,
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&device_index,
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&device_type)) {
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}
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auto stream = at::xpu::XPUStream::unpack3(
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stream_id, device_index, static_cast<c10::DeviceType>(device_type));
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auto device = c10::xpu::current_device();
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if (device != stream.device_index()) {
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c10::xpu::set_device(stream.device_index());
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}
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at::xpu::setCurrentXPUStream(stream);
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Py_RETURN_NONE;
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END_HANDLE_TH_ERRORS
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}
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PyObject* THXPModule_xpuSynchronize(PyObject* self, PyObject* arg) {
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HANDLE_TH_ERRORS
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TORCH_CHECK(THPUtils_checkLong(arg), "invalid argument to synchronize");
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auto device_index = THPUtils_unpackDeviceIndex(arg);
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{
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pybind11::gil_scoped_release no_gil;
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// Only the SYCL queues we have reserved will be synchronized, see Note
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// [Synchronize Streams on Device].
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c10::xpu::syncStreamsOnDevice(device_index);
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}
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Py_RETURN_NONE;
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END_HANDLE_TH_ERRORS
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}
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PyObject* THXPModule_emptyCache(PyObject* self, PyObject* noargs) {
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HANDLE_TH_ERRORS
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c10::xpu::XPUCachingAllocator::emptyCache();
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END_HANDLE_TH_ERRORS
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Py_RETURN_NONE;
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}
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// XPU module initialization
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static void registerXpuDeviceProperties(PyObject* module) {
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// Add _xpuDevicePropertires class to torch._C
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using namespace c10::xpu;
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auto get_device_type = [](const DeviceProp& prop) {
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std::ostringstream stream;
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using namespace sycl::info;
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switch (prop.device_type) {
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case device_type::cpu:
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stream << "cpu";
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break;
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case device_type::gpu:
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stream << "gpu";
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break;
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case device_type::accelerator:
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stream << "accelerator";
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break;
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case device_type::host:
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stream << "host";
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break;
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default:
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stream << "unknown device type:"
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<< static_cast<typename std::underlying_type<device_type>::type>(
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prop.device_type);
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break;
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}
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return stream.str();
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};
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auto gpu_subslice_count = [](const DeviceProp& prop) {
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return (prop.gpu_eu_count / prop.gpu_eu_count_per_subslice);
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};
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auto m = py::handle(module).cast<py::module>();
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py::class_<DeviceProp>(m, "_XpuDeviceProperties")
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.def_readonly("name", &DeviceProp::name)
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.def_readonly("platform_name", &DeviceProp::platform_name)
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.def_readonly("total_memory", &DeviceProp::global_mem_size)
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.def_readonly("max_compute_units", &DeviceProp::max_compute_units)
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.def_readonly("gpu_eu_count", &DeviceProp::gpu_eu_count)
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.def_property_readonly("gpu_subslice_count", gpu_subslice_count)
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.def_readonly("max_work_group_size", &DeviceProp::max_work_group_size)
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.def_readonly("max_num_sub_groups", &DeviceProp::max_num_sub_groups)
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.def_readonly("sub_group_sizes", &DeviceProp::sub_group_sizes)
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.def_property_readonly("type", get_device_type)
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.def(
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"__repr__",
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[&get_device_type, &gpu_subslice_count](const DeviceProp& prop) {
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std::ostringstream stream;
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stream << "_XpuDeviceProperties(name='" << prop.name
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<< "', platform_name='" << prop.platform_name << "', type='"
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<< get_device_type(prop)
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<< ", total_memory=" << prop.global_mem_size / (1024 * 1024)
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<< "MB, max_compute_units=" << prop.max_compute_units
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<< ", gpu_eu_count=" << prop.gpu_eu_count
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<< ", gpu_subslice_count=" << gpu_subslice_count(prop)
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<< ", max_work_group_size=" << prop.max_work_group_size
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<< ", max_num_sub_groups=" << prop.max_num_sub_groups
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<< ", sub_group_sizes=[" << prop.sub_group_sizes << "])";
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return stream.str();
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});
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}
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static void bindGetDeviceProperties(PyObject* module) {
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// Add method to torch.xpu
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auto m = py::handle(module).cast<py::module>();
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m.def(
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"_get_device_properties",
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[](int device) -> c10::xpu::DeviceProp* {
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return at::xpu::getDeviceProperties(device);
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},
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py::return_value_policy::reference);
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}
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// Callback for python part. Used for additional initialization of python
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// classes
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static PyObject* THXPModule_initExtension(PyObject* self, PyObject* noargs) {
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HANDLE_TH_ERRORS
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TORCH_INTERNAL_ASSERT(!in_bad_fork); // Handled at python level
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poison_fork();
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at::globalContext().lazyInitXPU();
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auto m = THPObjectPtr(PyImport_ImportModule("torch.xpu"));
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if (!m)
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throw python_error();
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bindGetDeviceProperties(m);
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Py_RETURN_NONE;
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END_HANDLE_TH_ERRORS
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}
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// NOLINTNEXTLINE(modernize-avoid-c-arrays,
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// cppcoreguidelines-avoid-non-const-global-variables,
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// cppcoreguidelines-avoid-c-arrays)
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static struct PyMethodDef _THXPModule_methods[] = {
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{"_xpu_init", THXPModule_initExtension, METH_NOARGS, nullptr},
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{"_xpu_setDevice", THXPModule_setDevice_wrap, METH_O, nullptr},
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{"_xpu_exchangeDevice", THXPModule_exchangeDevice_wrap, METH_O, nullptr},
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{"_xpu_maybeExchangeDevice",
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THXPModule_maybeExchangeDevice_wrap,
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METH_O,
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nullptr},
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{"_xpu_getDevice", THXPModule_getDevice_wrap, METH_NOARGS, nullptr},
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{"_xpu_getDeviceCount",
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THXPModule_getDeviceCount_wrap,
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METH_NOARGS,
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nullptr},
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{"_xpu_isInBadFork", THXPModule_isInBadFork_wrap, METH_NOARGS, nullptr},
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{"_xpu_getCurrentStream",
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THXPModule_getCurrentStream_wrap,
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METH_O,
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nullptr},
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{"_xpu_getCurrentRawStream",
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THXPModule_getCurrentStream_raw,
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METH_O,
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nullptr},
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{"_xpu_setStream",
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castPyCFunctionWithKeywords(THXPModule_setStream_wrap),
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METH_VARARGS | METH_KEYWORDS,
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nullptr},
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{"_xpu_synchronize", THXPModule_xpuSynchronize, METH_O, nullptr},
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{"_xpu_emptyCache", THXPModule_emptyCache, METH_NOARGS, nullptr},
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{nullptr}};
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PyMethodDef* THXPModule_methods() {
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return _THXPModule_methods;
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}
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namespace torch::xpu {
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void initModule(PyObject* module) {
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registerXpuDeviceProperties(module);
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}
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} // namespace torch::xpu
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