Files
pytorch/torch/csrc/DeviceAccelerator.cpp
can-gaa-hou 89e3bbcb5b [Accelerator] Add Accelerator Capabilities API (#165631)
# Motivation
There are several issues related to the data type and precision that an accelerator supports (see #165038 and #143112). Sometimes, we have to check for these capabilities in the document, and then hard-code.  This PR proposes a new unified API for users to check their accelerator capabilities.

# Changes
This PR creates a new data structure `DeviceCapability` containing the capabilities that an accelerator commonly has:
- Supporting DataType (set to be supported as default):
  - `fp16`, `int32`, `complex` ... etc
- Other capabilities (need to be discussed)

To access the structure, this PR defines a new Python API in the Accelerator module -- `get_device_capability`. It takes `device` as an input and returns a dictionary containing the capabilities (now we have `supported_dtypes` as the key).

# Usage
```python
>>> import torch
>>> import torch_openreg
>>> torch.accelerator.get_device_capability('openreg:0')
{'supported_dtypes': [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64, torch.float16, torch.float32, torch.float64, torch.complex32, torch.complex64, torch.complex128, torch.bool, torch.qint8, torch.quint8, torch.qint32, torch.bfloat16, torch.quint4x2, torch.quint2x4, torch.bits1x8, torch.bits2x4, torch.bits4x2, torch.bits8, torch.bits16, torch.float8_e5m2, torch.float8_e4m3fn, torch.float8_e5m2fnuz, torch.float8_e4m3fnuz, torch.uint16, torch.uint32, torch.uint64, torch.uint1, torch.uint2, torch.uint3, torch.uint4, torch.uint5, torch.uint6, torch.uint7, torch.int1, torch.int2, torch.int3, torch.int4, torch.int5, torch.int6, torch.int7, torch.float8_e8m0fnu, torch.float4_e2m1fn_x2]}
```
# TODO
- So far, precision is the only capability to track, based on my knowledge. But we can find more capabilities in common, and the API should be designed for good extension.
- It will support other in-tree accelerators, such as **cuda** and **mps**.
- Clarify whether the capabilities are software or hardware supported. (By @guangyey )

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165631
Approved by: https://github.com/guangyey, https://github.com/albanD

Co-authored-by: Yu, Guangye <106960996+guangyey@users.noreply.github.com>
Co-authored-by: Jiawei Li <ljw1101.vip@gmail.com>
2025-12-03 21:37:30 +00:00

173 lines
6.5 KiB
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#include <c10/core/AllocatorConfig.h>
#include <torch/csrc/DeviceAccelerator.h>
#include <torch/csrc/utils/device_lazy_init.h>
namespace torch::accelerator {
void initModule(PyObject* module) {
auto m = py::handle(module).cast<py::module>();
m.def("_accelerator_getAccelerator", []() -> std::optional<c10::Device> {
// If no accelerator was available at compile time, return None.
auto acc = at::getAccelerator(false);
if (acc.has_value()) {
return acc.value();
} else {
return std::nullopt;
}
});
m.def("_accelerator_setDeviceIndex", [](c10::DeviceIndex device_index) {
// If device index is negative, no-op
if (device_index < 0) {
return;
}
const auto device_type = at::accelerator::getAccelerator(true).value();
torch::utils::maybe_initialize_device(device_type);
at::accelerator::setDeviceIndex(device_index);
});
m.def("_accelerator_getDeviceIndex", []() {
const auto device_type = at::accelerator::getAccelerator(true).value();
torch::utils::maybe_initialize_device(device_type);
return at::accelerator::getDeviceIndex();
});
m.def("_accelerator_getDeviceCapability", [](c10::DeviceIndex device_index) {
const auto device_type = at::accelerator::getAccelerator(true).value();
torch::utils::maybe_initialize_device(device_type);
auto caps = at::accelerator::getDeviceCapability(device_index);
py::dict dict;
py::set dtype_set;
caps.forEachSupportedScalarType([&](c10::ScalarType dtype) {
THPDtype* thp_dtype = torch::getTHPDtype(dtype);
py::object dtype_obj =
py::reinterpret_borrow<py::object>((PyObject*)thp_dtype);
dtype_set.add(dtype_obj);
});
dict["supported_dtypes"] = dtype_set;
return dict;
});
m.def("_accelerator_setStream", [](c10::Stream stream) {
const auto device_type = at::accelerator::getAccelerator(true).value();
torch::utils::maybe_initialize_device(device_type);
// Set the current device to the device of stream
if (at::accelerator::getDeviceIndex() != stream.device_index()) {
at::accelerator::setDeviceIndex(stream.device_index());
}
at::accelerator::setCurrentStream(stream);
});
m.def("_accelerator_getStream", [](c10::DeviceIndex device_index) {
const auto device_type = at::accelerator::getAccelerator(true).value();
torch::utils::maybe_initialize_device(device_type);
return at::accelerator::getCurrentStream(device_index);
});
m.def("_accelerator_synchronizeDevice", [](c10::DeviceIndex device_index) {
const auto device_type = at::accelerator::getAccelerator(true).value();
if (torch::utils::is_device_lazy_init_supported(device_type) &&
!torch::utils::is_device_initialized(device_type)) {
return;
}
torch::utils::maybe_initialize_device(device_type);
{
py::gil_scoped_release no_gil;
at::accelerator::synchronizeDevice(device_index);
}
});
m.def("_accelerator_exchangeDevice", [](c10::DeviceIndex device_index) {
const auto device_type = at::accelerator::getAccelerator(true).value();
torch::utils::maybe_initialize_device(device_type);
return at::accelerator::exchangeDevice(device_index);
});
m.def("_accelerator_maybeExchangeDevice", [](c10::DeviceIndex device_index) {
const auto device_type = at::accelerator::getAccelerator(true).value();
torch::utils::maybe_initialize_device(device_type);
return at::accelerator::maybeExchangeDevice(device_index);
});
m.def("_accelerator_isAllocatorInitialized", []() {
const auto device_type = at::accelerator::getAccelerator(true).value();
return at::getDeviceAllocator(device_type)->initialized();
});
m.def("_accelerator_emptyCache", []() { at::accelerator::emptyCache(); });
m.def("_accelerator_getDeviceStats", [](c10::DeviceIndex device_index) {
using c10::CachingAllocator::Stat;
using c10::CachingAllocator::StatArray;
using c10::CachingAllocator::StatType;
using c10::CachingDeviceAllocator::DeviceStats;
const auto stats = at::accelerator::getDeviceStats(device_index);
const auto stat_to_dict = [](const Stat& stat) -> py::dict {
py::dict dict;
dict["current"] = stat.current;
dict["peak"] = stat.peak;
dict["allocated"] = stat.allocated;
dict["freed"] = stat.freed;
return dict;
};
const auto stat_array_to_dict = [=](const StatArray& stats) -> py::dict {
const std::array<const char*, static_cast<size_t>(StatType::NUM_TYPES)>
kStatTypeNames = {"all", "small_pool", "large_pool"};
py::dict dict;
for (const auto i : c10::irange(kStatTypeNames.size())) {
dict[kStatTypeNames[i]] = stat_to_dict(stats[i]);
}
return dict;
};
py::dict result;
result["num_alloc_retries"] = stats.num_alloc_retries;
result["num_ooms"] = stats.num_ooms;
result["max_split_size"] = stats.max_split_size;
result["num_sync_all_streams"] = stats.num_sync_all_streams;
result["num_device_alloc"] = stats.num_device_alloc;
result["num_device_free"] = stats.num_device_free;
result["allocated_bytes"] = stat_array_to_dict(stats.allocated_bytes);
result["reserved_bytes"] = stat_array_to_dict(stats.reserved_bytes);
result["active_bytes"] = stat_array_to_dict(stats.active_bytes);
result["requested_bytes"] = stat_array_to_dict(stats.requested_bytes);
result["allocation"] = stat_array_to_dict(stats.allocation);
result["segment"] = stat_array_to_dict(stats.segment);
result["active"] = stat_array_to_dict(stats.active);
result["inactive_split"] = stat_array_to_dict(stats.inactive_split);
result["inactive_split_bytes"] =
stat_array_to_dict(stats.inactive_split_bytes);
result["oversize_allocations"] = stat_to_dict(stats.oversize_allocations);
result["oversize_segments"] = stat_to_dict(stats.oversize_segments);
return result;
});
m.def(
"_accelerator_resetAccumulatedStats", [](c10::DeviceIndex device_index) {
at::accelerator::resetAccumulatedStats(device_index);
});
m.def("_accelerator_resetPeakStats", [](c10::DeviceIndex device_index) {
at::accelerator::resetPeakStats(device_index);
});
m.def("_accelerator_getMemoryInfo", [](c10::DeviceIndex device_index) {
const auto device_type = at::accelerator::getAccelerator(true).value();
torch::utils::maybe_initialize_device(device_type);
py::gil_scoped_release no_gil;
return at::accelerator::getMemoryInfo(device_index);
});
m.def("_accelerator_setAllocatorSettings", [](std::string env) {
c10::CachingAllocator::setAllocatorSettings(env);
});
}
} // namespace torch::accelerator