mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
cb72c9f5b1
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29131 caffe2_pb2.CUDA --> workspace.GpuDeviceType workspace.NumCudaDevices() --> workspace.NumGpuDevices() Also added the totalGlobalMem into get_device_properties(), which is needed by multi_gpu_utils.py Test Plan: sandcastle f148921769 Reviewed By: bddppq Differential Revision: D18290090 fbshipit-source-id: bde7c175d1fb6ff59a062266c1b17de39d113b24
101 lines
3.1 KiB
C++
101 lines
3.1 KiB
C++
#define NO_IMPORT_ARRAY
|
|
|
|
#include "pybind_state.h"
|
|
|
|
#include <pybind11/pybind11.h>
|
|
#include <pybind11/stl.h>
|
|
|
|
#include "caffe2/core/hip/common_miopen.h"
|
|
#include "caffe2/core/hip/context_gpu.h"
|
|
#include "caffe2/operators/hip/operator_fallback_gpu.h"
|
|
#include "caffe2/python/pybind_state_registry.h"
|
|
#include <c10/hip/HIPGuard.h>
|
|
|
|
namespace caffe2 {
|
|
namespace python {
|
|
|
|
REGISTER_HIP_OPERATOR(Python, GPUFallbackOp);
|
|
REGISTER_HIP_OPERATOR(
|
|
PythonGradient,
|
|
GPUFallbackOp);
|
|
|
|
REGISTER_HIP_OPERATOR(PythonDLPack, GPUFallbackOp);
|
|
REGISTER_HIP_OPERATOR(PythonDLPackGradient, GPUFallbackOp);
|
|
|
|
REGISTER_BLOB_FEEDER(HIP, TensorFeeder<HIPContext>);
|
|
|
|
namespace py = pybind11;
|
|
|
|
void addHIPGlobalMethods(py::module& m) {
|
|
m.def("num_hip_devices", &NumHipDevices);
|
|
m.def("get_hip_version", &HipVersion);
|
|
m.def("get_miopen_version", &miopenCompiledVersion);
|
|
m.def("get_gpu_memory_info", [](int device_id) {
|
|
HIPGuard guard(device_id);
|
|
size_t device_free, device_total;
|
|
HIP_CHECK(hipMemGetInfo(&device_free, &device_total));
|
|
return std::pair<size_t, size_t>{device_free, device_total};
|
|
});
|
|
m.def("get_hip_peer_access_pattern", []() {
|
|
std::vector<std::vector<bool>> pattern;
|
|
CAFFE_ENFORCE(caffe2::GetHipPeerAccessPattern(&pattern));
|
|
return pattern;
|
|
});
|
|
m.def("get_device_properties", [](int deviceid) {
|
|
auto& prop = GetDeviceProperty(deviceid);
|
|
std::map<std::string, py::object> obj;
|
|
obj["name"] = py::cast(prop.name);
|
|
obj["major"] = py::cast(prop.major);
|
|
obj["minor"] = py::cast(prop.minor);
|
|
obj["totalGlobalMem"] = py::cast(prop.totalGlobalMem);
|
|
return obj;
|
|
});
|
|
};
|
|
|
|
void addHIPObjectMethods(py::module& m) {
|
|
py::class_<DLPackWrapper<HIPContext>>(m, "DLPackTensorHIP")
|
|
.def_property_readonly(
|
|
"data",
|
|
[](DLPackWrapper<HIPContext>* t) -> py::object {
|
|
CAFFE_ENFORCE_EQ(
|
|
t->device_option.device_type(),
|
|
PROTO_HIP,
|
|
"Expected HIP device option for HIP tensor");
|
|
|
|
return t->data();
|
|
},
|
|
"Return DLPack tensor with tensor's data.")
|
|
.def(
|
|
"feed",
|
|
[](DLPackWrapper<HIPContext>* t, py::object obj) {
|
|
CAFFE_ENFORCE_EQ(
|
|
t->device_option.device_type(),
|
|
PROTO_HIP,
|
|
"Expected HIP device option for HIP tensor");
|
|
t->feed(obj);
|
|
},
|
|
"Copy data from given DLPack tensor into this tensor.")
|
|
.def_property_readonly(
|
|
"_shape",
|
|
[](const DLPackWrapper<HIPContext>& t) { return t.tensor->sizes(); })
|
|
.def(
|
|
"_reshape",
|
|
[](DLPackWrapper<HIPContext>* t, std::vector<int64_t> dims) {
|
|
t->tensor->Resize(dims);
|
|
});
|
|
}
|
|
|
|
PYBIND11_MODULE(caffe2_pybind11_state_hip, m) {
|
|
m.doc() = "pybind11 stateful interface to Caffe2 workspaces - GPU edition";
|
|
|
|
addGlobalMethods(m);
|
|
addHIPGlobalMethods(m);
|
|
addObjectMethods(m);
|
|
addHIPObjectMethods(m);
|
|
for (const auto& addition : PybindAdditionRegistry()->Keys()) {
|
|
PybindAdditionRegistry()->Create(addition, m);
|
|
}
|
|
}
|
|
} // namespace python
|
|
} // namespace caffe2
|