343 lines
21 KiB
C++
343 lines
21 KiB
C++
#include <pybind11/pybind11.h>
|
|
#include <pybind11/stl.h>
|
|
#include <pybind11/numpy.h>
|
|
|
|
#include <kompute/Kompute.hpp>
|
|
|
|
#include "fmt/ranges.h"
|
|
|
|
#include "utils.hpp"
|
|
#include "docstrings.hpp"
|
|
|
|
namespace py = pybind11;
|
|
|
|
//used in Core.hpp
|
|
py::object kp_debug, kp_info, kp_warning, kp_error;
|
|
|
|
std::unique_ptr<kp::OpAlgoDispatch> opAlgoDispatchPyInit(
|
|
std::shared_ptr<kp::Algorithm>& algorithm,
|
|
const py::array& push_consts) {
|
|
const py::buffer_info info = push_consts.request();
|
|
KP_LOG_DEBUG("Kompute Python Manager creating tensor_T with push_consts size {} dtype {}",
|
|
push_consts.size(), std::string(py::str(push_consts.dtype())));
|
|
|
|
|
|
if (push_consts.dtype() == py::dtype::of<std::float_t>()) {
|
|
std::vector<float> dataVec((float*)info.ptr, ((float*)info.ptr) + info.size);
|
|
return std::unique_ptr<kp::OpAlgoDispatch>{new kp::OpAlgoDispatch(algorithm, dataVec)};
|
|
} else if (push_consts.dtype() == py::dtype::of<std::uint32_t>()) {
|
|
std::vector<uint32_t> dataVec((uint32_t*)info.ptr, ((uint32_t*)info.ptr) + info.size);
|
|
return std::unique_ptr<kp::OpAlgoDispatch>{new kp::OpAlgoDispatch(algorithm, dataVec)};
|
|
} else if (push_consts.dtype() == py::dtype::of<std::int32_t>()) {
|
|
std::vector<int32_t> dataVec((int32_t*)info.ptr, ((int32_t*)info.ptr) + info.size);
|
|
return std::unique_ptr<kp::OpAlgoDispatch>{new kp::OpAlgoDispatch(algorithm, dataVec)};
|
|
} else if (push_consts.dtype() == py::dtype::of<std::double_t>()) {
|
|
std::vector<double> dataVec((double*)info.ptr, ((double*)info.ptr) + info.size);
|
|
return std::unique_ptr<kp::OpAlgoDispatch>{new kp::OpAlgoDispatch(algorithm, dataVec)};
|
|
} else {
|
|
throw std::runtime_error("Kompute Python no valid dtype supported");
|
|
}
|
|
}
|
|
|
|
PYBIND11_MODULE(kp, m) {
|
|
|
|
// The logging modules are used in the Kompute.hpp file
|
|
py::module_ logging = py::module_::import("logging");
|
|
py::object kp_logger = logging.attr("getLogger")("kp");
|
|
kp_debug = kp_logger.attr("debug");
|
|
kp_info = kp_logger.attr("info");
|
|
kp_warning = kp_logger.attr("warning");
|
|
kp_error = kp_logger.attr("error");
|
|
logging.attr("basicConfig")();
|
|
|
|
py::module_ np = py::module_::import("numpy");
|
|
|
|
py::enum_<kp::Tensor::TensorTypes>(m, "TensorTypes")
|
|
.value("device", kp::Tensor::TensorTypes::eDevice, DOC(kp, Tensor, TensorTypes, eDevice))
|
|
.value("host", kp::Tensor::TensorTypes::eHost, DOC(kp, Tensor, TensorTypes, eHost))
|
|
.value("storage", kp::Tensor::TensorTypes::eStorage, DOC(kp, Tensor, TensorTypes, eStorage))
|
|
.export_values();
|
|
|
|
py::class_<kp::OpBase, std::shared_ptr<kp::OpBase>>(m, "OpBase", DOC(kp, OpBase));
|
|
|
|
py::class_<kp::OpTensorSyncDevice, std::shared_ptr<kp::OpTensorSyncDevice>>(
|
|
m, "OpTensorSyncDevice", py::base<kp::OpBase>(), DOC(kp, OpTensorSyncDevice))
|
|
.def(py::init<const std::vector<std::shared_ptr<kp::Tensor>>&>(), DOC(kp, OpTensorSyncDevice, OpTensorSyncDevice));
|
|
|
|
py::class_<kp::OpTensorSyncLocal, std::shared_ptr<kp::OpTensorSyncLocal>>(
|
|
m, "OpTensorSyncLocal", py::base<kp::OpBase>(), DOC(kp, OpTensorSyncLocal))
|
|
.def(py::init<const std::vector<std::shared_ptr<kp::Tensor>>&>(), DOC(kp, OpTensorSyncLocal, OpTensorSyncLocal));
|
|
|
|
py::class_<kp::OpTensorCopy, std::shared_ptr<kp::OpTensorCopy>>(
|
|
m, "OpTensorCopy", py::base<kp::OpBase>(), DOC(kp, OpTensorCopy))
|
|
.def(py::init<const std::vector<std::shared_ptr<kp::Tensor>>&>(), DOC(kp, OpTensorCopy, OpTensorCopy));
|
|
|
|
py::class_<kp::OpAlgoDispatch, std::shared_ptr<kp::OpAlgoDispatch>>(
|
|
m, "OpAlgoDispatch", py::base<kp::OpBase>(), DOC(kp, OpAlgoDispatch))
|
|
.def(py::init<const std::shared_ptr<kp::Algorithm>&,const std::vector<float>&>(),
|
|
DOC(kp, OpAlgoDispatch, OpAlgoDispatch),
|
|
py::arg("algorithm"), py::arg("push_consts") = std::vector<float>())
|
|
.def(py::init(&opAlgoDispatchPyInit),
|
|
DOC(kp, OpAlgoDispatch, OpAlgoDispatch),
|
|
py::arg("algorithm"), py::arg("push_consts"));
|
|
|
|
py::class_<kp::OpMult, std::shared_ptr<kp::OpMult>>(
|
|
m, "OpMult", py::base<kp::OpBase>(), DOC(kp, OpMult))
|
|
.def(py::init<const std::vector<std::shared_ptr<kp::Tensor>>&,const std::shared_ptr<kp::Algorithm>&>(),
|
|
DOC(kp, OpMult, OpMult));
|
|
|
|
py::class_<kp::Algorithm, std::shared_ptr<kp::Algorithm>>(m, "Algorithm", DOC(kp, Algorithm, Algorithm))
|
|
.def("get_tensors", &kp::Algorithm::getTensors, DOC(kp, Algorithm, getTensors))
|
|
.def("destroy", &kp::Algorithm::destroy, DOC(kp, Algorithm, destroy))
|
|
.def("is_init", &kp::Algorithm::isInit, DOC(kp, Algorithm, isInit));
|
|
|
|
py::class_<kp::Tensor, std::shared_ptr<kp::Tensor>>(m, "Tensor", DOC(kp, Tensor))
|
|
.def("data", [](kp::Tensor& self) {
|
|
// Non-owning container exposing the underlying pointer
|
|
switch (self.dataType()) {
|
|
case kp::Tensor::TensorDataTypes::eFloat:
|
|
return py::array(self.size(), self.data<float>(), py::cast(&self));
|
|
case kp::Tensor::TensorDataTypes::eUnsignedInt:
|
|
return py::array(self.size(), self.data<uint32_t>(), py::cast(&self));
|
|
case kp::Tensor::TensorDataTypes::eInt:
|
|
return py::array(self.size(), self.data<int32_t>(), py::cast(&self));
|
|
case kp::Tensor::TensorDataTypes::eDouble:
|
|
return py::array(self.size(), self.data<double>(), py::cast(&self));
|
|
case kp::Tensor::TensorDataTypes::eBool:
|
|
return py::array(self.size(), self.data<bool>(), py::cast(&self));
|
|
default:
|
|
throw std::runtime_error("Kompute Python data type not supported");
|
|
}
|
|
}, DOC(kp, Tensor, data))
|
|
.def("size", &kp::Tensor::size, DOC(kp, Tensor, size))
|
|
.def("__len__", &kp::Tensor::size, DOC(kp, Tensor, size))
|
|
.def("tensor_type", &kp::Tensor::tensorType, DOC(kp, Tensor, tensorType))
|
|
.def("data_type", &kp::Tensor::dataType, DOC(kp, Tensor, dataType))
|
|
.def("is_init", &kp::Tensor::isInit, DOC(kp, Tensor, isInit))
|
|
.def("destroy", &kp::Tensor::destroy, DOC(kp, Tensor, destroy));
|
|
|
|
py::class_<kp::Sequence, std::shared_ptr<kp::Sequence>>(m, "Sequence")
|
|
.def("record", [](kp::Sequence& self, std::shared_ptr<kp::OpBase> op) { return self.record(op); },
|
|
DOC(kp, Sequence, record))
|
|
.def("eval", [](kp::Sequence& self) { return self.eval(); },
|
|
DOC(kp, Sequence, eval))
|
|
.def("eval", [](kp::Sequence& self, std::shared_ptr<kp::OpBase> op) { return self.eval(op); },
|
|
DOC(kp, Sequence, eval_2))
|
|
.def("eval_async", [](kp::Sequence& self) { return self.eval(); },
|
|
DOC(kp, Sequence, evalAwait))
|
|
.def("eval_async", [](kp::Sequence& self, std::shared_ptr<kp::OpBase> op) { return self.evalAsync(op); },
|
|
DOC(kp, Sequence, evalAsync))
|
|
.def("eval_await", [](kp::Sequence& self) { return self.evalAwait(); },
|
|
DOC(kp, Sequence, evalAwait))
|
|
.def("eval_await", [](kp::Sequence& self, uint32_t wait) { return self.evalAwait(wait); },
|
|
DOC(kp, Sequence, evalAwait))
|
|
.def("is_recording", &kp::Sequence::isRecording,
|
|
DOC(kp, Sequence, isRecording))
|
|
.def("is_running", &kp::Sequence::isRunning,
|
|
DOC(kp, Sequence, isRunning))
|
|
.def("is_init", &kp::Sequence::isInit,
|
|
DOC(kp, Sequence, isInit))
|
|
.def("clear", &kp::Sequence::clear,
|
|
DOC(kp, Sequence, clear))
|
|
.def("rerecord", &kp::Sequence::rerecord,
|
|
DOC(kp, Sequence, rerecord))
|
|
.def("get_timestamps", &kp::Sequence::getTimestamps,
|
|
DOC(kp, Sequence, getTimestamps))
|
|
.def("destroy", &kp::Sequence::destroy,
|
|
DOC(kp, Sequence, destroy));
|
|
|
|
py::class_<kp::Manager, std::shared_ptr<kp::Manager>>(m, "Manager", DOC(kp, Manager))
|
|
.def(py::init(), DOC(kp, Manager, Manager))
|
|
.def(py::init<uint32_t>(), DOC(kp, Manager, Manager_2))
|
|
.def(py::init<uint32_t,const std::vector<uint32_t>&,const std::vector<std::string>&>(),
|
|
DOC(kp, Manager, Manager_2),
|
|
py::arg("device") = 0,
|
|
py::arg("family_queue_indices") = std::vector<uint32_t>(),
|
|
py::arg("desired_extensions") = std::vector<std::string>())
|
|
.def("destroy", &kp::Manager::destroy,
|
|
DOC(kp, Manager, destroy))
|
|
.def("sequence", &kp::Manager::sequence, DOC(kp, Manager, sequence),
|
|
py::arg("queue_index") = 0, py::arg("total_timestamps") = 0)
|
|
.def("tensor", [np](kp::Manager& self,
|
|
const py::array_t<float>& data,
|
|
kp::Tensor::TensorTypes tensor_type) {
|
|
const py::array_t<float>& flatdata = np.attr("ravel")(data);
|
|
const py::buffer_info info = flatdata.request();
|
|
KP_LOG_DEBUG("Kompute Python Manager tensor() creating tensor float with data size {}", flatdata.size());
|
|
return self.tensor(
|
|
info.ptr,
|
|
flatdata.size(),
|
|
sizeof(float),
|
|
kp::Tensor::TensorDataTypes::eFloat,
|
|
tensor_type);
|
|
},
|
|
DOC(kp, Manager, tensor),
|
|
py::arg("data"), py::arg("tensor_type") = kp::Tensor::TensorTypes::eDevice)
|
|
.def("tensor_t", [np](kp::Manager& self,
|
|
const py::array& data,
|
|
kp::Tensor::TensorTypes tensor_type) {
|
|
// TODO: Suppport strides in numpy format
|
|
const py::array& flatdata = np.attr("ravel")(data);
|
|
const py::buffer_info info = flatdata.request();
|
|
KP_LOG_DEBUG("Kompute Python Manager creating tensor_T with data size {} dtype {}",
|
|
flatdata.size(), std::string(py::str(flatdata.dtype())));
|
|
if (flatdata.dtype() == py::dtype::of<std::float_t>()) {
|
|
return self.tensor(
|
|
info.ptr, flatdata.size(), sizeof(float), kp::Tensor::TensorDataTypes::eFloat, tensor_type);
|
|
} else if (flatdata.dtype() == py::dtype::of<std::uint32_t>()) {
|
|
return self.tensor(
|
|
info.ptr, flatdata.size(), sizeof(uint32_t), kp::Tensor::TensorDataTypes::eUnsignedInt, tensor_type);
|
|
} else if (flatdata.dtype() == py::dtype::of<std::int32_t>()) {
|
|
return self.tensor(
|
|
info.ptr, flatdata.size(), sizeof(int32_t), kp::Tensor::TensorDataTypes::eInt, tensor_type);
|
|
} else if (flatdata.dtype() == py::dtype::of<std::double_t>()) {
|
|
return self.tensor(
|
|
info.ptr, flatdata.size(), sizeof(double), kp::Tensor::TensorDataTypes::eDouble, tensor_type);
|
|
} else if (flatdata.dtype() == py::dtype::of<bool>()) {
|
|
return self.tensor(
|
|
info.ptr, flatdata.size(), sizeof(bool), kp::Tensor::TensorDataTypes::eBool, tensor_type);
|
|
} else {
|
|
throw std::runtime_error("Kompute Python no valid dtype supported");
|
|
}
|
|
},
|
|
DOC(kp, Manager, tensorT),
|
|
py::arg("data"), py::arg("tensor_type") = kp::Tensor::TensorTypes::eDevice)
|
|
.def("algorithm", [](kp::Manager& self,
|
|
const std::vector<std::shared_ptr<kp::Tensor>>& tensors,
|
|
const py::bytes& spirv,
|
|
const kp::Workgroup& workgroup,
|
|
const std::vector<float>& spec_consts,
|
|
const std::vector<float>& push_consts) {
|
|
py::buffer_info info(py::buffer(spirv).request());
|
|
const char *data = reinterpret_cast<const char *>(info.ptr);
|
|
size_t length = static_cast<size_t>(info.size);
|
|
std::vector<uint32_t> spirvVec((uint32_t*)data, (uint32_t*)(data + length));
|
|
return self.algorithm(tensors, spirvVec, workgroup, spec_consts, push_consts);
|
|
},
|
|
DOC(kp, Manager, algorithm),
|
|
py::arg("tensors"),
|
|
py::arg("spirv"),
|
|
py::arg("workgroup") = kp::Workgroup(),
|
|
py::arg("spec_consts") = std::vector<float>(),
|
|
py::arg("push_consts") = std::vector<float>())
|
|
.def("algorithm", [np](kp::Manager& self,
|
|
const std::vector<std::shared_ptr<kp::Tensor>>& tensors,
|
|
const py::bytes& spirv,
|
|
const kp::Workgroup& workgroup,
|
|
const py::array& spec_consts,
|
|
const py::array& push_consts) {
|
|
|
|
py::buffer_info info(py::buffer(spirv).request());
|
|
const char *data = reinterpret_cast<const char *>(info.ptr);
|
|
size_t length = static_cast<size_t>(info.size);
|
|
std::vector<uint32_t> spirvVec((uint32_t*)data, (uint32_t*)(data + length));
|
|
|
|
const py::buffer_info pushInfo = push_consts.request();
|
|
const py::buffer_info specInfo = spec_consts.request();
|
|
|
|
KP_LOG_DEBUG("Kompute Python Manager creating Algorithm_T with "
|
|
"push consts data size {} dtype {} and spec const data size {} dtype {}",
|
|
push_consts.size(), std::string(py::str(push_consts.dtype())),
|
|
spec_consts.size(), std::string(py::str(spec_consts.dtype())));
|
|
|
|
// We have to iterate across a combination of parameters due to the lack of support for templating
|
|
if (spec_consts.dtype() == py::dtype::of<std::float_t>()) {
|
|
std::vector<float> specConstsVec((float*)specInfo.ptr, ((float*)specInfo.ptr) + specInfo.size);
|
|
if (spec_consts.dtype() == py::dtype::of<std::float_t>()) {
|
|
std::vector<float> pushConstsVec((float*)pushInfo.ptr, ((float*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specConstsVec, pushConstsVec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::int32_t>()) {
|
|
std::vector<int32_t> pushConstsVec((int32_t*)pushInfo.ptr, ((int32_t*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specConstsVec, pushConstsVec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::uint32_t>()) {
|
|
std::vector<uint32_t> pushConstsVec((uint32_t*)pushInfo.ptr, ((uint32_t*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specConstsVec, pushConstsVec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::double_t>()) {
|
|
std::vector<double> pushConstsVec((double*)pushInfo.ptr, ((double*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specConstsVec, pushConstsVec);
|
|
}
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::int32_t>()) {
|
|
std::vector<int32_t> specconstsvec((int32_t*)specInfo.ptr, ((int32_t*)specInfo.ptr) + specInfo.size);
|
|
if (spec_consts.dtype() == py::dtype::of<std::float_t>()) {
|
|
std::vector<float> pushconstsvec((float*)pushInfo.ptr, ((float*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::int32_t>()) {
|
|
std::vector<int32_t> pushconstsvec((int32_t*)pushInfo.ptr, ((int32_t*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::uint32_t>()) {
|
|
std::vector<uint32_t> pushconstsvec((uint32_t*)pushInfo.ptr, ((uint32_t*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::double_t>()) {
|
|
std::vector<double> pushconstsvec((double*)pushInfo.ptr, ((double*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
}
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::uint32_t>()) {
|
|
std::vector<uint32_t> specconstsvec((uint32_t*)specInfo.ptr, ((uint32_t*)specInfo.ptr) + specInfo.size);
|
|
if (spec_consts.dtype() == py::dtype::of<std::float_t>()) {
|
|
std::vector<float> pushconstsvec((float*)pushInfo.ptr, ((float*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::int32_t>()) {
|
|
std::vector<int32_t> pushconstsvec((int32_t*)pushInfo.ptr, ((int32_t*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::uint32_t>()) {
|
|
std::vector<uint32_t> pushconstsvec((uint32_t*)pushInfo.ptr, ((uint32_t*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::double_t>()) {
|
|
std::vector<double> pushconstsvec((double*)pushInfo.ptr, ((double*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
}
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::double_t>()) {
|
|
std::vector<double> specconstsvec((double*)specInfo.ptr, ((double*)specInfo.ptr) + specInfo.size);
|
|
if (spec_consts.dtype() == py::dtype::of<std::float_t>()) {
|
|
std::vector<float> pushconstsvec((float*)pushInfo.ptr, ((float*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::int32_t>()) {
|
|
std::vector<float> pushconstsvec((int32_t*)pushInfo.ptr, ((int32_t*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::uint32_t>()) {
|
|
std::vector<float> pushconstsvec((uint32_t*)pushInfo.ptr, ((uint32_t*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
} else if (spec_consts.dtype() == py::dtype::of<std::double_t>()) {
|
|
std::vector<float> pushconstsvec((double*)pushInfo.ptr, ((double*)pushInfo.ptr) + pushInfo.size);
|
|
return self.algorithm(tensors, spirvVec, workgroup, specconstsvec, pushconstsvec);
|
|
}
|
|
} else {
|
|
// If reach then no valid dtype supported
|
|
throw std::runtime_error("Kompute Python no valid dtype supported");
|
|
}
|
|
},
|
|
DOC(kp, Manager, algorithm),
|
|
py::arg("tensors"),
|
|
py::arg("spirv"),
|
|
py::arg("workgroup") = kp::Workgroup(),
|
|
py::arg("spec_consts") = std::vector<float>(),
|
|
py::arg("push_consts") = std::vector<float>())
|
|
.def("list_devices", [](kp::Manager& self){
|
|
const std::vector<vk::PhysicalDevice> devices = self.listDevices();
|
|
py::list list;
|
|
for (const vk::PhysicalDevice& device : devices) {
|
|
list.append(kp::py::vkPropertiesToDict(device.getProperties()));
|
|
}
|
|
return list;
|
|
}, "Return a dict containing information about the device")
|
|
.def("get_device_properties", [](kp::Manager& self){
|
|
const vk::PhysicalDeviceProperties properties = self.getDeviceProperties();
|
|
|
|
return kp::py::vkPropertiesToDict(properties);
|
|
}, "Return a dict containing information about the device");
|
|
|
|
auto atexit = py::module_::import("atexit");
|
|
atexit.attr("register")(py::cpp_function([](){
|
|
kp_debug = py::none();
|
|
kp_info = py::none();
|
|
kp_warning = py::none();
|
|
kp_error = py::none();
|
|
}));
|
|
|
|
#ifdef VERSION_INFO
|
|
m.attr("__version__") = VERSION_INFO;
|
|
#else
|
|
m.attr("__version__") = "dev";
|
|
#endif
|
|
}
|
|
|