mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-12-19 19:06:16 +01:00
44d8cdbba1
* ROCm AMD/GPU based build and detector, WIP * detectors/rocm: separate yolov8 postprocessing into own function; fix box scaling; use cv2.dnn.blobForImage for preprocessing; assert on required model parameters * AMD/ROCm: add couple of more ultralytics models; comments * docker/rocm: make imported model files readable by all * docker/rocm: readme about running on AMD GPUs * docker/rocm: updated README * docker/rocm: updated README * docker/rocm: updated README * detectors/rocm: separated preprocessing functions into yolo_utils.py * detector/plugins: added onnx cpu plugin * docker/rocm: updated container with limite label sets * example detectors view * docker/rocm: updated README.md * docker/rocm: update README.md * docker/rocm: do not set HSA_OVERRIDE_GFX_VERSION at all for the general version as the empty value broke rocm * detectors: simplified/optimized yolov8_postprocess * detector/yolo_utils: indentation, remove unused variable * detectors/rocm: default option to conserve cpu usage at the expense of latency * detectors/yolo_utils: use nms to prefilter overlapping boxes if too many detected * detectors/edgetpu_tfl: add support for yolov8 * util/download_models: script to download yolov8 model files * docker/main: add download-models overlay into s6 startup * detectors/rocm: assume models are in /config/model_cache/yolov8/ * docker/rocm: compile onnx files into mxr files at startup * switch model download into bash script * detectors/rocm: automatically override HSA_OVERRIDE_GFX_VERSION for couple of known chipsets * docs: rocm detector first notes * typos * describe builds (harakas temporary) * docker/rocm: also build a version for gfx1100 * docker/rocm: use cp instead of tar * docker.rocm: remove README as it is now in detector config * frigate/detectors: renamed yolov8_preprocess->preprocess, pass input tensor element type * docker/main: use newer openvino (2023.3.0) * detectors: implement class aggregation * update yolov8 model * add openvino/yolov8 support for label aggregation * docker: remove pointless s6/timeout-up files * Revert "detectors: implement class aggregation" This reverts commitdcfe6bbf6f
. * detectors/openvino: remove class aggregation * detectors: increase yolov8 postprocessing score trershold to 0.5 * docker/rocm: separate rocm distributed files into its own build stage * Update object_detectors.md * updated CODEOWNERS file for rocm * updated build names for documentation * Revert "docker/main: use newer openvino (2023.3.0)" This reverts commitdee95de908
. * reverrted openvino detector * reverted edgetpu detector * scratched rocm docs from any mention of edgetpu or openvino * Update docs/docs/configuration/object_detectors.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * renamed frigate.detectors.yolo_utils.py -> frigate.detectors.util.py * clarified rocm example performance * Improved wording and clarified text * Mentioned rocm detector for AMD GPUs * applied ruff formating * applied ruff suggested fixes * docker/rocm: fix missing argument resulting in larger docker image sizes * docs/configuration/object_detectors: fix links to yolov8 release files --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
583 lines
22 KiB
C++
583 lines
22 KiB
C++
/*
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* The MIT License (MIT)
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*
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* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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* THE SOFTWARE.
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*/
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#include <pybind11/pybind11.h>
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#include <pybind11/stl.h>
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#include <pybind11/numpy.h>
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#include <migraphx/program.hpp>
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#include <migraphx/instruction_ref.hpp>
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#include <migraphx/operation.hpp>
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#include <migraphx/quantization.hpp>
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#include <migraphx/generate.hpp>
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#include <migraphx/instruction.hpp>
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#include <migraphx/ref/target.hpp>
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#include <migraphx/stringutils.hpp>
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#include <migraphx/tf.hpp>
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#include <migraphx/onnx.hpp>
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#include <migraphx/load_save.hpp>
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#include <migraphx/register_target.hpp>
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#include <migraphx/json.hpp>
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#include <migraphx/make_op.hpp>
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#include <migraphx/op/common.hpp>
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#ifdef HAVE_GPU
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#include <migraphx/gpu/hip.hpp>
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#endif
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using half = half_float::half;
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namespace py = pybind11;
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#ifdef __clang__
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#define MIGRAPHX_PUSH_UNUSED_WARNING \
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_Pragma("clang diagnostic push") \
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_Pragma("clang diagnostic ignored \"-Wused-but-marked-unused\"")
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#define MIGRAPHX_POP_WARNING _Pragma("clang diagnostic pop")
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#else
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#define MIGRAPHX_PUSH_UNUSED_WARNING
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#define MIGRAPHX_POP_WARNING
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#endif
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#define MIGRAPHX_PYBIND11_MODULE(...) \
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MIGRAPHX_PUSH_UNUSED_WARNING \
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PYBIND11_MODULE(__VA_ARGS__) \
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MIGRAPHX_POP_WARNING
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#define MIGRAPHX_PYTHON_GENERATE_SHAPE_ENUM(x, t) .value(#x, migraphx::shape::type_t::x)
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namespace migraphx {
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migraphx::value to_value(py::kwargs kwargs);
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migraphx::value to_value(py::list lst);
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template <class T, class F>
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void visit_py(T x, F f)
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{
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if(py::isinstance<py::kwargs>(x))
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{
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f(to_value(x.template cast<py::kwargs>()));
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}
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else if(py::isinstance<py::list>(x))
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{
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f(to_value(x.template cast<py::list>()));
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}
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else if(py::isinstance<py::bool_>(x))
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{
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f(x.template cast<bool>());
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}
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else if(py::isinstance<py::int_>(x) or py::hasattr(x, "__index__"))
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{
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f(x.template cast<int>());
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}
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else if(py::isinstance<py::float_>(x))
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{
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f(x.template cast<float>());
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}
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else if(py::isinstance<py::str>(x))
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{
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f(x.template cast<std::string>());
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}
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else if(py::isinstance<migraphx::shape::dynamic_dimension>(x))
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{
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f(migraphx::to_value(x.template cast<migraphx::shape::dynamic_dimension>()));
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}
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else
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{
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MIGRAPHX_THROW("VISIT_PY: Unsupported data type!");
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}
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}
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migraphx::value to_value(py::list lst)
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{
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migraphx::value v = migraphx::value::array{};
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for(auto val : lst)
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{
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visit_py(val, [&](auto py_val) { v.push_back(py_val); });
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}
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return v;
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}
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migraphx::value to_value(py::kwargs kwargs)
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{
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migraphx::value v = migraphx::value::object{};
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for(auto arg : kwargs)
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{
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auto&& key = py::str(arg.first);
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auto&& val = arg.second;
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visit_py(val, [&](auto py_val) { v[key] = py_val; });
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}
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return v;
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}
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} // namespace migraphx
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namespace pybind11 {
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namespace detail {
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template <>
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struct npy_format_descriptor<half>
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{
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static std::string format()
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{
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// following: https://docs.python.org/3/library/struct.html#format-characters
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return "e";
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}
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static constexpr auto name() { return _("half"); }
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};
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} // namespace detail
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} // namespace pybind11
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template <class F>
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void visit_type(const migraphx::shape& s, F f)
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{
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s.visit_type(f);
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}
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template <class T, class F>
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void visit(const migraphx::raw_data<T>& x, F f)
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{
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x.visit(f);
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}
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template <class F>
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void visit_types(F f)
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{
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migraphx::shape::visit_types(f);
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}
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template <class T>
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py::buffer_info to_buffer_info(T& x)
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{
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migraphx::shape s = x.get_shape();
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assert(s.type() != migraphx::shape::tuple_type);
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if(s.dynamic())
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MIGRAPHX_THROW("MIGRAPHX PYTHON: dynamic shape argument passed to to_buffer_info");
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auto strides = s.strides();
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std::transform(
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strides.begin(), strides.end(), strides.begin(), [&](auto i) { return i * s.type_size(); });
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py::buffer_info b;
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visit_type(s, [&](auto as) {
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// migraphx use int8_t data to store bool type, we need to
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// explicitly specify the data type as bool for python
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if(s.type() == migraphx::shape::bool_type)
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{
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b = py::buffer_info(x.data(),
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as.size(),
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py::format_descriptor<bool>::format(),
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s.ndim(),
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s.lens(),
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strides);
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}
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else
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{
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b = py::buffer_info(x.data(),
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as.size(),
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py::format_descriptor<decltype(as())>::format(),
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s.ndim(),
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s.lens(),
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strides);
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}
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});
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return b;
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}
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migraphx::shape to_shape(const py::buffer_info& info)
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{
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migraphx::shape::type_t t;
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std::size_t n = 0;
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visit_types([&](auto as) {
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if(info.format == py::format_descriptor<decltype(as())>::format() or
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(info.format == "l" and py::format_descriptor<decltype(as())>::format() == "q") or
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(info.format == "L" and py::format_descriptor<decltype(as())>::format() == "Q"))
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{
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t = as.type_enum();
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n = sizeof(as());
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}
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else if(info.format == "?" and py::format_descriptor<decltype(as())>::format() == "b")
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{
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t = migraphx::shape::bool_type;
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n = sizeof(bool);
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}
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});
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if(n == 0)
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{
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MIGRAPHX_THROW("MIGRAPHX PYTHON: Unsupported data type " + info.format);
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}
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auto strides = info.strides;
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std::transform(strides.begin(), strides.end(), strides.begin(), [&](auto i) -> std::size_t {
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return n > 0 ? i / n : 0;
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});
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// scalar support
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if(info.shape.empty())
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{
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return migraphx::shape{t};
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}
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else
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{
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return migraphx::shape{t, info.shape, strides};
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}
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}
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MIGRAPHX_PYBIND11_MODULE(migraphx, m)
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{
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py::class_<migraphx::shape> shape_cls(m, "shape");
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shape_cls
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.def(py::init([](py::kwargs kwargs) {
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auto v = migraphx::to_value(kwargs);
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auto t = migraphx::shape::parse_type(v.get("type", "float"));
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if(v.contains("dyn_dims"))
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{
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auto dyn_dims =
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migraphx::from_value<std::vector<migraphx::shape::dynamic_dimension>>(
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v.at("dyn_dims"));
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return migraphx::shape(t, dyn_dims);
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}
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auto lens = v.get<std::size_t>("lens", {1});
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if(v.contains("strides"))
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return migraphx::shape(t, lens, v.at("strides").to_vector<std::size_t>());
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else
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return migraphx::shape(t, lens);
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}))
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.def("type", &migraphx::shape::type)
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.def("lens", &migraphx::shape::lens)
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.def("strides", &migraphx::shape::strides)
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.def("ndim", &migraphx::shape::ndim)
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.def("elements", &migraphx::shape::elements)
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.def("bytes", &migraphx::shape::bytes)
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.def("type_string", &migraphx::shape::type_string)
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.def("type_size", &migraphx::shape::type_size)
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.def("dyn_dims", &migraphx::shape::dyn_dims)
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.def("packed", &migraphx::shape::packed)
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.def("transposed", &migraphx::shape::transposed)
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.def("broadcasted", &migraphx::shape::broadcasted)
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.def("standard", &migraphx::shape::standard)
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.def("scalar", &migraphx::shape::scalar)
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.def("dynamic", &migraphx::shape::dynamic)
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.def("__eq__", std::equal_to<migraphx::shape>{})
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.def("__ne__", std::not_equal_to<migraphx::shape>{})
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.def("__repr__", [](const migraphx::shape& s) { return migraphx::to_string(s); });
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py::enum_<migraphx::shape::type_t>(shape_cls, "type_t")
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MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_PYTHON_GENERATE_SHAPE_ENUM);
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py::class_<migraphx::shape::dynamic_dimension>(shape_cls, "dynamic_dimension")
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.def(py::init<>())
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.def(py::init<std::size_t, std::size_t>())
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.def(py::init<std::size_t, std::size_t, std::set<std::size_t>>())
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.def_readwrite("min", &migraphx::shape::dynamic_dimension::min)
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.def_readwrite("max", &migraphx::shape::dynamic_dimension::max)
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.def_readwrite("optimals", &migraphx::shape::dynamic_dimension::optimals)
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.def("is_fixed", &migraphx::shape::dynamic_dimension::is_fixed);
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py::class_<migraphx::argument>(m, "argument", py::buffer_protocol())
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.def_buffer([](migraphx::argument& x) -> py::buffer_info { return to_buffer_info(x); })
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.def(py::init([](py::buffer b) {
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py::buffer_info info = b.request();
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return migraphx::argument(to_shape(info), info.ptr);
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}))
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.def("get_shape", &migraphx::argument::get_shape)
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.def("data_ptr",
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[](migraphx::argument& x) { return reinterpret_cast<std::uintptr_t>(x.data()); })
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.def("tolist",
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[](migraphx::argument& x) {
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py::list l{x.get_shape().elements()};
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visit(x, [&](auto data) { l = py::cast(data.to_vector()); });
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return l;
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})
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.def("__eq__", std::equal_to<migraphx::argument>{})
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.def("__ne__", std::not_equal_to<migraphx::argument>{})
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.def("__repr__", [](const migraphx::argument& x) { return migraphx::to_string(x); });
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py::class_<migraphx::target>(m, "target");
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py::class_<migraphx::instruction_ref>(m, "instruction_ref")
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.def("shape", [](migraphx::instruction_ref i) { return i->get_shape(); })
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.def("op", [](migraphx::instruction_ref i) { return i->get_operator(); });
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py::class_<migraphx::module, std::unique_ptr<migraphx::module, py::nodelete>>(m, "module")
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.def("print", [](const migraphx::module& mm) { std::cout << mm << std::endl; })
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.def(
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"add_instruction",
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[](migraphx::module& mm,
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const migraphx::operation& op,
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std::vector<migraphx::instruction_ref>& args,
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std::vector<migraphx::module*>& mod_args) {
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return mm.add_instruction(op, args, mod_args);
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},
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py::arg("op"),
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py::arg("args"),
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py::arg("mod_args") = std::vector<migraphx::module*>{})
|
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.def(
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"add_literal",
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[](migraphx::module& mm, py::buffer data) {
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py::buffer_info info = data.request();
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auto literal_shape = to_shape(info);
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return mm.add_literal(literal_shape, reinterpret_cast<char*>(info.ptr));
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},
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py::arg("data"))
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.def(
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"add_parameter",
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[](migraphx::module& mm, const std::string& name, const migraphx::shape shape) {
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return mm.add_parameter(name, shape);
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},
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py::arg("name"),
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py::arg("shape"))
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.def(
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"add_return",
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[](migraphx::module& mm, std::vector<migraphx::instruction_ref>& args) {
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return mm.add_return(args);
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},
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py::arg("args"))
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.def("__repr__", [](const migraphx::module& mm) { return migraphx::to_string(mm); });
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py::class_<migraphx::program>(m, "program")
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.def(py::init([]() { return migraphx::program(); }))
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.def("get_parameter_names", &migraphx::program::get_parameter_names)
|
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.def("get_parameter_shapes", &migraphx::program::get_parameter_shapes)
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.def("get_output_shapes", &migraphx::program::get_output_shapes)
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.def("is_compiled", &migraphx::program::is_compiled)
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.def(
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"compile",
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[](migraphx::program& p,
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const migraphx::target& t,
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bool offload_copy,
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bool fast_math,
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bool exhaustive_tune) {
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migraphx::compile_options options;
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options.offload_copy = offload_copy;
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options.fast_math = fast_math;
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options.exhaustive_tune = exhaustive_tune;
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p.compile(t, options);
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},
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py::arg("t"),
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py::arg("offload_copy") = true,
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py::arg("fast_math") = true,
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py::arg("exhaustive_tune") = false)
|
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.def("get_main_module", [](const migraphx::program& p) { return p.get_main_module(); })
|
|
.def(
|
|
"create_module",
|
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[](migraphx::program& p, const std::string& name) { return p.create_module(name); },
|
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py::arg("name"))
|
|
.def("run",
|
|
[](migraphx::program& p, py::dict params) {
|
|
migraphx::parameter_map pm;
|
|
for(auto x : params)
|
|
{
|
|
std::string key = x.first.cast<std::string>();
|
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py::buffer b = x.second.cast<py::buffer>();
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|
py::buffer_info info = b.request();
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pm[key] = migraphx::argument(to_shape(info), info.ptr);
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}
|
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return p.eval(pm);
|
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})
|
|
.def("run_async",
|
|
[](migraphx::program& p,
|
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py::dict params,
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std::uintptr_t stream,
|
|
std::string stream_name) {
|
|
migraphx::parameter_map pm;
|
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for(auto x : params)
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|
{
|
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std::string key = x.first.cast<std::string>();
|
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py::buffer b = x.second.cast<py::buffer>();
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py::buffer_info info = b.request();
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pm[key] = migraphx::argument(to_shape(info), info.ptr);
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}
|
|
migraphx::execution_environment exec_env{
|
|
migraphx::any_ptr(reinterpret_cast<void*>(stream), stream_name), true};
|
|
return p.eval(pm, exec_env);
|
|
})
|
|
.def("sort", &migraphx::program::sort)
|
|
.def("print", [](const migraphx::program& p) { std::cout << p << std::endl; })
|
|
.def("__eq__", std::equal_to<migraphx::program>{})
|
|
.def("__ne__", std::not_equal_to<migraphx::program>{})
|
|
.def("__repr__", [](const migraphx::program& p) { return migraphx::to_string(p); });
|
|
|
|
py::class_<migraphx::operation> op(m, "op");
|
|
op.def(py::init([](const std::string& name, py::kwargs kwargs) {
|
|
migraphx::value v = migraphx::value::object{};
|
|
if(kwargs)
|
|
{
|
|
v = migraphx::to_value(kwargs);
|
|
}
|
|
return migraphx::make_op(name, v);
|
|
}))
|
|
.def("name", &migraphx::operation::name);
|
|
|
|
py::enum_<migraphx::op::pooling_mode>(op, "pooling_mode")
|
|
.value("average", migraphx::op::pooling_mode::average)
|
|
.value("max", migraphx::op::pooling_mode::max)
|
|
.value("lpnorm", migraphx::op::pooling_mode::lpnorm);
|
|
|
|
py::enum_<migraphx::op::rnn_direction>(op, "rnn_direction")
|
|
.value("forward", migraphx::op::rnn_direction::forward)
|
|
.value("reverse", migraphx::op::rnn_direction::reverse)
|
|
.value("bidirectional", migraphx::op::rnn_direction::bidirectional);
|
|
|
|
m.def(
|
|
"argument_from_pointer",
|
|
[](const migraphx::shape shape, const int64_t address) {
|
|
return migraphx::argument(shape, reinterpret_cast<void*>(address));
|
|
},
|
|
py::arg("shape"),
|
|
py::arg("address"));
|
|
|
|
m.def(
|
|
"parse_tf",
|
|
[](const std::string& filename,
|
|
bool is_nhwc,
|
|
unsigned int batch_size,
|
|
std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims,
|
|
std::vector<std::string> output_names) {
|
|
return migraphx::parse_tf(
|
|
filename, migraphx::tf_options{is_nhwc, batch_size, map_input_dims, output_names});
|
|
},
|
|
"Parse tf protobuf (default format is nhwc)",
|
|
py::arg("filename"),
|
|
py::arg("is_nhwc") = true,
|
|
py::arg("batch_size") = 1,
|
|
py::arg("map_input_dims") = std::unordered_map<std::string, std::vector<std::size_t>>(),
|
|
py::arg("output_names") = std::vector<std::string>());
|
|
|
|
m.def(
|
|
"parse_onnx",
|
|
[](const std::string& filename,
|
|
unsigned int default_dim_value,
|
|
migraphx::shape::dynamic_dimension default_dyn_dim_value,
|
|
std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims,
|
|
std::unordered_map<std::string, std::vector<migraphx::shape::dynamic_dimension>>
|
|
map_dyn_input_dims,
|
|
bool skip_unknown_operators,
|
|
bool print_program_on_error,
|
|
int64_t max_loop_iterations) {
|
|
migraphx::onnx_options options;
|
|
options.default_dim_value = default_dim_value;
|
|
options.default_dyn_dim_value = default_dyn_dim_value;
|
|
options.map_input_dims = map_input_dims;
|
|
options.map_dyn_input_dims = map_dyn_input_dims;
|
|
options.skip_unknown_operators = skip_unknown_operators;
|
|
options.print_program_on_error = print_program_on_error;
|
|
options.max_loop_iterations = max_loop_iterations;
|
|
return migraphx::parse_onnx(filename, options);
|
|
},
|
|
"Parse onnx file",
|
|
py::arg("filename"),
|
|
py::arg("default_dim_value") = 0,
|
|
py::arg("default_dyn_dim_value") = migraphx::shape::dynamic_dimension{1, 1},
|
|
py::arg("map_input_dims") = std::unordered_map<std::string, std::vector<std::size_t>>(),
|
|
py::arg("map_dyn_input_dims") =
|
|
std::unordered_map<std::string, std::vector<migraphx::shape::dynamic_dimension>>(),
|
|
py::arg("skip_unknown_operators") = false,
|
|
py::arg("print_program_on_error") = false,
|
|
py::arg("max_loop_iterations") = 10);
|
|
|
|
m.def(
|
|
"parse_onnx_buffer",
|
|
[](const std::string& onnx_buffer,
|
|
unsigned int default_dim_value,
|
|
migraphx::shape::dynamic_dimension default_dyn_dim_value,
|
|
std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims,
|
|
std::unordered_map<std::string, std::vector<migraphx::shape::dynamic_dimension>>
|
|
map_dyn_input_dims,
|
|
bool skip_unknown_operators,
|
|
bool print_program_on_error) {
|
|
migraphx::onnx_options options;
|
|
options.default_dim_value = default_dim_value;
|
|
options.default_dyn_dim_value = default_dyn_dim_value;
|
|
options.map_input_dims = map_input_dims;
|
|
options.map_dyn_input_dims = map_dyn_input_dims;
|
|
options.skip_unknown_operators = skip_unknown_operators;
|
|
options.print_program_on_error = print_program_on_error;
|
|
return migraphx::parse_onnx_buffer(onnx_buffer, options);
|
|
},
|
|
"Parse onnx file",
|
|
py::arg("filename"),
|
|
py::arg("default_dim_value") = 0,
|
|
py::arg("default_dyn_dim_value") = migraphx::shape::dynamic_dimension{1, 1},
|
|
py::arg("map_input_dims") = std::unordered_map<std::string, std::vector<std::size_t>>(),
|
|
py::arg("map_dyn_input_dims") =
|
|
std::unordered_map<std::string, std::vector<migraphx::shape::dynamic_dimension>>(),
|
|
py::arg("skip_unknown_operators") = false,
|
|
py::arg("print_program_on_error") = false);
|
|
|
|
m.def(
|
|
"load",
|
|
[](const std::string& name, const std::string& format) {
|
|
migraphx::file_options options;
|
|
options.format = format;
|
|
return migraphx::load(name, options);
|
|
},
|
|
"Load MIGraphX program",
|
|
py::arg("filename"),
|
|
py::arg("format") = "msgpack");
|
|
|
|
m.def(
|
|
"save",
|
|
[](const migraphx::program& p, const std::string& name, const std::string& format) {
|
|
migraphx::file_options options;
|
|
options.format = format;
|
|
return migraphx::save(p, name, options);
|
|
},
|
|
"Save MIGraphX program",
|
|
py::arg("p"),
|
|
py::arg("filename"),
|
|
py::arg("format") = "msgpack");
|
|
|
|
m.def("get_target", &migraphx::make_target);
|
|
m.def("create_argument", [](const migraphx::shape& s, const std::vector<double>& values) {
|
|
if(values.size() != s.elements())
|
|
MIGRAPHX_THROW("Values and shape elements do not match");
|
|
migraphx::argument a{s};
|
|
a.fill(values.begin(), values.end());
|
|
return a;
|
|
});
|
|
m.def("generate_argument", &migraphx::generate_argument, py::arg("s"), py::arg("seed") = 0);
|
|
m.def("fill_argument", &migraphx::fill_argument, py::arg("s"), py::arg("value"));
|
|
m.def("quantize_fp16",
|
|
&migraphx::quantize_fp16,
|
|
py::arg("prog"),
|
|
py::arg("ins_names") = std::vector<std::string>{"all"});
|
|
m.def("quantize_int8",
|
|
&migraphx::quantize_int8,
|
|
py::arg("prog"),
|
|
py::arg("t"),
|
|
py::arg("calibration") = std::vector<migraphx::parameter_map>{},
|
|
py::arg("ins_names") = std::vector<std::string>{"dot", "convolution"});
|
|
|
|
#ifdef HAVE_GPU
|
|
m.def("allocate_gpu", &migraphx::gpu::allocate_gpu, py::arg("s"), py::arg("host") = false);
|
|
m.def("to_gpu", &migraphx::gpu::to_gpu, py::arg("arg"), py::arg("host") = false);
|
|
m.def("from_gpu", &migraphx::gpu::from_gpu);
|
|
m.def("gpu_sync", [] { migraphx::gpu::gpu_sync(); });
|
|
#endif
|
|
|
|
#ifdef VERSION_INFO
|
|
m.attr("__version__") = VERSION_INFO;
|
|
#else
|
|
m.attr("__version__") = "dev";
|
|
#endif
|
|
}
|