Remove numpy dependency from its Python binary build (#657)
This commit is contained in:
Wenbing Li
GitHub
Родитель
972cb17a82
Коммит
69a08ffb1d
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@ -3,11 +3,11 @@ block(PROPAGATE Python3_FOUND)
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# if we don't set this to NEVER (or possibly LAST) the builds of the wheel for different python versions will fail
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# as it will find the system python version first and not the correct python version for the wheel.
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set(Python3_FIND_FRAMEWORK NEVER)
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find_package(Python3 COMPONENTS Interpreter Development.Module NumPy)
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find_package(Python3 COMPONENTS Interpreter Development.Module)
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endblock()
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if (NOT Python3_FOUND)
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message(FATAL_ERROR "Python3 or NumPy not found!")
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message(FATAL_ERROR "Python3 not found!")
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endif()
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file(GLOB TARGET_SRC_PYOPS "pyop/*.cc" "pyop/*.h" "shared/*.cc")
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@ -25,7 +25,6 @@ include(pybind11)
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target_include_directories(extensions_pydll PRIVATE
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${pybind11_INCLUDE_DIRS}
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$<TARGET_PROPERTY:Python3::Module,INTERFACE_INCLUDE_DIRECTORIES>
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$<TARGET_PROPERTY:Python3::NumPy,INTERFACE_INCLUDE_DIRECTORIES>
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$<TARGET_PROPERTY:ocos_operators,INTERFACE_INCLUDE_DIRECTORIES>)
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target_compile_definitions(extensions_pydll PRIVATE
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144
pyop/pyfunc.cc
144
pyop/pyfunc.cc
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@ -8,7 +8,6 @@
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#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
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#define PY_ARRAY_UNIQUE_SYMBOL ocos_python_ARRAY_API
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#include <numpy/arrayobject.h>
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#include <pybind11/iostream.h>
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#include <pybind11/pybind11.h>
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@ -43,42 +42,6 @@ const int PyCustomOpDef::dt_complex128 = ONNX_TENSOR_ELEMENT_DATA_TYPE_COMPLEX12
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// Non-IEEE floating-point format based on IEEE754 single-precision
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const int PyCustomOpDef::dt_bfloat16 = ONNX_TENSOR_ELEMENT_DATA_TYPE_BFLOAT16;
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static int to_numpy(ONNXTensorElementDataType dt) {
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switch (dt) {
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT:
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return NPY_FLOAT;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8:
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return NPY_UINT8;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8:
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return NPY_INT8;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT16:
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return NPY_UINT16;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT16:
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return NPY_INT16;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32:
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return NPY_INT32;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64:
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return NPY_INT64;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_BOOL:
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return NPY_BOOL;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16:
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return NPY_FLOAT16;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE:
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return NPY_DOUBLE;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT32:
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return NPY_UINT32;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT64:
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return NPY_UINT64;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_COMPLEX64:
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return NPY_COMPLEX64;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_COMPLEX128:
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return NPY_COMPLEX128;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_STRING:
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return NPY_OBJECT;
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default:
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throw std::runtime_error("No corresponding Numpy data type/Tensor data Type.");
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}
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}
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static size_t element_size(ONNXTensorElementDataType dt) {
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switch (dt) {
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@ -117,44 +80,6 @@ static size_t element_size(ONNXTensorElementDataType dt) {
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}
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}
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static ONNXTensorElementDataType from_numpy(int dt) {
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switch (dt) {
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case NPY_FLOAT:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT;
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case NPY_UINT8:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8;
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case NPY_INT8:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8;
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case NPY_UINT16:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT16;
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case NPY_INT16:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT16;
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case NPY_INT32:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32;
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case NPY_INT64:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64;
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case NPY_BOOL:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_BOOL;
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case NPY_FLOAT16:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16;
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case NPY_DOUBLE:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE;
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case NPY_UINT32:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT32;
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case NPY_UINT64:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT64;
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case NPY_COMPLEX64:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_COMPLEX64;
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case NPY_COMPLEX128:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_COMPLEX128;
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case NPY_OBJECT:
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case NPY_STRING:
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return ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_STRING;
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default:
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throw std::runtime_error("No corresponding ONNX data type/Tensor data Type.");
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}
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}
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struct PyCustomOpDefImpl : public PyCustomOpDef {
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typedef std::vector<int64_t> shape_t;
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static int64_t calc_size_from_shape(const shape_t& sp) {
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@ -165,19 +90,66 @@ struct PyCustomOpDefImpl : public PyCustomOpDef {
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return c;
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}
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static py::object BuildPyObjFromTensor(
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static py::object BuildPyArrayFromTensor(
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const OrtApi& api, OrtW::CustomOpApi& ort, OrtKernelContext* context, const OrtValue* value,
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const shape_t& shape, ONNXTensorElementDataType dtype) {
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std::vector<npy_intp> npy_dims;
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std::vector<std::size_t> npy_dims;
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for (auto n : shape) {
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npy_dims.push_back(n);
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}
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const int numpy_type = to_numpy(dtype);
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py::object obj = py::reinterpret_steal<py::object>(PyArray_SimpleNew(
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static_cast<int>(shape.size()), npy_dims.data(), numpy_type));
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void* out_ptr = static_cast<void*>(
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PyArray_DATA(reinterpret_cast<PyArrayObject*>(obj.ptr())));
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py::array obj;
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switch (dtype) {
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_STRING:
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obj = py::array(py::dtype("O"), npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT:
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obj = py::array_t<float>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8:
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obj = py::array_t<uint8_t>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8:
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obj = py::array_t<int8_t>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT16:
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obj = py::array_t<uint16_t>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT16:
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obj = py::array_t<int16_t>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32:
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obj = py::array_t<int32_t>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64:
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obj = py::array_t<int64_t>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_BOOL:
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obj = py::array_t<bool>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16:
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throw std::runtime_error(MakeString(
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"Type float16 not supported by python customops api"));
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE:
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obj = py::array_t<double>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT32:
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obj = py::array_t<uint32_t>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT64:
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obj = py::array_t<uint64_t>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_COMPLEX64:
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obj = py::array_t<std::complex<float>>(npy_dims);
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break;
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case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_COMPLEX128:
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obj = py::array_t<std::complex<double>>(npy_dims);
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break;
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default:
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throw std::runtime_error(MakeString("Unsupported ONNX Tensor type: ", dtype));
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}
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void* out_ptr = obj.mutable_data();
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if (dtype == ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_STRING) {
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py::object* outObj = static_cast<py::object*>(out_ptr);
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auto size = calc_size_from_shape(shape);
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@ -292,7 +264,7 @@ void PyCustomOpKernel::Compute(OrtKernelContext* context) {
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{
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py::list pyinputs;
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for (auto it = inputs.begin(); it != inputs.end(); ++it) {
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py::object input0 = PyCustomOpDefImpl::BuildPyObjFromTensor(
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py::object input0 = PyCustomOpDefImpl::BuildPyArrayFromTensor(
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api_, ort_, context, it->input_X, it->dimensions, it->dtype);
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pyinputs.append(input0);
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}
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@ -462,11 +434,6 @@ OrtStatusPtr RegisterPythonDomainAndOps(OrtSessionOptions* options, const OrtApi
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return status;
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}
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static int init_numpy() {
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import_array1(0);
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return 0;
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}
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uint64_t hash_64(const std::string& str, uint64_t num_buckets, bool fast) {
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if (fast) {
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return Hash64Fast(str.c_str(), str.size()) % static_cast<uint64_t>(num_buckets);
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@ -514,7 +481,6 @@ void AddObjectMethods(pybind11::module& m) {
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PYBIND11_MODULE(_extensions_pydll, m) {
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m.doc() = "pybind11 stateful interface to ONNXRuntime-Extensions";
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init_numpy();
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AddGlobalMethods(m);
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AddObjectMethods(m);
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auto atexit = py::module_::import("atexit");
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@ -1,6 +1,6 @@
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[build-system]
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# Minimum requirements for the build system to execute.
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requires = ["setuptools", "wheel", "numpy>=1.18.5", "ninja", "cmake"] # PEP 508 specifications.
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requires = ["setuptools", "wheel", "ninja", "cmake"] # PEP 508 specifications.
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build-backend = "backend"
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backend-path = [".pyproject"]
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@ -2,7 +2,7 @@ import os
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import onnx
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import unittest
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import numpy as np
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from numpy.testing import assert_almost_equal
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from numpy.testing import assert_almost_equal, assert_array_equal
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from onnx import helper, onnx_pb as onnx_proto
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import onnxruntime as _ort
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from onnxruntime_extensions import (
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@ -72,11 +72,11 @@ def _create_test_model_2outputs(prefix, domain='ai.onnx.contrib'):
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]
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input0 = helper.make_tensor_value_info(
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'x', onnx_proto.TensorProto.FLOAT, [])
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'x', onnx_proto.TensorProto.FLOAT, None)
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output1 = helper.make_tensor_value_info(
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'neg', onnx_proto.TensorProto.FLOAT, [])
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'neg', onnx_proto.TensorProto.FLOAT, None)
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output2 = helper.make_tensor_value_info(
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'pos', onnx_proto.TensorProto.FLOAT, [])
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'pos', onnx_proto.TensorProto.FLOAT, None)
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graph = helper.make_graph(nodes, 'test0', [input0], [output1, output2])
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model = make_onnx_model(graph)
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@ -225,7 +225,7 @@ class TestPythonOp(unittest.TestCase):
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arr = np.array([["a", "b"]], dtype=object)
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txout = sess.run(None, {'input_1': arr})
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exp = np.array(["a;b"], dtype=object)
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assert txout[0][0] == exp[0]
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assert_array_equal(txout[0][0], exp[0])
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if __name__ == "__main__":
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