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https://github.com/zebrajr/pytorch.git
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d80fe49de0
This is a re-land of #81372 and #81233 with the exception that it does not force the range-checks on older Python runtime versions and as such should not affect the internal workloads, which were the reason for revert, see https://github.com/pytorch/pytorch/pull/81372#issuecomment-1187516464 - [Py3.10] Allow floats to be imported as Long (#81372) - [CI] Move CUDA-11.6 to Python-3.10 configuration (#81233) - Don't do anything about range checks for pre-py3.10 Pull Request resolved: https://github.com/pytorch/pytorch/pull/82329 Approved by: https://github.com/kit1980
120 lines
3.7 KiB
C++
120 lines
3.7 KiB
C++
#pragma once
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#include <ATen/ATen.h>
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#include <c10/util/TypeCast.h>
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#include <torch/csrc/python_headers.h>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/utils/python_numbers.h>
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namespace torch {
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namespace utils {
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template <typename T>
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inline T unpackIntegral(PyObject* obj, const char* type) {
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#if PY_VERSION_HEX >= 0x030a00f0
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// In Python-3.10 floats can no longer be silently converted to integers
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// Keep backward compatible behavior for now
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if (PyFloat_Check(obj)) {
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return c10::checked_convert<T>(THPUtils_unpackDouble(obj), type);
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}
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return c10::checked_convert<T>(THPUtils_unpackLong(obj), type);
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#else
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return static_cast<T>(THPUtils_unpackLong(obj));
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#endif
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}
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inline void store_scalar(void* data, at::ScalarType scalarType, PyObject* obj) {
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switch (scalarType) {
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case at::kByte:
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*(uint8_t*)data = unpackIntegral<uint8_t>(obj, "uint8");
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break;
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case at::kChar:
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*(int8_t*)data = unpackIntegral<int8_t>(obj, "int8");
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break;
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case at::kShort:
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*(int16_t*)data = unpackIntegral<int16_t>(obj, "int16");
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break;
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case at::kInt:
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*(int32_t*)data = unpackIntegral<int32_t>(obj, "int32");
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break;
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case at::kLong:
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*(int64_t*)data = unpackIntegral<int64_t>(obj, "int64");
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break;
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case at::kHalf:
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*(at::Half*)data =
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at::convert<at::Half, double>(THPUtils_unpackDouble(obj));
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break;
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case at::kFloat:
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*(float*)data = (float)THPUtils_unpackDouble(obj);
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break;
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case at::kDouble:
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*(double*)data = THPUtils_unpackDouble(obj);
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break;
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case at::kComplexHalf:
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*(c10::complex<at::Half>*)data =
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(c10::complex<at::Half>)static_cast<c10::complex<float>>(
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THPUtils_unpackComplexDouble(obj));
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break;
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case at::kComplexFloat:
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*(c10::complex<float>*)data =
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(c10::complex<float>)THPUtils_unpackComplexDouble(obj);
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break;
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case at::kComplexDouble:
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*(c10::complex<double>*)data = THPUtils_unpackComplexDouble(obj);
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break;
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case at::kBool:
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*(bool*)data = THPUtils_unpackNumberAsBool(obj);
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break;
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case at::kBFloat16:
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*(at::BFloat16*)data =
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at::convert<at::BFloat16, double>(THPUtils_unpackDouble(obj));
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break;
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default:
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throw std::runtime_error("invalid type");
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}
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}
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inline PyObject* load_scalar(void* data, at::ScalarType scalarType) {
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switch (scalarType) {
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case at::kByte:
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return THPUtils_packInt64(*(uint8_t*)data);
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case at::kChar:
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return THPUtils_packInt64(*(int8_t*)data);
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case at::kShort:
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return THPUtils_packInt64(*(int16_t*)data);
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case at::kInt:
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return THPUtils_packInt64(*(int32_t*)data);
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case at::kLong:
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return THPUtils_packInt64(*(int64_t*)data);
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case at::kHalf:
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return PyFloat_FromDouble(
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at::convert<double, at::Half>(*(at::Half*)data));
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case at::kFloat:
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return PyFloat_FromDouble(*(float*)data);
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case at::kDouble:
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return PyFloat_FromDouble(*(double*)data);
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case at::kComplexHalf: {
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auto data_ = reinterpret_cast<c10::complex<at::Half>*>(data);
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return PyComplex_FromDoubles(data_->real(), data_->imag());
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}
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case at::kComplexFloat: {
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auto data_ = reinterpret_cast<c10::complex<float>*>(data);
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return PyComplex_FromDoubles(data_->real(), data_->imag());
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}
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case at::kComplexDouble:
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return PyComplex_FromCComplex(
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*reinterpret_cast<Py_complex*>((c10::complex<double>*)data));
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case at::kBool:
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return PyBool_FromLong(*(bool*)data);
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case at::kBFloat16:
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return PyFloat_FromDouble(
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at::convert<double, at::BFloat16>(*(at::BFloat16*)data));
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default:
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throw std::runtime_error("invalid type");
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}
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}
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} // namespace utils
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} // namespace torch
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