mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
Summary: Linting `torch/csrc/` (non-recursive) and `torch/csrc/autograd` (non-recursive). Fixed things like: - `typedef` vs `using` - Use `.empty()` instead of comparing with empty string/using `.size() == 0` - Use range for loops instead of old style loops (`modernize-`) - Remove some `virtual` + `override` - Replace `stdint.h` with `cstdint` - Replace `return Type(x, y)` with `return {x, y}` - Use boolean values (`true`/`false`) instead of numbers (1/0) - More ... ezyang apaszke cpuhrsch Pull Request resolved: https://github.com/pytorch/pytorch/pull/11050 Differential Revision: D9597505 Pulled By: goldsborough fbshipit-source-id: cb0fb4793ade885a8dbf4b10484487b84c64c7f2
129 lines
3.3 KiB
C++
129 lines
3.3 KiB
C++
#pragma once
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#include "torch/csrc/python_headers.h"
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#include <cstdint>
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#include <stdexcept>
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#include "torch/csrc/Exceptions.h"
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#include "torch/csrc/utils/tensor_numpy.h"
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#include "torch/csrc/jit/tracer.h"
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// largest integer that can be represented consecutively in a double
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const int64_t DOUBLE_INT_MAX = 9007199254740992;
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inline PyObject* THPUtils_packInt64(int64_t value) {
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#if PY_MAJOR_VERSION == 2
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if (sizeof(long) == sizeof(int64_t)) {
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return PyInt_FromLong(static_cast<long>(value));
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} else if (value <= INT32_MAX && value >= INT32_MIN) {
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return PyInt_FromLong(static_cast<long>(value));
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}
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#endif
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return PyLong_FromLongLong(value);
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}
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inline PyObject* THPUtils_packUInt64(uint64_t value) {
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#if PY_MAJOR_VERSION == 2
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if (value <= INT32_MAX) {
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return PyInt_FromLong(static_cast<long>(value));
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}
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#endif
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return PyLong_FromUnsignedLongLong(value);
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}
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inline PyObject* THPUtils_packDoubleAsInt(double value) {
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#if PY_MAJOR_VERSION == 2
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if (value <= INT32_MAX && value >= INT32_MIN) {
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return PyInt_FromLong(static_cast<long>(value));
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}
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#endif
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return PyLong_FromDouble(value);
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}
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inline bool THPUtils_checkLong(PyObject* obj) {
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#if PY_MAJOR_VERSION == 2
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return (PyLong_Check(obj) || PyInt_Check(obj)) && !PyBool_Check(obj);
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#else
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return PyLong_Check(obj) && !PyBool_Check(obj);
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#endif
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}
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inline int64_t THPUtils_unpackLong(PyObject* obj) {
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int overflow;
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long long value = PyLong_AsLongLongAndOverflow(obj, &overflow);
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if (value == -1 && PyErr_Occurred()) {
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throw python_error();
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}
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if (overflow != 0) {
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throw std::runtime_error("Overflow when unpacking long");
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}
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return (int64_t)value;
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}
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inline bool THPUtils_checkIndex(PyObject *obj) {
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if (PyBool_Check(obj)) {
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return false;
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}
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if (THPUtils_checkLong(obj)) {
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return true;
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}
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torch::jit::tracer::NoWarn no_warn_guard;
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auto index = THPObjectPtr(PyNumber_Index(obj));
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if (!index) {
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PyErr_Clear();
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return false;
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}
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return true;
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}
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inline int64_t THPUtils_unpackIndex(PyObject* obj) {
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if (!THPUtils_checkLong(obj)) {
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auto index = THPObjectPtr(PyNumber_Index(obj));
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if (index == nullptr) {
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throw python_error();
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}
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obj = index.get();
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}
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return THPUtils_unpackLong(obj);
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}
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inline bool THPUtils_checkDouble(PyObject* obj) {
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bool is_numpy_scalar;
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#ifdef USE_NUMPY
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is_numpy_scalar = torch::utils::is_numpy_scalar(obj);
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#else
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is_numpy_scalar = false;
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#endif
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#if PY_MAJOR_VERSION == 2
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return PyFloat_Check(obj) || PyLong_Check(obj) || PyInt_Check(obj) || is_numpy_scalar;
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#else
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return PyFloat_Check(obj) || PyLong_Check(obj) || is_numpy_scalar;
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#endif
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}
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inline double THPUtils_unpackDouble(PyObject* obj) {
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if (PyFloat_Check(obj)) {
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return PyFloat_AS_DOUBLE(obj);
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}
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if (PyLong_Check(obj)) {
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int overflow;
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long long value = PyLong_AsLongLongAndOverflow(obj, &overflow);
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if (overflow != 0) {
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throw std::runtime_error("Overflow when unpacking double");
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}
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if (value > DOUBLE_INT_MAX || value < -DOUBLE_INT_MAX) {
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throw std::runtime_error("Precision loss when unpacking double");
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}
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return (double)value;
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}
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#if PY_MAJOR_VERSION == 2
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if (PyInt_Check(obj)) {
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return (double)PyInt_AS_LONG(obj);
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}
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#endif
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double value = PyFloat_AsDouble(obj);
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if (value == -1 && PyErr_Occurred()) {
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throw python_error();
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}
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return value;
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}
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