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dbc7e919b8
This PR introduces **-Wmissing-prototypes** of clang-tidy to prevent further coding errors such as the one fixed by PR #96714. <!-- copilot:summary --> ### <samp>🤖 Generated by Copilot at fd2cf2a</samp> This pull request makes several internal functions static to improve performance and avoid name clashes. It also fixes some typos, formatting, and missing includes in various files. It adds a new .clang-tidy check to warn about missing prototypes for non-static functions. Pull Request resolved: https://github.com/pytorch/pytorch/pull/96805 Approved by: https://github.com/malfet, https://github.com/albanD
92 lines
2.9 KiB
C++
92 lines
2.9 KiB
C++
#include <ATen/ATen.h>
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#include <ATen/NestedTensorImpl.h>
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#include <c10/core/ScalarType.h>
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#include <torch/csrc/python_headers.h>
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#include <torch/csrc/utils/nested.h>
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#include <torch/csrc/utils/pybind.h>
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#include <torch/csrc/utils/tensor_new.h>
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#include <torch/torch.h>
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#include <stdexcept>
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#include <vector>
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namespace torch {
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namespace utils {
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// NB: device_idx here is NOT a DeviceIndex, but index into PythonArgs
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static c10::TensorOptions typeIdWithDefault(
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PythonArgs& r,
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int device_idx,
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c10::DispatchKey dispatch_key) {
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auto options = dispatchKeyToTensorOptions(dispatch_key);
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if (!r.isNone(device_idx)) {
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options = options.device(r.device(device_idx));
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}
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return options;
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}
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at::Tensor nested_tensor_ctor(
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c10::DispatchKey dispatch_key,
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at::ScalarType scalar_type,
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torch::PythonArgs& r) {
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TORCH_CHECK(r.idx == 0, "nested_tensor(): invalid arguments");
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PyObject* data = r.pyobject(0);
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// Check if data is a list: Only List[Tensor] and List[List...[Scalar]] are
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// accepted for now
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TORCH_CHECK_TYPE(
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PyList_Check(data),
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"Only lists (List[Tensor] and List[List...[Scalar]]) are accepted in nested_tensor");
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auto dtype_val = r.scalartypeWithDefault(1, scalar_type);
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auto tensor_options = typeIdWithDefault(r, 2, dispatch_key);
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bool pin_memory = r.toBool(3);
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bool args_requires_grad = r.toBool(4);
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TORCH_CHECK(
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PyList_Size(data) >= 0,
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"Something went really wrong and your list has negative size");
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// Check whether we are dealing with lists of tensors or not
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std::vector<at::Tensor> new_list(PyList_Size(data));
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for (const auto i : c10::irange(PyList_Size(data))) {
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PyObject* elem = PyList_GetItem(data, i);
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if (THPVariable_Check(elem)) {
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new_list[i] = THPVariable_Unpack(PyList_GetItem(data, i)).detach();
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TORCH_CHECK(
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!new_list[i].is_nested(),
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"We do not accept nested tensors as input to nested tensors");
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TORCH_CHECK(
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new_list[i].layout() == kStrided,
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"We do not accept non-strided layouts as input to nested tensors");
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} else {
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PythonArgs elem_r(r);
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std::array<PyObject*, 6> elem_args = {
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elem, // data
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r.args[1], // dtpye
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nullptr, // device (cpu)
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nullptr, // no pinned memory
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r.args[4], // requires grad
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nullptr // names
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};
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elem_r.args = elem_args.data();
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new_list[i] = tensor_ctor(dispatch_key, scalar_type, elem_r);
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}
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}
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at::ScalarType final_dtype = dtype_val;
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if (r.isNone(1) && !new_list.empty()) {
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final_dtype = c10::typeMetaToScalarType(new_list[0].dtype());
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}
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at::Device final_device = tensor_options.device();
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if (r.isNone(2) && !new_list.empty()) {
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final_device = new_list[0].device();
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}
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auto out = at::_nested_tensor_from_tensor_list(
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new_list, final_dtype, c10::nullopt, final_device, pin_memory);
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out.requires_grad_(args_requires_grad);
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return out;
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}
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} // namespace utils
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} // namespace torch
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