mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
7a61306031
Summary: This PR adds the final set of clang-tidy checks we should add for our codebase: a last set of performance-related checks. Most fixes here are around changing `auto` to `const auto&` in a few places where unnecessary copies were made, and adding `reserve()` calls before loops doing repeated `push_back()`. Also a few cases of calling `std::string::find` with a single-character string literal instead of a single char, which uses a less efficient string search algorithm meant for searching larger substrings.  ezyang apaszke Pull Request resolved: https://github.com/pytorch/pytorch/pull/15198 Differential Revision: D13468797 Pulled By: goldsborough fbshipit-source-id: 2bed1ea1c7c162b7f3e0e1026f17125e88c4d5b2
626 lines
24 KiB
C++
626 lines
24 KiB
C++
#include <torch/csrc/python_headers.h>
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#include <torch/csrc/utils/tensor_new.h>
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#include <torch/csrc/DynamicTypes.h>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/Size.h>
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#include <torch/csrc/autograd/variable.h>
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#include <torch/csrc/utils/auto_gil.h>
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#include <torch/csrc/utils/cuda_lazy_init.h>
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#include <torch/csrc/utils/numpy_stub.h>
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#include <torch/csrc/utils/python_arg_parser.h>
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#include <torch/csrc/utils/python_numbers.h>
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#include <torch/csrc/utils/python_scalars.h>
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#include <torch/csrc/utils/python_strings.h>
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#include <torch/csrc/utils/tensor_numpy.h>
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#include <torch/csrc/autograd/generated/variable_factories.h>
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#include <ATen/ATen.h>
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#include <ATen/InitialTensorOptions.h>
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#include <c10/util/Exception.h>
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#include <c10/util/Optional.h>
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#include <stdexcept>
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#include <vector>
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using at::Backend;
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using at::Device;
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using at::IntList;
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using at::kCPU;
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using at::kCUDA;
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using at::kLong;
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using at::Scalar;
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using at::ScalarType;
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using at::Storage;
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using at::Tensor;
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using at::TensorOptions;
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using at::Type;
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using c10::optional;
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namespace torch { namespace utils {
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namespace {
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const int MAX_DIMS = 128;
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void maybe_initialize_cuda(const Type &type) {
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if (type.is_cuda()) {
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torch::utils::cuda_lazy_init();
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}
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}
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void maybe_initialize_cuda(const Device device) {
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if (device.is_cuda()) {
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torch::utils::cuda_lazy_init();
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}
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}
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Tensor dispatch_zeros(const Type& type, optional<Device> device, IntList sizes) {
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maybe_initialize_cuda(type);
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AutoNoGIL no_gil;
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return torch::zeros(sizes, type.options(std::move(device)));
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}
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Tensor dispatch_ones(const Type& type, optional<Device> device, IntList sizes) {
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maybe_initialize_cuda(type);
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AutoNoGIL no_gil;
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return torch::ones(sizes, type.options(std::move(device)));
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}
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Tensor dispatch_full(const Type& type, Scalar fill_value, optional<Device> device, IntList sizes) {
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maybe_initialize_cuda(type);
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AutoNoGIL no_gil;
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return torch::full(sizes, fill_value, type.options(std::move(device)));
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}
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Tensor new_with_sizes(const Type& type, optional<Device> device, IntList sizes) {
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maybe_initialize_cuda(type);
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AutoNoGIL no_gil;
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return torch::empty(sizes, type.options(std::move(device)));
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}
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Tensor new_with_storage(const Type& type, Storage storage) {
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auto tensor = at::empty({}, type.options());
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tensor.set_(std::move(storage));
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return tensor;
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}
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Tensor new_with_tensor(const Type& type, const Tensor& other) {
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if (other.type() != type) {
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throw TypeError("expected %s (got %s)", type.toString(), other.type().toString());
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}
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return other.slice();
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}
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std::vector<int64_t> compute_sizes(PyObject* seq) {
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std::vector<int64_t> sizes;
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THPObjectPtr handle;
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while (PySequence_Check(seq)) {
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auto length = PySequence_Length(seq);
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if (length < 0) throw python_error();
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sizes.push_back(length);
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if (sizes.size() > MAX_DIMS) {
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throw ValueError("too many dimensions '%s'", Py_TYPE(seq)->tp_name);
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}
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if (length == 0) break;
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handle = THPObjectPtr(PySequence_GetItem(seq, 0));
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if (!handle) {
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throw ValueError("could not determine the shape of object type '%s'", Py_TYPE(seq)->tp_name);
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}
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seq = handle.get();
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}
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return sizes;
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}
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ScalarType infer_scalar_type(PyObject *obj) {
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if (PyFloat_Check(obj)) {
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// this is always guaranteed to be a floating-point type, and makes it more
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// convenient to write e.g. torch.tensor(0.) than torch.tensor(0., dtype=torch.Tensor.dtype).
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return torch::tensors::get_default_tensor_type().scalarType();
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}
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if (THPUtils_checkLong(obj)) {
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return ScalarType::Long;
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}
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if (PyBool_Check(obj)) {
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// TODO: infer Bool when we have Bool ScalarType
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return ScalarType::Byte;
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}
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if (THPVariable_Check(obj)) {
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auto var = reinterpret_cast<THPVariable*>(obj)->cdata;
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return var.type().scalarType();
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}
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#ifdef USE_NUMPY
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if (PyArray_Check(obj)) {
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return numpy_dtype_to_aten(PyArray_TYPE((PyArrayObject*)obj));
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}
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if (PyArray_CheckScalar(obj)) {
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return numpy_dtype_to_aten(PyArray_TYPE((PyArrayObject*)(PyArray_FromScalar(obj, nullptr))));
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}
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#endif
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if (THPUtils_checkString(obj)) {
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throw TypeError("new(): invalid data type '%s'", Py_TYPE(obj)->tp_name);
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}
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if (PySequence_Check(obj)) {
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c10::optional<ScalarType> scalarType;
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auto length = PySequence_Length(obj);
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if (length < 0) throw python_error();
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// match NumPy semantics, except use default tensor type instead of double.
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if (length == 0) return torch::tensors::get_default_tensor_type().scalarType();
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for (int i = 0; i < length; ++i) {
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THPObjectPtr handle(PySequence_GetItem(obj, i));
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if (!handle) throw python_error();
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auto cur_item = handle.get();
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if (cur_item == obj) throw TypeError("new(): self-referential lists are incompatible");
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ScalarType item_scalarType = infer_scalar_type(cur_item);
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scalarType = (scalarType) ?
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at::promoteTypes(*scalarType, item_scalarType) : item_scalarType;
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if (scalarType == ScalarType::Double) {
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// this won't change (unless we hit undefined, but that will fail later).
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return *scalarType;
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}
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}
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return *scalarType;
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}
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AT_ERROR("Could not infer dtype of ", Py_TYPE(obj)->tp_name);
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}
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void recursive_store(char* data, IntList sizes, IntList strides, int64_t dim,
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ScalarType scalarType, int elementSize, PyObject* obj) {
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int64_t ndim = sizes.size();
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if (dim == ndim) {
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torch::utils::store_scalar(data, scalarType, obj);
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return;
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}
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auto n = sizes[dim];
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auto seq = THPObjectPtr(PySequence_Fast(obj, "not a sequence"));
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if (!seq) throw python_error();
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auto seq_size = PySequence_Fast_GET_SIZE(seq.get());
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if (seq_size != n) {
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throw ValueError("expected sequence of length %lld at dim %lld (got %lld)",
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(long long)n, (long long)dim, (long long)seq_size);
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}
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PyObject** items = PySequence_Fast_ITEMS(seq.get());
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for (int64_t i = 0; i < n; i++) {
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recursive_store(data, sizes, strides, dim + 1, scalarType, elementSize, items[i]);
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data += strides[dim] * elementSize;
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}
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}
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Tensor internal_new_from_data(
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const Type& type,
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c10::optional<Device> device_opt,
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PyObject* data,
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bool copy_variables,
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bool copy_numpy,
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bool type_inference) {
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if (THPUtils_checkString(data)) {
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throw TypeError("new(): invalid data type '%s'", Py_TYPE(data)->tp_name);
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}
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if (THPVariable_Check(data)) {
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auto var = reinterpret_cast<THPVariable*>(data)->cdata;
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if (copy_variables) {
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var = var.detach();
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}
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// infer the scalar type and device type; it's not expected to infer the layout since these constructors
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// are defined per-layout-type (e.g. tensor vs sparse_coo_tensor).
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const auto& scalar_type = type_inference ? var.type().scalarType() : type.scalarType();
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auto device = device_opt.has_value() ? *device_opt : (type_inference ? var.device() : at::Device(torch::getDeviceType(type)));
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AutoNoGIL no_gil;
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maybe_initialize_cuda(device);
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return var.to(device, scalar_type, /*non_blocking=*/false, /*copy=*/copy_variables);
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}
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#ifdef USE_NUMPY
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if (PyArray_Check(data)) {
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auto tensor = autograd::make_variable(tensor_from_numpy(data), /*requires_grad=*/false);
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const auto& scalar_type = type_inference ? tensor.type().scalarType() : type.scalarType();
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auto device = device_opt.has_value() ? *device_opt : at::Device(type.device_type());
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AutoNoGIL no_gil;
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maybe_initialize_cuda(device);
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return tensor.to(device, scalar_type, /*non_blocking=*/false, /*copy=*/copy_numpy);
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}
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#endif
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auto sizes = compute_sizes(data);
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ScalarType scalar_type = type_inference ? infer_scalar_type(data) : type.scalarType();
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auto tensor = autograd::make_variable(at::empty(sizes, at::initialTensorOptions().dtype(scalar_type)), /*requires_grad=*/false);
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recursive_store(
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(char*)tensor.data_ptr(), tensor.sizes(), tensor.strides(), 0,
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scalar_type, tensor.type().elementSizeInBytes(), data);
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auto device = device_opt.has_value() ? *device_opt : at::Device(torch::getDeviceType(type));
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AutoNoGIL no_gil;
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maybe_initialize_cuda(device);
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return tensor.to(device, scalar_type, /*non_blocking=*/false, /*copy=*/false);
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}
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Tensor new_from_data_copy(
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const Type& type,
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c10::optional<Device> device,
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PyObject* data) {
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return internal_new_from_data(type, std::move(device), data, true, true, false);
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}
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Tensor legacy_new_from_sequence(
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const Type& type,
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c10::optional<Device> device,
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PyObject* data) {
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if (!PySequence_Check(data)) {
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throw TypeError("new(): data must be a sequence (got %s)", Py_TYPE(data)->tp_name);
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}
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return legacy_new_from_data(type, std::move(device), data);
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}
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void check_legacy_ctor_device(const Type& type, c10::optional<Device> device) {
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if (device.has_value()) {
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AT_CHECK(type.device_type() == device.value().type(),
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"legacy constructor for device type: ", type.device_type(),
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" was passed device type: ", device.value().type(),
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", but device type must be: ", type.device_type());
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}
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}
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Tensor legacy_sparse_tensor_ctor(const Type& type, PyObject* args, PyObject* kwargs) {
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static PythonArgParser parser({
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"new(*, Device? device=None)",
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"new(*, int64_t cdata)|hidden",
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"new(Tensor indices, Tensor values, *, Device? device=None)",
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"new(Tensor indices, Tensor values, IntList size, *, Device? device=None)",
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"new(IntList size, *, Device? device=None)",
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});
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ParsedArgs<4> parsed_args;
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auto r = parser.parse(args, kwargs, parsed_args);
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if (r.idx == 0) {
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auto deviceOptional = r.deviceOptional(0);
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check_legacy_ctor_device(type, deviceOptional);
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return at::empty({0}, type.options(r.deviceOptional(0)));
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} else if (r.idx == 1) {
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auto cdata = reinterpret_cast<void*>(r.toInt64(0));
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return type.unsafeTensorFromTH(cdata, true);
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} else if (r.idx == 2) {
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auto deviceOptional = r.deviceOptional(2);
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check_legacy_ctor_device(type, deviceOptional);
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at::OptionalDeviceGuard device_guard(deviceOptional);
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return at::sparse_coo_tensor(r.tensor(0), r.tensor(1));
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} else if (r.idx == 3) {
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auto deviceOptional = r.deviceOptional(3);
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check_legacy_ctor_device(type, deviceOptional);
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at::OptionalDeviceGuard device_guard(deviceOptional);
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return at::sparse_coo_tensor(r.tensor(0), r.tensor(1), r.intlist(2));
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} else if (r.idx == 4) {
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PyObject* arg = r.pyobject(0);
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auto deviceOptional = r.deviceOptional(1);
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check_legacy_ctor_device(type, deviceOptional);
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if (!THPSize_Check(arg) && PyTuple_GET_SIZE(args) >= 1 && arg == PyTuple_GET_ITEM(args, 0)) {
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// new(sequence) binds to this signature but should be treated differently
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// unless the sequences is a torch.Size
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return legacy_new_from_sequence(type, deviceOptional, r.pyobject(0));
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}
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return new_with_sizes(type, r.deviceOptional(1), r.intlist(0));
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}
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throw std::runtime_error("new(): invalid arguments");
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}
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Tensor legacy_sparse_tensor_new(const Type& type, PyObject* args, PyObject* kwargs) {
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static PythonArgParser parser({
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"new(*, Device? device=None)",
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"new(*, int64_t cdata)|hidden",
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"new(Tensor indices, Tensor values, *, Device? device=None)",
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"new(Tensor indices, Tensor values, IntList size, *, Device? device=None)",
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"new(IntList size, *, Device? device=None)",
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});
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ParsedArgs<5> parsed_args;
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auto r = parser.parse(args, kwargs, parsed_args);
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if (r.idx == 0) {
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auto deviceOptional = r.deviceOptional(0);
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check_legacy_ctor_device(type, deviceOptional);
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at::OptionalDeviceGuard device_guard(deviceOptional);
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return at::empty({0}, type.options());
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} else if (r.idx == 1) {
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auto cdata = reinterpret_cast<void*>(r.toInt64(0));
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return type.unsafeTensorFromTH(cdata, true);
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} else if (r.idx == 2) {
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// Note: this signature doesn't have a dtype, even though it has a device; it probably shouldn't
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// have a device (we should infer it).
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auto deviceOptional = r.deviceOptional(2);
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check_legacy_ctor_device(type, deviceOptional);
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at::OptionalDeviceGuard device_guard(deviceOptional);
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return at::sparse_coo_tensor(r.tensor(0), r.tensor(1));
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} else if (r.idx == 3) {
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// Note: this signature doesn't have a dtype, even though it has a device; it probably shouldn't
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// have a device (we should infer it).
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auto deviceOptional = r.deviceOptional(3);
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check_legacy_ctor_device(type, deviceOptional);
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at::OptionalDeviceGuard device_guard(deviceOptional);
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return at::sparse_coo_tensor(r.tensor(0), r.tensor(1), r.intlist(2));
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} else if (r.idx == 4) {
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PyObject* arg = r.pyobject(0);
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auto deviceOptional = r.deviceOptional(1);
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check_legacy_ctor_device(type, deviceOptional);
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if (!THPSize_Check(arg) && PyTuple_GET_SIZE(args) >= 1 && arg == PyTuple_GET_ITEM(args, 0)) {
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// new(sequence) binds to this signature but should be treated differently
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// unless the sequences is a torch.Size
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return legacy_new_from_sequence(type, deviceOptional, r.pyobject(0));
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}
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return new_with_sizes(type, r.deviceOptional(1), r.intlist(0));
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}
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throw std::runtime_error("new(): invalid arguments");
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}
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// NB: device_idx here is NOT a DeviceIndex, but index into PythonArgs
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const Type& typeWithDefault(PythonArgs& r, int64_t dtype_idx, int64_t device_idx, const Type& type) {
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const auto scalartype = r.scalartypeWithDefault(dtype_idx, type.scalarType());
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const Device types_device_type(type.device_type());
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const auto device_type = r.isNone(device_idx) ? types_device_type : r.device(device_idx).type();
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return torch::getVariableType(scalartype, *torch::getLayout(type.backend()), device_type);
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}
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} // namespace
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Tensor legacy_tensor_ctor(const Type& type, PyObject* args, PyObject* kwargs) {
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static PythonArgParser parser({
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"new(*, Device? device=None)",
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"new(Storage storage)",
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"new(*, int64_t cdata)|hidden",
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"new(Tensor other)",
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"new(IntList size, *, Device? device=None)",
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"new(PyObject* data, *, Device? device=None)",
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});
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if (type.is_sparse()) {
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return legacy_sparse_tensor_ctor(type, args, kwargs);
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}
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ParsedArgs<2> parsed_args;
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auto r = parser.parse(args, kwargs, parsed_args);
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if (r.idx == 0) {
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auto deviceOptional = r.deviceOptional(0);
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check_legacy_ctor_device(type, deviceOptional);
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at::OptionalDeviceGuard device_guard(deviceOptional);
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return at::empty({0}, type.options());
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} else if (r.idx == 1) {
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return new_with_storage(type, r.storage(0));
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} else if (r.idx == 2) {
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auto cdata = reinterpret_cast<void*>(r.toInt64(0));
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return type.unsafeTensorFromTH(cdata, true);
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} else if (r.idx == 3) {
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return new_with_tensor(type, r.tensor(0));
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} else if (r.idx == 4) {
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PyObject* arg = r.pyobject(0);
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auto deviceOptional = r.deviceOptional(1);
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check_legacy_ctor_device(type, deviceOptional);
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if (!THPSize_Check(arg) && PyTuple_GET_SIZE(args) >= 1 && arg == PyTuple_GET_ITEM(args, 0)) {
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// new(sequence) binds to this signature but should be treated differently
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// unless the sequences is a torch.Size
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return legacy_new_from_sequence(type, deviceOptional, r.pyobject(0));
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}
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return new_with_sizes(type, r.deviceOptional(1), r.intlist(0));
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} else if (r.idx == 5) {
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auto deviceOptional = r.deviceOptional(1);
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check_legacy_ctor_device(type, deviceOptional);
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return legacy_new_from_sequence(type, deviceOptional, r.pyobject(0));
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}
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throw std::runtime_error("new(): invalid arguments");
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}
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Tensor legacy_tensor_new(const Type& type, PyObject* args, PyObject* kwargs) {
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static PythonArgParser parser({
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"new(*, Device? device=None)",
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"new(Storage storage)",
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"new(*, int64_t cdata)|hidden",
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"new(Tensor other)", // this doesn't have a dtype/device because it creates an alias.
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"new(IntList size, *, Device? device=None)",
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"new(PyObject* data, *, Device? device=None)",
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});
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if (type.is_sparse()) {
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return legacy_sparse_tensor_new(type, args, kwargs);
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|
}
|
|
|
|
ParsedArgs<3> parsed_args;
|
|
auto r = parser.parse(args, kwargs, parsed_args);
|
|
if (r.idx == 0) {
|
|
auto deviceOptional = r.deviceOptional(0);
|
|
check_legacy_ctor_device(type, deviceOptional);
|
|
at::OptionalDeviceGuard device_guard(deviceOptional);
|
|
return at::empty({0}, type.options());
|
|
} else if (r.idx == 1) {
|
|
return new_with_storage(type, r.storage(0));
|
|
} else if (r.idx == 2) {
|
|
auto cdata = reinterpret_cast<void*>(r.toInt64(0));
|
|
return type.unsafeTensorFromTH(cdata, true);
|
|
} else if (r.idx == 3) {
|
|
return new_with_tensor(type, r.tensor(0));
|
|
} else if (r.idx == 4) {
|
|
PyObject* arg = r.pyobject(0);
|
|
auto deviceOptional = r.deviceOptional(1);
|
|
check_legacy_ctor_device(type, deviceOptional);
|
|
if (!THPSize_Check(arg) && PyTuple_GET_SIZE(args) >= 1 && arg == PyTuple_GET_ITEM(args, 0)) {
|
|
// new(sequence) binds to this signature but should be treated differently
|
|
// unless the sequences is a torch.Size
|
|
return legacy_new_from_sequence(type, deviceOptional, r.pyobject(0));
|
|
}
|
|
return new_with_sizes(type, r.deviceOptional(1), r.intlist(0));
|
|
} else if (r.idx == 5) {
|
|
auto deviceOptional = r.deviceOptional(1);
|
|
check_legacy_ctor_device(type, deviceOptional);
|
|
return legacy_new_from_sequence(type, r.deviceOptional(1), r.pyobject(0));
|
|
}
|
|
throw std::runtime_error("new(): invalid arguments");
|
|
}
|
|
|
|
Tensor legacy_new_from_data(
|
|
const Type& type,
|
|
c10::optional<Device> device,
|
|
PyObject* data) {
|
|
return internal_new_from_data(type, std::move(device), data, false, false, false);
|
|
}
|
|
|
|
Tensor sparse_coo_tensor_ctor(const Type& default_type, PyObject* args, PyObject* kwargs) {
|
|
static PythonArgParser parser({
|
|
"sparse_coo_tensor(PyObject* indices, PyObject* values, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
|
|
"sparse_coo_tensor(PyObject* indices, PyObject* values, IntList size, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
|
|
"sparse_coo_tensor(IntList size, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
|
|
});
|
|
|
|
ParsedArgs<6> parsed_args;
|
|
auto r = parser.parse(args, kwargs, parsed_args);
|
|
if (r.idx == 0) {
|
|
bool type_inference = r.isNone(2);
|
|
const auto& type = typeWithDefault(r, 2, 3, default_type);
|
|
const auto& values_type = type.toDense();
|
|
at::OptionalDeviceGuard device_guard(r.deviceOptional(3));
|
|
// if no dtype provided, infer type based on value type.
|
|
Tensor values = internal_new_from_data(values_type, r.deviceOptional(3), r.pyobject(1), false, true, type_inference);
|
|
const auto& indices_type = values.type().toScalarType(kLong);
|
|
Tensor indices = internal_new_from_data(indices_type, r.deviceOptional(3), r.pyobject(0), false, true, false);
|
|
return at::sparse_coo_tensor(indices, values, values.options().layout(at::kSparse)).set_requires_grad(r.toBool(4));
|
|
} else if (r.idx == 1) {
|
|
bool type_inference = r.isNone(3);
|
|
const auto& type = typeWithDefault(r, 3, 4, default_type);
|
|
const auto& values_type = type.toDense();
|
|
at::OptionalDeviceGuard device_guard(r.deviceOptional(4));
|
|
Tensor values = internal_new_from_data(values_type, r.deviceOptional(4), r.pyobject(1), false, true, type_inference);
|
|
const auto& indices_type = values.type().toScalarType(kLong);
|
|
Tensor indices = internal_new_from_data(indices_type, r.deviceOptional(4), r.pyobject(0), false, true, false);
|
|
return at::sparse_coo_tensor(indices, values, r.intlist(2), values.options().layout(at::kSparse)).set_requires_grad(r.toBool(5));
|
|
} else if (r.idx == 2) {
|
|
const auto& type = typeWithDefault(r, 1, 2, default_type);
|
|
at::OptionalDeviceGuard device_guard(r.deviceOptional(2));
|
|
return at::sparse_coo_tensor(r.intlist(0), type.options().layout(at::kSparse)).set_requires_grad(r.toBool(3));
|
|
}
|
|
throw std::runtime_error("sparse_coo_tensor(): invalid arguments");
|
|
}
|
|
|
|
Tensor tensor_ctor(const Type& type, PyObject* args, PyObject* kwargs) {
|
|
static PythonArgParser parser({
|
|
"tensor(PyObject* data, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
|
|
});
|
|
|
|
ParsedArgs<4> parsed_args;
|
|
auto r = parser.parse(args, kwargs, parsed_args);
|
|
if (r.idx == 0) {
|
|
PyObject* data = r.pyobject(0);
|
|
if (THPVariable_Check(data)) {
|
|
PyErr_WarnEx(PyExc_UserWarning,
|
|
"To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() "
|
|
"or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).", 1);
|
|
}
|
|
|
|
bool type_inference = r.isNone(1);
|
|
bool args_requires_grad = r.toBool(3);
|
|
auto new_tensor = internal_new_from_data(
|
|
typeWithDefault(r, 1, 2, type),
|
|
r.deviceOptional(2),
|
|
data,
|
|
true,
|
|
true,
|
|
type_inference);
|
|
new_tensor.detach_(); // ensure new_tensor a leaf node
|
|
new_tensor.set_requires_grad(args_requires_grad);
|
|
return new_tensor;
|
|
}
|
|
throw std::runtime_error("tensor(): invalid arguments");
|
|
}
|
|
|
|
Tensor as_tensor(const Type& type, PyObject* args, PyObject* kwargs) {
|
|
// TODO: add requires_grad once we decide on semantics for sharing data.
|
|
static PythonArgParser parser({
|
|
"as_tensor(PyObject* data, *, ScalarType dtype=None, Device? device=None)",
|
|
});
|
|
|
|
ParsedArgs<3> parsed_args;
|
|
auto r = parser.parse(args, kwargs, parsed_args);
|
|
if (r.idx == 0) {
|
|
bool type_inference = r.isNone(1);
|
|
return internal_new_from_data(
|
|
typeWithDefault(r, 1, 2, type), r.deviceOptional(2), r.pyobject(0), false, false, type_inference);
|
|
}
|
|
throw std::runtime_error("tensor(): invalid arguments");
|
|
}
|
|
|
|
Tensor new_tensor(const Type& type, PyObject* args, PyObject* kwargs) {
|
|
static PythonArgParser parser({
|
|
"new_tensor(PyObject* data, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
|
|
});
|
|
|
|
ParsedArgs<4> parsed_args;
|
|
auto r = parser.parse(args, kwargs, parsed_args);
|
|
if (r.idx == 0) {
|
|
PyObject* data = r.pyobject(0);
|
|
if (THPVariable_Check(data)) {
|
|
PyErr_WarnEx(PyExc_UserWarning,
|
|
"To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() "
|
|
"or sourceTensor.clone().detach().requires_grad_(True), rather than tensor.new_tensor(sourceTensor).", 1);
|
|
}
|
|
|
|
bool args_requires_grad = r.toBool(3);
|
|
auto new_tensor = new_from_data_copy(
|
|
typeWithDefault(r, 1, 2, type),
|
|
r.deviceOptional(2),
|
|
data);
|
|
new_tensor.detach_(); // ensure new_tensor a leaf node
|
|
new_tensor.set_requires_grad(args_requires_grad);
|
|
return new_tensor;
|
|
}
|
|
throw std::runtime_error("new_tensor(): invalid arguments");
|
|
}
|
|
|
|
Tensor new_empty(const Type& type, PyObject* args, PyObject* kwargs) {
|
|
static PythonArgParser parser({
|
|
"new_empty(IntList size, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
|
|
}, /*traceable=*/true);
|
|
|
|
ParsedArgs<4> parsed_args;
|
|
auto r = parser.parse(args, kwargs, parsed_args);
|
|
if (r.idx == 0) {
|
|
const auto& actual_type = typeWithDefault(r, 1, 2, type);
|
|
return new_with_sizes(actual_type, r.deviceOptional(2), r.intlist(0)).set_requires_grad(r.toBool(3));
|
|
}
|
|
throw std::runtime_error("new_empty(): invalid arguments");
|
|
}
|
|
|
|
Tensor new_full(const Type& type, PyObject* args, PyObject* kwargs) {
|
|
static PythonArgParser parser({
|
|
"new_full(IntList size, Scalar fill_value, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
|
|
}, /*traceable=*/true);
|
|
|
|
ParsedArgs<5> parsed_args;
|
|
auto r = parser.parse(args, kwargs, parsed_args);
|
|
if (r.idx == 0) {
|
|
const auto& actual_type = typeWithDefault(r, 2, 3, type);
|
|
return dispatch_full(actual_type, r.scalar(1), r.deviceOptional(3), r.intlist(0)).set_requires_grad(r.toBool(4));
|
|
}
|
|
throw std::runtime_error("new_full(): invalid arguments");
|
|
}
|
|
|
|
Tensor new_ones(const Type& type, PyObject* args, PyObject* kwargs) {
|
|
static PythonArgParser parser({
|
|
"new_ones(IntList size, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
|
|
}, /*traceable=*/true);
|
|
|
|
ParsedArgs<4> parsed_args;
|
|
auto r = parser.parse(args, kwargs, parsed_args);
|
|
if (r.idx == 0) {
|
|
const auto& actual_type = typeWithDefault(r, 1, 2, type);
|
|
return dispatch_ones(actual_type, r.deviceOptional(2), r.intlist(0)).set_requires_grad(r.toBool(3));
|
|
}
|
|
throw std::runtime_error("new_ones(): invalid arguments");
|
|
}
|
|
|
|
Tensor new_zeros(const Type& type, PyObject* args, PyObject* kwargs) {
|
|
static PythonArgParser parser({
|
|
"new_zeros(IntList size, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
|
|
}, /*traceable=*/true);
|
|
|
|
ParsedArgs<4> parsed_args;
|
|
auto r = parser.parse(args, kwargs, parsed_args);
|
|
if (r.idx == 0) {
|
|
const auto& actual_type = typeWithDefault(r, 1, 2, type);
|
|
return dispatch_zeros(actual_type, r.deviceOptional(2), r.intlist(0)).set_requires_grad(r.toBool(3));
|
|
}
|
|
throw std::runtime_error("new_zeros(): invalid arguments");
|
|
}
|
|
|
|
}} // namespace torch::utils
|