mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
1178851280
Summary: 1) Most usages are replaced by at::empty. 2) native_tensor has its namespace function removed 3) Type.tensor(sizes, strides) becomes at::empty_strided(sizes, strides). Pull Request resolved: https://github.com/pytorch/pytorch/pull/12002 Differential Revision: D10007201 Pulled By: gchanan fbshipit-source-id: 5e5647c050ed2ecb87a33e0b5ce4928fa3186c34
625 lines
25 KiB
C++
625 lines
25 KiB
C++
#include "torch/csrc/python_headers.h"
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#include "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_conversion_dispatch.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/core/Error.h>
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#include <ATen/core/optional.h>
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#include <stdexcept>
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#include <vector>
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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::optional;
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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 at::Backend;
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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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Tensor dispatch_zeros(const Type& type, int32_t device_index, 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(device_index));
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}
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Tensor dispatch_ones(const Type& type, int32_t device_index, 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(device_index));
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}
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Tensor dispatch_full(const Type& type, Scalar fill_value, int32_t device_index, 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(device_index));
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}
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Tensor new_with_sizes(const Type& type, int32_t device_index, 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(device_index));
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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_(storage);
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return tensor;
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}
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Tensor new_with_tensor(const Type& type, 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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Tensor new_with_type_conversion(const Type& type, Tensor other, int32_t device_index) {
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return dispatch_type_conversion(other, type, device_index, false);
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}
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Tensor new_with_tensor_copy(const Type& type, Tensor other, int32_t device_index) {
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maybe_initialize_cuda(type);
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AutoNoGIL no_gil;
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at::DeviceGuard device_guard;
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if (type.is_cuda()) {
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device_guard.set_index(device_index);
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}
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return type.copy(other);
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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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at::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(const Type & type, at::optional<Device> device_opt, PyObject* data,
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bool copy_variables, bool copy_numpy,
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bool type_inference) {
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int32_t device_index = -1;
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if (device_opt.has_value()) {
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device_index = device_opt->index();
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}
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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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auto type_inference_device_type = device_opt.has_value() ? device_opt->type()
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: torch::getDeviceType(var.type());
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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& type_inference_type = torch::getVariableType(var.type().scalarType(),
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*torch::getLayout(type.backend()),
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type_inference_device_type);
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const auto& type_to_use = type_inference ? type_inference_type : type;
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return copy_variables ? new_with_tensor_copy(type_to_use, var, device_index) :
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new_with_type_conversion(type_to_use, var, device_index);
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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& type_to_use = type_inference ? type.toScalarType(tensor.type().scalarType()) : type;
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return copy_numpy ? new_with_tensor_copy(type_to_use, tensor, device_index) :
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new_with_type_conversion(type_to_use, tensor, device_index);
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}
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#endif
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auto sizes = compute_sizes(data);
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ScalarType scalarType = type_inference ? infer_scalar_type(data) : type.scalarType();
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auto tensor = autograd::make_variable(CPU(scalarType).tensor(sizes), /*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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scalarType, tensor.type().elementSizeInBytes(), data);
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const auto& type_to_use = type_inference ? type.toScalarType(scalarType) : type;
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return new_with_type_conversion(type_to_use, tensor, device_index);
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}
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Tensor new_from_data_copy(const Type & type, at::optional<Device> device, PyObject *data) {
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return internal_new_from_data(type, device, data, true, true, false);
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}
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Tensor legacy_new_from_sequence(const Type & type, at::optional<Device> device, 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, device, data);
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}
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void check_legacy_ctor_device(const Type& type, at::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.device(0).index()));
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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::DeviceGuard device_guard(deviceOptional);
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return type.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::DeviceGuard device_guard(deviceOptional);
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return type.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.device(1).index(), 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::DeviceGuard 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::DeviceGuard device_guard(deviceOptional);
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return type.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::DeviceGuard device_guard(deviceOptional);
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return type.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.device(1).index(), 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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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::DeviceGuard 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.device(1).index(), 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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}
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ParsedArgs<3> parsed_args;
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auto r = parser.parse(args, kwargs, parsed_args);
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if (r.idx == 0) {
|
|
auto deviceOptional = r.deviceOptional(0);
|
|
check_legacy_ctor_device(type, deviceOptional);
|
|
at::DeviceGuard 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.device(1).index(), 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, at::optional<Device> device, PyObject *data) {
|
|
return internal_new_from_data(type, device, data, false, false, false);
|
|
}
|
|
|
|
Tensor sparse_coo_tensor_ctor(const Type& type, PyObject* args, PyObject* kwargs) {
|
|
const auto sparse_backend = type.is_cuda() ? Backend::SparseCUDA : Backend::SparseCPU;
|
|
const auto& default_sparse_type = type.toBackend(sparse_backend);
|
|
|
|
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& sparse_type = typeWithDefault(r, 2, 3, default_sparse_type);
|
|
const auto& dense_type = sparse_type.toBackend(sparse_type.is_cuda() ? Backend::CUDA : Backend::CPU);
|
|
at::DeviceGuard device_guard(r.device(3));
|
|
Tensor values = internal_new_from_data(dense_type, r.deviceOptional(3), r.pyobject(1), false, true, type_inference);
|
|
// if no dtype provided, infer type based on value type.
|
|
const auto& index_type = values.type().toScalarType(kLong);
|
|
Tensor indices = internal_new_from_data(index_type, r.deviceOptional(3), r.pyobject(0), false, true, false);
|
|
const auto& sparse_type_to_use = values.type().toBackend(values.type().is_cuda() ? Backend::SparseCUDA : Backend::SparseCPU);
|
|
return sparse_type_to_use.sparse_coo_tensor(indices, values).set_requires_grad(r.toBool(4));
|
|
} else if (r.idx == 1) {
|
|
bool type_inference = r.isNone(3);
|
|
const auto& sparse_type = typeWithDefault(r, 3, 4, default_sparse_type);
|
|
const auto& dense_type = sparse_type.toBackend(sparse_type.is_cuda() ? Backend::CUDA : Backend::CPU);
|
|
at::DeviceGuard device_guard(r.device(4));
|
|
Tensor values = internal_new_from_data(dense_type, r.deviceOptional(4), r.pyobject(1), false, true, type_inference);
|
|
const auto& index_type = values.type().toScalarType(kLong);
|
|
Tensor indices = internal_new_from_data(index_type, r.deviceOptional(4), r.pyobject(0), false, true, false);
|
|
const auto& sparse_type_to_use = values.type().toBackend(values.type().is_cuda() ? Backend::SparseCUDA : Backend::SparseCPU);
|
|
return sparse_type_to_use.sparse_coo_tensor(indices, values, r.intlist(2)).set_requires_grad(r.toBool(5));
|
|
} else if (r.idx == 2) {
|
|
const auto& sparse_type_to_use = typeWithDefault(r, 1, 2, default_sparse_type);
|
|
at::DeviceGuard device_guard(r.device(2));
|
|
return sparse_type_to_use.sparse_coo_tensor(r.intlist(0)).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.device(2).index(), 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.device(3).index(), 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.device(2).index(), 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.device(2).index(), r.intlist(0)).set_requires_grad(r.toBool(3));
|
|
}
|
|
throw std::runtime_error("new_zeros(): invalid arguments");
|
|
}
|
|
|
|
}} // namespace torch::utils
|