Bug Summary

File:build/../torch/csrc/utils/tensor_new.cpp
Warning:line 185, column 27
PyObject ownership leak with reference count of 1

Annotated Source Code

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clang -cc1 -cc1 -triple x86_64-unknown-linux-gnu -analyze -disable-free -disable-llvm-verifier -discard-value-names -main-file-name tensor_new.cpp -analyzer-store=region -analyzer-opt-analyze-nested-blocks -analyzer-checker=core -analyzer-checker=apiModeling -analyzer-checker=unix -analyzer-checker=deadcode -analyzer-checker=cplusplus -analyzer-checker=security.insecureAPI.UncheckedReturn -analyzer-checker=security.insecureAPI.getpw -analyzer-checker=security.insecureAPI.gets -analyzer-checker=security.insecureAPI.mktemp -analyzer-checker=security.insecureAPI.mkstemp -analyzer-checker=security.insecureAPI.vfork -analyzer-checker=nullability.NullPassedToNonnull -analyzer-checker=nullability.NullReturnedFromNonnull -analyzer-output plist -w -analyzer-output=html -analyzer-checker=python -analyzer-disable-checker=deadcode -analyzer-config prune-paths=true,suppress-c++-stdlib=true,suppress-inlined-defensive-checks=false,suppress-null-return-paths=false,crosscheck-with-z3=true,model-path=/opt/pyrefcon/lib/pyrefcon/models/models -analyzer-config experimental-enable-naive-ctu-analysis=true,ctu-dir=/tmp/pyrefcon/pytorch/csa-scan,ctu-index-name=/tmp/pyrefcon/pytorch/csa-scan/externalDefMap.txt,ctu-invocation-list=/tmp/pyrefcon/pytorch/csa-scan/invocations.yaml,display-ctu-progress=false -setup-static-analyzer -analyzer-config-compatibility-mode=true -mrelocation-model pic -pic-level 2 -fhalf-no-semantic-interposition -mframe-pointer=none -relaxed-aliasing -fno-rounding-math -ffp-exception-behavior=ignore -mconstructor-aliases -munwind-tables -target-cpu x86-64 -tune-cpu generic -debugger-tuning=gdb -fcoverage-compilation-dir=/tmp/pyrefcon/pytorch/build -resource-dir /opt/pyrefcon/lib/clang/13.0.0 -isystem third_party/gloo -isystem ../cmake/../third_party/gloo -isystem ../cmake/../third_party/googletest/googlemock/include -isystem ../cmake/../third_party/googletest/googletest/include -isystem ../third_party/protobuf/src -isystem ../third_party/gemmlowp -isystem ../third_party/neon2sse -isystem ../third_party/XNNPACK/include -isystem ../third_party -isystem ../cmake/../third_party/eigen -isystem /opt/pyrefcon/lib/pyrefcon/models/python3.8 -isystem /usr/lib/python3/dist-packages/numpy/core/include -isystem ../cmake/../third_party/pybind11/include -isystem /usr/lib/x86_64-linux-gnu/openmpi/include/openmpi -isystem /usr/lib/x86_64-linux-gnu/openmpi/include -isystem ../third_party/ideep/mkl-dnn/include -isystem ../third_party/ideep/include -D BUILDING_TESTS -D FMT_HEADER_ONLY=1 -D HAVE_MALLOC_USABLE_SIZE=1 -D HAVE_MMAP=1 -D HAVE_SHM_OPEN=1 -D HAVE_SHM_UNLINK=1 -D MINIZ_DISABLE_ZIP_READER_CRC32_CHECKS -D ONNXIFI_ENABLE_EXT=1 -D ONNX_ML=1 -D ONNX_NAMESPACE=onnx_torch -D THP_BUILD_MAIN_LIB -D USE_C10D -D USE_C10D_GLOO -D USE_C10D_MPI -D USE_DISTRIBUTED -D USE_EXTERNAL_MZCRC -D USE_NUMPY -D USE_RPC -D USE_TENSORPIPE -D USE_VALGRIND -D _FILE_OFFSET_BITS=64 -D torch_python_EXPORTS -I aten/src -I ../aten/src -I . -I ../ -I ../cmake/../third_party/benchmark/include -I caffe2/contrib/aten -I ../third_party/onnx -I third_party/onnx -I ../third_party/foxi -I third_party/foxi -I ../torch/.. -I ../torch/../aten/src -I ../torch/../aten/src/TH -I caffe2/aten/src -I third_party -I ../torch/../third_party/valgrind-headers -I ../torch/../third_party/gloo -I ../torch/../third_party/onnx -I ../torch/csrc -I ../torch/csrc/api/include -I ../torch/lib -I ../torch/lib/libshm -I ../torch/csrc/distributed -I ../torch/csrc/api -I ../c10/.. -I third_party/ideep/mkl-dnn/include -I ../third_party/ideep/mkl-dnn/src/../include -I ../torch/lib/libshm/../../../torch/lib -I ../third_party/fmt/include -D USE_PTHREADPOOL -D NDEBUG -D USE_KINETO -D LIBKINETO_NOCUPTI -D USE_FBGEMM -D USE_QNNPACK -D USE_PYTORCH_QNNPACK -D USE_XNNPACK -D SYMBOLICATE_MOBILE_DEBUG_HANDLE -D HAVE_AVX_CPU_DEFINITION -D HAVE_AVX2_CPU_DEFINITION -D NDEBUG -D NDEBUG -D CAFFE2_USE_GLOO -D HAVE_GCC_GET_CPUID -D USE_AVX -D USE_AVX2 -D TH_HAVE_THREAD -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10 -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/10/../../../../include/x86_64-linux-gnu/c++/10 -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/backward -internal-isystem /opt/pyrefcon/lib/clang/13.0.0/include -internal-isystem /usr/local/include -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/10/../../../../x86_64-linux-gnu/include -internal-externc-isystem /usr/include/x86_64-linux-gnu -internal-externc-isystem /include -internal-externc-isystem /usr/include -O3 -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -Wno-unused-but-set-variable -Wno-maybe-uninitialized -Werror=format -Werror=cast-function-type -Wno-stringop-overflow -Wno-write-strings -Wno-strict-aliasing -w -std=gnu++14 -fdeprecated-macro -fdebug-compilation-dir=/tmp/pyrefcon/pytorch/build -ferror-limit 19 -fvisibility-inlines-hidden -fopenmp -fopenmp-cuda-parallel-target-regions -pthread -fgnuc-version=4.2.1 -fcxx-exceptions -fexceptions -faligned-allocation -fcolor-diagnostics -vectorize-loops -vectorize-slp -faddrsig -D__GCC_HAVE_DWARF2_CFI_ASM=1 -o /tmp/pyrefcon/pytorch/csa-scan/reports -x c++ ../torch/csrc/utils/tensor_new.cpp

../torch/csrc/utils/tensor_new.cpp

→
1#include <torch/csrc/python_headers.h>
2#include <torch/csrc/utils/tensor_new.h>
3
4#include <pybind11/pybind11.h>
5#include <torch/csrc/DynamicTypes.h>
6#include <torch/csrc/Exceptions.h>
7#include <torch/csrc/Size.h>
8#include <torch/csrc/autograd/variable.h>
9#include <torch/csrc/utils/cuda_lazy_init.h>
10#include <torch/csrc/utils/numpy_stub.h>
11#include <torch/csrc/utils/python_arg_parser.h>
12#include <torch/csrc/utils/python_numbers.h>
13#include <torch/csrc/utils/python_scalars.h>
14#include <torch/csrc/utils/python_strings.h>
15#include <torch/csrc/utils/tensor_numpy.h>
16#include <torch/csrc/autograd/generated/variable_factories.h>
17
18#include <ATen/ATen.h>
19#include <ATen/InitialTensorOptions.h>
20#include <ATen/NamedTensorUtils.h>
21#include <ATen/TracerMode.h>
22#include <c10/core/Backend.h>
23#include <c10/core/Layout.h>
24#include <c10/util/Exception.h>
25#include <c10/util/irange.h>
26#include <c10/util/Optional.h>
27
28#include <stdexcept>
29#include <vector>
30
31using at::Backend;
32using at::Device;
33using at::IntArrayRef;
34using at::kCPU;
35using at::kCUDA;
36using at::kLong;
37using at::kInt;
38using at::Scalar;
39using at::ScalarType;
40using at::Storage;
41using at::Tensor;
42using at::TensorOptions;
43using at::Type;
44using c10::optional;
45
46namespace torch { namespace utils {
47namespace {
48const int MAX_DIMS = 128;
49
50TensorOptions build_options(c10::TensorOptions options, at::ScalarType scalar_type, const c10::optional<Device>& device=c10::nullopt) {
51 options = options.dtype(scalar_type);
52 if (device.has_value()) {
53 return options.device(device);
54 }
55 return options;
56}
57
58void maybe_initialize_cuda(const Device device) {
59 if (device.is_cuda()) {
60 torch::utils::cuda_lazy_init();
61 }
62}
63
64// NB: It appears there is some consistency invariant between options and device, where
65// if device is non-empty, its type must be consistent with the device type in
66// options.
67// TODO: Refactor this so we just pass everything in via options
68
69Tensor dispatch_ones(c10::TensorOptions options, at::ScalarType scalar_type, const optional<Device>& device, IntArrayRef sizes) {
70 maybe_initialize_cuda(options.device());
71 pybind11::gil_scoped_release no_gil;
72 return torch::ones(sizes, build_options(options, scalar_type, device));
73}
74
75Tensor new_with_sizes(c10::TensorOptions options, at::ScalarType scalar_type, const optional<Device>& device, IntArrayRef sizes) {
76 maybe_initialize_cuda(options.device());
77 pybind11::gil_scoped_release no_gil;
78 return torch::empty(sizes, build_options(options, scalar_type, device));
79}
80
81Tensor new_with_storage(c10::TensorOptions options, at::ScalarType scalar_type, Storage storage) {
82 auto tensor = at::empty({}, build_options(options, scalar_type));
83 tensor.set_(std::move(storage));
84 return tensor;
85}
86
87Tensor new_with_tensor(c10::TensorOptions options, at::ScalarType scalar_type, const Tensor& other) {
88 options = options.dtype(scalar_type);
89 TORCH_CHECK_TYPE(other.options().type_equal(options), "expected ",if ((__builtin_expect(static_cast<bool>(!(other.options
().type_equal(options))), 0))) { throw ::c10::TypeError( {__func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(90)}, (::c10::detail::torchCheckMsgImpl( "Expected " "other.options().type_equal(options)"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "expected "
, options, " (got ", other.options(), ")"))); }
90 options, " (got ", other.options(), ")")if ((__builtin_expect(static_cast<bool>(!(other.options
().type_equal(options))), 0))) { throw ::c10::TypeError( {__func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(90)}, (::c10::detail::torchCheckMsgImpl( "Expected " "other.options().type_equal(options)"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "expected "
, options, " (got ", other.options(), ")"))); };
91 return other.alias();
92}
93
94std::vector<int64_t> compute_sizes(PyObject* seq) {
95 std::vector<int64_t> sizes;
96 THPObjectPtr handle;
97 while (PySequence_Check(seq)) {
98 auto length = PySequence_LengthPySequence_Size(seq);
99 if (length < 0) throw python_error();
100 sizes.push_back(length);
101 if (sizes.size() > MAX_DIMS) {
102 throw ValueError("too many dimensions '%s'", Py_TYPE(seq)(((PyObject*)(seq))->ob_type)->tp_name);
103 }
104 if (length == 0) break;
105 handle = THPObjectPtr(PySequence_GetItem(seq, 0));
106 if (!handle) {
107 throw ValueError("could not determine the shape of object type '%s'", Py_TYPE(seq)(((PyObject*)(seq))->ob_type)->tp_name);
108 }
109 seq = handle.get();
110 }
111
112 return sizes;
113}
114
115ScalarType infer_scalar_type(PyObject *obj) {
116#ifdef USE_NUMPY1
117 if (is_numpy_available()) {
118 if (PyArray_Check(obj)((((PyObject*)(obj))->ob_type) == (&(*(PyTypeObject *)
__numpy_array_api[2])) || PyType_IsSubtype((((PyObject*)(obj)
)->ob_type), (&(*(PyTypeObject *)__numpy_array_api[2])
)))) {
119 return numpy_dtype_to_aten(PyArray_TYPE((PyArrayObject*)obj));
120 }
121 if (PyArray_CheckScalar(obj)((((((PyObject*)(obj))->ob_type) == (&(*(PyTypeObject *
)__numpy_array_api[10])) || PyType_IsSubtype((((PyObject*)(obj
))->ob_type), (&(*(PyTypeObject *)__numpy_array_api[10
]))))) || (((((PyObject*)(obj))->ob_type) == (&(*(PyTypeObject
*)__numpy_array_api[2])) || PyType_IsSubtype((((PyObject*)(obj
))->ob_type), (&(*(PyTypeObject *)__numpy_array_api[2]
)))) && (PyArray_NDIM((PyArrayObject *)obj) == 0)))) {
122 THPObjectPtr arr(PyArray_FromScalar(*(PyObject * (*)(PyObject *, PyArray_Descr *)) __numpy_array_api
[61])(obj, nullptr));
123 return numpy_dtype_to_aten(PyArray_TYPE((PyArrayObject*) arr.get()));
124 }
125 }
126#endif
127 if (PyFloat_Check(obj)((((PyObject*)(obj))->ob_type) == (&PyFloat_Type) || PyType_IsSubtype
((((PyObject*)(obj))->ob_type), (&PyFloat_Type)))) {
128 // this is always guaranteed to be a floating-point type, and makes it more
129 // convenient to write e.g. torch.tensor(0.) than torch.tensor(0., dtype=torch.Tensor.dtype).
130 return torch::tensors::get_default_scalar_type();
131 }
132 if (THPUtils_checkLong(obj)) {
133 return ScalarType::Long;
134 }
135 if (PyBool_Check(obj)((((PyObject*)(obj))->ob_type) == &PyBool_Type)) {
136 return ScalarType::Bool;
137 }
138 if (PyComplex_Check(obj)((((PyObject*)(obj))->ob_type) == (&PyComplex_Type) ||
PyType_IsSubtype((((PyObject*)(obj))->ob_type), (&PyComplex_Type
)))) {
139 switch (torch::tensors::get_default_scalar_type()) {
140 case ScalarType::Float: return ScalarType::ComplexFloat;
141 case ScalarType::Double: return ScalarType::ComplexDouble;
142 default: TORCH_CHECK(false, "invalid default scalar type for complex")if ((__builtin_expect(static_cast<bool>(!(false)), 0)))
{ ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(142), (::c10::detail::torchCheckMsgImpl
( "Expected " "false" " to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "invalid default scalar type for complex"
))); };
143 }
144 }
145 if (THPVariable_Check(obj)) {
146 const auto& var = THPVariable_Unpack(obj);
147 return var.scalar_type();
148 }
149 if (THPUtils_checkString(obj)) {
150 throw TypeError("new(): invalid data type '%s'", Py_TYPE(obj)(((PyObject*)(obj))->ob_type)->tp_name);
151 }
152 if (PySequence_Check(obj)) {
153 c10::optional<ScalarType> scalarType;
154 auto length = PySequence_LengthPySequence_Size(obj);
155 if (length < 0) throw python_error();
156 // match NumPy semantics, except use default tensor type instead of double.
157 if (length == 0) return torch::tensors::get_default_scalar_type();
158 for (const auto i : c10::irange(length)) {
159 THPObjectPtr handle(PySequence_GetItem(obj, i));
160 if (!handle) throw python_error();
161 auto cur_item = handle.get();
162 if (cur_item == obj) throw TypeError("new(): self-referential lists are incompatible");
163 ScalarType item_scalarType = infer_scalar_type(cur_item);
164 scalarType = (scalarType) ?
165 at::promoteTypes(*scalarType, item_scalarType) : item_scalarType;
166 if (scalarType == ScalarType::ComplexDouble) {
167 // this won't change (unless we hit undefined, but that will fail later).
168 return *scalarType;
169 }
170 }
171 return *scalarType;
172 }
173 AT_ERROR("Could not infer dtype of ", Py_TYPE(obj)->tp_name)do { ::c10::detail::deprecated_AT_ERROR(); if ((__builtin_expect
(static_cast<bool>(!(false)), 0))) { ::c10::detail::torchCheckFail
( __func__, "../torch/csrc/utils/tensor_new.cpp", static_cast
<uint32_t>(173), (::c10::detail::torchCheckMsgImpl( "Expected "
"false" " to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", ::c10::
str("Could not infer dtype of ", (((PyObject*)(obj))->ob_type
)->tp_name)))); }; } while (false);
174}
175
176void recursive_store(char* data, IntArrayRef sizes, IntArrayRef strides, int64_t dim,
177 ScalarType scalarType, int elementSize, PyObject* obj) {
178 int64_t ndim = sizes.size();
179 if (dim == ndim) {
13
←
Assuming 'dim' is not equal to 'ndim'
→
14
←
Taking false branch
→
180 torch::utils::store_scalar(data, scalarType, obj);
181 return;
182 }
183
184 auto n = sizes[dim];
185 auto seq = THPObjectPtr(PySequence_Fast(obj, "not a sequence"));
15
←
Calling 'PySequence_Fast'
→
17
←
Returning from 'PySequence_Fast'
→
24
←
PyObject ownership leak with reference count of 1
186 if (!seq) throw python_error();
18
←
Assuming the condition is false
→
19
←
Taking false branch
→
187 // NOLINTNEXTLINE(bugprone-branch-clone)
188 auto seq_size = PySequence_Fast_GET_SIZE(seq.get())(((((((PyObject*)(seq.get()))->ob_type))->tp_flags &
((1UL << 25))) != 0) ? ((((PyVarObject*)(seq.get()))->
ob_size)) : (((PyVarObject*)(((PyTupleObject *)(seq.get()))))
->ob_size));
20
←
Assuming the condition is true
→
21
←
'?' condition is true
→
189 if (seq_size != n) {
22
←
Assuming 'seq_size' is not equal to 'n'
→
23
←
Taking true branch
→
190 throw ValueError("expected sequence of length %lld at dim %lld (got %lld)",
191 (long long)n, (long long)dim, (long long)seq_size);
192 }
193
194 PyObject** items = PySequence_Fast_ITEMS(seq.get())(((((((PyObject*)(seq.get()))->ob_type))->tp_flags &
((1UL << 25))) != 0) ? ((PyListObject *)(seq.get()))->
ob_item : ((PyTupleObject *)(seq.get()))->ob_item);
195 for(const auto i : c10::irange(n)) {
196 recursive_store(data, sizes, strides, dim + 1, scalarType, elementSize, items[i]);
197 data += strides[dim] * elementSize;
198 }
199}
200
201Tensor internal_new_from_data(
202 c10::TensorOptions options,
203 at::ScalarType scalar_type,
204 c10::optional<Device> device_opt,
205 PyObject* data,
206 bool copy_variables,
207 bool copy_numpy,
208 bool type_inference,
209 bool pin_memory = false) {
210
211 if (THPUtils_checkString(data)) {
6
←
Taking false branch
→
212 throw TypeError("new(): invalid data type '%s'", Py_TYPE(data)(((PyObject*)(data))->ob_type)->tp_name);
213 }
214
215 if (THPVariable_Check(data)) {
7
←
Taking false branch
→
216 TORCH_CHECK(!pin_memory, "Can't pin tensor constructed from a variable")if ((__builtin_expect(static_cast<bool>(!(!pin_memory))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(216), (::c10::detail::torchCheckMsgImpl
( "Expected " "!pin_memory" " to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Can't pin tensor constructed from a variable"
))); };
217 // TODO: use MaybeOwned
218 auto var = THPVariable_Unpack(data);
219 if (copy_variables) {
220 var = var.detach();
221 }
222 // infer the scalar type and device type; it's not expected to infer the layout since these constructors
223 // are defined per-layout-type (e.g. tensor vs sparse_coo_tensor).
224 const auto& inferred_scalar_type = type_inference ? var.scalar_type() : scalar_type;
225 auto device = device_opt.has_value() ? *device_opt : var.device();
226 pybind11::gil_scoped_release no_gil;
227 maybe_initialize_cuda(device);
228 return var.to(device, inferred_scalar_type, /*non_blocking=*/false, /*copy=*/copy_variables);
229 }
230
231#ifdef USE_NUMPY1
232 if (PyObject_HasAttrString(data, "__cuda_array_interface__")) {
8
←
Assuming the condition is false
→
9
←
Taking false branch
→
233 TORCH_CHECK(!pin_memory, "Can't pin tensor constructed from __cuda_array_interface__")if ((__builtin_expect(static_cast<bool>(!(!pin_memory))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(233), (::c10::detail::torchCheckMsgImpl
( "Expected " "!pin_memory" " to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Can't pin tensor constructed from __cuda_array_interface__"
))); };
234 auto tensor = tensor_from_cuda_array_interface(data);
235 const auto& inferred_scalar_type = type_inference ? tensor.scalar_type() : scalar_type;
236 auto device = device_opt.has_value() ? *device_opt : options.device();
237 pybind11::gil_scoped_release no_gil;
238 maybe_initialize_cuda(device);
239 return tensor.to(device, inferred_scalar_type, /*non_blocking=*/false, /*copy=*/copy_numpy);
240 }
241
242 if (is_numpy_available() && PyArray_Check(data)((((PyObject*)(data))->ob_type) == (&(*(PyTypeObject *
)__numpy_array_api[2])) || PyType_IsSubtype((((PyObject*)(data
))->ob_type), (&(*(PyTypeObject *)__numpy_array_api[2]
))))) {
10
←
Assuming the condition is false
→
243 TORCH_CHECK(!pin_memory, "Can't pin tensor constructed from numpy")if ((__builtin_expect(static_cast<bool>(!(!pin_memory))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(243), (::c10::detail::torchCheckMsgImpl
( "Expected " "!pin_memory" " to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Can't pin tensor constructed from numpy"
))); };
244 auto tensor = tensor_from_numpy(data, /*warn_if_not_writeable=*/!copy_numpy);
245 const auto& inferred_scalar_type = type_inference ? tensor.scalar_type() : scalar_type;
246 auto device = device_opt.has_value() ? *device_opt : options.device();
247 pybind11::gil_scoped_release no_gil;
248 maybe_initialize_cuda(device);
249 return tensor.to(device, inferred_scalar_type, /*non_blocking=*/false, /*copy=*/copy_numpy);
250 }
251#endif
252
253 auto sizes = compute_sizes(data);
254 ScalarType inferred_scalar_type = type_inference
10.1
'type_inference' is false
10.1
'type_inference' is false
 ? infer_scalar_type(data) : scalar_type;
11
←
'?' condition is false
→
255 // This exists to prevent us from tracing the call to empty(). The actual
256 // autograd code doesn't really matter, because requires_grad is always false
257 // here.
258 Tensor tensor;
259 {
260 at::AutoDispatchBelowADInplaceOrView guard; // TODO: remove
261 at::tracer::impl::NoTracerDispatchMode tracer_guard;
262 tensor = at::empty(sizes, at::initialTensorOptions().dtype(inferred_scalar_type).pinned_memory(pin_memory));
263 recursive_store(
12
←
Calling 'recursive_store'
→
264 (char*)tensor.data_ptr(), tensor.sizes(), tensor.strides(), 0,
265 inferred_scalar_type, tensor.dtype().itemsize(), data);
266 }
267 auto device = device_opt.has_value() ? *device_opt : options.device();
268 pybind11::gil_scoped_release no_gil;
269 maybe_initialize_cuda(device);
270 // However, it is VERY important that we trace the to() call here (even
271 // though the reason this is important is a hack). Without *some* factory
272 // function call that is traced at construction time, we will consider
273 // a tensor constant as originating from "outside" the trace, and if you
274 // try to return it directly we will fail with the error saying no
275 // "no observable data dependence". In an ideal world, we wouldn't trace
276 // a to() call but I need to think harder about what exactly we should trace
277 // in this case.
278 return tensor.to(device, inferred_scalar_type, /*non_blocking=*/false, /*copy=*/false);
279}
280
281Tensor new_from_data_copy(
282 c10::TensorOptions options,
283 at::ScalarType scalar_type,
284 c10::optional<Device> device,
285 PyObject* data) {
286 return internal_new_from_data(options, scalar_type, device, data,
5
←
Calling 'internal_new_from_data'
→
287 /*copy_variables=*/true, /*copy_numpy=*/true,
288 /*type_inference=*/false);
289}
290
291Tensor legacy_new_from_sequence(
292 c10::TensorOptions options,
293 at::ScalarType scalar_type,
294 c10::optional<Device> device,
295 PyObject* data) {
296 if (!PySequence_Check(data)) {
297 throw TypeError("new(): data must be a sequence (got %s)", Py_TYPE(data)(((PyObject*)(data))->ob_type)->tp_name);
298 }
299 return internal_new_from_data(options, scalar_type, device, data,
300 /*copy_variables=*/false, /*copy_numpy=*/false,
301 /*type_inference=*/false);
302}
303
304// "base" here refers to the Tensor type on which the function was invoked, e.g.:
305// in x.new(y), 'x' is the base.
306// TODO: Rewrite this using dispatchKeyToTensorOptions
307void check_base_legacy_new(c10::DispatchKey dispatch_key, at::Layout expected_layout) {
308 if (expected_layout == c10::kStrided) {
309 TORCH_CHECK(if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
310 dispatch_key == c10::DispatchKey::CPU ||if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
311 dispatch_key == c10::DispatchKey::CUDA ||if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
312 dispatch_key == c10::DispatchKey::HIP ||if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
313 dispatch_key == c10::DispatchKey::XLA ||if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
314 dispatch_key == c10::DispatchKey::XPU,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
315 "new(): expected DispatchKey: ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
316 c10::DispatchKey::CPU,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
317 " or ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
318 c10::DispatchKey::CUDA,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
319 " or ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
320 c10::DispatchKey::HIP,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
321 " or ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
322 c10::DispatchKey::XLA,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
323 " or ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
324 c10::DispatchKey::XPU,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
325 " but got: ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); }
326 dispatch_key)if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA
|| dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10
::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU))
, 0))) { ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(326), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::CPU || dispatch_key == c10::DispatchKey::CUDA || dispatch_key == c10::DispatchKey::HIP || dispatch_key == c10::DispatchKey::XLA || dispatch_key == c10::DispatchKey::XPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::CPU, " or ", c10::DispatchKey::CUDA, " or "
, c10::DispatchKey::HIP, " or ", c10::DispatchKey::XLA, " or "
, c10::DispatchKey::XPU, " but got: ", dispatch_key))); };
327 } else if(expected_layout == c10::kSparse) {
328 // NOTE: no sparse XLA
329 TORCH_CHECK(if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
330 dispatch_key == c10::DispatchKey::SparseCPU ||if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
331 dispatch_key == c10::DispatchKey::SparseCUDA ||if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
332 dispatch_key == c10::DispatchKey::SparseHIP ||if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
333 dispatch_key == c10::DispatchKey::SparseXPU,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
334 "new(): expected DispatchKey: ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
335 c10::DispatchKey::SparseCPU,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
336 " or ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
337 c10::DispatchKey::SparseCUDA,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
338 " or ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
339 c10::DispatchKey::SparseHIP,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
340 " or ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
341 c10::DispatchKey::SparseXPU,if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
342 " but got: ",if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); }
343 dispatch_key)if ((__builtin_expect(static_cast<bool>(!(dispatch_key ==
c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey
::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP ||
dispatch_key == c10::DispatchKey::SparseXPU)), 0))) { ::c10::
detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(343), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatch_key == c10::DispatchKey::SparseCPU || dispatch_key == c10::DispatchKey::SparseCUDA || dispatch_key == c10::DispatchKey::SparseHIP || dispatch_key == c10::DispatchKey::SparseXPU"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "new(): expected DispatchKey: "
, c10::DispatchKey::SparseCPU, " or ", c10::DispatchKey::SparseCUDA
, " or ", c10::DispatchKey::SparseHIP, " or ", c10::DispatchKey
::SparseXPU, " but got: ", dispatch_key))); };
344 } else {
345 TORCH_INTERNAL_ASSERT(false, "unexpected layout")if ((__builtin_expect(static_cast<bool>(!(false)), 0)))
{ ::c10::detail::torchInternalAssertFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(345), "false" "INTERNAL ASSERT FAILED at "
"\"../torch/csrc/utils/tensor_new.cpp\"" ":" "345" ", please report a bug to PyTorch. "
, c10::str("unexpected layout")); };
346 }
347}
348
349// TODO: Make this accept options instead of dispatch key
350void check_legacy_ctor_device(c10::DispatchKey dispatch_key, c10::optional<Device> device) {
351 if (device.has_value()) {
352 TORCH_CHECK(dispatchKeyToDeviceType(dispatch_key) == device.value().type(),if ((__builtin_expect(static_cast<bool>(!(dispatchKeyToDeviceType
(dispatch_key) == device.value().type())), 0))) { ::c10::detail
::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(354), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatchKeyToDeviceType(dispatch_key) == device.value().type()"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "legacy constructor expects device type: "
, dispatchKeyToDeviceType(dispatch_key), " but device type: "
, device.value().type(), " was passed"))); }
353 "legacy constructor expects device type: ", dispatchKeyToDeviceType(dispatch_key),if ((__builtin_expect(static_cast<bool>(!(dispatchKeyToDeviceType
(dispatch_key) == device.value().type())), 0))) { ::c10::detail
::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(354), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatchKeyToDeviceType(dispatch_key) == device.value().type()"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "legacy constructor expects device type: "
, dispatchKeyToDeviceType(dispatch_key), " but device type: "
, device.value().type(), " was passed"))); }
354 " but device type: ", device.value().type(), " was passed")if ((__builtin_expect(static_cast<bool>(!(dispatchKeyToDeviceType
(dispatch_key) == device.value().type())), 0))) { ::c10::detail
::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(354), (::c10::detail::torchCheckMsgImpl
( "Expected " "dispatchKeyToDeviceType(dispatch_key) == device.value().type()"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "legacy constructor expects device type: "
, dispatchKeyToDeviceType(dispatch_key), " but device type: "
, device.value().type(), " was passed"))); };
355 }
356}
357
358Tensor legacy_sparse_tensor_ctor(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
359 auto options = dispatchKeyToTensorOptions(dispatch_key);
360 static PythonArgParser parser({
361 "new(*, Device? device=None)",
362 "new(*, int64_t cdata)|hidden",
363 "new(Tensor indices, Tensor values, *, Device? device=None)",
364 "new(Tensor indices, Tensor values, IntArrayRef size, *, Device? device=None)",
365 "new(IntArrayRef size, *, Device? device=None)",
366 });
367 ParsedArgs<4> parsed_args;
368 auto r = parser.parse(args, kwargs, parsed_args);
369 if (r.idx == 0) {
370 auto deviceOptional = r.deviceOptional(0);
371 check_legacy_ctor_device(dispatch_key, deviceOptional);
372 return at::empty({0}, build_options(options, scalar_type, deviceOptional));
373 } else if (r.idx == 1) {
374 auto cdata = reinterpret_cast<void*>(r.toInt64(0));
375 return at::unsafeTensorFromTH(cdata, true);
376 } else if (r.idx == 2) {
377 auto deviceOptional = r.deviceOptional(2);
378 check_legacy_ctor_device(dispatch_key, deviceOptional);
379 at::OptionalDeviceGuard device_guard(deviceOptional);
380 return at::sparse_coo_tensor(r.tensor(0), r.tensor(1));
381 } else if (r.idx == 3) {
382 auto deviceOptional = r.deviceOptional(3);
383 check_legacy_ctor_device(dispatch_key, deviceOptional);
384 at::OptionalDeviceGuard device_guard(deviceOptional);
385 return at::sparse_coo_tensor(r.tensor(0), r.tensor(1), r.intlist(2));
386 } else if (r.idx == 4) {
387 PyObject* arg = r.pyobject(0);
388 auto deviceOptional = r.deviceOptional(1);
389 check_legacy_ctor_device(dispatch_key, deviceOptional);
390 if (!THPSize_Check(arg)((((PyObject*)(arg))->ob_type) == &THPSizeType) && PyTuple_GET_SIZE(args)(((PyVarObject*)(((PyTupleObject *)(args))))->ob_size) >= 1 && arg == PyTuple_GET_ITEM(args, 0)(((PyTupleObject *)(args))->ob_item[0])) {
391 // new(sequence) binds to this signature but should be treated differently
392 // unless the sequences is a torch.Size
393 throw TypeError("torch.SparseTensor(sequence) only accepts sizes. Please use torch.sparse_coo_tensor() " \
394 "or construct a strided tensor and convert it to sparse via to_sparse.");
395 }
396 return new_with_sizes(options, scalar_type, r.deviceOptional(1), r.intlist(0));
397 }
398 throw std::runtime_error("new(): invalid arguments");
399}
400
401Tensor legacy_sparse_tensor_new(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
402 auto options = dispatchKeyToTensorOptions(dispatch_key);
403 static PythonArgParser parser({
404 "new(*, Device? device=None)",
405 "new(*, int64_t cdata)|hidden",
406 "new(Tensor indices, Tensor values, *, Device? device=None)",
407 "new(Tensor indices, Tensor values, IntArrayRef size, *, Device? device=None)",
408 "new(IntArrayRef size, *, Device? device=None)",
409 });
410 check_base_legacy_new(dispatch_key, c10::kSparse);
411 ParsedArgs<5> parsed_args;
412 auto r = parser.parse(args, kwargs, parsed_args);
413 if (r.idx == 0) {
414 auto deviceOptional = r.deviceOptional(0);
415 check_legacy_ctor_device(dispatch_key, deviceOptional);
416 at::OptionalDeviceGuard device_guard(deviceOptional);
417 return at::empty({0}, build_options(options, scalar_type));
418 } else if (r.idx == 1) {
419 auto cdata = reinterpret_cast<void*>(r.toInt64(0));
420 return at::unsafeTensorFromTH(cdata, true);
421 } else if (r.idx == 2) {
422 // Note: this signature doesn't have a dtype, even though it has a device; it probably shouldn't
423 // have a device (we should infer it).
424 auto deviceOptional = r.deviceOptional(2);
425 check_legacy_ctor_device(dispatch_key, deviceOptional);
426 at::OptionalDeviceGuard device_guard(deviceOptional);
427 return at::sparse_coo_tensor(r.tensor(0), r.tensor(1));
428 } else if (r.idx == 3) {
429 // Note: this signature doesn't have a dtype, even though it has a device; it probably shouldn't
430 // have a device (we should infer it).
431 auto deviceOptional = r.deviceOptional(3);
432 check_legacy_ctor_device(dispatch_key, deviceOptional);
433 at::OptionalDeviceGuard device_guard(deviceOptional);
434 return at::sparse_coo_tensor(r.tensor(0), r.tensor(1), r.intlist(2));
435 } else if (r.idx == 4) {
436 PyObject* arg = r.pyobject(0);
437 auto deviceOptional = r.deviceOptional(1);
438 check_legacy_ctor_device(dispatch_key, deviceOptional);
439 if (!THPSize_Check(arg)((((PyObject*)(arg))->ob_type) == &THPSizeType) && PyTuple_GET_SIZE(args)(((PyVarObject*)(((PyTupleObject *)(args))))->ob_size) >= 1 && arg == PyTuple_GET_ITEM(args, 0)(((PyTupleObject *)(args))->ob_item[0])) {
440 // new(sequence) binds to this signature but should be treated differently
441 // unless the sequences is a torch.Size
442 throw TypeError("SparseTensor.new(sequence) only accepts sizes. Please use torch.sparse_coo_tensor() " \
443 "or construct a strided tensor and convert it to sparse via to_sparse.");
444 }
445 return new_with_sizes(options, scalar_type, r.deviceOptional(1), r.intlist(0));
446 }
447 throw std::runtime_error("new(): invalid arguments");
448}
449
450// NB: device_idx here is NOT a DeviceIndex, but index into PythonArgs
451c10::TensorOptions typeIdWithDefault(PythonArgs& r, int64_t device_idx, c10::DispatchKey dispatch_key) {
452 auto options = dispatchKeyToTensorOptions(dispatch_key);
453 if (!r.isNone(device_idx)) {
454 // TODO: This line doesn't seem to be exercised at all in tests
455 options = options.device(r.device(device_idx).type());
456 }
457 return options;
458}
459
460} // namespace
461
462Tensor legacy_tensor_ctor(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
463 auto options = dispatchKeyToTensorOptions(dispatch_key);
464 static PythonArgParser parser({
465 "new(*, Device? device=None)",
466 "new(Storage storage)",
467 "new(*, int64_t cdata)|hidden",
468 "new(Tensor other)",
469 "new(Tensor other, *, Device? device=None)|hidden", // prevent Tensor matching with IntArrayRef, PyObject*
470 "new(IntArrayRef size, *, Device? device=None)",
471 "new(PyObject* data, *, Device? device=None)",
472 });
473
474 if (isSparse(dispatchKeyToBackend(dispatch_key))) {
475 return legacy_sparse_tensor_ctor(dispatch_key, scalar_type, args, kwargs);
476 }
477
478 ParsedArgs<2> parsed_args;
479 auto r = parser.parse(args, kwargs, parsed_args);
480 if (r.idx == 0) {
481 auto deviceOptional = r.deviceOptional(0);
482 check_legacy_ctor_device(dispatch_key, deviceOptional);
483 at::OptionalDeviceGuard device_guard(deviceOptional);
484 return at::empty({0}, build_options(options, scalar_type));
485 } else if (r.idx == 1) {
486 THPObjectPtr dtype_attr(PyObject_GetAttrString(r.pyobject(0), "dtype"));
487 if (!dtype_attr) throw python_error();
488 at::ScalarType storage_scalar_type = reinterpret_cast<THPDtype*>(
489 dtype_attr.get())->scalar_type;
490 TORCH_CHECK(if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(496), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
491 storage_scalar_type == scalar_type,if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(496), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
492 "Expected Storage of type ",if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(496), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
493 scalar_type,if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(496), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
494 " but got type ",if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(496), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
495 storage_scalar_type,if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(496), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
496 " for argument 1 'storage'")if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(496), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); };
497 return new_with_storage(options, scalar_type, r.storage(0));
498 } else if (r.idx == 2) {
499 auto cdata = reinterpret_cast<void*>(r.toInt64(0));
500 return at::unsafeTensorFromTH(cdata, true);
501 } else if (r.idx == 3) {
502 return new_with_tensor(options, scalar_type, r.tensor(0));
503 } else if (r.idx == 4) {
504 TORCH_CHECK(false, "Legacy tensor constructor of the form torch.Tensor(tensor, device=device) " \if ((__builtin_expect(static_cast<bool>(!(false)), 0)))
{ ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(505), (::c10::detail::torchCheckMsgImpl
( "Expected " "false" " to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Legacy tensor constructor of the form torch.Tensor(tensor, device=device) "
"is not supported. Use torch.tensor(...) or torch.as_tensor(...) instead."
))); }
505 "is not supported. Use torch.tensor(...) or torch.as_tensor(...) instead.")if ((__builtin_expect(static_cast<bool>(!(false)), 0)))
{ ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(505), (::c10::detail::torchCheckMsgImpl
( "Expected " "false" " to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Legacy tensor constructor of the form torch.Tensor(tensor, device=device) "
"is not supported. Use torch.tensor(...) or torch.as_tensor(...) instead."
))); };
506 } else if (r.idx == 5) {
507 PyObject* arg = r.pyobject(0);
508 auto deviceOptional = r.deviceOptional(1);
509 check_legacy_ctor_device(dispatch_key, deviceOptional);
510 if (!THPSize_Check(arg)((((PyObject*)(arg))->ob_type) == &THPSizeType) && PyTuple_GET_SIZE(args)(((PyVarObject*)(((PyTupleObject *)(args))))->ob_size) >= 1 && arg == PyTuple_GET_ITEM(args, 0)(((PyTupleObject *)(args))->ob_item[0])) {
511 // new(sequence) binds to this signature but should be treated differently
512 // unless the sequences is a torch.Size
513 return legacy_new_from_sequence(options, scalar_type, deviceOptional, r.pyobject(0));
514 }
515 return new_with_sizes(options, scalar_type, r.deviceOptional(1), r.intlist(0));
516 } else if (r.idx == 6) {
517 auto deviceOptional = r.deviceOptional(1);
518 check_legacy_ctor_device(dispatch_key, deviceOptional);
519 return legacy_new_from_sequence(options, scalar_type, deviceOptional, r.pyobject(0));
520 }
521 throw std::runtime_error("new(): invalid arguments");
522}
523
524Tensor legacy_tensor_new(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
525 auto options = dispatchKeyToTensorOptions(dispatch_key);
526 static PythonArgParser parser({
527 "new(*, Device? device=None)",
528 "new(Storage storage)",
529 "new(*, int64_t cdata)|hidden",
530 "new(Tensor other)", // this doesn't have a dtype/device because it creates an alias.
531 "new(Tensor other, *, Device? device=None)|hidden", // prevent Tensor matching with IntArrayRef, PyObject*
532 "new(IntArrayRef size, *, Device? device=None)",
533 "new(PyObject* data, *, Device? device=None)",
534 });
535
536 if (isSparse(dispatchKeyToBackend(dispatch_key))) {
537 return legacy_sparse_tensor_new(dispatch_key, scalar_type, args, kwargs);
538 }
539
540 check_base_legacy_new(dispatch_key, c10::kStrided);
541 ParsedArgs<3> parsed_args;
542 auto r = parser.parse(args, kwargs, parsed_args);
543 if (r.idx == 0) {
544 auto deviceOptional = r.deviceOptional(0);
545 check_legacy_ctor_device(dispatch_key, deviceOptional);
546 at::OptionalDeviceGuard device_guard(deviceOptional);
547 return at::empty({0}, build_options(options, scalar_type));
548 } else if (r.idx == 1) {
549 THPObjectPtr dtype_attr(PyObject_GetAttrString(r.pyobject(0), "dtype"));
550 if (!dtype_attr) throw python_error();
551 at::ScalarType storage_scalar_type = reinterpret_cast<THPDtype*>(
552 dtype_attr.get())->scalar_type;
553 TORCH_CHECK(if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(559), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
554 storage_scalar_type == scalar_type,if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(559), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
555 "Expected Storage of type ",if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(559), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
556 scalar_type,if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(559), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
557 " but got type ",if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(559), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
558 storage_scalar_type,if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(559), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); }
559 " for argument 1 'storage'")if ((__builtin_expect(static_cast<bool>(!(storage_scalar_type
== scalar_type)), 0))) { ::c10::detail::torchCheckFail( __func__
, "../torch/csrc/utils/tensor_new.cpp", static_cast<uint32_t
>(559), (::c10::detail::torchCheckMsgImpl( "Expected " "storage_scalar_type == scalar_type"
" to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Expected Storage of type "
, scalar_type, " but got type ", storage_scalar_type, " for argument 1 'storage'"
))); };
560 return new_with_storage(options, scalar_type, r.storage(0));
561 } else if (r.idx == 2) {
562 auto cdata = reinterpret_cast<void*>(r.toInt64(0));
563 return at::unsafeTensorFromTH(cdata, true);
564 } else if (r.idx == 3) {
565 return new_with_tensor(options, scalar_type, r.tensor(0));
566 } else if (r.idx == 4) {
567 TORCH_CHECK(false, "Legacy tensor new of the form tensor.new(tensor, device=device) " \if ((__builtin_expect(static_cast<bool>(!(false)), 0)))
{ ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(568), (::c10::detail::torchCheckMsgImpl
( "Expected " "false" " to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Legacy tensor new of the form tensor.new(tensor, device=device) "
"is not supported. Use torch.as_tensor(...) instead."))); }
568 "is not supported. Use torch.as_tensor(...) instead.")if ((__builtin_expect(static_cast<bool>(!(false)), 0)))
{ ::c10::detail::torchCheckFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(568), (::c10::detail::torchCheckMsgImpl
( "Expected " "false" " to be true, but got false. " "(Could this error message be improved? If so, "
"please report an enhancement request to PyTorch.)", "Legacy tensor new of the form tensor.new(tensor, device=device) "
"is not supported. Use torch.as_tensor(...) instead."))); };
569 } else if (r.idx == 5) {
570 PyObject* arg = r.pyobject(0);
571 auto deviceOptional = r.deviceOptional(1);
572 check_legacy_ctor_device(dispatch_key, deviceOptional);
573 if (!THPSize_Check(arg)((((PyObject*)(arg))->ob_type) == &THPSizeType) && PyTuple_GET_SIZE(args)(((PyVarObject*)(((PyTupleObject *)(args))))->ob_size) >= 1 && arg == PyTuple_GET_ITEM(args, 0)(((PyTupleObject *)(args))->ob_item[0])) {
574 // new(sequence) binds to this signature but should be treated differently
575 // unless the sequences is a torch.Size
576 return legacy_new_from_sequence(options, scalar_type, deviceOptional, r.pyobject(0));
577 }
578 return new_with_sizes(options, scalar_type, r.deviceOptional(1), r.intlist(0));
579 } else if (r.idx == 6) {
580 auto deviceOptional = r.deviceOptional(1);
581 check_legacy_ctor_device(dispatch_key, deviceOptional);
582 return legacy_new_from_sequence(options, scalar_type, r.deviceOptional(1), r.pyobject(0));
583 }
584 throw std::runtime_error("new(): invalid arguments");
585}
586
587Tensor indexing_tensor_from_data(
588 c10::TensorOptions options,
589 at::ScalarType scalar_type,
590 c10::optional<Device> device,
591 PyObject* data) {
592 // Specific to tensor indexing, converts an indexing list to an
593 // indexing tensor (type Byte or Long)
594 ScalarType inferred_scalar_type = infer_scalar_type(data);
595 if (inferred_scalar_type == ScalarType::Byte || inferred_scalar_type == ScalarType::Bool) {
596 return internal_new_from_data(options, inferred_scalar_type, device, data,
597 /*copy_variables=*/false, /*copy_numpy=*/false,
598 /*type_inference=*/false);
599 } else {
600 return internal_new_from_data(options, scalar_type, device, data,
601 /*copy_variables=*/false, /*copy_numpy=*/false,
602 /*type_inference=*/false);
603 }
604}
605
606Tensor sparse_csr_tensor_ctor(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
607 TORCH_INTERNAL_ASSERT(!isSparseCsr(dispatchKeyToBackend(dispatch_key)))if ((__builtin_expect(static_cast<bool>(!(!isSparseCsr(
dispatchKeyToBackend(dispatch_key)))), 0))) { ::c10::detail::
torchInternalAssertFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(607), "!isSparseCsr(dispatchKeyToBackend(dispatch_key))"
"INTERNAL ASSERT FAILED at " "\"../torch/csrc/utils/tensor_new.cpp\""
":" "607" ", please report a bug to PyTorch. ", c10::str());
};
608 TORCH_INTERNAL_ASSERT(!isSparse(dispatchKeyToBackend(dispatch_key)))if ((__builtin_expect(static_cast<bool>(!(!isSparse(dispatchKeyToBackend
(dispatch_key)))), 0))) { ::c10::detail::torchInternalAssertFail
( __func__, "../torch/csrc/utils/tensor_new.cpp", static_cast
<uint32_t>(608), "!isSparse(dispatchKeyToBackend(dispatch_key))"
"INTERNAL ASSERT FAILED at " "\"../torch/csrc/utils/tensor_new.cpp\""
":" "608" ", please report a bug to PyTorch. ", c10::str());
};
609 static PythonArgParser parser({
610 "sparse_csr_tensor(PyObject* crow_indices, PyObject* col_indices, PyObject* values, IntArrayRef size, *, ScalarType dtype=None, Layout? layout=None, Device? device=None, bool pin_memory=False, bool requires_grad=False)",
611 "sparse_csr_tensor(PyObject* crow_indices, PyObject* col_indices, PyObject* values, *, ScalarType dtype=None, Layout? layout=None, Device? device=None, bool pin_memory=False, bool requires_grad=False)",
612 });
613 const int NUM_ARGS = 9, CROW_INDICES_ARG = 0, COL_INDICES_ARG = 1, VALUES_ARG = 2;
614 ParsedArgs<NUM_ARGS> parsed_args;
615 auto r = parser.parse(args, kwargs, parsed_args);
616 auto safe_get_attr_string = [](PyObject *o, const char *attr_name) -> PyObject* {
617 // Clear error indicator if attribute does not exists.
618 // Otherwise subsequent Python C API calls might return bogus values.
619 // See https://github.com/pytorch/pytorch/issues/58520 for more details
620 auto rc = PyObject_GetAttrString(o, attr_name);
621 if (!rc) {
622 if (!PyErr_ExceptionMatches(PyExc_AttributeError)) {
623 throw python_error();
624 }
625 // Warning: a wrong attribute error may be suppressed here
626 PyErr_Clear();
627 }
628 return rc;
629 };
630 THPObjectPtr crow_indices_dtype_attr(safe_get_attr_string(r.pyobject(CROW_INDICES_ARG), "dtype"));
631 THPObjectPtr col_indices_dtype_attr(safe_get_attr_string(r.pyobject(COL_INDICES_ARG), "dtype"));
632 at::ScalarType crow_indices_scalar_type = crow_indices_dtype_attr ? reinterpret_cast<THPDtype*>(
633 crow_indices_dtype_attr.get())->scalar_type : kInt;
634 at::ScalarType col_indices_scalar_type = col_indices_dtype_attr ? reinterpret_cast<THPDtype*>(
635 col_indices_dtype_attr.get())->scalar_type : kInt;
636
637 if (r.idx == 0) {
638 const int SIZE_ARRAY_ARG = 3, TYPE_INFERENCE_ARG = 4, DEVICE_TYPE_ARG = 6, REQ_GRAD_ARG = 8;
639 bool type_inference = r.isNone(TYPE_INFERENCE_ARG);
640 const auto inferred_options = typeIdWithDefault(r, DEVICE_TYPE_ARG, dispatch_key);
641 const auto inferred_scalar_type = r.scalartypeWithDefault(TYPE_INFERENCE_ARG, scalar_type);
642 at::OptionalDeviceGuard device_guard(r.deviceOptional(DEVICE_TYPE_ARG));
643
644 Tensor values = internal_new_from_data(inferred_options, inferred_scalar_type, r.deviceOptional(DEVICE_TYPE_ARG),
645 r.pyobject(VALUES_ARG), /*copy_variables=*/false, /*copy_numpy=*/true,
646 /*type_inference=*/type_inference);
647 Tensor crow_indices = internal_new_from_data(values.options(),
648 crow_indices_scalar_type, r.deviceOptional(DEVICE_TYPE_ARG), r.pyobject(CROW_INDICES_ARG),
649 /*copy_variables=*/false, /*copy_numpy=*/true,
650 /*type_inference=*/true);
651 Tensor col_indices = internal_new_from_data(values.options(),
652 col_indices_scalar_type, r.deviceOptional(DEVICE_TYPE_ARG), r.pyobject(COL_INDICES_ARG),
653 /*copy_variables=*/false, /*copy_numpy=*/true,
654 /*type_inference=*/true);
655
656 return at::sparse_csr_tensor(crow_indices, col_indices, values, r.intlist(SIZE_ARRAY_ARG),
657 values.options().layout(at::kSparseCsr)).set_requires_grad(r.toBool(REQ_GRAD_ARG));
658 } else if (r.idx == 1) {
659 const int TYPE_INFERENCE_ARG = 3, DEVICE_TYPE_ARG = 5, REQ_GRAD_ARG = 7;
660 bool type_inference = r.isNone(TYPE_INFERENCE_ARG);
661 const auto inferred_options = typeIdWithDefault(r, DEVICE_TYPE_ARG, dispatch_key);
662 const auto inferred_scalar_type = r.scalartypeWithDefault(TYPE_INFERENCE_ARG, scalar_type);
663 at::OptionalDeviceGuard device_guard(r.deviceOptional(DEVICE_TYPE_ARG));
664
665 Tensor values = internal_new_from_data(inferred_options, inferred_scalar_type, r.deviceOptional(DEVICE_TYPE_ARG),
666 r.pyobject(VALUES_ARG), /*copy_variables=*/false, /*copy_numpy=*/true,
667 /*type_inference=*/type_inference);
668 Tensor crow_indices = internal_new_from_data(values.options(),
669 crow_indices_scalar_type, r.deviceOptional(DEVICE_TYPE_ARG),
670 r.pyobject(CROW_INDICES_ARG), /*copy_variables=*/false, /*copy_numpy=*/true,
671 /*type_inference=*/true);
672 Tensor col_indices = internal_new_from_data(values.options(), col_indices_scalar_type, r.deviceOptional(DEVICE_TYPE_ARG),
673 r.pyobject(COL_INDICES_ARG), /*copy_variables=*/false, /*copy_numpy=*/true,
674 /*type_inference=*/true);
675 return at::sparse_csr_tensor(crow_indices, col_indices, values,
676 values.options().layout(at::kSparseCsr)).set_requires_grad(r.toBool(REQ_GRAD_ARG));
677 }
678 throw std::runtime_error("sparse_csr_tensor(): invalid arguments");
679}
680
681Tensor _sparse_csr_tensor_unsafe_ctor(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
682 TORCH_INTERNAL_ASSERT(!isSparseCsr(dispatchKeyToBackend(dispatch_key)))if ((__builtin_expect(static_cast<bool>(!(!isSparseCsr(
dispatchKeyToBackend(dispatch_key)))), 0))) { ::c10::detail::
torchInternalAssertFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(682), "!isSparseCsr(dispatchKeyToBackend(dispatch_key))"
"INTERNAL ASSERT FAILED at " "\"../torch/csrc/utils/tensor_new.cpp\""
":" "682" ", please report a bug to PyTorch. ", c10::str());
};
683 TORCH_INTERNAL_ASSERT(!isSparse(dispatchKeyToBackend(dispatch_key)))if ((__builtin_expect(static_cast<bool>(!(!isSparse(dispatchKeyToBackend
(dispatch_key)))), 0))) { ::c10::detail::torchInternalAssertFail
( __func__, "../torch/csrc/utils/tensor_new.cpp", static_cast
<uint32_t>(683), "!isSparse(dispatchKeyToBackend(dispatch_key))"
"INTERNAL ASSERT FAILED at " "\"../torch/csrc/utils/tensor_new.cpp\""
":" "683" ", please report a bug to PyTorch. ", c10::str());
};
684 enum {
685 ARG_CROW_INDICES = 0,
686 ARG_COL_INDICES,
687 ARG_VALUES,
688 ARG_SIZE,
689 ARG_TYPE,
690 ARG_DEVICE,
691 ARG_REQUIRES_GRAD,
692 ARGS_COUNT
693 };
694 static PythonArgParser parser({
695 "_sparse_csr_tensor_unsafe(PyObject* crow_indices, PyObject* col_indices, PyObject* values, IntArrayRef size, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
696 });
697
698 ParsedArgs<ARGS_COUNT> parsed_args;
699 auto r = parser.parse(args, kwargs, parsed_args);
700 bool type_inference = r.isNone(ARG_TYPE);
701 const auto inferred_options = typeIdWithDefault(r, ARG_DEVICE, dispatch_key);
702 const auto inferred_scalar_type = r.scalartypeWithDefault(ARG_TYPE, scalar_type);
703 at::OptionalDeviceGuard device_guard(r.deviceOptional(ARG_DEVICE));
704 Tensor values = internal_new_from_data(inferred_options, inferred_scalar_type, r.deviceOptional(ARG_DEVICE), r.pyobject(ARG_VALUES),
705 /*copy_variables=*/false, /*copy_numpy=*/true,
706 /*type_inference=*/type_inference);
707
708 Tensor crow_indices = internal_new_from_data(values.options(), kInt, r.deviceOptional(ARG_DEVICE), r.pyobject(ARG_CROW_INDICES),
709 /*copy_variables=*/false, /*copy_numpy=*/true,
710 /*type_inference=*/true);
711
712 Tensor col_indices = internal_new_from_data(values.options(), kInt, r.deviceOptional(ARG_DEVICE), r.pyobject(ARG_COL_INDICES),
713 /*copy_variables=*/false, /*copy_numpy=*/true,
714 /*type_inference=*/true);
715
716 return at::_sparse_csr_tensor_unsafe(crow_indices, col_indices, values, r.intlist(ARG_SIZE), values.options().layout(at::kSparseCsr)).set_requires_grad(r.toBool(ARG_REQUIRES_GRAD));
717}
718
719// Note [Ensuring sparse values and indices match devices]
720// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
721// In all places where we construct indices, we read out options from values
722// (rather than use inferred_options). Why? This handles the case when
723// values is a CUDA tensor, but indices is a non-Tensor value (and the device
724// argument is not set). Example:
725//
726// torch.sparse_coo_tensor(([0, 1],), self.empty(2, 0).cuda(), (4, 0))
727//
728// Sparse tensors require both indices and values to live on the same device.
729// If values lives on CUDA, we can infer where the indices should live, and
730// should accept even ordinary index sequences (and just make sure we write them
731// into the correct device). values is the ONLY way we know that the index
732// tensor should go to CUDA, so we have to get the information in somehow.
733//
734// This code is kind of jank. For one, the dtype in options is silently ignored
735// by internal_new_from_data. Also, in classic janky code style, it used to
736// not work quite right: if values lives on "cuda:1", before all we said was
737// "this needs to be CUDA" and indices would be allocated on the wrong tensor.
738// Options is more right and gets this correct.
739
740Tensor sparse_coo_tensor_ctor(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
741 TORCH_INTERNAL_ASSERT(!isSparse(dispatchKeyToBackend(dispatch_key)))if ((__builtin_expect(static_cast<bool>(!(!isSparse(dispatchKeyToBackend
(dispatch_key)))), 0))) { ::c10::detail::torchInternalAssertFail
( __func__, "../torch/csrc/utils/tensor_new.cpp", static_cast
<uint32_t>(741), "!isSparse(dispatchKeyToBackend(dispatch_key))"
"INTERNAL ASSERT FAILED at " "\"../torch/csrc/utils/tensor_new.cpp\""
":" "741" ", please report a bug to PyTorch. ", c10::str());
};
742 TORCH_INTERNAL_ASSERT(!isSparseCsr(dispatchKeyToBackend(dispatch_key)))if ((__builtin_expect(static_cast<bool>(!(!isSparseCsr(
dispatchKeyToBackend(dispatch_key)))), 0))) { ::c10::detail::
torchInternalAssertFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(742), "!isSparseCsr(dispatchKeyToBackend(dispatch_key))"
"INTERNAL ASSERT FAILED at " "\"../torch/csrc/utils/tensor_new.cpp\""
":" "742" ", please report a bug to PyTorch. ", c10::str());
};
743 static PythonArgParser parser({
744 "sparse_coo_tensor(PyObject* indices, PyObject* values, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
745 "sparse_coo_tensor(PyObject* indices, PyObject* values, IntArrayRef size, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
746 "sparse_coo_tensor(IntArrayRef size, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
747 });
748
749 ParsedArgs<6> parsed_args;
750 auto r = parser.parse(args, kwargs, parsed_args);
751 if (r.idx == 0) {
752 bool type_inference = r.isNone(2);
753 const auto inferred_options = typeIdWithDefault(r, 3, dispatch_key);
754 const auto inferred_scalar_type = r.scalartypeWithDefault(2, scalar_type);
755 at::OptionalDeviceGuard device_guard(r.deviceOptional(3));
756 // if no dtype provided, infer type based on value type.
757 Tensor values = internal_new_from_data(inferred_options, inferred_scalar_type, r.deviceOptional(3), r.pyobject(1),
758 /*copy_variables=*/false, /*copy_numpy=*/true,
759 /*type_inference=*/type_inference);
760 // See Note [Ensuring sparse values and indices match devices]
761 Tensor indices = internal_new_from_data(values.options(), kLong, r.deviceOptional(3), r.pyobject(0),
762 /*copy_variables=*/false, /*copy_numpy=*/true,
763 /*type_inference=*/false);
764 return at::sparse_coo_tensor(indices, values, values.options().layout(at::kSparse)).set_requires_grad(r.toBool(4));
765 } else if (r.idx == 1) {
766 bool type_inference = r.isNone(3);
767 const auto inferred_options = typeIdWithDefault(r, 4, dispatch_key);
768 const auto inferred_scalar_type = r.scalartypeWithDefault(3, scalar_type);
769 at::OptionalDeviceGuard device_guard(r.deviceOptional(4));
770 Tensor values = internal_new_from_data(inferred_options, inferred_scalar_type, r.deviceOptional(4), r.pyobject(1),
771 /*copy_variables=*/false, /*copy_numpy=*/true,
772 /*type_inference=*/type_inference);
773 // See Note [Ensuring sparse values and indices match devices]
774 Tensor indices = internal_new_from_data(values.options(), kLong, r.deviceOptional(4), r.pyobject(0),
775 /*copy_variables=*/false, /*copy_numpy=*/true,
776 /*type_inference=*/false);
777 return at::sparse_coo_tensor(indices, values, r.intlist(2), values.options().layout(at::kSparse)).set_requires_grad(r.toBool(5));
778 } else if (r.idx == 2) {
779 const auto inferred_options = typeIdWithDefault(r, 2, dispatch_key);
780 const auto inferred_scalar_type = r.scalartypeWithDefault(1, scalar_type);
781 at::OptionalDeviceGuard device_guard(r.deviceOptional(2));
782 return at::sparse_coo_tensor(r.intlist(0), inferred_options.dtype(inferred_scalar_type).layout(at::kSparse)).set_requires_grad(r.toBool(3));
783 }
784 throw std::runtime_error("sparse_coo_tensor(): invalid arguments");
785}
786
787Tensor _sparse_coo_tensor_unsafe_ctor(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
788 TORCH_INTERNAL_ASSERT(!isSparse(dispatchKeyToBackend(dispatch_key)))if ((__builtin_expect(static_cast<bool>(!(!isSparse(dispatchKeyToBackend
(dispatch_key)))), 0))) { ::c10::detail::torchInternalAssertFail
( __func__, "../torch/csrc/utils/tensor_new.cpp", static_cast
<uint32_t>(788), "!isSparse(dispatchKeyToBackend(dispatch_key))"
"INTERNAL ASSERT FAILED at " "\"../torch/csrc/utils/tensor_new.cpp\""
":" "788" ", please report a bug to PyTorch. ", c10::str());
};
789 TORCH_INTERNAL_ASSERT(!isSparseCsr(dispatchKeyToBackend(dispatch_key)))if ((__builtin_expect(static_cast<bool>(!(!isSparseCsr(
dispatchKeyToBackend(dispatch_key)))), 0))) { ::c10::detail::
torchInternalAssertFail( __func__, "../torch/csrc/utils/tensor_new.cpp"
, static_cast<uint32_t>(789), "!isSparseCsr(dispatchKeyToBackend(dispatch_key))"
"INTERNAL ASSERT FAILED at " "\"../torch/csrc/utils/tensor_new.cpp\""
":" "789" ", please report a bug to PyTorch. ", c10::str());
};
790 enum {
791 ARG_INDICES = 0,
792 ARG_VALUES,
793 ARG_SIZE,
794 ARG_TYPE,
795 ARG_DEVICE,
796 ARG_REQUIRES_GRAD,
797 ARGS_COUNT
798 };
799 static PythonArgParser parser({
800 "_sparse_coo_tensor_unsafe(PyObject* indices, PyObject* values, IntArrayRef size, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
801 });
802
803 ParsedArgs<ARGS_COUNT> parsed_args;
804 auto r = parser.parse(args, kwargs, parsed_args);
805 bool type_inference = r.isNone(ARG_TYPE);
806 const auto inferred_options = typeIdWithDefault(r, ARG_DEVICE, dispatch_key);
807 const auto inferred_scalar_type = r.scalartypeWithDefault(ARG_TYPE, scalar_type);
808 at::OptionalDeviceGuard device_guard(r.deviceOptional(ARG_DEVICE));
809 Tensor values = internal_new_from_data(inferred_options, inferred_scalar_type, r.deviceOptional(ARG_DEVICE), r.pyobject(ARG_VALUES),
810 /*copy_variables=*/false, /*copy_numpy=*/true,
811 /*type_inference=*/type_inference);
812 // See Note [Ensuring sparse values and indices match devices]
813 Tensor indices = internal_new_from_data(values.options(), kLong, r.deviceOptional(ARG_DEVICE), r.pyobject(ARG_INDICES),
814 /*copy_variables=*/false, /*copy_numpy=*/true,
815 /*type_inference=*/false);
816 return at::_sparse_coo_tensor_unsafe(indices, values, r.intlist(ARG_SIZE), values.options().layout(at::kSparse)).set_requires_grad(r.toBool(ARG_REQUIRES_GRAD));
817}
818
819void _validate_sparse_coo_tensor_args(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
820 auto options = dispatchKeyToTensorOptions(dispatch_key);
821 static PythonArgParser parser({
822 "_validate_sparse_coo_tensor(PyObject* indices, PyObject* values, IntArrayRef size)",
823 });
824
825 ParsedArgs<3> parsed_args;
826 auto r = parser.parse(args, kwargs, parsed_args);
827 Tensor values = internal_new_from_data(
828 options, scalar_type, c10::nullopt, r.pyobject(1),
829 /*copy_variables=*/false, /*copy_numpy=*/true, /*type_inference=*/true);
830 // See Note [Ensuring sparse values and indices match devices]
831 Tensor indices = internal_new_from_data(
832 values.options(), kLong, c10::nullopt, r.pyobject(0),
833 /*copy_variables=*/false, /*copy_numpy=*/true, /*type_inference=*/false);
834 at::native::_validate_sparse_coo_tensor_args(indices, values, r.intlist(2));
835}
836
837
838void _validate_sparse_csr_tensor_args(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
839 auto options = dispatchKeyToTensorOptions(dispatch_key);
840 static PythonArgParser parser({
841 "_validate_sparse_csr_tensor(PyObject* crow_indices, PyObject* col_indices, PyObject* values, IntArrayRef size)",
842 });
843
844 ParsedArgs<4> parsed_args;
845 auto r = parser.parse(args, kwargs, parsed_args);
846 Tensor values = internal_new_from_data(
847 options, scalar_type, c10::nullopt, r.pyobject(2),
848 /*copy_variables=*/false, /*copy_numpy=*/true, /*type_inference=*/true);
849 // See Note [Ensuring sparse values and indices match devices]
850 Tensor crow_indices = internal_new_from_data(
851 values.options(), kInt, c10::nullopt, r.pyobject(0),
852 /*copy_variables=*/false, /*copy_numpy=*/true, /*type_inference=*/true);
853 Tensor col_indices = internal_new_from_data(
854 values.options(), kInt, c10::nullopt, r.pyobject(1),
855 /*copy_variables=*/false, /*copy_numpy=*/true, /*type_inference=*/true);
856
857 at::native::_validate_sparse_csr_tensor_args(crow_indices, col_indices, values, r.intlist(3));
858}
859
860Tensor tensor_ctor(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
861 static PythonArgParser parser({
862 "tensor(PyObject* data, *, ScalarType dtype=None, Device? device=None, bool pin_memory=False, bool requires_grad=False, DimnameList? names=None)",
863 });
864
865 constexpr int ctor_num_args = 6;
866 ParsedArgs<ctor_num_args> parsed_args;
867 auto r = parser.parse(args, kwargs, parsed_args);
868 if (r.idx == 0) {
869 PyObject* data = r.pyobject(0);
870 if (THPVariable_Check(data)) {
871 auto ret = PyErr_WarnEx(PyExc_UserWarning,
872 "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() "
873 "or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).", 1);
874 if (ret != 0) throw python_error();
875 }
876
877 bool type_inference = r.isNone(1);
878 bool pin_memory = r.toBool(3);
879 bool args_requires_grad = r.toBool(4);
880 auto new_tensor = internal_new_from_data(
881 typeIdWithDefault(r, 2, dispatch_key),
882 r.scalartypeWithDefault(1, scalar_type),
883 r.deviceOptional(2),
884 data,
885 /*copy_variables=*/true,
886 /*copy_numpy=*/true,
887 /*type_inference=*/type_inference,
888 pin_memory);
889 auto names = r.toDimnameListOptional(5);
890 if (names) {
891 at::namedinference::propagate_names(new_tensor, *names, /*validate_names=*/true);
892 }
893 new_tensor.detach_(); // ensure new_tensor a leaf node
894 new_tensor.set_requires_grad(args_requires_grad);
895 return new_tensor;
896 }
897 throw std::runtime_error("tensor(): invalid arguments");
898}
899
900Tensor as_tensor(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
901 // TODO: add requires_grad once we decide on semantics for sharing data.
902 static PythonArgParser parser({
903 "as_tensor(PyObject* data, *, ScalarType dtype=None, Device? device=None)",
904 });
905
906 ParsedArgs<3> parsed_args;
907 auto r = parser.parse(args, kwargs, parsed_args);
908 if (r.idx == 0) {
909 bool type_inference = r.isNone(1);
910 return internal_new_from_data(
911 typeIdWithDefault(r, 2, dispatch_key),
912 r.scalartypeWithDefault(1, scalar_type),
913 r.deviceOptional(2),
914 r.pyobject(0),
915 /*copy_variables=*/false,
916 /*copy_numpy=*/false,
917 /*type_inference=*/type_inference);
918 }
919 throw std::runtime_error("tensor(): invalid arguments");
920}
921
922Tensor new_tensor(c10::DispatchKey dispatch_key, at::ScalarType scalar_type, PyObject* args, PyObject* kwargs) {
923 static PythonArgParser parser({
924 "new_tensor(PyObject* data, *, ScalarType dtype=None, Device? device=None, bool requires_grad=False)",
925 });
926
927 ParsedArgs<4> parsed_args;
928 auto r = parser.parse(args, kwargs, parsed_args);
929 if (r.idx == 0) {
1
Assuming field 'idx' is equal to 0
→
2
←
Taking true branch
→
930 PyObject* data = r.pyobject(0);
931 if (THPVariable_Check(data)) {
3
←
Taking false branch
→
932 auto ret = PyErr_WarnEx(PyExc_UserWarning,
933 "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() "
934 "or sourceTensor.clone().detach().requires_grad_(True), rather than tensor.new_tensor(sourceTensor).", 1);
935 if (ret != 0) throw python_error();
936 }
937
938 bool args_requires_grad = r.toBool(3);
939 auto new_tensor = new_from_data_copy(
4
←
Calling 'new_from_data_copy'
→
940 typeIdWithDefault(r, 2, dispatch_key),
941 r.scalartypeWithDefault(1, scalar_type),
942 r.deviceOptional(2),
943 data);
944 new_tensor.detach_(); // ensure new_tensor a leaf node
945 new_tensor.set_requires_grad(args_requires_grad);
946 return new_tensor;
947 }
948 throw std::runtime_error("new_tensor(): invalid arguments");
949}
950
951}} // namespace torch::utils
←

/opt/pyrefcon/lib/pyrefcon/models/models/PySequence_Fast.model

1#ifndef PySequence_Fast
2struct _object;
3typedef struct _object PyObject;
4PyObject* clang_analyzer_PyObject_New_Reference();
5PyObject* PySequence_Fast(PyObject *o, const char *m) {
6 return clang_analyzer_PyObject_New_Reference();
16
←
Setting reference count to 1
→
7}
8#else
9#warning "API PySequence_Fast is defined as a macro."
10#endif