| |
| r""" |
| The torch package contains data structures for multi-dimensional |
| tensors and defines mathematical operations over these tensors. |
| Additionally, it provides many utilities for efficient serializing of |
| Tensors and arbitrary types, and other useful utilities. |
| |
| It has a CUDA counterpart, that enables you to run your tensor computations |
| on an NVIDIA GPU with compute capability >= 3.0. |
| """ |
| |
| import os |
| import sys |
| import platform |
| import textwrap |
| import ctypes |
| import warnings |
| import inspect |
| if sys.version_info < (3,): |
| raise Exception("Python 2 has reached end-of-life and is no longer supported by PyTorch.") |
| |
| from ._utils import _import_dotted_name, classproperty |
| from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \ |
| USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS |
| # TODO(torch_deploy) figure out how to freeze version.py in fbcode build |
| if sys.executable == 'torch_deploy': |
| __version__ = "torch-deploy-1.8" |
| else: |
| from .torch_version import __version__ as __version__ |
| |
| from ._six import string_classes as _string_classes |
| |
| from typing import Set, Type, TYPE_CHECKING, Union, Callable |
| import builtins |
| |
| __all__ = [ |
| 'typename', 'is_tensor', 'is_storage', 'set_default_tensor_type', |
| 'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed', |
| 'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul', |
| 'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode', |
| 'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage', |
| 'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage', |
| '_TypedStorage', |
| 'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor', |
| 'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor', |
| 'lobpcg', 'use_deterministic_algorithms', |
| 'are_deterministic_algorithms_enabled', |
| 'is_deterministic_algorithms_warn_only_enabled', |
| 'set_deterministic_debug_mode', 'get_deterministic_debug_mode', |
| 'set_float32_matmul_precision', 'get_float32_matmul_precision', |
| 'set_warn_always', 'is_warn_always_enabled', |
| ] |
| |
| ################################################################################ |
| # Load the extension module |
| ################################################################################ |
| |
| if sys.platform == 'win32': |
| pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files') |
| py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin') |
| th_dll_path = os.path.join(os.path.dirname(__file__), 'lib') |
| |
| # When users create a virtualenv that inherits the base environment, |
| # we will need to add the corresponding library directory into |
| # DLL search directories. Otherwise, it will rely on `PATH` which |
| # is dependent on user settings. |
| if sys.exec_prefix != sys.base_exec_prefix: |
| base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin') |
| else: |
| base_py_dll_path = '' |
| |
| dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path])) |
| |
| if all([not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths]): |
| nvtoolsext_dll_path = os.path.join( |
| os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64') |
| else: |
| nvtoolsext_dll_path = '' |
| |
| from .version import cuda as cuda_version |
| import glob |
| if cuda_version and all([not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths]): |
| cuda_version_1 = cuda_version.replace('.', '_') |
| cuda_path_var = 'CUDA_PATH_V' + cuda_version_1 |
| default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version) |
| cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin') |
| else: |
| cuda_path = '' |
| |
| dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path])) |
| |
| kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True) |
| with_load_library_flags = hasattr(kernel32, 'AddDllDirectory') |
| prev_error_mode = kernel32.SetErrorMode(0x0001) |
| |
| kernel32.LoadLibraryW.restype = ctypes.c_void_p |
| if with_load_library_flags: |
| kernel32.AddDllDirectory.restype = ctypes.c_void_p |
| kernel32.LoadLibraryExW.restype = ctypes.c_void_p |
| |
| for dll_path in dll_paths: |
| if sys.version_info >= (3, 8): |
| os.add_dll_directory(dll_path) |
| elif with_load_library_flags: |
| res = kernel32.AddDllDirectory(dll_path) |
| if res is None: |
| err = ctypes.WinError(ctypes.get_last_error()) |
| err.strerror += f' Error adding "{dll_path}" to the DLL directories.' |
| raise err |
| |
| try: |
| ctypes.CDLL('vcruntime140.dll') |
| ctypes.CDLL('msvcp140.dll') |
| ctypes.CDLL('vcruntime140_1.dll') |
| except OSError: |
| print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure. |
| It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''') |
| |
| dlls = glob.glob(os.path.join(th_dll_path, '*.dll')) |
| path_patched = False |
| for dll in dlls: |
| is_loaded = False |
| if with_load_library_flags: |
| res = kernel32.LoadLibraryExW(dll, None, 0x00001100) |
| last_error = ctypes.get_last_error() |
| if res is None and last_error != 126: |
| err = ctypes.WinError(last_error) |
| err.strerror += f' Error loading "{dll}" or one of its dependencies.' |
| raise err |
| elif res is not None: |
| is_loaded = True |
| if not is_loaded: |
| if not path_patched: |
| os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']]) |
| path_patched = True |
| res = kernel32.LoadLibraryW(dll) |
| if res is None: |
| err = ctypes.WinError(ctypes.get_last_error()) |
| err.strerror += f' Error loading "{dll}" or one of its dependencies.' |
| raise err |
| |
| kernel32.SetErrorMode(prev_error_mode) |
| |
| |
| # See Note [Global dependencies] |
| def _load_global_deps(): |
| if platform.system() == 'Windows' or sys.executable == 'torch_deploy': |
| return |
| |
| lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so') |
| here = os.path.abspath(__file__) |
| lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name) |
| |
| ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL) |
| |
| |
| if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \ |
| platform.system() != 'Windows': |
| # Do it the hard way. You might want to load libtorch with RTLD_GLOBAL in a |
| # few circumstances: |
| # |
| # 1. You're in a build environment (e.g., fbcode) where |
| # libtorch_global_deps is not available, but you still need |
| # to get mkl to link in with RTLD_GLOBAL or it will just |
| # not work. |
| # |
| # 2. You're trying to run PyTorch under UBSAN and you need |
| # to ensure that only one copy of libtorch is loaded, so |
| # vptr checks work properly |
| # |
| # If you're using this setting, you must verify that all the libraries |
| # you load consistently use the same libstdc++, or you may have |
| # mysterious segfaults. |
| # |
| import os as _dl_flags |
| if not hasattr(_dl_flags, 'RTLD_GLOBAL') or not hasattr(_dl_flags, 'RTLD_LAZY'): |
| try: |
| # next try if DLFCN exists |
| import DLFCN as _dl_flags # type: ignore[import, no-redef] |
| except ImportError: |
| # as a last attempt, use compile-time constants |
| import torch._dl as _dl_flags # type: ignore[import, no-redef] |
| old_flags = sys.getdlopenflags() |
| sys.setdlopenflags(_dl_flags.RTLD_GLOBAL | _dl_flags.RTLD_LAZY) |
| from torch._C import * # noqa: F403 |
| sys.setdlopenflags(old_flags) |
| del old_flags |
| del _dl_flags |
| |
| else: |
| # Easy way. You want this most of the time, because it will prevent |
| # C++ symbols from libtorch clobbering C++ symbols from other |
| # libraries, leading to mysterious segfaults. |
| # |
| # If building in an environment where libtorch_global_deps isn't available |
| # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will |
| # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False |
| # |
| # See Note [Global dependencies] |
| if USE_GLOBAL_DEPS: |
| _load_global_deps() |
| from torch._C import * # noqa: F403 |
| |
| # Appease the type checker; ordinarily this binding is inserted by the |
| # torch._C module initialization code in C |
| if TYPE_CHECKING: |
| import torch._C as _C |
| |
| # Check to see if we can load C extensions, and if not provide some guidance |
| # on what the problem might be. |
| try: |
| # _initExtension is chosen (arbitrarily) as a sentinel. |
| from torch._C import _initExtension |
| except ImportError: |
| import torch._C as _C_for_compiled_check |
| |
| # The __file__ check only works for Python 3.7 and above. |
| if sys.version_info >= (3, 7) and _C_for_compiled_check.__file__ is None: |
| raise ImportError(textwrap.dedent(''' |
| Failed to load PyTorch C extensions: |
| It appears that PyTorch has loaded the `torch/_C` folder |
| of the PyTorch repository rather than the C extensions which |
| are expected in the `torch._C` namespace. This can occur when |
| using the `install` workflow. e.g. |
| $ python setup.py install && python -c "import torch" |
| |
| This error can generally be solved using the `develop` workflow |
| $ python setup.py develop && python -c "import torch" # This should succeed |
| or by running Python from a different directory. |
| ''').strip()) from None |
| raise # If __file__ is not None the cause is unknown, so just re-raise. |
| |
| for name in dir(_C): |
| if name[0] != '_' and not name.endswith('Base'): |
| __all__.append(name) |
| obj = getattr(_C, name) |
| if (isinstance(obj, Callable) or inspect.isclass(obj)): # type: ignore[arg-type] |
| if (obj.__module__ != 'torch'): |
| # TODO: fix their module from C++ side |
| if name not in ['DisableTorchFunction', 'Generator']: |
| obj.__module__ = 'torch' |
| |
| if not TYPE_CHECKING: |
| # issue 38137 and python issue 43367. Submodules of a C extension are |
| # non-standard, and attributes of those submodules cannot be pickled since |
| # pickle expect to be able to import them as "from _C.sub import attr" |
| # which fails with "_C is not a package |
| for attr in dir(_C): |
| candidate = getattr(_C, attr) |
| if type(candidate) is type(_C): |
| # submodule |
| if f'torch._C.{attr}' not in sys.modules: |
| sys.modules[f'torch._C.{attr}'] = candidate |
| |
| |
| ################################################################################ |
| # Define basic utilities |
| ################################################################################ |
| |
| |
| def typename(o): |
| if isinstance(o, torch.Tensor): |
| return o.type() |
| |
| module = '' |
| class_name = '' |
| if hasattr(o, '__module__') and o.__module__ != 'builtins' \ |
| and o.__module__ != '__builtin__' and o.__module__ is not None: |
| module = o.__module__ + '.' |
| |
| if hasattr(o, '__qualname__'): |
| class_name = o.__qualname__ |
| elif hasattr(o, '__name__'): |
| class_name = o.__name__ |
| else: |
| class_name = o.__class__.__name__ |
| |
| return module + class_name |
| |
| |
| def is_tensor(obj): |
| r"""Returns True if `obj` is a PyTorch tensor. |
| |
| Note that this function is simply doing ``isinstance(obj, Tensor)``. |
| Using that ``isinstance`` check is better for typechecking with mypy, |
| and more explicit - so it's recommended to use that instead of |
| ``is_tensor``. |
| |
| Args: |
| obj (Object): Object to test |
| Example:: |
| |
| >>> x=torch.tensor([1,2,3]) |
| >>> torch.is_tensor(x) |
| True |
| |
| """ |
| return isinstance(obj, torch.Tensor) |
| |
| |
| def is_storage(obj): |
| r"""Returns True if `obj` is a PyTorch storage object. |
| |
| Args: |
| obj (Object): Object to test |
| """ |
| return type(obj) in _storage_classes |
| |
| |
| def set_default_tensor_type(t): |
| r"""Sets the default ``torch.Tensor`` type to floating point tensor type |
| ``t``. This type will also be used as default floating point type for |
| type inference in :func:`torch.tensor`. |
| |
| The default floating point tensor type is initially ``torch.FloatTensor``. |
| |
| Args: |
| t (type or string): the floating point tensor type or its name |
| |
| Example:: |
| |
| >>> torch.tensor([1.2, 3]).dtype # initial default for floating point is torch.float32 |
| torch.float32 |
| >>> torch.set_default_tensor_type(torch.DoubleTensor) |
| >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor |
| torch.float64 |
| |
| """ |
| if isinstance(t, _string_classes): |
| t = _import_dotted_name(t) |
| _C._set_default_tensor_type(t) |
| |
| |
| def set_default_dtype(d): |
| r""" |
| |
| Sets the default floating point dtype to :attr:`d`. Supports torch.float32 |
| and torch.float64 as inputs. Other dtypes may be accepted without complaint |
| but are not supported and are unlikely to work as expected. |
| |
| When PyTorch is initialized its default floating point dtype is torch.float32, |
| and the intent of set_default_dtype(torch.float64) is to facilitate NumPy-like |
| type inference. The default floating point dtype is used to: |
| |
| 1. Implicitly determine the default complex dtype. When the default floating point |
| type is float32 the default complex dtype is complex64, and when the default |
| floating point type is float64 the default complex type is complex128. |
| 2. Infer the dtype for tensors constructed using Python floats or complex Python |
| numbers. See examples below. |
| 3. Determine the result of type promotion between bool and integer tensors and |
| Python floats and complex Python numbers. |
| |
| Args: |
| d (:class:`torch.dtype`): the floating point dtype to make the default. |
| Either torch.float32 or torch.float64. |
| |
| Example: |
| >>> # initial default for floating point is torch.float32 |
| >>> # Python floats are interpreted as float32 |
| >>> torch.tensor([1.2, 3]).dtype |
| torch.float32 |
| >>> # initial default for floating point is torch.complex64 |
| >>> # Complex Python numbers are interpreted as complex64 |
| >>> torch.tensor([1.2, 3j]).dtype |
| torch.complex64 |
| |
| >>> torch.set_default_dtype(torch.float64) |
| |
| >>> # Python floats are now interpreted as float64 |
| >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor |
| torch.float64 |
| >>> # Complex Python numbers are now interpreted as complex128 |
| >>> torch.tensor([1.2, 3j]).dtype # a new complex tensor |
| torch.complex128 |
| |
| """ |
| _C._set_default_dtype(d) |
| |
| def use_deterministic_algorithms(mode, *, warn_only=False): |
| r""" Sets whether PyTorch operations must use "deterministic" |
| algorithms. That is, algorithms which, given the same input, and when |
| run on the same software and hardware, always produce the same output. |
| When enabled, operations will use deterministic algorithms when available, |
| and if only nondeterministic algorithms are available they will throw a |
| :class:`RuntimeError` when called. |
| |
| .. note:: :func:`torch.set_deterministic_debug_mode` offers an alternative |
| interface for this feature. |
| |
| The following normally-nondeterministic operations will act |
| deterministically when ``mode=True``: |
| |
| * :class:`torch.nn.Conv1d` when called on CUDA tensor |
| * :class:`torch.nn.Conv2d` when called on CUDA tensor |
| * :class:`torch.nn.Conv3d` when called on CUDA tensor |
| * :class:`torch.nn.ConvTranspose1d` when called on CUDA tensor |
| * :class:`torch.nn.ConvTranspose2d` when called on CUDA tensor |
| * :class:`torch.nn.ConvTranspose3d` when called on CUDA tensor |
| * :func:`torch.bmm` when called on sparse-dense CUDA tensors |
| * :func:`torch.Tensor.__getitem__` when attempting to differentiate a CPU tensor |
| and the index is a list of tensors |
| * :func:`torch.Tensor.index_put` with ``accumulate=False`` |
| * :func:`torch.Tensor.index_put` with ``accumulate=True`` when called on a CPU |
| tensor |
| * :func:`torch.Tensor.put_` with ``accumulate=True`` when called on a CPU |
| tensor |
| * :func:`torch.Tensor.scatter_add_` when ``input`` dimension is one and called |
| on a CUDA tensor |
| * :func:`torch.gather` when ``input`` dimension is one and called |
| on a CUDA tensor that requires grad |
| * :func:`torch.index_add` when called on CUDA tensor |
| * :func:`torch.index_select` when attempting to differentiate a CUDA tensor |
| * :func:`torch.repeat_interleave` when attempting to differentiate a CUDA tensor |
| * :func:`torch.Tensor.index_copy` when called on a CPU or CUDA tensor |
| |
| The following normally-nondeterministic operations will throw a |
| :class:`RuntimeError` when ``mode=True``: |
| |
| * :class:`torch.nn.AvgPool3d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.AdaptiveAvgPool2d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.AdaptiveAvgPool3d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.MaxPool3d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.AdaptiveMaxPool2d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.FractionalMaxPool2d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.FractionalMaxPool3d` when attempting to differentiate a CUDA tensor |
| * :func:`torch.nn.functional.interpolate` when attempting to differentiate a CUDA tensor |
| and one of the following modes is used: |
| |
| - ``linear`` |
| - ``bilinear`` |
| - ``bicubic`` |
| - ``trilinear`` |
| |
| * :class:`torch.nn.ReflectionPad1d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.ReflectionPad2d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.ReflectionPad3d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.ReplicationPad1d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.ReplicationPad2d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.ReplicationPad3d` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.NLLLoss` when called on a CUDA tensor |
| * :class:`torch.nn.CTCLoss` when attempting to differentiate a CUDA tensor |
| * :class:`torch.nn.EmbeddingBag` when attempting to differentiate a CUDA tensor when |
| ``mode='max'`` |
| * :func:`torch.Tensor.scatter_add_` when ``input`` dimension is larger than one |
| and called on a CUDA tensor |
| * :func:`torch.gather` when ``input`` dimension is larger than one |
| and called on a CUDA tensor that requires grad |
| * :func:`torch.Tensor.put_` when ``accumulate=False`` |
| * :func:`torch.Tensor.put_` when ``accumulate=True`` and called on a CUDA tensor |
| * :func:`torch.histc` when called on a CUDA tensor |
| * :func:`torch.bincount` when called on a CUDA tensor |
| * :func:`torch.kthvalue` with called on a CUDA tensor |
| * :func:`torch.median` with indices output when called on a CUDA tensor |
| * :func:`torch.nn.functional.grid_sample` when attempting to differentiate a CUDA tensor |
| |
| A handful of CUDA operations are nondeterministic if the CUDA version is |
| 10.2 or greater, unless the environment variable ``CUBLAS_WORKSPACE_CONFIG=:4096:8`` |
| or ``CUBLAS_WORKSPACE_CONFIG=:16:8`` is set. See the CUDA documentation for more |
| details: `<https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility>`_ |
| If one of these environment variable configurations is not set, a :class:`RuntimeError` |
| will be raised from these operations when called with CUDA tensors: |
| |
| * :func:`torch.mm` |
| * :func:`torch.mv` |
| * :func:`torch.bmm` |
| |
| Note that deterministic operations tend to have worse performance than |
| nondeterministic operations. |
| |
| .. note:: |
| |
| This flag does not detect or prevent nondeterministic behavior caused |
| by calling an inplace operation on a tensor with an internal memory |
| overlap or by giving such a tensor as the :attr:`out` argument for an |
| operation. In these cases, multiple writes of different data may target |
| a single memory location, and the order of writes is not guaranteed. |
| |
| Args: |
| mode (:class:`bool`): If True, makes potentially nondeterministic |
| operations switch to a deterministic algorithm or throw a runtime |
| error. If False, allows nondeterministic operations. |
| |
| Keyword args: |
| warn_only (:class:`bool`, optional): If True, operations that do not |
| have a deterministic implementation will throw a warning instead of |
| an error. Default: ``False`` |
| |
| Example:: |
| |
| >>> torch.use_deterministic_algorithms(True) |
| |
| # Forward mode nondeterministic error |
| >>> torch.randn(10).index_copy(0, torch.tensor([0]), torch.randn(1)) |
| ... |
| RuntimeError: index_copy does not have a deterministic implementation... |
| |
| # Backward mode nondeterministic error |
| >>> torch.randn(10, requires_grad=True, device='cuda').index_select(0, torch.tensor([0], device='cuda')).backward() |
| ... |
| RuntimeError: index_add_cuda_ does not have a deterministic implementation... |
| """ |
| _C._set_deterministic_algorithms(mode, warn_only=warn_only) |
| |
| def are_deterministic_algorithms_enabled(): |
| r"""Returns True if the global deterministic flag is turned on. Refer to |
| :func:`torch.use_deterministic_algorithms` documentation for more details. |
| """ |
| return _C._get_deterministic_algorithms() |
| |
| def is_deterministic_algorithms_warn_only_enabled(): |
| r"""Returns True if the global deterministic flag is set to warn only. |
| Refer to :func:`torch.use_deterministic_algorithms` documentation for more |
| details. |
| """ |
| return _C._get_deterministic_algorithms_warn_only() |
| |
| def set_deterministic_debug_mode(debug_mode: Union[builtins.int, str]) -> None: |
| r"""Sets the debug mode for deterministic operations. |
| |
| .. note:: This is an alternative interface for |
| :func:`torch.use_deterministic_algorithms`. Refer to that function's |
| documentation for details about affected operations. |
| |
| Args: |
| debug_mode(str or int): If "default" or 0, don't error or warn on |
| nondeterministic operations. If "warn" or 1, warn on |
| nondeterministic operations. If "error" or 2, error on |
| nondeterministic operations. |
| """ |
| |
| # NOTE: builtins.int is used here because int in this scope resolves |
| # to torch.int |
| if not isinstance(debug_mode, (builtins.int, str)): |
| raise TypeError(f'debug_mode must be str or int, but got {type(debug_mode)}') |
| |
| if isinstance(debug_mode, str): |
| if debug_mode == 'default': |
| debug_mode = 0 |
| elif debug_mode == 'warn': |
| debug_mode = 1 |
| elif debug_mode == 'error': |
| debug_mode = 2 |
| else: |
| raise RuntimeError( |
| 'invalid value of debug_mode, expected one of `default`, ' |
| f'`warn`, `error`, but got {debug_mode}') |
| |
| if debug_mode == 0: |
| _C._set_deterministic_algorithms(False) |
| elif debug_mode == 1: |
| _C._set_deterministic_algorithms(True, warn_only=True) |
| elif debug_mode == 2: |
| _C._set_deterministic_algorithms(True) |
| else: |
| raise RuntimeError( |
| 'invalid value of debug_mode, expected 0, 1, or 2, ' |
| f'but got {debug_mode}') |
| |
| def get_deterministic_debug_mode() -> builtins.int: |
| r"""Returns the current value of the debug mode for deterministic |
| operations. Refer to :func:`torch.set_deterministic_debug_mode` |
| documentation for more details. |
| """ |
| |
| if _C._get_deterministic_algorithms(): |
| if _C._get_deterministic_algorithms_warn_only(): |
| return 1 |
| else: |
| return 2 |
| else: |
| return 0 |
| |
| def get_float32_matmul_precision() -> builtins.str: |
| r"""Returns the current value of float32 matrix multiplication precision. Refer to |
| :func:`torch.set_float32_matmul_precision` documentation for more details. |
| """ |
| return _C._get_float32_matmul_precision() |
| |
| def set_float32_matmul_precision(precision): |
| r"""Sets the precision of float32 matrix multiplication (one of HIGHEST, HIGH, MEDIUM). |
| Original RFC: https://github.com/pytorch/pytorch/issues/76440 |
| Args: |
| precision(str): default "highest": avoid internally reducing precision with |
| formats such as TF32. |
| If "high," allow TF32. |
| If "medium," allow TF32. |
| """ |
| _C._set_float32_matmul_precision(precision) |
| |
| def set_warn_always(b): |
| r"""When this flag is False (default) then some PyTorch warnings may only |
| appear once per process. This helps avoid excessive warning information. |
| Setting it to True causes these warnings to always appear, which may be |
| helpful when debugging. |
| |
| Args: |
| b (:class:`bool`): If True, force warnings to always be emitted |
| If False, set to the default behaviour |
| """ |
| _C._set_warnAlways(b) |
| |
| def is_warn_always_enabled(): |
| r"""Returns True if the global warn_always flag is turned on. Refer to |
| :func:`torch.set_warn_always` documentation for more details. |
| """ |
| return _C._get_warnAlways() |
| |
| ################################################################################ |
| # Define numeric constants |
| ################################################################################ |
| |
| # For Python Array API (https://data-apis.org/array-api/latest/API_specification/constants.html) and |
| # NumPy consistency (https://numpy.org/devdocs/reference/constants.html) |
| from math import e , nan , inf , pi |
| __all__.extend(['e', 'pi', 'nan', 'inf']) |
| |
| ################################################################################ |
| # Define Storage and Tensor classes |
| ################################################################################ |
| |
| from ._tensor import Tensor |
| from .storage import _StorageBase, _TypedStorage, _LegacyStorage, _UntypedStorage |
| |
| # NOTE: New <type>Storage classes should never be added. When adding a new |
| # dtype, use torch.storage._TypedStorage directly. |
| |
| class ByteStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.uint8 |
| |
| class DoubleStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.double |
| |
| class FloatStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.float |
| |
| class HalfStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.half |
| |
| class LongStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.long |
| |
| class IntStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.int |
| |
| class ShortStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.short |
| |
| class CharStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.int8 |
| |
| class BoolStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.bool |
| |
| class BFloat16Storage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.bfloat16 |
| |
| class ComplexDoubleStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.cdouble |
| |
| class ComplexFloatStorage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.cfloat |
| |
| class QUInt8Storage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.quint8 |
| |
| class QInt8Storage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.qint8 |
| |
| class QInt32Storage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.qint32 |
| |
| class QUInt4x2Storage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.quint4x2 |
| |
| class QUInt2x4Storage(_LegacyStorage): |
| @classproperty |
| def dtype(self): |
| return torch.quint2x4 |
| |
| _storage_classes = { |
| _UntypedStorage, DoubleStorage, FloatStorage, LongStorage, IntStorage, |
| ShortStorage, CharStorage, ByteStorage, HalfStorage, BoolStorage, |
| QUInt8Storage, QInt8Storage, QInt32Storage, BFloat16Storage, |
| ComplexFloatStorage, ComplexDoubleStorage, QUInt4x2Storage, QUInt2x4Storage, |
| _TypedStorage |
| } |
| |
| # The _tensor_classes set is initialized by the call to _C._initialize_tensor_type_bindings() |
| _tensor_classes: Set[Type] = set() |
| |
| # If you edit these imports, please update torch/__init__.py.in as well |
| from .random import set_rng_state, get_rng_state, manual_seed, initial_seed, seed |
| from .serialization import save, load |
| from ._tensor_str import set_printoptions |
| |
| ################################################################################ |
| # Initialize extension |
| ################################################################################ |
| |
| def manager_path(): |
| if platform.system() == 'Windows' or sys.executable == 'torch_deploy': |
| return b"" |
| path = get_file_path('torch', 'bin', 'torch_shm_manager') |
| prepare_multiprocessing_environment(get_file_path('torch')) |
| if not os.path.exists(path): |
| raise RuntimeError("Unable to find torch_shm_manager at " + path) |
| return path.encode('utf-8') |
| |
| from torch.amp import autocast |
| |
| # Shared memory manager needs to know the exact location of manager executable |
| _C._initExtension(manager_path()) |
| del manager_path |
| |
| # Appease the type checker: it can't deal with direct setting of globals(). |
| # Note that we will see "too many" functions when reexporting this way; there |
| # is not a good way to fix this problem. Perhaps, try to redesign VariableFunctions |
| # so that this import is good enough |
| if TYPE_CHECKING: |
| # Some type signatures pulled in from _VariableFunctions here clash with |
| # signatures already imported. For now these clashes are ignored; see |
| # PR #43339 for details. |
| from torch._C._VariableFunctions import * # type: ignore[misc] # noqa: F403 |
| |
| # Ops not to be exposed in `torch` namespace, |
| # mostly helper ops. |
| PRIVATE_OPS = ( |
| 'unique_dim', |
| ) |
| |
| for name in dir(_C._VariableFunctions): |
| if name.startswith('__') or name in PRIVATE_OPS: |
| continue |
| obj = getattr(_C._VariableFunctions, name) |
| obj.__module__ = 'torch' |
| globals()[name] = obj |
| if not name.startswith("_"): |
| __all__.append(name) |
| |
| ################################################################################ |
| # Import interface functions defined in Python |
| ################################################################################ |
| |
| # needs to be after the above ATen bindings so we can overwrite from Python side |
| from .functional import * # noqa: F403 |
| |
| |
| ################################################################################ |
| # Remove unnecessary members |
| ################################################################################ |
| |
| del _StorageBase |
| del _LegacyStorage |
| |
| ################################################################################ |
| # Define _assert |
| ################################################################################ |
| |
| # needs to be before the submodule imports to avoid circular dependencies |
| def _assert(condition, message): |
| r"""A wrapper around Python's assert which is symbolically traceable. |
| """ |
| from .overrides import has_torch_function, handle_torch_function |
| |
| if type(condition) is not torch.Tensor and has_torch_function((condition,)): |
| return handle_torch_function(_assert, (condition,), condition, message) |
| assert condition, message |
| |
| ################################################################################ |
| # Import most common subpackages |
| ################################################################################ |
| |
| # Use the redundant form so that type checkers know that these are a part of |
| # the public API. The "regular" import lines are there solely for the runtime |
| # side effect of adding to the imported module's members for other users. |
| from torch import cuda as cuda |
| from torch import cpu as cpu |
| from torch import autograd as autograd |
| from torch.autograd import ( |
| no_grad as no_grad, |
| enable_grad as enable_grad, |
| set_grad_enabled as set_grad_enabled, |
| inference_mode as inference_mode, |
| ) |
| from torch import fft as fft |
| from torch import futures as futures |
| from torch import nn as nn |
| from torch import optim as optim |
| import torch.optim._multi_tensor |
| from torch import multiprocessing as multiprocessing |
| from torch import sparse as sparse |
| from torch import special as special |
| import torch.utils.backcompat |
| from torch import onnx as onnx |
| from torch import jit as jit |
| from torch import linalg as linalg |
| from torch import hub as hub |
| from torch import random as random |
| from torch import distributions as distributions |
| from torch import testing as testing |
| import torch.backends.cuda |
| import torch.backends.mps |
| import torch.backends.cudnn |
| import torch.backends.mkl |
| import torch.backends.mkldnn |
| import torch.backends.openmp |
| import torch.backends.quantized |
| import torch.utils.data |
| from torch import __config__ as __config__ |
| from torch import __future__ as __future__ |
| from torch import profiler as profiler |
| |
| # Quantized, sparse, AO, etc. should be last to get imported, as nothing |
| # is expected to depend on them. |
| import torch.nn.intrinsic |
| import torch.nn.quantizable |
| import torch.nn.quantized |
| # AO depends on nn, as well as quantized stuff -- so should be after those. |
| from torch import ao as ao |
| |
| _C._init_names(list(torch._storage_classes)) |
| |
| # attach docstrings to torch and tensor functions |
| from . import _torch_docs, _tensor_docs, _storage_docs |
| del _torch_docs, _tensor_docs, _storage_docs |
| |
| |
| def compiled_with_cxx11_abi(): |
| r"""Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1""" |
| return _C._GLIBCXX_USE_CXX11_ABI |
| |
| |
| # Import the ops "namespace" |
| from torch._ops import ops |
| from torch._classes import classes |
| |
| # quantization depends on torch.fx |
| # Import quantization |
| from torch import quantization as quantization |
| |
| # Import the quasi random sampler |
| from torch import quasirandom as quasirandom |
| |
| # If you are seeing this, it means that this call site was not checked if |
| # the memory format could be preserved, and it was switched to old default |
| # behaviour of contiguous |
| legacy_contiguous_format = contiguous_format |
| |
| # Register fork handler to initialize OpenMP in child processes (see gh-28389) |
| from torch.multiprocessing._atfork import register_after_fork |
| register_after_fork(torch.get_num_threads) |
| del register_after_fork |
| |
| # Import tools that require fully imported torch (for applying |
| # torch.jit.script as a decorator, for instance): |
| from ._lobpcg import lobpcg as lobpcg |
| |
| from ._vmap_internals import vmap as vmap |
| |
| # These were previously defined in native_functions.yaml and appeared on the |
| # `torch` namespace, but we moved them to c10 dispatch to facilitate custom |
| # class usage. We add these lines here to preserve backward compatibility. |
| quantized_lstm = torch.ops.aten.quantized_lstm |
| quantized_gru = torch.ops.aten.quantized_gru |
| |
| from torch.utils.dlpack import from_dlpack, to_dlpack |
| |
| # Import experimental masked operations support. See |
| # [RFC-0016](https://github.com/pytorch/rfcs/pull/27) for more |
| # information. |
| from . import _masked |
| |
| # Import removed ops with error message about removal |
| from ._linalg_utils import solve |
| |
| |
| def _register_device_module(device_type, module): |
| r"""Register an external runtime module of the specific :attr:`device_type` |
| supported by torch. |
| |
| After the :attr:`module` is registered correctly, the user can refer |
| the external runtime module as part of torch with attribute torch.xxx. |
| """ |
| # Make sure the device_type represent a supported device type for torch. |
| device_type = torch.device(device_type).type |
| m = sys.modules[__name__] |
| if hasattr(m, device_type): |
| raise RuntimeError("The runtime module of '{}' has already " |
| "been registered with '{}'".format(device_type, getattr(m, device_type))) |
| setattr(m, device_type, module) |
| torch_module_name = '.'.join([__name__, device_type]) |
| sys.modules[torch_module_name] = module |
| |
| # expose return_types |
| from . import return_types |
| if sys.executable != 'torch_deploy': |
| from . import library |
| from . import _meta_registrations |
