| from collections import OrderedDict |
| import enum |
| import functools |
| from numbers import Number |
| from typing import Any, Dict, Optional, Tuple, Union |
| import warnings |
| import copyreg |
| from copy import deepcopy |
| |
| import torch |
| import torch._C as _C |
| from torch._namedtensor_internals import ( |
| update_names, check_serializing_named_tensor, resolve_ellipsis, |
| unzip_namedshape, single_ellipsis_index, is_ellipsis) |
| from torch.overrides import ( |
| has_torch_function, has_torch_function_unary, has_torch_function_variadic, |
| handle_torch_function, get_default_nowrap_functions) |
| import torch.utils.hooks as hooks |
| |
| |
| def _handle_torch_function_and_wrap_type_error_to_not_implemented(f): |
| # functools.wraps doesn't work well with methods in python 2 |
| method_assignments = ('__name__', '__doc__') |
| assigned = functools.WRAPPER_ASSIGNMENTS |
| |
| @functools.wraps(f, assigned=assigned) |
| def wrapped(*args, **kwargs): |
| try: |
| # See https://github.com/pytorch/pytorch/issues/75462 |
| if has_torch_function(args): |
| return handle_torch_function(wrapped, args, *args, **kwargs) |
| return f(*args, **kwargs) |
| except TypeError: |
| return NotImplemented |
| return wrapped |
| |
| # Should not be used, this is kept only for BC of loading old serialized Tensor subclasses |
| def _rebuild_from_type(func, type, args, dict): |
| if type is Tensor: |
| return func(*args) |
| |
| ret = func(*args).as_subclass(type) |
| ret.__dict__ = dict |
| return ret |
| |
| def _rebuild_from_type_v2(func, new_type, args, state): |
| if new_type is Tensor: |
| return func(*args) |
| |
| ret = func(*args) |
| if type(ret) is not new_type: |
| ret = ret.as_subclass(new_type) |
| # Tensor does define __setstate__ even though it doesn't define |
| # __getstate__. So only use __setstate__ if it is NOT the one defined |
| # on Tensor |
| if getattr(ret.__class__, "__setstate__", Tensor.__setstate__) is not Tensor.__setstate__: |
| ret.__setstate__(state) |
| else: |
| if isinstance(state, tuple): |
| if not len(state) == 2: |
| raise RuntimeError(f"Invalid serialized state: {state}") |
| dict_state = state[0] |
| slots_state = state[1] |
| else: |
| dict_state = state |
| slots_state = None |
| |
| for k, v in dict_state.items(): |
| setattr(ret, k, v) |
| |
| if slots_state: |
| for k, v in slots_state.items(): |
| setattr(ret, k, v) |
| return ret |
| |
| |
| # NB: If you subclass Tensor, and want to share the subclassed class |
| # across processes, you must also update torch/multiprocessing/reductions.py |
| # to define a ForkingPickler serialization mode for the class. |
| # |
| # NB: If you add a new method to Tensor, you must update |
| # torch/__init__.py.in to add a type annotation for your method; |
| # otherwise, it will not show up in autocomplete. |
| class Tensor(torch._C._TensorBase): |
| def __deepcopy__(self, memo): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__deepcopy__, (self,), self, memo) |
| if not self.is_leaf: |
| raise RuntimeError("Only Tensors created explicitly by the user " |
| "(graph leaves) support the deepcopy protocol at the moment") |
| if id(self) in memo: |
| return memo[id(self)] |
| with torch.no_grad(): |
| # TODO: skipping storage copy is wrong for meta, as meta |
| # does accurate alias tracking; however, the code below |
| # doesn't work because of |
| # https://github.com/pytorch/pytorch/issues/47442 |
| # Update the test in test_serialization if you remove 'meta' from here |
| if self.is_sparse or self.device.type in ['lazy', 'xla', 'mps', 'ort', 'meta', 'hpu'] or \ |
| (type(self) is not Tensor and self.data_ptr() == 0): |
| new_tensor = self.clone() |
| if type(new_tensor) is not type(self): |
| raise RuntimeError("The default implementation of __deepcopy__() for wrapper subclasses " |
| "only works for subclass types that implement clone() and for which " |
| "cloning returns another instance of the same subclass. You should either " |
| "properly implement clone() for your subclass or override __deepcopy__() " |
| "if it is intended behavior for clone() to return an instance of a " |
| "different type.") |
| else: |
| new_storage = self.storage().__deepcopy__(memo) |
| if self.is_quantized: |
| # quantizer_params can be different type based on torch attribute |
| quantizer_params: Union[Tuple[torch.qscheme, float, int], Tuple[torch.qscheme, Tensor, Tensor, int]] |
| if self.qscheme() == torch.per_tensor_affine: |
| quantizer_params = self.qscheme(), self.q_scale(), self.q_zero_point() |
| elif self.qscheme() in (torch.per_channel_affine, torch.per_channel_affine_float_qparams): |
| quantizer_params = self.qscheme(), \ |
| self.q_per_channel_scales(), \ |
| self.q_per_channel_zero_points(), \ |
| self.q_per_channel_axis() |
| else: |
| raise RuntimeError(f"Unsupported qscheme {self.qscheme()} in deepcopy") |
| # TODO: Once we decide to break serialization FC, no longer |
| # need to wrap with _TypedStorage |
| new_tensor = torch._utils._rebuild_qtensor( |
| torch.storage._TypedStorage( |
| wrap_storage=new_storage._untyped(), |
| dtype=self.dtype), |
| self.storage_offset(), |
| self.size(), |
| self.stride(), |
| quantizer_params, |
| self.requires_grad, |
| self._backward_hooks) |
| if type(new_tensor) is not type(self): |
| raise RuntimeError("The default implementation of __deepcopy__() for quantized tensors " |
| "expects the tensor returned by torch._utils._rebuild_qtensor() to " |
| "match the type of the instance being copied. If you encounter this, " |
| "please open an issue on PyTorch's GitHub.") |
| else: |
| new_tensor = self.new_empty([]) |
| if type(new_tensor) is not type(self): |
| raise RuntimeError("The default implementation of __deepcopy__() for non-wrapper subclasses " |
| "only works for subclass types that implement new_empty() and for which " |
| "that function returns another instance of the same subclass. You should " |
| "either properly implement new_empty() for your subclass or override " |
| "__deepcopy__() if it is intended behavior for new_empty() to return " |
| "an instance of a different type.") |
| new_tensor.set_(new_storage, self.storage_offset(), self.size(), self.stride()) |
| if self.is_conj(): |
| new_tensor = new_tensor.conj_physical() |
| if self.is_neg(): |
| new_tensor = new_tensor.neg() |
| if self.requires_grad: |
| new_tensor.requires_grad_() |
| if self.grad is not None: |
| new_tensor.grad = self.grad.__deepcopy__(memo) |
| |
| if not type(self) is Tensor: |
| if type(new_tensor) is not type(self): |
| raise RuntimeError("Type of deepcopy result does not match the type of the source tensor. " |
| "If you encounter this, please open an issue on PyTorch's GitHub.") |
| |
| # Plain Tensors don't have slots |
| slots_to_save = copyreg._slotnames(self.__class__) # type: ignore[attr-defined] |
| for slot in slots_to_save: |
| if hasattr(self, slot): |
| setattr(new_tensor, slot, deepcopy(getattr(self, slot), memo)) |
| |
| new_tensor.__dict__ = deepcopy(self.__dict__, memo) |
| |
| memo[id(self)] = new_tensor |
| return new_tensor |
| |
| def __reduce_ex__(self, proto): |
| if type(self) is Tensor: |
| return self._reduce_ex_internal(proto) |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__reduce_ex__, (self,), self, proto) |
| func, args = self._reduce_ex_internal(proto) |
| # Get the state of the python subclass |
| # This loosely mimicks the function on the object class but since Tensor do not inherit |
| # from it, we cannot call that function directly |
| # https://github.com/python/cpython/blob/c83919bd635f4433f1c6ae8504996a9fe3c215e5/Objects/typeobject.c#L4891 |
| getstate_fn = getattr(self, "__getstate__", None) |
| if getstate_fn: |
| state = getstate_fn() |
| else: |
| slots_to_save = copyreg._slotnames(self.__class__) # type: ignore[attr-defined] |
| if slots_to_save: |
| state = (self.__dict__, {name: getattr(self, name) for name in slots_to_save if hasattr(self, name)}) |
| else: |
| state = self.__dict__ |
| return (_rebuild_from_type_v2, (func, type(self), args, state)) |
| |
| def storage(self): |
| r""" |
| storage() -> torch.Storage |
| |
| Returns the underlying storage. |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.storage, (self,), self) |
| |
| return torch._TypedStorage(wrap_storage=self._storage(), dtype=self.dtype) |
| |
| def _reduce_ex_internal(self, proto): |
| check_serializing_named_tensor(self) |
| # See Note [Don't serialize hooks] |
| torch.utils.hooks.warn_if_has_hooks(self) |
| backward_hooks: Dict[Any, Any] = OrderedDict() |
| # Note: Numpy array is chosen to be the rebuild component for XLA, ORT Tensors. |
| # We considered a few options: |
| # 1. CPU tensor can't be used here. |
| # Otherwise in torch.load CPU storage is reconstructed with randomly |
| # initialized data, moved onto backend device, and then storage is updated |
| # to the serialized content. This works perfectly for CPU/CUDA but not these backends; |
| # their tensors are disconnected with storage so they don't get the update. |
| # 2. Python list is not a good fit due to performance reason. |
| # `tolist()` converts every single element in the tensor into python objects |
| # and serialize them one by one. |
| if self.device.type in ['xla', 'ort', 'mps', 'hpu']: |
| # Convert BFloat16 tesors to Float32 before conversion to numpy, as numpy doesn't |
| # support BFloat16. The rebuild tensor from numpy takes in the original self.dtype, |
| # this would reconstruct the BFloat16 tensor from numpy. |
| numpy_tensor = self.cpu().numpy() if self.dtype != torch.bfloat16 else self.cpu().to(torch.float32).numpy() |
| return (torch._utils._rebuild_device_tensor_from_numpy, (numpy_tensor, |
| self.dtype, |
| str(self.device), |
| self.requires_grad)) |
| if self.device.type == 'meta': |
| # NB: This implementation BREAKS storage sharing. Current |
| # hypothesis is that no one cares for meta tensors. |
| arg_meta = ( |
| self.dtype, |
| tuple(self.size()), |
| self.stride(), |
| self.requires_grad, |
| ) |
| return (torch._utils._rebuild_meta_tensor_no_storage, arg_meta) |
| if self.is_quantized: |
| # quantizer_params can be different type based on torch attribute |
| quantizer_params: Union[Tuple[torch.qscheme, float, int], Tuple[Any, Tensor, Tensor, int]] |
| if self.qscheme() == torch.per_tensor_affine: |
| quantizer_params = (torch.per_tensor_affine, |
| self.q_scale(), |
| self.q_zero_point()) |
| elif self.qscheme() in (torch.per_channel_affine, torch.per_channel_affine_float_qparams): |
| # convert scales and zero points to tuple to avoid recursive calls |
| # when/if we get multi-axis quantized tensors in the future, the shape |
| # is recoverable from the main tensor shape |
| quantizer_params = (torch.per_channel_affine, |
| self.q_per_channel_scales(), |
| self.q_per_channel_zero_points(), |
| self.q_per_channel_axis()) |
| else: |
| raise RuntimeError(f"Serialization is not supported for tensors of type {self.qscheme()}") |
| # TODO: Once we decide to break serialization FC, no longer |
| # need to wrap with _TypedStorage |
| args_qtensor = ( |
| torch.storage._TypedStorage( |
| wrap_storage=self.storage()._untyped(), |
| dtype=self.dtype), |
| self.storage_offset(), |
| tuple(self.size()), |
| self.stride(), |
| quantizer_params, |
| self.requires_grad, |
| backward_hooks) |
| return (torch._utils._rebuild_qtensor, args_qtensor) |
| elif self.is_sparse: |
| if self.layout == torch.sparse_coo: |
| args_sparse = (self.layout, |
| (self._indices(), |
| self._values(), |
| self.size())) |
| else: |
| raise NotImplementedError( |
| 'sparse tensor __reduce_ex__ for layout `%s`' % (self.layout)) |
| return (torch._utils._rebuild_sparse_tensor, args_sparse) |
| elif self.is_sparse_csr: |
| if self.layout == torch.sparse_csr: |
| args_sparse_csr = (self.layout, |
| (self.crow_indices(), |
| self.col_indices(), |
| self.values(), |
| self.size())) |
| else: |
| raise NotImplementedError( |
| 'sparse csr tensor __reduce_ex__ for layout `%s`' % (self.layout)) |
| return (torch._utils._rebuild_sparse_csr_tensor, args_sparse_csr) |
| elif self.data_ptr() == 0 and type(self) is not torch.Tensor: |
| arg_wrapper_subclass = ( |
| type(self), |
| self.dtype, |
| tuple(self.size()), |
| self.stride(), |
| self.storage_offset(), |
| self.layout, |
| self.device, |
| self.requires_grad |
| ) |
| return (torch._utils._rebuild_wrapper_subclass, arg_wrapper_subclass) |
| else: |
| # TODO: Once we decide to break serialization FC, no longer |
| # need to wrap with _TypedStorage |
| args = ( |
| torch.storage._TypedStorage( |
| wrap_storage=self.storage()._untyped(), |
| dtype=self.dtype), |
| self.storage_offset(), |
| tuple(self.size()), |
| self.stride(), |
| self.requires_grad, |
| backward_hooks) # previously was self._backward_hooks |
| return (torch._utils._rebuild_tensor_v2, args) |
| |
| def __setstate__(self, state): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__setstate__, (self,), self, state) |
| # Warning: this method is NOT called when you torch.load() a tensor; |
| # that is managed by _rebuild_tensor_v2 |
| if not self.is_leaf: |
| raise RuntimeError('__setstate__ can be only called on leaf Tensors') |
| if len(state) == 4: |
| # legacy serialization of Tensor |
| self.set_(*state) |
| return |
| elif len(state) == 5: |
| # legacy serialization of Variable |
| self.data = state[0] |
| state = (state[3], state[4], state[2]) |
| # The setting of _backward_hooks is expected to be a no-op. |
| # See Note [Don't serialize hooks] |
| self.requires_grad, _, self._backward_hooks = state |
| |
| def __repr__(self, *, tensor_contents=None): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__repr__, (self,), self, |
| tensor_contents=tensor_contents) |
| # All strings are unicode in Python 3. |
| return torch._tensor_str._str(self, tensor_contents=tensor_contents) |
| |
| def backward(self, gradient=None, retain_graph=None, create_graph=False, inputs=None): |
| r"""Computes the gradient of current tensor w.r.t. graph leaves. |
| |
| The graph is differentiated using the chain rule. If the tensor is |
| non-scalar (i.e. its data has more than one element) and requires |
| gradient, the function additionally requires specifying ``gradient``. |
| It should be a tensor of matching type and location, that contains |
| the gradient of the differentiated function w.r.t. ``self``. |
| |
| This function accumulates gradients in the leaves - you might need to zero |
| ``.grad`` attributes or set them to ``None`` before calling it. |
| See :ref:`Default gradient layouts<default-grad-layouts>` |
| for details on the memory layout of accumulated gradients. |
| |
| .. note:: |
| |
| If you run any forward ops, create ``gradient``, and/or call ``backward`` |
| in a user-specified CUDA stream context, see |
| :ref:`Stream semantics of backward passes<bwd-cuda-stream-semantics>`. |
| |
| .. note:: |
| |
| When ``inputs`` are provided and a given input is not a leaf, |
| the current implementation will call its grad_fn (though it is not strictly needed to get this gradients). |
| It is an implementation detail on which the user should not rely. |
| See https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780 for more details. |
| |
| Args: |
| gradient (Tensor or None): Gradient w.r.t. the |
| tensor. If it is a tensor, it will be automatically converted |
| to a Tensor that does not require grad unless ``create_graph`` is True. |
| None values can be specified for scalar Tensors or ones that |
| don't require grad. If a None value would be acceptable then |
| this argument is optional. |
| retain_graph (bool, optional): If ``False``, the graph used to compute |
| the grads will be freed. Note that in nearly all cases setting |
| this option to True is not needed and often can be worked around |
| in a much more efficient way. Defaults to the value of |
| ``create_graph``. |
| create_graph (bool, optional): If ``True``, graph of the derivative will |
| be constructed, allowing to compute higher order derivative |
| products. Defaults to ``False``. |
| inputs (sequence of Tensor): Inputs w.r.t. which the gradient will be |
| accumulated into ``.grad``. All other Tensors will be ignored. If not |
| provided, the gradient is accumulated into all the leaf Tensors that were |
| used to compute the attr::tensors. |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function( |
| Tensor.backward, |
| (self,), |
| self, |
| gradient=gradient, |
| retain_graph=retain_graph, |
| create_graph=create_graph, |
| inputs=inputs) |
| torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) |
| |
| def register_hook(self, hook): |
| r"""Registers a backward hook. |
| |
| The hook will be called every time a gradient with respect to the |
| Tensor is computed. The hook should have the following signature:: |
| |
| hook(grad) -> Tensor or None |
| |
| |
| The hook should not modify its argument, but it can optionally return |
| a new gradient which will be used in place of :attr:`grad`. |
| |
| This function returns a handle with a method ``handle.remove()`` |
| that removes the hook from the module. |
| |
| Example:: |
| |
| >>> v = torch.tensor([0., 0., 0.], requires_grad=True) |
| >>> h = v.register_hook(lambda grad: grad * 2) # double the gradient |
| >>> v.backward(torch.tensor([1., 2., 3.])) |
| >>> v.grad |
| |
| 2 |
| 4 |
| 6 |
| [torch.FloatTensor of size (3,)] |
| |
| >>> h.remove() # removes the hook |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.register_hook, (self,), self, hook) |
| if not self.requires_grad: |
| raise RuntimeError("cannot register a hook on a tensor that " |
| "doesn't require gradient") |
| if self._backward_hooks is None: |
| self._backward_hooks = OrderedDict() |
| if self.grad_fn is not None: |
| self.grad_fn._register_hook_dict(self) |
| handle = hooks.RemovableHandle(self._backward_hooks) |
| self._backward_hooks[handle.id] = hook |
| return handle |
| |
| def reinforce(self, reward): |
| def trim(str): |
| return '\n'.join([line.strip() for line in str.split('\n')]) |
| |
| raise RuntimeError(trim(r"""reinforce() was removed. |
| Use torch.distributions instead. |
| See https://pytorch.org/docs/master/distributions.html |
| |
| Instead of: |
| |
| probs = policy_network(state) |
| action = probs.multinomial() |
| next_state, reward = env.step(action) |
| action.reinforce(reward) |
| action.backward() |
| |
| Use: |
| |
| probs = policy_network(state) |
| # NOTE: categorical is equivalent to what used to be called multinomial |
| m = torch.distributions.Categorical(probs) |
| action = m.sample() |
| next_state, reward = env.step(action) |
| loss = -m.log_prob(action) * reward |
| loss.backward() |
| """)) |
| |
| detach = _C._add_docstr(_C._TensorBase.detach, r""" |
| Returns a new Tensor, detached from the current graph. |
| |
| The result will never require gradient. |
| |
| This method also affects forward mode AD gradients and the result will never |
| have forward mode AD gradients. |
| |
| .. note:: |
| |
| Returned Tensor shares the same storage with the original one. |
| In-place modifications on either of them will be seen, and may trigger |
| errors in correctness checks. |
| IMPORTANT NOTE: Previously, in-place size / stride / storage changes |
| (such as `resize_` / `resize_as_` / `set_` / `transpose_`) to the returned tensor |
| also update the original tensor. Now, these in-place changes will not update the |
| original tensor anymore, and will instead trigger an error. |
| For sparse tensors: |
| In-place indices / values changes (such as `zero_` / `copy_` / `add_`) to the |
| returned tensor will not update the original tensor anymore, and will instead |
| trigger an error. |
| """) |
| |
| detach_ = _C._add_docstr(_C._TensorBase.detach_, r""" |
| Detaches the Tensor from the graph that created it, making it a leaf. |
| Views cannot be detached in-place. |
| |
| This method also affects forward mode AD gradients and the result will never |
| have forward mode AD gradients. |
| """) |
| |
| def is_shared(self): |
| r"""Checks if tensor is in shared memory. |
| |
| This is always ``True`` for CUDA tensors. |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.is_shared, (self,), self) |
| return self.storage().is_shared() |
| |
| def share_memory_(self): |
| r"""Moves the underlying storage to shared memory. |
| |
| This is a no-op if the underlying storage is already in shared memory |
| and for CUDA tensors. Tensors in shared memory cannot be resized. |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.share_memory_, (self,), self) |
| self.storage().share_memory_() |
| return self |
| |
| def __reversed__(self): |
| r"""Reverses the tensor along dimension 0.""" |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__reversed__, (self,), self) |
| if self.dim() == 0: |
| return self |
| else: |
| return self.flip(0) |
| |
| def norm(self, p="fro", dim=None, keepdim=False, dtype=None): |
| r"""See :func:`torch.norm`""" |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.norm, (self,), self, p=p, dim=dim, keepdim=keepdim, dtype=dtype) |
| return torch.norm(self, p, dim, keepdim, dtype=dtype) |
| |
| def solve(self, other): |
| from ._linalg_utils import solve |
| return solve(self, other) |
| |
| def lu(self, pivot=True, get_infos=False): |
| r"""See :func:`torch.lu`""" |
| # If get_infos is True, then we don't need to check for errors and vice versa |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.lu, (self,), self, pivot=pivot, get_infos=get_infos) |
| |
| LU, pivots, infos = torch._lu_with_info(self, pivot=pivot, check_errors=(not get_infos)) |
| if get_infos: |
| return LU, pivots, infos |
| else: |
| return LU, pivots |
| |
| def stft(self, n_fft: int, hop_length: Optional[int] = None, |
| win_length: Optional[int] = None, window: 'Optional[Tensor]' = None, |
| center: bool = True, pad_mode: str = 'reflect', normalized: bool = False, |
| onesided: Optional[bool] = None, return_complex: Optional[bool] = None): |
| r"""See :func:`torch.stft` |
| |
| .. warning:: |
| This function changed signature at version 0.4.1. Calling with |
| the previous signature may cause error or return incorrect result. |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function( |
| Tensor.stft, (self,), self, n_fft, hop_length=hop_length, |
| win_length=win_length, window=window, center=center, pad_mode=pad_mode, normalized=normalized, |
| onesided=onesided, return_complex=return_complex |
| ) |
| return torch.stft(self, n_fft, hop_length, win_length, window, center, |
| pad_mode, normalized, onesided, return_complex=return_complex) |
| |
| def istft(self, n_fft: int, hop_length: Optional[int] = None, |
| win_length: Optional[int] = None, window: 'Optional[Tensor]' = None, |
| center: bool = True, normalized: bool = False, |
| onesided: Optional[bool] = None, length: Optional[int] = None, |
| return_complex: bool = False): |
| r"""See :func:`torch.istft`""" |
| if has_torch_function_unary(self): |
| return handle_torch_function( |
| Tensor.istft, (self,), self, n_fft, hop_length=hop_length, win_length=win_length, |
| window=window, center=center, normalized=normalized, onesided=onesided, length=length, |
| return_complex=return_complex |
| ) |
| return torch.istft(self, n_fft, hop_length, win_length, window, center, |
| normalized, onesided, length, return_complex=return_complex) |
| |
| def resize(self, *sizes): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.resize, (self,), self, *sizes) |
| warnings.warn("non-inplace resize is deprecated") |
| from torch.autograd._functions import Resize |
| return Resize.apply(self, sizes) |
| |
| def resize_as(self, tensor): |
| if has_torch_function_variadic(self, tensor): |
| return handle_torch_function(Tensor.resize_as, (self, tensor), self, tensor) |
| warnings.warn("non-inplace resize_as is deprecated") |
| from torch.autograd._functions import Resize |
| return Resize.apply(self, tensor.size()) |
| |
| def split(self, split_size, dim=0): |
| r"""See :func:`torch.split` |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.split, (self,), self, split_size, dim=dim) |
| if isinstance(split_size, int): |
| return super(Tensor, self).split(split_size, dim) |
| elif isinstance(split_size, Tensor): |
| try: |
| split_size = int(split_size) |
| return super(Tensor, self).split(split_size, dim) |
| except ValueError: |
| return super(Tensor, self).split_with_sizes(split_size, dim) |
| else: |
| return super(Tensor, self).split_with_sizes(split_size, dim) |
| |
| def unique(self, sorted=True, return_inverse=False, return_counts=False, dim=None): |
| r"""Returns the unique elements of the input tensor. |
| |
| See :func:`torch.unique` |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function( |
| Tensor.unique, (self,), self, sorted=sorted, return_inverse=return_inverse, |
| return_counts=return_counts, dim=dim |
| ) |
| return torch.unique(self, sorted=sorted, return_inverse=return_inverse, return_counts=return_counts, dim=dim) |
| |
| def unique_consecutive(self, return_inverse=False, return_counts=False, dim=None): |
| r"""Eliminates all but the first element from every consecutive group of equivalent elements. |
| |
| See :func:`torch.unique_consecutive` |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function( |
| Tensor.unique_consecutive, (self,), self, return_inverse=return_inverse, |
| return_counts=return_counts, dim=dim |
| ) |
| return torch.unique_consecutive(self, return_inverse=return_inverse, return_counts=return_counts, dim=dim) |
| |
| @_handle_torch_function_and_wrap_type_error_to_not_implemented |
| def __rsub__(self, other): |
| return _C._VariableFunctions.rsub(self, other) |
| |
| @_handle_torch_function_and_wrap_type_error_to_not_implemented |
| def __rdiv__(self, other): |
| return self.reciprocal() * other |
| |
| __rtruediv__ = __rdiv__ |
| __itruediv__ = _C._TensorBase.__idiv__ |
| |
| __pow__ = _handle_torch_function_and_wrap_type_error_to_not_implemented(_C._TensorBase.pow) |
| __ipow__ = _handle_torch_function_and_wrap_type_error_to_not_implemented(_C._TensorBase.pow_) |
| |
| @_handle_torch_function_and_wrap_type_error_to_not_implemented |
| def __rmod__(self, other): |
| return torch.remainder(other, self) |
| |
| def __format__(self, format_spec): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__format__, (self,), self, format_spec) |
| if self.dim() == 0 and not self.is_meta: |
| return self.item().__format__(format_spec) |
| return object.__format__(self, format_spec) |
| |
| @_handle_torch_function_and_wrap_type_error_to_not_implemented |
| def __rpow__(self, other): |
| dtype = torch.result_type(other, self) |
| return torch.tensor(other, dtype=dtype, device=self.device) ** self |
| |
| @_handle_torch_function_and_wrap_type_error_to_not_implemented |
| def __floordiv__(self, other): |
| return torch.floor_divide(self, other) |
| |
| @_handle_torch_function_and_wrap_type_error_to_not_implemented |
| def __rfloordiv__(self, other): |
| return torch.floor_divide(other, self) |
| |
| @_handle_torch_function_and_wrap_type_error_to_not_implemented |
| def __rlshift__(self, other): |
| return torch.bitwise_left_shift(other, self) |
| |
| @_handle_torch_function_and_wrap_type_error_to_not_implemented |
| def __rrshift__(self, other): |
| return torch.bitwise_right_shift(other, self) |
| |
| @_handle_torch_function_and_wrap_type_error_to_not_implemented |
| def __rmatmul__(self, other): |
| return torch.matmul(other, self) |
| |
| __pos__ = _C._TensorBase.positive |
| __neg__ = _C._TensorBase.neg |
| __abs__ = _C._TensorBase.abs |
| |
| def __len__(self): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__len__, (self,), self) |
| if self.dim() == 0: |
| raise TypeError("len() of a 0-d tensor") |
| if torch._C._get_tracing_state(): |
| warnings.warn('Using len to get tensor shape might cause the trace to be incorrect. ' |
| 'Recommended usage would be tensor.shape[0]. ' |
| 'Passing a tensor of different shape might lead to errors or silently give ' |
| 'incorrect results.', category=torch.jit.TracerWarning, stacklevel=2) |
| return self.shape[0] |
| |
| def __iter__(self): |
| # NB: we use 'imap' and not 'map' here, so that in Python 2 we get a |
| # generator and don't eagerly perform all the indexes. This could |
| # save us work, and also helps keep trace ordering deterministic |
| # (e.g., if you zip(*hiddens), the eager map will force all the |
| # indexes of hiddens[0] before hiddens[1], while the generator |
| # map will interleave them.) |
| # NB: We have intentionally skipped __torch_function__ dispatch here. |
| # See gh-54457 |
| if self.dim() == 0: |
| raise TypeError('iteration over a 0-d tensor') |
| if torch._C._get_tracing_state(): |
| warnings.warn('Iterating over a tensor might cause the trace to be incorrect. ' |
| 'Passing a tensor of different shape won\'t change the number of ' |
| 'iterations executed (and might lead to errors or silently give ' |
| 'incorrect results).', category=torch.jit.TracerWarning, stacklevel=2) |
| return iter(self.unbind(0)) |
| |
| def __hash__(self): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__hash__, (self,), self) |
| return id(self) |
| |
| def __dir__(self): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__dir__, (self,), self) |
| tensor_methods = dir(self.__class__) |
| tensor_methods.remove('volatile') # deprecated |
| attrs = list(self.__dict__.keys()) |
| keys = tensor_methods + attrs |
| |
| # property only available dense, cuda tensors |
| if (not self.is_cuda) or self.is_sparse: |
| keys.remove("__cuda_array_interface__") |
| |
| return sorted(keys) |
| |
| # Numpy array interface, to support `numpy.asarray(tensor) -> ndarray` |
| __array_priority__ = 1000 # prefer Tensor ops over numpy ones |
| |
| def __array__(self, dtype=None): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__array__, (self,), self, dtype=dtype) |
| if dtype is None: |
| return self.numpy() |
| else: |
| return self.numpy().astype(dtype, copy=False) |
| |
| # Wrap Numpy array again in a suitable tensor when done, to support e.g. |
| # `numpy.sin(tensor) -> tensor` or `numpy.greater(tensor, 0) -> ByteTensor` |
| def __array_wrap__(self, array): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__array_wrap__, (self,), self, array=array) |
| if array.dtype == bool: |
| # Workaround, torch has no built-in bool tensor |
| array = array.astype('uint8') |
| return torch.from_numpy(array) |
| |
| def __contains__(self, element): |
| r"""Check if `element` is present in tensor |
| |
| Args: |
| element (Tensor or scalar): element to be checked |
| for presence in current tensor" |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__contains__, (self,), self, element) |
| if isinstance(element, (torch.Tensor, Number)): |
| # type hint doesn't understand the __contains__ result array |
| return (element == self).any().item() # type: ignore[union-attr] |
| |
| raise RuntimeError( |
| "Tensor.__contains__ only supports Tensor or scalar, but you passed in a %s." % |
| type(element) |
| ) |
| |
| @property |
| def __cuda_array_interface__(self): |
| """Array view description for cuda tensors. |
| |
| See: |
| https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html |
| """ |
| if has_torch_function_unary(self): |
| # TODO mypy doesn't support @property, see: https://github.com/python/mypy/issues/6185 |
| return handle_torch_function(Tensor.__cuda_array_interface__.__get__, (self,), self) # type: ignore[attr-defined] |
| |
| # raise AttributeError for unsupported tensors, so that |
| # hasattr(cpu_tensor, "__cuda_array_interface__") is False. |
| if not self.is_cuda: |
| raise AttributeError( |
| "Can't get __cuda_array_interface__ on non-CUDA tensor type: %s " |
| "If CUDA data is required use tensor.cuda() to copy tensor to device memory." % |
| self.type() |
| ) |
| |
| if self.is_sparse: |
| raise AttributeError( |
| "Can't get __cuda_array_interface__ on sparse type: %s " |
| "Use Tensor.to_dense() to convert to a dense tensor first." % |
| self.type() |
| ) |
| |
| # RuntimeError, matching tensor.__array__() behavior. |
| if self.requires_grad: |
| raise RuntimeError( |
| "Can't get __cuda_array_interface__ on Variable that requires grad. " |
| "If gradients aren't required, use var.detach() to get Variable that doesn't require grad." |
| ) |
| |
| # CUDA devices are little-endian and tensors are stored in native byte |
| # order. 1-byte entries are endian-agnostic. |
| typestr = { |
| torch.complex64: "<c8", |
| torch.complex128: "<c16", |
| torch.float16: "<f2", |
| torch.float32: "<f4", |
| torch.float64: "<f8", |
| torch.uint8: "|u1", |
| torch.int8: "|i1", |
| torch.int16: "<i2", |
| torch.int32: "<i4", |
| torch.int64: "<i8", |
| }[self.dtype] |
| |
| itemsize = self.storage().element_size() |
| |
| shape = tuple(self.shape) |
| if self.is_contiguous(): |
| # __cuda_array_interface__ v2 requires the strides to be omitted |
| # (either not set or set to None) for C-contiguous arrays. |
| strides = None |
| else: |
| strides = tuple(s * itemsize for s in self.stride()) |
| data_ptr = self.data_ptr() if self.numel() > 0 else 0 |
| data = (data_ptr, False) # read-only is false |
| |
| return dict(typestr=typestr, shape=shape, strides=strides, data=data, version=2) |
| |
| def storage_type(self): |
| r"""storage_type() -> type |
| |
| Returns the type of the underlying storage. |
| |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.storage_type, (self,), self) |
| |
| return self.storage()._get_legacy_storage_class() |
| |
| def refine_names(self, *names): |
| r"""Refines the dimension names of :attr:`self` according to :attr:`names`. |
| |
| Refining is a special case of renaming that "lifts" unnamed dimensions. |
| A ``None`` dim can be refined to have any name; a named dim can only be |
| refined to have the same name. |
| |
| Because named tensors can coexist with unnamed tensors, refining names |
| gives a nice way to write named-tensor-aware code that works with both |
| named and unnamed tensors. |
| |
| :attr:`names` may contain up to one Ellipsis (``...``). |
| The Ellipsis is expanded greedily; it is expanded in-place to fill |
| :attr:`names` to the same length as ``self.dim()`` using names from the |
| corresponding indices of ``self.names``. |
| |
| Python 2 does not support Ellipsis but one may use a string literal |
| instead (``'...'``). |
| |
| Args: |
| names (iterable of str): The desired names of the output tensor. May |
| contain up to one Ellipsis. |
| |
| Examples:: |
| |
| >>> imgs = torch.randn(32, 3, 128, 128) |
| >>> named_imgs = imgs.refine_names('N', 'C', 'H', 'W') |
| >>> named_imgs.names |
| ('N', 'C', 'H', 'W') |
| |
| >>> tensor = torch.randn(2, 3, 5, 7, 11) |
| >>> tensor = tensor.refine_names('A', ..., 'B', 'C') |
| >>> tensor.names |
| ('A', None, None, 'B', 'C') |
| |
| .. warning:: |
| The named tensor API is experimental and subject to change. |
| |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.refine_names, (self,), self, *names) |
| names = resolve_ellipsis(names, self.names, 'refine_names') |
| return super(Tensor, self).refine_names(names) |
| |
| def align_to(self, *names): |
| r"""Permutes the dimensions of the :attr:`self` tensor to match the order |
| specified in :attr:`names`, adding size-one dims for any new names. |
| |
| All of the dims of :attr:`self` must be named in order to use this method. |
| The resulting tensor is a view on the original tensor. |
| |
| All dimension names of :attr:`self` must be present in :attr:`names`. |
| :attr:`names` may contain additional names that are not in ``self.names``; |
| the output tensor has a size-one dimension for each of those new names. |
| |
| :attr:`names` may contain up to one Ellipsis (``...``). |
| The Ellipsis is expanded to be equal to all dimension names of :attr:`self` |
| that are not mentioned in :attr:`names`, in the order that they appear |
| in :attr:`self`. |
| |
| Python 2 does not support Ellipsis but one may use a string literal |
| instead (``'...'``). |
| |
| Args: |
| names (iterable of str): The desired dimension ordering of the |
| output tensor. May contain up to one Ellipsis that is expanded |
| to all unmentioned dim names of :attr:`self`. |
| |
| Examples:: |
| |
| >>> tensor = torch.randn(2, 2, 2, 2, 2, 2) |
| >>> named_tensor = tensor.refine_names('A', 'B', 'C', 'D', 'E', 'F') |
| |
| # Move the F and E dims to the front while keeping the rest in order |
| >>> named_tensor.align_to('F', 'E', ...) |
| |
| .. warning:: |
| The named tensor API is experimental and subject to change. |
| |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.align_to, (self,), self, *names) |
| ellipsis_idx = single_ellipsis_index(names, 'align_to') |
| if ellipsis_idx is None: |
| return super(Tensor, self).align_to(names) |
| return super(Tensor, self).align_to( |
| [name for name in names if not is_ellipsis(name)], |
| ellipsis_idx) |
| |
| def unflatten(self, dim, sizes): |
| r"""Expands the dimension :attr:`dim` of the :attr:`self` tensor over multiple dimensions |
| of sizes given by :attr:`sizes`. |
| |
| * :attr:`sizes` is the new shape of the unflattened dimension and it can be a `Tuple[int]` as well |
| as `torch.Size` if :attr:`self` is a `Tensor`, or `namedshape` (Tuple[(name: str, size: int)]) |
| if :attr:`self` is a `NamedTensor`. The total number of elements in sizes must match the number |
| of elements in the original dim being unflattened. |
| |
| Args: |
| dim (Union[int, str]): Dimension to unflatten |
| sizes (Union[Tuple[int] or torch.Size, Tuple[Tuple[str, int]]]): New shape of the unflattened dimension |
| |
| Examples: |
| >>> torch.randn(3, 4, 1).unflatten(1, (2, 2)).shape |
| torch.Size([3, 2, 2, 1]) |
| >>> torch.randn(3, 4, 1).unflatten(1, (-1, 2)).shape # the size -1 is inferred from the size of dimension 1 |
| torch.Size([3, 2, 2, 1]) |
| >>> torch.randn(2, 4, names=('A', 'B')).unflatten('B', (('B1', 2), ('B2', 2))) |
| tensor([[[-1.1772, 0.0180], |
| [ 0.2412, 0.1431]], |
| [[-1.1819, -0.8899], |
| [ 1.5813, 0.2274]]], names=('A', 'B1', 'B2')) |
| >>> torch.randn(2, names=('A',)).unflatten('A', (('B1', -1), ('B2', 1))) |
| tensor([[-0.8591], |
| [ 0.3100]], names=('B1', 'B2')) |
| |
| .. warning:: |
| The named tensor API is experimental and subject to change. |
| |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.unflatten, (self,), self, dim, sizes) |
| |
| if not sizes: |
| raise RuntimeError("unflatten: sizes must be non-empty") |
| |
| names = None |
| if isinstance(sizes, OrderedDict) or (isinstance(sizes, (tuple, list)) and isinstance(sizes[0], (tuple, list))): |
| names, sizes = unzip_namedshape(sizes) |
| return super(Tensor, self).unflatten(dim, sizes, names) |
| |
| |
| def rename_(self, *names, **rename_map): |
| """In-place version of :meth:`~Tensor.rename`.""" |
| |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.rename_, (self,), self, *names, **rename_map) |
| |
| # Note [rename_ / rename API] |
| # The Python API for these is different from the C++ API. In Python: |
| # 1) tensor.rename(*names) takes a vararglist of names |
| # 2) tensor.rename(**rename_map) takes a map of names to rename. |
| # C++ is static, making it difficult to implement similar behavior. |
| return update_names(self, names, rename_map, inplace=True) |
| |
| def rename(self, *names, **rename_map): |
| """Renames dimension names of :attr:`self`. |
| |
| There are two main usages: |
| |
| ``self.rename(**rename_map)`` returns a view on tensor that has dims |
| renamed as specified in the mapping :attr:`rename_map`. |
| |
| ``self.rename(*names)`` returns a view on tensor, renaming all |
| dimensions positionally using :attr:`names`. |
| Use ``self.rename(None)`` to drop names on a tensor. |
| |
| One cannot specify both positional args :attr:`names` and keyword args |
| :attr:`rename_map`. |
| |
| Examples:: |
| |
| >>> imgs = torch.rand(2, 3, 5, 7, names=('N', 'C', 'H', 'W')) |
| >>> renamed_imgs = imgs.rename(N='batch', C='channels') |
| >>> renamed_imgs.names |
| ('batch', 'channels', 'H', 'W') |
| |
| >>> renamed_imgs = imgs.rename(None) |
| >>> renamed_imgs.names |
| (None,) |
| |
| >>> renamed_imgs = imgs.rename('batch', 'channel', 'height', 'width') |
| >>> renamed_imgs.names |
| ('batch', 'channel', 'height', 'width') |
| |
| .. warning:: |
| The named tensor API is experimental and subject to change. |
| |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.rename, (self,), self, *names, **rename_map) |
| |
| # See Note [rename_ / rename API] |
| return update_names(self, names, rename_map, inplace=False) |
| |
| def to_sparse_coo(self): |
| """ Convert a tensor to :ref:`coordinate format <sparse-coo-docs>`. |
| |
| Examples:: |
| |
| >>> dense = torch.randn(5, 5) |
| >>> sparse = dense.to_sparse_coo() |
| >>> sparse._nnz() |
| 25 |
| |
| """ |
| return self.to_sparse() |
| |
| def _update_names(self, names, inplace): |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor._update_names, (self,), self, names, inplace) |
| |
| # See Note [rename_ / rename API] |
| if inplace: |
| return super(Tensor, self).rename_(names) |
| else: |
| return super(Tensor, self).rename(names) |
| |
| @classmethod |
| def __torch_function__(cls, func, types, args=(), kwargs=None): |
| """ |
| This __torch_function__ implementation wraps subclasses such that |
| methods called on subclasses return a subclass instance instead of |
| a ``torch.Tensor`` instance. |
| |
| One corollary to this is that you need coverage for torch.Tensor |
| methods if implementing __torch_function__ for subclasses. |
| |
| We recommend always calling ``super().__torch_function__`` as the base |
| case when doing the above. |
| |
| While not mandatory, we recommend making `__torch_function__` a classmethod. |
| """ |
| if kwargs is None: |
| kwargs = {} |
| |
| if not all(issubclass(cls, t) for t in types): |
| return NotImplemented |
| |
| with _C.DisableTorchFunction(): |
| ret = func(*args, **kwargs) |
| if func in get_default_nowrap_functions(): |
| return ret |
| else: |
| return _convert(ret, cls) |
| |
| __torch_dispatch__ = _C._disabled_torch_dispatch_impl |
| |
| def __dlpack__(self, stream=None): |
| """ |
| Creates a DLpack `capsule https://data-apis.org/array-api/latest/design_topics/data_interchange.html#data-interchange`_ |
| of the current tensor to be exported to other libraries. |
| |
| This function will be called from the `from_dlpack` method |
| of the library that will consume the capsule. `from_dlpack` passes the current |
| stream to this method as part of the specification. |
| |
| Args: |
| stream (integer or None): An optional Python integer representing a |
| pointer to a CUDA stream. The current stream is synchronized with |
| this stream before the capsule is created, and since the capsule |
| shares its storage with the tensor this make it safe to access from |
| both streams. If None or -1 is passed then no synchronization is performed. |
| """ |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__dlpack__, (self,), self, stream) |
| |
| # DLPack capsules can't capture all of PyTorch's semantics, |
| # so we prohibit exporting tensors that would lose their properties like |
| # requires_grad and having the conjugate bit set. |
| if self.requires_grad: |
| raise RuntimeError('Can\'t export tensors that require gradient, use tensor.detach()') |
| if self.is_conj(): |
| raise RuntimeError('Can\'t export tensors with the conjugate bit set') |
| if self.layout != torch.strided: |
| raise RuntimeError('Can\'t export tensors with layout other than torch.strided') |
| |
| if stream is not None and type(stream) is not int: |
| # Stream pointers in CUDA/ROCm are uniquely numbered and can |
| # be retrieved from their integer value. |
| raise TypeError('stream must be ``int`` or ``none``') |
| elif stream is not None and stream != -1: |
| if self.device.type == 'cuda': |
| stream = torch.cuda.ExternalStream(stream) |
| # Only synchronize on different streams |
| if stream != torch.cuda.current_stream: |
| event = torch.cuda.Event() |
| event.record(torch.cuda.current_stream()) |
| stream.wait_event(event) |
| return torch.to_dlpack(self) |
| |
| def __dlpack_device__(self) -> Tuple[enum.IntEnum, int]: |
| # Avoid circular import |
| from torch.utils.dlpack import DLDeviceType |
| if has_torch_function_unary(self): |
| return handle_torch_function(Tensor.__dlpack_device__, (self,), self) |
| idx = self.device.index if self.device.index is not None else 0 |
| if self.device.type == 'cuda' and torch.version.hip is not None: |
| device_type = DLDeviceType.kDLROCM |
| elif self.device.type == 'cpu' and self.is_pinned(): |
| device_type = DLDeviceType.kDLCPUPinned |
| elif self.device.type == 'cuda': |
| device_type = DLDeviceType.kDLGPU |
| elif self.device.type == 'cpu': |
| device_type = DLDeviceType.kDLCPU |
| else: |
| raise ValueError('Unknown device type {} for Dlpack'.format(self.device.type)) |
| return (device_type, idx) |
| |
| __module__ = 'torch' |
| |
| def _convert(ret, cls): |
| if cls is Tensor: |
| return ret |
| |
| if isinstance(ret, Tensor) and not isinstance(ret, cls): |
| ret = ret.as_subclass(cls) |
| |
| if isinstance(ret, (tuple, list)): |
| # Also handles things like namedtuples |
| ret = type(ret)(_convert(r, cls) for r in ret) |
| |
| return ret |
