980 lines
42 KiB
Python
980 lines
42 KiB
Python
from collections import OrderedDict
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import functools
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from numbers import Number
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from typing import Any, Dict, Optional, Tuple, Union
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import warnings
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import weakref
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import torch
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import torch._C as _C
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from torch._namedtensor_internals import (
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update_names, check_serializing_named_tensor, resolve_ellipsis,
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unzip_namedshape, single_ellipsis_index, is_ellipsis)
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from torch.overrides import (
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has_torch_function, has_torch_function_unary, has_torch_function_variadic,
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handle_torch_function)
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import torch.utils.hooks as hooks
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def _wrap_type_error_to_not_implemented(f):
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# functools.wraps doesn't work well with methods in python 2
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method_assignments = ('__name__', '__doc__')
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assigned = functools.WRAPPER_ASSIGNMENTS
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@functools.wraps(f, assigned=assigned)
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def wrapped(*args, **kwargs):
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if has_torch_function(args):
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return handle_torch_function(wrapped, args, *args, **kwargs)
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try:
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return f(*args, **kwargs)
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except TypeError:
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return NotImplemented
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return wrapped
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def _rebuild_from_type(func, type, args, dict):
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if type is Tensor:
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return func(*args)
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ret = func(*args).as_subclass(type)
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ret.__dict__ = dict
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return ret
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# NB: If you subclass Tensor, and want to share the subclassed class
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# across processes, you must also update torch/multiprocessing/reductions.py
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# to define a ForkingPickler serialization mode for the class.
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#
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# NB: If you add a new method to Tensor, you must update
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# torch/__init__.py.in to add a type annotation for your method;
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# otherwise, it will not show up in autocomplete.
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class Tensor(torch._C._TensorBase):
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def __deepcopy__(self, memo):
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.__deepcopy__, (self,), self, memo)
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if not self.is_leaf:
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raise RuntimeError("Only Tensors created explicitly by the user "
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"(graph leaves) support the deepcopy protocol at the moment")
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if id(self) in memo:
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return memo[id(self)]
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with torch.no_grad():
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if self.is_sparse or self.device.type == 'xla':
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new_tensor = self.clone()
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else:
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new_storage = self.storage().__deepcopy__(memo)
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if self.is_quantized:
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# quantizer_params can be different type based on torch attribute
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quantizer_params: Union[Tuple[torch.qscheme, float, int], Tuple[torch.qscheme, Tensor, Tensor, int]]
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if self.qscheme() == torch.per_tensor_affine:
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quantizer_params = self.qscheme(), self.q_scale(), self.q_zero_point()
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elif self.qscheme() in (torch.per_channel_affine, torch.per_channel_affine_float_qparams):
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quantizer_params = self.qscheme(), \
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self.q_per_channel_scales(), \
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self.q_per_channel_zero_points(), \
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self.q_per_channel_axis()
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else:
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raise RuntimeError(f"Unsupported qscheme {self.qscheme()} in deepcopy")
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new_tensor = torch._utils._rebuild_qtensor(
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new_storage,
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self.storage_offset(),
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self.size(),
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self.stride(),
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quantizer_params,
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self.requires_grad,
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self._backward_hooks)
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else:
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new_tensor = self.new()
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new_tensor.set_(new_storage, self.storage_offset(), self.size(), self.stride())
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new_tensor.requires_grad = self.requires_grad
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if self.grad is not None:
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new_tensor.grad = self.grad.__deepcopy__(memo)
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memo[id(self)] = new_tensor
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return new_tensor
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def __reduce_ex__(self, proto):
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if type(self) is Tensor:
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return self._reduce_ex_internal(proto)
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relevant_args = (self,)
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from torch.overrides import has_torch_function, handle_torch_function
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if type(self) is not Tensor and has_torch_function(relevant_args):
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return handle_torch_function(Tensor.__reduce_ex__, relevant_args, self, proto)
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func, args = self._reduce_ex_internal(proto)
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return (_rebuild_from_type, (func, type(self), args, self.__dict__))
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def _reduce_ex_internal(self, proto):
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.__reduce_ex__, (self,), self, proto)
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check_serializing_named_tensor(self)
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# See Note [Don't serialize hooks]
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torch.utils.hooks.warn_if_has_hooks(self)
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backward_hooks: Dict[Any, Any] = OrderedDict()
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# Note: Numpy array is chosen to be the rebuild component for XLA Tensor.
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# We considered a few options:
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# 1. CPU tensor can't be used here.
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# Otherwise in torch.load CPU storage is reconstructed with randomly
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# initialized data, moved onto XLA device, and then storage is updated
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# to the serialized content. This works perfectly for CPU/CUDA but not XLA.
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# XLA tensor is disconnected with storage so it doesn't get the update.
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# 2. Python list is not a good fit due to performance reason.
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# `tolist()` converts every single element in the tensor into python objects
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# and serialize them one by one.
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if self.device.type == 'xla':
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arg_xla = (self.cpu().numpy(),
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self.dtype,
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str(self.device),
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self.requires_grad)
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return (torch._utils._rebuild_xla_tensor, arg_xla)
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if self.is_quantized:
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# quantizer_params can be different type based on torch attribute
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quantizer_params: Union[Tuple[torch.qscheme, float, int], Tuple[Any, Tensor, Tensor, int]]
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if self.qscheme() == torch.per_tensor_affine:
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quantizer_params = (torch.per_tensor_affine,
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self.q_scale(),
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self.q_zero_point())
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elif self.qscheme() in (torch.per_channel_affine, torch.per_channel_affine_float_qparams):
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# convert scales and zero points to tuple to avoid recursive calls
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# when/if we get multi-axis quantized tensors in the future, the shape
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# is recoverable from the main tensor shape
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quantizer_params = (torch.per_channel_affine,
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self.q_per_channel_scales(),
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self.q_per_channel_zero_points(),
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self.q_per_channel_axis())
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else:
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raise RuntimeError(f"Serialization is not supported for tensors of type {self.qscheme()}")
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args_qtensor = (self.storage(),
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self.storage_offset(),
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tuple(self.size()),
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self.stride(),
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quantizer_params,
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self.requires_grad,
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backward_hooks)
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return (torch._utils._rebuild_qtensor, args_qtensor)
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elif self.is_sparse:
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if self.layout == torch.sparse_coo:
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args_sparse = (self.layout,
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(self._indices(),
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self._values(),
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self.size()))
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else:
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raise NotImplementedError(
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'sparse tensor __reduce_ex__ for layout `%s`' % (self.layout))
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return (torch._utils._rebuild_sparse_tensor, args_sparse)
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else:
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args = (self.storage(),
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self.storage_offset(),
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tuple(self.size()),
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self.stride(),
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self.requires_grad,
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backward_hooks) # previously was self._backward_hooks
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return (torch._utils._rebuild_tensor_v2, args)
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def __setstate__(self, state):
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.__setstate__, (self,), self, state)
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# Warning: this method is NOT called when you torch.load() a tensor;
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# that is managed by _rebuild_tensor_v2
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if not self.is_leaf:
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raise RuntimeError('__setstate__ can be only called on leaf Tensors')
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if len(state) == 4:
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# legacy serialization of Tensor
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self.set_(*state)
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return
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elif len(state) == 5:
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# legacy serialization of Variable
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self.data = state[0]
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state = (state[3], state[4], state[2])
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# The setting of _backward_hooks is expected to be a no-op.
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# See Note [Don't serialize hooks]
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self.requires_grad, _, self._backward_hooks = state
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def __repr__(self):
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.__repr__, (self,), self)
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# All strings are unicode in Python 3.
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return torch._tensor_str._str(self)
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def backward(self, gradient=None, retain_graph=None, create_graph=False, inputs=None):
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r"""Computes the gradient of current tensor w.r.t. graph leaves.
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The graph is differentiated using the chain rule. If the tensor is
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non-scalar (i.e. its data has more than one element) and requires
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gradient, the function additionally requires specifying ``gradient``.
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It should be a tensor of matching type and location, that contains
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the gradient of the differentiated function w.r.t. ``self``.
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This function accumulates gradients in the leaves - you might need to zero
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``.grad`` attributes or set them to ``None`` before calling it.
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See :ref:`Default gradient layouts<default-grad-layouts>`
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for details on the memory layout of accumulated gradients.
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.. note::
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If you run any forward ops, create ``gradient``, and/or call ``backward``
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in a user-specified CUDA stream context, see
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:ref:`Stream semantics of backward passes<bwd-cuda-stream-semantics>`.
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Args:
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gradient (Tensor or None): Gradient w.r.t. the
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tensor. If it is a tensor, it will be automatically converted
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to a Tensor that does not require grad unless ``create_graph`` is True.
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None values can be specified for scalar Tensors or ones that
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don't require grad. If a None value would be acceptable then
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this argument is optional.
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retain_graph (bool, optional): If ``False``, the graph used to compute
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the grads will be freed. Note that in nearly all cases setting
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this option to True is not needed and often can be worked around
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in a much more efficient way. Defaults to the value of
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``create_graph``.
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create_graph (bool, optional): If ``True``, graph of the derivative will
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be constructed, allowing to compute higher order derivative
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products. Defaults to ``False``.
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inputs (sequence of Tensor): Inputs w.r.t. which the gradient will be
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accumulated into ``.grad``. All other Tensors will be ignored. If not
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provided, the gradient is accumulated into all the leaf Tensors that were
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used to compute the attr::tensors. All the provided inputs must be leaf
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Tensors.
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"""
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if has_torch_function_unary(self):
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return handle_torch_function(
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Tensor.backward,
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(self,),
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self,
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gradient=gradient,
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retain_graph=retain_graph,
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create_graph=create_graph,
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inputs=inputs)
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torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
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def register_hook(self, hook):
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r"""Registers a backward hook.
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The hook will be called every time a gradient with respect to the
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Tensor is computed. The hook should have the following signature::
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hook(grad) -> Tensor or None
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The hook should not modify its argument, but it can optionally return
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a new gradient which will be used in place of :attr:`grad`.
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This function returns a handle with a method ``handle.remove()``
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that removes the hook from the module.
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Example::
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>>> v = torch.tensor([0., 0., 0.], requires_grad=True)
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>>> h = v.register_hook(lambda grad: grad * 2) # double the gradient
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>>> v.backward(torch.tensor([1., 2., 3.]))
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>>> v.grad
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2
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4
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6
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[torch.FloatTensor of size (3,)]
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>>> h.remove() # removes the hook
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"""
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.register_hook, (self,), self, hook)
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if not self.requires_grad:
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raise RuntimeError("cannot register a hook on a tensor that "
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"doesn't require gradient")
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if self._backward_hooks is None:
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self._backward_hooks = OrderedDict()
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if self.grad_fn is not None:
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self.grad_fn._register_hook_dict(self)
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handle = hooks.RemovableHandle(self._backward_hooks)
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self._backward_hooks[handle.id] = hook
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return handle
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def reinforce(self, reward):
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def trim(str):
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return '\n'.join([line.strip() for line in str.split('\n')])
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raise RuntimeError(trim(r"""reinforce() was removed.
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Use torch.distributions instead.
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See https://pytorch.org/docs/master/distributions.html
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Instead of:
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probs = policy_network(state)
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action = probs.multinomial()
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next_state, reward = env.step(action)
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action.reinforce(reward)
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action.backward()
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Use:
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probs = policy_network(state)
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# NOTE: categorical is equivalent to what used to be called multinomial
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m = torch.distributions.Categorical(probs)
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action = m.sample()
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next_state, reward = env.step(action)
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loss = -m.log_prob(action) * reward
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loss.backward()
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"""))
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detach = _C._add_docstr(_C._TensorBase.detach, r"""
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Returns a new Tensor, detached from the current graph.
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The result will never require gradient.
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.. note::
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Returned Tensor shares the same storage with the original one.
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In-place modifications on either of them will be seen, and may trigger
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errors in correctness checks.
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IMPORTANT NOTE: Previously, in-place size / stride / storage changes
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(such as `resize_` / `resize_as_` / `set_` / `transpose_`) to the returned tensor
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also update the original tensor. Now, these in-place changes will not update the
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original tensor anymore, and will instead trigger an error.
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For sparse tensors:
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In-place indices / values changes (such as `zero_` / `copy_` / `add_`) to the
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returned tensor will not update the original tensor anymore, and will instead
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trigger an error.
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""")
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detach_ = _C._add_docstr(_C._TensorBase.detach_, r"""
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Detaches the Tensor from the graph that created it, making it a leaf.
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Views cannot be detached in-place.
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""")
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def retain_grad(self):
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r"""Enables .grad attribute for non-leaf Tensors."""
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.retain_grad, (self,), self)
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if not self.requires_grad:
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raise RuntimeError("can't retain_grad on Tensor that has requires_grad=False")
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if self.is_leaf: # no-op for leaves
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return
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if hasattr(self, 'retains_grad'):
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return
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weak_self = weakref.ref(self)
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def retain_grad_hook(grad):
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var = weak_self()
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if var is None:
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return
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if var._grad is None:
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if grad.is_sparse:
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var._grad = grad.clone()
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else:
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var._grad = grad.clone(memory_format=torch.contiguous_format)
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else:
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var._grad = var._grad + grad
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self.register_hook(retain_grad_hook)
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self.retains_grad = True
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def is_shared(self):
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r"""Checks if tensor is in shared memory.
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This is always ``True`` for CUDA tensors.
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"""
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.is_shared, (self,), self)
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return self.storage().is_shared()
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def share_memory_(self):
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r"""Moves the underlying storage to shared memory.
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This is a no-op if the underlying storage is already in shared memory
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and for CUDA tensors. Tensors in shared memory cannot be resized.
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"""
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.share_memory_, (self,), self)
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self.storage().share_memory_()
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return self
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def __reversed__(self):
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r"""Reverses the tensor along dimension 0."""
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.__reversed__, (self,), self)
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if self.dim() == 0:
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return self
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else:
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return self.flip(0)
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def norm(self, p="fro", dim=None, keepdim=False, dtype=None):
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r"""See :func:`torch.norm`"""
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.norm, (self,), self, p=p, dim=dim, keepdim=keepdim, dtype=dtype)
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return torch.norm(self, p, dim, keepdim, dtype=dtype)
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def lu(self, pivot=True, get_infos=False):
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r"""See :func:`torch.lu`"""
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# If get_infos is True, then we don't need to check for errors and vice versa
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if has_torch_function_unary(self):
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return handle_torch_function(Tensor.lu, (self,), self, pivot=pivot, get_infos=get_infos)
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if not torch._jit_internal.is_scripting():
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if self.requires_grad:
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if not (self.size(-2) == self.size(-1) and self.dtype.is_floating_point):
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raise ValueError(
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'lu.backward works only with batches of squared full-rank matrices'
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' of floating types.'
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)
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from torch._autograd_functions import _LU
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LU, pivots, infos = _LU.apply(self, pivot, get_infos)
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if get_infos:
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return LU, pivots, infos
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else:
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return LU, pivots
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else:
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if self.requires_grad:
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raise RuntimeError(
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'Script and require gradients is not supported at the moment.'
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'If you just want to do the forward, use .detach()'
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'on the input before calling the function.'
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)
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LU, pivots, infos = torch._lu_with_info(self, pivot=pivot, check_errors=(not get_infos))
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if get_infos:
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return LU, pivots, infos
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else:
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return LU, pivots
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def stft(self, n_fft: int, hop_length: Optional[int] = None,
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win_length: Optional[int] = None, window: 'Optional[Tensor]' = None,
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center: bool = True, pad_mode: str = 'reflect', normalized: bool = False,
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onesided: Optional[bool] = None, return_complex: Optional[bool] = None):
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r"""See :func:`torch.stft`
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.. warning::
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This function changed signature at version 0.4.1. Calling with
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the previous signature may cause error or return incorrect result.
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"""
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if has_torch_function_unary(self):
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return handle_torch_function(
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Tensor.stft, (self,), self, n_fft, hop_length=hop_length,
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win_length=win_length, window=window, center=center, pad_mode=pad_mode, normalized=normalized,
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onesided=onesided, return_complex=return_complex
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)
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return torch.stft(self, n_fft, hop_length, win_length, window, center,
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pad_mode, normalized, onesided, return_complex=return_complex)
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def istft(self, n_fft: int, hop_length: Optional[int] = None,
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win_length: Optional[int] = None, window: 'Optional[Tensor]' = None,
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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)
|
|
|
|
def __rsub__(self, other):
|
|
if has_torch_function_variadic(self, other):
|
|
return handle_torch_function(Tensor.__rsub__, (self, other), self, other)
|
|
return _C._VariableFunctions.rsub(self, other)
|
|
|
|
def __rdiv__(self, other):
|
|
if has_torch_function_variadic(self, other):
|
|
return handle_torch_function(Tensor.__rdiv__, (self, other), self, other)
|
|
return self.reciprocal() * other
|
|
|
|
__rtruediv__ = __rdiv__
|
|
__itruediv__ = _C._TensorBase.__idiv__
|
|
|
|
__pow__ = _C._TensorBase.pow
|
|
|
|
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:
|
|
return self.item().__format__(format_spec)
|
|
return object.__format__(self, format_spec)
|
|
|
|
def __ipow__(self, other): # type: ignore[misc]
|
|
if has_torch_function_variadic(self, other):
|
|
return handle_torch_function(Tensor.__ipow__, (self, other), self, other)
|
|
return NotImplemented
|
|
|
|
@_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
|
|
|
|
@_wrap_type_error_to_not_implemented
|
|
def __floordiv__(self, other):
|
|
return torch.floor_divide(self, other)
|
|
|
|
@_wrap_type_error_to_not_implemented
|
|
def __rfloordiv__(self, other):
|
|
return torch.floor_divide(other, self)
|
|
|
|
__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")
|
|
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.)
|
|
if has_torch_function_unary(self):
|
|
return handle_torch_function(Tensor.__iter__, (self,), self)
|
|
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=RuntimeWarning)
|
|
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)
|
|
if self.is_quantized:
|
|
warnings.warn('Only a small subset of methods are supported for quantized tensors.')
|
|
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 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(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'))
|
|
|
|
.. 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 _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)
|
|
|
|
@property
|
|
def grad(self):
|
|
"""
|
|
This attribute is ``None`` by default and becomes a Tensor the first time a call to
|
|
:func:`backward` computes gradients for ``self``.
|
|
The attribute will then contain the gradients computed and future calls to
|
|
:func:`backward` will accumulate (add) gradients into it.
|
|
"""
|
|
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.grad.__get__, (self,), self) # type: ignore[attr-defined]
|
|
|
|
if self.requires_grad and not hasattr(self, "retains_grad") and not self.is_leaf and self._grad is None:
|
|
warnings.warn("The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad "
|
|
"attribute won't be populated during autograd.backward(). If you indeed want the gradient "
|
|
"for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the "
|
|
"non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See "
|
|
"github.com/pytorch/pytorch/pull/30531 for more informations.", stacklevel=2)
|
|
return self._grad
|
|
|
|
@grad.setter
|
|
def grad(self, new_grad):
|
|
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.grad.__set__, (self,), self, new_grad) # type: ignore[attr-defined]
|
|
self._grad = new_grad
|
|
|
|
@grad.deleter
|
|
def grad(self):
|
|
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.grad.__delete__, (self,), self) # type: ignore[attr-defined]
|
|
del self._grad
|
|
|
|
@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)
|
|
return _convert(ret, cls)
|
|
|
|
__module__ = 'torch'
|
|
|
|
|
|
def _convert(ret, cls):
|
|
if cls is Tensor:
|
|
return ret
|
|
|
|
if isinstance(ret, Tensor):
|
|
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
|