mirror of
https://github.com/zebrajr/pytorch.git
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5b815d980e
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/32238 Differential Revision: D19416791 Pulled By: anjali411 fbshipit-source-id: 5aadc0a7a55af40d76f444ab7d7d47ec822f55a5
385 lines
9.8 KiB
ReStructuredText
385 lines
9.8 KiB
ReStructuredText
torch
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===================================
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.. automodule:: torch
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Tensors
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----------------------------------
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.. autofunction:: is_tensor
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.. autofunction:: is_storage
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.. autofunction:: is_floating_point
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.. autofunction:: set_default_dtype
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.. autofunction:: get_default_dtype
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.. autofunction:: set_default_tensor_type
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.. autofunction:: numel
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.. autofunction:: set_printoptions
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.. autofunction:: set_flush_denormal
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.. _tensor-creation-ops:
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Creation Ops
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~~~~~~~~~~~~~~~~~~~~~~
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.. note::
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Random sampling creation ops are listed under :ref:`random-sampling` and
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include:
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:func:`torch.rand`
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:func:`torch.rand_like`
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:func:`torch.randn`
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:func:`torch.randn_like`
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:func:`torch.randint`
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:func:`torch.randint_like`
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:func:`torch.randperm`
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You may also use :func:`torch.empty` with the :ref:`inplace-random-sampling`
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methods to create :class:`torch.Tensor` s with values sampled from a broader
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range of distributions.
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.. autofunction:: tensor
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.. autofunction:: sparse_coo_tensor
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.. autofunction:: as_tensor
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.. autofunction:: as_strided
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.. autofunction:: from_numpy
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.. autofunction:: zeros
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.. autofunction:: zeros_like
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.. autofunction:: ones
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.. autofunction:: ones_like
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.. autofunction:: arange
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.. autofunction:: range
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.. autofunction:: linspace
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.. autofunction:: logspace
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.. autofunction:: eye
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.. autofunction:: empty
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.. autofunction:: empty_like
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.. autofunction:: empty_strided
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.. autofunction:: full
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.. autofunction:: full_like
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.. autofunction:: quantize_per_tensor
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.. autofunction:: quantize_per_channel
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Indexing, Slicing, Joining, Mutating Ops
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: cat
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.. autofunction:: chunk
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.. autofunction:: gather
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.. autofunction:: index_select
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.. autofunction:: masked_select
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.. autofunction:: narrow
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.. autofunction:: nonzero
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.. autofunction:: reshape
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.. autofunction:: split
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.. autofunction:: squeeze
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.. autofunction:: stack
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.. autofunction:: t
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.. autofunction:: take
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.. autofunction:: transpose
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.. autofunction:: unbind
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.. autofunction:: unsqueeze
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.. autofunction:: where
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.. _generators:
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Generators
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----------------------------------
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.. autoclass:: torch._C.Generator
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:members:
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.. _random-sampling:
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Random sampling
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----------------------------------
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.. autofunction:: seed
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.. autofunction:: manual_seed
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.. autofunction:: initial_seed
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.. autofunction:: get_rng_state
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.. autofunction:: set_rng_state
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.. autoattribute:: torch.default_generator
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:annotation: Returns the default CPU torch.Generator
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.. The following doesn't actually seem to exist.
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https://github.com/pytorch/pytorch/issues/27780
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.. autoattribute:: torch.cuda.default_generators
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:annotation: If cuda is available, returns a tuple of default CUDA torch.Generator-s.
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The number of CUDA torch.Generator-s returned is equal to the number of
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GPUs available in the system.
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.. autofunction:: bernoulli
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.. autofunction:: multinomial
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.. autofunction:: normal
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.. autofunction:: poisson
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.. autofunction:: rand
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.. autofunction:: rand_like
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.. autofunction:: randint
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.. autofunction:: randint_like
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.. autofunction:: randn
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.. autofunction:: randn_like
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.. autofunction:: randperm
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.. _inplace-random-sampling:
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In-place random sampling
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~~~~~~~~~~~~~~~~~~~~~~~~
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There are a few more in-place random sampling functions defined on Tensors as well. Click through to refer to their documentation:
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- :func:`torch.Tensor.bernoulli_` - in-place version of :func:`torch.bernoulli`
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- :func:`torch.Tensor.cauchy_` - numbers drawn from the Cauchy distribution
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- :func:`torch.Tensor.exponential_` - numbers drawn from the exponential distribution
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- :func:`torch.Tensor.geometric_` - elements drawn from the geometric distribution
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- :func:`torch.Tensor.log_normal_` - samples from the log-normal distribution
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- :func:`torch.Tensor.normal_` - in-place version of :func:`torch.normal`
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- :func:`torch.Tensor.random_` - numbers sampled from the discrete uniform distribution
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- :func:`torch.Tensor.uniform_` - numbers sampled from the continuous uniform distribution
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Quasi-random sampling
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: torch.quasirandom.SobolEngine
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:members:
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:exclude-members: MAXBIT, MAXDIM
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:undoc-members:
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Serialization
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----------------------------------
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.. autofunction:: save
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.. autofunction:: load
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Parallelism
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----------------------------------
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.. autofunction:: get_num_threads
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.. autofunction:: set_num_threads
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.. autofunction:: get_num_interop_threads
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.. autofunction:: set_num_interop_threads
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Locally disabling gradient computation
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--------------------------------------
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The context managers :func:`torch.no_grad`, :func:`torch.enable_grad`, and
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:func:`torch.set_grad_enabled` are helpful for locally disabling and enabling
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gradient computation. See :ref:`locally-disable-grad` for more details on
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their usage. These context managers are thread local, so they won't
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work if you send work to another thread using the ``threading`` module, etc.
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Examples::
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>>> x = torch.zeros(1, requires_grad=True)
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>>> with torch.no_grad():
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... y = x * 2
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>>> y.requires_grad
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False
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>>> is_train = False
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>>> with torch.set_grad_enabled(is_train):
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... y = x * 2
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>>> y.requires_grad
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False
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>>> torch.set_grad_enabled(True) # this can also be used as a function
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>>> y = x * 2
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>>> y.requires_grad
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True
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>>> torch.set_grad_enabled(False)
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>>> y = x * 2
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>>> y.requires_grad
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False
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Math operations
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----------------------------------
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Pointwise Ops
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~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: abs
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.. autofunction:: acos
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.. autofunction:: add
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.. autofunction:: addcdiv
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.. autofunction:: addcmul
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.. autofunction:: angle
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.. autofunction:: asin
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.. autofunction:: atan
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.. autofunction:: atan2
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.. autofunction:: bitwise_not
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.. autofunction:: bitwise_and
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.. autofunction:: bitwise_or
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.. autofunction:: bitwise_xor
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.. autofunction:: ceil
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.. autofunction:: clamp
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.. autofunction:: conj
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.. autofunction:: cos
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.. autofunction:: cosh
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.. autofunction:: div
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.. autofunction:: digamma
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.. autofunction:: erf
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.. autofunction:: erfc
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.. autofunction:: erfinv
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.. autofunction:: exp
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.. autofunction:: expm1
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.. autofunction:: floor
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.. autofunction:: floor_divide
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.. autofunction:: fmod
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.. autofunction:: frac
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.. autofunction:: imag
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.. autofunction:: lerp
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.. autofunction:: lgamma
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.. autofunction:: log
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.. autofunction:: log10
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.. autofunction:: log1p
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.. autofunction:: log2
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.. autofunction:: logical_and
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.. autofunction:: logical_not
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.. autofunction:: logical_or
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.. autofunction:: logical_xor
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.. autofunction:: mul
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.. autofunction:: mvlgamma
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.. autofunction:: neg
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.. autofunction:: polygamma
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.. autofunction:: pow
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.. autofunction:: real
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.. autofunction:: reciprocal
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.. autofunction:: remainder
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.. autofunction:: round
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.. autofunction:: rsqrt
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.. autofunction:: sigmoid
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.. autofunction:: sign
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.. autofunction:: sin
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.. autofunction:: sinh
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.. autofunction:: sqrt
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.. autofunction:: square
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.. autofunction:: tan
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.. autofunction:: tanh
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.. autofunction:: trunc
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Reduction Ops
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~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: argmax
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.. autofunction:: argmin
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.. autofunction:: dist
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.. autofunction:: logsumexp
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.. autofunction:: mean
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.. autofunction:: median
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.. autofunction:: mode
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.. autofunction:: norm
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.. autofunction:: prod
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.. autofunction:: std
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.. autofunction:: std_mean
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.. autofunction:: sum
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.. autofunction:: unique
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.. autofunction:: unique_consecutive
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.. autofunction:: var
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.. autofunction:: var_mean
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Comparison Ops
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~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: allclose
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.. autofunction:: argsort
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.. autofunction:: eq
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.. autofunction:: equal
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.. autofunction:: ge
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.. autofunction:: gt
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.. autofunction:: isfinite
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.. autofunction:: isinf
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.. autofunction:: isnan
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.. autofunction:: kthvalue
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.. autofunction:: le
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.. autofunction:: lt
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.. autofunction:: max
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.. autofunction:: min
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.. autofunction:: ne
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.. autofunction:: sort
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.. autofunction:: topk
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Spectral Ops
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~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: fft
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.. autofunction:: ifft
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.. autofunction:: rfft
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.. autofunction:: irfft
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.. autofunction:: stft
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.. autofunction:: bartlett_window
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.. autofunction:: blackman_window
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.. autofunction:: hamming_window
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.. autofunction:: hann_window
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Other Operations
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~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: bincount
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.. autofunction:: broadcast_tensors
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.. autofunction:: cartesian_prod
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.. autofunction:: cdist
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.. autofunction:: combinations
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.. autofunction:: cross
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.. autofunction:: cummax
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.. autofunction:: cummin
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.. autofunction:: cumprod
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.. autofunction:: cumsum
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.. autofunction:: diag
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.. autofunction:: diag_embed
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.. autofunction:: diagflat
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.. autofunction:: diagonal
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.. autofunction:: einsum
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.. autofunction:: flatten
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.. autofunction:: flip
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.. autofunction:: rot90
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.. autofunction:: histc
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.. autofunction:: meshgrid
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.. autofunction:: renorm
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.. autofunction:: repeat_interleave
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.. autofunction:: roll
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.. autofunction:: tensordot
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.. autofunction:: trace
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.. autofunction:: tril
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.. autofunction:: tril_indices
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.. autofunction:: triu
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.. autofunction:: triu_indices
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BLAS and LAPACK Operations
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: addbmm
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.. autofunction:: addmm
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.. autofunction:: addmv
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.. autofunction:: addr
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.. autofunction:: baddbmm
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.. autofunction:: bmm
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.. autofunction:: chain_matmul
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.. autofunction:: cholesky
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.. autofunction:: cholesky_inverse
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.. autofunction:: cholesky_solve
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.. autofunction:: dot
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.. autofunction:: eig
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.. autofunction:: geqrf
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.. autofunction:: ger
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.. autofunction:: inverse
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.. autofunction:: det
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.. autofunction:: logdet
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.. autofunction:: slogdet
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.. autofunction:: lstsq
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.. autofunction:: lu
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.. autofunction:: lu_solve
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.. autofunction:: lu_unpack
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.. autofunction:: matmul
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.. autofunction:: matrix_power
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.. autofunction:: matrix_rank
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.. autofunction:: mm
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.. autofunction:: mv
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.. autofunction:: orgqr
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.. autofunction:: ormqr
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.. autofunction:: pinverse
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.. autofunction:: qr
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.. autofunction:: solve
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.. autofunction:: svd
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.. autofunction:: symeig
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.. autofunction:: trapz
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.. autofunction:: triangular_solve
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Utilities
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----------------------------------
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.. autofunction:: compiled_with_cxx11_abi
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.. autofunction:: result_type
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.. autofunction:: can_cast
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.. autofunction:: promote_types
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