| torch |
| ===== |
| The torch package contains data structures for multi-dimensional |
| tensors and defines mathematical operations over these tensors. |
| Additionally, it provides many utilities for efficient serializing of |
| Tensors and arbitrary types, and other useful utilities. |
| |
| It has a CUDA counterpart, that enables you to run your tensor computations |
| on an NVIDIA GPU with compute capability >= 3.0 |
| |
| .. currentmodule:: torch |
| |
| Tensors |
| ------- |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| is_tensor |
| is_storage |
| is_complex |
| is_floating_point |
| is_nonzero |
| set_default_dtype |
| get_default_dtype |
| set_default_tensor_type |
| numel |
| set_printoptions |
| set_flush_denormal |
| |
| .. _tensor-creation-ops: |
| |
| Creation Ops |
| ~~~~~~~~~~~~~~~~~~~~~~ |
| |
| .. note:: |
| Random sampling creation ops are listed under :ref:`random-sampling` and |
| include: |
| :func:`torch.rand` |
| :func:`torch.rand_like` |
| :func:`torch.randn` |
| :func:`torch.randn_like` |
| :func:`torch.randint` |
| :func:`torch.randint_like` |
| :func:`torch.randperm` |
| You may also use :func:`torch.empty` with the :ref:`inplace-random-sampling` |
| methods to create :class:`torch.Tensor` s with values sampled from a broader |
| range of distributions. |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| tensor |
| sparse_coo_tensor |
| as_tensor |
| as_strided |
| from_numpy |
| zeros |
| zeros_like |
| ones |
| ones_like |
| arange |
| range |
| linspace |
| logspace |
| eye |
| empty |
| empty_like |
| empty_strided |
| full |
| full_like |
| quantize_per_tensor |
| quantize_per_channel |
| dequantize |
| complex |
| polar |
| heaviside |
| |
| Indexing, Slicing, Joining, Mutating Ops |
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| cat |
| chunk |
| column_stack |
| dstack |
| gather |
| hstack |
| index_select |
| masked_select |
| movedim |
| moveaxis |
| narrow |
| nonzero |
| reshape |
| row_stack |
| scatter |
| scatter_add |
| split |
| squeeze |
| stack |
| swapaxes |
| swapdims |
| t |
| take |
| tensor_split |
| tile |
| transpose |
| unbind |
| unsqueeze |
| vstack |
| where |
| |
| .. _generators: |
| |
| Generators |
| ---------------------------------- |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| Generator |
| |
| .. _random-sampling: |
| |
| Random sampling |
| ---------------------------------- |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| seed |
| manual_seed |
| initial_seed |
| get_rng_state |
| set_rng_state |
| |
| .. autoattribute:: torch.default_generator |
| :annotation: Returns the default CPU torch.Generator |
| |
| .. The following doesn't actually seem to exist. |
| https://github.com/pytorch/pytorch/issues/27780 |
| .. autoattribute:: torch.cuda.default_generators |
| :annotation: If cuda is available, returns a tuple of default CUDA torch.Generator-s. |
| The number of CUDA torch.Generator-s returned is equal to the number of |
| GPUs available in the system. |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| bernoulli |
| multinomial |
| normal |
| poisson |
| rand |
| rand_like |
| randint |
| randint_like |
| randn |
| randn_like |
| randperm |
| |
| .. _inplace-random-sampling: |
| |
| In-place random sampling |
| ~~~~~~~~~~~~~~~~~~~~~~~~ |
| |
| There are a few more in-place random sampling functions defined on Tensors as well. Click through to refer to their documentation: |
| |
| - :func:`torch.Tensor.bernoulli_` - in-place version of :func:`torch.bernoulli` |
| - :func:`torch.Tensor.cauchy_` - numbers drawn from the Cauchy distribution |
| - :func:`torch.Tensor.exponential_` - numbers drawn from the exponential distribution |
| - :func:`torch.Tensor.geometric_` - elements drawn from the geometric distribution |
| - :func:`torch.Tensor.log_normal_` - samples from the log-normal distribution |
| - :func:`torch.Tensor.normal_` - in-place version of :func:`torch.normal` |
| - :func:`torch.Tensor.random_` - numbers sampled from the discrete uniform distribution |
| - :func:`torch.Tensor.uniform_` - numbers sampled from the continuous uniform distribution |
| |
| Quasi-random sampling |
| ~~~~~~~~~~~~~~~~~~~~~ |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| :template: sobolengine.rst |
| |
| quasirandom.SobolEngine |
| |
| Serialization |
| ---------------------------------- |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| save |
| load |
| |
| Parallelism |
| ---------------------------------- |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| get_num_threads |
| set_num_threads |
| get_num_interop_threads |
| set_num_interop_threads |
| |
| Locally disabling gradient computation |
| -------------------------------------- |
| The context managers :func:`torch.no_grad`, :func:`torch.enable_grad`, and |
| :func:`torch.set_grad_enabled` are helpful for locally disabling and enabling |
| gradient computation. See :ref:`locally-disable-grad` for more details on |
| their usage. These context managers are thread local, so they won't |
| work if you send work to another thread using the ``threading`` module, etc. |
| |
| Examples:: |
| |
| >>> x = torch.zeros(1, requires_grad=True) |
| >>> with torch.no_grad(): |
| ... y = x * 2 |
| >>> y.requires_grad |
| False |
| |
| >>> is_train = False |
| >>> with torch.set_grad_enabled(is_train): |
| ... y = x * 2 |
| >>> y.requires_grad |
| False |
| |
| >>> torch.set_grad_enabled(True) # this can also be used as a function |
| >>> y = x * 2 |
| >>> y.requires_grad |
| True |
| |
| >>> torch.set_grad_enabled(False) |
| >>> y = x * 2 |
| >>> y.requires_grad |
| False |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| no_grad |
| enable_grad |
| set_grad_enabled |
| |
| Math operations |
| --------------- |
| |
| Pointwise Ops |
| ~~~~~~~~~~~~~~~~~~~~~~ |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| abs |
| absolute |
| acos |
| arccos |
| acosh |
| arccosh |
| add |
| addcdiv |
| addcmul |
| angle |
| asin |
| arcsin |
| asinh |
| arcsinh |
| atan |
| arctan |
| atanh |
| arctanh |
| atan2 |
| bitwise_not |
| bitwise_and |
| bitwise_or |
| bitwise_xor |
| ceil |
| clamp |
| clip |
| conj |
| copysign |
| cos |
| cosh |
| deg2rad |
| div |
| divide |
| digamma |
| erf |
| erfc |
| erfinv |
| exp |
| exp2 |
| expm1 |
| fix |
| float_power |
| floor |
| floor_divide |
| fmod |
| frac |
| imag |
| ldexp |
| lerp |
| lgamma |
| log |
| log10 |
| log1p |
| log2 |
| logaddexp |
| logaddexp2 |
| logical_and |
| logical_not |
| logical_or |
| logical_xor |
| logit |
| hypot |
| i0 |
| igamma |
| igammac |
| mul |
| multiply |
| mvlgamma |
| nan_to_num |
| neg |
| negative |
| nextafter |
| polygamma |
| pow |
| rad2deg |
| real |
| reciprocal |
| remainder |
| round |
| rsqrt |
| sigmoid |
| sign |
| signbit |
| sin |
| sinc |
| sinh |
| sqrt |
| square |
| sub |
| subtract |
| tan |
| tanh |
| true_divide |
| trunc |
| xlogy |
| |
| Reduction Ops |
| ~~~~~~~~~~~~~~~~~~~~~~ |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| argmax |
| argmin |
| amax |
| amin |
| all |
| any |
| max |
| min |
| dist |
| logsumexp |
| mean |
| median |
| nanmedian |
| mode |
| norm |
| nansum |
| prod |
| quantile |
| nanquantile |
| std |
| std_mean |
| sum |
| unique |
| unique_consecutive |
| var |
| var_mean |
| count_nonzero |
| |
| Comparison Ops |
| ~~~~~~~~~~~~~~~~~~~~~~ |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| allclose |
| argsort |
| eq |
| equal |
| ge |
| greater_equal |
| gt |
| greater |
| isclose |
| isfinite |
| isinf |
| isposinf |
| isneginf |
| isnan |
| isreal |
| kthvalue |
| le |
| less_equal |
| lt |
| less |
| maximum |
| minimum |
| fmax |
| fmin |
| ne |
| not_equal |
| sort |
| topk |
| msort |
| |
| |
| Spectral Ops |
| ~~~~~~~~~~~~~~~~~~~~~~ |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| stft |
| istft |
| bartlett_window |
| blackman_window |
| hamming_window |
| hann_window |
| kaiser_window |
| |
| |
| Other Operations |
| ~~~~~~~~~~~~~~~~~~~~~~ |
| |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| atleast_1d |
| atleast_2d |
| atleast_3d |
| bincount |
| block_diag |
| broadcast_tensors |
| broadcast_to |
| broadcast_shapes |
| bucketize |
| cartesian_prod |
| cdist |
| clone |
| combinations |
| cross |
| cummax |
| cummin |
| cumprod |
| cumsum |
| diag |
| diag_embed |
| diagflat |
| diagonal |
| einsum |
| flatten |
| flip |
| fliplr |
| flipud |
| kron |
| rot90 |
| gcd |
| histc |
| meshgrid |
| lcm |
| logcumsumexp |
| ravel |
| renorm |
| repeat_interleave |
| roll |
| searchsorted |
| tensordot |
| trace |
| tril |
| tril_indices |
| triu |
| triu_indices |
| vander |
| view_as_real |
| view_as_complex |
| |
| |
| BLAS and LAPACK Operations |
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| addbmm |
| addmm |
| addmv |
| addr |
| baddbmm |
| bmm |
| chain_matmul |
| cholesky |
| cholesky_inverse |
| cholesky_solve |
| dot |
| eig |
| geqrf |
| ger |
| inner |
| inverse |
| det |
| logdet |
| slogdet |
| lstsq |
| lu |
| lu_solve |
| lu_unpack |
| matmul |
| matrix_power |
| matrix_rank |
| matrix_exp |
| mm |
| mv |
| orgqr |
| ormqr |
| outer |
| pinverse |
| qr |
| solve |
| svd |
| svd_lowrank |
| pca_lowrank |
| symeig |
| lobpcg |
| trapz |
| triangular_solve |
| vdot |
| |
| Utilities |
| ---------------------------------- |
| .. autosummary:: |
| :toctree: generated |
| :nosignatures: |
| |
| compiled_with_cxx11_abi |
| result_type |
| can_cast |
| promote_types |
| set_deterministic |
| is_deterministic |
| vmap |
| _assert |
