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native_functions.yaml
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native_functions.yaml
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# See README.md in this directory for more guidance
# *********NB: _cast_* operators are DEPRECATED and will be removed
# eventually. These were previously used before TorchScript IR supported
# representing ScalarType's. They are now superseded by usage of
# `aten::to()`. The ops remain here for backward compatibility purposes.
# DEPRECATED. DO NOT USE
- func: _cast_Byte(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Char(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Double(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Float(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Int(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Long(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Short(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Half(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
# Computes the gradient of current tensor w.r.t. graph leaves.
- func: backward(Tensor self, Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False) -> ()
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
manual_kernel_registration: True
variants: method
# DEPRECATED. Sets the tensor data held by this `Variable` to be the same as
# `new_data`. It requires that `new_data` and `Variable` have compatible tensor
# type, by checking `_has_compatible_shallow_copy_type(this, new_data)`.
#
# This function is deprecated because it doesn't really make sense in a world
# where Variables *are* Tensors (as opposed to them containing tensors, which
# is what the previous interpretation was.)
- func: set_data(Tensor(a!) self, Tensor new_data) -> ()
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
- func: data(Tensor self) -> Tensor
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
# True if this `Variable` is a leaf and thus does not have a `grad_fn`.
- func: is_leaf(Tensor self) -> bool
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
# Returns the output index of this variable from the forward operation that
# produced it. Conversely, it returns the input index of the gradient `Node` to
# which this `Variable` is connected (because in the gradient computation,
# inputs and outputs switch meaning). For example:
#
# y0, y1, y2 = f(x)
# assert y0.output_nr == 0
# assert y1.output_nr == 1
# assert y2.output_nr == 2
#
- func: output_nr(Tensor self) -> int
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
- func: _version(Tensor self) -> int
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
- func: requires_grad_(Tensor(a!) self, bool requires_grad=True) -> Tensor(a!)
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
# Enables .grad attribute for non-leaf Tensors.
- func: retain_grad(Tensor(a!) self) -> ()
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
- func: rename_(Tensor(a!) self, Dimname[]? names) -> Tensor(a!)
variants: method
- func: rename(Tensor(a) self, Dimname[]? names) -> Tensor(a)
variants: method
- func: align_to(Tensor(a) self, Dimname[] names) -> Tensor(a)
variants: method
- func: align_to.ellipsis_idx(Tensor(a) self, Dimname[] order, int ellipsis_idx) -> Tensor(a)
variants: method
- func: align_as(Tensor self, Tensor other) -> Tensor
use_c10_dispatcher: full
variants: method
- func: align_tensors(Tensor[] tensors) -> Tensor[]
use_c10_dispatcher: full
- func: refine_names(Tensor(a) self, Dimname[] names) -> Tensor(a)
variants: method
- func: _use_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank) -> bool
use_c10_dispatcher: full
dispatch:
CUDA: _use_cudnn_ctc_loss
- func: _cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)
use_c10_dispatcher: full
dispatch:
CUDA: _cudnn_ctc_loss
- func: _use_cudnn_rnn_flatten_weight() -> bool
use_c10_dispatcher: full
- func: _cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, int input_size, int mode, int hidden_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor
use_c10_dispatcher: full
dispatch:
CUDA: _cudnn_rnn_flatten_weight
- func: _cudnn_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
CUDA: _cudnn_rnn
- func: _cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[])
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
CUDA: _cudnn_rnn_backward
- func: _cudnn_init_dropout_state(float dropout, bool train, int dropout_seed, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
CUDA: _cudnn_init_dropout_state
- func: _debug_has_internal_overlap(Tensor self) -> int
use_c10_dispatcher: full
variants: function
- func: _fused_dropout(Tensor self, float p, Generator? generator=None) -> (Tensor, Tensor)
variants: function
dispatch:
CUDA: fused_dropout_cuda
- func: _masked_scale(Tensor self, Tensor mask, float scale) -> Tensor
use_c10_dispatcher: full
variants: function
dispatch:
CUDA: masked_scale_cuda
- func: _sobol_engine_draw(Tensor quasi, int n, Tensor sobolstate, int dimension, int num_generated, ScalarType? dtype) -> (Tensor, Tensor)
use_c10_dispatcher: full
- func: _sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!)
use_c10_dispatcher: full
- func: _sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!)
use_c10_dispatcher: full
- func: _sobol_engine_initialize_state_(Tensor(a!) self, int dimension) -> Tensor(a!)
use_c10_dispatcher: full
- func: _reshape_from_tensor(Tensor self, Tensor shape) -> Tensor
use_c10_dispatcher: full
- func: _shape_as_tensor(Tensor self) -> Tensor
use_c10_dispatcher: full
- func: dropout(Tensor input, float p, bool train) -> Tensor
use_c10_dispatcher: full
- func: dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
use_c10_dispatcher: full
- func: feature_dropout(Tensor input, float p, bool train) -> Tensor
use_c10_dispatcher: full
- func: feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
use_c10_dispatcher: full
- func: alpha_dropout(Tensor input, float p, bool train) -> Tensor
use_c10_dispatcher: full
- func: alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
use_c10_dispatcher: full
- func: feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor
use_c10_dispatcher: full
- func: feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
use_c10_dispatcher: full
- func: abs(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: abs
- func: abs_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: abs_
- func: abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: abs_out
# Note [Adding an alias]
# To add an alias do the following:
#
# 1) Copy the original functions native_functions.yaml entry, but replace the
# original function's name with their own and delete any dispatch
# keys for the aliases. Specifying a dispatch key will prevent
# autograd from recording the operations the alias performs, which
# will stop it from "inheriting" the original operation's autograd behavior.
# 2) Implement the corresponding functions and have them redispatch to the
# original function.
# 3) Add entries for the alias (and original function, if needed) to
# aten/src/ATen/core/interned_strings.h
# (This may require removing an entry from ATen/core/aten_interned_strings.h.)
# 4) Add docstrings to the new function that reference the original function,
# and document the method as usual (if it exists.)
# (See torch/_torch_docs.py and docs/source/torch.rst if adding a function,
# torch/_tensor_docs.py and docs/source/tensors.rst if adding a method,
# or module-specific doc bindings (like torch/linalg/__init__.py) if
# adding an alias in a namespace.)
# 5) Update torch/overrides.py consistent with the original function.
# 6) Update the alias_map in torch/csrc/jit/passes/normalize_ops.cpp.
# 7) Add entries to test/test_op_aliases.py's "alias_infos"
#
# See torch.absolute, an alias for torch.abs, as an example.
# Absolute, alias for abs
- func: absolute(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: absolute_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: method
- func: absolute.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: angle(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: angle
- func: angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: angle_out
- func: view_as_real(Tensor(a) self) -> Tensor(a)
use_c10_dispatcher: full
variants: function
dispatch:
DefaultBackend: view_as_real
- func: view_as_complex(Tensor(a) self) -> Tensor(a)
use_c10_dispatcher: full
variants: function
dispatch:
DefaultBackend: view_as_complex
- func: sgn(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: sgn
- func: sgn_(Tensor(a!) self) -> Tensor(a!)
variants: method
dispatch:
DefaultBackend: sgn_
- func: sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: sgn_out
- func: real(Tensor(a) self) -> Tensor(a)
use_c10_dispatcher: full
variants: function
- func: imag(Tensor(a) self) -> Tensor(a)
use_c10_dispatcher: full
variants: function
- func: conj(Tensor(a) self) -> Tensor(a)
use_c10_dispatcher: full
variants: function, method
- func: conj.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: conj_out
- func: _conj(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function
dispatch:
DefaultBackend: _conj
- func: acos(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: acos
- func: acos_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: acos_
- func: acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: acos_out
# arccos, alias of acos
- func: arccos(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: arccos_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
- func: arccos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: avg_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, bool ceil_mode=False, bool count_include_pad=True) -> Tensor
use_c10_dispatcher: full
- func: adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor
use_c10_dispatcher: full
# Return: (Tensor output, Tensor indices)
- func: adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor)
use_c10_dispatcher: full
- func: add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU, CUDA: add
SparseCPU, SparseCUDA: add_sparse
MkldnnCPU: mkldnn_add
- func: add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
use_c10_dispatcher: full
variants: method
dispatch:
CPU, CUDA: add_
SparseCPU, SparseCUDA: add_sparse_
MkldnnCPU: mkldnn_add_
- func: add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: add_out
SparseCPU: add_out_sparse_cpu
SparseCUDA: add_out_sparse_cuda
MkldnnCPU: mkldnn_add_out
- func: add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
use_c10_dispatcher: full
variants: function
dispatch:
CPU: add_relu
- func: add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
use_c10_dispatcher: full
variants: function
dispatch:
CPU: add_relu_
- func: add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU: add_relu_out
# For C++ only, until we have conversion from C++ numbers to Tensor
- func: add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: add
- func: add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
use_c10_dispatcher: full
variants: method
dispatch:
DefaultBackend: add_
- func: addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU, CUDA: addmv
- func: addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU, CUDA: addmv_
- func: addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: addmv_out
- func: _addmv_impl_(Tensor(a!) self, Tensor self2, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
use_c10_dispatcher: full
dispatch:
CPU: addmv_impl_cpu
CUDA: addmv_impl_cuda
- func: addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: addr
- func: addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
use_c10_dispatcher: full
variants: method
dispatch:
DefaultBackend: addr_
- func: addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
DefaultBackend: addr_out
- func: affine_grid_generator(Tensor theta, int[] size, bool align_corners) -> Tensor
use_c10_dispatcher: full
variants: function
dispatch:
DefaultBackend: affine_grid_generator
- func: affine_grid_generator_backward(Tensor grad, int[] size, bool align_corners) -> Tensor
use_c10_dispatcher: full
variants: function
- func: all.dim(Tensor self, int dim, bool keepdim=False) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU, CUDA: all
- func: all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: all_out
- func: all.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor
variants: function, method
- func: all.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: allclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> bool
use_c10_dispatcher: full
variants: function, method
- func: any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU, CUDA: any
- func: any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: any_out
- func: any.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor
variants: function, method
- func: any.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
- func: arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
- func: arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
- func: arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!)
- func: arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: arange_cpu_out
CUDA: arange_cuda_out
# This function is a temporary hack to allow tracing of arange like constructs with dynamic
# bounds on arange. Normal arange is not traceable because it does not take any tensor inputs;
# if the range you need is based on another tensor, calling this function directly will
# preserve tracing. Get rid of this when arange can directly take tensors for bounds
# (so that it can be traced directly).
- func: _dim_arange(Tensor like, int dim) -> Tensor
use_c10_dispatcher: full
- func: argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU, CUDA: argmax
- func: argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU, CUDA: argmin
- func: acosh(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: acosh
- func: acosh_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: acosh_
- func: acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: acosh_out
# arccosh, alias for acosh
- func: arccosh(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: arccosh_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
- func: arccosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: asinh(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: asinh
- func: asinh_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: asinh_
- func: asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: asinh_out
# arcsinh, alias for asinh
- func: arcsinh(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: arcsinh_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
- func: arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: atanh(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: atanh
- func: atanh_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: atanh_
- func: atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: atanh_out
# arctanh, alias for atanh
- func: arctanh(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: arctanh_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
- func: arctanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: as_strided(Tensor(a) self, int[] size, int[] stride, int? storage_offset=None) -> Tensor(a)
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU, CUDA: as_strided_tensorimpl
QuantizedCPU, QuantizedCUDA: as_strided_qtensorimpl
device_guard: False
- func: as_strided_(Tensor(a!) self, int[] size, int[] stride, int? storage_offset=None) -> Tensor(a!)
variants: function, method
device_guard: False
dispatch:
DefaultBackend: as_strided_
- func: asin(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: asin
- func: asin_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU, CUDA: asin_
SparseCPU, SparseCUDA: asin_sparse_
- func: asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: asin_out
SparseCPU, SparseCUDA: asin_out_sparse
# arcsin, alias of asin
- func: arcsin(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: arcsin_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
- func: arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: atan(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: atan
- func: atan_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: atan_
- func: atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: atan_out
# arctan, alias of atan
- func: arctan(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: arctan_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
- func: arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: atleast_1d(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function
- func: atleast_1d.Sequence(Tensor[] tensors) -> Tensor[]
use_c10_dispatcher: full
- func: atleast_2d(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function
- func: atleast_2d.Sequence(Tensor[] tensors) -> Tensor[]
use_c10_dispatcher: full
variants: function
- func: atleast_3d(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function
- func: atleast_3d.Sequence(Tensor[] tensors) -> Tensor[]
use_c10_dispatcher: full
variants: function
- func: baddbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU: baddbmm_cpu
CUDA: baddbmm_cuda
- func: baddbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
use_c10_dispatcher: full
variants: method
dispatch:
CPU: baddbmm__cpu
CUDA: baddbmm__cuda
- func: _baddbmm_mkl_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
use_c10_dispatcher: full
variants: function
- func: baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU: baddbmm_out_cpu
CUDA: baddbmm_out_cuda
- func: bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
- func: bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
- func: batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
- func: quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
dispatch:
QuantizedCPU: quantized_batch_norm
- func: _batch_norm_impl_index(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> (Tensor, Tensor, Tensor, Tensor, int)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
- func: _batch_norm_impl_index_backward(int impl_index, Tensor input, Tensor grad_output, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var_transform, bool train, float eps, bool[3] output_mask, Tensor reservedSpace) -> (Tensor, Tensor, Tensor)
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
# Sample bernoulli with values in `self` as probability.
- func: bernoulli(Tensor self, *, Generator? generator=None) -> Tensor
variants: function, method
dispatch:
DefaultBackend: bernoulli
- func: bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: bernoulli_out
- func: bernoulli_.Tensor(Tensor(a!) self, Tensor p, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: bernoulli_
- func: bernoulli_.float(Tensor(a!) self, float p=0.5, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: bernoulli_
# This out-of-place version isn't used explicitly, but needed by jit.
# There is no default valid on `p` here because it would introduce ambiguity
# with `bernoulli(Tensor self, *, Generator? generator=None)` declaration.
- func: bernoulli.p(Tensor self, float p, *, Generator? generator=None) -> Tensor
variants: function, method
- func: bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
- func: binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
python_module: nn
variants: function
dispatch:
CPU: binary_cross_entropy_cpu
CUDA: binary_cross_entropy_cuda
- func: binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
variants: function
dispatch:
CPU: binary_cross_entropy_out_cpu
CUDA: binary_cross_entropy_out_cuda
- func: binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
python_module: nn
variants: function
dispatch:
CPU: binary_cross_entropy_backward_cpu
CUDA: binary_cross_entropy_backward_cuda
- func: binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
variants: function
dispatch:
CPU: binary_cross_entropy_backward_out_cpu
CUDA: binary_cross_entropy_backward_out_cuda
- func: binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
variants: function
dispatch:
DefaultBackend: binary_cross_entropy_with_logits
- func: binary_cross_entropy_with_logits_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
variants: function
- func: bincount(Tensor self, Tensor? weights=None, int minlength=0) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
variants: function, method
dispatch:
CPU: _bincount_cpu
CUDA: _bincount_cuda
- func: bitwise_not(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: bitwise_not_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: method
- func: bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: bitwise_not_out
- func: logical_not(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: logical_not_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: method
- func: logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: logical_not_out
- func: logical_xor(Tensor self, Tensor other) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: logical_xor_(Tensor(a!) self, Tensor other) -> Tensor(a!)
use_c10_dispatcher: full
variants: method
- func: logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: logical_xor_out
- func: logical_and(Tensor self, Tensor other) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: logical_and_(Tensor(a!) self, Tensor other) -> Tensor(a!)
use_c10_dispatcher: full
variants: method
- func: logical_and.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: logical_and_out
- func: logical_or(Tensor self, Tensor other) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: logical_or_(Tensor(a!) self, Tensor other) -> Tensor(a!)
use_c10_dispatcher: full
variants: method
- func: logical_or.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: logical_or_out
- func: blackman_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
- func: blackman_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
use_c10_dispatcher: hacky_wrapper_for_legacy_signatures
- func: bmm(Tensor self, Tensor mat2) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU: bmm_cpu
CUDA: bmm_cuda
SparseCPU: bmm_sparse_cpu
SparseCUDA: bmm_sparse_cuda
- func: _bmm(Tensor self, Tensor mat2, *, bool deterministic=False) -> Tensor
use_c10_dispatcher: full
variants: function
dispatch:
SparseCUDA: _bmm_sparse_cuda
- func: bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU: bmm_out_cpu
CUDA: bmm_out_cuda
SparseCPU: bmm_out_sparse_cpu
SparseCUDA: bmm_out_sparse_cuda
- func: _bmm.out(Tensor self, Tensor mat2, *, bool deterministic=False, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
SparseCUDA: _bmm_out_sparse_cuda
- func: broadcast_tensors(Tensor[] tensors) -> Tensor[]
use_c10_dispatcher: full
device_guard: False
- func: cat(Tensor[] tensors, int dim=0) -> Tensor
use_c10_dispatcher: full
dispatch:
DefaultBackend: cat
- func: cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
DefaultBackend: cat_out
- func: cat.names(Tensor[] tensors, Dimname dim) -> Tensor
- func: cat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
- func: block_diag(Tensor[] tensors) -> Tensor
use_c10_dispatcher: full
variants: function
- func: ceil(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: ceil
- func: ceil_(Tensor(a!) self) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: ceil_
- func: ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: ceil_out
- func: chain_matmul(Tensor[] matrices) -> Tensor
use_c10_dispatcher: full
variants: function
- func: unsafe_chunk(Tensor self, int chunks, int dim=0) -> Tensor[]
use_c10_dispatcher: full
variants: function, method
device_guard: False
- func: chunk(Tensor(a) self, int chunks, int dim=0) -> Tensor(a)[]
use_c10_dispatcher: full
variants: function, method
device_guard: False
- func: tensor_split.sections(Tensor(a) self, int sections, int dim=0) -> Tensor(a)[]
use_c10_dispatcher: full
variants: function, method
- func: tensor_split.indices(Tensor(a) self, int[] indices, int dim=0) -> Tensor(a)[]
use_c10_dispatcher: full
variants: function, method
- func: clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU, CUDA: clamp
QuantizedCPU: clamp_quantized_cpu
- func: clamp_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)
use_c10_dispatcher: full
variants: function, method
dispatch:
DefaultBackend: clamp_
- func: clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)