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android / platform / external / pytorch / dde2bc4818 / . / aten / src / ATen / native / native_functions.yaml
blob: 5d860c2ce5a228480e80fb499446d92dca203b7f [file] [log] [blame]
# 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# representing ScalarType's. They are now superseded by usage of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
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Char(Tensor self, bool non_blocking=False) -> Tensor
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Double(Tensor self, bool non_blocking=False) -> Tensor
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Float(Tensor self, bool non_blocking=False) -> Tensor
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Int(Tensor self, bool non_blocking=False) -> Tensor
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Long(Tensor self, bool non_blocking=False) -> Tensor
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Short(Tensor self, bool non_blocking=False) -> Tensor
variants: function
# DEPRECATED. DO NOT USE
- func: _cast_Half(Tensor self, bool non_blocking=False) -> Tensor
variants: function
# Computes the gradient of current tensor w.r.t. graph leaves.
- func: _backward(Tensor self, Tensor[] inputs, Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False) -> ()
manual_cpp_binding: 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) -> ()
manual_cpp_binding: True
variants: method
- func: data(Tensor self) -> Tensor
manual_cpp_binding: True
variants: method
# True if this `Variable` is a leaf and thus does not have a `grad_fn`.
- func: is_leaf(Tensor self) -> bool
manual_cpp_binding: 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
manual_cpp_binding: True
variants: method
- func: _version(Tensor self) -> int
manual_cpp_binding: True
variants: method
- func: requires_grad_(Tensor(a!) self, bool requires_grad=True) -> Tensor(a!)
manual_cpp_binding: True
variants: method
# Enables .grad attribute for non-leaf Tensors.
- func: retain_grad(Tensor(a!) self) -> ()
manual_cpp_binding: True
variants: method
- func: _fw_primal(Tensor(a) self, int level) -> Tensor(a)
variants: method
dispatch:
CompositeExplicitAutograd: _fw_primal
- func: _make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a)
variants: function
- func: _unpack_dual(Tensor(a) dual, int level) -> (Tensor(a) primal, Tensor tangent)
variants: function
- 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
variants: method
- func: align_tensors(Tensor[] tensors) -> Tensor[]
# Not assert because it's a keyword; not Assert because FX already
# took that syntax
# TODO: need to specify this is side-effectful somehow
- func: _assert_async(Tensor self) -> ()
dispatch:
CPU: _assert_async_cpu
CUDA: _assert_async_cuda
- 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
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)
dispatch:
CUDA: _cudnn_ctc_loss
- func: _use_cudnn_rnn_flatten_weight() -> bool
- func: _cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, int input_size, int mode, int hidden_size, int proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor
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 proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
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 proj_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[])
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
dispatch:
CUDA: _cudnn_init_dropout_state
- func: _debug_has_internal_overlap(Tensor self) -> int
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
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)
- func: _sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!)
- func: _sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!)
- func: _sobol_engine_initialize_state_(Tensor(a!) self, int dimension) -> Tensor(a!)
- func: _reshape_from_tensor(Tensor self, Tensor shape) -> Tensor
- func: _shape_as_tensor(Tensor self) -> Tensor
- func: dropout(Tensor input, float p, bool train) -> Tensor
- func: dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
- func: feature_dropout(Tensor input, float p, bool train) -> Tensor
- func: feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
- func: alpha_dropout(Tensor input, float p, bool train) -> Tensor
- func: alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
- func: feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor
- func: feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
- func: abs(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: abs
- func: abs_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: 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 aliases argument to existing OpInfo/UnaryUfuncInfo or create new OpInfo/UnaryUfuncInfo entry
# in op_db list in torch/testing/_internal/common_methods_invocations.py
#
# See torch.absolute, an alias for torch.abs, as an example.
# Absolute, alias for abs
- func: absolute(Tensor self) -> Tensor
variants: function, method
- func: absolute_(Tensor(a!) self) -> Tensor(a!)
variants: method
- func: absolute.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: angle(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: 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)
variants: function
dispatch:
CPU, CUDA: view_as_real
- func: view_as_complex(Tensor(a) self) -> Tensor(a)
variants: function
dispatch:
CPU, CUDA: view_as_complex
- func: sgn(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: sgn
- func: sgn_(Tensor(a!) self) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: sgn_
- func: sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: sgn_out
- func: real(Tensor(a) self) -> Tensor(a)
variants: function
- func: imag(Tensor(a) self) -> Tensor(a)
variants: function
- func: conj(Tensor(a) self) -> Tensor(a)
variants: function, method
- func: conj.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: conj_out
- func: _conj(Tensor self) -> Tensor
variants: function
dispatch:
CompositeExplicitAutograd: _conj
- func: acos(Tensor self) -> Tensor
variants: function, method
structured_delegate: acos.out
- func: acos_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
structured_delegate: acos.out
- func: acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: acos_out
# arccos, alias of acos
- func: arccos(Tensor self) -> Tensor
variants: function, method
- func: arccos_(Tensor(a!) self) -> Tensor(a!)
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
- func: adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor
# Return: (Tensor output, Tensor indices)
- func: adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor)
- func: add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
structured_delegate: add.out
variants: function, method
dispatch:
SparseCPU, SparseCUDA: add_sparse
SparseCsrCPU: add_sparse_csr
MkldnnCPU: mkldnn_add
- func: add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
variants: method
structured_delegate: add.out
dispatch:
SparseCPU, SparseCUDA: add_sparse_
SparseCsrCPU: add_sparse_csr_
MkldnnCPU: mkldnn_add_
- func: add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: add_out
SparseCPU: add_out_sparse_cpu
SparseCUDA: add_out_sparse_cuda
SparseCsrCPU: add_out_sparse_csr_cpu
MkldnnCPU: mkldnn_add_out
- func: _add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
variants: function
dispatch:
CPU: add_relu
- func: _add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
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
variants: function, method
dispatch:
CompositeExplicitAutograd: add
- func: add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: add_
- func: addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor
structured_delegate: addmv.out
variants: function, method
- func: addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
structured_delegate: addmv.out
variants: function, method
- func: addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
structured: True
dispatch:
CPU: addmv_out_cpu
CUDA: addmv_out_cuda
- func: addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: addr
CompositeImplicitAutograd: math_addr
- func: addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: addr_
- func: addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: addr_out
CompositeImplicitAutograd: math_addr_out
- func: affine_grid_generator(Tensor theta, int[] size, bool align_corners) -> Tensor
variants: function
dispatch:
CompositeExplicitAutograd: affine_grid_generator
- func: affine_grid_generator_backward(Tensor grad, int[] size, bool align_corners) -> Tensor
variants: function
- func: all.dim(Tensor self, int dim, bool keepdim=False) -> Tensor
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
variants: function, method
- func: any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor
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
- func: arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- 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
- func: argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: argmax
- func: argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: argmax_out
- func: argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: argmin
- func: argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: argmin_out
- func: acosh(Tensor self) -> Tensor
variants: function, method
structured_delegate: acosh.out
- func: acosh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
structured_delegate: acosh.out
- func: acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: acosh_out
# arccosh, alias for acosh
- func: arccosh(Tensor self) -> Tensor
variants: function, method
- func: arccosh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
- func: arccosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: asinh(Tensor self) -> Tensor
variants: function, method
structured_delegate: asinh.out
- func: asinh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
structured_delegate: asinh.out
- func: asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: asinh_out
# arcsinh, alias for asinh
- func: arcsinh(Tensor self) -> Tensor
variants: function, method
- func: arcsinh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
- func: arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: atanh(Tensor self) -> Tensor
structured_delegate: atanh.out
variants: function, method
- func: atanh_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: atanh.out
variants: function, method
- func: atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: atanh_out
# arctanh, alias for atanh
- func: arctanh(Tensor self) -> Tensor
variants: function, method
- func: arctanh_(Tensor(a!) self) -> Tensor(a!)
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)
variants: function, method
dispatch:
CPU, CUDA, Meta: 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:
CompositeExplicitAutograd: as_strided_
- func: asin(Tensor self) -> Tensor
variants: function, method
structured_delegate: asin.out
dispatch:
SparseCPU, SparseCUDA: asin_sparse
- func: asin_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
structured_delegate: asin.out
dispatch:
SparseCPU, SparseCUDA: asin_sparse_
- func: asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: asin_out
SparseCPU, SparseCUDA: asin_out_sparse
# arcsin, alias of asin
- func: arcsin(Tensor self) -> Tensor
variants: function, method
- func: arcsin_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
- func: arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: atan(Tensor self) -> Tensor
structured_delegate: atan.out
variants: function, method
- func: atan_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: atan.out
variants: function, method
- func: atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: atan_out
# arctan, alias of atan
- func: arctan(Tensor self) -> Tensor
variants: function, method
- func: arctan_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
- func: arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: atleast_1d(Tensor self) -> Tensor
variants: function
- func: atleast_1d.Sequence(Tensor[] tensors) -> Tensor[]
- func: atleast_2d(Tensor self) -> Tensor
variants: function
- func: atleast_2d.Sequence(Tensor[] tensors) -> Tensor[]
variants: function
- func: atleast_3d(Tensor self) -> Tensor
variants: function
- func: atleast_3d.Sequence(Tensor[] tensors) -> Tensor[]
variants: function
- func: baddbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
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!)
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!)
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
- func: bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- 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
- func: quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor
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)
- 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)
# Sample bernoulli with values in `self` as probability.
- func: bernoulli(Tensor self, *, Generator? generator=None) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: 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
- func: binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor
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
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
variants: function
dispatch:
CompositeExplicitAutograd: 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
variants: function
- func: bincount(Tensor self, Tensor? weights=None, int minlength=0) -> Tensor
variants: function, method
dispatch:
CPU: _bincount_cpu
CUDA: _bincount_cuda
- func: bitwise_not(Tensor self) -> Tensor
structured_delegate: bitwise_not.out
variants: function, method
- func: bitwise_not_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: bitwise_not.out
variants: method
- func: bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: bitwise_not_out
- func: copysign.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: copysign_out
- func: copysign.Tensor(Tensor self, Tensor other) -> Tensor
variants: function, method
structured_delegate: copysign.out
- func: copysign_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
structured_delegate: copysign.out
- func: copysign.Scalar(Tensor self, Scalar other) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: copysign
- func: copysign_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: copysign_
- func: copysign.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: copysign_out
- func: logical_not(Tensor self) -> Tensor
variants: function, method
- func: logical_not_(Tensor(a!) self) -> Tensor(a!)
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
variants: function, method
- func: logical_xor_(Tensor(a!) self, Tensor other) -> Tensor(a!)
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
variants: function, method
- func: logical_and_(Tensor(a!) self, Tensor other) -> Tensor(a!)
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
variants: function, method
- func: logical_or_(Tensor(a!) self, Tensor other) -> Tensor(a!)
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
- func: blackman_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: bmm(Tensor self, Tensor mat2) -> Tensor
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
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[]
device_guard: False
- func: broadcast_to(Tensor(a) self, int[] size) -> Tensor(a)
variants: function, method
- func: cat(Tensor[] tensors, int dim=0) -> Tensor
dispatch:
CompositeExplicitAutograd: cat
- func: cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: 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
variants: function
- func: ceil(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: ceil
- func: ceil_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: ceil_
- func: ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: ceil_out
# alias for torch.linalg.multi_dot
- func: chain_matmul(Tensor[] matrices) -> Tensor
variants: function
# alias for torch.linalg.multi_dot
- func: chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!)
- func: unsafe_chunk(Tensor self, int chunks, int dim=0) -> Tensor[]
variants: function, method
device_guard: False
- func: chunk(Tensor(a) self, int chunks, int dim=0) -> Tensor(a)[]
variants: function, method
device_guard: False
- func: tensor_split.sections(Tensor(a) self, int sections, int dim=0) -> Tensor(a)[]
variants: function, method
- func: tensor_split.indices(Tensor(a) self, int[] indices, int dim=0) -> Tensor(a)[]
variants: function, method
- func: tensor_split.tensor_indices_or_sections(Tensor(a) self, Tensor tensor_indices_or_sections, int dim=0) -> Tensor(a)[]
variants: function, method
- func: clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: clamp
QuantizedCPU: clamp_quantized_cpu
- func: clamp_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: clamp_
- func: clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: clamp_out
- func: clamp_max(Tensor self, Scalar max) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: clamp_max
- func: clamp_max_(Tensor(a!) self, Scalar max) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: clamp_max_
- func: clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: clamp_max_out
- func: clamp_min(Tensor self, Scalar min) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: clamp_min
- func: clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: clamp_min_
- func: clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: clamp_min_out
# clip is an alias for clamp
- func: clip(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
variants: function, method
- func: clip_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)
variants: function, method
- func: clip.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)
- func: cudnn_is_acceptable(Tensor self) -> bool
device_guard: False
- func: complex(Tensor real, Tensor imag) -> Tensor
variants: function
dispatch:
CompositeExplicitAutograd: complex
- func: complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: complex_out
- func: polar(Tensor abs, Tensor angle) -> Tensor
variants: function
dispatch:
CompositeExplicitAutograd: polar
- func: polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: polar_out
- func: constant_pad_nd(Tensor self, int[] pad, Scalar value=0) -> Tensor
variants: function
dispatch:
CompositeExplicitAutograd: constant_pad_nd
- func: contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a)
variants: method
manual_cpp_binding: True
- func: convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensor
- func: convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensor
dispatch:
CompositeExplicitAutograd: convolution_overrideable
- func: convolution_backward_overrideable(Tensor grad_output, Tensor input, Tensor weight, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
dispatch:
CompositeExplicitAutograd: convolution_backward_overrideable
- func: _convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor
- func: _convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor
- func: _convolution_mode(Tensor input, Tensor weight, Tensor? bias, int[] stride, str padding, int[] dilation, int groups) -> Tensor
- func: _convolution_nogroup(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding) -> Tensor
- func: _convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
- func: conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensor
- func: conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensor
- func: conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensor
- func: conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, str padding="valid", int[1] dilation=1, int groups=1) -> Tensor
cpp_no_default_args: ['bias', 'stride', 'padding']
- func: conv2d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, str padding="valid", int[2] dilation=1, int groups=1) -> Tensor
cpp_no_default_args: ['bias', 'stride', 'padding']
- func: conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, str padding="valid", int[3] dilation=1, int groups=1) -> Tensor
cpp_no_default_args: ['bias', 'stride', 'padding']
- func: conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor
dispatch:
CompositeExplicitAutograd: conv_tbc
- func: conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int pad) -> (Tensor, Tensor, Tensor)
# NB: we inherit the goofy argument order from PyTorch torch.nn.functional
- func: conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] output_padding=0, int groups=1, int[1] dilation=1) -> Tensor
- func: conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int groups=1, int[2] dilation=1) -> Tensor
- func: conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int groups=1, int[3] dilation=1) -> Tensor
- func: copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!)
variants: method
device_guard: False
dispatch:
MkldnnCPU: copy_mkldnn_
CompositeExplicitAutograd: copy_
- func: _copy_from(Tensor self, Tensor dst, bool non_blocking=False) -> Tensor
dispatch: {}
- func: cos(Tensor self) -> Tensor
variants: function, method
structured_delegate: cos.out
- func: cos_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
structured_delegate: cos.out
- func: cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: cos_out
- func: cosh(Tensor self) -> Tensor
variants: function, method
structured_delegate: cosh.out
- func: cosh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
structured_delegate: cosh.out
- func: cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: cosh_out
- func: cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor
- func: count_nonzero.dim_IntList(Tensor self, int[] dim) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: count_nonzero
- func: count_nonzero(Tensor self, int? dim=None) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: count_nonzero
- func: cudnn_affine_grid_generator(Tensor theta, int N, int C, int H, int W) -> Tensor grid
dispatch:
CUDA: cudnn_affine_grid_generator_forward
# TODO: Why do I have to call this grad?!
- func: cudnn_affine_grid_generator_backward(Tensor grad, int N, int C, int H, int W) -> Tensor grad_theta
dispatch:
CUDA: cudnn_affine_grid_generator_backward
- func: cudnn_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor, Tensor)
dispatch:
CUDA: cudnn_batch_norm
# NB: You can only use this if you used cudnn_batch_norm training=True
- func: cudnn_batch_norm_backward(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, Tensor reserveSpace) -> (Tensor, Tensor, Tensor)
dispatch:
CUDA: cudnn_batch_norm_backward
- func: cudnn_convolution.deprecated(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: cudnn_convolution_deprecated
- func: cudnn_convolution.deprecated2(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: cudnn_convolution_deprecated2
- func: cudnn_convolution(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor
dispatch:
CUDA: cudnn_convolution
- func: cudnn_convolution_backward_input(int[] self_size, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor
dispatch:
CUDA: cudnn_convolution_backward_input
- func: cudnn_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32, bool[2] output_mask) -> (Tensor, Tensor)
dispatch:
CUDA: cudnn_convolution_backward
- func: cudnn_convolution_backward_weight(int[] weight_size, Tensor grad_output, Tensor self, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor
dispatch:
CUDA: cudnn_convolution_backward_weight
- func: cudnn_convolution_transpose.deprecated(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: cudnn_convolution_transpose_deprecated
- func: cudnn_convolution_transpose.deprecated2(Tensor self, Tensor weight, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: cudnn_convolution_transpose_deprecated2
- func: cudnn_convolution_transpose(Tensor self, Tensor weight, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor
dispatch:
CUDA: cudnn_convolution_transpose
# NB: output_padding not strictly needed here, but it's helpful for the float
# backwards
- func: cudnn_convolution_transpose_backward(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32, bool[2] output_mask) -> (Tensor, Tensor)
dispatch:
CUDA: cudnn_convolution_transpose_backward
- func: cudnn_convolution_transpose_backward_input(Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor
dispatch:
CUDA: cudnn_convolution_transpose_backward_input
- func: cudnn_convolution_transpose_backward_weight(int[] weight_size, Tensor grad_output, Tensor self, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor
dispatch:
CUDA: cudnn_convolution_transpose_backward_weight
- func: cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor
dispatch:
CUDA: cudnn_convolution_relu
- func: cudnn_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor
dispatch:
CUDA: cudnn_convolution_add_relu
# NB: input is special cased in a way I don't quite understand
- func: cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output
dispatch:
CUDA: cudnn_grid_sampler_forward
- func: cudnn_grid_sampler_backward(Tensor self, Tensor grid, Tensor grad_output) -> (Tensor grad_self, Tensor grad_grid)
dispatch:
CUDA: cudnn_grid_sampler_backward
- func: cummax(Tensor self, int dim) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
CompositeExplicitAutograd: cummax
- func: cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CompositeExplicitAutograd: cummax_out
- func: cummax.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)
variants: function, method
- func: cummax.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
- func: _cummax_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> ()
variants: function
dispatch:
CPU: cummax_helper_cpu
CUDA: cummax_helper_cuda
- func: cummin(Tensor self, int dim) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
CompositeExplicitAutograd: cummin
- func: cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CompositeExplicitAutograd: cummin_out
- func: cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)
variants: function, method
- func: cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
- func: _cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> ()
variants: function
dispatch:
CPU: cummin_helper_cpu
CUDA: cummin_helper_cuda
- func: cummaxmin_backward(Tensor grad, Tensor input, Tensor indices, int dim) -> Tensor
variants: function
device_guard: False
- func: cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: cumprod
- func: cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: cumprod_
- func: cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: cumprod_out
- func: cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
variants: function, method
- func: cumprod_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!)
variants: method
- func: cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
- func: cumprod_backward(Tensor grad, Tensor input, int dim, Tensor output) -> Tensor
variants: function
device_guard: False
- func: cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: cumsum
- func: cumsum_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: cumsum_
- func: cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: cumsum_out
- func: cumsum.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
variants: function, method
- func: cumsum_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!)
variants: method
- func: cumsum.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
- func: ctc_loss.IntList(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor
# convenience function that converts to intlists for you
- func: ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor
- func: _ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)
dispatch:
CPU: ctc_loss_cpu
CUDA: ctc_loss_gpu
- func: _ctc_loss_backward(Tensor grad, Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False) -> Tensor
dispatch:
CPU: ctc_loss_backward_cpu
CUDA: ctc_loss_backward_gpu
- func: diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor
variants: function, method
- func: diagflat(Tensor self, int offset=0) -> Tensor
variants: function, method
- func: diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a)
variants: function, method
dispatch:
CompositeExplicitAutograd: diagonal
- func: diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a)
variants: function, method
- func: diagonal_backward(Tensor grad, int[] input_sizes, int offset, int dim1, int dim2) -> Tensor
variants: function
device_guard: False
- func: fill_diagonal_(Tensor(a!) self, Scalar fill_value, bool wrap=False) -> Tensor(a!)
variants: method
- func: diff(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None) -> Tensor
variants: function, method
- func: diff.out(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None, *, Tensor(a!) out) -> Tensor(a!)
variants: function
- func: div.Tensor(Tensor self, Tensor other) -> Tensor
variants: function, method
structured_delegate: div.out
dispatch:
SparseCPU, SparseCUDA: div_sparse
- func: div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
structured_delegate: div.out
dispatch:
SparseCPU, SparseCUDA: div_sparse_
- func: div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: div_out
SparseCPU, SparseCUDA: div_out_sparse_zerodim
- func: div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor
variants: function, method
structured_delegate: div.out_mode
- func: div_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!)
variants: method
structured_delegate: div.out_mode
- func: div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: div_out_mode
# For C++ only, until we have conversion from C++ numbers to Tensor
- func: div.Scalar(Tensor self, Scalar other) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: div
- func: div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: div_
- func: div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: div
- func: div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: div_
# divide, alias for div
- func: divide.Tensor(Tensor self, Tensor other) -> Tensor
variants: function, method
- func: divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: divide.Scalar(Tensor self, Scalar other) -> Tensor
variants: function, method
- func: divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor
variants: function, method
- func: divide_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!)
variants: method
- func: divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)
- func: divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor
variants: function, method
- func: divide_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!)
variants: method
# true_divide, an alias for div
- func: true_divide.Tensor(Tensor self, Tensor other) -> Tensor
variants: function, method
- func: true_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: true_divide.Scalar(Tensor self, Scalar other) -> Tensor
variants: function, method
- func: true_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: dot(Tensor self, Tensor tensor) -> Tensor
variants: function, method
dispatch:
CPU: dot
CUDA: dot_cuda
- func: dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: dot_out
- func: vdot(Tensor self, Tensor other) -> Tensor
variants: function, method
dispatch:
CPU: vdot
CUDA: vdot_cuda
- func: vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: vdot_out
- func: einsum(str equation, Tensor[] tensors) -> Tensor
- func: embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor
dispatch:
CompositeExplicitAutograd: embedding
- func: embedding_backward(Tensor grad, Tensor indices, int num_weights, int padding_idx, bool scale_grad_by_freq, bool sparse) -> Tensor
- func: embedding_dense_backward(Tensor grad_output, Tensor indices, int num_weights, int padding_idx, bool scale_grad_by_freq) -> Tensor
dispatch:
CPU: embedding_dense_backward_cpu
CUDA: embedding_dense_backward_cuda
- func: embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!)
dispatch:
CPU: embedding_renorm_cpu_
CUDA: embedding_renorm_cuda_
- func: embedding_sparse_backward(Tensor grad, Tensor indices, int num_weights, int padding_idx, bool scale_grad_by_freq) -> Tensor
# NOTE [ embedding_bag Native Functions ]
# The `_embedding_bag.*` variants assume that input tensors except for `weight`,
# e.g. `indices` and `offsets` (and `offset2bag`), are contiguous.
# We really only need to enforce this for `_embedding_bag` (the forward) because
# the backward inputs are the same as forward ones.
# The above `embedding_bag` wrapper is created to achieve this, e.g.,
# applying indices = indices.contiguous().
# The backward functions apply a check that these input tensors are contiguous.
- func: _embedding_bag_forward_only(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor)
dispatch:
CPU: _embedding_bag_forward_only_cpu
CUDA: _embedding_bag_forward_only_cuda
- func: _rowwise_prune(Tensor weight, Tensor mask, ScalarType compressed_indices_dtype) -> (Tensor, Tensor)
# row_stack is the alias of vstack
- func: row_stack(Tensor[] tensors) -> Tensor
- func: row_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
- func: embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> (Tensor, Tensor, Tensor, Tensor)
# To keep backward and forward compatibility, and to avoid ambiguity with the
# original signature above, scale_grad_by_freq, mode, sparse,
# per_sample_weights, and include_last_offset parameters do not have default
# values. Once the original signature is removed, default values can be added.
- func: embedding_bag.padding_idx(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int? padding_idx) -> (Tensor, Tensor, Tensor, Tensor)
- func: _embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor)
dispatch:
CPU: _embedding_bag_cpu
CUDA: _embedding_bag_cuda
- func: _embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, int num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
- func: _embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, int num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
- func: _embedding_bag_dense_backward(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, int num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
dispatch:
CPU: _embedding_bag_dense_backward_cpu
CUDA: _embedding_bag_dense_backward_cuda
- func: _embedding_bag_per_sample_weights_backward(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1) -> Tensor
dispatch:
CPU: _embedding_bag_per_sample_weights_backward_cpu
CUDA: _embedding_bag_per_sample_weights_backward_cuda
- func: empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
device_guard: False
- func: empty.memory_format(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
dispatch:
CPU: empty_cpu
CUDA: empty_cuda
Meta: empty_meta
MkldnnCPU: empty_mkldnn
SparseCPU, SparseCUDA: empty_sparse
- func: new_empty(Tensor self, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
variants: method
- func: new_empty_strided(Tensor self, int[] size, int[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
variants: method
- func: new_full(Tensor self, int[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
variants: method
- func: new_zeros(Tensor self, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
variants: method
# other overrides are to provide a more helpful error message that dtype is required
- func: _empty_affine_quantized(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor
dispatch:
CPU: empty_affine_quantized_other_backends_stub
QuantizedCPU, QuantizedCUDA: empty_affine_quantized
# it's a factory function receiving a tensor argument, thus overriding explicitly
# other overrides are to provide a more helpful error message that dtype is required
- func: _empty_per_channel_affine_quantized(int[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor
category_override: factory
dispatch:
CPU: empty_per_channel_affine_quantized_other_backends_stub
QuantizedCPU, QuantizedCUDA: empty_per_channel_affine_quantized
- func: resize_(Tensor(a!) self, int[] size, *, MemoryFormat? memory_format=None) -> Tensor(a!)
use_const_ref_for_mutable_tensors: True
variants: method
device_guard: False
dispatch:
CPU, Meta: resize_
CUDA: resize_cuda_
QuantizedCPU: quantized_resize_cpu_
- func: empty_quantized(int[] size, Tensor qtensor) -> Tensor
variants: function
dispatch:
QuantizedCPU, QuantizedCUDA: empty_quantized
- func: empty.out(int[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
device_guard: False
- func: empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
device_guard: False
- func: empty_strided(int[] size, int[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
dispatch:
CPU: empty_strided_cpu
CUDA: empty_strided_cuda
Meta: empty_strided_meta
- func: erf(Tensor self) -> Tensor
structured_delegate: erf.out
variants: function, method
- func: erf_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: erf.out
variants: function, method
- func: erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: erf_out
- func: erfc(Tensor self) -> Tensor
structured_delegate: erfc.out
variants: function, method
- func: erfc_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: erfc.out
variants: function, method
- func: erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: erfc_out
- func: exp(Tensor self) -> Tensor
structured_delegate: exp.out
variants: function, method
- func: exp_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: exp.out
variants: function, method
- func: exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: exp_out
- func: exp2(Tensor self) -> Tensor
structured_delegate: exp2.out
variants: function, method
- func: exp2_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: exp2.out
variants: function, method
- func: exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: exp2_out
- func: expm1(Tensor self) -> Tensor
structured_delegate: expm1.out
variants: function, method
- func: expm1_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: expm1.out
variants: function, method
- func: expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: expm1_out
- func: expand(Tensor(a) self, int[] size, *, bool implicit=False) -> Tensor(a)
variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too.
device_guard: False
dispatch:
CompositeExplicitAutograd: expand
- func: expand_as(Tensor(a) self, Tensor other) -> Tensor(a)
variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too.
device_guard: False
- func: eye(int n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: eye.m(int n, int m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: eye.out(int n, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: eye_out_cpu
CUDA: eye_out_cuda
- func: eye.m_out(int n, int m, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: eye_out_cpu
CUDA: eye_out_cuda
- func: flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)
variants: function, method
- func: flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a)
variants: function, method
- func: flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a)
variants: function, method
- func: flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a)
variants: function, method
- func: unflatten.int(Tensor(a) self, int dim, int[] sizes, Dimname[]? names=None) -> Tensor(a)
variants: method
- func: unflatten.Dimname(Tensor(a) self, Dimname dim, int[] sizes, Dimname[] names) -> Tensor(a)
variants: method
- func: fill_.Scalar(Tensor(a!) self, Scalar value) -> Tensor(a!)
variants: function, method
dispatch:
CPU, CUDA, QuantizedCPU, QuantizedCUDA: fill_
Meta: fill_meta_
- func: fill_.Tensor(Tensor(a!) self, Tensor value) -> Tensor(a!)
variants: function, method
dispatch:
CPU, CUDA, QuantizedCPU, QuantizedCUDA: fill_
Meta: fill_meta_
- func: floor(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: floor
- func: floor_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: floor_
- func: floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: floor_out
- func: floor_divide(Tensor self, Tensor other) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: floor_divide
SparseCPU, SparseCUDA: floor_divide_sparse
- func: floor_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: floor_divide_
SparseCPU, SparseCUDA: floor_divide_sparse_
- func: floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: floor_divide_out
SparseCPU, SparseCUDA: floor_divide_out_sparse_zerodim
- func: floor_divide.Scalar(Tensor self, Scalar other) -> Tensor
variants: function, method
- func: floor_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: frac(Tensor self) -> Tensor
structured_delegate: frac.out
variants: function, method
- func: frac_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: frac.out
variants: function, method
- func: frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: frac_out
- func: full.names(int[] size, Scalar fill_value, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
device_guard: False
- func: full(int[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: full.out(int[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)
- func: full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
- func: from_file(str filename, bool? shared=None, int? size=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
dispatch:
CPU: from_file
- func: gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: gcd_out
- func: gcd(Tensor self, Tensor other) -> Tensor
variants: function, method
- func: gcd_(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: function, method
- func: lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: lcm_out
- func: lcm(Tensor self, Tensor other) -> Tensor
variants: function, method
- func: lcm_(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: function, method
# NOTE [ grid_sampler Native Functions ]
# `grid_sampler` does all the shape checking and then dispatches to one of
# `cudnn_grid_sampler`, `grid_sampler_2d`, or `grid_sampler_3d`, each of which
# has the corresponding backward defined as native functions as well. Therefore,
# in these functions and their backwards, no more shape checking is done.
#
# There is also _grid_sampler_2d_backward_cpu_fallback which is an
# implementation detail of grid_sampler_2d and is only exposed here for testing
# purposes.
#
# Additionally, arguments `padding_mode` and `interpolation_mode` are cast to
# enums defined in `native/GridSampler.h`. `cudnn_grid_sampler` doesn't take in
# `interpolation_mode` because it only supports Bilinear interpolation mode.
# Nor does it take in `align_corners` because it only supports the mode
# `align_corners = True`.
- func: grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
- func: grid_sampler_2d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
dispatch:
CPU: grid_sampler_2d_cpu
CUDA: grid_sampler_2d_cuda
- func: grid_sampler_2d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> (Tensor, Tensor)
dispatch:
CPU: grid_sampler_2d_backward_cpu
CUDA: grid_sampler_2d_backward_cuda
# See NOTE [ grid_sample CPU fallback ]
- func: _grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
dispatch:
CompositeExplicitAutograd: _grid_sampler_2d_cpu_fallback
- func: _grid_sampler_2d_cpu_fallback_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> (Tensor, Tensor)
- func: grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
dispatch:
CPU: grid_sampler_3d_cpu
CUDA: grid_sampler_3d_cuda
- func: grid_sampler_3d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> (Tensor, Tensor)
dispatch:
CPU: grid_sampler_3d_backward_cpu
CUDA: grid_sampler_3d_backward_cuda
- func: hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: kaiser_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: kaiser_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: kaiser_window.beta(int window_length, bool periodic, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor
- func: group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor
- func: native_group_norm(Tensor input, Tensor? weight, Tensor? bias, int N, int C, int HxW, int group, float eps) -> (Tensor, Tensor, Tensor)
dispatch:
CPU, CUDA: native_group_norm
CompositeImplicitAutograd: math_group_norm
- func: native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, int N, int C, int HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
CPU, CUDA: native_group_norm_backward
# Real to complex forward FFT
- func: _fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor
variants: function
dispatch:
CPU: _fft_r2c_mkl
CUDA: _fft_r2c_cufft
- func: _fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU: _fft_r2c_mkl_out
CUDA: _fft_r2c_cufft_out
# Complex to real inverse FFT
- func: _fft_c2r(Tensor self, int[] dim, int normalization, int last_dim_size) -> Tensor
variants: function
dispatch:
CPU: _fft_c2r_mkl
CUDA: _fft_c2r_cufft
- func: _fft_c2r.out(Tensor self, int[] dim, int normalization, int last_dim_size, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU: _fft_c2r_mkl_out
CUDA: _fft_c2r_cufft_out
# Standard complex to complex FFT (forward or backward)
- func: _fft_c2c(Tensor self, int[] dim, int normalization, bool forward) -> Tensor
variants: function
dispatch:
CPU: _fft_c2c_mkl
CUDA: _fft_c2c_cufft
- func: _fft_c2c.out(Tensor self, int[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU: _fft_c2c_mkl_out
CUDA: _fft_c2c_cufft_out
- func: _cufft_get_plan_cache_size(int device_index) -> int
- func: _cufft_get_plan_cache_max_size(int device_index) -> int
- func: _cufft_set_plan_cache_max_size(int device_index, int max_size) -> ()
- func: _cufft_clear_plan_cache(int device_index) -> ()
- func: index.Tensor(Tensor self, Tensor?[] indices) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: index
QuantizedCPU: quantized_index
# NB: This function is special-cased in tools/autograd/gen_variable_type.py
# NB: The following functions are declared in aten/src/ATen/templates/TensorBody.h and defined in aten/src/ATen/TensorIndexing.cpp:
# - Tensor Tensor::index(ArrayRef<TensorIndex> indices)
# - Tensor Tensor::index(std::initializer_list<TensorIndex> indices)
- func: index_copy_(Tensor(a!) self, int dim, Tensor index, Tensor source) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: index_copy_
- func: index_copy(Tensor self, int dim, Tensor index, Tensor source) -> Tensor
variants: function, method
- func: index_copy_.dimname(Tensor(a!) self, Dimname dim, Tensor index, Tensor source) -> Tensor(a!)
variants: method
- func: index_copy.dimname(Tensor self, Dimname dim, Tensor index, Tensor source) -> Tensor
variants: function, method
- func: index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: index_put_
# NB: The following functions are declared in aten/src/ATen/templates/TensorBody.h and defined in aten/src/ATen/TensorIndexing.cpp:
# - Tensor & Tensor::index_put_(ArrayRef<TensorIndex> indices, Tensor const & rhs)
# - Tensor & Tensor::index_put_(ArrayRef<TensorIndex> indices, Scalar v)
# - Tensor & Tensor::index_put_(std::initializer_list<TensorIndex> indices, Tensor const & rhs)
# - Tensor & Tensor::index_put_(std::initializer_list<TensorIndex> indices, Scalar v)
- func: index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor
variants: function, method
- func: _index_put_impl_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: _index_put_impl_
- func: instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> Tensor
variants: function
- func: inverse(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: inverse
- func: inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: inverse_out
- func: _inverse_helper(Tensor self) -> Tensor
variants: function
dispatch:
CPU: _inverse_helper_cpu
CUDA: _inverse_helper_cuda
- func: isclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> Tensor
variants: function, method
- func: isnan(Tensor self) -> Tensor
variants: function, method
device_guard: False
dispatch:
CPU, CUDA: isnan
SparseCPU, SparseCUDA: isnan_sparse
- func: is_distributed(Tensor self) -> bool
variants: function, method
device_guard: False
- func: is_floating_point(Tensor self) -> bool
variants: function, method
device_guard: False
manual_cpp_binding: True
- func: is_complex(Tensor self) -> bool
variants: function, method
device_guard: False
manual_cpp_binding: True
- func: isreal(Tensor self) -> Tensor
variants: function, method
- func: is_nonzero(Tensor self) -> bool
variants: function, method
device_guard: False
- func: is_same_size(Tensor self, Tensor other) -> bool
variants: function, method
device_guard: False
- func: is_signed(Tensor self) -> bool
variants: function, method
device_guard: False
manual_cpp_binding: True
- func: kl_div(Tensor self, Tensor target, int reduction=Mean, *, bool log_target=False) -> Tensor
dispatch:
CompositeExplicitAutograd: kl_div
- func: kl_div_backward(Tensor grad_output, Tensor self, Tensor target, int reduction=Mean, *, bool log_target=False) -> Tensor
dispatch:
CPU: kl_div_backward_cpu
CUDA: kl_div_backward_cuda
- func: kron(Tensor self, Tensor other) -> Tensor
variants: function, method
- func: kron.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: kthvalue(Tensor self, int k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
CompositeExplicitAutograd: kthvalue
- func: kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CPU: kthvalue_out_cpu
CUDA: kthvalue_out_cuda
- func: kthvalue.dimname(Tensor self, int k, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
- func: kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
- func: layer_norm(Tensor input, int[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensor
- func: native_layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)
dispatch:
CPU: layer_norm_cpu
CUDA: layer_norm_cuda
CompositeImplicitAutograd: math_native_layer_norm
- func: native_layer_norm_backward(Tensor grad_out, Tensor input, int[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
CPU: layer_norm_backward_cpu
CUDA: layer_norm_backward_cuda
- func: nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: nan_to_num
- func: nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: nan_to_num_
- func: nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: nan_to_num_out
- func: linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor
python_module: nn
- func: mkldnn_linear(Tensor self, Tensor weight, Tensor? bias=None) -> Tensor
python_module: nn
dispatch:
MkldnnCPU: mkldnn_linear
- func: mkldnn_linear_backward_input(int[] input_size, Tensor grad_output, Tensor weight) -> Tensor
dispatch:
MkldnnCPU: mkldnn_linear_backward_input
- func: mkldnn_linear_backward_weights(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined) -> (Tensor, Tensor)
dispatch:
MkldnnCPU: mkldnn_linear_backward_weights
- func: mkldnn_linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
MkldnnCPU: mkldnn_linear_backward
- func: fbgemm_linear_int8_weight_fp32_activation(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor
- func: fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor
- func: fbgemm_linear_quantize_weight(Tensor input) -> (Tensor, Tensor, float, int)
- func: fbgemm_pack_gemm_matrix_fp16(Tensor input) -> Tensor
- func: fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor
- func: fbgemm_linear_fp16_weight(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor
- func: fbgemm_pack_quantized_matrix(Tensor input) -> Tensor
- func: fbgemm_pack_quantized_matrix.KN(Tensor input, int K, int N) -> Tensor
- func: ldexp.Tensor(Tensor self, Tensor other) -> Tensor
variants: function, method
- func: ldexp_(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: function, method
- func: ldexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: linspace(Scalar start, Scalar end, int? steps=None, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: linspace.out(Scalar start, Scalar end, int? steps=None, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: linspace_cpu_out
CUDA: linspace_cuda_out
- func: log(Tensor self) -> Tensor
structured_delegate: log.out
variants: function, method
- func: log_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: log.out
variants: function, method
- func: log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: log_out
- func: log10(Tensor self) -> Tensor
structured_delegate: log10.out
variants: function, method
- func: log10_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: log10.out
variants: function, method
- func: log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: log10_out
- func: log1p(Tensor self) -> Tensor
structured_delegate: log1p.out
variants: function, method
dispatch:
SparseCPU, SparseCUDA: log1p_sparse
- func: log1p_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: log1p.out
variants: function, method
dispatch:
SparseCPU, SparseCUDA: log1p_sparse_
- func: log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: log1p_out
SparseCPU, SparseCUDA: log1p_out_sparse
- func: log2(Tensor self) -> Tensor
structured_delegate: log2.out
variants: function, method
- func: log2_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: log2.out
variants: function, method
- func: log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: log2_out
- func: logaddexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: logaddexp_out
- func: logaddexp(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: logaddexp
- func: logaddexp2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: logaddexp2_out
- func: logaddexp2(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: logaddexp2
- func: xlogy.Tensor(Tensor self, Tensor other) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: xlogy
- func: xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor
variants: function
dispatch:
CPU, CUDA: xlogy
- func: xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: xlogy
# xlogy: inplace variant
- func: xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: function, method
dispatch:
CPU, CUDA: xlogy_
- func: xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: function, method
dispatch:
CPU, CUDA: xlogy_
# xlogy: out variant
- func: xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: xlogy_out
- func: xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: xlogy_out
- func: xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: xlogy_out
- func: logdet(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: logdet
- func: logspace(Scalar start, Scalar end, int? steps=None, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: logspace.out(Scalar start, Scalar end, int? steps=None, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: logspace_cpu_out
CUDA: logspace_cuda_out
# log_softmax allows positional dtype, unlike most operators, because kwonly is BC-breaking when loading jit models.
- func: log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
variants: function, method
- func: log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
variants: function, method
- func: _log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor
dispatch:
CPU: log_softmax_cpu
CUDA: log_softmax_cuda
- func: _log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor
dispatch:
CPU: log_softmax_backward_cpu
CUDA: log_softmax_backward_cuda
- func: _logcumsumexp(Tensor self, int dim) -> Tensor
dispatch:
CPU: _logcumsumexp_cpu
CUDA: _logcumsumexp_cuda
- func: _logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: _logcumsumexp_out_cpu
CUDA: _logcumsumexp_out_cuda
- func: logcumsumexp(Tensor self, int dim) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: logcumsumexp
- func: logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: logcumsumexp_out
- func: logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor
variants: function, method
- func: logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
- func: logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: logsumexp
- func: logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: logsumexp_out
- func: logsumexp.names(Tensor self, Dimname[1] dim, bool keepdim=False) -> Tensor
variants: function, method
- func: logsumexp.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor
- func: matmul(Tensor self, Tensor other) -> Tensor
variants: function, method
- func: matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: matrix_rank.tol(Tensor self, float tol, bool symmetric=False) -> Tensor
- func: matrix_rank(Tensor self, bool symmetric=False) -> Tensor
# Alias to linalg.matrix_power
- func: matrix_power(Tensor self, int n) -> Tensor
variants: function, method
# Alias to linalg.matrix_power
- func: matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!)
- func: matrix_exp(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: matrix_exp
- func: matrix_exp_backward(Tensor self, Tensor grad) -> Tensor
- func: _aminmax(Tensor self) -> (Tensor, Tensor)
variants: function
dispatch:
CPU, CUDA: _aminmax_all
- func: _aminmax.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor, Tensor)
variants: function
dispatch:
CPU, CUDA: _aminmax
- func: _compute_linear_combination(Tensor input, Tensor coefficients) -> Tensor
dispatch:
CPU, CUDA: _compute_linear_combination
- func: _compute_linear_combination.out(Tensor input, Tensor coefficients, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: _compute_linear_combination_out
- func: max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
CPU, CUDA, QuantizedCPU, QuantizedCUDA: max
- func: max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CPU, CUDA: max_out
- func: max.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
- func: max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)
- func: value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, int[] sizes, bool keepdim) -> Tensor
variants: function
device_guard: False
- func: amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: amax
- func: amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: amax_out
# Return: (Tensor output, Tensor indices)
- func: max_pool1d_with_indices(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)
- func: max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor
- func: max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor
- func: mkldnn_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor
dispatch:
MkldnnCPU: mkldnn_max_pool2d
- func: mkldnn_max_pool2d_backward(Tensor grad_output, Tensor output, Tensor input, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor
dispatch:
MkldnnCPU: mkldnn_max_pool2d_backward
- func: mkldnn_max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor
dispatch:
MkldnnCPU: mkldnn_max_pool3d
- func: mkldnn_max_pool3d_backward(Tensor grad_output, Tensor output, Tensor input, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor
dispatch:
MkldnnCPU: mkldnn_max_pool3d_backward
- func: quantized_max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor
dispatch:
QuantizedCPU: quantized_max_pool1d
- func: quantized_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor
dispatch:
QuantizedCPU: quantized_max_pool2d
- func: max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor
# The CPU and GPU dispatch variants are named weirdly here because otherwise there
# are namespacing issues in C++
- func: mean(Tensor self, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: mean_cpu_gpu
QuantizedCPU: mean_quantized_cpu
- func: mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: mean_cpu_gpu
QuantizedCPU: mean_quantized_cpu
- func: mean.out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: mean_out_cpu_gpu
QuantizedCPU: mean_out_quantized_cpu
- func: mean.names_dim(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
variants: function, method
- func: mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
- func: median(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: median_cpu
CUDA: median_cuda
- func: median.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
CompositeExplicitAutograd: median
- func: median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CPU: median_out_cpu
CUDA: median_out_cuda
- func: median.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
- func: median.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
- func: nanmedian(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: nanmedian_cpu
CUDA: nanmedian_cuda
- func: nanmedian.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
CompositeExplicitAutograd: nanmedian
- func: nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CPU: nanmedian_out_cpu
CUDA: nanmedian_out_cuda
- func: nanmedian.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
- func: nanmedian.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
- func: min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
CPU, CUDA, QuantizedCPU, QuantizedCUDA: min
- func: min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CPU, CUDA: min_out
- func: min.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
- func: min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)
- func: amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: amin
- func: amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: amin_out
- func: mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensor
dispatch:
CompositeExplicitAutograd: mkldnn_convolution
- func: mkldnn_convolution_backward_input(int[] self_size, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool bias_defined) -> Tensor
- func: mkldnn_convolution_backward_weights(int[] weight_size, Tensor grad_output, Tensor self, int[] padding, int[] stride, int[] dilation, int groups, bool bias_defined) -> (Tensor, Tensor)
- func: mkldnn_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
CompositeExplicitAutograd: mkldnn_convolution_backward
- func: miopen_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor)
dispatch:
CUDA: miopen_batch_norm
- func: miopen_batch_norm_backward(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon) -> (Tensor, Tensor, Tensor)
dispatch:
CUDA: miopen_batch_norm_backward
- func: miopen_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: miopen_convolution
- func: miopen_convolution_backward_input(int[] self_size, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: miopen_convolution_backward_input
- func: miopen_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
CUDA: miopen_convolution_backward
- func: miopen_convolution_backward_bias(Tensor grad_output) -> Tensor
dispatch:
CUDA: miopen_convolution_backward_bias
- func: miopen_convolution_backward_weight(int[] weight_size, Tensor grad_output, Tensor self, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: miopen_convolution_backward_weight
- func: miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: miopen_convolution_transpose
# NB: output_padding not strictly needed here, but it's helpful for the float
# backwards
- func: miopen_convolution_transpose_backward(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
CUDA: miopen_convolution_transpose_backward
- func: miopen_convolution_transpose_backward_input(Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: miopen_convolution_transpose_backward_input
- func: miopen_convolution_transpose_backward_weight(int[] weight_size, Tensor grad_output, Tensor self, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: miopen_convolution_transpose_backward_weight
- func: miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: miopen_depthwise_convolution
- func: miopen_depthwise_convolution_backward_input(int[] self_size, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: miopen_depthwise_convolution_backward_input
- func: miopen_depthwise_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
CUDA: miopen_depthwise_convolution_backward
- func: miopen_depthwise_convolution_backward_weight(int[] weight_size, Tensor grad_output, Tensor self, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: miopen_depthwise_convolution_backward_weight
- func: miopen_rnn(Tensor input, Tensor[] weight, int weight_stride0, 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)
dispatch:
CUDA: miopen_rnn
- func: miopen_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[])
dispatch:
CUDA: miopen_rnn_backward
- func: mm(Tensor self, Tensor mat2) -> Tensor
variants: function, method
dispatch:
CPU: mm_cpu
CUDA: mm_cuda
SparseCPU, SparseCUDA, SparseCsrCPU: _sparse_mm
- func: mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: mm_cpu_out
CUDA: mm_out_cuda
SparseCPU, SparseCUDA: _sparse_mm_out
SparseCsrCPU: _sparse_csr_mm_out
- func: _sparse_mm(Tensor sparse, Tensor dense) -> Tensor
- func: _sparse_sparse_matmul(Tensor self, Tensor other) -> Tensor
dispatch:
SparseCPU: sparse_sparse_matmul_cpu
SparseCUDA: sparse_sparse_matmul_cuda
- func: _sparse_mask_helper(Tensor t, Tensor mask_indices) -> Tensor
dispatch:
SparseCPU: sparse_mask_helper_cpu
SparseCUDA: sparse_mask_helper_cuda
- func: mode(Tensor self, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
dispatch:
CPU, CUDA: mode
- func: mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CompositeExplicitAutograd: mode_out
- func: mode.dimname(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
variants: function, method
- func: mode.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
- func: mul.Tensor(Tensor self, Tensor other) -> Tensor
structured_delegate: mul.out
variants: function, method
dispatch:
SparseCPU, SparseCUDA: mul_sparse
MkldnnCPU: mkldnn_mul
- func: mul_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
structured_delegate: mul.out
variants: method
dispatch:
SparseCPU, SparseCUDA: mul_sparse_
MkldnnCPU: mkldnn_mul_
- func: mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: mul_out
SparseCPU: mul_out_sparse_cpu
SparseCUDA: mul_out_sparse_cuda
MkldnnCPU: mkldnn_mul_out
# For C++ only, until we have conversion from C++ numbers to Tensor
- func: mul.Scalar(Tensor self, Scalar other) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: mul
- func: mul_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: mul_
# multiply, alias for mul
- func: multiply.Tensor(Tensor self, Tensor other) -> Tensor
variants: function, method
- func: multiply_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: multiply.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: multiply.Scalar(Tensor self, Scalar other) -> Tensor
variants: function, method
- func: multiply_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: mv(Tensor self, Tensor vec) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: mv
SparseCPU, SparseCUDA, SparseCsrCPU: mv_sparse
- func: mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: mv_out
- func: mvlgamma(Tensor self, int p) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: mvlgamma
- func: mvlgamma_(Tensor(a!) self, int p) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: mvlgamma_
- func: narrow_copy(Tensor self, int dim, int start, int length) -> Tensor
variants: function, method
dispatch:
CPU: narrow_copy_dense_cpu
SparseCPU, SparseCUDA: narrow_copy_sparse
CompositeExplicitAutograd: narrow_copy_dense
- func: narrow_copy.out(Tensor self, int dim, int start, int length, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: narrow_copy_dense_cpu_out
- func: narrow(Tensor(a) self, int dim, int start, int length) -> Tensor(a)
variants: function, method
device_guard: False
- func: narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -> Tensor(a)
variants: function, method
device_guard: False
- func: native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)
dispatch:
CPU: batch_norm_cpu
CUDA: batch_norm_cuda
MkldnnCPU: mkldnn_batch_norm
- func: native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!))
dispatch:
CUDA: batch_norm_cuda_out
- func: batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor)
dispatch:
CUDA: batch_norm_stats_cuda
- func: batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps) -> Tensor
dispatch:
CUDA: batch_norm_elemt_cuda
- func: batch_norm_elemt.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CUDA: batch_norm_elemt_cuda_out
# for backward compatibility
- func: batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor)
dispatch:
CUDA: batch_norm_gather_stats_cuda
- func: batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts) -> (Tensor, Tensor)
dispatch:
CUDA: batch_norm_gather_stats_with_counts_cuda
- func: native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
CPU: batch_norm_backward_cpu
CUDA: batch_norm_backward_cuda
MkldnnCPU: mkldnn_batch_norm_backward
- func: batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor)
dispatch:
CUDA: batch_norm_backward_reduce_cuda
- func: batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor mean_dy, Tensor mean_dy_xmu, Tensor count) -> Tensor
dispatch:
CUDA: batch_norm_backward_elemt_cuda
- func: batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor)
dispatch:
CPU: batch_norm_update_stats_cpu
CUDA: batch_norm_update_stats_cuda
- func: is_vulkan_available() -> bool
- func: _nnpack_available() -> bool
- func: _nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, int[2] padding, int[2] stride=1) -> Tensor
variants: function
dispatch:
CompositeExplicitAutograd: _nnpack_spatial_convolution
- func: _nnpack_spatial_convolution_backward(Tensor input, Tensor grad_output, Tensor weight, int[2] padding, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
variants: function
- func: _nnpack_spatial_convolution_backward_input(Tensor input, Tensor grad_output, Tensor weight, int[2] padding) -> Tensor
variants: function
- func: _nnpack_spatial_convolution_backward_weight(Tensor input, int[] weightsize, Tensor grad_output, int[2] padding) -> Tensor
variants: function
- func: ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
device_guard: False
- func: ones(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: ones.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)
- func: ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
- func: pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor
- func: cdist(Tensor x1, Tensor x2, float p=2, int? compute_mode=None) -> Tensor
- func: _euclidean_dist(Tensor x1, Tensor x2) -> Tensor
dispatch:
CompositeExplicitAutograd: _euclidean_dist
- func: _cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor
dispatch:
CPU, CUDA: _cdist_forward
- func: _cdist_backward(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist) -> Tensor
dispatch:
CPU, CUDA: _cdist_backward
- func: pdist(Tensor self, float p=2) -> Tensor
- func: _pdist_forward(Tensor self, float p=2) -> Tensor
dispatch:
CPU, CUDA: _pdist_forward
- func: _pdist_backward(Tensor grad, Tensor self, float p, Tensor pdist) -> Tensor
dispatch:
CPU, CUDA: _pdist_backward
- func: cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor
variants: function
- func: permute(Tensor(a) self, int[] dims) -> Tensor(a)
variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too.
dispatch:
CompositeExplicitAutograd: permute
- func: movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)
variants: function, method
- func: movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a)
variants: function, method
# moveaxis, alias for movedim
- func: moveaxis.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)
variants: function, method
- func: moveaxis.int(Tensor(a) self, int source, int destination) -> Tensor(a)
variants: function, method
# Only exposed from C++ -- in Python,
# we expose it as an attribute `T`, not a function.
#
# I'd like to name this "T" in C++ too, but
# calling a native function "T" causes undefined
# behavior on Windows, for reasons I don't understand
# (maybe related to capital letter collation somehow...)
- func: numpy_T(Tensor(a) self) -> Tensor(a)
variants: method
- func: pixel_shuffle(Tensor self, int upscale_factor) -> Tensor
- func: pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor
- func: channel_shuffle(Tensor self, int groups) -> Tensor
dispatch:
CPU: channel_shuffle
QuantizedCPU: channel_shuffle_quantized_cpu
- func: is_pinned(Tensor self) -> bool
variants: method
- func: pin_memory(Tensor(a) self) -> Tensor(a)
variants: method
- func: pinverse(Tensor self, float rcond=1e-15) -> Tensor
variants: function, method
- func: poisson_nll_loss(Tensor input, Tensor target, bool log_input, bool full, float eps, int reduction) -> Tensor
variants: function
- func: rad2deg(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: rad2deg
- func: rad2deg_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: rad2deg_
- func: rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: rad2deg_out
- func: deg2rad(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: deg2rad
- func: deg2rad_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: deg2rad_
- func: deg2rad.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: deg2rad_out
- func: scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: rand.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
device_guard: False
- func: rand.generator_with_names(int[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
device_guard: False
- func: rand(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: rand.generator(int[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: rand.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)
- func: rand.generator_out(int[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
- func: rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
- func: randint(int high, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: randint.generator(int high, int[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: randint.low(int low, int high, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: randint.low_generator(int low, int high, int[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: randint.out(int high, int[] size, *, Tensor(a!) out) -> Tensor(a!)
- func: randint.generator_out(int high, int[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
- func: randint.low_out(int low, int high, int[] size, *, Tensor(a!) out) -> Tensor(a!)
- func: randint.low_generator_out(int low, int high, int[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
- func: randint_like(Tensor self, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
- func: randint_like.low_dtype(Tensor self, int low, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
- func: randn(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: randn.generator(int[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: randn.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
device_guard: False
- func: randn.generator_with_names(int[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
device_guard: False
- func: randn.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)
- func: randn.generator_out(int[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
- func: randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
- func: randperm(int n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: randperm.generator(int n, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: randperm.out(int n, *, Tensor(a!) out) -> Tensor(a!)
- func: randperm.generator_out(int n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: randperm_out_cpu
CUDA: randperm_out_cuda
- func: range.step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: range(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: range_cpu_out
CUDA: range_cuda_out
- func: ravel(Tensor(a) self) -> Tensor(a)
variants: function, method
- func: reciprocal(Tensor self) -> Tensor
structured_delegate: reciprocal.out
variants: function, method
- func: reciprocal_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: reciprocal.out
variants: function, method
- func: reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: reciprocal_out
- func: neg(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU, CUDA, SparseCPU, SparseCUDA: neg
- func: neg_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
CPU, CUDA: neg_
SparseCPU, SparseCUDA: neg_sparse_
- func: neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: neg_out
SparseCPU, SparseCUDA: neg_out_sparse
# Alias for neg
- func: negative(Tensor self) -> Tensor
variants: function, method
- func: negative_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
- func: negative.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: repeat(Tensor self, int[] repeats) -> Tensor
variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too.
dispatch:
CompositeExplicitAutograd: repeat
- func: repeat_interleave.Tensor(Tensor repeats) -> Tensor
variants: function
dispatch:
CPU: repeat_interleave_cpu
CUDA: repeat_interleave_cuda
- func: repeat_interleave.self_Tensor(Tensor self, Tensor repeats, int? dim=None) -> Tensor
variants: function, method
- func: repeat_interleave.self_int(Tensor self, int repeats, int? dim=None) -> Tensor
variants: function, method
- func: reshape(Tensor(a) self, int[] shape) -> Tensor(a)
variants: function, method
device_guard: False
- func: _mkldnn_reshape(Tensor self, int[] shape) -> Tensor
device_guard: False
dispatch:
MkldnnCPU: mkldnn_reshape
- func: reshape_as(Tensor(a) self, Tensor other) -> Tensor(a)
variants: method
device_guard: False
- func: round(Tensor self) -> Tensor
structured_delegate: round.out
variants: function, method
- func: round_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: round.out
variants: function, method
- func: round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU: round_out
CUDA: round_out
- func: rrelu(Tensor self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor
- func: rrelu_(Tensor(a!) self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!)
- func: relu(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: relu
MkldnnCPU: mkldnn_relu
QuantizedCPU: relu_quantized_cpu
- func: relu_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
CPU, CUDA: relu_
MkldnnCPU: mkldnn_relu_
QuantizedCPU: relu_quantized_cpu_
- func: relu6(Tensor self) -> Tensor
python_module: nn
- func: relu6_(Tensor(a!) self) -> Tensor(a!)
python_module: nn
- func: prelu(Tensor self, Tensor weight) -> Tensor
variants: function, method
dispatch:
CPU: prelu_cpu
CUDA: prelu_cuda
- func: prelu_backward(Tensor grad_output, Tensor self, Tensor weight) -> (Tensor, Tensor)
variants: function, method
dispatch:
CPU: prelu_backward_cpu
CUDA: prelu_backward_cuda
- func: gelu(Tensor self) -> Tensor
python_module: nn
dispatch:
CPU: gelu_cpu
CUDA: gelu_cuda
- func: gelu_backward(Tensor grad, Tensor self) -> Tensor
python_module: nn
dispatch:
CPU: gelu_backward_cpu
CUDA: gelu_backward_cuda
- func: infinitely_differentiable_gelu_backward(Tensor grad, Tensor self) -> Tensor
variants: function
python_module: nn
device_guard: False
- func: hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: hardshrink
- func: hardshrink_backward(Tensor grad_out, Tensor self, Scalar lambd) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: hardshrink_backward
- func: rsqrt(Tensor self) -> Tensor
structured_delegate: rsqrt.out
variants: function, method
- func: rsqrt_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: rsqrt.out
variants: function, method
- func: rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: rsqrt_out
- func: select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a)
variants: function, method
device_guard: False
- func: select.int(Tensor(a) self, int dim, int index) -> Tensor(a)
variants: function, method
device_guard: False
dispatch:
CompositeExplicitAutograd: select
- func: select_backward(Tensor grad, int[] input_sizes, int dim, int index) -> Tensor
variants: function
device_guard: False
- func: selu(Tensor self) -> Tensor
- func: selu_(Tensor(a!) self) -> Tensor(a!)
- func: celu(Tensor self, Scalar alpha=1.0) -> Tensor
dispatch:
CompositeExplicitAutograd: celu
- func: celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: celu_
- func: silu(Tensor self) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: silu
- func: silu_(Tensor(a!) self) -> Tensor(a!)
python_module: nn
dispatch:
CompositeExplicitAutograd: silu_
- func: silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: silu_out
- func: silu_backward(Tensor grad_output, Tensor self) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: silu_backward
CompositeImplicitAutograd: math_silu_backward
- func: sigmoid(Tensor self) -> Tensor
structured_delegate: sigmoid.out
variants: function, method
dispatch:
QuantizedCPU: sigmoid_quantized_cpu
MkldnnCPU: mkldnn_sigmoid
- func: sigmoid_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: sigmoid.out
variants: function, method
dispatch:
MkldnnCPU: mkldnn_sigmoid_
- func: sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: sigmoid_out
- func: logit(Tensor self, float? eps=None) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: logit
- func: logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!)
variants: function, method
dispatch:
CPU, CUDA: logit_
- func: logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: logit_out
- func: sin(Tensor self) -> Tensor
structured_delegate: sin.out
variants: function, method
- func: sin_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: sin.out
variants: function, method
- func: sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: sin_out
- func: sinc(Tensor self) -> Tensor
structured_delegate: sinc.out
variants: function, method
- func: sinc_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: sinc.out
variants: function, method
- func: sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: sinc_out
- func: sinh(Tensor self) -> Tensor
structured_delegate: sinh.out
variants: function, method
- func: sinh_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: sinh.out
variants: function, method
- func: sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: sinh_out
# Returns a copy of this `Variable` that is detached from its autograd graph.
# This method is OK to call if the `Variable` is a view.
#
# NOTE: Previously, if we change the tensor metadata (e.g. sizes / strides /
# storage / storage_offset) of a tensor created from `detach()`, those metadata
# in the original tensor will also be updated. However, the new behavior is that
# those metadata changes to the detached tensor will not update the original tensor
# anymore, and in the `detach()` function we need to set `allow_tensor_metadata_change_`
# to false to make such changes explicitly illegal, in order to prevent users from
# changing metadata of the detached tensor and expecting the original tensor to also
# be updated.
- func: detach(Tensor(a) self) -> Tensor(a)
variants: function, method
dispatch:
CompositeExplicitAutograd: detach
# Like `detach()`, but modifies this `Variable` in-place. This method may
# only be called on non-view `Variable`s. You can use `is_view()` to check
# this. If this `Variable` is a view, throws an `std::runtime_error()`.
- func: detach_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: detach_
- func: size.int(Tensor self, int dim) -> int
variants: function
device_guard: False
manual_cpp_binding: True
- func: size.Dimname(Tensor self, Dimname dim) -> int
variants: function, method
device_guard: False
- func: slice.Tensor(Tensor(a) self, int dim=0, int? start=0, int? end=9223372036854775807, int step=1) -> Tensor(a)
variants: function, method
device_guard: False
dispatch:
CompositeExplicitAutograd: slice
- func: slice_backward(Tensor grad, int[] input_sizes, int dim, int start, int end, int step) -> Tensor
variants: function
device_guard: False
- func: slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet)
variants: function, method
dispatch:
CompositeExplicitAutograd: slogdet
- func: smm(Tensor self, Tensor mat2) -> Tensor
variants: function, method
# softmax allows positional dtype, unlike most operators, because kwonly is BC-breaking when loading jit models.
- func: softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
variants: function, method
- func: softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
variants: function, method
- func: _softmax(Tensor self, int dim, bool half_to_float) -> Tensor
dispatch:
CPU: softmax_cpu
CUDA: softmax_cuda
MkldnnCPU: mkldnn_softmax
- func: _softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor
dispatch:
CPU: softmax_backward_cpu
CUDA: softmax_backward_cuda
- func: unsafe_split.Tensor(Tensor self, int split_size, int dim=0) -> Tensor[]
variants: function, method
device_guard: False
dispatch:
CompositeExplicitAutograd: unsafe_split
- func: split.Tensor(Tensor(a) self, int split_size, int dim=0) -> Tensor(a)[]
variants: function, method
device_guard: False
dispatch:
CompositeExplicitAutograd: split
- func: unsafe_split_with_sizes(Tensor self, int[] split_sizes, int dim=0) -> Tensor[]
variants: function, method
device_guard: False
dispatch:
CompositeExplicitAutograd: unsafe_split_with_sizes
- func: split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -> Tensor(a)[]
variants: function, method
device_guard: False
dispatch:
CompositeExplicitAutograd: split_with_sizes
- func: squeeze(Tensor(a) self) -> Tensor(a)
variants: function, method
device_guard: False
dispatch:
CompositeExplicitAutograd: squeeze
- func: squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)
variants: function, method
device_guard: False
dispatch:
CompositeExplicitAutograd: squeeze
- func: squeeze.dimname(Tensor(a) self, Dimname dim) -> Tensor(a)
variants: function, method
device_guard: False
- func: squeeze_(Tensor(a!) self) -> Tensor(a!)
variants: method
device_guard: False
dispatch:
CompositeExplicitAutograd: squeeze_
- func: squeeze_.dim(Tensor(a!) self, int dim) -> Tensor(a!)
variants: method
device_guard: False
dispatch:
CompositeExplicitAutograd: squeeze_
- func: squeeze_.dimname(Tensor(a!) self, Dimname dim) -> Tensor(a!)
variants: method
device_guard: False
- func: sspaddmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: function, method
- func: sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: _sspaddmm_out_only_sparse
CUDA: _sspaddmm_out_only_sparse_cuda
SparseCPU: _sspaddmm_out_cpu
SparseCUDA: _sspaddmm_out_cuda
- func: stack(Tensor[] tensors, int dim=0) -> Tensor
dispatch:
CompositeExplicitAutograd: stack
- func: stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: stack_out
- func: _stack(Tensor[] tensors, int dim=0) -> Tensor
dispatch: # match the backends supported by _cat
CPU: _stack_cpu
CompositeExplicitAutograd: _stack
- func: _stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch: # match the backends supported by _cat_out
CPU: _stack_out_cpu
CompositeExplicitAutograd: _stack_out
- func: hstack(Tensor[] tensors) -> Tensor
- func: hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
- func: vstack(Tensor[] tensors) -> Tensor
- func: vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
- func: dstack(Tensor[] tensors) -> Tensor
- func: dstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
# The signature is designed to be consistent with librosa except that it is
# missing the `pad_mode` and `center` arguments, which are taken care of at
# `torch.functional.py`. They shall be moved here once we have mapping between
# Python strings and C++ Enum in codegen.
- func: stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor
variants: function, method
- func: istft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, bool normalized=False, bool? onesided=None, int? length=None, bool return_complex=False) -> Tensor
variants: function, method
- func: stride.int(Tensor self, int dim) -> int
variants: function
device_guard: False
manual_cpp_binding: True
- func: stride.Dimname(Tensor self, Dimname dim) -> int
variants: function, method
device_guard: False
- func: sum(Tensor self, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: sum
- func: sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: sum
- func: sum.dim_DimnameList(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
variants: function, method
- func: sum.IntList_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: sum_out
- func: sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
- func: nansum(Tensor self, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: nansum
- func: nansum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: nansum
- func: nansum.IntList_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: nansum_out
- func: sum_to_size(Tensor self, int[] size) -> Tensor
variants: method
device_guard: False
- func: sqrt(Tensor self) -> Tensor
structured_delegate: sqrt.out
variants: function, method
dispatch:
SparseCPU, SparseCUDA: sqrt_sparse
- func: sqrt_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: sqrt.out
variants: function, method
- func: sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: sqrt_out
SparseCPU, SparseCUDA: sqrt_out_sparse
- func: square(Tensor self) -> Tensor
variants: function, method
- func: square_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
- func: square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: square_out
- func: std(Tensor self, bool unbiased=True) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: std
- func: std.dim(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: std
- func: std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)
variants: function
dispatch:
CPU, CUDA: std_mean
- func: std_mean.dim(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
variants: function
dispatch:
CPU, CUDA: std_mean
- func: std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
variants: function
- func: std.out(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: std_out
- func: std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor
variants: function, method
- func: std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: prod(Tensor self, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: prod
- func: prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: prod
- func: prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: prod_out
- func: prod.dim_Dimname(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
variants: function, method
- func: prod.Dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
- func: t(Tensor(a) self) -> Tensor(a)
device_guard: False
variants: function, method
dispatch:
CompositeExplicitAutograd: t
- func: t_(Tensor(a!) self) -> Tensor(a!)
device_guard: False
variants: method
dispatch:
CompositeExplicitAutograd: t_
- func: tan(Tensor self) -> Tensor
structured_delegate: tan.out
variants: function, method
- func: tan_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: tan.out
variants: function, method
- func: tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: tan_out
- func: tanh(Tensor self) -> Tensor
structured_delegate: tanh.out
variants: function, method
dispatch:
QuantizedCPU: tanh_quantized_cpu
MkldnnCPU: mkldnn_tanh
- func: tanh_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: tanh.out
variants: function, method
dispatch:
MkldnnCPU: mkldnn_tanh_
- func: tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: tanh_out
- func: tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor
variants: function
- func: tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: tensordot_out
# TODO: namespace threshold in 'nn'
- func: threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor
variants: function
dispatch:
CPU: threshold
CUDA: threshold_cuda
QuantizedCPU: threshold_quantized_cpu
- func: threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!)
variants: function
dispatch:
CPU: threshold_
CUDA: threshold__cuda
- func: threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: threshold_out
CUDA: threshold_out_cuda
- func: threshold_backward(Tensor grad_output, Tensor self, Scalar threshold) -> Tensor
variants: function
dispatch:
CPU: threshold_backward
CUDA: threshold_backward_cuda
MkldnnCPU: mkldnn_relu_backward
- func: tile(Tensor self, int[] dims) -> Tensor
variants: function, method
- func: transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)
variants: function, method
device_guard: False
dispatch:
CompositeExplicitAutograd: transpose
- func: transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a)
variants: function, method
device_guard: False
- func: _mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor
device_guard: False
dispatch:
MkldnnCPU: mkldnn_transpose
- func: transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)
variants: method
device_guard: False
dispatch:
CompositeExplicitAutograd: transpose_
- func: _mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)
device_guard: False
dispatch:
MkldnnCPU: mkldnn_transpose_
- func: one_hot(Tensor self, int num_classes=-1) -> Tensor
python_module: nn
variants: function
- func: flip(Tensor self, int[] dims) -> Tensor
variants: function, method
dispatch:
CPU, QuantizedCPU: flip_cpu
CUDA: flip_cuda
- func: fliplr(Tensor self) -> Tensor
variants: function, method
- func: flipud(Tensor self) -> Tensor
variants: function, method
- func: roll(Tensor self, int[1] shifts, int[1] dims=[]) -> Tensor
variants: function, method
dispatch:
CPU: roll_cpu
CUDA: roll_cuda
# default int[] value [0,1] should not add space after comma, since codegen parser uses ', ' to split args
- func: rot90(Tensor self, int k=1, int[] dims=[0,1]) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: rot90
- func: trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor
- func: trapz.dx(Tensor y, *, float dx=1, int dim=-1) -> Tensor
- func: _trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor
dispatch:
CompositeExplicitAutograd: _trilinear
- func: triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, float margin=1.0, float p=2, float eps=1e-06, bool swap=False, int reduction=Mean) -> Tensor
- func: trunc(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: trunc
- func: trunc_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: trunc_
- func: trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: trunc_out
# Alias for trunc
- func: fix(Tensor self) -> Tensor
variants: function, method
- func: fix_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
- func: fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: type_as(Tensor self, Tensor other) -> Tensor
variants: method
- func: _has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool
variants: function
- func: _unique(Tensor self, bool sorted=True, bool return_inverse=False) -> (Tensor, Tensor)
variants: function
dispatch:
CPU: _unique_cpu
CUDA: _unique_cuda
- func: unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)
variants: function
dispatch:
CPU: unique_dim_cpu
CUDA: unique_dim_cuda
- func: unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor)
variants: function
dispatch:
CPU: unique_consecutive_cpu
CUDA: unique_consecutive_cuda
- func: unique_dim_consecutive(Tensor self, int dim, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)
variants: function
dispatch:
CPU: unique_dim_consecutive_cpu
CUDA: unique_dim_consecutive_cuda
# _unique and _unique_dim are fragile and modifying them easily cause internal break
# the below operator is a temporary hack for adding return_counts support
# Please don't rely on these two operators, they will be removed soon
- func: _unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)
variants: function
dispatch:
CPU: _unique2_cpu
CUDA: _unique2_cuda
- func: _unsafe_view(Tensor self, int[] size) -> Tensor
dispatch:
CompositeExplicitAutograd: _unsafe_view
- func: unsqueeze(Tensor(a) self, int dim) -> Tensor(a)
variants: function, method
device_guard: False
dispatch:
CompositeExplicitAutograd: unsqueeze
- func: unsqueeze_(Tensor(a!) self, int dim) -> Tensor(a!)
variants: method
device_guard: False
dispatch:
CompositeExplicitAutograd: unsqueeze_
- func: vander(Tensor x, int? N=None, bool increasing=False) -> Tensor
- func: var(Tensor self, bool unbiased=True) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: var
- func: var.dim(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: var
- func: var.out(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: var_out
- func: var.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor
variants: function, method
- func: var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: var_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)
variants: function
dispatch:
CPU, CUDA: var_mean
- func: var_mean.dim(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
variants: function
dispatch:
CPU, CUDA: var_mean
- func: var_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
variants: function
- func: view_as(Tensor(a) self, Tensor other) -> Tensor(a)
variants: method
device_guard: False
# we define both of these because 'where' does the broadcast and '_s_where' doesn't;
# this allows us to implicitly calculate the broadcast derivative, while only dealing with the
# _s_where derivative.
- func: where.self(Tensor condition, Tensor self, Tensor other) -> Tensor
variants: function, method
- func: where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor
variants: function
- func: where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor
variants: function
- func: where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor
variants: function
- func: where(Tensor condition) -> Tensor[]
variants: function
- func: _s_where(Tensor condition, Tensor self, Tensor other) -> Tensor
variants: function
dispatch:
CPU, CUDA: _s_where
- func: norm_except_dim(Tensor v, int pow=2, int dim=0) -> Tensor
variants: function
# VariableType::_weight_norm does not want to be given a gap in the autograd graph,
# so we don't define "dispatch" variants for it.
- func: _weight_norm(Tensor v, Tensor g, int dim=0) -> Tensor
variants: function
- func: _weight_norm_cuda_interface(Tensor v, Tensor g, int dim=0) -> (Tensor, Tensor)
variants: function
dispatch:
CUDA: weight_norm_cuda
- func: _weight_norm_cuda_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)
variants: function
dispatch:
CUDA: weight_norm_cuda_backward
- func: _weight_norm_differentiable_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)
variants: function
- func: zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
device_guard: False
- func: zeros(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: zeros.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)
- func: zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
- func: _standard_gamma_grad(Tensor self, Tensor output) -> Tensor
variants: function
dispatch:
CPU: _standard_gamma_grad_cpu
CUDA: _standard_gamma_grad_cuda
- func: _standard_gamma(Tensor self, Generator? generator=None) -> Tensor
variants: function
dispatch:
CPU: _s_gamma_cpu
CUDA: _s_gamma_cuda
- func: _dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor
dispatch:
CPU: _dirichlet_grad_cpu
CUDA: _dirichlet_grad_cuda
- func: _sample_dirichlet(Tensor self, Generator? generator=None) -> Tensor
variants: function
dispatch:
CPU: _s_dirichlet_cpu
CUDA: _s_dirichlet_cuda
- func: poisson(Tensor self, Generator? generator=None) -> Tensor
dispatch:
CPU: _s_poisson_cpu
CUDA: _s_poisson_cuda
- func: binomial(Tensor count, Tensor prob, Generator? generator=None) -> Tensor
dispatch:
CPU: _s_binomial_cpu
CUDA: _s_binomial_cuda
# When more variants get ported to native, this dispatch will get more
# complicated
- func: native_norm(Tensor self, Scalar p=2) -> Tensor
dispatch:
SparseCPU, SparseCUDA: norm_sparse
- func: native_norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype) -> Tensor
dispatch:
SparseCPU, SparseCUDA: norm_sparse
# TODO: reduce signatures down to one when optional args is available
- func: _sparse_sum(Tensor self) -> Tensor
- func: _sparse_sum.dtype(Tensor self, *, ScalarType dtype) -> Tensor
- func: _sparse_sum.dim(Tensor self, int[1] dim) -> Tensor
dispatch:
CompositeExplicitAutograd: _sparse_sum
- func: _sparse_sum.dim_dtype(Tensor self, int[1] dim, *, ScalarType dtype) -> Tensor
- func: _sparse_sum_backward(Tensor grad, Tensor self, int[] dim) -> Tensor
dispatch:
SparseCPU: _sparse_sum_backward_cpu
SparseCUDA: _sparse_sum_backward_cuda
- func: _sparse_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
variants: function
- func: _sparse_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
variants: function
- func: _sparse_softmax(Tensor self, int dim, bool half_to_float) -> Tensor
dispatch:
SparseCPU: softmax_sparse_cpu
SparseCUDA: softmax_sparse_cuda
- func: _sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor
dispatch:
SparseCPU: softmax_backward_sparse_cpu
SparseCUDA: softmax_backward_sparse_cuda
- func: _sparse_log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
variants: function
- func: _sparse_log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
variants: function
- func: _sparse_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor
dispatch:
SparseCPU: log_softmax_sparse_cpu
SparseCUDA: log_softmax_sparse_cuda
- func: _sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor
dispatch:
SparseCPU: log_softmax_backward_sparse_cpu
SparseCUDA: log_softmax_backward_sparse_cuda
- func: norm.ScalarOpt_dtype(Tensor self, Scalar? p, *, ScalarType dtype) -> Tensor
variants: function, method
dispatch:
CPU, CUDA, SparseCPU, SparseCUDA: norm
- func: norm.Scalar(Tensor self, Scalar p=2) -> Tensor
variants: function, method
dispatch:
CPU, CUDA, SparseCPU, SparseCUDA: norm
- func: norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor
variants: function, method
dispatch:
CPU, CUDA, SparseCPU, SparseCUDA: norm
- func: norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> Tensor
variants: function, method
dispatch:
CPU, CUDA, SparseCPU, SparseCUDA: norm
- func: norm.dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: norm_out
- func: norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: norm_out
# These four redispatch in their implementation, so OK to be CompositeImplicitAutograd
- func: norm.names_ScalarOpt_dim_dtype(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor
variants: function, method
- func: norm.names_ScalarOpt_dim(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False) -> Tensor
variants: function, method
- func: norm.names_dtype_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)
- func: norm.names_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent)
variants: method, function
dispatch:
CompositeExplicitAutograd: frexp
- func: frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent)
dispatch:
CPU, CUDA: frexp_out
- func: frobenius_norm(Tensor self) -> Tensor
variants: function
- func: frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor
variants: function
- func: frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
variants: function
- func: nuclear_norm(Tensor self, bool keepdim=False) -> Tensor
variants: function
- func: nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
variants: function
- func: nuclear_norm.dim(Tensor self, int[2] dim, bool keepdim=False) -> Tensor
variants: function
- func: nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
variants: function
- func: clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: clone
SparseCPU, SparseCUDA: clone_sparse
MkldnnCPU: mkldnn_clone
QuantizedCPU, QuantizedCUDA: quantized_clone
- func: resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!)
use_const_ref_for_mutable_tensors: True
variants: function, method
dispatch:
CompositeExplicitAutograd: resize_as_
- func: resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!)
use_const_ref_for_mutable_tensors: True
variants: function
dispatch:
SparseCPU, SparseCUDA: resize_as_sparse_
SparseCsrCPU: resize_as_sparse_csr_
- func: zero_(Tensor(a!) self) -> Tensor(a!)
variants: method, function
dispatch:
CPU, CUDA: zero_
Meta: zero_meta_
SparseCPU, SparseCUDA: zero_sparse_
MkldnnCPU: mkldnn_zero_
- func: sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: sub_out
SparseCPU, SparseCUDA: sub_out_sparse
- func: sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
variants: function, method
structured_delegate: sub.out
dispatch:
SparseCPU, SparseCUDA: sub_sparse
- func: sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
variants: method
structured_delegate: sub.out
dispatch:
SparseCPU, SparseCUDA: sub_sparse_
# For C++ only, until we have conversion from C++ numbers to Tensor
- func: sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: sub
- func: sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: sub_
# subtract, alias for sub
- func: subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
- func: subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
variants: function, method
- func: subtract_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
variants: method
# For C++ only, until we have conversion from C++ numbers to Tensor
- func: subtract.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
variants: function, method
- func: subtract_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
variants: method
- func: rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
variants: function
dispatch:
CPU, CUDA: rsub
- func: heaviside.out(Tensor self, Tensor values, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: heaviside_out
- func: heaviside(Tensor self, Tensor values) -> Tensor
variants: function, method
- func: heaviside_(Tensor(a!) self, Tensor values) -> Tensor(a!)
variants: method
# For C++ only, until we have conversion from C++ numbers to Tensor
- func: rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
variants: function
dispatch:
CompositeExplicitAutograd: rsub
# Functionally the same as addmm, but we give it a different derivative formula
# that doesn't propagate gradients to non-present entries on sparse.
- func: _sparse_addmm(Tensor self, Tensor sparse, Tensor dense, *, Scalar beta=1, Scalar alpha=1) -> Tensor
dispatch:
CompositeExplicitAutograd: _sparse_addmm
- func: addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: addmm_cpu_out
CUDA: addmm_out_cuda
SparseCPU: addmm_out_sparse_dense_cpu
SparseCUDA: addmm_out_sparse_dense_cuda
SparseCsrCPU: addmm_out_sparse_csr_dense_cpu
- func: addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: function, method
dispatch:
CPU: addmm_cpu
CUDA: addmm_cuda
SparseCPU: addmm_sparse_dense_cpu
SparseCUDA: addmm_sparse_dense_cuda
SparseCsrCPU: addmm_sparse_csr_dense_cpu
- func: addmm_(Tensor(a!) self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
variants: method
dispatch:
CPU: addmm_cpu_
CUDA: addmm__cuda
# Warning! For whatever reason, the inplace sparse addmm is NON
# broadcasting
SparseCPU: s_addmm_sparse_dense_cpu_
SparseCUDA: s_addmm_sparse_dense_cuda_
# NOTE [ Sparse: autograd and API ]
#
#
# Sparse Tensor Constructors
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# The API entry points to sparse tensor construction should be
# `sparse_coo tensor` and `_sparse_coo_tensor_unsafe`. Depending on whether the
# indices and values tensors are given, they eventually dispatch to either
# `sparse_coo_tensor_with_dims` or `sparse_coo_tensor_with_dims_and_tensors`.
#
# The autograd support for ctor is implement on `sparse_coo_tensor_with_dims_and_tensors`.
#
# The API methods `sparse_coo tensor` and `_sparse_coo_tensor_unsafe`
# **must not** have specific type dispatches because otherwise codegen will
# consider them as abstract methods (see Note [Abstract ATen methods]), dispatch
# using **Tensor** type, and thus lose autograd tracking on the actual method
# they dispatch to, e.g., `sparse_coo_tensor_with_dims_and_tensors`.
#
#
# Sparse Methods API Design
# ~~~~~~~~~~~~~~~~~~~~~~~~~
#
# Goals: 1. Flexible API for users to write custom sparse ops
# 2. ctor and member accessor with autograd support
#
# To achieve 1, we need to provide a set of *dangerous* APIs (dangerous in the
# sense that misusing them will break sparse tensor invariant and may out in
# unexpected behavior, e.g., crash). These methods are all prefixed with
# underscore "_" to indicate that they should be used with care. We provide:
#
# + `_indices()`: returns the *raw* indices within the sparse tensor (not just
# sharing storage). Any inplace operation will change the
# actual indices, including t_, set_, as_strided_, resize_,
# etc.
# + `_values()`: returns the *raw* values within the sparse tensor. Similar
# semantics as `_indices()`
# + `_nnz()`: returns the number of non-zero entries. This will always be
# determined by the shapes of indices and values.
# + `_coalesced_(bool)`: inplace sets whether the tensor is coalesced, and
# returns itself.
#
# These methods are very useful in writing new operations, e.g., a custom
# autograd Function.
#
# We also provide other public *safe* APIs:
# + `indices()`: returns a **view** of the indices tensor if the sparse tensor
# is **coalesced**.
# + `values()`: returns a **view** of the values tensor if the containing
# sparse tensor is **coalesced**.
# + `sparse_dim()`: number of sparse dimensions
# + `dense_dim()`: number of dense dimensions
# + `is_coalesced()`: whether the sparse tensor is coalesced
#
# `_indices()` and `_values()` should returns the raw indices and values dense
# tensors within a sparse tensor. They can be quite unsafe with inplace
# operations like `t_()`, and exposes uncoalesced indices and values. The public
# recommended API is `indices()` and `values()`, both of which first check that
# the tensor is coalesced and return views on those tensors.
#
#
# Autograd Support
# ~~~~~~~~~~~~~~~~
#
# Autograd is supported on `values()` and sparse tensor ctor with indices and
# values tensors. E.g., `torch.sparse_coo_tensor(i, v).values().sum()` is
# differentiable w.r.t. `v`.
#
# NB: The `values()` and `_values()` operators are special in that they are
# layout-aware, i.e., the output depends not just on the data it represents, but
# also on the input layout details (in this case, the `indices` tensor). See
# NOTE [ as_strided Backward and layout-aware/agnostic autograd ] in Functions.cpp
# for discussion on layout-aware vs layout-agnostic autograd. Since PyTorch ops
# operate in the layout-agnostic mode, similar to `as_strided`, backward of
# these two operators need to consider them in a layout-agnostic way:
# + `values()`:
# Input is coalesced.
# We just pretend having `input.indices()` as an additional argument
# `input_indices`, then forward is similar to
# `input.to(kStrided).index_select(input_indices)` regardless of the layout.
# Note that `values()` normally is layout-aware even if we constrain
# ourselves on sparse inputs since it may include all zeros values entries
# as "present" entries.
# + `_values()`:
# Input may be uncoalesced.
# It is not straightforward to construct a layout-agnostic version because
# duplicate indices entries may exist and additional parameterization is
# needed to distribute the value into different values entries. Furthermore,
# this op is intended to provide ways to write custom sparse ops, rather
# than being used in autograd graph, so it is marked as *non-differentiable*
# in derivatives.yaml.
#
# Before reading the following, see NOTE [ Autograd Variable Views ] in
# variable.h for details on views that are tracked by autograd, and views that
# are not.
#
# Moreover, these methods return tensors that share storage with inputs, so we
# mark these methods as view ops to support autograd history tracking.
# The sparse tensor ctor output should technically be view of both input indices
# and values tensors, but currently we only support setting as view of a single
# Variable, so it is only view of the values tensor.
# TODO: clone indices in sparse tensor ctor.
#
# For other methods that return outputs that share storage with inputs, i.e.,
# `indices()` and `_indices()`. We mark their outputs as non-differentiable, so
# the view relation is not tracked by autograd, but the version counter is still
# shared. In other words, their outputs are non-differentiable views of the
# sparse tensor.
# FIXME: would be nicer if TensorOptions was optional based; not adding default arguments for options given
# the default would never make sense.
- func: sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
- func: sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
- func: sparse_coo_tensor.size(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
- func: sparse_coo_tensor.indices(Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: sparse_coo_tensor.indices_size(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: _sparse_coo_tensor_unsafe(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: _validate_sparse_coo_tensor_args(Tensor indices, Tensor values, int[] size) -> ()
- func: _sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
dispatch:
SparseCPU, SparseCUDA: new_with_dims_sparse
- func: _sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, int[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
dispatch:
SparseCPU, SparseCUDA: new_with_dims_and_tensor_sparse
- func: sparse_resize_(Tensor(a!) self, int[] size, int sparse_dim, int dense_dim) -> Tensor(a!)
use_const_ref_for_mutable_tensors: True
variants: method
dispatch:
SparseCPU, SparseCUDA: sparse_resize_
- func: sparse_resize_and_clear_(Tensor(a!) self, int[] size, int sparse_dim, int dense_dim) -> Tensor(a!)
use_const_ref_for_mutable_tensors: True
variants: method
dispatch:
SparseCPU, SparseCUDA: sparse_resize_and_clear_
- func: sparse_mask(Tensor self, Tensor mask) -> Tensor
variants: method
dispatch:
SparseCPU: sparse_mask_cpu
SparseCUDA: sparse_mask_cuda
- func: to_dense(Tensor self, ScalarType? dtype=None) -> Tensor
variants: method
dispatch:
SparseCPU, SparseCUDA, SparseCsrCPU: sparse_to_dense
MkldnnCPU: mkldnn_to_dense
- func: to_dense_backward(Tensor grad, Tensor input) -> Tensor
- func: sparse_dim(Tensor self) -> int
variants: method
dispatch:
SparseCPU, SparseCUDA: sparse_dim_sparse
device_guard: False
# legacy method
- func: _dimI(Tensor self) -> int
variants: method
dispatch:
SparseCPU, SparseCUDA: sparse_dim_sparse
device_guard: False
- func: dense_dim(Tensor self) -> int
variants: method
dispatch:
SparseCPU, SparseCUDA: dense_dim_sparse
device_guard: False
# legacy method
- func: _dimV(Tensor self) -> int
variants: method
dispatch:
SparseCPU, SparseCUDA: dense_dim_sparse
device_guard: False
- func: _nnz(Tensor self) -> int
variants: method
dispatch:
SparseCPU, SparseCUDA: _nnz_sparse
SparseCsrCPU: _nnz_sparse_csr
device_guard: False
# NOTE: [ coalesce autograd ]
# coalesce returns self directly for already coalesced sparse tensors.
# This means coalesce cannot have a derivative registered, otherwise it creates
# circular references in the autograd graph (see gh-52874).
# Instead, the derivative is registered on the slow-path "_coalesce"
- func: coalesce(Tensor(a) self) -> Tensor(a)
variants: method
- func: _coalesce(Tensor self) -> Tensor
dispatch:
SparseCPU: _coalesce_sparse_cpu
SparseCUDA: _coalesce_sparse_cuda
- func: is_coalesced(Tensor self) -> bool
variants: method
dispatch:
SparseCPU, SparseCUDA: is_coalesced_sparse
device_guard: False
- func: _indices(Tensor(a) self) -> Tensor(a)
variants: method
dispatch:
SparseCPU, SparseCUDA: _indices_sparse
device_guard: False
- func: _values(Tensor(a) self) -> Tensor(a)
variants: method
dispatch:
SparseCPU, SparseCUDA: _values_sparse
device_guard: False
# This method doesn't do any check but only directly sets the flag. So it can be
# a bit unsafe. Similar to _indices and _values, this is useful for implementing
# custom sparse operations in Python/C++ extension.
- func: _coalesced_(Tensor(a!) self, bool coalesced) -> Tensor(a!)
variants: method
dispatch:
SparseCPU, SparseCUDA: _coalesced_sparse_
device_guard: False
- func: indices(Tensor(a) self) -> Tensor(a)
variants: method
dispatch:
SparseCPU, SparseCUDA: indices_sparse
device_guard: False
- func: values(Tensor(a) self) -> Tensor(a)
variants: method
dispatch:
SparseCPU, SparseCUDA: values_sparse
SparseCsrCPU: values_sparse_csr
device_guard: False
- func: crow_indices(Tensor(a) self) -> Tensor(a)
variants: method
dispatch:
SparseCsrCPU: crow_indices_sparse_csr
device_guard: False
- func: col_indices(Tensor(a) self) -> Tensor(a)
variants: method
dispatch:
SparseCsrCPU: col_indices_sparse_csr
device_guard: False
- func: hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
SparseCPU: hspmm_out_sparse_cpu
SparseCUDA: hspmm_out_sparse_cuda
- func: hspmm(Tensor mat1, Tensor mat2) -> Tensor
dispatch:
SparseCPU: hspmm_sparse_cpu
SparseCUDA: hspmm_sparse_cuda
- func: copy_sparse_to_sparse_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!)
variants: function
dispatch:
SparseCPU, SparseCUDA: copy_sparse_
- func: unbind.int(Tensor(a) self, int dim=0) -> Tensor(a)[]
variants: function, method
dispatch:
CompositeExplicitAutograd: unbind
- func: unbind.Dimname(Tensor(a) self, Dimname dim) -> Tensor(a)[]
variants: function, method
- func: to_sparse.sparse_dim(Tensor self, int sparse_dim) -> Tensor
variants: method
dispatch:
CPU, CUDA: dense_to_sparse
- func: to_sparse(Tensor self) -> Tensor
variants: method
dispatch:
CPU, CUDA: dense_to_sparse
- func: to_mkldnn(Tensor self, ScalarType? dtype=None) -> Tensor
variants: method
dispatch:
CPU: dense_to_mkldnn
- func: mkldnn_reorder_conv2d_weight(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1) -> Tensor
variants: function
python_module: nn
dispatch:
MkldnnCPU: mkldnn_reorder_conv2d_weight
- func: mkldnn_reorder_conv3d_weight(Tensor self, int[3] padding=0, int[3] stride=1, int[3] dilation=1, int groups=1) -> Tensor
variants: function
python_module: nn
dispatch:
MkldnnCPU: mkldnn_reorder_conv3d_weight
- func: to_mkldnn_backward(Tensor grad, Tensor input) -> Tensor
- func: quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensor
variants: function
dispatch:
CPU, CUDA: quantize_per_tensor
- func: quantize_per_tensor.tensors(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype) -> Tensor[]
variants: function
dispatch:
CPU: quantize_per_tensor_list_cpu
- func: quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor
variants: function
dispatch:
CPU: quantize_per_channel_cpu
- func: dequantize.self(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: dequantize_cpu
QuantizedCPU, QuantizedCUDA: dequantize_quantized_cpu
- func: dequantize.tensors(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
QuantizedCPU: dequantize_tensors_quantized_cpu
- func: q_scale(Tensor self) -> float
variants: function, method
dispatch:
QuantizedCPU, QuantizedCUDA: q_scale_quant
- func: q_zero_point(Tensor self) -> int
variants: function, method
dispatch:
QuantizedCPU, QuantizedCUDA: q_zero_point_quant
- func: q_per_channel_scales(Tensor self) -> Tensor
variants: function, method
dispatch:
QuantizedCPU, QuantizedCUDA: q_per_channel_scales
- func: q_per_channel_zero_points(Tensor self) -> Tensor
variants: function, method
dispatch:
QuantizedCPU, QuantizedCUDA: q_per_channel_zero_points
- func: q_per_channel_axis(Tensor self) -> int
variants: function, method
dispatch:
QuantizedCPU, QuantizedCUDA: q_per_channel_axis
- func: int_repr(Tensor self) -> Tensor
variants: function, method
dispatch:
QuantizedCPU: int_repr_quantized_cpu
QuantizedCUDA: int_repr_quantized_cuda
- func: _make_per_tensor_quantized_tensor(Tensor self, float scale, int zero_point) -> Tensor
dispatch:
CPU: make_per_tensor_quantized_tensor_cpu
CUDA: make_per_tensor_quantized_tensor_cuda
- func: _make_per_channel_quantized_tensor(Tensor self, Tensor scale, Tensor zero_point, int axis) -> Tensor
dispatch:
CPU: make_per_channel_quantized_tensor_cpu
- func: qscheme(Tensor self) -> QScheme
variants: method
dispatch:
QuantizedCPU, QuantizedCUDA: qscheme_quant
- func: fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor
variants: function
- func: fake_quantize_per_tensor_affine_cachemask(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> (Tensor output, Tensor mask)
variants: function
dispatch:
CPU, CUDA: fake_quantize_per_tensor_affine_cachemask
- func: fake_quantize_per_tensor_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor
variants: function
- func: _fake_quantize_learnable_per_tensor_affine(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor
variants: function
dispatch:
CPU, CUDA: _fake_quantize_learnable_per_tensor_affine
- func: _fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor)
variants: function
- func: fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensor
variants: function
- func: fake_quantize_per_channel_affine_cachemask(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> (Tensor output, Tensor mask)
variants: function
dispatch:
CPU, CUDA: fake_quantize_per_channel_affine_cachemask
- func: fake_quantize_per_channel_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor
variants: function
- func: _fake_quantize_learnable_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor
variants: function
dispatch:
CPU, CUDA: _fake_quantize_learnable_per_channel_affine
- func: _fake_quantize_learnable_per_channel_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor)
variants: function
- func: _choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int)
variants: function
- func: _saturate_weight_to_fp16(Tensor weight) -> Tensor
variants: function
- func: choose_qparams_optimized(Tensor input, int numel, int n_bins, float ratio, int bit_width) -> (Tensor, Tensor)
variants: function
# to(Device) must not exist because all constructors of Device also works for
# TensorOptions. Otherwise, an ambiguity error is thrown.
# See NOTE [ TensorOptions Constructors ].
- func: to.dtype_layout(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor
variants: method
device_guard: False
- func: to.device(Tensor self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor
variants: method
device_guard: False
- func: to.dtype(Tensor self, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor
variants: method
device_guard: False
- func: to.other(Tensor self, Tensor other, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor
variants: method
device_guard: False
- func: meshgrid(Tensor[] tensors) -> Tensor[]
- func: cartesian_prod(Tensor[] tensors) -> Tensor
variants: function
- func: combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor
variants: function
- func: item(Tensor self) -> Scalar
variants: method
- func: result_type.Tensor(Tensor tensor, Tensor other) -> ScalarType
variants: function
- func: result_type.Scalar(Tensor tensor, Scalar other) -> ScalarType
variants: function
- func: result_type.Scalar_Tensor(Scalar scalar, Tensor tensor) -> ScalarType
variants: function
- func: result_type.Scalar_Scalar(Scalar scalar1, Scalar scalar2) -> ScalarType
- func: can_cast(ScalarType from, ScalarType to) -> bool
variants: function
- func: promote_types(ScalarType type1, ScalarType type2) -> ScalarType
variants: function
# NB: Does NOT check precondition that numel == 1
- func: _local_scalar_dense(Tensor self) -> Scalar
dispatch:
CPU: _local_scalar_dense_cpu
CUDA: _local_scalar_dense_cuda
variants: function
# Fused RNN kernels
- func: _thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor)
dispatch:
CUDA: _thnn_fused_lstm_cell_cuda
- func: _thnn_fused_lstm_cell_backward(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
dispatch:
CUDA: _thnn_fused_lstm_cell_backward_cuda
- func: _thnn_differentiable_lstm_cell_backward(Tensor? grad_hy, Tensor? grad_cy, Tensor input_gates, Tensor hidden_gates, Tensor? input_bias, Tensor? hidden_bias, Tensor cx, Tensor cy) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
- func: _thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor)
dispatch:
CUDA: _thnn_fused_gru_cell_cuda
- func: _thnn_fused_gru_cell_backward(Tensor grad_hy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
dispatch:
CUDA: _thnn_fused_gru_cell_backward_cuda
- func: _thnn_differentiable_gru_cell_backward(Tensor grad_hy, Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias, Tensor? hidden_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
# RNN cells and layers
- func: lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor)
- func: lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor, Tensor)
- func: gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)
- func: gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)
- func: rnn_tanh.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)
- func: rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)
- func: rnn_relu.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)
- func: rnn_relu.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)
- func: lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)
- func: gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor
- func: rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor
- func: rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor
# Quantized RNN layer registration has been moved to C10 dispatch in `RNN.cpp`
# Quantized RNN layers
# - func: quantized_lstm(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, ScalarType? dtype=None, bool use_dynamic=False) -> (Tensor, Tensor, Tensor)
# - func: quantized_lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, *, ScalarType? dtype=None, bool use_dynamic=False) -> (Tensor, Tensor, Tensor)
# Quantized GRU layers
# - func: quantized_gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)
#
# - func: quantized_gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)
#
# Quantized RNN cells
- func: quantized_lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> (Tensor, Tensor)
- func: quantized_gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor
- func: quantized_rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor
- func: quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor
# PackedSequence utilities
- func: _pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor)
dispatch:
CompositeExplicitAutograd: _pack_padded_sequence
- func: _pack_padded_sequence_backward(Tensor grad, int[] input_size, Tensor batch_sizes, bool batch_first) -> Tensor
- func: _pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor)
# wrappers for legacy TH methods
- func: set_.source_Storage(Tensor(a!) self, Storage source) -> Tensor(a!)
variants: method
device_guard: False
dispatch:
CPU, CUDA: set_
- func: set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, int storage_offset, int[] size, int[] stride=[]) -> Tensor(a!)
variants: method
device_guard: False
dispatch:
CPU: set_storage_cpu_
CUDA: set_storage_cuda_
QuantizedCPU, QuantizedCUDA: set_storage_quantized_
- func: set_.source_Tensor(Tensor(a!) self, Tensor source) -> Tensor(a!)
variants: method
device_guard: False
dispatch:
CPU, CUDA: set_tensor_
- func: set_(Tensor(a!) self) -> Tensor(a!)
variants: method
dispatch:
CPU: set_cpu_
CUDA: set_cuda_
- func: is_set_to(Tensor self, Tensor tensor) -> bool
variants: method
device_guard: False
dispatch:
CPU, CUDA: is_set_to
- func: masked_fill_.Scalar(Tensor(a!) self, Tensor mask, Scalar value) -> Tensor(a!)
variants: method
dispatch:
CPU: masked_fill__cpu
CUDA: masked_fill__cuda
- func: masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor
variants: function, method
- func: masked_fill_.Tensor(Tensor(a!) self, Tensor mask, Tensor value) -> Tensor(a!)
variants: method
dispatch:
CPU: masked_fill__cpu
CUDA: masked_fill__cuda
- func: masked_fill.Tensor(Tensor self, Tensor mask, Tensor value) -> Tensor
variants: function, method
- func: masked_scatter_(Tensor(a!) self, Tensor mask, Tensor source) -> Tensor(a!)
variants: method
dispatch:
CPU: masked_scatter__cpu
CUDA: masked_scatter__cuda
- func: masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor
variants: function, method
- func: view(Tensor(a) self, int[] size) -> Tensor(a)
variants: method
device_guard: False
dispatch:
CPU, CUDA, Meta, QuantizedCPU, QuantizedCUDA: view
MkldnnCPU: mkldnn_view
# Warning: If you want to change the name or overload name of this
# operator, you might also want to change the `isBlockListedSchema`
# function in `torch/csrc/jit/frontend/schema_catching.cpp`.
# The name and overload name of this operator is hardcoded in that
# function in order to workaround a bug:
# https://github.com/pytorch/pytorch/issues/47964
- func: view.dtype(Tensor(a) self, ScalarType dtype) -> Tensor(a)
variants: method
device_guard: False
dispatch:
CompositeExplicitAutograd: view_dtype
- func: put_(Tensor(a!) self, Tensor index, Tensor source, bool accumulate=False) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: put_
- func: put(Tensor self, Tensor index, Tensor source, bool accumulate=False) -> Tensor
variants: function, method
- func: index_add_(Tensor(a!) self, int dim, Tensor index, Tensor source) -> Tensor(a!)
variants: method
- func: index_add_.alpha(Tensor(a!) self, int dim, Tensor index, Tensor source, *, Scalar alpha) -> Tensor(a!)
variants: method
dispatch:
CPU: index_add_cpu_
CUDA: index_add_cuda_
- func: index_add(Tensor self, int dim, Tensor index, Tensor source) -> Tensor
variants: function, method
- func: index_add.alpha(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha) -> Tensor
variants: function, method
- func: index_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor
variants: function, method
- func: index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!)
variants: method
dispatch:
CPU: index_fill_
CUDA: index_fill_
- func: index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor
variants: function, method
- func: index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: index_fill_
- func: index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor
variants: function, method
- func: index_fill_.Dimname_Scalar(Tensor(a!) self, Dimname dim, Tensor index, Scalar value) -> Tensor(a!)
variants: method
- func: index_fill_.Dimname_Tensor(Tensor(a!) self, Dimname dim, Tensor index, Tensor value) -> Tensor(a!)
variants: method
- func: index_fill.Dimname_Scalar(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor
variants: function, method
- func: index_fill.Dimname_Tensor(Tensor self, Dimname dim, Tensor index, Tensor value) -> Tensor
variants: function, method
- func: scatter_.src(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: scatter_
- func: scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor
variants: function, method
- func: scatter_.value(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: scatter_fill_
- func: scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor
variants: function, method
- func: scatter.dimname_src(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor
variants: function, method
- func: scatter.dimname_value(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor
variants: function, method
- func: scatter_.reduce(Tensor(a!) self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: scatter_reduce_
- func: scatter_.value_reduce(Tensor(a!) self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: scatter_scalar_reduce_
- func: scatter_add_(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: scatter_add_
- func: scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor
variants: function, method
- func: scatter_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor
variants: function, method
- func: eq_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: eq_
- func: eq_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: eq_
- func: bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: bitwise_and_out
- func: bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: bitwise_and_out
- func: bitwise_and.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: bitwise_and_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: bitwise_and_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: __and__.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: __and__.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: __iand__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: __iand__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: bitwise_or.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: bitwise_or_out
- func: bitwise_or.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: bitwise_or_out
- func: bitwise_or.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: bitwise_or_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: bitwise_or_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: __or__.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: __or__.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: __ior__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: __ior__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: bitwise_xor.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: bitwise_xor_out
- func: bitwise_xor.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU, CUDA: bitwise_xor_out
- func: bitwise_xor.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: bitwise_xor_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: bitwise_xor_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: __xor__.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: __xor__.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: __ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: __ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: __lshift__.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: __lshift__
- func: __lshift__.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: __lshift__
- func: __ilshift__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: __ilshift__
- func: __ilshift__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: __ilshift__
- func: __rshift__.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: __rshift__
- func: __rshift__.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: __rshift__
- func: __irshift__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: __irshift__
- func: __irshift__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: __irshift__
- func: tril_(Tensor(a!) self, int diagonal=0) -> Tensor(a!)
variants: method
dispatch:
CPU: tril_cpu_
CUDA: tril_cuda_
- func: triu_(Tensor(a!) self, int diagonal=0) -> Tensor(a!)
variants: method
dispatch:
CPU: triu_cpu_
CUDA: triu_cuda_
- func: digamma_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: digamma.out
variants: method
- func: renorm_(Tensor(a!) self, Scalar p, int dim, Scalar maxnorm) -> Tensor(a!)
variants: method
dispatch:
CPU: legacy::cpu::_th_renorm_
CUDA: legacy::cuda::_th_renorm_
- func: lerp_.Scalar(Tensor(a!) self, Tensor end, Scalar weight) -> Tensor(a!)
variants: method
dispatch:
CPU: lerp_cpu_scalar_
CUDA: lerp_cuda_scalar_
- func: lerp_.Tensor(Tensor(a!) self, Tensor end, Tensor weight) -> Tensor(a!)
variants: method
dispatch:
CPU: lerp_cpu_tensor_
CUDA: lerp_cuda_tensor_
- func: fmod_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: fmod_
- func: fmod_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: fmod_
- func: remainder_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: remainder_
- func: remainder_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: remainder_
- func: addbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: addbmm_
- func: addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: addbmm_out
- func: addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: addbmm
- func: addcdiv_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: addcdiv_
- func: random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: random_
Meta: random_meta_
- func: random_.to(Tensor(a!) self, int to, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: random_
Meta: random_meta_
- func: random_(Tensor(a!) self, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: random_
Meta: random_meta_
- func: uniform_(Tensor(a!) self, float from=0, float to=1, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: uniform_
Meta: uniform_meta_
- func: cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: cauchy_
- func: log_normal_(Tensor(a!) self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: log_normal_
- func: exponential_(Tensor(a!) self, float lambd=1, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: exponential_
- func: geometric_(Tensor(a!) self, float p, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: geometric_
# wrappers for TH functions
- func: diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: diag_cpu_out
CUDA: diag_cuda_out
- func: diag(Tensor self, int diagonal=0) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: diag
- func: diag_backward(Tensor grad, int[] input_sizes, int diagonal) -> Tensor
variants: function
device_guard: False
- func: cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: cross_out
- func: cross(Tensor self, Tensor other, int? dim=None) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: cross
- func: triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: triu_cpu_out
CUDA: triu_cuda_out
- func: triu(Tensor self, int diagonal=0) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: triu
- func: tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: tril_cpu_out
CUDA: tril_cuda_out
- func: tril(Tensor self, int diagonal=0) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: tril
- func: tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
dispatch:
CPU: tril_indices_cpu
CUDA: tril_indices_cuda
- func: triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
dispatch:
CPU: triu_indices_cpu
CUDA: triu_indices_cuda
- func: trace(Tensor self) -> Tensor
variants: method, function
dispatch:
CPU: trace_cpu
CUDA: trace_cuda
- func: trace_backward(Tensor grad, int[] sizes) -> Tensor
variants: function
device_guard: False
- func: ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: ne_out
QuantizedCPU: ne_out_quantized_cpu
- func: ne.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: ne
QuantizedCPU: ne_quantized_cpu
- func: ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: ne_out
QuantizedCPU: ne_out_quantized_cpu
- func: ne.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: ne
QuantizedCPU: ne_quantized_cpu
- func: ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: ne_
- func: ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: ne_
# not_equal, alias for torch.ne
- func: not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
- func: not_equal.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: not_equal.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: not_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: not_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: eq_out
QuantizedCPU: eq_out_quantized_cpu
- func: eq.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: eq
QuantizedCPU: eq_quantized_cpu
- func: eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: eq_out
QuantizedCPU: eq_out_quantized_cpu
- func: eq.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: eq
QuantizedCPU: eq_quantized_cpu
- func: ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: ge_out
QuantizedCPU: ge_out_quantized_cpu
- func: ge.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: ge
QuantizedCPU: ge_quantized_cpu
- func: ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: ge_out
QuantizedCPU: ge_out_quantized_cpu
- func: ge.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: ge
QuantizedCPU: ge_quantized_cpu
- func: ge_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: ge_
- func: ge_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: ge_
# greater_equal, alias for torch.ge
- func: greater_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
- func: greater_equal.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: greater_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: greater_equal.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: greater_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: greater_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: le_out
QuantizedCPU: le_out_quantized_cpu
- func: le.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: le
QuantizedCPU: le_quantized_cpu
- func: le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: le_out
QuantizedCPU: le_out_quantized_cpu
- func: le.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: le
QuantizedCPU: le_quantized_cpu
- func: le_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: le_
- func: le_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: le_
# less_equal, alias for torch.le
- func: less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
- func: less_equal.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: less_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: less_equal.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: less_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: less_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: gt_out
QuantizedCPU: gt_out_quantized_cpu
- func: gt.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: gt
QuantizedCPU: gt_quantized_cpu
- func: gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: gt_out
QuantizedCPU: gt_out_quantized_cpu
- func: gt.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: gt
QuantizedCPU: gt_quantized_cpu
- func: gt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: gt_
- func: gt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: gt_
# greater, alias for torch.gt
- func: greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
- func: greater.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: greater.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: greater_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: greater_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: lt_out
QuantizedCPU: lt_out_quantized_cpu
- func: lt.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: lt
QuantizedCPU: lt_quantized_cpu
- func: lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: lt_out
QuantizedCPU: lt_out_quantized_cpu
- func: lt.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: lt
QuantizedCPU: lt_quantized_cpu
- func: lt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: lt_
- func: lt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: lt_
# less, alias for torch.lt
- func: less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
- func: less.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
- func: less.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: less.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: less_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
variants: method
- func: less_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
- func: take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: take_out
- func: take(Tensor self, Tensor index) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: take
- func: take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
- func: take_along_dim(Tensor self, Tensor indices, int? dim=None) -> Tensor
variants: method, function
- func: index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: index_select_out_cpu_
CUDA: index_select_out_cuda
- func: index_select(Tensor self, int dim, Tensor index) -> Tensor
variants: method, function
dispatch:
CPU: index_select_cpu_
CUDA: index_select_cuda
SparseCPU: index_select_sparse
SparseCUDA: index_select_sparse
- func: index_select.dimname_out(Tensor self, Dimname dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
- func: index_select.dimname(Tensor self, Dimname dim, Tensor index) -> Tensor
variants: method, function
- func: index_select_backward(Tensor grad, int[] self_sizes, int dim, Tensor index) -> Tensor
variants: function
device_guard: False
- func: masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: masked_select_out_cpu
CUDA: masked_select_out_cuda
- func: masked_select(Tensor self, Tensor mask) -> Tensor
variants: method, function
dispatch:
CPU: masked_select_cpu
CUDA: masked_select_cuda
- func: masked_select_backward(Tensor grad, Tensor input, Tensor mask) -> Tensor
variants: function
device_guard: False
- func: nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: legacy::cpu::_th_nonzero_out
CUDA: nonzero_out_cuda
- func: nonzero(Tensor self) -> Tensor
variants: method, function
dispatch:
CPU: legacy::cpu::_th_nonzero
CUDA: nonzero_cuda
- func: nonzero_numpy(Tensor self) -> Tensor[]
variants: method, function
- func: gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: gather_out_cpu_cuda
CUDA: gather_out_cpu_cuda
- func: gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: gather
- func: gather_backward(Tensor grad, Tensor self, int dim, Tensor index, bool sparse_grad) -> Tensor
variants: function
device_guard: False
- func: gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)
- func: gather.dimname(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False) -> Tensor
variants: method, function
- func: _gather_sparse_backward(Tensor self, int dim, Tensor index, Tensor grad) -> Tensor
- func: addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: addcmul_out
- func: addcmul(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: addcmul
- func: addcmul_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: addcmul_
- func: addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: addcdiv_out
- func: addcdiv(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: addcdiv
- func: cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensor
python_module: nn
- func: lstsq.X(Tensor self, Tensor A, *, Tensor(a!) X, Tensor(b!) qr) -> (Tensor(a!) solution, Tensor(b!) QR)
dispatch:
CPU: legacy::cpu::_th_gels_out
CUDA: legacy::cuda::_th_gels_out
- func: lstsq(Tensor self, Tensor A) -> (Tensor solution, Tensor QR)
variants: method, function
dispatch:
CPU: legacy::cpu::_th_gels
CUDA: legacy::cuda::_th_gels
- func: triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)
dispatch:
CPU, CUDA: triangular_solve_out
- func: triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient)
variants: method, function
dispatch:
CPU, CUDA: triangular_solve
- func: symeig.e(Tensor self, bool eigenvectors=False, bool upper=True, *, Tensor(a!) e, Tensor(b!) V) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
dispatch:
CompositeExplicitAutograd: symeig_out
- func: symeig(Tensor self, bool eigenvectors=False, bool upper=True) -> (Tensor eigenvalues, Tensor eigenvectors)
variants: method, function
dispatch:
CompositeExplicitAutograd: symeig
- func: _symeig_helper(Tensor self, bool eigenvectors, bool upper) -> (Tensor, Tensor)
variants: function
dispatch:
CPU: _symeig_helper_cpu
CUDA: _symeig_helper_cuda
- func: eig.e(Tensor self, bool eigenvectors=False, *, Tensor(a!) e, Tensor(b!) v) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
dispatch:
CompositeExplicitAutograd: eig_out
- func: eig(Tensor self, bool eigenvectors=False) -> (Tensor eigenvalues, Tensor eigenvectors)
variants: method, function
dispatch:
CompositeExplicitAutograd: eig
- func: svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)
- func: svd(Tensor self, bool some=True, bool compute_uv=True) -> (Tensor U, Tensor S, Tensor V)
variants: method, function
- func: _svd_helper(Tensor self, bool some, bool compute_uv) -> (Tensor U, Tensor S, Tensor V)
variants: function
dispatch:
CPU: _svd_helper_cpu
CUDA: _svd_helper_cuda
# swapaxes, alias for transpose
- func: swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a)
variants: function, method
device_guard: False
- func: swapaxes_(Tensor(a!) self, int axis0, int axis1) -> Tensor(a!)
variants: method
device_guard: False
# swapdims, alias for transpose
- func: swapdims(Tensor(a) self, int dim0, int dim1) -> Tensor(a)
variants: function, method
device_guard: False
- func: swapdims_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)
variants: method
device_guard: False
- func: cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: cholesky_out
- func: cholesky(Tensor self, bool upper=False) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: cholesky
- func: _cholesky_helper(Tensor self, bool upper) -> Tensor
variants: function
dispatch:
CPU: _cholesky_helper_cpu
CUDA: _cholesky_helper_cuda
- func: cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: cholesky_solve_out
- func: cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: cholesky_solve
- func: _cholesky_solve_helper(Tensor self, Tensor A, bool upper) -> Tensor
variants: function
dispatch:
CPU: _cholesky_solve_helper_cpu
CUDA: _cholesky_solve_helper_cuda
- func: solve(Tensor self, Tensor A) -> (Tensor solution, Tensor LU)
variants: function, method
dispatch:
CompositeExplicitAutograd: solve
- func: solve.solution(Tensor self, Tensor A, *, Tensor(a!) solution, Tensor(b!) lu) -> (Tensor(a!) solution, Tensor(b!) LU)
dispatch:
CompositeExplicitAutograd: solve_out
- func: _solve_helper(Tensor self, Tensor A) -> (Tensor, Tensor)
variants: function
dispatch:
CPU: _solve_helper_cpu
CUDA: _solve_helper_cuda
- func: cholesky_inverse(Tensor self, bool upper=False) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: cholesky_inverse
- func: cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: cholesky_inverse_out
- func: qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)
- func: qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R)
variants: method, function
- func: geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau)
dispatch:
CPU: geqrf_out
CUDA: legacy::cuda::_th_geqrf_out
- func: geqrf(Tensor self) -> (Tensor a, Tensor tau)
variants: method, function
dispatch:
CPU: geqrf
CUDA: legacy::cuda::_th_geqrf
# orgqr, alias for linalg_householder_product
- func: orgqr(Tensor self, Tensor input2) -> Tensor
variants: method, function
- func: orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!)
- func: ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: legacy::cpu::_th_ormqr_out
- func: ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor
variants: method, function
dispatch:
CPU: legacy::cpu::_th_ormqr
- func: _lu_with_info(Tensor self, bool pivot=True, bool check_errors=True) -> (Tensor, Tensor, Tensor)
variants: function
dispatch:
CPU: _lu_with_info_cpu
CUDA: _lu_with_info_cuda
- func: lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: lu_solve_out
- func: lu_solve(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: lu_solve
- func: _lu_solve_helper(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor
variants: function
dispatch:
CPU: _lu_solve_helper_cpu
CUDA: _lu_solve_helper_cuda
# TODO: remove dispatch section when porting TH CUDA to ATen
- func: multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: multinomial_out
- func: multinomial(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: multinomial
- func: lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: lgamma_out
- func: lgamma_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: lgamma.out
variants: method
- func: lgamma(Tensor self) -> Tensor
structured_delegate: lgamma.out
variants: method, function
- func: digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: digamma_out
- func: digamma(Tensor self) -> Tensor
structured_delegate: digamma.out
variants: method, function
- func: polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: polygamma_out
- func: polygamma(int n, Tensor self) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: polygamma
- func: polygamma_(Tensor(a!) self, int n) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: polygamma_
- func: erfinv(Tensor self) -> Tensor
structured_delegate: erfinv.out
variants: method, function
- func: erfinv_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: erfinv.out
variants: method
- func: erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: erfinv_out
- func: i0(Tensor self) -> Tensor
structured_delegate: i0.out
variants: function, method
- func: i0_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: i0.out
variants: function, method
- func: i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: i0_out
- func: sign(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: sign
- func: sign_(Tensor(a!) self) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: sign_
- func: sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: sign_out
- func: signbit(Tensor self) -> Tensor
variants: function, method
- func: signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: signbit_out
CUDA: signbit_out
- func: dist(Tensor self, Tensor other, Scalar p=2) -> Tensor
variants: method, function
dispatch:
CompositeExplicitAutograd: dist
- func: atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: atan2_out
- func: atan2_(Tensor(a!) self, Tensor other) -> Tensor(a!)
structured_delegate: atan2.out
variants: method
- func: atan2(Tensor self, Tensor other) -> Tensor
structured_delegate: atan2.out
variants: method, function
- func: lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: lerp_cpu_scalar_out
CUDA: lerp_cuda_scalar_out
- func: lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: lerp_cpu_tensor_out
CUDA: lerp_cuda_tensor_out
- func: lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor
variants: method, function
dispatch:
CPU: lerp_cpu_scalar
CUDA: lerp_cuda_scalar
- func: lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor
variants: method, function
dispatch:
CPU: lerp_cpu_tensor
CUDA: lerp_cuda_tensor
- func: histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: legacy::cpu::_th_histc_out
CUDA: _histc_out_cuda
- func: histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor
variants: method, function
dispatch:
CPU: legacy::cpu::_th_histc
CUDA: _histc_cuda
- func: fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: fmod_out
- func: fmod.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: fmod
- func: fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: fmod_out
- func: fmod.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: fmod
- func: hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: hypot_out
- func: hypot(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: hypot
- func: hypot_(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: hypot_
- func: igamma.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: igamma_out
- func: igamma(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: igamma
- func: igamma_(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: igamma_
- func: igammac.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: igammac_out
- func: igammac(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: igammac
- func: igammac_(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: igammac_
- func: nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: nextafter_out
- func: nextafter(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: nextafter
- func: nextafter_(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
dispatch:
CompositeExplicitAutograd: nextafter_
- func: remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: remainder_out
- func: remainder.Scalar(Tensor self, Scalar other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: remainder
- func: remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: remainder_out
- func: remainder.Tensor(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: remainder
- func: min(Tensor self) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: min
QuantizedCPU: min_quantized_cpu
- func: fmin(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: fmin
- func: fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: fmin_out
- func: max(Tensor self) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: max
QuantizedCPU: max_quantized_cpu
- func: fmax(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: fmax
- func: fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: fmax_out
- func: maximum(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: maximum
- func: maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: maximum_out
# binary max, alias of maximum
# NOTE: max is not an alias for maximum, since there is also unary max
- func: max.other(Tensor self, Tensor other) -> Tensor
variants: method, function
- func: max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: minimum(Tensor self, Tensor other) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: minimum
- func: minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: minimum_out
# binary min, alias for minimum
# NOTE: min is not an alias for minimum, since there is also unary min
- func: min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
- func: min.other(Tensor self, Tensor other) -> Tensor
variants: method, function
# The following quantile signatures are DEPRECATED in favor of the new ones with the interpolation kwarg.
- func: quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False) -> Tensor
variants: method, function
- func: quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False) -> Tensor
variants: method, function
- func: nanquantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: nanquantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False) -> Tensor
variants: method, function
- func: nanquantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: nanquantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False) -> Tensor
variants: method, function
# To keep backward and forward compatibility, and to avoid ambiguity with the original signatures, dim, keepdim and interpolation
# parameters are required for now. Once the deprecated signatures are removed they will be made optional.
- func: quantile.new_scalar_out(Tensor self, float q, int? dim, bool keepdim, *, str interpolation, Tensor(a!) out) -> Tensor(a!)
- func: quantile.new_scalar(Tensor self, float q, int? dim, bool keepdim, *, str interpolation) -> Tensor
variants: method, function
- func: quantile.new_out(Tensor self, Tensor q, int? dim, bool keepdim, *, str interpolation, Tensor(a!) out) -> Tensor(a!)
- func: quantile.new(Tensor self, Tensor q, int? dim, bool keepdim, *, str interpolation) -> Tensor
variants: method, function
- func: nanquantile.new_scalar_out(Tensor self, float q, int? dim, bool keepdim, *, str interpolation, Tensor(a!) out) -> Tensor(a!)
- func: nanquantile.new_scalar(Tensor self, float q, int? dim, bool keepdim, *, str interpolation) -> Tensor
variants: method, function
- func: nanquantile.new_out(Tensor self, Tensor q, int? dim, bool keepdim, *, str interpolation, Tensor(a!) out) -> Tensor(a!)
- func: nanquantile.new(Tensor self, Tensor q, int? dim, bool keepdim, *, str interpolation) -> Tensor
variants: method, function
- func: sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CPU: sort_out_cpu
CUDA: sort_out_cuda
- func: sort.values_stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CPU: sort_out_cpu_stable
CUDA: sort_out_stable_cuda
- func: sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices)
variants: method, function
dispatch:
CPU: sort_cpu
CUDA: sort_cuda
QuantizedCPU: sort_quantized_cpu
- func: sort.stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices)
variants: method, function
dispatch:
CPU: sort_cpu_stable
CUDA: sort_stable_cuda
QuantizedCPU: sort_quantized_cpu_stable
- func: sort.dimname_values(Tensor self, Dimname dim, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
- func: sort.dimname_values_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
- func: sort.dimname(Tensor self, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices)
variants: method, function
- func: sort.dimname_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices)
variants: method, function
- func: msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
- func: msort(Tensor self) -> Tensor
variants: method, function
- func: argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor
variants: method, function
- func: argsort.dimname(Tensor self, Dimname dim, bool descending=False) -> Tensor
variants: method, function
- func: topk.values(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
dispatch:
CPU: topk_out_cpu
CUDA: legacy::cuda::_th_topk_out
- func: topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)
variants: method, function
dispatch:
CPU, CUDA: topk
QuantizedCPU: topk_quantized_cpu
- func: all(Tensor self) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: all
- func: any(Tensor self) -> Tensor
variants: method, function
dispatch:
CPU, CUDA: any
SparseCPU, SparseCUDA: any_sparse
- func: renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: legacy::cpu::_th_renorm_out
CUDA: legacy::cuda::_th_renorm_out
- func: renorm(Tensor self, Scalar p, int dim, Scalar maxnorm) -> Tensor
variants: method, function
dispatch:
CPU: legacy::cpu::_th_renorm
CUDA: legacy::cuda::_th_renorm
- func: unfold(Tensor(a) self, int dimension, int size, int step) -> Tensor(a)
variants: method
device_guard: False
dispatch:
CPU, CUDA: unfold
QuantizedCPU, QuantizedCUDA: unfold
- func: unfold_backward(Tensor grad_in, int[] input_sizes, int dim, int size, int step) -> Tensor
variants: function
dispatch:
CPU, CUDA: unfold_backward
- func: equal(Tensor self, Tensor other) -> bool
variants: method, function
dispatch:
CPU: cpu_equal
CUDA: cuda_equal
QuantizedCPU: equal_quantized_cpu
- func: pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: pow_Tensor_Tensor_out
- func: pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor
structured_delegate: pow.Tensor_Tensor_out
variants: method, function
- func: pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
structured: True
dispatch:
CPU, CUDA: pow_Scalar_out
- func: pow.Scalar(Scalar self, Tensor exponent) -> Tensor
structured_delegate: pow.Scalar_out
- func: pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: pow_Tensor_Scalar_out
SparseCPU, SparseCUDA: pow_out_sparse_scalar
- func: pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor
structured_delegate: pow.Tensor_Scalar_out
variants: function, method
dispatch:
SparseCPU, SparseCUDA: pow_sparse_scalar
- func: pow_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!)
structured_delegate: pow.Tensor_Scalar_out
variants: method
- func: pow_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!)
structured_delegate: pow.Tensor_Tensor_out
variants: method
- func: float_power.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
- func: float_power.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor
variants: function, method
- func: float_power.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
- func: float_power.Scalar(Scalar self, Tensor exponent) -> Tensor
- func: float_power.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)
- func: float_power.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor
variants: function, method
- func: float_power_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!)
variants: method
- func: float_power_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!)
variants: method
- func: normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU, CUDA: normal_
Meta: normal_meta_
- func: normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: normal_out
- func: normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor
dispatch:
CPU, CUDA: normal
- func: normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: normal_out
- func: normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor
dispatch:
CPU, CUDA: normal
- func: normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: normal_out
- func: normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor
dispatch:
CPU, CUDA: normal
- func: normal.float_float(float mean, float std, int[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
- func: normal.float_float_out(float mean, float std, int[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
- func: alias(Tensor(a) self) -> Tensor(a)
variants: method, function
dispatch:
CompositeExplicitAutograd: alias
- func: _index_copy_(Tensor(a!) self, int dim, Tensor index, Tensor source) -> Tensor(a!)
dispatch:
CPU: _index_copy_impl_
CUDA: _index_copy_impl_
- func: _cumsum(Tensor self, int dim) -> Tensor
dispatch:
CPU: _cumsum_cpu
CUDA: _cumsum_cuda
- func: _cumsum.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: _cumsum_out_cpu
CUDA: _cumsum_out_cuda
- func: _cumprod(Tensor self, int dim) -> Tensor
dispatch:
CPU: _cumprod_cpu
CUDA: _cumprod_cuda
- func: _cumprod.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: _cumprod_out_cpu
CUDA: _cumprod_out_cuda
- func: _var(Tensor self, bool unbiased=True) -> Tensor
dispatch:
CPU: legacy::cpu::_th_var
- func: _std(Tensor self, bool unbiased=True) -> Tensor
dispatch:
CPU: legacy::cpu::_th_std
- func: _amp_foreach_non_finite_check_and_unscale_(Tensor(a!)[] self, Tensor(b!) found_inf, Tensor inv_scale) -> ()
variants: function
dispatch:
CUDA: _amp_foreach_non_finite_check_and_unscale_cuda_
- func: _amp_update_scale(Tensor(a!) growth_tracker, Tensor current_scale, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> Tensor
variants: function
dispatch:
CUDA: _amp_update_scale_cuda
- func: _cat(Tensor[] tensors, int dim=0) -> Tensor
dispatch:
CPU: _cat_cpu
CUDA: cat_cuda
QuantizedCPU: cat_quantized_cpu
- func: _cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: _cat_out_cpu
CUDA: cat_out_cuda
QuantizedCPU: cat_out_quantized_cpu
- func: _foreach_add.Scalar(Tensor[] tensors, Scalar scalar) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_add_scalar_kernel_slow
CUDA: foreach_tensor_add_scalar_kernel_cuda
- func: _foreach_add_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
variants: function
dispatch:
CPU: foreach_tensor_add_scalar_kernel_slow_
CUDA: foreach_tensor_add_scalar_kernel_cuda_
- func: _foreach_sub.Scalar(Tensor[] tensors, Scalar scalar) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_sub_scalar_kernel_slow
CUDA: foreach_tensor_sub_scalar_kernel_cuda
- func: _foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
variants: function
dispatch:
CPU: foreach_tensor_sub_scalar_kernel_slow_
CUDA: foreach_tensor_sub_scalar_kernel_cuda_
- func: _foreach_mul.Scalar(Tensor[] tensors, Scalar scalar) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_mul_scalar_kernel_slow
CUDA: foreach_tensor_mul_scalar_kernel_cuda
- func: _foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
variants: function
dispatch:
CPU: foreach_tensor_mul_scalar_kernel_slow_
CUDA: foreach_tensor_mul_scalar_kernel_cuda_
- func: _foreach_div.Scalar(Tensor[] tensors, Scalar scalar) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_div_scalar_kernel_slow
CUDA: foreach_tensor_div_scalar_kernel_cuda
- func: _foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
variants: function
dispatch:
CPU: foreach_tensor_div_scalar_kernel_slow_
CUDA: foreach_tensor_div_scalar_kernel_cuda_
- func: _foreach_add.List(Tensor[] tensors1, Tensor[] tensors2, *, Scalar alpha=1) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_add_list_kernel_slow
CUDA: foreach_tensor_add_list_kernel_cuda
- func: _foreach_add_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> ()
variants: function
dispatch:
CPU: foreach_tensor_add_list_kernel_slow_
CUDA: foreach_tensor_add_list_kernel_cuda_
- func: _foreach_sub.List(Tensor[] tensors1, Tensor[] tensors2, *, Scalar alpha=1) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_sub_list_kernel_slow
CUDA: foreach_tensor_sub_list_kernel_cuda
- func: _foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> ()
variants: function
dispatch:
CPU: foreach_tensor_sub_list_kernel_slow_
CUDA: foreach_tensor_sub_list_kernel_cuda_
- func: _foreach_mul.List(Tensor[] tensors1, Tensor[] tensors2) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_mul_list_kernel_slow
CUDA: foreach_tensor_mul_list_kernel_cuda
- func: _foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> ()
variants: function
dispatch:
CPU: foreach_tensor_mul_list_kernel_slow_
CUDA: foreach_tensor_mul_list_kernel_cuda_
- func: _foreach_div.List(Tensor[] tensors1, Tensor[] tensors2) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_div_list_kernel_slow
CUDA: foreach_tensor_div_list_kernel_cuda
- func: _foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> ()
variants: function
dispatch:
CPU: foreach_tensor_div_list_kernel_slow_
CUDA: foreach_tensor_div_list_kernel_cuda_
- func: _foreach_add.ScalarList(Tensor[] tensors, Scalar[] scalars) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_add_scalarlist_kernel_slow
CUDA: foreach_tensor_add_scalarlist_kernel_cuda
- func: _foreach_add_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
variants: function
dispatch:
CPU: foreach_tensor_add_scalarlist_kernel_slow_
CUDA: foreach_tensor_add_scalarlist_kernel_cuda_
- func: _foreach_sub.ScalarList(Tensor[] tensors, Scalar[] scalars) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_sub_scalarlist_kernel_slow
CUDA: foreach_tensor_sub_scalarlist_kernel_cuda
- func: _foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
variants: function
dispatch:
CPU: foreach_tensor_sub_scalarlist_kernel_slow_
CUDA: foreach_tensor_sub_scalarlist_kernel_cuda_
- func: _foreach_div.ScalarList(Tensor[] tensors, Scalar[] scalars) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_div_scalarlist_kernel_slow
CUDA: foreach_tensor_div_scalarlist_kernel_cuda
- func: _foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
variants: function
dispatch:
CPU: foreach_tensor_div_scalarlist_kernel_slow_
CUDA: foreach_tensor_div_scalarlist_kernel_cuda_
- func: _foreach_mul.ScalarList(Tensor[] tensors, Scalar[] scalars) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_mul_scalarlist_kernel_slow
CUDA: foreach_tensor_mul_scalarlist_kernel_cuda
- func: _foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
variants: function
dispatch:
CPU: foreach_tensor_mul_scalarlist_kernel_slow_
CUDA: foreach_tensor_mul_scalarlist_kernel_cuda_
- func: _foreach_exp(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_exp_slow
CUDA: foreach_tensor_exp_cuda
- func: _foreach_zero_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_zero_slow_
CUDA: foreach_tensor_zero_cuda_
- func: _foreach_exp_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_exp_slow_
CUDA: foreach_tensor_exp_cuda_
- func: _foreach_sqrt(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_sqrt_slow
CUDA: foreach_tensor_sqrt_cuda
- func: _foreach_sqrt_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_sqrt_slow_
CUDA: foreach_tensor_sqrt_cuda_
- func: _foreach_abs(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_abs_slow
CUDA: foreach_tensor_abs_cuda
- func: _foreach_abs_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_abs_slow_
CUDA: foreach_tensor_abs_cuda_
- func: _foreach_acos(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_acos_slow
CUDA: foreach_tensor_acos_cuda
- func: _foreach_acos_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_acos_slow_
CUDA: foreach_tensor_acos_cuda_
- func: _foreach_asin(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_asin_slow
CUDA: foreach_tensor_asin_cuda
- func: _foreach_asin_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_asin_slow_
CUDA: foreach_tensor_asin_cuda_
- func: _foreach_atan(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_atan_slow
CUDA: foreach_tensor_atan_cuda
- func: _foreach_atan_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_atan_slow_
CUDA: foreach_tensor_atan_cuda_
- func: _foreach_ceil(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_ceil_slow
CUDA: foreach_tensor_ceil_cuda
- func: _foreach_ceil_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_ceil_slow_
CUDA: foreach_tensor_ceil_cuda_
- func: _foreach_cos(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_cos_slow
CUDA: foreach_tensor_cos_cuda
- func: _foreach_cos_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_cos_slow_
CUDA: foreach_tensor_cos_cuda_
- func: _foreach_cosh(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_cosh_slow
CUDA: foreach_tensor_cosh_cuda
- func: _foreach_cosh_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_cosh_slow_
CUDA: foreach_tensor_cosh_cuda_
- func: _foreach_erf(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_erf_slow
CUDA: foreach_tensor_erf_cuda
- func: _foreach_erf_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_erf_slow_
CUDA: foreach_tensor_erf_cuda_
- func: _foreach_erfc(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_erfc_slow
CUDA: foreach_tensor_erfc_cuda
- func: _foreach_erfc_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_erfc_slow_
CUDA: foreach_tensor_erfc_cuda_
- func: _foreach_expm1(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_expm1_slow
CUDA: foreach_tensor_expm1_cuda
- func: _foreach_expm1_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_expm1_slow_
CUDA: foreach_tensor_expm1_cuda_
- func: _foreach_floor(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_floor_slow
CUDA: foreach_tensor_floor_cuda
- func: _foreach_floor_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_floor_slow_
CUDA: foreach_tensor_floor_cuda_
- func: _foreach_log(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_log_slow
CUDA: foreach_tensor_log_cuda
- func: _foreach_log_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_log_slow_
CUDA: foreach_tensor_log_cuda_
- func: _foreach_log10(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_log10_slow
CUDA: foreach_tensor_log10_cuda
- func: _foreach_log10_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_log10_slow_
CUDA: foreach_tensor_log10_cuda_
- func: _foreach_log1p(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_log1p_slow
CUDA: foreach_tensor_log1p_cuda
- func: _foreach_log1p_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_log1p_slow_
CUDA: foreach_tensor_log1p_cuda_
- func: _foreach_log2(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_log2_slow
CUDA: foreach_tensor_log2_cuda
- func: _foreach_log2_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_log2_slow_
CUDA: foreach_tensor_log2_cuda_
- func: _foreach_neg(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_neg_slow
CUDA: foreach_tensor_neg_cuda
- func: _foreach_neg_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_neg_slow_
CUDA: foreach_tensor_neg_cuda_
- func: _foreach_tan(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_tan_slow
CUDA: foreach_tensor_tan_cuda
- func: _foreach_tan_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_tan_slow_
CUDA: foreach_tensor_tan_cuda_
- func: _foreach_tanh(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_tanh_slow
CUDA: foreach_tensor_tanh_cuda
- func: _foreach_tanh_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_tanh_slow_
CUDA: foreach_tensor_tanh_cuda_
- func: _foreach_sin(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_sin_slow
CUDA: foreach_tensor_sin_cuda
- func: _foreach_sin_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_sin_slow_
CUDA: foreach_tensor_sin_cuda_
- func: _foreach_sinh(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_sinh_slow
CUDA: foreach_tensor_sinh_cuda
- func: _foreach_sinh_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_sinh_slow_
CUDA: foreach_tensor_sinh_cuda_
- func: _foreach_round(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_round_slow
CUDA: foreach_tensor_round_cuda
- func: _foreach_round_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_round_slow_
CUDA: foreach_tensor_round_cuda_
- func: _foreach_lgamma(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_lgamma_slow
CUDA: foreach_tensor_lgamma_cuda
- func: _foreach_lgamma_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_lgamma_slow_
CUDA: foreach_tensor_lgamma_cuda_
- func: _foreach_frac(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_frac_slow
CUDA: foreach_tensor_frac_cuda
- func: _foreach_frac_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_frac_slow_
CUDA: foreach_tensor_frac_cuda_
- func: _foreach_reciprocal(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_reciprocal_slow
CUDA: foreach_tensor_reciprocal_cuda
- func: _foreach_reciprocal_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_reciprocal_slow_
CUDA: foreach_tensor_reciprocal_cuda_
- func: _foreach_sigmoid(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_sigmoid_slow
CUDA: foreach_tensor_sigmoid_cuda
- func: _foreach_sigmoid_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_sigmoid_slow_
CUDA: foreach_tensor_sigmoid_cuda_
- func: _foreach_trunc(Tensor[] tensors) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_trunc_slow
CUDA: foreach_tensor_trunc_cuda
- func: _foreach_trunc_(Tensor(a!)[] self) -> ()
variants: function
dispatch:
CPU: foreach_tensor_trunc_slow_
CUDA: foreach_tensor_trunc_cuda_
- func: _foreach_addcdiv_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> ()
variants: function
dispatch:
CPU: foreach_tensor_addcdiv_scalar_slow_
CUDA: foreach_tensor_addcdiv_scalar_cuda_
- func: _foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> ()
variants: function
dispatch:
CPU: foreach_tensor_addcmul_scalar_slow_
CUDA: foreach_tensor_addcmul_scalar_cuda_
- func: _foreach_addcdiv_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> ()
variants: function
dispatch:
CPU: foreach_tensor_addcdiv_scalarlist_slow_
CUDA: foreach_tensor_addcdiv_scalarlist_cuda_
- func: _foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> ()
variants: function
dispatch:
CPU: foreach_tensor_addcmul_scalarlist_slow_
CUDA: foreach_tensor_addcmul_scalarlist_cuda_
- func: _foreach_addcdiv.Scalar(Tensor[] input, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_addcdiv_scalar_slow
CUDA: foreach_tensor_addcdiv_scalar_cuda
- func: _foreach_addcmul.Scalar(Tensor[] input, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_addcmul_scalar_slow
CUDA: foreach_tensor_addcmul_scalar_cuda
- func: _foreach_addcdiv.ScalarList(Tensor[] input, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_addcdiv_scalarlist_slow
CUDA: foreach_tensor_addcdiv_scalarlist_cuda
- func: _foreach_addcmul.ScalarList(Tensor[] input, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_addcmul_scalarlist_slow
CUDA: foreach_tensor_addcmul_scalarlist_cuda
- func: _foreach_maximum.List(Tensor[] tensors1, Tensor[] tensors2) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_maximum_slow
CUDA: foreach_tensor_maximum_cuda
- func: _foreach_minimum.List(Tensor[] tensors1, Tensor[] tensors2) -> Tensor[]
variants: function
dispatch:
CPU: foreach_tensor_minimum_slow
CUDA: foreach_tensor_minimum_cuda
- func: bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor
dispatch:
CPU: bucketize_cpu
CUDA: bucketize_cuda
- func: bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: bucketize_out_cpu
CUDA: bucketize_out_cuda
- func: bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor
dispatch:
CPU: bucketize_cpu
CUDA: bucketize_cuda
- func: searchsorted.Tensor(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False) -> Tensor
dispatch:
CPU: searchsorted_cpu
CUDA: searchsorted_cuda
- func: searchsorted.Tensor_out(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: searchsorted_out_cpu
CUDA: searchsorted_out_cuda
- func: searchsorted.Scalar(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False) -> Tensor
dispatch:
CPU: searchsorted_cpu
CUDA: searchsorted_cuda
## NN wrappers
- func: mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: mse_loss_out
- func: mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: mse_loss
- func: mse_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: mse_loss_backward_out
- func: mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: mse_loss_backward
- func: l1_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CompositeExplicitAutograd: l1_loss_out
- func: l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: l1_loss
- func: l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: l1_loss_backward_out
- func: l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: l1_loss_backward
- func: multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: multi_margin_loss_cpu_out
CUDA: legacy::cuda::_thnn_multi_margin_loss_forward_out
- func: multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensor
python_module: nn
dispatch:
CPU: multi_margin_loss_cpu
CUDA: legacy::cuda::_thnn_multi_margin_loss_forward
- func: multi_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: multi_margin_loss_cpu_backward_out
CUDA: legacy::cuda::_thnn_multi_margin_loss_backward_out
- func: multi_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean) -> Tensor
python_module: nn
dispatch:
CPU: multi_margin_loss_cpu_backward
CUDA: legacy::cuda::_thnn_multi_margin_loss_backward
- func: multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
- func: multilabel_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
python_module: nn
- func: multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!))
python_module: nn
dispatch:
CPU: multilabel_margin_loss_forward_out_cpu
CUDA: legacy::cuda::_thnn_multilabel_margin_loss_forward_out
- func: multilabel_margin_loss_forward(Tensor self, Tensor target, int reduction) -> (Tensor output, Tensor is_target)
python_module: nn
dispatch:
CPU: multilabel_margin_loss_forward_cpu
CUDA: legacy::cuda::_thnn_multilabel_margin_loss_forward
- func: multilabel_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: multilabel_margin_loss_backward_cpu_out
CUDA: legacy::cuda::_thnn_multilabel_margin_loss_backward_out
- func: multilabel_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target) -> Tensor
python_module: nn
dispatch:
CPU: multilabel_margin_loss_backward_cpu
CUDA: legacy::cuda::_thnn_multilabel_margin_loss_backward
- func: nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
- func: nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensor
python_module: nn
- func: nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensor
python_module: nn
- func: nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))
python_module: nn
dispatch:
CPU: nll_loss_forward_out_cpu
CUDA: legacy::cuda::_thnn_nll_loss_forward_out
- func: nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)
python_module: nn
dispatch:
CPU: nll_loss_forward_cpu
CUDA: legacy::cuda::_thnn_nll_loss_forward
- func: nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: nll_loss_backward_out_cpu
CUDA: legacy::cuda::_thnn_nll_loss_backward_out
- func: nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, Tensor total_weight) -> Tensor
python_module: nn
dispatch:
CPU: nll_loss_backward_cpu
CUDA: legacy::cuda::_thnn_nll_loss_backward
- func: nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
- func: nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensor
python_module: nn
- func: nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))
python_module: nn
dispatch:
CPU: nll_loss2d_forward_out_cpu
CUDA: legacy::cuda::_thnn_nll_loss2d_forward_out
- func: nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)
python_module: nn
dispatch:
CPU: nll_loss2d_forward_cpu
CUDA: legacy::cuda::_thnn_nll_loss2d_forward
- func: nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: nll_loss2d_backward_out_cpu
CUDA: legacy::cuda::_thnn_nll_loss2d_backward_out
- func: nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, Tensor total_weight) -> Tensor
python_module: nn
dispatch:
CPU: nll_loss2d_backward_cpu
CUDA: legacy::cuda::_thnn_nll_loss2d_backward
- func: smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, float beta=1.0, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: smooth_l1_loss_out
CUDA: smooth_l1_loss_out
- func: smooth_l1_loss(Tensor self, Tensor target, int reduction=Mean, float beta=1.0) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: smooth_l1_loss
- func: smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: smooth_l1_loss_backward_out
CUDA: smooth_l1_loss_backward_out
- func: smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: smooth_l1_loss_backward
- func: huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: huber_loss_out
- func: huber_loss(Tensor self, Tensor target, int reduction=Mean, float delta=1.0) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: huber_loss
- func: huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: huber_loss_backward_out
- func: huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: huber_loss_backward
- func: soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CompositeExplicitAutograd: soft_margin_loss_out
- func: soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: soft_margin_loss
- func: soft_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CompositeExplicitAutograd: soft_margin_loss_backward_out
- func: soft_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: soft_margin_loss_backward
- func: elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: elu_out
- func: elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: elu
- func: elu_backward(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: elu_backward
- func: elu_(Tensor(a!) self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor(a!)
python_module: nn
dispatch:
CompositeExplicitAutograd: elu_
- func: glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: glu_out
CUDA: legacy::cuda::_thnn_glu_forward_out
- func: glu(Tensor self, int dim=-1) -> Tensor
python_module: nn
dispatch:
CPU: glu
CUDA: legacy::cuda::_thnn_glu_forward
- func: glu_backward.grad_input(Tensor grad_output, Tensor self, int dim, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: glu_backward_out
CUDA: legacy::cuda::_thnn_glu_backward_out
- func: glu_backward(Tensor grad_output, Tensor self, int dim) -> Tensor
python_module: nn
dispatch:
CPU: glu_backward
CUDA: legacy::cuda::_thnn_glu_backward
- func: hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: hardsigmoid_out
- func: hardsigmoid(Tensor self) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: hardsigmoid
QuantizedCPU: hardsigmoid_quantized_cpu
- func: hardsigmoid_(Tensor(a!) self) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: hardsigmoid_
- func: hardsigmoid_backward(Tensor grad_output, Tensor self) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: hardsigmoid_backward
- func: hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: hardtanh_out
QuantizedCPU: hardtanh_out_quantized_cpu
- func: hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: hardtanh
QuantizedCPU: hardtanh_quantized_cpu
- func: hardtanh_backward.grad_input(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: hardtanh_backward_out
- func: hardtanh_backward(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: hardtanh_backward
- func: hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: hardtanh_
QuantizedCPU: hardtanh_quantized_cpu_
- func: hardswish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: hardswish_out
- func: hardswish(Tensor self) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: hardswish
- func: hardswish_(Tensor(a!) self) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: hardswish_
- func: hardswish_backward(Tensor grad_output, Tensor self) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: hardswish_backward
- func: leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: leaky_relu_out
QuantizedCPU: leaky_relu_out_quantized_cpu
- func: leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: leaky_relu
QuantizedCPU: leaky_relu_quantized_cpu
- func: leaky_relu_backward(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: leaky_relu_backward
- func: leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: leaky_relu_
QuantizedCPU: leaky_relu_quantized_cpu_
- func: log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
- func: log_sigmoid(Tensor self) -> Tensor
python_module: nn
- func: log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!))
python_module: nn
dispatch:
CPU: log_sigmoid_forward_out_cpu
CUDA: legacy::cuda::_thnn_log_sigmoid_forward_out
- func: log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer)
python_module: nn
dispatch:
CPU: log_sigmoid_forward_cpu
CUDA: legacy::cuda::_thnn_log_sigmoid_forward
- func: log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: log_sigmoid_backward_out_cpu
CUDA: legacy::cuda::_thnn_log_sigmoid_backward_out
- func: log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor
python_module: nn
dispatch:
CPU: log_sigmoid_backward_cpu
CUDA: legacy::cuda::_thnn_log_sigmoid_backward
- func: rrelu_with_noise.out(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: rrelu_with_noise_out_cpu
CUDA: legacy::cuda::_thnn_rrelu_with_noise_forward_out
- func: rrelu_with_noise(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor
python_module: nn
dispatch:
CPU: rrelu_with_noise_cpu
CUDA: legacy::cuda::_thnn_rrelu_with_noise_forward
- func: rrelu_with_noise_backward(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: rrelu_with_noise_backward
- func: rrelu_with_noise_(Tensor(a!) self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!)
python_module: nn
dispatch:
CPU: rrelu_with_noise_cpu_
CUDA: legacy::cuda::_thnn_rrelu_with_noise_forward_
- func: softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: softplus_out
- func: softplus(Tensor self, Scalar beta=1, Scalar threshold=20) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: softplus
- func: softplus_backward.grad_input(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: softplus_backward_out
- func: softplus_backward(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, Tensor output) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: softplus_backward
- func: softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: softshrink_out
- func: softshrink(Tensor self, Scalar lambd=0.5) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: softshrink
- func: softshrink_backward.grad_input(Tensor grad_output, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: softshrink_backward_out
- func: softshrink_backward(Tensor grad_output, Tensor self, Scalar lambd) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: softshrink_backward
- func: adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: adaptive_avg_pool2d_out_cpu
MkldnnCPU: mkldnn_adaptive_avg_pool2d_out
- func: adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor
python_module: nn
- func: mkldnn_adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor
dispatch:
MkldnnCPU: mkldnn_adaptive_avg_pool2d
- func: mkldnn_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor
dispatch:
MkldnnCPU: mkldnn_adaptive_avg_pool2d_backward
- func: _adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor
dispatch:
CPU: adaptive_avg_pool2d_cpu
CUDA: adaptive_avg_pool2d_cuda
QuantizedCPU: adaptive_avg_pool2d_quantized_cpu
- func: _adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor
python_module: nn
dispatch:
CPU: adaptive_avg_pool2d_backward_cpu
CUDA: adaptive_avg_pool2d_backward_cuda
- func: adaptive_avg_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: adaptive_avg_pool3d_out_cpu
CUDA: adaptive_avg_pool3d_out_cuda
QuantizedCPU: adaptive_avg_pool3d_out_quantized_cpu
- func: adaptive_avg_pool3d(Tensor self, int[3] output_size) -> Tensor
python_module: nn
- func: _adaptive_avg_pool3d(Tensor self, int[3] output_size) -> Tensor
dispatch:
CPU: adaptive_avg_pool3d_cpu
CUDA: adaptive_avg_pool3d_cuda
QuantizedCPU: adaptive_avg_pool3d_quantized_cpu
- func: adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: adaptive_avg_pool3d_backward_out_cpu
CUDA: adaptive_avg_pool3d_backward_out_cuda
- func: _adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor
python_module: nn
dispatch:
CPU: adaptive_avg_pool3d_backward_cpu
CUDA: adaptive_avg_pool3d_backward_cuda
# Return: (Tensor output, Tensor indices)
- func: adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
python_module: nn
structured: True
dispatch:
CPU: adaptive_max_pool2d_out_cpu
CUDA: adaptive_max_pool2d_out_cuda
# Return: (Tensor output, Tensor indices)
- func: adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor)
python_module: nn
structured_delegate: adaptive_max_pool2d.out
- func: adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: adaptive_max_pool2d_backward_out_cpu
CUDA: adaptive_max_pool2d_backward_out_cuda
- func: adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor
python_module: nn
dispatch:
CPU: adaptive_max_pool2d_backward_cpu
CUDA: adaptive_max_pool2d_backward_cuda
# Return: (Tensor output, Tensor indices)
- func: adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
python_module: nn
structured: True
dispatch:
CPU: adaptive_max_pool3d_out_cpu
CUDA: adaptive_max_pool3d_out_cuda
# Return: (Tensor output, Tensor indices)
- func: adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor)
python_module: nn
structured_delegate: adaptive_max_pool3d.out
- func: adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: adaptive_max_pool3d_backward_out_cpu
CUDA: adaptive_max_pool3d_backward_out_cuda
- func: adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor
python_module: nn
dispatch:
CPU: adaptive_max_pool3d_backward_cpu
CUDA: adaptive_max_pool3d_backward_cuda
- func: avg_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: avg_pool2d_out_cpu
CUDA: avg_pool2d_out_cuda
MkldnnCPU: mkldnn_avg_pool2d_out
- func: avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor
python_module: nn
dispatch:
CPU: avg_pool2d_cpu
CUDA: avg_pool2d_cuda
MkldnnCPU: mkldnn_avg_pool2d
QuantizedCPU: avg_pool2d_quantized_cpu
- func: avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: avg_pool2d_backward_out_cpu
CUDA: avg_pool2d_backward_out_cuda
MkldnnCPU: mkldnn_avg_pool2d_backward_out
- func: avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor
python_module: nn
dispatch:
CPU: avg_pool2d_backward_cpu
CUDA: avg_pool2d_backward_cuda
MkldnnCPU: mkldnn_avg_pool2d_backward
- func: avg_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: avg_pool3d_out_cpu
CUDA: avg_pool3d_out_cuda
MkldnnCPU: mkldnn_avg_pool3d_out
- func: avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor
python_module: nn
dispatch:
CPU: avg_pool3d_cpu
CUDA: avg_pool3d_cuda
MkldnnCPU: mkldnn_avg_pool3d
QuantizedCPU: avg_pool3d_quantized_cpu
- func: avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: avg_pool3d_backward_out_cpu
CUDA: avg_pool3d_backward_out_cuda
MkldnnCPU: mkldnn_avg_pool3d_backward_out
- func: avg_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor
python_module: nn
dispatch:
CPU: avg_pool3d_backward_cpu
CUDA: avg_pool3d_backward_cuda
MkldnnCPU: mkldnn_avg_pool3d_backward
# Return: (Tensor output, Tensor indices)
- func: fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
python_module: nn
structured: True
dispatch:
CPU: fractional_max_pool2d_out_cpu
CUDA: fractional_max_pool2d_out_cuda
# Return: (Tensor output, Tensor indices)
- func: fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor)
python_module: nn
structured_delegate: fractional_max_pool2d.output
- func: fractional_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: fractional_max_pool2d_backward_out_cpu
CUDA: fractional_max_pool2d_backward_out_cuda
- func: fractional_max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices) -> Tensor
python_module: nn
dispatch:
CPU: fractional_max_pool2d_backward_cpu
CUDA: fractional_max_pool2d_backward_cuda
# Return: (Tensor output, Tensor indices)
- func: fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
python_module: nn
dispatch:
CPU: fractional_max_pool3d_out_cpu
CUDA: fractional_max_pool3d_out_cuda
# Return: (Tensor output, Tensor indices)
- func: fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor)
python_module: nn
dispatch:
CPU: fractional_max_pool3d_cpu
CUDA: fractional_max_pool3d_cuda
- func: fractional_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: fractional_max_pool3d_backward_out_cpu
CUDA: fractional_max_pool3d_backward_out_cuda
- func: fractional_max_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices) -> Tensor
python_module: nn
dispatch:
CPU: fractional_max_pool3d_backward_cpu
CUDA: fractional_max_pool3d_backward_cuda
# Return: (Tensor output, Tensor indices)
- func: max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
python_module: nn
dispatch:
CPU: max_pool2d_with_indices_out_cpu
CUDA: max_pool2d_with_indices_out_cuda
# Return: (Tensor output, Tensor indices)
- func: max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)
python_module: nn
dispatch:
CPU: max_pool2d_with_indices_cpu
CUDA: max_pool2d_with_indices_cuda
- func: max_pool2d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: max_pool2d_with_indices_backward_out_cpu
CUDA: max_pool2d_with_indices_backward_out_cuda
- func: max_pool2d_with_indices_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices) -> Tensor
python_module: nn
dispatch:
CPU: max_pool2d_with_indices_backward_cpu
CUDA: max_pool2d_with_indices_backward_cuda
# Return: (Tensor output, Tensor indices)
- func: max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
python_module: nn
dispatch:
CPU: max_pool3d_with_indices_out_cpu
CUDA: max_pool3d_with_indices_out_cuda
# Return: (Tensor output, Tensor indices)
- func: max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)
python_module: nn
dispatch:
CPU: max_pool3d_with_indices_cpu
CUDA: max_pool3d_with_indices_cuda
- func: max_pool3d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: max_pool3d_with_indices_backward_out_cpu
CUDA: max_pool3d_with_indices_backward_out_cuda
- func: max_pool3d_with_indices_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices) -> Tensor
python_module: nn
dispatch:
CPU: max_pool3d_with_indices_backward_cpu
CUDA: max_pool3d_with_indices_backward_cuda
- func: max_unpool2d.out(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: max_unpooling2d_forward_out_cpu
CUDA: max_unpooling2d_forward_out_cuda
- func: max_unpool2d(Tensor self, Tensor indices, int[2] output_size) -> Tensor
python_module: nn
dispatch:
CPU: max_unpooling2d_forward_cpu
CUDA: max_unpooling2d_forward_cuda
- func: max_unpool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: max_unpooling2d_backward_out_cpu
CUDA: max_unpooling2d_backward_out_cuda
- func: max_unpool2d_backward(Tensor grad_output, Tensor self, Tensor indices, int[2] output_size) -> Tensor
python_module: nn
dispatch:
CPU: max_unpooling2d_backward_cpu
CUDA: max_unpooling2d_backward_cuda
- func: max_unpool3d.out(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: max_unpooling3d_forward_out_cpu
CUDA: max_unpooling3d_forward_out_cuda
- func: max_unpool3d(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding) -> Tensor
python_module: nn
dispatch:
CPU: max_unpooling3d_forward_cpu
CUDA: max_unpooling3d_forward_cuda
- func: max_unpool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: max_unpooling3d_backward_out_cpu
CUDA: max_unpooling3d_backward_out_cuda
- func: max_unpool3d_backward(Tensor grad_output, Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding) -> Tensor
python_module: nn
dispatch:
CPU: max_unpooling3d_backward_cpu
CUDA: max_unpooling3d_backward_cuda
- func: reflection_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU, QuantizedCPU: reflection_pad1d_out_cpu
CUDA: reflection_pad1d_out_cuda
- func: reflection_pad1d(Tensor self, int[2] padding) -> Tensor
python_module: nn
structured_delegate: reflection_pad1d.out
dispatch:
QuantizedCPU: reflection_pad1d_cpu
- func: reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, int[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: reflection_pad1d_backward_out_cpu
CUDA: reflection_pad1d_backward_out_cuda
- func: reflection_pad1d_backward(Tensor grad_output, Tensor self, int[2] padding) -> Tensor
python_module: nn
dispatch:
CPU: reflection_pad1d_backward_cpu
CUDA: reflection_pad1d_backward_cuda
- func: reflection_pad2d.out(Tensor self, int[4] padding, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU, QuantizedCPU: reflection_pad2d_out_cpu
CUDA: reflection_pad2d_out_cuda
- func: reflection_pad2d(Tensor self, int[4] padding) -> Tensor
python_module: nn
dispatch:
CPU, QuantizedCPU: reflection_pad2d_cpu
CUDA: reflection_pad2d_cuda
- func: reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, int[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: reflection_pad2d_backward_out_cpu
CUDA: reflection_pad2d_backward_out_cuda
- func: reflection_pad2d_backward(Tensor grad_output, Tensor self, int[4] padding) -> Tensor
python_module: nn
dispatch:
CPU: reflection_pad2d_backward_cpu
CUDA: reflection_pad2d_backward_cuda
- func: replication_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: replication_pad1d_out_cpu
CUDA: replication_pad1d_out_cuda
- func: replication_pad1d(Tensor self, int[2] padding) -> Tensor
python_module: nn
structured_delegate: replication_pad1d.out
- func: replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, int[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: replication_pad1d_backward_out_cpu
CUDA: replication_pad1d_backward_out_cuda
- func: replication_pad1d_backward(Tensor grad_output, Tensor self, int[2] padding) -> Tensor
python_module: nn
structured_delegate: replication_pad1d_backward.grad_input
- func: replication_pad2d.out(Tensor self, int[4] padding, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: replication_pad2d_out_cpu
CUDA: replication_pad2d_out_cuda
- func: replication_pad2d(Tensor self, int[4] padding) -> Tensor
python_module: nn
structured_delegate: replication_pad2d.out
- func: replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, int[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: replication_pad2d_backward_out_cpu
CUDA: replication_pad2d_backward_out_cuda
- func: replication_pad2d_backward(Tensor grad_output, Tensor self, int[4] padding) -> Tensor
python_module: nn
dispatch:
CPU: replication_pad2d_backward_cpu
CUDA: replication_pad2d_backward_cuda
- func: replication_pad3d.out(Tensor self, int[6] padding, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: replication_pad3d_out_cpu
CUDA: replication_pad3d_out_cuda
- func: replication_pad3d(Tensor self, int[6] padding) -> Tensor
python_module: nn
structured_delegate: replication_pad3d.out
- func: replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, int[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: replication_pad3d_backward_out_cpu
CUDA: replication_pad3d_backward_out_cuda
- func: replication_pad3d_backward(Tensor grad_output, Tensor self, int[6] padding) -> Tensor
python_module: nn
dispatch:
CPU: replication_pad3d_backward_cpu
CUDA: replication_pad3d_backward_cuda
- func: upsample_linear1d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_linear1d
- func: upsample_linear1d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_linear1d_backward
- func: upsample_bilinear2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_bilinear2d
- func: upsample_bilinear2d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_bilinear2d_backward
- func: upsample_trilinear3d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_trilinear3d
- func: upsample_trilinear3d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_trilinear3d_backward
- func: upsample_bicubic2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_bicubic2d
- func: upsample_bicubic2d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, bool align_corners, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_bicubic2d_backward
- func: upsample_nearest1d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_nearest1d
- func: upsample_nearest1d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_nearest1d_backward
- func: upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_nearest2d
- func: upsample_nearest2d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CompositeExplicitAutograd: upsample_nearest2d_backward
- func: upsample_nearest3d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CPU: upsample_nearest3d_cpu
CUDA: upsample_nearest3d_cuda
QuantizedCPU: upsample_nearest3d_quantized_cpu
- func: upsample_nearest3d_backward.vec(Tensor grad_output, int[]? output_size, int[] input_size, float[]? scale_factors) -> Tensor
python_module: nn
dispatch:
CPU: upsample_nearest3d_backward_cpu
CUDA: upsample_nearest3d_backward_cuda
# NOTE: all of the non-"vec" upsample overloads are only kept for backward compatibility.
- func: upsample_linear1d.out(Tensor self, int[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_linear1d_out_cpu
CUDA: upsample_linear1d_out_cuda
- func: upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, float? scales=None) -> Tensor
python_module: nn
structured_delegate: upsample_linear1d.out
- func: upsample_linear1d_backward.grad_input(Tensor grad_output, int[1] output_size, int[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_linear1d_backward_out_cpu
CUDA: upsample_linear1d_backward_out_cuda
- func: upsample_linear1d_backward(Tensor grad_output, int[1] output_size, int[3] input_size, bool align_corners, float? scales=None) -> Tensor
python_module: nn
structured_delegate: upsample_linear1d_backward.grad_input
- func: upsample_bilinear2d.out(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_bilinear2d_out_cpu
CUDA: upsample_bilinear2d_out_cuda
- func: upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
python_module: nn
structured_delegate: upsample_bilinear2d.out
dispatch:
QuantizedCPU: upsample_bilinear2d_quantized_cpu
- func: upsample_bilinear2d_backward.grad_input(Tensor grad_output, int[2] output_size, int[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_bilinear2d_backward_out_cpu
CUDA: upsample_bilinear2d_backward_out_cuda
- func: upsample_bilinear2d_backward(Tensor grad_output, int[2] output_size, int[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
python_module: nn
structured_delegate: upsample_bilinear2d_backward.grad_input
- func: upsample_bicubic2d.out(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_bicubic2d_out_cpu
CUDA: upsample_bicubic2d_out_cuda
- func: upsample_bicubic2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
python_module: nn
structured_delegate: upsample_bicubic2d.out
- func: upsample_bicubic2d_backward.grad_input(Tensor grad_output, int[2] output_size, int[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_bicubic2d_backward_out_cpu
CUDA: upsample_bicubic2d_backward_out_cuda
- func: upsample_bicubic2d_backward(Tensor grad_output, int[2] output_size, int[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
python_module: nn
structured_delegate: upsample_bicubic2d_backward.grad_input
- func: upsample_trilinear3d.out(Tensor self, int[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_trilinear3d_out_cpu
CUDA: upsample_trilinear3d_out_cuda
- func: upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
python_module: nn
structured_delegate: upsample_trilinear3d.out
- func: upsample_trilinear3d_backward.grad_input(Tensor grad_output, int[3] output_size, int[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_trilinear3d_backward_out_cpu
CUDA: upsample_trilinear3d_backward_out_cuda
- func: upsample_trilinear3d_backward(Tensor grad_output, int[3] output_size, int[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
python_module: nn
structured_delegate: upsample_trilinear3d_backward.grad_input
- func: upsample_nearest1d.out(Tensor self, int[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_nearest1d_out_cpu
CUDA: upsample_nearest1d_out_cuda
- func: upsample_nearest1d(Tensor self, int[1] output_size, float? scales=None) -> Tensor
python_module: nn
structured_delegate: upsample_nearest1d.out
- func: upsample_nearest1d_backward.grad_input(Tensor grad_output, int[1] output_size, int[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_nearest1d_backward_out_cpu
CUDA: upsample_nearest1d_backward_out_cuda
- func: upsample_nearest1d_backward(Tensor grad_output, int[1] output_size, int[3] input_size, float? scales=None) -> Tensor
python_module: nn
structured_delegate: upsample_nearest1d_backward.grad_input
- func: upsample_nearest2d.out(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_nearest2d_out_cpu
CUDA: upsample_nearest2d_out_cuda
- func: upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor
python_module: nn
structured_delegate: upsample_nearest2d.out
dispatch:
QuantizedCPU: upsample_nearest2d_quantized_cpu
- func: upsample_nearest2d_backward.grad_input(Tensor grad_output, int[2] output_size, int[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_nearest2d_backward_out_cpu
CUDA: upsample_nearest2d_backward_out_cuda
- func: upsample_nearest2d_backward(Tensor grad_output, int[2] output_size, int[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor
python_module: nn
structured_delegate: upsample_nearest2d_backward.grad_input
- func: upsample_nearest3d.out(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_nearest3d_out_cpu
CUDA: upsample_nearest3d_out_cuda
- func: upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
python_module: nn
structured_delegate: upsample_nearest3d.out
dispatch:
QuantizedCPU: upsample_nearest3d_quantized_cpu
- func: upsample_nearest3d_backward.grad_input(Tensor grad_output, int[3] output_size, int[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
structured: True
dispatch:
CPU: upsample_nearest3d_backward_out_cpu
CUDA: upsample_nearest3d_backward_out_cuda
- func: upsample_nearest3d_backward(Tensor grad_output, int[3] output_size, int[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
python_module: nn
structured_delegate: upsample_nearest3d_backward.grad_input
- func: sigmoid_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: sigmoid_backward_out
- func: sigmoid_backward(Tensor grad_output, Tensor output) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: sigmoid_backward
- func: logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: logit_backward_out
- func: logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: logit_backward
- func: tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU, CUDA: tanh_backward_out
- func: tanh_backward(Tensor grad_output, Tensor output) -> Tensor
python_module: nn
dispatch:
CPU, CUDA: tanh_backward
# What's a thnn_conv_ versus a slow_conv_?
#
# Historically, we have inefficient implementations of convolutions
# coming from the THNN/THCUNN library. These convolutions typically
# operated by computing the Toeplitz matrix and then doing a matrix
# multiply with the input; this is very memory inefficient! However,
# occasionally, we really don't have anything better, so it's helpful
# to have these fallbacks when there is no more optimized implementation
# in cudnn or mkldnn, etc. Both thnn_ and slow_ convolutions fall
# into this bucket.
#
# The difference between these two designations, is that thnn_ refers
# to a convolution that is still written in the "legacy" style; that is,
# C code in the THNN/ or THCUNN/ directory. A slow_ convolution is
# one that is written in the native style: modern C++. Algorithmically,
# these are the same thing, but we give them different prefixes to
# make the operational distinction clear.
- func: slow_conv_transpose2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: slow_conv_transpose2d_out_cpu
CUDA: slow_conv_transpose2d_out_cuda
- func: slow_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1) -> Tensor
python_module: nn
dispatch:
CPU: slow_conv_transpose2d_cpu
CUDA: slow_conv_transpose2d_cuda
- func: slow_conv_transpose2d_backward.grad_output(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, int[2] output_padding, int[2] dilation, Tensor columns, Tensor ones, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))
python_module: nn
dispatch:
CPU: slow_conv_transpose2d_backward_out_cpu
CUDA: slow_conv_transpose2d_backward_out_cuda
- func: slow_conv_transpose2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, int[2] output_padding, int[2] dilation, Tensor columns, Tensor ones, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
python_module: nn
dispatch:
CPU: slow_conv_transpose2d_backward_cpu
CUDA: slow_conv_transpose2d_backward_cuda
- func: slow_conv_transpose3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: slow_conv_transpose3d_out_cpu
CUDA: slow_conv_transpose3d_out_cuda
- func: slow_conv_transpose3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1) -> Tensor
python_module: nn
dispatch:
CPU: slow_conv_transpose3d_cpu
CUDA: slow_conv_transpose3d_cuda
- func: slow_conv_transpose3d_backward.grad_output(Tensor grad_output, Tensor self, Tensor weight, int[3] kernel_size, int[3] stride, int[3] padding, int[3] output_padding, int[3] dilation, Tensor finput, Tensor fgrad_input, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))
python_module: nn
dispatch:
CPU: slow_conv_transpose3d_backward_out_cpu
CUDA: slow_conv_transpose3d_backward_out_cuda
- func: slow_conv_transpose3d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[3] kernel_size, int[3] stride, int[3] padding, int[3] output_padding, int[3] dilation, Tensor finput, Tensor fgrad_input, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
python_module: nn
dispatch:
CPU: slow_conv_transpose3d_backward_cpu
CUDA: slow_conv_transpose3d_backward_cuda
- func: thnn_conv2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
- func: thnn_conv2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0) -> Tensor
python_module: nn
- func: thnn_conv2d_forward.output(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, *, Tensor(a!) output, Tensor(b!) finput, Tensor(c!) fgrad_input) -> (Tensor(a!), Tensor(b!), Tensor(c!))
python_module: nn
dispatch:
CPU: slow_conv2d_forward_out_cpu
CUDA: legacy::cuda::_thnn_conv2d_forward_out
- func: thnn_conv2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding) -> (Tensor output, Tensor finput, Tensor fgrad_input)
python_module: nn
dispatch:
CPU: slow_conv2d_forward_cpu
CUDA: legacy::cuda::_thnn_conv2d_forward
- func: thnn_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, Tensor finput, Tensor fgrad_input, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))
python_module: nn
dispatch:
CPU: slow_conv2d_backward_out_cpu
CUDA: slow_conv2d_backward_out_cuda
- func: thnn_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, Tensor finput, Tensor fgrad_input, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
python_module: nn
dispatch:
CPU: slow_conv2d_backward_cpu
CUDA: slow_conv2d_backward_cuda
- func: thnn_conv_depthwise2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
- func: thnn_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1) -> Tensor
python_module: nn
- func: thnn_conv_depthwise2d_forward.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CUDA: legacy::cuda::_thnn_conv_depthwise2d_forward_out
- func: thnn_conv_depthwise2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation) -> Tensor
python_module: nn
dispatch:
CUDA: legacy::cuda::_thnn_conv_depthwise2d_forward
- func: thnn_conv_depthwise2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, *, Tensor(a!) grad_input, Tensor(b!) grad_weight) -> (Tensor(a!), Tensor(b!))
python_module: nn
dispatch:
CUDA: thnn_conv_depthwise2d_backward_out
- func: thnn_conv_depthwise2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool[2] output_mask) -> (Tensor grad_input, Tensor grad_weight)
python_module: nn
dispatch:
CUDA: thnn_conv_depthwise2d_backward
- func: conv_depthwise3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, int[3] padding, int[3] dilation) -> Tensor
python_module: nn
dispatch:
CUDA: conv_depthwise3d_cuda
- func: conv_depthwise3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))
python_module: nn
dispatch:
CUDA: conv_depthwise3d_backward_cuda_out
- func: conv_depthwise3d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
python_module: nn
dispatch:
CUDA: conv_depthwise3d_backward_cuda
- func: slow_conv3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
- func: slow_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0) -> Tensor
python_module: nn
- func: slow_conv3d_forward.output(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, int[3] padding, *, Tensor(a!) output, Tensor(b!) finput, Tensor(c!) fgrad_input) -> (Tensor(a!), Tensor(b!), Tensor(c!))
python_module: nn
dispatch:
CPU: slow_conv3d_forward_out_cpu
- func: slow_conv3d_forward(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, int[3] padding) -> (Tensor output, Tensor finput, Tensor fgrad_input)
python_module: nn
dispatch:
CPU: slow_conv3d_forward_cpu
- func: slow_conv3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[3] kernel_size, int[3] stride, int[3] padding, Tensor finput, Tensor fgrad_input, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))
python_module: nn
dispatch:
CPU: slow_conv3d_backward_out_cpu
- func: slow_conv3d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[3] kernel_size, int[3] stride, int[3] padding, Tensor finput, Tensor fgrad_input, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
python_module: nn
dispatch:
CPU: slow_conv3d_backward_cpu
- func: slow_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1) -> Tensor
python_module: nn
dispatch:
CPU: slow_conv_dilated2d_cpu
CUDA: slow_conv_dilated2d_cuda
- func: slow_conv_dilated2d_backward(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
python_module: nn
dispatch:
CPU: slow_conv_dilated2d_backward_cpu
CUDA: slow_conv_dilated2d_backward_cuda
- func: slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensor
python_module: nn
dispatch:
CPU: slow_conv_dilated3d_cpu
CUDA: slow_conv_dilated3d_cuda
- func: slow_conv_dilated3d_backward(Tensor grad_output, Tensor self, Tensor weight, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
python_module: nn
dispatch:
CPU: slow_conv_dilated3d_backward_cpu
CUDA: slow_conv_dilated3d_backward_cuda
- func: col2im.out(Tensor self, int[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: col2im_out_cpu
CUDA: col2im_out_cuda
- func: col2im(Tensor self, int[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor
python_module: nn
dispatch:
CPU: col2im_cpu
CUDA: col2im_cuda
- func: col2im_backward.grad_input(Tensor grad_output, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: col2im_backward_out_cpu
CUDA: col2im_backward_out_cuda
- func: col2im_backward(Tensor grad_output, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor
python_module: nn
dispatch:
CPU: col2im_backward_cpu
CUDA: col2im_backward_cuda
- func: column_stack(Tensor[] tensors) -> Tensor
- func: column_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
- func: im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)
python_module: nn
dispatch:
CPU: im2col_out_cpu
CUDA: im2col_out_cuda
- func: im2col(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor
python_module: nn
dispatch:
CPU: im2col_cpu
CUDA: im2col_cuda
- func: im2col_backward.grad_input(Tensor grad_output, int[2] input_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) grad_input) -> Tensor(a!)
python_module: nn
dispatch:
CPU: im2col_backward_out_cpu
CUDA: im2col_backward_out_cuda
- func: im2col_backward(Tensor grad_output, int[2] input_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor
python_module: nn
dispatch:
CPU: im2col_backward_cpu
CUDA: im2col_backward_cuda
- func: isfinite(Tensor self) -> Tensor
variants: function, method
device_guard: False
- func: isinf(Tensor self) -> Tensor
variants: function, method
device_guard: False
- func: record_stream(Tensor(a!) self, Stream s) -> ()
variants: method
dispatch:
CUDA: record_stream_cuda
- func: isposinf(Tensor self) -> Tensor
variants: function, method
- func: isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: isposinf_out
- func: isneginf(Tensor self) -> Tensor
variants: function, method
- func: isneginf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: isneginf_out
# NOTE [_add_batch_dim and _remove_batch_dim]
# _add_batch_dim and _remove_batch_dim are meant to be used in the implementation
# of the vmap frontend API (see torch/_vmap_internals.py). They are not
# user-facing, hence the leading underscore. Please don't use them them anywhere else.
- func: _add_batch_dim(Tensor self, int batch_dim, int level) -> Tensor
variants: function
# See NOTE [_add_batch_dim and _remove_batch_dim]
- func: _remove_batch_dim(Tensor self, int level, int batch_size, int out_dim) -> Tensor
variants: function
## Functions related to the `torch.special` namespace
# Note [special namespace binding]
# Functions in the special python module should have their names start with
# "special_" underscore and be bound to the desired Python name in
# torch/special/__init__.py, and the desired C++ name in torch/csrc/api/include/torch/special.h.
# The "special_" names should be hidden from the user and not documented.
- func: special_entr(Tensor self) -> Tensor
structured_delegate: special_entr.out
python_module: special
variants: function
- func: special_entr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
python_module: special
variants: function
dispatch:
CPU, CUDA: special_entr_out
- func: special_expm1(Tensor self) -> Tensor
python_module: special
variants: function
- func: special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: special
variants: function
- func: special_exp2(Tensor self) -> Tensor
python_module: special
variants: function
- func: special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: special
variants: function
- func: special_gammaln(Tensor self) -> Tensor
python_module: special
variants: function
- func: special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: special
variants: function
- func: special_erf(Tensor self) -> Tensor
python_module: special
variants: function
- func: special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: special
variants: function
- func: special_erfc(Tensor self) -> Tensor
python_module: special
variants: function
- func: special_erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: special
- func: special_erfinv(Tensor self) -> Tensor
python_module: special
variants: function
- func: special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: special
- func: special_i0e(Tensor self) -> Tensor
python_module: special
variants: function
structured_delegate: special_i0e.out
- func: special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: special
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: special_i0e_out
- func: special_logit(Tensor self, float? eps=None) -> Tensor
python_module: special
variants: function
- func: special_logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: special
- func: special_expit(Tensor self) -> Tensor
python_module: special
variants: function
- func: special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: special
variants: function
## Functions related to the fast Fourier transform and the torch.fft namespace
# Note [FFT namespace binding]
# Functions in the fft python module should have their names start with
# "fft_" underscore and be bound to the desired Python name in
# torch/fft/__init__.py, and the desired C++ name in torch/csrc/api/include/torch/fft.h.
# The "fft_" names should be hidden from the user and not documented.
#
# See fft_fft as an example.
# torch.fft.fft
# NOTE: NOT an alias for torch.fft, which has different semantics
- func: fft_fft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_fft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_ifft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_ifft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_rfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_rfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_irfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_irfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_hfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_hfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_ihfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_ihfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_fft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_fft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_ifft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_ifft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_rfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_rfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_irfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_irfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_fftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_fftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_ifftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_ifftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_rfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_rfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_irfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
python_module: fft
variants: function
- func: fft_irfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
python_module: fft
variants: function
- func: fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
python_module: fft
variants: function
- func: fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)
python_module: fft
variants: function
- func: fft_fftshift(Tensor self, int[1]? dim=None) -> Tensor
python_module: fft
variants: function
- func: fft_ifftshift(Tensor self, int[1]? dim=None) -> Tensor
python_module: fft
variants: function
## Functions for linear algebra and the torch.linalg namespace
# Note [linalg namespace binding]
# Functions in the linalg python module should have their names start with
# "linalg_" and be bound to the desired Python name in
# torch/linalg/__init__.py, and the desired C++ name in torch/csrc/api/include/torch/linalg.h.
# The "linalg_" names should be hidden from the user and not documented.
#
# See linalg_det as an example.
- func: linalg_cholesky(Tensor self) -> Tensor
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_cholesky
- func: linalg_cholesky.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_cholesky_out
- func: linalg_det(Tensor self) -> Tensor
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_det
- func: linalg_det.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
dispatch:
CompositeExplicitAutograd: linalg_det_out
# torch.det, alias for torch.linalg.det
- func: det(Tensor self) -> Tensor
variants: function, method
- func: linalg_lstsq(Tensor self, Tensor b, float? cond=None, *, str? driver=None) -> (Tensor solution, Tensor residuals, Tensor rank, Tensor singular_values)
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_lstsq
- func: linalg_lstsq.out(Tensor self, Tensor b, float? cond=None, *, str? driver=None, Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) -> (Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values)
python_module: linalg
variants: function
dispatch:
CPU, CUDA: linalg_lstsq_out
- func: _lstsq_helper_(Tensor(a!) self, Tensor(b!) rank, Tensor(c!) singular_values, Tensor(d!) infos, Tensor a, float cond, str driver_name) -> Tensor(a!)
variants: function
dispatch:
CPU: _lstsq_helper_cpu
CUDA: _lstsq_helper_cuda
- func: linalg_slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet)
python_module: linalg
variants: function
dispatch:
CPU, CUDA: linalg_slogdet
- func: linalg_slogdet.out(Tensor self, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet)
python_module: linalg
dispatch:
CPU, CUDA: linalg_slogdet_out
- func: linalg_eig(Tensor self) -> (Tensor eigenvalues, Tensor eigenvectors)
python_module: linalg
variants: function
dispatch:
CPU, CUDA: linalg_eig
- func: linalg_eig.out(Tensor self, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
python_module: linalg
dispatch:
CPU, CUDA: linalg_eig_out
- func: linalg_eigvals(Tensor self) -> Tensor
python_module: linalg
variants: function
dispatch:
CPU, CUDA: linalg_eigvals
- func: linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
dispatch:
CPU, CUDA: linalg_eigvals_out
- func: linalg_eigh(Tensor self, str UPLO="L") -> (Tensor eigenvalues, Tensor eigenvectors)
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_eigh
- func: linalg_eigh.eigvals(Tensor self, str UPLO="L", *, Tensor(a!) eigvals, Tensor(b!) eigvecs) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
python_module: linalg
dispatch:
CompositeExplicitAutograd: linalg_eigh_out
- func: linalg_eigvalsh(Tensor self, str UPLO="L") -> Tensor
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_eigvalsh
- func: linalg_eigvalsh.out(Tensor self, str UPLO='L', *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
dispatch:
CompositeExplicitAutograd: linalg_eigvalsh_out
- func: linalg_householder_product(Tensor input, Tensor tau) -> Tensor
python_module: linalg
variants: function
dispatch:
CPU, CUDA: linalg_householder_product
- func: linalg_householder_product.out(Tensor input, Tensor tau, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
dispatch:
CPU, CUDA: linalg_householder_product_out
- func: _linalg_inv_out_helper_(Tensor(a!) self, Tensor(b!) infos_lu, Tensor(c!) infos_getri) -> Tensor(a!)
variants: function
dispatch:
CPU: _linalg_inv_out_helper_cpu
CUDA: _linalg_inv_out_helper_cuda
- func: linalg_inv(Tensor self) -> Tensor
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_inv
- func: linalg_inv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_inv_out
- func: inner(Tensor self, Tensor other) -> Tensor
variants: function, method
- func: inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
# torch.outer, alias for torch.ger
- func: outer(Tensor self, Tensor vec2) -> Tensor
variants: function, method
- func: outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)
- func: ger(Tensor self, Tensor vec2) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: ger
- func: ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: ger_out
- func: linalg_norm(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
python_module: linalg
variants: function
- func: linalg_norm.ord_str(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
python_module: linalg
variants: function
- func: linalg_norm.out(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
- func: linalg_norm.ord_str_out(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
- func: linalg_vector_norm(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
python_module: linalg
variants: function
dispatch:
CPU, CUDA: linalg_vector_norm
- func: linalg_vector_norm.out(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
dispatch:
CPU, CUDA: linalg_vector_norm_out
- func: linalg_svd.U(Tensor self, bool full_matrices=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)
python_module: linalg
- func: linalg_svd(Tensor self, bool full_matrices=True, bool compute_uv=True) -> (Tensor U, Tensor S, Tensor V)
python_module: linalg
variants: function
- func: linalg_svdvals(Tensor input) -> Tensor
python_module: linalg
variants: function
- func: linalg_svdvals.out(Tensor input, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
- func: linalg_cond(Tensor self, Scalar? p=None) -> Tensor
python_module: linalg
variants: function
- func: linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
- func: linalg_cond.p_str(Tensor self, str p) -> Tensor
python_module: linalg
variants: function
- func: linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
- func: linalg_pinv(Tensor self, float rcond=1e-15, bool hermitian=False) -> Tensor
python_module: linalg
variants: function
- func: linalg_pinv.rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False) -> Tensor
python_module: linalg
variants: function
- func: linalg_pinv.out(Tensor self, float rcond=1e-15, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
- func: linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
- func: _linalg_solve_out_helper_(Tensor(a!) self, Tensor(b!) other, Tensor(c!) infos) -> Tensor(a!)
variants: function
dispatch:
CPU: _linalg_solve_out_helper_cpu
CUDA: _linalg_solve_out_helper_cuda
- func: linalg_solve(Tensor input, Tensor other) -> Tensor
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_solve
- func: linalg_solve.out(Tensor input, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
dispatch:
CompositeExplicitAutograd: linalg_solve_out
- func: linalg_tensorinv(Tensor self, int ind=2) -> Tensor
python_module: linalg
variants: function
- func: linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
- func: linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor
python_module: linalg
variants: function
- func: linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
- func: linalg_qr(Tensor self, str mode='reduced') -> (Tensor Q, Tensor R)
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_qr
- func: linalg_qr.out(Tensor self, str mode='reduced', *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)
python_module: linalg
variants: function
dispatch:
CompositeExplicitAutograd: linalg_qr_out
- func: _linalg_qr_helper(Tensor self, str mode) -> (Tensor, Tensor)
variants: function
dispatch:
CPU: _linalg_qr_helper_cpu
CUDA: _linalg_qr_helper_cuda
- func: linalg_matrix_power(Tensor self, int n) -> Tensor
python_module: linalg
- func: linalg_matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
- func: linalg_matrix_rank(Tensor self, float? tol=None, bool hermitian=False) -> Tensor
python_module: linalg
variants: function
- func: linalg_matrix_rank.out(Tensor self, float? tol=None, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
variants: function
- func: linalg_multi_dot(Tensor[] tensors) -> Tensor
python_module: linalg
- func: linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
python_module: linalg
## Functions that are only for testing
# It is undocumented and should not be used outside of tests.
- func: _test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=1) -> Tensor
# Note: this function is only for testing.
- func: _test_optional_intlist(Tensor values, int[]? addends) -> Tensor
python_module: nn
dispatch:
CPU: _test_optional_intlist
# Note: this function is only for testing.
- func: _test_optional_filled_intlist(Tensor values, int[2]? addends) -> Tensor
python_module: nn
dispatch:
CPU: _test_optional_intlist
# Note: this function is only for testing.
- func: _test_optional_floatlist(Tensor values, float[]? addends) -> Tensor
python_module: nn
dispatch:
CPU: _test_optional_floatlist
# Note: this function is only for testing.
- func: _test_string_default(Tensor dummy, str a="\"'\\", str b='"\'\\') -> Tensor
python_module: nn
# Note: this function is only for testing.
- func: _test_ambiguous_defaults.a(Tensor dummy, int a=1, int b=1) -> Tensor
python_module: nn
# Note: this function is only for testing.
- func: _test_ambiguous_defaults.b(Tensor dummy, int a=2, str b="2") -> Tensor
cpp_no_default_args: ['a', 'b']
python_module: nn
- func: segment_reduce(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, int axis=0, bool unsafe=False) -> Tensor
variants: function
dispatch:
CPU: _segment_reduce_cpu