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native_functions.yaml
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native_functions.yaml
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# See README.md in this directory for more guidance
# *********NB: _cast_* operators are DEPRECATED and will be removed
# eventually. These were previously used before TorchScript IR supported
# representing ScalarType's. They are now superseded by usage of
# `aten::to()`. The ops remain here for backward compatibility purposes.
# DEPRECATED. DO NOT USE
- func: _cast_Byte(Tensor self, bool non_blocking=False) -> Tensor
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: retains_grad(Tensor self) -> bool
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
dispatch:
CompositeExplicitAutograd: _make_dual
- func: _unpack_dual(Tensor(a) dual, int level) -> (Tensor(a) primal, Tensor tangent)
variants: function
# NOTE: [_new_zeros_with_same_feature_meta]
# This function creates a new tensor with the layout and TensorOptions
# of `other` but also takes into account the batch dimensions of `self`
#
# This function has a couple extra constraints because it is also used for `jvp`
# in functorch.
# - is used for forward AD because there is the restriction
# that the primal and tangent must have the same layout
# - We cannot assume that `self` and `other` have the same sizes or even dim
# because in the inplace over view case, `other` is the base tensor, and
# `self` is the forward grad with respect to the view, which can have an
# entirely different shape
# - takes the number of batch dims for `self` because we also handle
# some batching logic. We handle that here instead of a batching rule because
# we'd like to avoid calling as_strided in the batching rule (as to enable
# nested vmap in functorch).
# - needs to be CompositeExplicitAutograd for jvp support in functorch.
# functorch currently relies on TensorWrapper which does not have storage
# CompositeExplicitAutograd makes sure the TensorWrapper is unwrapped.
# - this function may eventually take on another int argument to store the
# the number of batch dims for other once we support that use case
- func: _new_zeros_with_same_feature_meta(Tensor self, Tensor other, *, int self_num_batch_dims=0) -> Tensor
variants: function
dispatch:
CompositeExplicitAutograd: _new_zeros_with_same_feature_meta
autogen: _new_zeros_with_same_feature_meta.out
# This function compares the storage numel of self with that of other, where
# storage numel is cumputed as: `other.storage().nbytes() / other.itemsize()`.
# We create this function for composite compliance purposes. The batching rule
# always returns true because vmapped as_strided does not support accessing
# storage locations not indexable by the input tensor.
# See the note above for more information.
- func: _has_same_storage_numel(Tensor self, Tensor other) -> bool
variants: function
dispatch:
CompositeExplicitAutograd: _has_same_storage_numel
- func: rename_(Tensor(a!) self, Dimname[]? names) -> Tensor(a!)
variants: method
tags: inplace_view
- 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: _assert_tensor_metadata(Tensor a, int[]? size=None, int[]? stride=None, ScalarType? dtype=None) -> ()
- 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
device_check: NoCheck # Tensor arguments allowed to be on different devices, see also _cudnn_ctc_loss
dispatch:
CUDA: _use_cudnn_ctc_loss
- func: _use_cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank) -> bool
device_check: NoCheck # Tensor arguments allowed to be on different devices, see also _cudnn_ctc_loss
dispatch:
CUDA: _use_cudnn_ctc_loss_tensor
- func: _cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)
device_check: NoCheck # log_probs is expected to be on CUDA while targets is expected to be on CPU
dispatch:
CUDA: _cudnn_ctc_loss
autogen: _cudnn_ctc_loss.out
- func: _cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)
device_check: NoCheck # log_probs is expected to be on CUDA while targets is expected to be on CPU
dispatch:
CUDA: _cudnn_ctc_loss_tensor
- func: _use_cudnn_rnn_flatten_weight() -> bool
- func: _cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor
dispatch:
CUDA: _cudnn_rnn_flatten_weight
autogen: _cudnn_rnn_flatten_weight.out
- func: _cudnn_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
# rnn_tanh may or may not redispatch to _cudnn_rnn based on algorithm and build. Thus it might hit dispatch or kernel device check.
# Disable dispatch time device check for consistent behavior.
device_check: NoCheck
dispatch:
CUDA: _cudnn_rnn
autogen: _cudnn_rnn.out
- 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, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[])
dispatch:
CUDA: _cudnn_rnn_backward
autogen: _cudnn_rnn_backward.out
- 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
autogen: _cudnn_init_dropout_state.out
- 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
tags: nondeterministic_seeded
autogen: _fused_dropout.out
- func: _masked_scale(Tensor self, Tensor mask, float scale) -> Tensor
variants: function
dispatch:
CUDA: masked_scale_cuda
autogen: _masked_scale.out
- func: native_dropout(Tensor input, float p, bool? train) -> (Tensor, Tensor)
variants: function
dispatch:
CPU: native_dropout_cpu
CUDA: native_dropout_cuda
NestedTensorCPU, NestedTensorCUDA: native_dropout_nested
tags: nondeterministic_seeded, canonical
autogen: native_dropout.out
- func: native_dropout_backward(Tensor grad_output, Tensor mask, float scale) -> Tensor
dispatch:
CPU, NestedTensorCPU, NestedTensorCUDA: native_dropout_backward
CUDA: native_dropout_backward_cuda
autogen: native_dropout_backward.out
- 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
tags: nondeterministic_seeded
- func: dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
tags: nondeterministic_seeded
- func: feature_dropout(Tensor input, float p, bool train) -> Tensor
tags: nondeterministic_seeded
- func: feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
tags: nondeterministic_seeded
- func: alpha_dropout(Tensor input, float p, bool train) -> Tensor
tags: nondeterministic_seeded
- func: alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
tags: nondeterministic_seeded
- func: feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor
tags: nondeterministic_seeded
- func: feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
tags: nondeterministic_seeded
- func: abs(Tensor self) -> Tensor
device_check: NoCheck # TensorIterator
variants: function, method
dispatch:
CompositeExplicitAutograd: abs
SparseCPU, SparseCUDA: abs_sparse
SparseCsrCPU, SparseCsrCUDA: abs_sparse_csr
tags: canonical
- func: abs_(Tensor(a!) self) -> Tensor(a!)
device_check: NoCheck # TensorIterator
variants: function, method
dispatch:
CompositeExplicitAutograd: abs_
SparseCPU, SparseCUDA: abs_sparse_
SparseCsrCPU, SparseCsrCUDA: abs_sparse_csr_
- func: abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
dispatch:
CPU, CUDA: abs_out
MPS: abs_out_mps
SparseCPU, SparseCUDA: abs_sparse_out
SparseCsrCPU, SparseCsrCUDA: abs_sparse_csr_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 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.)
# 4) Update torch/overrides.py consistent with the original function.
# 5) Update the alias_map in torch/csrc/jit/passes/normalize_ops.cpp.
# 6) 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
device_check: NoCheck # TensorIterator
variants: function, method
- func: absolute_(Tensor(a!) self) -> Tensor(a!)
device_check: NoCheck # TensorIterator
variants: method
- func: absolute.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
- func: angle(Tensor self) -> Tensor
device_check: NoCheck # TensorIterator
variants: function, method
dispatch:
CPU, CUDA: angle
SparseCsrCPU, SparseCsrCUDA: angle_sparse_csr
- func: angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
dispatch:
CPU, CUDA: angle_out
SparseCsrCPU, SparseCsrCUDA: angle_sparse_csr_out
- func: view_as_real(Tensor(a) self) -> Tensor(a)
variants: function
dispatch:
CPU, CUDA, MPS, Meta: view_as_real
- func: view_as_complex(Tensor(a) self) -> Tensor(a)
variants: function
dispatch:
CPU, CUDA, Meta: view_as_complex
- func: sgn(Tensor self) -> Tensor
variants: function, method
structured_delegate: sgn.out
dispatch:
SparseCPU, SparseCUDA: sgn_sparse
SparseCsrCPU, SparseCsrCUDA: sgn_sparse_csr
- func: sgn_(Tensor(a!) self) -> Tensor(a!)
variants: method
structured_delegate: sgn.out
dispatch:
SparseCPU, SparseCUDA: sgn_sparse_
SparseCsrCPU, SparseCsrCUDA: sgn_sparse_csr_
- func: sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: sgn_out
SparseCPU, SparseCUDA: sgn_sparse_out
SparseCsrCPU, SparseCsrCUDA: sgn_sparse_csr_out
- func: chalf(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor
variants: method
- func: real(Tensor(a) self) -> Tensor(a)
device_check: NoCheck # TensorIterator
variants: function
- func: imag(Tensor(a) self) -> Tensor(a)
device_check: NoCheck # TensorIterator
variants: function
- func: _conj(Tensor(a) self) -> Tensor(a)
variants: function, method
dispatch:
CompositeExplicitAutograd: _conj
- func: conj(Tensor(a) self) -> Tensor(a)
variants: function, method
manual_cpp_binding: True
- func: _conj_physical(Tensor self) -> Tensor
variants: function, method
dispatch:
CompositeExplicitAutograd: _conj_physical
SparseCsrCPU, SparseCsrCUDA: conj_physical_sparse_csr
autogen: _conj_physical.out
- func: conj_physical(Tensor self) -> Tensor
variants: function, method
- func: conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, CUDA: conj_physical_out
SparseCPU, SparseCUDA: conj_physical_out_sparse
SparseCsrCPU, SparseCsrCUDA: conj_physical_sparse_csr_out
- func: conj_physical_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
dispatch:
CompositeExplicitAutograd: conj_physical_
SparseCsrCPU, SparseCsrCUDA: conj_physical_sparse_csr_
- func: resolve_conj(Tensor(a) self) -> Tensor(a)
variants: function, method
- func: resolve_neg(Tensor(a) self) -> Tensor(a)
variants: function, method
- func: _neg_view(Tensor(a) self) -> Tensor(a)
variants: function, method
dispatch:
CompositeExplicitAutograd: _neg_view
- func: acos(Tensor self) -> Tensor
device_check: NoCheck # TensorIterator
variants: function, method
structured_delegate: acos.out
- func: acos_(Tensor(a!) self) -> Tensor(a!)
device_check: NoCheck # TensorIterator
variants: function, method
structured_delegate: acos.out
- func: acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: acos_out
MPS: acos_out_mps
# 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
device_check: NoCheck # TensorIterator
structured_delegate: add.out
variants: function, method
dispatch:
SparseCPU, SparseCUDA: add_sparse
SparseCsrCPU, SparseCsrCUDA: add_sparse_csr
MkldnnCPU: mkldnn_add
ZeroTensor: add_zerotensor
NestedTensorCPU, NestedTensorCUDA: NestedTensor_add_Tensor
tags: canonical
- func: add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
device_check: NoCheck # TensorIterator
variants: method
structured_delegate: add.out
dispatch:
SparseCPU, SparseCUDA: add_sparse_
SparseCsrCPU, SparseCsrCUDA: add_sparse_csr_
MkldnnCPU: mkldnn_add_
NestedTensorCPU, NestedTensorCUDA: NestedTensor_add__Tensor
- func: add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
structured: True
structured_inherits: TensorIteratorBase
ufunc_inner_loop:
Generic: add (AllAndComplex, BFloat16, Half, ComplexHalf)
ScalarOnly: add (Bool)
dispatch:
SparseCPU: add_out_sparse_cpu
SparseCUDA: add_out_sparse_cuda
SparseCsrCPU: add_out_sparse_csr_cpu
SparseCsrCUDA: add_out_sparse_csr_cuda
MkldnnCPU: mkldnn_add_out
MPS: add_out_mps
- 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
- func: _add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
variants: function
dispatch:
CPU: add_relu
- func: _add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
variants: function
dispatch:
CPU: add_relu_
autogen: _add_relu.Scalar_out
# For C++ only, until we have conversion from C++ numbers to Tensor
- func: add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
device_check: NoCheck # TensorIterator
variants: function, method
dispatch:
CompositeExplicitAutograd: add
tags: canonical
- func: add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
device_check: NoCheck # TensorIterator
variants: method
dispatch:
CompositeExplicitAutograd: add_
autogen: add.Scalar_out
- 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
MPS: addmv_out_mps
SparseCsrCPU: addmv_out_sparse_compressed
SparseCsrCUDA: addmv_out_sparse_compressed_cuda
- func: addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: function, method
dispatch:
CPU, CUDA: addr
CompositeExplicitAutograd: 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
CompositeExplicitAutograd: math_addr_out
- func: affine_grid_generator(Tensor theta, int[] size, bool align_corners) -> Tensor
variants: function
dispatch:
CompositeExplicitAutograd: affine_grid_generator
autogen: affine_grid_generator.out
- 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
device_check: NoCheck # TensorIterator
structured_delegate: all.out
variants: function, method
- func: all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
structured: True
precomputed:
- dim -> int dim
dispatch:
CPU, CUDA: all_out
MPS: all_out_mps
- func: all.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor
device_check: NoCheck # TensorIterator
variants: function, method
- func: all.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
- func: allclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> bool
variants: function, method
tags: data_dependent_output
dispatch:
CompositeExplicitAutograd: allclose
- func: any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor
device_check: NoCheck # TensorIterator
structured_delegate: any.out
variants: function, method
- func: any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
structured: True
precomputed:
- dim -> int dim
dispatch:
CPU, CUDA: any_out
MPS: any_out_mps
- func: any.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor
device_check: NoCheck # TensorIterator
variants: function, method
- func: any.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
- func: arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
dispatch:
CompositeExplicitAutograd: arange
- func: arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
dispatch:
CompositeExplicitAutograd: arange
# This operator should be named `aragne.start_out` if following the naming convention. However that
# name is already taken. Disabled because of CI job failures.
# FIXME: enable this
#- func: arange.start_out_(Scalar start, Scalar end, *, Tensor(a!) out) -> Tensor(a!)
# dispatch:
# CompositeExplicitAutograd: arange_start_out
- func: arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
dispatch:
CompositeExplicitAutograd: arange
cpp_no_default_args: ['step']
tags: canonical
- func: arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CompositeExplicitAutograd: arange_out
- func: arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU, Meta: arange_out
CUDA: arange_cuda_out
MPS: arange_mps_out
cpp_no_default_args: ['step']
# 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
structured_delegate: argmax.out
device_check: NoCheck # TensorIterator
variants: function, method
- func: argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
structured: True
dispatch:
CPU, CUDA: argmax_out
MPS: argmax_out_mps
- func: argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
structured_delegate: argmin.out
device_check: NoCheck # TensorIterator
variants: function, method
- func: argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
structured: True
dispatch:
CPU, CUDA: argmin_out
MPS: argmin_out_mps
- 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
MPS: acosh_out_mps
# 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
dispatch:
SparseCPU, SparseCUDA: asinh_sparse
SparseCsrCPU, SparseCsrCUDA: asinh_sparse_csr
- func: asinh_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
structured_delegate: asinh.out
dispatch:
SparseCPU, SparseCUDA: asinh_sparse_
SparseCsrCPU, SparseCsrCUDA: asinh_sparse_csr_
- func: asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: asinh_out
MPS: asinh_out_mps
SparseCPU, SparseCUDA: asinh_sparse_out
SparseCsrCPU, SparseCsrCUDA: asinh_sparse_csr_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
dispatch:
SparseCPU, SparseCUDA: atanh_sparse
SparseCsrCPU, SparseCsrCUDA: atanh_sparse_csr
- func: atanh_(Tensor(a!) self) -> Tensor(a!)
structured_delegate: atanh.out
variants: function, method
dispatch:
SparseCPU, SparseCUDA: atanh_sparse_
SparseCsrCPU, SparseCsrCUDA: atanh_sparse_csr_
- func: atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: atanh_out
MPS: atanh_out_mps
SparseCPU, SparseCUDA: atanh_sparse_out
SparseCsrCPU, SparseCsrCUDA: atanh_sparse_csr_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, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)
variants: function, method
dispatch:
ZeroTensor, CPU, CUDA: as_strided_tensorimpl
Meta: as_strided_tensorimpl_meta_symint
MPS: as_strided_tensorimpl_mps
QuantizedCPU, QuantizedCUDA: as_strided_qtensorimpl
device_check: NoCheck
device_guard: False
- func: as_strided_(Tensor(a!) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a!)
use_const_ref_for_mutable_tensors: True
variants: function, method
device_check: NoCheck
device_guard: False
tags: inplace_view
dispatch:
CompositeExplicitAutogradNonFunctional: as_strided__symint
- func: asin(Tensor self) -> Tensor
device_check: NoCheck # TensorIterator
variants: function, method
structured_delegate: asin.out
dispatch:
SparseCPU, SparseCUDA: asin_sparse
SparseCsrCPU, SparseCsrCUDA: asin_sparse_csr
- func: asin_(Tensor(a!) self) -> Tensor(a!)
device_check: NoCheck # TensorIterator
variants: function, method
structured_delegate: asin.out
dispatch:
SparseCPU, SparseCUDA: asin_sparse_
SparseCsrCPU, SparseCsrCUDA: asin_sparse_csr_
- func: asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: asin_out
MPS: asin_out_mps
SparseCPU, SparseCUDA: asin_sparse_out
SparseCsrCPU, SparseCsrCUDA: asin_sparse_csr_out
# 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
device_check: NoCheck # TensorIterator
structured_delegate: atan.out
variants: function, method
dispatch:
SparseCPU, SparseCUDA: atan_sparse
SparseCsrCPU, SparseCsrCUDA: atan_sparse_csr
- func: atan_(Tensor(a!) self) -> Tensor(a!)
device_check: NoCheck # TensorIterator
structured_delegate: atan.out
variants: function, method
dispatch:
SparseCPU, SparseCUDA: atan_sparse_
SparseCsrCPU, SparseCsrCUDA: atan_sparse_csr_
- func: atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
structured: True
structured_inherits: TensorIteratorBase
dispatch:
CPU, CUDA: atan_out
MPS: atan_out_mps
SparseCPU, SparseCUDA: atan_sparse_out
SparseCsrCPU, SparseCsrCUDA: atan_sparse_csr_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
structured_delegate: baddbmm.out
- func: baddbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
variants: method
structured_delegate: baddbmm.out
- func: baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
structured: True
variants: function
dispatch:
CPU: baddbmm_out_cpu
CUDA: baddbmm_out_cuda
MPS: baddbmm_out_mps
SparseCsrCUDA: baddbmm_out_sparse_csr_cuda
- func: bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
dispatch:
CompositeExplicitAutograd: bartlett_window
autogen: bartlett_window.out
- func: bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
dispatch:
CompositeExplicitAutograd: bartlett_window
autogen: bartlett_window.periodic_out
- 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
autogen: quantized_batch_norm.out
- 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
device_check: NoCheck # TensorIterator
variants: function, method
dispatch:
CompositeExplicitAutograd: bernoulli
tags: nondeterministic_seeded
- func: bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
device_check: NoCheck # TensorIterator
variants: function
tags: nondeterministic_seeded
dispatch:
CPU, CUDA: bernoulli_out
MPS: bernoulli_out_mps
- func: bernoulli_.Tensor(Tensor(a!) self, Tensor p, *, Generator? generator=None) -> Tensor(a!)
device_check: NoCheck # TensorIterator
variants: method
tags: nondeterministic_seeded
dispatch:
CPU, CUDA: bernoulli_
MPS: bernoulli_mps_
autogen: bernoulli.Tensor, bernoulli.Tensor_out
- func: bernoulli_.float(Tensor(a!) self, float p=0.5, *, Generator? generator=None) -> Tensor(a!)
device_check: NoCheck # TensorIterator
variants: method
tags: nondeterministic_seeded
dispatch:
CPU, CUDA: bernoulli_
MPS: bernoulli_mps_
autogen: bernoulli.float_out
# Note [bernoulli.p schema]
# We should probably just fix the overload ambiguity by appending a _functional to the C++ API name (BC breaking)
# 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
device_check: NoCheck # TensorIterator
variants: function, method
tags: nondeterministic_seeded
dispatch:
CompositeExplicitAutogradNonFunctional: bernoulli
- func: bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias=None) -> Tensor
- func: binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor
device_check: NoCheck # TensorIterator
python_module: nn
variants: function
dispatch:
CPU: binary_cross_entropy_cpu
CUDA: binary_cross_entropy_cuda
MPS: binary_cross_entropy_mps