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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
# Temporary type cast operators. These are needed to trace type-casts now since
# Type's are not supported in the IR. Instead, we call down to these
# specialized operators for each datatype.
# TODO: remove when we have Type support in the IR
- func: _cast_Byte(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
- func: _cast_Char(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
- func: _cast_Double(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
- func: _cast_Float(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
- func: _cast_Int(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
- func: _cast_Long(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
- func: _cast_Short(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
- func: _cast_Half(Tensor self, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
variants: function
# Computes the gradient of current tensor w.r.t. graph leaves.
- func: backward(Tensor self, Tensor? gradient=None, bool keep_graph=False, bool create_graph=False) -> ()
manual_kernel_registration: True
variants: method
# DEPRECATED. Sets the tensor data held by this `Variable` to be the same as
# `new_data`. It requires that `new_data` and `Variable` have compatible tensor
# type, by checking `_has_compatible_shallow_copy_type(this, new_data)`.
#
# This function is deprecated because it doesn't really make sense in a world
# where Variables *are* Tensors (as opposed to them containing tensors, which
# is what the previous interpretation was.)
- func: set_data(Tensor(a!) self, Tensor new_data) -> ()
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
- func: data(Tensor self) -> Tensor
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
# True if this `Variable` is a leaf and thus does not have a `grad_fn`.
- func: is_leaf(Tensor self) -> bool
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
# Returns the output index of this variable from the forward operation that
# produced it. Conversely, it returns the input index of the gradient `Node` to
# which this `Variable` is connected (because in the gradient computation,
# inputs and outputs switch meaning). For example:
#
# y0, y1, y2 = f(x)
# assert y0.output_nr == 0
# assert y1.output_nr == 1
# assert y2.output_nr == 2
#
- func: output_nr(Tensor self) -> int
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
supports_named_tensor: True
- func: _version(Tensor self) -> int
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
- func: requires_grad_(Tensor(a!) self, bool _requires_grad=True) -> Tensor(a!)
manual_kernel_registration: True
variants: method
# Enables .grad attribute for non-leaf Tensors.
- func: retain_grad(Tensor(a!) self) -> ()
use_c10_dispatcher: full
manual_kernel_registration: True
variants: method
- func: rename_(Tensor(a!) self, Dimname[]? names) -> Tensor(a!)
variants: method
supports_named_tensor: True
- func: rename(Tensor(a) self, Dimname[]? names) -> Tensor(a)
variants: method
supports_named_tensor: True
- func: align_to(Tensor(a) self, Dimname[] names) -> Tensor(a)
variants: method
supports_named_tensor: True
- func: align_to.ellipsis_idx(Tensor(a) self, Dimname[] order, int ellipsis_idx) -> Tensor(a)
variants: method
supports_named_tensor: True
- func: align_as(Tensor self, Tensor other) -> Tensor
variants: method
supports_named_tensor: True
- func: align_tensors(Tensor[] tensors) -> Tensor[]
supports_named_tensor: True
- func: refine_names(Tensor(a) self, Dimname[] names) -> Tensor(a)
variants: method
supports_named_tensor: True
- func: unflatten.Dimname(Tensor self, Dimname dim, int[] sizes, Dimname[] names) -> Tensor
variants: method
supports_named_tensor: True
- func: unflatten.int(Tensor self, int dim, int[] sizes, Dimname[] names) -> Tensor
variants: method
supports_named_tensor: True
- 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: _cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, int input_size, int mode, int hidden_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor
dispatch:
CUDA: _cudnn_rnn_flatten_weight
- func: _cudnn_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
dispatch:
CUDA: _cudnn_rnn
- func: _cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[])
dispatch:
CUDA: _cudnn_rnn_backward
- func: _cudnn_init_dropout_state(float dropout, bool train, int dropout_seed, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensor
dispatch:
CUDA: _cudnn_init_dropout_state
- func: _debug_has_internal_overlap(Tensor self) -> int
use_c10_dispatcher: full
variants: function
- func: _fused_dropout(Tensor self, float p, Generator? generator=None) -> (Tensor, Tensor)
variants: function
dispatch:
CUDA: fused_dropout_cuda
supports_named_tensor: True
- func: _masked_scale(Tensor self, Tensor mask, float scale) -> Tensor
use_c10_dispatcher: full
variants: function
dispatch:
CUDA: masked_scale_cuda
- func: _sobol_engine_draw(Tensor quasi, int n, Tensor sobolstate, int dimension, int num_generated, ScalarType? dtype) -> (Tensor, Tensor)
- 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
use_c10_dispatcher: full
- func: _shape_as_tensor(Tensor self) -> Tensor
use_c10_dispatcher: full
- func: dropout(Tensor input, float p, bool train) -> Tensor
use_c10_dispatcher: full
supports_named_tensor: True
- func: dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
supports_named_tensor: True
- func: feature_dropout(Tensor input, float p, bool train) -> Tensor
use_c10_dispatcher: full
- func: feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
- func: alpha_dropout(Tensor input, float p, bool train) -> Tensor
use_c10_dispatcher: full
- func: alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
- func: feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor
use_c10_dispatcher: full
- func: feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
- func: abs(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
supports_named_tensor: True
- func: abs_(Tensor(a!) self) -> Tensor(a!)
variants: function, method
supports_named_tensor: True
- func: abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: angle(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
supports_named_tensor: True
- func: angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: real(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
supports_named_tensor: True
- func: real.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: imag(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
supports_named_tensor: True
- func: imag.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: conj(Tensor self) -> Tensor
use_c10_dispatcher: full
variants: function, method
supports_named_tensor: True
- func: conj.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: acos(Tensor self) -> Tensor
use_c10_dispatcher: full
supports_named_tensor: True
variants: function, method
- func: acos_(Tensor(a!) self) -> Tensor(a!)
supports_named_tensor: True
variants: function, method
- func: acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- 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
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU: add
CUDA: add
SparseCPU: add_sparse
SparseCUDA: add_sparse
MkldnnCPU: mkldnn_add
supports_named_tensor: True
- func: add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
variants: method
dispatch:
CPU: add_
CUDA: add_
SparseCPU: add_sparse_
SparseCUDA: add_sparse_
MkldnnCPU: mkldnn_add_
supports_named_tensor: True
- func: add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: add_out
CUDA: add_out
SparseCPU: add_out_sparse_cpu
SparseCUDA: add_out_sparse_cuda
MkldnnCPU: mkldnn_add_out
supports_named_tensor: True
# For C++ only, until we have conversion from C++ numbers to Tensor
- func: add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
use_c10_dispatcher: full
variants: function, method
supports_named_tensor: True
- func: add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
variants: method
supports_named_tensor: True
- func: addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU: legacy::cpu::_th_addmv
CUDA: legacy::cuda::_th_addmv
supports_named_tensor: True
- func: addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
variants: function, method
dispatch:
CPU: legacy::cpu::_th_addmv_
CUDA: legacy::cuda::_th_addmv_
supports_named_tensor: True
- func: addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: legacy::cpu::_th_addmv_out
CUDA: legacy::cuda::_th_addmv_out
supports_named_tensor: True
- func: addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
variants: method
- func: addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
- func: affine_grid_generator(Tensor theta, int[] size, bool align_corners) -> Tensor
variants: function
- 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
use_c10_dispatcher: full
variants: function, method
- func: all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: all.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor
variants: function, method
- func: all.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- func: allclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> bool
use_c10_dispatcher: full
variants: function, method
- func: any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor
use_c10_dispatcher: full
variants: function, method
- func: any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
- 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
use_c10_dispatcher: full
- func: argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
variants: function, method
dispatch:
CPU: argmax
CUDA: argmax
- func: argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
variants: function, method
dispatch:
CPU: argmin
CUDA: argmin
- func: as_strided(Tensor(a) self, int[] size, int[] stride, int? storage_offset=None) -> Tensor(a)
variants: function, method
dispatch:
CPU: as_strided_tensorimpl
CUDA: as_strided_tensorimpl
QuantizedCPU: as_strided_qtensorimpl
device_guard: False
supports_named_tensor: True
- func: as_strided_(Tensor(a!) self, int[] size, int[] stride, int? storage_offset=None) -> Tensor(a!)
variants: function, method
device_guard: False
- func: asin(Tensor self) -> Tensor
use_c10_dispatcher: full
supports_named_tensor: True
variants: function, method
- func: asin_(Tensor(a!) self) -> Tensor(a!)
supports_named_tensor: True
variants: function, method
- func: asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: atan(Tensor self) -> Tensor
use_c10_dispatcher: full
supports_named_tensor: True
variants: function, method
- func: atan_(Tensor(a!) self) -> Tensor(a!)
supports_named_tensor: True
variants: function, method
dispatch:
CPU: _atan__cpu
CUDA: _atan__cuda
- func: atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
dispatch:
CPU: _atan_out_cpu
CUDA: _atan_out_cuda
- func: baddbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU: baddbmm_cpu
CUDA: baddbmm_cuda
- func: baddbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
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
requires_tensor: True
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
supports_named_tensor: True
- func: bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
variants: function
supports_named_tensor: True
- func: bernoulli_.Tensor(Tensor(a!) self, Tensor p, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU: bernoulli_tensor_cpu_
CUDA: bernoulli_tensor_cuda_
supports_named_tensor: True
- func: bernoulli_.float(Tensor(a!) self, float p=0.5, *, Generator? generator=None) -> Tensor(a!)
variants: method
dispatch:
CPU: bernoulli_scalar_cpu_
CUDA: bernoulli_scalar_cuda_
supports_named_tensor: True
# 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
- 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
use_c10_dispatcher: full
supports_named_tensor: True
variants: function, method
- func: bitwise_not_(Tensor(a!) self) -> Tensor(a!)
supports_named_tensor: True
variants: method
- func: bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
dispatch:
CPU: bitwise_not_out
CUDA: bitwise_not_out
- func: logical_not(Tensor self) -> Tensor
supports_named_tensor: True
variants: function, method
- func: logical_not_(Tensor(a!) self) -> Tensor(a!)
supports_named_tensor: True
variants: method
- func: logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
dispatch:
CPU: logical_not_out
CUDA: logical_not_out
- func: logical_xor(Tensor self, Tensor other) -> Tensor
variants: function, method
supports_named_tensor: True
- func: logical_xor_(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
supports_named_tensor: True
- func: logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: logical_xor_out
CUDA: logical_xor_out
supports_named_tensor: True
- func: logical_and(Tensor self, Tensor other) -> Tensor
variants: function, method
supports_named_tensor: True
- func: logical_and_(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
supports_named_tensor: True
- func: logical_and.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: logical_and_out
CUDA: logical_and_out
supports_named_tensor: True
- func: logical_or(Tensor self, Tensor other) -> Tensor
variants: function, method
supports_named_tensor: True
- func: logical_or_(Tensor(a!) self, Tensor other) -> Tensor(a!)
variants: method
supports_named_tensor: True
- func: logical_or.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: logical_or_out
CUDA: logical_or_out
supports_named_tensor: True
- 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
use_c10_dispatcher: full
variants: function, method
dispatch:
CPU: bmm_cpu
CUDA: bmm_cuda
supports_named_tensor: True
- func: bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
variants: function
dispatch:
CPU: bmm_out_cpu
CUDA: bmm_out_cuda
supports_named_tensor: True
- func: broadcast_tensors(Tensor[] tensors) -> Tensor[]
device_guard: False
- func: cat(Tensor[] tensors, int dim=0) -> Tensor
supports_named_tensor: True
- func: cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: cat.names(Tensor[] tensors, Dimname dim) -> Tensor
supports_named_tensor: True
- func: cat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: ceil(Tensor self) -> Tensor
use_c10_dispatcher: full
supports_named_tensor: True
variants: function, method
- func: ceil_(Tensor(a!) self) -> Tensor(a!)
supports_named_tensor: True
variants: function, method
- func: ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
dispatch:
CPU: ceil_out
CUDA: ceil_out
- func: chain_matmul(Tensor[] matrices) -> Tensor
variants: function
- func: chunk(Tensor(a) self, int chunks, int dim=0) -> Tensor(a)[]
variants: function, method
device_guard: False
supports_named_tensor: True
- func: clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
use_c10_dispatcher: full
supports_named_tensor: True
variants: function, method
dispatch:
CPU: clamp
CUDA: clamp
QuantizedCPU: quantized_clamp
- func: clamp_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)
supports_named_tensor: True
variants: function, method
dispatch:
CPU: _clamp__cpu
CUDA: _clamp__cuda
- func: clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
dispatch:
CPU: _clamp_out_cpu
CUDA: _clamp_out_cuda
- func: clamp_max(Tensor self, Scalar max) -> Tensor
use_c10_dispatcher: full
supports_named_tensor: True
variants: function, method
- func: clamp_max_(Tensor(a!) self, Scalar max) -> Tensor(a!)
supports_named_tensor: True
variants: function, method
dispatch:
CPU: _clamp_max__cpu
CUDA: _clamp_max__cuda
- func: clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
dispatch:
CPU: _clamp_max_out_cpu
CUDA: _clamp_max_out_cuda
- func: clamp_min(Tensor self, Scalar min) -> Tensor
use_c10_dispatcher: full
supports_named_tensor: True
variants: function, method
- func: clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!)
supports_named_tensor: True
variants: function, method
dispatch:
CPU: _clamp_min__cpu
CUDA: _clamp_min__cuda
- func: clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
dispatch:
CPU: _clamp_min_out_cpu
CUDA: _clamp_min_out_cuda
- func: cudnn_is_acceptable(Tensor self) -> bool
use_c10_dispatcher: full
device_guard: False
- func: constant_pad_nd(Tensor self, int[] pad, Scalar value=0) -> Tensor
variants: function
- func: contiguous(Tensor self, *, MemoryFormat memory_format=contiguous_format) -> Tensor
variants: method
supports_named_tensor: 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
- 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)
- 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) -> 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[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: _conv2d_prepack(Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1, float? output_min=None, float? output_max=None) -> Tensor
- func: _conv2d_packed(Tensor packed_weight, Tensor input) -> 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: conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor
use_c10_dispatcher: full
- 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!)
manual_kernel_registration: True
variants: method
device_guard: False
supports_named_tensor: True
- func: _copy_from(Tensor self, Tensor dst, bool non_blocking=False) -> Tensor
use_c10_dispatcher: full
dispatch: {}
- func: cos(Tensor self) -> Tensor
use_c10_dispatcher: full
supports_named_tensor: True
variants: function, method
- func: cos_(Tensor(a!) self) -> Tensor(a!)
supports_named_tensor: True
variants: function, method
dispatch:
CPU: _cos__cpu
CUDA: _cos__cuda
- func: cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
dispatch:
CPU: _cos_out_cpu
CUDA: _cos_out_cuda
- func: cosh(Tensor self) -> Tensor
use_c10_dispatcher: full
supports_named_tensor: True
variants: function, method
- func: cosh_(Tensor(a!) self) -> Tensor(a!)
supports_named_tensor: True
variants: function, method
dispatch:
CPU: _cosh__cpu
CUDA: _cosh__cuda
- func: cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
dispatch:
CPU: _cosh_out_cpu
CUDA: _cosh_out_cuda
- func: cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor
use_c10_dispatcher: full
- func: cudnn_affine_grid_generator(Tensor theta, int N, int C, int H, int W) -> Tensor grid
use_c10_dispatcher: full
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
use_c10_dispatcher: full
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(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> 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) -> 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[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) -> 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(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
# 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[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) -> 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) -> Tensor
dispatch:
CUDA: cudnn_convolution_transpose_backward_weight
# NB: input is special cased in a way I don't quite understand
- func: cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output
use_c10_dispatcher: full
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)
supports_named_tensor: True
variants: function, method
- func: cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
supports_named_tensor: True
- func: cummax.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)
supports_named_tensor: True
variants: function, method
- func: cummax.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
supports_named_tensor: True
- 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)
supports_named_tensor: True
variants: function, method
- func: cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
supports_named_tensor: True
- func: cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)
supports_named_tensor: True
variants: function, method
- func: cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
supports_named_tensor: True
- 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: cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
supports_named_tensor: True
variants: function, method
- func: cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
supports_named_tensor: True
variants: function, method
- func: cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
supports_named_tensor: True
variants: function, method
- func: cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- func: cumsum.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
supports_named_tensor: True
variants: function, method
- func: cumsum.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
supports_named_tensor: True
- 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
use_c10_dispatcher: full
- 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