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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
variants: function
- func: _cast_Char(Tensor self, bool non_blocking=false) -> Tensor
variants: function
- func: _cast_Double(Tensor self, bool non_blocking=false) -> Tensor
variants: function
- func: _cast_Float(Tensor self, bool non_blocking=false) -> Tensor
variants: function
- func: _cast_Int(Tensor self, bool non_blocking=false) -> Tensor
variants: function
- func: _cast_Long(Tensor self, bool non_blocking=false) -> Tensor
variants: function
- func: _cast_Short(Tensor self, bool non_blocking=false) -> Tensor
variants: function
- func: _cast_Half(Tensor self, bool non_blocking=false) -> Tensor
variants: function
- func: _cudnn_ctc_loss(Tensor log_probs, Tensor targets, IntList input_lengths, IntList target_lengths, int64_t blank, bool deterministic) -> (Tensor, Tensor)
dispatch:
CUDA: _cudnn_ctc_loss
- func: _cudnn_rnn_flatten_weight(TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, bool bidirectional) -> Tensor
dispatch:
CUDA: _cudnn_rnn_flatten_weight
- func: _cudnn_rnn(Tensor input, TensorList weight, int64_t weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntList batch_sizes, BoolTensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
dispatch:
CUDA: _cudnn_rnn
- func: _cudnn_rnn_backward(Tensor input, TensorList weight, int64_t weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntList batch_sizes, BoolTensor? dropout_state, Tensor reserve, std::array<bool,4> output_mask) -> (Tensor, Tensor, Tensor, TensorList)
dispatch:
CUDA: _cudnn_rnn_backward
- func: _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, TensorOptions options) -> Tensor
dispatch:
CUDA: _cudnn_init_dropout_state
- func: _fused_dropout(Tensor self, double p, Generator* generator=nullptr) -> (Tensor, Tensor)
variants: function
dispatch:
CUDA: fused_dropout_cuda
- func: _masked_scale(Tensor self, Tensor mask, double scale) -> Tensor
variants: function
dispatch:
CUDA: masked_scale_cuda
- func: _reshape_from_tensor(Tensor self, Tensor shape) -> Tensor
- func: _shape_as_tensor(Tensor self) -> Tensor
- func: dropout(Tensor input, double p, bool train) -> Tensor
- func: dropout_(Tensor self, double p, bool train) -> Tensor
- func: feature_dropout(Tensor input, double p, bool train) -> Tensor
- func: feature_dropout_(Tensor self, double p, bool train) -> Tensor
- func: alpha_dropout(Tensor input, double p, bool train) -> Tensor
- func: alpha_dropout_(Tensor self, double p, bool train) -> Tensor
- func: feature_alpha_dropout(Tensor input, double p, bool train) -> Tensor
- func: feature_alpha_dropout_(Tensor self, double p, bool train) -> Tensor
- func: abs(Tensor self) -> Tensor
variants: function, method
- func: abs_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _abs__cpu
CUDA: _abs__cuda
- func: abs_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _abs_out_cpu
CUDA: _abs_out_cuda
- func: acos(Tensor self) -> Tensor
variants: function, method
- func: acos_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _acos__cpu
CUDA: _acos__cuda
- func: acos_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _acos_out_cpu
CUDA: _acos_out_cuda
- func: avg_pool1d(Tensor self, IntList[1] kernel_size, IntList[1] stride={}, IntList[1] padding=0, bool ceil_mode=false, bool count_include_pad=true) -> Tensor
- func: adaptive_avg_pool1d(Tensor self, IntList[1] output_size) -> Tensor
- func: adaptive_max_pool1d(Tensor self, IntList[1] output_size) -> (Tensor, Tensor)
- func: add(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
variants: function, method
- func: add_(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
variants: method
- func: add_out(Tensor result, Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
# For C++ only, until we have conversion from C++ numbers to Tensor
- func: add(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
variants: function, method
- func: add_(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
variants: method
- func: addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: function, method
- func: addmv_(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: function, method
- func: addmv_out(Tensor result, Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor
- func: addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: function, method
- func: addr_(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: method
- func: addr_out(Tensor result, Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
- func: affine_grid_generator(Tensor theta, IntList size) -> Tensor
variants: function
- func: affine_grid_generator_backward(Tensor grad, IntList size) -> Tensor
variants: function
- func: all(Tensor self, int64_t dim, bool keepdim=false) -> Tensor
variants: function, method
- func: all_out(Tensor result, Tensor self, int64_t dim, bool keepdim=false) -> Tensor
- func: allclose(Tensor self, Tensor other, double rtol=1e-5, double atol=1e-8, bool equal_nan=False) -> bool
variants: function, method
- func: any(Tensor self, int64_t dim, bool keepdim=false) -> Tensor
variants: function, method
- func: any_out(Tensor result, Tensor self, int64_t dim, bool keepdim=false) -> Tensor
- func: arange(Scalar start, Scalar end, TensorOptions options={}) -> Tensor
- func: arange(Scalar start, Scalar end, Scalar step, TensorOptions options={}) -> Tensor
- func: arange_out(Tensor result, Scalar start, Scalar end) -> Tensor
- func: arange_out(Tensor result, Scalar start, Scalar end, Scalar step) -> Tensor
- func: arange(Scalar end, TensorOptions options={}) -> Tensor
- func: arange_out(Tensor result, Scalar end) -> Tensor
# 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, int64_t dim) -> Tensor
# `argmin` and `argmax` are exposed in C++ but not in Python, where we only
# expose `_argmin` and `_argmax` (which call the first versions). In Python, we
# then define our own `argmax` and `argmin` that handle passing `dim=None`,
# which gets the argmax/argmin of the flattened array.
- func: argmax(Tensor self, int64_t dim, bool keepdim=false) -> Tensor
variants: function, method
- func: argmax(Tensor self) -> Tensor
variants: function, method
- func: _argmax(Tensor self, int64_t dim, bool keepdim=false) -> Tensor
variants: function
- func: argmin(Tensor self, int64_t dim, bool keepdim=false) -> Tensor
variants: function, method
- func: argmin(Tensor self) -> Tensor
variants: function, method
- func: _argmin(Tensor self, int64_t dim, bool keepdim=false) -> Tensor
variants: function
- func: as_strided(Tensor self, IntList size, IntList stride) -> Tensor
variants: function, method
device_guard: false
- func: as_strided_(Tensor self, IntList size, IntList stride) -> Tensor
variants: function, method
device_guard: false
- func: as_strided(Tensor self, IntList size, IntList stride, int64_t storage_offset) -> Tensor
variants: function, method
device_guard: false
python_default_init:
storage_offset: self.storage_offset()
- func: as_strided_(Tensor self, IntList size, IntList stride, int64_t storage_offset) -> Tensor
variants: function, method
device_guard: false
python_default_init:
storage_offset: self.storage_offset()
- func: asin(Tensor self) -> Tensor
variants: function, method
- func: asin_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _asin__cpu
CUDA: _asin__cuda
- func: asin_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _asin_out_cpu
CUDA: _asin_out_cuda
- func: atan(Tensor self) -> Tensor
variants: function, method
- func: atan_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _atan__cpu
CUDA: _atan__cuda
- func: atan_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _atan_out_cpu
CUDA: _atan_out_cuda
- 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 self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: method
dispatch:
CPU: baddbmm__cpu
CUDA: baddbmm__cuda
- func: _baddbmm_mkl_(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: function
- func: baddbmm_out(Tensor result, Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
variants: function
dispatch:
CPU: baddbmm_out_cpu
CUDA: baddbmm_out_cuda
- func: bartlett_window(int64_t window_length, TensorOptions options={}) -> Tensor
- func: bartlett_window(int64_t window_length, bool periodic, TensorOptions options={}) -> Tensor
- func: batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, double momentum, double eps, bool cudnn_enabled) -> Tensor
# Sample bernoulli with values in `self` as probability.
- func: bernoulli(Tensor self, *, Generator* generator=nullptr) -> Tensor
variants: function, method
- func: bernoulli_out(Tensor result, Tensor self, *, Generator* generator=nullptr) -> Tensor
variants: function
- func: bernoulli_(Tensor self, Tensor p, *, Generator* generator=nullptr) -> Tensor
variants: method
dispatch:
CPU: bernoulli_tensor_cpu_
CUDA: bernoulli_tensor_cuda_
- func: bernoulli_(Tensor self, double p=0.5, *, Generator* generator=nullptr) -> Tensor
variants: method
dispatch:
CPU: bernoulli_scalar_cpu_
CUDA: bernoulli_scalar_cuda_
# 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=nullptr)` declaration.
- func: bernoulli(Tensor self, double p, *, Generator* generator=nullptr) -> Tensor
variants: function, method
- func: bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias) -> Tensor
- func: binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight, Tensor? pos_weight, int64_t reduction) -> Tensor
variants: function
- func: binary_cross_entropy_with_logits_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, Tensor? pos_weight, int64_t reduction) -> Tensor
variants: function
- func: bincount(Tensor self, Tensor? weights={}, int64_t minlength=0) -> Tensor
variants: function, method
dispatch:
CPU: _bincount_cpu
CUDA: _bincount_cuda
- func: blackman_window(int64_t window_length, TensorOptions options={}) -> Tensor
- func: blackman_window(int64_t window_length, bool periodic, TensorOptions options={}) -> Tensor
- func: bmm(Tensor self, Tensor mat2) -> Tensor
variants: function, method
dispatch:
CPU: bmm_cpu
CUDA: bmm_cuda
- func: bmm_out(Tensor result, Tensor self, Tensor mat2) -> Tensor
variants: function
dispatch:
CPU: bmm_out_cpu
CUDA: bmm_out_cuda
- func: broadcast_tensors(TensorList tensors) -> TensorList
device_guard: false
- func: cat(TensorList tensors, int64_t dim=0) -> Tensor
- func: cat_out(Tensor result, TensorList tensors, int64_t dim=0) -> Tensor
- func: ceil(Tensor self) -> Tensor
variants: function, method
- func: ceil_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _ceil__cpu
CUDA: _ceil__cuda
- func: ceil_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _ceil_out_cpu
CUDA: _ceil_out_cuda
- func: chain_matmul(TensorList matrices) -> Tensor
variants: function
- func: chunk(Tensor self, int64_t chunks, int64_t dim=0) -> TensorList
variants: function, method
device_guard: false
- func: clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
variants: function, method
- func: clamp_(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
variants: function, method
dispatch:
CPU: _clamp__cpu
CUDA: _clamp__cuda
- func: clamp_out(Tensor result, Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
dispatch:
CPU: _clamp_out_cpu
CUDA: _clamp_out_cuda
- func: clamp_max(Tensor self, Scalar max) -> Tensor
variants: function, method
- func: clamp_max_(Tensor self, Scalar max) -> Tensor
variants: function, method
dispatch:
CPU: _clamp_max__cpu
CUDA: _clamp_max__cuda
- func: clamp_max_out(Tensor result, Tensor self, Scalar max) -> Tensor
dispatch:
CPU: _clamp_max_out_cpu
CUDA: _clamp_max_out_cuda
- func: clamp_min(Tensor self, Scalar min) -> Tensor
variants: function, method
- func: clamp_min_(Tensor self, Scalar min) -> Tensor
variants: function, method
dispatch:
CPU: _clamp_min__cpu
CUDA: _clamp_min__cuda
- func: clamp_min_out(Tensor result, Tensor self, Scalar min) -> Tensor
dispatch:
CPU: _clamp_min_out_cpu
CUDA: _clamp_min_out_cuda
- func: cudnn_is_acceptable(Tensor self) -> bool
device_guard: false
- func: constant_pad_nd(Tensor self, IntList pad, Scalar value=0) -> Tensor
variants: function
- func: contiguous(Tensor self) -> Tensor
variants: method
- func: convolution(Tensor input, Tensor weight, Tensor? bias, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding, int64_t groups) -> Tensor
- func: _convolution(Tensor input, Tensor weight, Tensor? bias, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor
- func: _convolution_nogroup(Tensor input, Tensor weight, Tensor? bias, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding) -> Tensor
- func: _convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, std::array<bool,3> output_mask) -> (Tensor, Tensor, Tensor)
- func: conv1d(Tensor input, Tensor weight, Tensor? bias={}, IntList[1] stride=1, IntList[1] padding=0, IntList[1] dilation=1, int64_t groups=1) -> Tensor
- func: conv2d(Tensor input, Tensor weight, Tensor? bias={}, IntList[2] stride=1, IntList[2] padding=0, IntList[2] dilation=1, int64_t groups=1) -> Tensor
- func: conv3d(Tensor input, Tensor weight, Tensor? bias={}, IntList[3] stride=1, IntList[3] padding=0, IntList[3] dilation=1, int64_t groups=1) -> Tensor
- func: conv_tbc(Tensor self, Tensor weight, Tensor bias, int64_t pad=0) -> Tensor
- func: conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int64_t 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={}, IntList[1] stride=1, IntList[1] padding=0, IntList[1] output_padding=0, int64_t groups=1, IntList[1] dilation=1) -> Tensor
- func: conv_transpose2d(Tensor input, Tensor weight, Tensor? bias={}, IntList[2] stride=1, IntList[2] padding=0, IntList[2] output_padding=0, int64_t groups=1, IntList[2] dilation=1) -> Tensor
- func: conv_transpose3d(Tensor input, Tensor weight, Tensor? bias={}, IntList[3] stride=1, IntList[3] padding=0, IntList[3] output_padding=0, int64_t groups=1, IntList[3] dilation=1) -> Tensor
- func: s_copy_(Tensor self, Tensor src, bool non_blocking=false) -> Tensor
cpu_half: True
dispatch:
CPU: _s_copy__cpu
CUDA: _s_copy__cuda
- func: _s_copy_from(Tensor self, Tensor dst, bool non_blocking=false) -> Tensor
cpu_half: True
dispatch:
CUDA: _s_copy_from_cuda
- func: _copy_same_type_(Tensor self, Tensor src) -> void
cpu_half: True
dispatch:
CPU: _copy_same_type__cpu
- func: cos(Tensor self) -> Tensor
variants: function, method
- func: cos_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _cos__cpu
CUDA: _cos__cuda
- func: cos_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _cos_out_cpu
CUDA: _cos_out_cuda
- func: cosh(Tensor self) -> Tensor
variants: function, method
- func: cosh_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _cosh__cpu
CUDA: _cosh__cuda
- func: cosh_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _cosh_out_cpu
CUDA: _cosh_out_cuda
- func: cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, double margin=0.0, int64_t reduction=Reduction::Mean) -> Tensor
- func: cudnn_affine_grid_generator(Tensor theta, int64_t N, int64_t C, int64_t H, int64_t 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, int64_t N, int64_t C, int64_t H, int64_t 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, double exponential_average_factor, double epsilon) -> (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, double epsilon) -> (Tensor, Tensor, Tensor)
dispatch:
CUDA: cudnn_batch_norm_backward
- func: cudnn_convolution(Tensor self, Tensor weight, Tensor? bias, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: cudnn_convolution
- func: cudnn_convolution_backward_input(IntList self_size, Tensor grad_output, Tensor weight, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: cudnn_convolution_backward_input
- func: cudnn_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic, std::array<bool,3> output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
CUDA: cudnn_convolution_backward
- func: cudnn_convolution_backward_bias(Tensor grad_output) -> Tensor
dispatch:
CUDA: cudnn_convolution_backward_bias
- func: cudnn_convolution_backward_weight(IntList weight_size, Tensor grad_output, Tensor self, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: cudnn_convolution_backward_weight
- func: cudnn_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, IntList padding, IntList output_padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: cudnn_convolution_transpose
# NB: output_padding not strictly needed here, but it's helpful for the double
# backwards
- func: cudnn_convolution_transpose_backward(Tensor self, Tensor grad_output, Tensor weight, IntList padding, IntList output_padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic, std::array<bool,3> output_mask) -> (Tensor, Tensor, Tensor)
dispatch:
CUDA: cudnn_convolution_transpose_backward
- func: cudnn_convolution_transpose_backward_bias(Tensor grad_output) -> Tensor
dispatch:
CUDA: cudnn_convolution_backward_bias
- func: cudnn_convolution_transpose_backward_input(Tensor grad_output, Tensor weight, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) -> Tensor
dispatch:
CUDA: cudnn_convolution_transpose_backward_input
- func: cudnn_convolution_transpose_backward_weight(IntList weight_size, Tensor grad_output, Tensor self, IntList padding, IntList stride, IntList dilation, int64_t 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
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
# FIXME: These could be combined as optional<ScalarType> but for https://github.com/pytorch/pytorch/issues/6593.
- func: cumsum(Tensor self, int64_t dim, *, ScalarType dtype) -> Tensor
variants: function, method
- func: cumsum(Tensor self, int64_t dim) -> Tensor
variants: function, method
- func: cumsum_out(Tensor result, Tensor self, int64_t dim, *, ScalarType dtype) -> Tensor
- func: cumsum_out(Tensor result, Tensor self, int64_t dim) -> Tensor
# FIXME: These could be combined as optional<ScalarType> but for https://github.com/pytorch/pytorch/issues/6593.
- func: cumprod(Tensor self, int64_t dim, *, ScalarType dtype) -> Tensor
variants: function, method
- func: cumprod(Tensor self, int64_t dim) -> Tensor
variants: function, method
- func: cumprod_out(Tensor result, Tensor self, int64_t dim, *, ScalarType dtype) -> Tensor
- func: cumprod_out(Tensor result, Tensor self, int64_t dim) -> Tensor
- func: ctc_loss(Tensor log_probs, Tensor targets, IntList input_lengths, IntList target_lengths, int64_t blank=0, int64_t reduction=Reduction::Mean) -> Tensor
# convenience function that converts to intlists for you
- func: ctc_loss(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int64_t blank=0, int64_t reduction=Reduction::Mean) -> Tensor
- func: _ctc_loss(Tensor log_probs, Tensor targets, IntList input_lengths, IntList target_lengths, int64_t blank=0) -> (Tensor, Tensor)
dispatch:
CPU: ctc_loss_cpu
CUDA: ctc_loss_gpu
- func: _ctc_loss_backward(Tensor grad, Tensor log_probs, Tensor targets, IntList input_lengths, IntList target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int64_t blank) -> Tensor
dispatch:
CPU: ctc_loss_backward_cpu
CUDA: ctc_loss_backward_gpu
- func: det(Tensor self) -> Tensor
variants: function, method
- func: diag_embed(Tensor self, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1) -> Tensor
variants: function, method
- func: diagflat(Tensor self, int64_t offset=0) -> Tensor
variants: function, method
- func: diagonal(Tensor self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) -> Tensor
variants: function, method
- func: div(Tensor self, Tensor other) -> Tensor
variants: function, method
- func: div_(Tensor self, Tensor other) -> Tensor
variants: method
- func: div_out(Tensor result, Tensor self, Tensor other) -> Tensor
# For C++ only, until we have conversion from C++ numbers to Tensor
- func: div(Tensor self, Scalar other) -> Tensor
variants: function, method
- func: div_(Tensor self, Scalar other) -> Tensor
variants: method
- func: dot(Tensor self, Tensor tensor) -> Tensor
variants: function, method
- func: dot_out(Tensor result, Tensor self, Tensor tensor) -> Tensor
- func: einsum(std::string equation, TensorList tensors) -> Tensor
- func: embedding(Tensor weight, IndexTensor indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) -> Tensor
- func: embedding_backward(Tensor grad, IndexTensor indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) -> Tensor
- func: embedding_dense_backward(Tensor grad, IndexTensor indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) -> Tensor
dispatch:
CPU: embedding_dense_backward_cpu
CUDA: embedding_dense_backward_cuda
- func: embedding_renorm_(Tensor self, IndexTensor indices, double max_norm, double norm_type) -> Tensor
dispatch:
CPU: embedding_renorm_cpu_
CUDA: embedding_renorm_cuda_
- func: embedding_sparse_backward(Tensor grad, IndexTensor indices, int64_t num_weights, int64_t 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(Tensor weight, IndexTensor indices, IndexTensor offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false) -> (Tensor, Tensor, Tensor, Tensor)
- func: _embedding_bag(Tensor weight, IndexTensor indices, IndexTensor offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false) -> (Tensor, Tensor, Tensor, Tensor)
dispatch:
CPU: _embedding_bag_cpu
CUDA: _embedding_bag_cuda
- func: _embedding_bag_backward(Tensor grad, IndexTensor indices, IndexTensor offsets, IndexTensor offset2bag, IndexTensor bag_size, IndexTensor maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse) -> Tensor
- func: _embedding_bag_sparse_backward(Tensor grad, IndexTensor indices, IndexTensor offsets, IndexTensor offset2bag, IndexTensor bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode) -> Tensor
- func: _embedding_bag_dense_backward(Tensor grad, IndexTensor indices, IndexTensor offsets, IndexTensor offset2bag, IndexTensor bag_size, IndexTensor maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode) -> Tensor
dispatch:
CPU: _embedding_bag_dense_backward_cpu
CUDA: _embedding_bag_dense_backward_cuda
- func: empty(IntList size, TensorOptions options={}) -> Tensor
cpu_half: True
dispatch:
CPU: empty_cpu
CUDA: empty_cuda
SparseCPU: empty_sparse
SparseCUDA: empty_sparse
- func: resize_(Tensor self, IntList size) -> Tensor
variants: method
cpu_half: True
device_guard: False
dispatch:
CPU: resize_cpu_
CUDA: resize_cuda_
- func: empty_out(Tensor result, IntList size) -> Tensor
device_guard: False
- func: empty_like(Tensor self) -> Tensor
device_guard: False
- func: empty_like(Tensor self, *, TensorOptions options) -> Tensor
device_guard: False
- func: empty_strided(IntList size, IntList stride, *, TensorOptions options={}) -> Tensor
- func: erf(Tensor self) -> Tensor
variants: function, method
- func: erf_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _erf__cpu
CUDA: _erf__cuda
- func: erf_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _erf_out_cpu
CUDA: _erf_out_cuda
- func: erfc(Tensor self) -> Tensor
variants: function, method
- func: erfc_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _erfc__cpu
CUDA: _erfc__cuda
- func: erfc_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _erfc_out_cpu
CUDA: _erfc_out_cuda
- func: exp(Tensor self) -> Tensor
variants: function, method
- func: exp_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _exp__cpu
CUDA: _exp__cuda
- func: exp_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _exp_out_cpu
CUDA: _exp_out_cuda
- func: expm1(Tensor self) -> Tensor
variants: function, method
- func: expm1_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _expm1__cpu
CUDA: _expm1__cuda
- func: expm1_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _expm1_out_cpu
CUDA: _expm1_out_cuda
- func: expand(Tensor self, IntList size, *, bool implicit=false) -> Tensor
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: expand_as(Tensor self, Tensor other) -> Tensor
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(int64_t n, TensorOptions options={}) -> Tensor
- func: eye(int64_t n, int64_t m, TensorOptions options={}) -> Tensor
- func: eye_out(Tensor result, int64_t n) -> Tensor
dispatch:
CPU: eye_out_cpu
CUDA: eye_out_cuda
- func: eye_out(Tensor result, int64_t n, int64_t m) -> Tensor
dispatch:
CPU: eye_out_cpu
CUDA: eye_out_cuda
- func: flatten(Tensor self, int64_t start_dim=0, int64_t end_dim=-1) -> Tensor
variants: function, method
- func: fill_(Tensor self, Scalar value) -> Tensor
variants: function, method
- func: fill_(Tensor self, Tensor value) -> Tensor
variants: function, method
- func: floor(Tensor self) -> Tensor
variants: function, method
- func: floor_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _floor__cpu
CUDA: _floor__cuda
- func: floor_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _floor_out_cpu
CUDA: _floor_out_cuda
- func: full(IntList size, Scalar fill_value, TensorOptions options={}) -> Tensor
- func: full_out(Tensor result, IntList size, Scalar fill_value) -> Tensor
- func: full_like(Tensor self, Scalar fill_value) -> Tensor
- func: full_like(Tensor self, Scalar fill_value, *, TensorOptions options) -> Tensor
# 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.
#
# 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.
- func: grid_sampler(Tensor input, Tensor grid, int64_t interpolation_mode, int64_t padding_mode) -> Tensor
- func: grid_sampler_2d(Tensor input, Tensor grid, int64_t interpolation_mode, int64_t padding_mode) -> Tensor
dispatch:
CPU: grid_sampler_2d_cpu
CUDA: grid_sampler_2d_cuda
- func: grid_sampler_2d_backward(Tensor grad_output, Tensor input, Tensor grid, int64_t interpolation_mode, int64_t padding_mode) -> (Tensor, Tensor)
dispatch:
CPU: grid_sampler_2d_backward_cpu
CUDA: grid_sampler_2d_backward_cuda
- func: grid_sampler_3d(Tensor input, Tensor grid, int64_t interpolation_mode, int64_t padding_mode) -> Tensor
dispatch:
CPU: grid_sampler_3d_cpu
CUDA: grid_sampler_3d_cuda
- func: grid_sampler_3d_backward(Tensor grad_output, Tensor input, Tensor grid, int64_t interpolation_mode, int64_t padding_mode) -> (Tensor, Tensor)
dispatch:
CPU: grid_sampler_3d_backward_cpu
CUDA: grid_sampler_3d_backward_cuda
- func: hann_window(int64_t window_length, TensorOptions options={}) -> Tensor
- func: hann_window(int64_t window_length, bool periodic, TensorOptions options={}) -> Tensor
- func: hamming_window(int64_t window_length, TensorOptions options={}) -> Tensor
- func: hamming_window(int64_t window_length, bool periodic, TensorOptions options={}) -> Tensor
- func: hamming_window(int64_t window_length, bool periodic, double alpha, TensorOptions options={}) -> Tensor
- func: hamming_window(int64_t window_length, bool periodic, double alpha, double beta, TensorOptions options={}) -> Tensor
- func: hinge_embedding_loss(Tensor self, Tensor target, double margin=1.0, int64_t reduction=Reduction::Mean) -> Tensor
- func: ger(Tensor self, Tensor vec2) -> Tensor
variants: function, method
- func: ger_out(Tensor result, Tensor self, Tensor vec2) -> Tensor
- func: gesv(Tensor self, Tensor A) -> (Tensor, Tensor)
variants: function, method
- func: gesv_out(Tensor solution, Tensor lu, Tensor self, Tensor A) -> (Tensor, Tensor)
# gesv handles broadcasting of arbitrary batch dims while _gesv_helper does not.
- func: _gesv_helper(Tensor self, Tensor A) -> (Tensor, Tensor)
variants: function
dispatch:
CPU: _gesv_helper_cpu
CUDA: _gesv_helper_cuda
- func: group_norm(Tensor input, int64_t num_groups, Tensor? weight={}, Tensor? bias={}, double eps=1e-5, bool cudnn_enabled=True) -> Tensor
# FFT
- func: fft(Tensor self, int64_t signal_ndim, bool normalized=false) -> Tensor
variants: function, method
- func: ifft(Tensor self, int64_t signal_ndim, bool normalized=false) -> Tensor
variants: function, method
- func: rfft(Tensor self, int64_t signal_ndim, bool normalized=false, bool onesided=true) -> Tensor
variants: function, method
- func: irfft(Tensor self, int64_t signal_ndim, bool normalized=false, bool onesided=true, IntList signal_sizes={}) -> Tensor
variants: function, method
- func: _fft_with_size(Tensor self, int64_t signal_ndim, bool complex_input, bool complex_output, bool inverse, IntList checked_signal_sizes, bool normalized, bool onesided, IntList output_sizes) -> Tensor
variants: function
dispatch:
CPU: _fft_mkl
CUDA: _fft_cufft
- func: _cufft_get_plan_cache_size() -> int64_t
device_guard: false
- func: _cufft_get_plan_cache_max_size() -> int64_t
device_guard: false
- func: _cufft_set_plan_cache_max_size(int64_t max_size) -> void
device_guard: false
- func: _cufft_clear_plan_cache() -> void
device_guard: false
- func: index(Tensor self, TensorList indices) -> Tensor
variants: function, method
# NB: This function is special-cased in tools/autograd/gen_variable_type.py
- func: index_copy_(Tensor self, int64_t dim, IndexTensor index, Tensor source) -> Tensor
variants: method
- func: index_put(Tensor self, TensorList indices, Tensor values, bool accumulate=false) -> Tensor
variants: function, method
- func: index_put_(Tensor self, TensorList indices, Tensor values, bool accumulate=false) -> Tensor
variants: function, method
- func: instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled) -> Tensor
variants: function
- func: inverse(Tensor self) -> Tensor
variants: function, method
- func: inverse_out(Tensor result, Tensor self) -> Tensor
- func: _inverse_helper(Tensor self) -> Tensor
variants: function
dispatch:
CPU: _inverse_helper_cpu
CUDA: _inverse_helper_cuda
- func: isclose(Tensor self, Tensor other, double rtol=1e-5, double atol=1e-8, bool equal_nan=False) -> Tensor
variants: function, method
- 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
- func: is_complex(Tensor self) -> bool
variants: function, method
device_guard: false
- 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
- func: kl_div(Tensor self, Tensor target, int64_t reduction=Reduction::Mean) -> Tensor
- func: kl_div_backward(Tensor grad_output, Tensor self, Tensor target, int64_t reduction=Reduction::Mean) -> Tensor
dispatch:
CPU: kl_div_backward_cpu
CUDA: kl_div_backward_cuda
- func: kthvalue(Tensor self, int64_t k, int64_t dim=-1, bool keepdim=false) -> (Tensor, Tensor)
variants: function, method
- func: kthvalue_out(Tensor values, Tensor indices, Tensor self, int64_t k, int64_t dim=-1, bool keepdim=false) -> (Tensor, Tensor)
- func: layer_norm(Tensor input, IntList normalized_shape, Tensor? weight={}, Tensor? bias={}, double eps=1e-5, bool cudnn_enable=True) -> Tensor
- func: linear(Tensor input, Tensor weight, Tensor? bias={}) -> Tensor
- func: linspace(Scalar start, Scalar end, TensorOptions options={}) -> Tensor
- func: linspace(Scalar start, Scalar end, int64_t steps, TensorOptions options={}) -> Tensor
- func: linspace_out(Tensor result, Scalar start, Scalar end) -> Tensor
- func: linspace_out(Tensor result, Scalar start, Scalar end, int64_t steps) -> Tensor
- func: log(Tensor self) -> Tensor
variants: function, method
- func: log_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _log__cpu
CUDA: _log__cuda
- func: log_out(Tensor result, Tensor self) -> Tensor
dispatch:
CPU: _log_out_cpu
CUDA: _log_out_cuda
- func: log10(Tensor self) -> Tensor
variants: function, method
- func: log10_(Tensor self) -> Tensor
variants: function, method
dispatch:
CPU: _log10__cpu
CUDA: _log10__cuda