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[MPS] Implement logcumsumexp metal kernel (#156858)

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Jul 3, 2025

d56f11a · Jul 3, 2025

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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 These were previously used before TorchScript IR supported # representing ScalarType's. They are now superseded by usage of # `aten::to()`.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 computed 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_async.msg(Tensor self, str assert_msg) -> () dispatch: CPU: _assert_async_msg_cpu CUDA: _assert_async_msg_cuda - func: _assert_scalar(Scalar self, str assert_msg) -> () dispatch: CompositeExplicitAutograd: _assert_scalar - func: _functional_assert_scalar(Scalar self, str assert_msg, Tensor dep_token) -> Tensor dispatch: CompositeExplicitAutograd: _functional_assert_scalar - func: _functional_assert_async.msg(Tensor self, str assert_msg, Tensor dep_token) -> Tensor dispatch: CPU: _functional_assert_async_msg_cpu - func: _assert_tensor_metadata(Tensor a, SymInt[]? size=None, SymInt[]? stride=None, ScalarType? dtype=None, *, Device? device=None, Layout? layout=None) -> () dispatch: CompositeExplicitAutograd: _assert_tensor_metadata Meta: _assert_tensor_metadata_meta_symint - func: _print(str s) -> () dispatch: CompositeExplicitAutograd: _print - func: sym_constrain_range(Scalar size, *, int? min=None, int? max=None) -> () dispatch: CompositeExplicitAutograd: sym_constrain_range - func: sym_constrain_range_for_size(Scalar size, *, int? min=None, int? max=None) -> () dispatch: CompositeExplicitAutograd: sym_constrain_range_for_size - func: _functional_sym_constrain_range(Scalar size, int? min, int? max, Tensor dep_token) -> Tensor dispatch: CompositeExplicitAutograd: _functional_sym_constrain_range - func: _functional_sym_constrain_range_for_size(Scalar size, int? min, int? max, Tensor dep_token) -> Tensor dispatch: CompositeExplicitAutograd: _functional_sym_constrain_range_for_size - func: _make_dep_token(*, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor dispatch: CPU: _make_dep_token_cpu - 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 tags: nondeterministic_seeded - 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 tags: nondeterministic_seeded - 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, NestedTensorHPU, NestedTensorCUDA: native_dropout_nested tags: [nondeterministic_seeded, core] autogen: native_dropout.out - func: native_dropout_backward(Tensor grad_output, Tensor mask, float scale) -> Tensor dispatch: CPU, NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: native_dropout_backward CUDA: native_dropout_backward_cuda autogen: native_dropout_backward.out tags: pointwise - 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, maybe_aliasing_or_mutating] - 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, maybe_aliasing_or_mutating] - 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, maybe_aliasing_or_mutating] - 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, maybe_aliasing_or_mutating] - 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, SparseCsrMeta: abs_sparse_csr NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_abs tags: [core, pointwise] - func: abs_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: abs_ SparseCPU, SparseCUDA: abs_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: abs_sparse_csr_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_abs_ - func: abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MPS, MTIA: abs_out SparseCPU, SparseCUDA: abs_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: abs_sparse_csr_out tags: pointwise # 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 MPS: angle_mps SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: angle_sparse_csr tags: pointwise - func: angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: angle_out MPS: angle_out_mps SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: angle_sparse_csr_out tags: pointwise - 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, MPS, Meta: view_as_complex - func: sgn(Tensor self) -> Tensor variants: function, method structured_delegate: sgn.out dispatch: SparseCPU, SparseCUDA: sgn_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sgn_sparse_csr NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_sgn tags: pointwise - func: sgn_(Tensor(a!) self) -> Tensor(a!) variants: method structured_delegate: sgn.out dispatch: SparseCPU, SparseCUDA: sgn_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sgn_sparse_csr_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_sgn_ tags: pointwise - func: sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: sgn_out MPS: sgn_out_mps SparseCPU, SparseCUDA: sgn_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sgn_sparse_csr_out tags: pointwise - 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, SparseCsrMeta: conj_physical_sparse_csr autogen: _conj_physical.out - func: conj_physical(Tensor self) -> Tensor variants: function, method tags: [pointwise, maybe_aliasing_or_mutating] - func: conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: conj_physical_out MPS: conj_physical_out_mps SparseCPU, SparseCUDA: conj_physical_out_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: conj_physical_sparse_csr_out tags: pointwise - func: conj_physical_(Tensor(a!) self) -> Tensor(a!) variants: function, method dispatch: CompositeExplicitAutograd: conj_physical_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: conj_physical_sparse_csr_ tags: pointwise - 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 tags: [core, pointwise] - func: acos_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method structured_delegate: acos.out tags: pointwise - func: acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: acos_out tags: pointwise # 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 tags: core autogen: avg_pool1d.out - func: adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor tags: core autogen: adaptive_avg_pool1d.out # 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, SparseMeta: add_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: add_sparse_csr MkldnnCPU: mkldnn_add ZeroTensor: add_zerotensor NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_add_Tensor tags: [core, pointwise] - 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, SparseMeta: add_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: add_sparse_csr_ MkldnnCPU: mkldnn_add_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_add__Tensor tags: pointwise - 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, SparseMeta: add_out_sparse_cpu SparseCUDA: add_out_sparse_cuda SparseCsrCPU, SparseCsrMeta: add_out_sparse_compressed_cpu SparseCsrCUDA: add_out_sparse_compressed_cuda MkldnnCPU: mkldnn_add_out MPS: add_out_mps MTIA: add_out_mtia tags: pointwise - 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: [core, pointwise] - 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 tags: pointwise - 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 XPU: addmv_out_xpu 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 MPS: addr_mps 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 MPS: addr_out_mps CompositeExplicitAutograd: math_addr_out - func: affine_grid_generator(Tensor theta, SymInt[] size, bool align_corners) -> Tensor variants: function dispatch: CompositeExplicitAutograd: affine_grid_generator autogen: affine_grid_generator.out - func: affine_grid_generator_backward(Tensor grad, SymInt[] size, bool align_corners) -> Tensor variants: function - func: _is_all_true(Tensor self) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: _is_all_true - func: _is_any_true(Tensor self) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: _is_any_true # Note: this function is only for testing. - func: _test_check_tensor(Tensor self) -> Tensor variants: function # Note; this function is only for testing - func: _test_functorch_fallback(Tensor self, Tensor other) -> Tensor variants: function dispatch: CPU: _test_functorch_fallback autogen: _test_functorch_fallback.out - func: all.dim(Tensor self, int dim, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: all.out variants: function, method dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_all - func: all.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: all.dims_out variants: function, method cpp_no_default_args: ['dim'] dispatch: CompositeExplicitAutograd: all_dims_default - func: all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True dispatch: CPU, CUDA: all_out MPS: all_out_mps - func: all.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True dispatch: CPU, CUDA: all_dims_out CompositeExplicitAutograd: all_dims_out_default cpp_no_default_args: ['dim'] - 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 tags: core - func: any.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: any.dims_out variants: function, method cpp_no_default_args: ['dim'] tags: core dispatch: CompositeExplicitAutograd: any_dims_default - func: any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True dispatch: CPU, CUDA: any_out MPS: any_out_mps - func: any.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True dispatch: CPU, CUDA: any_dims_out CompositeExplicitAutograd: any_dims_out_default cpp_no_default_args: ['dim'] - 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 `arange.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: core - 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 tags: core - 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 tags: core - 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 tags: [core, pointwise] - func: acosh_(Tensor(a!) self) -> Tensor(a!) variants: function, method structured_delegate: acosh.out tags: pointwise - func: acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: acosh_out MPS: acosh_out_mps tags: pointwise # 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, SparseCsrMeta: asinh_sparse_csr tags: [core, pointwise] - func: asinh_(Tensor(a!) self) -> Tensor(a!) variants: function, method structured_delegate: asinh.out dispatch: SparseCPU, SparseCUDA: asinh_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: asinh_sparse_csr_ tags: pointwise - 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, SparseCsrMeta: asinh_sparse_csr_out tags: pointwise # 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, SparseCsrMeta: atanh_sparse_csr tags: [core, pointwise] - func: atanh_(Tensor(a!) self) -> Tensor(a!) structured_delegate: atanh.out variants: function, method dispatch: SparseCPU, SparseCUDA: atanh_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: atanh_sparse_csr_ tags: pointwise - 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, SparseCsrMeta: atanh_sparse_csr_out tags: pointwise # 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, MTIA: 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 tags: core - 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, SparseCsrMeta: asin_sparse_csr tags: [core, pointwise] - 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, SparseCsrMeta: asin_sparse_csr_ tags: pointwise - func: asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: asin_out SparseCPU, SparseCUDA: asin_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: asin_sparse_csr_out tags: pointwise # 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, SparseCsrMeta: atan_sparse_csr tags: [core, pointwise] - 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, SparseCsrMeta: atan_sparse_csr_ tags: pointwise - func: atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: atan_out SparseCPU, SparseCUDA: atan_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: atan_sparse_csr_out tags: pointwise # 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 tags: maybe_aliasing_or_mutating - func: atleast_1d.Sequence(Tensor[] tensors) -> Tensor[] - func: atleast_2d(Tensor self) -> Tensor variants: function tags: maybe_aliasing_or_mutating - func: atleast_2d.Sequence(Tensor[] tensors) -> Tensor[] variants: function - func: atleast_3d(Tensor self) -> Tensor variants: function tags: maybe_aliasing_or_mutating - 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 XPU: baddbmm_out_xpu SparseCsrCUDA: baddbmm_out_sparse_csr_cuda - func: baddbmm.dtype(Tensor self, Tensor batch1, Tensor batch2, ScalarType out_dtype, *, Scalar beta=1, Scalar alpha=1) -> Tensor variants: function dispatch: CUDA: _baddbmm_dtype_cuda - func: baddbmm.dtype_out(Tensor self, Tensor batch1, Tensor batch2, ScalarType out_dtype, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) variants: function dispatch: CUDA: _baddbmm_out_dtype_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 tags: maybe_aliasing_or_mutating - 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) tags: maybe_aliasing_or_mutating - 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 - func: binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: nn variants: function dispatch: CPU: binary_cross_entropy_out_cpu CUDA: binary_cross_entropy_out_cuda MPS: binary_cross_entropy_out_mps - 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 MPS: binary_cross_entropy_backward_mps - 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 MPS: binary_cross_entropy_backward_out_mps - func: binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> Tensor device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: binary_cross_entropy_with_logits autogen: binary_cross_entropy_with_logits.out - func: bincount(Tensor self, Tensor? weights=None, SymInt minlength=0) -> Tensor variants: function, method dispatch: CPU: _bincount_cpu CUDA: _bincount_cuda MPS: _bincount_mps tags: dynamic_output_shape autogen: bincount.out - func: bitwise_not(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: bitwise_not.out variants: function, method tags: [core, pointwise] - func: bitwise_not_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: bitwise_not.out variants: method tags: pointwise - func: bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: bitwise_not_out tags: pointwise - func: copysign.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: copysign_out tags: pointwise - func: copysign.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: copysign.out tags: pointwise - func: copysign_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: copysign.out - func: copysign.Scalar(Tensor self, Scalar other) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: copysign tags: pointwise - func: copysign_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method dispatch: CompositeExplicitAutograd: copysign_ - func: copysign.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: copysign_out tags: pointwise - func: _lazy_clone(Tensor self) -> Tensor # Like clone, but the copy takes place lazily, only if either the # input or the output are written. variants: function, method dispatch: CompositeExplicitAutograd: _lazy_clone - func: logical_not(Tensor self) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: logical_not NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_logical_not tags: [core, pointwise] - func: logical_not_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: logical_not_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_logical_not_ tags: pointwise - func: logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: logical_not_out MPS: logical_not_out_mps tags: pointwise - func: logical_xor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: logical_xor tags: [core, pointwise] - func: logical_xor_(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: logical_xor_ tags: pointwise - func: logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: logical_xor_out MPS: logical_xor_out_mps tags: pointwise - func: logical_and(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: logical_and tags: [core, pointwise] - func: logical_and_(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: logical_and_ tags: pointwise - func: logical_and.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: logical_and_out MPS: logical_and_out_mps tags: pointwise - func: logical_or(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: logical_or tags: [core, pointwise] - func: logical_or_(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: logical_or_ tags: pointwise - func: logical_or.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: logical_or_out MPS: logical_or_out_mps tags: pointwise - func: blackman_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: blackman_window autogen: blackman_window.out - func: blackman_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: blackman_window autogen: blackman_window.periodic_out - func: bmm(Tensor self, Tensor mat2) -> Tensor structured_delegate: bmm.out variants: function, method dispatch: SparseCPU: bmm_sparse_cpu SparseCUDA: bmm_sparse_cuda NestedTensorCPU: bmm_nested NestedTensorCUDA: bmm_nested_cuda tags: core - func: bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) structured: True variants: function dispatch: CPU: bmm_out_cpu CUDA: bmm_out_cuda MPS: bmm_out_mps XPU: bmm_out_xpu SparseCPU: bmm_out_sparse_cpu SparseCUDA: bmm_out_sparse_cuda SparseCsrCUDA: bmm_out_sparse_csr_cuda - func: bmm.dtype(Tensor self, Tensor mat2, ScalarType out_dtype) -> Tensor variants: function dispatch: CUDA: _bmm_dtype_cuda - func: bmm.dtype_out(Tensor self, Tensor mat2, ScalarType out_dtype, *, Tensor(a!) out) -> Tensor(a!) variants: function dispatch: CUDA: _bmm_out_dtype_cuda - func: broadcast_tensors(Tensor[] tensors) -> Tensor[] device_check: NoCheck device_guard: False - func: broadcast_to(Tensor(a) self, SymInt[] size) -> Tensor(a) variants: function, method dispatch: CompositeImplicitAutograd: broadcast_to_symint - func: _sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a) variants: function dispatch: SparseCPU, SparseCUDA: sparse_broadcast_to - func: cat(Tensor[] tensors, int dim=0) -> Tensor structured_delegate: cat.out dispatch: SparseCPU, SparseCUDA: cat_sparse QuantizedCPU: cat_quantized_cpu NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: cat_nested tags: core - func: cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) structured: True precomputed: - dim -> int dim, int valid, bool all_contiguous, bool all_same_dtype, bool all_same_sizes_and_stride, MemoryFormat memory_format dispatch: CPU: cat_out_cpu CUDA: cat_out_cuda MPS: cat_out_mps QuantizedCPU: cat_out_quantized_cpu - func: cat.names(Tensor[] tensors, Dimname dim) -> Tensor - func: cat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) # alias for torch.cat - func: concat(Tensor[] tensors, int dim=0) -> Tensor - func: concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) - func: concat.names(Tensor[] tensors, Dimname dim) -> Tensor - func: concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) # alias for torch.cat - func: concatenate(Tensor[] tensors, int dim=0) -> Tensor - func: concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) - func: concatenate.names(Tensor[] tensors, Dimname dim) -> Tensor - func: concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) - func: block_diag(Tensor[] tensors) -> Tensor variants: function dispatch: CompositeExplicitAutograd: block_diag autogen: block_diag.out - func: ceil(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: ceil.out variants: function, method dispatch: SparseCPU, SparseCUDA: ceil_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: ceil_sparse_csr tags: [core, pointwise] - func: ceil_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: ceil.out variants: function, method dispatch: SparseCPU, SparseCUDA: ceil_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: ceil_sparse_csr_ tags: pointwise - func: ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: ceil_out SparseCPU, SparseCUDA: ceil_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: ceil_sparse_csr_out tags: pointwise # alias for torch.linalg.multi_dot - func: chain_matmul(Tensor[] matrices) -> Tensor variants: function # alias for torch.linalg.multi_dot - func: chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!) - func: unsafe_chunk(Tensor self, int chunks, int dim=0) -> Tensor[] variants: function, method device_check: NoCheck device_guard: False tags: maybe_aliasing_or_mutating - func: chunk(Tensor(a -> *) self, int chunks, int dim=0) -> Tensor(a)[] variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeImplicitAutograd: chunk NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: chunk_nested_tensor - func: tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[] variants: function, method dispatch: CompositeImplicitAutograd: tensor_split_sections_symint - func: tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[] variants: function, method dispatch: CompositeImplicitAutograd: tensor_split_indices_symint - func: tensor_split.tensor_indices_or_sections(Tensor(a -> *) self, Tensor tensor_indices_or_sections, int dim=0) -> Tensor(a)[] variants: function, method - func: clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor device_check: NoCheck # TensorIterator variants: function, method cpp_no_default_args: ['min'] structured_delegate: clamp.out dispatch: QuantizedCPU: clamp_quantized_cpu tags: [core, pointwise] - func: clamp.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor variants: function, method structured_delegate: clamp.Tensor_out tags: [core, pointwise] - func: clamp_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method cpp_no_default_args: ['min'] structured_delegate: clamp.out tags: pointwise - func: clamp_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!) variants: function, method structured_delegate: clamp.Tensor_out tags: pointwise - func: clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator cpp_no_default_args: ['min'] structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MTIA: clamp_out MPS: clamp_out_mps tags: pointwise - func: clamp.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: clamp_Tensor_out MPS: clamp_Tensor_out_mps tags: pointwise - func: clamp_max(Tensor self, Scalar max) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: clamp_max.out tags: pointwise - func: clamp_max.Tensor(Tensor self, Tensor max) -> Tensor variants: function, method structured_delegate: clamp_max.Tensor_out tags: pointwise - func: clamp_max_(Tensor(a!) self, Scalar max) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method structured_delegate: clamp_max.out tags: pointwise - func: clamp_max_.Tensor(Tensor(a!) self, Tensor max) -> Tensor(a!) variants: function, method structured_delegate: clamp_max.Tensor_out tags: pointwise - func: clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MTIA: clamp_max_out MPS: clamp_max_out_mps tags: pointwise - func: clamp_max.Tensor_out(Tensor self, Tensor max, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: clamp_max_Tensor_out MPS: clamp_max_Tensor_out_mps tags: pointwise - func: clamp_min(Tensor self, Scalar min) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: clamp_min.out tags: pointwise - func: clamp_min.Tensor(Tensor self, Tensor min) -> Tensor variants: function, method structured_delegate: clamp_min.Tensor_out tags: pointwise - func: clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method structured_delegate: clamp_min.out tags: pointwise - func: clamp_min_.Tensor(Tensor(a!) self, Tensor min) -> Tensor(a!) variants: function, method structured_delegate: clamp_min.Tensor_out tags: pointwise - func: clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MTIA: clamp_min_out MPS: clamp_min_out_mps tags: pointwise - func: clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: clamp_min_Tensor_out MPS: clamp_min_Tensor_out_mps tags: pointwise # clip is an alias for clamp - func: clip(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor cpp_no_default_args: ['min'] variants: function, method tags: pointwise - func: clip.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor variants: function, method tags: pointwise - func: clip_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!) cpp_no_default_args: ['min'] variants: function, method tags: pointwise - func: clip_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!) variants: function, method tags: pointwise - func: clip.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!) cpp_no_default_args: ['min'] tags: pointwise - func: clip.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!) - func: cudnn_is_acceptable(Tensor self) -> bool device_check: NoCheck device_guard: False - func: complex(Tensor real, Tensor imag) -> Tensor variants: function dispatch: CompositeExplicitAutograd: complex - func: complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: complex_out - func: polar(Tensor abs, Tensor angle) -> Tensor variants: function dispatch: CompositeExplicitAutograd: polar - func: polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: polar_out - func: constant_pad_nd(Tensor self, SymInt[] pad, Scalar value=0) -> Tensor variants: function dispatch: CompositeExplicitAutograd: constant_pad_nd MPS: constant_pad_nd_mps autogen: constant_pad_nd.out tags: core - func: contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a) variants: method manual_cpp_binding: True - func: convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor dispatch: CompositeExplicitAutograd: convolution autogen: convolution.out tags: core - func: convolution_backward(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor) dispatch: CompositeExplicitAutograd, CUDA: convolution_backward autogen: convolution_backward.out tags: core - func: convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor dispatch: CompositeExplicitAutograd: convolution_overrideable autogen: convolution_overrideable.out - func: convolution_backward_overrideable(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias) dispatch: CompositeExplicitAutograd: convolution_backward_overrideable autogen: convolution_backward_overrideable.out - func: _convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor dispatch: CompositeExplicitAutograd: _convolution autogen: _convolution.out - func: _convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, int[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor - func: _convolution_mode(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, str padding, SymInt[] dilation, SymInt groups) -> Tensor dispatch: CompositeImplicitAutograd: _convolution_mode_symint - func: _convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor) - func: conv1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] dilation=1, SymInt groups=1) -> Tensor dispatch: CompositeImplicitAutograd: conv1d_symint - func: conv2d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, SymInt groups=1) -> Tensor dispatch: CompositeImplicitAutograd: conv2d_symint - func: conv3d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, SymInt groups=1) -> Tensor dispatch: CompositeImplicitAutograd: conv3d_symint - func: conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, str padding="valid", SymInt[1] dilation=1, SymInt groups=1) -> Tensor cpp_no_default_args: ['bias', 'stride', 'padding'] dispatch: CompositeImplicitAutograd: conv1d_padding_symint - func: conv2d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, str padding="valid", SymInt[2] dilation=1, SymInt groups=1) -> Tensor cpp_no_default_args: ['bias', 'stride', 'padding'] dispatch: CompositeImplicitAutograd: conv2d_padding_symint - func: conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, str padding="valid", SymInt[3] dilation=1, SymInt groups=1) -> Tensor cpp_no_default_args: ['bias', 'stride', 'padding'] dispatch: CompositeImplicitAutograd: conv3d_padding_symint - func: conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor dispatch: CompositeExplicitAutograd: conv_tbc autogen: conv_tbc.out - 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, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] output_padding=0, SymInt groups=1, SymInt[1] dilation=1) -> Tensor dispatch: CompositeImplicitAutograd: conv_transpose1d_symint - func: conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt groups=1, SymInt[2] dilation=1) -> Tensor dispatch: CompositeImplicitAutograd: conv_transpose2d_symint - func: conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt groups=1, SymInt[3] dilation=1) -> Tensor dispatch: CompositeImplicitAutograd: conv_transpose3d_symint - func: copy(Tensor self, Tensor src, bool non_blocking=False) -> Tensor variants: function dispatch: Meta: copy_meta CompositeExplicitAutogradNonFunctional: copy tags: core - func: copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False dispatch: MkldnnCPU: copy_mkldnn_ SparseCPU, SparseCUDA: copy_sparse_wrapper_ CompositeExplicitAutograd: copy_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: copy_sparse_compressed_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: copy_nested_ autogen: copy.out - func: _copy_from(Tensor self, Tensor dst, bool non_blocking=False) -> Tensor dispatch: MPS: _copy_from_mps autogen: _copy_from.out # We need this to be able to properly copy from a CPU to an XLA tensor with different sizes. # See https://github.com/pytorch/xla/issues/2881 - func: _copy_from_and_resize(Tensor self, Tensor dst) -> Tensor dispatch: MPS: _copy_from_and_resize_mps autogen: _copy_from_and_resize.out - func: cos(Tensor self) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: cos.out dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_cos tags: [core, pointwise] - func: cos_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method structured_delegate: cos.out tags: pointwise - func: cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: cos_out tags: pointwise - func: cosh(Tensor self) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: cosh.out tags: [core, pointwise] - func: cosh_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method structured_delegate: cosh.out tags: pointwise - func: cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: cosh_out tags: pointwise - func: cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor - func: count_nonzero.dim_IntList(Tensor self, int[] dim) -> Tensor variants: function, method dispatch: CPU: count_nonzero_cpu CUDA: count_nonzero_cuda MPS: count_nonzero_mps autogen: count_nonzero.dim_IntList_out - func: count_nonzero(Tensor self, int? dim=None) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: count_nonzero autogen: count_nonzero.out - func: cov(Tensor self, *, int correction=1, Tensor? fweights=None, Tensor? aweights=None) -> Tensor variants: function, method - func: corrcoef(Tensor self) -> Tensor variants: function, method - func: cudnn_affine_grid_generator(Tensor theta, int N, int C, int H, int W) -> Tensor grid dispatch: CUDA: cudnn_affine_grid_generator_forward autogen: cudnn_affine_grid_generator.out # TODO: Why do I have to call this grad?! - func: cudnn_affine_grid_generator_backward(Tensor grad, int N, int C, int H, int W) -> Tensor grad_theta dispatch: CUDA: cudnn_affine_grid_generator_backward autogen: cudnn_affine_grid_generator_backward.out - 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 autogen: cudnn_batch_norm.out # 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 autogen: cudnn_batch_norm_backward.out - func: cudnn_convolution(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor dispatch: CUDA: cudnn_convolution - func: cudnn_convolution.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) dispatch: CUDA: cudnn_convolution_out - func: cudnn_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor dispatch: CUDA: cudnn_convolution_transpose autogen: cudnn_convolution_transpose.out - func: _mps_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor dispatch: MPS: _mps_convolution_transpose autogen: _mps_convolution_transpose.out - func: mps_convolution_transpose_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask) -> (Tensor, Tensor) dispatch: MPS: mps_convolution_transpose_backward autogen: mps_convolution_transpose_backward.out - func: cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor dispatch: CUDA: cudnn_convolution_relu autogen: cudnn_convolution_relu.out - func: cudnn_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor dispatch: CUDA: cudnn_convolution_add_relu autogen: cudnn_convolution_add_relu.out # 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 autogen: cudnn_grid_sampler.out - func: cudnn_grid_sampler_backward(Tensor self, Tensor grid, Tensor grad_output) -> (Tensor grad_self, Tensor grad_grid) dispatch: CUDA: cudnn_grid_sampler_backward autogen: cudnn_grid_sampler_backward.out - func: cummax(Tensor self, int dim) -> (Tensor values, Tensor indices) device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: cummax - func: cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) device_check: NoCheck # TensorIterator dispatch: CompositeExplicitAutograd: cummax_out - func: cummax.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices) device_check: NoCheck # TensorIterator variants: function, method - func: cummax.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) device_check: NoCheck # TensorIterator - func: _cummax_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> () variants: function dispatch: CPU: cummax_helper_cpu CUDA: cummax_helper_cuda MPS: cummax_helper_mps - func: cummin(Tensor self, int dim) -> (Tensor values, Tensor indices) device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: cummin - func: cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) device_check: NoCheck # TensorIterator dispatch: CompositeExplicitAutograd: cummin_out - func: cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices) device_check: NoCheck # TensorIterator variants: function, method - func: cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) device_check: NoCheck # TensorIterator - func: _cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> () variants: function dispatch: CPU: cummin_helper_cpu CUDA: cummin_helper_cuda MPS: cummin_helper_mps - func: cummaxmin_backward(Tensor grad, Tensor input, Tensor indices, int dim) -> Tensor variants: function device_check: NoCheck device_guard: False - func: cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor structured_delegate: cumprod.out device_check: NoCheck # TensorIterator variants: function, method - func: cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!) structured_delegate: cumprod.out variants: method - func: cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) structured: True device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: cumprod_out MPS: cumprod_out_mps - func: cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: cumprod_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!) variants: method - func: cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator - func: cumprod_backward(Tensor grad, Tensor input, int dim, Tensor output) -> Tensor variants: function device_check: NoCheck device_guard: False - func: cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor structured_delegate: cumsum.out device_check: NoCheck # TensorIterator variants: function, method tags: core - func: cumsum_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!) structured_delegate: cumsum.out variants: method - func: cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) structured: True device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: cumsum_out MPS: cumsum_out_mps - func: cumsum.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: cumsum_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!) variants: method - func: cumsum.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator - func: cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor - func: cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor - func: ctc_loss.IntList(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor # convenience function that converts to intlists for you - func: ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor - func: _ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor) dispatch: CPU: ctc_loss_cpu CUDA: ctc_loss_gpu Meta: ctc_loss_meta autogen: _ctc_loss.out tags: dynamic_output_shape # the shape of second output is data dependent - func: _ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor) dispatch: CPU, CUDA: ctc_loss_tensor autogen: _ctc_loss.Tensor_out tags: dynamic_output_shape # the shape of second output is data dependent - 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 autogen: _ctc_loss_backward.out - func: _ctc_loss_backward.Tensor(Tensor grad, Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False) -> Tensor dispatch: CPU, CUDA: ctc_loss_backward_tensor - func: diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor variants: function, method dispatch: CompositeExplicitAutogradNonFunctional: diag_embed autogen: diag_embed.out - func: diagflat(Tensor self, int offset=0) -> Tensor variants: function, method - func: diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a) variants: function, method dispatch: CompositeExplicitAutograd: diagonal tags: core - func: linalg_diagonal(Tensor(a) A, *, int offset=0, int dim1=-2, int dim2=-1) -> Tensor(a) python_module: linalg variants: function - func: diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a) variants: function, method - func: diagonal_backward(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2) -> Tensor variants: function device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: diagonal_backward_symint autogen: diagonal_backward.out - func: fill_diagonal_(Tensor(a!) self, Scalar fill_value, bool wrap=False) -> Tensor(a!) variants: method - func: diff(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None) -> Tensor variants: function, method - func: diff.out(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None, *, Tensor(a!) out) -> Tensor(a!) variants: function - func: gradient.scalarint(Tensor self, *, Scalar? spacing=None, int? dim=None, int edge_order=1) -> Tensor[] variants: function - func: gradient.scalararray(Tensor self, *, Scalar spacing, int[] dim, int edge_order=1) -> Tensor[] variants: function - func: gradient.array(Tensor self, *, int[] dim, int edge_order=1) -> Tensor[] variants: function - func: gradient.scalarrayint(Tensor self, *, Scalar[] spacing, int? dim=None, int edge_order=1) -> Tensor[] variants: function - func: gradient.scalarrayarray(Tensor self, *, Scalar[] spacing, int[] dim, int edge_order=1) -> Tensor[] variants: function - func: gradient.tensorarrayint(Tensor self, *, Tensor[] spacing, int? dim=None, int edge_order=1) -> Tensor[] variants: function - func: gradient.tensorarray(Tensor self, *, Tensor[] spacing, int[] dim, int edge_order=1) -> Tensor[] variants: function - func: div.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: div.out dispatch: SparseCPU, SparseCUDA: div_sparse ZeroTensor: div_zerotensor NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_div_Tensor tags: [core, pointwise] - func: div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: div.out dispatch: SparseCPU, SparseCUDA: div_sparse_ tags: pointwise - func: div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: div_out SparseCPU, SparseCUDA: div_out_sparse_zerodim tags: pointwise - func: div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: div.out_mode dispatch: SparseCPU, SparseCUDA: div_sparse tags: [core, pointwise] - func: div_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: div.out_mode dispatch: SparseCPU, SparseCUDA: div_sparse_ tags: pointwise - func: div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: div_out_mode SparseCPU, SparseCUDA: div_out_sparse_zerodim tags: pointwise # For C++ only, until we have conversion from C++ numbers to Tensor - func: div.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: div NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_div_Scalar tags: [core, pointwise] - func: div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: div_ autogen: div.Scalar_out tags: pointwise - func: div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: div tags: [core, pointwise] - func: div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!) variants: method dispatch: CompositeExplicitAutograd: div_ autogen: div.Scalar_mode_out tags: pointwise # divide, alias for div - func: divide.Tensor(Tensor self, Tensor other) -> Tensor variants: function, method - func: divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) variants: method - func: divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) - func: divide.Scalar(Tensor self, Scalar other) -> Tensor variants: function, method - func: divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method - func: divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor variants: function, method - func: divide_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!) variants: method - func: divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) - func: divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor variants: function, method - func: divide_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!) variants: method # true_divide, an alias for div - func: true_divide.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method tags: pointwise - func: true_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method - func: true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator - func: true_divide.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: true_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method - func: dot(Tensor self, Tensor tensor) -> Tensor variants: function, method dispatch: CPU: dot CUDA: dot_cuda MPS: dot_mps - func: dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: dot_out - func: vdot(Tensor self, Tensor other) -> Tensor variants: function, method dispatch: CPU: vdot CUDA: vdot_cuda - func: vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: vdot_out - func: einsum(str equation, Tensor[] tensors, *, int[]? path=None) -> Tensor - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor dispatch: CompositeExplicitAutograd: embedding_symint NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_embedding autogen: embedding.out tags: core - func: embedding_backward(Tensor grad, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, bool sparse) -> Tensor dispatch: CompositeImplicitAutograd: embedding_backward_symint - func: embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor dispatch: CPU: embedding_dense_backward_cpu CUDA: embedding_dense_backward_cuda MPS: embedding_dense_backward_mps autogen: embedding_dense_backward.out tags: core - func: embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!) dispatch: CPU: embedding_renorm_cpu_ CUDA: embedding_renorm_cuda_ autogen: embedding_renorm, embedding_renorm.out - func: embedding_sparse_backward(Tensor grad, Tensor indices, int num_weights, int padding_idx, bool scale_grad_by_freq) -> Tensor # NOTE [ embedding_bag Native Functions ] # The `_embedding_bag.*` variants assume that input tensors except for `weight`, # e.g. `indices` and `offsets` (and `offset2bag`), are contiguous. # We really only need to enforce this for `_embedding_bag` (the forward) because # the backward inputs are the same as forward ones. # The above `embedding_bag` wrapper is created to achieve this, e.g., # applying indices = indices.contiguous(). # The backward functions apply a check that these input tensors are contiguous. - func: _embedding_bag_forward_only(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor) dispatch: CPU: _embedding_bag_forward_only_cpu CUDA: _embedding_bag_forward_only_cuda autogen: _embedding_bag_forward_only.out - func: _rowwise_prune(Tensor weight, Tensor mask, ScalarType compressed_indices_dtype) -> (Tensor, Tensor) # row_stack is the alias of vstack - func: row_stack(Tensor[] tensors) -> Tensor - func: row_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) - func: embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> (Tensor, Tensor, Tensor, Tensor) # To keep backward and forward compatibility, and to avoid ambiguity with the # original signature above, scale_grad_by_freq, mode, sparse, # per_sample_weights, and include_last_offset parameters do not have default # values. Once the original signature is removed, default values can be added. - func: embedding_bag.padding_idx(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int? padding_idx) -> (Tensor, Tensor, Tensor, Tensor) - func: _embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor) dispatch: CPU: _embedding_bag_cpu CUDA: _embedding_bag_cuda autogen: _embedding_bag.out tags: core - func: _embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor dispatch: CPU, CUDA: _embedding_bag_backward_symint - func: _embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor dispatch: CompositeImplicitAutograd: _embedding_bag_sparse_backward_symint - func: _embedding_bag_dense_backward(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor dispatch: CPU: _embedding_bag_dense_backward_cpu CUDA: _embedding_bag_dense_backward_cuda autogen: _embedding_bag_dense_backward.out - func: _embedding_bag_per_sample_weights_backward(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1) -> Tensor dispatch: CPU: _embedding_bag_per_sample_weights_backward_cpu CUDA: _embedding_bag_per_sample_weights_backward_cuda autogen: _embedding_bag_per_sample_weights_backward.out - func: empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: empty_names autogen: empty.names_out - func: empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor dispatch: CPU: empty_cpu CUDA: empty_cuda MPS: empty_mps Meta: empty_meta_symint MkldnnCPU: empty_mkldnn SparseCPU, SparseCUDA: empty_sparse SparseMeta: empty_sparse_symint SparseCsrCPU, SparseCsrCUDA: empty_sparse_compressed SparseCsrMeta: empty_sparse_compressed_symint QuantizedCPU, QuantizedCUDA, QuantizedMeta: empty_unknown_quantized tags: core - func: empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: empty_permuted_symint autogen: empty_permuted.out # We do not make new_empty a composite that calls into new_empty_strided, as the strided version # is significantly more difficult to implement by different backends - func: new_empty(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor variants: method dispatch: CompositeExplicitAutograd: new_empty_symint autogen: new_empty.out - func: new_empty_strided(Tensor self, SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor variants: method dispatch: CompositeExplicitAutogradNonFunctional: new_empty_strided_symint autogen: new_empty_strided.out - func: new_full(Tensor self, SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor variants: method dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd: new_full autogen: new_full.out - func: new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor variants: method dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd: new_zeros autogen: new_zeros.out - func: new_ones(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor variants: method dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd: new_ones autogen: new_ones.out # other overrides are to provide a more helpful error message that dtype is required - func: _empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor dispatch: CPU: empty_affine_quantized_other_backends_stub QuantizedCPU, QuantizedCUDA: empty_affine_quantized autogen: _empty_affine_quantized.out # it's a factory function receiving a tensor argument, thus overriding explicitly # other overrides are to provide a more helpful error message that dtype is required - func: _empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor category_override: factory dispatch: CPU: empty_per_channel_affine_quantized_other_backends_stub QuantizedCPU, QuantizedCUDA: empty_per_channel_affine_quantized autogen: _empty_per_channel_affine_quantized.out - func: resize_(Tensor(a!) self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor(a!) use_const_ref_for_mutable_tensors: True variants: method device_check: NoCheck device_guard: False tags: [core, inplace_view] dispatch: Meta: resize__symint CPU: resize_ CUDA: resize_cuda_ MPS: resize_mps_ QuantizedCPU: quantized_resize_cpu_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: resize_sparse_csr_ autogen: resize, resize.out # This is a utility function to enable users to resize out tensor while registering kernels for out variants. # Eventually, we can consider exposing `resize_output` as a public API to ship it with python op registration # to make it easy to register out variants for ops. - func: _resize_output_(Tensor(a!) self, SymInt[] size, Device device) -> Tensor(a!) use_const_ref_for_mutable_tensors: True variants: function dispatch: Meta: _resize_output_ autogen: _resize_output, _resize_output.out - func: empty_quantized(int[] size, Tensor qtensor, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor category_override: factory variants: function dispatch: QuantizedCPU, QuantizedCUDA: empty_quantized autogen: empty_quantized.out - func: empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck device_guard: False - func: empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: empty_like QuantizedCPU, QuantizedCUDA: empty_like_quantized SparseCPU, SparseCUDA, SparseMeta: empty_like_sparse_coo SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: empty_like_sparse_csr NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: empty_like_nested autogen: empty_like.out - func: empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CPU: empty_strided_cpu CUDA: empty_strided_cuda MPS: empty_strided_mps Meta: empty_strided_meta_symint QuantizedCPU, QuantizedCUDA: empty_strided_unknown_quantized autogen: empty_strided.out tags: core - func: erf(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: erf.out variants: function, method dispatch: SparseCPU, SparseCUDA: erf_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: erf_sparse_csr tags: [core, pointwise] - func: erf_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: erf.out variants: function, method dispatch: SparseCPU, SparseCUDA: erf_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: erf_sparse_csr_ tags: pointwise - func: erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: erf_out SparseCPU, SparseCUDA: erf_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: erf_sparse_csr_out tags: pointwise - func: erfc(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: erfc.out variants: function, method tags: pointwise - func: erfc_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: erfc.out variants: function, method tags: pointwise - func: erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: erfc_out tags: pointwise - func: exp(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: exp.out variants: function, method tags: [core, pointwise] - func: exp_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: exp.out variants: function, method tags: pointwise - func: exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: exp_out tags: pointwise - func: exp2(Tensor self) -> Tensor structured_delegate: exp2.out variants: function, method tags: pointwise - func: exp2_(Tensor(a!) self) -> Tensor(a!) structured_delegate: exp2.out variants: function, method tags: pointwise - func: exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: exp2_out tags: pointwise - func: expm1(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: expm1.out variants: function, method dispatch: SparseCPU, SparseCUDA: expm1_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: expm1_sparse_csr tags: [core, pointwise] - func: expm1_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: expm1.out variants: function, method dispatch: SparseCPU, SparseCUDA: expm1_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: expm1_sparse_csr_ tags: pointwise - func: expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: expm1_out SparseCPU, SparseCUDA: expm1_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: expm1_sparse_csr_out tags: pointwise - func: expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a) variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too. device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: expand tags: core - func: expand_as(Tensor(a) self, Tensor other) -> Tensor(a) variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too. device_check: NoCheck device_guard: False # decomposes to eye.m - func: eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: eye - func: eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: eye - func: eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, Meta: eye_out_cpu CUDA: eye_out_cuda MPS: eye_out_mps - func: eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, Meta: eye_out_cpu CUDA: eye_out_cuda MPS: eye_out_mps - func: flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a) variants: function, method - func: flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a) variants: function, method - func: flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a) variants: function, method - func: flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a) variants: function, method - func: unflatten.int(Tensor(a) self, int dim, SymInt[] sizes) -> Tensor(a) variants: function, method dispatch: CompositeImplicitAutograd: unflatten_symint - func: unflatten.Dimname(Tensor(a) self, Dimname dim, SymInt[] sizes, Dimname[] names) -> Tensor(a) variants: function, method dispatch: CompositeImplicitAutograd: unflatten_dimname_symint - func: fill.Scalar(Tensor self, Scalar value) -> Tensor variants: function dispatch: CompositeExplicitAutograd: fill tags: core - func: fill.Tensor(Tensor self, Tensor value) -> Tensor variants: function dispatch: CompositeExplicitAutograd: fill - func: fill_.Scalar(Tensor(a!) self, Scalar value) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method dispatch: CPU, CUDA: fill_ MPS: fill_scalar_mps QuantizedCPU, QuantizedCUDA: fill_quantized_ Meta: fill_meta_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: fill_sparse_csr_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: fill_nested_ autogen: fill.Scalar_out - func: fill_.Tensor(Tensor(a!) self, Tensor value) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method dispatch: CPU, CUDA: fill_ MPS: fill_tensor_mps_ QuantizedCPU, QuantizedCUDA: fill_quantized_ Meta: fill_meta_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: fill_nested_ autogen: fill.Tensor_out - func: floor(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: floor.out variants: function, method dispatch: SparseCPU, SparseCUDA: floor_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: floor_sparse_csr tags: [core, pointwise] - func: floor_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: floor.out variants: function, method dispatch: SparseCPU, SparseCUDA: floor_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: floor_sparse_csr_ tags: pointwise - func: floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: floor_out SparseCPU, SparseCUDA: floor_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: floor_sparse_csr_out tags: pointwise - func: floor_divide(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CPU, CUDA, MPS: floor_divide SparseCPU, SparseCUDA: floor_divide_sparse - func: floor_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CPU, CUDA, MPS: floor_divide_ SparseCPU, SparseCUDA: floor_divide_sparse_ - func: floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MPS: floor_divide_out SparseCPU, SparseCUDA: floor_divide_out_sparse_zerodim - func: floor_divide.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: floor_divide - func: floor_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: floor_divide_ autogen: floor_divide.Scalar_out - func: frac(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: frac.out variants: function, method dispatch: SparseCPU, SparseCUDA: frac_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: frac_sparse_csr tags: pointwise - func: frac_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: frac.out variants: function, method dispatch: SparseCPU, SparseCUDA: frac_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: frac_sparse_csr_ tags: pointwise - func: frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: frac_out MPS: frac_out_mps SparseCPU, SparseCUDA: frac_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: frac_sparse_csr_out tags: pointwise - func: full.names(int[] size, Scalar fill_value, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: full autogen: full.names_out - func: full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: full tags: core - func: full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: full_out - func: full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd: full_like autogen: full_like.out tags: core - func: from_file(str filename, bool? shared=None, int? size=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CPU: from_file autogen: from_file.out - func: gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: gcd_out tags: pointwise - func: gcd(Tensor self, Tensor other) -> Tensor structured_delegate: gcd.out variants: function, method tags: pointwise - func: gcd_(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: gcd.out variants: function, method - func: lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: lcm_out tags: pointwise - func: lcm(Tensor self, Tensor other) -> Tensor structured_delegate: lcm.out variants: function, method tags: pointwise - func: lcm_(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: lcm.out variants: function, method # NOTE [ grid_sampler Native Functions ] # `grid_sampler` is _supposed to_ do all the shape checking and then dispatch 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. # However, we do shape checking everywhere for now since each of the mentioned # functions can be called directly, which will lead to crashes otherwise. # See https://github.com/pytorch/pytorch/issues/73187 for more information. # # There is also _grid_sampler_2d_backward_cpu_fallback which is an # implementation detail of grid_sampler_2d and is only exposed here for testing # purposes. # # Additionally, arguments `padding_mode` and `interpolation_mode` are cast to # enums defined in `native/GridSampler.h`. `cudnn_grid_sampler` doesn't take in # `interpolation_mode` because it only supports Bilinear interpolation mode. # Nor does it take in `align_corners` because it only supports the mode # `align_corners = True`. - func: grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor - func: grid_sampler_2d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor dispatch: CPU, QuantizedCPU: grid_sampler_2d_cpu CUDA: grid_sampler_2d_cuda MPS: grid_sampler_2d_mps autogen: grid_sampler_2d.out tags: core # `grid_sampler_2d_backward` takes in `output_mask` to optimize performance for # the case where `input` doesn't require gradient. Gradient for `grid` is always # computed (only `output_mask[0]` is checked by the implementations). - func: grid_sampler_2d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor) dispatch: CPU: grid_sampler_2d_backward_cpu CUDA: grid_sampler_2d_backward_cuda autogen: grid_sampler_2d_backward.out # See NOTE [ grid_sample CPU fallback ] - func: _grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor dispatch: CompositeExplicitAutograd: _grid_sampler_2d_cpu_fallback autogen: _grid_sampler_2d_cpu_fallback.out - func: _grid_sampler_2d_cpu_fallback_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> (Tensor, Tensor) - func: grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor dispatch: CPU: grid_sampler_3d_cpu CUDA: grid_sampler_3d_cuda autogen: grid_sampler_3d.out # `grid_sampler_3d_backward` takes in `output_mask` to optimize performance for # the case where `input` doesn't require gradient. Gradient for `grid` is always # computed (only `output_mask[0]` is checked by the implementations). - func: grid_sampler_3d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor) dispatch: CPU: grid_sampler_3d_backward_cpu CUDA: grid_sampler_3d_backward_cuda autogen: grid_sampler_3d_backward.out - func: hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: hann_window autogen: hann_window.out - func: hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: hann_window autogen: hann_window.periodic_out - func: hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: hamming_window autogen: hamming_window.out - func: hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: hamming_window autogen: hamming_window.periodic_out - func: hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: hamming_window autogen: hamming_window.periodic_alpha_out - func: hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: hamming_window autogen: hamming_window.periodic_alpha_beta_out - func: kaiser_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: kaiser_window autogen: kaiser_window.out - func: kaiser_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: kaiser_window autogen: kaiser_window.periodic_out - func: kaiser_window.beta(int window_length, bool periodic, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: kaiser_window autogen: kaiser_window.beta_out - func: hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor - func: group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor - func: native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor) dispatch: CPU, CUDA: native_group_norm CompositeExplicitAutograd: math_group_norm autogen: native_group_norm.out tags: core - func: native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor) dispatch: CPU, CUDA: native_group_norm_backward autogen: native_group_norm_backward.out tags: core # Real to complex forward FFT - func: _fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor variants: function dispatch: CPU: _fft_r2c_mkl CUDA: _fft_r2c_cufft MPS: _fft_r2c_mps tags: core - func: _fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!) variants: function dispatch: CPU: _fft_r2c_mkl_out CUDA: _fft_r2c_cufft_out MPS: _fft_r2c_mps_out # Complex to real inverse FFT - func: _fft_c2r(Tensor self, int[] dim, int normalization, SymInt last_dim_size) -> Tensor variants: function dispatch: CPU: _fft_c2r_mkl CUDA: _fft_c2r_cufft MPS: _fft_c2r_mps - func: _fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!) variants: function dispatch: CPU: _fft_c2r_mkl_out CUDA: _fft_c2r_cufft_out MPS: _fft_c2r_mps_out # Standard complex to complex FFT (forward or backward) - func: _fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor variants: function dispatch: CPU: _fft_c2c_mkl CUDA: _fft_c2c_cufft MPS: _fft_c2c_mps - func: _fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!) variants: function dispatch: CPU: _fft_c2c_mkl_out CUDA: _fft_c2c_cufft_out MPS: _fft_c2c_mps_out - func: _validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> () device_check: NoCheck variants: function dispatch: CPU: _validate_compressed_sparse_indices_cpu CUDA: _validate_compressed_sparse_indices_cuda - func: _cufft_get_plan_cache_size(DeviceIndex device_index) -> int - func: _cufft_get_plan_cache_max_size(DeviceIndex device_index) -> int - func: _cufft_set_plan_cache_max_size(DeviceIndex device_index, int max_size) -> () - func: _cufft_clear_plan_cache(DeviceIndex device_index) -> () - func: index.Tensor(Tensor self, Tensor?[] indices) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: index.Tensor_out variants: function, method dispatch: QuantizedCPU: quantized_index tags: [core, dynamic_output_shape] # NB: This function is special-cased in tools/autograd/gen_variable_type.py # NB: The following functions are declared in aten/src/ATen/templates/TensorBody.h and defined in aten/src/ATen/TensorIndexing.cpp: # - Tensor Tensor::index(ArrayRef<TensorIndex> indices) # - Tensor Tensor::index(std::initializer_list<TensorIndex> indices) - func: index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck structured: True structured_inherits: TensorIteratorBase precomputed: - indices -> DimVector sizes, DimVector strides dispatch: CPU, CUDA, MPS: index_out # Used by inductor to signal indexing without bounds checks # Note that we don't support boolean indexing, to avoid dynamic output shapes - func: _unsafe_index.Tensor(Tensor self, Tensor?[] indices) -> Tensor variants: function dispatch: CompositeExplicitAutograd: _unsafe_index # Used by inductor to generate masked loads # Note that we don't support boolean indexing, to avoid dynamic output shapes - func: _unsafe_masked_index(Tensor self, Tensor mask, Tensor?[] indices, Scalar fill) -> Tensor variants: function dispatch: CompositeExplicitAutograd: _unsafe_masked_index - func: _unsafe_masked_index_put_accumulate(Tensor self, Tensor mask, Tensor?[] indices, Tensor values) -> Tensor variants: function dispatch: CompositeExplicitAutograd: _unsafe_masked_index_put_accumulate - func: index_copy.out(Tensor self, int dim, Tensor index, Tensor source, *, Tensor(a!) out) -> Tensor(a!) structured: True variants: function precomputed: - dim -> int dim dispatch: CPU, CUDA: index_copy_out MPS: index_copy_out_mps - func: index_copy_(Tensor(a!) self, int dim, Tensor index, Tensor source) -> Tensor(a!) variants: method structured_delegate: index_copy.out - func: index_copy(Tensor self, int dim, Tensor index, Tensor source) -> Tensor variants: function, method structured_delegate: index_copy.out - func: index_copy_.dimname(Tensor(a!) self, Dimname dim, Tensor index, Tensor source) -> Tensor(a!) variants: method - func: index_copy.dimname(Tensor self, Dimname dim, Tensor index, Tensor source) -> Tensor variants: function, method - func: index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!) device_check: NoCheck # delegate to _index_put_impl_, which leverages TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: index_put_ autogen: index_put.out # NB: The following functions are declared in aten/src/ATen/templates/TensorBody.h and defined in aten/src/ATen/TensorIndexing.cpp: # - Tensor & Tensor::index_put_(ArrayRef<TensorIndex> indices, Tensor const & rhs) # - Tensor & Tensor::index_put_(ArrayRef<TensorIndex> indices, Scalar v) # - Tensor & Tensor::index_put_(std::initializer_list<TensorIndex> indices, Tensor const & rhs) # - Tensor & Tensor::index_put_(std::initializer_list<TensorIndex> indices, Scalar v) - func: index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor device_check: NoCheck # delegate to _index_put_impl_ after clone, which leverages TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: index_put tags: core - func: _unsafe_index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor device_check: NoCheck # delegate to _index_put_impl_ after clone, which leverages TensorIterator variants: function dispatch: CompositeExplicitAutograd: _unsafe_index_put - func: _index_put_impl_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function dispatch: CPU, CUDA, MPS: _index_put_impl_ QuantizedCPU: _index_put_impl_quantized_cpu_ QuantizedCUDA: _index_put_impl_quantized_cuda_ autogen: _index_put_impl, _index_put_impl.out - func: instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> Tensor variants: function - func: isclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> Tensor variants: function, method - func: isin.Tensor_Tensor_out(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) variants: function structured: True dispatch: CPU, CUDA: isin_Tensor_Tensor_out MPS: isin_Tensor_Tensor_out_mps - func: isin.Tensor_Tensor(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor variants: function structured_delegate: isin.Tensor_Tensor_out - func: isin.Tensor_Scalar_out(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) variants: function structured: True dispatch: CPU, CUDA, MPS: isin_Tensor_Scalar_out - func: isin.Tensor_Scalar(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False) -> Tensor variants: function structured_delegate: isin.Tensor_Scalar_out - func: isin.Scalar_Tensor_out(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) variants: function structured: True dispatch: CPU, CUDA: isin_Scalar_Tensor_out MPS: isin_Scalar_Tensor_out_mps - func: isin.Scalar_Tensor(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor variants: function structured_delegate: isin.Scalar_Tensor_out - func: isnan(Tensor self) -> Tensor variants: function, method device_check: NoCheck device_guard: False dispatch: CPU, CUDA, MPS, MTIA: isnan NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_isnan SparseCPU, SparseCUDA: isnan_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: isnan_sparse_csr autogen: isnan.out tags: [core, pointwise] - func: is_distributed(Tensor self) -> bool variants: function, method device_check: NoCheck device_guard: False - func: is_floating_point(Tensor self) -> bool variants: function, method device_check: NoCheck device_guard: False manual_cpp_binding: True - func: is_complex(Tensor self) -> bool variants: function, method device_check: NoCheck device_guard: False manual_cpp_binding: True - func: is_conj(Tensor self) -> bool variants: function, method device_guard: False manual_cpp_binding: True - func: _is_zerotensor(Tensor self) -> bool variants: function, method device_guard: False manual_cpp_binding: True - func: is_neg(Tensor self) -> bool variants: function, method device_guard: False manual_cpp_binding: True - func: isreal(Tensor self) -> Tensor variants: function, method - func: is_nonzero(Tensor self) -> bool variants: function, method device_check: NoCheck device_guard: False - func: is_same_size(Tensor self, Tensor other) -> bool variants: function, method device_check: NoCheck device_guard: False dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: nested_is_same_size CompositeExplicitAutograd: is_same_size - func: is_signed(Tensor self) -> bool variants: function, method device_check: NoCheck device_guard: False manual_cpp_binding: True - func: is_inference(Tensor self) -> bool variants: function, method device_check: NoCheck device_guard: False manual_cpp_binding: True - func: kl_div(Tensor self, Tensor target, int reduction=Mean, *, bool log_target=False) -> Tensor - func: kron(Tensor self, Tensor other) -> Tensor variants: function, method - func: kron.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) - func: kthvalue(Tensor self, SymInt k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices) variants: function, method dispatch: CompositeExplicitAutograd: kthvalue - func: kthvalue.values(Tensor self, SymInt k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) dispatch: CPU: kthvalue_out_cpu CUDA: kthvalue_out_cuda - func: kthvalue.dimname(Tensor self, SymInt k, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) variants: function, method - func: kthvalue.dimname_out(Tensor self, SymInt k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) - func: layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensor dispatch: CompositeImplicitAutograd: layer_norm_symint - func: native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor) dispatch: CPU: layer_norm_cpu CUDA: layer_norm_cuda MPS: layer_norm_mps CompositeExplicitAutograd: math_native_layer_norm NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: nested_layer_norm autogen: native_layer_norm.out tags: core - func: native_layer_norm_backward(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask) -> (Tensor, Tensor, Tensor) dispatch: CPU: layer_norm_backward_cpu CUDA: layer_norm_backward_cuda MPS: layer_norm_backward_mps NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: layer_norm_backward_nested autogen: native_layer_norm_backward.out tags: core - func: rms_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, float? eps=None) -> Tensor dispatch: CompositeImplicitAutograd: rms_norm_symint - func: _fused_rms_norm(Tensor input, int normalized_shape_ndim, Tensor weight, float eps) -> Tensor dispatch: MPS: _fused_rms_norm_mps - func: nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: nan_to_num SparseCPU, SparseCUDA: nan_to_num_sparse tags: pointwise - func: nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!) variants: function, method dispatch: CompositeExplicitAutograd: nan_to_num_ SparseCPU, SparseCUDA: nan_to_num_sparse_ tags: pointwise - func: nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MTIA: nan_to_num_out MPS: nan_to_num_out_mps SparseCPU, SparseCUDA: nan_to_num_sparse_out tags: pointwise - func: linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor python_module: nn dispatch: CompositeImplicitAutograd: linear NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: nested_linear MPS: _mps_linear - func: linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor) dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: nested_linear_backward MPS: mps_linear_backward autogen: linear_backward.out - func: linear.out(Tensor input, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CompositeExplicitAutograd: linear_out - func: mkldnn_linear(Tensor self, Tensor weight, Tensor? bias=None) -> Tensor python_module: nn dispatch: MkldnnCPU: mkldnn_linear autogen: mkldnn_linear.out - func: mkldnn_linear_backward_input(int[] input_size, Tensor grad_output, Tensor weight) -> Tensor dispatch: MkldnnCPU: mkldnn_linear_backward_input autogen: mkldnn_linear_backward_input.out - func: mkldnn_linear_backward_weights(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined) -> (Tensor, Tensor) dispatch: MkldnnCPU: mkldnn_linear_backward_weights autogen: mkldnn_linear_backward_weights.out - func: mkldnn_linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor) dispatch: MkldnnCPU: mkldnn_linear_backward autogen: mkldnn_linear_backward.out - func: _cslt_compress(Tensor input) -> Tensor dispatch: CUDA: _cslt_compress - func: _cslt_sparse_mm(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False, int alg_id=0, int split_k=1, int split_k_mode=-1) -> Tensor dispatch: CUDA: _cslt_sparse_mm tags: needs_fixed_stride_order - func: _cslt_sparse_mm_search(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False) -> int dispatch: CUDA: _cslt_sparse_mm_search - func: _sparse_semi_structured_tile(Tensor input, str algorithm="", bool use_cutlass=True) -> (Tensor, Tensor, Tensor, Tensor, Tensor) dispatch: CUDA: _sparse_semi_structured_tile - func: _sparse_semi_structured_apply(Tensor input, Tensor thread_masks) -> (Tensor, Tensor) dispatch: CUDA: _sparse_semi_structured_apply - func: _sparse_semi_structured_apply_dense(Tensor input, Tensor thread_masks) -> Tensor dispatch: CUDA: _sparse_semi_structured_apply_dense # DEPRECATED: Use torch.__sparse_semi_structured_mm/torch._sparse_semi_structured_addmm instead - func: _sparse_semi_structured_linear(Tensor input, Tensor weight, Tensor meta, *, Tensor? bias=None, str? activation=None, ScalarType? out_dtype=None) -> Tensor dispatch: CUDA: _sparse_semi_structured_linear - func: _sparse_semi_structured_mm(Tensor mat1, Tensor mat1_meta, Tensor mat2, *, ScalarType? out_dtype=None) -> Tensor dispatch: CUDA: _sparse_semi_structured_mm - func: _sparse_semi_structured_addmm(Tensor input, Tensor mat1, Tensor mat1_meta, Tensor mat2, *, Scalar alpha=1, Scalar beta=1, ScalarType? out_dtype=None) -> Tensor dispatch: CUDA: _sparse_semi_structured_addmm - func: _mixed_dtypes_linear(Tensor input, Tensor weight, Tensor scale, *, Tensor? bias=None, str? activation=None) -> Tensor dispatch: CUDA: _mixed_dtypes_linear - func: fbgemm_linear_int8_weight_fp32_activation(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor - func: fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor - func: fbgemm_linear_quantize_weight(Tensor input) -> (Tensor, Tensor, float, int) - func: fbgemm_pack_gemm_matrix_fp16(Tensor input) -> Tensor - func: _wrapped_linear_prepack(Tensor weight, Tensor weight_scale, Tensor weight_zero_point, Tensor bias) -> Tensor - func: _wrapped_quantized_linear_prepacked(Tensor input, Tensor input_scale, Tensor input_zero_point, Tensor packed_weight, Tensor output_scale, Tensor output_zero_point, int out_channel) -> Tensor - func: fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor - func: fbgemm_linear_fp16_weight(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor - func: fbgemm_pack_quantized_matrix(Tensor input) -> Tensor - func: fbgemm_pack_quantized_matrix.KN(Tensor input, int K, int N) -> Tensor - func: ldexp.Tensor(Tensor self, Tensor other) -> Tensor variants: function, method - func: ldexp_(Tensor(a!) self, Tensor other) -> Tensor(a!) variants: function, method tags: pointwise - func: ldexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) tags: pointwise - func: linspace(Scalar start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: linspace - func: linspace.Tensor_Tensor(Tensor start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor category_override: factory dispatch: CompositeExplicitAutograd: linspace - func: linspace.Tensor_Scalar(Tensor start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor category_override: factory dispatch: CompositeExplicitAutograd: linspace - func: linspace.Scalar_Tensor(Scalar start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor category_override: factory dispatch: CompositeExplicitAutograd: linspace - func: linspace.out(Scalar start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, Meta: linspace_out CUDA: linspace_cuda_out MPS: linspace_out_mps - func: linspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!) category_override: factory dispatch: CompositeExplicitAutograd: linspace_out - func: linspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!) category_override: factory dispatch: CompositeExplicitAutograd: linspace_out - func: linspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!) category_override: factory dispatch: CompositeExplicitAutograd: linspace_out - func: log(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: log.out variants: function, method tags: [core, pointwise] - func: log_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: log.out variants: function, method tags: pointwise - func: log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: log_out tags: pointwise - func: log10(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: log10.out variants: function, method tags: [core, pointwise] - func: log10_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: log10.out variants: function, method tags: pointwise - func: log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: log10_out tags: pointwise - func: log1p(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: log1p.out variants: function, method dispatch: SparseCPU, SparseCUDA: log1p_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: log1p_sparse_csr tags: [core, pointwise] - func: log1p_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: log1p.out variants: function, method dispatch: SparseCPU, SparseCUDA: log1p_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: log1p_sparse_csr_ tags: pointwise - func: log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: log1p_out SparseCPU, SparseCUDA: log1p_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: log1p_sparse_csr_out tags: pointwise - func: log2(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: log2.out variants: function, method tags: [core, pointwise] - func: log2_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: log2.out variants: function, method tags: pointwise - func: log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: log2_out tags: pointwise - func: logaddexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: logaddexp_out MPS: logaddexp_out_mps tags: pointwise - func: logaddexp(Tensor self, Tensor other) -> Tensor variants: method, function structured_delegate: logaddexp.out tags: pointwise - func: logaddexp2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: logaddexp2_out MPS: logaddexp2_out_mps tags: pointwise - func: logaddexp2(Tensor self, Tensor other) -> Tensor variants: method, function structured_delegate: logaddexp2.out tags: pointwise - func: xlogy.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: xlogy.OutTensor variants: function, method tags: pointwise - func: xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: xlogy tags: pointwise - func: xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: xlogy tags: pointwise # xlogy: inplace variant - func: xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method structured_delegate: xlogy.OutTensor tags: pointwise - func: xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: xlogy_ # xlogy: out variant - func: xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase variants: function dispatch: CPU, CUDA: xlogy_out MPS: xlogy_out_mps tags: pointwise - func: xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: xlogy_out tags: pointwise - func: xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: xlogy_out tags: pointwise - func: logspace(Scalar start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: logspace - func: logspace.Tensor_Tensor(Tensor start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor category_override: factory dispatch: CompositeExplicitAutograd: logspace - func: logspace.Tensor_Scalar(Tensor start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor category_override: factory dispatch: CompositeExplicitAutograd: logspace - func: logspace.Scalar_Tensor(Scalar start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor category_override: factory dispatch: CompositeExplicitAutograd: logspace - func: logspace.out(Scalar start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, Meta: logspace_out CUDA: logspace_cuda_out - func: logspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) category_override: factory dispatch: CompositeExplicitAutograd: logspace_out - func: logspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) category_override: factory dispatch: CompositeExplicitAutograd: logspace_out - func: logspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) category_override: factory dispatch: CompositeExplicitAutograd: logspace_out # log_softmax allows positional dtype, unlike most operators, because kwonly is BC-breaking when loading jit models. - func: log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor variants: function, method - func: log_softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) variants: function dispatch: CompositeExplicitAutograd: log_softmax_out - func: log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor variants: function, method - func: _log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor structured_delegate: _log_softmax.out tags: core - func: _log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU: log_softmax_cpu_out CUDA: log_softmax_cuda_out MTIA: log_softmax_mtia_out MPS: log_softmax_mps_out - func: _log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor structured_delegate: _log_softmax_backward_data.out - func: _log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU: log_softmax_backward_cpu_out CUDA: log_softmax_backward_cuda_out MTIA: log_softmax_backward_mtia_out MPS: log_softmax_backward_mps_out - func: _logcumsumexp(Tensor self, int dim) -> Tensor dispatch: CPU: _logcumsumexp_cpu CUDA: _logcumsumexp_cuda MPS: _logcumsumexp_mps - func: _logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU: _logcumsumexp_out_cpu CUDA: _logcumsumexp_out_cuda MPS: _logcumsumexp_out_mps - func: logcumsumexp(Tensor self, int dim) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: logcumsumexp - func: logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: logcumsumexp_out - func: logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor variants: function, method - func: logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) - func: logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: logsumexp - func: logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: # calls squeeze CompositeExplicitAutogradNonFunctional: logsumexp_out - func: logsumexp.names(Tensor self, Dimname[1] dim, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: logsumexp.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator - func: margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor - func: matmul(Tensor self, Tensor other) -> Tensor variants: function, method dispatch: CompositeImplicitAutograd: matmul NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: matmul_nested - func: matmul_backward(Tensor grad, Tensor self, Tensor other, bool[2] mask) -> (Tensor, Tensor) dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: matmul_backward_nested autogen: matmul_backward.out - func: matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeImplicitAutograd: matmul_out NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: matmul_out_nested # Alias to linalg.matrix_power - func: matrix_power(Tensor self, int n) -> Tensor variants: function, method # Alias to linalg.matrix_power - func: matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!) # Alias to linalg.matrix_exp - func: matrix_exp(Tensor self) -> Tensor variants: function, method # This function should be deprecated in favor of differential_analytic_matrix_function in FunctionsManual.cpp - func: matrix_exp_backward(Tensor self, Tensor grad) -> Tensor # DEPRECATED: Use torch.aminmax instead - func: _aminmax(Tensor self) -> (Tensor, Tensor) dispatch: CPU, CUDA: _aminmax_all autogen: _aminmax.out # DEPRECATED: Use torch.aminmax instead - func: _aminmax.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor, Tensor) dispatch: CPU, CUDA: _aminmax autogen: _aminmax.dim_out - func: aminmax(Tensor self, *, int? dim=None, bool keepdim=False) -> (Tensor min, Tensor max) device_check: NoCheck # TensorIterator structured_delegate: aminmax.out variants: function, method - func: aminmax.out(Tensor self, *, int? dim=None, bool keepdim=False, Tensor(a!) min, Tensor(b!) max) -> (Tensor(a!) min, Tensor(b!) max) device_check: NoCheck # TensorIterator structured: True dispatch: CPU, CUDA: aminmax_out MPS: aminmax_out_mps - func: _compute_linear_combination(Tensor input, Tensor coefficients) -> Tensor dispatch: CPU, CUDA: _compute_linear_combination - func: _compute_linear_combination.out(Tensor input, Tensor coefficients, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: _compute_linear_combination_out - func: max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) device_check: NoCheck # TensorIterator structured_delegate: max.dim_max variants: function, method dispatch: QuantizedCPU, QuantizedCUDA: qmax tags: core - func: max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices) device_check: NoCheck # TensorIterator structured: True precomputed: - dim -> int dim dispatch: CPU, CUDA, MTIA: max_out MPS: max_out_mps - func: max.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) device_check: NoCheck # TensorIterator variants: function, method - func: max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices) device_check: NoCheck # TensorIterator - func: value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor variants: function device_check: NoCheck device_guard: False dispatch: CompositeImplicitAutograd: value_selecting_reduction_backward_symint NestedTensorCPU, NestedTensorCUDA: value_selecting_reduction_backward_nested_symint - func: amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor variants: function, method structured_delegate: amax.out tags: core - func: amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU, CUDA: amax_out MPS: amax_out_mps # Return: (Tensor output, Tensor indices) - func: max_pool1d_with_indices(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) - func: max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor - func: max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor dispatch: CompositeImplicitAutograd: max_pool2d MPS: mps_max_pool2d - func: max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor dispatch: MPS: mps_max_pool2d_backward autogen: max_pool2d_backward.out - func: mkldnn_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor dispatch: MkldnnCPU: mkldnn_max_pool2d autogen: mkldnn_max_pool2d.out - func: mkldnn_max_pool2d_backward(Tensor grad_output, Tensor output, Tensor input, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor dispatch: MkldnnCPU: mkldnn_max_pool2d_backward autogen: mkldnn_max_pool2d_backward.out - func: mkldnn_max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor dispatch: MkldnnCPU: mkldnn_max_pool3d autogen: mkldnn_max_pool3d.out - func: mkldnn_max_pool3d_backward(Tensor grad_output, Tensor output, Tensor input, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor dispatch: MkldnnCPU: mkldnn_max_pool3d_backward autogen: mkldnn_max_pool3d_backward.out - func: quantized_max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor dispatch: QuantizedCPU: quantized_max_pool1d autogen: quantized_max_pool1d.out - func: quantized_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor dispatch: QuantizedCPU: quantized_max_pool2d QuantizedCUDA: quantized_max_pool2d_cudnn autogen: quantized_max_pool2d.out - func: quantized_max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor dispatch: QuantizedCPU: quantized_max_pool3d autogen: quantized_max_pool3d.out - func: max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor # The CPU and GPU dispatch variants are named weirdly here because otherwise there # are namespacing issues in C++ - func: mean(Tensor self, *, ScalarType? dtype=None) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: mean tags: core # For normal naming convention this should be `mean.out`. However since we already have `mean.out` we have to rename this. - func: mean.dtype_out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CompositeExplicitAutograd: mean_dtype_out - func: mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor structured_delegate: mean.out device_check: NoCheck # TensorIterator variants: function, method dispatch: QuantizedCPU: mean_quantized_cpu tags: core - func: mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) structured: True device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: mean_out MPS: mean_out_mps QuantizedCPU: mean_out_quantized_cpu - func: mean.names_dim(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator - func: nanmean(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor device_check: NoCheck # Composite variants: function, method - func: nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # Composite - func: median(Tensor self) -> Tensor variants: function, method dispatch: CPU: median_cpu CUDA: median_cuda MPS: median_mps autogen: median.out - func: median.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) variants: function, method dispatch: CompositeExplicitAutograd: median - func: median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) dispatch: CPU: median_out_cpu CUDA: median_out_cuda MPS: median_out_mps - func: median.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) variants: function, method - func: median.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) - func: nanmedian(Tensor self) -> Tensor variants: function, method dispatch: CPU: nanmedian_cpu CUDA: nanmedian_cuda MPS: nanmedian_mps autogen: nanmedian.out - func: nanmedian.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) variants: function, method dispatch: CompositeExplicitAutograd: nanmedian - func: nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) dispatch: CPU: nanmedian_out_cpu CUDA: nanmedian_out_cuda MPS: nanmedian_out_mps - func: nanmedian.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) variants: function, method - func: nanmedian.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) - func: min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) device_check: NoCheck # TensorIterator structured_delegate: min.dim_min variants: function, method dispatch: QuantizedCPU, QuantizedCUDA: qmin tags: core - func: min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) device_check: NoCheck # TensorIterator structured: True precomputed: - dim -> int dim dispatch: CPU, CUDA, MTIA: min_out MPS: min_out_mps - func: min.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) device_check: NoCheck # TensorIterator variants: function, method - func: min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) device_check: NoCheck # TensorIterator - func: amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor variants: function, method structured_delegate: amin.out tags: core - func: amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU, CUDA: amin_out MPS: amin_out_mps # TODO: Add this function to MPS dispatch key so that we avoid declaring it in # native_functions.yaml # https://github.com/pytorch/pytorch/issues/77394 - func: _mps_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor dispatch: MPS: _mps_convolution autogen: _mps_convolution.out - func: mps_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor) dispatch: MPS: mps_convolution_backward autogen: mps_convolution_backward.out - func: mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor dispatch: CompositeExplicitAutograd: mkldnn_convolution autogen: mkldnn_convolution.out - func: mkldnn_rnn_layer(Tensor input, Tensor weight0, Tensor weight1, Tensor weight2, Tensor weight3, Tensor hx_, Tensor cx_, bool reverse, int[] batch_sizes, int mode, int hidden_size, int num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) -> (Tensor, Tensor, Tensor, Tensor) dispatch: CPU: mkldnn_rnn_layer MkldnnCPU: mkldnn_rnn_layer autogen: mkldnn_rnn_layer.out - func: mkldnn_rnn_layer_backward(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) dispatch: CPU: mkldnn_rnn_layer_backward autogen: mkldnn_rnn_layer_backward.out - func: miopen_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor) dispatch: CUDA: miopen_batch_norm autogen: miopen_batch_norm.out - func: miopen_batch_norm_backward(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon) -> (Tensor, Tensor, Tensor) dispatch: CUDA: miopen_batch_norm_backward autogen: miopen_batch_norm_backward.out - func: miopen_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor dispatch: CUDA: miopen_convolution autogen: miopen_convolution.out - func: miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor dispatch: CUDA: miopen_convolution_transpose autogen: miopen_convolution_transpose.out - func: miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor dispatch: CUDA: miopen_depthwise_convolution autogen: miopen_depthwise_convolution.out - func: miopen_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor dispatch: CUDA: miopen_convolution_relu - func: miopen_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor dispatch: CUDA: miopen_convolution_add_relu - func: miopen_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor) dispatch: CUDA: miopen_rnn autogen: miopen_rnn.out tags: nondeterministic_seeded - func: miopen_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[]) dispatch: CUDA: miopen_rnn_backward autogen: miopen_rnn_backward.out - func: mm(Tensor self, Tensor mat2) -> Tensor structured_delegate: mm.out variants: function, method dispatch: SparseCPU, SparseCUDA: _sparse_mm SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: _sparse_csr_mm tags: core - func: mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU: mm_out_cpu CUDA: mm_out_cuda MTIA: mm_out_mtia MPS: mm_out_mps XPU: mm_out_xpu SparseCPU, SparseCUDA: _sparse_mm_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: _sparse_csr_mm_out - func: mm.dtype(Tensor self, Tensor mat2, ScalarType out_dtype) -> Tensor dispatch: CUDA: _mm_dtype_cuda - func: mm.dtype_out(Tensor self, Tensor mat2, ScalarType out_dtype, *, Tensor(a!) out) -> Tensor(a!) dispatch: CUDA: _mm_dtype_out_cuda - func: _int_mm(Tensor self, Tensor mat2) -> Tensor dispatch: CPU: _int_mm_cpu CUDA: _int_mm_cuda - func: _int_mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU: _int_mm_out_cpu CUDA: _int_mm_out_cuda - func: _convert_weight_to_int4pack(Tensor self, int innerKTiles) -> Tensor dispatch: CUDA: _convert_weight_to_int4pack_cuda MPS: _convert_weight_to_int4pack_mps - func: _weight_int4pack_mm(Tensor self, Tensor mat2, int qGroupSize, Tensor qScaleAndZeros) -> Tensor dispatch: MPS: _weight_int4pack_mm_mps CUDA: _weight_int4pack_mm_cuda - func: _weight_int4pack_mm_with_scales_and_zeros(Tensor self, Tensor mat2, int qGroupSize, Tensor qScale, Tensor qZeros) -> Tensor dispatch: XPU: _weight_int4pack_mm_xpu # Split int4 pack weight between cpu and other devices due to # https://github.com/pytorch/ao/issues/1117#issuecomment-2451252756. - func: _convert_weight_to_int4pack_for_cpu(Tensor self, int innerKTiles) -> Tensor dispatch: CPU: _convert_weight_to_int4pack_cpu - func: _weight_int4pack_mm_for_cpu(Tensor self, Tensor mat2, int qGroupSize, Tensor qScaleAndZeros) -> Tensor dispatch: CPU: _weight_int4pack_mm_cpu - func: _dyn_quant_pack_4bit_weight(Tensor weights, Tensor scales_zeros, Tensor? bias, int block_size, int in_features, int out_features) -> Tensor dispatch: CPU: _dyn_quant_pack_4bit_weight_cpu - func: _dyn_quant_matmul_4bit(Tensor inp, Tensor packed_weights, int block_size, int in_features, int out_features) -> Tensor dispatch: CPU: _dyn_quant_matmul_4bit_cpu - func: _weight_int8pack_mm(Tensor self, Tensor mat2, Tensor scales) -> Tensor dispatch: CPU: _weight_int8pack_mm_cpu MPS: _weight_int8pack_mm_mps - func: _sparse_mm(Tensor sparse, Tensor dense) -> Tensor python_module: sparse - func: _sparse_mm.reduce(Tensor sparse, Tensor dense, str reduce) -> Tensor python_module: sparse - func: _sparse_sparse_matmul(Tensor self, Tensor other) -> Tensor dispatch: SparseCPU: sparse_sparse_matmul_cpu SparseCUDA: sparse_sparse_matmul_cuda autogen: _sparse_sparse_matmul.out - func: mode(Tensor self, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices) variants: function, method dispatch: CPU, CUDA: mode - func: mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) dispatch: CompositeExplicitAutograd: mode_out - func: mode.dimname(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) variants: function, method - func: mode.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) - func: mul.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: mul.out variants: function, method dispatch: SparseCPU, SparseCUDA: mul_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: mul_sparse_csr MkldnnCPU: mkldnn_mul ZeroTensor: mul_zerotensor NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_mul_Tensor tags: [core, pointwise] - func: mul_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: mul.out variants: method dispatch: SparseCPU, SparseCUDA: mul_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: mul_sparse_csr_ MkldnnCPU: mkldnn_mul_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_mul__Tensor tags: pointwise - func: mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: mul_out SparseCPU: mul_out_sparse_cpu SparseCUDA: mul_out_sparse_cuda SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: mul_out_sparse_csr MkldnnCPU: mkldnn_mul_out tags: pointwise # For C++ only, until we have conversion from C++ numbers to Tensor - func: mul.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: mul SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: mul_scalar_sparse_csr NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_mul_Scalar tags: [core, pointwise] - func: mul_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: mul_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: mul__scalar_sparse_csr NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_mul__Scalar autogen: mul.Scalar_out tags: pointwise # multiply, alias for mul - func: multiply.Tensor(Tensor self, Tensor other) -> Tensor variants: function, method - func: multiply_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) variants: method - func: multiply.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) - func: multiply.Scalar(Tensor self, Scalar other) -> Tensor variants: function, method - func: multiply_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method - func: mv(Tensor self, Tensor vec) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: mv SparseCPU, SparseCUDA: mv_sparse - func: mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: mv_out - func: mvlgamma.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: mvlgamma_out tags: pointwise - func: mvlgamma(Tensor self, int p) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: mvlgamma tags: pointwise - func: mvlgamma_(Tensor(a!) self, int p) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: mvlgamma_ tags: pointwise - func: narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor variants: function, method dispatch: CPU: narrow_copy_dense_cpu SparseCPU, SparseCUDA: narrow_copy_sparse CompositeExplicitAutogradNonFunctional: narrow_copy_dense_symint tags: view_copy - func: narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU: narrow_copy_dense_cpu_out - func: narrow(Tensor(a) self, int dim, SymInt start, SymInt length) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeImplicitAutograd: narrow_symint NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: narrow_nested_symint - func: narrow.Tensor(Tensor(a) self, int dim, Tensor start, SymInt length) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeImplicitAutograd: narrow_tensor_symint - func: native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) dispatch: CPU: batch_norm_cpu CUDA: batch_norm_cuda MPS: batch_norm_mps MkldnnCPU: mkldnn_batch_norm - func: native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) dispatch: CUDA: batch_norm_cuda_out MPS: batch_norm_mps_out CPU: batch_norm_cpu_out # TODO: In 2 weeks, we should make native_batch_norm composite implicit so that this correct schema percolates correctly through our dispatching - func: _native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) dispatch: CPU: _batch_norm_legit_cpu CUDA: _batch_norm_legit_cuda MPS: _batch_norm_legit_mps MkldnnCPU: _mkldnn_batch_norm_legit autogen: _native_batch_norm_legit_functional tags: core # HACK: identical to _native_batch_norm_legit, but training is known to be False, # So we known that running stats will not be mutated. # The real fix here is batch norm consolidation. - func: _native_batch_norm_legit_no_training(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor) dispatch: CompositeExplicitAutograd: _batch_norm_legit_no_training autogen: _native_batch_norm_legit_no_training.out tags: core - func: _native_batch_norm_legit.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd) -> (Tensor(d!), Tensor(e!), Tensor(f!)) dispatch: CPU: _batch_norm_legit_cpu_out CUDA: _batch_norm_legit_cuda_out MPS: _batch_norm_legit_mps_out - func: _native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) dispatch: CPU: _batch_norm_legit_no_stats_cpu CUDA: _batch_norm_legit_no_stats_cuda MPS: _batch_norm_legit_no_stats_mps MkldnnCPU: _mkldnn_batch_norm_legit_no_stats tags: core - func: _native_batch_norm_legit.no_stats_out(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) dispatch: CPU: _batch_norm_legit_no_stats_cpu_out CUDA: _batch_norm_legit_no_stats_cuda_out MPS: _batch_norm_legit_no_stats_mps_out - func: batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor) dispatch: CUDA: batch_norm_stats_cuda autogen: batch_norm_stats.out - func: batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps) -> Tensor dispatch: CUDA: batch_norm_elemt_cuda - func: batch_norm_elemt.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps, *, Tensor(a!) out) -> Tensor(a!) dispatch: CUDA: batch_norm_elemt_cuda_out # for backward compatibility - func: batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor) dispatch: CUDA: batch_norm_gather_stats_cuda autogen: batch_norm_gather_stats.out - func: batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts) -> (Tensor, Tensor) dispatch: CUDA: batch_norm_gather_stats_with_counts_cuda autogen: batch_norm_gather_stats_with_counts.out - func: native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor) dispatch: CPU: batch_norm_backward_cpu CUDA: batch_norm_backward_cuda MPS: batch_norm_backward_mps MkldnnCPU: mkldnn_batch_norm_backward autogen: native_batch_norm_backward.out - func: batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor) dispatch: CUDA: batch_norm_backward_reduce_cuda autogen: batch_norm_backward_reduce.out - func: batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count) -> Tensor dispatch: CUDA: batch_norm_backward_elemt_cuda autogen: batch_norm_backward_elemt.out - func: batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor) dispatch: CPU: batch_norm_update_stats_cpu CUDA: batch_norm_update_stats_cuda autogen: batch_norm_update_stats.out - func: is_vulkan_available() -> bool - func: _nnpack_available() -> bool - func: _nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1) -> Tensor variants: function dispatch: CompositeExplicitAutograd: _nnpack_spatial_convolution autogen: _nnpack_spatial_convolution.out - func: ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: ones autogen: ones.names_out - func: ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: ones - func: ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: ones_out - func: ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd: ones_like NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: ones_like autogen: ones_like.out - func: pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor - func: cdist(Tensor x1, Tensor x2, float p=2, int? compute_mode=None) -> Tensor - func: _euclidean_dist(Tensor x1, Tensor x2) -> Tensor dispatch: CompositeExplicitAutograd: _euclidean_dist autogen: _euclidean_dist.out - func: _cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor dispatch: CPU, CUDA: _cdist_forward MPS: _cdist_forward_mps autogen: _cdist_forward.out tags: core - func: _cdist_backward(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist) -> Tensor dispatch: CPU, CUDA: _cdist_backward autogen: _cdist_backward.out - func: pdist(Tensor self, float p=2) -> Tensor - func: _pdist_forward(Tensor self, float p=2) -> Tensor dispatch: CPU, CUDA: _pdist_forward autogen: _pdist_forward.out tags: core - func: _pdist_backward(Tensor grad, Tensor self, float p, Tensor pdist) -> Tensor dispatch: CPU, CUDA: _pdist_backward autogen: _pdist_backward.out - func: cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor variants: function - func: permute(Tensor(a) self, int[] dims) -> Tensor(a) variants: function, method dispatch: CompositeExplicitAutograd: permute MPS: permute_mps SparseCPU, SparseCUDA: permute_sparse_coo tags: core - func: movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a) variants: function, method - func: movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a) variants: function, method # moveaxis, alias for movedim - func: moveaxis.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a) variants: function, method - func: moveaxis.int(Tensor(a) self, int source, int destination) -> Tensor(a) variants: function, method # Only exposed from C++ -- in Python, # we expose it as an attribute `T`, not a function. # # I'd like to name this "T" in C++ too, but # calling a native function "T" causes undefined # behavior on Windows, for reasons I don't understand # (maybe related to capital letter collation somehow...) - func: numpy_T(Tensor(a) self) -> Tensor(a) variants: method # Exposed on Python as an attribute 'H' - func: matrix_H(Tensor(a) self) -> Tensor(a) variants: method # Exposed on Python as an attribute 'mT' - func: mT(Tensor(a) self) -> Tensor(a) variants: method # Exposed on Python as an attribute 'mH' - func: mH(Tensor(a) self) -> Tensor(a) variants: method - func: adjoint(Tensor(a) self) -> Tensor(a) variants: function, method - func: pixel_shuffle(Tensor self, int upscale_factor) -> Tensor dispatch: CPU: pixel_shuffle_cpu MPS: pixel_shuffle_mps CompositeExplicitAutogradNonFunctional: math_pixel_shuffle autogen: pixel_shuffle.out - func: pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor dispatch: CPU: pixel_unshuffle_cpu MPS: pixel_unshuffle_mps CompositeExplicitAutogradNonFunctional: math_pixel_unshuffle autogen: pixel_unshuffle.out - func: channel_shuffle(Tensor self, SymInt groups) -> Tensor dispatch: CPU, CUDA: channel_shuffle QuantizedCPU: channel_shuffle_quantized_cpu autogen: channel_shuffle.out - func: native_channel_shuffle(Tensor self, SymInt groups) -> Tensor dispatch: CPU: channel_shuffle_cpu CompositeImplicitAutograd: math_channel_shuffle - func: is_pinned(Tensor self, Device? device=None) -> bool variants: method dispatch: # the NestedTensor keys are necessary because NestedTensor has been removed # from the CompositeExplicitAutograd keyset see Note [NestedTensor Not Included in Backend Keys] CompositeExplicitAutograd, NestedTensorCPU: is_pinned SparseCsrCPU: is_pinned_sparse_compressed SparseCPU: is_pinned_sparse_coo # TODO: add a copy kwarg that guarantees that the tensor is put into fresh # pinned memory - func: pin_memory(Tensor(a) self, Device? device=None) -> Tensor(a) variants: method # Unlike pin_memory, this is guaranteed to give a new non-aliasing tensor - func: _pin_memory(Tensor self, Device? device=None) -> Tensor dispatch: CompositeExplicitAutograd: _pin_memory NestedTensorCPU: _pin_memory_nested SparseCPU: _pin_memory_sparse_coo SparseCsrCPU: _pin_memory_sparse_compressed autogen: _pin_memory.out - func: pinverse(Tensor self, float rcond=1e-15) -> Tensor variants: function, method - func: poisson_nll_loss(Tensor input, Tensor target, bool log_input, bool full, float eps, int reduction) -> Tensor variants: function - func: rad2deg(Tensor self) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: rad2deg SparseCPU, SparseCUDA: rad2deg_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: rad2deg_sparse_csr tags: pointwise - func: rad2deg_(Tensor(a!) self) -> Tensor(a!) variants: function, method dispatch: CompositeExplicitAutograd: rad2deg_ SparseCPU, SparseCUDA: rad2deg_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: rad2deg_sparse_csr_ tags: pointwise - func: rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: rad2deg_out SparseCPU, SparseCUDA: rad2deg_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: rad2deg_sparse_csr_out tags: pointwise - func: deg2rad(Tensor self) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: deg2rad SparseCPU, SparseCUDA: deg2rad_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: deg2rad_sparse_csr tags: pointwise - func: deg2rad_(Tensor(a!) self) -> Tensor(a!) variants: function, method dispatch: CompositeExplicitAutograd: deg2rad_ SparseCPU, SparseCUDA: deg2rad_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: deg2rad_sparse_csr_ tags: pointwise - func: deg2rad.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: deg2rad_out SparseCPU, SparseCUDA: deg2rad_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: deg2rad_sparse_csr_out tags: pointwise - func: scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: scalar_tensor autogen: scalar_tensor.out tags: core - func: rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: rand autogen: rand.names_out tags: nondeterministic_seeded - func: rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor device_check: NoCheck device_guard: False tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: rand autogen: rand.generator_with_names_out - func: rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: [core, nondeterministic_seeded] dispatch: CompositeExplicitAutograd: rand - func: rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: rand - func: rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: rand_out - func: rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded - func: rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor tags: nondeterministic_seeded dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd: rand_like autogen: rand_like.out - func: randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randint - func: randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randint - func: randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randint - func: randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randint - func: randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randint_out - func: randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randint_out - func: randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randint_out - func: randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randint_out - func: randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor tags: nondeterministic_seeded dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd: randint_like autogen: randint_like.out - func: randint_like.Tensor(Tensor self, Tensor high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor tags: nondeterministic_seeded dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd: randint_like autogen: randint_like.Tensor_out - func: randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor tags: nondeterministic_seeded dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd: randint_like autogen: randint_like.low_dtype_out - func: randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: [core, nondeterministic_seeded] dispatch: CompositeExplicitAutograd: randn - func: randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randn - func: randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: nondeterministic_seeded device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: randn autogen: randn.names_out - func: randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: nondeterministic_seeded device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: randn autogen: randn.generator_with_names_out - func: randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded - func: randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded - func: randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor tags: nondeterministic_seeded dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd, CompositeImplicitAutogradNestedTensor: randn_like autogen: randn_like.out - func: randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: [core, nondeterministic_seeded] dispatch: CompositeExplicitAutograd: randperm - func: randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randperm - func: randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: randperm_out - func: randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded dispatch: CPU: randperm_out_cpu CUDA: randperm_out_cuda MPS: randperm_out_mps - func: range.step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: range - func: range(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: range - func: range.out_(Scalar start, Scalar end, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: range_out_no_step - func: range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, Meta: range_out CUDA: range_cuda_out MPS: range_mps_out cpp_no_default_args: ['step'] - func: ravel(Tensor(a) self) -> Tensor(a) variants: function, method - func: reciprocal(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: reciprocal.out variants: function, method tags: [core, pointwise] - func: reciprocal_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: reciprocal.out variants: function, method tags: pointwise - func: reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MTIA: reciprocal_out MPS: reciprocal_out_mps tags: pointwise - func: neg(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: neg.out variants: function, method dispatch: SparseCPU, SparseCUDA: neg_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: neg_sparse_csr NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_neg tags: [core, pointwise] - func: neg_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: neg.out variants: function, method dispatch: SparseCPU, SparseCUDA: neg_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: neg_sparse_csr_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_neg_ tags: pointwise - func: neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: neg_out SparseCPU, SparseCUDA: neg_out_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: neg_sparse_csr_out tags: pointwise # Alias for neg - func: negative(Tensor self) -> Tensor variants: function, method - func: negative_(Tensor(a!) self) -> Tensor(a!) variants: function, method - func: negative.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) - func: repeat(Tensor self, SymInt[] repeats) -> Tensor variants: method # This is method-only to match the previous tensor API. In the future we could make this a function too. dispatch: CompositeExplicitAutograd: repeat MPS: repeat_mps autogen: repeat.out tags: core - func: repeat_interleave.Tensor(Tensor repeats, *, SymInt? output_size=None) -> Tensor variants: function dispatch: CPU: repeat_interleave_cpu CUDA: repeat_interleave_cuda MPS: repeat_interleave_mps tags: dynamic_output_shape autogen: repeat_interleave.Tensor_out - func: repeat_interleave.self_Tensor(Tensor self, Tensor repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor variants: function, method dispatch: CompositeImplicitAutograd: repeat_interleave_symint - func: repeat_interleave.self_int(Tensor self, SymInt repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor variants: function, method dispatch: CompositeImplicitAutograd: repeat_interleave_symint - func: reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeImplicitAutograd: reshape_symint CompositeImplicitAutogradNestedTensor: reshape_nested_symint - func: _reshape_copy(Tensor self, SymInt[] size) -> Tensor variants: function dispatch: CompositeExplicitAutograd: _reshape_copy_symint # NOTE [ _reshape_alias ] is meant to be used in the implementation of reshape. # They are not user-facing, hence the leading underscore. Please don't use it # anywhere else. - func: _reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CPU, CUDA, Meta, QuantizedCPU, QuantizedCUDA, ZeroTensor, MPS, MTIA: _reshape_alias # We don't need to support mkldnn since this is handled explicitly by the reshape operator. - func: _mkldnn_reshape(Tensor self, int[] shape) -> Tensor device_check: NoCheck device_guard: False dispatch: MkldnnCPU: mkldnn_reshape autogen: _mkldnn_reshape.out - func: reshape_as(Tensor(a) self, Tensor other) -> Tensor(a) variants: method device_check: NoCheck device_guard: False dispatch: CompositeImplicitAutograd: reshape_as CompositeImplicitAutogradNestedTensor: reshape_as_nested - func: round(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: round.out variants: function, method dispatch: SparseCPU, SparseCUDA: round_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: round_sparse_csr tags: [core, pointwise] - func: round_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: round.out variants: function, method dispatch: SparseCPU, SparseCUDA: round_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: round_sparse_csr_ tags: pointwise - func: round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: round_out SparseCPU, SparseCUDA: round_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: round_sparse_csr_out tags: pointwise - func: round.decimals(Tensor self, *, int decimals) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: round.decimals_out variants: function, method tags: pointwise - func: round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: round.decimals_out variants: function, method tags: pointwise - func: round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: round_decimals_out tags: pointwise - func: rrelu(Tensor self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor device_check: NoCheck # TensorIterator tags: [pointwise, nondeterministic_seeded] - func: rrelu_(Tensor(a!) self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!) tags: nondeterministic_seeded device_check: NoCheck # TensorIterator - func: relu(Tensor self) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CPU, CUDA, MTIA: relu MPS: relu_mps MkldnnCPU: mkldnn_relu QuantizedCPU: relu_quantized_cpu QuantizedCUDA: relu_quantized_cuda NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_relu SparseCPU, SparseCUDA: relu_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: relu_sparse_csr tags: [core, pointwise] - func: relu_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method dispatch: CPU, CUDA, MTIA: relu_ MPS: relu_mps_ MkldnnCPU: mkldnn_relu_ QuantizedCPU: relu_quantized_cpu_ QuantizedCUDA: relu_quantized_cuda_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_relu_ SparseCPU, SparseCUDA: relu_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: relu_sparse_csr_ autogen: relu.out tags: pointwise - func: relu6(Tensor self) -> Tensor python_module: nn tags: pointwise - func: relu6_(Tensor(a!) self) -> Tensor(a!) python_module: nn - func: prelu(Tensor self, Tensor weight) -> Tensor variants: function, method autogen: prelu.out - func: _prelu_kernel(Tensor self, Tensor weight) -> Tensor dispatch: CPU, CUDA: _prelu_kernel QuantizedCPU: _prelu_kernel_quantized_cpu MkldnnCPU: mkldnn_prelu MPS: prelu_mps - func: _prelu_kernel_backward(Tensor grad_output, Tensor self, Tensor weight) -> (Tensor, Tensor) dispatch: CPU, CUDA: _prelu_kernel_backward MkldnnCPU: mkldnn_prelu_backward MPS: prelu_backward_mps - func: gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU: gelu_out_cpu CUDA: gelu_out_cuda MPS: gelu_out_mps - func: gelu_(Tensor(a!) self, *, str approximate='none') -> Tensor(a!) structured_delegate: gelu.out device_check: NoCheck # TensorIterator python_module: nn dispatch: QuantizedCPU: gelu_quantized_cpu_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_gelu_ - func: gelu(Tensor self, *, str approximate='none') -> Tensor structured_delegate: gelu.out device_check: NoCheck # TensorIterator python_module: nn dispatch: MkldnnCPU: mkldnn_gelu QuantizedCPU: gelu_quantized_cpu QuantizedCUDA: gelu_quantized_cuda NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_gelu tags: [core, pointwise] - func: gelu_backward.grad_input(Tensor grad_output, Tensor self, *, str approximate='none', Tensor(a!) grad_input) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU: gelu_backward_out_cpu CUDA: gelu_backward_out_cuda MPS: gelu_backward_out_mps - func: gelu_backward(Tensor grad_output, Tensor self, *, str approximate='none') -> Tensor structured_delegate: gelu_backward.grad_input python_module: nn dispatch: MkldnnCPU: mkldnn_gelu_backward NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: gelu_backwards_nested tags: pointwise - func: infinitely_differentiable_gelu_backward(Tensor grad, Tensor self) -> Tensor variants: function python_module: nn device_check: NoCheck device_guard: False - func: hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MPS: hardshrink_out - func: hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor structured_delegate: hardshrink.out device_check: NoCheck # TensorIterator variants: function, method tags: pointwise - func: hardshrink_backward.grad_input(Tensor grad_out, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: hardshrink_backward_out - func: hardshrink_backward(Tensor grad_out, Tensor self, Scalar lambd) -> Tensor structured_delegate: hardshrink_backward.grad_input variants: function, method - func: rsqrt(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: rsqrt.out variants: function, method tags: [core, pointwise] - func: rsqrt_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: rsqrt.out variants: function, method tags: pointwise - func: rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: rsqrt_out tags: pointwise - func: select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False - func: select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: select_symint SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: select_sparse_csr NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: select_nested tags: core - func: select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor variants: function device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutogradNonFunctional: select_backward_symint autogen: select_backward.out - func: _nested_select_backward(Tensor grad_output, Tensor self, int dim, SymInt index) -> Tensor variants: function device_check: NoCheck device_guard: False dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: _nested_select_backward_symint - func: selu(Tensor self) -> Tensor device_check: NoCheck # TensorIterator tags: pointwise - func: selu_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator - func: celu(Tensor self, Scalar alpha=1.0) -> Tensor device_check: NoCheck # TensorIterator dispatch: CompositeExplicitAutograd: celu tags: pointwise - func: celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CompositeExplicitAutograd: celu_ autogen: celu.out - func: silu(Tensor self) -> Tensor structured_delegate: silu.out python_module: nn dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_silu tags: pointwise - func: silu_(Tensor(a!) self) -> Tensor(a!) structured_delegate: silu.out python_module: nn dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_silu_ tags: pointwise - func: silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA, MTIA: silu_out MPS: silu_out_mps tags: pointwise - func: silu_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA: silu_backward_out MPS: silu_backward_out_mps tags: pointwise - func: silu_backward(Tensor grad_output, Tensor self) -> Tensor structured_delegate: silu_backward.grad_input python_module: nn dispatch: CompositeImplicitAutograd: math_silu_backward NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: silu_backward_nested tags: pointwise - func: mish(Tensor self) -> Tensor structured_delegate: mish.out python_module: nn tags: pointwise - func: mish_(Tensor(a!) self) -> Tensor(a!) structured_delegate: mish.out python_module: nn - func: mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA: mish_out MPS: mish_out_mps - func: mish_backward(Tensor grad_output, Tensor self) -> Tensor python_module: nn dispatch: CPU, CUDA: mish_backward MPS: mish_backward_mps CompositeImplicitAutograd: math_mish_backward - func: sigmoid(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: sigmoid.out variants: function, method dispatch: QuantizedCPU: sigmoid_quantized_cpu MkldnnCPU: mkldnn_sigmoid tags: [core, pointwise] - func: sigmoid_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: sigmoid.out variants: function, method dispatch: MkldnnCPU: mkldnn_sigmoid_ tags: pointwise - func: sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: sigmoid_out tags: pointwise - func: logit(Tensor self, float? eps=None) -> Tensor variants: function, method dispatch: CPU, CUDA, MTIA: logit MPS: logit_mps tags: pointwise - func: logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!) variants: function, method dispatch: CPU, CUDA: logit_ tags: pointwise - func: logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: logit_out MPS: logit_out_mps tags: pointwise - func: sin(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: sin.out variants: function, method dispatch: SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sin_sparse_csr SparseCPU, SparseCUDA: sin_sparse NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_sin tags: [core, pointwise] - func: sin_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: sin.out variants: function, method dispatch: SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sin_sparse_csr_ SparseCPU, SparseCUDA: sin_sparse_ tags: pointwise - func: sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: sin_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sin_sparse_csr_out SparseCPU, SparseCUDA: sin_sparse_out tags: pointwise - func: sinc(Tensor self) -> Tensor structured_delegate: sinc.out variants: function, method tags: pointwise - func: sinc_(Tensor(a!) self) -> Tensor(a!) structured_delegate: sinc.out variants: function, method tags: pointwise - func: sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: sinc_out tags: pointwise - func: sinh(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: sinh.out variants: function, method dispatch: SparseCPU, SparseCUDA: sinh_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sinh_sparse_csr tags: [core, pointwise] - func: sinh_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: sinh.out variants: function, method dispatch: SparseCPU, SparseCUDA: sinh_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sinh_sparse_csr_ tags: pointwise - func: sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: sinh_out SparseCPU, SparseCUDA: sinh_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sinh_sparse_csr_out # Returns a copy of this `Variable` that is detached from its autograd graph. # This method is OK to call if the `Variable` is a view. # # NOTE: Previously, if we change the tensor metadata (e.g. sizes / strides / # storage / storage_offset) of a tensor created from `detach()`, those metadata # in the original tensor will also be updated. However, the new behavior is that # those metadata changes to the detached tensor will not update the original tensor # anymore, and in the `detach()` function we need to set `allow_tensor_metadata_change_` # to false to make such changes explicitly illegal, in order to prevent users from # changing metadata of the detached tensor and expecting the original tensor to also # be updated. tags: pointwise - func: detach(Tensor(a) self) -> Tensor(a) variants: function, method dispatch: CompositeExplicitAutograd: detach NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: detach # Like `detach()`, but modifies this `Variable` in-place. This method may # only be called on non-view `Variable`s. You can use `is_view()` to check # this. If this `Variable` is a view, throws an `std::runtime_error()`. - func: detach_(Tensor(a!) self) -> Tensor(a!) variants: function, method tags: inplace_view dispatch: CompositeExplicitAutograd: detach_ - func: size.int(Tensor self, int dim) -> int variants: function device_check: NoCheck device_guard: False manual_cpp_binding: True - func: size.Dimname(Tensor self, Dimname dim) -> int variants: function, method device_check: NoCheck device_guard: False - func: sym_size.int(Tensor self, int dim) -> SymInt variants: function device_check: NoCheck device_guard: False tags: core manual_cpp_binding: True - func: sym_numel(Tensor self) -> SymInt variants: function device_check: NoCheck device_guard: False tags: core manual_cpp_binding: True - func: sym_storage_offset(Tensor self) -> SymInt variants: function device_check: NoCheck device_guard: False tags: core manual_cpp_binding: True - func: slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: slice tags: core # NOTE: The implementation of split_with_sizes bypasses the dispatcher to call this; undo # that if adding specific implementations here! - func: slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor variants: function device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: slice_backward autogen: slice_backward.out # NB: This op exists to back the implementation of reverse view_funcs for various views (chunk, # slice.Tensor, split_with_sizes, et al.). Currently, these are only used during fake-ification # of PT2 graph input subclass instances that are views. This means: # * This op shouldn't really show up in eager mode (so e.g. XLA shouldn't have to implement it) # * This op shouldn't show up in a PT2 graph (so a PT2 backend shouldn't have to implement it) # * A subclass will have to implement this to work in PT2 if a subclass view is used as a graph # input AND the view utilizes this op in its inverse. The idea is that slice_inverse() is # easier to implement for a subclass than as_strided() - func: slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: slice_inverse_symint - func: slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutogradNonFunctional: slice_scatter autogen: slice_scatter.out tags: [core, view_copy] - func: select_scatter(Tensor self, Tensor src, int dim, SymInt index) -> Tensor variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutogradNonFunctional: select_scatter_symint autogen: select_scatter.out tags: core - func: diagonal_scatter(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1) -> Tensor variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutogradNonFunctional: diagonal_scatter autogen: diagonal_scatter.out - func: as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutogradNonFunctional: as_strided_scatter_symint autogen: as_strided_scatter.out - func: smm(Tensor self, Tensor mat2) -> Tensor variants: function, method # softmax allows positional dtype, unlike most operators, because kwonly is BC-breaking when loading jit models. - func: softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor variants: function, method - func: softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) variants: function dispatch: CompositeExplicitAutograd: softmax_out - func: softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor variants: function, method - func: _softmax(Tensor self, int dim, bool half_to_float) -> Tensor structured_delegate: _softmax.out dispatch: MkldnnCPU: mkldnn_softmax NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: softmax_nested tags: core - func: _softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU: softmax_cpu_out CUDA: softmax_cuda_out MPS: softmax_mps_out - func: _softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor structured_delegate: _softmax_backward_data.out dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: nested_softmax_backward - func: _softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!) structured: True dispatch: CPU: softmax_backward_cpu_out CUDA: softmax_backward_cuda_out MPS: softmax_backward_mps_out - func: unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: unsafe_split autogen: unsafe_split.Tensor_out - func: split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[] variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: split - func: split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[] variants: function, method device_guard: False dispatch: CompositeImplicitAutograd: split_symint - func: unsafe_split_with_sizes(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[] variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: unsafe_split_with_sizes autogen: unsafe_split_with_sizes.out - func: split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[] variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: split_with_sizes NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: split_with_sizes_nested tags: core - func: hsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] variants: function, method - func: hsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] variants: function, method - func: vsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] variants: function, method - func: vsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] variants: function, method - func: dsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] variants: function, method - func: dsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] variants: function, method - func: squeeze(Tensor(a) self) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: squeeze QuantizedCPU, QuantizedCUDA: squeeze_quantized NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: squeeze_nested - func: squeeze.dim(Tensor(a) self, int dim) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: squeeze QuantizedCPU, QuantizedCUDA: squeeze_quantized NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: squeeze_dim_nested tags: core - func: squeeze.dimname(Tensor(a) self, Dimname dim) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False - func: squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: squeeze QuantizedCPU, QuantizedCUDA: squeeze_quantized NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: squeeze_dim_nested tags: core - func: squeeze_(Tensor(a!) self) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False tags: inplace_view dispatch: CompositeExplicitAutograd: squeeze_ - func: squeeze_.dim(Tensor(a!) self, int dim) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False tags: inplace_view dispatch: CompositeExplicitAutograd: squeeze_ - func: squeeze_.dims(Tensor(a!) self, int[] dim) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False tags: inplace_view dispatch: CompositeExplicitAutograd: squeeze_ - func: squeeze_.dimname(Tensor(a!) self, Dimname dim) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False tags: inplace_view - func: sspaddmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor variants: function, method - func: sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) dispatch: CPU: _sspaddmm_out_only_sparse CUDA: _sspaddmm_out_only_sparse_cuda SparseCPU: _sspaddmm_out_cpu SparseCUDA: _sspaddmm_out_cuda - func: _chunk_cat(Tensor[] tensors, int dim, int num_chunks) -> Tensor dispatch: CompositeExplicitAutograd: _chunk_cat CUDA: _chunk_cat_cuda - func: _chunk_cat.out(Tensor[] tensors, int dim, int num_chunks, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: _chunk_cat_out CUDA: _chunk_cat_out_cuda - func: stack(Tensor[] tensors, int dim=0) -> Tensor dispatch: CompositeExplicitAutograd: stack - func: stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: stack_out - func: _stack(Tensor[] tensors, int dim=0) -> Tensor dispatch: # match the backends supported by _cat CPU: _stack_cpu CompositeExplicitAutograd: _stack - func: _stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) dispatch: # match the backends supported by _cat_out CPU: _stack_out_cpu CompositeExplicitAutograd: _stack_out - func: hstack(Tensor[] tensors) -> Tensor - func: hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) - func: vstack(Tensor[] tensors) -> Tensor - func: vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) - func: dstack(Tensor[] tensors) -> Tensor - func: dstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) # Overload without center & pad mode, needed for forward-compatibility - func: stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None, bool? align_to_window=None) -> Tensor variants: function, method cpp_no_default_args: ['hop_length', 'win_length', 'window', 'normalized'] - func: stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode="reflect", bool normalized=False, bool? onesided=None, bool? return_complex=None, bool? align_to_window=None) -> Tensor variants: function, method - func: istft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, bool normalized=False, bool? onesided=None, int? length=None, bool return_complex=False) -> Tensor variants: function, method - func: stride.int(Tensor self, int dim) -> int variants: function device_check: NoCheck device_guard: False manual_cpp_binding: True - func: stride.Dimname(Tensor self, Dimname dim) -> int variants: function, method device_check: NoCheck device_guard: False - func: sym_stride.int(Tensor self, int dim) -> SymInt variants: function device_check: NoCheck device_guard: False tags: core manual_cpp_binding: True - func: sum(Tensor self, *, ScalarType? dtype=None) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: sum SparseCPU, SparseCUDA, SparseMeta: sum_coo SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sum_csr autogen: sum.out - func: sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor # TODO: Align the signature of sum.dim_IntList and _sparse_csr_sum.dim_dtype structured_delegate: sum.IntList_out device_check: NoCheck # TensorIterator variants: function, method dispatch: NestedTensorCPU: NestedTensor_sum_dim_CPU SparseCPU, SparseCUDA: sum_sparse_coo SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sum_sparse_compressed tags: core - func: sum.dim_DimnameList(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) structured: True device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: sum_out MPS: sum_out_mps - func: sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator # TODO: this function will be replaced once nested expand semantics have been settled on - func: _nested_sum_backward(Tensor grad, Tensor self, int[1]? dim, bool keepdim=False) -> Tensor dispatch: NestedTensorCPU: _nested_sum_backward_cpu - func: nansum(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor variants: function, method dispatch: CPU, CUDA: nansum MPS: nansum_mps - func: nansum.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: nansum_out MPS: nansum_out_mps - func: sum_to_size(Tensor self, SymInt[] size) -> Tensor variants: method device_check: NoCheck device_guard: False dispatch: CompositeImplicitAutograd: sum_to_size_symint - func: sqrt(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: sqrt.out variants: function, method dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_sqrt SparseCPU, SparseCUDA: sqrt_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sqrt_sparse_csr tags: [core, pointwise] - func: sqrt_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: sqrt.out variants: function, method dispatch: SparseCPU, SparseCUDA: sqrt_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sqrt_sparse_csr_ tags: pointwise - func: sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: sqrt_out SparseCPU, SparseCUDA: sqrt_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sqrt_sparse_csr_out tags: pointwise - func: square(Tensor self) -> Tensor device_check: NoCheck # TensorIterator variants: function, method tags: pointwise - func: square_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function, method tags: pointwise - func: square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) tags: pointwise - func: std(Tensor self, bool unbiased=True) -> Tensor device_check: NoCheck # TensorIterator variants: function, method cpp_no_default_args: ["unbiased"] - func: std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method cpp_no_default_args: ["unbiased"] - func: std.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CPU, CUDA: std MPS: std_mps QuantizedCPU: std_quantized_cpu - func: std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor) device_check: NoCheck # TensorIterator variants: function cpp_no_default_args: ["unbiased"] - func: std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) device_check: NoCheck # TensorIterator variants: function cpp_no_default_args: ["unbiased"] - func: std_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) device_check: NoCheck # TensorIterator variants: function dispatch: CPU, CUDA: std_mean MPS: std_mean_mps autogen: std_mean.correction_out - func: std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) device_check: NoCheck # TensorIterator variants: function cpp_no_default_args: ["unbiased"] - func: std_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) device_check: NoCheck # TensorIterator variants: function - func: std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator cpp_no_default_args: ["unbiased"] - func: std.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: std_out QuantizedCPU: std_out_quantized_cpu - func: std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method cpp_no_default_args: ["unbiased"] - func: std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator cpp_no_default_args: ["unbiased"] - func: std.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: std.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function - func: prod(Tensor self, *, ScalarType? dtype=None) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CPU, CUDA: prod MPS: prod_mps autogen: prod.out tags: core - func: prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor structured_delegate: prod.int_out device_check: NoCheck # TensorIterator variants: function, method tags: core - func: prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) structured: True device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: prod_out MPS: prod_out_mps - func: prod.dim_Dimname(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: prod.Dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator - func: t(Tensor(a) self) -> Tensor(a) device_check: NoCheck device_guard: False variants: function, method dispatch: CompositeExplicitAutograd: t - func: t_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck device_guard: False variants: method tags: inplace_view dispatch: CompositeExplicitAutograd: t_ - func: tan(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: tan.out variants: function, method dispatch: SparseCPU, SparseCUDA: tan_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: tan_sparse_csr tags: [core, pointwise] - func: tan_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: tan.out variants: function, method dispatch: SparseCPU, SparseCUDA: tan_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: tan_sparse_csr_ tags: pointwise - func: tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: tan_out SparseCPU, SparseCUDA: tan_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: tan_sparse_csr_out tags: pointwise - func: tanh(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: tanh.out variants: function, method dispatch: QuantizedCPU: tanh_quantized_cpu MkldnnCPU: mkldnn_tanh SparseCPU, SparseCUDA: tanh_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: tanh_sparse_csr NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_tanh tags: [core, pointwise] - func: tanh_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: tanh.out variants: function, method dispatch: MkldnnCPU: mkldnn_tanh_ SparseCPU, SparseCUDA: tanh_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: tanh_sparse_csr_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_tanh_ tags: pointwise - func: tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: tanh_out SparseCPU, SparseCUDA: tanh_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: tanh_sparse_csr_out tags: pointwise - func: tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor variants: function - func: tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!) variants: function # TODO: namespace threshold in 'nn' - func: threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor device_check: NoCheck # TensorIterator variants: function structured_delegate: threshold.out dispatch: QuantizedCPU: threshold_quantized_cpu tags: pointwise - func: threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function structured_delegate: threshold.out - func: threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: threshold_out MPS: threshold_out_mps - func: threshold_backward.grad_input(Tensor grad_output, Tensor self, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: threshold_backward_out MPS: threshold_backward_out_mps SparseCPU, SparseCUDA: threshold_backward_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: threshold_backward_sparse_compressed_out - func: threshold_backward(Tensor grad_output, Tensor self, Scalar threshold) -> Tensor variants: function structured_delegate: threshold_backward.grad_input dispatch: MkldnnCPU: mkldnn_relu_backward SparseCPU, SparseCUDA: threshold_backward_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: threshold_backward_sparse_compressed NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: threshold_backwards_nested tags: pointwise - func: tile(Tensor self, SymInt[] dims) -> Tensor variants: function, method dispatch: CompositeImplicitAutograd: tile_symint - func: transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: transpose NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: transpose_nested - func: transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False - func: _mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor device_check: NoCheck device_guard: False dispatch: MkldnnCPU: mkldnn_transpose - func: transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False tags: inplace_view dispatch: CompositeExplicitAutograd: transpose_ - func: _mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) device_check: NoCheck device_guard: False dispatch: MkldnnCPU: mkldnn_transpose_ autogen: _mkldnn_transpose.out - func: one_hot(Tensor self, int num_classes=-1) -> Tensor python_module: nn variants: function tags: dynamic_output_shape - func: flip(Tensor self, int[] dims) -> Tensor variants: function, method dispatch: CPU, QuantizedCPU, CUDA, QuantizedCUDA: flip MPS: flip_mps autogen: flip.out tags: core - func: fliplr(Tensor self) -> Tensor variants: function, method - func: flipud(Tensor self) -> Tensor variants: function, method - func: roll(Tensor self, SymInt[1] shifts, int[1] dims=[]) -> Tensor variants: function, method dispatch: CPU, MPS: roll CUDA: roll_cuda autogen: roll.out # default int[] value [0,1] should not add space after comma, since codegen parser uses ', ' to split args - func: rot90(Tensor self, int k=1, int[] dims=[0,1]) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: rot90 autogen: rot90.out - func: trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor - func: trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor - func: trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor - func: trapz.dx(Tensor y, *, float dx=1, int dim=-1) -> Tensor # Fused implementation detail for transformers. Adds in-projection bias to QKV and divides Q by sqrt(D/num_heads). - func: _transform_bias_rescale_qkv(Tensor qkv, Tensor qkv_bias, int num_heads) -> (Tensor, Tensor, Tensor) dispatch: CPU, NestedTensorCPU: transform_bias_rescale_qkv_cpu CUDA, NestedTensorCUDA: transform_bias_rescale_qkv_cuda autogen: _transform_bias_rescale_qkv.out - func: _nested_tensor_from_mask(Tensor t, Tensor mask, bool mask_check=True) -> Tensor dispatch: CPU, CUDA: NestedTensor_nested_tensor_from_mask autogen: _nested_tensor_from_mask.out - func: _nested_tensor_from_mask_left_aligned(Tensor t, Tensor mask) -> bool dispatch: CPU, CUDA: NestedTensor_nested_tensor_from_mask_left_aligned - func: _nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False) -> Tensor device_check: NoCheck # cpu_nested_shape_example will always be on CPU dispatch: CPU: nested_from_padded_generic CUDA: nested_from_padded_cuda autogen: _nested_from_padded.out # These private functions are temporary. They will be updated/deleted when nested tensors switch to using SymInts for their metadata representation - func: _nested_tensor_size(Tensor self) -> Tensor variants: method dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: _nested_tensor_size autogen: _nested_tensor_size.out - func: _nested_tensor_strides(Tensor self) -> Tensor variants: method dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: _nested_tensor_strides autogen: _nested_tensor_strides.out - func: _nested_tensor_storage_offsets(Tensor self) -> Tensor variants: method dispatch: NestedTensorCPU, NestedTensorCUDA, NestedTensorMeta: _nested_tensor_storage_offsets autogen: _nested_tensor_storage_offsets.out # _nested_from_padded is not usable from Python, so # _nested_from_padded_and_nested_example is available for testing. - func: _nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example) -> Tensor dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_from_padded_and_nested_example autogen: _nested_from_padded_and_nested_example.out # The input arguments' types to this functions are temporary. When nested tensors switch to using SymInts for their metadata representation # this will need to be updated - func: _nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor(a) variants: function device_check: NoCheck dispatch: CPU, CUDA: _nested_view_from_buffer - func: _nested_view_from_buffer_copy(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor variants: function device_check: NoCheck tags: view_copy dispatch: CompositeExplicitAutogradNonFunctional: _nested_view_from_buffer_copy autogen: _nested_view_from_buffer_copy.out - func: _nested_view_from_jagged(Tensor(a) self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None) -> Tensor(a) variants: function device_check: NoCheck dispatch: {} - func: _nested_view_from_jagged_copy(Tensor self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None) -> Tensor variants: function device_check: NoCheck tags: view_copy dispatch: CompositeExplicitAutogradNonFunctional: _nested_view_from_jagged_copy autogen: _nested_view_from_jagged_copy.out - func: _nested_get_values(Tensor(a) self) -> Tensor(a) variants: function device_check: NoCheck dispatch: {} - func: _nested_get_values_copy(Tensor self) -> Tensor variants: function device_check: NoCheck tags: view_copy dispatch: CompositeExplicitAutogradNonFunctional: _nested_get_values_copy autogen: _nested_get_values_copy.out - func: _nested_get_offsets(Tensor self) -> Tensor variants: function device_check: NoCheck dispatch: {} # returns undefined Tensor if no lengths present - func: _nested_get_lengths(Tensor self) -> Tensor variants: function device_check: NoCheck dispatch: {} - func: _nested_get_ragged_idx(Tensor self) -> int variants: function device_check: NoCheck dispatch: {} - func: _nested_get_min_seqlen(Tensor self) -> Tensor variants: function device_check: NoCheck dispatch: {} - func: _nested_get_max_seqlen(Tensor self) -> Tensor variants: function device_check: NoCheck dispatch: {} - func: _nested_get_jagged_dummy(Tensor any) -> Tensor category_override: dummy dispatch: {} - func: _nested_compute_contiguous_strides_offsets(Tensor nested_size) -> (Tensor, Tensor) variants: function device_check: NoCheck dispatch: CPU, CUDA: _nested_compute_contiguous_strides_offsets - func: _trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor dispatch: # calls unsqueeze CompositeExplicitAutogradNonFunctional: _trilinear autogen: _trilinear.out - func: triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, float margin=1.0, float p=2, float eps=1e-06, bool swap=False, int reduction=Mean) -> Tensor - func: trunc(Tensor self) -> Tensor structured_delegate: trunc.out device_check: NoCheck # TensorIterator variants: function, method dispatch: SparseCPU, SparseCUDA: trunc_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: trunc_sparse_csr tags: [core, pointwise] - func: trunc_(Tensor(a!) self) -> Tensor(a!) structured_delegate: trunc.out device_check: NoCheck # TensorIterator variants: function, method dispatch: SparseCPU, SparseCUDA: trunc_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: trunc_sparse_csr_ tags: pointwise - func: trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MPS: trunc_out SparseCPU, SparseCUDA: trunc_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: trunc_sparse_csr_out tags: pointwise # Alias for trunc - func: fix(Tensor self) -> Tensor variants: function, method - func: fix_(Tensor(a!) self) -> Tensor(a!) variants: function, method - func: fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) - func: type_as(Tensor self, Tensor other) -> Tensor variants: method - func: _has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool variants: function - func: _unique(Tensor self, bool sorted=True, bool return_inverse=False) -> (Tensor, Tensor) variants: function dispatch: CPU: _unique_cpu CUDA: _unique_cuda autogen: _unique.out - func: unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) variants: function dispatch: CPU: unique_dim_cpu CUDA: unique_dim_cuda tags: dynamic_output_shape autogen: unique_dim.out - func: unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor) variants: function dispatch: CPU: unique_consecutive_cpu CUDA: unique_consecutive_cuda MPS: unique_consecutive_mps tags: dynamic_output_shape autogen: unique_consecutive.out - func: unique_dim_consecutive(Tensor self, int dim, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) variants: function dispatch: CPU: unique_dim_consecutive_cpu CUDA: unique_dim_consecutive_cuda MPS: unique_dim_consecutive_mps tags: dynamic_output_shape autogen: unique_dim_consecutive.out # _unique and _unique_dim are fragile and modifying them easily cause internal break # the below operator is a temporary hack for adding return_counts support # Please don't rely on these two operators, they will be removed soon - func: _unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) variants: function dispatch: CPU: _unique2_cpu CUDA: _unique2_cuda MPS: _unique2_mps tags: dynamic_output_shape autogen: _unique2.out - func: _unsafe_view(Tensor self, SymInt[] size) -> Tensor dispatch: CompositeExplicitAutograd: _unsafe_view autogen: _unsafe_view.out - func: unsqueeze(Tensor(a) self, int dim) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: unsqueeze SparseCPU, SparseCUDA: unsqueeze_sparse QuantizedCPU, QuantizedCUDA: unsqueeze_quantized NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: unsqueeze_nested tags: core - func: unsqueeze_(Tensor(a!) self, int dim) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False tags: inplace_view dispatch: CompositeExplicitAutograd: unsqueeze_ - func: vander(Tensor x, int? N=None, bool increasing=False) -> Tensor - func: var(Tensor self, bool unbiased=True) -> Tensor device_check: NoCheck # TensorIterator variants: function, method cpp_no_default_args: ["unbiased"] - func: var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method tags: core cpp_no_default_args: ["unbiased"] - func: var.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CPU, CUDA: var MPS: var_mps tags: core - func: var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator cpp_no_default_args: ["unbiased"] - func: var.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: var_out - func: var.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method cpp_no_default_args: ["unbiased"] - func: var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator cpp_no_default_args: ["unbiased"] - func: var.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: var.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function - func: var_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor) device_check: NoCheck # TensorIterator variants: function cpp_no_default_args: ["unbiased"] - func: var_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) device_check: NoCheck # TensorIterator variants: function cpp_no_default_args: ["unbiased"] - func: var_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) device_check: NoCheck # TensorIterator variants: function dispatch: CPU, CUDA: var_mean MPS: var_mean_mps autogen: var_mean.correction_out - func: var_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) device_check: NoCheck # TensorIterator variants: function cpp_no_default_args: ["unbiased"] - func: var_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) device_check: NoCheck # TensorIterator variants: function - func: view_as(Tensor(a) self, Tensor other) -> Tensor(a) variants: method device_check: NoCheck device_guard: False - func: where.self(Tensor condition, Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CPU, CUDA, MPS, MTIA: where NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_where tags: [core, pointwise] - func: where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MPS, MTIA: where_self_out NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_where_out - func: where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor variants: function - func: where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor variants: function, method - func: where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor variants: function - func: where(Tensor condition) -> Tensor[] device_check: NoCheck # TensorIterator variants: function - func: norm_except_dim(Tensor v, int pow=2, int dim=0) -> Tensor variants: function # VariableType::_weight_norm does not want to be given a gap in the autograd graph, # so we don't define "dispatch" variants for it. - func: _weight_norm(Tensor v, Tensor g, int dim=0) -> Tensor variants: function - func: _weight_norm_interface(Tensor v, Tensor g, int dim=0) -> (Tensor, Tensor) variants: function dispatch: CPU: weight_norm_cpu CUDA: weight_norm_cuda MPS: weight_norm_mps autogen: _weight_norm_interface.out - func: _weight_norm_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor) variants: function dispatch: CPU: weight_norm_backward_cpu CUDA: weight_norm_backward_cuda MPS: weight_norm_backward_mps autogen: _weight_norm_interface_backward.out - func: _weight_norm_differentiable_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor) variants: function - func: zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: zeros autogen: zeros.names_out - func: _efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CPU: _efficientzerotensor CUDA: _efficientzerotensor_cuda MPS: _efficientzerotensor_mps Meta: _efficientzerotensor_meta_symint autogen: _efficientzerotensor.out - func: zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: zeros_symint - func: zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: zeros_out SparseCPU, SparseCUDA, SparseMeta: zeros_sparse_out - func: zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor dispatch: # NB: Although this composite mutates on the inside, it is # non-differentiable so NonFunctional doesn't apply CompositeExplicitAutograd, CompositeImplicitAutogradNestedTensor: zeros_like autogen: zeros_like.out - func: _standard_gamma_grad(Tensor self, Tensor output) -> Tensor variants: function dispatch: CPU: _standard_gamma_grad_cpu CUDA: _standard_gamma_grad_cuda autogen: _standard_gamma_grad.out - func: _standard_gamma(Tensor self, Generator? generator=None) -> Tensor variants: function dispatch: CPU: _s_gamma_cpu CUDA: _s_gamma_cuda tags: nondeterministic_seeded autogen: _standard_gamma.out - func: _dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor dispatch: CPU: _dirichlet_grad_cpu CUDA: _dirichlet_grad_cuda autogen: _dirichlet_grad.out - func: _sample_dirichlet(Tensor self, Generator? generator=None) -> Tensor tags: nondeterministic_seeded variants: function dispatch: CPU: _s_dirichlet_cpu CUDA: _s_dirichlet_cuda autogen: _sample_dirichlet.out - func: poisson(Tensor self, Generator? generator=None) -> Tensor device_check: NoCheck # TensorIterator dispatch: CPU: _s_poisson_cpu CUDA: _s_poisson_cuda tags: nondeterministic_seeded autogen: poisson.out - func: binomial(Tensor count, Tensor prob, Generator? generator=None) -> Tensor device_check: NoCheck # TensorIterator dispatch: CPU: _s_binomial_cpu CUDA: _s_binomial_cuda tags: nondeterministic_seeded autogen: binomial.out # When more variants get ported to native, this dispatch will get more # complicated - func: native_norm(Tensor self, Scalar p=2) -> Tensor dispatch: SparseCPU, SparseCUDA: norm_sparse autogen: native_norm.out - func: native_norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype) -> Tensor dispatch: SparseCPU, SparseCUDA: norm_sparse autogen: native_norm.ScalarOpt_dim_dtype_out - func: _batch_norm_with_update(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor) dispatch: CPU: _batch_norm_with_update_cpu CUDA: _batch_norm_with_update_cuda MPS: _batch_norm_with_update_mps MkldnnCPU: _batch_norm_with_update_mkldnn autogen: _batch_norm_with_update_functional - func: _batch_norm_with_update.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd, Tensor(g!) reserve) -> (Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!)) dispatch: CPU: _batch_norm_with_update_cpu_out CUDA: _batch_norm_with_update_cuda_out MPS: _batch_norm_with_update_mps_out - func: _batch_norm_no_update(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor) dispatch: CompositeExplicitAutograd: _batch_norm_no_update autogen: _batch_norm_no_update.out - func: batch_norm_backward(Tensor grad_out, Tensor input, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, bool update, float eps, bool[3] output_mask, Tensor reserve) -> (Tensor, Tensor, Tensor) dispatch: CPU: _new_batch_norm_backward_cpu CUDA: _new_batch_norm_backward_cuda MPS: _new_batch_norm_backward_mps MkldnnCPU: _new_batch_norm_backward_mkldnn # TODO: reduce signatures down to one when optional args is available - func: _sparse_sum(Tensor self) -> Tensor - func: _sparse_sum.dtype(Tensor self, *, ScalarType dtype) -> Tensor - func: _sparse_sum.dim(Tensor self, int[1] dim) -> Tensor dispatch: CompositeExplicitAutograd: _sparse_sum autogen: _sparse_sum.dim_out - func: _sparse_sum.dim_dtype(Tensor self, int[1] dim, *, ScalarType dtype) -> Tensor - func: _sparse_sum_backward(Tensor grad, Tensor self, int[] dim) -> Tensor dispatch: SparseCPU: _sparse_sum_backward_cpu SparseCUDA: _sparse_sum_backward_cuda autogen: _sparse_sum_backward.out - func: _sparse_csr_sum.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor dispatch: SparseCsrCPU: _sparse_csr_sum_cpu SparseCsrCUDA: _sparse_csr_sum_cuda autogen: _sparse_csr_sum.dim_dtype_out - func: _sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor dispatch: SparseCsrCPU: _sparse_csr_prod_cpu SparseCsrCUDA: _sparse_csr_prod_cuda autogen: _sparse_csr_prod.dim_dtype_out - func: _sparse_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor python_module: sparse variants: function - func: _sparse_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor python_module: sparse variants: function - func: _sparse_softmax(Tensor self, int dim, bool half_to_float) -> Tensor python_module: sparse dispatch: SparseCPU: softmax_sparse_cpu SparseCUDA: softmax_sparse_cuda autogen: _sparse_softmax.out - func: _sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor dispatch: SparseCPU: softmax_backward_sparse_cpu SparseCUDA: softmax_backward_sparse_cuda autogen: _sparse_softmax_backward_data.out - func: _sparse_log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor python_module: sparse variants: function - func: _sparse_log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor python_module: sparse variants: function - func: _sparse_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor python_module: sparse dispatch: SparseCPU: log_softmax_sparse_cpu SparseCUDA: log_softmax_sparse_cuda autogen: _sparse_log_softmax.out - func: _sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor dispatch: SparseCPU: log_softmax_backward_sparse_cpu SparseCUDA: log_softmax_backward_sparse_cuda autogen: _sparse_log_softmax_backward_data.out - func: _spdiags(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None) -> Tensor python_module: sparse dispatch: CPU: spdiags autogen: _spdiags.out - func: norm.ScalarOpt_dtype(Tensor self, Scalar? p, *, ScalarType dtype) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: norm autogen: norm.ScalarOpt_dtype_out - func: norm.Scalar(Tensor self, Scalar p=2) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: norm autogen: norm.Scalar_out - func: norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor structured_delegate: norm.dtype_out device_check: NoCheck # TensorIterator variants: function, method dispatch: SparseCPU, SparseCUDA: sparse_dtype_norm - func: norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> Tensor structured_delegate: norm.out device_check: NoCheck # TensorIterator variants: function, method dispatch: SparseCPU, SparseCUDA: sparse_norm - func: norm.dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!) structured: True device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: norm_dtype_out MPS: norm_dtype_out_mps - func: norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) structured: True device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: norm_out MPS: norm_out_mps # These four redispatch in their implementation, so OK to be CompositeImplicitAutograd - func: norm.names_ScalarOpt_dim_dtype(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: norm.names_ScalarOpt_dim(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: norm.names_dtype_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator - func: norm.names_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator - func: frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent) variants: method, function dispatch: CompositeExplicitAutograd: frexp tags: pointwise - func: frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent) dispatch: CPU, CUDA: frexp_out tags: pointwise # Deprecated (v.1.12) - func: frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor variants: function # Deprecated (v.1.12) - func: frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) variants: function # Deprecated (v.1.12) - func: nuclear_norm(Tensor self, bool keepdim=False) -> Tensor variants: function # Deprecated (v.1.12) - func: nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) variants: function # Deprecated (v.1.12) - func: nuclear_norm.dim(Tensor self, int[2] dim, bool keepdim=False) -> Tensor variants: function # Deprecated (v.1.12) - func: nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) variants: function - func: clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: clone SparseCPU, SparseCUDA: clone_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: clone_sparse_compressed MkldnnCPU: mkldnn_clone QuantizedCPU, QuantizedCUDA: quantized_clone NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: clone_nested autogen: clone.out tags: [core, pointwise] - func: positive(Tensor(a) self) -> Tensor(a) variants: function, method tags: pointwise - func: resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!) use_const_ref_for_mutable_tensors: True variants: function, method dispatch: CompositeExplicitAutograd: resize_as_ autogen: resize_as, resize_as.out tags: inplace_view - func: resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!) use_const_ref_for_mutable_tensors: True variants: function, method dispatch: SparseCPU, SparseCUDA: resize_as_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: resize_as_sparse_compressed_ autogen: resize_as_sparse, resize_as_sparse.out - func: zero_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method, function dispatch: CPU, CUDA: zero_ MPS: zero_mps_ Meta: zero_meta_ SparseCPU, SparseCUDA, SparseMeta: zero_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: zero_sparse_csr_ MkldnnCPU: mkldnn_zero_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: zero_nested_ autogen: zero, zero.out - func: sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: sub_out MPS: sub_out_mps MTIA: sub_out_mtia SparseCPU, SparseCUDA: sub_out_sparse tags: pointwise - func: sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: sub.out dispatch: SparseCPU, SparseCUDA: sub_sparse ZeroTensor: sub_zerotensor NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_sub_Tensor tags: [core, pointwise] - func: sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: sub.out dispatch: SparseCPU, SparseCUDA: sub_sparse_ tags: pointwise # For C++ only, until we have conversion from C++ numbers to Tensor - func: sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: sub tags: [core, pointwise] - func: sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: sub_ autogen: sub.Scalar_out tags: pointwise # subtract, alias for sub - func: subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) - func: subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor variants: function, method - func: subtract_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) variants: method # For C++ only, until we have conversion from C++ numbers to Tensor - func: subtract.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor variants: function, method - func: subtract_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) variants: method - func: rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor device_check: NoCheck # TensorIterator variants: function dispatch: CPU, CUDA, MPS, MTIA: rsub autogen: rsub.Tensor_out - func: heaviside.out(Tensor self, Tensor values, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: heaviside_out tags: pointwise - func: heaviside(Tensor self, Tensor values) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: heaviside.out tags: pointwise - func: heaviside_(Tensor(a!) self, Tensor values) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: heaviside.out # For C++ only, until we have conversion from C++ numbers to Tensor - func: rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: rsub autogen: rsub.Scalar_out # Functionally the same as addmm, but we give it a different derivative formula # that doesn't propagate gradients to non-present entries on sparse. tags: pointwise - func: _sparse_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor python_module: sparse dispatch: CompositeExplicitAutograd: _sparse_addmm autogen: _sparse_addmm.out - func: sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) python_module: sparse dispatch: SparseCsrCUDA: sparse_sampled_addmm_out_sparse_csr_cuda SparseCsrCPU: sparse_sampled_addmm_out_sparse_csr_cpu - func: sparse_sampled_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor python_module: sparse dispatch: SparseCsrCUDA: sparse_sampled_addmm_sparse_csr_cuda SparseCsrCPU: sparse_sampled_addmm_sparse_csr_cpu - func: _sparse_mm_reduce_impl(Tensor self, Tensor other, str reduce) -> (Tensor, Tensor) python_module: sparse dispatch: SparseCsrCPU: _sparse_mm_reduce_impl_sparse_csr_cpu - func: _sparse_mm_reduce_impl_backward(Tensor self, Tensor grad_out, Tensor weight, str reduce, Tensor arg_out, bool[2] output_mask) -> (Tensor, Tensor) python_module: sparse dispatch: SparseCsrCPU: _sparse_mm_reduce_impl_backward_sparse_csr_cpu - func: addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU: addmm_out_cpu CUDA: addmm_out_cuda MPS: addmm_out_mps XPU: addmm_out_xpu SparseCPU: addmm_out_sparse_dense_cpu SparseCUDA: addmm_out_sparse_dense_cuda SparseCsrCPU: addmm_out_sparse_compressed_cpu SparseCsrCUDA: addmm_out_sparse_compressed_cuda - func: addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor structured_delegate: addmm.out variants: function, method dispatch: SparseCPU: addmm_sparse_dense_cpu SparseCUDA: addmm_sparse_dense_cuda SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: addmm_sparse_compressed_dense tags: core - func: addmm.dtype(Tensor self, Tensor mat1, Tensor mat2, ScalarType out_dtype, *, Scalar beta=1, Scalar alpha=1) -> Tensor dispatch: CUDA: _addmm_dtype_cuda - func: addmm.dtype_out(Tensor self, Tensor mat1, Tensor mat2, ScalarType out_dtype, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) dispatch: CUDA: _addmm_dtype_out_cuda - func: addmm_(Tensor(a!) self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) structured_delegate: addmm.out variants: method dispatch: # Warning! For whatever reason, the inplace sparse addmm is NON # broadcasting SparseCPU: s_addmm_sparse_dense_cpu_ SparseCUDA: s_addmm_sparse_dense_cuda_ - func: _addmm_activation.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU: addmm_activation_out_cpu CUDA: addmm_activation_out_cuda XPU: addmm_activation_out_xpu - func: _addmm_activation(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False) -> Tensor structured_delegate: _addmm_activation.out variants: function, method - func: _scaled_mm(Tensor self, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False) -> Tensor variants: function dispatch: CPU: _scaled_mm_cpu CUDA: _scaled_mm_cuda - func: _scaled_mm.out(Tensor self, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False, *, Tensor(a!) out) -> Tensor(a!) variants: function dispatch: CPU: _scaled_mm_out_cpu CUDA: _scaled_mm_out_cuda - func: _scaled_grouped_mm(Tensor self, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? offs=None, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False) -> Tensor variants: function dispatch: CUDA: _scaled_grouped_mm_cuda - func: _grouped_mm(Tensor self, Tensor mat2, Tensor? offs=None, Tensor? bias=None, ScalarType? out_dtype=None) -> Tensor variants: function dispatch: CUDA: _grouped_mm_cuda # NOTE [ Sparse: autograd and API ] # # # Sparse Tensor Constructors # ~~~~~~~~~~~~~~~~~~~~~~~~~~ # # The API entry points to sparse tensor construction should be # `sparse_coo tensor` and `_sparse_coo_tensor_unsafe`. Depending on whether the # indices and values tensors are given, they eventually dispatch to either # `sparse_coo_tensor_with_dims` or `sparse_coo_tensor_with_dims_and_tensors`. # # The autograd support for ctor is implement on `sparse_coo_tensor_with_dims_and_tensors`. # # The API methods `sparse_coo tensor` and `_sparse_coo_tensor_unsafe` # **must not** have specific type dispatches because otherwise codegen will # consider them as abstract methods (see Note [Abstract ATen methods]), dispatch # using **Tensor** type, and thus lose autograd tracking on the actual method # they dispatch to, e.g., `sparse_coo_tensor_with_dims_and_tensors`. # # # Sparse Methods API Design # ~~~~~~~~~~~~~~~~~~~~~~~~~ # # Goals: 1. Flexible API for users to write custom sparse ops # 2. ctor and member accessor with autograd support # # To achieve 1, we need to provide a set of *dangerous* APIs (dangerous in the # sense that misusing them will break sparse tensor invariant and may out in # unexpected behavior, e.g., crash). These methods are all prefixed with # underscore "_" to indicate that they should be used with care. We provide: # # + `_indices()`: returns the *raw* indices within the sparse tensor (not just # sharing storage). Any inplace operation will change the # actual indices, including t_, set_, as_strided_, resize_, # etc. # + `_values()`: returns the *raw* values within the sparse tensor. Similar # semantics as `_indices()` # + `_nnz()`: returns the number of non-zero entries. This will always be # determined by the shapes of indices and values. # + `_coalesced_(bool)`: inplace sets whether the tensor is coalesced, and # returns itself. # # These methods are very useful in writing new operations, e.g., a custom # autograd Function. # # We also provide other public *safe* APIs: # + `indices()`: returns a **view** of the indices tensor if the sparse tensor # is **coalesced**. # + `values()`: returns a **view** of the values tensor if the containing # sparse tensor is **coalesced**. # + `sparse_dim()`: number of sparse dimensions # + `dense_dim()`: number of dense dimensions # + `is_coalesced()`: whether the sparse tensor is coalesced # # `_indices()` and `_values()` should returns the raw indices and values dense # tensors within a sparse tensor. They can be quite unsafe with inplace # operations like `t_()`, and exposes uncoalesced indices and values. The public # recommended API is `indices()` and `values()`, both of which first check that # the tensor is coalesced and return views on those tensors. # # # Autograd Support # ~~~~~~~~~~~~~~~~ # # Autograd is supported on `values()` and sparse tensor ctor with indices and # values tensors. E.g., `torch.sparse_coo_tensor(i, v).values().sum()` is # differentiable w.r.t. `v`. # # NB: The `values()` and `_values()` operators are special in that they are # layout-aware, i.e., the output depends not just on the data it represents, but # also on the input layout details (in this case, the `indices` tensor). See # NOTE [ as_strided Backward and layout-aware/agnostic autograd ] in Functions.cpp # for discussion on layout-aware vs layout-agnostic autograd. Since PyTorch ops # operate in the layout-agnostic mode, similar to `as_strided`, backward of # these two operators need to consider them in a layout-agnostic way: # + `values()`: # Input is coalesced. # We just pretend having `input.indices()` as an additional argument # `input_indices`, then forward is similar to # `input.to(kStrided).index_select(input_indices)` regardless of the layout. # Note that `values()` normally is layout-aware even if we constrain # ourselves on sparse inputs since it may include all zeros values entries # as "present" entries. # + `_values()`: # Input may be uncoalesced. # It is not straightforward to construct a layout-agnostic version because # duplicate indices entries may exist and additional parameterization is # needed to distribute the value into different values entries. Furthermore, # this op is intended to provide ways to write custom sparse ops, rather # than being used in autograd graph, so it is marked as *non-differentiable* # in derivatives.yaml. # # Before reading the following, see NOTE [ Autograd Variable Views ] in # variable.h for details on views that are tracked by autograd, and views that # are not. # # Moreover, these methods return tensors that share storage with inputs, so we # mark these methods as view ops to support autograd history tracking. # The sparse tensor ctor output should technically be view of both input indices # and values tensors, but currently we only support setting as view of a single # Variable, so it is only view of the values tensor. # TODO: clone indices in sparse tensor ctor. # # For other methods that return outputs that share storage with inputs, i.e., # `indices()` and `_indices()`. We mark their outputs as non-differentiable, so # the view relation is not tracked by autograd, but the version counter is still # shared. In other words, their outputs are non-differentiable views of the # sparse tensor. # FIXME: would be nicer if TensorOptions was optional based; not adding default arguments for options given # the default would never make sense. - func: _sparse_compressed_tensor_with_dims(int nnz, int dense_dim, int[] size, int[] blocksize, ScalarType index_dtype, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor dispatch: CompositeExplicitAutograd: sparse_compressed_tensor_with_dims - func: sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor dispatch: CompositeExplicitAutograd: sparse_compressed_tensor - func: sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor - func: sparse_csc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor - func: sparse_bsr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor - func: sparse_bsc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor - func: sparse_compressed_tensor.comp_plain_value(Tensor compressed_indices, Tensor plain_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor dispatch: CompositeExplicitAutograd: sparse_compressed_tensor - func: sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor - func: sparse_csc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor - func: sparse_bsr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor - func: sparse_bsc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor - func: _sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeImplicitAutograd: _sparse_compressed_tensor_unsafe_symint - func: _sparse_csr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor - func: _sparse_csc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor - func: _sparse_bsr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor - func: _sparse_bsc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor - func: sparse_coo_tensor.size(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor dispatch: CompositeExplicitAutograd: sparse_coo_tensor autogen: sparse_coo_tensor.size_out - func: sparse_coo_tensor.indices(Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor - func: sparse_coo_tensor.indices_size(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor - func: _sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor dispatch: CompositeImplicitAutograd: _sparse_coo_tensor_unsafe_symint - func: _validate_sparse_coo_tensor_args(Tensor indices, Tensor values, int[] size, bool? is_coalesced=None, bool? check_pinning=None) -> () - func: _validate_sparse_compressed_tensor_args(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, Layout layout, bool? check_pinning=None) -> () - func: _validate_sparse_csr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, bool? check_pinning=None) -> () - func: _validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, bool? check_pinning=None) -> () - func: _validate_sparse_bsr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, bool? check_pinning=None) -> () - func: _validate_sparse_bsc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, bool? check_pinning=None) -> () - func: _sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor dispatch: SparseCPU, SparseCUDA, SparseMeta, SparseMPS, Meta: new_with_dims_sparse autogen: _sparse_coo_tensor_with_dims.out - func: _sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? is_coalesced=None) -> Tensor dispatch: SparseCPU, SparseCUDA, SparseMeta, SparseMPS, Meta: new_with_dims_and_tensor_sparse_symint autogen: _sparse_coo_tensor_with_dims_and_tensors.out - func: sparse_resize_(Tensor(a!) self, int[] size, int sparse_dim, int dense_dim) -> Tensor(a!) use_const_ref_for_mutable_tensors: True variants: method dispatch: SparseCPU, SparseCUDA, SparseMPS, SparseMeta: sparse_resize_ autogen: sparse_resize, sparse_resize.out - func: sparse_resize_and_clear_(Tensor(a!) self, int[] size, int sparse_dim, int dense_dim) -> Tensor(a!) use_const_ref_for_mutable_tensors: True variants: method dispatch: SparseCPU, SparseCUDA, SparseMPS, SparseMeta: sparse_resize_and_clear_ autogen: sparse_resize_and_clear, sparse_resize_and_clear.out - func: sparse_mask(Tensor self, Tensor mask) -> Tensor variants: method dispatch: SparseCPU, SparseCUDA: sparse_mask SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sparse_mask_sparse_compressed autogen: sparse_mask.out - func: _sparse_mask_projection(Tensor self, Tensor mask, bool accumulate_matches=False) -> Tensor variants: method dispatch: SparseCPU, SparseCUDA: sparse_mask_projection autogen: _sparse_mask_projection.out - func: _to_cpu(Tensor[] tensors) -> Tensor[] variants: function - func: to_dense(Tensor self, ScalarType? dtype=None, *, bool? masked_grad=None) -> Tensor variants: method # Special case of to_dense with custom derivative - func: _to_dense(Tensor self, ScalarType? dtype=None, bool? masked_grad=None) -> Tensor variants: method dispatch: SparseCPU, SparseCUDA: sparse_to_dense SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sparse_compressed_to_dense MkldnnCPU: mkldnn_to_dense autogen: _to_dense.out - func: to_dense_backward(Tensor grad, Tensor input, bool? masked_grad=None) -> Tensor - func: sparse_dim(Tensor self) -> int variants: method dispatch: SparseCPU, SparseCUDA, SparseMPS, SparseMeta: sparse_dim_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMPS, SparseCsrMeta: sparse_dim_sparse_csr CompositeExplicitAutograd: sparse_dim_default device_check: NoCheck device_guard: False # legacy method - func: _dimI(Tensor self) -> int variants: method dispatch: SparseCPU, SparseCUDA: sparse_dim_sparse device_check: NoCheck device_guard: False - func: dense_dim(Tensor self) -> int variants: method dispatch: SparseCPU, SparseCUDA, SparseMeta: dense_dim_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: dense_dim_sparse_csr CompositeExplicitAutograd: dense_dim_default device_check: NoCheck device_guard: False # legacy method - func: _dimV(Tensor self) -> int variants: method dispatch: SparseCPU, SparseCUDA, SparseMeta: dense_dim_sparse device_check: NoCheck device_guard: False - func: _nnz(Tensor self) -> int variants: method dispatch: SparseCPU, SparseCUDA, SparseMPS, SparseMeta: _nnz_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMPS, SparseCsrMeta: _nnz_sparse_csr device_check: NoCheck device_guard: False # NOTE: [ coalesce autograd ] # coalesce returns self directly for already coalesced sparse tensors. # This means coalesce cannot have a derivative registered, otherwise it creates # circular references in the autograd graph (see gh-52874). # Instead, the derivative is registered on the slow-path "_coalesce" - func: coalesce(Tensor(a) self) -> Tensor(a) variants: method - func: _coalesce(Tensor self) -> Tensor dispatch: SparseCPU: _coalesce_sparse_cpu SparseCUDA: _coalesce_sparse_cuda autogen: _coalesce.out - func: is_coalesced(Tensor self) -> bool variants: method dispatch: SparseCPU, SparseCUDA, SparseMPS, SparseMeta: is_coalesced_sparse CompositeExplicitAutograd: is_coalesced_default device_check: NoCheck device_guard: False - func: _indices(Tensor(a) self) -> Tensor(a) variants: method dispatch: SparseCPU, SparseCUDA, SparseMPS, SparseMeta: _indices_sparse device_check: NoCheck device_guard: False - func: _values(Tensor(a) self) -> Tensor(a) variants: method dispatch: SparseCPU, SparseCUDA, SparseMPS, SparseMeta: _values_sparse device_check: NoCheck device_guard: False # This method doesn't do any check but only directly sets the flag. So it can be # a bit unsafe. Similar to _indices and _values, this is useful for implementing # custom sparse operations in Python/C++ extension. - func: _coalesced_(Tensor(a!) self, bool coalesced) -> Tensor(a!) variants: method dispatch: SparseCPU, SparseCUDA, SparseMPS, SparseMeta: _coalesced_sparse_ device_check: NoCheck device_guard: False autogen: _coalesced, _coalesced.out - func: indices(Tensor(a) self) -> Tensor(a) variants: method dispatch: SparseCPU, SparseCUDA, SparseMeta: indices_sparse CompositeExplicitAutograd: indices_default device_check: NoCheck device_guard: False - func: values(Tensor(a) self) -> Tensor(a) variants: method dispatch: SparseCPU, SparseCUDA, SparseMeta: values_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: values_sparse_csr NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: values_nested CompositeExplicitAutograd: values_default device_check: NoCheck device_guard: False - func: crow_indices(Tensor(a) self) -> Tensor(a) variants: method dispatch: SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: crow_indices_sparse_csr CompositeExplicitAutograd: crow_indices_default device_check: NoCheck device_guard: False - func: col_indices(Tensor(a) self) -> Tensor(a) variants: method dispatch: SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: col_indices_sparse_csr CompositeExplicitAutograd: col_indices_default device_check: NoCheck device_guard: False - func: ccol_indices(Tensor(a) self) -> Tensor(a) variants: method dispatch: SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: ccol_indices_sparse_csr CompositeExplicitAutograd: ccol_indices_default device_check: NoCheck device_guard: False - func: row_indices(Tensor(a) self) -> Tensor(a) variants: method dispatch: SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: row_indices_sparse_csr CompositeExplicitAutograd: row_indices_default device_check: NoCheck device_guard: False - func: hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) dispatch: SparseCPU: hspmm_out_sparse_cpu SparseCUDA: hspmm_out_sparse_cuda - func: hspmm(Tensor mat1, Tensor mat2) -> Tensor dispatch: SparseCPU: hspmm_sparse_cpu SparseCUDA: hspmm_sparse_cuda - func: copy_sparse_to_sparse_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!) device_check: NoCheck # Allows copy into different device variants: function dispatch: SparseCPU, SparseCUDA, SparseMeta: copy_sparse_ autogen: copy_sparse_to_sparse, copy_sparse_to_sparse.out # By adding the AutogradNestedTensor this makes this function CompositeImplicit-like for nested tensors - func: unbind.int(Tensor(a -> *) self, int dim=0) -> Tensor(a)[] variants: function, method dispatch: CompositeExplicitAutograd: unbind NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_unbind - func: unbind.Dimname(Tensor(a -> *) self, Dimname dim) -> Tensor(a)[] variants: function, method - func: to_sparse.sparse_dim(Tensor self, int sparse_dim) -> Tensor variants: method # Special case of to_sparse.sparse_dim with custom derivative - func: _to_sparse.sparse_dim(Tensor self, int sparse_dim) -> Tensor variants: method dispatch: CPU, CUDA, MPS: dense_to_sparse SparseCPU, SparseCUDA, SparseMPS: sparse_coo_to_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta, SparseCsrMPS: sparse_compressed_to_sparse autogen: _to_sparse.sparse_dim_out - func: to_sparse(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None) -> Tensor variants: method # Special case of to_sparse with custom derivative - func: _to_sparse(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None) -> Tensor variants: method dispatch: CPU, CUDA, MPS: dense_to_sparse SparseCPU, SparseCUDA, SparseMPS: sparse_coo_to_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sparse_compressed_to_sparse autogen: _to_sparse.out - func: to_sparse_csr(Tensor self, int? dense_dim=None) -> Tensor variants: method # Special case of to_sparse_csr with custom derivative - func: _to_sparse_csr(Tensor self, int? dense_dim=None) -> Tensor variants: method dispatch: CPU, CUDA: dense_to_sparse_csr SparseCPU, SparseCUDA: coo_to_sparse_csr SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sparse_compressed_to_sparse_csr autogen: _to_sparse_csr.out - func: to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor variants: method # Special case of to_sparse_csc with custom derivative - func: _to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor variants: method dispatch: CPU, CUDA: dense_to_sparse_csc SparseCPU, SparseCUDA: coo_to_sparse_csc SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sparse_compressed_to_sparse_csc autogen: _to_sparse_csc.out - func: to_sparse_bsr(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor variants: method # Special case of to_sparse_bsr with custom derivative - func: _to_sparse_bsr(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor variants: method dispatch: CPU, CUDA: dense_to_sparse_bsr SparseCPU, SparseCUDA: coo_to_sparse_bsr SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sparse_compressed_to_sparse_bsr autogen: _to_sparse_bsr.out - func: to_sparse_bsc(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor variants: method # Special case of to_sparse_bsc with custom derivative - func: _to_sparse_bsc(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor variants: method dispatch: CPU, CUDA: dense_to_sparse_bsc SparseCPU, SparseCUDA: coo_to_sparse_bsc SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sparse_compressed_to_sparse_bsc autogen: _to_sparse_bsc.out - func: _to_sparse_semi_structured(Tensor dense) -> (Tensor, Tensor) variants: function dispatch: CUDA: _to_sparse_semi_structured - func: to_mkldnn(Tensor self, ScalarType? dtype=None) -> Tensor variants: method dispatch: CPU: dense_to_mkldnn autogen: to_mkldnn.out - func: mkldnn_reorder_conv2d_weight(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None) -> Tensor variants: function python_module: nn dispatch: MkldnnCPU: mkldnn_reorder_conv2d_weight autogen: mkldnn_reorder_conv2d_weight.out - func: mkldnn_reorder_conv3d_weight(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, SymInt[]? input_size=None) -> Tensor variants: function python_module: nn dispatch: MkldnnCPU: mkldnn_reorder_conv3d_weight autogen: mkldnn_reorder_conv3d_weight.out - func: to_mkldnn_backward(Tensor grad, Tensor input) -> Tensor - func: quantize_per_tensor_dynamic(Tensor self, ScalarType dtype, bool reduce_range) -> Tensor variants: function dispatch: CPU, CUDA: quantize_per_tensor_dynamic autogen: quantize_per_tensor_dynamic.out - func: quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensor variants: function dispatch: CPU, CUDA: quantize_per_tensor autogen: quantize_per_tensor.out - func: quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensor variants: function dispatch: CPU, CUDA: quantize_per_tensor_tensor_qparams autogen: quantize_per_tensor.tensor_qparams_out - func: quantize_per_tensor.tensors(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype) -> Tensor[] variants: function dispatch: CPU: quantize_per_tensor_list_cpu autogen: quantize_per_tensor.tensors_out - func: quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor variants: function dispatch: CPU, CUDA: quantize_per_channel autogen: quantize_per_channel.out - func: dequantize.self(Tensor self) -> Tensor variants: function, method dispatch: CPU, CUDA: dequantize_cpu_or_cuda QuantizedCPU, QuantizedCUDA: dequantize_quantized autogen: dequantize.self_out - func: dequantize.tensors(Tensor[] tensors) -> Tensor[] variants: function dispatch: QuantizedCPU: dequantize_tensors_quantized_cpu autogen: dequantize.tensors_out - func: q_scale(Tensor self) -> float variants: function, method dispatch: QuantizedCPU, QuantizedCUDA: q_scale_quant - func: q_zero_point(Tensor self) -> int variants: function, method dispatch: QuantizedCPU, QuantizedCUDA: q_zero_point_quant - func: q_per_channel_scales(Tensor self) -> Tensor variants: function, method dispatch: QuantizedCPU, QuantizedCUDA: q_per_channel_scales autogen: q_per_channel_scales.out - func: q_per_channel_zero_points(Tensor self) -> Tensor variants: function, method dispatch: QuantizedCPU, QuantizedCUDA: q_per_channel_zero_points autogen: q_per_channel_zero_points.out - func: q_per_channel_axis(Tensor self) -> int variants: function, method dispatch: QuantizedCPU, QuantizedCUDA: q_per_channel_axis - func: int_repr(Tensor self) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: QuantizedCPU: int_repr_quantized_cpu QuantizedCUDA: int_repr_quantized_cuda autogen: int_repr.out - func: _make_per_tensor_quantized_tensor(Tensor self, float scale, int zero_point) -> Tensor dispatch: CPU: make_per_tensor_quantized_tensor_cpu CUDA: make_per_tensor_quantized_tensor_cuda autogen: _make_per_tensor_quantized_tensor.out - func: _make_per_channel_quantized_tensor(Tensor self, Tensor scale, Tensor zero_point, int axis) -> Tensor dispatch: CPU: make_per_channel_quantized_tensor_cpu CUDA: make_per_channel_quantized_tensor_cuda autogen: _make_per_channel_quantized_tensor.out - func: qscheme(Tensor self) -> QScheme variants: method dispatch: QuantizedCPU, QuantizedCUDA: qscheme_quant - func: fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor device_check: NoCheck # TensorIterator variants: function - func: fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> Tensor device_check: NoCheck # TensorIterator variants: function - func: fake_quantize_per_tensor_affine_cachemask(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> (Tensor output, Tensor mask) variants: function dispatch: CPU, CUDA: fake_quantize_per_tensor_affine_cachemask autogen: fake_quantize_per_tensor_affine_cachemask.out - func: _fake_quantize_per_tensor_affine_cachemask_tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int quant_min, int quant_max) -> (Tensor output, Tensor mask) variants: function dispatch: CPU, CUDA: _fake_quantize_per_tensor_affine_cachemask_tensor_qparams autogen: _fake_quantize_per_tensor_affine_cachemask_tensor_qparams.out - func: fake_quantize_per_tensor_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor variants: function - func: _fake_quantize_learnable_per_tensor_affine(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor variants: function dispatch: CPU, CUDA: _fake_quantize_learnable_per_tensor_affine autogen: _fake_quantize_learnable_per_tensor_affine.out - func: _fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) variants: function dispatch: CPU, CUDA: _fake_quantize_learnable_per_tensor_affine_backward - func: fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensor device_check: NoCheck # TensorIterator variants: function - func: fake_quantize_per_channel_affine_cachemask(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> (Tensor output, Tensor mask) variants: function dispatch: CPU, CUDA: fake_quantize_per_channel_affine_cachemask autogen: fake_quantize_per_channel_affine_cachemask.out - func: fake_quantize_per_channel_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor variants: function - func: _fake_quantize_learnable_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor variants: function dispatch: CPU, CUDA: _fake_quantize_learnable_per_channel_affine autogen: _fake_quantize_learnable_per_channel_affine.out - func: _fake_quantize_learnable_per_channel_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) variants: function dispatch: CPU, CUDA: _fake_quantize_learnable_per_channel_affine_backward - func: fused_moving_avg_obs_fake_quant(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> Tensor variants: function - func: _fused_moving_avg_obs_fq_helper(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> (Tensor output, Tensor mask) dispatch: CPU: fused_moving_avg_obs_fake_quant_cpu CUDA: fused_moving_avg_obs_fake_quant_cuda autogen: _fused_moving_avg_obs_fq_helper_functional, _fused_moving_avg_obs_fq_helper.out - func: _choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int) variants: function - func: _saturate_weight_to_fp16(Tensor weight) -> Tensor variants: function - func: choose_qparams_optimized(Tensor input, int numel, int n_bins, float ratio, int bit_width) -> (Tensor, Tensor) variants: function - func: _autocast_to_reduced_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled, ScalarType cuda_dtype, ScalarType cpu_dtype) -> Tensor(a) variants: method device_guard: False - func: _autocast_to_full_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled) -> Tensor(a) variants: method device_guard: False - func: _to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: _to_copy NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: _to_copy_nested autogen: _to_copy.out tags: core # to(Device) must not exist because all constructors of Device also works for # TensorOptions. Otherwise, an ambiguity error is thrown. # See NOTE [ TensorOptions Constructors ]. - func: to.dtype_layout(Tensor(a) self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) variants: method device_check: NoCheck device_guard: False - func: to.device(Tensor(a) self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) variants: method device_check: NoCheck device_guard: False - func: to.dtype(Tensor(a) self, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) variants: method device_check: NoCheck device_guard: False - func: to.other(Tensor(a) self, Tensor other, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) variants: method device_check: NoCheck device_guard: False - func: meshgrid(Tensor[] tensors) -> Tensor[] # TODO: Two weeks after this lands, combine these two overloads, # making "indexing" optional. These are temporarily distinct for # forward-compatibility reasons. - func: meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[] - func: cartesian_prod(Tensor[] tensors) -> Tensor variants: function tags: maybe_aliasing_or_mutating - func: combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor variants: function - func: item(Tensor self) -> Scalar tags: data_dependent_output variants: method - func: result_type.Tensor(Tensor tensor, Tensor other) -> ScalarType variants: function - func: result_type.Scalar(Tensor tensor, Scalar other) -> ScalarType variants: function - func: result_type.Scalar_Tensor(Scalar scalar, Tensor tensor) -> ScalarType variants: function - func: result_type.Scalar_Scalar(Scalar scalar1, Scalar scalar2) -> ScalarType - func: can_cast(ScalarType from_, ScalarType to) -> bool variants: function - func: promote_types(ScalarType type1, ScalarType type2) -> ScalarType variants: function # NB: Does NOT check precondition that numel == 1 - func: _local_scalar_dense(Tensor self) -> Scalar tags: [core, data_dependent_output] dispatch: CPU: _local_scalar_dense_cpu CUDA: _local_scalar_dense_cuda MPS: _local_scalar_dense_mps variants: function # MPS LSTM implementation - func: _lstm_mps(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) dispatch: MPS: _lstm_mps autogen: _lstm_mps.out tags: nondeterministic_seeded - func: lstm_mps_backward(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor[], Tensor[]) dispatch: MPS: lstm_mps_backward autogen: lstm_mps_backward.out # Fused RNN kernels - func: _thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor) dispatch: CUDA: _thnn_fused_lstm_cell_cuda autogen: _thnn_fused_lstm_cell.out # NB: The composite version of this function below is a simple wrapper that duplicates some of the outputs # It is necessary to avoid triggering TensorImpl use count checks in debug mode # NB: this is function is NOT differentiable - func: _thnn_fused_lstm_cell_backward_impl(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor) dispatch: CUDA: _thnn_fused_lstm_cell_backward_impl_cuda autogen: _thnn_fused_lstm_cell_backward_impl.out - func: _thnn_fused_lstm_cell_backward(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor) - func: _thnn_differentiable_lstm_cell_backward(Tensor? grad_hy, Tensor? grad_cy, Tensor input_gates, Tensor hidden_gates, Tensor? input_bias, Tensor? hidden_bias, Tensor cx, Tensor cy) -> (Tensor, Tensor, Tensor, Tensor, Tensor) - func: _thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor) dispatch: CUDA: _thnn_fused_gru_cell_cuda autogen: _thnn_fused_gru_cell.out - func: _thnn_fused_gru_cell_backward(Tensor grad_hy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor) dispatch: CUDA: _thnn_fused_gru_cell_backward_cuda autogen: _thnn_fused_gru_cell_backward.out - func: _thnn_differentiable_gru_cell_backward(Tensor grad_hy, Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias, Tensor? hidden_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor) # RNN cells and layers - func: lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor) tags: nondeterministic_seeded - func: lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor, Tensor) tags: nondeterministic_seeded - func: gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) tags: nondeterministic_seeded - func: gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) tags: nondeterministic_seeded - func: rnn_tanh.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) tags: nondeterministic_seeded - func: rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) tags: nondeterministic_seeded - func: rnn_relu.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) tags: nondeterministic_seeded - func: rnn_relu.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) tags: nondeterministic_seeded - func: lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor) - func: gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor - func: rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor - func: rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor # Quantized RNN layer registration has been moved to C10 dispatch in `RNN.cpp` # Quantized RNN layers # - func: quantized_lstm(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, ScalarType? dtype=None, bool use_dynamic=False) -> (Tensor, Tensor, Tensor) # - func: quantized_lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, *, ScalarType? dtype=None, bool use_dynamic=False) -> (Tensor, Tensor, Tensor) # Quantized GRU layers # - func: quantized_gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) # # - func: quantized_gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) # # Quantized RNN cells - func: quantized_lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> (Tensor, Tensor) - func: quantized_gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor - func: quantized_rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor - func: quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor # PackedSequence utilities - func: _pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor) dispatch: CompositeExplicitAutograd: _pack_padded_sequence autogen: _pack_padded_sequence.out - func: _pack_padded_sequence_backward(Tensor grad, SymInt[] input_size, Tensor batch_sizes, bool batch_first) -> Tensor dispatch: CompositeImplicitAutograd: _pack_padded_sequence_backward_symint - func: _pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor) # wrappers for legacy TH methods - func: set_.source_Storage(Tensor(a!) self, Storage source) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False dispatch: CPU, CUDA, Meta, MPS: set_ autogen: set.source_Storage, set.source_Storage_out tags: inplace_view - func: set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False dispatch: CPU: set_storage_cpu_ Meta: set_storage_meta__symint CUDA: set_storage_cuda_ MPS: set_storage_mps_ QuantizedCPU, QuantizedCUDA: set_storage_quantized_ autogen: set.source_Storage_storage_offset, set.source_Storage_storage_offset_out tags: inplace_view - func: set_.source_Tensor_storage_offset(Tensor(a!) self, Tensor source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False dispatch: CompositeImplicitAutograd: set__symint tags: inplace_view - func: set_.source_Tensor(Tensor(a!) self, Tensor source) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False dispatch: CPU, CUDA, Meta, MPS: set_tensor_ autogen: set.source_Tensor, set.source_Tensor_out tags: inplace_view - func: set_(Tensor(a!) self) -> Tensor(a!) variants: method dispatch: CPU: set_cpu_ CUDA: set_cuda_ Meta: set_meta_ MPS: set_mps_ autogen: set, set.out tags: inplace_view # Not making it CompositeImplicitAutograd because lift # should be a primitive w.r.t. functorch # TODO: this should have a view annotation # TODO: shouldn't be a method - func: lift(Tensor self) -> Tensor dispatch: CompositeExplicitAutograd: lift autogen: lift.out # lift_fresh is called with an argument that is guaranteed to be # fresh (i.e., newly allocated). This is ONLY called from a # torch.tensor call; if you FX trace a lift_fresh, you are obligated # to convert this into a lift_fresh_copy (because FX will violate the # freshness invariant when tracing). - func: lift_fresh(Tensor(a) self) -> Tensor(a) dispatch: CompositeExplicitAutograd: lift_fresh # Like lift, but it clones the input. - func: lift_fresh_copy(Tensor self) -> Tensor tags: view_copy dispatch: CompositeExplicitAutogradNonFunctional: lift_fresh_copy autogen: lift_fresh_copy.out - func: is_set_to(Tensor self, Tensor tensor) -> bool variants: method device_check: NoCheck device_guard: False dispatch: CPU, CUDA, MPS: is_set_to - func: masked_fill_.Scalar(Tensor(a!) self, Tensor mask, Scalar value) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CPU: masked_fill__cpu CUDA: masked_fill__cuda QuantizedCPU: masked_fill__quantized_cpu QuantizedCUDA: masked_fill__quantized_cuda MPS: masked_fill__mps autogen: masked_fill.Scalar_out - func: masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: masked_fill NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_masked_fill tags: pointwise - func: masked_fill_.Tensor(Tensor(a!) self, Tensor mask, Tensor value) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CPU: masked_fill__cpu CUDA: masked_fill__cuda QuantizedCPU: masked_fill__quantized_cpu QuantizedCUDA: masked_fill__quantized_cuda MPS: masked_fill__mps autogen: masked_fill.Tensor_out - func: masked_fill.Tensor(Tensor self, Tensor mask, Tensor value) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: masked_fill - func: masked_scatter_(Tensor(a!) self, Tensor mask, Tensor source) -> Tensor(a!) variants: method dispatch: CPU: masked_scatter__cpu CUDA: masked_scatter__cuda MPS: masked_scatter__mps autogen: masked_scatter.out - func: masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: masked_scatter tags: core - func: masked_scatter_backward(Tensor grad_output, Tensor mask, SymInt[] sizes) -> Tensor dispatch: CompositeExplicitAutograd: masked_scatter_backward_symint - func: _masked_softmax(Tensor self, Tensor mask, int? dim=None, int? mask_type=None) -> Tensor dispatch: CUDA: masked_softmax_cuda CPU: masked_softmax_cpu autogen: _masked_softmax.out - func: _masked_softmax_backward(Tensor grad_output, Tensor output, Tensor mask, int? dim=None) -> Tensor dispatch: CUDA: masked_softmax_backward_cuda CPU: masked_softmax_backward_cpu autogen: _masked_softmax_backward.out - func: view(Tensor(a) self, SymInt[] size) -> Tensor(a) variants: method device_check: NoCheck device_guard: False dispatch: ZeroTensor, Meta, CPU, CUDA, QuantizedCPU, QuantizedCUDA, MPS, MTIA: view MkldnnCPU: mkldnn_view NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: view_nested tags: core # Warning: If you want to change the name or overload name of this # operator, you might also want to change the `isBlockListedSchema` # function in `torch/csrc/jit/frontend/schema_catching.cpp`. # The name and overload name of this operator is hardcoded in that # function in order to workaround a bug: # https://github.com/pytorch/pytorch/issues/47964 - func: view.dtype(Tensor(a) self, ScalarType dtype) -> Tensor(a) variants: method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: view_dtype - func: put_(Tensor(a!) self, Tensor index, Tensor source, bool accumulate=False) -> Tensor(a!) variants: method dispatch: CPU, CUDA: put_ autogen: put.out - func: put(Tensor self, Tensor index, Tensor source, bool accumulate=False) -> Tensor variants: function, method dispatch: CompositeExplicitAutograd: put - func: index_add.out(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) structured: True variants: function precomputed: - dim -> int dim dispatch: CPU: index_add_cpu_out CUDA: index_add_cuda_out MPS: index_add_mps_out - func: index_add_(Tensor(a!) self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor(a!) structured_delegate: index_add.out variants: method - func: index_add(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor structured_delegate: index_add.out variants: function, method - func: index_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor variants: function, method - func: index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) structured: True variants: function precomputed: - dim -> int dim dispatch: CPU: index_reduce_cpu_out CUDA: index_reduce_cuda_out - func: index_reduce_(Tensor(a!) self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor(a!) structured_delegate: index_reduce.out variants: method - func: index_reduce(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor structured_delegate: index_reduce.out variants: function, method - func: index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CPU: index_fill_ CUDA: index_fill_ MPS: index_fill_mps_ autogen: index_fill.int_Scalar_out - func: index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: index_fill - func: index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CPU, CUDA: index_fill_ MPS: index_fill_mps_ autogen: index_fill.int_Tensor_out - func: index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor device_check: NoCheck # TensorIterator variants: function, method dispatch: CompositeExplicitAutograd: index_fill - func: index_fill_.Dimname_Scalar(Tensor(a!) self, Dimname dim, Tensor index, Scalar value) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method - func: index_fill_.Dimname_Tensor(Tensor(a!) self, Dimname dim, Tensor index, Tensor value) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method - func: index_fill.Dimname_Scalar(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: index_fill.Dimname_Tensor(Tensor self, Dimname dim, Tensor index, Tensor value) -> Tensor device_check: NoCheck # TensorIterator variants: function, method - func: scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor structured_delegate: scatter.src_out variants: function, method tags: core - func: scatter_.src(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!) structured_delegate: scatter.src_out variants: method - func: scatter.src_out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) structured: True variants: function dispatch: CPU, CUDA: scatter_src_out MPS: scatter_src_out_mps - func: scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor structured_delegate: scatter.value_out variants: function, method tags: core - func: scatter_.value(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!) structured_delegate: scatter.value_out variants: method - func: scatter.value_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!) structured: True variants: function dispatch: CPU, CUDA: scatter_value_out MPS: scatter_value_out_mps - func: scatter.reduce(Tensor self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor structured_delegate: scatter.reduce_out variants: function, method - func: scatter_.reduce(Tensor(a!) self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor(a!) structured_delegate: scatter.reduce_out variants: method - func: scatter.reduce_out(Tensor self, int dim, Tensor index, Tensor src, *, str reduce, Tensor(a!) out) -> Tensor(a!) structured: True variants: function dispatch: CPU, CUDA: scatter_reduce_out MPS: scatter_reduce_out_mps - func: scatter.value_reduce(Tensor self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor structured_delegate: scatter.value_reduce_out variants: function, method - func: scatter_.value_reduce(Tensor(a!) self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor(a!) structured_delegate: scatter.value_reduce_out variants: method - func: scatter.value_reduce_out(Tensor self, int dim, Tensor index, Scalar value, *, str reduce, Tensor(a!) out) -> Tensor(a!) structured: True variants: function dispatch: CPU, CUDA: scatter_value_reduce_out MPS: scatter_value_reduce_out_mps - func: scatter.dimname_src(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor variants: function, method - func: scatter.dimname_value(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor variants: function, method - func: scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor structured_delegate: scatter_add.out variants: function, method tags: core - func: scatter_add_(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!) structured_delegate: scatter_add.out variants: method - func: scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) structured: True variants: function dispatch: CPU, CUDA: scatter_add MPS: scatter_add_mps_out - func: scatter_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor variants: function, method - func: scatter_reduce.two(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor structured_delegate: scatter_reduce.two_out variants: function, method tags: core - func: scatter_reduce_.two(Tensor(a!) self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor(a!) structured_delegate: scatter_reduce.two_out variants: method - func: scatter_reduce.two_out(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) structured: True variants: function dispatch: CPU, CUDA, MPS: scatter_reduce_two - func: eq_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) structured_delegate: eq.Scalar_out device_check: NoCheck # TensorIterator variants: method - func: eq_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: eq.Tensor_out device_check: NoCheck # TensorIterator variants: method - func: bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase variants: function dispatch: CPU, CUDA, MTIA: bitwise_and_out MPS: bitwise_and_out_mps tags: pointwise - func: bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: bitwise_and_out tags: pointwise - func: bitwise_and.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CompositeExplicitAutograd: bitwise_and tags: [core, pointwise] - func: bitwise_and.Scalar_Tensor(Scalar self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: bitwise_and autogen: bitwise_and.Scalar_Tensor_out tags: pointwise - func: bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function structured_delegate: bitwise_and.Tensor_out tags: [core, pointwise] - func: bitwise_and_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: bitwise_and_ tags: pointwise - func: bitwise_and_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: bitwise_and.Tensor_out tags: pointwise - func: __and__.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function - func: __and__.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function - func: __iand__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method - func: __iand__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method - func: bitwise_or.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase variants: function dispatch: CPU, CUDA, MTIA: bitwise_or_out MPS: bitwise_or_out_mps tags: pointwise - func: bitwise_or.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: bitwise_or_out tags: pointwise - func: bitwise_or.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CompositeExplicitAutograd: bitwise_or tags: [core, pointwise] - func: bitwise_or.Scalar_Tensor(Scalar self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: bitwise_or autogen: bitwise_or.Scalar_Tensor_out tags: pointwise - func: bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function structured_delegate: bitwise_or.Tensor_out tags: [core, pointwise] - func: bitwise_or_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: bitwise_or_ tags: pointwise - func: bitwise_or_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: bitwise_or.Tensor_out tags: pointwise - func: __or__.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function - func: __or__.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function - func: __ior__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method - func: __ior__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method - func: bitwise_xor.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase variants: function dispatch: CPU, CUDA: bitwise_xor_out MPS: bitwise_xor_out_mps tags: pointwise - func: bitwise_xor.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: bitwise_xor_out tags: pointwise - func: bitwise_xor.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CompositeExplicitAutograd: bitwise_xor tags: [core, pointwise] - func: bitwise_xor.Scalar_Tensor(Scalar self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: bitwise_xor autogen: bitwise_xor.Scalar_Tensor_out tags: pointwise - func: bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function structured_delegate: bitwise_xor.Tensor_out tags: [core, pointwise] - func: bitwise_xor_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: bitwise_xor_ tags: pointwise - func: bitwise_xor_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: bitwise_xor.Tensor_out tags: pointwise - func: __xor__.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function tags: pointwise - func: __xor__.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function tags: pointwise - func: __ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method tags: pointwise - func: __ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method tags: pointwise - func: __lshift__.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CPU, CUDA, MPS: __lshift__ tags: pointwise - func: __lshift__.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CPU, CUDA, MPS: __lshift__ tags: pointwise - func: __ilshift__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CPU, CUDA, MPS: __ilshift__ autogen: __lshift__.Scalar_out tags: pointwise - func: __ilshift__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CPU, CUDA, MPS: __ilshift__ autogen: __lshift__.Tensor_out tags: pointwise - func: bitwise_left_shift.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: bitwise_left_shift.Tensor_out tags: pointwise - func: bitwise_left_shift_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: bitwise_left_shift.Tensor_out tags: pointwise - func: bitwise_left_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: bitwise_left_shift_out tags: pointwise - func: bitwise_left_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CompositeExplicitAutograd: bitwise_left_shift tags: pointwise - func: bitwise_left_shift_.Tensor_Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: bitwise_left_shift_ tags: pointwise - func: bitwise_left_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: bitwise_left_shift_out tags: pointwise - func: bitwise_left_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: bitwise_left_shift autogen: bitwise_left_shift.Scalar_Tensor_out tags: pointwise - func: __rshift__.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CPU, CUDA, MPS: __rshift__ tags: pointwise - func: __rshift__.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CPU, CUDA, MPS: __rshift__ tags: pointwise - func: __irshift__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CPU, CUDA, MPS: __irshift__ autogen: __rshift__.Scalar_out - func: __irshift__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CPU, CUDA, MPS: __irshift__ autogen: __rshift__.Tensor_out - func: bitwise_right_shift.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function, method structured_delegate: bitwise_right_shift.Tensor_out tags: pointwise - func: bitwise_right_shift_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: bitwise_right_shift.Tensor_out tags: pointwise - func: bitwise_right_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: bitwise_right_shift_out tags: pointwise - func: bitwise_right_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CompositeExplicitAutograd: bitwise_right_shift tags: pointwise - func: bitwise_right_shift_.Tensor_Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: bitwise_right_shift_ tags: pointwise - func: bitwise_right_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: bitwise_right_shift_out tags: pointwise - func: bitwise_right_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function dispatch: CompositeExplicitAutograd: bitwise_right_shift autogen: bitwise_right_shift.Scalar_Tensor_out tags: pointwise - func: tril_(Tensor(a!) self, int diagonal=0) -> Tensor(a!) structured_delegate: tril.out variants: method - func: triu_(Tensor(a!) self, int diagonal=0) -> Tensor(a!) structured_delegate: triu.out variants: method - func: digamma_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: digamma.out variants: method tags: pointwise - func: lerp_.Scalar(Tensor(a!) self, Tensor end, Scalar weight) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: lerp.Scalar_out tags: pointwise - func: lerp_.Tensor(Tensor(a!) self, Tensor end, Tensor weight) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: lerp.Tensor_out tags: pointwise - func: addbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) variants: method dispatch: CPU, CUDA, XPU: addbmm_ MPS: addbmm_mps_ - func: addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, XPU: addbmm_out MPS: addbmm_out_mps - func: addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor variants: method, function dispatch: CPU, CUDA, XPU: addbmm MPS: addbmm_mps - func: random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method tags: nondeterministic_seeded dispatch: CPU, CUDA: random_ Meta: random_meta_ MPS: random_mps_ autogen: random.from, random.from_out - func: random_.to(Tensor(a!) self, int to, *, Generator? generator=None) -> Tensor(a!) device_check: NoCheck # TensorIterator tags: nondeterministic_seeded variants: method dispatch: CPU, CUDA: random_ Meta: random_meta_ MPS: random_mps_ autogen: random.to, random.to_out - func: random_(Tensor(a!) self, *, Generator? generator=None) -> Tensor(a!) device_check: NoCheck # TensorIterator tags: nondeterministic_seeded variants: method dispatch: CPU, CUDA: random_ MPS: random_mps_ Meta: random_meta_ autogen: random, random.out - func: uniform_(Tensor(a!) self, float from=0, float to=1, *, Generator? generator=None) -> Tensor(a!) device_check: NoCheck # TensorIterator tags: nondeterministic_seeded variants: method dispatch: CPU, CUDA: uniform_ MPS: uniform_mps_ Meta: uniform_meta_ autogen: uniform, uniform.out - func: cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method tags: nondeterministic_seeded dispatch: CPU, CUDA: cauchy_ autogen: cauchy, cauchy.out - func: log_normal_(Tensor(a!) self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor(a!) device_check: NoCheck # TensorIterator tags: nondeterministic_seeded variants: method dispatch: CPU, CUDA: log_normal_ autogen: log_normal, log_normal.out - func: exponential_(Tensor(a!) self, float lambd=1, *, Generator? generator=None) -> Tensor(a!) device_check: NoCheck # TensorIterator tags: nondeterministic_seeded variants: method dispatch: CPU, CUDA: exponential_ MPS: exponential_mps_ autogen: exponential, exponential.out - func: geometric_(Tensor(a!) self, float p, *, Generator? generator=None) -> Tensor(a!) device_check: NoCheck # TensorIterator tags: nondeterministic_seeded variants: method dispatch: CPU, CUDA: geometric_ # wrappers for TH functions autogen: geometric, geometric.out - func: diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) - func: diag(Tensor self, int diagonal=0) -> Tensor variants: method, function - func: cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) - func: cross(Tensor self, Tensor other, int? dim=None) -> Tensor variants: method, function - func: triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU: triu_cpu CUDA: triu_cuda MPS: triu_mps_out - func: triu(Tensor self, int diagonal=0) -> Tensor structured_delegate: triu.out variants: method, function - func: tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU: tril_cpu CUDA: tril_cuda MPS: tril_mps_out - func: tril(Tensor self, int diagonal=0) -> Tensor structured_delegate: tril.out variants: method, function - func: tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CPU: tril_indices_cpu CUDA: tril_indices_cuda MPS: tril_indices_mps autogen: tril_indices.out - func: triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CPU: triu_indices_cpu CUDA: triu_indices_cuda MPS: triu_indices_mps autogen: triu_indices.out - func: trace(Tensor self) -> Tensor variants: method, function dispatch: CPU: trace_cpu CUDA: trace_cuda MPS: trace_mps autogen: trace.out - func: trace_backward(Tensor grad, SymInt[] sizes) -> Tensor variants: function device_check: NoCheck device_guard: False dispatch: CompositeImplicitAutograd: trace_backward_symint - func: ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: ne_Scalar_out MPS: ne_scalar_out_mps QuantizedCPU: ne_out_quantized_cpu tags: pointwise - func: ne.Scalar(Tensor self, Scalar other) -> Tensor structured_delegate: ne.Scalar_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: ne_quantized_cpu tags: [core, pointwise] - func: ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: ne_Tensor_out MPS: ne_tensor_out_mps QuantizedCPU: ne_out_quantized_cpu tags: pointwise - func: ne.Tensor(Tensor self, Tensor other) -> Tensor structured_delegate: ne.Tensor_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: ne_quantized_cpu tags: [core, pointwise] - func: ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) structured_delegate: ne.Scalar_out device_check: NoCheck # TensorIterator variants: method - func: ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: ne.Tensor_out device_check: NoCheck # TensorIterator variants: method # not_equal, alias for torch.ne - func: not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) - func: not_equal.Scalar(Tensor self, Scalar other) -> Tensor variants: method, function - func: not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) - func: not_equal.Tensor(Tensor self, Tensor other) -> Tensor variants: method, function - func: not_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method - func: not_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) variants: method - func: eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: eq_Scalar_out MPS: eq_scalar_out_mps QuantizedCPU: eq_out_quantized_cpu tags: pointwise - func: eq.Scalar(Tensor self, Scalar other) -> Tensor structured_delegate: eq.Scalar_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: eq_quantized_cpu NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: eq_scalar_nested tags: [core, pointwise] - func: eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: eq_Tensor_out MPS: eq_tensor_out_mps QuantizedCPU: eq_out_quantized_cpu tags: pointwise - func: eq.Tensor(Tensor self, Tensor other) -> Tensor structured_delegate: eq.Tensor_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: eq_quantized_cpu NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: eq_tensor_nested tags: [core, pointwise] - func: ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: ge_Scalar_out MPS: ge_scalar_out_mps QuantizedCPU: ge_out_quantized_cpu tags: pointwise - func: ge.Scalar(Tensor self, Scalar other) -> Tensor structured_delegate: ge.Scalar_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: ge_quantized_cpu NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: ge_scalar_nested tags: [core, pointwise] - func: ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: ge_Tensor_out MPS: ge_tensor_out_mps QuantizedCPU: ge_out_quantized_cpu tags: pointwise - func: ge.Tensor(Tensor self, Tensor other) -> Tensor structured_delegate: ge.Tensor_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: ge_quantized_cpu tags: [core, pointwise] - func: ge_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) structured_delegate: ge.Scalar_out device_check: NoCheck # TensorIterator variants: method - func: ge_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: ge.Tensor_out device_check: NoCheck # TensorIterator variants: method # greater_equal, alias for torch.ge - func: greater_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) - func: greater_equal.Scalar(Tensor self, Scalar other) -> Tensor variants: method, function - func: greater_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) - func: greater_equal.Tensor(Tensor self, Tensor other) -> Tensor variants: method, function - func: greater_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method - func: greater_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) variants: method - func: le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: le_Scalar_out MPS: le_scalar_out_mps QuantizedCPU: le_out_quantized_cpu tags: pointwise - func: le.Scalar(Tensor self, Scalar other) -> Tensor structured_delegate: le.Scalar_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: le_quantized_cpu tags: [core, pointwise] - func: le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: le_Tensor_out MPS: le_tensor_out_mps QuantizedCPU: le_out_quantized_cpu tags: pointwise - func: le.Tensor(Tensor self, Tensor other) -> Tensor structured_delegate: le.Tensor_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: le_quantized_cpu tags: [core, pointwise] - func: le_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) structured_delegate: le.Scalar_out device_check: NoCheck # TensorIterator variants: method - func: le_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: le.Tensor_out device_check: NoCheck # TensorIterator variants: method # less_equal, alias for torch.le - func: less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) - func: less_equal.Scalar(Tensor self, Scalar other) -> Tensor variants: method, function - func: less_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) - func: less_equal.Tensor(Tensor self, Tensor other) -> Tensor variants: method, function - func: less_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method - func: less_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) variants: method - func: gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA,MTIA: gt_Scalar_out MPS: gt_scalar_out_mps QuantizedCPU: gt_out_quantized_cpu tags: pointwise - func: gt.Scalar(Tensor self, Scalar other) -> Tensor structured_delegate: gt.Scalar_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: gt_quantized_cpu NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: gt_scalar_nested tags: [core, pointwise] - func: gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: gt_Tensor_out MPS: gt_tensor_out_mps QuantizedCPU: gt_out_quantized_cpu tags: pointwise - func: gt.Tensor(Tensor self, Tensor other) -> Tensor structured_delegate: gt.Tensor_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: gt_quantized_cpu tags: [core, pointwise] - func: gt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) structured_delegate: gt.Scalar_out device_check: NoCheck # TensorIterator variants: method - func: gt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: gt.Tensor_out device_check: NoCheck # TensorIterator variants: method # greater, alias for torch.gt - func: greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) - func: greater.Scalar(Tensor self, Scalar other) -> Tensor variants: method, function - func: greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) - func: greater.Tensor(Tensor self, Tensor other) -> Tensor variants: method, function - func: greater_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method - func: greater_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) variants: method - func: lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: lt_Scalar_out MPS: lt_scalar_out_mps QuantizedCPU: lt_out_quantized_cpu tags: pointwise - func: lt.Scalar(Tensor self, Scalar other) -> Tensor structured_delegate: lt.Scalar_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: lt_quantized_cpu tags: [core, pointwise] - func: lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: lt_Tensor_out MPS: lt_tensor_out_mps QuantizedCPU: lt_out_quantized_cpu tags: pointwise - func: lt.Tensor(Tensor self, Tensor other) -> Tensor structured_delegate: lt.Tensor_out device_check: NoCheck # TensorIterator variants: method, function dispatch: QuantizedCPU: lt_quantized_cpu tags: [core, pointwise] - func: lt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) structured_delegate: lt.Scalar_out device_check: NoCheck # TensorIterator variants: method - func: lt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: lt.Tensor_out device_check: NoCheck # TensorIterator variants: method # less, alias for torch.lt - func: less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) - func: less.Scalar(Tensor self, Scalar other) -> Tensor variants: method, function - func: less.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) - func: less.Tensor(Tensor self, Tensor other) -> Tensor variants: method, function - func: less_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method - func: less_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) variants: method - func: take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: take_out - func: take(Tensor self, Tensor index) -> Tensor variants: method, function dispatch: CPU, CUDA: take - func: take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) - func: take_along_dim(Tensor self, Tensor indices, int? dim=None) -> Tensor variants: method, function - func: index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, QuantizedCPU: index_select_out_cpu_ CUDA, QuantizedCUDA: index_select_out_cuda MPS: index_select_out_mps - func: index_select(Tensor self, int dim, Tensor index) -> Tensor variants: method, function dispatch: CPU: index_select_cpu_ QuantizedCPU: index_select_quantized_cpu_ CUDA: index_select_cuda QuantizedCUDA: index_select_quantized_cuda SparseCPU: index_select_sparse_cpu SparseCUDA: index_select_sparse_cuda MPS: index_select_mps tags: core - func: index_select.dimname_out(Tensor self, Dimname dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!) - func: index_select.dimname(Tensor self, Dimname dim, Tensor index) -> Tensor variants: method, function - func: index_select_backward(Tensor grad, SymInt[] self_sizes, int dim, Tensor index) -> Tensor variants: function device_check: NoCheck device_guard: False dispatch: CompositeImplicitAutograd: index_select_backward_symint - func: masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU: masked_select_out_cpu CUDA: masked_select_out_cuda MPS: masked_select_out_mps tags: dynamic_output_shape - func: masked_select(Tensor self, Tensor mask) -> Tensor variants: method, function dispatch: CPU: masked_select_cpu CUDA: masked_select_cuda MPS: masked_select_mps tags: dynamic_output_shape - func: masked_select_backward(Tensor grad, Tensor input, Tensor mask) -> Tensor variants: function device_check: NoCheck device_guard: False - func: nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU: nonzero_out_cpu CUDA: nonzero_out_cuda MPS: nonzero_out_mps tags: dynamic_output_shape - func: nonzero(Tensor self) -> Tensor variants: method, function dispatch: CPU: nonzero_cpu CUDA: nonzero_cuda MPS: nonzero_mps tags: [dynamic_output_shape, core] - func: nonzero_static.out(Tensor self, *, SymInt size, int fill_value=-1, Tensor(a!) out) -> Tensor(a!) dispatch: CPU: nonzero_static_out_cpu CUDA: nonzero_static_out_cuda - func: nonzero_static(Tensor self, *, SymInt size, int fill_value=-1) -> Tensor variants: method, function dispatch: CPU: nonzero_static_cpu CUDA: nonzero_static_cuda - func: nonzero_numpy(Tensor self) -> Tensor[] variants: method, function - func: argwhere(Tensor self) -> Tensor variants: method, function tags: dynamic_output_shape - func: gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU, CUDA: gather_out MPS: gather_out_mps - func: gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor variants: method, function structured_delegate: gather.out tags: core - func: gather_backward(Tensor grad, Tensor self, int dim, Tensor index, bool sparse_grad) -> Tensor variants: function device_check: NoCheck device_guard: False - func: gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) - func: gather.dimname(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False) -> Tensor variants: method, function - func: _gather_sparse_backward(Tensor self, int dim, Tensor index, Tensor grad) -> Tensor - func: addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: addcmul_out MPS: addcmul_out_mps tags: pointwise - func: addcmul(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor structured_delegate: addcmul.out device_check: NoCheck # TensorIterator variants: method, function tags: pointwise - func: addcmul_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!) structured_delegate: addcmul.out device_check: NoCheck # TensorIterator variants: method tags: pointwise - func: addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: addcdiv_out MPS: addcdiv_out_mps tags: pointwise - func: addcdiv(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor structured_delegate: addcdiv.out device_check: NoCheck # TensorIterator variants: method, function tags: pointwise - func: addcdiv_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!) structured_delegate: addcdiv.out device_check: NoCheck # TensorIterator variants: method tags: pointwise - func: cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor python_module: nn dispatch: CompositeImplicitAutograd: cross_entropy_loss_symint - func: triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient) structured: True dispatch: CPU, CUDA: triangular_solve_out MPS: triangular_solve_mps_out SparseCsrCPU: triangular_solve_out_sparse_csr_cpu SparseCsrCUDA: triangular_solve_out_sparse_csr_cuda - func: triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient) structured_delegate: triangular_solve.X variants: method, function - func: _linalg_check_errors(Tensor info, str api_name, *, bool is_matrix) -> () dispatch: CompositeExplicitAutograd: _linalg_check_errors - func: linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!) python_module: linalg dispatch: CPU, CUDA: linalg_solve_triangular_out MPS: linalg_solve_triangular_mps_out - func: linalg_solve_triangular(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False) -> Tensor python_module: linalg variants: function dispatch: CPU, CUDA: linalg_solve_triangular MPS: linalg_solve_triangular_mps - func: linalg_vander(Tensor x, *, SymInt? N=None) -> Tensor python_module: linalg dispatch: CompositeImplicitAutograd: linalg_vander_symint - func: svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) - func: svd(Tensor self, bool some=True, bool compute_uv=True) -> (Tensor U, Tensor S, Tensor V) variants: method, function # swapaxes, alias for transpose - func: swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False - func: swapaxes_(Tensor(a!) self, int axis0, int axis1) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False tags: inplace_view # swapdims, alias for transpose - func: swapdims(Tensor(a) self, int dim0, int dim1) -> Tensor(a) variants: function, method device_check: NoCheck device_guard: False - func: swapdims_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) variants: method device_check: NoCheck device_guard: False tags: inplace_view - func: cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: cholesky_out - func: cholesky(Tensor self, bool upper=False) -> Tensor variants: method, function dispatch: CPU, CUDA, MPS: cholesky - func: cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: cholesky_solve_out - func: cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor variants: method, function dispatch: CompositeExplicitAutograd: cholesky_solve - func: _cholesky_solve_helper(Tensor self, Tensor A, bool upper) -> Tensor variants: function dispatch: CPU: _cholesky_solve_helper_cpu CUDA: _cholesky_solve_helper_cuda autogen: _cholesky_solve_helper.out - func: cholesky_inverse(Tensor self, bool upper=False) -> Tensor variants: method, function dispatch: CPU, CUDA: cholesky_inverse - func: cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: cholesky_inverse_out - func: qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) - func: qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R) variants: method, function - func: geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau) dispatch: CPU, CUDA: geqrf_out - func: geqrf(Tensor self) -> (Tensor a, Tensor tau) variants: method, function dispatch: CPU, CUDA: geqrf # orgqr, alias for linalg_householder_product - func: orgqr(Tensor self, Tensor input2) -> Tensor variants: method, function - func: orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!) - func: ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: ormqr_out - func: ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor variants: method, function dispatch: CPU, CUDA: ormqr - func: _lu_with_info(Tensor self, bool pivot=True, bool check_errors=True) -> (Tensor LU, Tensor pivots, Tensor info) variants: function - func: lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!) - func: lu_solve(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor variants: method, function # lu_unpack - func: lu_unpack(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True) -> (Tensor P, Tensor L, Tensor U) structured_delegate: lu_unpack.out variants: function - func: lu_unpack.out(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True, *, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) variants: function structured: True dispatch: CPU, CUDA: lu_unpack_out MPS: lu_unpack_out_mps # TODO: remove dispatch section when porting TH CUDA to ATen - func: multinomial.out(Tensor self, SymInt num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded dispatch: CPU, CUDA: multinomial_out MPS: multinomial_out_mps - func: multinomial(Tensor self, SymInt num_samples, bool replacement=False, *, Generator? generator=None) -> Tensor variants: method, function dispatch: CPU, CUDA: multinomial MPS: multinomial_mps tags: nondeterministic_seeded - func: lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: lgamma_out MPS: lgamma_out_mps tags: pointwise - func: lgamma_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: lgamma.out variants: method tags: pointwise - func: lgamma(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: lgamma.out variants: method, function tags: pointwise - func: digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: digamma_out MPS: digamma_out_mps tags: pointwise - func: digamma(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: digamma.out variants: method, function tags: pointwise - func: polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: polygamma_out MPS: polygamma_out_mps tags: pointwise - func: polygamma(int n, Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: polygamma.out variants: method, function tags: pointwise - func: polygamma_(Tensor(a!) self, int n) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: polygamma_ tags: pointwise - func: erfinv(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: erfinv.out variants: method, function dispatch: SparseCPU, SparseCUDA: erfinv_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: erfinv_sparse_csr tags: pointwise - func: erfinv_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: erfinv.out variants: method dispatch: SparseCPU, SparseCUDA: erfinv_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: erfinv_sparse_csr_ tags: pointwise - func: erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: erfinv_out SparseCPU, SparseCUDA: erfinv_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: erfinv_sparse_csr_out tags: pointwise - func: i0(Tensor self) -> Tensor structured_delegate: i0.out variants: function, method tags: pointwise - func: i0_(Tensor(a!) self) -> Tensor(a!) structured_delegate: i0.out variants: function, method tags: pointwise - func: i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: i0_out tags: pointwise - func: sign(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: sign.out variants: function, method dispatch: SparseCPU, SparseCUDA: sign_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sign_sparse_csr tags: [core, pointwise] - func: sign_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: sign.out variants: method dispatch: SparseCPU, SparseCUDA: sign_sparse_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sign_sparse_csr_ tags: pointwise - func: sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: sign_out MPS: sign_out_mps SparseCPU, SparseCUDA: sign_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: sign_sparse_csr_out tags: pointwise - func: signbit(Tensor self) -> Tensor variants: function, method structured_delegate: signbit.out dispatch: SparseCPU, SparseCUDA: signbit_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: signbit_sparse_csr tags: pointwise - func: signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU: signbit_out CUDA: signbit_out MPS: signbit_out_mps SparseCPU, SparseCUDA: signbit_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: signbit_sparse_csr_out tags: pointwise - func: dist(Tensor self, Tensor other, Scalar p=2) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CompositeExplicitAutograd: dist autogen: dist.out - func: atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: atan2_out MPS: atan2_out_mps tags: [core, pointwise] - func: atan2_(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: atan2.out variants: method tags: pointwise - func: atan2(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: atan2.out variants: method, function tags: [core, pointwise] # arctan2, alias of atan2 - func: arctan2(Tensor self, Tensor other) -> Tensor variants: method, function - func: arctan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator - func: arctan2_(Tensor(a!) self, Tensor other) -> Tensor(a!) variants: method - func: lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: lerp_Scalar tags: pointwise - func: lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: lerp_Tensor MPS: lerp_Tensor_mps tags: pointwise - func: lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor device_check: NoCheck # TensorIterator variants: method, function structured_delegate: lerp.Scalar_out tags: pointwise - func: lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor device_check: NoCheck # TensorIterator variants: method, function structured_delegate: lerp.Tensor_out tags: pointwise - func: histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, MPS: histogram_histc_out CUDA: _histc_out_cuda - func: histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor variants: method, function dispatch: CPU, MPS: histogram_histc CUDA: _histc_cuda - func: histogram.bins_tensor_out(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges) dispatch: CPU, MPS: histogram_out - func: histogram.bins_tensor(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges) variants: method, function dispatch: CPU, MPS: histogram - func: histogram.bin_ct_out(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges) dispatch: CPU, MPS: histogram_out - func: histogram.bin_ct(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges) variants: method, function dispatch: CPU, MPS: histogram - func: _histogramdd_bin_edges(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor[] dispatch: CPU, MPS: histogramdd_bin_edges autogen: _histogramdd_bin_edges.out - func: _histogramdd_from_bin_cts(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor dispatch: CPU, MPS: _histogramdd autogen: _histogramdd_from_bin_cts.out - func: _histogramdd_from_bin_tensors(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False) -> Tensor dispatch: CPU, MPS: _histogramdd autogen: _histogramdd_from_bin_tensors.out - func: histogramdd(Tensor self, int[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges) - func: histogramdd.int_bins(Tensor self, int bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges) - func: histogramdd.TensorList_bins(Tensor self, Tensor[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges) - func: fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CompositeExplicitAutograd: fmod_out tags: pointwise - func: fmod.Scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CompositeExplicitAutograd: fmod tags: [core, pointwise] - func: fmod_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method dispatch: CompositeExplicitAutograd: fmod_ tags: pointwise - func: fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: fmod_out tags: pointwise - func: fmod.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: fmod.Tensor_out variants: method, function tags: [core, pointwise] - func: fmod_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: fmod.Tensor_out tags: pointwise - func: hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: hypot_out MPS: hypot_out_mps tags: pointwise - func: hypot(Tensor self, Tensor other) -> Tensor structured_delegate: hypot.out variants: method, function tags: pointwise - func: hypot_(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: hypot.out variants: method tags: pointwise - func: igamma.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: igamma_out tags: pointwise - func: igamma(Tensor self, Tensor other) -> Tensor structured_delegate: igamma.out variants: method, function tags: pointwise - func: igamma_(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: igamma.out variants: method tags: pointwise - func: igammac.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: igammac_out tags: pointwise - func: igammac(Tensor self, Tensor other) -> Tensor structured_delegate: igammac.out variants: method, function tags: pointwise - func: igammac_(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: igammac.out variants: method tags: pointwise - func: nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: nextafter_out tags: pointwise - func: nextafter(Tensor self, Tensor other) -> Tensor structured_delegate: nextafter.out variants: method, function tags: pointwise - func: nextafter_(Tensor(a!) self, Tensor other) -> Tensor(a!) structured_delegate: nextafter.out variants: method tags: pointwise - func: remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: remainder_out tags: pointwise - func: remainder.Scalar(Tensor self, Scalar other) -> Tensor variants: method, function dispatch: CompositeExplicitAutograd: remainder tags: [core, pointwise] - func: remainder_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) variants: method dispatch: CompositeExplicitAutograd: remainder_ tags: pointwise - func: remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS, MTIA: remainder_out tags: pointwise - func: remainder.Tensor(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: remainder.Tensor_out variants: method, function tags: [core, pointwise] - func: remainder_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: remainder.Tensor_out variants: method tags: pointwise - func: remainder.Scalar_Tensor(Scalar self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: function dispatch: CPU, CUDA, MPS: remainder autogen: remainder.Scalar_Tensor_out tags: pointwise - func: min(Tensor self) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CPU, CUDA: min MPS: min_mps QuantizedCPU: min_quantized_cpu - func: min.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: min_unary_out QuantizedCPU: min_quantized_unary_out - func: fmin(Tensor self, Tensor other) -> Tensor structured_delegate: fmin.out device_check: NoCheck # TensorIterator variants: method, function tags: pointwise - func: fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MPS: fmin_out tags: pointwise - func: max(Tensor self) -> Tensor device_check: NoCheck # TensorIterator variants: method, function dispatch: CPU, CUDA: max MPS: max_mps QuantizedCPU: max_quantized_cpu - func: fmax(Tensor self, Tensor other) -> Tensor structured_delegate: fmax.out device_check: NoCheck # TensorIterator variants: method, function tags: pointwise - func: fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MPS: fmax_out tags: pointwise - func: maximum(Tensor self, Tensor other) -> Tensor structured_delegate: maximum.out device_check: NoCheck # TensorIterator variants: method, function tags: [core, pointwise] - func: maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: maximum_out MPS: maximum_out_mps tags: pointwise # binary max, alias of maximum # NOTE: max is not an alias for maximum, since there is also unary max - func: max.other(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function tags: pointwise - func: max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator tags: pointwise - func: max.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator dispatch: CPU, CUDA: max_unary_out QuantizedCPU: max_quantized_unary_out - func: minimum(Tensor self, Tensor other) -> Tensor structured_delegate: minimum.out device_check: NoCheck # TensorIterator variants: method, function tags: [core, pointwise] - func: minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator dispatch: CPU, CUDA, MTIA: minimum_out MPS: minimum_out_mps tags: pointwise # binary min, alias for minimum # NOTE: min is not an alias for minimum, since there is also unary min - func: min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator tags: pointwise - func: min.other(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator variants: method, function tags: pointwise - func: quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor variants: method, function - func: quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) - func: quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor variants: method, function - func: quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) - func: nanquantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor variants: method, function - func: nanquantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) - func: nanquantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor variants: method, function - func: nanquantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) - func: sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) device_check: NoCheck # TensorIterator dispatch: CompositeExplicitAutograd: sort_out - func: sort.values_stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) structured: True dispatch: CPU, CUDA: sort_stable_out MPS: sort_stable_out_mps - func: sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices) device_check: NoCheck # TensorIterator variants: method, function dispatch: CompositeExplicitAutograd: sort tags: core - func: sort.stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices) structured_delegate: sort.values_stable variants: method, function dispatch: QuantizedCPU: sort_quantized_cpu_stable - func: sort.dimname_values(Tensor self, Dimname dim, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) - func: sort.dimname_values_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) - func: sort.dimname(Tensor self, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices) variants: method, function - func: sort.dimname_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices) variants: method, function - func: msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) - func: msort(Tensor self) -> Tensor variants: method, function - func: argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor device_check: NoCheck # TensorIterator variants: method, function - func: argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor device_check: NoCheck # TensorIterator variants: method, function - func: argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: function - func: argsort.dimname(Tensor self, Dimname dim, bool descending=False) -> Tensor variants: method, function - func: topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) structured: True dispatch: CPU: topk_out_cpu CUDA: topk_out_cuda MPS: topk_out_mps - func: topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices) variants: method, function structured_delegate: topk.values dispatch: QuantizedCPU: topk_quantized_cpu tags: core - func: all(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: all.all_out variants: method, function - func: all.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck structured: True dispatch: CPU, CUDA: all_all_out MPS: all_all_out_mps - func: any(Tensor self) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: any.all_out variants: method, function dispatch: SparseCPU, SparseCUDA: any_sparse tags: core - func: any.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck structured: True dispatch: CPU, CUDA: any_all_out MPS: any_all_out_mps - func: renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True dispatch: CPU, CUDA: renorm_out MPS: renorm_out_mps - func: renorm(Tensor self, Scalar p, int dim, Scalar maxnorm) -> Tensor device_check: NoCheck # TensorIterator variants: method, function structured_delegate: renorm.out - func: renorm_(Tensor(a!) self, Scalar p, int dim, Scalar maxnorm) -> Tensor(a!) device_check: NoCheck # TensorIterator variants: method structured_delegate: renorm.out - func: unfold(Tensor(a) self, int dimension, int size, int step) -> Tensor(a) variants: method device_check: NoCheck device_guard: False dispatch: CPU, CUDA, Meta, MPS, MTIA: unfold QuantizedCPU, QuantizedCUDA: unfold - func: unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor variants: function dispatch: CPU, CUDA, MPS: unfold_backward autogen: unfold_backward.out - func: equal(Tensor self, Tensor other) -> bool tags: [data_dependent_output, pointwise] variants: method, function dispatch: CPU: cpu_equal CUDA: cuda_equal MPS: mps_equal QuantizedCPU: equal_quantized_cpu - func: pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: pow_Tensor_Tensor_out MPS: pow_tensor_tensor_out_mps tags: pointwise - func: pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: pow.Tensor_Tensor_out variants: method, function tags: [core, pointwise] - func: pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True dispatch: CPU, CUDA: pow_Scalar_out MPS: pow_Scalar_out_mps tags: pointwise - func: pow.Scalar(Scalar self, Tensor exponent) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: pow.Scalar_out tags: [core, pointwise] - func: pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: pow_Tensor_Scalar_out SparseCPU, SparseCUDA: pow_out_sparse_scalar MPS: pow_tensor_scalar_out_mps tags: pointwise - func: pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: pow.Tensor_Scalar_out variants: function, method dispatch: SparseCPU, SparseCUDA: pow_sparse_scalar tags: [core, pointwise] - func: pow_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: pow.Tensor_Scalar_out variants: method tags: pointwise - func: pow_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!) device_check: NoCheck # TensorIterator structured_delegate: pow.Tensor_Tensor_out variants: method tags: pointwise - func: float_power.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) tags: pointwise - func: float_power.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor variants: function, method tags: pointwise - func: float_power.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) tags: pointwise - func: float_power.Scalar(Scalar self, Tensor exponent) -> Tensor tags: pointwise - func: float_power.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!) tags: pointwise - func: float_power.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor variants: function, method tags: pointwise - func: float_power_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!) variants: method tags: pointwise - func: float_power_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!) variants: method tags: pointwise - func: normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!) device_check: NoCheck # TensorIterator tags: nondeterministic_seeded variants: method dispatch: CPU, CUDA: normal_ MPS: normal_mps_ Meta: normal_meta_ SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: normal_sparse_csr_ NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: normal_nested_ autogen: normal.out # Only used by the functionalization pass. # Normally, the codegen would be able to generate a normal() NativeFunction, # but we can't due to overload ambiguity with normal.Tensor_float. - func: normal_functional(Tensor self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor device_check: NoCheck # TensorIterator tags: nondeterministic_seeded dispatch: CompositeExplicitAutograd: normal_functional - func: normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) tags: nondeterministic_seeded dispatch: CPU, CUDA: normal_out MPS: normal_mps_out Meta: normal_out_meta - func: normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor dispatch: CPU, CUDA: normal MPS: normal_mps Meta: normal_meta tags: nondeterministic_seeded - func: normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: normal_out Meta: normal_out_meta MPS: normal_mps_out tags: nondeterministic_seeded - func: normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor dispatch: CPU, CUDA: normal MPS: normal_mps Meta: normal_meta tags: nondeterministic_seeded - func: normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: normal_out Meta: normal_out_meta MPS: normal_mps_out tags: nondeterministic_seeded - func: normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor dispatch: CPU, CUDA: normal MPS: normal_mps Meta: normal_meta tags: nondeterministic_seeded - func: normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor dispatch: CompositeExplicitAutograd: normal tags: nondeterministic_seeded - func: normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: normal_out tags: nondeterministic_seeded - func: alias(Tensor(a) self) -> Tensor(a) variants: method, function dispatch: CompositeExplicitAutograd: alias NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: alias_nested tags: core - func: _amp_foreach_non_finite_check_and_unscale_(Tensor(a!)[] self, Tensor(b!) found_inf, Tensor inv_scale) -> () variants: function dispatch: CUDA: _amp_foreach_non_finite_check_and_unscale_cuda_ CPU: _amp_foreach_non_finite_check_and_unscale_cpu_ MPS: _amp_foreach_non_finite_check_and_unscale_mps_ autogen: _amp_foreach_non_finite_check_and_unscale, _amp_foreach_non_finite_check_and_unscale.out - func: _amp_update_scale_(Tensor(a!) self, Tensor(b!) growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> Tensor(a!) variants: function dispatch: CUDA: _amp_update_scale_cuda_ CPU: _amp_update_scale_cpu_ MPS: _amp_update_scale_mps_ autogen: _amp_update_scale, _amp_update_scale.out #- func: _cat(Tensor[] tensors, int dim=0) -> Tensor #dispatch: #CPU: _cat_cpu #CUDA: cat_cuda #MPS: cat_mps #QuantizedCPU: cat_quantized_cpu #- func: _cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) #dispatch: #CPU: _cat_out_cpu #CUDA: cat_out_cuda #QuantizedCPU: cat_out_quantized_cpu - func: _foreach_add.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_add_scalar_kernel_slow CUDA: foreach_tensor_add_scalar_kernel_cuda - func: _foreach_add_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_add_scalar_kernel_slow_ CUDA: foreach_tensor_add_scalar_kernel_cuda_ autogen: _foreach_add.Scalar_out - func: _foreach_add.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_add_list_kernel_slow CUDA: foreach_tensor_add_list_kernel_cuda - func: _foreach_add_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_add_list_kernel_slow_ CUDA: foreach_tensor_add_list_kernel_cuda_ autogen: _foreach_add.List_out - func: _foreach_add.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_add_scalarlist_kernel_slow CUDA: foreach_tensor_add_scalarlist_kernel_cuda - func: _foreach_add_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_add_scalarlist_kernel_slow_ CUDA: foreach_tensor_add_scalarlist_kernel_cuda_ autogen: _foreach_add.ScalarList_out - func: _foreach_add.Tensor(Tensor[] self, Tensor other, *, Scalar alpha=1) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_add_tensor_kernel_slow CUDA: foreach_tensor_add_tensor_kernel_cuda - func: _foreach_add_.Tensor(Tensor(a!)[] self, Tensor other, *, Scalar alpha=1) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_add_tensor_kernel_slow_ CUDA: foreach_tensor_add_tensor_kernel_cuda_ autogen: _foreach_add.Tensor_out - func: _foreach_sub.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sub_scalar_kernel_slow CUDA: foreach_tensor_sub_scalar_kernel_cuda - func: _foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sub_scalar_kernel_slow_ CUDA: foreach_tensor_sub_scalar_kernel_cuda_ autogen: _foreach_sub.Scalar_out - func: _foreach_sub.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sub_list_kernel_slow CUDA: foreach_tensor_sub_list_kernel_cuda - func: _foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sub_list_kernel_slow_ CUDA: foreach_tensor_sub_list_kernel_cuda_ autogen: _foreach_sub.List_out - func: _foreach_sub.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sub_scalarlist_kernel_slow CUDA: foreach_tensor_sub_scalarlist_kernel_cuda - func: _foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sub_scalarlist_kernel_slow_ CUDA: foreach_tensor_sub_scalarlist_kernel_cuda_ autogen: _foreach_sub.ScalarList_out - func: _foreach_mul.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_mul_scalar_kernel_slow CUDA: foreach_tensor_mul_scalar_kernel_cuda - func: _foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_mul_scalar_kernel_slow_ CUDA: foreach_tensor_mul_scalar_kernel_cuda_ autogen: _foreach_mul.Scalar_out - func: _foreach_mul.List(Tensor[] self, Tensor[] other) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_mul_list_kernel_slow CUDA: foreach_tensor_mul_list_kernel_cuda - func: _foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_mul_list_kernel_slow_ CUDA: foreach_tensor_mul_list_kernel_cuda_ autogen: _foreach_mul.List_out - func: _foreach_mul.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_mul_scalarlist_kernel_slow CUDA: foreach_tensor_mul_scalarlist_kernel_cuda - func: _foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_mul_scalarlist_kernel_slow_ CUDA: foreach_tensor_mul_scalarlist_kernel_cuda_ autogen: _foreach_mul.ScalarList_out - func: _foreach_mul.Tensor(Tensor[] self, Tensor other) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_mul_tensor_kernel_slow CUDA: foreach_tensor_mul_tensor_kernel_cuda - func: _foreach_mul_.Tensor(Tensor(a!)[] self, Tensor other) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_mul_tensor_kernel_slow_ CUDA: foreach_tensor_mul_tensor_kernel_cuda_ autogen: _foreach_mul.Tensor_out - func: _foreach_div.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_div_scalar_kernel_slow CUDA: foreach_tensor_div_scalar_kernel_cuda - func: _foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_div_scalar_kernel_slow_ CUDA: foreach_tensor_div_scalar_kernel_cuda_ autogen: _foreach_div.Scalar_out - func: _foreach_div.List(Tensor[] self, Tensor[] other) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_div_list_kernel_slow CUDA: foreach_tensor_div_list_kernel_cuda - func: _foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_div_list_kernel_slow_ CUDA: foreach_tensor_div_list_kernel_cuda_ autogen: _foreach_div.List_out - func: _foreach_div.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_div_scalarlist_kernel_slow CUDA: foreach_tensor_div_scalarlist_kernel_cuda - func: _foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_div_scalarlist_kernel_slow_ CUDA: foreach_tensor_div_scalarlist_kernel_cuda_ autogen: _foreach_div.ScalarList_out - func: _foreach_div.Tensor(Tensor[] self, Tensor other) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_div_tensor_kernel_slow CUDA: foreach_tensor_div_tensor_kernel_cuda - func: _foreach_div_.Tensor(Tensor(a!)[] self, Tensor other) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_div_tensor_kernel_slow_ CUDA: foreach_tensor_div_tensor_kernel_cuda_ autogen: _foreach_div.Tensor_out - func: _foreach_clamp_max.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_scalar_kernel_slow CUDA: foreach_tensor_clamp_max_scalar_kernel_cuda - func: _foreach_clamp_max_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_scalar_kernel_slow_ CUDA: foreach_tensor_clamp_max_scalar_kernel_cuda_ autogen: _foreach_clamp_max.Scalar_out - func: _foreach_clamp_max.List(Tensor[] self, Tensor[] other) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_list_kernel_slow CUDA: foreach_tensor_clamp_max_list_kernel_cuda - func: _foreach_clamp_max_.List(Tensor(a!)[] self, Tensor[] other) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_list_kernel_slow_ CUDA: foreach_tensor_clamp_max_list_kernel_cuda_ autogen: _foreach_clamp_max.List_out - func: _foreach_clamp_max.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_scalarlist_kernel_slow CUDA: foreach_tensor_clamp_max_scalarlist_kernel_cuda - func: _foreach_clamp_max_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_scalarlist_kernel_slow_ CUDA: foreach_tensor_clamp_max_scalarlist_kernel_cuda_ autogen: _foreach_clamp_max.ScalarList_out - func: _foreach_clamp_min.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_scalar_kernel_slow CUDA: foreach_tensor_clamp_min_scalar_kernel_cuda - func: _foreach_clamp_min_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_scalar_kernel_slow_ CUDA: foreach_tensor_clamp_min_scalar_kernel_cuda_ autogen: _foreach_clamp_min.Scalar_out - func: _foreach_clamp_min.List(Tensor[] self, Tensor[] other) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_list_kernel_slow CUDA: foreach_tensor_clamp_min_list_kernel_cuda - func: _foreach_clamp_min_.List(Tensor(a!)[] self, Tensor[] other) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_list_kernel_slow_ CUDA: foreach_tensor_clamp_min_list_kernel_cuda_ autogen: _foreach_clamp_min.List_out - func: _foreach_clamp_min.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_scalarlist_kernel_slow CUDA: foreach_tensor_clamp_min_scalarlist_kernel_cuda - func: _foreach_clamp_min_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_scalarlist_kernel_slow_ CUDA: foreach_tensor_clamp_min_scalarlist_kernel_cuda_ autogen: _foreach_clamp_min.ScalarList_out # foreach_minimum/maximum dispatches to clamp_max/min - func: _foreach_maximum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_scalar_kernel_slow CUDA: foreach_tensor_clamp_min_scalar_kernel_cuda - func: _foreach_maximum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_scalar_kernel_slow_ CUDA: foreach_tensor_clamp_min_scalar_kernel_cuda_ autogen: _foreach_maximum.Scalar_out # foreach_minimum/maximum dispatches to clamp_max/min - func: _foreach_maximum.List(Tensor[] self, Tensor[] other) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_list_kernel_slow CUDA: foreach_tensor_clamp_min_list_kernel_cuda - func: _foreach_maximum_.List(Tensor(a!)[] self, Tensor[] other) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_list_kernel_slow_ CUDA: foreach_tensor_clamp_min_list_kernel_cuda_ autogen: _foreach_maximum.List_out # foreach_minimum/maximum dispatches to clamp_max/min - func: _foreach_maximum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_scalarlist_kernel_slow CUDA: foreach_tensor_clamp_min_scalarlist_kernel_cuda - func: _foreach_maximum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_min_scalarlist_kernel_slow_ CUDA: foreach_tensor_clamp_min_scalarlist_kernel_cuda_ autogen: _foreach_maximum.ScalarList_out - func: _foreach_minimum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_scalar_kernel_slow CUDA: foreach_tensor_clamp_max_scalar_kernel_cuda - func: _foreach_minimum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_scalar_kernel_slow_ CUDA: foreach_tensor_clamp_max_scalar_kernel_cuda_ autogen: _foreach_minimum.Scalar_out - func: _foreach_minimum.List(Tensor[] self, Tensor[] other) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_list_kernel_slow CUDA: foreach_tensor_clamp_max_list_kernel_cuda - func: _foreach_minimum_.List(Tensor(a!)[] self, Tensor[] other) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_list_kernel_slow_ CUDA: foreach_tensor_clamp_max_list_kernel_cuda_ autogen: _foreach_minimum.List_out - func: _foreach_minimum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_scalarlist_kernel_slow CUDA: foreach_tensor_clamp_max_scalarlist_kernel_cuda - func: _foreach_minimum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_clamp_max_scalarlist_kernel_slow_ CUDA: foreach_tensor_clamp_max_scalarlist_kernel_cuda_ autogen: _foreach_minimum.ScalarList_out - func: _foreach_addcdiv.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcdiv_scalar_slow CUDA: foreach_tensor_addcdiv_scalar_cuda - func: _foreach_addcdiv.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcdiv_scalarlist_slow CUDA: foreach_tensor_addcdiv_scalarlist_cuda - func: _foreach_addcdiv.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcdiv_tensor_slow CUDA: foreach_tensor_addcdiv_tensor_cuda - func: _foreach_addcdiv_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcdiv_scalar_slow_ CUDA: foreach_tensor_addcdiv_scalar_cuda_ autogen: _foreach_addcdiv.Scalar_out - func: _foreach_addcdiv_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcdiv_scalarlist_slow_ CUDA: foreach_tensor_addcdiv_scalarlist_cuda_ autogen: _foreach_addcdiv.ScalarList_out - func: _foreach_addcdiv_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcdiv_tensor_slow_ CUDA: foreach_tensor_addcdiv_tensor_cuda_ autogen: _foreach_addcdiv.Tensor_out - func: _foreach_addcmul.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcmul_scalar_slow CUDA: foreach_tensor_addcmul_scalar_cuda - func: _foreach_addcmul.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcmul_scalarlist_slow CUDA: foreach_tensor_addcmul_scalarlist_cuda - func: _foreach_addcmul.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcmul_tensor_slow CUDA: foreach_tensor_addcmul_tensor_cuda - func: _foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcmul_scalar_slow_ CUDA: foreach_tensor_addcmul_scalar_cuda_ autogen: _foreach_addcmul.Scalar_out - func: _foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcmul_scalarlist_slow_ CUDA: foreach_tensor_addcmul_scalarlist_cuda_ autogen: _foreach_addcmul.ScalarList_out - func: _foreach_addcmul_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_addcmul_tensor_slow_ CUDA: foreach_tensor_addcmul_tensor_cuda_ autogen: _foreach_addcmul.Tensor_out - func: _foreach_abs(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_abs_slow CUDA: foreach_tensor_abs_cuda - func: _foreach_abs_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_abs_slow_ CUDA: foreach_tensor_abs_cuda_ autogen: _foreach_abs.out - func: _foreach_acos(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_acos_slow CUDA: foreach_tensor_acos_cuda - func: _foreach_acos_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_acos_slow_ CUDA: foreach_tensor_acos_cuda_ autogen: _foreach_acos.out - func: _foreach_asin(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_asin_slow CUDA: foreach_tensor_asin_cuda - func: _foreach_asin_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_asin_slow_ CUDA: foreach_tensor_asin_cuda_ autogen: _foreach_asin.out - func: _foreach_atan(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_atan_slow CUDA: foreach_tensor_atan_cuda - func: _foreach_atan_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_atan_slow_ CUDA: foreach_tensor_atan_cuda_ autogen: _foreach_atan.out - func: _foreach_ceil(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_ceil_slow CUDA: foreach_tensor_ceil_cuda - func: _foreach_ceil_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_ceil_slow_ CUDA: foreach_tensor_ceil_cuda_ autogen: _foreach_ceil.out - func: _foreach_cos(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_cos_slow CUDA: foreach_tensor_cos_cuda - func: _foreach_cos_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_cos_slow_ CUDA: foreach_tensor_cos_cuda_ autogen: _foreach_cos.out - func: _foreach_cosh(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_cosh_slow CUDA: foreach_tensor_cosh_cuda - func: _foreach_cosh_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_cosh_slow_ CUDA: foreach_tensor_cosh_cuda_ autogen: _foreach_cosh.out - func: _foreach_erf(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_erf_slow CUDA: foreach_tensor_erf_cuda - func: _foreach_erf_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_erf_slow_ CUDA: foreach_tensor_erf_cuda_ autogen: _foreach_erf.out - func: _foreach_erfc(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_erfc_slow CUDA: foreach_tensor_erfc_cuda - func: _foreach_erfc_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_erfc_slow_ CUDA: foreach_tensor_erfc_cuda_ autogen: _foreach_erfc.out - func: _foreach_exp(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_exp_slow CUDA: foreach_tensor_exp_cuda - func: _foreach_exp_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_exp_slow_ CUDA: foreach_tensor_exp_cuda_ autogen: _foreach_exp.out - func: _foreach_expm1(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_expm1_slow CUDA: foreach_tensor_expm1_cuda - func: _foreach_expm1_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_expm1_slow_ CUDA: foreach_tensor_expm1_cuda_ autogen: _foreach_expm1.out - func: _foreach_floor(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_floor_slow CUDA: foreach_tensor_floor_cuda - func: _foreach_floor_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_floor_slow_ CUDA: foreach_tensor_floor_cuda_ autogen: _foreach_floor.out - func: _foreach_frac(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_frac_slow CUDA: foreach_tensor_frac_cuda - func: _foreach_frac_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_frac_slow_ CUDA: foreach_tensor_frac_cuda_ autogen: _foreach_frac.out - func: _foreach_lerp.List(Tensor[] self, Tensor[] tensors1, Tensor[] weights) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensors are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_ternary_lerp_slow CUDA: foreach_tensor_lerp_ternary_cuda autogen: _foreach_lerp.List_out - func: _foreach_lerp_.List(Tensor(a!)[] self, Tensor[] tensors1, Tensor[] weights) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensors are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_ternary_lerp_slow_ CUDA: foreach_tensor_lerp_ternary_cuda_ autogen: _foreach_lerp.List_out - func: _foreach_lerp.Scalar(Tensor[] self, Tensor[] tensors1, Scalar weight) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensors are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_lerp_list_kernel_slow CUDA: foreach_tensor_lerp_list_cuda autogen: _foreach_lerp.Scalar_out - func: _foreach_lerp_.Scalar(Tensor(a!)[] self, Tensor[] tensors1, Scalar weight) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensors are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_lerp_list_kernel_slow_ CUDA: foreach_tensor_lerp_list_cuda_ autogen: _foreach_lerp.Scalar_out - func: _foreach_lerp.ScalarList(Tensor[] self, Tensor[] tensors1, Scalar[] weight) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensors are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_lerp_scalarlist_kernel_slow CUDA: foreach_tensor_lerp_scalarlist_cuda autogen: _foreach_lerp.ScalarList_out - func: _foreach_lerp_.ScalarList(Tensor(a!)[] self, Tensor[] tensors1, Scalar[] weight) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensors are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_lerp_scalarlist_kernel_slow_ CUDA: foreach_tensor_lerp_scalarlist_cuda_ autogen: _foreach_lerp.ScalarList_out - func: _foreach_lgamma(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_lgamma_slow CUDA: foreach_tensor_lgamma_cuda - func: _foreach_lgamma_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_lgamma_slow_ CUDA: foreach_tensor_lgamma_cuda_ autogen: _foreach_lgamma.out - func: _foreach_log(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_log_slow CUDA: foreach_tensor_log_cuda - func: _foreach_log_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_log_slow_ CUDA: foreach_tensor_log_cuda_ autogen: _foreach_log.out - func: _foreach_log10(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_log10_slow CUDA: foreach_tensor_log10_cuda - func: _foreach_log10_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_log10_slow_ CUDA: foreach_tensor_log10_cuda_ autogen: _foreach_log10.out - func: _foreach_log1p(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_log1p_slow CUDA: foreach_tensor_log1p_cuda - func: _foreach_log1p_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_log1p_slow_ CUDA: foreach_tensor_log1p_cuda_ autogen: _foreach_log1p.out - func: _foreach_log2(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_log2_slow CUDA: foreach_tensor_log2_cuda - func: _foreach_log2_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_log2_slow_ CUDA: foreach_tensor_log2_cuda_ autogen: _foreach_log2.out - func: _foreach_max(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_max_slow CUDA: foreach_tensor_max_cuda autogen: _foreach_max.out - func: _foreach_neg(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_neg_slow CUDA: foreach_tensor_neg_cuda - func: _foreach_neg_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_neg_slow_ CUDA: foreach_tensor_neg_cuda_ autogen: _foreach_neg.out - func: _foreach_norm.Scalar(Tensor[] self, Scalar ord=2, ScalarType? dtype=None) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_norm_slow CUDA: foreach_tensor_norm_cuda autogen: _foreach_norm.Scalar_out - func: _foreach_pow.List(Tensor[] self, Tensor[] exponent) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_pow_list_kernel_slow CUDA: foreach_tensor_pow_list_kernel_cuda - func: _foreach_pow.Scalar(Tensor[] self, Scalar exponent) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_pow_scalar_kernel_slow CUDA: foreach_tensor_pow_scalar_kernel_cuda - func: _foreach_pow.ScalarList(Tensor[] self, Scalar[] exponent) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_pow_scalarlist_kernel_slow CUDA: foreach_tensor_pow_scalarlist_kernel_cuda - func: _foreach_pow.ScalarAndTensor(Scalar self, Tensor[] exponent) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_scalar_pow_list_kernel_slow CUDA: foreach_scalar_pow_list_kernel_cuda - func: _foreach_pow_.List(Tensor(a!)[] self, Tensor[] exponent) -> () device_check: NoCheck variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_pow_list_kernel_slow_ CUDA: foreach_tensor_pow_list_kernel_cuda_ autogen: _foreach_pow.List_out - func: _foreach_pow_.Scalar(Tensor(a!)[] self, Scalar exponent) -> () device_check: NoCheck variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_pow_scalar_kernel_slow_ CUDA: foreach_tensor_pow_scalar_kernel_cuda_ autogen: _foreach_pow.Scalar_out - func: _foreach_pow_.ScalarList(Tensor(a!)[] self, Scalar[] exponent) -> () device_check: NoCheck variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_pow_scalarlist_kernel_slow_ CUDA: foreach_tensor_pow_scalarlist_kernel_cuda_ autogen: _foreach_pow.ScalarList_out - func: _foreach_reciprocal(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_reciprocal_slow CUDA: foreach_tensor_reciprocal_cuda - func: _foreach_reciprocal_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_reciprocal_slow_ CUDA: foreach_tensor_reciprocal_cuda_ autogen: _foreach_reciprocal.out - func: _foreach_round(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_round_slow CUDA: foreach_tensor_round_cuda - func: _foreach_round_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_round_slow_ CUDA: foreach_tensor_round_cuda_ autogen: _foreach_round.out - func: _foreach_rsqrt(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_rsqrt_slow CUDA: foreach_tensor_rsqrt_cuda - func: _foreach_rsqrt_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_rsqrt_slow_ CUDA: foreach_tensor_rsqrt_cuda_ autogen: _foreach_rsqrt.out - func: _foreach_sigmoid(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sigmoid_slow CUDA: foreach_tensor_sigmoid_cuda - func: _foreach_sigmoid_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sigmoid_slow_ CUDA: foreach_tensor_sigmoid_cuda_ autogen: _foreach_sigmoid.out - func: _foreach_sign(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sign_slow CUDA: foreach_tensor_sign_cuda - func: _foreach_sign_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sign_slow_ CUDA: foreach_tensor_sign_cuda_ autogen: _foreach_sign.out - func: _foreach_sin(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sin_slow CUDA: foreach_tensor_sin_cuda - func: _foreach_sin_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sin_slow_ CUDA: foreach_tensor_sin_cuda_ autogen: _foreach_sin.out - func: _foreach_sinh(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sinh_slow CUDA: foreach_tensor_sinh_cuda - func: _foreach_sinh_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sinh_slow_ CUDA: foreach_tensor_sinh_cuda_ autogen: _foreach_sinh.out - func: _foreach_sqrt(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sqrt_slow CUDA: foreach_tensor_sqrt_cuda - func: _foreach_sqrt_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_sqrt_slow_ CUDA: foreach_tensor_sqrt_cuda_ autogen: _foreach_sqrt.out - func: _foreach_tan(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_tan_slow CUDA: foreach_tensor_tan_cuda - func: _foreach_tan_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_tan_slow_ CUDA: foreach_tensor_tan_cuda_ autogen: _foreach_tan.out - func: _foreach_tanh(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_tanh_slow CUDA: foreach_tensor_tanh_cuda - func: _foreach_tanh_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_tanh_slow_ CUDA: foreach_tensor_tanh_cuda_ autogen: _foreach_tanh.out - func: _foreach_trunc(Tensor[] self) -> Tensor[] device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_trunc_slow CUDA: foreach_tensor_trunc_cuda - func: _foreach_trunc_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_trunc_slow_ CUDA: foreach_tensor_trunc_cuda_ autogen: _foreach_trunc.out - func: _foreach_zero_(Tensor(a!)[] self) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_zero_slow_ CUDA: foreach_tensor_zero_cuda_ autogen: _foreach_zero, _foreach_zero.out - func: _foreach_copy_(Tensor(a!)[] self, Tensor[] src, bool non_blocking=False) -> () device_check: NoCheck # foreach kernels fall back to slow path when tensor are on different devices variants: function dispatch: CompositeExplicitAutograd: foreach_tensor_copy_list_kernel_slow_ CUDA: foreach_tensor_copy_list_kernel_cuda_ autogen: _foreach_copy.out - func: _foreach_copy(Tensor[] self, Tensor[] src, bool non_blocking=False) -> Tensor[] self_out device_check: NoCheck variants: function dispatch: CompositeExplicitAutograd: _foreach_copy - func: bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor dispatch: CPU: bucketize_cpu CUDA: bucketize_cuda MPS: bucketize_mps - func: bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) dispatch: CPU: bucketize_out_cpu CUDA: bucketize_out_cuda MPS: bucketize_out_mps - func: bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor dispatch: CPU: bucketize_cpu CUDA: bucketize_cuda MPS: bucketize_mps autogen: bucketize.Scalar_out - func: searchsorted.Tensor(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor dispatch: CPU: searchsorted_cpu CUDA: searchsorted_cuda MPS: searchsorted_mps - func: searchsorted.Tensor_out(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!) dispatch: CPU: searchsorted_out_cpu CUDA: searchsorted_out_cuda MPS: searchsorted_out_mps - func: searchsorted.Scalar(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor dispatch: CPU: searchsorted_cpu CUDA: searchsorted_cuda MPS: searchsorted_mps - func: searchsorted.Scalar_out(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!) dispatch: CPU: searchsorted_out_cpu CUDA: searchsorted_out_cuda MPS: searchsorted_out_mps - func: _convert_indices_from_coo_to_csr(Tensor self, int size, *, bool out_int32=False) -> Tensor structured_delegate: _convert_indices_from_coo_to_csr.out - func: _convert_indices_from_coo_to_csr.out(Tensor self, int size, *, bool out_int32=False, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU: _convert_indices_from_coo_to_csr_structured_cpu CUDA: _convert_indices_from_coo_to_csr_structured_cuda - func: _convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False) -> Tensor structured_delegate: _convert_indices_from_csr_to_coo.out - func: _convert_indices_from_csr_to_coo.out(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor(a!) out) -> Tensor(a!) structured: True dispatch: CPU: _convert_indices_from_csr_to_coo_structured_cpu CUDA: _convert_indices_from_csr_to_coo_structured_cuda ## NN wrappers - func: mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA: mse_loss_out MPS: mse_loss_out_mps - func: mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: mse_loss.out python_module: nn - func: mse_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU, CUDA: mse_loss_backward_out MPS: mse_loss_backward_out_mps - func: mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor python_module: nn dispatch: CPU, CUDA: mse_loss_backward MPS: mse_loss_backward_mps - func: l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor python_module: nn - func: multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CPU: multi_margin_loss_cpu_out CUDA: multi_margin_loss_cuda_out - func: multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensor python_module: nn dispatch: CPU: multi_margin_loss_cpu CUDA: multi_margin_loss_cuda - func: multi_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: multi_margin_loss_cpu_backward_out CUDA: multi_margin_loss_cuda_backward_out - func: multi_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean) -> Tensor python_module: nn dispatch: CPU: multi_margin_loss_cpu_backward CUDA: multi_margin_loss_cuda_backward - func: multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) python_module: nn - func: multilabel_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor python_module: nn - func: multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!)) python_module: nn dispatch: CPU: multilabel_margin_loss_forward_out_cpu CUDA: multilabel_margin_loss_forward_out_cuda - func: multilabel_margin_loss_forward(Tensor self, Tensor target, int reduction) -> (Tensor output, Tensor is_target) python_module: nn dispatch: CPU: multilabel_margin_loss_forward_cpu CUDA: multilabel_margin_loss_forward_cuda - func: multilabel_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: multilabel_margin_loss_backward_cpu_out CUDA: multilabel_margin_loss_backward_cuda_out - func: multilabel_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target) -> Tensor python_module: nn dispatch: CPU: multilabel_margin_loss_backward_cpu CUDA: multilabel_margin_loss_backward_cuda - func: nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) python_module: nn - func: nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor python_module: nn dispatch: CompositeImplicitAutograd: nll_loss_nd_symint - func: nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor python_module: nn dispatch: CompositeImplicitAutograd: nll_loss_symint - func: nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) python_module: nn structured: True dispatch: CPU: nll_loss_forward_out_cpu CUDA: nll_loss_forward_out_cuda MPS: nll_loss_forward_out_mps - func: nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight) python_module: nn structured_delegate: nll_loss_forward.output - func: nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: nll_loss_backward_out_cpu CUDA: nll_loss_backward_out_cuda MPS: nll_loss_backward_out_mps - func: nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor python_module: nn structured_delegate: nll_loss_backward.grad_input - func: nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) python_module: nn - func: nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor python_module: nn dispatch: CompositeImplicitAutograd: nll_loss2d_symint - func: nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) python_module: nn dispatch: CPU: nll_loss2d_forward_out_cpu CUDA: nll_loss2d_forward_out_cuda MPS: nll_loss2d_forward_out_mps - func: nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight) python_module: nn dispatch: CPU: nll_loss2d_forward_cpu CUDA: nll_loss2d_forward_cuda MPS: nll_loss2d_forward_mps - func: nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: nll_loss2d_backward_out_cpu CUDA: nll_loss2d_backward_out_cuda MPS: nll_loss2d_backward_out_mps - func: nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor python_module: nn dispatch: CPU: nll_loss2d_backward_cpu CUDA: nll_loss2d_backward_cuda MPS: nll_loss2d_backward_mps - func: smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, float beta=1.0, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA: smooth_l1_loss_out MPS: smooth_l1_loss_out_mps - func: smooth_l1_loss(Tensor self, Tensor target, int reduction=Mean, float beta=1.0) -> Tensor device_check: NoCheck # TensorIterator structured_delegate: smooth_l1_loss.out python_module: nn - func: smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: smooth_l1_loss_backward_out CUDA: smooth_l1_loss_backward_out MPS: smooth_l1_loss_backward_out_mps - func: smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor python_module: nn dispatch: CompositeExplicitAutograd: smooth_l1_loss_backward - func: huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CPU, CUDA: huber_loss_out MPS: huber_loss_out_mps - func: huber_loss(Tensor self, Tensor target, int reduction=Mean, float delta=1.0) -> Tensor python_module: nn dispatch: CPU, CUDA: huber_loss MPS: huber_loss_mps - func: huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU, CUDA: huber_loss_backward_out MPS: huber_loss_backward_out_mps - func: huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor python_module: nn dispatch: CompositeExplicitAutograd: huber_loss_backward - func: soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CompositeExplicitAutograd: soft_margin_loss_out - func: soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor python_module: nn dispatch: CompositeExplicitAutograd: soft_margin_loss - func: soft_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CompositeExplicitAutograd: soft_margin_loss_backward_out - func: soft_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor python_module: nn dispatch: CompositeExplicitAutograd: soft_margin_loss_backward - func: elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA: elu_out MPS: elu_out_mps - func: elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor structured_delegate: elu.out device_check: NoCheck # TensorIterator python_module: nn tags: [core, pointwise] - func: elu_backward.grad_input(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result, *, Tensor(a!) grad_input) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA: elu_backward_out MPS: elu_backward_out_mps - func: elu_backward(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result) -> Tensor structured_delegate: elu_backward.grad_input python_module: nn - func: elu_(Tensor(a!) self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor(a!) structured_delegate: elu.out device_check: NoCheck # TensorIterator python_module: nn - func: glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA: glu_out MPS: glu_out_mps - func: glu(Tensor self, int dim=-1) -> Tensor structured_delegate: glu.out device_check: NoCheck # TensorIterator python_module: nn - func: glu_backward.grad_input(Tensor grad_output, Tensor self, int dim, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: glu_backward_cpu_out CUDA: glu_backward_cuda_out MPS: glu_backward_mps_out - func: glu_backward(Tensor grad_output, Tensor self, int dim) -> Tensor python_module: nn dispatch: CPU: glu_backward_cpu CUDA: glu_backward_cuda MPS: glu_backward_mps - func: glu_jvp(Tensor glu, Tensor x, Tensor dx, int dim) -> Tensor python_module: nn dispatch: CPU, CUDA: glu_jvp autogen: glu_jvp.out - func: glu_backward_jvp(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim) -> Tensor python_module: nn dispatch: CPU, CUDA: glu_backward_jvp autogen: glu_backward_jvp.out - func: hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA, MPS: hardsigmoid_out QuantizedCPU: hardsigmoid_out_quantized_cpu - func: hardsigmoid(Tensor self) -> Tensor structured_delegate: hardsigmoid.out device_check: NoCheck # TensorIterator python_module: nn dispatch: QuantizedCPU: hardsigmoid_quantized_cpu tags: pointwise - func: hardsigmoid_(Tensor(a!) self) -> Tensor(a!) structured_delegate: hardsigmoid.out device_check: NoCheck # TensorIterator python_module: nn - func: hardsigmoid_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA, MPS: hardsigmoid_backward_out - func: hardsigmoid_backward(Tensor grad_output, Tensor self) -> Tensor structured_delegate: hardsigmoid_backward.grad_input python_module: nn - func: hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA, MPS: hardtanh_out QuantizedCPU: hardtanh_out_quantized_cpu - func: hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA, MPS: hardtanh QuantizedCPU: hardtanh_quantized_cpu tags: [pointwise, core] - func: hardtanh_backward.grad_input(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU, CUDA: hardtanh_backward_out MPS: hardtanh_backward_out_mps - func: hardtanh_backward(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val) -> Tensor python_module: nn dispatch: CPU, CUDA: hardtanh_backward MPS: hardtanh_backward_mps - func: hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA, MPS: hardtanh_ QuantizedCPU: hardtanh_quantized_cpu_ - func: hardswish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA, MPS: hardswish_out - func: hardswish(Tensor self) -> Tensor device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA, MPS: hardswish - func: hardswish_(Tensor(a!) self) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA, MPS: hardswish_ - func: hardswish_backward(Tensor grad_output, Tensor self) -> Tensor python_module: nn dispatch: CPU, CUDA, MPS: hardswish_backward autogen: hardswish_backward.out - func: leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA, MPS: leaky_relu_out QuantizedCPU: leaky_relu_out_quantized_cpu - func: leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor structured_delegate: leaky_relu.out device_check: NoCheck # TensorIterator python_module: nn dispatch: QuantizedCPU: leaky_relu_quantized_cpu tags: core - func: leaky_relu_backward.grad_input(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result, *, Tensor(a!) grad_input) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA, MPS: leaky_relu_backward_out - func: leaky_relu_backward(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result) -> Tensor structured_delegate: leaky_relu_backward.grad_input python_module: nn - func: leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!) structured_delegate: leaky_relu.out device_check: NoCheck # TensorIterator python_module: nn dispatch: QuantizedCPU: leaky_relu_quantized_cpu_ - func: log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: nn - func: log_sigmoid(Tensor self) -> Tensor device_check: NoCheck # TensorIterator python_module: nn - func: log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!)) device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU: log_sigmoid_forward_out_cpu CUDA: log_sigmoid_forward_out_cuda MPS: log_sigmoid_forward_out_mps - func: log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer) device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU: log_sigmoid_forward_cpu CUDA: log_sigmoid_forward_cuda MPS: log_sigmoid_forward_mps - func: log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: log_sigmoid_backward_cpu_out CUDA: log_sigmoid_backward_cuda_out MPS: log_sigmoid_backward_mps_out - func: log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor python_module: nn dispatch: CPU: log_sigmoid_backward_cpu CUDA: log_sigmoid_backward_cuda MPS: log_sigmoid_backward_mps - func: rrelu_with_noise.out(Tensor self, Tensor(b!) noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn tags: nondeterministic_seeded dispatch: CPU: rrelu_with_noise_out_cpu CUDA: rrelu_with_noise_out_cuda - func: rrelu_with_noise(Tensor self, Tensor(b!) noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor python_module: nn dispatch: CPU: rrelu_with_noise_cpu CUDA: rrelu_with_noise_cuda tags: nondeterministic_seeded autogen: rrelu_with_noise_functional - func: rrelu_with_noise_backward(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result) -> Tensor python_module: nn dispatch: CompositeExplicitAutograd: rrelu_with_noise_backward autogen: rrelu_with_noise_backward.out - func: rrelu_with_noise_(Tensor(a!) self, Tensor(b!) noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!) python_module: nn tags: nondeterministic_seeded dispatch: CPU: rrelu_with_noise_cpu_ CUDA: rrelu_with_noise_cuda_ - func: softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA: softplus_out MPS: softplus_out_mps - func: softplus(Tensor self, Scalar beta=1, Scalar threshold=20) -> Tensor structured_delegate: softplus.out device_check: NoCheck # TensorIterator python_module: nn tags: pointwise - func: softplus_backward.grad_input(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA: softplus_backward_out MPS: softplus_backward_out_mps - func: softplus_backward(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold) -> Tensor structured_delegate: softplus_backward.grad_input python_module: nn - func: softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase device_check: NoCheck # TensorIterator python_module: nn dispatch: CPU, CUDA, MPS: softshrink_out - func: softshrink(Tensor self, Scalar lambd=0.5) -> Tensor structured_delegate: softshrink.out device_check: NoCheck # TensorIterator python_module: nn tags: pointwise - func: softshrink_backward.grad_input(Tensor grad_output, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: nn dispatch: CPU, CUDA, MPS: softshrink_backward_out - func: softshrink_backward(Tensor grad_output, Tensor self, Scalar lambd) -> Tensor structured_delegate: softshrink_backward.grad_input python_module: nn - func: adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CPU: adaptive_avg_pool2d_out_cpu CUDA: adaptive_avg_pool2d_out_cuda MPS: adaptive_avg_pool2d_out_mps MkldnnCPU: mkldnn_adaptive_avg_pool2d_out_stub - func: adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor python_module: nn dispatch: CompositeImplicitAutograd: adaptive_avg_pool2d_symint - func: mkldnn_adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor dispatch: MkldnnCPU: mkldnn_adaptive_avg_pool2d - func: mkldnn_adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!) dispatch: MkldnnCPU: mkldnn_adaptive_avg_pool2d_out - func: mkldnn_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor dispatch: MkldnnCPU: mkldnn_adaptive_avg_pool2d_backward autogen: mkldnn_adaptive_avg_pool2d_backward.out - func: _adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor dispatch: CPU: adaptive_avg_pool2d_cpu CUDA: adaptive_avg_pool2d_cuda MPS: adaptive_avg_pool2d_mps QuantizedCPU: adaptive_avg_pool2d_quantized_cpu QuantizedCUDA: adaptive_avg_pool2d_quantized_cuda autogen: _adaptive_avg_pool2d.out tags: core - func: _adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor python_module: nn dispatch: CPU: adaptive_avg_pool2d_backward_cpu CUDA: adaptive_avg_pool2d_backward_cuda MPS: adaptive_avg_pool2d_backward_mps autogen: _adaptive_avg_pool2d_backward.out tags: core - func: adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CPU: adaptive_avg_pool3d_out_cpu CUDA: adaptive_avg_pool3d_out_cuda QuantizedCPU: adaptive_avg_pool3d_out_quantized_cpu - func: adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor python_module: nn dispatch: CompositeImplicitAutograd: adaptive_avg_pool3d_symint - func: _adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor dispatch: CPU: adaptive_avg_pool3d_cpu CUDA: adaptive_avg_pool3d_cuda QuantizedCPU: adaptive_avg_pool3d_quantized_cpu autogen: _adaptive_avg_pool3d.out tags: core - func: adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: adaptive_avg_pool3d_backward_out_cpu CUDA: adaptive_avg_pool3d_backward_out_cuda - func: _adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor python_module: nn dispatch: CPU: adaptive_avg_pool3d_backward_cpu CUDA: adaptive_avg_pool3d_backward_cuda autogen: _adaptive_avg_pool3d_backward.out # Return: (Tensor output, Tensor indices) - func: adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) python_module: nn structured: True dispatch: CPU: adaptive_max_pool2d_out_cpu CUDA: adaptive_max_pool2d_out_cuda MPS: adaptive_max_pool2d_out_mps # Return: (Tensor output, Tensor indices) - func: adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor) python_module: nn structured_delegate: adaptive_max_pool2d.out - func: adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: adaptive_max_pool2d_backward_out_cpu CUDA: adaptive_max_pool2d_backward_out_cuda MPS: adaptive_max_pool2d_backward_out_mps - func: adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor python_module: nn structured_delegate: adaptive_max_pool2d_backward.grad_input # Return: (Tensor output, Tensor indices) - func: adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) python_module: nn structured: True dispatch: CPU: adaptive_max_pool3d_out_cpu CUDA: adaptive_max_pool3d_out_cuda # Return: (Tensor output, Tensor indices) - func: adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor) python_module: nn structured_delegate: adaptive_max_pool3d.out - func: adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: adaptive_max_pool3d_backward_out_cpu CUDA: adaptive_max_pool3d_backward_out_cuda - func: adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor python_module: nn structured_delegate: adaptive_max_pool3d_backward.grad_input - func: avg_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True precomputed: - kernel_size -> int kH, int kW - stride -> int dH, int dW - padding -> int padH, int padW dispatch: CPU: avg_pool2d_out_cpu CUDA: avg_pool2d_out_cuda MPS: avg_pool2d_out_mps MkldnnCPU: mkldnn_avg_pool2d_out - func: avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor python_module: nn structured_delegate: avg_pool2d.out dispatch: MkldnnCPU: mkldnn_avg_pool2d QuantizedCPU: avg_pool2d_quantized_cpu tags: core - func: avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: avg_pool2d_backward_out_cpu CUDA: avg_pool2d_backward_out_cuda MPS: avg_pool2d_backward_out_mps MkldnnCPU: mkldnn_avg_pool2d_backward_out - func: avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor python_module: nn structured_delegate: avg_pool2d_backward.grad_input dispatch: MkldnnCPU: mkldnn_avg_pool2d_backward tags: core - func: avg_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: avg_pool3d_out_cpu CUDA: avg_pool3d_out_cuda MkldnnCPU: mkldnn_avg_pool3d_out - func: avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor python_module: nn structured_delegate: avg_pool3d.out dispatch: MkldnnCPU: mkldnn_avg_pool3d QuantizedCPU: avg_pool3d_quantized_cpu tags: core - func: avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: avg_pool3d_backward_out_cpu CUDA: avg_pool3d_backward_out_cuda MkldnnCPU: mkldnn_avg_pool3d_backward_out - func: avg_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor python_module: nn structured_delegate: avg_pool3d_backward.grad_input dispatch: MkldnnCPU: mkldnn_avg_pool3d_backward # Return: (Tensor output, Tensor indices) - func: fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) python_module: nn structured: True dispatch: CPU: fractional_max_pool2d_out_cpu CUDA: fractional_max_pool2d_out_cuda # Return: (Tensor output, Tensor indices) - func: fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor) python_module: nn structured_delegate: fractional_max_pool2d.output - func: fractional_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: fractional_max_pool2d_backward_cpu CUDA: fractional_max_pool2d_backward_cuda - func: fractional_max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices) -> Tensor python_module: nn structured_delegate: fractional_max_pool2d_backward.grad_input # Return: (Tensor output, Tensor indices) - func: fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) python_module: nn structured: True precomputed: - kernel_size -> int poolSizeT, int poolSizeH, int poolSizeW - output_size -> int outputT, int outputH, int outputW - int numBatch, int numPlanes, int inputT, int inputH, int inputW dispatch: CPU: fractional_max_pool3d_out_cpu CUDA: fractional_max_pool3d_out_cuda # Return: (Tensor output, Tensor indices) - func: fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor) python_module: nn structured_delegate: fractional_max_pool3d.output - func: fractional_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: fractional_max_pool3d_backward_out_cpu CUDA: fractional_max_pool3d_backward_out_cuda - func: fractional_max_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices) -> Tensor python_module: nn dispatch: CPU: fractional_max_pool3d_backward_cpu CUDA: fractional_max_pool3d_backward_cuda # Return: (Tensor output, Tensor indices) - func: max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) python_module: nn structured: True dispatch: CPU: max_pool2d_with_indices_out_cpu CUDA: max_pool2d_with_indices_out_cuda MPS: max_pool2d_with_indices_out_mps # Return: (Tensor output, Tensor indices) - func: max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) python_module: nn structured_delegate: max_pool2d_with_indices.out tags: core - func: max_pool2d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: max_pool2d_with_indices_backward_out_cpu CUDA: max_pool2d_with_indices_backward_out_cuda MPS: max_pool2d_with_indices_backward_out_mps - func: max_pool2d_with_indices_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices) -> Tensor python_module: nn structured_delegate: max_pool2d_with_indices_backward.grad_input tags: core # Return: (Tensor output, Tensor indices) - func: max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) python_module: nn dispatch: CPU: max_pool3d_with_indices_out_cpu CUDA: max_pool3d_with_indices_out_cuda MPS: max_pool3d_with_indices_out_mps # Return: (Tensor output, Tensor indices) - func: max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) python_module: nn dispatch: CPU: max_pool3d_with_indices_cpu CUDA: max_pool3d_with_indices_cuda MPS: max_pool3d_with_indices_mps tags: core - func: max_pool3d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: max_pool3d_with_indices_backward_out_cpu CUDA: max_pool3d_with_indices_backward_out_cuda - func: max_pool3d_with_indices_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices) -> Tensor python_module: nn dispatch: CPU: max_pool3d_with_indices_backward_cpu CUDA: max_pool3d_with_indices_backward_cuda - func: max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CPU: max_unpooling2d_forward_out_cpu CUDA: max_unpooling2d_forward_out_cuda - func: max_unpool2d(Tensor self, Tensor indices, SymInt[2] output_size) -> Tensor python_module: nn dispatch: CPU: max_unpooling2d_forward_cpu CUDA: max_unpooling2d_forward_cuda - func: max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CPU: max_unpooling3d_forward_out_cpu CUDA: max_unpooling3d_forward_out_cuda - func: max_unpool3d(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding) -> Tensor python_module: nn dispatch: CPU: max_unpooling3d_forward_cpu CUDA: max_unpooling3d_forward_cuda - func: reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: reflection_pad1d_out_cpu QuantizedCPU: reflection_pad1d_out_quantized_cpu CUDA: reflection_pad1d_out_cuda MPS: reflection_pad1d_out_mps - func: reflection_pad1d(Tensor self, SymInt[2] padding) -> Tensor python_module: nn structured_delegate: reflection_pad1d.out tags: core - func: reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: reflection_pad1d_backward_out_cpu CUDA: reflection_pad1d_backward_out_cuda MPS: reflection_pad1d_backward_out_mps - func: reflection_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor python_module: nn structured_delegate: reflection_pad1d_backward.grad_input - func: reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CPU, QuantizedCPU: reflection_pad2d_out_cpu CUDA: reflection_pad2d_out_cuda MPS: reflection_pad2d_out_mps - func: reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor python_module: nn dispatch: CPU: reflection_pad2d_cpu QuantizedCPU: reflection_pad2d_quantized_cpu CUDA: reflection_pad2d_cuda MPS: reflection_pad2d_mps tags: core - func: reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: reflection_pad2d_backward_out_cpu CUDA: reflection_pad2d_backward_out_cuda MPS: reflection_pad2d_backward_out_mps - func: reflection_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor python_module: nn dispatch: CPU: reflection_pad2d_backward_cpu CUDA: reflection_pad2d_backward_cuda MPS: reflection_pad2d_backward_mps - func: reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: reflection_pad3d_out_cpu CUDA: reflection_pad3d_out_cuda MPS: reflection_pad3d_out_mps - func: reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor python_module: nn structured_delegate: reflection_pad3d.out tags: core - func: reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: reflection_pad3d_backward_out_cpu CUDA: reflection_pad3d_backward_out_cuda MPS: reflection_pad3d_backward_out_mps - func: reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor python_module: nn structured_delegate: reflection_pad3d_backward.grad_input - func: replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: replication_pad1d_out_cpu CUDA: replication_pad1d_out_cuda MPS: replication_pad1d_out_mps - func: replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor python_module: nn structured_delegate: replication_pad1d.out - func: replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: replication_pad1d_backward_out_cpu CUDA: replication_pad1d_backward_out_cuda MPS: replication_pad1d_backward_out_mps - func: replication_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor python_module: nn structured_delegate: replication_pad1d_backward.grad_input - func: replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: replication_pad2d_out_cpu CUDA: replication_pad2d_out_cuda MPS: replication_pad2d_out_mps - func: replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor python_module: nn structured_delegate: replication_pad2d.out tags: core - func: replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: replication_pad2d_backward_out_cpu CUDA: replication_pad2d_backward_out_cuda MPS: replication_pad2d_backward_out_mps - func: replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor python_module: nn dispatch: CPU: replication_pad2d_backward_cpu CUDA: replication_pad2d_backward_cuda MPS: replication_pad2d_backward_mps - func: replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: replication_pad3d_out_cpu CUDA: replication_pad3d_out_cuda MPS: replication_pad3d_out_mps - func: replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor python_module: nn structured_delegate: replication_pad3d.out tags: core - func: replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn dispatch: CPU: replication_pad3d_backward_out_cpu CUDA: replication_pad3d_backward_out_cuda MPS: replication_pad3d_backward_out_mps - func: replication_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor python_module: nn dispatch: CPU: replication_pad3d_backward_cpu CUDA: replication_pad3d_backward_cuda MPS: replication_pad3d_backward_mps - func: _pad_circular(Tensor self, SymInt[] pad) -> Tensor python_module: nn dispatch: CompositeImplicitAutograd: _pad_circular_symint - func: _pad_enum(Tensor self, SymInt[] pad, int mode, float? value=None) -> Tensor python_module: nn dispatch: CompositeImplicitAutograd: _pad_enum_symint - func: pad(Tensor self, SymInt[] pad, str mode="constant", float? value=None) -> Tensor python_module: nn dispatch: CompositeImplicitAutograd: pad_symint - func: upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor python_module: nn autogen: upsample_linear1d.vec_out - func: upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor python_module: nn autogen: upsample_bilinear2d.vec_out tags: core - func: _upsample_bilinear2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor python_module: nn autogen: _upsample_bilinear2d_aa.vec_out - func: upsample_trilinear3d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor python_module: nn autogen: upsample_trilinear3d.vec_out - func: upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor python_module: nn autogen: upsample_bicubic2d.vec_out - func: _upsample_bicubic2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor python_module: nn autogen: _upsample_bicubic2d_aa.vec_out - func: upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor python_module: nn autogen: upsample_nearest1d.vec_out - func: _upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor python_module: nn autogen: _upsample_nearest_exact1d.vec_out - func: upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor python_module: nn autogen: upsample_nearest2d.vec_out tags: core - func: _upsample_nearest_exact2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor python_module: nn autogen: _upsample_nearest_exact2d.vec_out - func: upsample_nearest3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor python_module: nn autogen: upsample_nearest3d.vec_out - func: _upsample_nearest_exact3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor python_module: nn autogen: _upsample_nearest_exact3d.vec_out # NOTE: all of the non-"vec" upsample overloads are only kept for backward compatibility. - func: upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_linear1d_out_cpu CUDA: upsample_linear1d_out_cuda MPS: upsample_linear1d_out_mps - func: upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor python_module: nn structured_delegate: upsample_linear1d.out - func: upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_linear1d_backward_out_cpu CUDA: upsample_linear1d_backward_out_cuda MPS: upsample_linear1d_backward_out_mps - func: upsample_linear1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None) -> Tensor python_module: nn structured_delegate: upsample_linear1d_backward.grad_input - func: upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_bilinear2d_out_cpu CUDA: upsample_bilinear2d_out_cuda MPS: upsample_bilinear2d_out_mps - func: upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: upsample_bilinear2d.out dispatch: QuantizedCPU: upsample_bilinear2d_quantized_cpu - func: upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_bilinear2d_backward_out_cpu CUDA: upsample_bilinear2d_backward_out_cuda MPS: upsample_bilinear2d_backward_out_mps - func: upsample_bilinear2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: upsample_bilinear2d_backward.grad_input - func: _upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: _upsample_bilinear2d_aa_out_cpu CUDA: _upsample_bilinear2d_aa_out_cuda MPS: _upsample_bilinear2d_aa_out_mps - func: _upsample_bilinear2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: _upsample_bilinear2d_aa.out - func: _upsample_bilinear2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: _upsample_bilinear2d_aa_backward_out_cpu CUDA: _upsample_bilinear2d_aa_backward_out_cuda - func: _upsample_bilinear2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: _upsample_bilinear2d_aa_backward.grad_input - func: upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_bicubic2d_out_cpu CUDA: upsample_bicubic2d_out_cuda MPS: upsample_bicubic2d_out_mps - func: upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: upsample_bicubic2d.out - func: upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_bicubic2d_backward_out_cpu CUDA: upsample_bicubic2d_backward_out_cuda MPS: upsample_bicubic2d_backward_out_mps - func: upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: upsample_bicubic2d_backward.grad_input - func: _upsample_bicubic2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: _upsample_bicubic2d_aa_out_cpu CUDA: _upsample_bicubic2d_aa_out_cuda MPS: _upsample_bicubic2d_aa_out_mps - func: _upsample_bicubic2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: _upsample_bicubic2d_aa.out - func: _upsample_bicubic2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: _upsample_bicubic2d_aa_backward_out_cpu CUDA: _upsample_bicubic2d_aa_backward_out_cuda - func: _upsample_bicubic2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: _upsample_bicubic2d_aa_backward.grad_input - func: upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_trilinear3d_out_cpu CUDA: upsample_trilinear3d_out_cuda MPS: upsample_trilinear3d_out_mps - func: upsample_trilinear3d(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: upsample_trilinear3d.out - func: upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_trilinear3d_backward_out_cpu CUDA: upsample_trilinear3d_backward_out_cuda MPS: upsample_trilinear3d_backward_out_mps - func: upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: upsample_trilinear3d_backward.grad_input - func: upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_nearest1d_out_cpu CUDA: upsample_nearest1d_out_cuda MPS: upsample_nearest1d_out_mps - func: _upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: _upsample_nearest_exact1d_out_cpu CUDA: _upsample_nearest_exact1d_out_cuda MPS: _upsample_nearest_exact1d_out_mps - func: upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor python_module: nn structured_delegate: upsample_nearest1d.out - func: _upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor python_module: nn structured_delegate: _upsample_nearest_exact1d.out - func: upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_nearest1d_backward_out_cpu CUDA: upsample_nearest1d_backward_out_cuda MPS: upsample_nearest1d_backward_out_mps - func: _upsample_nearest_exact1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: _upsample_nearest_exact1d_backward_out_cpu CUDA: _upsample_nearest_exact1d_backward_out_cuda MPS: _upsample_nearest_exact1d_backward_out_mps - func: upsample_nearest1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor python_module: nn structured_delegate: upsample_nearest1d_backward.grad_input - func: _upsample_nearest_exact1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor python_module: nn structured_delegate: _upsample_nearest_exact1d_backward.grad_input - func: upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_nearest2d_out_cpu CUDA: upsample_nearest2d_out_cuda MPS: upsample_nearest2d_out_mps - func: _upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: _upsample_nearest_exact2d_out_cpu CUDA: _upsample_nearest_exact2d_out_cuda MPS: _upsample_nearest_exact2d_out_mps - func: upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: upsample_nearest2d.out dispatch: QuantizedCPU: upsample_nearest2d_quantized_cpu - func: _upsample_nearest_exact2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: _upsample_nearest_exact2d.out dispatch: QuantizedCPU: _upsample_nearest_exact2d_quantized_cpu - func: upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_nearest2d_backward_out_cpu CUDA: upsample_nearest2d_backward_out_cuda MPS: upsample_nearest2d_backward_out_mps - func: _upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: _upsample_nearest_exact2d_backward_out_cpu CUDA: _upsample_nearest_exact2d_backward_out_cuda MPS: _upsample_nearest_exact2d_backward_out_mps - func: upsample_nearest2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: upsample_nearest2d_backward.grad_input - func: _upsample_nearest_exact2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: _upsample_nearest_exact2d_backward.grad_input - func: upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_nearest3d_out_cpu CUDA: upsample_nearest3d_out_cuda MPS: upsample_nearest3d_out_mps - func: _upsample_nearest_exact3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: _upsample_nearest_exact3d_out_cpu CUDA: _upsample_nearest_exact3d_out_cuda MPS: _upsample_nearest_exact3d_out_mps - func: upsample_nearest3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: upsample_nearest3d.out dispatch: QuantizedCPU: upsample_nearest3d_quantized_cpu - func: _upsample_nearest_exact3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: _upsample_nearest_exact3d.out dispatch: QuantizedCPU: _upsample_nearest_exact3d_quantized_cpu - func: upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: upsample_nearest3d_backward_out_cpu CUDA: upsample_nearest3d_backward_out_cuda MPS: upsample_nearest3d_backward_out_mps - func: _upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: _upsample_nearest_exact3d_backward_out_cpu CUDA: _upsample_nearest_exact3d_backward_out_cuda MPS: _upsample_nearest_exact3d_backward_out_mps - func: upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: upsample_nearest3d_backward.grad_input - func: _upsample_nearest_exact3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor python_module: nn structured_delegate: _upsample_nearest_exact3d_backward.grad_input - func: sigmoid_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: sigmoid_backward_out MPS: sigmoid_backward_out_mps tags: pointwise - func: sigmoid_backward(Tensor grad_output, Tensor output) -> Tensor python_module: nn structured_delegate: sigmoid_backward.grad_input tags: pointwise - func: logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: logit_backward_out MPS: logit_backward_out_mps tags: pointwise - func: logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor python_module: nn structured_delegate: logit_backward.grad_input tags: pointwise - func: tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!) python_module: nn structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MTIA: tanh_backward_out MPS: tanh_backward_out_mps tags: pointwise - func: tanh_backward(Tensor grad_output, Tensor output) -> Tensor python_module: nn structured_delegate: tanh_backward.grad_input # What's a thnn_conv_ versus a slow_conv_? # # Historically, we have inefficient implementations of convolutions # coming from the THNN/THCUNN library. These convolutions typically # operated by computing the Toeplitz matrix and then doing a matrix # multiply with the input; this is very memory inefficient! However, # occasionally, we really don't have anything better, so it's helpful # to have these fallbacks when there is no more optimized implementation # in cudnn or mkldnn, etc. Both thnn_ and slow_ convolutions fall # into this bucket. # # The difference between these two designations, is that thnn_ refers # to a convolution that is still written in the "legacy" style; that is, # C code in the THNN/ or THCUNN/ directory. A slow_ convolution is # one that is written in the native style: modern C++. Algorithmically, # these are the same thing, but we give them different prefixes to # make the operational distinction clear. tags: pointwise - func: slow_conv_transpose2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) python_module: nn structured: True dispatch: CPU: slow_conv_transpose2d_structured_cpu CUDA: slow_conv_transpose2d_structured_cuda - func: slow_conv_transpose2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1) -> Tensor python_module: nn structured_delegate: slow_conv_transpose2d.out - func: slow_conv_transpose3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CPU: slow_conv_transpose3d_out_cpu CUDA: slow_conv_transpose3d_out_cuda - func: slow_conv_transpose3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1) -> Tensor python_module: nn dispatch: CPU: slow_conv_transpose3d_cpu CUDA: slow_conv_transpose3d_cuda - func: thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!) python_module: nn - func: thnn_conv2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0) -> Tensor python_module: nn - func: _slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!) python_module: nn dispatch: CPU: slow_conv2d_forward_out_cpu CUDA: slow_conv2d_forward_out_cuda - func: _slow_conv2d_forward(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding) -> Tensor python_module: nn dispatch: CPU: slow_conv2d_forward_cpu CUDA: slow_conv2d_forward_cuda - func: _slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) python_module: nn dispatch: CPU: slow_conv2d_backward_out_cpu CUDA: slow_conv2d_backward_out_cuda - func: _slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias) python_module: nn dispatch: CPU: slow_conv2d_backward_cpu CUDA: slow_conv2d_backward_cuda autogen: _slow_conv2d_backward.output_mask_out - func: _conv_depthwise2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CUDA: conv_depthwise2d_cuda_out - func: _conv_depthwise2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation) -> Tensor python_module: nn dispatch: CUDA: conv_depthwise2d_cuda - func: conv_depthwise3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation) -> Tensor python_module: nn dispatch: CUDA: conv_depthwise3d_cuda autogen: conv_depthwise3d.out - func: slow_conv3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!) python_module: nn - func: slow_conv3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0) -> Tensor python_module: nn - func: slow_conv3d_forward.output(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!) python_module: nn dispatch: CPU: slow_conv3d_forward_out_cpu - func: slow_conv3d_forward(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding) -> Tensor python_module: nn dispatch: CPU: slow_conv3d_forward_cpu - func: slow_conv_dilated2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1) -> Tensor python_module: nn dispatch: CPU: slow_conv_dilated2d_cpu CUDA: slow_conv_dilated2d_cuda autogen: slow_conv_dilated2d.out - func: slow_conv_dilated3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1) -> Tensor python_module: nn dispatch: CPU: slow_conv_dilated3d_cpu CUDA: slow_conv_dilated3d_cuda autogen: slow_conv_dilated3d.out - func: col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CPU: col2im_out_cpu CUDA: col2im_out_cuda MPS: col2im_out_mps - func: col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor python_module: nn dispatch: CPU: col2im_cpu CUDA: col2im_cuda MPS: col2im_mps tags: core - func: column_stack(Tensor[] tensors) -> Tensor - func: column_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) - func: im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) python_module: nn dispatch: CPU: im2col_out_cpu CUDA: im2col_out_cuda MPS: im2col_out_mps - func: im2col(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor python_module: nn dispatch: CPU: im2col_cpu CUDA: im2col_cuda MPS: im2col_mps - func: isfinite(Tensor self) -> Tensor variants: function, method device_check: NoCheck device_guard: False tags: pointwise - func: isinf(Tensor self) -> Tensor variants: function, method device_check: NoCheck device_guard: False dispatch: CompositeExplicitAutograd: isinf NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_isinf SparseCPU, SparseCUDA: isinf_sparse SparseMeta: isinf_sparse_meta SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: isinf_sparse_csr autogen: isinf.out tags: [core, pointwise] - func: record_stream(Tensor(a!) self, Stream s) -> () variants: method dispatch: CUDA: record_stream_cuda - func: isposinf(Tensor self) -> Tensor variants: function, method structured_delegate: isposinf.out dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_isposinf SparseCPU, SparseCUDA: isposinf_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: isposinf_sparse_csr tags: pointwise - func: isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: isposinf_out SparseCPU, SparseCUDA: isposinf_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: isposinf_sparse_csr_out tags: pointwise - func: isneginf(Tensor self) -> Tensor variants: function, method structured_delegate: isneginf.out dispatch: NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: NestedTensor_isneginf SparseCPU, SparseCUDA: isneginf_sparse SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: isneginf_sparse_csr tags: pointwise - func: isneginf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: isneginf_out SparseCPU, SparseCUDA: isneginf_sparse_out SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: isneginf_sparse_csr_out tags: pointwise # NOTE [_add_batch_dim and _remove_batch_dim] # _add_batch_dim and _remove_batch_dim are meant to be used in the implementation # of the vmap frontend API (see torch/_vmap_internals.py). They are not # user-facing, hence the leading underscore. Please don't use them them anywhere else. - func: _add_batch_dim(Tensor self, int batch_dim, int level) -> Tensor variants: function # See NOTE [_add_batch_dim and _remove_batch_dim] - func: _remove_batch_dim(Tensor self, int level, SymInt batch_size, int out_dim) -> Tensor variants: function ## Functions related to the `torch.special` namespace # Note [special namespace binding] # Functions in the special python module should have their names start with # "special_" underscore and be bound to the desired Python name in # torch/special/__init__.py, and the desired C++ name in torch/csrc/api/include/torch/special.h. # The "special_" names should be hidden from the user and not documented. - func: special_entr(Tensor self) -> Tensor structured_delegate: special_entr.out python_module: special variants: function tags: pointwise - func: special_entr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: special variants: function dispatch: CPU, CUDA, MPS: special_entr_out tags: pointwise - func: special_ndtri(Tensor self) -> Tensor structured_delegate: special_ndtri.out python_module: special variants: function tags: pointwise - func: special_ndtri.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: special variants: function dispatch: CPU, CUDA: special_ndtri_out tags: pointwise - func: special_log_ndtr(Tensor self) -> Tensor structured_delegate: special_log_ndtr.out python_module: special variants: function tags: pointwise - func: special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) structured: True structured_inherits: TensorIteratorBase python_module: special variants: function dispatch: CPU, CUDA: special_log_ndtr_out tags: pointwise - func: special_expm1(Tensor self) -> Tensor python_module: special variants: function - func: special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_exp2(Tensor self) -> Tensor python_module: special variants: function - func: special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_psi(Tensor self) -> Tensor python_module: special variants: function - func: special_psi.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_digamma(Tensor self) -> Tensor python_module: special variants: function - func: special_digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_gammaln(Tensor self) -> Tensor python_module: special variants: function - func: special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_erf(Tensor self) -> Tensor python_module: special variants: function - func: special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_erfc(Tensor self) -> Tensor python_module: special variants: function - func: special_erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special - func: special_erfcx(Tensor self) -> Tensor python_module: special variants: function structured_delegate: special_erfcx.out tags: pointwise - func: special_erfcx.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA: special_erfcx_out tags: pointwise - func: special_erfinv(Tensor self) -> Tensor python_module: special variants: function - func: special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special - func: special_ndtr(Tensor self) -> Tensor python_module: special variants: function - func: special_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_xlog1py(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator python_module: special variants: function structured_delegate: special_xlog1py.out tags: pointwise - func: special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator python_module: special variants: function dispatch: CompositeExplicitAutograd: special_xlog1py tags: pointwise - func: special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator python_module: special variants: function dispatch: CompositeExplicitAutograd: special_xlog1py tags: pointwise - func: special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase python_module: special variants: function dispatch: CPU, CUDA, MPS: special_xlog1py_out tags: pointwise - func: special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: special variants: function dispatch: CompositeExplicitAutograd: special_xlog1py_out tags: pointwise - func: special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: special variants: function dispatch: CompositeExplicitAutograd: special_xlog1py_out tags: pointwise - func: special_xlogy(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator python_module: special variants: function - func: special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator python_module: special variants: function - func: special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator python_module: special variants: function - func: special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: special variants: function - func: special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: special variants: function - func: special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: special variants: function - func: special_zeta(Tensor self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator python_module: special variants: function structured_delegate: special_zeta.out tags: pointwise - func: special_zeta.self_scalar(Scalar self, Tensor other) -> Tensor device_check: NoCheck # TensorIterator python_module: special variants: function dispatch: CompositeExplicitAutograd: special_zeta tags: pointwise - func: special_zeta.other_scalar(Tensor self, Scalar other) -> Tensor device_check: NoCheck # TensorIterator python_module: special variants: function dispatch: CompositeExplicitAutograd: special_zeta tags: pointwise - func: special_zeta.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator structured: True structured_inherits: TensorIteratorBase python_module: special variants: function dispatch: CPU, CUDA, MPS: special_zeta_out tags: pointwise - func: special_zeta.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: special variants: function dispatch: CompositeExplicitAutograd: special_zeta_out tags: pointwise - func: special_zeta.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck # TensorIterator python_module: special variants: function dispatch: CompositeExplicitAutograd: special_zeta_out tags: pointwise - func: special_i0(Tensor self) -> Tensor python_module: special variants: function - func: special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_i0e(Tensor self) -> Tensor python_module: special variants: function structured_delegate: special_i0e.out tags: pointwise - func: special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: special_i0e_out tags: pointwise - func: special_i1(Tensor self) -> Tensor python_module: special variants: function structured_delegate: special_i1.out tags: pointwise - func: special_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: special_i1_out tags: pointwise - func: special_i1e(Tensor self) -> Tensor python_module: special variants: function structured_delegate: special_i1e.out tags: pointwise - func: special_i1e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special structured: True structured_inherits: TensorIteratorBase dispatch: CPU, CUDA, MPS: special_i1e_out tags: pointwise - func: special_logit(Tensor self, float? eps=None) -> Tensor python_module: special variants: function - func: special_logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!) python_module: special - func: special_polygamma(int n, Tensor self) -> Tensor python_module: special variants: function - func: special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special - func: special_logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor python_module: special variants: function - func: special_logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) python_module: special - func: special_expit(Tensor self) -> Tensor python_module: special variants: function - func: special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_sinc(Tensor self) -> Tensor python_module: special variants: function - func: special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_round(Tensor self, *, int decimals=0) -> Tensor python_module: special variants: function - func: special_round.out(Tensor self, *, int decimals=0, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_log1p(Tensor self) -> Tensor python_module: special variants: function - func: special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_log_softmax(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor python_module: special variants: function - func: special_gammainc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_gammainc(Tensor self, Tensor other) -> Tensor python_module: special variants: function - func: special_gammaincc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_gammaincc(Tensor self, Tensor other) -> Tensor python_module: special variants: function - func: special_multigammaln(Tensor self, int p) -> Tensor python_module: special variants: function - func: special_multigammaln.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!) python_module: special variants: function - func: special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor python_module: special variants: function ## Functions related to the fast Fourier transform and the torch.fft namespace # Note [FFT namespace binding] # Functions in the fft python module should have their names start with # "fft_" underscore and be bound to the desired Python name in # torch/fft/__init__.py, and the desired C++ name in torch/csrc/api/include/torch/fft.h. # The "fft_" names should be hidden from the user and not documented. # # See fft_fft as an example. # torch.fft.fft # NOTE: NOT an alias for torch.fft, which has different semantics - func: fft_fft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_fft_symint - func: fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_fft_symint_out - func: fft_ifft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ifft_symint - func: fft_ifft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ifft_symint_out - func: fft_rfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_rfft_symint - func: fft_rfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_rfft_symint_out - func: fft_irfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_irfft_symint - func: fft_irfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_irfft_symint_out - func: fft_hfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_hfft_symint - func: fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_hfft_symint_out - func: fft_ihfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ihfft_symint - func: fft_ihfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ihfft_symint_out - func: fft_fft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_fft2_symint - func: fft_fft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_fft2_symint_out - func: fft_ifft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ifft2_symint - func: fft_ifft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ifft2_symint_out - func: fft_rfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_rfft2_symint - func: fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_rfft2_symint_out - func: fft_irfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_irfft2_symint - func: fft_irfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_irfft2_symint_out - func: fft_hfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor use_const_ref_for_mutable_tensors: True python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_hfft2_symint - func: fft_hfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_hfft2_symint_out - func: fft_ihfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor use_const_ref_for_mutable_tensors: True python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ihfft2_symint - func: fft_ihfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ihfft2_symint_out - func: fft_fftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_fftn_symint - func: fft_fftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_fftn_symint_out - func: fft_ifftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ifftn_symint - func: fft_ifftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ifftn_symint_out - func: fft_rfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_rfftn_symint - func: fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_rfftn_symint_out - func: fft_irfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_irfftn_symint - func: fft_irfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_irfftn_symint_out - func: fft_hfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor use_const_ref_for_mutable_tensors: True python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_hfftn_symint - func: fft_hfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_hfftn_symint_out - func: fft_ihfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor use_const_ref_for_mutable_tensors: True python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ihfftn_symint - func: fft_ihfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeImplicitAutograd: fft_ihfftn_symint_out - func: fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor python_module: fft variants: function dispatch: CompositeExplicitAutograd: fft_fftfreq - func: fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeExplicitAutograd: fft_fftfreq_out - func: fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor python_module: fft variants: function dispatch: CompositeExplicitAutograd: fft_rfftfreq - func: fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) python_module: fft variants: function dispatch: CompositeExplicitAutograd: fft_rfftfreq_out - func: fft_fftshift(Tensor self, int[1]? dim=None) -> Tensor python_module: fft variants: function - func: fft_ifftshift(Tensor self, int[1]? dim=None) -> Tensor python_module: fft variants: function ## Functions for linear algebra and the torch.linalg namespace # Note [linalg namespace binding] # Functions in the linalg python module should have their names start with # "linalg_" and be bound to the desired Python name in # torch/linalg/__init__.py, and the desired C++ name in torch/csrc/api/include/torch/linalg.h. # The "linalg_" names should be hidden from the user and not documented. # # See linalg_det as an example. # "_ex" stands for experimental - func: linalg_cholesky_ex(Tensor self, *, bool upper=False, bool check_errors=False) -> (Tensor L, Tensor info) python_module: linalg structured_delegate: linalg_cholesky_ex.L - func: linalg_cholesky_ex.L(Tensor self, *, bool upper=False, bool check_errors=False, Tensor(a!) L, Tensor(b!) info) -> (Tensor(a!) L, Tensor(b!) info) python_module: linalg structured: True dispatch: CPU, CUDA, MPS: linalg_cholesky_ex_out - func: linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor python_module: linalg - func: linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!) python_module: linalg - func: linalg_cross(Tensor self, Tensor other, *, int dim=-1) -> Tensor python_module: linalg variants: function structured_delegate: linalg_cross.out dispatch: ZeroTensor: linalg_cross_zerotensor - func: linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!) python_module: linalg structured: True dispatch: CPU, CUDA, MPS: linalg_cross_out # linalg.lu_factor - func: linalg_lu_factor(Tensor A, *, bool pivot=True) -> (Tensor LU, Tensor pivots) python_module: linalg variants: function dispatch: CompositeImplicitAutograd: linalg_lu_factor MPS: linalg_lu_factor_mps - func: linalg_lu_factor.out(Tensor A, *, bool pivot=True, Tensor(a!) LU, Tensor(b!) pivots) -> (Tensor(a!) LU, Tensor(b!) pivots) python_module: linalg variants: function dispatch: CompositeImplicitAutograd: linalg_lu_factor_out MPS: linalg_lu_factor_out_mps - func: linalg_lu_factor_ex(Tensor A, *, bool pivot=True, bool check_errors=False) -> (Tensor LU, Tensor pivots, Tensor info) python_module: linalg structured_delegate: linalg_lu_factor_ex.out variants: function - func: linalg_lu_factor_ex.out(Tensor A, *, bool pivot=True, bool check_errors=False, Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) python_module: linalg variants: function structured: True dispatch: CPU, CUDA: linalg_lu_factor_ex_out MPS: linalg_lu_factor_ex_out_mps # linalg.lu - func: linalg_lu(Tensor A, *, bool pivot=True) -> (Tensor P, Tensor L, Tensor U) python_module: linalg structured_delegate: linalg_lu.out variants: function - func: linalg_lu.out(Tensor A, *, bool pivot=True, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) python_module: linalg variants: function structured: True dispatch: CPU, CUDA: linalg_lu_out # linalg.lu_solve - func: linalg_lu_solve(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False) -> Tensor python_module: linalg structured_delegate: linalg_lu_solve.out variants: function - func: linalg_lu_solve.out(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function structured: True dispatch: CPU, CUDA: linalg_lu_solve_out # linalg.det - func: _linalg_det(Tensor A) -> (Tensor result, Tensor LU, Tensor pivots) structured_delegate: _linalg_det.result - func: _linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) structured: True dispatch: CPU, CUDA, MPS: _linalg_det_out - func: linalg_det(Tensor A) -> Tensor python_module: linalg variants: function - func: linalg_det.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg # torch.det, alias for torch.linalg.det - func: det(Tensor self) -> Tensor variants: function, method - func: linalg_ldl_factor_ex(Tensor self, *, bool hermitian=False, bool check_errors=False) -> (Tensor LD, Tensor pivots, Tensor info) structured_delegate: linalg_ldl_factor_ex.out python_module: linalg variants: function - func: linalg_ldl_factor_ex.out(Tensor self, *, bool hermitian=False, bool check_errors=False, Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info) structured: True python_module: linalg variants: function dispatch: CPU, CUDA: linalg_ldl_factor_ex_out - func: linalg_ldl_factor(Tensor self, *, bool hermitian=False) -> (Tensor LD, Tensor pivots) python_module: linalg variants: function - func: linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots) python_module: linalg variants: function - func: linalg_ldl_solve(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False) -> Tensor structured_delegate: linalg_ldl_solve.out python_module: linalg variants: function - func: linalg_ldl_solve.out(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) structured: True python_module: linalg variants: function dispatch: CPU, CUDA: linalg_ldl_solve_out - func: linalg_lstsq(Tensor self, Tensor b, float? rcond=None, *, str? driver=None) -> (Tensor solution, Tensor residuals, Tensor rank, Tensor singular_values) python_module: linalg variants: function dispatch: CompositeExplicitAutograd: linalg_lstsq tags: dynamic_output_shape - func: linalg_lstsq.out(Tensor self, Tensor b, float? rcond=None, *, str? driver=None, Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) -> (Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) python_module: linalg variants: function dispatch: CPU, CUDA: linalg_lstsq_out tags: dynamic_output_shape # torch.linalg.matmul, alias for torch.matmul - func: linalg_matmul(Tensor self, Tensor other) -> Tensor python_module: linalg variants: function - func: linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg - func: linalg_vecdot(Tensor x, Tensor y, *, int dim=-1) -> Tensor python_module: linalg variants: function - func: linalg_vecdot.out(Tensor x, Tensor y, *, int dim=-1, Tensor(a!) out) -> Tensor(a!) python_module: linalg - func: linalg_matrix_exp(Tensor self) -> Tensor python_module: linalg variants: function dispatch: CPU, CUDA: linalg_matrix_exp autogen: linalg_matrix_exp.out - func: _linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet, Tensor LU, Tensor pivots) structured_delegate: _linalg_slogdet.sign - func: _linalg_slogdet.sign(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots) -> (Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots) structured: True dispatch: CPU, CUDA, MPS: _linalg_slogdet_out - func: linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet) python_module: linalg - func: linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet) python_module: linalg - func: slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet) variants: function, method - func: slogdet.out(Tensor self, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet) variants: function - func: logdet(Tensor self) -> Tensor variants: function, method - func: linalg_eig(Tensor self) -> (Tensor eigenvalues, Tensor eigenvectors) python_module: linalg variants: function dispatch: CPU, CUDA: linalg_eig - func: linalg_eig.out(Tensor self, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) python_module: linalg dispatch: CPU, CUDA: linalg_eig_out - func: _linalg_eigvals(Tensor self) -> Tensor python_module: linalg dispatch: CPU, CUDA: _linalg_eigvals - func: linalg_eigvals(Tensor self) -> Tensor python_module: linalg - func: linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg dispatch: CPU, CUDA: linalg_eigvals_out # This function is exposes the `compute_v` flag, which is then used to implement `linalg.eigh` and # `linalg.eigvalsh` as composite functions that call this one - func: _linalg_eigh(Tensor A, str UPLO="L", bool compute_v=True) -> (Tensor eigenvalues, Tensor eigenvectors) structured_delegate: _linalg_eigh.eigenvalues - func: _linalg_eigh.eigenvalues(Tensor A, str UPLO="L", bool compute_v=True, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) structured: True dispatch: CPU, CUDA: _linalg_eigh_out - func: linalg_eigh(Tensor self, str UPLO="L") -> (Tensor eigenvalues, Tensor eigenvectors) python_module: linalg - func: linalg_eigh.eigvals(Tensor self, str UPLO="L", *, Tensor(a!) eigvals, Tensor(b!) eigvecs) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) python_module: linalg - func: linalg_eigvalsh(Tensor self, str UPLO="L") -> Tensor python_module: linalg - func: linalg_eigvalsh.out(Tensor self, str UPLO="L", *, Tensor(a!) out) -> Tensor(a!) python_module: linalg - func: linalg_householder_product(Tensor input, Tensor tau) -> Tensor python_module: linalg variants: function dispatch: CPU, CUDA: linalg_householder_product - func: linalg_householder_product.out(Tensor input, Tensor tau, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg dispatch: CPU, CUDA: linalg_householder_product_out - func: linalg_inv_ex(Tensor A, *, bool check_errors=False) -> (Tensor inverse, Tensor info) python_module: linalg structured_delegate: linalg_inv_ex.inverse - func: linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info) python_module: linalg structured: True dispatch: CPU, CUDA: linalg_inv_ex_out MPS: linalg_inv_ex_out_mps - func: linalg_inv(Tensor A) -> Tensor python_module: linalg - func: linalg_inv.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg - func: inverse(Tensor self) -> Tensor variants: function, method - func: inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) - func: inner(Tensor self, Tensor other) -> Tensor variants: function, method - func: inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) - func: outer(Tensor self, Tensor vec2) -> Tensor variants: function, method - func: outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) # torch.ger, alias for torch.outer - func: ger(Tensor self, Tensor vec2) -> Tensor variants: function, method - func: ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) - func: linalg_norm(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor python_module: linalg variants: function - func: linalg_norm.ord_str(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor python_module: linalg variants: function - func: linalg_norm.out(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_norm.ord_str_out(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_vector_norm(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor python_module: linalg variants: function structured_delegate: linalg_vector_norm.out - func: linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) python_module: linalg structured: True dispatch: CPU, CUDA: linalg_vector_norm_out MPS: linalg_vector_norm_out_mps - func: linalg_matrix_norm(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor python_module: linalg - func: linalg_matrix_norm.out(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) python_module: linalg - func: linalg_matrix_norm.str_ord(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor python_module: linalg - func: linalg_matrix_norm.str_ord_out(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) python_module: linalg # This function is exposes the `compute_uv` flag, which is then used to implement `linalg.svd` and # `linalg.svdvals` as composite functions that call this one - func: _linalg_svd(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh) variants: function structured_delegate: _linalg_svd.U - func: _linalg_svd.U(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) structured: True dispatch: CPU, CUDA: _linalg_svd_out - func: linalg_svd(Tensor A, bool full_matrices=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh) python_module: linalg variants: function - func: linalg_svd.U(Tensor A, bool full_matrices=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) python_module: linalg variants: function - func: linalg_svdvals(Tensor A, *, str? driver=None) -> Tensor python_module: linalg variants: function - func: linalg_svdvals.out(Tensor A, *, str? driver=None, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_cond(Tensor self, Scalar? p=None) -> Tensor python_module: linalg variants: function - func: linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_cond.p_str(Tensor self, str p) -> Tensor python_module: linalg variants: function - func: linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_pinv.atol_rtol_tensor(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor python_module: linalg variants: function dispatch: # calls svd, which calls mH() (view op) # also calls narrow() CompositeExplicitAutogradNonFunctional: linalg_pinv - func: linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function dispatch: CompositeExplicitAutograd: linalg_pinv_out - func: linalg_pinv.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor cpp_no_default_args: ['atol', 'rtol'] python_module: linalg variants: function - func: linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) cpp_no_default_args: ['atol', 'rtol'] python_module: linalg variants: function - func: linalg_pinv(Tensor self, float rcond, bool hermitian=False) -> Tensor python_module: linalg variants: function - func: linalg_pinv.rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False) -> Tensor python_module: linalg variants: function - func: linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: _linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor LU, Tensor pivots, Tensor info) structured_delegate: _linalg_solve_ex.result - func: _linalg_solve_ex.result(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info) structured: True dispatch: CPU, CUDA: _linalg_solve_ex_out MPS: _linalg_solve_ex_out_mps - func: linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor info) python_module: linalg - func: linalg_solve_ex.out(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) info) -> (Tensor(a!) result, Tensor(b!) info) python_module: linalg - func: linalg_solve(Tensor A, Tensor B, *, bool left=True) -> Tensor python_module: linalg - func: _spsolve(Tensor A, Tensor B, *, bool left=True) -> Tensor python_module: sparse dispatch: SparseCsrCUDA: _sparse_csr_linear_solve - func: linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!) python_module: linalg - func: linalg_tensorinv(Tensor self, int ind=2) -> Tensor python_module: linalg variants: function - func: linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor python_module: linalg variants: function - func: linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_qr(Tensor A, str mode='reduced') -> (Tensor Q, Tensor R) python_module: linalg variants: function structured_delegate: linalg_qr.out - func: linalg_qr.out(Tensor A, str mode='reduced', *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) python_module: linalg structured: True dispatch: CPU, CUDA: linalg_qr_out - func: linalg_matrix_power(Tensor self, int n) -> Tensor python_module: linalg - func: linalg_matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg - func: linalg_matrix_rank.atol_rtol_tensor(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor python_module: linalg variants: function - func: linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_matrix_rank.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor cpp_no_default_args: ['atol', 'rtol'] python_module: linalg variants: function - func: linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) cpp_no_default_args: ['atol', 'rtol'] python_module: linalg variants: function - func: linalg_matrix_rank(Tensor self, float tol, bool hermitian=False) -> Tensor python_module: linalg variants: function - func: linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_matrix_rank.tol_tensor(Tensor input, Tensor tol, bool hermitian=False) -> Tensor python_module: linalg variants: function - func: linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg variants: function - func: linalg_multi_dot(Tensor[] tensors) -> Tensor python_module: linalg - func: linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) python_module: linalg ## Functions related to the `torch.nested` namespace # Note [nested namespace binding] # Functions in the nested python module should have their names start with # "nested_" underscore and be bound to the desired Python name in # torch/nested/__init__.py, and the desired C++ name in torch/csrc/api/include/torch/nested.h. # The "nested_" names should be hidden from the user and not documented. - func: nested_to_padded_tensor(Tensor self, float padding, int[]? output_size=None) -> Tensor python_module: nested variants: function ## Functions that are only for testing # It is undocumented and should not be used outside of tests. - func: _test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=1) -> Tensor # Note: for testing COW materialization within `at::parallel_for` loop function - func: _test_parallel_materialize(Tensor self, int num_parallel, bool skip_first=False) -> Tensor variants: function dispatch: CompositeExplicitAutograd: _test_parallel_materialize # Note: this function is only for testing. - func: _test_optional_intlist(Tensor values, int[]? addends) -> Tensor python_module: nn dispatch: CPU: _test_optional_intlist autogen: _test_optional_intlist.out # Note: this function is only for testing. - func: _test_optional_filled_intlist(Tensor values, int[2]? addends) -> Tensor python_module: nn dispatch: CPU: _test_optional_intlist autogen: _test_optional_filled_intlist.out # Note: this function is only for testing. - func: _test_optional_floatlist(Tensor values, float[]? addends) -> Tensor python_module: nn dispatch: CPU: _test_optional_floatlist autogen: _test_optional_floatlist.out # Note: this function is only for testing. - func: _test_string_default(Tensor dummy, str a="\"'\\", str b='"\'\\') -> Tensor python_module: nn # Note: this function is only for testing. - func: _test_ambiguous_defaults.a(Tensor dummy, int a=1, int b=1) -> Tensor python_module: nn # Note: this function is only for testing. - func: _test_ambiguous_defaults.b(Tensor dummy, int a=2, str b="2") -> Tensor cpp_no_default_args: ['a', 'b'] python_module: nn # Note: this function is only for testing. - func: _test_warn_in_autograd(Tensor self) -> Tensor python_module: nn dispatch: CompositeExplicitAutograd: _test_warn_in_autograd autogen: _test_warn_in_autograd.out # Note: this function is only for testing. - func: _test_autograd_multiple_dispatch.fullcoverage(Tensor self) -> Tensor dispatch: # the NestedTensor keys are necessary because NestedTensor has been removed # from the CompositeExplicitAutograd keyset see Note [NestedTensor Not Included in Backend Keys] CompositeExplicitAutograd, NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: _test_autograd_multiple_dispatch_fullcoverage autogen: _test_autograd_multiple_dispatch.fullcoverage_out # Note: this function is only for testing. - func: _test_autograd_multiple_dispatch.ntonly(Tensor self, bool b) -> Tensor dispatch: CompositeImplicitAutograd, NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: _test_autograd_multiple_dispatch_ntonly # Note: this function is only for testing. - func: _test_autograd_multiple_dispatch_view(Tensor(a) self) -> Tensor(a) dispatch: CompositeExplicitAutograd: _test_autograd_multiple_dispatch_view # Note: this function is only for testing. - func: _test_autograd_multiple_dispatch_view_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: _test_autograd_multiple_dispatch_view_copy tags: view_copy autogen: _test_autograd_multiple_dispatch_view_copy.out - func: segment_reduce(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int axis=0, bool unsafe=False, Scalar? initial=None) -> Tensor variants: function dispatch: CPU, CUDA: segment_reduce_kernel autogen: segment_reduce.out - func: _segment_reduce_backward(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None) -> Tensor variants: function dispatch: CPU, CUDA: _segment_reduce_backward_kernel autogen: _segment_reduce_backward.out - func: pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0.0, str padding_side="right") -> Tensor python_module: nn variants: function - func: flatten_dense_tensors(Tensor[] tensors) -> Tensor variants: function python_module: nn - func: unflatten_dense_tensors(Tensor flat, Tensor[] tensors) -> Tensor[] variants: function python_module: nn - func: _nested_tensor_from_tensor_list(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor variants: function dispatch: CompositeExplicitAutograd: _nested_tensor_from_tensor_list autogen: _nested_tensor_from_tensor_list.out - func: _fw_primal_copy(Tensor self, int level) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: _fw_primal_copy tags: view_copy autogen: _fw_primal_copy.out - func: _make_dual_copy(Tensor primal, Tensor tangent, int level) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: _make_dual_copy tags: view_copy autogen: _make_dual_copy.out - func: view_as_real_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: view_as_real_copy tags: view_copy autogen: view_as_real_copy.out - func: view_as_complex_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: view_as_complex_copy tags: view_copy autogen: view_as_complex_copy.out - func: _conj_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: _conj_copy tags: view_copy autogen: _conj_copy.out - func: _neg_view_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: _neg_view_copy tags: view_copy autogen: _neg_view_copy.out - func: as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: as_strided_copy_symint tags: view_copy autogen: as_strided_copy.out - func: _sparse_broadcast_to_copy(Tensor self, int[] size) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: _sparse_broadcast_to_copy tags: view_copy autogen: _sparse_broadcast_to_copy.out - func: diagonal_copy(Tensor self, int offset=0, int dim1=0, int dim2=1) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: diagonal_copy tags: view_copy autogen: diagonal_copy.out - func: expand_copy(Tensor self, SymInt[] size, *, bool implicit=False) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: expand_copy_symint tags: view_copy autogen: expand_copy.out - func: permute_copy(Tensor self, int[] dims) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: permute_copy tags: view_copy autogen: permute_copy.out - func: _reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: _reshape_alias_copy_symint tags: view_copy autogen: _reshape_alias_copy.out - func: select_copy.int(Tensor self, int dim, SymInt index) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: select_copy_symint SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: select_copy_sparse_csr tags: view_copy autogen: select_copy.int_out - func: detach_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: detach_copy tags: view_copy autogen: detach_copy.out - func: slice_copy.Tensor(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: slice_copy_Tensor_symint tags: view_copy autogen: slice_copy.Tensor_out - func: split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] variants: function dispatch: CompositeExplicitAutogradNonFunctional: split_copy_Tensor_symint tags: view_copy - func: split_with_sizes_copy(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[] variants: function dispatch: CompositeExplicitAutogradNonFunctional: split_with_sizes_copy_symint tags: view_copy - func: squeeze_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: squeeze_copy tags: view_copy autogen: squeeze_copy.out - func: squeeze_copy.dim(Tensor self, int dim) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: squeeze_copy_dim tags: view_copy autogen: squeeze_copy.dim_out - func: squeeze_copy.dims(Tensor self, int[] dim) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: squeeze_copy_dims tags: view_copy autogen: squeeze_copy.dims_out - func: t_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: t_copy tags: view_copy autogen: t_copy.out - func: transpose_copy.int(Tensor self, int dim0, int dim1) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: transpose_copy_int tags: view_copy autogen: transpose_copy.int_out - func: unsqueeze_copy(Tensor self, int dim) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: unsqueeze_copy tags: view_copy autogen: unsqueeze_copy.out - func: _indices_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: _indices_copy tags: view_copy autogen: _indices_copy.out - func: _values_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: _values_copy tags: view_copy autogen: _values_copy.out - func: indices_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: indices_copy tags: view_copy autogen: indices_copy.out - func: values_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: values_copy tags: view_copy autogen: values_copy.out - func: crow_indices_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: crow_indices_copy tags: view_copy autogen: crow_indices_copy.out - func: col_indices_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: col_indices_copy tags: view_copy autogen: col_indices_copy.out - func: ccol_indices_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: ccol_indices_copy tags: view_copy autogen: ccol_indices_copy.out - func: row_indices_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: row_indices_copy tags: view_copy autogen: row_indices_copy.out - func: unbind_copy.int(Tensor self, int dim=0) -> Tensor[] variants: function dispatch: CompositeExplicitAutogradNonFunctional: unbind_copy_int tags: view_copy - func: unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> () variants: function dispatch: CompositeExplicitAutograd: unbind_copy_int_out - func: split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () variants: function dispatch: CompositeExplicitAutograd: split_copy_Tensor_out - func: split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> () variants: function dispatch: CompositeExplicitAutograd: split_with_sizes_copy_out CUDA: split_with_sizes_copy_out_cuda - func: view_copy(Tensor self, SymInt[] size) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: view_copy_symint tags: view_copy autogen: view_copy.out - func: view_copy.dtype(Tensor self, ScalarType dtype) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: view_copy_dtype tags: view_copy autogen: view_copy.dtype_out - func: unfold_copy(Tensor self, int dimension, int size, int step) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: unfold_copy tags: view_copy autogen: unfold_copy.out - func: alias_copy(Tensor self) -> Tensor variants: function dispatch: CompositeExplicitAutogradNonFunctional: alias_copy tags: view_copy autogen: alias_copy.out - func: to_padded_tensor(Tensor self, float padding, SymInt[]? output_size=None) -> Tensor variants: method dispatch: NestedTensorCPU: NestedTensor_to_padded_tensor_generic NestedTensorCUDA: NestedTensor_to_padded_tensor_cuda autogen: to_padded_tensor.out - func: _jagged_to_padded_dense_forward(Tensor values, Tensor[] offsets, SymInt[] max_lengths, float padding_value=0.0) -> Tensor variants: function dispatch: CUDA: _fbgemm_jagged_to_padded_dense_forward CPU: _jagged_to_padded_dense_forward_cpu - func: _padded_dense_to_jagged_forward(Tensor dense, Tensor[] offsets, SymInt? total_L=None) -> Tensor variants: function dispatch: CUDA: _fbgemm_dense_to_jagged_forward_symint CPU: _padded_dense_to_jagged_forward_cpu - func: _nested_from_padded_tensor(Tensor padded, Tensor offsets, Tensor dummy, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None, SymInt? sum_S=None) -> Tensor variants: function device_check: NoCheck dispatch: {} - func: _nested_tensor_softmax_with_shape(Tensor self, Tensor query) -> Tensor dispatch: NestedTensorCPU: NestedTensor_softmax_dropout NestedTensorCUDA: NestedTensor_softmax_dropout_cuda tags: nondeterministic_seeded - func: _safe_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor dispatch: CompositeExplicitAutograd: _safe_softmax NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: _safe_softmax # Apparently, putting "forward" in the name will cause Python bindings to be skipped, so "fwd" it is. - func: _transformer_encoder_layer_fwd(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int? mask_type=None) -> Tensor variants: function dispatch: CPU, CUDA, NestedTensorCPU, NestedTensorHPU, NestedTensorCUDA: transformer_encoder_layer_forward autogen: _transformer_encoder_layer_fwd.out - func: _native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor) variants: function dispatch: CPU, NestedTensorCPU: native_multi_head_attention_cpu CUDA, NestedTensorCUDA: native_multi_head_attention_cuda autogen: _native_multi_head_attention.out - func: scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, *, float? scale=None, bool enable_gqa=False) -> Tensor python_module: nn variants: function autogen: scaled_dot_product_attention.out tags: nondeterministic_seeded # This aten function is kept so that we can test the choice function from Python - func: _fused_sdp_choice(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, *, float? scale=None, bool enable_gqa=False) -> int dispatch: Meta: _fused_sdp_choice_meta CPU, NestedTensorCPU: _fused_sdp_choice_cpp CUDA, NestedTensorCUDA: _fused_sdp_choice_cuda XPU: _fused_sdp_choice_xpu tags: nondeterministic_seeded - func: _scaled_dot_product_attention_math(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, Tensor? dropout_mask=None, *, float? scale=None, bool enable_gqa=False) -> (Tensor, Tensor) variants: function tags: nondeterministic_seeded - func: _scaled_dot_product_attention_math_for_mps(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, Tensor? dropout_mask=None, *, float? scale=None) -> (Tensor, Tensor) dispatch: MPS: _scaled_dot_product_attention_math_mps tags: nondeterministic_seeded - func: _scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor rng_state, Tensor unused, Tensor debug_attn_mask) dispatch: CUDA: _scaled_dot_product_flash_attention_cuda NestedTensorCUDA: _scaled_dot_product_flash_attention_nestedtensor_cuda tags: nondeterministic_seeded - func: _scaled_dot_product_flash_attention_for_cpu(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor output, Tensor logsumexp) dispatch: CPU: _scaled_dot_product_flash_attention_cpu tags: nondeterministic_seeded - func: _scaled_dot_product_fused_attention_overrideable(Tensor query, Tensor key, Tensor value, Tensor? attn_bias=None, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask) dispatch: CompositeExplicitAutograd: _scaled_dot_product_fused_attention_overrideable XPU: _scaled_dot_product_fused_attention_overrideable_xpu tags: nondeterministic_seeded - func: _scaled_dot_product_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value) device_check: NoCheck variants: function dispatch: CUDA: _scaled_dot_product_flash_attention_backward_cuda NestedTensorCUDA: _scaled_dot_product_flash_attention_backward_nested - func: _scaled_dot_product_flash_attention_for_cpu_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, float dropout_p, bool is_causal, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value) device_check: NoCheck variants: function dispatch: CPU: _scaled_dot_product_flash_attention_cpu_backward - func: _scaled_dot_product_fused_attention_overrideable_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor attn_bias, bool[4] grad_input_mask, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value, Tensor grad_attn_bias) device_check: NoCheck variants: function dispatch: CompositeExplicitAutograd: _scaled_dot_product_fused_attention_overrideable_backward - func: _scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0.0, bool is_causal=False, *, float? scale=None) -> (Tensor output, Tensor log_sumexp, Tensor philox_seed, Tensor philox_offset) dispatch: CUDA: _scaled_dot_product_efficient_attention_cuda NestedTensorCUDA: _scaled_dot_product_efficient_attention_nestedtensor_cuda tags: nondeterministic_seeded - func: _scaled_dot_product_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor attn_bias, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, float dropout_p, bool[4] grad_input_mask, bool is_causal=False, *, float? scale=None) -> (Tensor, Tensor, Tensor, Tensor) device_check: NoCheck dispatch: CUDA: _scaled_dot_product_efficient_attention_backward_cuda tags: nondeterministic_seeded - func: _scaled_dot_product_cudnn_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask) dispatch: CUDA: _scaled_dot_product_cudnn_attention_cuda NestedTensorCUDA: _scaled_dot_product_cudnn_attention_nestedtensor_cuda tags: nondeterministic_seeded - func: _scaled_dot_product_cudnn_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor attn_bias, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, *, float? scale=None) -> (Tensor, Tensor, Tensor) dispatch: CUDA: _scaled_dot_product_cudnn_attention_backward_cuda tags: nondeterministic_seeded - func: _flash_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, bool return_debug_mask, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None, Tensor? seqused_k=None, Tensor? alibi_slopes=None) -> (Tensor output, Tensor softmax_logsumexp, Tensor rng_state, Tensor unused, Tensor debug_attn_mask) variants: function dispatch: CUDA: _flash_attention_forward tags: nondeterministic_seeded - func: _flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor rng_state, Tensor unused, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None) -> (Tensor, Tensor, Tensor) device_check: NoCheck variants: function dispatch: CUDA: _flash_attention_backward # Returns output, logsumexp if compute_logsumexp - func: _efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, SymInt? max_seqlen_q, SymInt? max_seqlen_k, float dropout_p, int custom_mask_type, bool compute_log_sumexp=False, *, float? scale=None, Tensor? seqlen_k=None, int? window_size=None) -> (Tensor output, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, SymInt max_seqlen_batch_q, SymInt max_seqlen_batch_k) variants: function dispatch: CUDA: _efficient_attention_forward tags: nondeterministic_seeded - func: _efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor out, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, SymInt max_seqlen_q, SymInt max_seqlen_k, Tensor logsumexp, float dropout_p, Tensor philox_seed, Tensor philox_offset, int custom_mask_type, bool bias_requires_grad, *, float? scale=None, int? num_splits_key=None, int? window_size=None, bool shared_storage_dqdkdv=False) -> (Tensor, Tensor, Tensor, Tensor) device_check: NoCheck variants: function dispatch: CUDA: _efficient_attention_backward - func: _cudnn_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, bool compute_log_sumexp, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask) dispatch: CUDA: _cudnn_attention_forward tags: nondeterministic_seeded - func: _triton_scaled_dot_attention(Tensor q, Tensor k, Tensor v, float dropout_p=0.0) -> Tensor variants: function dispatch: CUDA: triton_scaled_dot_attention tags: nondeterministic_seeded autogen: _triton_scaled_dot_attention.out - func: _fill_mem_eff_dropout_mask_(Tensor(a!) self, float dropout_p, int seed, int offset) -> Tensor(a!) variants: function dispatch: CUDA: _fill_mem_eff_dropout_mask_ tags: nondeterministic_seeded - func: _triton_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None) -> Tensor variants: function dispatch: CUDA: triton_multi_head_attention autogen: _triton_multi_head_attention.out - func: special_airy_ai(Tensor x) -> Tensor python_module: special structured_delegate: special_airy_ai.out variants: function tags: pointwise - func: special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA: special_airy_ai_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_bessel_j0(Tensor self) -> Tensor python_module: special structured_delegate: special_bessel_j0.out variants: function tags: pointwise - func: special_bessel_j0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_bessel_j0_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_bessel_j1(Tensor self) -> Tensor python_module: special structured_delegate: special_bessel_j1.out variants: function tags: pointwise - func: special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_bessel_j1_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_bessel_y0(Tensor self) -> Tensor python_module: special structured_delegate: special_bessel_y0.out variants: function tags: pointwise - func: special_bessel_y0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_bessel_y0_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_bessel_y1(Tensor self) -> Tensor python_module: special structured_delegate: special_bessel_y1.out variants: function tags: pointwise - func: special_bessel_y1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_bessel_y1_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_chebyshev_polynomial_t.out variants: function tags: pointwise - func: special_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_t device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_t device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA, MPS: special_chebyshev_polynomial_t_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_t_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_t_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_chebyshev_polynomial_u.out variants: function tags: pointwise - func: special_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_u device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_u device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA, MPS: special_chebyshev_polynomial_u_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_u_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_u_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_chebyshev_polynomial_v.out variants: function tags: pointwise - func: special_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_v device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_v device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA, MPS: special_chebyshev_polynomial_v_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_v_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_v_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_chebyshev_polynomial_w.out variants: function tags: pointwise - func: special_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_w device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_w device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA, MPS: special_chebyshev_polynomial_w_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_w_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_chebyshev_polynomial_w_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_hermite_polynomial_h.out variants: function tags: pointwise - func: special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_hermite_polynomial_h device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_hermite_polynomial_h device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA, MPS: special_hermite_polynomial_h_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_hermite_polynomial_h_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_hermite_polynomial_h_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_hermite_polynomial_he(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_hermite_polynomial_he.out variants: function tags: pointwise - func: special_hermite_polynomial_he.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_hermite_polynomial_he device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_hermite_polynomial_he.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_hermite_polynomial_he device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_hermite_polynomial_he.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA, MPS: special_hermite_polynomial_he_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_hermite_polynomial_he.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_hermite_polynomial_he_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_hermite_polynomial_he.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_hermite_polynomial_he_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_laguerre_polynomial_l(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_laguerre_polynomial_l.out variants: function tags: pointwise - func: special_laguerre_polynomial_l.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_laguerre_polynomial_l device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_laguerre_polynomial_l.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_laguerre_polynomial_l device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_laguerre_polynomial_l.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA: special_laguerre_polynomial_l_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_laguerre_polynomial_l.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_laguerre_polynomial_l_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_laguerre_polynomial_l.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_laguerre_polynomial_l_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_legendre_polynomial_p(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_legendre_polynomial_p.out variants: function tags: pointwise - func: special_legendre_polynomial_p.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_legendre_polynomial_p device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_legendre_polynomial_p.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_legendre_polynomial_p device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_legendre_polynomial_p.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA: special_legendre_polynomial_p_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_legendre_polynomial_p.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_legendre_polynomial_p_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_legendre_polynomial_p.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_legendre_polynomial_p_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_modified_bessel_i0(Tensor self) -> Tensor python_module: special structured_delegate: special_modified_bessel_i0.out variants: function tags: pointwise - func: special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_modified_bessel_i0_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_modified_bessel_i1(Tensor self) -> Tensor python_module: special structured_delegate: special_modified_bessel_i1.out variants: function tags: pointwise - func: special_modified_bessel_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_modified_bessel_i1_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_modified_bessel_k0(Tensor self) -> Tensor python_module: special structured_delegate: special_modified_bessel_k0.out variants: function tags: pointwise - func: special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_modified_bessel_k0_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_modified_bessel_k1(Tensor self) -> Tensor python_module: special structured_delegate: special_modified_bessel_k1.out variants: function tags: pointwise - func: special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_modified_bessel_k1_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_scaled_modified_bessel_k0(Tensor x) -> Tensor python_module: special structured_delegate: special_scaled_modified_bessel_k0.out variants: function tags: pointwise - func: special_scaled_modified_bessel_k0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_scaled_modified_bessel_k0_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_scaled_modified_bessel_k1(Tensor x) -> Tensor python_module: special structured_delegate: special_scaled_modified_bessel_k1.out variants: function tags: pointwise - func: special_scaled_modified_bessel_k1.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_scaled_modified_bessel_k1_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_shifted_chebyshev_polynomial_t.out variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_t device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_t device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA, MPS: special_shifted_chebyshev_polynomial_t_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_t_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_t_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_shifted_chebyshev_polynomial_u.out variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_u device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_u device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA, MPS: special_shifted_chebyshev_polynomial_u_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_u_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_u_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_shifted_chebyshev_polynomial_v.out variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_v device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_v device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA, MPS: special_shifted_chebyshev_polynomial_v_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_v_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_v_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor device_check: NoCheck python_module: special structured_delegate: special_shifted_chebyshev_polynomial_w.out variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_w device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_w device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) device_check: NoCheck dispatch: CPU, CUDA, MPS: special_shifted_chebyshev_polynomial_w_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_w_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_shifted_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) dispatch: CompositeExplicitAutograd: special_shifted_chebyshev_polynomial_w_out device_check: NoCheck python_module: special variants: function tags: pointwise - func: special_spherical_bessel_j0(Tensor x) -> Tensor python_module: special structured_delegate: special_spherical_bessel_j0.out variants: function tags: pointwise - func: special_spherical_bessel_j0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) dispatch: CPU, CUDA, MPS: special_spherical_bessel_j0_out python_module: special structured_inherits: TensorIteratorBase structured: True variants: function tags: pointwise # Aux function used in the test TestPythonDispatch.test_kwarg_only_and_positional_default # within test/test_python_dispatch.py - func: _foobar(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True) -> Tensor dispatch: CPU: foobar autogen: _foobar.out - func: _fused_adam_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () # Unlike "foreach" functions, lists of tensors should be guaranteed to be on the same device (for now). variants: function dispatch: CPU: _fused_adam_kernel_cpu_ CUDA: _fused_adam_kernel_cuda_ MPS: _fused_adam_kernel_mps_ autogen: _fused_adam, _fused_adam.out - func: _fused_adam_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () # Unlike "foreach" functions, lists of tensors should be guaranteed to be on the same device (for now), # but still skip the device check as the Tensor LR can be on CPU device_check: NoCheck variants: function dispatch: CPU: _fused_adam_kernel_cpu_ CUDA: _fused_adam_kernel_cuda_ MPS: _fused_adam_kernel_mps_ autogen: _fused_adam.tensor_lr, _fused_adam.tensor_lr_out - func: _fused_adamw_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () # Unlike "foreach" functions, lists of tensors should be guaranteed to be on the same device (for now). variants: function dispatch: CPU: _fused_adamw_kernel_cpu_ CUDA: _fused_adamw_kernel_cuda_ MPS: _fused_adamw_kernel_mps_ autogen: _fused_adamw, _fused_adamw.out - func: _fused_adamw_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () # Unlike "foreach" functions, lists of tensors should be guaranteed to be on the same device (for now), # but still skip the device check as the Tensor LR can be on CPU device_check: NoCheck variants: function dispatch: CPU: _fused_adamw_kernel_cpu_ CUDA: _fused_adamw_kernel_cuda_ MPS: _fused_adamw_kernel_mps_ autogen: _fused_adamw.tensor_lr, _fused_adamw.tensor_lr_out - func: _fused_sgd_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> () # Unlike "foreach" functions, lists of tensors should be guaranteed to be on the same device (for now). variants: function dispatch: CPU: _fused_sgd_kernel_cpu_ CUDA: _fused_sgd_kernel_cuda_ MPS: _fused_sgd_kernel_mps_ autogen: _fused_sgd, _fused_sgd.out - func: _fused_sgd_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> () # Unlike "foreach" functions, lists of tensors should be guaranteed to be on the same device (for now). # but still skip the device check as the Tensor LR can be on CPU device_check: NoCheck variants: function dispatch: CPU: _fused_sgd_kernel_cpu_ CUDA: _fused_sgd_kernel_cuda_ MPS: _fused_sgd_kernel_mps_ autogen: _fused_sgd.tensor_lr, _fused_sgd.tensor_lr_out - func: _fused_adagrad_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] state_sums, Tensor(d!)[] state_steps, *, float lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () variants: function dispatch: CPU: _fused_adagrad_kernel_cpu_ autogen: _fused_adagrad, _fused_adagrad.out - func: _fused_adagrad_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] state_sums, Tensor[] state_steps, *, Tensor lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () device_check: NoCheck variants: function dispatch: CPU: _fused_adagrad_kernel_cpu_ autogen: _fused_adagrad.tensor_lr, _fused_adagrad.tensor_lr_out # This op is ONLY used by pytorch/XLA in functionalization, and should never show up in vanilla eager mode or in any pytorch tracing contexts. - func: _propagate_xla_data(Tensor input, Tensor output) -> () variants: function

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