1 | // @generated by torchgen/gen.py from RegisterSchema.cpp |
2 | #define TORCH_ASSERT_ONLY_METHOD_OPERATORS |
3 | #include <torch/library.h> |
4 | |
5 | namespace at { |
6 | TORCH_LIBRARY(aten, m) { |
7 | m.def("_cast_Byte(Tensor self, bool non_blocking=False) -> Tensor" , {}); |
8 | m.def("_cast_Char(Tensor self, bool non_blocking=False) -> Tensor" , {}); |
9 | m.def("_cast_Double(Tensor self, bool non_blocking=False) -> Tensor" , {}); |
10 | m.def("_cast_Float(Tensor self, bool non_blocking=False) -> Tensor" , {}); |
11 | m.def("_cast_Int(Tensor self, bool non_blocking=False) -> Tensor" , {}); |
12 | m.def("_cast_Long(Tensor self, bool non_blocking=False) -> Tensor" , {}); |
13 | m.def("_cast_Short(Tensor self, bool non_blocking=False) -> Tensor" , {}); |
14 | m.def("_cast_Half(Tensor self, bool non_blocking=False) -> Tensor" , {}); |
15 | m.def("_backward(Tensor self, Tensor[] inputs, Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False) -> ()" , {}); |
16 | m.def("set_data(Tensor(a!) self, Tensor new_data) -> ()" , {}); |
17 | m.def("data(Tensor self) -> Tensor" , {}); |
18 | m.def("is_leaf(Tensor self) -> bool" , {}); |
19 | m.def("output_nr(Tensor self) -> int" , {}); |
20 | m.def("_version(Tensor self) -> int" , {}); |
21 | m.def("requires_grad_(Tensor(a!) self, bool requires_grad=True) -> Tensor(a!)" , {}); |
22 | m.def("retain_grad(Tensor(a!) self) -> ()" , {}); |
23 | m.def("retains_grad(Tensor self) -> bool" , {}); |
24 | m.def("_fw_primal(Tensor(a) self, int level) -> Tensor(a)" , {}); |
25 | m.def("_make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a)" , {}); |
26 | m.def("_unpack_dual(Tensor(a) dual, int level) -> (Tensor(a) primal, Tensor tangent)" , {}); |
27 | m.def("_new_zeros_with_same_feature_meta(Tensor self, Tensor other, *, int self_num_batch_dims=0) -> Tensor" , {}); |
28 | m.def("_has_same_storage_numel(Tensor self, Tensor other) -> bool" , {}); |
29 | m.def("rename_(Tensor(a!) self, Dimname[]? names) -> Tensor(a!)" , {at::Tag::inplace_view}); |
30 | m.def("rename(Tensor(a) self, Dimname[]? names) -> Tensor(a)" , {}); |
31 | m.def("align_to(Tensor(a) self, Dimname[] names) -> Tensor(a)" , {}); |
32 | m.def("align_to.ellipsis_idx(Tensor(a) self, Dimname[] order, int ellipsis_idx) -> Tensor(a)" , {}); |
33 | m.def("align_as(Tensor self, Tensor other) -> Tensor" , {}); |
34 | m.def("align_tensors(Tensor[] tensors) -> Tensor[]" , {}); |
35 | m.def("_assert_async(Tensor self) -> ()" , {}); |
36 | m.def("_assert_tensor_metadata(Tensor a, int[]? size=None, int[]? stride=None, ScalarType? dtype=None) -> ()" , {}); |
37 | m.def("refine_names(Tensor(a) self, Dimname[] names) -> Tensor(a)" , {}); |
38 | m.def("_use_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank) -> bool" , {}); |
39 | m.def("_use_cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank) -> bool" , {}); |
40 | m.def("_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)" , {}); |
41 | m.def("_cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)" , {}); |
42 | m.def("_use_cudnn_rnn_flatten_weight() -> bool" , {}); |
43 | m.def("_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" , {}); |
44 | m.def("_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)" , {}); |
45 | m.def("_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[])" , {}); |
46 | m.def("_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" , {}); |
47 | m.def("_debug_has_internal_overlap(Tensor self) -> int" , {}); |
48 | m.def("_fused_dropout(Tensor self, float p, Generator? generator=None) -> (Tensor, Tensor)" , {at::Tag::nondeterministic_seeded}); |
49 | m.def("_masked_scale(Tensor self, Tensor mask, float scale) -> Tensor" , {}); |
50 | m.def("native_dropout(Tensor input, float p, bool? train) -> (Tensor, Tensor)" , {at::Tag::core, at::Tag::nondeterministic_seeded}); |
51 | m.def("native_dropout_backward(Tensor grad_output, Tensor mask, float scale) -> Tensor" , {at::Tag::pointwise}); |
52 | m.def("_sobol_engine_draw(Tensor quasi, int n, Tensor sobolstate, int dimension, int num_generated, ScalarType? dtype) -> (Tensor, Tensor)" , {}); |
53 | m.def("_sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!)" , {}); |
54 | m.def("_sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!)" , {}); |
55 | m.def("_sobol_engine_initialize_state_(Tensor(a!) self, int dimension) -> Tensor(a!)" , {}); |
56 | m.def("_reshape_from_tensor(Tensor self, Tensor shape) -> Tensor" , {}); |
57 | m.def("_shape_as_tensor(Tensor self) -> Tensor" , {}); |
58 | m.def("dropout(Tensor input, float p, bool train) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
59 | m.def("dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
60 | m.def("feature_dropout(Tensor input, float p, bool train) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
61 | m.def("feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
62 | m.def("alpha_dropout(Tensor input, float p, bool train) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
63 | m.def("alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
64 | m.def("feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
65 | m.def("feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
66 | m.def("abs(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
67 | m.def("abs_(Tensor(a!) self) -> Tensor(a!)" , {}); |
68 | m.def("abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
69 | m.def("absolute(Tensor self) -> Tensor" , {}); |
70 | m.def("absolute_(Tensor(a!) self) -> Tensor(a!)" , {}); |
71 | m.def("absolute.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
72 | m.def("angle(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
73 | m.def("angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
74 | m.def("view_as_real(Tensor(a) self) -> Tensor(a)" , {}); |
75 | m.def("view_as_complex(Tensor(a) self) -> Tensor(a)" , {}); |
76 | m.def("sgn(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
77 | m.def("sgn_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
78 | m.def("sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
79 | m.def("chalf(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor" , {}); |
80 | m.def("real(Tensor(a) self) -> Tensor(a)" , {}); |
81 | m.def("imag(Tensor(a) self) -> Tensor(a)" , {}); |
82 | m.def("_conj(Tensor(a) self) -> Tensor(a)" , {}); |
83 | m.def("conj(Tensor(a) self) -> Tensor(a)" , {}); |
84 | m.def("_conj_physical(Tensor self) -> Tensor" , {}); |
85 | m.def("conj_physical(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
86 | m.def("conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
87 | m.def("conj_physical_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
88 | m.def("resolve_conj(Tensor(a) self) -> Tensor(a)" , {}); |
89 | m.def("resolve_neg(Tensor(a) self) -> Tensor(a)" , {}); |
90 | m.def("_neg_view(Tensor(a) self) -> Tensor(a)" , {}); |
91 | m.def("acos(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
92 | m.def("acos_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
93 | m.def("acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
94 | m.def("arccos(Tensor self) -> Tensor" , {}); |
95 | m.def("arccos_(Tensor(a!) self) -> Tensor(a!)" , {}); |
96 | m.def("arccos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
97 | m.def("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" , {}); |
98 | m.def("adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor" , {}); |
99 | m.def("adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor)" , {}); |
100 | m.def("add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
101 | m.def("add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)" , {at::Tag::pointwise}); |
102 | m.def("add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
103 | m.def("_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor" , {}); |
104 | m.def("_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)" , {}); |
105 | m.def("_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {}); |
106 | m.def("_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor" , {}); |
107 | m.def("_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)" , {}); |
108 | m.def("add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
109 | m.def("add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)" , {at::Tag::pointwise}); |
110 | m.def("addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor" , {}); |
111 | m.def("addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)" , {}); |
112 | m.def("addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {}); |
113 | m.def("addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor" , {}); |
114 | m.def("addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)" , {}); |
115 | m.def("addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {}); |
116 | m.def("affine_grid_generator(Tensor theta, int[] size, bool align_corners) -> Tensor" , {}); |
117 | m.def("affine_grid_generator_backward(Tensor grad, int[] size, bool align_corners) -> Tensor" , {}); |
118 | m.def("_is_all_true(Tensor self) -> Tensor" , {}); |
119 | m.def("_is_any_true(Tensor self) -> Tensor" , {}); |
120 | m.def("_test_check_tensor(Tensor self) -> Tensor" , {}); |
121 | m.def("all.dim(Tensor self, int dim, bool keepdim=False) -> Tensor" , {}); |
122 | m.def("all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
123 | m.def("all.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor" , {}); |
124 | m.def("all.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
125 | m.def("allclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> bool" , {at::Tag::data_dependent_output}); |
126 | m.def("any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor" , {}); |
127 | m.def("any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
128 | m.def("any.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor" , {}); |
129 | m.def("any.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
130 | m.def("arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
131 | m.def("arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
132 | m.def("arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::core}); |
133 | m.def("arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
134 | m.def("arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
135 | m.def("_dim_arange(Tensor like, int dim) -> Tensor" , {}); |
136 | m.def("argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor" , {at::Tag::core}); |
137 | m.def("argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
138 | m.def("argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor" , {at::Tag::core}); |
139 | m.def("argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
140 | m.def("acosh(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
141 | m.def("acosh_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
142 | m.def("acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
143 | m.def("arccosh(Tensor self) -> Tensor" , {}); |
144 | m.def("arccosh_(Tensor(a!) self) -> Tensor(a!)" , {}); |
145 | m.def("arccosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
146 | m.def("asinh(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
147 | m.def("asinh_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
148 | m.def("asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
149 | m.def("arcsinh(Tensor self) -> Tensor" , {}); |
150 | m.def("arcsinh_(Tensor(a!) self) -> Tensor(a!)" , {}); |
151 | m.def("arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
152 | m.def("atanh(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
153 | m.def("atanh_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
154 | m.def("atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
155 | m.def("arctanh(Tensor self) -> Tensor" , {}); |
156 | m.def("arctanh_(Tensor(a!) self) -> Tensor(a!)" , {}); |
157 | m.def("arctanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
158 | m.def("as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)" , {at::Tag::core}); |
159 | m.def("as_strided_(Tensor(a!) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a!)" , {at::Tag::inplace_view}); |
160 | m.def("asin(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
161 | m.def("asin_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
162 | m.def("asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
163 | m.def("arcsin(Tensor self) -> Tensor" , {}); |
164 | m.def("arcsin_(Tensor(a!) self) -> Tensor(a!)" , {}); |
165 | m.def("arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
166 | m.def("atan(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
167 | m.def("atan_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
168 | m.def("atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
169 | m.def("arctan(Tensor self) -> Tensor" , {}); |
170 | m.def("arctan_(Tensor(a!) self) -> Tensor(a!)" , {}); |
171 | m.def("arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
172 | m.def("atleast_1d(Tensor self) -> Tensor" , {}); |
173 | m.def("atleast_1d.Sequence(Tensor[] tensors) -> Tensor[]" , {}); |
174 | m.def("atleast_2d(Tensor self) -> Tensor" , {}); |
175 | m.def("atleast_2d.Sequence(Tensor[] tensors) -> Tensor[]" , {}); |
176 | m.def("atleast_3d(Tensor self) -> Tensor" , {}); |
177 | m.def("atleast_3d.Sequence(Tensor[] tensors) -> Tensor[]" , {}); |
178 | m.def("baddbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor" , {}); |
179 | m.def("baddbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)" , {}); |
180 | m.def("baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {}); |
181 | m.def("bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
182 | m.def("bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
183 | m.def("batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensor" , {}); |
184 | m.def("quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor" , {}); |
185 | m.def("_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)" , {}); |
186 | m.def("_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)" , {}); |
187 | m.def("bernoulli(Tensor self, *, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
188 | m.def("bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
189 | m.def("bernoulli_.Tensor(Tensor(a!) self, Tensor p, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
190 | m.def("bernoulli_.float(Tensor(a!) self, float p=0.5, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
191 | m.def("bernoulli.p(Tensor self, float p, *, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
192 | m.def("bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias=None) -> Tensor" , {}); |
193 | m.def("binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor" , {}); |
194 | m.def("binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
195 | m.def("binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor" , {}); |
196 | m.def("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!)" , {}); |
197 | m.def("binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> Tensor" , {}); |
198 | m.def("bincount(Tensor self, Tensor? weights=None, int minlength=0) -> Tensor" , {at::Tag::dynamic_output_shape}); |
199 | m.def("bitwise_not(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
200 | m.def("bitwise_not_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
201 | m.def("bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
202 | m.def("copysign.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
203 | m.def("copysign.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
204 | m.def("copysign_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
205 | m.def("copysign.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
206 | m.def("copysign_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
207 | m.def("copysign.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
208 | m.def("logical_not(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
209 | m.def("logical_not_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
210 | m.def("logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
211 | m.def("logical_xor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
212 | m.def("logical_xor_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
213 | m.def("logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
214 | m.def("logical_and(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
215 | m.def("logical_and_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
216 | m.def("logical_and.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
217 | m.def("logical_or(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
218 | m.def("logical_or_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
219 | m.def("logical_or.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
220 | m.def("blackman_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
221 | m.def("blackman_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
222 | m.def("bmm(Tensor self, Tensor mat2) -> Tensor" , {at::Tag::core}); |
223 | m.def("bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
224 | m.def("broadcast_tensors(Tensor[] tensors) -> Tensor[]" , {}); |
225 | m.def("broadcast_to(Tensor(a) self, SymInt[] size) -> Tensor(a)" , {}); |
226 | m.def("_sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a)" , {}); |
227 | m.def("cat(Tensor[] tensors, int dim=0) -> Tensor" , {at::Tag::core}); |
228 | m.def("cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
229 | m.def("cat.names(Tensor[] tensors, Dimname dim) -> Tensor" , {}); |
230 | m.def("cat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
231 | m.def("concat(Tensor[] tensors, int dim=0) -> Tensor" , {}); |
232 | m.def("concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
233 | m.def("concat.names(Tensor[] tensors, Dimname dim) -> Tensor" , {}); |
234 | m.def("concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
235 | m.def("concatenate(Tensor[] tensors, int dim=0) -> Tensor" , {}); |
236 | m.def("concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
237 | m.def("concatenate.names(Tensor[] tensors, Dimname dim) -> Tensor" , {}); |
238 | m.def("concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
239 | m.def("block_diag(Tensor[] tensors) -> Tensor" , {}); |
240 | m.def("ceil(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
241 | m.def("ceil_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
242 | m.def("ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
243 | m.def("chain_matmul(Tensor[] matrices) -> Tensor" , {}); |
244 | m.def("chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
245 | m.def("unsafe_chunk(Tensor self, int chunks, int dim=0) -> Tensor[]" , {}); |
246 | m.def("chunk(Tensor(a -> *) self, int chunks, int dim=0) -> Tensor(a)[]" , {}); |
247 | m.def("tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[]" , {}); |
248 | m.def("tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[]" , {}); |
249 | m.def("tensor_split.tensor_indices_or_sections(Tensor(a -> *) self, Tensor tensor_indices_or_sections, int dim=0) -> Tensor(a)[]" , {}); |
250 | m.def("clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
251 | m.def("clamp.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor" , {at::Tag::pointwise}); |
252 | m.def("clamp_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)" , {at::Tag::pointwise}); |
253 | m.def("clamp_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!)" , {at::Tag::pointwise}); |
254 | m.def("clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
255 | m.def("clamp.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
256 | m.def("clamp_max(Tensor self, Scalar max) -> Tensor" , {at::Tag::pointwise}); |
257 | m.def("clamp_max.Tensor(Tensor self, Tensor max) -> Tensor" , {at::Tag::pointwise}); |
258 | m.def("clamp_max_(Tensor(a!) self, Scalar max) -> Tensor(a!)" , {at::Tag::pointwise}); |
259 | m.def("clamp_max_.Tensor(Tensor(a!) self, Tensor max) -> Tensor(a!)" , {at::Tag::pointwise}); |
260 | m.def("clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
261 | m.def("clamp_max.Tensor_out(Tensor self, Tensor max, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
262 | m.def("clamp_min(Tensor self, Scalar min) -> Tensor" , {at::Tag::pointwise}); |
263 | m.def("clamp_min.Tensor(Tensor self, Tensor min) -> Tensor" , {at::Tag::pointwise}); |
264 | m.def("clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!)" , {at::Tag::pointwise}); |
265 | m.def("clamp_min_.Tensor(Tensor(a!) self, Tensor min) -> Tensor(a!)" , {at::Tag::pointwise}); |
266 | m.def("clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
267 | m.def("clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
268 | m.def("clip(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor" , {at::Tag::pointwise}); |
269 | m.def("clip.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor" , {at::Tag::pointwise}); |
270 | m.def("clip_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)" , {at::Tag::pointwise}); |
271 | m.def("clip_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!)" , {at::Tag::pointwise}); |
272 | m.def("clip.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
273 | m.def("clip.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
274 | m.def("cudnn_is_acceptable(Tensor self) -> bool" , {}); |
275 | m.def("complex(Tensor real, Tensor imag) -> Tensor" , {}); |
276 | m.def("complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
277 | m.def("polar(Tensor abs, Tensor angle) -> Tensor" , {}); |
278 | m.def("polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
279 | m.def("constant_pad_nd(Tensor self, SymInt[] pad, Scalar value=0) -> Tensor" , {at::Tag::core}); |
280 | m.def("contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a)" , {}); |
281 | m.def("convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, SymInt[] padding, int[] dilation, bool transposed, SymInt[] output_padding, int groups) -> Tensor" , {at::Tag::core}); |
282 | m.def("convolution_backward(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, int[] stride, SymInt[] padding, int[] dilation, bool transposed, SymInt[] output_padding, int groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)" , {at::Tag::core}); |
283 | m.def("convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensor" , {}); |
284 | m.def("convolution_backward_overrideable(Tensor grad_output, Tensor input, Tensor weight, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)" , {}); |
285 | m.def("_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, SymInt[] padding, int[] dilation, bool transposed, SymInt[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor" , {}); |
286 | m.def("_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor" , {}); |
287 | m.def("_convolution_mode(Tensor input, Tensor weight, Tensor? bias, int[] stride, str padding, int[] dilation, int groups) -> Tensor" , {}); |
288 | m.def("_convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, int[] stride, SymInt[] padding, int[] dilation, bool transposed, SymInt[] output_padding, int groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)" , {}); |
289 | m.def("conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensor" , {}); |
290 | m.def("conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensor" , {}); |
291 | m.def("conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensor" , {}); |
292 | m.def("conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, str padding=\"valid\", int[1] dilation=1, int groups=1) -> Tensor" , {}); |
293 | m.def("conv2d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, str padding=\"valid\", int[2] dilation=1, int groups=1) -> Tensor" , {}); |
294 | m.def("conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, str padding=\"valid\", int[3] dilation=1, int groups=1) -> Tensor" , {}); |
295 | m.def("conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor" , {}); |
296 | m.def("conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int pad) -> (Tensor, Tensor, Tensor)" , {}); |
297 | m.def("conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] output_padding=0, int groups=1, int[1] dilation=1) -> Tensor" , {}); |
298 | m.def("conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int groups=1, int[2] dilation=1) -> Tensor" , {}); |
299 | m.def("conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int groups=1, int[3] dilation=1) -> Tensor" , {}); |
300 | m.def("copy(Tensor self, Tensor src, bool non_blocking=False) -> Tensor" , {}); |
301 | m.def("copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!)" , {}); |
302 | m.def("_copy_from(Tensor self, Tensor dst, bool non_blocking=False) -> Tensor" , {}); |
303 | m.def("_copy_from_and_resize(Tensor self, Tensor dst) -> Tensor" , {}); |
304 | m.def("cos(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
305 | m.def("cos_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
306 | m.def("cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
307 | m.def("cosh(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
308 | m.def("cosh_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
309 | m.def("cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
310 | m.def("cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor" , {}); |
311 | m.def("count_nonzero.dim_IntList(Tensor self, int[] dim) -> Tensor" , {}); |
312 | m.def("count_nonzero(Tensor self, int? dim=None) -> Tensor" , {}); |
313 | m.def("cov(Tensor self, *, int correction=1, Tensor? fweights=None, Tensor? aweights=None) -> Tensor" , {}); |
314 | m.def("corrcoef(Tensor self) -> Tensor" , {}); |
315 | m.def("cudnn_affine_grid_generator(Tensor theta, int N, int C, int H, int W) -> Tensor grid" , {}); |
316 | m.def("cudnn_affine_grid_generator_backward(Tensor grad, int N, int C, int H, int W) -> Tensor grad_theta" , {}); |
317 | m.def("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)" , {}); |
318 | m.def("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)" , {}); |
319 | m.def("cudnn_convolution(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor" , {}); |
320 | m.def("cudnn_convolution_transpose(Tensor self, Tensor weight, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor" , {}); |
321 | m.def("_mps_convolution_transpose(Tensor self, Tensor weight, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups) -> Tensor" , {}); |
322 | m.def("mps_convolution_transpose_backward(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool[2] output_mask) -> (Tensor, Tensor)" , {}); |
323 | m.def("cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor" , {}); |
324 | m.def("cudnn_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor" , {}); |
325 | m.def("cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output" , {}); |
326 | m.def("cudnn_grid_sampler_backward(Tensor self, Tensor grid, Tensor grad_output) -> (Tensor grad_self, Tensor grad_grid)" , {}); |
327 | m.def("cummax(Tensor self, int dim) -> (Tensor values, Tensor indices)" , {}); |
328 | m.def("cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
329 | m.def("cummax.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)" , {}); |
330 | m.def("cummax.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
331 | m.def("_cummax_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> ()" , {}); |
332 | m.def("cummin(Tensor self, int dim) -> (Tensor values, Tensor indices)" , {}); |
333 | m.def("cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
334 | m.def("cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)" , {}); |
335 | m.def("cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
336 | m.def("_cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> ()" , {}); |
337 | m.def("cummaxmin_backward(Tensor grad, Tensor input, Tensor indices, int dim) -> Tensor" , {}); |
338 | m.def("cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor" , {}); |
339 | m.def("cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!)" , {}); |
340 | m.def("cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
341 | m.def("cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor" , {}); |
342 | m.def("cumprod_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!)" , {}); |
343 | m.def("cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
344 | m.def("cumprod_backward(Tensor grad, Tensor input, int dim, Tensor output) -> Tensor" , {}); |
345 | m.def("cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor" , {}); |
346 | m.def("cumsum_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!)" , {}); |
347 | m.def("cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
348 | m.def("cumsum.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor" , {}); |
349 | m.def("cumsum_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!)" , {}); |
350 | m.def("cumsum.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
351 | m.def("cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor" , {}); |
352 | m.def("cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor" , {}); |
353 | m.def("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" , {}); |
354 | m.def("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" , {}); |
355 | m.def("_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)" , {at::Tag::dynamic_output_shape}); |
356 | m.def("_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)" , {at::Tag::dynamic_output_shape}); |
357 | m.def("_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" , {}); |
358 | m.def("_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" , {}); |
359 | m.def("diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor" , {}); |
360 | m.def("diagflat(Tensor self, int offset=0) -> Tensor" , {}); |
361 | m.def("diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a)" , {}); |
362 | m.def("linalg_diagonal(Tensor(a) A, *, int offset=0, int dim1=-2, int dim2=-1) -> Tensor(a)" , {}); |
363 | m.def("diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a)" , {}); |
364 | m.def("diagonal_backward(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2) -> Tensor" , {}); |
365 | m.def("fill_diagonal_(Tensor(a!) self, Scalar fill_value, bool wrap=False) -> Tensor(a!)" , {}); |
366 | m.def("diff(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None) -> Tensor" , {}); |
367 | m.def("diff.out(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
368 | m.def("gradient.scalarint(Tensor self, *, Scalar? spacing=None, int? dim=None, int edge_order=1) -> Tensor[]" , {}); |
369 | m.def("gradient.scalararray(Tensor self, *, Scalar spacing, int[] dim, int edge_order=1) -> Tensor[]" , {}); |
370 | m.def("gradient.array(Tensor self, *, int[] dim, int edge_order=1) -> Tensor[]" , {}); |
371 | m.def("gradient.scalarrayint(Tensor self, *, Scalar[] spacing, int? dim=None, int edge_order=1) -> Tensor[]" , {}); |
372 | m.def("gradient.scalarrayarray(Tensor self, *, Scalar[] spacing, int[] dim, int edge_order=1) -> Tensor[]" , {}); |
373 | m.def("gradient.tensorarrayint(Tensor self, *, Tensor[] spacing, int? dim=None, int edge_order=1) -> Tensor[]" , {}); |
374 | m.def("gradient.tensorarray(Tensor self, *, Tensor[] spacing, int[] dim, int edge_order=1) -> Tensor[]" , {}); |
375 | m.def("div.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
376 | m.def("div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
377 | m.def("div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
378 | m.def("div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor" , {at::Tag::pointwise}); |
379 | m.def("div_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!)" , {at::Tag::pointwise}); |
380 | m.def("div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
381 | m.def("div.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
382 | m.def("div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
383 | m.def("div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor" , {at::Tag::pointwise}); |
384 | m.def("div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!)" , {at::Tag::pointwise}); |
385 | m.def("divide.Tensor(Tensor self, Tensor other) -> Tensor" , {}); |
386 | m.def("divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
387 | m.def("divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
388 | m.def("divide.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
389 | m.def("divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
390 | m.def("divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor" , {}); |
391 | m.def("divide_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!)" , {}); |
392 | m.def("divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)" , {}); |
393 | m.def("divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor" , {}); |
394 | m.def("divide_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!)" , {}); |
395 | m.def("true_divide.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
396 | m.def("true_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
397 | m.def("true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
398 | m.def("true_divide.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
399 | m.def("true_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
400 | m.def("dot(Tensor self, Tensor tensor) -> Tensor" , {}); |
401 | m.def("dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
402 | m.def("vdot(Tensor self, Tensor other) -> Tensor" , {}); |
403 | m.def("vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
404 | m.def("einsum(str equation, Tensor[] tensors, *, int[]? path=None) -> Tensor" , {}); |
405 | m.def("embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor" , {}); |
406 | m.def("embedding_backward(Tensor grad, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, bool sparse) -> Tensor" , {}); |
407 | m.def("embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor" , {at::Tag::core}); |
408 | m.def("embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!)" , {}); |
409 | m.def("embedding_sparse_backward(Tensor grad, Tensor indices, int num_weights, int padding_idx, bool scale_grad_by_freq) -> Tensor" , {}); |
410 | m.def("_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)" , {}); |
411 | m.def("_rowwise_prune(Tensor weight, Tensor mask, ScalarType compressed_indices_dtype) -> (Tensor, Tensor)" , {}); |
412 | m.def("row_stack(Tensor[] tensors) -> Tensor" , {}); |
413 | m.def("row_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
414 | m.def("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)" , {}); |
415 | m.def("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)" , {}); |
416 | m.def("_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)" , {}); |
417 | m.def("_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" , {}); |
418 | m.def("_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" , {}); |
419 | m.def("_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" , {}); |
420 | m.def("_embedding_bag_per_sample_weights_backward(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1) -> Tensor" , {}); |
421 | m.def("empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor" , {}); |
422 | m.def("empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor" , {}); |
423 | m.def("new_empty(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
424 | m.def("new_empty_strided(Tensor self, SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
425 | m.def("new_full(Tensor self, SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
426 | m.def("new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
427 | m.def("new_ones(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
428 | m.def("_empty_affine_quantized(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor" , {}); |
429 | m.def("_empty_per_channel_affine_quantized(int[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor" , {}); |
430 | m.def("resize_(Tensor(a!) self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor(a!)" , {at::Tag::inplace_view}); |
431 | m.def("_resize_output_(Tensor(a!) self, int[] size, Device device) -> Tensor(a!)" , {}); |
432 | m.def("empty_quantized(int[] size, Tensor qtensor, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor" , {}); |
433 | m.def("empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
434 | m.def("empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor" , {}); |
435 | m.def("empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::core}); |
436 | m.def("erf(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
437 | m.def("erf_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
438 | m.def("erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
439 | m.def("erfc(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
440 | m.def("erfc_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
441 | m.def("erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
442 | m.def("exp(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
443 | m.def("exp_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
444 | m.def("exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
445 | m.def("exp2(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
446 | m.def("exp2_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
447 | m.def("exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
448 | m.def("expm1(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
449 | m.def("expm1_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
450 | m.def("expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
451 | m.def("expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a)" , {at::Tag::core}); |
452 | m.def("expand_as(Tensor(a) self, Tensor other) -> Tensor(a)" , {}); |
453 | m.def("eye(int n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
454 | m.def("eye.m(int n, int m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
455 | m.def("eye.out(int n, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
456 | m.def("eye.m_out(int n, int m, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
457 | m.def("flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)" , {}); |
458 | m.def("flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a)" , {}); |
459 | m.def("flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a)" , {}); |
460 | m.def("flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a)" , {}); |
461 | m.def("unflatten.int(Tensor(a) self, int dim, int[] sizes) -> Tensor(a)" , {}); |
462 | m.def("unflatten.Dimname(Tensor(a) self, Dimname dim, int[] sizes, Dimname[] names) -> Tensor(a)" , {}); |
463 | m.def("fill.Scalar(Tensor self, Scalar value) -> Tensor" , {at::Tag::core}); |
464 | m.def("fill.Tensor(Tensor self, Tensor value) -> Tensor" , {}); |
465 | m.def("fill_.Scalar(Tensor(a!) self, Scalar value) -> Tensor(a!)" , {}); |
466 | m.def("fill_.Tensor(Tensor(a!) self, Tensor value) -> Tensor(a!)" , {}); |
467 | m.def("floor(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
468 | m.def("floor_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
469 | m.def("floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
470 | m.def("floor_divide(Tensor self, Tensor other) -> Tensor" , {}); |
471 | m.def("floor_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
472 | m.def("floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
473 | m.def("floor_divide.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
474 | m.def("floor_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
475 | m.def("frac(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
476 | m.def("frac_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
477 | m.def("frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
478 | m.def("full.names(int[] size, Scalar fill_value, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
479 | m.def("full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::core}); |
480 | m.def("full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
481 | m.def("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" , {}); |
482 | m.def("from_file(str filename, bool? shared=None, int? size=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
483 | m.def("gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
484 | m.def("gcd(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
485 | m.def("gcd_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
486 | m.def("lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
487 | m.def("lcm(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
488 | m.def("lcm_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
489 | m.def("grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor" , {}); |
490 | m.def("grid_sampler_2d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor" , {at::Tag::core}); |
491 | m.def("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)" , {}); |
492 | m.def("_grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor" , {}); |
493 | m.def("_grid_sampler_2d_cpu_fallback_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> (Tensor, Tensor)" , {}); |
494 | m.def("grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor" , {}); |
495 | m.def("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)" , {}); |
496 | m.def("hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
497 | m.def("hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
498 | m.def("hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
499 | m.def("hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
500 | m.def("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" , {}); |
501 | m.def("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" , {}); |
502 | m.def("kaiser_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
503 | m.def("kaiser_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
504 | m.def("kaiser_window.beta(int window_length, bool periodic, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
505 | m.def("hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor" , {}); |
506 | m.def("group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor" , {}); |
507 | m.def("native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor)" , {at::Tag::core}); |
508 | m.def("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)" , {at::Tag::core}); |
509 | m.def("_fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor" , {}); |
510 | m.def("_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
511 | m.def("_fft_c2r(Tensor self, int[] dim, int normalization, int last_dim_size) -> Tensor" , {}); |
512 | m.def("_fft_c2r.out(Tensor self, int[] dim, int normalization, int last_dim_size, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
513 | m.def("_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor" , {}); |
514 | m.def("_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
515 | m.def("_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> ()" , {}); |
516 | m.def("_cufft_get_plan_cache_size(int device_index) -> int" , {}); |
517 | m.def("_cufft_get_plan_cache_max_size(int device_index) -> int" , {}); |
518 | m.def("_cufft_set_plan_cache_max_size(int device_index, int max_size) -> ()" , {}); |
519 | m.def("_cufft_clear_plan_cache(int device_index) -> ()" , {}); |
520 | m.def("index.Tensor(Tensor self, Tensor?[] indices) -> Tensor" , {at::Tag::dynamic_output_shape}); |
521 | m.def("index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
522 | m.def("index_copy.out(Tensor self, int dim, Tensor index, Tensor source, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
523 | m.def("index_copy_(Tensor(a!) self, int dim, Tensor index, Tensor source) -> Tensor(a!)" , {}); |
524 | m.def("index_copy(Tensor self, int dim, Tensor index, Tensor source) -> Tensor" , {}); |
525 | m.def("index_copy_.dimname(Tensor(a!) self, Dimname dim, Tensor index, Tensor source) -> Tensor(a!)" , {}); |
526 | m.def("index_copy.dimname(Tensor self, Dimname dim, Tensor index, Tensor source) -> Tensor" , {}); |
527 | m.def("index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!)" , {}); |
528 | m.def("index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor" , {}); |
529 | m.def("_index_put_impl_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor(a!)" , {}); |
530 | m.def("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" , {}); |
531 | m.def("isclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> Tensor" , {}); |
532 | m.def("isin.Tensor_Tensor_out(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
533 | m.def("isin.Tensor_Tensor(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor" , {}); |
534 | m.def("isin.Tensor_Scalar_out(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
535 | m.def("isin.Tensor_Scalar(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False) -> Tensor" , {}); |
536 | m.def("isin.Scalar_Tensor_out(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
537 | m.def("isin.Scalar_Tensor(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor" , {}); |
538 | m.def("isnan(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
539 | m.def("is_distributed(Tensor self) -> bool" , {}); |
540 | m.def("is_floating_point(Tensor self) -> bool" , {}); |
541 | m.def("is_complex(Tensor self) -> bool" , {}); |
542 | m.def("is_conj(Tensor self) -> bool" , {}); |
543 | m.def("_is_zerotensor(Tensor self) -> bool" , {}); |
544 | m.def("is_neg(Tensor self) -> bool" , {}); |
545 | m.def("isreal(Tensor self) -> Tensor" , {}); |
546 | m.def("is_nonzero(Tensor self) -> bool" , {}); |
547 | m.def("is_same_size(Tensor self, Tensor other) -> bool" , {}); |
548 | m.def("is_signed(Tensor self) -> bool" , {}); |
549 | m.def("is_inference(Tensor self) -> bool" , {}); |
550 | m.def("kl_div(Tensor self, Tensor target, int reduction=Mean, *, bool log_target=False) -> Tensor" , {}); |
551 | m.def("kron(Tensor self, Tensor other) -> Tensor" , {}); |
552 | m.def("kron.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
553 | m.def("kthvalue(Tensor self, int k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)" , {}); |
554 | m.def("kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
555 | m.def("kthvalue.dimname(Tensor self, int k, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)" , {}); |
556 | m.def("kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
557 | m.def("layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensor" , {}); |
558 | m.def("native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)" , {at::Tag::core}); |
559 | m.def("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)" , {at::Tag::core}); |
560 | m.def("nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor" , {at::Tag::pointwise}); |
561 | m.def("nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!)" , {at::Tag::pointwise}); |
562 | m.def("nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
563 | m.def("linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor" , {}); |
564 | m.def("linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor)" , {}); |
565 | m.def("linear.out(Tensor input, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
566 | m.def("mkldnn_linear(Tensor self, Tensor weight, Tensor? bias=None) -> Tensor" , {}); |
567 | m.def("mkldnn_linear_backward_input(int[] input_size, Tensor grad_output, Tensor weight) -> Tensor" , {}); |
568 | m.def("mkldnn_linear_backward_weights(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined) -> (Tensor, Tensor)" , {}); |
569 | m.def("mkldnn_linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor)" , {}); |
570 | m.def("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" , {}); |
571 | m.def("fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor" , {}); |
572 | m.def("fbgemm_linear_quantize_weight(Tensor input) -> (Tensor, Tensor, float, int)" , {}); |
573 | m.def("fbgemm_pack_gemm_matrix_fp16(Tensor input) -> Tensor" , {}); |
574 | m.def("fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor" , {}); |
575 | m.def("fbgemm_linear_fp16_weight(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor" , {}); |
576 | m.def("fbgemm_pack_quantized_matrix(Tensor input) -> Tensor" , {}); |
577 | m.def("fbgemm_pack_quantized_matrix.KN(Tensor input, int K, int N) -> Tensor" , {}); |
578 | m.def("ldexp.Tensor(Tensor self, Tensor other) -> Tensor" , {}); |
579 | m.def("ldexp_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
580 | m.def("ldexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
581 | m.def("linspace(Scalar start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
582 | m.def("linspace.out(Scalar start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
583 | m.def("log(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
584 | m.def("log_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
585 | m.def("log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
586 | m.def("log10(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
587 | m.def("log10_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
588 | m.def("log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
589 | m.def("log1p(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
590 | m.def("log1p_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
591 | m.def("log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
592 | m.def("log2(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
593 | m.def("log2_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
594 | m.def("log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
595 | m.def("logaddexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
596 | m.def("logaddexp(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
597 | m.def("logaddexp2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
598 | m.def("logaddexp2(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
599 | m.def("xlogy.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
600 | m.def("xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
601 | m.def("xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
602 | m.def("xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
603 | m.def("xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
604 | m.def("xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
605 | m.def("xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
606 | m.def("xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
607 | m.def("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" , {}); |
608 | m.def("logspace.out(Scalar start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
609 | m.def("log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor" , {}); |
610 | m.def("log_softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
611 | m.def("log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor" , {}); |
612 | m.def("_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor" , {at::Tag::core}); |
613 | m.def("_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
614 | m.def("_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor" , {}); |
615 | m.def("_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
616 | m.def("_logcumsumexp(Tensor self, int dim) -> Tensor" , {}); |
617 | m.def("_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
618 | m.def("logcumsumexp(Tensor self, int dim) -> Tensor" , {}); |
619 | m.def("logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
620 | m.def("logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor" , {}); |
621 | m.def("logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
622 | m.def("logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor" , {}); |
623 | m.def("logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
624 | m.def("logsumexp.names(Tensor self, Dimname[1] dim, bool keepdim=False) -> Tensor" , {}); |
625 | m.def("logsumexp.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
626 | m.def("margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor" , {}); |
627 | m.def("matmul(Tensor self, Tensor other) -> Tensor" , {}); |
628 | m.def("matmul_backward(Tensor grad, Tensor self, Tensor other, bool[2] mask) -> (Tensor, Tensor)" , {}); |
629 | m.def("matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
630 | m.def("matrix_power(Tensor self, int n) -> Tensor" , {}); |
631 | m.def("matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
632 | m.def("matrix_exp(Tensor self) -> Tensor" , {}); |
633 | m.def("matrix_exp_backward(Tensor self, Tensor grad) -> Tensor" , {}); |
634 | m.def("_aminmax(Tensor self) -> (Tensor, Tensor)" , {}); |
635 | m.def("_aminmax.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor, Tensor)" , {}); |
636 | m.def("aminmax(Tensor self, *, int? dim=None, bool keepdim=False) -> (Tensor min, Tensor max)" , {}); |
637 | m.def("aminmax.out(Tensor self, *, int? dim=None, bool keepdim=False, Tensor(a!) min, Tensor(b!) max) -> (Tensor(a!) min, Tensor(b!) max)" , {}); |
638 | m.def("_compute_linear_combination(Tensor input, Tensor coefficients) -> Tensor" , {}); |
639 | m.def("_compute_linear_combination.out(Tensor input, Tensor coefficients, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
640 | m.def("max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)" , {at::Tag::core}); |
641 | m.def("max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
642 | m.def("max.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)" , {}); |
643 | m.def("max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
644 | m.def("value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor" , {}); |
645 | m.def("amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor" , {at::Tag::core}); |
646 | m.def("amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
647 | m.def("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)" , {}); |
648 | m.def("max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor" , {}); |
649 | m.def("max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor" , {}); |
650 | m.def("_mps_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor" , {}); |
651 | m.def("mps_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" , {}); |
652 | m.def("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" , {}); |
653 | m.def("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" , {}); |
654 | m.def("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" , {}); |
655 | m.def("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" , {}); |
656 | m.def("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" , {}); |
657 | m.def("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" , {}); |
658 | m.def("max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor" , {}); |
659 | m.def("mean(Tensor self, *, ScalarType? dtype=None) -> Tensor" , {}); |
660 | m.def("mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {at::Tag::core}); |
661 | m.def("mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
662 | m.def("mean.names_dim(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
663 | m.def("mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
664 | m.def("nanmean(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
665 | m.def("nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
666 | m.def("median(Tensor self) -> Tensor" , {}); |
667 | m.def("median.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)" , {}); |
668 | m.def("median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
669 | m.def("median.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)" , {}); |
670 | m.def("median.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
671 | m.def("nanmedian(Tensor self) -> Tensor" , {}); |
672 | m.def("nanmedian.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)" , {}); |
673 | m.def("nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
674 | m.def("nanmedian.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)" , {}); |
675 | m.def("nanmedian.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
676 | m.def("min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)" , {at::Tag::core}); |
677 | m.def("min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
678 | m.def("min.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)" , {}); |
679 | m.def("min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
680 | m.def("amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor" , {at::Tag::core}); |
681 | m.def("amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
682 | m.def("_mps_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensor" , {}); |
683 | m.def("mps_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)" , {}); |
684 | m.def("mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups) -> Tensor" , {}); |
685 | m.def("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)" , {}); |
686 | m.def("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)" , {}); |
687 | m.def("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)" , {}); |
688 | m.def("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)" , {}); |
689 | m.def("miopen_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor" , {}); |
690 | m.def("miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor" , {}); |
691 | m.def("miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor" , {}); |
692 | m.def("miopen_convolution_relu(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor" , {}); |
693 | m.def("miopen_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor" , {}); |
694 | m.def("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)" , {}); |
695 | m.def("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[])" , {}); |
696 | m.def("mm(Tensor self, Tensor mat2) -> Tensor" , {at::Tag::core}); |
697 | m.def("mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
698 | m.def("_sparse_mm(Tensor sparse, Tensor dense) -> Tensor" , {}); |
699 | m.def("_sparse_mm.reduce(Tensor sparse, Tensor dense, str reduce) -> Tensor" , {}); |
700 | m.def("_sparse_sparse_matmul(Tensor self, Tensor other) -> Tensor" , {}); |
701 | m.def("mode(Tensor self, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)" , {}); |
702 | m.def("mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
703 | m.def("mode.dimname(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)" , {}); |
704 | m.def("mode.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
705 | m.def("mul.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
706 | m.def("mul_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
707 | m.def("mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
708 | m.def("mul.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
709 | m.def("mul_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
710 | m.def("multiply.Tensor(Tensor self, Tensor other) -> Tensor" , {}); |
711 | m.def("multiply_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
712 | m.def("multiply.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
713 | m.def("multiply.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
714 | m.def("multiply_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
715 | m.def("mv(Tensor self, Tensor vec) -> Tensor" , {}); |
716 | m.def("mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
717 | m.def("mvlgamma.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
718 | m.def("mvlgamma(Tensor self, int p) -> Tensor" , {at::Tag::pointwise}); |
719 | m.def("mvlgamma_(Tensor(a!) self, int p) -> Tensor(a!)" , {at::Tag::pointwise}); |
720 | m.def("narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor" , {at::Tag::view_copy}); |
721 | m.def("narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
722 | m.def("narrow(Tensor(a) self, int dim, SymInt start, SymInt length) -> Tensor(a)" , {}); |
723 | m.def("narrow.Tensor(Tensor(a) self, int dim, Tensor start, SymInt length) -> Tensor(a)" , {}); |
724 | m.def("native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)" , {at::Tag::core}); |
725 | m.def("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!))" , {}); |
726 | m.def("_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)" , {}); |
727 | m.def("_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!))" , {}); |
728 | m.def("_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)" , {at::Tag::core}); |
729 | m.def("_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!))" , {}); |
730 | m.def("batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor)" , {}); |
731 | m.def("batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps) -> Tensor" , {}); |
732 | m.def("batch_norm_elemt.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
733 | m.def("batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor)" , {}); |
734 | m.def("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)" , {}); |
735 | m.def("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)" , {}); |
736 | m.def("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)" , {}); |
737 | m.def("batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor mean_dy, Tensor mean_dy_xmu, Tensor count) -> Tensor" , {}); |
738 | m.def("batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor)" , {}); |
739 | m.def("is_vulkan_available() -> bool" , {}); |
740 | m.def("_nnpack_available() -> bool" , {}); |
741 | m.def("_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, int[2] stride=1) -> Tensor" , {}); |
742 | m.def("ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
743 | m.def("ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
744 | m.def("ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
745 | m.def("ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor" , {}); |
746 | m.def("pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor" , {}); |
747 | m.def("cdist(Tensor x1, Tensor x2, float p=2, int? compute_mode=None) -> Tensor" , {}); |
748 | m.def("_euclidean_dist(Tensor x1, Tensor x2) -> Tensor" , {}); |
749 | m.def("_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor" , {}); |
750 | m.def("_cdist_backward(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist) -> Tensor" , {}); |
751 | m.def("pdist(Tensor self, float p=2) -> Tensor" , {}); |
752 | m.def("_pdist_forward(Tensor self, float p=2) -> Tensor" , {}); |
753 | m.def("_pdist_backward(Tensor grad, Tensor self, float p, Tensor pdist) -> Tensor" , {}); |
754 | m.def("cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor" , {}); |
755 | m.def("permute(Tensor(a) self, int[] dims) -> Tensor(a)" , {at::Tag::core}); |
756 | m.def("movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)" , {}); |
757 | m.def("movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a)" , {}); |
758 | m.def("moveaxis.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)" , {}); |
759 | m.def("moveaxis.int(Tensor(a) self, int source, int destination) -> Tensor(a)" , {}); |
760 | m.def("numpy_T(Tensor(a) self) -> Tensor(a)" , {}); |
761 | m.def("matrix_H(Tensor(a) self) -> Tensor(a)" , {}); |
762 | m.def("mT(Tensor(a) self) -> Tensor(a)" , {}); |
763 | m.def("mH(Tensor(a) self) -> Tensor(a)" , {}); |
764 | m.def("adjoint(Tensor(a) self) -> Tensor(a)" , {}); |
765 | m.def("pixel_shuffle(Tensor self, int upscale_factor) -> Tensor" , {}); |
766 | m.def("pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor" , {}); |
767 | m.def("channel_shuffle(Tensor self, int groups) -> Tensor" , {}); |
768 | m.def("native_channel_shuffle(Tensor self, int groups) -> Tensor" , {}); |
769 | m.def("is_pinned(Tensor self, Device? device=None) -> bool" , {}); |
770 | m.def("pin_memory(Tensor(a) self, Device? device=None) -> Tensor(a)" , {}); |
771 | m.def("_pin_memory(Tensor self, Device? device=None) -> Tensor" , {}); |
772 | m.def("pinverse(Tensor self, float rcond=1e-15) -> Tensor" , {}); |
773 | m.def("poisson_nll_loss(Tensor input, Tensor target, bool log_input, bool full, float eps, int reduction) -> Tensor" , {}); |
774 | m.def("rad2deg(Tensor self) -> Tensor" , {}); |
775 | m.def("rad2deg_(Tensor(a!) self) -> Tensor(a!)" , {}); |
776 | m.def("rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
777 | m.def("deg2rad(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
778 | m.def("deg2rad_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
779 | m.def("deg2rad.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
780 | m.def("scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::core}); |
781 | m.def("rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
782 | m.def("rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
783 | m.def("rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
784 | m.def("rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
785 | m.def("rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
786 | m.def("rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
787 | m.def("rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
788 | m.def("randint(int high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
789 | m.def("randint.generator(int high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
790 | m.def("randint.low(int low, int high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
791 | m.def("randint.low_generator(int low, int high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
792 | m.def("randint.out(int high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
793 | m.def("randint.generator_out(int high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
794 | m.def("randint.low_out(int low, int high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
795 | m.def("randint.low_generator_out(int low, int high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
796 | m.def("randint_like(Tensor self, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
797 | m.def("randint_like.low_dtype(Tensor self, int low, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
798 | m.def("randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
799 | m.def("randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
800 | m.def("randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
801 | m.def("randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
802 | m.def("randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
803 | m.def("randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
804 | m.def("randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
805 | m.def("randperm(int n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
806 | m.def("randperm.generator(int n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
807 | m.def("randperm.out(int n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
808 | m.def("randperm.generator_out(int n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
809 | m.def("range.step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
810 | m.def("range(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
811 | m.def("range.out_(Scalar start, Scalar end, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
812 | m.def("range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
813 | m.def("ravel(Tensor(a) self) -> Tensor(a)" , {}); |
814 | m.def("reciprocal(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
815 | m.def("reciprocal_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
816 | m.def("reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
817 | m.def("neg(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
818 | m.def("neg_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
819 | m.def("neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
820 | m.def("negative(Tensor self) -> Tensor" , {}); |
821 | m.def("negative_(Tensor(a!) self) -> Tensor(a!)" , {}); |
822 | m.def("negative.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
823 | m.def("repeat(Tensor self, SymInt[] repeats) -> Tensor" , {at::Tag::core}); |
824 | m.def("repeat_interleave.Tensor(Tensor repeats, *, int? output_size=None) -> Tensor" , {at::Tag::dynamic_output_shape}); |
825 | m.def("repeat_interleave.self_Tensor(Tensor self, Tensor repeats, int? dim=None, *, int? output_size=None) -> Tensor" , {}); |
826 | m.def("repeat_interleave.self_int(Tensor self, SymInt repeats, int? dim=None, *, int? output_size=None) -> Tensor" , {}); |
827 | m.def("reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a)" , {}); |
828 | m.def("_reshape_copy(Tensor self, SymInt[] size) -> Tensor" , {}); |
829 | m.def("_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a)" , {}); |
830 | m.def("_mkldnn_reshape(Tensor self, int[] shape) -> Tensor" , {}); |
831 | m.def("reshape_as(Tensor(a) self, Tensor other) -> Tensor(a)" , {}); |
832 | m.def("round(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
833 | m.def("round_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
834 | m.def("round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
835 | m.def("round.decimals(Tensor self, *, int decimals) -> Tensor" , {at::Tag::pointwise}); |
836 | m.def("round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!)" , {at::Tag::pointwise}); |
837 | m.def("round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
838 | m.def("rrelu(Tensor self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
839 | m.def("rrelu_(Tensor(a!) self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
840 | m.def("relu(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
841 | m.def("relu_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
842 | m.def("relu6(Tensor self) -> Tensor" , {}); |
843 | m.def("relu6_(Tensor(a!) self) -> Tensor(a!)" , {}); |
844 | m.def("prelu(Tensor self, Tensor weight) -> Tensor" , {}); |
845 | m.def("_prelu_kernel(Tensor self, Tensor weight) -> Tensor" , {}); |
846 | m.def("_prelu_kernel_backward(Tensor grad_output, Tensor self, Tensor weight) -> (Tensor, Tensor)" , {}); |
847 | m.def("gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!)" , {}); |
848 | m.def("gelu_(Tensor(a!) self, *, str approximate='none') -> Tensor(a!)" , {}); |
849 | m.def("gelu(Tensor self, *, str approximate='none') -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
850 | m.def("gelu_backward.grad_input(Tensor grad_output, Tensor self, *, str approximate='none', Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
851 | m.def("gelu_backward(Tensor grad_output, Tensor self, *, str approximate='none') -> Tensor" , {at::Tag::pointwise}); |
852 | m.def("infinitely_differentiable_gelu_backward(Tensor grad, Tensor self) -> Tensor" , {}); |
853 | m.def("hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
854 | m.def("hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor" , {}); |
855 | m.def("hardshrink_backward.grad_input(Tensor grad_out, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
856 | m.def("hardshrink_backward(Tensor grad_out, Tensor self, Scalar lambd) -> Tensor" , {}); |
857 | m.def("rsqrt(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
858 | m.def("rsqrt_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
859 | m.def("rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
860 | m.def("select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a)" , {}); |
861 | m.def("select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a)" , {at::Tag::core}); |
862 | m.def("select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor" , {}); |
863 | m.def("_nested_select_backward(Tensor grad_output, Tensor self, int dim, SymInt index) -> Tensor" , {}); |
864 | m.def("selu(Tensor self) -> Tensor" , {}); |
865 | m.def("selu_(Tensor(a!) self) -> Tensor(a!)" , {}); |
866 | m.def("celu(Tensor self, Scalar alpha=1.0) -> Tensor" , {}); |
867 | m.def("celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!)" , {}); |
868 | m.def("silu(Tensor self) -> Tensor" , {}); |
869 | m.def("silu_(Tensor(a!) self) -> Tensor(a!)" , {}); |
870 | m.def("silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
871 | m.def("silu_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
872 | m.def("silu_backward(Tensor grad_output, Tensor self) -> Tensor" , {}); |
873 | m.def("mish(Tensor self) -> Tensor" , {}); |
874 | m.def("mish_(Tensor(a!) self) -> Tensor(a!)" , {}); |
875 | m.def("mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
876 | m.def("mish_backward(Tensor grad_output, Tensor self) -> Tensor" , {}); |
877 | m.def("sigmoid(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
878 | m.def("sigmoid_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
879 | m.def("sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
880 | m.def("logit(Tensor self, float? eps=None) -> Tensor" , {at::Tag::pointwise}); |
881 | m.def("logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!)" , {at::Tag::pointwise}); |
882 | m.def("logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
883 | m.def("sin(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
884 | m.def("sin_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
885 | m.def("sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
886 | m.def("sinc(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
887 | m.def("sinc_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
888 | m.def("sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
889 | m.def("sinh(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
890 | m.def("sinh_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
891 | m.def("sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
892 | m.def("detach(Tensor(a) self) -> Tensor(a)" , {}); |
893 | m.def("detach_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::inplace_view}); |
894 | m.def("size.int(Tensor self, int dim) -> int" , {}); |
895 | m.def("size.Dimname(Tensor self, Dimname dim) -> int" , {}); |
896 | m.def("slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a)" , {at::Tag::core}); |
897 | m.def("slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor" , {}); |
898 | m.def("slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor" , {at::Tag::core}); |
899 | m.def("select_scatter(Tensor self, Tensor src, int dim, SymInt index) -> Tensor" , {}); |
900 | m.def("diagonal_scatter(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1) -> Tensor" , {}); |
901 | m.def("as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor" , {}); |
902 | m.def("smm(Tensor self, Tensor mat2) -> Tensor" , {}); |
903 | m.def("softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor" , {}); |
904 | m.def("softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
905 | m.def("softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor" , {}); |
906 | m.def("_softmax(Tensor self, int dim, bool half_to_float) -> Tensor" , {at::Tag::core}); |
907 | m.def("_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
908 | m.def("_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor" , {}); |
909 | m.def("_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
910 | m.def("unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]" , {}); |
911 | m.def("split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[]" , {}); |
912 | m.def("split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[]" , {}); |
913 | m.def("unsafe_split_with_sizes(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]" , {}); |
914 | m.def("split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[]" , {}); |
915 | m.def("hsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]" , {}); |
916 | m.def("hsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]" , {}); |
917 | m.def("vsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]" , {}); |
918 | m.def("vsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]" , {}); |
919 | m.def("dsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]" , {}); |
920 | m.def("dsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]" , {}); |
921 | m.def("squeeze(Tensor(a) self) -> Tensor(a)" , {}); |
922 | m.def("squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)" , {at::Tag::core}); |
923 | m.def("squeeze.dimname(Tensor(a) self, Dimname dim) -> Tensor(a)" , {}); |
924 | m.def("squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)" , {at::Tag::core}); |
925 | m.def("squeeze_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::inplace_view}); |
926 | m.def("squeeze_.dim(Tensor(a!) self, int dim) -> Tensor(a!)" , {at::Tag::inplace_view}); |
927 | m.def("squeeze_.dims(Tensor(a!) self, int[] dim) -> Tensor(a!)" , {at::Tag::inplace_view}); |
928 | m.def("squeeze_.dimname(Tensor(a!) self, Dimname dim) -> Tensor(a!)" , {at::Tag::inplace_view}); |
929 | m.def("sspaddmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor" , {}); |
930 | m.def("sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {}); |
931 | m.def("stack(Tensor[] tensors, int dim=0) -> Tensor" , {}); |
932 | m.def("stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
933 | m.def("_stack(Tensor[] tensors, int dim=0) -> Tensor" , {}); |
934 | m.def("_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
935 | m.def("hstack(Tensor[] tensors) -> Tensor" , {}); |
936 | m.def("hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
937 | m.def("vstack(Tensor[] tensors) -> Tensor" , {}); |
938 | m.def("vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
939 | m.def("dstack(Tensor[] tensors) -> Tensor" , {}); |
940 | m.def("dstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
941 | m.def("stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor" , {}); |
942 | m.def("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) -> Tensor" , {}); |
943 | m.def("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" , {}); |
944 | m.def("stride.int(Tensor self, int dim) -> int" , {}); |
945 | m.def("stride.Dimname(Tensor self, Dimname dim) -> int" , {}); |
946 | m.def("sum(Tensor self, *, ScalarType? dtype=None) -> Tensor" , {}); |
947 | m.def("sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {at::Tag::core}); |
948 | m.def("sum.dim_DimnameList(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
949 | m.def("sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
950 | m.def("sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
951 | m.def("_nested_sum_backward(Tensor grad, Tensor self, int[1]? dim, bool keepdim=False) -> Tensor" , {}); |
952 | m.def("nansum(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
953 | m.def("nansum.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
954 | m.def("sum_to_size(Tensor self, int[] size) -> Tensor" , {}); |
955 | m.def("sqrt(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
956 | m.def("sqrt_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
957 | m.def("sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
958 | m.def("square(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
959 | m.def("square_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
960 | m.def("square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
961 | m.def("std(Tensor self, bool unbiased=True) -> Tensor" , {}); |
962 | m.def("std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor" , {}); |
963 | m.def("std.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> Tensor" , {}); |
964 | m.def("std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)" , {}); |
965 | m.def("std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)" , {}); |
966 | m.def("std_mean.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor)" , {}); |
967 | m.def("std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)" , {}); |
968 | m.def("std_mean.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor)" , {}); |
969 | m.def("std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
970 | m.def("std.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
971 | m.def("std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor" , {}); |
972 | m.def("std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
973 | m.def("std.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> Tensor" , {}); |
974 | m.def("std.correction_names_out(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
975 | m.def("prod(Tensor self, *, ScalarType? dtype=None) -> Tensor" , {}); |
976 | m.def("prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
977 | m.def("prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
978 | m.def("prod.dim_Dimname(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
979 | m.def("prod.Dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
980 | m.def("t(Tensor(a) self) -> Tensor(a)" , {}); |
981 | m.def("t_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::inplace_view}); |
982 | m.def("tan(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
983 | m.def("tan_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
984 | m.def("tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
985 | m.def("tanh(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
986 | m.def("tanh_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
987 | m.def("tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
988 | m.def("tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor" , {}); |
989 | m.def("tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
990 | m.def("threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor" , {}); |
991 | m.def("threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!)" , {}); |
992 | m.def("threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
993 | m.def("threshold_backward.grad_input(Tensor grad_output, Tensor self, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
994 | m.def("threshold_backward(Tensor grad_output, Tensor self, Scalar threshold) -> Tensor" , {at::Tag::pointwise}); |
995 | m.def("tile(Tensor self, int[] dims) -> Tensor" , {}); |
996 | m.def("transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)" , {}); |
997 | m.def("transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a)" , {}); |
998 | m.def("_mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor" , {}); |
999 | m.def("transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)" , {at::Tag::inplace_view}); |
1000 | m.def("_mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)" , {}); |
1001 | m.def("one_hot(Tensor self, int num_classes=-1) -> Tensor" , {at::Tag::dynamic_output_shape}); |
1002 | m.def("flip(Tensor self, int[] dims) -> Tensor" , {at::Tag::core}); |
1003 | m.def("fliplr(Tensor self) -> Tensor" , {}); |
1004 | m.def("flipud(Tensor self) -> Tensor" , {}); |
1005 | m.def("roll(Tensor self, int[1] shifts, int[1] dims=[]) -> Tensor" , {}); |
1006 | m.def("rot90(Tensor self, int k=1, int[] dims=[0,1]) -> Tensor" , {}); |
1007 | m.def("trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor" , {}); |
1008 | m.def("trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor" , {}); |
1009 | m.def("trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor" , {}); |
1010 | m.def("trapz.dx(Tensor y, *, float dx=1, int dim=-1) -> Tensor" , {}); |
1011 | m.def("_transform_bias_rescale_qkv(Tensor qkv, Tensor qkv_bias, int num_heads) -> (Tensor, Tensor, Tensor)" , {}); |
1012 | m.def("_nested_tensor_from_mask(Tensor t, Tensor mask, bool mask_check=True) -> Tensor" , {}); |
1013 | m.def("_nested_tensor_from_mask_left_aligned(Tensor t, Tensor mask) -> bool" , {}); |
1014 | m.def("_nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False) -> Tensor" , {}); |
1015 | m.def("_nested_tensor_size(Tensor self) -> Tensor" , {}); |
1016 | m.def("_nested_tensor_strides(Tensor self) -> Tensor" , {}); |
1017 | m.def("_nested_tensor_offsets(Tensor self) -> int[]" , {}); |
1018 | m.def("_nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example) -> Tensor" , {}); |
1019 | m.def("_nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, int[] offsets) -> Tensor(a)" , {}); |
1020 | m.def("_nested_view_from_buffer_copy(Tensor self, Tensor nested_size, Tensor nested_strides, int[] offsets) -> Tensor" , {at::Tag::view_copy}); |
1021 | m.def("_trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor" , {}); |
1022 | m.def("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" , {}); |
1023 | m.def("trunc(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
1024 | m.def("trunc_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
1025 | m.def("trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1026 | m.def("fix(Tensor self) -> Tensor" , {}); |
1027 | m.def("fix_(Tensor(a!) self) -> Tensor(a!)" , {}); |
1028 | m.def("fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1029 | m.def("type_as(Tensor self, Tensor other) -> Tensor" , {}); |
1030 | m.def("_has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool" , {}); |
1031 | m.def("_unique(Tensor self, bool sorted=True, bool return_inverse=False) -> (Tensor, Tensor)" , {}); |
1032 | m.def("unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)" , {at::Tag::dynamic_output_shape}); |
1033 | m.def("unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor)" , {at::Tag::dynamic_output_shape}); |
1034 | m.def("unique_dim_consecutive(Tensor self, int dim, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)" , {at::Tag::dynamic_output_shape}); |
1035 | m.def("_unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)" , {at::Tag::dynamic_output_shape}); |
1036 | m.def("_unsafe_view(Tensor self, SymInt[] size) -> Tensor" , {}); |
1037 | m.def("unsqueeze(Tensor(a) self, int dim) -> Tensor(a)" , {at::Tag::core}); |
1038 | m.def("unsqueeze_(Tensor(a!) self, int dim) -> Tensor(a!)" , {at::Tag::inplace_view}); |
1039 | m.def("vander(Tensor x, int? N=None, bool increasing=False) -> Tensor" , {}); |
1040 | m.def("var(Tensor self, bool unbiased=True) -> Tensor" , {}); |
1041 | m.def("var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor" , {at::Tag::core}); |
1042 | m.def("var.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> Tensor" , {}); |
1043 | m.def("var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1044 | m.def("var.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
1045 | m.def("var.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor" , {}); |
1046 | m.def("var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1047 | m.def("var.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> Tensor" , {}); |
1048 | m.def("var.correction_names_out(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
1049 | m.def("var_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)" , {}); |
1050 | m.def("var_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)" , {}); |
1051 | m.def("var_mean.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor)" , {}); |
1052 | m.def("var_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)" , {}); |
1053 | m.def("var_mean.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor)" , {}); |
1054 | m.def("view_as(Tensor(a) self, Tensor other) -> Tensor(a)" , {}); |
1055 | m.def("where.self(Tensor condition, Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1056 | m.def("where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1057 | m.def("where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor" , {}); |
1058 | m.def("where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor" , {}); |
1059 | m.def("where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor" , {}); |
1060 | m.def("where(Tensor condition) -> Tensor[]" , {}); |
1061 | m.def("norm_except_dim(Tensor v, int pow=2, int dim=0) -> Tensor" , {}); |
1062 | m.def("_weight_norm(Tensor v, Tensor g, int dim=0) -> Tensor" , {}); |
1063 | m.def("_weight_norm_interface(Tensor v, Tensor g, int dim=0) -> (Tensor, Tensor)" , {}); |
1064 | m.def("_weight_norm_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)" , {}); |
1065 | m.def("_weight_norm_differentiable_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)" , {}); |
1066 | m.def("zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
1067 | m.def("_efficientzerotensor(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
1068 | m.def("zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
1069 | m.def("zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1070 | m.def("zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor" , {}); |
1071 | m.def("_standard_gamma_grad(Tensor self, Tensor output) -> Tensor" , {}); |
1072 | m.def("_standard_gamma(Tensor self, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
1073 | m.def("_dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor" , {}); |
1074 | m.def("_sample_dirichlet(Tensor self, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
1075 | m.def("poisson(Tensor self, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
1076 | m.def("binomial(Tensor count, Tensor prob, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
1077 | m.def("native_norm(Tensor self, Scalar p=2) -> Tensor" , {}); |
1078 | m.def("native_norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype) -> Tensor" , {}); |
1079 | m.def("_sparse_sum(Tensor self) -> Tensor" , {}); |
1080 | m.def("_sparse_sum.dtype(Tensor self, *, ScalarType dtype) -> Tensor" , {}); |
1081 | m.def("_sparse_sum.dim(Tensor self, int[1] dim) -> Tensor" , {}); |
1082 | m.def("_sparse_sum.dim_dtype(Tensor self, int[1] dim, *, ScalarType dtype) -> Tensor" , {}); |
1083 | m.def("_sparse_sum_backward(Tensor grad, Tensor self, int[] dim) -> Tensor" , {}); |
1084 | m.def("_sparse_csr_sum.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
1085 | m.def("_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
1086 | m.def("_sparse_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor" , {}); |
1087 | m.def("_sparse_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor" , {}); |
1088 | m.def("_sparse_softmax(Tensor self, int dim, bool half_to_float) -> Tensor" , {}); |
1089 | m.def("_sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor" , {}); |
1090 | m.def("_sparse_log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor" , {}); |
1091 | m.def("_sparse_log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor" , {}); |
1092 | m.def("_sparse_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor" , {}); |
1093 | m.def("_sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor" , {}); |
1094 | m.def("_spdiags(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None) -> Tensor" , {}); |
1095 | m.def("norm.ScalarOpt_dtype(Tensor self, Scalar? p, *, ScalarType dtype) -> Tensor" , {}); |
1096 | m.def("norm.Scalar(Tensor self, Scalar p=2) -> Tensor" , {}); |
1097 | m.def("norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor" , {}); |
1098 | m.def("norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> Tensor" , {}); |
1099 | m.def("norm.dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)" , {}); |
1100 | m.def("norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1101 | m.def("norm.names_ScalarOpt_dim_dtype(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor" , {}); |
1102 | m.def("norm.names_ScalarOpt_dim(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False) -> Tensor" , {}); |
1103 | m.def("norm.names_dtype_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)" , {}); |
1104 | m.def("norm.names_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1105 | m.def("frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent)" , {at::Tag::pointwise}); |
1106 | m.def("frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent)" , {at::Tag::pointwise}); |
1107 | m.def("frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor" , {}); |
1108 | m.def("frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1109 | m.def("nuclear_norm(Tensor self, bool keepdim=False) -> Tensor" , {}); |
1110 | m.def("nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1111 | m.def("nuclear_norm.dim(Tensor self, int[2] dim, bool keepdim=False) -> Tensor" , {}); |
1112 | m.def("nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1113 | m.def("clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor" , {at::Tag::core}); |
1114 | m.def("positive(Tensor(a) self) -> Tensor(a)" , {at::Tag::pointwise}); |
1115 | m.def("resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!)" , {}); |
1116 | m.def("resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!)" , {}); |
1117 | m.def("zero_(Tensor(a!) self) -> Tensor(a!)" , {}); |
1118 | m.def("sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1119 | m.def("sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1120 | m.def("sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)" , {at::Tag::pointwise}); |
1121 | m.def("sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1122 | m.def("sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)" , {at::Tag::pointwise}); |
1123 | m.def("subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {}); |
1124 | m.def("subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor" , {}); |
1125 | m.def("subtract_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)" , {}); |
1126 | m.def("subtract.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor" , {}); |
1127 | m.def("subtract_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)" , {}); |
1128 | m.def("rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor" , {}); |
1129 | m.def("heaviside.out(Tensor self, Tensor values, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1130 | m.def("heaviside(Tensor self, Tensor values) -> Tensor" , {at::Tag::pointwise}); |
1131 | m.def("heaviside_(Tensor(a!) self, Tensor values) -> Tensor(a!)" , {}); |
1132 | m.def("rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor" , {at::Tag::pointwise}); |
1133 | m.def("_sparse_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor" , {}); |
1134 | m.def("sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {}); |
1135 | m.def("sparse_sampled_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor" , {}); |
1136 | m.def("_sparse_mm_reduce_impl(Tensor self, Tensor other, str reduce) -> (Tensor, Tensor)" , {}); |
1137 | m.def("_sparse_mm_reduce_impl_backward(Tensor self, Tensor grad_out, Tensor weight, str reduce, Tensor arg_out, bool[2] output_mask) -> (Tensor, Tensor)" , {}); |
1138 | m.def("addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {}); |
1139 | m.def("addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor" , {at::Tag::core}); |
1140 | m.def("addmm_(Tensor(a!) self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)" , {}); |
1141 | m.def("_addmm_activation.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
1142 | m.def("_addmm_activation(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False) -> Tensor" , {}); |
1143 | m.def("sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor" , {}); |
1144 | m.def("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" , {}); |
1145 | m.def("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" , {}); |
1146 | m.def("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" , {}); |
1147 | m.def("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" , {}); |
1148 | m.def("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" , {}); |
1149 | m.def("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" , {}); |
1150 | m.def("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" , {}); |
1151 | m.def("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" , {}); |
1152 | m.def("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" , {}); |
1153 | m.def("_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
1154 | m.def("_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" , {}); |
1155 | m.def("_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" , {}); |
1156 | m.def("_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" , {}); |
1157 | m.def("_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" , {}); |
1158 | m.def("sparse_coo_tensor.size(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor" , {}); |
1159 | m.def("sparse_coo_tensor.indices(Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
1160 | m.def("sparse_coo_tensor.indices_size(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
1161 | m.def("_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
1162 | m.def("_validate_sparse_coo_tensor_args(Tensor indices, Tensor values, int[] size) -> ()" , {}); |
1163 | m.def("_validate_sparse_compressed_tensor_args(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, Layout layout) -> ()" , {}); |
1164 | m.def("_validate_sparse_csr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size) -> ()" , {}); |
1165 | m.def("_validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> ()" , {}); |
1166 | m.def("_validate_sparse_bsr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size) -> ()" , {}); |
1167 | m.def("_validate_sparse_bsc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> ()" , {}); |
1168 | m.def("_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" , {}); |
1169 | m.def("_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) -> Tensor" , {}); |
1170 | m.def("sparse_resize_(Tensor(a!) self, int[] size, int sparse_dim, int dense_dim) -> Tensor(a!)" , {}); |
1171 | m.def("sparse_resize_and_clear_(Tensor(a!) self, int[] size, int sparse_dim, int dense_dim) -> Tensor(a!)" , {}); |
1172 | m.def("sparse_mask(Tensor self, Tensor mask) -> Tensor" , {}); |
1173 | m.def("_to_cpu(Tensor[] tensors) -> Tensor[]" , {}); |
1174 | m.def("to_dense(Tensor self, ScalarType? dtype=None) -> Tensor" , {}); |
1175 | m.def("_to_dense(Tensor self, ScalarType? dtype=None) -> Tensor" , {}); |
1176 | m.def("to_dense_backward(Tensor grad, Tensor input) -> Tensor" , {}); |
1177 | m.def("sparse_dim(Tensor self) -> int" , {}); |
1178 | m.def("_dimI(Tensor self) -> int" , {}); |
1179 | m.def("dense_dim(Tensor self) -> int" , {}); |
1180 | m.def("_dimV(Tensor self) -> int" , {}); |
1181 | m.def("_nnz(Tensor self) -> int" , {}); |
1182 | m.def("coalesce(Tensor(a) self) -> Tensor(a)" , {}); |
1183 | m.def("_coalesce(Tensor self) -> Tensor" , {}); |
1184 | m.def("is_coalesced(Tensor self) -> bool" , {}); |
1185 | m.def("_indices(Tensor(a) self) -> Tensor(a)" , {}); |
1186 | m.def("_values(Tensor(a) self) -> Tensor(a)" , {}); |
1187 | m.def("_coalesced_(Tensor(a!) self, bool coalesced) -> Tensor(a!)" , {}); |
1188 | m.def("indices(Tensor(a) self) -> Tensor(a)" , {}); |
1189 | m.def("values(Tensor(a) self) -> Tensor(a)" , {}); |
1190 | m.def("crow_indices(Tensor(a) self) -> Tensor(a)" , {}); |
1191 | m.def("col_indices(Tensor(a) self) -> Tensor(a)" , {}); |
1192 | m.def("ccol_indices(Tensor(a) self) -> Tensor(a)" , {}); |
1193 | m.def("row_indices(Tensor(a) self) -> Tensor(a)" , {}); |
1194 | m.def("hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1195 | m.def("hspmm(Tensor mat1, Tensor mat2) -> Tensor" , {}); |
1196 | m.def("copy_sparse_to_sparse_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!)" , {}); |
1197 | m.def("unbind.int(Tensor(a -> *) self, int dim=0) -> Tensor(a)[]" , {}); |
1198 | m.def("unbind.Dimname(Tensor(a -> *) self, Dimname dim) -> Tensor(a)[]" , {}); |
1199 | m.def("to_sparse.sparse_dim(Tensor self, int sparse_dim) -> Tensor" , {}); |
1200 | m.def("to_sparse(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None) -> Tensor" , {}); |
1201 | m.def("to_sparse_csr(Tensor self, int? dense_dim=None) -> Tensor" , {}); |
1202 | m.def("to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor" , {}); |
1203 | m.def("to_sparse_bsr(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor" , {}); |
1204 | m.def("to_sparse_bsc(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor" , {}); |
1205 | m.def("to_mkldnn(Tensor self, ScalarType? dtype=None) -> Tensor" , {}); |
1206 | m.def("mkldnn_reorder_conv2d_weight(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1, int[]? input_size=None) -> Tensor" , {}); |
1207 | m.def("mkldnn_reorder_conv3d_weight(Tensor self, int[3] padding=0, int[3] stride=1, int[3] dilation=1, int groups=1) -> Tensor" , {}); |
1208 | m.def("to_mkldnn_backward(Tensor grad, Tensor input) -> Tensor" , {}); |
1209 | m.def("quantize_per_tensor_dynamic(Tensor self, ScalarType dtype, bool reduce_range) -> Tensor" , {}); |
1210 | m.def("quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensor" , {}); |
1211 | m.def("quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensor" , {}); |
1212 | m.def("quantize_per_tensor.tensors(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype) -> Tensor[]" , {}); |
1213 | m.def("quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor" , {}); |
1214 | m.def("dequantize.self(Tensor self) -> Tensor" , {}); |
1215 | m.def("dequantize.tensors(Tensor[] tensors) -> Tensor[]" , {}); |
1216 | m.def("q_scale(Tensor self) -> float" , {}); |
1217 | m.def("q_zero_point(Tensor self) -> int" , {}); |
1218 | m.def("q_per_channel_scales(Tensor self) -> Tensor" , {}); |
1219 | m.def("q_per_channel_zero_points(Tensor self) -> Tensor" , {}); |
1220 | m.def("q_per_channel_axis(Tensor self) -> int" , {}); |
1221 | m.def("int_repr(Tensor self) -> Tensor" , {}); |
1222 | m.def("_make_per_tensor_quantized_tensor(Tensor self, float scale, int zero_point) -> Tensor" , {}); |
1223 | m.def("_make_per_channel_quantized_tensor(Tensor self, Tensor scale, Tensor zero_point, int axis) -> Tensor" , {}); |
1224 | m.def("qscheme(Tensor self) -> QScheme" , {}); |
1225 | m.def("fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor" , {}); |
1226 | m.def("fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> Tensor" , {}); |
1227 | m.def("fake_quantize_per_tensor_affine_cachemask(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> (Tensor output, Tensor mask)" , {}); |
1228 | m.def("_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)" , {}); |
1229 | m.def("fake_quantize_per_tensor_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor" , {}); |
1230 | m.def("_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" , {}); |
1231 | m.def("_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)" , {}); |
1232 | m.def("fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensor" , {}); |
1233 | m.def("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)" , {}); |
1234 | m.def("fake_quantize_per_channel_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor" , {}); |
1235 | m.def("_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" , {}); |
1236 | m.def("_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)" , {}); |
1237 | m.def("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" , {}); |
1238 | m.def("_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)" , {}); |
1239 | m.def("_choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int)" , {}); |
1240 | m.def("_saturate_weight_to_fp16(Tensor weight) -> Tensor" , {}); |
1241 | m.def("choose_qparams_optimized(Tensor input, int numel, int n_bins, float ratio, int bit_width) -> (Tensor, Tensor)" , {}); |
1242 | m.def("_autocast_to_reduced_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled, ScalarType cuda_dtype, ScalarType cpu_dtype) -> Tensor(a)" , {}); |
1243 | m.def("_autocast_to_full_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled) -> Tensor(a)" , {}); |
1244 | m.def("_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" , {at::Tag::core}); |
1245 | m.def("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)" , {}); |
1246 | m.def("to.device(Tensor(a) self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)" , {}); |
1247 | m.def("to.dtype(Tensor(a) self, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)" , {}); |
1248 | m.def("to.other(Tensor(a) self, Tensor other, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)" , {}); |
1249 | m.def("meshgrid(Tensor[] tensors) -> Tensor[]" , {}); |
1250 | m.def("meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[]" , {}); |
1251 | m.def("cartesian_prod(Tensor[] tensors) -> Tensor" , {}); |
1252 | m.def("combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor" , {}); |
1253 | m.def("item(Tensor self) -> Scalar" , {at::Tag::data_dependent_output}); |
1254 | m.def("result_type.Tensor(Tensor tensor, Tensor other) -> ScalarType" , {}); |
1255 | m.def("result_type.Scalar(Tensor tensor, Scalar other) -> ScalarType" , {}); |
1256 | m.def("result_type.Scalar_Tensor(Scalar scalar, Tensor tensor) -> ScalarType" , {}); |
1257 | m.def("result_type.Scalar_Scalar(Scalar scalar1, Scalar scalar2) -> ScalarType" , {}); |
1258 | m.def("can_cast(ScalarType from, ScalarType to) -> bool" , {}); |
1259 | m.def("promote_types(ScalarType type1, ScalarType type2) -> ScalarType" , {}); |
1260 | m.def("_local_scalar_dense(Tensor self) -> Scalar" , {at::Tag::data_dependent_output}); |
1261 | m.def("_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)" , {}); |
1262 | m.def("lstm_mps_backward(Tensor grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor[], Tensor[])" , {}); |
1263 | m.def("_thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor)" , {}); |
1264 | m.def("_thnn_fused_lstm_cell_backward_impl(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor)" , {}); |
1265 | m.def("_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)" , {}); |
1266 | m.def("_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)" , {}); |
1267 | m.def("_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor)" , {}); |
1268 | m.def("_thnn_fused_gru_cell_backward(Tensor grad_hy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor)" , {}); |
1269 | m.def("_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)" , {}); |
1270 | m.def("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)" , {}); |
1271 | m.def("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)" , {}); |
1272 | m.def("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)" , {}); |
1273 | m.def("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)" , {}); |
1274 | m.def("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)" , {}); |
1275 | m.def("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)" , {}); |
1276 | m.def("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)" , {}); |
1277 | m.def("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)" , {}); |
1278 | m.def("lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)" , {}); |
1279 | m.def("gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor" , {}); |
1280 | m.def("rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor" , {}); |
1281 | m.def("rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor" , {}); |
1282 | m.def("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)" , {}); |
1283 | m.def("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" , {}); |
1284 | m.def("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" , {}); |
1285 | m.def("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" , {}); |
1286 | m.def("_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor)" , {}); |
1287 | m.def("_pack_padded_sequence_backward(Tensor grad, SymInt[] input_size, Tensor batch_sizes, bool batch_first) -> Tensor" , {}); |
1288 | m.def("_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor)" , {}); |
1289 | m.def("set_.source_Storage(Tensor(a!) self, Storage source) -> Tensor(a!)" , {}); |
1290 | m.def("set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor(a!)" , {}); |
1291 | m.def("set_.source_Tensor_storage_offset(Tensor(a!) self, Tensor source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor(a!)" , {}); |
1292 | m.def("set_.source_Tensor(Tensor(a!) self, Tensor source) -> Tensor(a!)" , {}); |
1293 | m.def("set_(Tensor(a!) self) -> Tensor(a!)" , {}); |
1294 | m.def("lift(Tensor self) -> Tensor" , {}); |
1295 | m.def("lift_fresh(Tensor(a) self) -> Tensor(a)" , {}); |
1296 | m.def("lift_fresh_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
1297 | m.def("is_set_to(Tensor self, Tensor tensor) -> bool" , {}); |
1298 | m.def("masked_fill_.Scalar(Tensor(a!) self, Tensor mask, Scalar value) -> Tensor(a!)" , {}); |
1299 | m.def("masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor" , {at::Tag::pointwise}); |
1300 | m.def("masked_fill_.Tensor(Tensor(a!) self, Tensor mask, Tensor value) -> Tensor(a!)" , {}); |
1301 | m.def("masked_fill.Tensor(Tensor self, Tensor mask, Tensor value) -> Tensor" , {}); |
1302 | m.def("masked_scatter_(Tensor(a!) self, Tensor mask, Tensor source) -> Tensor(a!)" , {}); |
1303 | m.def("masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor" , {}); |
1304 | m.def("_masked_softmax(Tensor self, Tensor mask, int? dim=None, int? mask_type=None) -> Tensor" , {}); |
1305 | m.def("_masked_softmax_backward(Tensor grad_output, Tensor output, Tensor mask, int? dim=None) -> Tensor" , {}); |
1306 | m.def("view(Tensor(a) self, SymInt[] size) -> Tensor(a)" , {at::Tag::core}); |
1307 | m.def("view.dtype(Tensor(a) self, ScalarType dtype) -> Tensor(a)" , {}); |
1308 | m.def("put_(Tensor(a!) self, Tensor index, Tensor source, bool accumulate=False) -> Tensor(a!)" , {}); |
1309 | m.def("put(Tensor self, Tensor index, Tensor source, bool accumulate=False) -> Tensor" , {}); |
1310 | m.def("index_add.out(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {}); |
1311 | m.def("index_add_(Tensor(a!) self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor(a!)" , {}); |
1312 | m.def("index_add(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor" , {}); |
1313 | m.def("index_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor" , {}); |
1314 | m.def("index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!)" , {}); |
1315 | m.def("index_reduce_(Tensor(a!) self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor(a!)" , {}); |
1316 | m.def("index_reduce(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor" , {}); |
1317 | m.def("index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!)" , {}); |
1318 | m.def("index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor" , {}); |
1319 | m.def("index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!)" , {}); |
1320 | m.def("index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor" , {}); |
1321 | m.def("index_fill_.Dimname_Scalar(Tensor(a!) self, Dimname dim, Tensor index, Scalar value) -> Tensor(a!)" , {}); |
1322 | m.def("index_fill_.Dimname_Tensor(Tensor(a!) self, Dimname dim, Tensor index, Tensor value) -> Tensor(a!)" , {}); |
1323 | m.def("index_fill.Dimname_Scalar(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor" , {}); |
1324 | m.def("index_fill.Dimname_Tensor(Tensor self, Dimname dim, Tensor index, Tensor value) -> Tensor" , {}); |
1325 | m.def("scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor" , {}); |
1326 | m.def("scatter_.src(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!)" , {}); |
1327 | m.def("scatter.src_out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1328 | m.def("scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor" , {}); |
1329 | m.def("scatter_.value(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!)" , {}); |
1330 | m.def("scatter.value_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1331 | m.def("scatter.reduce(Tensor self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor" , {}); |
1332 | m.def("scatter_.reduce(Tensor(a!) self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor(a!)" , {}); |
1333 | m.def("scatter.reduce_out(Tensor self, int dim, Tensor index, Tensor src, *, str reduce, Tensor(a!) out) -> Tensor(a!)" , {}); |
1334 | m.def("scatter.value_reduce(Tensor self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor" , {}); |
1335 | m.def("scatter_.value_reduce(Tensor(a!) self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor(a!)" , {}); |
1336 | m.def("scatter.value_reduce_out(Tensor self, int dim, Tensor index, Scalar value, *, str reduce, Tensor(a!) out) -> Tensor(a!)" , {}); |
1337 | m.def("scatter.dimname_src(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor" , {}); |
1338 | m.def("scatter.dimname_value(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor" , {}); |
1339 | m.def("scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor" , {at::Tag::core}); |
1340 | m.def("scatter_add_(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!)" , {}); |
1341 | m.def("scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1342 | m.def("scatter_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor" , {}); |
1343 | m.def("scatter_reduce.two(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor" , {at::Tag::core}); |
1344 | m.def("scatter_reduce_.two(Tensor(a!) self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor(a!)" , {}); |
1345 | m.def("scatter_reduce.two_out(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!)" , {}); |
1346 | m.def("eq_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1347 | m.def("eq_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1348 | m.def("bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1349 | m.def("bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1350 | m.def("bitwise_and.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
1351 | m.def("bitwise_and.Scalar_Tensor(Scalar self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1352 | m.def("bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1353 | m.def("bitwise_and_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1354 | m.def("bitwise_and_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1355 | m.def("__and__.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
1356 | m.def("__and__.Tensor(Tensor self, Tensor other) -> Tensor" , {}); |
1357 | m.def("__iand__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1358 | m.def("__iand__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1359 | m.def("bitwise_or.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1360 | m.def("bitwise_or.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1361 | m.def("bitwise_or.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
1362 | m.def("bitwise_or.Scalar_Tensor(Scalar self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1363 | m.def("bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1364 | m.def("bitwise_or_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1365 | m.def("bitwise_or_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1366 | m.def("__or__.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
1367 | m.def("__or__.Tensor(Tensor self, Tensor other) -> Tensor" , {}); |
1368 | m.def("__ior__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1369 | m.def("__ior__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1370 | m.def("bitwise_xor.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1371 | m.def("bitwise_xor.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1372 | m.def("bitwise_xor.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
1373 | m.def("bitwise_xor.Scalar_Tensor(Scalar self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1374 | m.def("bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1375 | m.def("bitwise_xor_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1376 | m.def("bitwise_xor_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1377 | m.def("__xor__.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
1378 | m.def("__xor__.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1379 | m.def("__ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1380 | m.def("__ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1381 | m.def("__lshift__.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
1382 | m.def("__lshift__.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1383 | m.def("__ilshift__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1384 | m.def("__ilshift__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1385 | m.def("bitwise_left_shift.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1386 | m.def("bitwise_left_shift_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1387 | m.def("bitwise_left_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1388 | m.def("bitwise_left_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
1389 | m.def("bitwise_left_shift_.Tensor_Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1390 | m.def("bitwise_left_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1391 | m.def("bitwise_left_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1392 | m.def("__rshift__.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
1393 | m.def("__rshift__.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1394 | m.def("__irshift__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1395 | m.def("__irshift__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1396 | m.def("bitwise_right_shift.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1397 | m.def("bitwise_right_shift_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1398 | m.def("bitwise_right_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1399 | m.def("bitwise_right_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
1400 | m.def("bitwise_right_shift_.Tensor_Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1401 | m.def("bitwise_right_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1402 | m.def("bitwise_right_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1403 | m.def("tril_(Tensor(a!) self, int diagonal=0) -> Tensor(a!)" , {}); |
1404 | m.def("triu_(Tensor(a!) self, int diagonal=0) -> Tensor(a!)" , {}); |
1405 | m.def("digamma_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
1406 | m.def("lerp_.Scalar(Tensor(a!) self, Tensor end, Scalar weight) -> Tensor(a!)" , {at::Tag::pointwise}); |
1407 | m.def("lerp_.Tensor(Tensor(a!) self, Tensor end, Tensor weight) -> Tensor(a!)" , {at::Tag::pointwise}); |
1408 | m.def("addbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)" , {}); |
1409 | m.def("addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {}); |
1410 | m.def("addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor" , {}); |
1411 | m.def("random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1412 | m.def("random_.to(Tensor(a!) self, int to, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1413 | m.def("random_(Tensor(a!) self, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1414 | m.def("uniform_(Tensor(a!) self, float from=0, float to=1, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1415 | m.def("cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1416 | m.def("log_normal_(Tensor(a!) self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1417 | m.def("exponential_(Tensor(a!) self, float lambd=1, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1418 | m.def("geometric_(Tensor(a!) self, float p, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1419 | m.def("diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1420 | m.def("diag(Tensor self, int diagonal=0) -> Tensor" , {}); |
1421 | m.def("cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1422 | m.def("cross(Tensor self, Tensor other, int? dim=None) -> Tensor" , {}); |
1423 | m.def("triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1424 | m.def("triu(Tensor self, int diagonal=0) -> Tensor" , {}); |
1425 | m.def("tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1426 | m.def("tril(Tensor self, int diagonal=0) -> Tensor" , {}); |
1427 | m.def("tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
1428 | m.def("triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
1429 | m.def("trace(Tensor self) -> Tensor" , {}); |
1430 | m.def("trace_backward(Tensor grad, SymInt[] sizes) -> Tensor" , {}); |
1431 | m.def("ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1432 | m.def("ne.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1433 | m.def("ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1434 | m.def("ne.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1435 | m.def("ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1436 | m.def("ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1437 | m.def("not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1438 | m.def("not_equal.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
1439 | m.def("not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1440 | m.def("not_equal.Tensor(Tensor self, Tensor other) -> Tensor" , {}); |
1441 | m.def("not_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1442 | m.def("not_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1443 | m.def("eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1444 | m.def("eq.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1445 | m.def("eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1446 | m.def("eq.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1447 | m.def("ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1448 | m.def("ge.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1449 | m.def("ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1450 | m.def("ge.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1451 | m.def("ge_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1452 | m.def("ge_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1453 | m.def("greater_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1454 | m.def("greater_equal.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
1455 | m.def("greater_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1456 | m.def("greater_equal.Tensor(Tensor self, Tensor other) -> Tensor" , {}); |
1457 | m.def("greater_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1458 | m.def("greater_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1459 | m.def("le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1460 | m.def("le.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1461 | m.def("le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1462 | m.def("le.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1463 | m.def("le_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1464 | m.def("le_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1465 | m.def("less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1466 | m.def("less_equal.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
1467 | m.def("less_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1468 | m.def("less_equal.Tensor(Tensor self, Tensor other) -> Tensor" , {}); |
1469 | m.def("less_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1470 | m.def("less_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1471 | m.def("gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1472 | m.def("gt.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1473 | m.def("gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1474 | m.def("gt.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1475 | m.def("gt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1476 | m.def("gt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1477 | m.def("greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1478 | m.def("greater.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
1479 | m.def("greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1480 | m.def("greater.Tensor(Tensor self, Tensor other) -> Tensor" , {}); |
1481 | m.def("greater_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1482 | m.def("greater_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1483 | m.def("lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1484 | m.def("lt.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1485 | m.def("lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1486 | m.def("lt.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1487 | m.def("lt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1488 | m.def("lt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1489 | m.def("less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1490 | m.def("less.Scalar(Tensor self, Scalar other) -> Tensor" , {}); |
1491 | m.def("less.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1492 | m.def("less.Tensor(Tensor self, Tensor other) -> Tensor" , {}); |
1493 | m.def("less_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {}); |
1494 | m.def("less_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1495 | m.def("take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1496 | m.def("take(Tensor self, Tensor index) -> Tensor" , {}); |
1497 | m.def("take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1498 | m.def("take_along_dim(Tensor self, Tensor indices, int? dim=None) -> Tensor" , {}); |
1499 | m.def("index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1500 | m.def("index_select(Tensor self, int dim, Tensor index) -> Tensor" , {at::Tag::core}); |
1501 | m.def("index_select.dimname_out(Tensor self, Dimname dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1502 | m.def("index_select.dimname(Tensor self, Dimname dim, Tensor index) -> Tensor" , {}); |
1503 | m.def("index_select_backward(Tensor grad, SymInt[] self_sizes, int dim, Tensor index) -> Tensor" , {}); |
1504 | m.def("masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::dynamic_output_shape}); |
1505 | m.def("masked_select(Tensor self, Tensor mask) -> Tensor" , {at::Tag::dynamic_output_shape}); |
1506 | m.def("masked_select_backward(Tensor grad, Tensor input, Tensor mask) -> Tensor" , {}); |
1507 | m.def("nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::dynamic_output_shape}); |
1508 | m.def("nonzero(Tensor self) -> Tensor" , {at::Tag::core, at::Tag::dynamic_output_shape}); |
1509 | m.def("nonzero_numpy(Tensor self) -> Tensor[]" , {}); |
1510 | m.def("argwhere(Tensor self) -> Tensor" , {at::Tag::dynamic_output_shape}); |
1511 | m.def("gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
1512 | m.def("gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor" , {at::Tag::core}); |
1513 | m.def("gather_backward(Tensor grad, Tensor self, int dim, Tensor index, bool sparse_grad) -> Tensor" , {}); |
1514 | m.def("gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
1515 | m.def("gather.dimname(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False) -> Tensor" , {}); |
1516 | m.def("_gather_sparse_backward(Tensor self, int dim, Tensor index, Tensor grad) -> Tensor" , {}); |
1517 | m.def("addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1518 | m.def("addcmul(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor" , {at::Tag::pointwise}); |
1519 | m.def("addcmul_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!)" , {at::Tag::pointwise}); |
1520 | m.def("addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1521 | m.def("addcdiv(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor" , {at::Tag::pointwise}); |
1522 | m.def("addcdiv_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!)" , {at::Tag::pointwise}); |
1523 | m.def("cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor" , {}); |
1524 | m.def("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)" , {}); |
1525 | m.def("triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient)" , {}); |
1526 | m.def("_linalg_check_errors(Tensor info, str api_name, *, bool is_matrix) -> ()" , {}); |
1527 | m.def("linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
1528 | m.def("linalg_solve_triangular(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False) -> Tensor" , {}); |
1529 | m.def("linalg_vander(Tensor x, *, int? N=None) -> Tensor" , {}); |
1530 | m.def("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)" , {}); |
1531 | m.def("svd(Tensor self, bool some=True, bool compute_uv=True) -> (Tensor U, Tensor S, Tensor V)" , {}); |
1532 | m.def("swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a)" , {}); |
1533 | m.def("swapaxes_(Tensor(a!) self, int axis0, int axis1) -> Tensor(a!)" , {at::Tag::inplace_view}); |
1534 | m.def("swapdims(Tensor(a) self, int dim0, int dim1) -> Tensor(a)" , {}); |
1535 | m.def("swapdims_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)" , {at::Tag::inplace_view}); |
1536 | m.def("cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1537 | m.def("cholesky(Tensor self, bool upper=False) -> Tensor" , {}); |
1538 | m.def("cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1539 | m.def("cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor" , {}); |
1540 | m.def("_cholesky_solve_helper(Tensor self, Tensor A, bool upper) -> Tensor" , {}); |
1541 | m.def("cholesky_inverse(Tensor self, bool upper=False) -> Tensor" , {}); |
1542 | m.def("cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1543 | m.def("qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)" , {}); |
1544 | m.def("qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R)" , {}); |
1545 | m.def("geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau)" , {}); |
1546 | m.def("geqrf(Tensor self) -> (Tensor a, Tensor tau)" , {}); |
1547 | m.def("orgqr(Tensor self, Tensor input2) -> Tensor" , {}); |
1548 | m.def("orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1549 | m.def("ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1550 | m.def("ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor" , {}); |
1551 | m.def("_lu_with_info(Tensor self, bool pivot=True, bool check_errors=True) -> (Tensor LU, Tensor pivots, Tensor info)" , {}); |
1552 | m.def("lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1553 | m.def("lu_solve(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor" , {}); |
1554 | m.def("lu_unpack(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True) -> (Tensor P, Tensor L, Tensor U)" , {}); |
1555 | m.def("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)" , {}); |
1556 | m.def("multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1557 | m.def("multinomial(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
1558 | m.def("lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1559 | m.def("lgamma_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
1560 | m.def("lgamma(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
1561 | m.def("digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1562 | m.def("digamma(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
1563 | m.def("polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1564 | m.def("polygamma(int n, Tensor self) -> Tensor" , {at::Tag::pointwise}); |
1565 | m.def("polygamma_(Tensor(a!) self, int n) -> Tensor(a!)" , {at::Tag::pointwise}); |
1566 | m.def("erfinv(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
1567 | m.def("erfinv_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
1568 | m.def("erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1569 | m.def("i0(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
1570 | m.def("i0_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
1571 | m.def("i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1572 | m.def("sign(Tensor self) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1573 | m.def("sign_(Tensor(a!) self) -> Tensor(a!)" , {at::Tag::pointwise}); |
1574 | m.def("sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1575 | m.def("signbit(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
1576 | m.def("signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1577 | m.def("dist(Tensor self, Tensor other, Scalar p=2) -> Tensor" , {}); |
1578 | m.def("atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1579 | m.def("atan2_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1580 | m.def("atan2(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1581 | m.def("arctan2(Tensor self, Tensor other) -> Tensor" , {}); |
1582 | m.def("arctan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1583 | m.def("arctan2_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {}); |
1584 | m.def("lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1585 | m.def("lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1586 | m.def("lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor" , {at::Tag::pointwise}); |
1587 | m.def("lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor" , {at::Tag::pointwise}); |
1588 | m.def("histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1589 | m.def("histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor" , {}); |
1590 | m.def("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)" , {}); |
1591 | m.def("histogram.bins_tensor(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges)" , {}); |
1592 | m.def("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)" , {}); |
1593 | m.def("histogram.bin_ct(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges)" , {}); |
1594 | m.def("_histogramdd_bin_edges(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor[]" , {}); |
1595 | m.def("_histogramdd_from_bin_cts(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor" , {}); |
1596 | m.def("_histogramdd_from_bin_tensors(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False) -> Tensor" , {}); |
1597 | m.def("histogramdd(Tensor self, int[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges)" , {}); |
1598 | m.def("histogramdd.int_bins(Tensor self, int bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges)" , {}); |
1599 | m.def("histogramdd.TensorList_bins(Tensor self, Tensor[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges)" , {}); |
1600 | m.def("fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1601 | m.def("fmod.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
1602 | m.def("fmod_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1603 | m.def("fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1604 | m.def("fmod.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1605 | m.def("fmod_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1606 | m.def("hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1607 | m.def("hypot(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1608 | m.def("hypot_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1609 | m.def("igamma.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1610 | m.def("igamma(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1611 | m.def("igamma_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1612 | m.def("igammac.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1613 | m.def("igammac(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1614 | m.def("igammac_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1615 | m.def("nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1616 | m.def("nextafter(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1617 | m.def("nextafter_(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1618 | m.def("remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1619 | m.def("remainder.Scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
1620 | m.def("remainder_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1621 | m.def("remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1622 | m.def("remainder.Tensor(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1623 | m.def("remainder_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)" , {at::Tag::pointwise}); |
1624 | m.def("remainder.Scalar_Tensor(Scalar self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1625 | m.def("min(Tensor self) -> Tensor" , {}); |
1626 | m.def("fmin(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1627 | m.def("fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1628 | m.def("max(Tensor self) -> Tensor" , {}); |
1629 | m.def("fmax(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1630 | m.def("fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1631 | m.def("maximum(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1632 | m.def("maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1633 | m.def("max.other(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1634 | m.def("max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1635 | m.def("max.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1636 | m.def("minimum(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1637 | m.def("minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1638 | m.def("min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1639 | m.def("min.other(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
1640 | m.def("quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor" , {}); |
1641 | m.def("quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)" , {}); |
1642 | m.def("quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor" , {}); |
1643 | m.def("quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)" , {}); |
1644 | m.def("nanquantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor" , {}); |
1645 | m.def("nanquantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)" , {}); |
1646 | m.def("nanquantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor" , {}); |
1647 | m.def("nanquantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)" , {}); |
1648 | m.def("sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
1649 | m.def("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)" , {}); |
1650 | m.def("sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices)" , {}); |
1651 | m.def("sort.stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices)" , {}); |
1652 | m.def("sort.dimname_values(Tensor self, Dimname dim, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
1653 | m.def("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)" , {}); |
1654 | m.def("sort.dimname(Tensor self, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices)" , {}); |
1655 | m.def("sort.dimname_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices)" , {}); |
1656 | m.def("msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1657 | m.def("msort(Tensor self) -> Tensor" , {}); |
1658 | m.def("argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor" , {}); |
1659 | m.def("argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor" , {}); |
1660 | m.def("argsort.dimname(Tensor self, Dimname dim, bool descending=False) -> Tensor" , {}); |
1661 | m.def("topk.values(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)" , {}); |
1662 | m.def("topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)" , {at::Tag::core}); |
1663 | m.def("all(Tensor self) -> Tensor" , {}); |
1664 | m.def("all.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1665 | m.def("any(Tensor self) -> Tensor" , {}); |
1666 | m.def("any.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1667 | m.def("renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1668 | m.def("renorm(Tensor self, Scalar p, int dim, Scalar maxnorm) -> Tensor" , {}); |
1669 | m.def("renorm_(Tensor(a!) self, Scalar p, int dim, Scalar maxnorm) -> Tensor(a!)" , {}); |
1670 | m.def("unfold(Tensor(a) self, int dimension, int size, int step) -> Tensor(a)" , {}); |
1671 | m.def("unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor" , {}); |
1672 | m.def("equal(Tensor self, Tensor other) -> bool" , {at::Tag::pointwise, at::Tag::data_dependent_output}); |
1673 | m.def("pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1674 | m.def("pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1675 | m.def("pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1676 | m.def("pow.Scalar(Scalar self, Tensor exponent) -> Tensor" , {at::Tag::pointwise}); |
1677 | m.def("pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1678 | m.def("pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor" , {at::Tag::pointwise, at::Tag::core}); |
1679 | m.def("pow_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!)" , {at::Tag::pointwise}); |
1680 | m.def("pow_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!)" , {at::Tag::pointwise}); |
1681 | m.def("float_power.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1682 | m.def("float_power.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor" , {at::Tag::pointwise}); |
1683 | m.def("float_power.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1684 | m.def("float_power.Scalar(Scalar self, Tensor exponent) -> Tensor" , {at::Tag::pointwise}); |
1685 | m.def("float_power.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
1686 | m.def("float_power.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor" , {at::Tag::pointwise}); |
1687 | m.def("float_power_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!)" , {at::Tag::pointwise}); |
1688 | m.def("float_power_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!)" , {at::Tag::pointwise}); |
1689 | m.def("normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1690 | m.def("normal_functional(Tensor self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
1691 | m.def("normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1692 | m.def("normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
1693 | m.def("normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1694 | m.def("normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
1695 | m.def("normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1696 | m.def("normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
1697 | m.def("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" , {at::Tag::nondeterministic_seeded}); |
1698 | m.def("normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1699 | m.def("alias(Tensor(a) self) -> Tensor(a)" , {at::Tag::core}); |
1700 | m.def("_amp_foreach_non_finite_check_and_unscale_(Tensor(a!)[] self, Tensor(b!) found_inf, Tensor inv_scale) -> ()" , {}); |
1701 | m.def("_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!)" , {}); |
1702 | m.def("_foreach_add.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]" , {}); |
1703 | m.def("_foreach_add_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()" , {}); |
1704 | m.def("_foreach_sub.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]" , {}); |
1705 | m.def("_foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()" , {}); |
1706 | m.def("_foreach_mul.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]" , {}); |
1707 | m.def("_foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()" , {}); |
1708 | m.def("_foreach_div.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]" , {}); |
1709 | m.def("_foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()" , {}); |
1710 | m.def("_foreach_clamp_min.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]" , {}); |
1711 | m.def("_foreach_clamp_min_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()" , {}); |
1712 | m.def("_foreach_clamp_max.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]" , {}); |
1713 | m.def("_foreach_clamp_max_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()" , {}); |
1714 | m.def("_foreach_maximum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]" , {}); |
1715 | m.def("_foreach_maximum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()" , {}); |
1716 | m.def("_foreach_minimum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]" , {}); |
1717 | m.def("_foreach_minimum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()" , {}); |
1718 | m.def("_foreach_add.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[]" , {}); |
1719 | m.def("_foreach_add_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> ()" , {}); |
1720 | m.def("_foreach_sub.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[]" , {}); |
1721 | m.def("_foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> ()" , {}); |
1722 | m.def("_foreach_mul.List(Tensor[] self, Tensor[] other) -> Tensor[]" , {}); |
1723 | m.def("_foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> ()" , {}); |
1724 | m.def("_foreach_div.List(Tensor[] self, Tensor[] other) -> Tensor[]" , {}); |
1725 | m.def("_foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> ()" , {}); |
1726 | m.def("_foreach_clamp_min.List(Tensor[] self, Tensor[] other) -> Tensor[]" , {}); |
1727 | m.def("_foreach_clamp_min_.List(Tensor(a!)[] self, Tensor[] other) -> ()" , {}); |
1728 | m.def("_foreach_clamp_max.List(Tensor[] self, Tensor[] other) -> Tensor[]" , {}); |
1729 | m.def("_foreach_clamp_max_.List(Tensor(a!)[] self, Tensor[] other) -> ()" , {}); |
1730 | m.def("_foreach_maximum.List(Tensor[] self, Tensor[] other) -> Tensor[]" , {}); |
1731 | m.def("_foreach_maximum_.List(Tensor(a!)[] self, Tensor[] other) -> ()" , {}); |
1732 | m.def("_foreach_minimum.List(Tensor[] self, Tensor[] other) -> Tensor[]" , {}); |
1733 | m.def("_foreach_minimum_.List(Tensor(a!)[] self, Tensor[] other) -> ()" , {}); |
1734 | m.def("_foreach_add.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]" , {}); |
1735 | m.def("_foreach_add_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()" , {}); |
1736 | m.def("_foreach_sub.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]" , {}); |
1737 | m.def("_foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()" , {}); |
1738 | m.def("_foreach_div.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]" , {}); |
1739 | m.def("_foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()" , {}); |
1740 | m.def("_foreach_mul.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]" , {}); |
1741 | m.def("_foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()" , {}); |
1742 | m.def("_foreach_clamp_min.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]" , {}); |
1743 | m.def("_foreach_clamp_min_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()" , {}); |
1744 | m.def("_foreach_clamp_max.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]" , {}); |
1745 | m.def("_foreach_clamp_max_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()" , {}); |
1746 | m.def("_foreach_maximum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]" , {}); |
1747 | m.def("_foreach_maximum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()" , {}); |
1748 | m.def("_foreach_minimum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]" , {}); |
1749 | m.def("_foreach_minimum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()" , {}); |
1750 | m.def("_foreach_exp(Tensor[] self) -> Tensor[]" , {}); |
1751 | m.def("_foreach_zero_(Tensor(a!)[] self) -> ()" , {}); |
1752 | m.def("_foreach_exp_(Tensor(a!)[] self) -> ()" , {}); |
1753 | m.def("_foreach_sqrt(Tensor[] self) -> Tensor[]" , {}); |
1754 | m.def("_foreach_sqrt_(Tensor(a!)[] self) -> ()" , {}); |
1755 | m.def("_foreach_abs(Tensor[] self) -> Tensor[]" , {}); |
1756 | m.def("_foreach_abs_(Tensor(a!)[] self) -> ()" , {}); |
1757 | m.def("_foreach_acos(Tensor[] self) -> Tensor[]" , {}); |
1758 | m.def("_foreach_acos_(Tensor(a!)[] self) -> ()" , {}); |
1759 | m.def("_foreach_asin(Tensor[] self) -> Tensor[]" , {}); |
1760 | m.def("_foreach_asin_(Tensor(a!)[] self) -> ()" , {}); |
1761 | m.def("_foreach_atan(Tensor[] self) -> Tensor[]" , {}); |
1762 | m.def("_foreach_atan_(Tensor(a!)[] self) -> ()" , {}); |
1763 | m.def("_foreach_ceil(Tensor[] self) -> Tensor[]" , {}); |
1764 | m.def("_foreach_ceil_(Tensor(a!)[] self) -> ()" , {}); |
1765 | m.def("_foreach_cos(Tensor[] self) -> Tensor[]" , {}); |
1766 | m.def("_foreach_cos_(Tensor(a!)[] self) -> ()" , {}); |
1767 | m.def("_foreach_cosh(Tensor[] self) -> Tensor[]" , {}); |
1768 | m.def("_foreach_cosh_(Tensor(a!)[] self) -> ()" , {}); |
1769 | m.def("_foreach_erf(Tensor[] self) -> Tensor[]" , {}); |
1770 | m.def("_foreach_erf_(Tensor(a!)[] self) -> ()" , {}); |
1771 | m.def("_foreach_erfc(Tensor[] self) -> Tensor[]" , {}); |
1772 | m.def("_foreach_erfc_(Tensor(a!)[] self) -> ()" , {}); |
1773 | m.def("_foreach_expm1(Tensor[] self) -> Tensor[]" , {}); |
1774 | m.def("_foreach_expm1_(Tensor(a!)[] self) -> ()" , {}); |
1775 | m.def("_foreach_floor(Tensor[] self) -> Tensor[]" , {}); |
1776 | m.def("_foreach_floor_(Tensor(a!)[] self) -> ()" , {}); |
1777 | m.def("_foreach_log(Tensor[] self) -> Tensor[]" , {}); |
1778 | m.def("_foreach_log_(Tensor(a!)[] self) -> ()" , {}); |
1779 | m.def("_foreach_log10(Tensor[] self) -> Tensor[]" , {}); |
1780 | m.def("_foreach_log10_(Tensor(a!)[] self) -> ()" , {}); |
1781 | m.def("_foreach_log1p(Tensor[] self) -> Tensor[]" , {}); |
1782 | m.def("_foreach_log1p_(Tensor(a!)[] self) -> ()" , {}); |
1783 | m.def("_foreach_log2(Tensor[] self) -> Tensor[]" , {}); |
1784 | m.def("_foreach_log2_(Tensor(a!)[] self) -> ()" , {}); |
1785 | m.def("_foreach_neg(Tensor[] self) -> Tensor[]" , {}); |
1786 | m.def("_foreach_neg_(Tensor(a!)[] self) -> ()" , {}); |
1787 | m.def("_foreach_tan(Tensor[] self) -> Tensor[]" , {}); |
1788 | m.def("_foreach_tan_(Tensor(a!)[] self) -> ()" , {}); |
1789 | m.def("_foreach_tanh(Tensor[] self) -> Tensor[]" , {}); |
1790 | m.def("_foreach_tanh_(Tensor(a!)[] self) -> ()" , {}); |
1791 | m.def("_foreach_sin(Tensor[] self) -> Tensor[]" , {}); |
1792 | m.def("_foreach_sin_(Tensor(a!)[] self) -> ()" , {}); |
1793 | m.def("_foreach_sinh(Tensor[] self) -> Tensor[]" , {}); |
1794 | m.def("_foreach_sinh_(Tensor(a!)[] self) -> ()" , {}); |
1795 | m.def("_foreach_round(Tensor[] self) -> Tensor[]" , {}); |
1796 | m.def("_foreach_round_(Tensor(a!)[] self) -> ()" , {}); |
1797 | m.def("_foreach_lgamma(Tensor[] self) -> Tensor[]" , {}); |
1798 | m.def("_foreach_lgamma_(Tensor(a!)[] self) -> ()" , {}); |
1799 | m.def("_foreach_frac(Tensor[] self) -> Tensor[]" , {}); |
1800 | m.def("_foreach_frac_(Tensor(a!)[] self) -> ()" , {}); |
1801 | m.def("_foreach_reciprocal(Tensor[] self) -> Tensor[]" , {}); |
1802 | m.def("_foreach_reciprocal_(Tensor(a!)[] self) -> ()" , {}); |
1803 | m.def("_foreach_sigmoid(Tensor[] self) -> Tensor[]" , {}); |
1804 | m.def("_foreach_sigmoid_(Tensor(a!)[] self) -> ()" , {}); |
1805 | m.def("_foreach_trunc(Tensor[] self) -> Tensor[]" , {}); |
1806 | m.def("_foreach_trunc_(Tensor(a!)[] self) -> ()" , {}); |
1807 | m.def("_foreach_addcdiv_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> ()" , {}); |
1808 | m.def("_foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> ()" , {}); |
1809 | m.def("_foreach_addcdiv_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> ()" , {}); |
1810 | m.def("_foreach_addcdiv_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> ()" , {}); |
1811 | m.def("_foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> ()" , {}); |
1812 | m.def("_foreach_addcmul_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> ()" , {}); |
1813 | m.def("_foreach_addcdiv.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[]" , {}); |
1814 | m.def("_foreach_addcmul.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[]" , {}); |
1815 | m.def("_foreach_addcdiv.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[]" , {}); |
1816 | m.def("_foreach_addcdiv.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[]" , {}); |
1817 | m.def("_foreach_addcmul.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[]" , {}); |
1818 | m.def("_foreach_addcmul.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[]" , {}); |
1819 | m.def("_foreach_norm.Scalar(Tensor[] self, Scalar ord=2) -> Tensor[]" , {}); |
1820 | m.def("_foreach_lerp.List(Tensor[] self, Tensor[] tensors1, Tensor[] weights) -> Tensor[]" , {}); |
1821 | m.def("_foreach_lerp_.List(Tensor(a!)[] self, Tensor[] tensors1, Tensor[] weights) -> ()" , {}); |
1822 | m.def("_foreach_lerp.Scalar(Tensor[] self, Tensor[] tensors1, Scalar weight) -> Tensor[]" , {}); |
1823 | m.def("_foreach_lerp_.Scalar(Tensor(a!)[] self, Tensor[] tensors1, Scalar weight) -> ()" , {}); |
1824 | m.def("bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor" , {}); |
1825 | m.def("bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
1826 | m.def("bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor" , {}); |
1827 | m.def("searchsorted.Tensor(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor" , {}); |
1828 | m.def("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!)" , {}); |
1829 | m.def("searchsorted.Scalar(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor" , {}); |
1830 | m.def("_convert_indices_from_coo_to_csr(Tensor self, int size, *, bool out_int32=False) -> Tensor" , {}); |
1831 | m.def("_convert_indices_from_coo_to_csr.out(Tensor self, int size, *, bool out_int32=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
1832 | m.def("_convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False) -> Tensor" , {}); |
1833 | m.def("_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!)" , {}); |
1834 | m.def("mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1835 | m.def("mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor" , {}); |
1836 | m.def("mse_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1837 | m.def("mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor" , {}); |
1838 | m.def("l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor" , {}); |
1839 | m.def("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!)" , {}); |
1840 | m.def("multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensor" , {}); |
1841 | m.def("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!)" , {}); |
1842 | m.def("multi_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean) -> Tensor" , {}); |
1843 | m.def("multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1844 | m.def("multilabel_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor" , {}); |
1845 | m.def("multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!))" , {}); |
1846 | m.def("multilabel_margin_loss_forward(Tensor self, Tensor target, int reduction) -> (Tensor output, Tensor is_target)" , {}); |
1847 | m.def("multilabel_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1848 | m.def("multilabel_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target) -> Tensor" , {}); |
1849 | m.def("nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1850 | m.def("nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor" , {}); |
1851 | m.def("nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor" , {}); |
1852 | m.def("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!))" , {}); |
1853 | m.def("nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight)" , {}); |
1854 | m.def("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!)" , {}); |
1855 | m.def("nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor" , {}); |
1856 | m.def("nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1857 | m.def("nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor" , {}); |
1858 | m.def("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!))" , {}); |
1859 | m.def("nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight)" , {}); |
1860 | m.def("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!)" , {}); |
1861 | m.def("nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor" , {}); |
1862 | m.def("smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, float beta=1.0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1863 | m.def("smooth_l1_loss(Tensor self, Tensor target, int reduction=Mean, float beta=1.0) -> Tensor" , {}); |
1864 | m.def("smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1865 | m.def("smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor" , {}); |
1866 | m.def("huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1867 | m.def("huber_loss(Tensor self, Tensor target, int reduction=Mean, float delta=1.0) -> Tensor" , {}); |
1868 | m.def("huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1869 | m.def("huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor" , {}); |
1870 | m.def("soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1871 | m.def("soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor" , {}); |
1872 | m.def("soft_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1873 | m.def("soft_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor" , {}); |
1874 | m.def("elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1875 | m.def("elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor" , {}); |
1876 | m.def("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!)" , {}); |
1877 | m.def("elu_backward(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result) -> Tensor" , {}); |
1878 | m.def("elu_(Tensor(a!) self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor(a!)" , {}); |
1879 | m.def("glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1880 | m.def("glu(Tensor self, int dim=-1) -> Tensor" , {}); |
1881 | m.def("glu_backward.grad_input(Tensor grad_output, Tensor self, int dim, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1882 | m.def("glu_backward(Tensor grad_output, Tensor self, int dim) -> Tensor" , {}); |
1883 | m.def("glu_jvp(Tensor glu, Tensor x, Tensor dx, int dim) -> Tensor" , {}); |
1884 | m.def("glu_backward_jvp(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim) -> Tensor" , {}); |
1885 | m.def("hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1886 | m.def("hardsigmoid(Tensor self) -> Tensor" , {}); |
1887 | m.def("hardsigmoid_(Tensor(a!) self) -> Tensor(a!)" , {}); |
1888 | m.def("hardsigmoid_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1889 | m.def("hardsigmoid_backward(Tensor grad_output, Tensor self) -> Tensor" , {}); |
1890 | m.def("hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1891 | m.def("hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor" , {at::Tag::core}); |
1892 | m.def("hardtanh_backward.grad_input(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1893 | m.def("hardtanh_backward(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val) -> Tensor" , {}); |
1894 | m.def("hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!)" , {}); |
1895 | m.def("hardswish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1896 | m.def("hardswish(Tensor self) -> Tensor" , {}); |
1897 | m.def("hardswish_(Tensor(a!) self) -> Tensor(a!)" , {}); |
1898 | m.def("hardswish_backward(Tensor grad_output, Tensor self) -> Tensor" , {}); |
1899 | m.def("leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1900 | m.def("leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor" , {at::Tag::core}); |
1901 | m.def("leaky_relu_backward.grad_input(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1902 | m.def("leaky_relu_backward(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result) -> Tensor" , {}); |
1903 | m.def("leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!)" , {}); |
1904 | m.def("log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1905 | m.def("log_sigmoid(Tensor self) -> Tensor" , {}); |
1906 | m.def("log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!))" , {}); |
1907 | m.def("log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer)" , {}); |
1908 | m.def("log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1909 | m.def("log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor" , {}); |
1910 | m.def("rrelu_with_noise.out(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1911 | m.def("rrelu_with_noise(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor" , {at::Tag::nondeterministic_seeded}); |
1912 | m.def("rrelu_with_noise_backward(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result) -> Tensor" , {}); |
1913 | m.def("rrelu_with_noise_(Tensor(a!) self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!)" , {at::Tag::nondeterministic_seeded}); |
1914 | m.def("softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1915 | m.def("softplus(Tensor self, Scalar beta=1, Scalar threshold=20) -> Tensor" , {}); |
1916 | m.def("softplus_backward.grad_input(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1917 | m.def("softplus_backward(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold) -> Tensor" , {}); |
1918 | m.def("softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1919 | m.def("softshrink(Tensor self, Scalar lambd=0.5) -> Tensor" , {}); |
1920 | m.def("softshrink_backward.grad_input(Tensor grad_output, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1921 | m.def("softshrink_backward(Tensor grad_output, Tensor self, Scalar lambd) -> Tensor" , {}); |
1922 | m.def("adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1923 | m.def("adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor" , {}); |
1924 | m.def("mkldnn_adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor" , {}); |
1925 | m.def("mkldnn_adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1926 | m.def("mkldnn_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor" , {}); |
1927 | m.def("_adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor" , {at::Tag::core}); |
1928 | m.def("_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor" , {at::Tag::core}); |
1929 | m.def("adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1930 | m.def("adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor" , {}); |
1931 | m.def("_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor" , {}); |
1932 | m.def("adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1933 | m.def("_adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor" , {}); |
1934 | m.def("adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))" , {}); |
1935 | m.def("adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor)" , {}); |
1936 | m.def("adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1937 | m.def("adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor" , {}); |
1938 | m.def("adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))" , {}); |
1939 | m.def("adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor)" , {}); |
1940 | m.def("adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1941 | m.def("adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor" , {}); |
1942 | m.def("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!)" , {}); |
1943 | m.def("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" , {at::Tag::core}); |
1944 | m.def("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!)" , {}); |
1945 | m.def("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" , {at::Tag::core}); |
1946 | m.def("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!)" , {}); |
1947 | m.def("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" , {}); |
1948 | m.def("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!)" , {}); |
1949 | m.def("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" , {}); |
1950 | m.def("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!))" , {}); |
1951 | m.def("fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor)" , {}); |
1952 | m.def("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!)" , {}); |
1953 | m.def("fractional_max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices) -> Tensor" , {}); |
1954 | m.def("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!))" , {}); |
1955 | m.def("fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor)" , {}); |
1956 | m.def("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!)" , {}); |
1957 | m.def("fractional_max_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices) -> Tensor" , {}); |
1958 | m.def("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!))" , {}); |
1959 | m.def("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)" , {at::Tag::core}); |
1960 | m.def("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!)" , {}); |
1961 | m.def("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" , {at::Tag::core}); |
1962 | m.def("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!))" , {}); |
1963 | m.def("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)" , {at::Tag::core}); |
1964 | m.def("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!)" , {}); |
1965 | m.def("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" , {}); |
1966 | m.def("max_unpool2d.out(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1967 | m.def("max_unpool2d(Tensor self, Tensor indices, int[2] output_size) -> Tensor" , {}); |
1968 | m.def("max_unpool3d.out(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1969 | m.def("max_unpool3d(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding) -> Tensor" , {}); |
1970 | m.def("reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1971 | m.def("reflection_pad1d(Tensor self, SymInt[2] padding) -> Tensor" , {}); |
1972 | m.def("reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1973 | m.def("reflection_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor" , {}); |
1974 | m.def("reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1975 | m.def("reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor" , {at::Tag::core}); |
1976 | m.def("reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1977 | m.def("reflection_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor" , {}); |
1978 | m.def("reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1979 | m.def("reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor" , {}); |
1980 | m.def("reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1981 | m.def("reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor" , {}); |
1982 | m.def("replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1983 | m.def("replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor" , {}); |
1984 | m.def("replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1985 | m.def("replication_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor" , {}); |
1986 | m.def("replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1987 | m.def("replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor" , {at::Tag::core}); |
1988 | m.def("replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1989 | m.def("replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor" , {}); |
1990 | m.def("replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
1991 | m.def("replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor" , {at::Tag::core}); |
1992 | m.def("replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)" , {}); |
1993 | m.def("replication_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor" , {}); |
1994 | m.def("_pad_circular(Tensor self, SymInt[] pad) -> Tensor" , {}); |
1995 | m.def("_pad_enum(Tensor self, SymInt[] pad, int mode, float? value=None) -> Tensor" , {}); |
1996 | m.def("pad(Tensor self, SymInt[] pad, str mode=\"constant\", float? value=None) -> Tensor" , {}); |
1997 | m.def("upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor" , {}); |
1998 | m.def("upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor" , {at::Tag::core}); |
1999 | m.def("_upsample_bilinear2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor" , {}); |
2000 | m.def("upsample_trilinear3d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor" , {}); |
2001 | m.def("upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor" , {}); |
2002 | m.def("_upsample_bicubic2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor" , {}); |
2003 | m.def("upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor" , {}); |
2004 | m.def("_upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor" , {}); |
2005 | m.def("upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor" , {at::Tag::core}); |
2006 | m.def("_upsample_nearest_exact2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor" , {}); |
2007 | m.def("upsample_nearest3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor" , {}); |
2008 | m.def("_upsample_nearest_exact3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor" , {}); |
2009 | m.def("upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2010 | m.def("upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor" , {}); |
2011 | m.def("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!)" , {}); |
2012 | m.def("upsample_linear1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None) -> Tensor" , {}); |
2013 | m.def("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!)" , {}); |
2014 | m.def("upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2015 | m.def("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!)" , {}); |
2016 | m.def("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" , {}); |
2017 | m.def("_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!)" , {}); |
2018 | m.def("_upsample_bilinear2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2019 | m.def("_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!)" , {}); |
2020 | m.def("_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" , {}); |
2021 | m.def("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!)" , {}); |
2022 | m.def("upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2023 | m.def("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!)" , {}); |
2024 | m.def("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" , {}); |
2025 | m.def("_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!)" , {}); |
2026 | m.def("_upsample_bicubic2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2027 | m.def("_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!)" , {}); |
2028 | m.def("_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" , {}); |
2029 | m.def("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!)" , {}); |
2030 | m.def("upsample_trilinear3d(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2031 | m.def("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!)" , {}); |
2032 | m.def("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" , {}); |
2033 | m.def("upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2034 | m.def("_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2035 | m.def("upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor" , {}); |
2036 | m.def("_upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor" , {}); |
2037 | m.def("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!)" , {}); |
2038 | m.def("_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!)" , {}); |
2039 | m.def("upsample_nearest1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor" , {}); |
2040 | m.def("_upsample_nearest_exact1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor" , {}); |
2041 | m.def("upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2042 | m.def("_upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2043 | m.def("upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2044 | m.def("_upsample_nearest_exact2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2045 | m.def("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!)" , {}); |
2046 | m.def("_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!)" , {}); |
2047 | m.def("upsample_nearest2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2048 | m.def("_upsample_nearest_exact2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2049 | m.def("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!)" , {}); |
2050 | m.def("_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!)" , {}); |
2051 | m.def("upsample_nearest3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2052 | m.def("_upsample_nearest_exact3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor" , {}); |
2053 | m.def("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!)" , {}); |
2054 | m.def("_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!)" , {}); |
2055 | m.def("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" , {}); |
2056 | m.def("_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" , {}); |
2057 | m.def("sigmoid_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)" , {at::Tag::pointwise}); |
2058 | m.def("sigmoid_backward(Tensor grad_output, Tensor output) -> Tensor" , {at::Tag::pointwise}); |
2059 | m.def("logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!)" , {at::Tag::pointwise}); |
2060 | m.def("logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor" , {at::Tag::pointwise}); |
2061 | m.def("tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)" , {at::Tag::pointwise}); |
2062 | m.def("tanh_backward(Tensor grad_output, Tensor output) -> Tensor" , {at::Tag::pointwise}); |
2063 | m.def("slow_conv_transpose2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2064 | m.def("slow_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, int[2] dilation=1) -> Tensor" , {}); |
2065 | m.def("slow_conv_transpose3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, int[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2066 | m.def("slow_conv_transpose3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, int[3] dilation=1) -> Tensor" , {}); |
2067 | m.def("thnn_conv2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2068 | m.def("thnn_conv2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0) -> Tensor" , {}); |
2069 | m.def("_slow_conv2d_forward.output(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, *, Tensor(a!) output) -> Tensor(a!)" , {}); |
2070 | m.def("_slow_conv2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding) -> Tensor" , {}); |
2071 | m.def("_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {}); |
2072 | m.def("_slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)" , {}); |
2073 | m.def("_conv_depthwise2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, SymInt[2] padding, int[2] dilation, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2074 | m.def("_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, SymInt[2] padding, int[2] dilation) -> Tensor" , {}); |
2075 | m.def("conv_depthwise3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, int[3] dilation) -> Tensor" , {}); |
2076 | m.def("slow_conv3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2077 | m.def("slow_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0) -> Tensor" , {}); |
2078 | m.def("slow_conv3d_forward.output(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!)" , {}); |
2079 | m.def("slow_conv3d_forward(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding) -> Tensor" , {}); |
2080 | m.def("slow_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, int[2] dilation=1) -> Tensor" , {}); |
2081 | m.def("slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0, int[3] dilation=1) -> Tensor" , {}); |
2082 | m.def("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!)" , {}); |
2083 | m.def("col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor" , {at::Tag::core}); |
2084 | m.def("column_stack(Tensor[] tensors) -> Tensor" , {}); |
2085 | m.def("column_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2086 | m.def("im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2087 | m.def("im2col(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor" , {}); |
2088 | m.def("isfinite(Tensor self) -> Tensor" , {}); |
2089 | m.def("isinf(Tensor self) -> Tensor" , {at::Tag::core}); |
2090 | m.def("record_stream(Tensor(a!) self, Stream s) -> ()" , {}); |
2091 | m.def("isposinf(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2092 | m.def("isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2093 | m.def("isneginf(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2094 | m.def("isneginf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2095 | m.def("_add_batch_dim(Tensor self, int batch_dim, int level) -> Tensor" , {}); |
2096 | m.def("_remove_batch_dim(Tensor self, int level, int batch_size, int out_dim) -> Tensor" , {}); |
2097 | m.def("special_entr(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2098 | m.def("special_entr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2099 | m.def("special_ndtri(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2100 | m.def("special_ndtri.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2101 | m.def("special_log_ndtr(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2102 | m.def("special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2103 | m.def("special_expm1(Tensor self) -> Tensor" , {}); |
2104 | m.def("special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2105 | m.def("special_exp2(Tensor self) -> Tensor" , {}); |
2106 | m.def("special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2107 | m.def("special_psi(Tensor self) -> Tensor" , {}); |
2108 | m.def("special_psi.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2109 | m.def("special_digamma(Tensor self) -> Tensor" , {}); |
2110 | m.def("special_digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2111 | m.def("special_gammaln(Tensor self) -> Tensor" , {}); |
2112 | m.def("special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2113 | m.def("special_erf(Tensor self) -> Tensor" , {}); |
2114 | m.def("special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2115 | m.def("special_erfc(Tensor self) -> Tensor" , {}); |
2116 | m.def("special_erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2117 | m.def("special_erfcx(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2118 | m.def("special_erfcx.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2119 | m.def("special_erfinv(Tensor self) -> Tensor" , {}); |
2120 | m.def("special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2121 | m.def("special_ndtr(Tensor self) -> Tensor" , {}); |
2122 | m.def("special_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2123 | m.def("special_xlog1py(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
2124 | m.def("special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
2125 | m.def("special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
2126 | m.def("special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2127 | m.def("special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2128 | m.def("special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2129 | m.def("special_xlogy(Tensor self, Tensor other) -> Tensor" , {}); |
2130 | m.def("special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor" , {}); |
2131 | m.def("special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor" , {}); |
2132 | m.def("special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2133 | m.def("special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2134 | m.def("special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2135 | m.def("special_zeta(Tensor self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
2136 | m.def("special_zeta.self_scalar(Scalar self, Tensor other) -> Tensor" , {at::Tag::pointwise}); |
2137 | m.def("special_zeta.other_scalar(Tensor self, Scalar other) -> Tensor" , {at::Tag::pointwise}); |
2138 | m.def("special_zeta.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2139 | m.def("special_zeta.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2140 | m.def("special_zeta.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2141 | m.def("special_i0(Tensor self) -> Tensor" , {}); |
2142 | m.def("special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2143 | m.def("special_i0e(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2144 | m.def("special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2145 | m.def("special_i1(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2146 | m.def("special_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2147 | m.def("special_i1e(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2148 | m.def("special_i1e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2149 | m.def("special_logit(Tensor self, float? eps=None) -> Tensor" , {}); |
2150 | m.def("special_logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2151 | m.def("special_polygamma(int n, Tensor self) -> Tensor" , {}); |
2152 | m.def("special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2153 | m.def("special_logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor" , {}); |
2154 | m.def("special_logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2155 | m.def("special_expit(Tensor self) -> Tensor" , {}); |
2156 | m.def("special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2157 | m.def("special_sinc(Tensor self) -> Tensor" , {}); |
2158 | m.def("special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2159 | m.def("special_round(Tensor self, *, int decimals=0) -> Tensor" , {}); |
2160 | m.def("special_round.out(Tensor self, *, int decimals=0, Tensor(a!) out) -> Tensor(a!)" , {}); |
2161 | m.def("special_log1p(Tensor self) -> Tensor" , {}); |
2162 | m.def("special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2163 | m.def("special_log_softmax(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor" , {}); |
2164 | m.def("special_gammainc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2165 | m.def("special_gammainc(Tensor self, Tensor other) -> Tensor" , {}); |
2166 | m.def("special_gammaincc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2167 | m.def("special_gammaincc(Tensor self, Tensor other) -> Tensor" , {}); |
2168 | m.def("special_multigammaln(Tensor self, int p) -> Tensor" , {}); |
2169 | m.def("special_multigammaln.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2170 | m.def("special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor" , {}); |
2171 | m.def("fft_fft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor" , {}); |
2172 | m.def("fft_fft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2173 | m.def("fft_ifft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor" , {}); |
2174 | m.def("fft_ifft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2175 | m.def("fft_rfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor" , {}); |
2176 | m.def("fft_rfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2177 | m.def("fft_irfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor" , {}); |
2178 | m.def("fft_irfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2179 | m.def("fft_hfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor" , {}); |
2180 | m.def("fft_hfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2181 | m.def("fft_ihfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor" , {}); |
2182 | m.def("fft_ihfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2183 | m.def("fft_fft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor" , {}); |
2184 | m.def("fft_fft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2185 | m.def("fft_ifft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor" , {}); |
2186 | m.def("fft_ifft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2187 | m.def("fft_rfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor" , {}); |
2188 | m.def("fft_rfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2189 | m.def("fft_irfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor" , {}); |
2190 | m.def("fft_irfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2191 | m.def("fft_hfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor" , {}); |
2192 | m.def("fft_hfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2193 | m.def("fft_ihfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor" , {}); |
2194 | m.def("fft_ihfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2195 | m.def("fft_fftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor" , {}); |
2196 | m.def("fft_fftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2197 | m.def("fft_ifftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor" , {}); |
2198 | m.def("fft_ifftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2199 | m.def("fft_rfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor" , {}); |
2200 | m.def("fft_rfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2201 | m.def("fft_irfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor" , {}); |
2202 | m.def("fft_irfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2203 | m.def("fft_hfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor" , {}); |
2204 | m.def("fft_hfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2205 | m.def("fft_ihfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor" , {}); |
2206 | m.def("fft_ihfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2207 | m.def("fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
2208 | m.def("fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2209 | m.def("fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
2210 | m.def("fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2211 | m.def("fft_fftshift(Tensor self, int[1]? dim=None) -> Tensor" , {}); |
2212 | m.def("fft_ifftshift(Tensor self, int[1]? dim=None) -> Tensor" , {}); |
2213 | m.def("linalg_cholesky_ex(Tensor self, *, bool upper=False, bool check_errors=False) -> (Tensor L, Tensor info)" , {}); |
2214 | m.def("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)" , {}); |
2215 | m.def("linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor" , {}); |
2216 | m.def("linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
2217 | m.def("linalg_cross(Tensor self, Tensor other, *, int dim=-1) -> Tensor" , {}); |
2218 | m.def("linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)" , {}); |
2219 | m.def("linalg_lu_factor(Tensor A, *, bool pivot=True) -> (Tensor LU, Tensor pivots)" , {}); |
2220 | m.def("linalg_lu_factor.out(Tensor A, *, bool pivot=True, Tensor(a!) LU, Tensor(b!) pivots) -> (Tensor(a!) LU, Tensor(b!) pivots)" , {}); |
2221 | m.def("linalg_lu_factor_ex(Tensor A, *, bool pivot=True, bool check_errors=False) -> (Tensor LU, Tensor pivots, Tensor info)" , {}); |
2222 | m.def("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)" , {}); |
2223 | m.def("linalg_lu(Tensor A, *, bool pivot=True) -> (Tensor P, Tensor L, Tensor U)" , {}); |
2224 | m.def("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)" , {}); |
2225 | m.def("linalg_lu_solve(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False) -> Tensor" , {}); |
2226 | m.def("linalg_lu_solve.out(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
2227 | m.def("_linalg_det(Tensor A) -> (Tensor result, Tensor LU, Tensor pivots)" , {}); |
2228 | m.def("_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots)" , {}); |
2229 | m.def("linalg_det(Tensor A) -> Tensor" , {}); |
2230 | m.def("linalg_det.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2231 | m.def("det(Tensor self) -> Tensor" , {}); |
2232 | m.def("linalg_ldl_factor_ex(Tensor self, *, bool hermitian=False, bool check_errors=False) -> (Tensor LD, Tensor pivots, Tensor info)" , {}); |
2233 | m.def("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)" , {}); |
2234 | m.def("linalg_ldl_factor(Tensor self, *, bool hermitian=False) -> (Tensor LD, Tensor pivots)" , {}); |
2235 | m.def("linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots)" , {}); |
2236 | m.def("linalg_ldl_solve(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False) -> Tensor" , {}); |
2237 | m.def("linalg_ldl_solve.out(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
2238 | m.def("linalg_lstsq(Tensor self, Tensor b, float? rcond=None, *, str? driver=None) -> (Tensor solution, Tensor residuals, Tensor rank, Tensor singular_values)" , {at::Tag::dynamic_output_shape}); |
2239 | m.def("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)" , {at::Tag::dynamic_output_shape}); |
2240 | m.def("linalg_matmul(Tensor self, Tensor other) -> Tensor" , {}); |
2241 | m.def("linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2242 | m.def("linalg_vecdot(Tensor x, Tensor y, *, int dim=-1) -> Tensor" , {}); |
2243 | m.def("linalg_vecdot.out(Tensor x, Tensor y, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)" , {}); |
2244 | m.def("linalg_matrix_exp(Tensor self) -> Tensor" , {}); |
2245 | m.def("_linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet, Tensor LU, Tensor pivots)" , {}); |
2246 | m.def("_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)" , {}); |
2247 | m.def("linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet)" , {}); |
2248 | m.def("linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet)" , {}); |
2249 | m.def("slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet)" , {}); |
2250 | m.def("slogdet.out(Tensor self, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet)" , {}); |
2251 | m.def("logdet(Tensor self) -> Tensor" , {}); |
2252 | m.def("linalg_eig(Tensor self) -> (Tensor eigenvalues, Tensor eigenvectors)" , {}); |
2253 | m.def("linalg_eig.out(Tensor self, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)" , {}); |
2254 | m.def("linalg_eigvals(Tensor self) -> Tensor" , {}); |
2255 | m.def("linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2256 | m.def("_linalg_eigh(Tensor A, str UPLO=\"L\", bool compute_v=True) -> (Tensor eigenvalues, Tensor eigenvectors)" , {}); |
2257 | m.def("_linalg_eigh.eigenvalues(Tensor A, str UPLO=\"L\", bool compute_v=True, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)" , {}); |
2258 | m.def("linalg_eigh(Tensor self, str UPLO=\"L\") -> (Tensor eigenvalues, Tensor eigenvectors)" , {}); |
2259 | m.def("linalg_eigh.eigvals(Tensor self, str UPLO=\"L\", *, Tensor(a!) eigvals, Tensor(b!) eigvecs) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)" , {}); |
2260 | m.def("linalg_eigvalsh(Tensor self, str UPLO=\"L\") -> Tensor" , {}); |
2261 | m.def("linalg_eigvalsh.out(Tensor self, str UPLO=\"L\", *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2262 | m.def("linalg_householder_product(Tensor input, Tensor tau) -> Tensor" , {}); |
2263 | m.def("linalg_householder_product.out(Tensor input, Tensor tau, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2264 | m.def("linalg_inv_ex(Tensor A, *, bool check_errors=False) -> (Tensor inverse, Tensor info)" , {}); |
2265 | m.def("linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info)" , {}); |
2266 | m.def("linalg_inv(Tensor A) -> Tensor" , {}); |
2267 | m.def("linalg_inv.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2268 | m.def("inverse(Tensor self) -> Tensor" , {}); |
2269 | m.def("inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2270 | m.def("inner(Tensor self, Tensor other) -> Tensor" , {}); |
2271 | m.def("inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2272 | m.def("outer(Tensor self, Tensor vec2) -> Tensor" , {}); |
2273 | m.def("outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2274 | m.def("ger(Tensor self, Tensor vec2) -> Tensor" , {}); |
2275 | m.def("ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2276 | m.def("linalg_norm(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
2277 | m.def("linalg_norm.ord_str(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
2278 | m.def("linalg_norm.out(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
2279 | m.def("linalg_norm.ord_str_out(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
2280 | m.def("linalg_vector_norm(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
2281 | m.def("linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
2282 | m.def("linalg_matrix_norm(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
2283 | m.def("linalg_matrix_norm.out(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
2284 | m.def("linalg_matrix_norm.str_ord(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor" , {}); |
2285 | m.def("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!)" , {}); |
2286 | m.def("_linalg_svd(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh)" , {}); |
2287 | m.def("_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)" , {}); |
2288 | m.def("linalg_svd(Tensor A, bool full_matrices=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh)" , {}); |
2289 | m.def("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)" , {}); |
2290 | m.def("linalg_svdvals(Tensor A, *, str? driver=None) -> Tensor" , {}); |
2291 | m.def("linalg_svdvals.out(Tensor A, *, str? driver=None, Tensor(a!) out) -> Tensor(a!)" , {}); |
2292 | m.def("linalg_cond(Tensor self, Scalar? p=None) -> Tensor" , {}); |
2293 | m.def("linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2294 | m.def("linalg_cond.p_str(Tensor self, str p) -> Tensor" , {}); |
2295 | m.def("linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2296 | m.def("linalg_pinv.atol_rtol_tensor(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor" , {}); |
2297 | m.def("linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
2298 | m.def("linalg_pinv.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor" , {}); |
2299 | m.def("linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
2300 | m.def("linalg_pinv(Tensor self, float rcond, bool hermitian=False) -> Tensor" , {}); |
2301 | m.def("linalg_pinv.rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False) -> Tensor" , {}); |
2302 | m.def("linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2303 | m.def("linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2304 | m.def("_linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor LU, Tensor pivots, Tensor info)" , {}); |
2305 | m.def("_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)" , {}); |
2306 | m.def("linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor info)" , {}); |
2307 | m.def("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)" , {}); |
2308 | m.def("linalg_solve(Tensor A, Tensor B, *, bool left=True) -> Tensor" , {}); |
2309 | m.def("linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!)" , {}); |
2310 | m.def("linalg_tensorinv(Tensor self, int ind=2) -> Tensor" , {}); |
2311 | m.def("linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2312 | m.def("linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor" , {}); |
2313 | m.def("linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2314 | m.def("linalg_qr(Tensor A, str mode='reduced') -> (Tensor Q, Tensor R)" , {}); |
2315 | m.def("linalg_qr.out(Tensor A, str mode='reduced', *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)" , {}); |
2316 | m.def("linalg_matrix_power(Tensor self, int n) -> Tensor" , {}); |
2317 | m.def("linalg_matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2318 | m.def("linalg_matrix_rank.atol_rtol_tensor(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor" , {}); |
2319 | m.def("linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
2320 | m.def("linalg_matrix_rank.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor" , {}); |
2321 | m.def("linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)" , {}); |
2322 | m.def("linalg_matrix_rank(Tensor self, float tol, bool hermitian=False) -> Tensor" , {}); |
2323 | m.def("linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2324 | m.def("linalg_matrix_rank.tol_tensor(Tensor input, Tensor tol, bool hermitian=False) -> Tensor" , {}); |
2325 | m.def("linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2326 | m.def("linalg_multi_dot(Tensor[] tensors) -> Tensor" , {}); |
2327 | m.def("linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)" , {}); |
2328 | m.def("nested_to_padded_tensor(Tensor self, float padding, int[]? output_size=None) -> Tensor" , {}); |
2329 | m.def("_test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=1) -> Tensor" , {}); |
2330 | m.def("_test_optional_intlist(Tensor values, int[]? addends) -> Tensor" , {}); |
2331 | m.def("_test_optional_filled_intlist(Tensor values, int[2]? addends) -> Tensor" , {}); |
2332 | m.def("_test_optional_floatlist(Tensor values, float[]? addends) -> Tensor" , {}); |
2333 | m.def("_test_string_default(Tensor dummy, str a=\"\\\"'\\\\\", str b='\"\\'\\\\') -> Tensor" , {}); |
2334 | m.def("_test_ambiguous_defaults.a(Tensor dummy, int a=1, int b=1) -> Tensor" , {}); |
2335 | m.def("_test_ambiguous_defaults.b(Tensor dummy, int a=2, str b=\"2\") -> Tensor" , {}); |
2336 | m.def("_test_warn_in_autograd(Tensor self) -> Tensor" , {}); |
2337 | m.def("_test_autograd_multiple_dispatch.fullcoverage(Tensor self) -> Tensor" , {}); |
2338 | m.def("_test_autograd_multiple_dispatch.ntonly(Tensor self, bool b) -> Tensor" , {}); |
2339 | m.def("_test_autograd_multiple_dispatch_view(Tensor(a) self) -> Tensor(a)" , {}); |
2340 | m.def("_test_autograd_multiple_dispatch_view_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2341 | m.def("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" , {}); |
2342 | m.def("_segment_reduce_backward(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None) -> Tensor" , {}); |
2343 | m.def("pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0.0) -> Tensor" , {}); |
2344 | m.def("flatten_dense_tensors(Tensor[] tensors) -> Tensor" , {}); |
2345 | m.def("unflatten_dense_tensors(Tensor flat, Tensor[] tensors) -> Tensor[]" , {}); |
2346 | m.def("_nested_tensor_from_tensor_list(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor" , {}); |
2347 | m.def("_fw_primal_copy(Tensor self, int level) -> Tensor" , {at::Tag::view_copy}); |
2348 | m.def("_make_dual_copy(Tensor primal, Tensor tangent, int level) -> Tensor" , {at::Tag::view_copy}); |
2349 | m.def("view_as_real_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2350 | m.def("view_as_complex_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2351 | m.def("_conj_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2352 | m.def("_neg_view_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2353 | m.def("as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor" , {at::Tag::view_copy}); |
2354 | m.def("_sparse_broadcast_to_copy(Tensor self, int[] size) -> Tensor" , {at::Tag::view_copy}); |
2355 | m.def("diagonal_copy(Tensor self, int offset=0, int dim1=0, int dim2=1) -> Tensor" , {at::Tag::view_copy}); |
2356 | m.def("expand_copy(Tensor self, SymInt[] size, *, bool implicit=False) -> Tensor" , {at::Tag::view_copy}); |
2357 | m.def("permute_copy(Tensor self, int[] dims) -> Tensor" , {at::Tag::view_copy}); |
2358 | m.def("_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor" , {at::Tag::view_copy}); |
2359 | m.def("select_copy.int(Tensor self, int dim, SymInt index) -> Tensor" , {at::Tag::view_copy}); |
2360 | m.def("detach_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2361 | m.def("slice_copy.Tensor(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor" , {at::Tag::view_copy}); |
2362 | m.def("split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]" , {at::Tag::view_copy}); |
2363 | m.def("split_with_sizes_copy(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]" , {at::Tag::view_copy}); |
2364 | m.def("squeeze_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2365 | m.def("squeeze_copy.dim(Tensor self, int dim) -> Tensor" , {at::Tag::view_copy}); |
2366 | m.def("squeeze_copy.dims(Tensor self, int[] dim) -> Tensor" , {at::Tag::view_copy}); |
2367 | m.def("t_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2368 | m.def("transpose_copy.int(Tensor self, int dim0, int dim1) -> Tensor" , {at::Tag::view_copy}); |
2369 | m.def("unsqueeze_copy(Tensor self, int dim) -> Tensor" , {at::Tag::view_copy}); |
2370 | m.def("_indices_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2371 | m.def("_values_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2372 | m.def("indices_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2373 | m.def("values_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2374 | m.def("crow_indices_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2375 | m.def("col_indices_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2376 | m.def("ccol_indices_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2377 | m.def("row_indices_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2378 | m.def("unbind_copy.int(Tensor self, int dim=0) -> Tensor[]" , {at::Tag::view_copy}); |
2379 | m.def("unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> ()" , {}); |
2380 | m.def("split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()" , {}); |
2381 | m.def("split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()" , {}); |
2382 | m.def("view_copy(Tensor self, SymInt[] size) -> Tensor" , {at::Tag::view_copy}); |
2383 | m.def("view_copy.dtype(Tensor self, ScalarType dtype) -> Tensor" , {at::Tag::view_copy}); |
2384 | m.def("unfold_copy(Tensor self, int dimension, int size, int step) -> Tensor" , {at::Tag::view_copy}); |
2385 | m.def("alias_copy(Tensor self) -> Tensor" , {at::Tag::view_copy}); |
2386 | m.def("to_padded_tensor(Tensor self, float padding, SymInt[]? output_size=None) -> Tensor" , {}); |
2387 | m.def("_nested_tensor_softmax_with_shape(Tensor self, Tensor query) -> Tensor" , {}); |
2388 | m.def("_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" , {}); |
2389 | m.def("_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)" , {}); |
2390 | m.def("scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False) -> Tensor" , {}); |
2391 | m.def("_scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool need_attn_weights=False, bool is_causal=False) -> (Tensor, Tensor)" , {}); |
2392 | m.def("_fused_sdp_choice(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False) -> int" , {}); |
2393 | m.def("_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) -> (Tensor, Tensor)" , {}); |
2394 | m.def("_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) -> (Tensor ouput, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, int philox_seed, int philox_offset, Tensor debug_attn_mask)" , {}); |
2395 | m.def("_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, int max_q, int max_k, float dropout_p, bool is_causal, int philox_seed, int philox_offset) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value)" , {}); |
2396 | m.def("_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, bool compute_log_sumexp, bool is_causal=False) -> (Tensor, Tensor)" , {}); |
2397 | m.def("_scaled_dot_product_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, bool is_causal=False, bool chunk_grad_outputs=False) -> (Tensor, Tensor, Tensor)" , {}); |
2398 | m.def("_chunk_grad_outputs_efficient_attention(Tensor query, Tensor key, Tensor value, bool is_causal=False) -> bool" , {}); |
2399 | m.def("_flash_attention_forward(Tensor query, Tensor key, Tensor value, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, float dropout_p, bool is_causal, bool return_debug_mask) -> (Tensor output, Tensor softmax_logsumexp, int philox_seed, int philox_offset, Tensor debug_attn_mask)" , {}); |
2400 | m.def("_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, float dropout_p, bool is_causal, int philox_seed, int philox_offset) -> (Tensor, Tensor, Tensor)" , {}); |
2401 | m.def("_efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, int? max_seqlen_q, bool compute_log_sumexp=False, bool causal=False) -> (Tensor, Tensor)" , {}); |
2402 | m.def("_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, bool is_causal=False, bool chunk_grad_outputs=False) -> (Tensor, Tensor, Tensor)" , {}); |
2403 | m.def("_triton_scaled_dot_attention(Tensor q, Tensor k, Tensor v, float dropout_p=0.0) -> Tensor" , {}); |
2404 | m.def("_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" , {}); |
2405 | m.def("special_airy_ai(Tensor x) -> Tensor" , {at::Tag::pointwise}); |
2406 | m.def("special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2407 | m.def("_transformer_decoder_only_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, Tensor? incr_key=None, Tensor? incr_value=None) -> (Tensor, Tensor, Tensor)" , {}); |
2408 | m.def("_native_decoder_only_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? incr_key=None, Tensor? incr_value=None, bool need_weights=True, bool average_attn_weights=True) -> (Tensor, Tensor, Tensor, Tensor)" , {}); |
2409 | m.def("special_bessel_j0(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2410 | m.def("special_bessel_j0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2411 | m.def("special_bessel_j1(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2412 | m.def("special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2413 | m.def("special_bessel_y0(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2414 | m.def("special_bessel_y0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2415 | m.def("special_bessel_y1(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2416 | m.def("special_bessel_y1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2417 | m.def("special_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2418 | m.def("special_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2419 | m.def("special_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2420 | m.def("special_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2421 | m.def("special_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2422 | m.def("special_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2423 | m.def("special_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2424 | m.def("special_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2425 | m.def("special_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2426 | m.def("special_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2427 | m.def("special_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2428 | m.def("special_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2429 | m.def("special_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2430 | m.def("special_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2431 | m.def("special_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2432 | m.def("special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2433 | m.def("special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2434 | m.def("special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2435 | m.def("special_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2436 | m.def("special_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2437 | m.def("special_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2438 | m.def("special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2439 | m.def("special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2440 | m.def("special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2441 | m.def("special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2442 | m.def("special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2443 | m.def("special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2444 | m.def("special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2445 | m.def("special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2446 | m.def("special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2447 | m.def("special_hermite_polynomial_he(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2448 | m.def("special_hermite_polynomial_he.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2449 | m.def("special_hermite_polynomial_he.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2450 | m.def("special_hermite_polynomial_he.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2451 | m.def("special_hermite_polynomial_he.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2452 | m.def("special_hermite_polynomial_he.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2453 | m.def("special_laguerre_polynomial_l(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2454 | m.def("special_laguerre_polynomial_l.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2455 | m.def("special_laguerre_polynomial_l.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2456 | m.def("special_laguerre_polynomial_l.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2457 | m.def("special_laguerre_polynomial_l.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2458 | m.def("special_laguerre_polynomial_l.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2459 | m.def("special_legendre_polynomial_p(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2460 | m.def("special_legendre_polynomial_p.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2461 | m.def("special_legendre_polynomial_p.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2462 | m.def("special_legendre_polynomial_p.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2463 | m.def("special_legendre_polynomial_p.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2464 | m.def("special_legendre_polynomial_p.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2465 | m.def("special_modified_bessel_i0(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2466 | m.def("special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2467 | m.def("special_modified_bessel_i1(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2468 | m.def("special_modified_bessel_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2469 | m.def("special_modified_bessel_k0(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2470 | m.def("special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2471 | m.def("special_modified_bessel_k1(Tensor self) -> Tensor" , {at::Tag::pointwise}); |
2472 | m.def("special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2473 | m.def("special_scaled_modified_bessel_k0(Tensor x) -> Tensor" , {at::Tag::pointwise}); |
2474 | m.def("special_scaled_modified_bessel_k0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2475 | m.def("special_scaled_modified_bessel_k1(Tensor x) -> Tensor" , {at::Tag::pointwise}); |
2476 | m.def("special_scaled_modified_bessel_k1.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2477 | m.def("special_shifted_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2478 | m.def("special_shifted_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2479 | m.def("special_shifted_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2480 | m.def("special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2481 | m.def("special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2482 | m.def("special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2483 | m.def("special_shifted_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2484 | m.def("special_shifted_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2485 | m.def("special_shifted_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2486 | m.def("special_shifted_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2487 | m.def("special_shifted_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2488 | m.def("special_shifted_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2489 | m.def("special_shifted_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2490 | m.def("special_shifted_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2491 | m.def("special_shifted_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2492 | m.def("special_shifted_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2493 | m.def("special_shifted_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2494 | m.def("special_shifted_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2495 | m.def("special_shifted_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2496 | m.def("special_shifted_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor" , {at::Tag::pointwise}); |
2497 | m.def("special_shifted_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor" , {at::Tag::pointwise}); |
2498 | m.def("special_shifted_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2499 | m.def("special_shifted_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2500 | m.def("special_shifted_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2501 | m.def("special_spherical_bessel_j0(Tensor x) -> Tensor" , {at::Tag::pointwise}); |
2502 | m.def("special_spherical_bessel_j0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::pointwise}); |
2503 | m.def("_foobar(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True) -> Tensor" , {}); |
2504 | m.def("_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) -> ()" , {}); |
2505 | m.def("_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) -> ()" , {}); |
2506 | m.def("_new_zeros_with_same_feature_meta.out(Tensor self, Tensor other, *, int self_num_batch_dims=0, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2507 | m.def("_cudnn_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2508 | m.def("_cudnn_rnn_flatten_weight.out(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(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2509 | m.def("_cudnn_rnn.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))" , {at::Tag::generated}); |
2510 | m.def("_cudnn_rnn_backward.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()" , {at::Tag::generated}); |
2511 | m.def("_cudnn_init_dropout_state.out(float dropout, bool train, int dropout_seed, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2512 | m.def("_fused_dropout.out(Tensor self, float p, Generator? generator=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2513 | m.def("_masked_scale.out(Tensor self, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2514 | m.def("native_dropout.out(Tensor input, float p, bool? train, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2515 | m.def("native_dropout_backward.out(Tensor grad_output, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2516 | m.def("_conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2517 | m.def("_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2518 | m.def("add.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2519 | m.def("affine_grid_generator.out(Tensor theta, int[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2520 | m.def("bartlett_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2521 | m.def("bartlett_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2522 | m.def("quantized_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2523 | m.def("bernoulli.Tensor_out(Tensor self, Tensor p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2524 | m.def("bernoulli.Tensor(Tensor self, Tensor p, *, Generator? generator=None) -> Tensor" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2525 | m.def("bernoulli.float_out(Tensor self, float p=0.5, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2526 | m.def("binary_cross_entropy_with_logits.out(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2527 | m.def("bincount.out(Tensor self, Tensor? weights=None, int minlength=0, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2528 | m.def("blackman_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2529 | m.def("blackman_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2530 | m.def("block_diag.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2531 | m.def("constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2532 | m.def("convolution.out(Tensor input, Tensor weight, Tensor? bias, int[] stride, SymInt[] padding, int[] dilation, bool transposed, SymInt[] output_padding, int groups, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2533 | m.def("convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, int[] stride, SymInt[] padding, int[] dilation, bool transposed, SymInt[] output_padding, int groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2534 | m.def("convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2535 | m.def("convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2536 | m.def("_convolution.out(Tensor input, Tensor weight, Tensor? bias, int[] stride, SymInt[] padding, int[] dilation, bool transposed, SymInt[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2537 | m.def("conv_tbc.out(Tensor self, Tensor weight, Tensor bias, int pad=0, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2538 | m.def("copy.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2539 | m.def("_copy_from.out(Tensor self, Tensor dst, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2540 | m.def("_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2541 | m.def("count_nonzero.dim_IntList_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2542 | m.def("count_nonzero.out(Tensor self, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2543 | m.def("cudnn_affine_grid_generator.out(Tensor theta, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2544 | m.def("cudnn_affine_grid_generator_backward.out(Tensor grad, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2545 | m.def("cudnn_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))" , {at::Tag::generated}); |
2546 | m.def("cudnn_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, Tensor reserveSpace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2547 | m.def("cudnn_convolution.out(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2548 | m.def("cudnn_convolution_transpose.out(Tensor self, Tensor weight, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2549 | m.def("_mps_convolution_transpose.out(Tensor self, Tensor weight, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2550 | m.def("mps_convolution_transpose_backward.out(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2551 | m.def("cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2552 | m.def("cudnn_convolution_add_relu.out(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2553 | m.def("cudnn_grid_sampler.out(Tensor self, Tensor grid, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2554 | m.def("cudnn_grid_sampler_backward.out(Tensor self, Tensor grid, Tensor grad_output, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2555 | m.def("_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2556 | m.def("_ctc_loss.Tensor_out(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2557 | m.def("_ctc_loss_backward.out(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(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2558 | m.def("diag_embed.out(Tensor self, int offset=0, int dim1=-2, int dim2=-1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2559 | m.def("diagonal_backward.out(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2560 | m.def("div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2561 | m.def("div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2562 | m.def("embedding.out(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2563 | m.def("embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2564 | m.def("embedding_renorm.out(Tensor self, Tensor indices, float max_norm, float norm_type, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2565 | m.def("embedding_renorm(Tensor self, Tensor indices, float max_norm, float norm_type) -> Tensor" , {at::Tag::generated}); |
2566 | m.def("_embedding_bag_forward_only.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))" , {at::Tag::generated}); |
2567 | m.def("_embedding_bag.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))" , {at::Tag::generated}); |
2568 | m.def("_embedding_bag_dense_backward.out(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(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2569 | m.def("_embedding_bag_per_sample_weights_backward.out(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2570 | m.def("empty.names_out(int[] size, *, Dimname[]? names, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2571 | m.def("new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2572 | m.def("new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2573 | m.def("new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2574 | m.def("new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2575 | m.def("new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2576 | m.def("_empty_affine_quantized.out(int[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2577 | m.def("_empty_per_channel_affine_quantized.out(int[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2578 | m.def("resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2579 | m.def("resize(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor" , {at::Tag::generated}); |
2580 | m.def("_resize_output.out(Tensor self, int[] size, Device device, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2581 | m.def("_resize_output(Tensor self, int[] size, Device device) -> Tensor" , {at::Tag::generated}); |
2582 | m.def("empty_quantized.out(int[] size, Tensor qtensor, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2583 | m.def("empty_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2584 | m.def("empty_strided.out(SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2585 | m.def("fill.Scalar_out(Tensor self, Scalar value, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2586 | m.def("fill.Tensor_out(Tensor self, Tensor value, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2587 | m.def("full.names_out(int[] size, Scalar fill_value, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2588 | m.def("full_like.out(Tensor self, Scalar fill_value, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2589 | m.def("from_file.out(str filename, bool? shared=None, int? size=0, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2590 | m.def("grid_sampler_2d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2591 | m.def("grid_sampler_2d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2592 | m.def("_grid_sampler_2d_cpu_fallback.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2593 | m.def("grid_sampler_3d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2594 | m.def("grid_sampler_3d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2595 | m.def("hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2596 | m.def("hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2597 | m.def("hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2598 | m.def("hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2599 | m.def("hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2600 | m.def("hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2601 | m.def("kaiser_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2602 | m.def("kaiser_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2603 | m.def("kaiser_window.beta_out(int window_length, bool periodic, float beta, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2604 | m.def("native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2605 | m.def("native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2606 | m.def("index_put.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2607 | m.def("_index_put_impl.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2608 | m.def("_index_put_impl(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor" , {at::Tag::generated}); |
2609 | m.def("isnan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2610 | m.def("native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2611 | m.def("native_layer_norm_backward.out(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2612 | m.def("linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2613 | m.def("mkldnn_linear.out(Tensor self, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2614 | m.def("mkldnn_linear_backward_input.out(int[] input_size, Tensor grad_output, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2615 | m.def("mkldnn_linear_backward_weights.out(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2616 | m.def("mkldnn_linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2617 | m.def("matmul_backward.out(Tensor grad, Tensor self, Tensor other, bool[2] mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2618 | m.def("_aminmax.out(Tensor self, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2619 | m.def("_aminmax.dim_out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2620 | m.def("_mps_max_pool2d.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(a!)" , {at::Tag::generated}); |
2621 | m.def("mps_max_pool2d_backward.out(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(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2622 | m.def("mkldnn_max_pool2d.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(a!)" , {at::Tag::generated}); |
2623 | m.def("mkldnn_max_pool2d_backward.out(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(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2624 | m.def("mkldnn_max_pool3d.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(a!)" , {at::Tag::generated}); |
2625 | m.def("mkldnn_max_pool3d_backward.out(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(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2626 | m.def("quantized_max_pool1d.out(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2627 | m.def("quantized_max_pool2d.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(a!)" , {at::Tag::generated}); |
2628 | m.def("median.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2629 | m.def("nanmedian.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2630 | m.def("_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2631 | m.def("mps_convolution_backward.out(Tensor self, Tensor grad_output, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2632 | m.def("mkldnn_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2633 | m.def("mkldnn_rnn_layer.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))" , {at::Tag::generated}); |
2634 | m.def("mkldnn_rnn_layer_backward.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5, Tensor(g!) out6) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!))" , {at::Tag::generated}); |
2635 | m.def("miopen_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2636 | m.def("miopen_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2637 | m.def("miopen_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2638 | m.def("miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2639 | m.def("miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2640 | m.def("miopen_rnn.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))" , {at::Tag::generated}); |
2641 | m.def("miopen_rnn_backward.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()" , {at::Tag::generated}); |
2642 | m.def("_sparse_sparse_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2643 | m.def("mul.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2644 | m.def("_native_batch_norm_legit_functional(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor running_mean_out, Tensor running_var_out)" , {at::Tag::generated}); |
2645 | m.def("batch_norm_stats.out(Tensor input, float eps, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2646 | m.def("batch_norm_gather_stats.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2647 | m.def("batch_norm_gather_stats_with_counts.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2648 | m.def("native_batch_norm_backward.out(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(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2649 | m.def("batch_norm_backward_reduce.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))" , {at::Tag::generated}); |
2650 | m.def("batch_norm_backward_elemt.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor mean_dy, Tensor mean_dy_xmu, Tensor count, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2651 | m.def("batch_norm_update_stats.out(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2652 | m.def("_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, int[2] stride=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2653 | m.def("ones.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2654 | m.def("ones_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2655 | m.def("_euclidean_dist.out(Tensor x1, Tensor x2, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2656 | m.def("_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2657 | m.def("_cdist_backward.out(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2658 | m.def("_pdist_forward.out(Tensor self, float p=2, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2659 | m.def("_pdist_backward.out(Tensor grad, Tensor self, float p, Tensor pdist, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2660 | m.def("pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2661 | m.def("pixel_unshuffle.out(Tensor self, int downscale_factor, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2662 | m.def("channel_shuffle.out(Tensor self, int groups, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2663 | m.def("_pin_memory.out(Tensor self, Device? device=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2664 | m.def("scalar_tensor.out(Scalar s, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2665 | m.def("rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2666 | m.def("rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2667 | m.def("rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2668 | m.def("randint_like.out(Tensor self, int high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2669 | m.def("randint_like.low_dtype_out(Tensor self, int low, int high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2670 | m.def("randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2671 | m.def("randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2672 | m.def("randn_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2673 | m.def("repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2674 | m.def("repeat_interleave.Tensor_out(Tensor repeats, *, int? output_size=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2675 | m.def("_mkldnn_reshape.out(Tensor self, int[] shape, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2676 | m.def("relu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2677 | m.def("select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2678 | m.def("celu.out(Tensor self, Scalar alpha=1.0, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2679 | m.def("slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2680 | m.def("slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2681 | m.def("select_scatter.out(Tensor self, Tensor src, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2682 | m.def("diagonal_scatter.out(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2683 | m.def("as_strided_scatter.out(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2684 | m.def("unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2685 | m.def("unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2686 | m.def("sum.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2687 | m.def("std_mean.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2688 | m.def("prod.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2689 | m.def("_mkldnn_transpose.out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2690 | m.def("flip.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2691 | m.def("roll.out(Tensor self, int[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2692 | m.def("rot90.out(Tensor self, int k=1, int[] dims=[0,1], *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2693 | m.def("_transform_bias_rescale_qkv.out(Tensor qkv, Tensor qkv_bias, int num_heads, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2694 | m.def("_nested_tensor_from_mask.out(Tensor t, Tensor mask, bool mask_check=True, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2695 | m.def("_nested_from_padded.out(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2696 | m.def("_nested_tensor_size.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2697 | m.def("_nested_tensor_strides.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2698 | m.def("_nested_from_padded_and_nested_example.out(Tensor padded, Tensor nt_example, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2699 | m.def("_nested_view_from_buffer_copy.out(Tensor self, Tensor nested_size, Tensor nested_strides, int[] offsets, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2700 | m.def("_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2701 | m.def("_unique.out(Tensor self, bool sorted=True, bool return_inverse=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2702 | m.def("unique_dim.out(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2703 | m.def("unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2704 | m.def("unique_dim_consecutive.out(Tensor self, int dim, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2705 | m.def("_unique2.out(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2706 | m.def("_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2707 | m.def("var_mean.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2708 | m.def("_weight_norm_interface.out(Tensor v, Tensor g, int dim=0, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2709 | m.def("_weight_norm_interface_backward.out(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2710 | m.def("zeros.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2711 | m.def("_efficientzerotensor.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2712 | m.def("zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2713 | m.def("_standard_gamma_grad.out(Tensor self, Tensor output, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2714 | m.def("_standard_gamma.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2715 | m.def("_dirichlet_grad.out(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2716 | m.def("_sample_dirichlet.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2717 | m.def("poisson.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2718 | m.def("binomial.out(Tensor count, Tensor prob, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2719 | m.def("native_norm.out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2720 | m.def("native_norm.ScalarOpt_dim_dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2721 | m.def("_sparse_sum.dim_out(Tensor self, int[1] dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2722 | m.def("_sparse_sum_backward.out(Tensor grad, Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2723 | m.def("_sparse_csr_sum.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2724 | m.def("_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2725 | m.def("_sparse_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2726 | m.def("_sparse_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2727 | m.def("_sparse_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2728 | m.def("_sparse_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2729 | m.def("_spdiags.out(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2730 | m.def("norm.ScalarOpt_dtype_out(Tensor self, Scalar? p, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2731 | m.def("norm.Scalar_out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2732 | m.def("clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2733 | m.def("resize_as.out(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2734 | m.def("resize_as(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor" , {at::Tag::generated}); |
2735 | m.def("resize_as_sparse.out(Tensor self, Tensor the_template, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2736 | m.def("resize_as_sparse(Tensor self, Tensor the_template) -> Tensor" , {at::Tag::generated}); |
2737 | m.def("zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2738 | m.def("zero(Tensor self) -> Tensor" , {at::Tag::generated}); |
2739 | m.def("sub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2740 | m.def("rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2741 | m.def("rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2742 | m.def("_sparse_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2743 | m.def("sparse_coo_tensor.size_out(int[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2744 | m.def("_sparse_coo_tensor_with_dims.out(int sparse_dim, int dense_dim, int[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2745 | m.def("_sparse_coo_tensor_with_dims_and_tensors.out(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2746 | m.def("sparse_resize.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2747 | m.def("sparse_resize(Tensor self, int[] size, int sparse_dim, int dense_dim) -> Tensor" , {at::Tag::generated}); |
2748 | m.def("sparse_resize_and_clear.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2749 | m.def("sparse_resize_and_clear(Tensor self, int[] size, int sparse_dim, int dense_dim) -> Tensor" , {at::Tag::generated}); |
2750 | m.def("sparse_mask.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2751 | m.def("_to_dense.out(Tensor self, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2752 | m.def("_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2753 | m.def("_coalesced.out(Tensor self, bool coalesced, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2754 | m.def("_coalesced(Tensor self, bool coalesced) -> Tensor" , {at::Tag::generated}); |
2755 | m.def("copy_sparse_to_sparse.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2756 | m.def("copy_sparse_to_sparse(Tensor self, Tensor src, bool non_blocking=False) -> Tensor" , {at::Tag::generated}); |
2757 | m.def("to_sparse.sparse_dim_out(Tensor self, int sparse_dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2758 | m.def("to_sparse.out(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2759 | m.def("to_sparse_csr.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2760 | m.def("to_sparse_csc.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2761 | m.def("to_sparse_bsr.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2762 | m.def("to_sparse_bsc.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2763 | m.def("to_mkldnn.out(Tensor self, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2764 | m.def("mkldnn_reorder_conv2d_weight.out(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1, int[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2765 | m.def("mkldnn_reorder_conv3d_weight.out(Tensor self, int[3] padding=0, int[3] stride=1, int[3] dilation=1, int groups=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2766 | m.def("quantize_per_tensor_dynamic.out(Tensor self, ScalarType dtype, bool reduce_range, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2767 | m.def("quantize_per_tensor.out(Tensor self, float scale, int zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2768 | m.def("quantize_per_tensor.tensor_qparams_out(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2769 | m.def("quantize_per_tensor.tensors_out(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2770 | m.def("quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2771 | m.def("dequantize.self_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2772 | m.def("dequantize.tensors_out(Tensor[] tensors, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2773 | m.def("q_per_channel_scales.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2774 | m.def("q_per_channel_zero_points.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2775 | m.def("int_repr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2776 | m.def("_make_per_tensor_quantized_tensor.out(Tensor self, float scale, int zero_point, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2777 | m.def("_make_per_channel_quantized_tensor.out(Tensor self, Tensor scale, Tensor zero_point, int axis, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2778 | m.def("fake_quantize_per_tensor_affine_cachemask.out(Tensor self, float scale, int zero_point, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2779 | m.def("_fake_quantize_per_tensor_affine_cachemask_tensor_qparams.out(Tensor self, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2780 | m.def("_fake_quantize_learnable_per_tensor_affine.out(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2781 | m.def("fake_quantize_per_channel_affine_cachemask.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2782 | m.def("_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2783 | m.def("_fused_moving_avg_obs_fq_helper.out(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(e!) out0, Tensor(f!) out1) -> (Tensor(e!), Tensor(f!))" , {at::Tag::generated}); |
2784 | m.def("_fused_moving_avg_obs_fq_helper_functional(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor running_min, Tensor running_max, Tensor scale, Tensor 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, Tensor running_min_out, Tensor running_max_out, Tensor scale_out, Tensor zero_point_out)" , {at::Tag::generated}); |
2785 | m.def("_to_copy.out(Tensor self, *, bool non_blocking=False, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2786 | m.def("_lstm_mps.out(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))" , {at::Tag::generated}); |
2787 | m.def("lstm_mps_backward.out(Tensor grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!)[] out1, Tensor(c!)[] out2) -> ()" , {at::Tag::generated}); |
2788 | m.def("_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2789 | m.def("_thnn_fused_lstm_cell_backward_impl.out(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2790 | m.def("_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2791 | m.def("_thnn_fused_gru_cell_backward.out(Tensor grad_hy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))" , {at::Tag::generated}); |
2792 | m.def("_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2793 | m.def("set.source_Storage_out(Tensor self, Storage source, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2794 | m.def("set.source_Storage(Tensor self, Storage source) -> Tensor" , {at::Tag::generated}); |
2795 | m.def("set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2796 | m.def("set.source_Storage_storage_offset(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor" , {at::Tag::generated}); |
2797 | m.def("set.source_Tensor_out(Tensor self, Tensor source, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2798 | m.def("set.source_Tensor(Tensor self, Tensor source) -> Tensor" , {at::Tag::generated}); |
2799 | m.def("set.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2800 | m.def("set(Tensor self) -> Tensor" , {at::Tag::generated}); |
2801 | m.def("lift.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2802 | m.def("lift_fresh_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2803 | m.def("masked_fill.Scalar_out(Tensor self, Tensor mask, Scalar value, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2804 | m.def("masked_fill.Tensor_out(Tensor self, Tensor mask, Tensor value, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2805 | m.def("masked_scatter.out(Tensor self, Tensor mask, Tensor source, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2806 | m.def("_masked_softmax.out(Tensor self, Tensor mask, int? dim=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2807 | m.def("_masked_softmax_backward.out(Tensor grad_output, Tensor output, Tensor mask, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2808 | m.def("put.out(Tensor self, Tensor index, Tensor source, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2809 | m.def("index_fill.int_Scalar_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2810 | m.def("index_fill.int_Tensor_out(Tensor self, int dim, Tensor index, Tensor value, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2811 | m.def("bitwise_and.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2812 | m.def("bitwise_or.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2813 | m.def("bitwise_xor.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2814 | m.def("__lshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2815 | m.def("__lshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2816 | m.def("bitwise_left_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2817 | m.def("__rshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2818 | m.def("__rshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2819 | m.def("bitwise_right_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2820 | m.def("random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2821 | m.def("random.from(Tensor self, int from, int? to, *, Generator? generator=None) -> Tensor" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2822 | m.def("random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2823 | m.def("random.to(Tensor self, int to, *, Generator? generator=None) -> Tensor" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2824 | m.def("random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2825 | m.def("random(Tensor self, *, Generator? generator=None) -> Tensor" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2826 | m.def("uniform.out(Tensor self, float from=0, float to=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2827 | m.def("uniform(Tensor self, float from=0, float to=1, *, Generator? generator=None) -> Tensor" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2828 | m.def("cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2829 | m.def("cauchy(Tensor self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2830 | m.def("log_normal.out(Tensor self, float mean=1, float std=2, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2831 | m.def("log_normal(Tensor self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2832 | m.def("exponential.out(Tensor self, float lambd=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2833 | m.def("exponential(Tensor self, float lambd=1, *, Generator? generator=None) -> Tensor" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2834 | m.def("geometric.out(Tensor self, float p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2835 | m.def("geometric(Tensor self, float p, *, Generator? generator=None) -> Tensor" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2836 | m.def("tril_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2837 | m.def("triu_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2838 | m.def("trace.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2839 | m.def("_cholesky_solve_helper.out(Tensor self, Tensor A, bool upper, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2840 | m.def("dist.out(Tensor self, Tensor other, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2841 | m.def("_histogramdd_bin_edges.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2842 | m.def("_histogramdd_from_bin_cts.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2843 | m.def("_histogramdd_from_bin_tensors.out(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2844 | m.def("remainder.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2845 | m.def("argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2846 | m.def("unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2847 | m.def("normal.out(Tensor self, float mean=0, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::nondeterministic_seeded}); |
2848 | m.def("_amp_foreach_non_finite_check_and_unscale.out(Tensor[] self, Tensor(b!) found_inf, Tensor inv_scale, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2849 | m.def("_amp_foreach_non_finite_check_and_unscale(Tensor[] self, Tensor found_inf, Tensor inv_scale) -> (Tensor[] self_out, Tensor found_inf_out)" , {at::Tag::generated}); |
2850 | m.def("_amp_update_scale.out(Tensor self, Tensor(b!) growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2851 | m.def("_amp_update_scale(Tensor self, Tensor growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> (Tensor, Tensor growth_tracker_out)" , {at::Tag::generated}); |
2852 | m.def("_foreach_add.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2853 | m.def("_foreach_sub.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2854 | m.def("_foreach_mul.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2855 | m.def("_foreach_div.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2856 | m.def("_foreach_clamp_min.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2857 | m.def("_foreach_clamp_max.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2858 | m.def("_foreach_maximum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2859 | m.def("_foreach_minimum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2860 | m.def("_foreach_add.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2861 | m.def("_foreach_sub.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2862 | m.def("_foreach_mul.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2863 | m.def("_foreach_div.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2864 | m.def("_foreach_clamp_min.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2865 | m.def("_foreach_clamp_max.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2866 | m.def("_foreach_maximum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2867 | m.def("_foreach_minimum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2868 | m.def("_foreach_add.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2869 | m.def("_foreach_sub.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2870 | m.def("_foreach_div.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2871 | m.def("_foreach_mul.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2872 | m.def("_foreach_clamp_min.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2873 | m.def("_foreach_clamp_max.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2874 | m.def("_foreach_maximum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2875 | m.def("_foreach_minimum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2876 | m.def("_foreach_exp.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2877 | m.def("_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2878 | m.def("_foreach_zero(Tensor[] self) -> Tensor[] self_out" , {at::Tag::generated}); |
2879 | m.def("_foreach_sqrt.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2880 | m.def("_foreach_abs.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2881 | m.def("_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2882 | m.def("_foreach_asin.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2883 | m.def("_foreach_atan.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2884 | m.def("_foreach_ceil.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2885 | m.def("_foreach_cos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2886 | m.def("_foreach_cosh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2887 | m.def("_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2888 | m.def("_foreach_erfc.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2889 | m.def("_foreach_expm1.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2890 | m.def("_foreach_floor.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2891 | m.def("_foreach_log.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2892 | m.def("_foreach_log10.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2893 | m.def("_foreach_log1p.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2894 | m.def("_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2895 | m.def("_foreach_neg.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2896 | m.def("_foreach_tan.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2897 | m.def("_foreach_tanh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2898 | m.def("_foreach_sin.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2899 | m.def("_foreach_sinh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2900 | m.def("_foreach_round.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2901 | m.def("_foreach_lgamma.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2902 | m.def("_foreach_frac.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2903 | m.def("_foreach_reciprocal.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2904 | m.def("_foreach_sigmoid.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2905 | m.def("_foreach_trunc.out(Tensor[] self, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2906 | m.def("_foreach_addcdiv.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2907 | m.def("_foreach_addcmul.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2908 | m.def("_foreach_addcdiv.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2909 | m.def("_foreach_addcdiv.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2910 | m.def("_foreach_addcmul.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2911 | m.def("_foreach_addcmul.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2912 | m.def("_foreach_norm.Scalar_out(Tensor[] self, Scalar ord=2, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2913 | m.def("_foreach_lerp.List_out(Tensor[] self, Tensor[] tensors1, Tensor[] weights, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2914 | m.def("_foreach_lerp.Scalar_out(Tensor[] self, Tensor[] tensors1, Scalar weight, *, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2915 | m.def("bucketize.Scalar_out(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2916 | m.def("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!)" , {at::Tag::generated}); |
2917 | m.def("glu_jvp.out(Tensor glu, Tensor x, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2918 | m.def("glu_backward_jvp.out(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2919 | m.def("hardswish_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2920 | m.def("rrelu_with_noise_backward.out(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2921 | m.def("mkldnn_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2922 | m.def("_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2923 | m.def("_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2924 | m.def("_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2925 | m.def("_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2926 | m.def("_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2927 | m.def("conv_depthwise3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, int[3] dilation, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2928 | m.def("slow_conv_dilated2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2929 | m.def("slow_conv_dilated3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0, int[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2930 | m.def("isinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2931 | m.def("linalg_matrix_exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2932 | m.def("_test_optional_intlist.out(Tensor values, int[]? addends, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2933 | m.def("_test_optional_filled_intlist.out(Tensor values, int[2]? addends, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2934 | m.def("_test_optional_floatlist.out(Tensor values, float[]? addends, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2935 | m.def("_test_warn_in_autograd.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2936 | m.def("_test_autograd_multiple_dispatch.fullcoverage_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2937 | m.def("_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2938 | m.def("segment_reduce.out(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int axis=0, bool unsafe=False, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2939 | m.def("_segment_reduce_backward.out(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2940 | m.def("_nested_tensor_from_tensor_list.out(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2941 | m.def("_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2942 | m.def("_make_dual_copy.out(Tensor primal, Tensor tangent, int level, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2943 | m.def("view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2944 | m.def("view_as_complex_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2945 | m.def("_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2946 | m.def("_neg_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2947 | m.def("as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2948 | m.def("_sparse_broadcast_to_copy.out(Tensor self, int[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2949 | m.def("diagonal_copy.out(Tensor self, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2950 | m.def("expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2951 | m.def("permute_copy.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2952 | m.def("_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2953 | m.def("select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2954 | m.def("detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2955 | m.def("slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2956 | m.def("squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2957 | m.def("squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2958 | m.def("squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2959 | m.def("t_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2960 | m.def("transpose_copy.int_out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2961 | m.def("unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2962 | m.def("_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2963 | m.def("_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2964 | m.def("indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2965 | m.def("values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2966 | m.def("crow_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2967 | m.def("col_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2968 | m.def("ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2969 | m.def("row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2970 | m.def("view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2971 | m.def("view_copy.dtype_out(Tensor self, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2972 | m.def("unfold_copy.out(Tensor self, int dimension, int size, int step, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2973 | m.def("alias_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated, at::Tag::view_copy}); |
2974 | m.def("to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2975 | m.def("_transformer_encoder_layer_fwd.out(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(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2976 | m.def("_native_multi_head_attention.out(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(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))" , {at::Tag::generated}); |
2977 | m.def("_triton_scaled_dot_attention.out(Tensor q, Tensor k, Tensor v, float dropout_p=0.0, *, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2978 | m.def("_triton_multi_head_attention.out(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(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2979 | m.def("_transformer_decoder_only_layer_fwd.out(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, Tensor? incr_key=None, Tensor? incr_value=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))" , {at::Tag::generated}); |
2980 | m.def("_native_decoder_only_multi_head_attention.out(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? incr_key=None, Tensor? incr_value=None, bool need_weights=True, bool average_attn_weights=True, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))" , {at::Tag::generated}); |
2981 | m.def("_foobar.out(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True, Tensor(a!) out) -> Tensor(a!)" , {at::Tag::generated}); |
2982 | m.def("_fused_adam.out(Tensor[] 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, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2983 | m.def("_fused_adam(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] 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) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)" , {at::Tag::generated}); |
2984 | m.def("_fused_adamw.out(Tensor[] 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, Tensor(a!)[] out) -> ()" , {at::Tag::generated}); |
2985 | m.def("_fused_adamw(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] 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) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)" , {at::Tag::generated});; |
2986 | // Distributed Ops |
2987 | // Implementations located in torch/csrc/jit/runtime/register_distributed_ops.cpp |
2988 | m.def("get_gradients(int context_id) -> Dict(Tensor, Tensor)" ); |
2989 | } |
2990 | |
2991 | } // namespace at |
2992 | |