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let _cudnn_rnn input ~ weight ~ weight_stride0 ~ weight_buf ~ hx ~ cx ~ mode ~ hidden_size ~ proj_size ~ num_layers ~ batch_first ~ dropout ~ train ~ bidirectional ~ batch_sizes ~ dropout_state = let out__ = CArray . make t 5 in stubs__cudnn_rnn ( CArray . start out__ ) input ( CArray . of_list t weight |> CArray . start ) ( List . length weight ) ( Int64 . of_int weight_stride0 ) ( match weight_buf with | Some v -> v | None -> null ) hx ( match cx with | Some v -> v | None -> null ) ( Int64 . of_int mode ) ( Int64 . of_int hidden_size ) ( Int64 . of_int proj_size ) ( Int64 . of_int num_layers ) ( if batch_first then 1 else 0 ) dropout ( if train then 1 else 0 ) ( if bidirectional then 1 else 0 ) ( List . map Int64 . of_int batch_sizes |> CArray . of_list int64_t |> CArray . start ) ( List . length batch_sizes ) ( match dropout_state with | Some v -> v | None -> null ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; let t3 = CArray . get out__ 3 in Gc . finalise C . Tensor . free t3 ; let t4 = CArray . get out__ 4 in Gc . finalise C . Tensor . free t4 ; t0 , t1 , t2 , t3 , t4 |
let _cudnn_rnn_flatten_weight ~ weight_arr ~ weight_stride0 ~ input_size ~ mode ~ hidden_size ~ proj_size ~ num_layers ~ batch_first ~ bidirectional = let out__ = CArray . make t 1 in stubs__cudnn_rnn_flatten_weight ( CArray . start out__ ) ( CArray . of_list t weight_arr |> CArray . start ) ( List . length weight_arr ) ( Int64 . of_int weight_stride0 ) ( Int64 . of_int input_size ) ( Int64 . of_int mode ) ( Int64 . of_int hidden_size ) ( Int64 . of_int proj_size ) ( Int64 . of_int num_layers ) ( if batch_first then 1 else 0 ) ( if bidirectional then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _det_lu_based_helper self = let out__ = CArray . make t 3 in stubs__det_lu_based_helper ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; t0 , t1 , t2 |
let _det_lu_based_helper_backward_helper ~ det_grad ~ det self ~ lu ~ pivs = let out__ = CArray . make t 1 in stubs__det_lu_based_helper_backward_helper ( CArray . start out__ ) det_grad det self lu pivs ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _dim_arange ~ like ~ dim = let out__ = CArray . make t 1 in stubs__dim_arange ( CArray . start out__ ) like ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _dirichlet_grad ~ x ~ alpha ~ total = let out__ = CArray . make t 1 in stubs__dirichlet_grad ( CArray . start out__ ) x alpha total ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _embedding_bag ~ weight ~ indices ~ offsets ~ scale_grad_by_freq ~ mode ~ sparse ~ per_sample_weights ~ include_last_offset ~ padding_idx = let out__ = CArray . make t 4 in stubs__embedding_bag ( CArray . start out__ ) weight indices offsets ( if scale_grad_by_freq then 1 else 0 ) ( Int64 . of_int mode ) ( if sparse then 1 else 0 ) ( match per_sample_weights with | Some v -> v | None -> null ) ( if include_last_offset then 1 else 0 ) ( Int64 . of_int padding_idx ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; let t3 = CArray . get out__ 3 in Gc . finalise C . Tensor . free t3 ; t0 , t1 , t2 , t3 |
let _embedding_bag_backward ~ grad ~ indices ~ offsets ~ offset2bag ~ bag_size ~ maximum_indices ~ num_weights ~ scale_grad_by_freq ~ mode ~ sparse ~ per_sample_weights ~ padding_idx = let out__ = CArray . make t 1 in stubs__embedding_bag_backward ( CArray . start out__ ) grad indices offsets offset2bag bag_size maximum_indices ( Int64 . of_int num_weights ) ( if scale_grad_by_freq then 1 else 0 ) ( Int64 . of_int mode ) ( if sparse then 1 else 0 ) ( match per_sample_weights with | Some v -> v | None -> null ) ( Int64 . of_int padding_idx ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _embedding_bag_dense_backward ~ grad ~ indices ~ offset2bag ~ bag_size ~ maximum_indices ~ num_weights ~ scale_grad_by_freq ~ mode ~ per_sample_weights ~ padding_idx = let out__ = CArray . make t 1 in stubs__embedding_bag_dense_backward ( CArray . start out__ ) grad indices offset2bag bag_size maximum_indices ( Int64 . of_int num_weights ) ( if scale_grad_by_freq then 1 else 0 ) ( Int64 . of_int mode ) ( match per_sample_weights with | Some v -> v | None -> null ) ( Int64 . of_int padding_idx ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _embedding_bag_forward_only ~ weight ~ indices ~ offsets ~ scale_grad_by_freq ~ mode ~ sparse ~ per_sample_weights ~ include_last_offset ~ padding_idx = let out__ = CArray . make t 4 in stubs__embedding_bag_forward_only ( CArray . start out__ ) weight indices offsets ( if scale_grad_by_freq then 1 else 0 ) ( Int64 . of_int mode ) ( if sparse then 1 else 0 ) ( match per_sample_weights with | Some v -> v | None -> null ) ( if include_last_offset then 1 else 0 ) ( Int64 . of_int padding_idx ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; let t3 = CArray . get out__ 3 in Gc . finalise C . Tensor . free t3 ; t0 , t1 , t2 , t3 |
let _embedding_bag_per_sample_weights_backward ~ grad ~ weight ~ indices ~ offsets ~ offset2bag ~ mode ~ padding_idx = let out__ = CArray . make t 1 in stubs__embedding_bag_per_sample_weights_backward ( CArray . start out__ ) grad weight indices offsets offset2bag ( Int64 . of_int mode ) ( Int64 . of_int padding_idx ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _embedding_bag_sparse_backward ~ grad ~ indices ~ offsets ~ offset2bag ~ bag_size ~ num_weights ~ scale_grad_by_freq ~ mode ~ per_sample_weights ~ padding_idx = let out__ = CArray . make t 1 in stubs__embedding_bag_sparse_backward ( CArray . start out__ ) grad indices offsets offset2bag bag_size ( Int64 . of_int num_weights ) ( if scale_grad_by_freq then 1 else 0 ) ( Int64 . of_int mode ) ( match per_sample_weights with | Some v -> v | None -> null ) ( Int64 . of_int padding_idx ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _empty_affine_quantized ~ size ~ options ~ scale ~ zero_point = let out__ = CArray . make t 1 in stubs__empty_affine_quantized ( CArray . start out__ ) ( List . map Int64 . of_int size |> CArray . of_list int64_t |> CArray . start ) ( List . length size ) ( Kind . packed_to_int ( fst options ) ) ( Device . to_int ( snd options ) ) scale ( Int64 . of_int zero_point ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _empty_per_channel_affine_quantized ~ size ~ scales ~ zero_points ~ axis ~ options = let out__ = CArray . make t 1 in stubs__empty_per_channel_affine_quantized ( CArray . start out__ ) ( List . map Int64 . of_int size |> CArray . of_list int64_t |> CArray . start ) ( List . length size ) scales zero_points ( Int64 . of_int axis ) ( Kind . packed_to_int ( fst options ) ) ( Device . to_int ( snd options ) ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _euclidean_dist ~ x1 ~ x2 = let out__ = CArray . make t 1 in stubs__euclidean_dist ( CArray . start out__ ) x1 x2 ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _fake_quantize_learnable_per_channel_affine self ~ scale ~ zero_point ~ axis ~ quant_min ~ quant_max ~ grad_factor = let out__ = CArray . make t 1 in stubs__fake_quantize_learnable_per_channel_affine ( CArray . start out__ ) self scale zero_point ( Int64 . of_int axis ) ( Int64 . of_int quant_min ) ( Int64 . of_int quant_max ) grad_factor ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _fake_quantize_learnable_per_channel_affine_backward ~ grad self ~ scale ~ zero_point ~ axis ~ quant_min ~ quant_max ~ grad_factor = let out__ = CArray . make t 3 in stubs__fake_quantize_learnable_per_channel_affine_backward ( CArray . start out__ ) grad self scale zero_point ( Int64 . of_int axis ) ( Int64 . of_int quant_min ) ( Int64 . of_int quant_max ) grad_factor ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; t0 , t1 , t2 |
let _fake_quantize_learnable_per_tensor_affine self ~ scale ~ zero_point ~ quant_min ~ quant_max ~ grad_factor = let out__ = CArray . make t 1 in stubs__fake_quantize_learnable_per_tensor_affine ( CArray . start out__ ) self scale zero_point ( Int64 . of_int quant_min ) ( Int64 . of_int quant_max ) grad_factor ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _fake_quantize_learnable_per_tensor_affine_backward ~ grad self ~ scale ~ zero_point ~ quant_min ~ quant_max ~ grad_factor = let out__ = CArray . make t 3 in stubs__fake_quantize_learnable_per_tensor_affine_backward ( CArray . start out__ ) grad self scale zero_point ( Int64 . of_int quant_min ) ( Int64 . of_int quant_max ) grad_factor ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; t0 , t1 , t2 |
let _fake_quantize_per_tensor_affine_cachemask_tensor_qparams self ~ scale ~ zero_point ~ fake_quant_enabled ~ quant_min ~ quant_max = let out__ = CArray . make t 2 in stubs__fake_quantize_per_tensor_affine_cachemask_tensor_qparams ( CArray . start out__ ) self scale zero_point fake_quant_enabled ( Int64 . of_int quant_min ) ( Int64 . of_int quant_max ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let _fft_c2c self ~ dim ~ normalization ~ forward = let out__ = CArray . make t 1 in stubs__fft_c2c ( CArray . start out__ ) self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( Int64 . of_int normalization ) ( if forward then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _fft_c2c_out ~ out self ~ dim ~ normalization ~ forward = let out__ = CArray . make t 1 in stubs__fft_c2c_out ( CArray . start out__ ) out self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( Int64 . of_int normalization ) ( if forward then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _fft_c2r self ~ dim ~ normalization ~ last_dim_size = let out__ = CArray . make t 1 in stubs__fft_c2r ( CArray . start out__ ) self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( Int64 . of_int normalization ) ( Int64 . of_int last_dim_size ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _fft_c2r_out ~ out self ~ dim ~ normalization ~ last_dim_size = let out__ = CArray . make t 1 in stubs__fft_c2r_out ( CArray . start out__ ) out self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( Int64 . of_int normalization ) ( Int64 . of_int last_dim_size ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _fft_r2c self ~ dim ~ normalization ~ onesided = let out__ = CArray . make t 1 in stubs__fft_r2c ( CArray . start out__ ) self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( Int64 . of_int normalization ) ( if onesided then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _fft_r2c_out ~ out self ~ dim ~ normalization ~ onesided = let out__ = CArray . make t 1 in stubs__fft_r2c_out ( CArray . start out__ ) out self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( Int64 . of_int normalization ) ( if onesided then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _fused_dropout self ~ p = let out__ = CArray . make t 2 in stubs__fused_dropout ( CArray . start out__ ) self p ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let _fused_moving_avg_obs_fq_helper self ~ observer_on ~ fake_quant_on ~ running_min ~ running_max ~ scale ~ zero_point ~ averaging_const ~ quant_min ~ quant_max ~ ch_axis ~ per_row_fake_quant ~ symmetric_quant = let out__ = CArray . make t 2 in stubs__fused_moving_avg_obs_fq_helper ( CArray . start out__ ) self observer_on fake_quant_on running_min running_max scale zero_point averaging_const ( Int64 . of_int quant_min ) ( Int64 . of_int quant_max ) ( Int64 . of_int ch_axis ) ( if per_row_fake_quant then 1 else 0 ) ( if symmetric_quant then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let _fw_primal self ~ level = let out__ = CArray . make t 1 in stubs__fw_primal ( CArray . start out__ ) self ( Int64 . of_int level ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _gather_sparse_backward self ~ dim ~ index ~ grad = let out__ = CArray . make t 1 in stubs__gather_sparse_backward ( CArray . start out__ ) self ( Int64 . of_int dim ) index grad ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _grid_sampler_2d_cpu_fallback input ~ grid ~ interpolation_mode ~ padding_mode ~ align_corners = let out__ = CArray . make t 1 in stubs__grid_sampler_2d_cpu_fallback ( CArray . start out__ ) input grid ( Int64 . of_int interpolation_mode ) ( Int64 . of_int padding_mode ) ( if align_corners then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _grid_sampler_2d_cpu_fallback_backward ~ grad_output input ~ grid ~ interpolation_mode ~ padding_mode ~ align_corners = let out__ = CArray . make t 2 in stubs__grid_sampler_2d_cpu_fallback_backward ( CArray . start out__ ) grad_output input grid ( Int64 . of_int interpolation_mode ) ( Int64 . of_int padding_mode ) ( if align_corners then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let _index_copy_ self ~ dim ~ index ~ source = let out__ = CArray . make t 1 in stubs__index_copy_ ( CArray . start out__ ) self ( Int64 . of_int dim ) index source ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _index_put_impl_ self ~ indices ~ values ~ accumulate ~ unsafe = let out__ = CArray . make t 1 in stubs__index_put_impl_ ( CArray . start out__ ) self ( List . map ( function | Some x -> x | None -> null ) indices |> CArray . of_list t |> CArray . start ) ( List . length indices ) values ( if accumulate then 1 else 0 ) ( if unsafe then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _indices self = let out__ = CArray . make t 1 in stubs__indices ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _inverse_helper self = let out__ = CArray . make t 1 in stubs__inverse_helper ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _linalg_inv_out_helper_ self ~ infos_lu ~ infos_getri = let out__ = CArray . make t 1 in stubs__linalg_inv_out_helper_ ( CArray . start out__ ) self infos_lu infos_getri ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _linalg_qr_helper self ~ mode = let out__ = CArray . make t 2 in stubs__linalg_qr_helper ( CArray . start out__ ) self mode ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let _log_softmax self ~ dim ~ half_to_float = let out__ = CArray . make t 1 in stubs__log_softmax ( CArray . start out__ ) self ( Int64 . of_int dim ) ( if half_to_float then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _log_softmax_backward_data ~ grad_output ~ output ~ dim self = let out__ = CArray . make t 1 in stubs__log_softmax_backward_data ( CArray . start out__ ) grad_output output ( Int64 . of_int dim ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _log_softmax_backward_data_out ~ out ~ grad_output ~ output ~ dim self = let out__ = CArray . make t 1 in stubs__log_softmax_backward_data_out ( CArray . start out__ ) out grad_output output ( Int64 . of_int dim ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _log_softmax_out ~ out self ~ dim ~ half_to_float = let out__ = CArray . make t 1 in stubs__log_softmax_out ( CArray . start out__ ) out self ( Int64 . of_int dim ) ( if half_to_float then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _logcumsumexp self ~ dim = let out__ = CArray . make t 1 in stubs__logcumsumexp ( CArray . start out__ ) self ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _logcumsumexp_out ~ out self ~ dim = let out__ = CArray . make t 1 in stubs__logcumsumexp_out ( CArray . start out__ ) out self ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _lu_with_info self ~ pivot ~ check_errors = let out__ = CArray . make t 3 in stubs__lu_with_info ( CArray . start out__ ) self ( if pivot then 1 else 0 ) ( if check_errors then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; t0 , t1 , t2 |
let _make_dual ~ primal ~ tangent ~ level = let out__ = CArray . make t 1 in stubs__make_dual ( CArray . start out__ ) primal tangent ( Int64 . of_int level ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _make_per_channel_quantized_tensor self ~ scale ~ zero_point ~ axis = let out__ = CArray . make t 1 in stubs__make_per_channel_quantized_tensor ( CArray . start out__ ) self scale zero_point ( Int64 . of_int axis ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _make_per_tensor_quantized_tensor self ~ scale ~ zero_point = let out__ = CArray . make t 1 in stubs__make_per_tensor_quantized_tensor ( CArray . start out__ ) self scale ( Int64 . of_int zero_point ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _masked_scale self ~ mask ~ scale = let out__ = CArray . make t 1 in stubs__masked_scale ( CArray . start out__ ) self mask scale ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _mkldnn_reshape self ~ shape = let out__ = CArray . make t 1 in stubs__mkldnn_reshape ( CArray . start out__ ) self ( List . map Int64 . of_int shape |> CArray . of_list int64_t |> CArray . start ) ( List . length shape ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _mkldnn_transpose self ~ dim0 ~ dim1 = let out__ = CArray . make t 1 in stubs__mkldnn_transpose ( CArray . start out__ ) self ( Int64 . of_int dim0 ) ( Int64 . of_int dim1 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _mkldnn_transpose_ self ~ dim0 ~ dim1 = let out__ = CArray . make t 1 in stubs__mkldnn_transpose_ ( CArray . start out__ ) self ( Int64 . of_int dim0 ) ( Int64 . of_int dim1 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _neg_view self = let out__ = CArray . make t 1 in stubs__neg_view ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _nnpack_spatial_convolution input ~ weight ~ bias ~ padding ~ stride = let out__ = CArray . make t 1 in stubs__nnpack_spatial_convolution ( CArray . start out__ ) input weight ( match bias with | Some v -> v | None -> null ) ( List . map Int64 . of_int padding |> CArray . of_list int64_t |> CArray . start ) ( List . length padding ) ( List . map Int64 . of_int stride |> CArray . of_list int64_t |> CArray . start ) ( List . length stride ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _nnpack_spatial_convolution_backward_input input ~ grad_output ~ weight ~ padding = let out__ = CArray . make t 1 in stubs__nnpack_spatial_convolution_backward_input ( CArray . start out__ ) input grad_output weight ( List . map Int64 . of_int padding |> CArray . of_list int64_t |> CArray . start ) ( List . length padding ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _nnpack_spatial_convolution_backward_weight input ~ weightsize ~ grad_output ~ padding = let out__ = CArray . make t 1 in stubs__nnpack_spatial_convolution_backward_weight ( CArray . start out__ ) input ( List . map Int64 . of_int weightsize |> CArray . of_list int64_t |> CArray . start ) ( List . length weightsize ) grad_output ( List . map Int64 . of_int padding |> CArray . of_list int64_t |> CArray . start ) ( List . length padding ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _pack_padded_sequence input ~ lengths ~ batch_first = let out__ = CArray . make t 2 in stubs__pack_padded_sequence ( CArray . start out__ ) input lengths ( if batch_first then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let _pack_padded_sequence_backward ~ grad ~ input_size ~ batch_sizes ~ batch_first = let out__ = CArray . make t 1 in stubs__pack_padded_sequence_backward ( CArray . start out__ ) grad ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) batch_sizes ( if batch_first then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _pad_packed_sequence ~ data ~ batch_sizes ~ batch_first ~ padding_value ~ total_length = let out__ = CArray . make t 2 in stubs__pad_packed_sequence ( CArray . start out__ ) data batch_sizes ( if batch_first then 1 else 0 ) padding_value ( Int64 . of_int total_length ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let _pdist_backward ~ grad self ~ p ~ pdist = let out__ = CArray . make t 1 in stubs__pdist_backward ( CArray . start out__ ) grad self p pdist ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _pin_memory self ~ device = let out__ = CArray . make t 1 in stubs__pin_memory ( CArray . start out__ ) self ( Device . to_int device ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _remove_batch_dim self ~ level ~ batch_size ~ out_dim = let out__ = CArray . make t 1 in stubs__remove_batch_dim ( CArray . start out__ ) self ( Int64 . of_int level ) ( Int64 . of_int batch_size ) ( Int64 . of_int out_dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _reshape_alias self ~ size ~ stride = let out__ = CArray . make t 1 in stubs__reshape_alias ( CArray . start out__ ) self ( List . map Int64 . of_int size |> CArray . of_list int64_t |> CArray . start ) ( List . length size ) ( List . map Int64 . of_int stride |> CArray . of_list int64_t |> CArray . start ) ( List . length stride ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _reshape_from_tensor self ~ shape = let out__ = CArray . make t 1 in stubs__reshape_from_tensor ( CArray . start out__ ) self shape ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _rowwise_prune ~ weight ~ mask ~ compressed_indices_dtype = let out__ = CArray . make t 2 in stubs__rowwise_prune ( CArray . start out__ ) weight mask ( Kind . packed_to_int compressed_indices_dtype ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let _s_where ~ condition self other = let out__ = CArray . make t 1 in stubs__s_where ( CArray . start out__ ) condition self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sample_dirichlet self = let out__ = CArray . make t 1 in stubs__sample_dirichlet ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _saturate_weight_to_fp16 ~ weight = let out__ = CArray . make t 1 in stubs__saturate_weight_to_fp16 ( CArray . start out__ ) weight ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _segment_reduce_backward ~ grad ~ output ~ data ~ reduce ~ lengths ~ axis = let out__ = CArray . make t 1 in stubs__segment_reduce_backward ( CArray . start out__ ) grad output data reduce ( match lengths with | Some v -> v | None -> null ) ( Int64 . of_int axis ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _shape_as_tensor self = let out__ = CArray . make t 1 in stubs__shape_as_tensor ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sobol_engine_draw ~ quasi ~ n ~ sobolstate ~ dimension ~ num_generated ~ dtype = let out__ = CArray . make t 2 in stubs__sobol_engine_draw ( CArray . start out__ ) quasi ( Int64 . of_int n ) sobolstate ( Int64 . of_int dimension ) ( Int64 . of_int num_generated ) ( Kind . packed_to_int dtype ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let _sobol_engine_ff_ self ~ n ~ sobolstate ~ dimension ~ num_generated = let out__ = CArray . make t 1 in stubs__sobol_engine_ff_ ( CArray . start out__ ) self ( Int64 . of_int n ) sobolstate ( Int64 . of_int dimension ) ( Int64 . of_int num_generated ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sobol_engine_initialize_state_ self ~ dimension = let out__ = CArray . make t 1 in stubs__sobol_engine_initialize_state_ ( CArray . start out__ ) self ( Int64 . of_int dimension ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sobol_engine_scramble_ self ~ ltm ~ dimension = let out__ = CArray . make t 1 in stubs__sobol_engine_scramble_ ( CArray . start out__ ) self ltm ( Int64 . of_int dimension ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _softmax self ~ dim ~ half_to_float = let out__ = CArray . make t 1 in stubs__softmax ( CArray . start out__ ) self ( Int64 . of_int dim ) ( if half_to_float then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _softmax_backward_data ~ grad_output ~ output ~ dim self = let out__ = CArray . make t 1 in stubs__softmax_backward_data ( CArray . start out__ ) grad_output output ( Int64 . of_int dim ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _softmax_backward_data_out ~ grad_input ~ grad_output ~ output ~ dim self = let out__ = CArray . make t 1 in stubs__softmax_backward_data_out ( CArray . start out__ ) grad_input grad_output output ( Int64 . of_int dim ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _softmax_out ~ out self ~ dim ~ half_to_float = let out__ = CArray . make t 1 in stubs__softmax_out ( CArray . start out__ ) out self ( Int64 . of_int dim ) ( if half_to_float then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _solve_helper self ~ a = let out__ = CArray . make t 2 in stubs__solve_helper ( CArray . start out__ ) self a ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let _sparse_addmm self ~ sparse ~ dense = let out__ = CArray . make t 1 in stubs__sparse_addmm ( CArray . start out__ ) self sparse dense ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_coo_tensor_unsafe ~ indices ~ values ~ size ~ options = let out__ = CArray . make t 1 in stubs__sparse_coo_tensor_unsafe ( CArray . start out__ ) indices values ( List . map Int64 . of_int size |> CArray . of_list int64_t |> CArray . start ) ( List . length size ) ( Kind . packed_to_int ( fst options ) ) ( Device . to_int ( snd options ) ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_coo_tensor_with_dims ~ sparse_dim ~ dense_dim ~ size ~ options = let out__ = CArray . make t 1 in stubs__sparse_coo_tensor_with_dims ( CArray . start out__ ) ( Int64 . of_int sparse_dim ) ( Int64 . of_int dense_dim ) ( List . map Int64 . of_int size |> CArray . of_list int64_t |> CArray . start ) ( List . length size ) ( Kind . packed_to_int ( fst options ) ) ( Device . to_int ( snd options ) ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_coo_tensor_with_dims_and_tensors ~ sparse_dim ~ dense_dim ~ size ~ indices ~ values ~ options = let out__ = CArray . make t 1 in stubs__sparse_coo_tensor_with_dims_and_tensors ( CArray . start out__ ) ( Int64 . of_int sparse_dim ) ( Int64 . of_int dense_dim ) ( List . map Int64 . of_int size |> CArray . of_list int64_t |> CArray . start ) ( List . length size ) indices values ( Kind . packed_to_int ( fst options ) ) ( Device . to_int ( snd options ) ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_csr_tensor_unsafe ~ crow_indices ~ col_indices ~ values ~ size ~ options = let out__ = CArray . make t 1 in stubs__sparse_csr_tensor_unsafe ( CArray . start out__ ) crow_indices col_indices values ( List . map Int64 . of_int size |> CArray . of_list int64_t |> CArray . start ) ( List . length size ) ( Kind . packed_to_int ( fst options ) ) ( Device . to_int ( snd options ) ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_log_softmax self ~ dim ~ half_to_float = let out__ = CArray . make t 1 in stubs__sparse_log_softmax ( CArray . start out__ ) self ( Int64 . of_int dim ) ( if half_to_float then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_log_softmax_backward_data ~ grad_output ~ output ~ dim self = let out__ = CArray . make t 1 in stubs__sparse_log_softmax_backward_data ( CArray . start out__ ) grad_output output ( Int64 . of_int dim ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_log_softmax_int self ~ dim ~ dtype = let out__ = CArray . make t 1 in stubs__sparse_log_softmax_int ( CArray . start out__ ) self ( Int64 . of_int dim ) ( Kind . packed_to_int dtype ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_mask_helper ~ tr ~ mask_indices = let out__ = CArray . make t 1 in stubs__sparse_mask_helper ( CArray . start out__ ) tr mask_indices ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_mm ~ sparse ~ dense = let out__ = CArray . make t 1 in stubs__sparse_mm ( CArray . start out__ ) sparse dense ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_softmax self ~ dim ~ half_to_float = let out__ = CArray . make t 1 in stubs__sparse_softmax ( CArray . start out__ ) self ( Int64 . of_int dim ) ( if half_to_float then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_softmax_backward_data ~ grad_output ~ output ~ dim self = let out__ = CArray . make t 1 in stubs__sparse_softmax_backward_data ( CArray . start out__ ) grad_output output ( Int64 . of_int dim ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_softmax_int self ~ dim ~ dtype = let out__ = CArray . make t 1 in stubs__sparse_softmax_int ( CArray . start out__ ) self ( Int64 . of_int dim ) ( Kind . packed_to_int dtype ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_sparse_matmul self other = let out__ = CArray . make t 1 in stubs__sparse_sparse_matmul ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_sum self = let out__ = CArray . make t 1 in stubs__sparse_sum ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_sum_backward ~ grad self ~ dim = let out__ = CArray . make t 1 in stubs__sparse_sum_backward ( CArray . start out__ ) grad self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_sum_dim self ~ dim = let out__ = CArray . make t 1 in stubs__sparse_sum_dim ( CArray . start out__ ) self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_sum_dim_dtype self ~ dim ~ dtype = let out__ = CArray . make t 1 in stubs__sparse_sum_dim_dtype ( CArray . start out__ ) self ( List . map Int64 . of_int dim |> CArray . of_list int64_t |> CArray . start ) ( List . length dim ) ( Kind . packed_to_int dtype ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _sparse_sum_dtype self ~ dtype = let out__ = CArray . make t 1 in stubs__sparse_sum_dtype ( CArray . start out__ ) self ( Kind . packed_to_int dtype ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _stack tensors ~ dim = let out__ = CArray . make t 1 in stubs__stack ( CArray . start out__ ) ( CArray . of_list t tensors |> CArray . start ) ( List . length tensors ) ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let _stack_out ~ out tensors ~ dim = let out__ = CArray . make t 1 in stubs__stack_out ( CArray . start out__ ) out ( CArray . of_list t tensors |> CArray . start ) ( List . length tensors ) ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
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