package torch
Torch bindings for OCaml
Install
Dune Dependency
Authors
Maintainers
Sources
torch-v0.16.0.tar.gz
sha256=ccd9ef3b630bdc7c41e363e71d8ecb86c316460cbf79afe67546c6ff22c19da4
doc/src/torch.core/wrapper.ml.html
Source file wrapper.ml
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open Ctypes let ptr_of_string str = let len = String.length str in let carray = CArray.make Ctypes.char (1 + len) in String.iteri (fun i char -> CArray.set carray i char) str; CArray.set carray len '\x00'; CArray.start carray ;; let ptr_of_strings strings = let strings = List.map ptr_of_string strings in let start = CArray.(of_list (ptr char) strings |> start) in Gc.finalise (fun _ -> ignore (Sys.opaque_identity strings : _ list)) start; start ;; module Tensor = struct include Wrapper_generated open! C.Tensor type nonrec t = t let float_vec ?(kind = `float) values = let values_len = List.length values in let values = CArray.of_list double values |> CArray.start in let kind = match kind with | `float -> Kind.T Float | `double -> Kind.T Double | `half -> Kind.T Half in let t = float_vec values values_len (Kind.packed_to_int kind) in Gc.finalise free t; t ;; let int_vec ?(kind = `int) values = let values_len = List.length values in let values = List.map Int64.of_int values |> CArray.of_list int64_t |> CArray.start in let kind = match kind with | `uint8 -> Kind.T Uint8 | `int8 -> Kind.T Int8 | `int16 -> Kind.T Int16 | `int -> Kind.T Int | `int64 -> Kind.T Int64 in let t = int_vec values values_len (Kind.packed_to_int kind) in Gc.finalise free t; t ;; let of_bigarray (type a b) (ga : (b, a, Bigarray.c_layout) Bigarray.Genarray.t) = let dims = Bigarray.Genarray.dims ga in let kind = Bigarray.Genarray.kind ga in let tensor_kind = match kind with | Bigarray.Float32 -> Kind.T Float | Bigarray.Float64 -> Kind.T Double | Bigarray.Int8_signed -> Kind.T Int8 | Bigarray.Int8_unsigned -> Kind.T Uint8 | Bigarray.Char -> Kind.T Uint8 | Bigarray.Int16_signed -> Kind.T Int16 | Bigarray.Int32 -> Kind.T Int | Bigarray.Int -> Kind.T Int64 | Bigarray.Int64 -> Kind.T Int64 | _ -> failwith "unsupported bigarray kind" in let t = tensor_of_data (bigarray_start genarray ga |> to_voidp) (Array.to_list dims |> List.map Int64.of_int |> CArray.of_list int64_t |> CArray.start) (Array.length dims) (Bigarray.kind_size_in_bytes kind) (Kind.packed_to_int tensor_kind) in Gc.finalise free t; t ;; let copy_to_bigarray (type a b) t (ga : (b, a, Bigarray.c_layout) Bigarray.Genarray.t) = let kind = Bigarray.Genarray.kind ga in copy_data t (bigarray_start genarray ga |> to_voidp) (Bigarray.Genarray.dims ga |> Array.fold_left ( * ) 1 |> Int64.of_int) (Bigarray.kind_size_in_bytes kind) ;; let shape t = let num_dims = num_dims t in let carray = CArray.make int num_dims in shape t (CArray.start carray); CArray.to_list carray ;; let size = shape let unexpected_shape shape = let shape = String.concat ", " (List.map string_of_int shape) in Printf.sprintf "unexpected shape <%s>" shape |> failwith ;; let shape1_exn t = match shape t with | [ s1 ] -> s1 | shape -> unexpected_shape shape ;; let shape2_exn t = match shape t with | [ s1; s2 ] -> s1, s2 | shape -> unexpected_shape shape ;; let shape3_exn t = match shape t with | [ s1; s2; s3 ] -> s1, s2, s3 | shape -> unexpected_shape shape ;; let shape4_exn t = match shape t with | [ s1; s2; s3; s4 ] -> s1, s2, s3, s4 | shape -> unexpected_shape shape ;; let kind t = scalar_type t |> Kind.of_int_exn let requires_grad t = if requires_grad t <> 0 then true else false let grad_set_enabled b = grad_set_enabled (if b then 1 else 0) <> 0 let get t index = let t = get t index in Gc.finalise free t; t ;; let float_value t = double_value t (from_voidp int null) 0 let int_value t = int64_value t (from_voidp int null) 0 |> Int64.to_int let float_get t indexes = double_value t (CArray.of_list int indexes |> CArray.start) (List.length indexes) ;; let int_get t indexes = int64_value t (CArray.of_list int indexes |> CArray.start) (List.length indexes) |> Int64.to_int ;; let float_set t indexes v = double_value_set t (CArray.of_list int indexes |> CArray.start) (List.length indexes) v ;; let int_set t indexes v = int64_value_set t (CArray.of_list int indexes |> CArray.start) (List.length indexes) (Int64.of_int v) ;; let fill_float t v = fill_double t v let fill_int t i = fill_int64 t (Int64.of_int i) let backward ?(keep_graph = false) ?(create_graph = false) t = backward t (if keep_graph then 1 else 0) (if create_graph then 1 else 0) ;; let print = print let to_string t ~line_size = to_string t line_size let argmax ?dim ?(keepdim = false) t = argmax t ~dim ~keepdim let max = maximum let min = minimum let copy_ t ~src = copy_ t src let set_data t ~src = set_data t src let defined = defined let device t = device t |> Device.of_int let new_tensor () = let t = new_tensor () in Gc.finalise free t; t ;; let run_backward ?keep_graph ?(create_graph = false) tensors inputs = let keep_graph = match keep_graph with | None -> create_graph | Some keep_graph -> keep_graph in let out_ = CArray.make t (List.length inputs) in run_backward (CArray.of_list t tensors |> CArray.start) (List.length tensors) (CArray.of_list t inputs |> CArray.start) (List.length inputs) (CArray.start out_) (if keep_graph then 1 else 0) (if create_graph then 1 else 0); let out_ = CArray.to_list out_ in List.iter (Gc.finalise free) out_; out_ ;; let sum t = sum t ~dtype:(kind t) let mean t = mean t ~dtype:(kind t) end module Scalar = struct module S = Wrapper_generated.C.Scalar include ( S : module type of struct include S end with type t := S.t) type nonrec _ t = S.t let int i = let t = int (Int64.of_int i) in Gc.finalise free t; t ;; let float f = let t = float f in Gc.finalise free t; t ;; end module Optimizer = struct include Wrapper_generated.C.Optimizer let adam ~learning_rate ~beta1 ~beta2 ~weight_decay ~eps = let t = adam learning_rate beta1 beta2 weight_decay eps in Gc.finalise free t; t ;; let rmsprop ~learning_rate ~alpha ~eps ~weight_decay ~momentum ~centered = let centered = if centered then 1 else 0 in let t = rmsprop learning_rate alpha eps weight_decay momentum centered in Gc.finalise free t; t ;; let sgd ~learning_rate ~momentum ~dampening ~weight_decay ~nesterov = let t = sgd learning_rate momentum dampening weight_decay nesterov in Gc.finalise free t; t ;; let add_parameters t tensors = add_parameters t CArray.(of_list Wrapper_generated.C.Tensor.t tensors |> start) (List.length tensors) ;; end module Serialize = struct include Wrapper_generated.C.Serialize let save t ~filename = save t filename let escape s = String.map (function | '.' -> '|' | c -> c) s ;; let unescape s = String.map (function | '|' -> '.' | c -> c) s ;; let load ~filename = let t = load filename in Gc.finalise Wrapper_generated.C.Tensor.free t; t ;; let save_multi ~named_tensors ~filename = let names, tensors = List.split named_tensors in let names = List.map escape names in save_multi CArray.(of_list Wrapper_generated.C.Tensor.t tensors |> start) (ptr_of_strings names) (List.length named_tensors) filename ;; let load_multi ~names ~filename = let names = List.map escape names in let ntensors = List.length names in let tensors = CArray.make Wrapper_generated.C.Tensor.t ntensors in load_multi (CArray.start tensors) (ptr_of_strings names) ntensors filename; let tensors = CArray.to_list tensors in List.iter (Gc.finalise Wrapper_generated.C.Tensor.free) tensors; tensors ;; let load_multi_ ~named_tensors ~filename = let names, tensors = List.split named_tensors in let names = List.map escape names in load_multi_ CArray.(of_list Wrapper_generated.C.Tensor.t tensors |> start) (ptr_of_strings names) (List.length named_tensors) filename ;; let load_all ~filename = let all_tensors = ref [] in let callback = coerce (Foreign.funptr (string @-> Wrapper_generated.C.Tensor.t @-> returning void)) (static_funptr (string @-> Wrapper_generated.C.Tensor.t @-> returning void)) (fun tensor_name tensor -> Gc.finalise Wrapper_generated.C.Tensor.free tensor; all_tensors := (unescape tensor_name, tensor) :: !all_tensors) [@alert "-deprecated"] in load_callback filename callback; !all_tensors ;; end module Cuda = struct include Wrapper_generated.C.Cuda let is_available () = is_available () <> 0 let cudnn_is_available () = cudnn_is_available () <> 0 let set_benchmark_cudnn b = set_benchmark_cudnn (if b then 1 else 0) end module Ivalue = struct module Tag = struct type t = | None | Tensor | Double | Int | Bool | Tuple | IntList | DoubleList | BoolList | String | TensorList | GenericList | GenericDict end include Wrapper_generated.C.Ivalue let none () = let t = none () in Gc.finalise free t; t ;; let bool b = let t = bool (if b then 1 else 0) in Gc.finalise free t; t ;; let tensor tensor_ = let t = tensor tensor_ in Gc.finalise free t; t ;; let double d = let t = double d in Gc.finalise free t; t ;; let int64 i = let t = int64 i in Gc.finalise free t; t ;; let tuple ts = let t = tuple CArray.(of_list t ts |> start) (List.length ts) in Gc.finalise free t; t ;; let string s = let t = string s in Gc.finalise free t; t ;; let tag t : Tag.t = match tag t with | 0 -> None | 1 -> Tensor | 2 -> Double | 3 -> Int | 4 -> Bool | 5 -> Tuple | 6 -> IntList | 7 -> DoubleList | 8 -> BoolList | 9 -> String | 10 -> TensorList | 12 -> GenericList | 13 -> GenericDict | otherwise -> Printf.sprintf "unexpected tag %d" otherwise |> failwith ;; let to_bool t = to_bool t <> 0 let to_tensor t = let tensor = to_tensor t in Gc.finalise Wrapper_generated.C.Tensor.free tensor; tensor ;; let to_tuple t = let noutputs = tuple_length t in let outputs = CArray.make Wrapper_generated.C.Tensor.t noutputs in to_tuple t (CArray.start outputs) noutputs; let outputs = CArray.to_list outputs in List.iter (Gc.finalise free) outputs; outputs ;; end module Module = struct include Wrapper_generated.C.Module let forward t tensors = let tensor = forward t CArray.(of_list Wrapper_generated.C.Tensor.t tensors |> start) (List.length tensors) in Gc.finalise Wrapper_generated.C.Tensor.free tensor; tensor ;; let forward_ t ivalues = let ivalue = forward_ t CArray.(of_list Wrapper_generated.C.Ivalue.t ivalues |> start) (List.length ivalues) in Gc.finalise Wrapper_generated.C.Ivalue.free ivalue; ivalue ;; let named_buffers t = let wrapper_ivalue = named_buffers t in let names_and_tensors = CArray.make Wrapper_generated.C.Ivalue.t 2 in Wrapper_generated.C.Ivalue.to_tuple wrapper_ivalue (CArray.start names_and_tensors) 2; let names_ivalue = CArray.get names_and_tensors 0 and tensors_ivalue = CArray.get names_and_tensors 1 in let len = Wrapper_generated.C.Ivalue.list_length tensors_ivalue in let names = CArray.make Wrapper_generated.C.Ivalue.t len and tensors = CArray.make Wrapper_generated.C.Ivalue.t len in Wrapper_generated.C.Ivalue.to_generic_list names_ivalue (CArray.start names) len; Wrapper_generated.C.Ivalue.to_generic_list tensors_ivalue (CArray.start tensors) len; let names = names |> CArray.to_list |> List.map Wrapper_generated.C.Ivalue.to_string and tensors = tensors |> CArray.to_list |> List.map Wrapper_generated.C.Ivalue.to_tensor in List.combine names tensors |> Base.Map.of_alist_exn (module Base.String) ;; let load filename = let m = load filename in Gc.finalise free m; m ;; let load_str str = let m = load_str str (String.length str) in Gc.finalise free m; m ;; end let manual_seed seed = Wrapper_generated.C.manual_seed (Int64.of_int seed) let set_num_threads = Wrapper_generated.C.set_num_threads let get_num_threads = Wrapper_generated.C.get_num_threads
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