tensor.ml

open Base
module Nd = Arrayjit.Ndarray
module Tn = Arrayjit.Tnode
module Asgns = Arrayjit.Assignments
module Idx = Arrayjit.Indexing
module Debug_runtime = Arrayjit.Utils.Debug_runtime

type tn = Tn.t
type tn_set = Set.M(Arrayjit.Tnode).t
type asgns = Asgns.t
type comp = Arrayjit.Assignments.comp
type init_op = Arrayjit.Ops.init_op
type fetch_op = Asgns.fetch_op
type projections = Arrayjit.Indexing.projections

let _get_local_debug_runtime = Arrayjit.Utils._get_local_debug_runtime

[%%global_debug_log_level 9]
[%%global_debug_log_level_from_env_var "OCANNL_LOG_LEVEL"]

type diff = { grad : (Tn.t[@sexp.opaque]); zero_grads : Asgns.t; backprop : Asgns.comp }
[@@deriving sexp_of]

type t = {
  forward : comp;
  diff : diff option;
  id : int;
  value : tn;
  shape : Shape.t;
  children : subtensor list;
}

and subtensor = { subtensor : t; embedded : bool }

let rec sexp_of_t t =
  Sexp.message "Tensor"
    [
      ("id", sexp_of_int t.id);
      ("label", [%sexp_of: string list] t.value.label);
      ("forward", [%sexp_of: Asgns.comp] t.forward);
      ("diff", [%sexp_of: diff option] t.diff);
      ("children", [%sexp_of: subtensor list] t.children);
    ]

and sexp_of_subtensor ch =
  Sexp.message "child"
    [
      (if ch.embedded then ("", sexp_of_t ch.subtensor) else ("ref-id", sexp_of_int ch.subtensor.id));
    ]

module Compare = struct
  type nonrec t = t

  let compare t1 t2 = Int.compare t1.id t2.id
  let sexp_of_t = sexp_of_t
end

module Self_comparator = Comparator.Make (Compare)
include Self_comparator

module Self = struct
  include Compare
  include Self_comparator
end

type session_state = {
  mutable next_id : int;
  mutable forward_roots : t Map.M(Int).t;
  mutable backprop_roots : t Map.M(Int).t;
}

let session_state =
  { next_id = 0; forward_roots = Map.empty (module Int); backprop_roots = Map.empty (module Int) }

let%track5_sexp unsafe_reinitialize () =
  session_state.next_id <- 0;
  session_state.forward_roots <- Map.empty (module Int);
  session_state.backprop_roots <- Map.empty (module Int);
  Tn.Registry.clear Tn.registry;
  Arrayjit.Rand.Random_for_tests.rand := (1l : Int32.t);
  Shape.unsafe_reinitialize ()

let is_fwd_root t = Map.mem session_state.forward_roots t.id
let remove_fwd_root t = session_state.forward_roots <- Map.remove session_state.forward_roots t.id
let is_bprop_root t = Map.mem session_state.backprop_roots t.id

let remove_bprop_root t =
  session_state.backprop_roots <- Map.remove session_state.backprop_roots t.id

let with_unchanged_roots ~f =
  let fwd_roots = session_state.forward_roots in
  let bprop_roots = session_state.backprop_roots in
  let finally () =
    session_state.forward_roots <- fwd_roots;
    session_state.backprop_roots <- bprop_roots
  in
  Exn.protectx ~f ~finally ()

let iter_embedded ~f t =
  Set.iter ~f t.forward.embedded_nodes;
  Option.iter t.diff ~f:(fun diff -> Set.iter ~f diff.backprop.embedded_nodes)

let default_value_prec = ref Arrayjit.Ops.single
let default_grad_prec = ref Arrayjit.Ops.single

exception Session_error of string * t option [@@deriving sexp]

let session_error_printer = function
  | Session_error (msg, None) -> Some msg
  | Session_error (msg, Some m) ->
      Some [%string "For #%{m.id#Int} %{Tn.debug_name m.value}: %{msg}"]
  | _ -> None

let () = Stdlib.Printexc.register_printer session_error_printer
let lazy_to_dims shape = lazy (Shape.to_dims shape)

let fetch_zeros array shape =
  Asgns.Fetch { array; fetch_op = Constant 0.; dims = lazy_to_dims shape }

let default_init_op = Arrayjit.Ops.Constant_fill { values = [| 0.0 |]; strict = false }
let max_sublabel_length = ref 25

let raw_binop ~initialize_neutral ~accum ~(t : t) ~(lhs_is_grad : bool) ~op ~(t1 : t)
    ~(rhs1_is_grad : bool) ~(rhs1_is_merge : bool) ~(t2 : t) ~rhs2_is_grad ~rhs2_is_merge ~logic :
    Asgns.t =
  let shape = t.shape in
  let shape_logic = Shape.Broadcast (logic, t1.shape, t2.shape) in
  let local_shape_update = Shape.{ shape; logic = shape_logic; id = get_update_id () } in
  Shape.propagate_shapes local_shape_update;
  let projections = lazy (Shape.derive_projections local_shape_update) in
  let lhs = if lhs_is_grad then (Option.value_exn ~here:[%here] t.diff).grad else t.value in
  let rhs1 = if rhs1_is_grad then (Option.value_exn ~here:[%here] t1.diff).grad else t1.value in
  let rhs1 = if rhs1_is_merge then Asgns.Merge_buffer rhs1 else Node rhs1 in
  let rhs2 = if rhs2_is_grad then (Option.value_exn ~here:[%here] t2.diff).grad else t2.value in
  let rhs2 = if rhs2_is_merge then Asgns.Merge_buffer rhs2 else Node rhs2 in
  Asgns.Accum_binop { initialize_neutral; accum; lhs; op; rhs1; rhs2; projections }

let raw_unop ~initialize_neutral ~accum ~(t : t) ~(lhs_is_grad : bool) ~op ~(t1 : t)
    ~(rhs_is_grad : bool) ~(rhs_is_merge : bool) ~logic =
  let shape = t.shape in
  let shape_logic = Shape.Transpose (logic, t1.shape) in
  let local_shape_update = Shape.{ shape; logic = shape_logic; id = get_update_id () } in
  Shape.propagate_shapes local_shape_update;
  let projections = lazy (Shape.derive_projections local_shape_update) in
  let lhs = if lhs_is_grad then (Option.value_exn ~here:[%here] t.diff).grad else t.value in
  let rhs = if rhs_is_grad then (Option.value_exn ~here:[%here] t1.diff).grad else t1.value in
  let rhs = if rhs_is_merge then Asgns.Merge_buffer rhs else Node rhs in
  Asgns.Accum_unop { initialize_neutral; accum; lhs; op; rhs; projections }

type grad_spec = Require_grad | Prohibit_grad | If_needed [@@deriving sexp, equal, variants]

let op ~(label : string list) ?(compose_op = Shape.Pointwise_bin)
    ?(transpose_op = Shape.Pointwise_un) ?(init_op = default_init_op) ~op_asn ~grad_asn
    ?(grad_spec = If_needed) make_shape (orig_ts : t list) : t =
  (* The code needs to be included in the order it was computed due to potential non-tree DAGs. *)
  let ordered_ts = List.dedup_and_sort orig_ts ~compare:(fun t1 t2 -> Int.ascending t1.id t2.id) in
  let id = session_state.next_id in
  session_state.next_id <- session_state.next_id + 1;
  let shape = make_shape ~debug_name:(Tn.get_debug_name ~id ~label ()) ~id in
  let default_prec =
    let lazy_v_precs = List.map orig_ts ~f:(fun ti -> ti.value.prec) in
    let default = !default_value_prec in
    lazy
      (List.map lazy_v_precs ~f:Lazy.force
      |> List.reduce ~f:Arrayjit.Ops.promote_prec
      |> Option.value ~default)
  in
  let rec shape_logics = function
    | [] -> [ Shape.Terminal init_op ]
    | [ t1 ] -> [ Shape.Transpose (transpose_op, t1.shape) ]
    | [ t1; t2 ] -> [ Shape.Broadcast (compose_op, t1.shape, t2.shape) ]
    | t1 :: (t2 :: _ as ts) -> Shape.Broadcast (compose_op, t1.shape, t2.shape) :: shape_logics ts
  in
  let local_shape_updates =
    List.map ~f:(fun logic -> Shape.{ shape; logic; id = get_update_id () }) @@ shape_logics orig_ts
  in
  let dims = lazy_to_dims shape in
  List.iter ~f:Shape.propagate_shapes local_shape_updates;
  let projections = lazy (Shape.derive_projections @@ List.hd_exn local_shape_updates) in
  let v = Tn.create ~default_prec ~id ~label ~dims init_op in
  let embedded_nodes = ref @@ Set.singleton (module Tn) v in
  let children =
    List.folding_map orig_ts
      ~init:(Set.empty (module Int))
      ~f:(fun used ti ->
        let embedded = is_fwd_root ti && not (Set.mem used ti.id) in
        if embedded then
          embedded_nodes := Set.add (Set.union !embedded_nodes ti.forward.embedded_nodes) ti.value;
        (Set.add used ti.id, { subtensor = ti; embedded }))
  in
  let fwds =
    List.filter_map ordered_ts ~f:(fun ti -> if is_fwd_root ti then Some ti.forward else None)
  in
  let forward = Asgns.sequence @@ fwds @ [ op_asn ~v ~projections ] in
  let forward =
    Asgns.
      { asgns = forward.asgns; embedded_nodes = Set.union forward.embedded_nodes !embedded_nodes }
  in
  List.iter ordered_ts ~f:(fun ti -> remove_fwd_root ti);
  if
    is_prohibit_grad grad_spec
    || Fn.non is_require_grad grad_spec
       && List.for_all orig_ts ~f:(fun ti -> Option.is_none ti.diff)
  then (
    let tensor = { forward; diff = None; id; value = v; shape; children } in
    session_state.forward_roots <- Map.add_exn session_state.forward_roots ~key:id ~data:tensor;
    tensor)
  else
    let default_prec =
      let f ti = Option.map ti.diff ~f:(fun d -> d.grad.Tn.prec) in
      let lazy_g_precs = List.filter_map orig_ts ~f in
      let default = !default_grad_prec in
      lazy
        (List.map lazy_g_precs ~f:Lazy.force
        |> List.reduce ~f:Arrayjit.Ops.promote_prec
        |> Option.value ~default)
    in
    let grad_id = session_state.next_id in
    session_state.next_id <- session_state.next_id + 1;
    let g = Tn.create ~default_prec ~id:grad_id ~label:("grad" :: label) ~dims default_init_op in
    let is_bck_root ti = Map.mem session_state.backprop_roots ti.id in
    let zero_grads =
      let zero_g ti =
        Option.value_map ti.diff ~default:Asgns.Noop ~f:(fun diff -> diff.zero_grads)
      in
      let zeros =
        List.map ordered_ts ~f:(fun ti -> if is_bck_root ti then zero_g ti else Asgns.Noop)
      in
      Asgns.sequential @@ zeros @ [ fetch_zeros g shape ]
    in
    let embedded_nodes = ref @@ Set.singleton (module Tn) g in
    (* The code needs to be included in the reverse order to which it was computed. This guarantees
       that all ancestors of a node are backpropagated before the node is backpropagated, even for
       non-tree DAGs. For repeating subtensors, the first one to-be-included should actually be
       included! That's why we reverse the order up-front prior to processing. *)
    let ordered_ts = List.rev ordered_ts in
    let bprop ti =
      Option.map ti.diff ~f:(fun diff ->
          embedded_nodes :=
            Set.add (Set.union !embedded_nodes diff.backprop.embedded_nodes) diff.grad;
          diff.backprop)
    in
    let bcks =
      List.filter_map ordered_ts ~f:(fun ti -> if is_bck_root ti then bprop ti else None)
    in
    let backprop = Asgns.sequence @@ (grad_asn ~v ~g ~projections :: bcks) in
    let backprop =
      {
        Asgns.asgns = backprop.asgns;
        embedded_nodes = Set.union backprop.embedded_nodes !embedded_nodes;
      }
    in
    List.iter ordered_ts ~f:(fun ti ->
        session_state.backprop_roots <- Map.remove session_state.backprop_roots ti.id);
    (* The order is not relevant, we keep the same order as in backprop for readability. *)
    let diff = Some { grad = g; zero_grads; backprop } in
    let tensor = { forward; diff; id; value = v; shape; children } in
    session_state.forward_roots <- Map.add_exn session_state.forward_roots ~key:id ~data:tensor;
    session_state.backprop_roots <- Map.add_exn session_state.backprop_roots ~key:id ~data:tensor;
    tensor

let binop ~label ?compose_op ~op_asn ~grad_asn ?grad_spec t1 t2 =
  let op_asn ~v ~projections = op_asn ~v ~t1 ~t2 ~projections in
  let grad_asn ~v ~g ~projections = grad_asn ~v ~g ~t1 ~t2 ~projections in
  op ~label ?compose_op ?transpose_op:None ~op_asn ~grad_asn ?grad_spec (Shape.make ()) [ t1; t2 ]

let unop ~label ?transpose_op ~op_asn ~grad_asn ?grad_spec t1 =
  let op_asn ~v ~projections = op_asn ~v ~t1 ~projections in
  let grad_asn ~v ~g ~projections = grad_asn ~v ~g ~t1 ~projections in
  op ~label ?compose_op:None ?transpose_op ~op_asn ~grad_asn ?grad_spec (Shape.make ()) [ t1 ]

let term ~label ~grad_spec ?batch_dims ?input_dims ?output_dims ?batch_axes ?input_axes ?output_axes
    ?deduced ?init_op ?fetch_op () =
  let op_asn ~v ~projections =
    let open Asgns in
    let dims = lazy (Lazy.force projections).Idx.lhs_dims in
    match (fetch_op, init_op) with
    | None, Some (Arrayjit.Ops.Constant_fill { values = [| _ |]; strict = _ })
      when not (is_require_grad grad_spec) ->
        (* The scalar literal case. *)
        let fetch_op =
          match init_op with
          | Some (Arrayjit.Ops.Constant_fill { values = [| c |]; _ }) -> Constant c
          | _ -> assert false
        in
        Asgns.to_comp @@ Fetch { array = v; fetch_op; dims }
    | None, _ -> Asgns.empty_comp
    | Some fetch_op, _ ->
        let fetch_op = fetch_op ~v in
        (match fetch_op with
        | Constant _ | Slice _ | Embed_symbol _ -> ()
        | Imported _ ->
            (* Note: [Imported] can be used for merging across devices. But, some use cases of
               [Imported] will require a hosted tensor node. *)
            Tn.update_memory_mode v Materialized 22);
        Asgns.to_comp @@ Fetch { array = v; fetch_op; dims }
  in
  let grad_asn ~v:_ ~g:_ ~projections:_ = Asgns.empty_comp in
  let make_shape =
    Shape.make ?batch_dims ?input_dims ?output_dims ?batch_axes ?input_axes ?output_axes ?deduced ()
  in
  op ~label ?compose_op:None ?transpose_op:None ?init_op ~op_asn ~grad_asn ~grad_spec make_shape []

let float_to_label v = Float.to_string v

let number ?(label = []) ?axis_label ?(grad_spec = Prohibit_grad) c =
  (* Note: no axis label so that we do not conflict with user labels. *)
  let label = float_to_label c :: label in
  let init_op = Arrayjit.Ops.Constant_fill { values = [| c |]; strict = true } in
  let t = term ~label ~grad_spec ~batch_dims:[] ~input_dims:[] ~init_op in
  let t =
    match axis_label with
    | None -> t ~output_dims:[ 1 ] ()
    | Some axis_label -> t ~output_axes:[ (axis_label, 1) ] ()
  in
  Tn.update_memory_mode t.value Effectively_constant 24;
  Arrayjit.Ops.(
    if Tn.exceeds_fp16_cutoff t.value c then Tn.update_prec ~only_if:is_up_to_fp16 t.value single);
  t

let ndarray ?(label = []) ?(grad_spec = Prohibit_grad) ?batch_dims ?input_dims ?output_dims
    ?batch_axes ?input_axes ?output_axes ?(strict = true) values =
  let to_dim_list dims axes =
    Option.value ~default:[] @@ Option.first_some dims @@ Option.map axes ~f:(List.map ~f:snd)
  in
  let batch_ds = to_dim_list batch_dims batch_axes in
  let output_ds = to_dim_list output_dims output_axes in
  let input_ds = to_dim_list input_dims input_axes in
  let op_label =
    Stdlib.Format.pp_set_geometry Stdlib.Format.str_formatter ~max_indent:!max_sublabel_length
      ~margin:(!max_sublabel_length * 2);
    let dims = Array.concat_map [| batch_ds; output_ds; input_ds |] ~f:Array.of_list in
    let debug = "Temporary array for pretty-printing" in
    let ndarr =
      Nd.create_array ~debug Arrayjit.Ops.double ~dims (Constant_fill { values; strict })
    in
    let ( ! ) = List.length in
    Nd.pp_array_inline ~num_batch_axes:!batch_ds ~num_output_axes:!output_ds
      ~num_input_axes:!input_ds Stdlib.Format.str_formatter ndarr;
    Stdlib.Format.flush_str_formatter ()
  in
  let op_label =
    if String.contains op_label '\n' then
      "c" ^ Idx.dims_to_string
      @@ Array.concat_map [| batch_ds; output_ds; input_ds |] ~f:Array.of_list
    else op_label
  in
  let label = op_label :: label in
  let batch_dims = Option.first_some batch_dims @@ Option.some_if (Option.is_none batch_axes) [] in
  let input_dims = Option.first_some input_dims @@ Option.some_if (Option.is_none input_axes) [] in
  let output_dims =
    Option.first_some output_dims @@ Option.some_if (Option.is_none output_axes) []
  in
  let t =
    term ~label ~grad_spec ?batch_dims ?input_dims ?output_dims ?batch_axes ?input_axes ?output_axes
      ~deduced:Not_constrained
      ~init_op:(Constant_fill { values; strict })
      ()
  in
  Tn.update_memory_mode t.value Effectively_constant 24;
  let max_abs = Array.fold values ~init:0. ~f:(fun acc v -> Float.(max acc @@ abs v)) in
  Arrayjit.Ops.(
    if Tn.exceeds_fp16_cutoff t.value max_abs then
      Tn.update_prec ~only_if:is_up_to_fp16 t.value single);
  t

let param ?(more_label = []) ?input_dims ?output_dims ?input_axes ?output_axes ?deduced
    ?(strict = false) ?values label =
  let init_op =
    match values with
    | Some values -> Arrayjit.Ops.Constant_fill { values; strict }
    | None -> Standard_uniform
  in
  let t =
    term ~label:(label :: more_label) ~grad_spec:Require_grad ~batch_dims:[] ?input_dims
      ?output_dims ?input_axes ?output_axes ?deduced ~init_op ()
  in
  let v = t.value in
  (* It is convenient to use the param syntax for volatiles (mutable embedded_nodes). *)
  Tn.update_memory_mode v (Hosted Nonconstant) 24;
  (* In principle, gradients can even be local, if a single jitted block does forward, backprop, and
     update computations. *)
  let g = (Option.value_exn ~here:[%here] t.diff).grad in
  Tn.update_memory_mode g Never_virtual 26;
  t

let debug_name t = Tn.debug_name t.value
let debug_grad t = Tn.debug_name (Option.value_exn t.diff).grad

let consume_forward_code t =
  if not @@ is_fwd_root t then
    raise
    @@ Session_error
         ( "Tensor.consume_forward_code: tensor is not a root for tnode: " ^ Tn.debug_name t.value,
           Some t );
  let unsafe_roots =
    Map.data session_state.forward_roots
    |> List.filter ~f:(fun r -> not (List.is_empty r.children || r.id = t.id))
  in
  if not @@ List.is_empty unsafe_roots then
    raise
    @@ Session_error
         ( [%string
             {|Tensor.consume_forward_code for %{debug_name t}:
found potentially unsafe roots: %{String.concat ~sep:", " @@ List.map ~f:debug_name unsafe_roots}|}],
           Some t );
  remove_fwd_root t;
  t.forward

let consume_backprop_code t =
  let diff =
    Option.value_or_thunk t.diff ~default:(fun () ->
        raise
        @@ Session_error
             ( "Tensor.consume_backprop_code: tensor is not differentiable for value: "
               ^ debug_name t,
               Some t ))
  in
  if not @@ is_bprop_root t then
    raise
    @@ Session_error
         ("Tensor.consume_backprop_code: tensor is not a root for tnode: " ^ debug_grad t, Some t);
  let unsafe_roots =
    Map.data session_state.backprop_roots
    |> List.filter ~f:(fun r -> not (List.is_empty r.children || r.id = t.id))
  in
  if not @@ List.is_empty unsafe_roots then
    raise
    @@ Session_error
         ( [%string
             {|Tensor.consume_backprop_code for %{debug_grad t}:
found potentially unsafe roots: %{String.concat ~sep:", " @@ List.map ~f:debug_name unsafe_roots}|}],
           Some t );
  remove_bprop_root t;
  (diff.zero_grads, diff.backprop)

let header t =
  let v_dims_s = Tn.dims_to_string t.value in
  let g_dims_s =
    match t.diff with None -> "<no-grad>" | Some diff -> Tn.dims_to_string diff.grad
  in
  let dims_s =
    if String.equal v_dims_s g_dims_s then "dims " ^ v_dims_s
    else "dims val " ^ v_dims_s ^ " grad " ^ g_dims_s
  in
  "#" ^ Int.to_string t.id ^ " " ^ Tn.label t.value ^ " " ^ dims_s ^ " ["
  ^ String.concat ~sep:","
      (List.map t.children ~f:(fun { subtensor = { id; _ }; _ } -> Int.to_string id))
  ^ "]"
(*^" "^PrintBox_text.to_string (PrintBox.Simple.to_box v.label)*)

let lazy_optional_payload ~spy ~present ~missing v =
  if Lazy.is_val v || not spy then
    match Lazy.force v with
    | Some p -> present p
    | None -> `Vlist (false, [ `Text (missing ()); `Text "<void>" ])
  else `Vlist (false, [ `Text (missing ()); `Text "<not-in-yet> " ])

type array_print_style =
  [ `Default | `Inline | `Label_layout of (string * int) list | `N5_layout of string ]

let to_dag ?(single_node = false) ?entries_per_axis ~spy ~with_shape ~with_id ~with_value ~with_grad
    t =
  let rec to_dag { subtensor = t; embedded } : PrintBox_utils.dag =
    let id = Int.to_string t.id in
    let children = if single_node then [] else List.map ~f:to_dag t.children in
    let indices = Shape.default_display_indices t.shape in
    let labels = Shape.to_labels t.shape in
    let where_located a = Tn.(debug_memory_mode a.memory_mode) in
    let txt =
      if with_id then "#" ^ id ^ " " ^ Tn.label t.value (* ^ " DEBUG: " ^ where_located t.value *)
      else Tn.label t.value
    in
    let grad_txt diff =
      let label = Tn.label diff.grad in
      assert (String.is_prefix label ~prefix:"grad");
      if with_id then
        "#" ^ Int.to_string diff.grad.id ^ " " ^ label (* ^ " DEBUG: " ^ where_located diff.grad *)
      else label
    in
    let add_shape nodes =
      if with_shape then
        let shape = `Box (PrintBox.asprintf "%a" Sexp.pp_hum ([%sexp_of: Shape.t] t.shape)) in
        `Vlist (false, nodes @ [ shape ])
      else `Vlist (false, nodes)
    in
    match (not embedded, with_value, with_grad, t.diff) with
    | true, _, _, _ -> `Embed_subtree_ID (Int.to_string t.id)
    | _, false, false, _ | _, false, true, None ->
        `Subtree_with_ID (id, `Tree (add_shape [ `Text txt ], children))
    | _, true, false, _ | _, true, true, None ->
        let node =
          lazy_optional_payload t.value.array ~spy
            ~present:(fun v_array ->
              Tn.do_read t.value;
              `Box
                (Nd.render_array ~brief:true ~prefix:txt ?entries_per_axis ~labels ~indices v_array))
            ~missing:(fun () -> txt ^ " " ^ where_located t.value)
        in
        `Subtree_with_ID (id, `Tree (add_shape [ node ], children))
    | _, false, true, Some diff ->
        let prefix = grad_txt diff in
        let node =
          match Lazy.force diff.grad.array with
          | Some g_array ->
              Tn.do_read diff.grad;
              `Box (Nd.render_array ~brief:true ~prefix ?entries_per_axis ~labels ~indices g_array)
          | None -> `Text (prefix ^ " " ^ where_located diff.grad)
        in
        `Subtree_with_ID (id, `Tree (add_shape [ node ], children))
    | _, true, true, Some diff ->
        let node =
          let value =
            lazy_optional_payload t.value.array ~spy
              ~present:(fun v_array ->
                Tn.do_read t.value;
                `Box
                  (Nd.render_array ~brief:true ~prefix:txt ?entries_per_axis ~labels ~indices
                     v_array))
              ~missing:(fun () -> txt ^ " " ^ where_located t.value)
          in
          let grad =
            lazy_optional_payload diff.grad.array ~spy
              ~present:(fun g_array ->
                Tn.do_read diff.grad;
                `Box
                  (Nd.render_array ~brief:true ~prefix:(grad_txt diff) ?entries_per_axis ~labels
                     ~indices g_array))
              ~missing:(fun () -> grad_txt diff ^ " " ^ where_located diff.grad)
          in
          `Vlist (false, [ value; grad ])
        in
        `Subtree_with_ID (id, `Tree (add_shape [ node ], children))
  in
  to_dag { subtensor = t; embedded = true }

let to_printbox ?single_node ?entries_per_axis ?(with_id = false) ?(spy = false)
    ?(with_shape = false) ?(with_value = true) ~with_grad ~depth t =
  to_dag ?single_node ?entries_per_axis ~with_id ~spy ~with_shape ~with_value ~with_grad t
  |> PrintBox_utils.reformat_dag depth

let log_debug_info ~from_log_level t =
  let%diagn_sexp log_child { subtensor = _subtensor; embedded = _embedded } =
    [%logN_block
      from_log_level
        ((if _embedded then "Embedded " else "Non-embedded ") ^ Tn.debug_name _subtensor.value);
      Tn.log_debug_info ~from_log_level _subtensor.value]
  in
  [%diagn_sexp
    [%logN_block
      from_log_level ("Tensor " ^ Tn.dims_to_string t.value);
      Tn.log_debug_info ~from_log_level t.value;
      Option.iter t.diff ~f:(fun diff ->
          [%log_block
            "Gradient";
            Tn.log_debug_info ~from_log_level diff.grad]);
      List.iter ~f:log_child t.children]]

let print ?(spy = false) ~with_grad ~with_code ?(with_low_level = false) (style : array_print_style)
    t =
  let sh = t.shape in
  let label = Tn.label t.value in
  let prefix =
    "[" ^ Int.to_string t.id ^ "]: " ^ label ^ " shape "
    ^ Shape.to_string_hum ~style:`Axis_number_and_size sh
    ^ " "
  in
  let grad_txt diff =
    let label = Tn.label diff.grad in
    assert (String.is_prefix label ~prefix:"grad");
    label
  in
  let labels = Shape.to_labels t.shape in
  let indices =
    match style with
    | `Default -> Shape.default_display_indices sh
    | `N5_layout priorities ->
        let f : (string, int) Either.t -> int = function
          | Either.Second i -> i
          | First _ -> invalid_arg "`N5_layout requires integer-only labels"
        in
        let p_labels = Shape.(axis_labels @@ axis_labels_of_spec priorities) in
        (Shape.axis_map_to_dims_index p_labels : (string, int) Either.t array) |> Array.map ~f
    | `Label_layout label_idcs ->
        let inv_labels =
          Array.mapi labels ~f:(fun i l -> (l, i)) |> Array.to_list |> Map.of_alist (module String)
        in
        let inv_labels =
          match inv_labels with
          | `Duplicate_key l ->
              raise @@ Session_error ("`Label_layout found a repeating label: " ^ l, Some t)
          | `Ok inv_labels -> inv_labels
        in
        let result = Array.create ~len:(Array.length labels) 0 in
        List.iter label_idcs ~f:(fun (l, priority) ->
            match Map.find inv_labels l with
            | Some pos -> result.(pos) <- priority
            | None -> raise @@ Session_error ("`Label_layout label not found in shape: " ^ l, Some t));
        result
    | `Inline -> [||]
  in
  let needs_spec =
    Array.exists ~f:(Fn.non String.is_empty) labels
    || Shape.(List.exists ~f:Row.(equal_dim @@ get_dim ~d:1 ()) sh.input.dims)
  in
  let axes_spec = if needs_spec then Some (Shape.to_string_hum ~style:`Only_labels sh) else None in
  let num_batch_axes = List.length sh.batch.dims in
  let num_input_axes = List.length sh.input.dims in
  let num_output_axes = List.length sh.output.dims in
  (* TODO: code sharing with [to_dag] *)
  (if spy && not (Lazy.is_val t.value.array) then Stdlib.Format.printf "%s <not-in-yet>@ " prefix
   else
     match (style, t.value.array) with
     | `Inline, (lazy None) -> Stdlib.Format.printf "<virtual>@ "
     | `Inline, (lazy (Some arr)) ->
         Tn.do_read t.value;
         Nd.pp_array_inline
           (Stdlib.Format.get_std_formatter ())
           ~num_batch_axes ~num_input_axes ~num_output_axes ?axes_spec arr
     | _, (lazy None) -> Stdlib.Format.printf "<virtual>@ "
     | _, (lazy (Some arr)) ->
         Tn.do_read t.value;
         Nd.pp_array (Stdlib.Format.get_std_formatter ()) ~prefix ~labels ~indices arr;
         Stdlib.Format.print_char '\n');
  if with_grad then
    Option.iter t.diff ~f:(fun diff ->
        if spy && not (Lazy.is_val diff.grad.array) then
          Stdlib.Format.printf "%s <not-in-yet>@ " (grad_txt diff)
        else
          match (style, diff.grad.array) with
          | `Inline, (lazy (Some arr)) ->
              Tn.do_read diff.grad;
              Nd.pp_array_inline
                (Stdlib.Format.get_std_formatter ())
                ~num_batch_axes ~num_input_axes ~num_output_axes ?axes_spec arr;
              Stdlib.Format.print_char '\n'
          | _, (lazy (Some arr)) ->
              Tn.do_read diff.grad;
              Nd.pp_array
                (Stdlib.Format.get_std_formatter ())
                ~prefix:(prefix ^ " " ^ grad_txt diff)
                ~labels ~indices arr;
              Stdlib.Format.print_char '\n'
          | _, (lazy None) -> Stdlib.Format.printf "%s <virtual>@ " (grad_txt diff));
  if with_code then (
    (match t.forward.asgns with
    | Noop -> ()
    | fwd_code ->
        Stdlib.Format.printf "@[<v 2>Current forward body:%a@]@," (Asgns.fprint_hum ()) fwd_code);
    match t.diff with
    | Some { backprop = { asgns = Noop; _ }; _ } -> ()
    | Some { backprop = bwd_code; _ } ->
        Stdlib.Format.printf "@[<v 2>Current backprop body:%a@]@," (Asgns.fprint_hum ())
          bwd_code.asgns
    | None -> ());
  if with_low_level then (
    (match t.forward.asgns with
    | Noop -> ()
    | fwd_code ->
        Stdlib.Format.printf "@[<v 2>Current forward low-level body:%a@]@,"
          (Arrayjit.Low_level.fprint_hum ())
        @@ Asgns.to_low_level fwd_code);
    match t.diff with
    | Some { backprop = { asgns = Noop; _ }; _ } -> ()
    | Some { backprop = bwd_code; _ } ->
        Stdlib.Format.printf "@[<v 2>Current backprop low-level body:%a@]@,"
          (Arrayjit.Low_level.fprint_hum ())
        @@ Asgns.to_low_level bwd_code.asgns
    | None -> ());
  Stdlib.Format.printf "\n"

let print_forward_roots ~with_grad ~with_code (style : array_print_style) =
  List.iter (Map.to_alist ~key_order:`Increasing session_state.forward_roots) ~f:(fun (id, root) ->
      assert (id = root.id);
      print ~with_grad ~with_code style root)

let print_tree ?entries_per_axis ?(with_backend_info = false) ?(with_id = true) ?(spy = false)
    ?(with_shape = false) ?(with_value = true) ~with_grad ~depth t =
  (* FIXME: print backend info *)
  ignore with_backend_info;
  PrintBox_text.output Stdio.stdout @@ PrintBox_utils.dag_to_box @@ PrintBox_utils.boxify depth
  @@ to_dag ?entries_per_axis ~with_id ~spy ~with_shape ~with_value ~with_grad t