neural_nets_lib 0.3.3 (latest)

Tensor shape types, shape inference, projection inference.

Labels specifications and einsum notation.

Definition and properties of the syntax of labels specifications and einsum notation:

Whitespace-insensitive except that whitespace separates identifiers.
Comes in two variants: single-character and multicharacter;
if there is a comma ',' anywhere in the initial text, the multicharacter version is used,
otherwise the single character version is used.
Currently, the only non-whitespace, non-alphanumeric characters that make sense / are allowed in a spec are: '>', '|', '-', ',', '=', ';'.
identifier: single alphanum character or '_' in single-char mode, a sequence of alphanum characters or '_' otherwise (whitespace not allowed).
separators: a sequence of commas and whitespaces.
separators_with_comma: commas and whitespaces containing at least one comma.
axes_spec_single_char: separators? identifier+ separators?
axes_spec_multichar: separators? (identifier separators_with_comma)* identifier separators?
ellipsis_spec: '...' <|> '..' identifier '..'
row_spec: axes_spec <|> ellipsis_spec axes_spec <|> axes_spec ellipsis_spec axes_spec
labels_spec: row_spec <|> row_spec '|' row_spec <|> row_spec '->' row_spec <|> row_spec '|' row_spec '->' row_spec.
permute_spec: labels_spec '=>' labels_spec
einsum_spec: labels_spec ';' labels_spec '=>' labels_spec

If labels_spec does not contain "|" nor "->", each label is of the kind Output. If the spec doesn't contain "|", labels to the left of "->" are Input and to the right Output. Labels to the left of "|" are Batch, and between "|" and "->" are Input.

The labels ".."ident"..", "..." (where ident does not contain any of the special characters) are only allowed once for a kind. They are used to enable (in-the-middle) broadcasting for the axis kind in the einsum-related shape inference (like the ellipsis "..." in numpy.einsum), and are translated to row variables. The ellipsis "..." is context dependent: in the batch row it is the same as "..batch..", in the input row the same as "..input..", in the output row the same as "..output..". When the same row variable is used in multiple rows, the corresponding broadcasted axes are matched pointwise in the resulting operation.

The label "_" is a place-holder: it is not output to the resulting map but aligns the axes of other labels.

User-ish API.

type t = {

mutable batch : Row.t;
mutable input : Row.t;
mutable output : Row.t;
id : Base.int;
(*
A node that has the same shape as this shape.
*)
debug_name : Base.string;

}

include Ppx_compare_lib.Equal.S with type t := t

val equal : t -> t -> bool

include Sexplib0.Sexpable.S with type t := t

val t_of_sexp : Sexplib0.Sexp.t -> t

val sexp_of_t : t -> Sexplib0.Sexp.t

type deduce_within_shape =

| Not_constrained
| Input_equals_output

val compare_deduce_within_shape : 
  deduce_within_shape ->
  deduce_within_shape ->
  Base.int

val sexp_of_deduce_within_shape : deduce_within_shape -> Sexplib0.Sexp.t

val deduce_within_shape_of_sexp : Sexplib0.Sexp.t -> deduce_within_shape

type compose_type =

| Pointwise_bin
(*
NumPy-style broadcast matching batch, input and output axes, e.g. as in s1 + s2.
*)
| Compose
(*
Compose the outputs of the second shape with the inputs of the first shape, i.e. the shape of fun x -> s1(s2(x)), or s1 * s2 where * is the inner product (e.g. matrix multiply).
*)
| Einsum of Base.string
(*
The binary "einsum" syntax: RHS1;RHS2=>LHS, where RHSi, LHS are labels specifications. Since OCANNL's extended einsum notation supports both axis variables and row variables, it makes other compose types redundant. The axis_labels use pseudo-labels local to the notation, to line up the axes and row variables. The symmetric difference / disjunctive union of RHS1 and RHS2's pseudo-labels should be equal to LHS pseudo-labels.
Note: The "right-hand-side" is on the left! I.e. the syntax is "rhs=>lhs", "rhs1;rhs2=>lhs".
*)

val sexp_of_compose_type : compose_type -> Sexplib0.Sexp.t

val compose_type_of_sexp : Sexplib0.Sexp.t -> compose_type

val equal_compose_type : compose_type -> compose_type -> Base.bool

type transpose_type =

| Transpose
(*
Swaps inputs and outputs of a shape, preserves batch axes.
*)
| Pointwise_un
(*
Preserves the shape.
*)
| Permute of Base.string
(*
The unary "einsum" syntax: RHS1=>LHS.
*)
| Batch_slice of Arrayjit.Indexing.static_symbol
(*
Removes the leftmost batch axis.
*)

val equal_transpose_type : transpose_type -> transpose_type -> Base.bool

val sexp_of_transpose_type : transpose_type -> Sexplib0.Sexp.t

val transpose_type_of_sexp : Sexplib0.Sexp.t -> transpose_type

val make : 
  ?batch_dims:Base.int Base.list ->
  ?input_dims:Base.int Base.list ->
  ?output_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:deduce_within_shape ->
  debug_name:Base.string ->
  id:Base.int ->
  Base.unit ->
  t

Creates a shape. id should be the id the associated tensor (if any). At most one of the pairs batch_dims, batch_axes etc. should be given: if none, the corresponding row will be inferred. batch_axes etc. provide labels for the dimensions of the corresponding axes. Note that these are dimensions labels and not axis labels: they need not be unique for a row, are inferred when provided, and must match whenever the axis sizes must match.

val to_string_hum : 
  ?style:
    [< `Axis_number_and_size
    | `Axis_size
    | `Only_labels Axis_size Only_labels ] ->
  t ->
  Base.string

Internal-ish API.

type logic =

| Broadcast of compose_type * t * t
(*
Matches the shapes for a binary operation.
For Broadcast (Einsum (ls1, ls2, ls3), s1, s2), the labels of s1 and s2 must match according to the ls1, ls2 lineup, and the resulting shape inherits the labels according to the ls3 lineup.
*)
| Transpose of transpose_type * t
(*
Permutes the axes of a shape. One case of Transpose is to swap inputs with outputs of s1, hence the name.
*)
| Terminal of Arrayjit.Ops.init_op
(*
Extracts any available shape information from the initialization. E.g. for File_mapped fn, opens the file fn to check its length.
*)

How to propagate shape updates and do the last update of Tensor.t.shape when finalizing the tensor. Axes are broadcast-expanded on a bottom-up update to fit the incoming shape.

val equal_logic : logic -> logic -> Base.bool

val sexp_of_logic : logic -> Sexplib0.Sexp.t

val logic_of_sexp : Sexplib0.Sexp.t -> logic

type update_id

val equal_update_id : update_id -> update_id -> Base.bool

val compare_update_id : update_id -> update_id -> Base.int

val hash_fold_update_id : Base.Hash.state -> update_id -> Base.Hash.state

val hash_update_id : update_id -> Base.Hash.hash_value

val sexp_of_update_id : update_id -> Sexplib0.Sexp.t

val update_id_of_sexp : Sexplib0.Sexp.t -> update_id

val get_update_id : Base.unit -> update_id

type update_step = {

shape : t;
logic : logic;
id : update_id;

}

Data required for a shape inference update step. Ideally, an update should be performed at least twice, the second time after all the other relevant updates have been performed for the first time. In OCANNL, this is achieved by performing updates both as the tensors are constructed, and via lazy callbacks as the corresponding Arrayjit.Indexing dimensions and projections are first accessed.

val sexp_of_update_step : update_step -> Sexplib0.Sexp.t

val update_step_of_sexp : Sexplib0.Sexp.t -> update_step

val to_dims : t -> Base.int Base.array

val propagate_shapes : update_step -> Base.unit

val derive_projections : update_step -> Arrayjit.Indexing.projections

Computes the indexing into subtensors given the shape information of a tensor. derive_projections should only be invoked when the shapes are fully inferred already!

val backprop_ith_arg : 
  from_1:Base.int ->
  Arrayjit.Indexing.projections ->
  Arrayjit.Indexing.projections

val of_spec : 
  ?deduced:deduce_within_shape ->
  debug_name:Base.string ->
  id:Base.int ->
  Base.string ->
  t

val default_display_indices : t -> Base.int Base.array

val to_labels : t -> Base.string Base.array

type 'a axis_map

type parsed_axis_labels

val axis_labels : 
  parsed_axis_labels ->
  (Base.string, Base.int) Base.Either.t axis_map

val axis_labels_of_spec : Base.string -> parsed_axis_labels

val axis_map_to_dims_index : ?default:'a -> 'a axis_map -> 'a Base.array

package neural_nets_lib

Tensor shape types, shape inference, projection inference.

Labels specifications and einsum notation.

User-ish API.

Internal-ish API.