Source file bert.ml
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open Rune
type config = {
vocab_size : int;
hidden_size : int;
num_hidden_layers : int;
num_attention_heads : int;
intermediate_size : int;
hidden_act : [ `gelu | `relu | `swish | `gelu_new ];
hidden_dropout_prob : float;
attention_probs_dropout_prob : float;
max_position_embeddings : int;
type_vocab_size : int;
layer_norm_eps : float;
pad_token_id : int;
position_embedding_type : [ `absolute | `relative ];
use_cache : bool;
classifier_dropout : float option;
}
let default_config =
{
vocab_size = 30522;
hidden_size = 768;
num_hidden_layers = 12;
num_attention_heads = 12;
intermediate_size = 3072;
hidden_act = `gelu;
hidden_dropout_prob = 0.1;
attention_probs_dropout_prob = 0.1;
max_position_embeddings = 512;
type_vocab_size = 2;
layer_norm_eps = 1e-12;
pad_token_id = 0;
position_embedding_type = `absolute;
use_cache = true;
classifier_dropout = None;
}
let bert_base_uncased = default_config
let bert_large_uncased =
{
default_config with
hidden_size = 1024;
num_hidden_layers = 24;
num_attention_heads = 16;
intermediate_size = 4096;
}
let bert_base_cased =
default_config
let bert_base_multilingual =
{
default_config with
vocab_size = 105879;
}
type inputs = {
input_ids : (int32, int32_elt) Rune.t;
attention_mask : (int32, int32_elt) Rune.t;
token_type_ids : (int32, int32_elt) Rune.t option;
position_ids : (int32, int32_elt) Rune.t option;
}
module Tokenizer = struct
type t = {
vocab : (string, int) Hashtbl.t;
inv_vocab : (int, string) Hashtbl.t;
unk_token_id : int;
cls_token_id : int;
sep_token_id : int;
pad_token_id : int;
max_input_chars_per_word : int;
}
let download_vocab_file model_id =
let cache_dir =
match Sys.getenv_opt "XDG_CACHE_HOME" with
| Some dir -> dir
| None -> (
match Sys.getenv_opt "HOME" with
| Some home -> Filename.concat home ".cache"
| None -> "/tmp/.cache")
in
let kaun_cache = Filename.concat cache_dir "kaun" in
let vocab_cache = Filename.concat kaun_cache "vocab" in
let vocab_file = Filename.concat vocab_cache (model_id ^ "-vocab.txt") in
if not (Sys.file_exists vocab_cache) then
Sys.command (Printf.sprintf "mkdir -p %s" vocab_cache) |> ignore;
if not (Sys.file_exists vocab_file) then (
Printf.printf "Downloading vocab file for %s...\n%!" model_id;
let url =
Printf.sprintf "https://huggingface.co/%s/resolve/main/vocab.txt"
model_id
in
let cmd =
Printf.sprintf
"curl -L -o %s %s 2>/dev/null || wget -O %s %s 2>/dev/null" vocab_file
url vocab_file url
in
let exit_code = Sys.command cmd in
if exit_code <> 0 then
failwith
(Printf.sprintf "Failed to download vocab file for %s" model_id));
vocab_file
let load_vocab vocab_file =
let vocab = Hashtbl.create 30000 in
let inv_vocab = Hashtbl.create 30000 in
let ic = open_in vocab_file in
let idx = ref 0 in
(try
while true do
let line = input_line ic in
let token = String.trim line in
if String.length token > 0 then (
Hashtbl.add vocab token !idx;
Hashtbl.add inv_vocab !idx token;
incr idx)
done
with End_of_file -> ());
close_in ic;
(vocab, inv_vocab)
let create ?vocab_file ?model_id () =
let vocab_file =
match (vocab_file, model_id) with
| Some file, _ -> file
| None, Some id -> download_vocab_file id
| None, None -> download_vocab_file "bert-base-uncased"
in
let vocab, inv_vocab = load_vocab vocab_file in
{
vocab;
inv_vocab;
unk_token_id = 100;
cls_token_id = 101;
sep_token_id = 102;
pad_token_id = 0;
max_input_chars_per_word = 100;
}
let basic_tokenize text =
let text = String.lowercase_ascii text in
let tokens = ref [] in
let current = Buffer.create 16 in
String.iter
(fun c ->
match c with
| 'a' .. 'z' | '0' .. '9' -> Buffer.add_char current c
| _ ->
if Buffer.length current > 0 then (
tokens := Buffer.contents current :: !tokens;
Buffer.clear current);
if c <> ' ' && c <> '\t' && c <> '\n' && c <> '\r' then
tokens := String.make 1 c :: !tokens)
text;
if Buffer.length current > 0 then
tokens := Buffer.contents current :: !tokens;
List.rev !tokens
let wordpiece_tokenize_word t word =
let n = String.length word in
if n > t.max_input_chars_per_word then [ t.unk_token_id ]
else
let output = ref [] in
let start = ref 0 in
while !start < n do
let end_idx = ref n in
let found = ref false in
while !start < !end_idx && not !found do
let substr =
if !start > 0 then "##" ^ String.sub word !start (!end_idx - !start)
else String.sub word !start (!end_idx - !start)
in
match Hashtbl.find_opt t.vocab substr with
| Some token_id ->
output := token_id :: !output;
found := true;
start := !end_idx
| None -> decr end_idx
done;
if not !found then (
output := [ t.unk_token_id ];
start := n )
done;
List.rev !output
let encode_to_array t text =
let basic_tokens = basic_tokenize text in
let token_ids = ref [ t.cls_token_id ] in
List.iter
(fun word ->
let word_tokens = wordpiece_tokenize_word t word in
token_ids := !token_ids @ word_tokens)
basic_tokens;
token_ids := !token_ids @ [ t.sep_token_id ];
Array.of_list !token_ids
let encode t text =
let token_ids = encode_to_array t text in
let seq_len = Array.length token_ids in
let input_ids =
Rune.create Int32 [| 1; seq_len |] (Array.map Int32.of_int token_ids)
in
let attention_mask = Rune.ones Int32 [| 1; seq_len |] in
{ input_ids; attention_mask; token_type_ids = None; position_ids = None }
let encode_batch t ?(max_length = 512) ?(padding = true) texts =
let encoded = List.map (encode_to_array t) texts in
let actual_max =
if padding then max_length
else
List.fold_left (fun acc arr -> Int.max acc (Array.length arr)) 0 encoded
in
let padded =
List.map
(fun arr ->
let len = Array.length arr in
if len >= actual_max then Array.sub arr 0 actual_max
else Array.append arr (Array.make (actual_max - len) t.pad_token_id))
encoded
in
let batch_size = List.length padded in
let flat_data = Array.concat padded in
let nx_tensor =
let data = Array.map Int32.of_int flat_data in
Nx.create Int32 [| batch_size; max_length |] data
in
Rune.of_nx nx_tensor
let decode t token_ids =
let tokens = ref [] in
Array.iter
(fun id ->
if id <> t.cls_token_id && id <> t.sep_token_id && id <> t.pad_token_id
then
match Hashtbl.find_opt t.inv_vocab id with
| Some token ->
let token =
if String.length token > 2 && String.sub token 0 2 = "##" then
String.sub token 2 (String.length token - 2)
else token ^ " "
in
tokens := token :: !tokens
| None -> ())
token_ids;
String.concat "" (List.rev !tokens) |> String.trim
let create_wordpiece ?vocab_file ?model_id () =
create ?vocab_file ?model_id ()
end
let embeddings ~config () =
let open Kaun.Layer in
let token_embeddings =
embedding ~vocab_size:config.vocab_size ~embed_dim:config.hidden_size ()
in
let position_embeddings =
embedding ~vocab_size:config.max_position_embeddings
~embed_dim:config.hidden_size ()
in
let token_type_embeddings =
embedding ~vocab_size:config.type_vocab_size ~embed_dim:config.hidden_size
()
in
let layer_norm =
layer_norm ~dim:config.hidden_size ~eps:config.layer_norm_eps ()
in
let dropout = dropout ~rate:config.hidden_dropout_prob () in
{
Kaun.init =
(fun ~rngs ~dtype ->
let keys = Rune.Rng.split ~n:5 rngs in
Kaun.Ptree.record_of
[
("token_embeddings", token_embeddings.init ~rngs:keys.(0) ~dtype);
( "position_embeddings",
position_embeddings.init ~rngs:keys.(1) ~dtype );
( "token_type_embeddings",
token_type_embeddings.init ~rngs:keys.(2) ~dtype );
("layer_norm", layer_norm.init ~rngs:keys.(3) ~dtype);
("dropout", dropout.init ~rngs:keys.(4) ~dtype);
]);
Kaun.apply =
(fun params ~training ?rngs x ->
let input_ids = Rune.cast Rune.int32 x in
match params with
| Kaun.Ptree.Record fields ->
let get_params name =
match Kaun.Ptree.Record.find_opt name fields with
| Some p -> p
| None -> failwith ("Embeddings: missing " ^ name)
in
let get_embedding_table name =
match get_params name with
| Tensor t -> t
| _ -> failwith ("Expected tensor for " ^ name)
in
let token_embeddings_table =
get_embedding_table "token_embeddings"
in
let position_embeddings_table =
get_embedding_table "position_embeddings"
in
let token_type_embeddings_table =
get_embedding_table "token_type_embeddings"
in
let lookup_embeddings embedding_table indices =
let batch_size = (Rune.shape indices).(0) in
let seq_len = (Rune.shape indices).(1) in
let embed_dim = (Rune.shape embedding_table).(1) in
let indices_flat =
Rune.reshape [| batch_size * seq_len |] indices
in
let gathered = Rune.take ~axis:0 indices_flat embedding_table in
Rune.reshape [| batch_size; seq_len; embed_dim |] gathered
in
let token_embeds =
lookup_embeddings token_embeddings_table input_ids
in
let seq_len =
(Rune.shape input_ids).(Array.length (Rune.shape input_ids) - 1)
in
let batch_size = (Rune.shape input_ids).(0) in
let position_ids =
let pos_ids = Rune.zeros Rune.int32 [| batch_size; seq_len |] in
for b = 0 to batch_size - 1 do
for s = 0 to seq_len - 1 do
Rune.set [ b; s ] pos_ids
(Rune.scalar Rune.int32 (Int32.of_int s))
done
done;
pos_ids
in
let position_embeds =
lookup_embeddings position_embeddings_table position_ids
in
let token_type_ids = Rune.zeros Rune.int32 (Rune.shape input_ids) in
let token_type_embeds =
lookup_embeddings token_type_embeddings_table token_type_ids
in
let embeddings =
Rune.add token_embeds (Rune.add position_embeds token_type_embeds)
in
let embeddings =
layer_norm.apply (get_params "layer_norm") ~training ?rngs
embeddings
in
let embeddings =
dropout.apply (get_params "dropout") ~training ?rngs embeddings
in
embeddings
| _ -> failwith "Embeddings: invalid params");
}
let pooler ~hidden_size () =
{
Kaun.init =
(fun ~rngs ~dtype ->
let key = Rune.Rng.to_int rngs in
let init_fn = (Kaun.Initializers.normal ~stddev:0.02 ()).f in
let dense_weight = init_fn key [| hidden_size; hidden_size |] dtype in
let dense_bias = Rune.zeros dtype [| hidden_size |] in
Kaun.Ptree.(
Record
(Record.of_seq
@@ List.to_seq
[
("dense_weight", Tensor dense_weight);
("dense_bias", Tensor dense_bias);
])));
Kaun.apply =
(fun params ~training:_ ?rngs:_ x ->
let open Rune in
let cls_token = slice [ A; I 0; A ] x in
let dense_weight =
match params with
| Record fields -> (
match Kaun.Ptree.Record.find_opt "dense_weight" fields with
| Some (Tensor t) -> t
| _ -> failwith "Pooler: missing dense_weight")
| _ -> failwith "Pooler: invalid params"
in
let dense_bias =
match params with
| Record fields -> (
match Kaun.Ptree.Record.find_opt "dense_bias" fields with
| Some (Tensor t) -> t
| _ -> failwith "Pooler: missing dense_bias")
| _ -> failwith "Pooler: invalid params"
in
let pooled = add (matmul cls_token dense_weight) dense_bias in
tanh pooled);
}
type 'a bert = {
model : Kaun.Layer.module_;
params : 'a Kaun.Ptree.t;
config : config;
dtype : (float, 'a) Rune.dtype;
}
type 'a output = {
last_hidden_state : (float, 'a) Rune.t;
pooler_output : (float, 'a) Rune.t option;
hidden_states : (float, 'a) Rune.t list option;
attentions : (float, 'a) Rune.t list option;
}
let create_bert_layers ~config ~add_pooling_layer =
let layers =
[
embeddings ~config ();
Kaun.Layer.transformer_encoder ~num_layers:config.num_hidden_layers
~hidden_size:config.hidden_size
~num_attention_heads:config.num_attention_heads
~intermediate_size:config.intermediate_size
~hidden_dropout_prob:config.hidden_dropout_prob
~attention_probs_dropout_prob:config.attention_probs_dropout_prob
~layer_norm_eps:config.layer_norm_eps
~hidden_act:
(match config.hidden_act with
| `gelu | `gelu_new -> `gelu
| `relu -> `relu
| `swish -> `swish)
~use_bias:true
();
]
@
if add_pooling_layer then [ pooler ~hidden_size:config.hidden_size () ]
else []
in
layers
let create ?(config = default_config) ?(add_pooling_layer = true) () =
let open Kaun.Layer in
sequential (create_bert_layers ~config ~add_pooling_layer)
let from_pretrained ?(model_id = "bert-base-uncased") ?revision ?cache_config
~dtype () =
let cache_config =
Option.value cache_config ~default:Kaun_huggingface.Config.default
in
let revision = Option.value revision ~default:Kaun_huggingface.Latest in
let config_json =
match
Kaun_huggingface.load_config ~config:cache_config ~revision ~model_id ()
with
| Cached json | Downloaded (json, _) -> json
in
let bert_config =
let open Yojson.Safe.Util in
{
vocab_size = config_json |> member "vocab_size" |> to_int;
hidden_size = config_json |> member "hidden_size" |> to_int;
num_hidden_layers = config_json |> member "num_hidden_layers" |> to_int;
num_attention_heads =
config_json |> member "num_attention_heads" |> to_int;
intermediate_size = config_json |> member "intermediate_size" |> to_int;
hidden_act =
(match config_json |> member "hidden_act" |> to_string_option with
| Some "gelu" | Some "gelu_new" -> `gelu
| Some "relu" -> `relu
| Some "swish" | Some "silu" -> `swish
| _ -> `gelu);
hidden_dropout_prob =
config_json
|> member "hidden_dropout_prob"
|> to_float_option |> Option.value ~default:0.1;
attention_probs_dropout_prob =
config_json
|> member "attention_probs_dropout_prob"
|> to_float_option |> Option.value ~default:0.1;
max_position_embeddings =
config_json |> member "max_position_embeddings" |> to_int;
type_vocab_size =
config_json |> member "type_vocab_size" |> to_int_option
|> Option.value ~default:2;
layer_norm_eps =
config_json |> member "layer_norm_eps" |> to_float_option
|> Option.value ~default:1e-12;
pad_token_id = 0;
position_embedding_type = `absolute;
use_cache = true;
classifier_dropout = None;
}
in
let hf_params =
Kaun_huggingface.from_pretrained ~config:cache_config ~revision ~model_id
~dtype ()
in
let map_huggingface_to_kaun hf_params =
let rec flatten_ptree prefix tree =
match tree with
| Kaun.Ptree.Tensor t -> [ (prefix, t) ]
| Kaun.Ptree.List lst ->
List.concat
(List.mapi
(fun i subtree ->
flatten_ptree (prefix ^ "." ^ string_of_int i) subtree)
lst)
| Kaun.Ptree.Record fields ->
Kaun.Ptree.Record.fold
(fun name subtree acc ->
let new_prefix =
if prefix = "" then name else prefix ^ "." ^ name
in
flatten_ptree new_prefix subtree @ acc)
fields []
in
let flat_params = flatten_ptree "" hf_params in
let embeddings_params = ref Kaun.Ptree.Record.empty in
let encoder_layers = ref [] in
let pooler_params = ref Kaun.Ptree.Record.empty in
List.iter
(fun (hf_name, tensor) ->
let name =
if String.starts_with ~prefix:"bert." hf_name then
String.sub hf_name 5 (String.length hf_name - 5)
else hf_name
in
match name with
| s
when String.starts_with ~prefix:"embeddings.word_embeddings.weight" s
->
embeddings_params :=
Kaun.Ptree.Record.add "token_embeddings"
(Kaun.Ptree.Tensor tensor) !embeddings_params
| s
when String.starts_with
~prefix:"embeddings.position_embeddings.weight" s ->
embeddings_params :=
Kaun.Ptree.Record.add "position_embeddings"
(Kaun.Ptree.Tensor tensor) !embeddings_params
| s
when String.starts_with
~prefix:"embeddings.token_type_embeddings.weight" s ->
embeddings_params :=
Kaun.Ptree.Record.add "token_type_embeddings"
(Kaun.Ptree.Tensor tensor) !embeddings_params
| s when String.starts_with ~prefix:"embeddings.LayerNorm" s ->
let ln_params =
match
Kaun.Ptree.Record.find_opt "layer_norm" !embeddings_params
with
| Some (Kaun.Ptree.Record r) -> r
| _ -> Kaun.Ptree.Record.empty
in
let field =
if String.ends_with ~suffix:"weight" s then "gamma"
else if String.ends_with ~suffix:"bias" s then "beta"
else if String.ends_with ~suffix:"gamma" s then "gamma"
else "beta"
in
let updated_ln =
Kaun.Ptree.Record.add field (Kaun.Ptree.Tensor tensor) ln_params
in
embeddings_params :=
Kaun.Ptree.Record.add "layer_norm" (Kaun.Ptree.Record updated_ln)
!embeddings_params
| s when String.starts_with ~prefix:"encoder.layer." s -> (
let rest = String.sub s 14 (String.length s - 14) in
match String.split_on_char '.' rest with
| layer_idx :: params ->
let layer_idx_int = int_of_string layer_idx in
let param_name = String.concat "." params in
let kaun_param, needs_transpose =
match param_name with
| "attention.self.query.weight" -> ("q_weight", true)
| "attention.self.key.weight" -> ("k_weight", true)
| "attention.self.value.weight" -> ("v_weight", true)
| "attention.output.dense.weight" -> ("attn_out_weight", true)
| "intermediate.dense.weight" -> ("inter_weight", true)
| "output.dense.weight" -> ("out_weight", true)
| "attention.self.query.bias" -> ("q_bias", false)
| "attention.self.key.bias" -> ("k_bias", false)
| "attention.self.value.bias" -> ("v_bias", false)
| "attention.output.dense.bias" -> ("attn_out_bias", false)
| "intermediate.dense.bias" -> ("inter_bias", false)
| "output.dense.bias" -> ("out_bias", false)
| "attention.output.LayerNorm.weight" -> ("attn_gamma", false)
| "attention.output.LayerNorm.bias" -> ("attn_beta", false)
| "output.LayerNorm.weight" -> ("ffn_gamma", false)
| "output.LayerNorm.bias" -> ("ffn_beta", false)
| "attention.output.LayerNorm.gamma" -> ("attn_gamma", false)
| "attention.output.LayerNorm.beta" -> ("attn_beta", false)
| "output.LayerNorm.gamma" -> ("ffn_gamma", false)
| "output.LayerNorm.beta" -> ("ffn_beta", false)
| _ -> (param_name, false)
in
while List.length !encoder_layers <= layer_idx_int do
encoder_layers :=
!encoder_layers @ [ ref Kaun.Ptree.Record.empty ]
done;
let layer_params = List.nth !encoder_layers layer_idx_int in
let final_tensor =
if needs_transpose then Rune.transpose tensor ~axes:[ 1; 0 ]
else tensor
in
layer_params :=
Kaun.Ptree.Record.add kaun_param
(Kaun.Ptree.Tensor final_tensor) !layer_params
| _ -> ())
| s when String.starts_with ~prefix:"pooler.dense.weight" s ->
let transposed_tensor = Rune.transpose tensor ~axes:[ 1; 0 ] in
pooler_params :=
Kaun.Ptree.Record.add "dense_weight"
(Kaun.Ptree.Tensor transposed_tensor) !pooler_params
| s when String.starts_with ~prefix:"pooler.dense.bias" s ->
pooler_params :=
Kaun.Ptree.Record.add "dense_bias" (Kaun.Ptree.Tensor tensor)
!pooler_params
| _ -> () )
flat_params;
let encoder_list =
List.map (fun r -> Kaun.Ptree.Record !r) !encoder_layers
in
embeddings_params :=
Kaun.Ptree.Record.add "dropout" (Kaun.Ptree.List []) !embeddings_params;
let encoder_params =
Kaun.Ptree.List
[ Kaun.Ptree.Record !embeddings_params; Kaun.Ptree.List encoder_list ]
in
Kaun.Ptree.record_of
[
("encoder", encoder_params); ("pooler", Kaun.Ptree.Record !pooler_params);
]
in
let mapped_params = map_huggingface_to_kaun hf_params in
let model = create ~config:bert_config ~add_pooling_layer:false () in
{ model; params = mapped_params; config = bert_config; dtype }
let forward bert inputs ?(training = false) ?(output_hidden_states = false)
?(output_attentions = false) () =
let { model; params; _ } = bert in
let { input_ids; attention_mask; token_type_ids; position_ids = _ } =
inputs
in
let open Rune in
let input_shape = shape input_ids in
let batch_size = input_shape.(0) in
let seq_len = input_shape.(1) in
let _token_type_ids =
match token_type_ids with
| Some ids -> ids
| None -> zeros int32 [| batch_size; seq_len |]
in
let _ = attention_mask in
let encoder_params =
try Kaun.Ptree.get_by_path "encoder" params
with Invalid_argument _ -> failwith "forward: missing encoder params"
in
let pooler_params =
try Kaun.Ptree.get_by_path "pooler" params
with Invalid_argument _ -> Kaun.Ptree.Record Kaun.Ptree.Record.empty
in
let dtype_tensor =
match Kaun.Ptree.flatten_with_paths encoder_params with
| [] -> failwith "Empty encoder params"
| (_, t) :: _ -> t
in
let target_dtype = dtype dtype_tensor in
let float_input = cast target_dtype input_ids in
let model_output = Kaun.apply model encoder_params ~training float_input in
let last_hidden_state = model_output in
let hidden_states =
if output_hidden_states then Some [ last_hidden_state ] else None
in
let attentions =
if output_attentions then None
else None
in
let pooler_output =
match pooler_params with
| Kaun.Ptree.Record fields when Kaun.Ptree.Record.cardinal fields > 0 ->
let pooler_weight =
match
Kaun.Ptree.get_by_path "dense_weight" pooler_params
|> Kaun.Ptree.get_tensor
with
| Some t -> t
| None -> failwith "Pooler: missing dense_weight"
in
let pooler_bias =
match
Kaun.Ptree.get_by_path "dense_bias" pooler_params
|> Kaun.Ptree.get_tensor
with
| Some t -> t
| None -> failwith "Pooler: missing dense_bias"
in
let cls_token = slice [ A; I 0; A ] last_hidden_state in
let pooled = add (matmul cls_token pooler_weight) pooler_bias in
Some (tanh pooled)
| _ -> None
in
{ last_hidden_state; pooler_output; hidden_states; attentions }
module For_masked_lm = struct
let create ?(config = default_config) () =
let open Kaun.Layer in
sequential
[
create ~config ~add_pooling_layer:false ();
linear ~in_features:config.hidden_size ~out_features:config.hidden_size
();
gelu ();
layer_norm ~dim:config.hidden_size ~eps:config.layer_norm_eps ();
linear ~in_features:config.hidden_size ~out_features:config.vocab_size
();
]
let forward ~model ~params ~input_ids ?attention_mask:_ ?token_type_ids:_
?labels ~training () =
let open Rune in
let dtype_tensor =
match Kaun.Ptree.flatten_with_paths params with
| [] -> failwith "Empty params"
| (_, t) :: _ -> t
in
let target_dtype = dtype dtype_tensor in
let float_input = cast target_dtype input_ids in
let logits = Kaun.apply model params ~training float_input in
let loss =
match labels with
| Some labels ->
let batch_size = (shape logits).(0) in
let seq_length = (shape logits).(1) in
let vocab_size = (shape logits).(2) in
let flat_logits =
Rune.reshape [| batch_size * seq_length; vocab_size |] logits
in
let flat_labels = Rune.reshape [| batch_size * seq_length |] labels in
Some
(Kaun.Loss.softmax_cross_entropy_with_indices flat_logits
flat_labels)
| None -> None
in
(logits, loss)
end
module For_sequence_classification = struct
let create ?(config = default_config) ~num_labels () =
let open Kaun.Layer in
sequential
[
create ~config ~add_pooling_layer:true ();
dropout
~rate:
(Option.value config.classifier_dropout
~default:config.hidden_dropout_prob)
();
linear ~in_features:config.hidden_size ~out_features:num_labels ();
]
let forward ~model ~params ~input_ids ?attention_mask:_ ?token_type_ids:_
?labels ~training () =
let open Rune in
let dtype_tensor =
match Kaun.Ptree.flatten_with_paths params with
| [] -> failwith "Empty params"
| (_, t) :: _ -> t
in
let target_dtype = dtype dtype_tensor in
let float_input = cast target_dtype input_ids in
let logits = Kaun.apply model params ~training float_input in
let loss =
match labels with
| Some labels ->
Some (Kaun.Loss.softmax_cross_entropy_with_indices logits labels)
| None -> None
in
(logits, loss)
end
module For_token_classification = struct
let create ?(config = default_config) ~num_labels () =
let open Kaun.Layer in
sequential
[
create ~config ~add_pooling_layer:false ();
dropout ~rate:config.hidden_dropout_prob ();
linear ~in_features:config.hidden_size ~out_features:num_labels ();
]
let forward ~model ~params ~input_ids ?attention_mask:_ ?token_type_ids:_
?labels ~training () =
let open Rune in
let dtype_tensor =
match Kaun.Ptree.flatten_with_paths params with
| [] -> failwith "Empty params"
| (_, t) :: _ -> t
in
let target_dtype = dtype dtype_tensor in
let float_input = cast target_dtype input_ids in
let logits = Kaun.apply model params ~training float_input in
let loss =
match labels with
| Some labels ->
let batch_size = (shape logits).(0) in
let seq_length = (shape logits).(1) in
let num_labels = (shape logits).(2) in
let flat_logits =
Rune.reshape [| batch_size * seq_length; num_labels |] logits
in
let flat_labels = Rune.reshape [| batch_size * seq_length |] labels in
Some
(Kaun.Loss.softmax_cross_entropy_with_indices flat_logits
flat_labels)
| None -> None
in
(logits, loss)
end
let parse_bert_config json =
let open Yojson.Safe.Util in
{
vocab_size = json |> member "vocab_size" |> to_int;
hidden_size = json |> member "hidden_size" |> to_int;
num_hidden_layers = json |> member "num_hidden_layers" |> to_int;
num_attention_heads = json |> member "num_attention_heads" |> to_int;
intermediate_size = json |> member "intermediate_size" |> to_int;
hidden_act =
(match json |> member "hidden_act" |> to_string_option with
| Some "gelu" | Some "gelu_new" -> `gelu
| Some "relu" -> `relu
| Some "swish" | Some "silu" -> `swish
| _ -> `gelu);
hidden_dropout_prob =
json
|> member "hidden_dropout_prob"
|> to_float_option |> Option.value ~default:0.1;
attention_probs_dropout_prob =
json
|> member "attention_probs_dropout_prob"
|> to_float_option |> Option.value ~default:0.1;
max_position_embeddings = json |> member "max_position_embeddings" |> to_int;
type_vocab_size =
json |> member "type_vocab_size" |> to_int_option
|> Option.value ~default:2;
layer_norm_eps =
json |> member "layer_norm_eps" |> to_float_option
|> Option.value ~default:1e-12;
pad_token_id = 0;
position_embedding_type = `absolute;
use_cache = true;
classifier_dropout = None;
}
let create_attention_mask (type a) ~(input_ids : (int32, int32_elt) Rune.t)
~pad_token_id ~(dtype : (float, a) dtype) : (float, a) Rune.t =
let input_dtype = Rune.dtype input_ids in
let pad_tensor = Rune.scalar input_dtype (Int32.of_int pad_token_id) in
let mask = Rune.not_equal input_ids pad_tensor in
Rune.cast dtype mask
let get_embeddings ~model:_ ~params:_ ~input_ids:_ ?attention_mask:_
~layer_index:_ () =
failwith "get_embeddings not fully implemented"
let num_parameters params =
let tensors = Kaun.Ptree.flatten_with_paths params in
List.fold_left
(fun acc (_, t) -> acc + Array.fold_left ( * ) 1 (Rune.shape t))
0 tensors
let parameter_stats params =
let total_params = num_parameters params in
let total_bytes = total_params * 4 in
Printf.sprintf "BERT parameters: %d (%.2f MB)" total_params
(float_of_int total_bytes /. 1024. /. 1024.)
let load_bert_base_uncased ~dtype () =
from_pretrained ~model_id:"bert-base-uncased" ~dtype ()
let load_bert_large_uncased ~dtype () =
from_pretrained ~model_id:"bert-large-uncased" ~dtype ()
let load_bert_base_cased ~dtype () =
from_pretrained ~model_id:"bert-base-cased" ~dtype ()
let load_bert_base_multilingual_cased ~dtype () =
from_pretrained ~model_id:"bert-base-multilingual-cased" ~dtype ()