package kaun
Flax-inspired neural network library for OCaml
Install
dune-project
Dependency
Authors
Maintainers
Sources
raven-1.0.0.alpha1.tbz
sha256=8e277ed56615d388bc69c4333e43d1acd112b5f2d5d352e2453aef223ff59867
sha512=369eda6df6b84b08f92c8957954d107058fb8d3d8374082e074b56f3a139351b3ae6e3a99f2d4a4a2930dd950fd609593467e502368a13ad6217b571382da28c
doc/src/kaun.models/gpt2.ml.html
Source file gpt2.ml
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open Rune (* Configuration *) type config = { vocab_size : int; n_positions : int; n_embd : int; n_layer : int; n_head : int; n_inner : int option; activation_function : [ `gelu | `relu | `swish | `gelu_new ]; resid_pdrop : float; embd_pdrop : float; attn_pdrop : float; layer_norm_epsilon : float; initializer_range : float; scale_attn_weights : bool; use_cache : bool; scale_attn_by_inverse_layer_idx : bool; reorder_and_upcast_attn : bool; bos_token_id : int option; eos_token_id : int option; pad_token_id : int option; } let default_config = { vocab_size = 50257; n_positions = 1024; n_embd = 768; n_layer = 12; n_head = 12; n_inner = None; (* Defaults to 4 * n_embd *) activation_function = `gelu_new; resid_pdrop = 0.1; embd_pdrop = 0.1; attn_pdrop = 0.1; layer_norm_epsilon = 1e-5; initializer_range = 0.02; scale_attn_weights = true; use_cache = true; scale_attn_by_inverse_layer_idx = false; reorder_and_upcast_attn = false; bos_token_id = Some 50256; eos_token_id = Some 50256; pad_token_id = None; } let gpt2_small = default_config let gpt2_medium = { default_config with n_embd = 1024; n_layer = 24; n_head = 16 } let gpt2_large = { default_config with n_embd = 1280; n_layer = 36; n_head = 20 } let gpt2_xl = { default_config with n_embd = 1600; n_layer = 48; n_head = 25 } (* Input type *) type inputs = { input_ids : (int32, int32_elt) Rune.t; attention_mask : (int32, int32_elt) Rune.t option; position_ids : (int32, int32_elt) Rune.t option; } (* GPT-2 specific tokenizer with BPE *) module Tokenizer = struct type t = { bpe_model : Saga.Bpe.t; (* Store the actual BPE model for encoding/decoding *) vocab_size : int; bos_token_id : int; eos_token_id : int; pad_token_id : int option; } let download_vocab_and_merges model_id = (* Download vocab and merges files from HuggingFace if not present *) 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 gpt2_cache = Filename.concat kaun_cache "gpt2" in let model_cache = Filename.concat gpt2_cache model_id in let vocab_file = Filename.concat model_cache "vocab.json" in let merges_file = Filename.concat model_cache "merges.txt" in (* Create cache directories if they don't exist *) if not (Sys.file_exists model_cache) then Sys.command (Printf.sprintf "mkdir -p %s" model_cache) |> ignore; (* Download vocab.json if it doesn't exist *) if not (Sys.file_exists vocab_file) then ( Printf.printf "Downloading vocab.json for %s...\n%!" model_id; let url = Printf.sprintf "https://huggingface.co/%s/resolve/main/vocab.json" 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.json for %s" model_id)); (* Download merges.txt if it doesn't exist *) if not (Sys.file_exists merges_file) then ( Printf.printf "Downloading merges.txt for %s...\n%!" model_id; let url = Printf.sprintf "https://huggingface.co/%s/resolve/main/merges.txt" model_id in let cmd = Printf.sprintf "curl -L -o %s %s 2>/dev/null || wget -O %s %s 2>/dev/null" merges_file url merges_file url in let exit_code = Sys.command cmd in if exit_code <> 0 then failwith (Printf.sprintf "Failed to download merges.txt for %s" model_id)); (vocab_file, merges_file) let create ?vocab_file ?merges_file ?model_id () = (* Either provide vocab_file and merges_file paths, or a model_id to download from *) let vocab_file, merges_file = match (vocab_file, merges_file, model_id) with | Some vf, Some mf, _ -> (vf, mf) | None, None, Some id -> download_vocab_and_merges id | None, None, None -> download_vocab_and_merges "gpt2" (* Default *) | _ -> failwith "Either provide both vocab_file and merges_file, or model_id" in (* Create GPT-2 tokenizer with ByteLevel pre-tokenizer Use use_regex:true to enable GPT-2 pattern splitting *) let bpe_model = Saga.Bpe.from_files ~vocab_file ~merges_file in { bpe_model; vocab_size = 50257; (* GPT-2 vocab size *) bos_token_id = 50256; (* <|endoftext|> *) eos_token_id = 50256; (* <|endoftext|> *) pad_token_id = None; (* GPT-2 doesn't use padding by default *) } let encode_to_array t text = (* Use pre-tokenizer to split text, then apply BPE tokenization *) let pre_tokenizer = Saga.Pre_tokenizers.byte_level ~add_prefix_space:false ~use_regex:true () in let pre_tokens = pre_tokenizer text |> List.map fst in (* Tokenize each pre-token with BPE and collect IDs *) let token_ids = List.concat_map (fun pre_token -> Saga.Bpe.tokenize t.bpe_model pre_token |> List.map (fun token -> token.Saga.Bpe.id)) pre_tokens in 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 (* Convert to tensors *) let input_ids = Rune.create Int32 [| 1; seq_len |] (Array.map Int32.of_int token_ids) in (* Return inputs record *) { input_ids; attention_mask = None; position_ids = None } let encode_batch t ?(max_length = 1024) ?(padding = false) texts = let encoded = List.map (encode_to_array t) texts in (* Find max length or use specified max_length *) let actual_max = if padding then max_length else List.fold_left (fun acc arr -> Int.max acc (Array.length arr)) 0 encoded in (* Ensure we don't exceed vocabulary size *) let vocab_size = t.vocab_size in let validate_tokens arr = Array.map (fun token_id -> if token_id >= 0 && token_id < vocab_size then token_id else t.eos_token_id (* Replace invalid tokens with EOS *)) arr in (* Pad sequences if padding is enabled *) let padded = if padding then let pad_token_id = Option.value t.pad_token_id ~default:t.eos_token_id in List.map (fun arr -> let validated = validate_tokens arr in let len = Array.length validated in if len >= actual_max then Array.sub validated 0 actual_max else Array.append validated (Array.make (actual_max - len) pad_token_id)) encoded else (* Truncate to max length if no padding *) List.map (fun arr -> let validated = validate_tokens arr in let len = Array.length validated in if len > actual_max then Array.sub validated 0 actual_max else validated) encoded in (* Convert to tensor *) 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; actual_max |] data in (* Convert to Rune tensor *) Rune.of_nx nx_tensor let decode t token_ids = (* Decode token IDs back to text using BPE model *) let tokens = Array.to_list token_ids |> List.filter_map (fun id -> Saga.Bpe.id_to_token t.bpe_model id) in String.concat "" tokens (* Get special token IDs *) let get_bos_token_id t = t.bos_token_id let get_eos_token_id t = t.eos_token_id let get_pad_token_id t = t.pad_token_id let get_vocab_size t = t.vocab_size end (* GPT-2 Embeddings *) let embeddings ~config () = let open Kaun.Layer in (* GPT-2 embeddings: token + position *) let token_embeddings = embedding ~vocab_size:config.vocab_size ~embed_dim:config.n_embd () in let position_embeddings = embedding ~vocab_size:config.n_positions ~embed_dim:config.n_embd () in let dropout = dropout ~rate:config.embd_pdrop () in (* Custom module that applies both embeddings and sums them *) { Kaun.init = (fun ~rngs ~dtype -> let keys = Rune.Rng.split ~n:3 rngs in Kaun.Ptree.record_of [ ("token_embeddings", token_embeddings.init ~rngs:keys.(0) ~dtype); ( "position_embeddings", position_embeddings.init ~rngs:keys.(1) ~dtype ); ("dropout", dropout.init ~rngs:keys.(2) ~dtype); ]); Kaun.apply = (fun params ~training ?rngs x -> (* x is expected to be float tensor, but we need int indices *) let input_ids = Rune.cast Rune.int32 x in match params with | Kaun.Ptree.Record fields -> (* Get each embedding layer's params *) let get_params name = match Kaun.Ptree.Record.find_opt name fields with | Some p -> p | None -> failwith ("Embeddings: missing " ^ name) in (* Manually perform embedding lookups *) 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 (* Perform embedding lookups using differentiable operations *) let lookup_embeddings embedding_table indices = let batch_size = (Rune.shape indices).(0) in let seq_len = (Rune.shape indices).(1) in let table_shape = Rune.shape embedding_table in if Array.length table_shape <> 2 then failwith (Printf.sprintf "Embedding table has wrong shape: %d dims, expected 2" (Array.length table_shape)); let embed_dim = table_shape.(1) in (* Flatten indices for gather operation *) let indices_flat = Rune.reshape [| batch_size * seq_len |] indices in (* Use take to gather embeddings - this is differentiable *) let gathered = Rune.take ~axis:0 indices_flat embedding_table in (* Reshape to [batch_size, seq_len, embed_dim] *) Rune.reshape [| batch_size; seq_len; embed_dim |] gathered in (* Apply token embeddings *) let token_embeds = lookup_embeddings token_embeddings_table input_ids in (* Create position ids: [0, 1, 2, ..., seq_len-1] *) 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 (* Sum embeddings *) let embeddings = Rune.add token_embeds position_embeds in (* Apply dropout *) let embeddings = dropout.apply (get_params "dropout") ~training ?rngs embeddings in embeddings | _ -> failwith "Embeddings: invalid params"); } (* Main Model *) type 'a gpt2 = { 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; hidden_states : (float, 'a) Rune.t list option; attentions : (float, 'a) Rune.t list option; } module Gpt2_block = struct (* Generate causal attention mask *) let causal_mask ~seq_len ~dtype = (* Create a lower triangular matrix for causal masking *) let mask = ones dtype [| seq_len; seq_len |] in tril mask ~k:0 (* GPT-2 uses GELU activation *) let gelu x = (* Use exact GELU with erf for numerical stability *) (* GELU(x) = 0.5 * x * (1 + erf(x / sqrt(2))) *) Rune.gelu x (* Multi-head attention with causal masking *) let causal_attention ~n_head ~ ~params x = let batch_size = (shape x).(0) in let seq_len = (shape x).(1) in let head_dim = hidden_size / n_head in let dtype = dtype x in (* Get Q, K, V weights and biases *) let get_param name = match Kaun.Ptree.Record.find_opt name params with | Some (Kaun.Ptree.Tensor t) -> t | _ -> failwith ("Missing parameter: " ^ name) in let qkv_weight = get_param "qkv_weight" in let qkv_bias = get_param "qkv_bias" in let out_weight = get_param "attn_out_weight" in let out_bias = get_param "attn_out_bias" in (* Compute combined QKV *) let qkv = add (matmul x qkv_weight) (reshape [| 1; 1; 3 * hidden_size |] qkv_bias) in (* Split into Q, K, V *) let query = slice [ A; A; R (0, hidden_size) ] qkv in let key = slice [ A; A; R (hidden_size, 2 * hidden_size) ] qkv in let value = slice [ A; A; R (2 * hidden_size, 3 * hidden_size) ] qkv in (* Reshape for multi-head attention: [batch, seq, n_head, head_dim] *) let reshape_for_heads t = let t = reshape [| batch_size; seq_len; n_head; head_dim |] t in (* Transpose to [batch, n_head, seq, head_dim] *) transpose ~axes:[ 0; 2; 1; 3 ] t in let query = reshape_for_heads query in let key = reshape_for_heads key in let value = reshape_for_heads value in (* Scaled dot-product attention with causal mask *) (* scores = Q @ K^T / sqrt(head_dim) *) let key_t = transpose ~axes:[ 0; 1; 3; 2 ] key in let scores = matmul query key_t in let scores = div_s scores (Float.sqrt (Float.of_int head_dim)) in (* Apply causal mask *) let mask = causal_mask ~seq_len ~dtype in (* Expand mask for batch and heads: [1, 1, seq_len, seq_len] *) let mask = reshape [| 1; 1; seq_len; seq_len |] mask in (* Where mask is 0, set score to large negative value *) let neg_inf = full dtype [| 1; 1; 1; 1 |] (-1e10) in let scores = where (equal_s mask 0.0) neg_inf scores in (* Softmax over last dimension *) let attn_weights = softmax scores ~axes:[ 3 ] in (* Apply attention to values *) let attn_output = matmul attn_weights value in (* Transpose back and reshape *) let attn_output = transpose ~axes:[ 0; 2; 1; 3 ] attn_output in (* Make contiguous before reshaping - force a copy to ensure it's contiguous *) let attn_output = copy attn_output in let attn_output = reshape [| batch_size; seq_len; hidden_size |] attn_output in (* Output projection *) add (matmul attn_output out_weight) (reshape [| 1; 1; hidden_size |] out_bias) (* Feed-forward network *) let mlp ~n_inner ~ ~params x = let get_param name = match Kaun.Ptree.Record.find_opt name params with | Some (Kaun.Ptree.Tensor t) -> t | _ -> failwith ("Missing parameter: " ^ name) in let inter_weight = get_param "inter_weight" in let inter_bias = get_param "inter_bias" in let out_weight = get_param "out_weight" in let out_bias = get_param "out_bias" in (* First linear layer *) let h = add (matmul x inter_weight) (reshape [| 1; 1; n_inner |] inter_bias) in (* GELU activation *) let h = gelu h in (* Second linear layer *) add (matmul h out_weight) (reshape [| 1; 1; hidden_size |] out_bias) (* GPT-2 transformer block with pre-layer normalization *) let gpt2_block ~config ~params x = let get_param name = match Kaun.Ptree.Record.find_opt name params with | Some (Kaun.Ptree.Tensor t) -> t | _ -> failwith ("Missing parameter: " ^ name) in let ln1_weight = get_param "attn_gamma" in let ln1_bias = get_param "attn_beta" in let ln2_weight = get_param "ffn_gamma" in let ln2_bias = get_param "ffn_beta" in (* Pre-layer norm for attention *) let normed = Kaun.Ops.layer_norm ~gamma:ln1_weight ~beta:ln1_bias ~dim:(-1) ~eps:config.layer_norm_epsilon ~elementwise_affine:true x in (* Self-attention with residual *) let attn_out = causal_attention ~n_head:config.n_head ~hidden_size:config.n_embd ~params normed in let x = add x attn_out in (* Pre-layer norm for FFN *) let normed = Kaun.Ops.layer_norm ~gamma:ln2_weight ~beta:ln2_bias ~dim:(-1) ~eps:config.layer_norm_epsilon ~elementwise_affine:true x in (* FFN with residual *) let n_inner = Option.value config.n_inner ~default:(4 * config.n_embd) in let ffn_out = mlp ~n_inner ~hidden_size:config.n_embd ~params normed in add x ffn_out (* Stack of GPT-2 blocks *) let gpt2_transformer ~config ~layer_params x = (* Apply each transformer block sequentially *) let rec apply_layers x = function | [] -> x | params :: rest -> let x = gpt2_block ~config ~params x in apply_layers x rest in apply_layers x layer_params end let create ?(config = default_config) () = (* GPT-2 uses a custom architecture with causal attention *) (* We'll implement it as a custom module *) { Kaun.init = (fun ~rngs ~dtype -> (* Initialize embeddings *) let embeddings_layer = embeddings ~config () in let embeddings_params = Kaun.init embeddings_layer ~rngs ~dtype in (* Initialize transformer blocks *) let layer_params = List.init config.n_layer (fun _ -> (* Each layer needs initialized parameters *) (* For now, return empty - will be filled by from_pretrained *) Kaun.Ptree.Record.empty) in (* Initialize final layer norm *) let ln_f_gamma = Rune.ones dtype [| config.n_embd |] in let ln_f_beta = Rune.zeros dtype [| config.n_embd |] in (* Return params structure *) Kaun.Ptree.List [ embeddings_params; Kaun.Ptree.List (List.map (fun p -> Kaun.Ptree.Record p) layer_params); Kaun.Ptree.Record (Kaun.Ptree.Record.empty |> Kaun.Ptree.Record.add "gamma" (Kaun.Ptree.Tensor ln_f_gamma) |> Kaun.Ptree.Record.add "beta" (Kaun.Ptree.Tensor ln_f_beta)); ]); Kaun.apply = (fun params ~training:_ ?rngs:_ x -> match params with | Kaun.Ptree.List [ embeddings_params; layer_params_list; ln_f_params ] -> ( (* Apply embeddings *) let embeddings_layer = embeddings ~config () in let x = Kaun.apply embeddings_layer embeddings_params ~training:false x in (* Extract layer params *) let layer_params = match layer_params_list with | Kaun.Ptree.List lst -> List.map (function | Kaun.Ptree.Record r -> r | _ -> failwith "Invalid layer params structure") lst | _ -> failwith "Invalid layer params list" in (* Apply GPT-2 transformer blocks *) let x = Gpt2_block.gpt2_transformer ~config ~layer_params x in (* Apply final layer norm *) match ln_f_params with | Kaun.Ptree.Record fields -> let gamma = match Kaun.Ptree.Record.find_opt "gamma" fields with | Some (Kaun.Ptree.Tensor t) -> t | _ -> failwith "Missing ln_f gamma" in let beta = match Kaun.Ptree.Record.find_opt "beta" fields with | Some (Kaun.Ptree.Tensor t) -> t | _ -> failwith "Missing ln_f beta" in Kaun.Ops.layer_norm x ~gamma ~beta ~eps:config.layer_norm_epsilon ~dim:(-1) ~elementwise_affine:true | _ -> failwith "Invalid ln_f params") | _ -> failwith "Invalid params structure"); } let from_pretrained ?(model_id = "gpt2") ?revision ?cache_config ~dtype () = (* Load config and weights from HuggingFace *) let cache_config = Option.value cache_config ~default:Kaun_huggingface.Config.default in let revision = Option.value revision ~default:Kaun_huggingface.Latest in (* Load config JSON from HuggingFace *) let config_json = match Kaun_huggingface.load_config ~config:cache_config ~revision ~model_id () with | Cached json | Downloaded (json, _) -> json in (* Parse GPT-2 specific config *) let gpt2_config = let open Yojson.Safe.Util in { vocab_size = config_json |> member "vocab_size" |> to_int; n_positions = config_json |> member "n_positions" |> to_int; n_embd = config_json |> member "n_embd" |> to_int; n_layer = config_json |> member "n_layer" |> to_int; n_head = config_json |> member "n_head" |> to_int; n_inner = config_json |> member "n_inner" |> to_int_option; activation_function = (match config_json |> member "activation_function" |> to_string_option with | Some "gelu_new" -> `gelu_new | Some "gelu" -> `gelu | Some "relu" -> `relu | Some "swish" | Some "silu" -> `swish | _ -> `gelu_new); resid_pdrop = config_json |> member "resid_pdrop" |> to_float_option |> Option.value ~default:0.1; embd_pdrop = config_json |> member "embd_pdrop" |> to_float_option |> Option.value ~default:0.1; attn_pdrop = config_json |> member "attn_pdrop" |> to_float_option |> Option.value ~default:0.1; layer_norm_epsilon = config_json |> member "layer_norm_epsilon" |> to_float_option |> Option.value ~default:1e-5; initializer_range = config_json |> member "initializer_range" |> to_float_option |> Option.value ~default:0.02; scale_attn_weights = config_json |> member "scale_attn_weights" |> to_bool_option |> Option.value ~default:true; use_cache = config_json |> member "use_cache" |> to_bool_option |> Option.value ~default:true; scale_attn_by_inverse_layer_idx = config_json |> member "scale_attn_by_inverse_layer_idx" |> to_bool_option |> Option.value ~default:false; reorder_and_upcast_attn = config_json |> member "reorder_and_upcast_attn" |> to_bool_option |> Option.value ~default:false; bos_token_id = config_json |> member "bos_token_id" |> to_int_option; eos_token_id = config_json |> member "eos_token_id" |> to_int_option; pad_token_id = config_json |> member "pad_token_id" |> to_int_option; } in (* Load weights using HuggingFace infrastructure *) let hf_params = Kaun_huggingface.from_pretrained ~config:cache_config ~revision ~model_id ~dtype () in (* Map HuggingFace parameter names to our expected structure *) let map_huggingface_to_kaun hf_params = (* Flatten the nested HuggingFace structure *) 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 decoder_layers = ref [] in let final_layer_norm_params = ref Kaun.Ptree.Record.empty in List.iter (fun (hf_name, tensor) -> match hf_name with (* Embeddings *) | s when String.starts_with ~prefix:"wte.weight" s -> embeddings_params := Kaun.Ptree.Record.add "token_embeddings" (Kaun.Ptree.Tensor tensor) !embeddings_params | s when String.starts_with ~prefix:"wpe.weight" s -> embeddings_params := Kaun.Ptree.Record.add "position_embeddings" (Kaun.Ptree.Tensor tensor) !embeddings_params (* Transformer blocks *) | s when String.starts_with ~prefix:"h." s -> ( let rest = String.sub s 2 (String.length s - 2) 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 (* Ensure we have enough layers *) while List.length !decoder_layers <= layer_idx_int do decoder_layers := !decoder_layers @ [ ref Kaun.Ptree.Record.empty ] done; (* Get the layer params ref *) let layer_params = List.nth !decoder_layers layer_idx_int in (* Handle different parameter types *) match param_name with | "attn.c_attn.weight" -> (* Keep combined QKV weight, will split after matmul *) (* GPT-2 stores as [in_features, out_features] = [768, 2304] *) layer_params := Kaun.Ptree.Record.add "qkv_weight" (Kaun.Ptree.Tensor tensor) !layer_params | "attn.c_attn.bias" -> (* Keep combined QKV bias *) layer_params := Kaun.Ptree.Record.add "qkv_bias" (Kaun.Ptree.Tensor tensor) !layer_params | "attn.c_proj.weight" -> (* GPT-2 stores as [in, out] which is [768, 768] *) (* We need [768, 768] for x[*,*,768] @ W -> [*,*,768] *) (* So NO transpose needed! *) layer_params := Kaun.Ptree.Record.add "attn_out_weight" (Kaun.Ptree.Tensor tensor) !layer_params | "attn.c_proj.bias" -> layer_params := Kaun.Ptree.Record.add "attn_out_bias" (Kaun.Ptree.Tensor tensor) !layer_params | "mlp.c_fc.weight" -> (* GPT-2 stores as [in, out] which is [768, 3072] *) (* We need [768, 3072] for x[*,*,768] @ W -> [*,*,3072] *) (* So NO transpose needed! *) layer_params := Kaun.Ptree.Record.add "inter_weight" (Kaun.Ptree.Tensor tensor) !layer_params | "mlp.c_proj.weight" -> (* GPT-2 stores as [in, out] which is [3072, 768] *) (* We need [3072, 768] for h[*,*,3072] @ W -> [*,*,768] *) (* So NO transpose needed! *) layer_params := Kaun.Ptree.Record.add "out_weight" (Kaun.Ptree.Tensor tensor) !layer_params | "mlp.c_fc.bias" -> layer_params := Kaun.Ptree.Record.add "inter_bias" (Kaun.Ptree.Tensor tensor) !layer_params | "mlp.c_proj.bias" -> layer_params := Kaun.Ptree.Record.add "out_bias" (Kaun.Ptree.Tensor tensor) !layer_params | "ln_1.weight" -> layer_params := Kaun.Ptree.Record.add "attn_gamma" (Kaun.Ptree.Tensor tensor) !layer_params | "ln_1.bias" -> layer_params := Kaun.Ptree.Record.add "attn_beta" (Kaun.Ptree.Tensor tensor) !layer_params | "ln_2.weight" -> layer_params := Kaun.Ptree.Record.add "ffn_gamma" (Kaun.Ptree.Tensor tensor) !layer_params | "ln_2.bias" -> layer_params := Kaun.Ptree.Record.add "ffn_beta" (Kaun.Ptree.Tensor tensor) !layer_params | _ -> () (* Ignore other parameters like attn.bias *)) | _ -> ()) (* Final layer norm *) | s when String.starts_with ~prefix:"ln_f.weight" s -> final_layer_norm_params := Kaun.Ptree.Record.add "gamma" (Kaun.Ptree.Tensor tensor) !final_layer_norm_params | s when String.starts_with ~prefix:"ln_f.bias" s -> final_layer_norm_params := Kaun.Ptree.Record.add "beta" (Kaun.Ptree.Tensor tensor) !final_layer_norm_params | _ -> () (* Ignore other parameters *)) flat_params; (* Build the final sequential structure *) let decoder_list = List.map (fun r -> Kaun.Ptree.Record !r) !decoder_layers in (* Add dropout placeholder for embeddings *) embeddings_params := Kaun.Ptree.Record.add "dropout" (Kaun.Ptree.List []) !embeddings_params; (* Create sequential structure: embeddings, decoder layers, final layer norm *) Kaun.Ptree.List [ Kaun.Ptree.Record !embeddings_params; Kaun.Ptree.List decoder_list; Kaun.Ptree.Record !final_layer_norm_params; ] in let mapped_params = map_huggingface_to_kaun hf_params in let model = create ~config:gpt2_config () in { model; params = mapped_params; config = gpt2_config; dtype } let forward gpt2 inputs ?(training = false) ?( = false) ?(output_attentions = false) () = let { model; params; _ } = gpt2 in let { input_ids; attention_mask = _; position_ids = _ } = inputs in (* GPT-2 forward pass using the Kaun model *) let open Rune in (* Get dtype from params to maintain polymorphism *) 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 (* Apply the model using Kaun *) let model_output = Kaun.apply model params ~training float_input in (* The model output is the final hidden state *) let = model_output in (* For output_hidden_states and output_attentions, we would need to modify the model architecture to return intermediate values. For now, return minimal info *) let = if output_hidden_states then Some [ last_hidden_state ] else None in let attentions = if output_attentions then None else None in (* Return structured output *) { last_hidden_state; hidden_states; attentions } (* Language Modeling Head *) module For_causal_lm = struct let create ?(config = default_config) () = let open Kaun.Layer in sequential [ (* GPT-2 base model *) create ~config (); (* LM head: project to vocabulary *) linear ~in_features:config.n_embd ~out_features:config.vocab_size (); ] let forward ~model ~params ~input_ids ?attention_mask:_ ?position_ids:_ ?labels ~training () = let open Rune in (* Get the dtype from params to maintain polymorphism *) 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 = Kaun.apply model params ~training float_input in (* Apply LM head: project hidden states to vocabulary GPT-2 uses weight tying, so we use the transposed token embeddings *) let logits = (* Try to find token embeddings in params structure *) match params with | Kaun.Ptree.List param_list -> ( (* params is a list where first element is embeddings module *) match List.nth_opt param_list 0 with | Some (Kaun.Ptree.Record emb_fields) -> ( match Kaun.Ptree.Record.find_opt "token_embeddings" emb_fields with | Some (Kaun.Ptree.Tensor wte) -> (* Token embeddings have shape [vocab_size, hidden_size] We need to use them as [hidden_size, vocab_size] for the LM head *) let wte_transposed = transpose ~axes:[ 1; 0 ] wte in matmul hidden_states wte_transposed | _ -> hidden_states) | _ -> hidden_states) | _ -> hidden_states in (* Compute loss if labels provided *) let loss = match labels with | Some labels -> (* Shift labels for next token prediction *) let batch_size = (shape labels).(0) in let seq_length = (shape labels).(1) in let vocab_size = (shape logits).(2) in (* Shift logits and labels: predict next token *) let shift_logits = slice [ A; R (0, seq_length - 1); A ] logits in let shift_labels = slice [ A; R (1, seq_length); A ] labels in let flat_logits = Rune.reshape [| batch_size * (seq_length - 1); vocab_size |] shift_logits in let flat_labels = Rune.reshape [| batch_size * (seq_length - 1) |] shift_labels in Some (Kaun.Loss.softmax_cross_entropy_with_indices flat_logits flat_labels) | None -> None in (logits, loss) end (* Utilities *) let parse_gpt2_config json = (* Parse GPT-2 specific configuration from HuggingFace JSON *) let open Yojson.Safe.Util in { vocab_size = json |> member "vocab_size" |> to_int; n_positions = json |> member "n_positions" |> to_int; n_embd = json |> member "n_embd" |> to_int; n_layer = json |> member "n_layer" |> to_int; n_head = json |> member "n_head" |> to_int; n_inner = json |> member "n_inner" |> to_int_option; activation_function = (match json |> member "activation_function" |> to_string_option with | Some "gelu_new" -> `gelu_new | Some "gelu" -> `gelu | Some "relu" -> `relu | Some "swish" | Some "silu" -> `swish | _ -> `gelu_new); resid_pdrop = json |> member "resid_pdrop" |> to_float_option |> Option.value ~default:0.1; embd_pdrop = json |> member "embd_pdrop" |> to_float_option |> Option.value ~default:0.1; attn_pdrop = json |> member "attn_pdrop" |> to_float_option |> Option.value ~default:0.1; layer_norm_epsilon = json |> member "layer_norm_epsilon" |> to_float_option |> Option.value ~default:1e-5; initializer_range = json |> member "initializer_range" |> to_float_option |> Option.value ~default:0.02; scale_attn_weights = json |> member "scale_attn_weights" |> to_bool_option |> Option.value ~default:true; use_cache = json |> member "use_cache" |> to_bool_option |> Option.value ~default:true; scale_attn_by_inverse_layer_idx = json |> member "scale_attn_by_inverse_layer_idx" |> to_bool_option |> Option.value ~default:false; reorder_and_upcast_attn = json |> member "reorder_and_upcast_attn" |> to_bool_option |> Option.value ~default:false; bos_token_id = json |> member "bos_token_id" |> to_int_option; eos_token_id = json |> member "eos_token_id" |> to_int_option; pad_token_id = json |> member "pad_token_id" |> to_int_option; } 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 (* Assuming float32 *) Printf.sprintf "GPT-2 parameters: %d (%.2f MB)" total_params (float_of_int total_bytes /. 1024. /. 1024.) (* Common GPT-2 model configurations *) let load_gpt2_small ~dtype () = from_pretrained ~model_id:"gpt2" ~dtype () let load_gpt2_medium ~dtype () = from_pretrained ~model_id:"gpt2-medium" ~dtype () let load_gpt2_large ~dtype () = from_pretrained ~model_id:"gpt2-large" ~dtype () let load_gpt2_xl ~dtype () = from_pretrained ~model_id:"gpt2-xl" ~dtype ()
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