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-#!/usr/bin/env python3
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-# HF llama --> gguf conversion
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-
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-from __future__ import annotations
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-
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-import argparse
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-import json
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-import os
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-import struct
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-import sys
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-from pathlib import Path
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-from typing import TYPE_CHECKING, Any
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-
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-import gguf
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-import numpy as np
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-import torch
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-from sentencepiece import SentencePieceProcessor # type: ignore[import]
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-
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-if TYPE_CHECKING:
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- from typing import TypeAlias
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-
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-NDArray: TypeAlias = 'np.ndarray[Any, Any]'
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-
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-# reverse HF permute back to original pth layout
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-# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py
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-
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-
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-def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray:
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- if n_kv_head is not None and n_head != n_kv_head:
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- n_head //= n_kv_head
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-
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- return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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- .swapaxes(1, 2)
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- .reshape(weights.shape))
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-
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-
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-def count_model_parts(dir_model: str) -> int:
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- num_parts = 0
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-
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- for filename in os.listdir(dir_model):
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- if filename.startswith("pytorch_model-"):
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- num_parts += 1
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-
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- if num_parts > 0:
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- print("gguf: found " + str(num_parts) + " model parts")
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-
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- return num_parts
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-
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-
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-def parse_args() -> argparse.Namespace:
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- parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file")
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- parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
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- parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
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- parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
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- parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1)
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- return parser.parse_args()
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-
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-args = parse_args()
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-
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-dir_model = args.model
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-ftype = args.ftype
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-if not dir_model.is_dir():
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- print(f'Error: {args.model} is not a directory', file = sys.stderr)
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- sys.exit(1)
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-
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-# possible tensor data types
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-# ftype == 0 -> float32
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-# ftype == 1 -> float16
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-
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-# map from ftype to string
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-ftype_str = ["f32", "f16"]
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-
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-if args.outfile is not None:
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- fname_out = args.outfile
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-else:
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- # output in the same directory as the model by default
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- fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
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-
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-print("gguf: loading model "+dir_model.name)
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-
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-with open(dir_model / "config.json", "r", encoding="utf-8") as f:
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- hparams = json.load(f)
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-
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-if hparams["architectures"][0] != "LlamaForCausalLM":
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- print("Model architecture not supported: " + hparams["architectures"][0])
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-
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- sys.exit()
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-
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-# get number of model parts
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-num_parts = count_model_parts(dir_model)
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-
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-ARCH=gguf.MODEL_ARCH.LLAMA
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-gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
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-
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-print("gguf: get model metadata")
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-
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-block_count = hparams["num_hidden_layers"]
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-head_count = hparams["num_attention_heads"]
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-
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-if "num_key_value_heads" in hparams:
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- head_count_kv = hparams["num_key_value_heads"]
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-else:
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- head_count_kv = head_count
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-
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-if "_name_or_path" in hparams:
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- hf_repo = hparams["_name_or_path"]
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-else:
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- hf_repo = ""
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-
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-if "max_sequence_length" in hparams:
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- ctx_length = hparams["max_sequence_length"]
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-elif "max_position_embeddings" in hparams:
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- ctx_length = hparams["max_position_embeddings"]
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-else:
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- print("gguf: can not find ctx length parameter.")
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-
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- sys.exit()
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-
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-
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-gguf_writer.add_name(dir_model.name)
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-gguf_writer.add_source_hf_repo(hf_repo)
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-gguf_writer.add_tensor_data_layout("Meta AI original pth")
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-gguf_writer.add_context_length(ctx_length)
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-gguf_writer.add_embedding_length(hparams["hidden_size"])
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-gguf_writer.add_block_count(block_count)
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-gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
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-gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
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-gguf_writer.add_head_count(head_count)
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-gguf_writer.add_head_count_kv(head_count_kv)
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-gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
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-
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-if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]:
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- if "type" in hparams["rope_scaling"]:
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- if hparams["rope_scaling"]["type"] == "linear":
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- gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"])
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-
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-
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-# TOKENIZATION
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-
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-print("gguf: get tokenizer metadata")
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-
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-tokens: list[bytes] = []
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-scores: list[float] = []
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-toktypes: list[int] = []
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-
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-tokenizer_model_file = dir_model / 'tokenizer.model'
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-if not tokenizer_model_file.is_file():
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- print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
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- sys.exit(1)
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-
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-# vocab type sentencepiece
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-print("gguf: get sentencepiece tokenizer vocab, scores and token types")
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-
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-tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
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-
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-for i in range(tokenizer.vocab_size()):
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- text: bytes
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- score: float
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-
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- piece = tokenizer.id_to_piece(i)
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- text = piece.encode("utf-8")
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- score = tokenizer.get_score(i)
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-
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- toktype = 1 # defualt to normal token type
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- if tokenizer.is_unknown(i):
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- toktype = 2
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- if tokenizer.is_control(i):
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- toktype = 3
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-
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- # toktype = 4 is user-defined = tokens from added_tokens.json
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-
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- if tokenizer.is_unused(i):
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- toktype = 5
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- if tokenizer.is_byte(i):
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- toktype = 6
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-
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- tokens.append(text)
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- scores.append(score)
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- toktypes.append(toktype)
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-
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-added_tokens_file = dir_model / 'added_tokens.json'
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-if added_tokens_file.is_file():
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- with open(added_tokens_file, "r", encoding="utf-8") as f:
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- addtokens_json = json.load(f)
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-
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- print("gguf: get added tokens")
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-
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- for key in addtokens_json:
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- tokens.append( key.encode("utf-8") )
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- scores.append(-1000.0)
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- toktypes.append(4) # user-defined token type
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-
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-
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-gguf_writer.add_tokenizer_model("llama")
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-gguf_writer.add_token_list(tokens)
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-gguf_writer.add_token_scores(scores)
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-gguf_writer.add_token_types(toktypes)
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-
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-special_vocab = gguf.SpecialVocab(dir_model)
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-special_vocab.add_to_gguf(gguf_writer)
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-
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-# TENSORS
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-
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-tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
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-
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-# tensor info
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-print("gguf: get tensor metadata")
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-
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-if num_parts == 0:
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- part_names = iter(("pytorch_model.bin",))
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-else:
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- part_names = (
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- f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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- )
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-
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-for part_name in part_names:
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- if args.vocab_only:
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- break
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- print("gguf: loading model part '" + part_name + "'")
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- model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
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-
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- for name in model_part.keys():
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- data = model_part[name]
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-
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- # we don't need these
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- if name.endswith(".rotary_emb.inv_freq"):
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- continue
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-
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- old_dtype = data.dtype
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-
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- # convert any unsupported data types to float32
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- if data.dtype != torch.float16 and data.dtype != torch.float32:
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- data = data.to(torch.float32)
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-
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- data = data.squeeze().numpy()
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-
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- # reverse permute these
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- if name.endswith(".q_proj.weight"):
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- data = reverse_hf_permute(data, head_count)
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- if name.endswith(".k_proj.weight"):
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- data = reverse_hf_permute(data, head_count, head_count_kv)
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-
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- # map tensor names
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- new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
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- if new_name is None:
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- print("Can not map tensor '" + name + "'")
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- sys.exit()
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-
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- n_dims = len(data.shape)
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- data_dtype = data.dtype
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-
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- # if f32 desired, convert any float16 to float32
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- if ftype == 0 and data_dtype == np.float16:
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- data = data.astype(np.float32)
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-
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- # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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- if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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- data = data.astype(np.float32)
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-
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- # if f16 desired, convert any float32 2-dim weight tensors to float16
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- if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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- data = data.astype(np.float16)
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-
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- print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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-
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- gguf_writer.add_tensor(new_name, data)
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-
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-
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-print("gguf: write header")
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-gguf_writer.write_header_to_file()
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-print("gguf: write metadata")
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-gguf_writer.write_kv_data_to_file()
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-if not args.vocab_only:
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- print("gguf: write tensors")
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- gguf_writer.write_tensors_to_file()
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-
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-gguf_writer.close()
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-
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-print(f"gguf: model successfully exported to '{fname_out}'")
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-print("")
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slaren