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Diffstat (limited to 'convert-baichuan-hf-to-gguf.py')
| -rwxr-xr-x | convert-baichuan-hf-to-gguf.py | 292 |
1 files changed, 292 insertions, 0 deletions
diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py new file mode 100755 index 00000000..5b301de2 --- /dev/null +++ b/convert-baichuan-hf-to-gguf.py @@ -0,0 +1,292 @@ +#!/usr/bin/env python3 +# HF baichuan --> gguf conversion + +from __future__ import annotations + +import argparse +import json +import os +import struct +import sys +from pathlib import Path +from typing import TYPE_CHECKING, Any +import itertools +import gguf +import numpy as np +import torch +from sentencepiece import SentencePieceProcessor # type: ignore[import] + + +if TYPE_CHECKING: + from typing import TypeAlias + +NDArray: TypeAlias = 'np.ndarray[Any, Any]' + +# reverse HF permute back to original pth layout + + +def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head + + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + +def reverse_hf_permute_part(weights: NDArray, n_part: int, n_head: int, n_head_kv: int| None = None) -> NDArray: + r = weights.shape[0] // 3 + return (reverse_hf_permute(weights[r * n_part : r * n_part + r, ...], n_head, n_head_kv)) + +def reverse_hf_part(weights: NDArray, n_part: int) -> NDArray: + r = weights.shape[0] // 3 + return weights[r * n_part : r * n_part + r, ...] + +def count_model_parts(dir_model: str) -> int: + num_parts = 0 + + for filename in os.listdir(dir_model): + if filename.startswith("pytorch_model-"): + num_parts += 1 + + if num_parts > 0: + print("gguf: found " + str(num_parts) + " model parts") + + return num_parts + + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file") + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") + parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) + return parser.parse_args() + +args = parse_args() + +dir_model = args.model +ftype = args.ftype +if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file = sys.stderr) + sys.exit(1) + +# possible tensor data types +# ftype == 0 -> float32 +# ftype == 1 -> float16 + +# map from ftype to string +ftype_str = ["f32", "f16"] + +if args.outfile is not None: + fname_out = args.outfile +else: + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' + +print("gguf: loading model "+dir_model.name) + +with open(dir_model / "config.json", "r", encoding="utf-8") as f: + hparams = json.load(f) +print("hello print: ",hparams["architectures"][0]) +if hparams["architectures"][0] != "BaichuanForCausalLM": + print("Model architecture not supported: " + hparams["architectures"][0]) + + sys.exit() + +# get number of model parts +num_parts = count_model_parts(dir_model) +print(f"num_parts:{num_parts}\n") +ARCH=gguf.MODEL_ARCH.BAICHUAN +gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) + +print("gguf: get model metadata") + +block_count = hparams["num_hidden_layers"] +head_count = hparams["num_attention_heads"] + +if "num_key_value_heads" in hparams: + head_count_kv = hparams["num_key_value_heads"] +else: + head_count_kv = head_count + +if "_name_or_path" in hparams: + hf_repo = hparams["_name_or_path"] +else: + hf_repo = "" + +if "max_sequence_length" in hparams: + ctx_length = hparams["max_sequence_length"] +elif "max_position_embeddings" in hparams: + ctx_length = hparams["max_position_embeddings"] +elif "model_max_length" in hparams: + ctx_length = hparams["model_max_length"] +else: + print("gguf: can not find ctx length parameter.") + + sys.exit() + + +gguf_writer.add_name(dir_model.name) +gguf_writer.add_source_hf_repo(hf_repo) +gguf_writer.add_tensor_data_layout("Meta AI original pth") +gguf_writer.add_context_length(ctx_length) +gguf_writer.add_embedding_length(hparams["hidden_size"]) +gguf_writer.add_block_count(block_count) +gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) +gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) +gguf_writer.add_head_count(head_count) +gguf_writer.add_head_count_kv(head_count_kv) +gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) + +if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]: + if "type" in hparams["rope_scaling"]: + if hparams["rope_scaling"]["type"] == "linear": + gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"]) + + +# TOKENIZATION + +print("gguf: get tokenizer metadata") + +tokens: list[bytes] = [] +scores: list[float] = [] +toktypes: list[int] = [] + +tokenizer_model_file = dir_model / 'tokenizer.model' +if not tokenizer_model_file.is_file(): + print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) + sys.exit(1) + +# vocab type sentencepiece +print("gguf: get sentencepiece tokenizer vocab, scores and token types") + +tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) + +for i in range(tokenizer.vocab_size()): + text: bytes + score: float + + piece = tokenizer.id_to_piece(i) + text = piece.encode("utf-8") + score = tokenizer.get_score(i) + + toktype = 1 # defualt to normal token type + if tokenizer.is_unknown(i): + toktype = 2 + if tokenizer.is_control(i): + toktype = 3 + + # toktype = 4 is user-defined = tokens from added_tokens.json + + if tokenizer.is_unused(i): + toktype = 5 + if tokenizer.is_byte(i): + toktype = 6 + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + +added_tokens_file = dir_model / 'added_tokens.json' +if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + addtokens_json = json.load(f) + + print("gguf: get added tokens") + + for key in addtokens_json: + tokens.append( key.encode("utf-8") ) + scores.append(-1000.0) + toktypes.append(4) # user-defined token type + + +gguf_writer.add_tokenizer_model("llama") +gguf_writer.add_token_list(tokens) +gguf_writer.add_token_scores(scores) +gguf_writer.add_token_types(toktypes) + +special_vocab = gguf.SpecialVocab(dir_model) +special_vocab.add_to_gguf(gguf_writer) + +# TENSORS + +tensor_map = gguf.get_tensor_name_map(ARCH,block_count) + +# tensor info +print("gguf: get tensor metadata") + +if num_parts == 0: + part_names = iter(("pytorch_model.bin",)) +else: + part_names = ( + f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) + ) + + +for part_name in part_names: + if args.vocab_only: + break + print("gguf: loading model part '" + part_name + "'") + model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") + + tmp=model_part + for i in range(block_count): + if f"model.layers.{i}.self_attn.W_pack.weight" in model_part: + print(f"Unpacking and permuting layer {i}") + tmp[f"model.layers.{i}.self_attn.q_proj.weight"]=reverse_hf_permute_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],0,head_count,head_count) + tmp[f"model.layers.{i}.self_attn.k_proj.weight"]=reverse_hf_permute_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],1,head_count,head_count_kv) + tmp[f"model.layers.{i}.self_attn.v_proj.weight"]=reverse_hf_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],2) + del tmp[f"model.layers.{i}.self_attn.W_pack.weight"] + + for name in model_part.keys(): + data = model_part[name] + # we don't need these + if name.endswith(".rotary_emb.inv_freq"): + continue + + old_dtype = data.dtype + + # convert any unsupported data types to float32 + if data.dtype != torch.float16 and data.dtype != torch.float32: + data = data.to(torch.float32) + + data = data.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) + if new_name is None: + print("Can not map tensor '" + name + "'") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(name + " -> " + new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) + gguf_writer.add_tensor(new_name, data) + + +print("gguf: write header") +gguf_writer.write_header_to_file() +print("gguf: write metadata") +gguf_writer.write_kv_data_to_file() +if not args.vocab_only: + print("gguf: write tensors") + gguf_writer.write_tensors_to_file() + +gguf_writer.close() + +print(f"gguf: model successfully exported to '{fname_out}'") +print("") |