#!/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 numpy as np import torch from sentencepiece import SentencePieceProcessor # type: ignore[import] if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf 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], default=1, nargs='?', help="output format - use 0 for float32, 1 for float16", ) parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine") 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) endianess = gguf.GGUFEndian.LITTLE if args.bigendian: endianess = gguf.GGUFEndian.BIG endianess_str = "Big Endian" if args.bigendian else "Little Endian" print(f"gguf: Conversion Endianess {endianess}") # 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" and 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], endianess=endianess) 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_scaling_type(gguf.RopeScalingType.LINEAR) gguf_writer.add_rope_scaling_factor(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)) vocab_size = hparams.get('vocab_size') if vocab_size is None: vocab_size = tokenizer.vocab_size() for i in range(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, n_vocab = len(tokens)) 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("")