mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 03:14:35 +00:00
317 lines
9.9 KiB
Python
Executable File
317 lines
9.9 KiB
Python
Executable File
#!/usr/bin/env python3
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# HF baichuan --> gguf conversion
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from __future__ import annotations
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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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import itertools
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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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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
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import gguf
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if TYPE_CHECKING:
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from typing import TypeAlias
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NDArray: TypeAlias = 'np.ndarray[Any, Any]'
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# reverse HF permute back to original pth layout
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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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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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def reverse_hf_permute_part(weights: NDArray, n_part: int, n_head: int, n_head_kv: int| None = None) -> NDArray:
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r = weights.shape[0] // 3
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return (reverse_hf_permute(weights[r * n_part : r * n_part + r, ...], n_head, n_head_kv))
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def reverse_hf_part(weights: NDArray, n_part: int) -> NDArray:
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r = weights.shape[0] // 3
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return weights[r * n_part : r * n_part + r, ...]
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def count_model_parts(dir_model: str) -> int:
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num_parts = 0
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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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if num_parts > 0:
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print("gguf: found " + str(num_parts) + " model parts")
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return num_parts
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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(
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"--vocab-only", action="store_true",
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help="extract only the vocab",
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)
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parser.add_argument(
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"--outfile", type=Path,
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help="path to write to; default: based on input",
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)
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parser.add_argument(
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"model", type=Path,
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help="directory containing model file, or model file itself (*.bin)",
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)
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parser.add_argument(
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"ftype", type=int, choices=[0, 1], default=1, nargs='?',
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help="output format - use 0 for float32, 1 for float16",
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)
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parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
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return parser.parse_args()
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args = parse_args()
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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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endianess = gguf.GGUFEndian.LITTLE
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if args.bigendian:
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endianess = gguf.GGUFEndian.BIG
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endianess_str = "Big Endian" if args.bigendian else "Little Endian"
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print(f"gguf: Conversion Endianess {endianess}")
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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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# map from ftype to string
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ftype_str = ["f32", "f16"]
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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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print("gguf: loading model "+dir_model.name)
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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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print("hello print: ",hparams["architectures"][0])
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if hparams["architectures"][0] != "BaichuanForCausalLM" and hparams["architectures"][0] != "BaiChuanForCausalLM":
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print("Model architecture not supported: " + hparams["architectures"][0])
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sys.exit()
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# get number of model parts
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num_parts = count_model_parts(dir_model)
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print(f"num_parts:{num_parts}\n")
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ARCH=gguf.MODEL_ARCH.BAICHUAN
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gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
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print("gguf: get model metadata")
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block_count = hparams["num_hidden_layers"]
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head_count = hparams["num_attention_heads"]
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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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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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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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elif "model_max_length" in hparams:
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ctx_length = hparams["model_max_length"]
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else:
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print("gguf: can not find ctx length parameter.")
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sys.exit()
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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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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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# TOKENIZATION
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print("gguf: get tokenizer metadata")
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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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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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# vocab type sentencepiece
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print("gguf: get sentencepiece tokenizer vocab, scores and token types")
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tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
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vocab_size = hparams.get('vocab_size')
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if vocab_size is None:
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vocab_size = tokenizer.vocab_size()
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for i in range(vocab_size):
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text: bytes
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score: float
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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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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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# toktype = 4 is user-defined = tokens from added_tokens.json
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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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tokens.append(text)
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scores.append(score)
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toktypes.append(toktype)
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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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print("gguf: get added tokens")
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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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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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special_vocab = gguf.SpecialVocab(dir_model, n_vocab = len(tokens))
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special_vocab.add_to_gguf(gguf_writer)
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# TENSORS
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tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
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# tensor info
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print("gguf: get tensor metadata")
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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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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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tmp=model_part
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for i in range(block_count):
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if f"model.layers.{i}.self_attn.W_pack.weight" in model_part:
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print(f"Unpacking and permuting layer {i}")
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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)
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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)
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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)
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del tmp[f"model.layers.{i}.self_attn.W_pack.weight"]
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for name in model_part.keys():
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data = model_part[name]
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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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old_dtype = data.dtype
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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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data = data.squeeze().numpy()
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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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n_dims = len(data.shape)
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data_dtype = data.dtype
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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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# 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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# 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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print(name + " -> " + new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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gguf_writer.add_tensor(new_name, data)
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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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gguf_writer.close()
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print(f"gguf: model successfully exported to '{fname_out}'")
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print("")
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