mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 02:44:36 +00:00
scripts : Remove missed baichuan convert script (#4127)
This commit is contained in:
Galunid
GitHub
parent
2923f17f6f
commit
0b5c3b0457
@ -1,315 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# HF baichuan --> gguf conversion
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any
|
||||
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("")
|
||||
Loading…
Reference in New Issue
Block a user