vbatts/llama.cpp
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Refactoring: more concise and readable

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qunash 2023-03-14 03:57:35 +03:00
parent d8aba05a62
commit c2af31149f
1 changed files with 81 additions and 103 deletions
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184
convert-pth-to-ggml.py
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@ -17,6 +17,7 @@
# and vocabulary. # and vocabulary.
# #
import argparse
import sys import sys
import json import json
import struct import struct
@ -24,45 +25,29 @@ import numpy as np
import torch import torch
from sentencepiece import SentencePieceProcessor from sentencepiece import SentencePieceProcessor
if len(sys.argv) < 3: parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file')
print("Usage: convert-ckpt-to-ggml.py dir-model ftype\n") parser.add_argument('dir_model', help='directory containing the model checkpoint')
print(" ftype == 0 -> float32") parser.add_argument('ftype', type=int, choices=[0, 1], default=1, help='file type (0: float32, 1: float16)')
print(" ftype == 1 -> float16") args = parser.parse_args()
sys.exit(1)
# output in the same directory as the model # output in the same directory as the model
dir_model = sys.argv[1] dir_model = args.dir_model
ftype = args.ftype
fname_hparams = f"{dir_model}/params.json" fname_hparams = f"{dir_model}/params.json"
fname_tokenizer = f"{dir_model}/../tokenizer.model" fname_tokenizer = f"{dir_model}/../tokenizer.model"
def get_n_parts(dim): def get_n_parts(dim):
mappings = { mappings = {4096: 1, 5120: 2, 6656: 4, 8192: 8}
4096: 1, n_parts = mappings.get(dim)
5120: 2, if n_parts is None:
6656: 4,
8192: 8
}
if dim not in mappings:
print(f"Invalid dim: {dim}") print(f"Invalid dim: {dim}")
sys.exit(1) sys.exit(1)
return mappings[dim] return n_parts
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string # map from ftype to string
ftype_str = ["f32", "f16"] ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print(f"Invalid ftype: {ftype}")
sys.exit(1)
fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin"
with open(fname_hparams, "r") as f: with open(fname_hparams, "r") as f:
hparams = json.load(f) hparams = json.load(f)
@ -78,98 +63,91 @@ print(f"n_parts = {n_parts}\n")
for p in range(n_parts): for p in range(n_parts):
print(f"Processing part {p}\n") print(f"Processing part {p}\n")
#fname_model = sys.argv[1] + "/consolidated.00.pth" #fname_model = args[1] + "/consolidated.00.pth"
fname_model = f"{dir_model}/consolidated.0{p}.pth" fname_model = f"{dir_model}/consolidated.0{p}.pth"
fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin" fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin{'' if p == 0 else '.' + str(p)}"
if (p > 0):
fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin.{p}"
model = torch.load(fname_model, map_location="cpu") model = torch.load(fname_model, map_location="cpu")
fout = open(fname_out, "wb") with open(fname_out, "wb") as fout:
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
fout.write(struct.pack("i", hparams["vocab_size"])) fout.write(struct.pack("i", hparams["vocab_size"]))
fout.write(struct.pack("i", hparams["dim"])) fout.write(struct.pack("i", hparams["dim"]))
fout.write(struct.pack("i", hparams["multiple_of"])) fout.write(struct.pack("i", hparams["multiple_of"]))
fout.write(struct.pack("i", hparams["n_heads"])) fout.write(struct.pack("i", hparams["n_heads"]))
fout.write(struct.pack("i", hparams["n_layers"])) fout.write(struct.pack("i", hparams["n_layers"]))
fout.write(struct.pack("i", hparams["dim"] // hparams["n_heads"])) # rot (obsolete) fout.write(struct.pack("i", hparams["dim"] // hparams["n_heads"])) # rot (obsolete)
fout.write(struct.pack("i", ftype)) fout.write(struct.pack("i", ftype))
# Is this correct?? # Is this correct??
for i in range(32000): for i in range(32000):
if tokenizer.is_unknown(i): if tokenizer.is_unknown(i):
# "<unk>" token (translated as ??) # "<unk>" token (translated as ??)
text = " \u2047 ".encode("utf-8") text = " \u2047 ".encode("utf-8")
fout.write(struct.pack("i", len(text))) fout.write(struct.pack("i", len(text)))
fout.write(text) fout.write(text)
elif tokenizer.is_control(i): elif tokenizer.is_control(i):
# "<s>"/"</s>" tokens # "<s>"/"</s>" tokens
fout.write(struct.pack("i", 0)) fout.write(struct.pack("i", 0))
elif tokenizer.is_byte(i): elif tokenizer.is_byte(i):
# "<U+XX>" tokens (which may be invalid UTF-8) # "<U+XX>" tokens (which may be invalid UTF-8)
piece = tokenizer.id_to_piece(i) piece = tokenizer.id_to_piece(i)
if len(piece) != 6: if len(piece) != 6:
print(f"Invalid token: {piece}") print(f"Invalid token: {piece}")
sys.exit(1) sys.exit(1)
byte_value = int(piece[3:-1], 16) byte_value = int(piece[3:-1], 16)
fout.write(struct.pack("i", 1)) fout.write(struct.pack("i", 1))
fout.write(struct.pack("B", byte_value)) fout.write(struct.pack("B", byte_value))
else: else:
# normal token. Uses U+2581 (LOWER ONE EIGHTH BLOCK) to represent spaces. # normal token. Uses U+2581 (LOWER ONE EIGHTH BLOCK) to represent spaces.
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8") text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
fout.write(struct.pack("i", len(text))) fout.write(struct.pack("i", len(text)))
fout.write(text) fout.write(text)
for k, v in model.items(): for name, data in model.items():
name = k shape = data.shape
shape = v.shape
# skip layers.X.attention.inner_attention.rope.freqs # skip layers.X.attention.inner_attention.rope.freqs
if name[-5:] == "freqs": if name.endswith("freqs"):
continue continue
print(f"Processing variable: {name} with shape: {data.shape} and type: {data.dtype}\n") print(f"Processing variable: {name} with shape: {shape} and type: {data.dtype}\n")
#data = tf.train.load_variable(dir_model, name).squeeze() #data = tf.train.load_variable(dir_model, name).squeeze()
data = v.numpy().squeeze() data = np.squeeze(data)
n_dims = len(data.shape); n_dims = len(data.shape)
# for efficiency - transpose some matrices # for efficiency - transpose some matrices
# "model/h.*/attn/c_attn/w" # "model/h.*/attn/c_attn/w"
# "model/h.*/attn/c_proj/w" # "model/h.*/attn/c_proj/w"
# "model/h.*/mlp/c_fc/w" # "model/h.*/mlp/c_fc/w"
# "model/h.*/mlp/c_proj/w" # "model/h.*/mlp/c_proj/w"
#if name[-14:] == "/attn/c_attn/w" or \ #if name[-14:] == "/attn/c_attn/w" or \
# name[-14:] == "/attn/c_proj/w" or \ # name[-14:] == "/attn/c_proj/w" or \
# name[-11:] == "/mlp/c_fc/w" or \ # name[-11:] == "/mlp/c_fc/w" or \
# name[-13:] == "/mlp/c_proj/w": # name[-13:] == "/mlp/c_proj/w":
# print(" Transposing") # print(" Transposing")
# data = data.transpose() # data = data.transpose()
dshape = data.shape # default type is fp16
ftype_cur = 1
if ftype == 0 or n_dims == 1:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
# default type is fp16 # header
ftype_cur = 1 sname = name.encode('utf-8')
if ftype == 0 or n_dims == 1: fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
print(" Converting to float32") for dim in reversed(data.shape):
data = data.astype(np.float32) fout.write(struct.pack("i", dim))
ftype_cur = 0
# header fout.write(sname)
sname = name.encode('utf-8')
fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
for i in range(n_dims):
fout.write(struct.pack("i", dshape[n_dims - 1 - i]))
fout.write(sname);
# data # data
data.tofile(fout) data.tofile(fout)
# I hope this deallocates the memory .. del model
model = None
fout.close()
print(f"Done. Output file: {fname_out}, (part {p})\n") print(f"Done. Output file: {fname_out}, (part {p})\n")