modularize
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qunash
parent
c2af31149f
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e1b1e12a41
1 changed files with 100 additions and 96 deletions
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@ -16,7 +16,6 @@
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# At the start of the ggml file we write the model parameters
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# and vocabulary.
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#
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import argparse
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import sys
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import json
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@ -25,129 +24,134 @@ import numpy as np
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import torch
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from sentencepiece import SentencePieceProcessor
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parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file')
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parser.add_argument('dir_model', help='directory containing the model checkpoint')
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parser.add_argument('ftype', type=int, choices=[0, 1], default=1, help='file type (0: float32, 1: float16)')
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args = parser.parse_args()
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def parse_args():
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# output in the same directory as the model
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dir_model = args.dir_model
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ftype = args.ftype
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fname_hparams = f"{dir_model}/params.json"
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fname_tokenizer = f"{dir_model}/../tokenizer.model"
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parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file')
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parser.add_argument('dir_model', help='directory containing the model checkpoint')
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parser.add_argument('ftype', type=int, choices=[0, 1], default=1, help='file type (0: float32, 1: float16)')
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return parser.parse_args()
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def get_n_parts(dim):
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mappings = {4096: 1, 5120: 2, 6656: 4, 8192: 8}
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n_parts = mappings.get(dim)
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if n_parts is None:
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print(f"Invalid dim: {dim}")
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sys.exit(1)
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print(f"n_parts = {n_parts}\n")
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return n_parts
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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def load_hparams_and_tokenizer(dir_model):
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fname_hparams = f"{dir_model}/params.json"
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fname_tokenizer = f"{dir_model}/../tokenizer.model"
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with open(fname_hparams, "r") as f:
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hparams = json.load(f)
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with open(fname_hparams, "r") as f:
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hparams = json.load(f)
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print(hparams)
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tokenizer = SentencePieceProcessor(fname_tokenizer)
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tokenizer = SentencePieceProcessor(fname_tokenizer)
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hparams.update({"vocab_size": tokenizer.vocab_size()})
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hparams.update({"vocab_size": tokenizer.vocab_size()})
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return hparams, tokenizer
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n_parts = get_n_parts(hparams["dim"])
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def write_header(fout, hparams, ftype):
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keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"]
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values = [
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0x67676d6c, # magic: ggml in hex
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*[hparams[key] for key in keys],
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hparams["dim"] // hparams["n_heads"], # rot (obsolete)
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ftype
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]
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fout.write(struct.pack("i" * len(values), *values))
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print(hparams)
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print(f"n_parts = {n_parts}\n")
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def write_tokens(fout, tokenizer):
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for p in range(n_parts):
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print(f"Processing part {p}\n")
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for i in range(32000):
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if tokenizer.is_unknown(i):
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text = " \u2047 ".encode("utf-8")
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elif tokenizer.is_control(i):
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text = b""
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elif tokenizer.is_byte(i):
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piece = tokenizer.id_to_piece(i)
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if len(piece) != 6:
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print(f"Invalid token: {piece}")
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sys.exit(1)
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byte_value = int(piece[3:-1], 16)
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text = struct.pack("B", byte_value)
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else:
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text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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#fname_model = args[1] + "/consolidated.00.pth"
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fname_model = f"{dir_model}/consolidated.0{p}.pth"
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fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin{'' if p == 0 else '.' + str(p)}"
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def process_and_write_variables(fout, model, ftype):
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model = torch.load(fname_model, map_location="cpu")
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for name, data in model.items():
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if name.endswith("freqs"):
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continue
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shape = data.shape
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print(f"Processing variable: {name} with shape: {shape} and type: {data.dtype}\n")
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data = np.squeeze(data)
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n_dims = len(shape)
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with open(fname_out, "wb") as fout:
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# for efficiency - transpose some matrices
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# "model/h.*/attn/c_attn/w"
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# "model/h.*/attn/c_proj/w"
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# "model/h.*/mlp/c_fc/w"
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# "model/h.*/mlp/c_proj/w"
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#if name.endswith(("/attn/c_attn/w", "/attn/c_proj/w", "/mlp/c_fc/w", "/mlp/c_proj/w")):
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# print("Transposing")
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# data = data.transpose()
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fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
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fout.write(struct.pack("i", hparams["vocab_size"]))
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fout.write(struct.pack("i", hparams["dim"]))
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fout.write(struct.pack("i", hparams["multiple_of"]))
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fout.write(struct.pack("i", hparams["n_heads"]))
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fout.write(struct.pack("i", hparams["n_layers"]))
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fout.write(struct.pack("i", hparams["dim"] // hparams["n_heads"])) # rot (obsolete)
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fout.write(struct.pack("i", ftype))
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# default type is fp16
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ftype_cur = 1
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if ftype == 0 or n_dims == 1:
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print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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# Is this correct??
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for i in range(32000):
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if tokenizer.is_unknown(i):
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# "<unk>" token (translated as ??)
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text = " \u2047 ".encode("utf-8")
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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elif tokenizer.is_control(i):
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# "<s>"/"</s>" tokens
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fout.write(struct.pack("i", 0))
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elif tokenizer.is_byte(i):
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# "<U+XX>" tokens (which may be invalid UTF-8)
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piece = tokenizer.id_to_piece(i)
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if len(piece) != 6:
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print(f"Invalid token: {piece}")
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sys.exit(1)
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byte_value = int(piece[3:-1], 16)
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fout.write(struct.pack("i", 1))
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fout.write(struct.pack("B", byte_value))
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else:
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# normal token. Uses U+2581 (LOWER ONE EIGHTH BLOCK) to represent spaces.
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text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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# header
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sname = name.encode('utf-8')
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fout.write(struct.pack("iii", len(data.shape), len(sname), ftype_cur))
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for dim in reversed(data.shape):
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fout.write(struct.pack("i", dim))
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fout.write(sname)
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# data
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data.tofile(fout)
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for name, data in model.items():
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shape = data.shape
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def main():
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# skip layers.X.attention.inner_attention.rope.freqs
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if name.endswith("freqs"):
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continue
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args = parse_args()
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dir_model = args.dir_model
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ftype = args.ftype
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ftype_str = ["f32", "f16"]
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print(f"Processing variable: {name} with shape: {shape} and type: {data.dtype}\n")
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hparams, tokenizer = load_hparams_and_tokenizer(dir_model)
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n_parts = get_n_parts(hparams["dim"])
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#data = tf.train.load_variable(dir_model, name).squeeze()
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data = np.squeeze(data)
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n_dims = len(data.shape)
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for p in range(n_parts):
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# for efficiency - transpose some matrices
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# "model/h.*/attn/c_attn/w"
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# "model/h.*/attn/c_proj/w"
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# "model/h.*/mlp/c_fc/w"
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# "model/h.*/mlp/c_proj/w"
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#if name[-14:] == "/attn/c_attn/w" or \
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# name[-14:] == "/attn/c_proj/w" or \
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# name[-11:] == "/mlp/c_fc/w" or \
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# name[-13:] == "/mlp/c_proj/w":
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# print(" Transposing")
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# data = data.transpose()
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print(f"Processing part {p}\n")
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fname_model = f"{dir_model}/consolidated.0{p}.pth"
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fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin{'' if p == 0 else '.' + str(p)}"
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# default type is fp16
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ftype_cur = 1
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if ftype == 0 or n_dims == 1:
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print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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model = torch.load(fname_model, map_location="cpu")
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# header
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sname = name.encode('utf-8')
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fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
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for dim in reversed(data.shape):
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fout.write(struct.pack("i", dim))
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fout.write(sname)
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# data
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data.tofile(fout)
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with open(fname_out, "wb") as fout:
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write_header(fout, hparams, ftype)
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write_tokens(fout, tokenizer)
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process_and_write_variables(fout, model, ftype)
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del model
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print(f"Done. Output file: {fname_out}, (part {p})\n")
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print(f"Done. Output file: {fname_out}, (part {p})\n")
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if __name__ == "__main__":
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main()
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