vbatts/llama.cpp
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Support all LLaMA models + change Q4_0 quantization storage

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Georgi Gerganov 2023-03-11 10:47:09 +02:00
parent 5f2f970d51
commit 007a8f6f45
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GPG key ID: 449E073F9DC10735
5 changed files with 404 additions and 205 deletions
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144
convert-pth-to-ggml.py
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@ -33,12 +33,23 @@ if len(sys.argv) < 3:
# output in the same directory as the model
dir_model = sys.argv[1]
fname_out = sys.argv[1] + "/ggml-model.bin"
fname_hparams = sys.argv[1] + "/params.json"
fname_model = sys.argv[1] + "/consolidated.00.pth"
fname_tokenizer = sys.argv[1] + "/../tokenizer.model"
def get_n_parts(dim):
if dim == 4096:
return 1
elif dim == 5120:
return 2
elif dim == 6656:
return 4
elif dim == 8192:
return 8
else:
print("Invalid dim: " + str(dim))
sys.exit(1)
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
@ -61,76 +72,91 @@ tokenizer = SentencePieceProcessor(fname_tokenizer)
hparams.update({"vocab_size": tokenizer.vocab_size()})
n_parts = get_n_parts(hparams["dim"])
print(hparams)
print('n_parts = ', n_parts)
model = torch.load(fname_model, map_location="cpu")
for p in range(n_parts):
print('Processing part ', p)
fout = open(fname_out, "wb")
#fname_model = sys.argv[1] + "/consolidated.00.pth"
fname_model = sys.argv[1] + "/consolidated.0" + str(p) + ".pth"
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
if (p > 0):
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin" + "." + str(p)
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
fout.write(struct.pack("i", hparams["vocab_size"]))
fout.write(struct.pack("i", hparams["dim"]))
fout.write(struct.pack("i", hparams["multiple_of"]))
fout.write(struct.pack("i", hparams["n_heads"]))
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", ftype))
model = torch.load(fname_model, map_location="cpu")
# Is this correct??
for i in range(32000):
# TODO: this is probably wrong - not sure how this tokenizer works
text = tokenizer.decode([29889, i]).encode('utf-8')
# remove the first byte (it's always '.')
text = text[1:]
fout.write(struct.pack("i", len(text)))
fout.write(text)
fout = open(fname_out, "wb")
for k, v in model.items():
name = k
shape = v.shape
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
fout.write(struct.pack("i", hparams["vocab_size"]))
fout.write(struct.pack("i", hparams["dim"]))
fout.write(struct.pack("i", hparams["multiple_of"]))
fout.write(struct.pack("i", hparams["n_heads"]))
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", ftype))
# skip layers.X.attention.inner_attention.rope.freqs
if name[-5:] == "freqs":
continue
# Is this correct??
for i in range(32000):
# TODO: this is probably wrong - not sure how this tokenizer works
text = tokenizer.decode([29889, i]).encode('utf-8')
# remove the first byte (it's always '.')
text = text[1:]
fout.write(struct.pack("i", len(text)))
fout.write(text)
print("Processing variable: " + name + " with shape: ", shape, " and type: ", v.dtype)
for k, v in model.items():
name = k
shape = v.shape
#data = tf.train.load_variable(dir_model, name).squeeze()
data = v.numpy().squeeze()
n_dims = len(data.shape);
# skip layers.X.attention.inner_attention.rope.freqs
if name[-5:] == "freqs":
continue
# for efficiency - transpose some matrices
# "model/h.*/attn/c_attn/w"
# "model/h.*/attn/c_proj/w"
# "model/h.*/mlp/c_fc/w"
# "model/h.*/mlp/c_proj/w"
#if name[-14:] == "/attn/c_attn/w" or \
# name[-14:] == "/attn/c_proj/w" or \
# name[-11:] == "/mlp/c_fc/w" or \
# name[-13:] == "/mlp/c_proj/w":
# print(" Transposing")
# data = data.transpose()
print("Processing variable: " + name + " with shape: ", shape, " and type: ", v.dtype)
dshape = data.shape
#data = tf.train.load_variable(dir_model, name).squeeze()
data = v.numpy().squeeze()
n_dims = len(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
# for efficiency - transpose some matrices
# "model/h.*/attn/c_attn/w"
# "model/h.*/attn/c_proj/w"
# "model/h.*/mlp/c_fc/w"
# "model/h.*/mlp/c_proj/w"
#if name[-14:] == "/attn/c_attn/w" or \
# name[-14:] == "/attn/c_proj/w" or \
# name[-11:] == "/mlp/c_fc/w" or \
# name[-13:] == "/mlp/c_proj/w":
# print(" Transposing")
# data = data.transpose()
# header
str = name.encode('utf-8')
fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
for i in range(n_dims):
fout.write(struct.pack("i", dshape[n_dims - 1 - i]))
fout.write(str);
dshape = data.shape
# data
data.tofile(fout)
# 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
fout.close()
# header
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);
print("Done. Output file: " + fname_out)
print("")
# data
data.tofile(fout)
# I hope this deallocates the memory ..
model = None
fout.close()
print("Done. Output file: " + fname_out + ", (part ", p, ")")
print("")