vbatts/llama.cpp
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Use f-strings instead of concatenation

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qunash 2023-03-14 01:55:51 +03:00
parent 94f368fd53
commit d8aba05a62
1 changed files with 12 additions and 13 deletions
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25
convert-pth-to-ggml.py
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@ -33,8 +33,8 @@ if len(sys.argv) < 3:
# output in the same directory as the model
dir_model = sys.argv[1]
fname_hparams = sys.argv[1] + "/params.json"
fname_tokenizer = sys.argv[1] + "/../tokenizer.model"
fname_hparams = f"{dir_model}/params.json"
fname_tokenizer = f"{dir_model}/../tokenizer.model"
def get_n_parts(dim):
mappings = {
@ -59,9 +59,9 @@ ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
print(f"Invalid ftype: {ftype}")
sys.exit(1)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin"
with open(fname_hparams, "r") as f:
hparams = json.load(f)
@ -73,16 +73,16 @@ hparams.update({"vocab_size": tokenizer.vocab_size()})
n_parts = get_n_parts(hparams["dim"])
print(hparams)
print('n_parts = ', n_parts)
print(f"n_parts = {n_parts}\n")
for p in range(n_parts):
print('Processing part ', p)
print(f"Processing part {p}\n")
#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"
fname_model = f"{dir_model}/consolidated.0{p}.pth"
fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin"
if (p > 0):
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin" + "." + str(p)
fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin.{p}"
model = torch.load(fname_model, map_location="cpu")
@ -111,7 +111,7 @@ for p in range(n_parts):
# "<U+XX>" tokens (which may be invalid UTF-8)
piece = tokenizer.id_to_piece(i)
if len(piece) != 6:
print("Invalid token: " + piece)
print(f"Invalid token: {piece}")
sys.exit(1)
byte_value = int(piece[3:-1], 16)
fout.write(struct.pack("i", 1))
@ -130,7 +130,7 @@ for p in range(n_parts):
if name[-5:] == "freqs":
continue
print("Processing variable: " + name + " with shape: ", shape, " and type: ", v.dtype)
print(f"Processing variable: {name} with shape: {data.shape} and type: {data.dtype}\n")
#data = tf.train.load_variable(dir_model, name).squeeze()
data = v.numpy().squeeze()
@ -172,5 +172,4 @@ for p in range(n_parts):
fout.close()
print("Done. Output file: " + fname_out + ", (part ", p, ")")
print("")
print(f"Done. Output file: {fname_out}, (part {p})\n")