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llama.cpp
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py : cleanup the code

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- use f-strings where possible
- drop first param of encode/decode functions since "utf-8" is the default
pull/656/head
Pavol Rusnak 1 year ago
parent 9733104be5
commit cbef542879
6 changed files with 27 additions and 29 deletions
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16
convert-ggml-to-pth.py
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@ -27,9 +27,9 @@ def read_tokens(fin, vocab_size):
text_len = struct.unpack("i", fin.read(4))[0]
text_bytes = fin.read(text_len)
try:
text = text_bytes.decode("utf-8")
text = text_bytes.decode()
except UnicodeDecodeError:
text = text_bytes.decode("utf-8", "replace")
text = text_bytes.decode(errors="replace")
score = struct.unpack("f", fin.read(4))[0]
tokens.append((text, score))
return tokens
@ -82,7 +82,7 @@ def read_variables(fin):
shape = tuple(struct.unpack("i" * n_dims, fin.read(4 * n_dims)))
shape = shape[::-1]
name = fin.read(name_length).decode("utf-8")
name = fin.read(name_length).decode()
# ensure tensor data is aligned
tensor_data_offset = fin.tell()
@ -199,7 +199,7 @@ def chat(model, hparams, llama_dir):
device = torch.device("cpu")
llama = llama.to(device)
ctx = """You are AI.
ctx = """You are AI.
This is a dialog, where User interacts with AI. AI is helpful, kind, obedient, honest, respectful, direct, concise, should try to protect User's privacy, and knows its own limits. Also, AI must answer User and AI cannot stop the conversation by itself.
User: Hello, AI.
AI: Hello! How can I assist you today?
@ -207,11 +207,11 @@ AI: Hello! How can I assist you today?
print(ctx.rstrip("\n"))
while True:
print("-" * 60)
prompt = input(f"User: ")
prompt = input("User: ")
if ctx != "":
ctx = ctx + "User: " + prompt + "\n"
ctx = f"{ctx}User: {prompt}\n"
else:
ctx = prompt + "\nAI:"
ctx = f"{prompt}\nAI:"
ctx = (ctx[-1920:]) if len(ctx) >= 2048 else ctx
@ -236,7 +236,7 @@ AI: Hello! How can I assist you today?
)
s = generation_output.sequences[0]
decoded = tokenizer.decode(s)
ctx = decoded + "\n"
ctx = f"{decoded}\n"
def main():

6
convert-gpt4all-to-ggml.py
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@ -49,7 +49,7 @@ def write_header(f_out, header):
def write_tokens(fout, tokenizer):
for i in range(tokenizer.vocab_size()):
if tokenizer.is_unknown(i):
text = " \u2047 ".encode("utf-8")
text = " \u2047 ".encode()
elif tokenizer.is_control(i):
text = b""
elif tokenizer.is_byte(i):
@ -60,13 +60,13 @@ def write_tokens(fout, tokenizer):
byte_value = int(piece[3:-1], 16)
text = struct.pack("B", byte_value)
else:
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode()
fout.write(struct.pack("i", len(text)))
fout.write(text)
fout.write(struct.pack("f", tokenizer.get_score(i)))
# TODO: GPT4All - add extra <pad> token
text = "<pad>".encode("utf-8")
text = "<pad>".encode()
fout.write(struct.pack("i", len(text)))
fout.write(text)
fout.write(struct.pack("f", 0.0))

14
convert-gptq-to-ggml.py
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@ -50,7 +50,7 @@ fout.write(struct.pack("i", 4))
# This loop unchanged from convert-pth-to-ggml.py:
for i in range(tokenizer.vocab_size()):
if tokenizer.is_unknown(i):
text = " \u2047 ".encode("utf-8")
text = " \u2047 ".encode()
elif tokenizer.is_control(i):
text = b""
elif tokenizer.is_byte(i):
@ -61,13 +61,13 @@ for i in range(tokenizer.vocab_size()):
byte_value = int(piece[3:-1], 16)
text = struct.pack("B", byte_value)
else:
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode()
fout.write(struct.pack("i", len(text)))
fout.write(text)
fout.write(struct.pack("f", tokenizer.get_score(i)))
def write_header(shape, dst_name, ftype_cur):
sname = dst_name.encode('utf-8')
sname = dst_name.encode()
fout.write(struct.pack("iii", len(shape), len(sname), ftype_cur))
fout.write(struct.pack("i" * len(shape), *shape[::-1]))
fout.write(sname)
@ -80,7 +80,7 @@ def write_header(shape, dst_name, ftype_cur):
def convert_non_q4(src_name, dst_name):
v = model[src_name]
shape = v.shape
print("Processing non-Q4 variable: " + src_name + " with shape: ", shape, " and type: ", v.dtype)
print(f"Processing non-Q4 variable: {src_name} with shape: {shape} and type: {v.dtype}")
if len(shape) == 1:
print(" Converting to float32")
v = v.to(torch.float32)
@ -105,7 +105,7 @@ def convert_q4(src_name, dst_name, permute=False):
# Each int32 item is actually 8 int4 items packed together, and it's transposed.
shape = (qweight.shape[0], qweight.shape[1] * 8)
print("Processing Q4 variable: " + src_name + " with shape: ", shape)
print(f"Processing Q4 variable: {src_name} with shape: {shape}")
# The output format has the int4 weights in groups of 32 rather than 8.
# It looks like this:
@ -168,5 +168,5 @@ for i in range(n_layer):
fout.close()
print("Done. Output file: " + fname_out)
print("")
print(f"Done. Output file: {fname_out}")
print()

6
convert-pth-to-ggml.py
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@ -120,7 +120,7 @@ def write_header(fout, hparams, ftype):
def write_tokens(fout, tokenizer):
for i in range(tokenizer.vocab_size()):
if tokenizer.is_unknown(i):
text = " \u2047 ".encode("utf-8")
text = " \u2047 ".encode()
elif tokenizer.is_control(i):
text = b""
elif tokenizer.is_byte(i):
@ -131,7 +131,7 @@ def write_tokens(fout, tokenizer):
byte_value = int(piece[3:-1], 16)
text = struct.pack("B", byte_value)
else:
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode()
fout.write(struct.pack("i", len(text)))
fout.write(text)
fout.write(struct.pack("f", tokenizer.get_score(i)))
@ -191,7 +191,7 @@ def process_and_write_variables(fout, model, ftype, part_id, n_parts):
fullshape = list(partshape)
if n_dims > 1:
fullshape[split_dim] *= n_parts
sname = name.encode('utf-8')
sname = name.encode()
fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
for dim in reversed(fullshape):
fout.write(struct.pack("i", dim))

4
convert-unversioned-ggml-to-ggml.py
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@ -44,7 +44,7 @@ def write_header(f_out, header):
def write_tokens(fout, tokenizer):
for i in range(tokenizer.vocab_size()):
if tokenizer.is_unknown(i):
text = " \u2047 ".encode("utf-8")
text = " \u2047 ".encode()
elif tokenizer.is_control(i):
text = b""
elif tokenizer.is_byte(i):
@ -55,7 +55,7 @@ def write_tokens(fout, tokenizer):
byte_value = int(piece[3:-1], 16)
text = struct.pack("B", byte_value)
else:
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode()
fout.write(struct.pack("i", len(text)))
fout.write(text)
fout.write(struct.pack("f", tokenizer.get_score(i)))

10
migrate-ggml-2023-03-30-pr613.py
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@ -272,13 +272,11 @@ def main():
tokens = read_tokens(fin, hparams)
if hparams['magic'] == 0x67676a74: # ggjt
print("%s: input ggml has already been converted to 'ggjt' magic\n" %
(args.fin_path))
print(f"{args.fin_path}: input ggml has already been converted to 'ggjt' magic\n")
sys.exit(1)
if hparams['magic'] != 0x67676d66: # ggmf
print("%s: input ggml file doesn't have expected 'ggmf' magic: %#x\n" %
(args.fin_path, hparams['magic']))
print(f"{args.fin_path}: input ggml file doesn't have expected 'ggmf' magic: {hparams['magic']:#x}\n")
sys.exit(1)
hparams['magic'] = 0x67676a74 # ggjt
@ -286,7 +284,7 @@ def main():
# count number of multipart files by convention
n_parts = 1
while True:
if os.path.exists("%s.%d" % (args.fin_path, n_parts)):
if os.path.exists(f"{args.fin_path}.{n_parts}"):
n_parts += 1
else:
break
@ -302,7 +300,7 @@ def main():
print(f"Processing part {part_id+1} of {n_parts}\n")
fin_path = args.fin_path
if part_id > 0:
fin_path += ".%d" % (part_id)
fin_path += f".{part_id}"
with open(fin_path, "rb") as fin:
read_tokens(fin, read_hparams(fin))
copy_tensors(fin, fout, part_id, n_parts)

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