mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 02:44:36 +00:00
py : cleanup the code
- use f-strings where possible - drop first param of encode/decode functions since "utf-8" is the default
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@ -27,9 +27,9 @@ def read_tokens(fin, vocab_size):
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text_len = struct.unpack("i", fin.read(4))[0]
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text_bytes = fin.read(text_len)
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try:
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text = text_bytes.decode("utf-8")
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text = text_bytes.decode()
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except UnicodeDecodeError:
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text = text_bytes.decode("utf-8", "replace")
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text = text_bytes.decode(errors="replace")
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score = struct.unpack("f", fin.read(4))[0]
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tokens.append((text, score))
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return tokens
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@ -82,7 +82,7 @@ def read_variables(fin):
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shape = tuple(struct.unpack("i" * n_dims, fin.read(4 * n_dims)))
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shape = shape[::-1]
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name = fin.read(name_length).decode("utf-8")
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name = fin.read(name_length).decode()
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# ensure tensor data is aligned
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tensor_data_offset = fin.tell()
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@ -199,7 +199,7 @@ def chat(model, hparams, llama_dir):
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device = torch.device("cpu")
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llama = llama.to(device)
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ctx = """You are AI.
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ctx = """You are AI.
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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.
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User: Hello, AI.
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AI: Hello! How can I assist you today?
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@ -207,11 +207,11 @@ AI: Hello! How can I assist you today?
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print(ctx.rstrip("\n"))
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while True:
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print("-" * 60)
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prompt = input(f"User: ")
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prompt = input("User: ")
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if ctx != "":
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ctx = ctx + "User: " + prompt + "\n"
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ctx = f"{ctx}User: {prompt}\n"
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else:
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ctx = prompt + "\nAI:"
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ctx = f"{prompt}\nAI:"
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ctx = (ctx[-1920:]) if len(ctx) >= 2048 else ctx
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@ -236,7 +236,7 @@ AI: Hello! How can I assist you today?
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)
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s = generation_output.sequences[0]
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decoded = tokenizer.decode(s)
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ctx = decoded + "\n"
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ctx = f"{decoded}\n"
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def main():
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@ -49,7 +49,7 @@ def write_header(f_out, header):
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def write_tokens(fout, tokenizer):
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for i in range(tokenizer.vocab_size()):
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if tokenizer.is_unknown(i):
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text = " \u2047 ".encode("utf-8")
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text = " \u2047 ".encode()
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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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@ -60,13 +60,13 @@ def write_tokens(fout, tokenizer):
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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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text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode()
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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fout.write(struct.pack("f", tokenizer.get_score(i)))
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# TODO: GPT4All - add extra <pad> token
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text = "<pad>".encode("utf-8")
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text = "<pad>".encode()
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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fout.write(struct.pack("f", 0.0))
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@ -50,7 +50,7 @@ fout.write(struct.pack("i", 4))
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# This loop unchanged from convert-pth-to-ggml.py:
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for i in range(tokenizer.vocab_size()):
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if tokenizer.is_unknown(i):
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text = " \u2047 ".encode("utf-8")
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text = " \u2047 ".encode()
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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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@ -61,13 +61,13 @@ for i in range(tokenizer.vocab_size()):
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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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text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode()
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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fout.write(struct.pack("f", tokenizer.get_score(i)))
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def write_header(shape, dst_name, ftype_cur):
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sname = dst_name.encode('utf-8')
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sname = dst_name.encode()
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fout.write(struct.pack("iii", len(shape), len(sname), ftype_cur))
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fout.write(struct.pack("i" * len(shape), *shape[::-1]))
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fout.write(sname)
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@ -80,7 +80,7 @@ def write_header(shape, dst_name, ftype_cur):
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def convert_non_q4(src_name, dst_name):
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v = model[src_name]
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shape = v.shape
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print("Processing non-Q4 variable: " + src_name + " with shape: ", shape, " and type: ", v.dtype)
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print(f"Processing non-Q4 variable: {src_name} with shape: {shape} and type: {v.dtype}")
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if len(shape) == 1:
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print(" Converting to float32")
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v = v.to(torch.float32)
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@ -105,7 +105,7 @@ def convert_q4(src_name, dst_name, permute=False):
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# Each int32 item is actually 8 int4 items packed together, and it's transposed.
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shape = (qweight.shape[0], qweight.shape[1] * 8)
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print("Processing Q4 variable: " + src_name + " with shape: ", shape)
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print(f"Processing Q4 variable: {src_name} with shape: {shape}")
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# The output format has the int4 weights in groups of 32 rather than 8.
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# It looks like this:
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@ -168,5 +168,5 @@ for i in range(n_layer):
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fout.close()
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print("Done. Output file: " + fname_out)
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print("")
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print(f"Done. Output file: {fname_out}")
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print()
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@ -120,7 +120,7 @@ def write_header(fout, hparams, ftype):
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def write_tokens(fout, tokenizer):
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for i in range(tokenizer.vocab_size()):
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if tokenizer.is_unknown(i):
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text = " \u2047 ".encode("utf-8")
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text = " \u2047 ".encode()
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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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@ -131,7 +131,7 @@ def write_tokens(fout, tokenizer):
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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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text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode()
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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fout.write(struct.pack("f", tokenizer.get_score(i)))
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@ -191,7 +191,7 @@ def process_and_write_variables(fout, model, ftype, part_id, n_parts):
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fullshape = list(partshape)
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if n_dims > 1:
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fullshape[split_dim] *= n_parts
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sname = name.encode('utf-8')
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sname = name.encode()
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fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
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for dim in reversed(fullshape):
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fout.write(struct.pack("i", dim))
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@ -44,7 +44,7 @@ def write_header(f_out, header):
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def write_tokens(fout, tokenizer):
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for i in range(tokenizer.vocab_size()):
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if tokenizer.is_unknown(i):
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text = " \u2047 ".encode("utf-8")
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text = " \u2047 ".encode()
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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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@ -55,7 +55,7 @@ def write_tokens(fout, tokenizer):
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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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text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode()
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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fout.write(struct.pack("f", tokenizer.get_score(i)))
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@ -272,13 +272,11 @@ def main():
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tokens = read_tokens(fin, hparams)
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if hparams['magic'] == 0x67676a74: # ggjt
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print("%s: input ggml has already been converted to 'ggjt' magic\n" %
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(args.fin_path))
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print(f"{args.fin_path}: input ggml has already been converted to 'ggjt' magic\n")
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sys.exit(1)
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if hparams['magic'] != 0x67676d66: # ggmf
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print("%s: input ggml file doesn't have expected 'ggmf' magic: %#x\n" %
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(args.fin_path, hparams['magic']))
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print(f"{args.fin_path}: input ggml file doesn't have expected 'ggmf' magic: {hparams['magic']:#x}\n")
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sys.exit(1)
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hparams['magic'] = 0x67676a74 # ggjt
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@ -286,7 +284,7 @@ def main():
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# count number of multipart files by convention
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n_parts = 1
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while True:
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if os.path.exists("%s.%d" % (args.fin_path, n_parts)):
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if os.path.exists(f"{args.fin_path}.{n_parts}"):
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n_parts += 1
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else:
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break
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@ -302,7 +300,7 @@ def main():
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print(f"Processing part {part_id+1} of {n_parts}\n")
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fin_path = args.fin_path
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if part_id > 0:
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fin_path += ".%d" % (part_id)
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fin_path += f".{part_id}"
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with open(fin_path, "rb") as fin:
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read_tokens(fin, read_hparams(fin))
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copy_tensors(fin, fout, part_id, n_parts)
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