diff --git a/convert-ggml-to-pth.py b/convert-ggml-to-pth.py index 8ab1741..7ddfe3a 100644 --- a/convert-ggml-to-pth.py +++ b/convert-ggml-to-pth.py @@ -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(): diff --git a/convert-gpt4all-to-ggml.py b/convert-gpt4all-to-ggml.py index f1d9d7a..b1a5e05 100644 --- a/convert-gpt4all-to-ggml.py +++ b/convert-gpt4all-to-ggml.py @@ -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 token - text = "".encode("utf-8") + text = "".encode() fout.write(struct.pack("i", len(text))) fout.write(text) fout.write(struct.pack("f", 0.0)) diff --git a/convert-gptq-to-ggml.py b/convert-gptq-to-ggml.py index 860eb14..42e99c2 100644 --- a/convert-gptq-to-ggml.py +++ b/convert-gptq-to-ggml.py @@ -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() diff --git a/convert-pth-to-ggml.py b/convert-pth-to-ggml.py index df42e76..dcef2f6 100644 --- a/convert-pth-to-ggml.py +++ b/convert-pth-to-ggml.py @@ -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)) diff --git a/convert-unversioned-ggml-to-ggml.py b/convert-unversioned-ggml-to-ggml.py index 33b6243..5151d90 100644 --- a/convert-unversioned-ggml-to-ggml.py +++ b/convert-unversioned-ggml-to-ggml.py @@ -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))) diff --git a/migrate-ggml-2023-03-30-pr613.py b/migrate-ggml-2023-03-30-pr613.py index 5596f6c..b6ef247 100644 --- a/migrate-ggml-2023-03-30-pr613.py +++ b/migrate-ggml-2023-03-30-pr613.py @@ -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)