diff --git a/convert-ggml-to-pth.py b/convert-ggml-to-pth.py index 8ab17410d7929..7ddfe3a1b20a6 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 __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, stops 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 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, stops 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 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, stops ) 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 f1d9d7aefe3e0..b1a5e0560e083 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 860eb148b2a86..42e99c2ff8d3b 100644 --- a/convert-gptq-to-ggml.py +++ b/convert-gptq-to-ggml.py @@ -50,7 +50,7 @@ # 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 @@ 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 @@ def convert_q4(src_name, dst_name, permute=False): 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 df42e76bdd0d2..dcef2f6a32213 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 33b6243bd94e0..5151d9081a6d3 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 5596f6c5513a5..b6ef2476e052d 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)