diff options
Diffstat (limited to 'convert-lora-to-ggml.py')
| -rwxr-xr-x | convert-lora-to-ggml.py | 183 |
1 files changed, 92 insertions, 91 deletions
diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index 53bb8a3d..35ce152f 100755 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -47,95 +47,96 @@ def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_ty fout.seek((fout.tell() + 31) & -32) -if len(sys.argv) < 2: - print(f"Usage: python {sys.argv[0]} <path> [arch]") - print( - "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" - ) - print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") - sys.exit(1) - -input_json = os.path.join(sys.argv[1], "adapter_config.json") -input_model = os.path.join(sys.argv[1], "adapter_model.bin") -output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") - -model = torch.load(input_model, map_location="cpu") -arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" - -if arch_name not in gguf.MODEL_ARCH_NAMES.values(): - print(f"Error: unsupported architecture {arch_name}") - sys.exit(1) - -arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] -name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone - -with open(input_json, "r") as f: - params = json.load(f) - -if params["peft_type"] != "LORA": - print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") - sys.exit(1) - -if params["fan_in_fan_out"] is True: - print("Error: param fan_in_fan_out is not supported") - sys.exit(1) - -if params["bias"] is not None and params["bias"] != "none": - print("Error: param bias is not supported") - sys.exit(1) - -# TODO: these seem to be layers that have been trained but without lora. -# doesn't seem widely used but eventually should be supported -if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: - print("Error: param modules_to_save is not supported") - sys.exit(1) - -with open(output_path, "wb") as fout: - fout.truncate() - - write_file_header(fout, params) - for k, v in model.items(): - orig_k = k - if k.endswith(".default.weight"): - k = k.replace(".default.weight", ".weight") - if k in ["llama_proj.weight", "llama_proj.bias"]: - continue - if k.endswith("lora_A.weight"): - if v.dtype != torch.float16 and v.dtype != torch.float32: +if __name__ == '__main__': + if len(sys.argv) < 2: + print(f"Usage: python {sys.argv[0]} <path> [arch]") + print( + "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" + ) + print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") + sys.exit(1) + + input_json = os.path.join(sys.argv[1], "adapter_config.json") + input_model = os.path.join(sys.argv[1], "adapter_model.bin") + output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") + + model = torch.load(input_model, map_location="cpu") + arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" + + if arch_name not in gguf.MODEL_ARCH_NAMES.values(): + print(f"Error: unsupported architecture {arch_name}") + sys.exit(1) + + arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] + name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone + + with open(input_json, "r") as f: + params = json.load(f) + + if params["peft_type"] != "LORA": + print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") + sys.exit(1) + + if params["fan_in_fan_out"] is True: + print("Error: param fan_in_fan_out is not supported") + sys.exit(1) + + if params["bias"] is not None and params["bias"] != "none": + print("Error: param bias is not supported") + sys.exit(1) + + # TODO: these seem to be layers that have been trained but without lora. + # doesn't seem widely used but eventually should be supported + if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: + print("Error: param modules_to_save is not supported") + sys.exit(1) + + with open(output_path, "wb") as fout: + fout.truncate() + + write_file_header(fout, params) + for k, v in model.items(): + orig_k = k + if k.endswith(".default.weight"): + k = k.replace(".default.weight", ".weight") + if k in ["llama_proj.weight", "llama_proj.bias"]: + continue + if k.endswith("lora_A.weight"): + if v.dtype != torch.float16 and v.dtype != torch.float32: + v = v.float() + v = v.T + else: v = v.float() - v = v.T - else: - v = v.float() - - t = v.detach().numpy() - - prefix = "base_model.model." - if k.startswith(prefix): - k = k[len(prefix) :] - - lora_suffixes = (".lora_A.weight", ".lora_B.weight") - if k.endswith(lora_suffixes): - suffix = k[-len(lora_suffixes[0]):] - k = k[: -len(lora_suffixes[0])] - else: - print(f"Error: unrecognized tensor name {orig_k}") - sys.exit(1) - - tname = name_map.get_name(k) - if tname is None: - print(f"Error: could not map tensor name {orig_k}") - print(" Note: the arch parameter must be specified if the model is not llama") - sys.exit(1) - - if suffix == ".lora_A.weight": - tname += ".weight.loraA" - elif suffix == ".lora_B.weight": - tname += ".weight.loraB" - else: - assert False - - print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") - write_tensor_header(fout, tname, t.shape, t.dtype) - t.tofile(fout) - -print(f"Converted {input_json} and {input_model} to {output_path}") + + t = v.detach().numpy() + + prefix = "base_model.model." + if k.startswith(prefix): + k = k[len(prefix) :] + + lora_suffixes = (".lora_A.weight", ".lora_B.weight") + if k.endswith(lora_suffixes): + suffix = k[-len(lora_suffixes[0]):] + k = k[: -len(lora_suffixes[0])] + else: + print(f"Error: unrecognized tensor name {orig_k}") + sys.exit(1) + + tname = name_map.get_name(k) + if tname is None: + print(f"Error: could not map tensor name {orig_k}") + print(" Note: the arch parameter must be specified if the model is not llama") + sys.exit(1) + + if suffix == ".lora_A.weight": + tname += ".weight.loraA" + elif suffix == ".lora_B.weight": + tname += ".weight.loraB" + else: + assert False + + print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") + write_tensor_header(fout, tname, t.shape, t.dtype) + t.tofile(fout) + + print(f"Converted {input_json} and {input_model} to {output_path}") |