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Diffstat (limited to 'convert-lora-to-ggml.py')
-rw-r--r-- | convert-lora-to-ggml.py | 124 |
1 files changed, 124 insertions, 0 deletions
diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py new file mode 100644 index 0000000..8a2085c --- /dev/null +++ b/convert-lora-to-ggml.py @@ -0,0 +1,124 @@ +import json +import os +import re +import struct +import sys +from typing import Any, Dict, Sequence, TextIO + +import torch + +from convert import DATA_TYPE_TO_FTYPE, NUMPY_TYPE_TO_DATA_TYPE, DataType + +HF_SUBLAYER_TO_GGML = { + "self_attn.q_proj": "attention.wq", + "self_attn.k_proj": "attention.wk", + "self_attn.v_proj": "attention.wv", + "self_attn.o_proj": "attention.wo", + "mlp.gate_proj": "feed_forward.w1", + "mlp.down_proj": "feed_forward.w2", + "mlp.up_proj": "feed_forward.w3", + "input_layernorm": "attention_norm", + "post_attention_layernorm": "ffn_norm", + # "norm": "norm", + # "embed_tokens": "tok_embeddings", + # "lm_head": "output", +} + + +def translate_tensor_name(t: str) -> str: + match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t) + if match: + nn = match.group(1) + sub_layer = match.group(2) + lora_type = match.group(3) + + sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer) + if sub_layer_renamed is None: + print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}") + sys.exit(1) + + output_string = ( + f"layers.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}" + ) + return output_string + else: + print(f"Error: unrecognized tensor {t}") + sys.exit(1) + + +def write_file_header(fout: TextIO, params: Dict[str, Any]) -> None: + fout.write(b"ggla"[::-1]) # magic (ggml lora) + fout.write(struct.pack("i", 1)) # file version + fout.write(struct.pack("ii", params["r"], params["lora_alpha"])) + + +def write_tensor_header( + self, name: str, shape: Sequence[int], data_type: DataType +) -> None: + sname = name.encode("utf-8") + fout.write( + struct.pack( + "iii", + len(shape), + len(sname), + DATA_TYPE_TO_FTYPE[NUMPY_TYPE_TO_DATA_TYPE[data_type]], + ) + ) + fout.write(struct.pack("i" * len(shape), *shape[::-1])) + fout.write(sname) + fout.seek((fout.tell() + 31) & -32) + + +if len(sys.argv) != 2: + print(f"Usage: python {sys.argv[0]} <path>") + print( + "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" + ) + 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") + +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"] == 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(): + 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() + + t = v.numpy() + tname = translate_tensor_name(k) + 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}") |