mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-14 00:59:43 +00:00
124 lines
3.7 KiB
Python
124 lines
3.7 KiB
Python
import json
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import os
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import re
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import struct
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import sys
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from typing import Any, Dict, Sequence, TextIO
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import torch
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from convert import DATA_TYPE_TO_FTYPE, NUMPY_TYPE_TO_DATA_TYPE, DataType
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HF_SUBLAYER_TO_GGML = {
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"self_attn.q_proj": "attention.wq",
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"self_attn.k_proj": "attention.wk",
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"self_attn.v_proj": "attention.wv",
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"self_attn.o_proj": "attention.wo",
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"mlp.gate_proj": "feed_forward.w1",
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"mlp.down_proj": "feed_forward.w2",
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"mlp.up_proj": "feed_forward.w3",
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"input_layernorm": "attention_norm",
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"post_attention_layernorm": "ffn_norm",
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# "norm": "norm",
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# "embed_tokens": "tok_embeddings",
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# "lm_head": "output",
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}
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def translate_tensor_name(t: str) -> str:
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match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t)
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if match:
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nn = match.group(1)
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sub_layer = match.group(2)
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lora_type = match.group(3)
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sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer)
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if sub_layer_renamed is None:
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print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}")
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sys.exit(1)
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output_string = (
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f"layers.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
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)
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return output_string
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else:
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print(f"Error: unrecognized tensor {t}")
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sys.exit(1)
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def write_file_header(fout: TextIO, params: Dict[str, Any]) -> None:
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fout.write(b"ggla"[::-1]) # magic (ggml lora)
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fout.write(struct.pack("i", 1)) # file version
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fout.write(struct.pack("ii", params["r"], params["lora_alpha"]))
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def write_tensor_header(
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self, name: str, shape: Sequence[int], data_type: DataType
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) -> None:
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sname = name.encode("utf-8")
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fout.write(
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struct.pack(
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"iii",
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len(shape),
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len(sname),
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DATA_TYPE_TO_FTYPE[NUMPY_TYPE_TO_DATA_TYPE[data_type]],
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)
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)
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fout.write(struct.pack("i" * len(shape), *shape[::-1]))
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fout.write(sname)
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fout.seek((fout.tell() + 31) & -32)
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if len(sys.argv) != 2:
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print(f"Usage: python {sys.argv[0]} <path>")
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print(
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"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
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)
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sys.exit(1)
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input_json = os.path.join(sys.argv[1], "adapter_config.json")
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input_model = os.path.join(sys.argv[1], "adapter_model.bin")
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output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
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model = torch.load(input_model, map_location="cpu")
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with open(input_json, "r") as f:
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params = json.load(f)
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if params["peft_type"] != "LORA":
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print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
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sys.exit(1)
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if params["fan_in_fan_out"] == True:
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print("Error: param fan_in_fan_out is not supported")
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sys.exit(1)
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if params["bias"] is not None and params["bias"] != "none":
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print("Error: param bias is not supported")
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sys.exit(1)
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# TODO: these seem to be layers that have been trained but without lora.
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# doesn't seem widely used but eventually should be supported
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if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
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print("Error: param modules_to_save is not supported")
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sys.exit(1)
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with open(output_path, "wb") as fout:
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fout.truncate()
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write_file_header(fout, params)
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for k, v in model.items():
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if k.endswith("lora_A.weight"):
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if v.dtype != torch.float16 and v.dtype != torch.float32:
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v = v.float()
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v = v.T
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else:
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v = v.float()
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t = v.numpy()
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tname = translate_tensor_name(k)
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print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
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write_tensor_header(fout, tname, t.shape, t.dtype)
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t.tofile(fout)
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print(f"Converted {input_json} and {input_model} to {output_path}")
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