alpaca-lora/export_state_dict_checkpoint.py

import os
import json

import torch
from peft import PeftModel, LoraConfig
from transformers import LlamaTokenizer, LlamaForCausalLM

tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")

base_model = LlamaForCausalLM.from_pretrained(
    "decapoda-research/llama-7b-hf",
    load_in_8bit=False,
    torch_dtype=torch.float16,
    device_map={"": "cpu"},
)

lora_model = PeftModel.from_pretrained(
    base_model,
    "tloen/alpaca-lora-7b",
    device_map={"": "cpu"},
    torch_dtype=torch.float16,
)

# merge weights
for layer in lora_model.base_model.model.model.layers:
    layer.self_attn.q_proj.merge_weights = True
    layer.self_attn.v_proj.merge_weights = True
    
lora_model.train(False)

lora_model_sd = lora_model.state_dict()

params = {
    "dim": 4096,
    "multiple_of": 256,
    "n_heads": 32,
    "n_layers": 32,
    "norm_eps": 1e-06,
    "vocab_size": -1,
}
n_layers = params["n_layers"]
n_heads = params["n_heads"]
dim = params["dim"]
dims_per_head = dim // n_heads
base = 10000.0
inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))


def permute(w):
    return (
        w.view(n_heads, dim // n_heads // 2, 2, dim).transpose(1, 2).reshape(dim, dim)
    )


def unpermute(w):
    return (
        w.view(n_heads, 2, dim // n_heads // 2, dim).transpose(1, 2).reshape(dim, dim)
    )


def translate_state_dict_key(k):
    k = k.replace("base_model.model.", "")
    if k == "model.embed_tokens.weight":
        return "tok_embeddings.weight"
    elif k == "model.norm.weight":
        return "norm.weight"
    elif k == "lm_head.weight":
        return "output.weight"
    elif k.startswith("model.layers."):
        layer = k.split(".")[2]
        if k.endswith(".self_attn.q_proj.weight"):
            return f"layers.{layer}.attention.wq.weight"
        elif k.endswith(".self_attn.k_proj.weight"):
            return f"layers.{layer}.attention.wk.weight"
        elif k.endswith(".self_attn.v_proj.weight"):
            return f"layers.{layer}.attention.wv.weight"
        elif k.endswith(".self_attn.o_proj.weight"):
            return f"layers.{layer}.attention.wo.weight"
        elif k.endswith(".mlp.gate_proj.weight"):
            return f"layers.{layer}.feed_forward.w1.weight"
        elif k.endswith(".mlp.down_proj.weight"):
            return f"layers.{layer}.feed_forward.w2.weight"
        elif k.endswith(".mlp.up_proj.weight"):
            return f"layers.{layer}.feed_forward.w3.weight"
        elif k.endswith(".input_layernorm.weight"):
            return f"layers.{layer}.attention_norm.weight"
        elif k.endswith(".post_attention_layernorm.weight"):
            return f"layers.{layer}.ffn_norm.weight"
        elif k.endswith("rotary_emb.inv_freq") or "lora" in k:
            return None
        else:
            print(layer, k)
            raise NotImplementedError
    else:
        print(k)
        raise NotImplementedError


new_state_dict = {}
for k, v in lora_model_sd.items():
    new_k = translate_state_dict_key(k)
    if new_k is not None:
        if "wq" in new_k or "wk" in new_k:
            new_state_dict[new_k] = unpermute(v)
        else:
            new_state_dict[new_k] = v

os.makedirs("./ckpt", exist_ok=True)

torch.save(new_state_dict, "./ckpt/consolidated.00.pth")

with open("./ckpt/params.json", "w") as f:
    json.dump(params, f)