mirror of
https://github.com/tloen/alpaca-lora.git
synced 2024-10-01 01:05:56 -04:00
1310547f9f
* add HF dataset loading, add linters, pyproject.toml - applied markdownlint - add black, black[jupyter], isort - fix noqa codes - add .github workflow linting - update README.md * restore default settings * resume_from_checkpoint Co-authored-by: AngainorDev <54739135+AngainorDev@users.noreply.github.com> * Print warning on checkpoint not found * add HF dataset loading, add linters, pyproject.toml - applied markdownlint - add black, black[jupyter], isort - fix noqa codes - add .github workflow linting - update README.md * Default to local copy and update it * Typo * Remove duplicate code block --------- Co-authored-by: Eric Wang <eric.james.wang@gmail.com> Co-authored-by: AngainorDev <54739135+AngainorDev@users.noreply.github.com>
134 lines
3.8 KiB
Python
134 lines
3.8 KiB
Python
import json
|
|
import os
|
|
|
|
import torch
|
|
import transformers
|
|
|
|
# Unused imports
|
|
# from peft import LoraConfig
|
|
from peft import PeftModel
|
|
|
|
assert (
|
|
"LlamaTokenizer" in transformers._import_structure["models.llama"]
|
|
), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git" # noqa: E501
|
|
|
|
from transformers import LlamaForCausalLM, LlamaTokenizer # noqa: E402
|
|
|
|
BASE_MODEL = os.environ.get("BASE_MODEL", None)
|
|
assert (
|
|
BASE_MODEL
|
|
), "Please specify a value for BASE_MODEL environment variable, e.g. `export BASE_MODEL=decapoda-research/llama-7b-hf`" # noqa: E501
|
|
|
|
tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)
|
|
|
|
base_model = LlamaForCausalLM.from_pretrained(
|
|
BASE_MODEL,
|
|
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): # noqa: C901
|
|
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)
|