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weight_diff.py
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weight_diff.py
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# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import fire
import torch
import tqdm
import transformers
from train import smart_tokenizer_and_embedding_resize
@torch.inference_mode()
def make_diff(
path_raw: str, path_tuned: str, path_diff: str, device="cpu", # "cuda" or "cpu"
):
"""Make the weight diff.
This function is given to present full transparency of how the weight diff was created.
Run:
python weight_diff.py make_diff --path_raw <your_path_raw> --path_tuned <your_path_tuned> --path_diff <your_path_diff>
"""
model_tuned: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
path_tuned,
device_map={"": torch.device(device)},
torch_dtype=torch.float32,
low_cpu_mem_usage=True,
)
model_raw: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
path_raw,
device_map={"": torch.device(device)},
torch_dtype=torch.float32,
low_cpu_mem_usage=True,
)
tokenizer_tuned: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
path_tuned
)
tokenizer_raw: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
path_raw
)
if tokenizer_raw.pad_token is None:
smart_tokenizer_and_embedding_resize(
special_tokens_dict=dict(pad_token="[PAD]"),
model=model_raw,
tokenizer=tokenizer_raw,
)
state_dict_tuned = model_tuned.state_dict()
state_dict_raw = model_raw.state_dict()
for key in tqdm.tqdm(state_dict_tuned):
state_dict_tuned[key].add_(-state_dict_raw[key])
model_tuned.save_pretrained(path_diff)
tokenizer_tuned.save_pretrained(path_diff)
@torch.inference_mode()
def recover(
path_raw,
path_diff,
path_tuned: Optional[str] = None,
device="cpu",
test_inference=True,
check_integrity_naively=True,
):
"""Recover the original weights from the released weight diff.
This function is given for you to run.
Things to do before running this:
1. Convert Meta's released weights into huggingface format. Follow this guide:
https://huggingface.co/docs/transformers/main/model_doc/llama
2. Make sure you cloned the released weight diff into your local machine. The weight diff is located at:
https://huggingface.co/tatsu-lab/alpaca-7b/tree/main
3. Run this function with the correct paths. E.g.,
python weight_diff.py recover --path_raw <path_to_step_1_dir> --path_diff <path_to_step_2_dir>
Additional notes:
- If things run too slowly, and you have an 80G GPU lying around, let GPU go brrr by setting `--device "cuda"`.
- If you want to save the recovered weights, set `--path_tuned <your_path_tuned>`.
Next time you can load the recovered weights directly from `<your_path_tuned>`.
"""
model_raw: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
path_raw,
device_map={"": torch.device(device)},
torch_dtype=torch.float32,
low_cpu_mem_usage=True,
)
model_recovered: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
path_diff,
device_map={"": torch.device(device)},
torch_dtype=torch.float32,
low_cpu_mem_usage=True,
)
tokenizer_raw: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
path_raw
)
if tokenizer_raw.pad_token is None:
smart_tokenizer_and_embedding_resize(
special_tokens_dict=dict(pad_token="[PAD]"),
model=model_raw,
tokenizer=tokenizer_raw,
)
tokenizer_recovered: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
path_diff
)
state_dict_recovered = model_recovered.state_dict()
state_dict_raw = model_raw.state_dict()
for key in tqdm.tqdm(state_dict_recovered):
state_dict_recovered[key].add_(state_dict_raw[key])
if check_integrity_naively:
# This is not a rigorous, cryptographically strong integrity check :)
allsum = sum(state_dict_recovered[key].sum() for key in state_dict_recovered)
assert torch.allclose(
allsum, torch.full_like(allsum, fill_value=50637.1836), atol=1e-2, rtol=0
), "Naive integrity check failed. This could imply that some of the checkpoint files are corrupted."
if path_tuned is not None:
model_recovered.save_pretrained(path_tuned)
tokenizer_recovered.save_pretrained(path_tuned)
if test_inference:
input_text = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\r\n\r\n"
"### Instruction:\r\nList three technologies that make life easier.\r\n\r\n### Response:"
)
inputs = tokenizer_recovered(input_text, return_tensors="pt")
out = model_recovered.generate(inputs=inputs.input_ids, max_new_tokens=100)
output_text = tokenizer_recovered.batch_decode(out, skip_special_tokens=True)[0]
output_text = output_text[len(input_text) :]
print(f"Input: {input_text}\nCompletion: {output_text}")
return model_recovered, tokenizer_recovered
def main(task, **kwargs):
globals()[task](**kwargs)
if __name__ == "__main__":
fire.Fire(main)