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# Basic imports
import os
from functools import partial
from argparse import Namespace
import numpy as np
# HF classses
from transformers import AutoTokenizer
from datasets import Dataset, concatenate_datasets
# watermarking micro lib
from watermark import (BlacklistLogitsProcessor,
compute_bl_metrics)
# some file i/o helpers
from io_utils import read_jsonlines, read_json
from watermark import compute_bl_metrics, BlacklistLogitsProcessor
###########################################################################
# Compute E[wl] for each example
###########################################################################
def expected_whitelist(example,
idx,
exp_wl_coef: float == None,
drop_spike_entropies: bool = False):
assert "spike_entropies" in example, "Need to construct bl processor with store_spike_ents=True to compute them in post"
num_toks_gend = example["w_bl_num_tokens_generated"]
avg_spike_ent = np.mean(example["spike_entropies"])
example.update({"avg_spike_entropy":avg_spike_ent})
if drop_spike_entropies: del example["spike_entropies"]
exp_num_wl = (exp_wl_coef*num_toks_gend)*avg_spike_ent
var_num_wl = num_toks_gend*exp_wl_coef*avg_spike_ent*(1-(exp_wl_coef*avg_spike_ent))
example.update({"w_bl_exp_num_wl_tokens":exp_num_wl})
example.update({"w_bl_var_num_wl_tokens":var_num_wl})
example.update({"exp_wl_coef":exp_wl_coef})
if num_toks_gend > 0:
example.update({"w_bl_exp_whitelist_fraction":exp_num_wl/num_toks_gend,
"w_bl_var_whitelist_fraction":var_num_wl/num_toks_gend})
else:
example.update({"w_bl_exp_whitelist_fraction":-1,
"w_bl_var_whitelist_fraction":-1})
return example
from typing import Callable
def add_metadata(ex, meta_table=None):
ex.update(meta_table)
return ex
def str_replace_bug_check(example,idx):
baseline_before = example["baseline_completion"]
example["baseline_completion"] = baseline_before.replace(example["truncated_input"][:-1],"")
if example["baseline_completion"] != baseline_before:
print("baseline input replacement bug occurred, skipping row!")
return False
else:
return True
def load_all_datasets(run_names: list[str]=None,
base_run_dir: str=None,
meta_name: str=None,
gen_name: str=None,
apply_metric_func: bool=False,
convert_to_pandas: bool = False,
drop_buggy_rows: bool = False,
limit_output_tokens: int = 0,
save_ds: bool = True,
save_dir: str=None):
print(f"Loading {len(run_names)} datasets from {base_run_dir}...")
if not isinstance(gen_name, Callable):
file_check = lambda name: os.path.exists(f"{base_run_dir}/{name}/{gen_name}")
assert all([file_check(name) for name in run_names]), f"Make sure all the run dirs contain the required data files: {meta_name} and {gen_name}"
all_datasets = []
for i,run_name in enumerate(run_names):
print(f"[{i}] Loading dataset")
run_base_dir = f"{base_run_dir}/{run_name}"
gen_table_meta_path = f"{run_base_dir}/{meta_name}"
if isinstance(gen_name, Callable):
gen_table_path = f"{run_base_dir}/{gen_name(run_name)}"
else:
gen_table_path = f"{run_base_dir}/{gen_name}"
# load the raw files
gen_table_meta = read_json(gen_table_meta_path)
gen_table_lst = [ex for ex in read_jsonlines(gen_table_path)]
gen_table_ds = Dataset.from_list(gen_table_lst)
print(f"Original dataset length={len(gen_table_ds)}")
# drop the rows where the string replace thing happens
if drop_buggy_rows:
gen_table_ds_filtered = gen_table_ds.filter(str_replace_bug_check,batched=False,with_indices=True)
else:
gen_table_ds_filtered = gen_table_ds
# enrich all rows with the run metadata
add_meta = partial(
add_metadata,
meta_table=gen_table_meta
)
gen_table_w_meta = gen_table_ds_filtered.map(add_meta, batched=False)
# optionally, apply the metric function(s) - somewhat expensive
# want to do this here rather than at end because you need each run's tokenizer
# though tbh it would be odd if they're not the same, but you can check that at the end
if apply_metric_func:
tokenizer = AutoTokenizer.from_pretrained(gen_table_meta["model_name"])
comp_bl_metrics = partial(
compute_bl_metrics,
tokenizer=tokenizer,
hf_model_name=gen_table_meta["model_name"],
initial_seed=gen_table_meta["initial_seed"],
dynamic_seed=gen_table_meta["dynamic_seed"],
bl_proportion=gen_table_meta["bl_proportion"],
use_cuda=True, # this is obvi critical to match the pseudorandomness
record_hits=True,
limit_output_tokens=limit_output_tokens,
)
gen_table_w_bl_metrics = gen_table_w_meta.map(comp_bl_metrics, batched=False, with_indices=True)
# Construct the blacklist processor so you can get the expectation coef
all_token_ids = list(tokenizer.get_vocab().values())
vocab_size = len(all_token_ids)
args = Namespace()
args.__dict__.update(gen_table_meta)
bl_processor = BlacklistLogitsProcessor(bad_words_ids=None,
store_bl_ids=False,
store_spike_ents=True,
eos_token_id=tokenizer.eos_token_id,
vocab=all_token_ids,
vocab_size=vocab_size,
bl_proportion=args.bl_proportion,
bl_logit_bias=args.bl_logit_bias,
bl_type=args.bl_type,
initial_seed= args.initial_seed,
dynamic_seed=args.dynamic_seed)
if "spike_entropies" in gen_table_w_bl_metrics.column_names:
comp_exp_num_wl = partial(
expected_whitelist,
exp_wl_coef=bl_processor.expected_wl_coef,
drop_spike_entropies=False,
# drop_spike_entropies=True,
)
gen_table_w_spike_ents = gen_table_w_bl_metrics.map(comp_exp_num_wl, batched=False, with_indices=True)
final_single_run_ds = gen_table_w_spike_ents
else:
final_single_run_ds = gen_table_w_bl_metrics
else:
final_single_run_ds = gen_table_w_meta
all_datasets.append(final_single_run_ds)
ds = concatenate_datasets(all_datasets)
if save_ds:
ds.save_to_disk(save_dir)
if convert_to_pandas:
df = ds.to_pandas()
return df
else:
return ds
output_dir = "/cmlscratch/jkirchen/spiking-root/lm-blacklisting/output_large_sweep"
# output_dir = "/cmlscratch/jkirchen/spiking-root/lm-blacklisting/output_large_sweep_downsize"
# output_dir = "/cmlscratch/jkirchen/spiking-root/lm-blacklisting/output_large_sweep_downsize"
# output_dir = "/cmlscratch/jkirchen/spiking-root/lm-blacklisting/output_greedy_redo"
# output_dir = "/cmlscratch/jkirchen/spiking-root/lm-blacklisting/output_greedy_gamma_0-25"
run_names = list(filter(lambda name: os.path.exists(f"{output_dir}/{name}/gen_table_w_metrics.jsonl"), sorted(os.listdir(output_dir))))
run_names = list(filter(lambda name: "realnewslike" in name, run_names))
# run_names = list(filter(lambda name: "pile" in name, run_names))
# run_names = list(filter(lambda name: "c4_en" in name, run_names))
# output_dir = "/cmlscratch/jkirchen/spiking-root/lm-blacklisting/output_attacked_greedy_updated"
# # output_dir = "/cmlscratch/jkirchen/spiking-root/lm-blacklisting/output_attacked_new"
# run_names = list(filter(lambda name: os.path.exists(f"{output_dir}/{name}/gen_table_w{('_'+name) if 't5' in name else ''}_attack_metrics.jsonl"), sorted(os.listdir(output_dir))))
# run_names = list(filter(lambda name: os.path.exists(f"{output_dir}/{name}/gen_table_w_attack_metrics.jsonl"), sorted(os.listdir(output_dir))))
runs_to_load = run_names
print(len(run_names))
for name in run_names: print(name)
runs_ready = [os.path.exists(f"{output_dir}/{name}/gen_table_w_metrics.jsonl") for name in runs_to_load]
# runs_ready = [os.path.exists(f"{output_dir}/{name}/gen_table_w_attack_metrics.jsonl") for name in runs_to_load]
print(f"all runs ready? {all(runs_ready)}\n{runs_ready}")
# save_name = "analysis_ds_1-21_greedy_redo"
# save_name = "analysis_ds_1-21_greedy_redo_truncated"
# save_name = "analysis_ds_1-21_greedy_redo_truncated_sanity_check"
# save_name = "analysis_ds_1-19_realnews_1-3_v2_hitlist_check"
# save_name = "analysis_ds_1-20_more_attack"
# save_name = "analysis_ds_1-23_greedy_gamma_0-25_truncated"
# save_name = "analysis_ds_1-21_greedy_attacked_updated_truncated"
# save_name = "analysis_ds_1-23_pile_1-3"
# save_name = "analysis_ds_1-23_en_1-3"
save_name = "analysis_ds_1-30_realnews_2-7"
save_dir = f"input/{save_name}"
raw_data = load_all_datasets(run_names=runs_to_load,
base_run_dir=output_dir,
meta_name="gen_table_meta.json",
gen_name="gen_table_w_metrics.jsonl",
# gen_name="gen_table_w_attack_metrics.jsonl",
apply_metric_func=True,
# drop_buggy_rows=True,
drop_buggy_rows=False,
# limit_output_tokens=200,
convert_to_pandas=False,
save_ds=True,
save_dir=save_dir)
print(f"All finished with {save_dir}!!")
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