Аудит упереджень у вподобаннях і донавчання мовних моделей за допомогою Direct Preference Optimization на Anthropic HH-RLHF з використанням TRL і LoRA

import dataclasses
import importlib.util
import inspect
import os
import re
import subprocess
import sys
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
REQUIRED = ["trl>=0.12", "transformers>=4.45", "accelerate", "datasets", "peft", "scikit-learn"]
def ensure_deps():
   """Install in ONE pip call so the resolver picks a mutually compatible set."""
   try:
       import trl
       import transformers
       return False
   except ImportError:
       print("Installing dependencies...")
       subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "-U", *REQUIRED])
       return True
def drop_broken_torchao():
   """Colab ships torchao 0.10.0; peft demands >0.16 and raises rather than skipping.
   Nothing here uses torchao, so removing it is safer than upgrading (an upgrade can
   drag in a torch build that does not match this runtime)."""
   if importlib.util.find_spec("torchao") is None:
       return False
   try:
       from peft.import_utils import is_torchao_available
       is_torchao_available()
       return False
   except ImportError:
       print("Removing incompatible torchao (unused, but peft raises on it)...")
       subprocess.call([sys.executable, "-m", "pip", "uninstall", "-y", "-q", "torchao"])
       return True
   except Exception:
       return False
_installed = ensure_deps()
_removed = drop_broken_torchao() if not _installed else False
if _installed or _removed:
   print("\nEnvironment changed. RESTART THE RUNTIME (Runtime > Restart session), "
         "then run this cell again.")
   raise SystemExit(0)
import numpy as np
import pandas as pd
import torch
import matplotlib.pyplot as plt
from datasets import load_dataset, concatenate_datasets
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, roc_auc_score
import transformers
import trl
from trl import DPOConfig, DPOTrainer
def patch_peft_torchao():
   """Belt and braces: if torchao survived the uninstall, stop peft raising on it."""
   try:
       from peft import import_utils
       from peft.tuners.lora import torchao as lora_torchao
   except ImportError:
       return
   try:
       import_utils.is_torchao_available()
   except ImportError as exc:
       print(f"  neutralising peft's torchao check ({exc})")
       import_utils.is_torchao_available = lambda: False
       lora_torchao.is_torchao_available = lambda: False
MODEL_ID = "Qwen/Qwen2.5-0.5B-Instruct"
SUBSETS = ["helpful-base", "helpful-rejection-sampled", "helpful-online", "harmless-base"]
N_TRAIN_PER_SUBSET = 120
N_TEST_PER_SUBSET = 30
MAX_LENGTH = 512
MAX_PROMPT_LENGTH = 256
BETA = 0.1
MAX_STEPS = 30
BATCH_SIZE = 1
GRAD_ACCUM = 8
LEARNING_RATE = 5e-6
WARMUP_RATIO = 0.1
LOGGING_STEPS = 5
USE_LORA = True
N_REWARD_EVAL = 40
SEED = 17
OUTPUT_DIR = "/content/dpo-hh" if os.path.isdir("/content") else "./dpo-hh"
set_seed(SEED)
rng = np.random.default_rng(SEED)
def report_environment():
   from transformers import TrainingArguments
   cuda = torch.cuda.is_available()
   bf16 = bool(cuda and torch.cuda.is_bf16_supported())
   fp16 = bool(cuda and not bf16)
   device = "cuda" if cuda else "cpu"
   print(f"python       : {sys.version.split()[0]}")
   print(f"torch        : {torch.__version__}")
   print(f"transformers : {transformers.__version__}")
   print(f"trl          : {trl.__version__}")
   print(f"Device: {device} | bf16={bf16} | fp16={fp16}")
   if not cuda:
       print("CPU fallback is enabled; training is intentionally shortened.")
   cfg_fields = {f.name for f in dataclasses.fields(DPOConfig)}
   trainer_params = set(inspect.signature(DPOTrainer.__init__).parameters)
   print(f"DPOConfig subclasses TrainingArguments : {issubclass(DPOConfig, TrainingArguments)}")
   print(f"DPOConfig fields                       : {len(cfg_fields)}")
   for probe in ("warmup_ratio", "warmup_steps", "beta", "max_length", "max_prompt_length"):
       where = [c for c, s in (("DPOConfig", cfg_fields), ("DPOTrainer", trainer_params))
                if probe in s]
       print(f"  {probe:<20} -> {', '.join(where) if where else 'NOT ACCEPTED ANYWHERE'}")
   if not issubclass(DPOConfig, TrainingArguments) or "per_device_train_batch_size" not in cfg_fields:
       print("\n!! DPOConfig looks broken. Reinstall in one command, then restart:")
       print("   pip install -U trl transformers accelerate datasets peft")
   return device, bf16, fp16, cfg_fields, trainer_params
DEVICE, BF16, FP16, CFG_FIELDS, TRAINER_PARAMS = report_environment()

def sample_split(ds, n, seed):
   return ds.shuffle(seed=seed).select(range(min(n, len(ds)))).flatten_indices()
def load_hh():
   train_parts, test_parts = [], []
   for i, subset in enumerate(SUBSETS):
       ds = load_dataset("Anthropic/hh-rlhf", data_dir=subset)
       tr = sample_split(ds["train"], N_TRAIN_PER_SUBSET, SEED + i)
       te = sample_split(ds["test"], N_TEST_PER_SUBSET, SEED + i)
       train_parts.append(tr.add_column("source", [subset] * len(tr)))
       test_parts.append(te.add_column("source", [subset] * len(te)))
   return concatenate_datasets(train_parts), concatenate_datasets(test_parts)
raw_train, raw_test = load_hh()
print(f"\nRaw sampled rows -> train={len(raw_train)}, test={len(raw_test)}")
print(pd.Series(raw_train["source"]).value_counts().sort_index().to_string())
TURN_RE = re.compile(r"\n\n(Human|Assistant):[ ]?")
def parse_transcript(text):
   if not isinstance(text, str) or not text.strip():
       return None
   parts = TURN_RE.split(text)
   if parts[0].strip():
       return None
   roles, contents = parts[1::2], parts[2::2]
   if len(roles) != len(contents) or len(roles) < 2:
       return None
   msgs = [{"role": "user" if r == "Human" else "assistant", "content": c.strip()}
           for r, c in zip(roles, contents)]
   if msgs[0]["role"] != "user" or msgs[-1]["role"] != "assistant":
       return None
   if any(a["role"] == b["role"] for a, b in zip(msgs, msgs[1:])):
       return None
   if any(not m["content"] for m in msgs):
       return None
   return msgs
def to_pair(row):
   ch = parse_transcript(row["chosen"])
   rj = parse_transcript(row["rejected"])
   ok = ch is not None and rj is not None and ch[:-1] == rj[:-1]
   return {
       "ok": bool(ok),
       "prompt": ch[:-1] if ok else [],
       "chosen": [ch[-1]] if ok else [],
       "rejected": [rj[-1]] if ok else [],
       "prompt_turns": len(ch) - 1 if ok else 0,
       "source": row["source"],
   }
parsed_train = raw_train.map(to_pair, remove_columns=raw_train.column_names).filter(lambda r: r["ok"])
parsed_test = raw_test.map(to_pair, remove_columns=raw_test.column_names).filter(lambda r: r["ok"])
print(f"\nValid parsed rows -> train={len(parsed_train)}, test={len(parsed_test)}")
identical = sum(1 for c, r in zip(parsed_train["chosen"], parsed_train["rejected"])
               if c[0]["content"] == r[0]["content"])
print(f"Identical completion pairs in sampled train: {identical}")

audit = pd.DataFrame({
   "source": parsed_train["source"],
   "prompt_turns": parsed_train["prompt_turns"],
   "chosen_words": [len(c[0]["content"].split()) for c in parsed_train["chosen"]],
   "rejected_words": [len(r[0]["content"].split()) for r in parsed_train["rejected"]],
})
audit["length_delta"] = audit["chosen_words"] - audit["rejected_words"]
summary = audit.groupby("source").agg(
   pairs=("chosen_words", "size"),
   chosen_words_mean=("chosen_words", "mean"),
   rejected_words_mean=("rejected_words", "mean"),
   median_turns=("prompt_turns", "median"),
   mean_length_delta=("length_delta", "mean"),
).round(2)
print("\nPreference-pair audit:")
print(summary.to_string())
fig, axes = plt.subplots(1, 2, figsize=(11, 4))
summary["mean_length_delta"].plot(kind="barh", ax=axes[0], color="#4c72b0")
axes[0].axvline(0, color="0.3", lw=1)
axes[0].set_title("mean(chosen − rejected) words")
axes[0].set_ylabel("")
for src, grp in audit.groupby("source"):
   axes[1].hist(grp["length_delta"], bins=30, histtype="step", lw=1.6, label=src)
axes[1].axvline(0, color="0.3", lw=1)
axes[1].set_title("per-pair length delta")
axes[1].legend(fontsize=7)
plt.tight_layout()
plt.show()
print("\nSanitized structural preview (user text is not printed):")
for i in range(min(3, len(audit))):
   r = audit.iloc[i]
   print({"source": r["source"], "prompt_turns": int(r["prompt_turns"]),
          "chosen_words": int(r["chosen_words"]), "rejected_words": int(r["rejected_words"])})
def build_lexical_dataset(ds):
   chosen_txt = [c[0]["content"] for c in ds["chosen"]]
   rejected_txt = [r[0]["content"] for r in ds["rejected"]]
   texts = chosen_txt + rejected_txt
   labels = np.concatenate([np.ones(len(chosen_txt), int), np.zeros(len(rejected_txt), int)])
   pair_id = np.concatenate([np.arange(len(chosen_txt)), np.arange(len(rejected_txt))])
   assert texts[: len(chosen_txt)] == chosen_txt and labels[: len(chosen_txt)].all()
   assert not labels[len(chosen_txt):].any()
   return np.array(texts, dtype=object), labels, pair_id
def run_lexical_diagnostic(texts, labels, pair_id, tag="observed"):
   pairs = np.unique(pair_id)
   shuffled = rng.permutation(pairs)
   test_pairs = set(shuffled[: len(shuffled) // 2].tolist())
   is_test = np.array([p in test_pairs for p in pair_id])
   vec = TfidfVectorizer(ngram_range=(1, 2), min_df=2, max_features=20000, sublinear_tf=True)
   Xtr = vec.fit_transform(texts[~is_test])
   Xte = vec.transform(texts[is_test])
   clf = LogisticRegression(max_iter=2000).fit(Xtr, labels[~is_test])
   pred = clf.predict(Xte)
   prob = clf.predict_proba(Xte)[:, 1]
   acc = accuracy_score(labels[is_test], pred)
   auc = roc_auc_score(labels[is_test], prob)
   print(f"Lexical diagnostic ({tag}) accuracy: {acc:.3f}")
   print(f"Lexical diagnostic ({tag}) ROC-AUC:  {auc:.3f}")
   return acc, auc, clf, labels[is_test], pred
print("\nTraining a lexical diagnostic to detect easy preference shortcuts...")
texts, labels, pair_id = build_lexical_dataset(parsed_train)
acc, auc, clf, y_true, y_pred = run_lexical_diagnostic(texts, labels, pair_id)
print(classification_report(y_true, y_pred, target_names=["rejected", "chosen"], digits=3))
perm = rng.permutation(len(labels))
_, auc_perm, _, _, _ = run_lexical_diagnostic(texts, labels[perm], pair_id, tag="permuted labels")
print(f"Chance baseline from permuted labels: AUC {auc_perm:.3f}")
if abs(auc - 0.5) <= abs(auc_perm - 0.5) + 0.02:
   print("-> observed AUC is within permutation noise: no detectable lexical shortcut.")
elif auc < 0.5:
   print("-> observed AUC is BELOW chance beyond noise: inspect label ordering upstream.")
else:
   print("-> observed AUC is ABOVE chance: a real lexical shortcut exists in this sample.")
coefs = np.sort(np.abs(clf.coef_.ravel()))[-20:]
print(f"Top-20 absolute lexical coefficient range: {coefs[0]:.3f} to {coefs[-1]:.3f}")
print("Feature strings are intentionally not printed because the source corpus may contain offensive text.")

print("\nPreparing conversational DPO data...")
tok = AutoTokenizer.from_pretrained(MODEL_ID)
if tok.pad_token is None:
   tok.pad_token = tok.eos_token
CHATML = (
   "{% for m in messages %}"
   "{{ '<|im_start|>' + m['role'] + '\n' + m['content'] + '<|im_end|>\n' }}"
   "{% endfor %}"
   "{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
)
if getattr(tok, "chat_template", None) is None:
   tok.chat_template = CHATML
   print("Tokenizer had no chat template; installed a ChatML fallback.")
def add_lengths(row):
   prompt_txt = tok.apply_chat_template(row["prompt"], tokenize=False, add_generation_prompt=True)
   n_prompt = len(tok(prompt_txt, add_special_tokens=False)["input_ids"])
   n_ch = len(tok(row["chosen"][0]["content"], add_special_tokens=False)["input_ids"])
   n_rj = len(tok(row["rejected"][0]["content"], add_special_tokens=False)["input_ids"])
   return {"n_prompt": n_prompt, "n_total": n_prompt + max(n_ch, n_rj)}
def fits(row):
   return row["n_prompt"] <= MAX_PROMPT_LENGTH and row["n_total"] <= MAX_LENGTH
dpo_train_full = parsed_train.map(add_lengths).filter(fits)
dpo_test_full = parsed_test.map(add_lengths).filter(fits)
test_sources = list(dpo_test_full["source"])
test_prompts = list(dpo_test_full["prompt"])
test_chosen = list(dpo_test_full["chosen"])
test_rejected = list(dpo_test_full["rejected"])
DPO_COLS = ["prompt", "chosen", "rejected"]
dpo_train = dpo_train_full.remove_columns([c for c in dpo_train_full.column_names if c not in DPO_COLS])
dpo_test = dpo_test_full.remove_columns([c for c in dpo_test_full.column_names if c not in DPO_COLS])
print(f"DPO-ready rows after {MAX_LENGTH}-token filter -> train={len(dpo_train)}, test={len(dpo_test)}")
print("DPO schema:", dict(dpo_train.features))
def split_kwargs(wanted, valid):
   return ({k: v for k, v in wanted.items() if k in valid},
           {k: v for k, v in wanted.items() if k not in valid})
def build_dpo_config(wanted):
   kept, dropped = split_kwargs(wanted, CFG_FIELDS)
   if "warmup_ratio" in dropped and "warmup_steps" in CFG_FIELDS:
       steps = max(1, int(dropped.pop("warmup_ratio") * wanted.get("max_steps", 100)))
       kept["warmup_steps"] = steps
       print(f"  warmup_ratio unsupported here -> converted to warmup_steps={steps}")
   forwarded, truly_dropped = split_kwargs(dropped, TRAINER_PARAMS)
   if forwarded:
       print("  forwarded to DPOTrainer:", sorted(forwarded))
   if truly_dropped:
       print("  dropped (accepted nowhere in this build):", sorted(truly_dropped))
       if "max_prompt_length" in truly_dropped:
           print("    -> harmless: the token filter in section 7 already caps prompts")
   return DPOConfig(**kept), forwarded
wanted_args = dict(
   output_dir=OUTPUT_DIR,
   max_steps=MAX_STEPS,
   per_device_train_batch_size=BATCH_SIZE,
   per_device_eval_batch_size=BATCH_SIZE,
   gradient_accumulation_steps=GRAD_ACCUM,
   learning_rate=LEARNING_RATE,
   warmup_ratio=WARMUP_RATIO,
   logging_steps=LOGGING_STEPS,
   save_strategy="no",
   report_to=[],
   remove_unused_columns=False,
   bf16=BF16,
   fp16=FP16,
   seed=SEED,
   beta=BETA,
   max_length=MAX_LENGTH,
   max_prompt_length=MAX_PROMPT_LENGTH,
)
print("\nBuilding DPOConfig for the installed TRL...")
args, forwarded_to_trainer = build_dpo_config(wanted_args)
print("  DPOConfig built OK")
def build_model():
   dtype = torch.bfloat16 if BF16 else torch.float32
   try:
       model = AutoModelForCausalLM.from_pretrained(MODEL_ID, dtype=dtype)
   except TypeError:
       model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype=dtype)
   model.config.use_cache = False
   return model
peft_config = None
if USE_LORA:
   try:
       from peft import LoraConfig
       peft_config = LoraConfig(
           r=16, lora_alpha=32, lora_dropout=0.05, bias="none",
           task_type="CAUSAL_LM",
       )
       print("  LoRA enabled (the frozen base doubles as the reference model)")
   except ImportError:
       print("  peft not installed -> full fine-tune with an explicit reference model")
def build_trainer(model, args, train_ds, eval_ds, tokenizer, peft_config, extra):
   kwargs = dict(model=model, args=args, train_dataset=train_ds, eval_dataset=eval_ds)
   if "processing_class" in TRAINER_PARAMS:
       kwargs["processing_class"] = tokenizer
   elif "tokenizer" in TRAINER_PARAMS:
       kwargs["tokenizer"] = tokenizer
   if peft_config is not None and "peft_config" in TRAINER_PARAMS:
       kwargs["peft_config"] = peft_config
   elif peft_config is None and "ref_model" in TRAINER_PARAMS:
       kwargs["ref_model"] = None
   kwargs.update(extra)
   print("  DPOTrainer kwargs:", sorted(kwargs))
   return DPOTrainer(**kwargs)
print("\nBuilding DPOTrainer...")
patch_peft_torchao()
model = build_model()
trainer = build_trainer(model, args, dpo_train, dpo_test, tok, peft_config, forwarded_to_trainer)
print("  DPOTrainer built OK")

print(f"\nTraining for {MAX_STEPS} steps on {DEVICE} "
     f"(effective batch {BATCH_SIZE * GRAD_ACCUM})...")
train_result = trainer.train()
print("\nTraining metrics:")
for k, v in sorted(train_result.metrics.items()):
   print(f"  {k:<28} {v}")
print("\nEvaluating on held-out pairs...")
eval_metrics = trainer.evaluate()
for k, v in sorted(eval_metrics.items()):
   if any(t in k for t in ("accuracies", "margins", "rewards", "loss")):
       print(f"  {k:<34} {v:.4f}" if isinstance(v, float) else f"  {k:<34} {v}")
log_df = pd.DataFrame(trainer.state.log_history)
if "loss" in log_df.columns:
   fig, ax = plt.subplots(figsize=(7, 3.5))
   d = log_df.dropna(subset=["loss"])
   ax.plot(d["step"], d["loss"], marker="o", ms=3, label="train loss")
   acc_col = next((c for c in log_df.columns if c.endswith("rewards/accuracies")), None)
   if acc_col:
       d2 = log_df.dropna(subset=[acc_col])
       ax.plot(d2["step"], d2[acc_col], marker="s", ms=3, label="reward accuracy")
       ax.axhline(0.5, color="0.6", lw=0.8, ls="--")
   ax.set_xlabel("step")
   ax.legend(fontsize=8)
   ax.set_title("DPO training")
   plt.tight_layout()
   plt.show()

@torch.no_grad()
def completion_logprob(policy, messages_prompt, message_completion, use_ref=False):
   prompt_txt = tok.apply_chat_template(messages_prompt, tokenize=False, add_generation_prompt=True)
   full_txt = prompt_txt + message_completion["content"] + tok.eos_token
   p_ids = tok(prompt_txt, add_special_tokens=False, return_tensors="pt")["input_ids"]
   f_ids = tok(full_txt, add_special_tokens=False, return_tensors="pt",
               truncation=True, max_length=MAX_LENGTH)["input_ids"].to(policy.device)
   start = min(p_ids.shape[1], f_ids.shape[1] - 1)
   ctx = policy.disable_adapter() if (use_ref and hasattr(policy, "disable_adapter")) else None
   if ctx is not None:
       with ctx:
           logits = policy(f_ids).logits
   else:
       logits = policy(f_ids).logits
   logprobs = torch.log_softmax(logits[:, :-1].float(), dim=-1)
   targets = f_ids[:, 1:]
   picked = logprobs.gather(-1, targets.unsqueeze(-1)).squeeze(-1)
   return picked[:, start:].sum().item()
def per_source_reward_accuracy(n=N_REWARD_EVAL):
   policy = trainer.model
   policy.eval()
   if not hasattr(policy, "disable_adapter") and getattr(trainer, "ref_model", None) is None:
       print("  no reference model reachable; skipping per-source analysis")
       return None
   idx = rng.permutation(len(test_sources))[:min(n, len(test_sources))]
   rows = []
   for i in idx:
       i = int(i)
       rc = completion_logprob(policy, test_prompts[i], test_chosen[i][0])
       rr = completion_logprob(policy, test_prompts[i], test_rejected[i][0])
       refc = completion_logprob(policy, test_prompts[i], test_chosen[i][0], use_ref=True)
       refr = completion_logprob(policy, test_prompts[i], test_rejected[i][0], use_ref=True)
       rows.append({
           "source": test_sources[i],
           "margin": BETA * ((rc - refc) - (rr - refr)),
           "correct": BETA * ((rc - refc) - (rr - refr)) > 0,
           "len_delta": len(test_chosen[i][0]["content"].split())
                        - len(test_rejected[i][0]["content"].split()),
       })
   df = pd.DataFrame(rows)
   out = df.groupby("source").agg(
       n=("correct", "size"),
       reward_accuracy=("correct", "mean"),
       mean_margin=("margin", "mean"),
       mean_len_delta=("len_delta", "mean"),
   ).round(3)
   print(out.to_string())
   longer_wins = (df["correct"] == (df["len_delta"] > 0)).mean()
   print(f"\n  agreement between 'model prefers chosen' and 'chosen is longer': {longer_wins:.3f}")
   print("  (near 0.5 = no length shortcut; near 1.0 = the policy is mostly ranking by length)")
   return out
print(f"\nPer-source reward accuracy on {N_REWARD_EVAL} held-out pairs:")
try:
   per_source = per_source_reward_accuracy()
except Exception as exc:
   print(f"  skipped: {type(exc).__name__}: {exc}")
   per_source = None
def generate(messages, max_new_tokens=96):
   text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
   enc = tok(text, return_tensors="pt").to(trainer.model.device)
   with torch.no_grad():
       out = trainer.model.generate(**enc, max_new_tokens=max_new_tokens, do_sample=False,
                                    pad_token_id=tok.pad_token_id)
   return tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True).strip()
probes = [
   [{"role": "user", "content": "My laptop fan is suddenly very loud. What should I check first?"}],
   [{"role": "user", "content": "Explain in two sentences why DPO does not need a separate reward model."}],
]
print("\nSample generations from the tuned policy:")
for p in probes:
   print(f"\n  user      : {p[0]['content']}")
   print(f"  assistant : {generate(p)}")
trainer.save_model(OUTPUT_DIR)
tok.save_pretrained(OUTPUT_DIR)
print(f"\nSaved to {OUTPUT_DIR}")
print("""
Reading the results
 * At MAX_STEPS=30 on CPU this is a smoke test, not a trained model. Reward accuracy
   near 0.5 is the expected outcome; raise MAX_STEPS on a GPU before concluding anything.
 * The number to watch is the per-source table, not the aggregate. If harmless-base
   reward accuracy drops while the helpful subsets rise, the policy is learning the
   length asymmetry visible in the section 5 audit rather than the intended preference.
 * If a TRL call fails after an upgrade, the environment report at the top names which
   class accepts which argument in your build; extend `wanted_args` from that list.
""")