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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForCausalLM, T5EncoderModel, RobertaTokenizer
from huggingface_hub import hf_hub_download
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MAX_LEN = 256
THRESHOLD = 0.475
REPO_ID = "santh-cpu/ai_code_detect"
class PolyglotMetricEngine(nn.Module):
def __init__(self, base_model):
super().__init__()
self.model = base_model
@torch.no_grad()
def forward(self, input_ids, attention_mask):
B, L = input_ids.shape
with torch.autocast(device_type='cuda', dtype=torch.float16):
logits_raw = self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=False).logits
logits_raw = logits_raw.float()
shift_logits = logits_raw[:, :-1, :].contiguous()
shift_labels = input_ids[:, 1:].contiguous()
shift_mask = attention_mask[:, 1:].float()
log_probs_all = F.log_softmax(shift_logits, dim=-1)
probs_all = log_probs_all.exp()
log_prob = log_probs_all.gather(2, shift_labels.unsqueeze(-1)).squeeze(-1)
K_max = min(1001, shift_logits.size(-1))
topk_vals, topk_idx = torch.topk(shift_logits, K_max, dim=-1)
rank_approx = (log_probs_all.gather(2, topk_idx) > log_prob.unsqueeze(-1)).sum(-1).float() + 1.0
true_rank_log = torch.log1p(rank_approx)
top10_mass = log_probs_all.gather(2, topk_idx[:, :, :10]).exp().sum(dim=-1)
lp_topk = log_probs_all.gather(2, topk_idx[:, :, :2])
gap_1_2 = (lp_topk[:, :, 0] - lp_topk[:, :, 1]).clamp(-20, 20)
entropy = -(probs_all * log_probs_all).sum(dim=-1)
varentropy = (probs_all * (-log_probs_all - entropy.unsqueeze(-1))**2).sum(dim=-1)
r10_flag = (true_rank_log <= math.log1p(10)).float()
r100_flag = ((true_rank_log > math.log1p(10)) & (true_rank_log <= math.log1p(100))).float()
r1k_flag = ((true_rank_log > math.log1p(100)) & (true_rank_log <= math.log1p(1000))).float()
rtail_flag = (true_rank_log > math.log1p(1000)).float()
valid_n = shift_mask.sum(dim=1, keepdim=True).clamp(min=1)
lp_mean = (log_prob * shift_mask).sum(1, keepdim=True) / valid_n
lp_var = ((log_prob - lp_mean)**2 * shift_mask).sum(1, keepdim=True) / valid_n
lp_std = lp_var.sqrt().clamp(min=1e-4)
surprisal_z = ((log_prob - lp_mean) / lp_std) * shift_mask
entropy_shift = F.pad(entropy[:, :-1], (1, 0), value=0.)
entropy_delta = (entropy - entropy_shift) * shift_mask
cum_positions = torch.arange(1, L, device=input_ids.device).unsqueeze(0).float()
cum_rank = (true_rank_log * shift_mask).cumsum(dim=1) / cum_positions
is_special = torch.zeros_like(shift_mask)
m = shift_mask
token_feats_12 = torch.stack([
log_prob*m, true_rank_log*m, entropy*m, varentropy*m,
top10_mass*m, gap_1_2*m, surprisal_z, entropy_delta,
cum_rank*m, is_special*m, r10_flag*m, r100_flag*m,
], dim=-1)
out_token = torch.zeros(B, MAX_LEN, 12, device=input_ids.device)
out_token[:, :L-1, :] = token_feats_12
seq_feats = self._compute_seq_feats(
log_prob, entropy, varentropy, top10_mass,
gap_1_2, surprisal_z, r10_flag, r100_flag,
r1k_flag, rtail_flag, shift_mask, valid_n
)
return out_token.detach(), seq_feats.detach()
def _compute_seq_feats(self, log_prob, entropy, varentropy, top10_mass,
gap_1_2, surprisal_z, r10_flag, r100_flag,
r1k_flag, rtail_flag, mask, valid_n):
feats = []
def masked_moments(x):
n = valid_n.squeeze(1)
mu = (x * mask).sum(1) / n
dev = (x - mu.unsqueeze(1)) * mask
var = (dev**2).sum(1) / n
std = var.sqrt().clamp(min=1e-6)
skew = (dev**3).sum(1) / (n * std**3 + 1e-8)
kurt = (dev**4).sum(1) / (n * var**2 + 1e-8)
return mu, std, skew.clamp(-10, 10), kurt.clamp(0, 50)
for feat in [log_prob, entropy, varentropy, top10_mass]:
feats += list(masked_moments(feat))
for lag in [1, 5]:
e_shift = F.pad(entropy[:, lag:], (0, lag)) * mask
e_norm = entropy - (entropy*mask).sum(1, keepdim=True)/valid_n
e_shift_norm = e_shift - (e_shift*mask).sum(1, keepdim=True)/valid_n
num = (e_norm * e_shift_norm * mask).sum(1)
denom = (((e_norm**2*mask).sum(1)+1e-8).sqrt() * ((e_shift_norm**2*mask).sum(1)+1e-8).sqrt())
feats.append((num/denom).clamp(-1, 1))
n = valid_n.squeeze(1)
for flag in [r10_flag, r100_flag, r1k_flag, rtail_flag]:
feats.append((flag*mask).sum(1)/n)
feats += list(masked_moments(gap_1_2)[:2])
feats += list(masked_moments(surprisal_z)[:2])
pos = torch.arange(entropy.shape[1], device=entropy.device).float().unsqueeze(0)
pos_mu = (pos*mask).sum(1)/n
ent_mu = (entropy*mask).sum(1)/n
cov = ((pos - pos_mu.unsqueeze(1)) * (entropy - ent_mu.unsqueeze(1)) * mask).sum(1)
var_pos = ((pos - pos_mu.unsqueeze(1))**2 * mask).sum(1)
feats.append((cov/(var_pos+1e-8)).clamp(-5, 5))
abs_surp = surprisal_z.abs()
mu_b, std_b, _, _ = masked_moments(abs_surp)
feats.append((std_b/(mu_b+1e-6)).clamp(0, 20))
feats.append((top10_mass*mask).sum(1)/n)
feats.append((((top10_mass-(top10_mass*mask).sum(1,keepdim=True)/valid_n)**2*mask).sum(1)/n).sqrt())
ent_median = entropy.median(dim=1).values.unsqueeze(1)
feats.append(((entropy < ent_median)*mask).sum(1)/n)
lp_std = masked_moments(log_prob)[1]
ent_mu2 = (entropy*mask).sum(1)/n
feats.append((lp_std/(ent_mu2+1e-4)).clamp(0, 20))
return torch.nan_to_num(torch.stack(feats, dim=1), nan=0., posinf=20., neginf=-20.)
class GatedTemporalMixer(nn.Module):
def __init__(self, dim, kernel_size=7):
super().__init__()
self.conv = nn.Conv1d(dim, dim*2, kernel_size, padding=(kernel_size-1), groups=dim)
self.norm = nn.LayerNorm(dim)
def forward(self, x):
h = self.conv(x.transpose(1,2))[:, :, :x.shape[1]]
gate, val = h.chunk(2, dim=1)
return self.norm((torch.sigmoid(gate)*val).transpose(1,2) + x)
class PerTokenEncoder(nn.Module):
def __init__(self):
super().__init__()
self.feat_norm = nn.LayerNorm(12)
self.proj_in = nn.Linear(12, 128)
self.mixer1 = GatedTemporalMixer(128, 7)
self.mixer2 = GatedTemporalMixer(128, 15)
self.ff = nn.Sequential(
nn.Linear(128, 256), nn.GELU(), nn.Dropout(0.1),
nn.Linear(256, 128), nn.LayerNorm(128)
)
self.attn_q = nn.Linear(128, 1, bias=False)
self.proj_out = nn.Linear(128, 256)
def forward(self, x, mask):
x_proj = F.gelu(self.proj_in(self.feat_norm(x)))
mixed = self.mixer2(self.mixer1(x_proj))
hidden = mixed + self.ff(mixed)
scores = self.attn_q(hidden).squeeze(-1).masked_fill(mask==0, float('-inf'))
return self.proj_out((hidden * torch.softmax(scores, dim=-1).unsqueeze(-1)).sum(1))
class SeqFeatMLP(nn.Module):
def __init__(self):
super().__init__()
self.net = nn.Sequential(
nn.LayerNorm(32), nn.Linear(32, 128), nn.GELU(),
nn.Dropout(0.1), nn.Linear(128, 64), nn.LayerNorm(64)
)
def forward(self, x): return self.net(x)
class PolyglotClassifierV3(nn.Module):
def __init__(self, base):
super().__init__()
self.encoder = base
self.token_enc = PerTokenEncoder()
self.seq_mlp = SeqFeatMLP()
fused = base.config.hidden_size + 256 + 64
self.classifier = nn.Sequential(
nn.LayerNorm(fused), nn.Linear(fused, 512),
nn.GELU(), nn.Dropout(0.2),
nn.Linear(512, 128), nn.GELU(),
nn.Dropout(0.1), nn.Linear(128, 1)
)
def forward(self, ids, mask, tf, sf):
hs = self.encoder(input_ids=ids, attention_mask=mask).last_hidden_state
sem = (hs * mask.unsqueeze(-1)).sum(1) / mask.unsqueeze(-1).sum(1).clamp(min=1e-4)
return self.classifier(torch.cat([sem, self.token_enc(tf, mask), self.seq_mlp(sf)], dim=-1)).squeeze(-1)
gen_tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
if gen_tokenizer.pad_token is None:
gen_tokenizer.pad_token = gen_tokenizer.eos_token
t5_tokenizer = RobertaTokenizer.from_pretrained("Salesforce/codet5-base", extra_special_tokens=None)
gen_base = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono", torch_dtype=torch.float16).to(DEVICE)
metric_engine = PolyglotMetricEngine(gen_base).eval()
t5_base = T5EncoderModel.from_pretrained("Salesforce/codet5-base")
detector = PolyglotClassifierV3(t5_base).to(DEVICE)
weights_path = hf_hub_download(repo_id=REPO_ID, filename="model_weights.pt")
detector.load_state_dict(torch.load(weights_path, map_location=DEVICE))
detector.eval()
def predict(code: str, threshold: float = THRESHOLD) -> dict:
if len(code.strip()) < 120:
return {"pred": "too short", "prob": None}
with torch.no_grad():
g = gen_tokenizer(code, return_tensors="pt", padding="max_length", truncation=True, max_length=MAX_LEN).to(DEVICE)
tf, sf = metric_engine(g["input_ids"], g["attention_mask"])
t = t5_tokenizer(code, return_tensors="pt", padding="max_length", truncation=True, max_length=MAX_LEN).to(DEVICE)
logits = detector(t["input_ids"], t["attention_mask"], tf.float(), sf.float())
prob = torch.sigmoid(logits).item()
return {
"pred": "ai-generated" if prob >= threshold else "human",
"prob": (prob * 100, 2)
} |