File size: 7,224 Bytes
63519e7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | """Standalone inference for Grounded Pointer QA (proqa).
Self-contained: everything needed to load the checkpoint and ask questions
over your own documents. Requires: torch, transformers, scikit-learn, numpy.
from modeling_proqa import GroundedQA
qa = GroundedQA("proqa.pt") # or a hf_hub_download path
qa.load_folder(r"C:\\my\\notes") # .txt / .md files
print(qa.ask("who approved the budget?")) # quote + source, or None
"""
import os
from dataclasses import dataclass
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.feature_extraction.text import TfidfVectorizer
from transformers import AutoConfig, AutoModel, AutoTokenizer
@dataclass
class ProConfig:
backbone: str = "roberta-base"
max_len: int = 384
k_passages: int = 4
class ProReaderQA(nn.Module):
def __init__(self, cfg: ProConfig):
super().__init__()
self.cfg = cfg
self.backbone = AutoModel.from_config(AutoConfig.from_pretrained(cfg.backbone))
h = self.backbone.config.hidden_size
self.span_head = nn.Linear(h, 2)
self.abstain_head = nn.Sequential(nn.Linear(h, h), nn.Tanh(), nn.Linear(h, 1))
def forward(self, input_ids, attention_mask, context_mask):
b, k, L = input_ids.shape
out = self.backbone(input_ids=input_ids.view(b * k, L),
attention_mask=attention_mask.view(b * k, L)
).last_hidden_state
start_logits, end_logits = self.span_head(out).unbind(dim=-1)
neg_inf = torch.finfo(start_logits.dtype).min
cm = context_mask.view(b * k, L)
start_logits = start_logits.masked_fill(~cm, neg_inf).view(b, k * L)
end_logits = end_logits.masked_fill(~cm, neg_inf).view(b, k * L)
cls = out[:, 0].view(b, k, -1).mean(dim=1)
return start_logits, end_logits, self.abstain_head(cls).squeeze(-1)
def chunk_text(text, chunk_words=150, overlap=40):
words = text.split()
if not words:
return []
chunks, step = [], max(chunk_words - overlap, 1)
for i in range(0, len(words), step):
chunks.append(" ".join(words[i:i + chunk_words]))
if i + chunk_words >= len(words):
break
return chunks
class GroundedQA:
def __init__(self, checkpoint_path: str, device: str = None):
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
ckpt = torch.load(checkpoint_path, map_location=self.device)
self.cfg = ProConfig(**ckpt["config"])
self.model = ProReaderQA(self.cfg).to(self.device).eval()
self.model.load_state_dict(ckpt["state_dict"])
self.gate = ckpt.get("gate_threshold", 0.5)
self.tok = AutoTokenizer.from_pretrained(self.cfg.backbone)
self.passages, self._vec, self._mat = [], None, None
# ---------- knowledge ----------
def load_passages(self, passages):
self.passages = list(passages)
self._vec = TfidfVectorizer(lowercase=True, ngram_range=(1, 2),
sublinear_tf=True, min_df=1)
self._mat = self._vec.fit_transform(self.passages)
def load_folder(self, folder, chunk_words=150):
passages = []
for root, _, files in os.walk(folder):
for name in sorted(files):
path = os.path.join(root, name)
if name.lower().endswith((".txt", ".md")):
with open(path, encoding="utf-8", errors="ignore") as f:
passages.extend(chunk_text(f.read(), chunk_words))
elif name.lower().endswith(".pdf"):
try:
from pypdf import PdfReader
text = "\n".join(p.extract_text() or ""
for p in PdfReader(path).pages)
passages.extend(chunk_text(text, chunk_words))
except ImportError:
pass # pip install pypdf for PDF support
if not passages:
raise ValueError(f"no readable documents under {folder}")
self.load_passages(passages)
# ---------- ask ----------
@torch.no_grad()
def ask(self, question: str, gate: float = None):
"""Returns dict(answer, source, confidence) or dict(answer=None, ...)."""
assert self.passages, "load knowledge first (load_folder / load_passages)"
gate = self.gate if gate is None else gate
k, L = self.cfg.k_passages, self.cfg.max_len
q = self._vec.transform([question])
sims = (q @ self._mat.T).toarray()[0]
top = list(np.argsort(-sims)[:k])
while len(top) < k:
top.append(top[-1])
passages = [self.passages[j] for j in top]
q_ids = self.tok(question, add_special_tokens=False, truncation=True,
max_length=64)["input_ids"]
question = self.tok.decode(q_ids)
enc = self.tok([question] * k, passages, truncation="only_second",
max_length=L, padding="max_length",
return_offsets_mapping=True, return_tensors="pt")
cm = torch.zeros(k, L, dtype=torch.bool)
for s in range(k):
cm[s] = torch.tensor([sid == 1 for sid in enc.sequence_ids(s)])
if top[s] in top[:s]: # dedup tiny indexes
cm[s] = False
with torch.autocast(device_type="cuda", dtype=torch.bfloat16,
enabled=self.device == "cuda"):
s_log, e_log, a_log = self.model(
enc["input_ids"].unsqueeze(0).to(self.device),
enc["attention_mask"].bool().unsqueeze(0).to(self.device),
cm.unsqueeze(0).to(self.device))
s_lp = F.log_softmax(s_log.float(), -1).view(1, k, L)
e_lp = F.log_softmax(e_log.float(), -1).view(1, k, L)
scores = s_lp.unsqueeze(3) + e_lp.unsqueeze(2)
valid = torch.ones(L, L, dtype=torch.bool, device=scores.device).triu()
valid &= ~torch.ones(L, L, dtype=torch.bool, device=scores.device).triu(80)
flat = scores.masked_fill(~valid, float("-inf")).view(1, -1)
best, idx = flat.max(-1)
pi, rem = int(idx // (L * L)), int(idx % (L * L))
s, e = rem // L, rem % L
conf = float(a_log.float().sigmoid() * best.exp())
if conf < gate:
return {"answer": None, "source": None, "confidence": conf}
o = enc["offset_mapping"][pi]
return {"answer": passages[pi][int(o[s][0]): int(o[e][1])],
"source": passages[pi], "confidence": conf}
if __name__ == "__main__":
import sys
qa = GroundedQA(sys.argv[1] if len(sys.argv) > 1 else "proqa.pt")
qa.load_folder(sys.argv[2] if len(sys.argv) > 2 else ".")
print(f"loaded {len(qa.passages)} passages; gate={qa.gate:.2f}")
while True:
q = input("question> ").strip()
if q in ("", "/quit"):
break
r = qa.ask(q)
if r["answer"] is None:
print(f" [abstains] (conf={r['confidence']:.2f})")
else:
print(f" \"{r['answer']}\" (conf={r['confidence']:.2f})")
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