| from __future__ import annotations |
| import gzip,json,pickle,time |
| from pathlib import Path |
| from concurrent.futures import ProcessPoolExecutor,as_completed |
| import multiprocessing as mp |
| import numpy as np |
| from sklearn.feature_extraction.text import CountVectorizer |
| from sklearn.preprocessing import normalize |
| from numba import njit,prange,set_num_threads |
| ROOT=Path('/mnt/data'); W=ROOT/'msmarco_scale_work'; G=W/'geometry_uniform1m_s32'; OLD=W/'full_index'; NEW=W/'full_index_uniform1m_s32'; NEW.mkdir(exist_ok=True) |
| N=8_841_823; M=50_000; F=4; S=32; SENT=np.uint16(65535) |
|
|
| def shard_path(i): |
| hits=list(ROOT.glob(f'corpus_{i:04d}.jsonl*.gz')); assert len(hits)==1; return hits[0] |
| def load_vocab(): |
| with gzip.open(W/'final_vocab_50k.pkl.gz','rb') as g:z=pickle.load(g) |
| terms=z['terms'].tolist(); idf=np.asarray(z['idf'],np.float32); return idf,{t:i for i,t in enumerate(terms)} |
| @njit(cache=False) |
| def lookup(ct,cv,t): |
| lo=0; hi=ct.size |
| while lo<hi: |
| mid=(lo+hi)//2; x=ct[mid] |
| if x==65535 or x>=t: hi=mid |
| else: lo=mid+1 |
| if lo<ct.size and ct[lo]==t:return cv[lo] |
| return 0.0 |
| @njit(parallel=True,cache=False) |
| def kernel(indptr,indices,data,ct,cv): |
| n=indptr.size-1; br=np.full((n,F),SENT,np.uint16); rt=np.full((n,F,S),SENT,np.uint16); sb=np.zeros((n,F),np.uint32) |
| for d in prange(n): |
| a=indptr[d]; b=indptr[d+1]; tv=np.zeros(F,np.float32); tt=np.full(F,SENT,np.uint16) |
| for p in range(a,b): |
| v=data[p]; t=np.uint16(indices[p]); pos=F |
| for r in range(F): |
| if v>tv[r]:pos=r;break |
| if pos<F: |
| for r in range(F-1,pos,-1):tv[r]=tv[r-1];tt[r]=tt[r-1] |
| tv[pos]=v;tt[pos]=t |
| den=0.0 |
| for s in range(F):den+=tv[s] |
| if den<=0:continue |
| for s in range(F):br[d,s]=tt[s] |
| for sl in range(F): |
| j=int(tt[sl]); best=np.zeros(S,np.float32); bt=np.full(S,SENT,np.uint16); bp=np.zeros(S,np.uint8) |
| for p in range(a,b): |
| t=np.uint16(indices[p]); rr=data[p]-lookup(ct[j],cv[j],t); ar=abs(rr); mi=0; mv=best[0] |
| for q in range(1,S): |
| if best[q]<mv:mi=q;mv=best[q] |
| if ar>mv:best[mi]=ar;bt[mi]=t;bp[mi]=1 if rr>=0 else 0 |
| for x in range(S): |
| mx=x |
| for y in range(x+1,S): |
| if best[y]>best[mx]:mx=y |
| if mx!=x: |
| zz=best[x];best[x]=best[mx];best[mx]=zz; zt=bt[x];bt[x]=bt[mx];bt[mx]=zt; zp=bp[x];bp[x]=bp[mx];bp[mx]=zp |
| bits=np.uint32(0) |
| for q in range(S): |
| rt[d,sl,q]=bt[q] |
| if bt[q]!=SENT and bp[q]:bits|=np.uint32(1)<<np.uint32(q) |
| sb[d,sl]=bits |
| return br,rt,sb |
|
|
| def work(sid): |
| set_num_threads(1); t=time.time(); idf,vocab=load_vocab(); ct=np.load(G/'center_terms.npy',mmap_mode='r'); cvv=np.load(G/'center_values.npy',mmap_mode='r'); oldbr=np.memmap(OLD/'branches.u16',np.uint16,'r',shape=(N,F)); rtg=np.memmap(NEW/'res_terms.u16',np.uint16,'r+',shape=(N,F,S)); sbg=np.memmap(NEW/'signbits.u32',np.uint32,'r+',shape=(N,F)) |
| texts=[] |
| with gzip.open(shard_path(sid),'rt',encoding='utf-8') as f: |
| for line in f: |
| o=json.loads(line);texts.append(((o.get('title') or '')+' '+(o.get('text') or '')).strip()) |
| cv=CountVectorizer(vocabulary=vocab,lowercase=True,token_pattern=r'(?u)\b\w\w+\b',dtype=np.int32);X=cv.transform(texts).tocsr().astype(np.float32);X.data*=idf[X.indices];normalize(X,norm='l2',axis=1,copy=False);X.sort_indices(); br,rt,sb=kernel(X.indptr.astype(np.int64),X.indices.astype(np.int32),X.data.astype(np.float32),ct,cvv) |
| n=len(texts);off=sid*250_000;sl=slice(off,off+n);mism=int(np.sum(br!=np.asarray(oldbr[sl])));assert mism==0,(sid,mism);rtg[sl]=rt;sbg[sl]=sb;rtg.flush();sbg.flush();sec=time.time()-t;json.dump({'offset':off,'n':n,'nnz':int(X.nnz),'seconds':sec,'branch_mismatches':mism},open(NEW/f'shard_{sid:04d}.json','w'));return sid,n,sec |
| if __name__=='__main__': |
| if not (NEW/'res_terms.u16').exists():a=np.memmap(NEW/'res_terms.u16',np.uint16,'w+',shape=(N,F,S));a[:]=SENT;a.flush();del a |
| if not (NEW/'signbits.u32').exists():a=np.memmap(NEW/'signbits.u32',np.uint32,'w+',shape=(N,F));a[:]=0;a.flush();del a |
| done={int(p.stem.split('_')[1]) for p in NEW.glob('shard_*.json')};missing=[i for i in range(36) if i not in done];print('missing',missing,flush=True);t=time.time() |
| with ProcessPoolExecutor(max_workers=2,mp_context=mp.get_context('spawn')) as ex: |
| fs=[ex.submit(work,i) for i in missing] |
| for k,fu in enumerate(as_completed(fs),1):sid,n,sec=fu.result();print(f'[{k:02d}/{len(missing):02d}] shard {sid:04d} n={n:,} sec={sec:.1f}',flush=True) |
| print('S32 ENCODE DONE',time.time()-t,flush=True) |
|
|
