File size: 11,206 Bytes
8948114 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | """AksharaLP v3 tiny sentence LM: Devanagari subword (unigram-LM pieces, Viterbi segmentation reimplemented here so Python and
JS tokenize identically) + 2-layer LSTM with projection (LSTMP) + tied input/output embedding, trained with a self-normalisation
penalty (alpha * logZ^2) so the runtime may skip the softmax normaliser (norm='self') or compute it exactly (norm='exact').
File format (same low-bit scheme as bhc/xlit_q.py): u32 header length | JSON header {cfg, bits, G, tensors, vocab:[[piece, score]...]} | data.
Sentence = '</s>' w1 w2 ... wn '</s>' ; digits runs are the word '<n>'."""
import io, json, math
import numpy as np
SP = "\u2581"
class Tok:
"""Viterbi over piece log-probs (identical to sentencepiece unigram encode for single whitespace-free words, no sampling)."""
def __init__(self, vocab):
self.pieces = [p for p, _ in vocab]; self.score = {p: s for p, s in vocab}; self.id = {p: i for i, p in enumerate(self.pieces)}
self.maxlen = max(len(p) for p in self.pieces); self.unk = self.id["<unk>"]; self.eos = self.id["</s>"]
self.unk_score = min(s for _, s in vocab) - 10.0; self.cache = {}
def encode_word(self, w):
r = self.cache.get(w)
if r is not None: return r
if w in self.id and w.startswith("<"): r = [self.id[w]]; self.cache[w] = r; return r
s = SP + w; n = len(s); best = [-1e30] * (n + 1); back = [None] * (n + 1); best[0] = 0.0
for i in range(n):
if best[i] <= -1e29: continue
hit = False
for j in range(i + 1, min(n, i + self.maxlen) + 1):
p = s[i:j]; sc = self.score.get(p)
if sc is not None and p not in ("<unk>", "<s>", "</s>", "<n>"):
hit = True
if best[i] + sc > best[j]: best[j] = best[i] + sc; back[j] = (i, self.id[p])
if not hit or (back[i + 1] is None): # single unknown character -> <unk>
if best[i] + self.unk_score > best[i + 1]: best[i + 1] = best[i] + self.unk_score; back[i + 1] = (i, self.unk)
out = []; j = n
while j > 0: i, pid = back[j]; out.append(pid); j = i
r = out[::-1]
if len(self.cache) < 200000: self.cache[w] = r
return r
def _dq(data, off, shape, kind, G):
r, c = shape; ng = (c + G - 1) // G
if kind == "q8":
q = np.frombuffer(data, np.int8, r * c, off).astype(np.float32).reshape(r, c); off += r * c
else:
nb = (r * c + 1) // 2; u = np.frombuffer(data, np.uint8, nb, off); off += nb
q = np.empty(nb * 2, np.int8); q[0::2] = (u >> 4).astype(np.int8) - 8; q[1::2] = (u & 15).astype(np.int8) - 8
q = q[:r * c].astype(np.float32).reshape(r, c)
off += (-off) % 4
sc = np.frombuffer(data, np.float16, r * ng, off).astype(np.float32).reshape(r, ng)
return q * np.repeat(sc, G, 1)[:, :c]
class NLM:
"""numpy float32 inference. state = (h[L,E], c[L,H]) per hypothesis, stacked into arrays for batching."""
def __init__(self, cfg, W, vocab, norm="exact"):
self.cfg = cfg; self.W = W; self.tok = Tok(vocab); self.L = cfg["layers"]; self.norm = norm
self.emb = W["emb.weight"]; self.ob = W["out_bias"]
self._init = None; L = self.L
self._wihT = [np.ascontiguousarray(W[f"lstm.weight_ih_l{l}"].T) for l in range(L)]; self._wih0T = self._wihT[0]
self._whhT = [np.ascontiguousarray(W[f"lstm.weight_hh_l{l}"].T) for l in range(L)]
self._whrT = [np.ascontiguousarray(W[f"lstm.weight_hr_l{l}"].T) for l in range(L)]
self._b = [W[f"lstm.bias_ih_l{l}"] + W[f"lstm.bias_hh_l{l}"] for l in range(L)]; self._embT = np.ascontiguousarray(self.emb.T)
@staticmethod
def load(path, norm=None):
b = open(path, "rb").read(); n = int.from_bytes(b[:4], "little"); meta = json.loads(b[4:4 + n]); data = b[4 + n:]
W = {}
for k, shape, kind, off, ln in meta["tensors"]:
if kind == "f16":
cnt = int(np.prod(shape)); W[k] = np.frombuffer(data, np.float16, cnt, off).astype(np.float32).reshape(shape)
elif kind == "f32":
cnt = int(np.prod(shape)); W[k] = np.frombuffer(data, np.float32, cnt, off).reshape(shape).copy()
else: W[k] = _dq(data, off, shape, kind, meta["G"])
return NLM(meta["cfg"], W, meta["vocab"], norm or meta["cfg"].get("norm", "exact"))
def _step(self, h, c, ids, g0=None):
"""h: [L,B,E], c: [L,B,H], ids: [B] -> new h, c. g0: optional precomputed layer-0 gate pre-activations [B,4H]."""
hs = []; cs = []
if g0 is None:
u, inv = np.unique(ids, return_inverse=True) # layer-0 input projection once per distinct piece
g0 = (self.emb[u] @ self._wih0T + self._b[0])[inv] + h[0] @ self._whhT[0]
for l in range(self.L):
g = g0 if l == 0 else x @ self._wihT[l] + h[l] @ self._whhT[l] + self._b[l]
H = g.shape[1] // 4
i_ = 1 / (1 + np.exp(-g[:, :H])); f_ = 1 / (1 + np.exp(-g[:, H:2 * H])); g_ = np.tanh(g[:, 2 * H:3 * H]); o_ = 1 / (1 + np.exp(-g[:, 3 * H:]))
cn = f_ * c[l] + i_ * g_; hn = (o_ * np.tanh(cn)) @ self._whrT[l]
hs.append(hn); cs.append(cn); x = hn
return np.stack(hs), np.stack(cs)
def _logp(self, h, ids):
"""log P(ids[b] | state b) using the top-layer output h[-1]: [B]"""
top = h[-1]; z = (top * self.emb[ids]).sum(1) + self.ob[ids]
if self.norm == "exact":
lg = top @ self._embT + self.ob; m = lg.max(1); z = z - (m + np.log(np.exp(lg - m[:, None]).sum(1)))
return z
def init_state(self):
if self._init is None:
E = self.emb.shape[1]; H = self.W["lstm.weight_hh_l0"].shape[1] * 0 + self.W["lstm.weight_ih_l0"].shape[0] // 4
h = np.zeros((self.L, 1, E), np.float32); c = np.zeros((self.L, 1, H), np.float32)
h, c = self._step(h, c, np.array([self.tok.eos])); self._init = (h[:, 0], c[:, 0])
return self._init
def score_words(self, states, words):
"""states: list of B states; words: list of K Devanagari words -> (lp[B][K], new_states[B][K]).
Exact chain rule over each word's pieces from each state, batched over all (b,k) pairs. First piece: one softmax
normaliser per beam state and the layer-0 recurrent term once per beam (broadcast over the K candidates)."""
B, K = len(states), len(words); seqs = [self.tok.encode_word(w) for w in words]
h = np.stack([s[0] for s in states], 1); c = np.stack([s[1] for s in states], 1) # [L,B,E], [L,B,H]
id0 = np.array([q[0] for q in seqs]); top = h[-1]
lp = top @ self.emb[id0].T + self.ob[id0][None, :] # [B,K]
if self.norm == "exact":
lg = top @ self._embT + self.ob; m = lg.max(1); lp = lp - (m + np.log(np.exp(lg - m[:, None]).sum(1)))[:, None]
g0 = ((self.emb[id0] @ self._wih0T + self._b[0])[None, :, :] + (h[0] @ self._whhT[0])[:, None, :]).reshape(B * K, -1)
hb, cb = self._step(np.repeat(h, K, 1), np.repeat(c, K, 1), None, g0=g0) # pair j = b*K + k
flat = lp.reshape(-1).astype(np.float32); maxp = max(len(q) for q in seqs)
for t in range(1, maxp):
ks = [k for k in range(K) if len(seqs[k]) > t]
idx = (np.arange(B)[:, None] * K + np.array(ks)[None, :]).reshape(-1)
ids = np.tile(np.array([seqs[k][t] for k in ks]), B)
flat[idx] += self._logp(hb[:, idx], ids)
hn, cn = self._step(hb[:, idx], cb[:, idx], ids); hb[:, idx] = hn; cb[:, idx] = cn
ns = [[(hb[:, b * K + k], cb[:, b * K + k]) for k in range(K)] for b in range(B)]
return flat.reshape(B, K).tolist(), ns
def advance_many(self, states, words):
lp, ns = self.score_words(states, words); return ns
def eos_logp(self, state):
h = state[0][:, None, :]
return float(self._logp(h, np.array([self.tok.eos]))[0])
def sent_logp(self, words):
st = self.init_state(); tot = 0.0
for w in words:
lp, ns = self.score_words([st], [w]); tot += lp[0][0]; st = ns[0][0]
return tot + self.eos_logp(st)
# ---------------- torch model + export (training side)
def torch_model(cfg):
import torch, torch.nn as nn
class LSTMP_LM(nn.Module):
def __init__(s, cfg):
super().__init__(); s.cfg = cfg; V, E, H, L = cfg["vocab"], cfg["emb"], cfg["hid"], cfg["layers"]
s.emb = nn.Embedding(V, E); nn.init.normal_(s.emb.weight, 0, E ** -0.5)
s.lstm = nn.LSTM(E, H, num_layers=L, proj_size=E, batch_first=True, dropout=cfg.get("dropout", 0.0))
s.out_bias = nn.Parameter(torch.zeros(V)); s.drop = nn.Dropout(cfg.get("dropout", 0.0))
def forward(s, x, state=None):
h, st = s.lstm(s.drop(s.emb(x)), state)
return s.drop(h) @ s.emb.weight.T + s.out_bias, st
return LSTMP_LM(cfg)
def _q(v, bits, G):
"""symmetric b-bit ints, one fp16 scale per G consecutive inputs per row, MSE-optimal clipping for 4-bit (same as bhc/xlit_q.py)."""
r, c = v.shape; pad = (-c) % G; vp = np.pad(v, ((0, 0), (0, pad))).reshape(r, -1, G)
L = 2 ** (bits - 1) - 1; base = np.abs(vp).max(2, keepdims=True) / L; base[base == 0] = 1
best_err = None
for al in ((1.0,) if bits >= 8 else (1.0, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65)):
s_ = (base * al).astype(np.float16).astype(np.float32); q_ = np.clip(np.round(vp / s_), -L, L)
e = ((q_ * s_ - vp) ** 2).sum(2, keepdims=True)
if best_err is None: best_err, sc, q = e, s_, q_
else: m = e < best_err; best_err = np.where(m, e, best_err); sc = np.where(m, s_, sc); q = np.where(m, q_, q)
return q.astype(np.int8).reshape(r, -1)[:, :c], sc[..., 0].astype(np.float16)
def export(model, vocab, path, bits=4, G=32, emb_bits=8, norm="self"):
"""bits/emb_bits in {4, 8} or 0 (= float32, exact reference)."""
sd = model.state_dict(); cfg = dict(model.cfg); cfg["norm"] = norm
meta = {"cfg": cfg, "bits": bits, "G": G, "tensors": [], "vocab": vocab}; buf = io.BytesIO()
for k, v in sd.items():
v = v.float().cpu().numpy(); buf.write(b"\0" * ((-buf.tell()) % 4)) # 4-byte aligned tensors (JS typed arrays)
b_ = emb_bits if k.startswith("emb") else bits
if v.ndim == 2 and b_:
q, sc = _q(v, b_, G); flat = q.reshape(-1)
if b_ == 4:
u = (flat + 8).astype(np.uint8); u = np.r_[u, np.zeros(len(u) % 2, np.uint8)]; data = (u[0::2] << 4 | u[1::2]).astype(np.uint8).tobytes()
else: data = flat.astype(np.int8).tobytes()
meta["tensors"].append([k, list(v.shape), "q%d" % b_, buf.tell(), len(data)]); buf.write(data); buf.write(b"\0" * ((-buf.tell()) % 4)); buf.write(sc.tobytes())
else:
meta["tensors"].append([k, list(v.shape), "f32", buf.tell(), 0]); buf.write(v.astype(np.float32).tobytes())
hdr = json.dumps(meta, separators=(",", ":"), ensure_ascii=False).encode(); hdr += b" " * ((-len(hdr) - 4) % 4)
with open(path, "wb") as f: f.write(len(hdr).to_bytes(4, "little")); f.write(hdr); f.write(buf.getvalue())
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