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981ee48 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 | """HF-Hub-compatible model for Stoicheia-meter (macronization + metrical scansion).
Self-contained: vendors the same transformer primitives as modeling_char_bert.py, plus
the fine-tune-only additions meter/model.py::MeterModel and meter/backbone.py::
CharBertWithHidden make on top of the plain backbone:
- a zero-init `cap_emb` capitalization input embedding (fine-tune-only; base
pretraining treats capitalization as output-only)
- an ELMo-style learned scalar mix over every block's output (+ the final normed
hidden state) instead of using only the last layer
- two extra per-letter heads: `head_mac` (2-way: long/short vowel quantity) and
`head_scan` (4-way: none/heavy/light/verse-final syllable weight)
The submodule layout (`self.encoder.*` for the frozen backbone, `head_mac`/
`head_scan`/`mix_w` at the top level) matches meter.model.MeterModel's real state
dict exactly -- converted checkpoints load with strict=True and no key remapping.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import ModelOutput
from .configuration_char_bert_meter import CharBertMeterConfig
class RMSNorm(nn.Module):
def __init__(self, d, eps=1e-6):
super().__init__()
self.w = nn.Parameter(torch.ones(d))
self.eps = eps
def forward(self, x):
x = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
return x * self.w
class RoPE(nn.Module):
def __init__(self, dim, base=10000.0):
super().__init__()
self.dim = dim
self.base = base
def cos_sin(self, pos):
# Recomputed on every call rather than cached in a registered buffer: a
# persistent=False buffer is never covered by the checkpoint's state dict,
# so it depends entirely on __init__-time materialization -- which some
# transformers versions' meta-device/low_cpu_mem_usage loading path can
# skip, silently leaving this tensor uninitialized. Recomputing here is
# immune to that regardless of how the model was constructed/loaded.
inv = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=pos.device).float() / self.dim))
f = torch.outer(pos.float(), inv)
emb = torch.cat([f, f], -1)
return emb.cos(), emb.sin()
def _rotate_half(x):
d = x.shape[-1] // 2
return torch.cat([-x[..., d:], x[..., :d]], -1)
def apply_rope(q, k, cos, sin):
cos = cos[None, None]
sin = sin[None, None]
return q * cos + _rotate_half(q) * sin, k * cos + _rotate_half(k) * sin
class Attention(nn.Module):
def __init__(self, d, n_heads, rope: RoPE, qk_norm=False):
super().__init__()
self.h = n_heads
self.dh = d // n_heads
self.qkv = nn.Linear(d, 3 * d, bias=False)
self.o = nn.Linear(d, d, bias=False)
self.rope = rope
self.qk_norm = qk_norm
if qk_norm:
self.q_norm = RMSNorm(self.dh)
self.k_norm = RMSNorm(self.dh)
def forward(self, x, pos, attn_mask):
B, T, D = x.shape
qkv = self.qkv(x).view(B, T, 3, self.h, self.dh).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
if self.qk_norm:
q, k = self.q_norm(q), self.k_norm(k)
cos, sin = self.rope.cos_sin(pos)
cos, sin = cos.to(x.dtype), sin.to(x.dtype)
q, k = apply_rope(q, k, cos, sin)
out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
out = out.transpose(1, 2).reshape(B, T, D)
return self.o(out)
class GeGLU(nn.Module):
def __init__(self, d, mult=8 / 3):
super().__init__()
hidden = int(d * mult)
hidden = (hidden + 63) // 64 * 64
self.wi = nn.Linear(d, 2 * hidden, bias=False)
self.wo = nn.Linear(hidden, d, bias=False)
def forward(self, x):
a, b = self.wi(x).chunk(2, -1)
return self.wo(F.gelu(a) * b)
class Block(nn.Module):
def __init__(self, d, n_heads, rope, window=0, qk_norm=False):
super().__init__()
self.n1 = RMSNorm(d)
self.attn = Attention(d, n_heads, rope, qk_norm=qk_norm)
self.n2 = RMSNorm(d)
self.mlp = GeGLU(d)
self.window = window # 0 = global; >0 = local sliding window (characters)
def forward(self, x, pos, base_mask):
x = x + self.attn(self.n1(x), pos, base_mask)
x = x + self.mlp(self.n2(x))
return x
def build_attn_mask(seg_id, window, device, dtype):
"""Additive mask (B,1,T,T): same-segment AND (window==0 or |i-j|<window)."""
B, T = seg_id.shape
same = seg_id[:, None, :] == seg_id[:, :, None]
if window and window > 0:
idx = torch.arange(T, device=device)
near = (idx[None, :] - idx[:, None]).abs() < window
same = same & near[None]
mask = torch.zeros(B, 1, T, T, dtype=dtype, device=device)
mask.masked_fill_(~same[:, None], float("-inf"))
return mask
class _MeterEncoder(nn.Module):
"""Same submodule names/shapes as a plain CharBertEncoder (so a pretraining
backbone loads into it with no remapping), plus an optional zero-init cap_emb
and per-layer output collection for the scalar mix -- mirrors
meter.backbone.CharBertWithHidden exactly."""
def __init__(self, config: CharBertMeterConfig):
super().__init__()
self.e_char = nn.Embedding(config.n_char_ids, config.d_model)
self.e_bnd = nn.Embedding(config.n_boundary, config.d_model)
self.e_dia = nn.Embedding(config.n_dia, config.d_model)
self.e_punct = nn.Embedding(config.n_punct, config.d_model)
if config.use_cap:
self.cap_emb = nn.Embedding(2, config.d_model)
rope = RoPE(config.d_model // config.n_heads)
blocks = []
for i in range(config.depth):
win = 0 if i % 4 == 3 else config.char_window # 3 local : 1 global
blocks.append(Block(config.d_model, config.n_heads, rope, window=win, qk_norm=config.qk_norm))
self.blocks = nn.ModuleList(blocks)
self.norm_out = RMSNorm(config.d_model)
# frozen pretraining output heads: not used by the meter heads, but part of
# the backbone's real state dict (kept so a pretraining checkpoint -- or this
# converted meter checkpoint -- loads with strict=True)
self.head_char = nn.Linear(config.d_model, config.n_char_ids, bias=False)
self.head_bnd = nn.Linear(config.d_model, 3, bias=False)
self.head_dia = nn.Linear(config.d_model, 48, bias=False)
self.head_cap = nn.Linear(config.d_model, 2, bias=False)
self.head_punct = nn.Linear(config.d_model, 6, bias=False)
self.cfg = config
def forward(self, input_ids, boundary, dia, punct, cap=None, seg_id=None, collect_layers=False):
cfg = self.cfg
B, T = input_ids.shape
pos = torch.arange(T, device=input_ids.device)
seg = seg_id if seg_id is not None else torch.zeros(B, T, dtype=torch.long, device=input_ids.device)
x = self.e_char(input_ids) + self.e_bnd(boundary) + self.e_dia(dia) + self.e_punct(punct)
cap_emb = getattr(self, "cap_emb", None)
if cap_emb is not None and cap is not None:
x = x + cap_emb(cap)
attn_mask = build_attn_mask(seg, cfg.char_window, input_ids.device, x.dtype)
glob_mask = build_attn_mask(seg, 0, input_ids.device, x.dtype)
layers = []
for blk in self.blocks:
m = glob_mask if blk.window == 0 else attn_mask
x = blk(x, pos, m)
if collect_layers:
layers.append(x)
x = self.norm_out(x)
return layers, x
@dataclass
class CharBertMeterOutput(ModelOutput):
mac: torch.FloatTensor = None
scan: torch.FloatTensor = None
class CharBertMeterModel(PreTrainedModel):
config_class = CharBertMeterConfig
def __init__(self, config: CharBertMeterConfig):
super().__init__(config)
self.encoder = _MeterEncoder(config)
self.head_mac = nn.Linear(config.d_model, 2, bias=False) # 0=long, 1=short
self.head_scan = nn.Linear(config.d_model, 4, bias=False) # 0=none,1=heavy,2=light,3=verse-final
if config.scalar_mix:
self.mix_w = nn.Parameter(torch.zeros(config.depth + 1))
self.post_init()
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, std=0.02)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=0.02)
def forward(
self,
input_ids: torch.LongTensor,
boundary: torch.LongTensor,
dia: torch.LongTensor,
punct: torch.LongTensor,
cap: Optional[torch.LongTensor] = None,
seg_id: Optional[torch.LongTensor] = None,
return_dict: bool = True,
**kwargs,
):
collect = bool(self.config.scalar_mix)
layers, x = self.encoder(input_ids, boundary, dia, punct, cap=cap, seg_id=seg_id,
collect_layers=collect)
if self.config.scalar_mix:
h = torch.stack(layers + [x]) # (L+1, B, T, D)
mix = torch.softmax(self.mix_w, 0)
h = torch.einsum("l,lbtd->btd", mix.to(h.dtype), h)
else:
h = x
mac = self.head_mac(h)
scan = self.head_scan(h)
if not return_dict:
return (mac, scan)
return CharBertMeterOutput(mac=mac, scan=scan)
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