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2643cac | 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 | """HF-Hub-compatible model for Stoicheia (CharBertEncoder).
Self-contained: vendors the transformer primitives (RMSNorm/RoPE/Attention/GeGLU/Block)
so this file has no dependency on the original research repo. Uses the SDPA attention
path only (portable to CPU and any CUDA GPU) -- the original training code also supports
a compiled FlexAttention block-sparse path for long packed sequences on GPU, which is not
needed for standalone inference on single passages and is omitted here for portability.
"""
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 import CharBertConfig
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
@dataclass
class CharBertOutput(ModelOutput):
char: torch.FloatTensor = None
boundary: torch.FloatTensor = None
dia: torch.FloatTensor = None
cap: torch.FloatTensor = None
punct: torch.FloatTensor = None
hidden_states: Optional[tuple] = None
class CharBertModel(PreTrainedModel):
config_class = CharBertConfig
def __init__(self, config: CharBertConfig):
super().__init__(config)
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)
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)
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.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,
seg_id: Optional[torch.LongTensor] = None,
output_hidden_states: bool = False,
return_dict: bool = True,
**kwargs,
):
cfg = self.config
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)
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)
hidden_states = [] if output_hidden_states else None
for blk in self.blocks:
m = glob_mask if blk.window == 0 else attn_mask
x = blk(x, pos, m)
if output_hidden_states:
hidden_states.append(x)
x = self.norm_out(x)
out = dict(
char=self.head_char(x),
boundary=self.head_bnd(x),
dia=self.head_dia(x),
cap=self.head_cap(x),
punct=self.head_punct(x),
)
if output_hidden_states:
out["hidden_states"] = tuple(hidden_states) + (x,)
if not return_dict:
return tuple(v for v in out.values() if v is not None)
return CharBertOutput(**out)
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