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d435fba | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 | """Dihya-5M: byte-level Berber language identification.
Module attribute names are the published checkpoint's state_dict keys. Renaming one
breaks `from_pretrained` for everybody who downloaded the release.
The contrastive projection head the model was trained with is not here. It shapes the
trunk during training and is never read at inference, so shipping it would hand every
downloader 98,304 parameters that no forward pass touches.
"""
from __future__ import annotations
import math
from typing import Any
import torch
from torch import Tensor, nn
from torch.nn import functional
from transformers import PreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput
from .configuration_dihya import DihyaConfig
MASK_FILL = -1e4
"""Finite rather than `-inf`: a row that is entirely padding would otherwise softmax to
NaN, and an empty string is a real input to a language identifier."""
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6) -> None:
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: Tensor) -> Tensor:
variance = x.pow(2).mean(-1, keepdim=True)
normed: Tensor = x * torch.rsqrt(variance + self.eps) * self.weight
return normed
class SwiGLU(nn.Module):
def __init__(self, dim: int, intermediate_dim: int) -> None:
super().__init__()
self.w1 = nn.Linear(dim, intermediate_dim, bias=False)
self.w2 = nn.Linear(dim, intermediate_dim, bias=False)
self.w3 = nn.Linear(intermediate_dim, dim, bias=False)
def forward(self, x: Tensor) -> Tensor:
projected: Tensor = self.w3(functional.silu(self.w1(x)) * self.w2(x))
return projected
class ConvStem(nn.Module):
"""Parallel depthwise-separable convolutions over the byte embeddings.
Three widths because the discriminating evidence sits at three scales: a grapheme
cluster, an affix, and a clitic chain. One kernel width picks one of the three.
"""
def __init__(self, config: DihyaConfig) -> None:
super().__init__()
dim, branch_dim = config.hidden_size, config.conv_dim
self.branches = nn.ModuleList(
nn.Sequential(
nn.Conv1d(dim, dim, kernel_size=k, padding=k // 2, groups=dim, bias=False),
nn.Conv1d(dim, branch_dim, kernel_size=1, bias=False),
)
for k in config.conv_kernels
)
self.branch_norms = nn.ModuleList(
RMSNorm(branch_dim, eps=config.rms_norm_eps) for _ in config.conv_kernels
)
self.proj = nn.Linear(branch_dim * len(config.conv_kernels), dim, bias=False)
self.norm = RMSNorm(dim, eps=config.rms_norm_eps)
self.dropout = nn.Dropout(config.dropout_prob)
def forward(self, x: Tensor) -> Tensor:
transposed = x.transpose(1, 2)
outputs = [
functional.silu(norm(branch(transposed).transpose(1, 2)))
for branch, norm in zip(self.branches, self.branch_norms, strict=True)
]
stemmed: Tensor = x + self.dropout(self.norm(self.proj(torch.cat(outputs, dim=-1))))
return stemmed
def rope_freqs(head_dim: int, length: int, base: float, device: torch.device) -> Tensor:
"""Complex rotary frequencies, derived on call and never stored.
A registered non-persistent buffer comes back from `from_pretrained` as uninitialised
memory, because it is deliberately absent from the checkpoint.
"""
theta = 1.0 / (base ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim))
positions = torch.arange(length, device=device).float()
angles = torch.outer(positions, theta)
return torch.polar(torch.ones_like(angles), angles)
def apply_rope(x: Tensor, freqs: Tensor) -> Tensor:
batch, heads, length, head_dim = x.shape
paired = torch.view_as_complex(x.float().reshape(batch, heads, length, -1, 2))
rotated = torch.view_as_real(paired * freqs[:length].unsqueeze(0).unsqueeze(0))
return rotated.reshape(batch, heads, length, head_dim).type_as(x)
class Attention(nn.Module):
def __init__(self, config: DihyaConfig) -> None:
super().__init__()
dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = config.head_size
self.dropout = config.dropout_prob
self.q_proj = nn.Linear(dim, dim, bias=False)
self.k_proj = nn.Linear(dim, dim, bias=False)
self.v_proj = nn.Linear(dim, dim, bias=False)
self.out_proj = nn.Linear(dim, dim, bias=False)
def forward(self, x: Tensor, freqs: Tensor, mask: Tensor | None = None) -> Tensor:
batch, length, dim = x.shape
shape = (batch, length, self.num_heads, self.head_dim)
query = apply_rope(self.q_proj(x).view(shape).transpose(1, 2), freqs)
key = apply_rope(self.k_proj(x).view(shape).transpose(1, 2), freqs)
value = self.v_proj(x).view(shape).transpose(1, 2)
attended = functional.scaled_dot_product_attention(
query,
key,
value,
attn_mask=mask.unsqueeze(1).unsqueeze(2) if mask is not None else None,
dropout_p=self.dropout if self.training else 0.0,
)
merged: Tensor = self.out_proj(attended.transpose(1, 2).reshape(batch, length, dim))
return merged
class EncoderLayer(nn.Module):
def __init__(self, config: DihyaConfig) -> None:
super().__init__()
self.norm1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.attn = Attention(config)
self.norm2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.ffn = SwiGLU(config.hidden_size, config.intermediate_size)
self.dropout = nn.Dropout(config.dropout_prob)
def forward(self, x: Tensor, freqs: Tensor, mask: Tensor | None = None) -> Tensor:
hidden: Tensor = x + self.dropout(self.attn(self.norm1(x), freqs, mask=mask))
residual: Tensor = self.dropout(self.ffn(self.norm2(hidden)))
return hidden + residual
class AttentivePooling(nn.Module):
"""Weighted sum over positions, so a short discriminating affix is not averaged away."""
def __init__(self, config: DihyaConfig) -> None:
super().__init__()
self.score = nn.Linear(config.hidden_size, 1, bias=False)
def forward(self, x: Tensor, mask: Tensor | None = None) -> Tensor:
scores = self.score(x).squeeze(-1) / math.sqrt(x.size(-1))
if mask is not None:
scores = scores.masked_fill(~mask, MASK_FILL)
weights = functional.softmax(scores, dim=-1).unsqueeze(-1)
pooled: Tensor = (x * weights).sum(dim=1)
return pooled
class MarginHead(nn.Module):
"""Scaled cosine classifier.
The per-class additive margins the head was trained with apply to the target logit
only, so they exist during training and are identity at inference. The margin buffer
is therefore not part of the release.
"""
def __init__(self, config: DihyaConfig) -> None:
super().__init__()
self.scale = config.logit_scale
self.weight = nn.Parameter(torch.empty(len(config.classes), config.hidden_size))
def forward(self, x: Tensor) -> Tensor:
cosine = functional.linear(
functional.normalize(x, p=2, dim=1), functional.normalize(self.weight, p=2, dim=1)
)
return cosine * self.scale
class DihyaPreTrainedModel(PreTrainedModel):
config_class = DihyaConfig
base_model_prefix = "dihya"
supports_gradient_checkpointing = False
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear | nn.Conv1d):
nn.init.xavier_uniform_(module.weight)
if getattr(module, "bias", None) is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=0.02)
if module.padding_idx is not None:
with torch.no_grad():
module.weight[module.padding_idx].fill_(0)
elif isinstance(module, RMSNorm):
nn.init.ones_(module.weight)
elif isinstance(module, MarginHead):
nn.init.xavier_uniform_(module.weight)
class DihyaForSequenceClassification(DihyaPreTrainedModel):
"""Byte-level classifier over six Berber varieties and an explicit rejection class."""
def __init__(self, config: DihyaConfig) -> None:
super().__init__(config)
self.embed = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)
self.stem = ConvStem(config)
self.layers = nn.ModuleList(EncoderLayer(config) for _ in range(config.num_hidden_layers))
self.final_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.pool = AttentivePooling(config)
self.head = MarginHead(config)
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.embed
def set_input_embeddings(self, value: nn.Module) -> None:
self.embed = value # type: ignore[assignment]
def forward(
self,
input_ids: Tensor,
attention_mask: Tensor | None = None,
labels: Tensor | None = None,
return_dict: bool | None = None,
**kwargs: Any,
) -> SequenceClassifierOutput | tuple[Tensor, ...]:
mask = attention_mask.bool() if attention_mask is not None else None
hidden = self.stem(self.embed(input_ids))
freqs = rope_freqs(
self.config.head_size,
input_ids.size(1),
self.config.rope_theta,
input_ids.device,
)
for layer in self.layers:
hidden = layer(hidden, freqs, mask=mask)
pooled = self.pool(self.final_norm(hidden), mask=mask)
logits = self.head(pooled)
loss = None
if labels is not None:
loss = functional.cross_entropy(logits, labels)
if return_dict is False:
return (logits,) if loss is None else (loss, logits)
return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=(pooled,))
def prior_shift(self, device: torch.device, dtype: torch.dtype) -> Tensor:
return torch.tensor(self.config.prior_shift, device=device, dtype=dtype)
@torch.inference_mode()
def identify(
self,
texts: str | list[str],
max_length: int | None = None,
batch_size: int = 128,
) -> list[dict[str, Any]]:
"""Classify text. One dict per input: `language`, `confidence`, `prediction_set`.
The tokenizer is not needed: the vocabulary is the 256 UTF-8 byte values, so the
encoding is the input's own bytes. `prediction_set` is the split-conformal set
`{k : p_k >= 1 - q_hat}` when the repository carries a calibrated `q_hat`, and the
argmax alone when it does not — never a singleton dressed up as a guarantee.
"""
wanted = [texts] if isinstance(texts, str) else list(texts)
if not wanted:
return []
limit = max_length or self.config.max_position_embeddings
device = next(self.parameters()).device
classes = list(self.config.classes)
threshold = None if self.config.q_hat is None else 1.0 - float(self.config.q_hat)
results: list[dict[str, Any]] = []
for start in range(0, len(wanted), batch_size):
chunk = wanted[start : start + batch_size]
rows = [
[b + self.config.byte_offset for b in text.encode("utf-8")[:limit]]
or [self.config.pad_token_id]
for text in chunk
]
width = max(len(row) for row in rows)
input_ids = torch.full(
(len(rows), width), self.config.pad_token_id, dtype=torch.long, device=device
)
attention = torch.zeros((len(rows), width), dtype=torch.long, device=device)
for i, row in enumerate(rows):
input_ids[i, : len(row)] = torch.tensor(row, dtype=torch.long, device=device)
attention[i, : len(row)] = 1
logits = self(input_ids, attention_mask=attention).logits
shifted = logits - self.prior_shift(logits.device, logits.dtype)
for probabilities in torch.softmax(shifted, dim=-1).tolist():
top = max(range(len(probabilities)), key=probabilities.__getitem__)
members = (
tuple(c for c, p in zip(classes, probabilities, strict=True) if p >= threshold)
if threshold is not None
else (classes[top],)
)
results.append(
{
"language": classes[top],
"confidence": probabilities[top],
"prediction_set": members or (classes[top],),
"probabilities": dict(zip(classes, probabilities, strict=True)),
}
)
return results
__all__ = [
"DihyaConfig",
"DihyaForSequenceClassification",
"DihyaPreTrainedModel",
]
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