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46144df | 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 | """OxMini hybrid KDA-lite/MLA-lite causal language model."""
from __future__ import annotations
from dataclasses import dataclass
import json
from pathlib import Path
from typing import Any
import torch
from torch import nn
from torch.nn import functional as F
from .attention_kda import KDALiteAttention
from .attention_mla import MLALiteAttention
from .config import OxMiniConfig
from .layers import RMSNorm, SwiGLU
from .mhc import MHCResidual, StreamCollapse
@dataclass
class CausalLMOutput:
logits: torch.Tensor
loss: torch.Tensor | None = None
class OxMiniBlock(nn.Module):
def __init__(self, config: OxMiniConfig, attention_type: str) -> None:
super().__init__()
self.use_mhc = config.use_mhc
self.norm_attn = RMSNorm(config.n_embd, config.rms_norm_eps)
self.norm_mlp = RMSNorm(config.n_embd, config.rms_norm_eps)
if attention_type == "kda":
self.attention = KDALiteAttention(
config.n_embd, config.n_head, config.dropout, config.bias
)
elif attention_type == "mla":
self.attention = MLALiteAttention(
config.n_embd,
config.n_head,
config.mla_latent_dim,
config.dropout,
config.bias,
)
else:
raise ValueError(f"unknown attention type: {attention_type}")
self.mlp = SwiGLU(
config.n_embd,
config.n_embd * config.ffn_multiplier,
config.dropout,
config.bias,
)
if self.use_mhc:
self.attn_residual = MHCResidual(config.hc_streams, config.use_sinkhorn_mhc)
self.mlp_residual = MHCResidual(config.hc_streams, config.use_sinkhorn_mhc)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.use_mhc:
# Attention and MLP each have an independent routing matrix, just as
# a standard pre-norm block has two independent residual additions.
x = self.attn_residual(x, lambda value: self.attention(self.norm_attn(value)))
return self.mlp_residual(x, lambda value: self.mlp(self.norm_mlp(value)))
x = x + self.attention(self.norm_attn(x))
return x + self.mlp(self.norm_mlp(x))
class OxMiniForCausalLM(nn.Module):
def __init__(self, config: OxMiniConfig) -> None:
super().__init__()
self.config = config
self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd)
self.blocks = nn.ModuleList(
[OxMiniBlock(config, attention_type) for attention_type in config.layer_types]
)
self.collapse = StreamCollapse(config.hc_streams) if config.use_mhc else nn.Identity()
self.final_norm = RMSNorm(config.n_embd, config.rms_norm_eps)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.apply(self._init_weights)
if config.tie_embeddings:
self.lm_head.weight = self.token_embedding.weight
@staticmethod
def _init_weights(module: nn.Module) -> None:
if isinstance(module, (nn.Linear, nn.Embedding)):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if isinstance(module, nn.Linear) and module.bias is not None:
nn.init.zeros_(module.bias)
@property
def num_parameters(self) -> int:
return sum(parameter.numel() for parameter in self.parameters())
def forward(
self,
input_ids: torch.Tensor,
targets: torch.Tensor | None = None,
) -> CausalLMOutput:
if input_ids.ndim != 2:
raise ValueError("input_ids must have shape [batch, sequence]")
if input_ids.shape[1] > self.config.block_size:
raise ValueError(
f"sequence length {input_ids.shape[1]} exceeds block_size {self.config.block_size}"
)
x = self.token_embedding(input_ids)
if self.config.use_mhc:
# Broadcast, do not concatenate: every stream starts as the same
# token representation and subsequently diverges through learned
# per-sublayer post-routing coefficients.
x = x.unsqueeze(2).expand(-1, -1, self.config.hc_streams, -1)
for block in self.blocks:
x = block(x)
x = self.collapse(x)
logits = self.lm_head(self.final_norm(x))
loss = None
if targets is not None:
# Every position predicts the next character supplied in ``targets``;
# data batching performs the one-token shift before this call.
loss = F.cross_entropy(logits.reshape(-1, logits.shape[-1]), targets.reshape(-1))
return CausalLMOutput(logits=logits, loss=loss)
@torch.no_grad()
def generate(
self,
input_ids: torch.Tensor,
max_new_tokens: int,
temperature: float = 1.0,
top_k: int | None = None,
generator: torch.Generator | None = None,
) -> torch.Tensor:
if input_ids.ndim != 2 or input_ids.shape[1] == 0:
raise ValueError("input_ids must be a non-empty [batch, sequence] tensor")
was_training = self.training
self.eval()
generated = input_ids
for _ in range(max_new_tokens):
# This correctness-first implementation recomputes the cropped
# context each step. It does not claim a production KV/state cache.
context = generated[:, -self.config.block_size :]
logits = self(context).logits[:, -1, :]
if not torch.isfinite(logits).all():
raise FloatingPointError("non-finite logits encountered during generation")
if temperature <= 0:
next_token = logits.argmax(dim=-1, keepdim=True)
else:
logits = logits / temperature
if top_k is not None:
k = min(top_k, logits.shape[-1])
cutoff = torch.topk(logits, k).values[:, [-1]]
logits = logits.masked_fill(logits < cutoff, float("-inf"))
probabilities = torch.softmax(logits, dim=-1)
next_token = torch.multinomial(probabilities, 1, generator=generator)
generated = torch.cat((generated, next_token), dim=1)
if was_training:
self.train()
return generated
def save_pretrained(self, directory: str | Path) -> Path:
from safetensors.torch import save_file
directory = Path(directory)
directory.mkdir(parents=True, exist_ok=True)
values: dict[str, Any] = self.config.to_dict()
values.update({"architectures": [self.__class__.__name__], "model_type": "oxmini"})
with (directory / "config.json").open("w", encoding="utf-8") as handle:
json.dump(values, handle, indent=2, sort_keys=True)
handle.write("\n")
# Clone tied tensors so safetensors sees independent storage for both
# state-dict keys while preserving strict-load compatibility.
state = {
key: value.detach().cpu().clone().contiguous()
for key, value in self.state_dict().items()
}
save_file(state, str(directory / "pytorch_model.safetensors"))
return directory
@classmethod
def from_pretrained(
cls,
model_id_or_path: str | Path,
map_location: str | torch.device = "cpu",
revision: str | None = None,
) -> "OxMiniForCausalLM":
from safetensors.torch import load_file
path = Path(model_id_or_path)
if not path.exists():
from huggingface_hub import snapshot_download
path = Path(snapshot_download(str(model_id_or_path), revision=revision))
config = OxMiniConfig.from_file(path / "config.json")
model = cls(config)
state = load_file(str(path / "pytorch_model.safetensors"), device=str(map_location))
model.load_state_dict(state)
return model.to(map_location)
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