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9492702 | 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 | """LFM2 backbone with bidirectional attention + non-causal short-conv.
Wired into the HF repo via `auto_map` in config.json so that
AutoModel.from_pretrained(repo, trust_remote_code=True)
AutoModelForMaskedLM.from_pretrained(repo, trust_remote_code=True)
both return a model with the encoder-style patches already applied.
Supports `attn_implementation` in {"eager", "sdpa", "flash_attention_2"}:
eager/sdpa consume a 4D additive pad-only mask and reproduce the exact
training-time behavior; flash_attention_2 receives the 2D padding mask (or
None) and runs the kernel non-causally via `Lfm2Attention.is_causal = False`,
yielding outputs equivalent to the unpadded forward.
"""
import math
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_outputs import BaseModelOutput, MaskedLMOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.models.lfm2 import modeling_lfm2 as _lfm2_mod
from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
from transformers.models.lfm2.modeling_lfm2 import (
Lfm2Attention,
Lfm2Model,
Lfm2PreTrainedModel,
Lfm2ShortConv,
apply_mask_to_padding_states,
)
def _bidirectional_mask(
config,
input_embeds: torch.Tensor = None,
attention_mask: Optional[torch.Tensor] = None,
cache_position: Optional[torch.LongTensor] = None,
past_key_values=None,
position_ids: Optional[torch.LongTensor] = None,
**kwargs,
) -> Optional[torch.Tensor]:
# transformers has renamed the embeds kwarg across versions
# (input_embeds <-> inputs_embeds); accept either to stay forward-compatible.
if input_embeds is None:
input_embeds = kwargs.get("inputs_embeds")
if config._attn_implementation == "flash_attention_2":
# FA2 only uses the 2D padding mask to unpad sequences; causality is
# controlled by `Lfm2Attention.is_causal` (set to False below).
if attention_mask is not None and not attention_mask.all():
return attention_mask
return None
device = input_embeds.device
dtype = input_embeds.dtype
bsz, q_len = input_embeds.shape[:2]
past = past_key_values.get_seq_length() if past_key_values is not None else 0
kv_len = past + q_len
mask = torch.zeros((bsz, 1, q_len, kv_len), device=device, dtype=dtype)
if attention_mask is not None:
cur_len = attention_mask.size(-1)
key_pad_flags = (attention_mask == 0).to(device=device, dtype=torch.float32)
pad_vec = torch.zeros((bsz, kv_len), device=device, dtype=torch.float32)
if cur_len > 0:
pad_vec[:, past:past + cur_len] = key_pad_flags * -1e9
mask = mask + pad_vec.to(dtype)[:, None, None, :]
return mask
def _noncausal_shortconv_forward(
self,
hidden_states: torch.Tensor,
past_key_values=None,
cache_position=None,
attention_mask: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
x = apply_mask_to_padding_states(hidden_states, attention_mask)
BCx = self.in_proj(x).transpose(-1, -2)
B, C, x = BCx.chunk(3, dim=-2)
Bx = B * x
k = self.conv.weight.shape[-1]
pad = k // 2
conv_out = F.conv1d(
Bx, weight=self.conv.weight, bias=self.conv.bias,
stride=1, padding=pad, dilation=1, groups=Bx.shape[1],
)
if conv_out.shape[-1] > Bx.shape[-1]:
conv_out = conv_out[..., :Bx.shape[-1]]
elif conv_out.shape[-1] < Bx.shape[-1]:
conv_out = F.pad(conv_out, (0, Bx.shape[-1] - conv_out.shape[-1]))
y = C * conv_out
y = y.transpose(-1, -2).contiguous()
return self.out_proj(y)
def _shortconv_forward(self, *args, **kwargs):
return self.slow_forward(*args, **kwargs)
_PATCHED = False
def _install_patches() -> None:
global _PATCHED
if _PATCHED:
return
_lfm2_mod.create_causal_mask = _bidirectional_mask
Lfm2ShortConv.slow_forward = _noncausal_shortconv_forward
Lfm2ShortConv.forward = _shortconv_forward
_PATCHED = True
_install_patches()
def _set_attention_noncausal(model) -> None:
for module in model.modules():
if isinstance(module, Lfm2Attention):
module.is_causal = False
class Lfm2BidirectionalModel(Lfm2Model):
"""LFM2 patched for encoder-style use:
full bidirectional attention + non-causal short-conv."""
def __init__(self, config):
_install_patches()
super().__init__(config)
_set_attention_noncausal(self)
class Lfm2BidirectionalForMaskedLM(Lfm2PreTrainedModel):
"""LFM2 bidirectional encoder with a tied masked-LM head."""
config_class = Lfm2Config
base_model_prefix = "lfm2"
_tied_weights_keys = {"lm_head.weight": "lfm2.embed_tokens.weight"}
def __init__(self, config: Lfm2Config):
_install_patches()
config = type(config).from_dict({**config.to_dict(), "use_cache": False})
super().__init__(config)
self.lfm2 = Lfm2BidirectionalModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
self.lm_head.weight = self.lfm2.embed_tokens.weight
def get_input_embeddings(self):
return self.lfm2.embed_tokens
def set_input_embeddings(self, value):
self.lfm2.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> MaskedLMOutput:
return_dict = True if return_dict is None else return_dict
outputs = self.lfm2(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
use_cache=False,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
hidden = outputs.last_hidden_state
logits = self.lm_head(hidden)
loss = None
if labels is not None:
loss = F.cross_entropy(
logits.view(-1, self.config.vocab_size),
labels.view(-1),
ignore_index=-100,
)
if not return_dict:
out = (logits,) + outputs[1:]
return ((loss,) + out) if loss is not None else out
return MaskedLMOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class Lfm2BidirForSequenceRouting(Lfm2PreTrainedModel):
"""Zero-shot prompt router built on the bidirectional LFM2 encoder."""
config_class = Lfm2Config
base_model_prefix = "lfm2"
def __init__(self, config: Lfm2Config):
_install_patches()
config = type(config).from_dict({**config.to_dict(), "use_cache": False})
super().__init__(config)
self.lfm2 = Lfm2BidirectionalModel(config)
proj_dim = getattr(config, "rule_proj_dim", 256)
self.tok_proj = nn.Linear(config.hidden_size, proj_dim)
self.rule_proj = nn.Linear(config.hidden_size, proj_dim)
self.score_bias = nn.Parameter(torch.tensor(0.0))
self.logit_scale = nn.Parameter(torch.tensor(1.0))
self.post_init()
def get_input_embeddings(self):
return self.lfm2.embed_tokens
def set_input_embeddings(self, value):
self.lfm2.embed_tokens = value
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
text_pool: Optional[torch.Tensor] = None,
category_pool: Optional[torch.Tensor] = None,
**kwargs,
):
outputs = self.lfm2(
input_ids=input_ids,
attention_mask=attention_mask,
use_cache=False,
return_dict=True,
)
hidden = outputs.last_hidden_state
text_rep = torch.bmm(text_pool, hidden).squeeze(1)
category_rep = torch.bmm(category_pool, hidden)
query = F.normalize(self.tok_proj(text_rep), dim=-1)
categories = F.normalize(self.rule_proj(category_rep), dim=-1)
scale = torch.clamp(self.logit_scale.exp(), max=30.0)
logits = torch.einsum("bd,brd->br", query, categories) * scale + self.score_bias
return {"logits": logits}
@staticmethod
def _prefix(routes):
body = "\n".join(f"- {route}" for route in routes) if routes else "- (none)"
return f"Categories:\n{body}\n\nText:\n"
@staticmethod
def _category_ranges(routes):
ranges = []
pos = len("Categories:\n")
for route in routes:
start = pos + 2
end = start + len(route)
ranges.append((start, end))
pos = end + 1
return ranges
@torch.no_grad()
def route(self, text, routes, tokenizer, threshold=None):
prefix = self._prefix(routes)
full_text = prefix + text
enc = tokenizer(full_text, return_offsets_mapping=True, return_tensors="pt")
offsets = enc.pop("offset_mapping")[0].tolist()
enc = {k: v.to(self.device) for k, v in enc.items()}
text_start = len(prefix)
text_idxs = [
i for i, (start, end) in enumerate(offsets)
if end > text_start and start != end
]
text_pool = torch.zeros(1, 1, len(offsets), device=self.device)
if text_idxs:
text_pool[0, 0, text_idxs] = 1 / len(text_idxs)
category_pool = torch.zeros(1, len(routes), len(offsets), device=self.device)
for route_idx, (start, end) in enumerate(self._category_ranges(routes)):
token_idxs = [
i for i, (tok_start, tok_end) in enumerate(offsets)
if tok_start < end and tok_end > start and tok_start != tok_end
]
if token_idxs:
category_pool[0, route_idx, token_idxs] = 1 / len(token_idxs)
logits = self(**enc, text_pool=text_pool, category_pool=category_pool)["logits"][0]
probs = logits.softmax(dim=-1).detach().cpu()
results = [
{"route": route, "score": float(prob)}
for route, prob in zip(routes, probs)
if threshold is None or prob >= threshold
]
return sorted(results, key=lambda item: item["score"], reverse=True)
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