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ac509ef 8b16d8f ac509ef 8b16d8f ac509ef | 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 | """LFM2 backbone with bidirectional attention + non-causal short-conv, for retrieval/embedding use.
Wired into the HF repo via `auto_map` in config.json so that
AutoModel.from_pretrained(repo, trust_remote_code=True)
SentenceTransformer(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.
Repos may set `"disable_flash_attention": true` in config.json to reject
flash_attention_2 at load time (used for ColBERT, where PyLate query expansion
tokens — attention_mask=0 but scored in MaxSim — are incompatible with FA2
unpadding and severely degrade retrieval quality).
"""
from typing import Optional
import torch
import torch.nn.functional as F
from transformers.models.lfm2 import modeling_lfm2 as _lfm2_mod
from transformers.models.lfm2.modeling_lfm2 import (
Lfm2Attention,
Lfm2Model,
Lfm2ShortConv,
apply_mask_to_padding_states,
)
def _bidirectional_mask(config, **kwargs) -> Optional[torch.Tensor]:
# transformers has renamed the embeds kwarg across versions
# (input_embeds <-> inputs_embeds); accept either to stay forward-compatible.
embeds = kwargs.get("inputs_embeds")
if embeds is None:
embeds = kwargs.get("input_embeds")
attention_mask = kwargs.get("attention_mask")
past_key_values = kwargs.get("past_key_values")
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 = embeds.device
dtype = embeds.dtype
bsz, q_len = 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:
# transformers >=5.x passes seq_idx (packed-sample conv-state reset) to the conv. This full
# sequence non-causal conv has no cache and no packing, so it is ignored, like the cache args
# above. **kwargs absorbs it and any future additions rather than breaking on each new one.
# Only the flash_attention_2 path expects padding states zeroed before the
# conv. On eager/sdpa the checkpoints were trained WITHOUT zeroing: under
# transformers 4.56 the conv received the 4D additive mask, on which
# apply_mask_to_padding_states is a no-op. transformers >=5.x routes the raw
# 2D padding mask here instead, which would zero padding/query-expansion
# states and shift per-token embeddings (hurts ColBERT MaxSim). Gate on the
# attention implementation so behavior matches training on every version.
if getattr(self.config, "_attn_implementation", None) == "flash_attention_2":
x = apply_mask_to_padding_states(hidden_states, attention_mask)
else:
x = hidden_states
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()
class Lfm2BidirectionalModel(Lfm2Model):
"""LFM2 patched for encoder-style use: full bidirectional attention + non-causal short-conv."""
def __init__(self, config):
if (
getattr(config, "_attn_implementation", None) == "flash_attention_2"
and getattr(config, "disable_flash_attention", False)
):
raise ValueError(
"flash_attention_2 is disabled for this model: query expansion "
"tokens (attention_mask=0 but scored in MaxSim) are incompatible "
"with FA2 unpadding and severely degrade retrieval quality. "
"Load with attn_implementation='sdpa' (default) or 'eager'."
)
_install_patches()
super().__init__(config)
for module in self.modules():
if isinstance(module, Lfm2Attention):
module.is_causal = False
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