from __future__ import annotations
import re
from collections.abc import Iterable
from typing import TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf, logger
@ModelBase.register("LagunaForCausalLM")
@ModelBase.example("poolside/Laguna-XS.2", "poolside/Laguna-S-2.1")
class LagunaModel(TextModel):
model_arch = gguf.MODEL_ARCH.LAGUNA
_experts: list[dict] | None = None
_gate_types: list[str] | None = None
# --- vocab ---------------------------------------------------------------
def set_vocab(self) -> None:
self._set_vocab_gpt2()
# Some Laguna releases wrap the chat template in tokenizer_config.json as
# "{% include 'chat_template.jinja' %}", which SpecialVocab embeds verbatim
# and llama.cpp's jinja engine cannot process. Prefer the resolved template
# from the chat_template.jinja file so the GGUF is self-contained.
tmpl_file = self.dir_model / "chat_template.jinja"
if tmpl_file.is_file():
self.gguf_writer.add_chat_template(tmpl_file.read_text(encoding="utf-8"))
logger.info("gguf: embedded resolved chat_template.jinja (overriding include directive)")
# eos_token_id is a list [2, 24]: token 2 (EOS, also BOS) and token 24
# (</assistant>, the turn-end). _set_vocab_gpt2 only records the scalar
# eos, so register the extra id as eot; llama.cpp folds eot into its EOG
# set, so the model halts on </assistant> natively.
eos_ids = self.hparams.get("eos_token_id")
if isinstance(eos_ids, list):
bos_id = self.hparams.get("bos_token_id")
extra = [e for e in eos_ids if e != bos_id]
if extra:
self.gguf_writer.add_eot_token_id(extra[0])
logger.info(f"gguf: registered eot_token_id={extra[0]} from eos list {eos_ids}")
def get_vocab_base(self) -> tuple[list[str], list[int], str]:
# </assistant> is the assistant turn-end (registered as eot below). The
# HF tokenizer flags it special=false, so the base classifies it as
# USER_DEFINED and llama.cpp renders its text into generated content,
# leaking "</assistant>" and breaking response parsing. It is a control
# marker, so promote it to CONTROL: llama.cpp then treats it as
# end-of-generation and suppresses its text.
tokens, toktypes, tokpre = super().get_vocab_base()
for i, tok in enumerate(tokens):
if tok == "</assistant>":
toktypes[i] = gguf.TokenType.CONTROL
logger.info(f"gguf: marked </assistant> (id {i}) as CONTROL token")
return tokens, toktypes, tokpre
# --- hparams -------------------------------------------------------------
def set_gguf_parameters(self) -> None:
super().set_gguf_parameters()
hparams = self.hparams
# super() does not emit vocab_size for the gpt2 vocab path; head_count is
# overridden with a per-layer array (XS.2 varies heads per layer via
# num_attention_heads_per_layer; M.1 is uniform and omits it).
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
per_layer_heads = hparams.get("num_attention_heads_per_layer")
if not per_layer_heads:
per_layer_heads = [hparams["num_attention_heads"]] * hparams["num_hidden_layers"]
assert len(per_layer_heads) == hparams["num_hidden_layers"], (
f"num_attention_heads_per_layer length {len(per_layer_heads)} != "
f"num_hidden_layers {hparams['num_hidden_layers']}"
)
self.gguf_writer.add_head_count(per_layer_heads)
# Resolve + validate the attention gate type now so an inconsistent
# `gating` field fails at conversion time. See _attn_gate_types.
self._attn_gate_types()
# SWA window size (M.1 has none -> key omitted, swa_type stays NONE).
sliding_window = hparams.get("sliding_window") or 0
if sliding_window > 0:
self.gguf_writer.add_sliding_window(sliding_window)
# MoE (expert_count / expert_used_count come from super().set_gguf_parameters())
self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
self.gguf_writer.add_expert_shared_feed_forward_length(hparams["shared_expert_intermediate_size"])
self.gguf_writer.add_expert_weights_norm(True) # HF reference always sum-normalises after top-k
self.gguf_writer.add_expert_weights_scale(float(hparams["moe_routed_scaling_factor"]))
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
# Leading dense layers (XS.2 has 1, M.1 has 3) before the MoE layers.
mlp_layer_types: list[str] = hparams["mlp_layer_types"]
leading_dense = 0
for t in mlp_layer_types:
if t == "dense":
leading_dense += 1
else:
break
self.gguf_writer.add_leading_dense_block_count(leading_dense)
# Per-layer-type RoPE dimension count (partial rotary). base emits
# rope_freq_base(_swa) and the YaRN params from self.rope_parameters.
head_dim = hparams["head_dim"]
full_rope = self.rope_parameters["full_attention"]
self.gguf_writer.add_rope_dimension_count(
int(head_dim * float(full_rope.get("partial_rotary_factor", 1.0))))
swa_rope = self.rope_parameters.get("sliding_attention")
if swa_rope is not None:
self.gguf_writer.add_rope_dimension_count_swa(
int(head_dim * float(swa_rope.get("partial_rotary_factor", 1.0))))
def _attn_gate_types(self) -> list[str]:
"""Per-layer attention output gate type: "per_head" or "per_element".
`gating_types` (per layer) is authoritative when present; otherwise the
scalar `gating` field is used (the "per-element"/"per-head" string, or
the legacy boolean True == per-head, as in Laguna-XS.2).
Fails loudly when the model is per-element but the `gating` field does
not declare that as a string: runtimes that key off `gating` (vLLM,
transformers) ignore gating_types and read a bare boolean True as
per-head, silently corrupting the model. Surfacing it here keeps a
broken checkpoint from being packaged as if it were fine.
"""
if self._gate_types is not None:
return self._gate_types
hparams = self.hparams
n_layer = hparams["num_hidden_layers"]
gating = hparams.get("gating")
gating_types = hparams.get("gating_types")
def _norm(t: object) -> str:
sval = str(t).replace("-", "_")
if sval in ("per_element", "per_head"):
return sval
raise ValueError(f"Laguna: unrecognised attention gate type {t!r}")
if gating_types:
assert len(gating_types) == n_layer, (
f"gating_types length {len(gating_types)} != num_hidden_layers {n_layer}")
types = [_norm(t) for t in gating_types]
elif isinstance(gating, str):
types = [_norm(gating)] * n_layer
elif gating is True:
types = ["per_head"] * n_layer
else:
raise ValueError(
f"Laguna: cannot determine attention gate type "
f"(gating={gating!r}, gating_types={gating_types!r})")
if any(t == "per_element" for t in types) and not (
isinstance(gating, str) and _norm(gating) == "per_element"):
raise ValueError(
f"Laguna config declares a per-element attention gate but "
f"`gating`={gating!r} is not the string \"per-element\". Runtimes that "
f"read `gating` (vLLM, transformers) will mis-handle this checkpoint as "
f"per-head. Set gating=\"per-element\" in the source config.")
self._gate_types = types
return types
# --- tensor handling -----------------------------------------------------
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# Per-expert MoE weights: model.layers.{bid}.mlp.experts.{xid}.{w}.weight.
# Only the NUMBERED per-expert weights are stacked; the router bias
# (mlp.experts.e_score_correction_bias) takes the normal mapping path.
if re.search(r"mlp\.experts\.\d+\.", name):
n_experts = self.find_hparam(["num_local_experts", "num_experts"])
assert bid is not None
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
self._experts[bid][name] = data_torch
needed = [f"model.layers.{bid}.mlp.experts.{x}.{w}.weight"
for x in range(n_experts) for w in ("gate_proj", "up_proj", "down_proj")]
if all(e in self._experts[bid] for e in needed):
for w_name in ["gate_proj", "up_proj", "down_proj"]:
datas = [self._experts[bid][f"model.layers.{bid}.mlp.experts.{x}.{w_name}.weight"]
for x in range(n_experts)]
stacked = torch.stack(datas, dim=0)
merged = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
yield from TextModel.modify_tensors(self, stacked, merged, bid)
self._experts[bid].clear()
return
return
# Cross-check the gate projection width against the declared gate type;
# a mismatch means the weights and config disagree -> fail, do not guess.
if bid is not None and name.endswith("self_attn.g_proj.weight"):
heads = (self.hparams.get("num_attention_heads_per_layer")
or [self.hparams["num_attention_heads"]] * self.hparams["num_hidden_layers"])
n_head = heads[bid]
head_dim = self.hparams["head_dim"]
gate_type = self._attn_gate_types()[bid]
expected = n_head * head_dim if gate_type == "per_element" else n_head
out_features = int(data_torch.shape[0])
if out_features != expected:
raise ValueError(
f"Laguna layer {bid}: g_proj output width {out_features} contradicts the "
f"declared {gate_type} gate (expected {expected}); weights and config disagree.")
yield from TextModel.modify_tensors(self, data_torch, name, bid)