from __future__ import annotations
from typing import Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf, logger
@ModelBase.register("MellumForCausalLM")
@ModelBase.example("JetBrains/Mellum2-12B-A2.5B-Base")
class MellumModel(TextModel):
model_arch = gguf.MODEL_ARCH.MELLUM
def set_gguf_parameters(self):
super().set_gguf_parameters()
if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
use_sliding_window = self.hparams.get("use_sliding_window")
sliding_window = self.hparams.get("sliding_window")
if (use_sliding_window is True or use_sliding_window is None) and sliding_window is not None:
self.gguf_writer.add_sliding_window(sliding_window)
logger.info(f"gguf: sliding window = {sliding_window}")
self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in self.hparams["layer_types"]])
logger.info(f"gguf: sliding window pattern length = {len(self.hparams['layer_types'])}")
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.find("experts") != -1:
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
if len(self._experts[bid]) >= n_experts * 3:
for w_name in ["down_proj", "gate_proj", "up_proj"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
yield from super().modify_tensors(data_torch, merged_name, bid)
return
else:
return
yield from super().modify_tensors(data_torch, name, bid)