mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-06-26 06:10:19 +00:00
Mikhail Podvitskii
4fb16eccce
* model: support for Mellum architecture * model: improve mellum.py formatting * model: improve mellum.py formatting once again * deps: downgrade transformers to 4.57.6 (to fix CI) * deps: remove huggingface_hub dependency * deps: remove huggingface_hub from test requirements --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
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")
|
|
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)
|