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("SmallThinkerForCausalLM")
@ModelBase.example("PowerInfer/SmallThinker-4BA0.6B-Instruct")
class SmallThinkerModel(TextModel):
model_arch = gguf.MODEL_ARCH.SMALLTHINKER
def set_gguf_parameters(self):
super().set_gguf_parameters()
if (n_experts := self.hparams.get("moe_num_primary_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
if (n_experts_used := self.hparams.get("moe_num_active_primary_experts")) is not None:
self.gguf_writer.add_expert_used_count(n_experts_used)
if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None:
self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
self.gguf_writer.add_feed_forward_length(moe_intermediate_size)
logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
if (self.hparams.get('moe_primary_router_apply_softmax')):
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
else:
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
sliding_window_layout = self.hparams.get("sliding_window_layout")
if sliding_window_layout:
for i in sliding_window_layout:
if i != 0:
sliding_window = self.hparams.get("sliding_window_size")
if sliding_window:
self.gguf_writer.add_sliding_window(sliding_window)
break
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# process the experts separately
if name.find("experts") != -1:
n_experts = self.hparams.get("moe_num_primary_experts") or 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:
# merge the experts into a single 3d tensor
for w_name in ["down", "gate", "up"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.block_sparse_moe.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}.block_sparse_moe.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)
def prepare_tensors(self):
super().prepare_tensors()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
experts = [k for d in self._experts for k in d.keys()]
if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts}")