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cc7200bf12
* move conversion code to a dedicated conversion directory and split the files akin to the src/models architecture --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
from __future__ import annotations
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from typing import Iterable, TYPE_CHECKING
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import torch
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if TYPE_CHECKING:
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from torch import Tensor
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from .base import ModelBase, TextModel, gguf, logger
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@ModelBase.register("SmallThinkerForCausalLM")
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class SmallThinkerModel(TextModel):
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model_arch = gguf.MODEL_ARCH.SMALLTHINKER
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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if (n_experts := self.hparams.get("moe_num_primary_experts")) is not None:
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self.gguf_writer.add_expert_count(n_experts)
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if (n_experts_used := self.hparams.get("moe_num_active_primary_experts")) is not None:
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self.gguf_writer.add_expert_used_count(n_experts_used)
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if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None:
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self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
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self.gguf_writer.add_feed_forward_length(moe_intermediate_size)
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logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
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if (self.hparams.get('moe_primary_router_apply_softmax')):
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self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
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else:
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self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
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sliding_window_layout = self.hparams.get("sliding_window_layout")
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if sliding_window_layout:
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for i in sliding_window_layout:
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if i != 0:
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sliding_window = self.hparams.get("sliding_window_size")
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if sliding_window:
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self.gguf_writer.add_sliding_window(sliding_window)
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break
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_experts: list[dict[str, Tensor]] | None = None
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# process the experts separately
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if name.find("experts") != -1:
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n_experts = self.hparams.get("moe_num_primary_experts") or self.find_hparam(["num_local_experts", "num_experts"])
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assert bid is not None
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if self._experts is None:
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self._experts = [{} for _ in range(self.block_count)]
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self._experts[bid][name] = data_torch
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if len(self._experts[bid]) >= n_experts * 3:
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# merge the experts into a single 3d tensor
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for w_name in ["down", "gate", "up"]:
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datas: list[Tensor] = []
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for xid in range(n_experts):
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ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
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datas.append(self._experts[bid][ename])
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del self._experts[bid][ename]
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
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yield from super().modify_tensors(data_torch, merged_name, bid)
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return
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else:
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return
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yield from super().modify_tensors(data_torch, name, bid)
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def prepare_tensors(self):
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super().prepare_tensors()
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if self._experts is not None:
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# flatten `list[dict[str, Tensor]]` into `list[str]`
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experts = [k for d in self._experts for k in d.keys()]
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if len(experts) > 0:
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raise ValueError(f"Unprocessed experts: {experts}")
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