llama.cpp/conversion/smallthinker.py

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")
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}")