char-fork/llama.cpp

git clone https://git.t4t.associates/char-fork/llama.cpp

Xuan-Son Nguyenconvert: add @ModelBase.example (#27208)fa88ae936

char/aliceai-t5-moe
brancheschar/aliceai-t5-moeall refs →
3.4 KiB83 linesraw
1from __future__ import annotations
2
3from typing import Iterable, TYPE_CHECKING
4
5import torch
6
7if TYPE_CHECKING:
8    from torch import Tensor
9
10from .base import ModelBase, TextModel, gguf, logger
11
12
13@ModelBase.register("SmallThinkerForCausalLM")
14@ModelBase.example("PowerInfer/SmallThinker-4BA0.6B-Instruct")
15class SmallThinkerModel(TextModel):
16    model_arch = gguf.MODEL_ARCH.SMALLTHINKER
17
18    def set_gguf_parameters(self):
19        super().set_gguf_parameters()
20        if (n_experts := self.hparams.get("moe_num_primary_experts")) is not None:
21            self.gguf_writer.add_expert_count(n_experts)
22        if (n_experts_used := self.hparams.get("moe_num_active_primary_experts")) is not None:
23            self.gguf_writer.add_expert_used_count(n_experts_used)
24        if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None:
25            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
26            self.gguf_writer.add_feed_forward_length(moe_intermediate_size)
27            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
28        if (self.hparams.get('moe_primary_router_apply_softmax')):
29            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
30        else:
31            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
32
33        sliding_window_layout = self.hparams.get("sliding_window_layout")
34        if sliding_window_layout:
35            for i in sliding_window_layout:
36                if i != 0:
37                    sliding_window = self.hparams.get("sliding_window_size")
38                    if sliding_window:
39                        self.gguf_writer.add_sliding_window(sliding_window)
40                    break
41
42    _experts: list[dict[str, Tensor]] | None = None
43
44    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
45        # process the experts separately
46        if name.find("experts") != -1:
47            n_experts = self.hparams.get("moe_num_primary_experts") or self.find_hparam(["num_local_experts", "num_experts"])
48            assert bid is not None
49
50            if self._experts is None:
51                self._experts = [{} for _ in range(self.block_count)]
52
53            self._experts[bid][name] = data_torch
54
55            if len(self._experts[bid]) >= n_experts * 3:
56                # merge the experts into a single 3d tensor
57                for w_name in ["down", "gate", "up"]:
58                    datas: list[Tensor] = []
59
60                    for xid in range(n_experts):
61                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
62                        datas.append(self._experts[bid][ename])
63                        del self._experts[bid][ename]
64
65                    data_torch = torch.stack(datas, dim=0)
66
67                    merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
68
69                    yield from super().modify_tensors(data_torch, merged_name, bid)
70                return
71            else:
72                return
73
74        yield from super().modify_tensors(data_torch, name, bid)
75
76    def prepare_tensors(self):
77        super().prepare_tensors()
78
79        if self._experts is not None:
80            # flatten `list[dict[str, Tensor]]` into `list[str]`
81            experts = [k for d in self._experts for k in d.keys()]
82            if len(experts) > 0:
83                raise ValueError(f"Unprocessed experts: {experts}")