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 →
3.6 KiB84 linesraw
1from __future__ import annotations
2
3from typing import Any, Iterable, TYPE_CHECKING
4
5if TYPE_CHECKING:
6    from torch import Tensor
7
8from .base import ModelBase, TextModel, gguf
9
10
11@ModelBase.register("OpenELMForCausalLM")
12@ModelBase.example("apple/OpenELM-270M")
13class OpenELMModel(TextModel):
14    model_arch = gguf.MODEL_ARCH.OPENELM
15
16    @staticmethod
17    def _make_divisible(v: float | int, divisor: int) -> int:
18        # ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38
19        new_v = max(divisor, int(v + divisor / 2) // divisor * divisor)
20        # Make sure that round down does not go down by more than 10%.
21        if new_v < 0.9 * v:
22            new_v += divisor
23        return new_v
24
25    def __init__(self, *args, **kwargs):
26        super().__init__(*args, **kwargs)
27
28        ffn_multipliers: list[float] = self.hparams["ffn_multipliers"]
29        ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"]
30        self._n_embd: int = self.hparams["model_dim"]
31        self._num_kv_heads: list[int] = self.hparams["num_kv_heads"]
32        self._num_query_heads: list[int] = self.hparams["num_query_heads"]
33        self._ffn_dims: list[int] = [
34            OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor)
35            for multiplier in ffn_multipliers
36        ]
37        assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
38        assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int)
39
40    # Uses the tokenizer from meta-llama/Llama-2-7b-hf
41    def set_vocab(self):
42        try:
43            self._set_vocab_sentencepiece()
44        except FileNotFoundError:
45            self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"])
46
47    def set_gguf_parameters(self):
48        n_embd = self._n_embd
49        head_dim = self.hparams["head_dim"]
50        rot_pct = 1.0
51        assert self.block_count == len(self._num_kv_heads)
52        assert self.block_count == len(self._num_query_heads)
53        assert self.block_count == len(self._ffn_dims)
54
55        self.gguf_writer.add_block_count(self.block_count)
56        self.gguf_writer.add_context_length(self.hparams["max_context_length"])
57        self.gguf_writer.add_embedding_length(n_embd)
58        self.gguf_writer.add_feed_forward_length(self._ffn_dims)
59        self.gguf_writer.add_head_count(self._num_query_heads)
60        self.gguf_writer.add_head_count_kv(self._num_kv_heads)
61        self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"])
62        # https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30
63        self.gguf_writer.add_layer_norm_rms_eps(1e-6)
64        self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim))
65        self.gguf_writer.add_key_length(head_dim)
66        self.gguf_writer.add_value_length(head_dim)
67        self.gguf_writer.add_file_type(self.ftype)
68
69    def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
70        if "n_layers" in keys:
71            return self.hparams["num_transformer_layers"]
72
73        return super().find_hparam(keys, optional)
74
75    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
76
77        # split ff
78        if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight":
79            ff_dim = self._ffn_dims[bid]
80            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])
81            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])
82            return
83
84        yield (self.map_tensor_name(name), data_torch)