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 →
5.1 KiB117 linesraw
1from __future__ import annotations
2
3import sys
4
5from typing import Iterable, TYPE_CHECKING
6
7import torch
8
9if TYPE_CHECKING:
10    from torch import Tensor
11
12from .base import ModelBase, TextModel, gguf, logger
13
14
15@ModelBase.register("GrokForCausalLM", "Grok1ForCausalLM")
16@ModelBase.example("keyfan/grok-1-hf")
17class GrokModel(TextModel):
18    model_arch = gguf.MODEL_ARCH.GROK
19
20    def set_vocab(self):
21        if (self.dir_model / 'tokenizer.model').is_file():
22            self._set_vocab_sentencepiece()
23            return
24
25        if not (self.dir_model / 'tokenizer.json').is_file() or not (self.dir_model / 'chat_template.jinja').is_file():
26            logger.error('Error: Missing vocab and chat template, download files from https://huggingface.co/alvarobartt/grok-2-tokenizer')
27            sys.exit(1)
28
29        self._set_vocab_gpt2()
30
31    def __init__(self, *args, **kwargs):
32        super().__init__(*args, **kwargs)
33
34    def set_gguf_parameters(self):
35        super().set_gguf_parameters()
36
37        self.gguf_writer.add_attn_logit_softcapping(self.hparams.get("attn_logit_softcapping", 30.0))
38        self.gguf_writer.add_router_logit_softcapping(self.hparams.get("router_logit_softcapping", 30.0))
39        if (final_logit_softcap := self.hparams.get("final_logit_softcapping")):
40            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
41
42        if (rope_dim := self.hparams.get("head_dim")) is None:
43            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
44
45        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
46            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
47
48        # Treat "original" as "yarn", seems to have been a mistake
49        if self.hparams.get("rope_type") in ("yarn", "original"):
50            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
51            self.gguf_writer.add_rope_scaling_factor(self.hparams["scaling_factor"])
52            self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["original_max_position_embeddings"])
53            self.gguf_writer.add_rope_scaling_yarn_ext_factor(self.hparams["extrapolation_factor"])
54            self.gguf_writer.add_rope_scaling_yarn_attn_factor(self.hparams["attn_factor"])
55            self.gguf_writer.add_rope_scaling_yarn_beta_fast(self.hparams["beta_fast"])
56            self.gguf_writer.add_rope_scaling_yarn_beta_slow(self.hparams["beta_slow"])
57
58        if temp_len := self.hparams.get("attn_temperature_len"):
59            self.gguf_writer.add_attn_temperature_length(temp_len)
60
61        self.gguf_writer.add_attn_output_scale(self.hparams.get("attn_output_multiplier", rope_dim**-0.5))
62        self.gguf_writer.add_embedding_scale(self.hparams["embedding_multiplier_scale"])
63        self.gguf_writer.add_logit_scale(self.hparams["output_multiplier_scale"])
64
65    _experts: list[dict[str, list[Tensor]]] | None = None
66    _cur_expert = ""
67
68    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
69        deferred: list[tuple[Tensor, str, int | None]] = []
70        is_expert = ".moe." in name or ".block_sparse_moe.experts." in name
71
72        if not is_expert:
73            deferred.append((data_torch, name, bid))
74
75        # process the experts separately
76        if is_expert or self._cur_expert:
77            n_experts = self.hparams["num_local_experts"]
78
79            assert bid is not None
80
81            if self._experts is None:
82                self._experts = [{} for _ in range(self.block_count)]
83
84            # concatenate split tensors
85            if name in self._experts[bid]:
86                self._cur_expert = name
87                self._experts[bid][name].append(data_torch)
88                return
89            elif is_expert:
90                self._cur_expert = name
91                self._experts[bid][name] = [data_torch]
92                return
93            else:
94                self._cur_expert = ""
95
96            for bid in range(self.block_count):
97                if len(self._experts[bid]) >= n_experts * 3:
98                    # merge the experts into a single 3d tensor
99                    for wid in [("linear", "w1", 0), ("linear_1", "w2", 1), ("linear_v", "w3", 0)]:
100                        datas: list[Tensor] = []
101
102                        for xid in range(n_experts):
103                            ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid[0]}.weight"
104                            if ename not in self._experts[bid]:
105                                ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid[1]}.weight"
106                            tensor_list = self._experts[bid][ename]
107                            datas.append(torch.cat(tensor_list, dim=wid[2]) if len(tensor_list) > 1 else tensor_list[0])
108                            del self._experts[bid][ename]
109
110                        data_torch = torch.stack(datas, dim=0)
111
112                        merged_name = f"transformer.decoder_layer.{bid}.moe.{wid[0]}.weight"
113
114                        yield from super().modify_tensors(data_torch, merged_name, bid)
115
116        for t in deferred:
117            yield from super().modify_tensors(*t)