Felipe97/llama-cpp-compiled

1from __future__ import annotations2 3from typing import Iterable, TYPE_CHECKING4 5if TYPE_CHECKING:6    from torch import Tensor7 8from .base import ModelBase, TextModel, gguf9 10 11@ModelBase.register("GPTRefactForCausalLM")12@ModelBase.example("smallcloudai/Refact-1_6-base")13class RefactModel(TextModel):14    model_arch = gguf.MODEL_ARCH.REFACT15 16    def set_vocab(self):17        super().set_vocab()18 19        # TODO: how to determine special FIM tokens automatically?20        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,21                                          special_token_types = ['prefix', 'suffix', 'middle', 'eot'])22        special_vocab._set_special_token("prefix", 1)23        special_vocab._set_special_token("suffix", 3)24        special_vocab._set_special_token("middle", 2)25        special_vocab.chat_template = None  # do not add it twice26        special_vocab.add_to_gguf(self.gguf_writer)27 28    def set_gguf_parameters(self):29        hidden_dim = self.hparams["n_embd"]30        inner_dim = 4 * hidden_dim31        hidden_dim = int(2 * inner_dim / 3)32        multiple_of = 25633        ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)34 35        # refact uses Alibi. So this is from config.json which might be used by training.36        self.gguf_writer.add_context_length(self.hparams["n_positions"])37        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])38 39        self.gguf_writer.add_feed_forward_length(ff_dim)40        self.gguf_writer.add_block_count(self.block_count)41        self.gguf_writer.add_head_count(self.hparams["n_head"])42        self.gguf_writer.add_head_count_kv(1)43        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])44        self.gguf_writer.add_file_type(self.ftype)45 46    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:47        hidden_dim = self.hparams["n_embd"]48        inner_dim = 4 * hidden_dim49        hidden_dim = int(2 * inner_dim / 3)50        multiple_of = 25651        ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)52        n_head = self.hparams["n_head"]53        n_head_kv = 154        head_dim = self.hparams["n_embd"] // n_head55 56        if bid is not None:57            if name == f"transformer.h.{bid}.attn.kv.weight":58                yield from super().modify_tensors(data_torch[:n_head_kv * head_dim], self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), bid)59                yield from super().modify_tensors(data_torch[n_head_kv * head_dim:], self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), bid)60                return61            if name == f"transformer.h.{bid}.attn.q.weight":62                yield from super().modify_tensors(data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), bid)63                return64            if name == f"transformer.h.{bid}.mlp.gate_up_proj.weight":65                yield from super().modify_tensors(data_torch[:ff_dim], self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid)66                yield from super().modify_tensors(data_torch[ff_dim:], self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid)67                return68 69        yield from super().modify_tensors(data_torch, name, bid)70