Felipe97/llama-cpp-compiled

1from __future__ import annotations2 3from typing import Iterable, TYPE_CHECKING4 5import torch6 7if TYPE_CHECKING:8    from torch import Tensor9 10from .base import ModelBase, TextModel, gguf, logger11 12 13@ModelBase.register("GPT2LMHeadModel")14@ModelBase.example("openai-community/gpt2")15class GPT2Model(TextModel):16    model_arch = gguf.MODEL_ARCH.GPT217 18    def set_gguf_parameters(self):19        self.gguf_writer.add_block_count(self.block_count)20        self.gguf_writer.add_context_length(self.hparams["n_ctx"])21        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])22        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])23        self.gguf_writer.add_head_count(self.hparams["n_head"])24        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])25        self.gguf_writer.add_file_type(self.ftype)26 27    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:28        # we don't need these29        if name.endswith((".attn.bias", ".attn.masked_bias")):30            yield from super().modify_tensors(data_torch, name, bid)31            return32 33        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):34            data_torch = data_torch.transpose(1, 0)35 36        new_name = self.map_tensor_name(name)37 38        yield from super().modify_tensors(data_torch, new_name, bid)39 40 41@ModelBase.register("RuGPT3XLForCausalLM")42@ModelBase.example("evilfreelancer/ruGPT3XL")43class RuGPT3XLModel(TextModel):44    model_arch = gguf.MODEL_ARCH.GPT245 46    _qkv_parts: list[dict[str, Tensor]] | None = None47 48    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:49        # Fuse separate Q, K, V projections into a single QKV tensor50        if ".self_attn.q_proj." in name or ".self_attn.k_proj." in name or ".self_attn.v_proj." in name:51            suffix = "weight" if name.endswith(".weight") else "bias"52            part = "q" if ".q_proj." in name else ("k" if ".k_proj." in name else "v")53            key = f"{part}.{suffix}"54 55            assert bid is not None56            if self._qkv_parts is None:57                self._qkv_parts = [{} for _ in range(self.block_count)]58            self._qkv_parts[bid][key] = data_torch59 60            q_key, k_key, v_key = f"q.{suffix}", f"k.{suffix}", f"v.{suffix}"61            if all(k in self._qkv_parts[bid] for k in [q_key, k_key, v_key]):62                q = self._qkv_parts[bid].pop(q_key)63                k = self._qkv_parts[bid].pop(k_key)64                v = self._qkv_parts[bid].pop(v_key)65                data_torch = torch.cat([q, k, v], dim=0)66                name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, bid, f".{suffix}")67                logger.debug(f"Fused Q/K/V {suffix} for layer {bid} -> {name}")68            else:69                return70 71        yield from super().modify_tensors(data_torch, name, bid)72 73    def prepare_tensors(self):74        super().prepare_tensors()75 76        if self._qkv_parts is not None:77            # flatten `list[dict[str, Tensor]]` into `list[str]`78            parts = [f"({i}){k}" for i, d in enumerate(self._qkv_parts) for k in d.keys()]79            if len(parts) > 0:80                raise ValueError(f"Unprocessed Q/K/V parts: {parts}")81