Felipe97/llama-cpp-compiled

1from __future__ import annotations2 3import math4 5from typing import Callable, Iterable, TYPE_CHECKING6 7if TYPE_CHECKING:8    from torch import Tensor9 10from .base import ModelBase, TextModel, gguf11 12 13@ModelBase.register("Jais2ForCausalLM")14# [TAG_HF_EXAMPLE_GATED] inceptionai/Jais-2-8B-Chat is gated15# [TAG_HF_EXAMPLE_MISSING]16class Jais2Model(TextModel):17    model_arch = gguf.MODEL_ARCH.JAIS218 19    def set_gguf_parameters(self):20        super().set_gguf_parameters()21        hparams = self.hparams22        head_dim = hparams.get("head_dim", hparams["hidden_size"] // hparams["num_attention_heads"])23        self.gguf_writer.add_rope_dimension_count(head_dim)24 25 26@ModelBase.register("JAISLMHeadModel")27@ModelBase.example("inceptionai/jais-family-590m")28class JaisModel(TextModel):29    model_arch = gguf.MODEL_ARCH.JAIS30 31    def __init__(self, *args, **kwargs):32        super().__init__(*args, **kwargs)33 34        # SwigLU activation35        assert self.hparams["activation_function"] == "swiglu"36        # ALiBi position embedding37        assert self.hparams["position_embedding_type"] == "alibi"38 39        # Embeddings scale40        self.embeddings_scale = 1.041        if 'mup_embeddings_scale' in self.hparams:42            self.embeddings_scale = self.hparams['mup_embeddings_scale']43        elif 'embeddings_scale' in self.hparams:44            self.embeddings_scale = self.hparams['embeddings_scale']45        else:46            assert False47 48        self.width_scale = 1.049        if 'mup_output_alpha' in self.hparams:50            assert 'mup_width_scale' in self.hparams51            self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale']52        elif 'width_scale' in self.hparams:53            self.width_scale = self.hparams['width_scale']54        else:55            assert False56 57        self.max_alibi_bias = 8.058 59    def set_vocab(self):60        self._set_vocab_gpt2()61 62    def set_gguf_parameters(self):63        self.gguf_writer.add_block_count(self.block_count)64        self.gguf_writer.add_context_length(self.hparams["n_positions"])65        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])66        self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"])67        self.gguf_writer.add_head_count(self.hparams["n_head"])68        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])69        self.gguf_writer.add_file_type(self.ftype)70 71    @classmethod72    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:73        name, gen = item74 75        # we don't need these76        if name.endswith((".attn.bias")):77            return None78 79        return super().filter_tensors(item)80 81    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:82        if name.endswith(("relative_pe.slopes")):83            # Calculate max ALiBi bias (this is the inverse of the ALiBi calculation)84            # Some other models has max_alibi_bias spelled out explicitly in the hyperparams,85            # but Jais's PyTorch model simply precalculates the slope values and places them86            # in relative_pes.slopes87            n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))88            first_val = float(data_torch[0].item())89            self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)90 91            return92 93        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")):94            data_torch = data_torch.transpose(1, 0)95 96        new_name = self.map_tensor_name(name)97 98        if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):99            yield from super().modify_tensors(data_torch * self.embeddings_scale, new_name, bid)100        elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT):101            yield from super().modify_tensors(data_torch * self.width_scale, new_name, bid)102        else:103            yield from super().modify_tensors(data_torch, new_name, bid)104 105    def prepare_tensors(self):106        super().prepare_tensors()107        self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)108