Felipe97/llama-cpp-compiled

1from __future__ import annotations2 3import json4 5from typing import Iterable, TYPE_CHECKING6 7import torch8 9if TYPE_CHECKING:10    from torch import Tensor11 12from .base import ModelBase, TextModel, gguf13 14 15@ModelBase.register("PlamoForCausalLM")16@ModelBase.example("pfnet/plamo-13b")17class PlamoModel(TextModel):18    model_arch = gguf.MODEL_ARCH.PLAMO19 20    def set_vocab(self):21        self._set_vocab_sentencepiece()22 23    def set_gguf_parameters(self):24        hparams = self.hparams25 26        self.gguf_writer.add_context_length(4096)  # not in config.json27        self.gguf_writer.add_embedding_length(hparams["hidden_size"])28        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])29        self.gguf_writer.add_block_count(self.block_count)30        self.gguf_writer.add_head_count(hparams["num_attention_heads"])31        self.gguf_writer.add_head_count_kv(5)  # hparams["num_key_value_heads"]) is wrong32        self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])33        self.gguf_writer.add_file_type(self.ftype)34 35    def shuffle_attn_q_weight(self, data_torch):36        assert data_torch.size() == (5120, 5120)37        data_torch = data_torch.reshape(8, 5, 128, 5120)38        data_torch = torch.permute(data_torch, (1, 0, 2, 3))39        data_torch = torch.reshape(data_torch, (5120, 5120))40        return data_torch41 42    def shuffle_attn_output_weight(self, data_torch):43        assert data_torch.size() == (5120, 5120)44        data_torch = data_torch.reshape(5120, 8, 5, 128)45        data_torch = torch.permute(data_torch, (0, 2, 1, 3))46        data_torch = torch.reshape(data_torch, (5120, 5120))47        return data_torch48 49    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:50        new_name = self.map_tensor_name(name)51 52        # shuffle for broadcasting of gqa in ggml_mul_mat53        if new_name.endswith("attn_q.weight"):54            data_torch = self.shuffle_attn_q_weight(data_torch)55        elif new_name.endswith("attn_output.weight"):56            data_torch = self.shuffle_attn_output_weight(data_torch)57 58        yield from super().modify_tensors(data_torch, name, bid)59 60 61@ModelBase.register("Plamo2ForCausalLM", "PLaMo2ForCausalLM")62@ModelBase.example("pfnet/plamo-2-1b")63class Plamo2Model(TextModel):64    model_arch = gguf.MODEL_ARCH.PLAMO265 66    def set_vocab(self):67        self._set_vocab_plamo()68 69    def set_gguf_parameters(self):70        hparams = self.hparams71        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])72 73        # Which layers are Mamba layers74        # PLaMo 2 uses mamba_step to indicate the pattern (e.g., 2 means every other layer)75        # This logic matches modeling_plamo.py's is_mamba function76        mamba_step = hparams.get("mamba_step", 2)77        mamba_enabled = hparams.get("mamba_enabled", True)78        num_key_value_heads = []79        num_attention_heads = []80 81        if mamba_enabled:82            for i in range(self.block_count):83                if self.block_count <= (mamba_step // 2):84                    # use attention in last layer85                    is_mamba = (i != self.block_count - 1)86                else:87                    is_mamba = (i % mamba_step) != (mamba_step // 2)88                if is_mamba:89                    num_key_value_heads.append(0)90                    num_attention_heads.append(0)91                else:92                    num_key_value_heads.append(hparams.get("num_key_value_heads", 4))93                    num_attention_heads.append(hparams.get("num_attention_heads", 32))94 95        if num_key_value_heads and num_attention_heads:96            self.gguf_writer.add_head_count_kv(num_key_value_heads)97            self.gguf_writer.add_head_count(num_attention_heads)98 99        self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 2048))100        self.gguf_writer.add_embedding_length(hparams.get("hidden_size", 4096))101        self.gguf_writer.add_key_length(hparams.get("hidden_size_per_head", 128))102        self.gguf_writer.add_value_length(hparams.get("hidden_size_per_head", 128))103        self.gguf_writer.add_block_count(self.block_count)104        self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06))105        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("rope_theta", 10000))106 107        # Mamba parameters108        self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64))109        self.gguf_writer.add_ssm_conv_kernel(hparams.get("mamba_d_conv", 4))110        self.gguf_writer.add_ssm_time_step_rank(hparams.get("mamba_num_heads", 64))111        intermediate_size = hparams.get("mamba_num_heads", 64) * hparams.get("hidden_size_per_head", 128)112        self.gguf_writer.add_ssm_inner_size(intermediate_size)113        self.gguf_writer.add_ssm_group_count(0)114 115        # MLP feed forward parameters (for attention layers)116        self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 13312))117        self.gguf_writer.add_file_type(self.ftype)118 119    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:120        if name.endswith(".A_log"):121            data_torch = -torch.exp(data_torch)122        elif name.endswith(".dt_bias"):123            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"124        elif name.endswith(".dt_norm_weight"):125            name = name.rpartition(".dt_norm_weight")[0] + ".dt_norm.weight"126        elif name.endswith(".B_norm_weight"):127            name = name.rpartition(".B_norm_weight")[0] + ".B_norm.weight"128        elif name.endswith(".C_norm_weight"):129            name = name.rpartition(".C_norm_weight")[0] + ".C_norm.weight"130        elif name.endswith(".k_weight"):131            name = name.rpartition(".k_weight")[0] + ".k.weight"132        elif name.endswith(".q_weight"):133            name = name.rpartition(".q_weight")[0] + ".q.weight"134        elif name.endswith(".conv1d.weight"):135            data_torch = torch.squeeze(data_torch)  # remove (, 1, )136            assert data_torch.ndim == 2137        elif name.endswith(".pre_mixer_norm.weight"):138            data_torch += 1.0139        elif name.endswith(".post_mixer_norm.weight"):140            data_torch += 1.0 / 5141        elif name.endswith(".pre_mlp_norm.weight"):142            data_torch += 1.0143        elif name.endswith(".post_mlp_norm.weight"):144            data_torch += 1.0 / (5**1.5)145        elif name.endswith(".norm.weight"):146            data_torch += 1.0147 148        yield from super().modify_tensors(data_torch, name, bid)149 150 151@ModelBase.register("Plamo3ForCausalLM", "PLaMo3ForCausalLM")152# [TAG_HF_EXAMPLE_GATED] pfnet/plamo-3-nict-2b-base is gated153@ModelBase.example("midorin-Linux/plamo-3-12b-self-merged-base")154class Plamo3Model(TextModel):155    model_arch = gguf.MODEL_ARCH.PLAMO3156 157    def set_vocab(self):158        self._set_vocab_plamo()159 160        tokenizer_config_path = self.dir_model / "tokenizer_config.json"161        tokenizer_config = {}162 163        if tokenizer_config_path.is_file():164            with open(tokenizer_config_path, encoding="utf-8") as f:165                tokenizer_config = json.load(f)166 167        chat_template = tokenizer_config.get("chat_template")168        chat_template_jinja = self.dir_model / "chat_template.jinja"169 170        if chat_template_jinja.is_file():171            with open(chat_template_jinja, encoding="utf-8") as f:172                chat_template = f.read()173 174        if chat_template:175            self.gguf_writer.add_chat_template(chat_template)176 177    def set_gguf_parameters(self):178        super().set_gguf_parameters()179        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])180        if (sliding_window := self.find_hparam(["window_size", "sliding_window"], optional=True)) is not None:181            self.gguf_writer.add_sliding_window(sliding_window)182            self.gguf_writer.add_sliding_window_pattern(self.hparams["sliding_window_pattern"])183 184    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:185 186        if name.endswith(".pre_mixer_norm.weight"):187            data_torch = data_torch + 1.0188        elif name.endswith(".post_mixer_norm.weight"):189            data_torch = data_torch + 1.0 / 5190        elif name.endswith(".pre_mlp_norm.weight"):191            data_torch = data_torch + 1.0192        elif name.endswith(".post_mlp_norm.weight"):193            data_torch = data_torch + 1.0 / (5**1.5)194        elif name.endswith((".mixer.q_norm.weight", ".mixer.k_norm.weight")):195            data_torch = data_torch + 1.0196        elif name.endswith(".norm.weight"):197            data_torch = data_torch + 1.0198 199        yield from super().modify_tensors(data_torch, name, bid)200