[Bug] 自定义模型评测配置

Prerequisite

• I have searched Issues and Discussions but cannot get the expected help.
• The bug has not been fixed in the latest version.

Type

I'm evaluating with the officially supported tasks/models/datasets.

Environment

aaa

Reproduces the problem - code/configuration sample

aaa

Reproduces the problem - command or script

aaa

Reproduces the problem - error message

/mnt/workspace/aaa/opencompass/opencompass/init.py:17: UserWarning: Starting from v0.4.0, all AMOTIC configuration files currently located in ./configs/datasets, ./configs/models, and ./configs/summarizers will be migrated to the opencompass/configs/ package. Please update your configuration file paths accordingly.
_warn_about_config_migration()
12/09 17:12:04 - OpenCompass - INFO - Task [PhiLanguageModel/demo_gsm8k]
Traceback (most recent call last):
File "/mnt/workspace/aaa/opencompass/opencompass/tasks/openicl_infer.py", line 161, in
inferencer.run()
File "/mnt/workspace/aaa/opencompass/opencompass/tasks/openicl_infer.py", line 73, in run
self.model = build_model_from_cfg(model_cfg)
File "/mnt/workspace/aaa/opencompass/opencompass/utils/build.py", line 25, in build_model_from_cfg
return MODELS.build(model_cfg)
File "/mnt/workspace/anaconda3/envs/opencompass/lib/python3.10/site-packages/mmengine/registry/registry.py", line 570, in build
return self.build_func(cfg, *args, **kwargs, registry=self)
File "/mnt/workspace/anaconda3/envs/opencompass/lib/python3.10/site-packages/mmengine/registry/build_functions.py", line 98, in build_from_cfg
obj_cls = registry.get(obj_type)
File "/mnt/workspace/anaconda3/envs/opencompass/lib/python3.10/site-packages/mmengine/registry/registry.py", line 470, in get
import_module(f'{scope}.registry')
File "/mnt/workspace/anaconda3/envs/opencompass/lib/python3.10/importlib/init.py", line 121, in import_module
raise TypeError(msg.format(name))
TypeError: the 'package' argument is required to perform a relative import for '.registry'

Other information

我正在评测自定义的模型，根据官方教程和网上能查到的资料进行了配置。
自定义模型在这个文件：/mnt/workspace/gaojunqi/opencompass/opencompass/models/mymodel.py

import torch
from torch.nn import functional as F
from typing import List, Optional, Dict
from transformers import PreTrainedTokenizerFast
# from your_model_file import CustomLanguageModel, load_custom_model  # 替换为正确的路径
from .base import BaseModel

import json
import torch
import torch.nn as nn
from transformers import PreTrainedTokenizerFast

# 定义自定义模块
class PhiBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        d_model = config["core_input"]["d_state"]  # 从配置中获取模型维度

        # 定义核心计算模块
        self.z_bias = nn.Parameter(torch.zeros(d_model))  # 确保 z_bias 被初始化
        self.D = nn.Parameter(torch.zeros(d_model))
        self.in_proj = nn.Linear(d_model, d_model, bias=False)
        self.conv1d = nn.Conv1d(d_model, d_model, kernel_size=1, bias=True)
        self.out_proj = nn.Linear(d_model, d_model, bias=False)

        # 定义 MLP 和 LayerNorm
        self.mlp = nn.Sequential(
            nn.Linear(d_model, d_model),
            nn.ReLU(),
            nn.Linear(d_model, d_model)
        )
        self.norm = nn.LayerNorm(d_model)

    def forward(self, x):
        if x.size(-1) != self.z_bias.size(0):
            raise ValueError(
                f"Mismatch in tensor dimensions: x has {x.size(-1)}, "
                f"but z_bias has {self.z_bias.size(0)}."
            )
        # 添加偏置
        x = x + self.z_bias

        # 转换形状以适配 Conv1d
        x = self.in_proj(x)
        x = x.transpose(1, 2)  # [batch_size, seq_length, d_model] -> [batch_size, d_model, seq_length]
        x = self.conv1d(x)  # [batch_size, d_model, seq_length]
        x = x.transpose(1, 2)  # 转回 [batch_size, seq_length, d_model]

        x = self.out_proj(x)
        x = self.mlp(self.norm(x))
        return x


# 主语言模型类
class CustomLanguageModel(nn.Module):
    def __init__(self, config_path):
        super().__init__()
        # 加载配置
        with open(config_path, "r") as f:
            config = json.load(f)

        # 初始化参数
        vocab_size = config["LanguageModel"]["input"]["vocab_size"]
        d_model = config["MixerModel"]["input"]["d_model"]
        n_layers = config["Block1"]["n_layers"]

        # 定义词嵌入层
        self.embedding = nn.Embedding(vocab_size, d_model)

        # 定义 Transformer 块
        self.blocks = nn.ModuleList(
            [PhiBlock(config["Block1"]) for _ in range(n_layers)]
        )

        # 定义 LayerNorm 和输出层
        self.final_layernorm = nn.LayerNorm(d_model)
        self.lm_head = nn.Linear(d_model, vocab_size)

    def forward(self, input_ids):
        x = self.embedding(input_ids)
        for block in self.blocks:
            x = block(x)
        x = self.final_layernorm(x)
        return self.lm_head(x)


# 重映射 state_dict 的键名
def remap_state_dict_keys(state_dict):
    new_state_dict = {}
    for key, value in state_dict.items():
        new_state_dict[key] = value
    return new_state_dict


# 模型加载函数
def load_custom_model(model_path):
    config_path = f"{model_path}/config.json"
    model_weights_path = f"{model_path}/pytorch_model.bin"
    tokenizer_path = model_path

    # 加载模型配置
    model = CustomLanguageModel(config_path)

    # 加载权重并重映射键名
    state_dict = torch.load(model_weights_path, map_location="cpu")
    remapped_state_dict = remap_state_dict_keys(state_dict)
    model.load_state_dict(remapped_state_dict, strict=False)  # strict=False 忽略不匹配的层
    model.eval()  # 设置为推理模式

    # 加载分词器
    tokenizer = PreTrainedTokenizerFast.from_pretrained(tokenizer_path)

    # 设置 pad_token
    if tokenizer.pad_token is None:
        if tokenizer.eos_token:  # 使用 eos_token 作为 pad_token
            tokenizer.pad_token = tokenizer.eos_token
        else:  # 如果没有 eos_token，手动添加 [PAD]
            tokenizer.add_special_tokens({'pad_token': '[PAD]'})

    return model, tokenizer

# from mmengine.registry import MODELS

# @MODELS.register_module()

from mmengine.registry import OPTIMIZERS

@OPTIMIZERS.register_module()
class PhiLanguageModel(BaseModel):
    def __init__(self,
                 pkg_root: str,
                 ckpt_path: str,
                 tokenizer_only: bool = False,
                 meta_template: Optional[Dict] = None,
                 **kwargs):
        """
        初始化模型和分词器。

        Args:
            pkg_root (str): 根路径。
            ckpt_path (str): 模型权重路径。
            tokenizer_only (bool): 是否仅加载分词器。
            meta_template (Optional[Dict]): 元信息模板。
        """
        self.pkg_root = pkg_root
        self.ckpt_path = ckpt_path
        self.tokenizer_only = tokenizer_only
        self.meta_template = meta_template

        # 加载模型和分词器
        self.model, self.tokenizer = load_custom_model(ckpt_path)

        # 设置设备
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.model.to(self.device)

    def get_token_len(self, prompt: str) -> int:
        """
        获取分词后的 token 长度。

        Args:
            prompt (str): 输入的字符串。

        Returns:
            int: token 数量。
        """
        tokens = self.tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
        return len(tokens["input_ids"][0])

    def generate(self, inputs: List[str], max_out_len: int) -> List[str]:
        """
        给定输入生成输出。

        Args:
            inputs (List[str]): 输入文本列表。
            max_out_len (int): 最大生成长度。

        Returns:
            List[str]: 生成的文本列表。
        """
        results = []
        for input_text in inputs:
            # 分词并转移到设备
            tokens = self.tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).to(self.device)

            # 推理
            with torch.no_grad():
                outputs = self.model(tokens["input_ids"])
                logits = outputs
                predicted_ids = torch.argmax(logits, dim=-1)

            # 解码生成文本
            generated_text = self.tokenizer.decode(predicted_ids[0], skip_special_tokens=True)
            results.append(generated_text[:max_out_len])

        return results

    def get_ppl(self, inputs: List[str], mask_length: Optional[List[int]] = None) -> List[float]:
        """
        计算困惑度 (Perplexity)。

        Args:
            inputs (List[str]): 输入文本列表。
            mask_length (Optional[List[int]]): 掩码长度列表，用于计算特定范围的困惑度。

        Returns:
            List[float]: 每个输入的困惑度。
        """
        ppl_scores = []
        for input_text in inputs:
            tokens = self.tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).to(self.device)

            with torch.no_grad():
                outputs = self.model(tokens["input_ids"])
                logits = outputs

            # 获取交叉熵损失
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = tokens["input_ids"][..., 1:].contiguous()

            loss_fct = F.cross_entropy(
                shift_logits.view(-1, shift_logits.size(-1)),
                shift_labels.view(-1),
                ignore_index=self.tokenizer.pad_token_id,
                reduction="none"
            )

            loss = loss_fct.view(tokens["input_ids"].size(0), -1).mean(dim=1)
            perplexity = torch.exp(loss)
            ppl_scores.append(perplexity.item())

        return ppl_scores

/mnt/workspace/gaojunqi/opencompass/configs/models/qwen2_5/mymodels.py

from ....opencompass.models.mymodel import PhiLanguageModel

models = [
    dict(
        type=PhiLanguageModel,
        abbr='PhiLanguageModel',
        path='***',
        max_out_len=4096,
        batch_size=8,
        run_cfg=dict(num_gpus=1),
    )
]

/mnt/workspace/gaojunqi/opencompass/configs/eval_phi_mamba.py

from mmengine.config import read_base

with read_base():
    from .datasets.demo.demo_gsm8k_chat_gen import gsm8k_datasets
    from .datasets.demo.demo_math_chat_gen import math_datasets
    from .models.qwen.hf_qwen2_1_5b_instruct import models as hf_qwen2_1_5b_instruct_models
    from .models.hf_internlm.hf_internlm2_chat_1_8b import models as hf_internlm2_chat_1_8b_models
    from .models.qwen2_5.mymodels import models as PhiLanguageModel
    

datasets = gsm8k_datasets

models = PhiLanguageModel

提交命令：

python run.py configs/eval_phi_mamba.py

报错如下：

/mnt/workspace/gaojunqi/opencompass/opencompass/__init__.py:17: UserWarning: Starting from v0.4.0, all AMOTIC configuration files currently located in `./configs/datasets`, `./configs/models`, and `./configs/summarizers` will be migrated to the `opencompass/configs/` package. Please update your configuration file paths accordingly.
  _warn_about_config_migration()
12/09 17:12:04 - OpenCompass - INFO - Task [PhiLanguageModel/demo_gsm8k]
Traceback (most recent call last):
  File "/mnt/workspace/gaojunqi/opencompass/opencompass/tasks/openicl_infer.py", line 161, in <module>
    inferencer.run()
  File "/mnt/workspace/gaojunqi/opencompass/opencompass/tasks/openicl_infer.py", line 73, in run
    self.model = build_model_from_cfg(model_cfg)
  File "/mnt/workspace/gaojunqi/opencompass/opencompass/utils/build.py", line 25, in build_model_from_cfg
    return MODELS.build(model_cfg)
  File "/mnt/workspace/anaconda3/envs/opencompass/lib/python3.10/site-packages/mmengine/registry/registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "/mnt/workspace/anaconda3/envs/opencompass/lib/python3.10/site-packages/mmengine/registry/build_functions.py", line 98, in build_from_cfg
    obj_cls = registry.get(obj_type)
  File "/mnt/workspace/anaconda3/envs/opencompass/lib/python3.10/site-packages/mmengine/registry/registry.py", line 470, in get
    import_module(f'{scope}.registry')
  File "/mnt/workspace/anaconda3/envs/opencompass/lib/python3.10/importlib/__init__.py", line 121, in import_module
    raise TypeError(msg.format(name))
TypeError: the 'package' argument is required to perform a relative import for '.registry'

请问上述问题应该如何解决