[Feature] Support turbomind (#166)

* support turbomind

* update doc

* Update docs/en/advanced_guides/evaluation_turbomind.md
Co-authored-by: Tong Gao <gaotongxiao@gmail.com>

* Update docs/zh_cn/advanced_guides/evaluation_turbomind.md
Co-authored-by: Tong Gao <gaotongxiao@gmail.com>

* Update docs/zh_cn/advanced_guides/evaluation_turbomind.md
Co-authored-by: Tong Gao <gaotongxiao@gmail.com>

* Update docs/en/advanced_guides/evaluation_turbomind.md
Co-authored-by: Tong Gao <gaotongxiao@gmail.com>

* update

---------
Co-authored-by: Tong Gao <gaotongxiao@gmail.com>

configs/eval_internlm_chat_7b_turbomind.py

+from mmengine.config import read_base
+from opencompass.models.turbomind import TurboMindModel
+
+with read_base():
+    # choose a list of datasets
+    from .datasets.SuperGLUE_CB.SuperGLUE_CB_gen import CB_datasets
+    # and output the results in a choosen format
+    from .summarizers.medium import summarizer
+
+datasets = [*CB_datasets]
+
+
+_meta_template = dict(
+    round=[
+        dict(role='HUMAN', begin='<|User|>:', end='<eoh>\n'),
+        dict(role='BOT', begin='<|Bot|>:', end='<eoa>\n', generate=True),
+    ],
+)
+
+models = [
+    dict(
+        type=TurboMindModel,
+        abbr='internlm-chat-7b-tb',
+        path="internlm-chat-7b",
+        model_path='./workspace',
+        max_out_len=100,
+        max_seq_len=2048,
+        batch_size=16,
+        meta_template=_meta_template,
+        run_cfg=dict(num_gpus=1, num_procs=1),
+    )
+]

configs/models/hf_internlm_chat_7b.py

@@ -19,12 +19,16 @@ models = [
             truncation_side='left',
             use_fast=False,
             trust_remote_code=True,
+            revision="1a6328795c6e207904e1eb58177e03ad24ae06f3"
         ),
         max_out_len=100,
         max_seq_len=2048,
         batch_size=8,
         meta_template=_meta_template,
-        model_kwargs=dict(trust_remote_code=True, device_map='auto'),
+        model_kwargs=dict(
+            trust_remote_code=True,
+            device_map='auto',
+            revision="1a6328795c6e207904e1eb58177e03ad24ae06f3"),
         run_cfg=dict(num_gpus=1, num_procs=1),
     )
 ]

docs/en/advanced_guides/evaluation_turbomind.md

+# Evaluation with LMDeploy
+
+We now support evaluation of models accelerated by the [LMDeploy](https://github.com/InternLM/lmdeploy). LMDeploy is a toolkit designed for compressing, deploying, and serving LLM. **TurboMind** is an efficient inference engine proposed by LMDeploy. OpenCompass is compatible with TurboMind. We now illustrate how to evaluate a model with the support of TurboMind in OpenCompass.
+
+# Setup
+
+## Install OpenCompass
+
+Please follow the [instructions](https://opencompass.readthedocs.io/en/latest/get_started.html) to install the OpenCompass and prepare the evaluation datasets.
+
+## Install LMDeploy
+
+Install lmdeploy via pip (python 3.8+)
+
+```shell
+pip install lmdeploy
+```
+
+# Evaluation
+
+We take the InternLM as example.
+
+## Step-1: Get InternLM model
+
+```shell
+# 1. Download InternLM model(or use the cached model's checkpoint)
+
+# Make sure you have git-lfs installed (https://git-lfs.com)
+git lfs install
+git clone https://huggingface.co/internlm/internlm-chat-7b /path/to/internlm-chat-7b
+
+# if you want to clone without large files – just their pointers
+# prepend your git clone with the following env var:
+GIT_LFS_SKIP_SMUDGE=1
+
+# 2. Convert InternLM model to turbomind's format, which will be in "./workspace" by default
+python3 -m lmdeploy.serve.turbomind.deploy internlm-chat-7b /path/to/internlm-chat-7b
+
+```
+
+## Step-2: Verify the Converted Model
+
+```shell
+python -m lmdeploy.turbomind.chat ./workspace
+```
+
+## Step-3: Evaluate the Converted Model
+
+In the home folder of OpenCompass
+
+```shell
+python run.py configs/eval_internlm_chat_7b_turbomind.py -w outputs/turbomind
+```
+
+You are expected to get the evaluation results after the inference and evaluation.

docs/en/index.rst

@@ -44,6 +44,7 @@ We always welcome *PRs* and *Issues* for the betterment of OpenCompass.
 
    advanced_guides/new_dataset.md
    advanced_guides/new_model.md
+   advanced_guides/evaluation_turbomind.md
 
 .. _Prompt:
 .. toctree::

docs/zh_cn/advanced_guides/evaluation_turbomind.md

+# 评测LMDeploy模型
+
+我们支持评测使用[LMDeploy](https://github.com/InternLM/lmdeploy)加速过的大语言模型。LMDeploy 由 MMDeploy 和 MMRazor 团队联合开发，是涵盖了 LLM 任务的全套轻量化、部署和服务解决方案。 **TurboMind** 是 LMDeploy 推出的高效推理引擎。OpenCompass 对 TurboMind 进行了适配，本教程将介绍如何使用 OpenCompass 来对 TurboMind 加速后的模型进行评测。
+
+# 环境配置
+
+## 安装OpenCompass
+
+请根据OpenCompass[安装指南](https://opencompass.readthedocs.io/en/latest/get_started.html) 来安装算法库和准备数据集。
+
+## 安装LMDeploy
+
+使用pip安装LMDeploy( python 3.8+)
+
+```shell
+pip install lmdeploy
+```
+
+# 评测
+
+我们使用InternLM作为例子来介绍如何评测
+
+## 第一步: 获取InternLM模型
+
+```shell
+# 1. Download InternLM model(or use the cached model's checkpoint)
+
+# Make sure you have git-lfs installed (https://git-lfs.com)
+git lfs install
+git clone https://huggingface.co/internlm/internlm-chat-7b /path/to/internlm-chat-7b
+
+# if you want to clone without large files – just their pointers
+# prepend your git clone with the following env var:
+GIT_LFS_SKIP_SMUDGE=1
+
+# 2. Convert InternLM model to turbomind's format, which will be in "./workspace" by default
+python3 -m lmdeploy.serve.turbomind.deploy internlm-chat-7b /path/to/internlm-chat-7b
+
+```
+
+## 第二步: 验证转换后的模型
+
+```shell
+python -m lmdeploy.turbomind.chat ./workspace
+```
+
+## 第三步: 评测转换后的模型
+
+在OpenCompass项目文件执行：
+
+```shell
+python run.py configs/eval_internlm_chat_7b_turbomind.py -w outputs/turbomind
+```
+
+当模型完成推理和指标计算后，我们便可获得模型的评测结果

docs/zh_cn/index.rst

@@ -54,6 +54,7 @@ OpenCompass 上手路线
 
    advanced_guides/new_dataset.md
    advanced_guides/new_model.md
+   advanced_guides/evaluation_turbomind.md
 
 .. _工具:
 .. toctree::

opencompass/models/turbomind.py

+import os.path as osp
+import random
+from concurrent.futures import ThreadPoolExecutor
+from typing import Dict, List, Optional, Union
+
+from opencompass.models.base import BaseModel
+from opencompass.models.base_api import TokenBucket
+from opencompass.utils.logging import get_logger
+from opencompass.utils.prompt import PromptList
+
+PromptType = Union[PromptList, str]
+
+
+def valid_str(string, coding='utf-8'):
+    """decode text according to its encoding type."""
+    invalid_chars = [b'\xef\xbf\xbd']
+    bstr = bytes(string, coding)
+    for invalid_char in invalid_chars:
+        bstr = bstr.replace(invalid_char, b'')
+    ret = bstr.decode(encoding=coding, errors='ignore')
+    return ret
+
+
+class TurboMindModel(BaseModel):
+    """Model wrapper for TurboMind API.
+
+    Args:
+        path (str): The name of OpenAI's model.
+        model_path (str): folder of the turbomind model's path
+        max_seq_len (int): The maximum allowed sequence length of a model.
+            Note that the length of prompt + generated tokens shall not exceed
+            this value. Defaults to 2048.
+        query_per_second (int): The maximum queries allowed per second
+            between two consecutive calls of the API. Defaults to 1.
+        retry (int): Number of retires if the API call fails. Defaults to 2.
+        meta_template (Dict, optional): The model's meta prompt
+            template if needed, in case the requirement of injecting or
+            wrapping of any meta instructions.
+    """
+
+    is_api: bool = True
+
+    def __init__(
+        self,
+        path: str,
+        model_path: str,
+        max_seq_len: int = 2048,
+        query_per_second: int = 1,
+        retry: int = 2,
+        meta_template: Optional[Dict] = None,
+    ):
+
+        super().__init__(path=path,
+                         max_seq_len=max_seq_len,
+                         meta_template=meta_template)
+        self.logger = get_logger()
+
+        from lmdeploy import turbomind as tm
+        from lmdeploy.model import MODELS as LMMODELS
+        from lmdeploy.turbomind.tokenizer import Tokenizer as LMTokenizer
+
+        self.retry = retry
+
+        tokenizer_model_path = osp.join(model_path, 'triton_models',
+                                        'tokenizer')
+        self.tokenizer = LMTokenizer(tokenizer_model_path)
+        tm_model = tm.TurboMind(model_path, eos_id=self.tokenizer.eos_token_id)
+        self.model_name = tm_model.model_name
+        self.model = LMMODELS.get(self.model_name)()
+        self.generator = tm_model.create_instance()
+        self.token_bucket = TokenBucket(query_per_second)
+
+    def generate(
+        self,
+        inputs: List[str or PromptList],
+        max_out_len: int = 512,
+        temperature: float = 0.0,
+    ) -> List[str]:
+        """Generate results given a list of inputs.
+
+        Args:
+            inputs (List[str or PromptList]): A list of strings or PromptDicts.
+                The PromptDict should be organized in OpenCompass'
+                API format.
+            max_out_len (int): The maximum length of the output.
+            temperature (float): What sampling temperature to use,
+                between 0 and 2. Higher values like 0.8 will make the output
+                more random, while lower values like 0.2 will make it more
+                focused and deterministic. Defaults to 0.7.
+
+        Returns:
+            List[str]: A list of generated strings.
+        """
+        prompts = inputs
+        with ThreadPoolExecutor() as executor:
+            results = list(
+                executor.map(self._generate, prompts,
+                             [max_out_len] * len(inputs),
+                             [temperature] * len(inputs)))
+        return results
+
+    def wait(self):
+        """Wait till the next query can be sent.
+
+        Applicable in both single-thread and multi-thread environments.
+        """
+        return self.token_bucket.get_token()
+
+    def _generate(self, input: str or PromptList, max_out_len: int,
+                  temperature: float) -> str:
+        """Generate results given a list of inputs.
+
+        Args:
+            inputs (str or PromptList): A string or PromptDict.
+                The PromptDict should be organized in OpenCompass'
+                API format.
+            max_out_len (int): The maximum length of the output.
+            temperature (float): What sampling temperature to use,
+                between 0 and 2. Higher values like 0.8 will make the output
+                more random, while lower values like 0.2 will make it more
+                focused and deterministic.
+
+        Returns:
+            str: The generated string.
+        """
+        assert isinstance(input, (str, PromptList))
+
+        assert type(
+            input
+        ) is str, 'We only support string for TurboMind Python API now'
+
+        intput_token_ids = self.tokenizer.encode(input)
+
+        for _ in range(self.retry):
+            self.wait()
+            session_id = random.randint(1, 100000)
+            nth_round = 0
+            for outputs in self.generator.stream_infer(
+                    session_id=session_id,
+                    input_ids=[intput_token_ids],
+                    stream_output=False,
+                    request_output_len=max_out_len,
+                    sequence_start=(nth_round == 0),
+                    sequence_end=False,
+                    step=0,
+                    stop=False,
+                    top_k=40,
+                    top_p=0.8,
+                    temperature=temperature,
+                    repetition_penalty=1.0,
+                    ignore_eos=False,
+                    random_seed=random.getrandbits(64)
+                    if nth_round == 0 else None):
+                pass
+
+        output_token_ids, _ = outputs[0]
+        # decode output_token_ids
+        response = self.tokenizer.decode(output_token_ids)
+        response = valid_str(response)
+
+        return response