[Feature] Support LLaMA2-Accessory (#732)

* Support LLaMA2-Accessory

* remove strip

* clear imports

* reformat

* fix lint

* fix lint

* update readme

* update readme

* update readme

* update readme

README.md

@@ -50,6 +50,7 @@ Just like a compass guides us on our journey, OpenCompass will guide you through
 
 ## 🚀 What's New <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
 
+- **\[2023.12.28\]** We have enabled seamless evaluation of all models developed using [LLaMA2-Accessory](https://github.com/Alpha-VLLM/LLaMA2-Accessory), a powerful toolkit for comprehensive LLM development. 🔥🔥🔥.
 - **\[2023.12.22\]** We have released [T-Eval](https://github.com/open-compass/T-Eval), a step-by-step evaluation benchmark to gauge your LLMs on tool utilization. Welcome to our [Leaderboard](https://open-compass.github.io/T-Eval/leaderboard.html) for more details! 🔥🔥🔥.
 - **\[2023.12.10\]** We have released [VLMEvalKit](https://github.com/open-compass/VLMEvalKit), a toolkit for evaluating vision-language models (VLMs), currently support 20+ VLMs and 7 multi-modal benchmarks (including MMBench series). 🔥🔥🔥.
 - **\[2023.12.10\]** We have supported Mistral AI's MoE LLM: **Mixtral-8x7B-32K**. Welcome to [MixtralKit](https://github.com/open-compass/MixtralKit) for more details about inference and evaluation. 🔥🔥🔥.

README_zh-CN.md

@@ -50,6 +50,7 @@
 
 ## 🚀 最新进展 <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
 
+- **\[2023.12.28\]** 我们支持了对使用[LLaMA2-Accessory](https://github.com/Alpha-VLLM/LLaMA2-Accessory)（一款强大的LLM开发工具箱）开发的所有模型的无缝评估! 🔥🔥🔥.
 - **\[2023.12.22\]** 我们开源了[T-Eval](https://github.com/open-compass/T-Eval)用于评测大语言模型工具调用能力。欢迎访问T-Eval的官方[Leaderboard](https://open-compass.github.io/T-Eval/leaderboard.html)获取更多信息! 🔥🔥🔥.
 - **\[2023.12.10\]** 我们开源了多模评测框架 [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)，目前已支持 20+ 个多模态大模型与包括 MMBench 系列在内的 7 个多模态评测集. 🔥🔥🔥.
 - **\[2023.12.10\]** 我们已经支持了Mistral AI的MoE模型 **Mixtral-8x7B-32K**。欢迎查阅[MixtralKit](https://github.com/open-compass/MixtralKit)以获取更多关于推理和评测的详细信息。🔥🔥🔥。

configs/models/accessory/accessory_llama2_7b.py

+from opencompass.models import LLaMA2AccessoryModel
+
+# Please follow the LLaMA2-Accessory installation document
+# https://llama2-accessory.readthedocs.io/en/latest/install.html
+# to install LLaMA2-Accessory
+
+models = [
+    dict(
+        abbr="Accessory_llama2_7b",
+        type=LLaMA2AccessoryModel,
+
+        # additional_stop_symbols=["###"],  # for models tuned with chat template  # noqa
+        additional_stop_symbols=[],
+
+        # <begin> kwargs for accessory.MetaModel.from_pretrained
+        # download https://huggingface.co/meta-llama/Llama-2-7b/tree/main to
+        # 'path/to/Llama-2-7b/', which should contain:
+        #   - consolidated.00.pth
+        #   - params.json
+        #   - tokenizer.model
+        pretrained_path="path/to/Llama-2-7b/",
+        llama_type="llama",
+        llama_config="path/to/Llama-2-7b/params.json",
+        tokenizer_path="path/to/Llama-2-7b/tokenizer.model",
+        with_visual=False,
+        max_seq_len=4096,
+        quant=False,
+        # <end>
+
+        batch_size=2,
+        # LLaMA2-Accessory needs num_gpus==num_procs
+        run_cfg=dict(num_gpus=1, num_procs=1),
+    ),
+]

configs/models/accessory/accessory_mixtral_8x7b.py

+from opencompass.models import LLaMA2AccessoryModel
+
+# Please follow the LLaMA2-Accessory installation document
+# https://llama2-accessory.readthedocs.io/en/latest/install.html
+# to install LLaMA2-Accessory
+
+models = [
+    dict(
+        abbr="Accessory_mixtral_8x7b",
+        type=LLaMA2AccessoryModel,
+
+        # additional_stop_symbols=["###"],  # for models tuned with chat template  # noqa
+        additional_stop_symbols=[],
+
+        # <begin> kwargs for accessory.MetaModel.from_pretrained
+        # download from https://huggingface.co/Alpha-VLLM/MoE-Mixtral-7B-8Expert/tree/main/converted_sparse  # noqa
+        # see https://llama2-accessory.readthedocs.io/en/latest/projects/mixtral-8x7b.html for more details  # noqa
+        pretrained_path="path/to/MoE-Mixtral-7B-8Expert/converted_sparse",
+        llama_type=None,  # None for automatic probe from pretrained_path
+        llama_config=None,  # None for automatic probe from pretrained_path
+        tokenizer_path=None,  # None for automatic probe from pretrained_path
+        with_visual=False,
+        max_seq_len=4096,
+        quant=False,
+        # <end>
+
+        batch_size=2,
+        # LLaMA2-Accessory needs num_gpus==num_procs
+        run_cfg=dict(num_gpus=2, num_procs=2),
+    ),
+]

configs/models/accessory/accessory_sphinx_v2_1k.py

+from opencompass.models import LLaMA2AccessoryModel
+
+# Please follow the LLaMA2-Accessory installation document
+# https://llama2-accessory.readthedocs.io/en/latest/install.html
+# to install LLaMA2-Accessory
+
+models = [
+    dict(
+        abbr="Accessory_sphinx_v2_1k",
+        type=LLaMA2AccessoryModel,
+
+        additional_stop_symbols=["###"],  # for models tuned with chat template
+
+        # <begin> kwargs for accessory.MetaModel.from_pretrained
+        # download from https://huggingface.co/Alpha-VLLM/LLaMA2-Accessory/tree/main/finetune/mm/SPHINX/SPHINX-v2-1k  # noqa
+        pretrained_path="path/to/sphinx_v2_1k",
+        llama_type=None,  # None for automatic probe from pretrained_path
+        llama_config=None,  # None for automatic probe from pretrained_path
+        tokenizer_path=None,  # None for automatic probe from pretrained_path
+        with_visual=False,  # currently only support single-modal evaluation
+        max_seq_len=4096,
+        quant=False,
+        # <end>
+
+        batch_size=2,
+        # LLaMA2-Accessory needs num_gpus==num_procs
+        run_cfg=dict(num_gpus=1, num_procs=1),
+    ),
+]

opencompass/models/__init__.py

+from .accessory import LLaMA2AccessoryModel  # noqa: F401
 from .ai360_api import AI360GPT  # noqa: F401
 from .alaya import AlayaLM  # noqa: F401
 from .baichuan_api import BaiChuan  # noqa: F401

opencompass/models/accessory.py

+from typing import Dict, Iterable, List, Optional, Union
+
+import numpy as np
+import torch.distributed as dist
+
+from opencompass.models.base import BaseModel
+from opencompass.models.base_api import APITemplateParser
+from opencompass.utils.logging import get_logger
+from opencompass.utils.prompt import PromptList
+
+PromptType = Union[PromptList, str]
+
+
+class LLaMA2AccessoryModel(BaseModel):
+    """LLaMA2-Accessory model wrapper.
+
+    Project: https://github.com/Alpha-VLLM/LLaMA2-Accessory
+
+    Args:
+        tokenizer_only (bool): whether to load tokenizer only
+        meta_template (dict): meta template for the model
+        additional_stop_symbols: (Iterable[str]): additional symbols that mark
+            the end of generation, e.g. the "###" symbol for separating turns
+            in the chat template.
+        from_pretrained_kwargs: kwargs that will be passed to
+            `accessory.MetaModel.from_pretrained` for model instantiation.
+    """
+
+    def __init__(self,
+                 tokenizer_only: bool = False,
+                 meta_template: Optional[Dict] = None,
+                 additional_stop_symbols: Iterable[str] = (),
+                 **from_pretrained_kwargs):
+        if tokenizer_only:
+            self._load_tokenizer(from_pretrained_kwargs)
+        else:
+            self._load_model(from_pretrained_kwargs)
+
+        self.additional_stop_symbols = additional_stop_symbols
+        self.max_seq_len = from_pretrained_kwargs.get('max_seq_len', 4096)
+        self.template_parser = APITemplateParser(meta_template)
+        self.logger = get_logger()
+
+    def _load_model(self, from_pretrained_kwargs):
+        from accessory.model.meta import MetaModel
+        from accessory.util.misc import init_distributed_mode
+        if not dist.is_initialized():
+            init_distributed_mode()
+
+        model_parallel_group = dist.GroupMember.WORLD
+        from_pretrained_kwargs['mp_group'] = model_parallel_group
+
+        self.model = MetaModel.from_pretrained(**from_pretrained_kwargs)
+        self.tokenizer = self.model.tokenizer
+        self.logger = get_logger()
+
+    def _load_tokenizer(self, from_pretrained_kwargs):
+        from accessory.model.tokenizer import (
+            Tokenizer, probe_tokenizer_path_from_pretrained)
+        if 'tokenizer_path' in from_pretrained_kwargs:
+            tokenizer_path = from_pretrained_kwargs['tokenizer_path']
+        else:
+            pretrained_path = from_pretrained_kwargs['pretrained_path']
+            if isinstance(pretrained_path, str):
+                pretrained_path = [pretrained_path]
+            tokenizer_path = probe_tokenizer_path_from_pretrained(
+                pretrained_path[-1])
+
+        self.tokenizer = Tokenizer(tokenizer_path)
+
+    def generate(self, inputs: List[str], max_out_len: int) -> List[str]:
+        results = self.model.generate(
+            prompts=inputs,
+            max_gen_len=max_out_len,
+            temperature=0.,
+            additional_stop_symbols=self.additional_stop_symbols)
+        return results
+
+    def get_ppl(self,
+                inputs: List[str],
+                mask_length: Optional[List[int]] = None):
+        assert mask_length is None, 'mask_length is not supported'
+        evaluation_results = self.model.evaluate_examples(examples=inputs)
+        ppl = evaluation_results['ppl']
+        return np.array(ppl, dtype=np.float32)
+
+    def get_token_len(self, prompt: str) -> int:
+        return len(self.tokenizer.encode(prompt, True, True))