Integrate lmdeploy pipeline api

configs/models/hf_internlm/lmdeploy_internlm2_chat_7b.py

@@ -1,15 +1,24 @@
 from opencompass.models import TurboMindModelwithChatTemplate
 
+
 models = [
     dict(
         type=TurboMindModelwithChatTemplate,
-        abbr='internlm2-chat-7b-turbomind',
+        abbr=f'internlm2-chat-7b-lmdeploy',
         path='internlm/internlm2-chat-7b',
-        engine_config=dict(session_len=8192, max_batch_size=16, tp=1),
-        gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096),
+        # inference backend of LMDeploy. It can be either 'turbomind' or 'pytorch'.
+        # If the model is not supported by 'turbomind', it will fallback to
+        # 'pytorch'
+        backend='turbomind',
+        # For the detailed engine config and generation config, please refer to
+        # https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/messages.py
+        engine_config=dict(tp=1),
+        gen_config=dict(do_sample=False),
         max_seq_len=8192,
         max_out_len=4096,
-        batch_size=16,
+        # the max number of prompts that LMDeploy receives
+        # in `generate` function
+        batch_size=32,
         run_cfg=dict(num_gpus=1),
     )
 ]

docs/en/advanced_guides/evaluation_lmdeploy.md

@@ -0,0 +1,88 @@
+# 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. It has a remarkable inference performance. We now illustrate how to evaluate a model with the support of LMDeploy in OpenCompass.
+
+## Setup
+
+### Install OpenCompass
+
+Please follow the [instructions](https://opencompass.readthedocs.io/en/latest/get_started/installation.html) to install the OpenCompass and prepare the evaluation datasets.
+
+### Install LMDeploy
+
+Install lmdeploy via pip (python 3.8+)
+
+```shell
+pip install lmdeploy
+```
+
+The default prebuilt package is compiled on CUDA 12. However, if CUDA 11+ is required, you can install lmdeploy by:
+
+```shell
+export LMDEPLOY_VERSION=0.6.0
+export PYTHON_VERSION=310
+pip install https://github.com/InternLM/lmdeploy/releases/download/v${LMDEPLOY_VERSION}/lmdeploy-${LMDEPLOY_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux2014_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
+```
+
+## Evaluation
+
+When evaluating a model, it is necessary to prepare an evaluation configuration that specifies information such as the evaluation dataset, the model, and inference parameters.
+
+Taking [internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) as an example, the evaluation config is as follows:
+
+```python
+# configure the dataset
+from mmengine.config import read_base
+
+
+with read_base():
+    # choose a list of datasets
+    from .datasets.mmlu.mmlu_gen_a484b3 import mmlu_datasets
+    from .datasets.ceval.ceval_gen_5f30c7 import ceval_datasets
+    from .datasets.triviaqa.triviaqa_gen_2121ce import triviaqa_datasets
+    from opencompass.configs.datasets.gsm8k.gsm8k_0shot_v2_gen_a58960 import \
+        gsm8k_datasets
+    # and output the results in a chosen format
+    from .summarizers.medium import summarizer
+
+datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
+
+# configure lmdeploy
+from opencompass.models import TurboMindModelwithChatTemplate
+
+
+
+# configure the model
+models = [
+    dict(
+        type=TurboMindModelwithChatTemplate,
+        abbr=f'internlm2-chat-7b-lmdeploy',
+        # model path, which can be the address of a model repository on the Hugging Face Hub or a local path
+        path='internlm/internlm2-chat-7b',
+        # inference backend of LMDeploy. It can be either 'turbomind' or 'pytorch'.
+        # If the model is not supported by 'turbomind', it will fallback to
+        # 'pytorch'
+        backend='turbomind',
+        # For the detailed engine config and generation config, please refer to
+        # https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/messages.py
+        engine_config=dict(tp=1),
+        gen_config=dict(do_sample=False),
+        # the max size of the context window
+        max_seq_len=7168,
+        # the max number of new tokens
+        max_out_len=1024,
+        # the max number of prompts that LMDeploy receives
+        # in `generate` function
+        batch_size=5000,
+        run_cfg=dict(num_gpus=1),
+    )
+]
+```
+
+Place the aforementioned configuration in a file, such as "configs/eval_internlm2_lmdeploy.py". Then, in the home folder of OpenCompass, start evaluation by the following command:
+
+```shell
+python run.py configs/eval_internlm2_lmdeploy.py -w outputs
+```
+
+You are expected to get the evaluation results after the inference and evaluation.