[Feature] Update evaluate turbomind (#804)

* update * fix * fix * fix
2025-05-30 16:03:24 +08:00 · 2024-01-17 11:09:50 +08:00 · 2024-01-17 11:09:50 +08:00 · 0836aec67b
commit 0836aec67b
parent 814b3f73bd
5 changed files with 58 additions and 54 deletions
--- a/configs/eval_internlm_chat_turbomind.py
+++ b/configs/eval_internlm_chat_turbomind.py
@ -48,7 +48,7 @@ baichuan2_meta_template = dict(round=[
 internlm_chat_7b = dict(
    type=TurboMindModel,
    abbr='internlm-chat-7b-turbomind',
-    path='./turbomind',
+    path='internlm/internlm-chat-7b',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=32,
@ -60,7 +60,7 @@ internlm_chat_7b = dict(
 internlm_chat_7b_w4 = dict(
    type=TurboMindModel,
    abbr='internlm-chat-7b-w4-turbomind',
-    path='./turbomind',
+    path='internlm/internlm-chat-7b-w4',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=32,
@ -73,7 +73,7 @@ internlm_chat_7b_w4 = dict(
 internlm_chat_7b_w4kv8 = dict(
    type=TurboMindModel,
    abbr='internlm-chat-7b-w4kv8-turbomind',
-    path='./turbomind',
+    path='internlm/internlm-chat-7b-w4kv8',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=32,
@ -86,7 +86,7 @@ internlm_chat_7b_w4kv8 = dict(
 internlm_chat_20b = dict(
    type=TurboMindModel,
    abbr='internlm-chat-20b-turbomind',
-    path='./turbomind',
+    path='internlm/internlm-chat-20b',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=8,
@ -99,7 +99,7 @@ internlm_chat_20b = dict(
 internlm_chat_20b_w4 = dict(
    type=TurboMindModel,
    abbr='internlm-chat-20b-w4-turbomind',
-    path='./turbomind',
+    path='internlm/internlm-chat-20b-w4',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=16,
@ -112,7 +112,7 @@ internlm_chat_20b_w4 = dict(
 internlm_chat_20b_w4kv8 = dict(
    type=TurboMindModel,
    abbr='internlm-chat-20b-w4kv8-turbomind',
-    path='./turbomind',
+    path='internlm/internlm-chat-20b-w4kv8',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=16,
@ -125,7 +125,7 @@ internlm_chat_20b_w4kv8 = dict(
 llama2_chat_7b = dict(
    type=TurboMindModel,
    abbr='llama2-chat-7b-turbomind',
-    path='./turbomind',
+    path='meta-llama/Llama-2-7b-chat-hf',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=16,
@ -138,7 +138,7 @@ llama2_chat_7b = dict(
 llama2_chat_13b = dict(
    type=TurboMindModel,
    abbr='llama2-chat-13b-turbomind',
-    path='./turbomind',
+    path='meta-llama/Llama-2-13b-chat-hf',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=16,
@ -151,7 +151,7 @@ llama2_chat_13b = dict(
 llama2_chat_70b = dict(
    type=TurboMindModel,
    abbr='llama2-chat-70b-turbomind',
-    path='./turbomind',
+    path='meta-llama/Llama-2-70b-chat-hf',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=8,
@ -164,7 +164,7 @@ llama2_chat_70b = dict(
 qwen_chat_7b = dict(
    type=TurboMindModel,
    abbr='qwen-chat-7b-turbomind',
-    path='./turbomind',
+    path='Qwen/Qwen-7B-Chat',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=16,
@ -177,7 +177,7 @@ qwen_chat_7b = dict(
 qwen_chat_14b = dict(
    type=TurboMindModel,
    abbr='qwen-chat-14b-turbomind',
-    path='./turbomind',
+    path='Qwen/Qwen-14B-Chat',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=16,
@ -190,7 +190,7 @@ qwen_chat_14b = dict(
 baichuan2_chat_7b = dict(
    type=TurboMindModel,
    abbr='baichuan2-chat-7b-turbomind',
-    path='./turbomind',
+    path='baichuan-inc/Baichuan2-7B-Chat',
    max_out_len=100,
    max_seq_len=2048,
    batch_size=16,
--- a/configs/eval_internlm_turbomind.py
+++ b/configs/eval_internlm_turbomind.py
@ -19,7 +19,7 @@ datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
 internlm_7b = dict(
        type=TurboMindModel,
        abbr='internlm-7b-turbomind',
-        path="./turbomind",
+        path="internlm/internlm-7b",
        max_out_len=100,
        max_seq_len=2048,
        batch_size=32,
@ -31,7 +31,7 @@ internlm_7b = dict(
 internlm_7b_w4 = dict(
        type=TurboMindModel,
        abbr='internlm-7b-w4-turbomind',
-        path="./turbomind",
+        path="internlm/internlm-7b-w4",
        max_out_len=100,
        max_seq_len=2048,
        batch_size=32,
@ -43,7 +43,7 @@ internlm_7b_w4 = dict(
 internlm_7b_w4kv8 = dict(
        type=TurboMindModel,
        abbr='internlm-7b-w4kv8-turbomind',
-        path="./turbomind",
+        path="internlm/internlm-7b-w4kv8",
        max_out_len=100,
        max_seq_len=2048,
        batch_size=32,
@ -55,7 +55,7 @@ internlm_7b_w4kv8 = dict(
 internlm_20b = dict(
        type=TurboMindModel,
        abbr='internlm-20b-turbomind',
-        path="./turbomind",
+        path="internlm/internlm-20b",
        max_out_len=100,
        max_seq_len=2048,
        batch_size=8,
@ -67,7 +67,7 @@ internlm_20b = dict(
 internlm_20b_w4 = dict(
        type=TurboMindModel,
        abbr='internlm-20b-w4-turbomind',
-        path="./turbomind",
+        path="internlm/internlm-20b-w4",
        max_out_len=100,
        max_seq_len=2048,
        batch_size=16,
@ -80,7 +80,7 @@ internlm_20b_w4 = dict(
 internlm_20b_w4kv8 = dict(
        type=TurboMindModel,
        abbr='internlm-20b-w4kv8-turbomind',
-        path="./turbomind",
+        path="internlm/internlm-20b-w4kv8",
        max_out_len=100,
        max_seq_len=2048,
        batch_size=16,
--- a/docs/en/advanced_guides/evaluation_turbomind.md
+++ b/docs/en/advanced_guides/evaluation_turbomind.md
@ -20,27 +20,14 @@ pip install lmdeploy

 OpenCompass integrates both turbomind's python API and gRPC API for evaluation. And the former is highly recommended.

-We take the InternLM-20B as example. Please download it from huggingface and convert it to turbomind's model format:
+We take the InternLM-20B as example. Please download it from huggingface:

 ```shell
-# 1. Download InternLM model(or use the cached model's checkpoint)
+# 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-20b /path/to/internlm-20b
-
-# 2. Convert InternLM model to turbomind's format, and save it in the home folder of opencompass
-lmdeploy convert internlm /path/to/internlm-20b \
-    --dst-path {/home/folder/of/opencompass}/turbomind
-```
-
-**Note**:
-
-If evaluating the InternLM Chat model, make sure to pass `internlm-chat` as the model name instead of `internlm` when converting the model format. The specific command is:
-
-```shell
-lmdeploy convert internlm-chat /path/to/internlm-20b-chat \
-    --dst-path {/home/folder/of/opencompass}/turbomind
 ```

 ### Evaluation with Turbomind Python API (recommended)
@ -61,6 +48,22 @@ You are expected to get the evaluation results after the inference and evaluatio

 ### Evaluation with Turbomind gPRC API (optional)

+Convert model to TurboMind format using lmdeploy
+
+```shell
+lmdeploy convert internlm /path/to/internlm-20b \
+    --dst-path {/home/folder/of/opencompass}/turbomind
+```
+
+**Note**:
+
+If evaluating the InternLM Chat model, make sure to pass `internlm-chat` as the model name instead of `internlm` when converting the model format. The specific command is:
+
+```shell
+lmdeploy convert internlm-chat /path/to/internlm-20b-chat \
+    --dst-path {/home/folder/of/opencompass}/turbomind
+```
+
 In the home folder of OpenCompass, launch the Triton Inference Server:

 ```shell
--- a/docs/zh_cn/advanced_guides/evaluation_turbomind.md
+++ b/docs/zh_cn/advanced_guides/evaluation_turbomind.md
@ -20,25 +20,14 @@ pip install lmdeploy

 OpenCompass 支持分别通过 turbomind python API 和 gRPC API 评测数据集。我们强烈推荐使用前者进行评测。

-下文以 InternLM-20B 模型为例，介绍如何评测。首先，从 huggingface 上下载 InternLM 模型，并转换为 turbomind 模型格式：
+下文以 InternLM-20B 模型为例，介绍如何评测。首先，从 huggingface 上下载 InternLM 模型：

 ```shell
-# 1. Download InternLM model(or use the cached model's checkpoint)
+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-20b /path/to/internlm-20b
-
-# 2. Convert InternLM model to turbomind's format, and save it in the home folder of opencompass
-lmdeploy convert internlm /path/to/internlm-20b \
-    --dst-path {/home/folder/of/opencompass}/turbomind
-```
-
-注意：如果评测 InternLM Chat 模型，那么在转换模型格式的时候，模型名字要填写 `internlm-chat`。具体命令是：
-
-```shell
-lmdeploy convert internlm-chat /path/to/internlm-20b-chat \
-    --dst-path {/home/folder/of/opencompass}/turbomind
 ```

 ### 通过 TurboMind Python API 评测（推荐）
@ -57,6 +46,20 @@ python run.py configs/eval_internlm_turbomind.py -w outputs/turbomind/internlm-2

 ### 通过 TurboMind gPRC API 评测（可选）

+首先需要将模型转换为 turbomind 格式
+
+```shell script
+lmdeploy convert internlm /path/to/internlm-20b \
+    --dst-path {/home/folder/of/opencompass}/turbomind
+```
+
+注意：如果评测 InternLM Chat 模型，那么在转换模型格式的时候，模型名字要填写 `internlm-chat`。具体命令是：
+
+```shell
+lmdeploy convert internlm-chat /path/to/internlm-20b-chat \
+    --dst-path {/home/folder/of/opencompass}/turbomind
+```
+
 在 OpenCompass 的项目目录下，启动 triton inference server：

 ```shell
--- a/opencompass/models/turbomind.py
+++ b/opencompass/models/turbomind.py
@ -1,4 +1,3 @@
-import os.path as osp
 from concurrent.futures import ThreadPoolExecutor
 from typing import Dict, List, Optional, Union

@ -40,16 +39,14 @@ class TurboMindModel(BaseModel):
        max_seq_len: int = 2048,
        meta_template: Optional[Dict] = None,
    ):
-        from lmdeploy import turbomind as tm
-        from lmdeploy.tokenizer import Tokenizer
+        from lmdeploy.turbomind import TurboMind

        super().__init__(path=path,
                         max_seq_len=max_seq_len,
                         meta_template=meta_template)
        self.logger = get_logger()
-        tokenizer_model_path = osp.join(path, 'triton_models', 'tokenizer')
-        self.tokenizer = Tokenizer(tokenizer_model_path)
-        tm_model = tm.TurboMind(path)
+        tm_model = TurboMind.from_pretrained(path)
+        self.tokenizer = tm_model.tokenizer
        self.generators = [
            tm_model.create_instance() for i in range(concurrency)
        ]
@ -134,9 +131,10 @@ class TurboMindModel(BaseModel):
                                              sequence_start=True,
                                              sequence_end=True,
                                              top_k=1,
+                                              top_p=0.8,
                                              step=0,
                                              stream_output=False):
-            output_ids, _ = outputs[0]
-            response = self.tokenizer.decode(output_ids.tolist())
+            _, output_ids, _ = outputs
+            response = self.tokenizer.decode(output_ids)
            response = valid_str(response)
        return response