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[Doc] update wint2 doc (#3819)

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# WINT2 Quantization
Weights are compressed offline using the CCQ (Convolutional Coding Quantization) method. The actual stored numerical type of weights is INT8, with 4 weights packed into each INT8 value, equivalent to 2 bits per weight. Activations are not quantized. During inference, weights are dequantized and decoded in real-time to BF16 numerical type, and calculations are performed using BF16 numerical type.
Weights are compressed offline using the [CCQ (Convolutional Coding Quantization)](https://arxiv.org/pdf/2507.07145) method. The actual stored numerical type of weights is INT8, with 4 weights packed into each INT8 value, equivalent to 2 bits per weight. Activations are not quantized. During inference, weights are dequantized and decoded in real-time to BF16 numerical type, and calculations are performed using BF16 numerical type.
- **Supported Hardware**: GPU
- **Supported Architecture**: MoE architecture
This method relies on the convolution algorithm to use overlapping bits to map 2-bit values ​​to a larger numerical representation space, so that the model weight quantization retains more information of the original data while compressing the true value to an extremely low 2-bit size. The general principle can be seen in the figure below:
[卷积编码量化示意图](./wint2.png)
CCQ WINT2 is generally used in resource-constrained and low-threshold scenarios. Taking ERNIE-4.5-300B-A47B as an example, weights are compressed to 89GB, supporting single-card deployment on 141GB H20.
## Run WINT2 Inference Service
## Executing WINT2 Offline Inference
- When executing TP2/TP4 models, you can change the `model_name_or_path` and `tensor_parallel_size` parameters.
```
model_name_or_path = "baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle"
prompts = ["解析三首李白的诗"]
from fastdeploy import LLM, SamplingParams
sampling_params = SamplingParams(temperature=0.7, top_p=0, max_tokens=128)
llm = LLM(model=model_name_or_path, tensor_parallel_size=1, use_cudagraph=True,)
outputs = llm.generate(prompts, sampling_params)
print(outputs)
```
## Run WINT2 Inference Service
- When executing TP2/TP4 models, you can change the `--model` and `tensor-parallel-size` parameters.
```
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle \
--port 8180 --engine-worker-queue-port 8181 \
--cache-queue-port 8182 --metrics-port 8182 \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--cache-queue-port 8183 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 32
--use-cudagraph \
--enable-prefix-caching \
--enable-chunked-prefill \
--max-num-seqs 256
```
## Request the Service
After starting the service, the following output indicates successful initialization:
```shell
api_server.py[line:91] Launching metrics service at http://0.0.0.0:8181/metrics
api_server.py[line:94] Launching chat completion service at http://0.0.0.0:8180/v1/chat/completions
api_server.py[line:97] Launching completion service at http://0.0.0.0:8180/v1/completions
INFO: Started server process [13909]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8180 (Press CTRL+C to quit)
```
### Health Check
Verify service status (HTTP 200 indicates success):
```shell
curl -i http://0.0.0.0:8180/health
```
### cURL Request
Send requests to the service with the following command:
```shell
curl -X POST "http://0.0.0.0:1822/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": "Write me a poem about large language model."}
],
"stream": true
}'
```
### Python Client (OpenAI-compatible API)
FastDeploy's API is OpenAI-compatible. You can also use Python for requests:
```python
import openai
host = "0.0.0.0"
port = "8180"
client = openai.Client(base_url=f"http://{host}:{port}/v1", api_key="null")
response = client.chat.completions.create(
model="null",
messages=[
{"role": "system", "content": "I'm a helpful AI assistant."},
{"role": "user", "content": "Write me a poem about large language model."},
],
stream=True,
)
for chunk in response:
if chunk.choices[0].delta:
print(chunk.choices[0].delta.content, end='')
print('\n')
```
By specifying `--model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle`, the offline quantized WINT2 model can be automatically downloaded from AIStudio. In the config.json file of this model, there will be WINT2 quantization-related configuration information, so there's no need to set `--quantization` when starting the inference service.
@@ -54,9 +134,7 @@ On the ERNIE-4.5-300B-A47B model, comparison of WINT2 vs WINT4 performance:
| Test Set | Dataset Size | WINT4 | WINT2 |
|---------|---------|---------|---------|
| IFEval |500|88.17 | 85.40 |
|BBH|6511|94.43|92.02|
|DROP|9536|91.17|89.97|
|GSM8K|1319|96.21|95.98|
|CMath|600|96.50|96.00|
|CMMLU|11477|89.92|86.22|
| IFEval |500|88.17 | 85.95 |
|BBH|6511|94.43|90.06|
|DROP|9536|91.17|89.32|
|CMMLU|11477|89.92|86.55|

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# WINT2量化
权重经过CCQ（Convolutional Coding Quantization）方法离线压缩。权重实际存储的数值类型是INT8，每个INT8数值中打包了4个权重，等价于每个权重2bits. 激活不做量化，计算时将权重实时地反量化、解码为BF16数值类型，并用BF16数值类型计算。
权重经过 [CCQ（卷积编码量化）](https://arxiv.org/pdf/2507.07145) 方法进行离线压缩。权重的实际存储数值类型为INT8，每个INT8数值中打包了4个权重，等价于每个权重2bits。激活不做量化。在推理过程中，权重会被实时反量化并解码为BF16数值类型，并使用BF16数值类型进行计算。
- **支持硬件**：GPU
- **支持结构**：MoE结构
该方法依托卷积算法利用重叠的Bit位将2Bit的数值映射到更大的数值表示空间，使得模型权重量化后既保留原始数据更多的信息，同时将真实数值压缩到极低的2Bit大小，大致原理可参考下图：
[卷积编码量化示意图](./wint2.png)
CCQ WINT2一般用于资源受限的低门槛场景，以ERNIE-4.5-300B-A47B为例，将权重压缩到89GB，可支持141GB H20单卡部署。
## 启动WINT2推理服务
## 执行WINT2离线推理
- 执行TP2/TP4模型时，可更换`model_name_or_path`以及`tensor_parallel_size`参数。
```
model_name_or_path = "baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle"
prompts = ["解析三首李白的诗"]
from fastdeploy import LLM, SamplingParams
sampling_params = SamplingParams(temperature=0.7, top_p=0, max_tokens=128)
llm = LLM(model=model_name_or_path, tensor_parallel_size=1, use_cudagraph=True,)
outputs = llm.generate(prompts, sampling_params)
print(outputs)
```
## 启动WINT2推理服务
- 执行TP2/TP4模型时，可更换`--model`以及`tensor-parallel-size`参数；
```
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle \
--port 8180 --engine-worker-queue-port 8181 \
--cache-queue-port 8182 --metrics-port 8182 \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--cache-queue-port 8183 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 32
--use-cudagraph \
--enable-prefix-caching \
--enable-chunked-prefill \
--max-num-seqs 256
```
## 用户发起服务请求
执行启动服务指令后，当终端打印如下信息，说明服务已经启动成功。
```
api_server.py[line:91] Launching metrics service at http://0.0.0.0:8181/metrics
api_server.py[line:94] Launching chat completion service at http://0.0.0.0:8180/v1/chat/completions
api_server.py[line:97] Launching completion service at http://0.0.0.0:8180/v1/completions
INFO: Started server process [13909]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8180 (Press CTRL+C to quit)
```
FastDeploy提供服务探活接口，用以判断服务的启动状态，执行如下命令返回 ```HTTP/1.1 200 OK``` 即表示服务启动成功。
```shell
curl -i http://0.0.0.0:8180/health
```
通过如下命令发起服务请求
```shell
curl -X POST "http://0.0.0.0:8180/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": "把李白的静夜思改写为现代诗"}
]
}'
```
FastDeploy服务接口兼容OpenAI协议，可以通过如下Python代码发起服务请求。
```python
import openai
host = "0.0.0.0"
port = "8180"
client = openai.Client(base_url=f"http://{host}:{port}/v1", api_key="null")
response = client.chat.completions.create(
model="null",
messages=[
{"role": "system", "content": "I'm a helpful AI assistant."},
{"role": "user", "content": "把李白的静夜思改写为现代诗"},
],
stream=True,
)
for chunk in response:
if chunk.choices[0].delta:
print(chunk.choices[0].delta.content, end='')
print('\n')
```
通过指定 `--model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle` 可自动从AIStudio下载已离线量化好的WINT2模型，在该模型的config.json文件中，会有WINT2量化相关的配置信息，不用再在启动推理服务时设置 `--quantization`.
@@ -54,8 +129,7 @@ python -m fastdeploy.entrypoints.openai.api_server \
| 测试集 |数据集大小| WINT4 | WINT2 |
|---------|---------|---------|---------|
| IFEval |500|88.17 | 85.40 |
|BBH|6511|94.43|92.02|
|DROP|9536|91.17|89.97|
## WINT2推理性能
| IFEval |500|88.17 | 85.95 |
|BBH|6511|94.43|90.06|
|DROP|9536|91.17|89.32|
|CMMLU|11477|89.92|86.55|

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