diff --git a/docs/quantization/wint2.md b/docs/quantization/wint2.md index 82dd60609..f82b7da73 100644 --- a/docs/quantization/wint2.md +++ b/docs/quantization/wint2.md @@ -1,21 +1,101 @@ # 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 \ - --tensor-parallel-size 1 \ - --max-model-len 32768 \ - --max-num-seqs 32 + --model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle \ + --port 8180 \ + --metrics-port 8181 \ + --engine-worker-queue-port 8182 \ + --cache-queue-port 8183 \ + --tensor-parallel-size 1 \ + --max-model-len 32768 \ + --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| diff --git a/docs/quantization/wint2.png b/docs/quantization/wint2.png new file mode 100644 index 000000000..a117ea8af Binary files /dev/null and b/docs/quantization/wint2.png differ diff --git a/docs/zh/quantization/wint2.md b/docs/zh/quantization/wint2.md index 91c1441bf..cc224aabb 100644 --- a/docs/zh/quantization/wint2.md +++ b/docs/zh/quantization/wint2.md @@ -1,21 +1,96 @@ # 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 \ - --tensor-parallel-size 1 \ - --max-model-len 32768 \ - --max-num-seqs 32 + --model baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle \ + --port 8180 \ + --metrics-port 8181 \ + --engine-worker-queue-port 8182 \ + --cache-queue-port 8183 \ + --tensor-parallel-size 1 \ + --max-model-len 32768 \ + --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| diff --git a/docs/zh/quantization/wint2.png b/docs/zh/quantization/wint2.png new file mode 100644 index 000000000..a117ea8af Binary files /dev/null and b/docs/zh/quantization/wint2.png differ