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101 lines
4.0 KiB
Markdown
101 lines
4.0 KiB
Markdown
[简体中文](../zh/features/data_parallel_service.md)
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# Data Parallelism (DP)
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Data Parallelism (DP) is a distributed inference approach in which incoming requests are distributed across multiple **identical model replicas**, with each replica independently handling inference for its assigned requests.
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In practice, especially when deploying **Mixture-of-Experts (MoE)** models, **Data Parallelism (DP)** is often combined with **Expert Parallelism (EP)**.
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Each DP service independently performs the Attention computation, while all DP services collaboratively participate in the MoE computation, thereby improving overall inference performance.
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FastDeploy supports DP-based inference and provides the `multi_api_server` interface to launch multiple inference services simultaneously.
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---
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## Launching FastDeploy Services
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Taking the **ERNIE-4.5-300B** model as an example, the following command launches a service with **DP=8, TP=1, EP=8**:
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```shell
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export FD_ENABLE_MULTI_API_SERVER=1
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python -m fastdeploy.entrypoints.openai.multi_api_server \
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--num-servers 8 \
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--ports "1811,1822,1833,1844,1855,1866,1877,1888" \
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--metrics-ports "3101,3201,3301,3401,3501,3601,3701,3801" \
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--args --model ERNIE-4_5-300B-A47B-FP8-Paddle \
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--engine-worker-queue-port "25611,25621,25631,25641,25651,25661,25671,25681" \
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--tensor-parallel-size 1 \
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--data-parallel-size 8 \
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--max-model-len 12288 \
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--max-num-seqs 64 \
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--num-gpu-blocks-override 256 \
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--enable-expert-parallel
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```
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### Parameter Description
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* `num-servers`: Number of DP service instances to launch.
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* `ports`: API server ports for the DP service instances. The number of ports must match `num-servers`.
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* `metrics-ports`: Metrics server ports for the DP service instances. The number must match `num-servers`. If not specified, available ports will be allocated automatically.
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* `args`: Arguments passed to each DP service instance. Refer to the [parameter documentation](../parameters.md) for details.
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---
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## Request Scheduling
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After launching multiple DP services using the data parallel strategy, incoming user requests must be distributed across the services by a scheduler to achieve load balancing.
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### Web Server–Based Scheduling
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Once the IP addresses and ports of the DP service instances are known, common web servers (such as **Nginx**) can be used to implement request scheduling. Details are omitted here.
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### FastDeploy Router
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FastDeploy provides a Python-based [Router](https://github.com/PaddlePaddle/FastDeploy/tree/develop/fastdeploy/router) to handle request reception and scheduling.
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A high-performance Router implementation is currently under development.
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The usage and request scheduling workflow is as follows:
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* Start the Router
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* Start FastDeploy service instances (either single-DP or multi-DP), which register themselves with the Router
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* User requests are sent to the Router
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* The Router selects an appropriate service instance based on the global load status
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* The Router forwards the request to the selected instance for inference
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* The Router receives the generated result from the instance and returns it to the user
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---
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## Quick Start Example
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### Launching the Router
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Start the Router service. Logs are written to `log_router/router.log`.
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```shell
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export FD_LOG_DIR="log_router"
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python -m fastdeploy.router.launch \
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--host 0.0.0.0 \
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--port 30000
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```
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### Launching DP Services with Router
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Again using the **ERNIE-4.5-300B** model as an example, the following command launches **DP=8, TP=1, EP=8** services and registers them with the Router via the `--router` argument:
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```shell
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export FD_ENABLE_MULTI_API_SERVER=1
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python -m fastdeploy.entrypoints.openai.multi_api_server \
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--num-servers 8 \
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--ports "1811,1822,1833,1844,1855,1866,1877,1888" \
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--metrics-ports "3101,3201,3301,3401,3501,3601,3701,3801" \
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--args --model ERNIE-4_5-300B-A47B-FP8-Paddle \
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--tensor-parallel-size 1 \
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--data-parallel-size 8 \
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--max-model-len 12288 \
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--max-num-seqs 64 \
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--num-gpu-blocks-override 256 \
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--enable-expert-parallel \
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--router "0.0.0.0:30000"
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```
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