[Feature] support pooling model runner

[lizexu123]

This PR supports actual inference for pooling models

Usage

online serving

• launch the serving
• Embeddings can be obtained via the EmbeddingCompletionRequest API or the EmbeddingChatRequest API

# Start the service demo
model_path=Qwen3-Embedding-0.6B
# 以下三个参数是必须的 
export ENABLE_V1_KVCACHE_SCHEDULER=1 #启用 KV Cache 块调度器的 V1 版本
export FD_DISABLE_CHUNKED_PREFILL=1 #强制禁用默认的分块预填充（Chunked Prefill）功能
export FD_USE_GET_SAVE_OUTPUT_V1=1  # 是否使用新版本的 get_output 和 save_output 方法

python -m fastdeploy.entrypoints.openai.api_server --model ${model_path} \
    --max-num-seqs 256 --max-model-len 32768 \
    --port 13331 --engine-worker-queue-port 7132 \
    --metrics-port 7431 --tensor-parallel-size 1 \
    --gpu-memory-utilization 0.9 \
    --load-choices "default_v1" \
    --runner pooling \
    --graph-optimization-config '{"use_cudagraph":false}'

Request Method (curl example)

A. EmbeddingCompletionRequest 示例（标准文本输入）

curl -X POST 'YOUR_SERVICE_URL/v1/embeddings' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "text-embedding-chat-model",
    "input": [
      "This is a sentence for pooling embedding.",
      "Another input text."
    ],
    "user": "test_client"
  }'

B. EmbeddingChatRequest 示例（消息序列输入）

curl -X POST 'YOUR_SERVICE_URL/v1/embeddings' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "text-embedding-chat-model",
    "messages": [
      {"role": "user", "content": "Generate embedding for user query."}
    ]
  }'

[paddle-bot]

Thanks for your contribution!