516 lines
15 KiB
Plaintext
516 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# SGLang Native APIs\n",
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"\n",
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"Apart from the OpenAI compatible APIs, the SGLang Runtime also provides its native server APIs. We introduce these following APIs:\n",
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"\n",
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"- `/generate` (text generation model)\n",
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"- `/get_model_info`\n",
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"- `/get_server_info`\n",
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"- `/health`\n",
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"- `/health_generate`\n",
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"- `/flush_cache`\n",
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"- `/update_weights`\n",
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"- `/encode`(embedding model)\n",
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"- `/classify`(reward model)\n",
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"- `/start_expert_distribution_record`\n",
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"- `/stop_expert_distribution_record`\n",
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"- `/dump_expert_distribution_record`\n",
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"\n",
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"We mainly use `requests` to test these APIs in the following examples. You can also use `curl`."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Launch A Server"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"from sglang.test.test_utils import is_in_ci\n",
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"\n",
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"if is_in_ci():\n",
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" from patch import launch_server_cmd\n",
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"else:\n",
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" from sglang.utils import launch_server_cmd\n",
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"\n",
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"from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
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"\n",
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"\n",
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"server_process, port = launch_server_cmd(\n",
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" \"python -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --host 0.0.0.0\"\n",
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")\n",
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"\n",
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"wait_for_server(f\"http://localhost:{port}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate (text generation model)\n",
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"Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](./sampling_params.md)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = f\"http://localhost:{port}/generate\"\n",
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"data = {\"text\": \"What is the capital of France?\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"print_highlight(response.json())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get Model Info\n",
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"\n",
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"Get the information of the model.\n",
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"\n",
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"- `model_path`: The path/name of the model.\n",
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"- `is_generation`: Whether the model is used as generation model or embedding model.\n",
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"- `tokenizer_path`: The path/name of the tokenizer."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = f\"http://localhost:{port}/get_model_info\"\n",
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"\n",
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"response = requests.get(url)\n",
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"response_json = response.json()\n",
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"print_highlight(response_json)\n",
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"assert response_json[\"model_path\"] == \"meta-llama/Llama-3.2-1B-Instruct\"\n",
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"assert response_json[\"is_generation\"] is True\n",
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"assert response_json[\"tokenizer_path\"] == \"meta-llama/Llama-3.2-1B-Instruct\"\n",
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"assert response_json.keys() == {\"model_path\", \"is_generation\", \"tokenizer_path\"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get Server Info\n",
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"Gets the server information including CLI arguments, token limits, and memory pool sizes.\n",
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"- Note: `get_server_info` merges the following deprecated endpoints:\n",
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" - `get_server_args`\n",
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" - `get_memory_pool_size` \n",
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" - `get_max_total_num_tokens`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# get_server_info\n",
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"\n",
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"url = f\"http://localhost:{port}/get_server_info\"\n",
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"\n",
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"response = requests.get(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Health Check\n",
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"- `/health`: Check the health of the server.\n",
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"- `/health_generate`: Check the health of the server by generating one token."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = f\"http://localhost:{port}/health_generate\"\n",
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"\n",
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"response = requests.get(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = f\"http://localhost:{port}/health\"\n",
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"\n",
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"response = requests.get(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Flush Cache\n",
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"\n",
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"Flush the radix cache. It will be automatically triggered when the model weights are updated by the `/update_weights` API."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# flush cache\n",
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"\n",
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"url = f\"http://localhost:{port}/flush_cache\"\n",
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"\n",
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"response = requests.post(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Update Weights From Disk\n",
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"\n",
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"Update model weights from disk without restarting the server. Only applicable for models with the same architecture and parameter size.\n",
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"\n",
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"SGLang support `update_weights_from_disk` API for continuous evaluation during training (save checkpoint to disk and update weights from disk).\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# successful update with same architecture and size\n",
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"\n",
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"url = f\"http://localhost:{port}/update_weights_from_disk\"\n",
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"data = {\"model_path\": \"meta-llama/Llama-3.2-1B\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"print_highlight(response.text)\n",
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"assert response.json()[\"success\"] is True\n",
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"assert response.json()[\"message\"] == \"Succeeded to update model weights.\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# failed update with different parameter size or wrong name\n",
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"\n",
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"url = f\"http://localhost:{port}/update_weights_from_disk\"\n",
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"data = {\"model_path\": \"meta-llama/Llama-3.2-1B-wrong\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"response_json = response.json()\n",
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"print_highlight(response_json)\n",
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"assert response_json[\"success\"] is False\n",
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"assert response_json[\"message\"] == (\n",
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" \"Failed to get weights iterator: \"\n",
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" \"meta-llama/Llama-3.2-1B-wrong\"\n",
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" \" (repository not found).\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Encode (embedding model)\n",
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"\n",
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"Encode text into embeddings. Note that this API is only available for [embedding models](openai_api_embeddings.html#openai-apis-embedding) and will raise an error for generation models.\n",
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"Therefore, we launch a new server to server an embedding model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"terminate_process(server_process)\n",
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"\n",
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"embedding_process, port = launch_server_cmd(\n",
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" \"\"\"\n",
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"python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n",
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" --host 0.0.0.0 --is-embedding\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"wait_for_server(f\"http://localhost:{port}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# successful encode for embedding model\n",
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"\n",
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"url = f\"http://localhost:{port}/encode\"\n",
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"data = {\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"text\": \"Once upon a time\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"response_json = response.json()\n",
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"print_highlight(f\"Text embedding (first 10): {response_json['embedding'][:10]}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"terminate_process(embedding_process)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Classify (reward model)\n",
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"\n",
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"SGLang Runtime also supports reward models. Here we use a reward model to classify the quality of pairwise generations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"terminate_process(embedding_process)\n",
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"\n",
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"# Note that SGLang now treats embedding models and reward models as the same type of models.\n",
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"# This will be updated in the future.\n",
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"\n",
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"reward_process, port = launch_server_cmd(\n",
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" \"\"\"\n",
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"python -m sglang.launch_server --model-path Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 --host 0.0.0.0 --is-embedding\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"wait_for_server(f\"http://localhost:{port}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from transformers import AutoTokenizer\n",
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"\n",
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"PROMPT = (\n",
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" \"What is the range of the numeric output of a sigmoid node in a neural network?\"\n",
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")\n",
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"\n",
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"RESPONSE1 = \"The output of a sigmoid node is bounded between -1 and 1.\"\n",
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"RESPONSE2 = \"The output of a sigmoid node is bounded between 0 and 1.\"\n",
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"\n",
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"CONVS = [\n",
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" [{\"role\": \"user\", \"content\": PROMPT}, {\"role\": \"assistant\", \"content\": RESPONSE1}],\n",
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" [{\"role\": \"user\", \"content\": PROMPT}, {\"role\": \"assistant\", \"content\": RESPONSE2}],\n",
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"]\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"Skywork/Skywork-Reward-Llama-3.1-8B-v0.2\")\n",
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"prompts = tokenizer.apply_chat_template(CONVS, tokenize=False)\n",
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"\n",
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"url = f\"http://localhost:{port}/classify\"\n",
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"data = {\"model\": \"Skywork/Skywork-Reward-Llama-3.1-8B-v0.2\", \"text\": prompts}\n",
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"\n",
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"responses = requests.post(url, json=data).json()\n",
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"for response in responses:\n",
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" print_highlight(f\"reward: {response['embedding'][0]}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"terminate_process(reward_process)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Capture expert selection distribution in MoE models\n",
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"\n",
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"SGLang Runtime supports recording the number of times an expert is selected in a MoE model run for each expert in the model. This is useful when analyzing the throughput of the model and plan for optimization."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"expert_record_server_process, port = launch_server_cmd(\n",
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" \"python -m sglang.launch_server --model-path Qwen/Qwen1.5-MoE-A2.7B --host 0.0.0.0\"\n",
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")\n",
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"\n",
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"wait_for_server(f\"http://localhost:{port}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"response = requests.post(f\"http://localhost:{port}/start_expert_distribution_record\")\n",
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"print_highlight(response)\n",
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"\n",
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"url = f\"http://localhost:{port}/generate\"\n",
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"data = {\"text\": \"What is the capital of France?\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"print_highlight(response.json())\n",
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"\n",
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"response = requests.post(f\"http://localhost:{port}/stop_expert_distribution_record\")\n",
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"print_highlight(response)\n",
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"\n",
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"response = requests.post(f\"http://localhost:{port}/dump_expert_distribution_record\")\n",
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"print_highlight(response)\n",
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"\n",
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"import glob\n",
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"\n",
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"output_file = glob.glob(\"expert_distribution_*.csv\")[0]\n",
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"with open(output_file, \"r\") as f:\n",
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" print_highlight(\"Content of dumped record:\")\n",
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" for line in f:\n",
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" print_highlight(line.strip())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"terminate_process(expert_record_server_process)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Skip Tokenizer and Detokenizer\n",
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"\n",
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"SGLang Runtime also supports skip tokenizer and detokenizer. This is useful in cases like integrating with RLHF workflow."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"tokenizer_free_server_process, port = launch_server_cmd(\n",
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" \"\"\"\n",
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"python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --skip-tokenizer-init\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"wait_for_server(f\"http://localhost:{port}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from transformers import AutoTokenizer\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"meta-llama/Llama-3.2-1B-Instruct\")\n",
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"\n",
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"input_text = \"What is the capital of France?\"\n",
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"\n",
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"input_tokens = tokenizer.encode(input_text)\n",
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"print_highlight(f\"Input Text: {input_text}\")\n",
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"print_highlight(f\"Tokenized Input: {input_tokens}\")\n",
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"\n",
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"response = requests.post(\n",
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" f\"http://localhost:{port}/generate\",\n",
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" json={\n",
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" \"input_ids\": input_tokens,\n",
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" \"sampling_params\": {\n",
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" \"temperature\": 0,\n",
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" \"max_new_tokens\": 256,\n",
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" \"stop_token_ids\": [tokenizer.eos_token_id],\n",
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" },\n",
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" \"stream\": False,\n",
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" },\n",
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")\n",
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"output = response.json()\n",
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"output_tokens = output[\"output_ids\"]\n",
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"\n",
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"output_text = tokenizer.decode(output_tokens, skip_special_tokens=False)\n",
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"print_highlight(f\"Tokenized Output: {output_tokens}\")\n",
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"print_highlight(f\"Decoded Output: {output_text}\")\n",
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"print_highlight(f\"Output Text: {output['meta_info']['finish_reason']}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"terminate_process(tokenizer_free_server_process)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|