send_request.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick Start: Launch A Server and Send Requests\n",
    "\n",
    "This notebook provides a quick-start guide for using SGLang after installation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Launch a server\n",
    "\n",
    "This code block is equivalent to executing \n",
    "\n",
    "```bash\n",
    "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \\\n",
    "--port 30000 --host 0.0.0.0\n",
    "```\n",
    "\n",
    "in your command line and wait for the server to be ready."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-01T02:46:13.611212Z",
     "iopub.status.busy": "2024-11-01T02:46:13.611093Z",
     "iopub.status.idle": "2024-11-01T02:46:42.810261Z",
     "shell.execute_reply": "2024-11-01T02:46:42.809147Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:18] server_args=ServerArgs(model_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Meta-Llama-3.1-8B-Instruct', chat_template=None, is_embedding=False, host='0.0.0.0', port=30000, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=706578968, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
      "  warnings.warn(\n",
      "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:24 TP0] Init torch distributed begin.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:24 TP0] Load weight begin. avail mem=47.27 GB\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:25 TP0] lm_eval is not installed, GPTQ may not be usable\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO 10-31 19:46:26 weight_utils.py:243] Using model weights format ['*.safetensors']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading safetensors checkpoint shards:   0% Completed | 0/4 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading safetensors checkpoint shards:  25% Completed | 1/4 [00:00<00:01,  2.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading safetensors checkpoint shards:  50% Completed | 2/4 [00:00<00:00,  2.39it/s]\n",
      "\r",
      "Loading safetensors checkpoint shards:  75% Completed | 3/4 [00:00<00:00,  3.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:01<00:00,  2.95it/s]\n",
      "\r",
      "Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:01<00:00,  2.90it/s]\n",
      "\n",
      "[2024-10-31 19:46:28 TP0] Load weight end. type=LlamaForCausalLM, dtype=torch.bfloat16, avail mem=32.22 GB\n",
      "[2024-10-31 19:46:28 TP0] Memory pool end. avail mem=4.60 GB\n",
      "[2024-10-31 19:46:28 TP0] Capture cuda graph begin. This can take up to several minutes.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:36 TP0] max_total_num_tokens=217512, max_prefill_tokens=16384, max_running_requests=2049, context_len=131072\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:36] INFO:     Started server process [1548791]\n",
      "[2024-10-31 19:46:36] INFO:     Waiting for application startup.\n",
      "[2024-10-31 19:46:36] INFO:     Application startup complete.\n",
      "[2024-10-31 19:46:36] INFO:     Uvicorn running on http://0.0.0.0:30000 (Press CTRL+C to quit)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:37] INFO:     127.0.0.1:46022 - \"GET /v1/models HTTP/1.1\" 200 OK\n",
      "[2024-10-31 19:46:37] INFO:     127.0.0.1:46028 - \"GET /get_model_info HTTP/1.1\" 200 OK\n",
      "[2024-10-31 19:46:37 TP0] Prefill batch. #new-seq: 1, #new-token: 7, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:38] INFO:     127.0.0.1:46042 - \"POST /generate HTTP/1.1\" 200 OK\n",
      "[2024-10-31 19:46:38] The server is fired up and ready to roll!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong style='color: #00008B;'><br><br>                    NOTE: Typically, the server runs in a separate terminal.<br>                    In this notebook, we run the server and notebook code together, so their outputs are combined.<br>                    To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.<br>                    </strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sglang.utils import (\n",
    "    execute_shell_command,\n",
    "    wait_for_server,\n",
    "    terminate_process,\n",
    "    print_highlight,\n",
    ")\n",
    "\n",
    "server_process = execute_shell_command(\n",
    "\"\"\"\n",
    "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \\\n",
    "--port 30000 --host 0.0.0.0\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "wait_for_server(\"http://localhost:30000\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Send a Request\n",
    "\n",
    "Once the server is running, you can send test requests using curl. The server implements the [OpenAI-compatible API](https://platform.openai.com/docs/api-reference/chat)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-01T02:46:42.813656Z",
     "iopub.status.busy": "2024-11-01T02:46:42.813354Z",
     "iopub.status.idle": "2024-11-01T02:46:51.436613Z",
     "shell.execute_reply": "2024-11-01T02:46:51.435965Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:42 TP0] Prefill batch. #new-seq: 1, #new-token: 46, #cached-token: 1, cache hit rate: 1.85%, token usage: 0.00, #running-req: 0, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:43 TP0] Decode batch. #running-req: 1, #token: 80, token usage: 0.00, gen throughput (token/s): 5.40, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:44 TP0] Decode batch. #running-req: 1, #token: 120, token usage: 0.00, gen throughput (token/s): 42.48, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:45 TP0] Decode batch. #running-req: 1, #token: 160, token usage: 0.00, gen throughput (token/s): 42.37, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:46 TP0] Decode batch. #running-req: 1, #token: 200, token usage: 0.00, gen throughput (token/s): 42.33, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:47 TP0] Decode batch. #running-req: 1, #token: 240, token usage: 0.00, gen throughput (token/s): 42.34, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:48 TP0] Decode batch. #running-req: 1, #token: 280, token usage: 0.00, gen throughput (token/s): 42.28, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:49 TP0] Decode batch. #running-req: 1, #token: 320, token usage: 0.00, gen throughput (token/s): 42.28, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:50 TP0] Decode batch. #running-req: 1, #token: 360, token usage: 0.00, gen throughput (token/s): 42.24, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:51] INFO:     127.0.0.1:46046 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n",
      "{\"id\":\"f9761ee1b1444bd7a640286884a90842\",\"object\":\"chat.completion\",\"created\":1730429211,\"model\":\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"LLM stands for Large Language Model. It's a type of artificial intelligence (AI) designed to process and comprehend human language in a way that's similar to how humans do.\\n\\nLarge Language Models are trained on massive amounts of text data, which allows them to learn patterns and relationships in language. This training enables them to generate text, answer questions, summarize content, and even engage in conversation.\\n\\nSome key characteristics of LLMs include:\\n\\n1. **Language understanding**: LLMs can comprehend the meaning of text, including nuances like idioms, sarcasm, and figurative language.\\n2. **Contextual awareness**: LLMs can understand the context in which a piece of text is written, including the topic, tone, and intent.\\n3. **Generative capabilities**: LLMs can generate text, including entire articles, conversations, or even creative writing like stories or poetry.\\n4. **Continuous learning**: LLMs can learn from new data and update their understanding of language over time.\\n\\nLLMs are used in a wide range of applications, including:\\n\\n1. **Virtual assistants**: LLMs power virtual assistants like Siri, Alexa, and Google Assistant.\\n2. **Chatbots**: LLMs are used to create chatbots that can engage with customers and provide support.\\n3. **Language translation**: LLMs can translate text from one language to another with high accuracy.\\n4. **Content generation**: LLMs can generate content, such as articles, social media posts, and product descriptions.\\n5. **Research and analysis**: LLMs can help researchers analyze and understand large amounts of text data.\\n\\nIn the context of our conversation, I'm a Large Language Model designed to provide helpful and informative responses to your questions!\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":128009}],\"usage\":{\"prompt_tokens\":47,\"total_tokens\":400,\"completion_tokens\":353,\"prompt_tokens_details\":null}}"
     ]
    }
   ],
   "source": [
    "!curl http://localhost:30000/v1/chat/completions \\\n",
    "  -H \"Content-Type: application/json\" \\\n",
    "  -H \"Authorization: Bearer None\" \\\n",
    "  -d '{\"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\", \"messages\": [{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}, {\"role\": \"user\", \"content\": \"What is a LLM?\"}]}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using OpenAI Python Client\n",
    "\n",
    "You can also use the OpenAI Python API library to send requests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-01T02:46:51.439372Z",
     "iopub.status.busy": "2024-11-01T02:46:51.439178Z",
     "iopub.status.idle": "2024-11-01T02:46:52.895776Z",
     "shell.execute_reply": "2024-11-01T02:46:52.895318Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:51 TP0] Prefill batch. #new-seq: 1, #new-token: 20, #cached-token: 29, cache hit rate: 29.13%, token usage: 0.00, #running-req: 0, #queue-req: 0\n",
      "[2024-10-31 19:46:51 TP0] Decode batch. #running-req: 1, #token: 50, token usage: 0.00, gen throughput (token/s): 27.57, #queue-req: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-10-31 19:46:52 TP0] Decode batch. #running-req: 1, #token: 90, token usage: 0.00, gen throughput (token/s): 42.69, #queue-req: 0\n",
      "[2024-10-31 19:46:52] INFO:     127.0.0.1:40952 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong style='color: #00008B;'>ChatCompletion(id='c563abb8fe74496f83203fe21ec4ff61', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', function_call=None, tool_calls=None), matched_stop=128009)], created=1730429212, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, prompt_tokens_details=None))</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import openai\n",
    "\n",
    "client = openai.Client(base_url=\"http://127.0.0.1:30000/v1\", api_key=\"None\")\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
    "    messages=[\n",
    "        {\"role\": \"system\", \"content\": \"You are a helpful AI assistant\"},\n",
    "        {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n",
    "    ],\n",
    "    temperature=0,\n",
    "    max_tokens=64,\n",
    ")\n",
    "print_highlight(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-01T02:46:52.898411Z",
     "iopub.status.busy": "2024-11-01T02:46:52.898149Z",
     "iopub.status.idle": "2024-11-01T02:46:54.398382Z",
     "shell.execute_reply": "2024-11-01T02:46:54.397564Z"
    }
   },
   "outputs": [],
   "source": [
    "terminate_process(server_process)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}