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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a6221e83-9d8f-4716-aeda-b40847931f56",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%bash\n",
"git clone https://github.com/philschmid/llmperf.git\n",
"cd llmperf\n",
"pip install -e . -q"
]
},
{
"cell_type": "markdown",
"id": "602a8c54-b434-4d8e-bc72-824c642fbdb5",
"metadata": {},
"source": [
"# Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "73b1aa22-a1e3-4a1e-9dd2-042ab0f5939a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import sys\n",
"import json\n",
"from getpass import getpass\n",
"import subprocess\n",
"import os\n",
"from datetime import datetime\n",
"import pandas as pd\n",
"import numpy as np\n",
"from huggingface_hub import notebook_login, create_inference_endpoint, list_inference_endpoints, whoami, get_inference_endpoint, get_token\n",
"from pathlib import Path\n",
"from tqdm.notebook import tqdm"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "772897cb-c2b1-4f9a-8143-ad64aed40b5b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f951213-46a1-4db9-be2c-51c2291ecdc2",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"proj_dir = Path.cwd()\n",
"print(proj_dir)\n",
"LLMPerf_path = proj_dir/'llmperf'"
]
},
{
"cell_type": "markdown",
"id": "267ea96b-b756-4e16-b41a-fee2119edf76",
"metadata": {
"tags": []
},
"source": [
"# Config"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d3341f2-217e-42a5-89fb-1653fd418c48",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Endpoint\n",
"ENDPOINT_NAME=\"mixtral-exp\"\n",
"NAMESPACE = 'HF-test-lab'\n",
"MODEL = 'TheBloke/mixtral-8x7b-v0.1-GPTQ'\n",
"INSTANCE_TYPE = 'nvidia-l4_AWQ'\n",
"\n",
"# Simulation\n",
"RESULTS_DIR = proj_dir/'tgi_benchmark_results'/INSTANCE_TYPE\n",
"tgi_bss = [1]\n",
"INPUT_TOKENS = 800\n",
"OUTPUT_TOKENS = 1600"
]
},
{
"cell_type": "markdown",
"id": "f6bbb792-b168-42b8-bff1-c6ea9f6daf79",
"metadata": {},
"source": [
"# Endpoint setup"
]
},
{
"cell_type": "markdown",
"id": "8610e033-8586-495a-943e-539b7c8304d0",
"metadata": {},
"source": [
"Be sure to configure your endpoint how you desire, I made some guesses on what you might want in the `env`. You can see some settings in the [pricing section](https://huggingface.co/docs/inference-endpoints/en/pricing#gpu-instances) of the docs. I would also recommend manually deploying once and using `get_inference_endpoint().__dict__` to double check your settings just to double check."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae923833-8ca1-4d16-85be-a78ffb386c43",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def create_endpoint(MAX_BATCH_SIZE, name, instance_type):\n",
" try:\n",
" endpoint = get_inference_endpoint(name=name, namespace=NAMESPACE)\n",
" endpoint.wait()\n",
" return endpoint\n",
" except:\n",
" pass\n",
" try:\n",
" endpoint = create_inference_endpoint(\n",
" name,\n",
" repository=MODEL,\n",
" task=\"text-generation\",\n",
" framework=\"pytorch\",\n",
" region=\"us-east-1\",\n",
" vendor=\"aws\",\n",
" accelerator=\"gpu\",\n",
" instance_size=\"x4\",\n",
" instance_type='nvidia-l4',\n",
" min_replica=0,\n",
" max_replica=1,\n",
" namespace=NAMESPACE,\n",
" custom_image={\n",
" \"health_route\": \"/health\",\n",
" \"env\": {\n",
" \"MAX_INPUT_LENGTH\": f\"{INPUT_TOKENS+50}\",\n",
" \"MAX_TOTAL_TOKENS\": f\"{INPUT_TOKENS + OUTPUT_TOKENS}\",\n",
" \"MAX_BATCH_SIZE\": f\"{MAX_BATCH_SIZE}\",\n",
" \"HF_TOKEN\": get_token(),\n",
" \"QUANTIZE\":\"awq\",\n",
" \"MODEL_ID\": \"/repository\",\n",
" },\n",
" \"url\": \"ghcr.io/huggingface/text-generation-inference:2.2.0\",\n",
" },\n",
" type=\"protected\",\n",
" )\n",
" endpoint.wait()\n",
" except Exception as create_error:\n",
" print(f\"Failed to create inference endpoint: {str(create_error)}\")\n",
" return None\n",
"\n",
" return endpoint"
]
},
{
"cell_type": "markdown",
"id": "5e55710d-fa77-41b7-ae9c-a4826140f6b6",
"metadata": {},
"source": [
"Make sure to check the command to make sure it matches what you expect. Also check the summary stats json to see what actually happened."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "491b82b3-4db8-4409-85ce-7c003a6c2f6f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def run_command(batch_size, endpoint, tgi_bs):\n",
" prefix = f'tgibs_{tgi_bs}__bs_{batch_size}'\n",
" vu = batch_size\n",
"\n",
" # Set environment variables\n",
" env = os.environ.copy()\n",
" env['HUGGINGFACE_API_BASE'] = endpoint.url\n",
" env['HUGGINGFACE_API_TOKEN'] = get_token()\n",
" env['MODEL_ID'] = MODEL\n",
" # Convert pathlib.Path to string and append to PYTHONPATH\n",
" env['PYTHONPATH'] = str(LLMPerf_path) + (os.pathsep + env.get('PYTHONPATH', ''))\n",
"\n",
" # Define the benchmark script path\n",
" benchmark_script = str(LLMPerf_path / \"token_benchmark_ray.py\")\n",
"\n",
" if not os.path.isfile(benchmark_script):\n",
" print(f\"LLMPerf script not found at {benchmark_script}, please ensure the path is correct.\")\n",
" return \"Script not found\", False\n",
"\n",
" # Calculate the max number of completed requests\n",
" max_requests = vu * 8\n",
"\n",
" # Generate the results directory name\n",
" date_str = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')\n",
" results_dir = RESULTS_DIR / f\"{date_str}_{prefix}\"\n",
"\n",
" # Construct the command to run the benchmark script\n",
" command = [\n",
" \"python\", benchmark_script,\n",
" \"--model\", f\"{MODEL}\",\n",
" \"--mean-input-tokens\", f\"{INPUT_TOKENS}\",\n",
" \"--stddev-input-tokens\", \"10\",\n",
" \"--mean-output-tokens\", f\"{OUTPUT_TOKENS}\",\n",
" \"--stddev-output-tokens\", \"5\",\n",
" \"--max-num-completed-requests\", str(min(max_requests, 1500)),\n",
" \"--timeout\", \"7200\",\n",
" \"--num-concurrent-requests\", str(vu),\n",
" \"--results-dir\", str(results_dir),\n",
" \"--llm-api\", \"huggingface\",\n",
" \"--additional-sampling-params\", '{}'\n",
" ]\n",
"\n",
" # Run the command with the modified environment\n",
" try:\n",
" result = subprocess.check_output(command, stderr=subprocess.STDOUT, env=env).decode('utf-8')\n",
" return result, True\n",
" except subprocess.CalledProcessError as e:\n",
" print(f\"Error with batch size {batch_size}: {e.output.decode()}\")\n",
" return e.output.decode(), False\n",
"\n",
"def find_max_working_batch_size(endpoint, tgi_bs):\n",
" batch_sizes = [8, 16, 32]\n",
" max_working = None\n",
" for size in tqdm(batch_sizes):\n",
" tqdm.write(f\"Running: TGIBS {tgi_bs} Client Requests {size}\")\n",
" output, success = run_command(size, endpoint, tgi_bs)\n",
" if success:\n",
" max_working = size\n",
" else:\n",
" break\n",
" if max_working is None:\n",
" return \"No working batch size found in the provided list\"\n",
" return max_working"
]
},
{
"cell_type": "markdown",
"id": "d32b71a7-371f-4f80-a9f2-2cfc65e04afd",
"metadata": {},
"source": [
"Here Im creating the endpoint and then running the simulation."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "70a11c08-0bea-43d6-85eb-ef014473c9f1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"for tgi_bs in tqdm(tgi_bss):\n",
" name = f\"{ENDPOINT_NAME}--tgibs-{tgi_bs}\"\n",
" try:\n",
" endpoint = get_inference_endpoint(name, namespace=NAMESPACE)\n",
" except:\n",
" endpoint = create_endpoint(MAX_BATCH_SIZE=tgi_bs, name=name, instance_type=INSTANCE_TYPE) \n",
" pass\n",
" endpoint.wait()\n",
" tqdm.write(f\"Endpoint Created: {name}\")\n",
" max_batch_size = find_max_working_batch_size(endpoint=endpoint, tgi_bs=tgi_bs)\n",
" endpoint.delete()\n",
" tqdm.write(f\"Endpoint Deleted: {name}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "70a5f441-3da7-4888-9943-112750681067",
"metadata": {},
"outputs": [],
"source": []
}
],
"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",
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}
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"nbformat": 4,
"nbformat_minor": 5
}
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