File size: 7,241 Bytes
a402b9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | import concurrent.futures
import os
import random
import time
from concurrent.futures import ProcessPoolExecutor
from statistics import mean
import requests
from tqdm import tqdm
from transformers import AutoTokenizer
from sglang.lang.backend.runtime_endpoint import RuntimeEndpoint
###############################################################################
# CONFIG
###############################################################################
ENDPOINT_URL = "http://127.0.0.1:30000"
TOKENIZER_DIR = "/models/meta-llama/Llama-3.2-3B"
# Benchmark configurations
NUM_REQUESTS = 10 # Total number of requests (each with BATCH_SIZE prompts)
NUM_TOKENS = 32000 # Tokens per prompt
BATCH_SIZE = 8 # Number of prompts per request
GEN_TOKENS = 0 # Tokens to generate per prompt
###############################################################################
# REQUEST GENERATION (in parallel)
###############################################################################
def generate_random_prompt(index, tokenizer_dir, num_tokens):
"""Generate a single random prompt with specified token count."""
tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir)
vocab_size = tokenizer.vocab_size
def generate_random_text(num_toks):
random_token_ids = [random.randint(0, vocab_size - 1) for _ in range(num_toks)]
return tokenizer.decode(random_token_ids, clean_up_tokenization_spaces=True)
random_text = generate_random_text(num_tokens)
return f"Prompt {index}: {random_text}"
def prepare_all_prompts(num_requests, batch_size, num_tokens, tokenizer_dir):
"""Generate prompts for all requests in parallel."""
total_prompts = num_requests * batch_size
all_prompts = [None] * total_prompts
max_workers = min(os.cpu_count() or 1, total_prompts)
with ProcessPoolExecutor(max_workers=max_workers) as executor:
futures = [
executor.submit(generate_random_prompt, i, tokenizer_dir, num_tokens)
for i in range(total_prompts)
]
for future in tqdm(
concurrent.futures.as_completed(futures),
total=total_prompts,
desc="Generating prompts",
):
index = futures.index(future)
all_prompts[index] = future.result()
batched_prompts = [
all_prompts[i * batch_size : (i + 1) * batch_size] for i in range(num_requests)
]
print(
f"Generated {total_prompts} prompts with {num_tokens} tokens each, grouped into {num_requests} requests of {batch_size} prompts.\n"
)
return batched_prompts
###############################################################################
# HTTP CALLS
###############################################################################
def send_batch_request(endpoint, prompts, gen_tokens, request_id):
"""Send a batch of prompts to the /generate endpoint synchronously."""
sampling_params = {
"max_new_tokens": gen_tokens,
"temperature": 0.7,
"stop": "\n",
}
data = {"text": prompts, "sampling_params": sampling_params}
start_time = time.perf_counter()
try:
response = requests.post(
endpoint.base_url + "/generate", json=data, timeout=3600
)
if response.status_code != 200:
error = response.json()
raise RuntimeError(f"Request {request_id} failed: {error}")
result = response.json()
elapsed_time = (time.perf_counter() - start_time) * 1000 # Convert to ms
avg_per_prompt = elapsed_time / len(prompts) if prompts else 0
return request_id, elapsed_time, avg_per_prompt, True, len(prompts)
except Exception as e:
print(f"[Request] Error for request {request_id}: {e}")
return request_id, 0, 0, False, len(prompts)
def run_benchmark(endpoint, batched_prompts, batch_size, gen_tokens):
"""Run the benchmark sequentially."""
results = []
num_requests = len(batched_prompts)
# Record start time for total latency
benchmark_start_time = time.perf_counter()
for i, batch_prompts in enumerate(batched_prompts):
request_id = i + 1
assert (
len(batch_prompts) == batch_size
), f"Request {request_id} should have {batch_size} prompts, got {len(batch_prompts)}"
print(
f"[Request] Sending request {request_id}/{num_requests} with {len(batch_prompts)} prompts at {int(time.time()*1000)}"
)
result = send_batch_request(endpoint, batch_prompts, gen_tokens, request_id)
results.append(result)
# Calculate total latency
total_latency = (time.perf_counter() - benchmark_start_time) * 1000 # Convert to ms
return results, total_latency
###############################################################################
# RESULTS
###############################################################################
def process_results(results, total_latency, num_requests):
"""Process and display benchmark results."""
total_time = 0
successful_requests = 0
failed_requests = 0
request_latencies = []
per_prompt_latencies = []
total_prompts = 0
for request_id, elapsed_time, avg_per_prompt, success, batch_size in results:
if success:
successful_requests += 1
total_prompts += batch_size
request_latencies.append(elapsed_time)
per_prompt_latencies.append(avg_per_prompt)
total_time += elapsed_time / 1000 # Convert to seconds
else:
failed_requests += 1
avg_request_latency = mean(request_latencies) if request_latencies else 0
avg_per_prompt_latency = mean(per_prompt_latencies) if per_prompt_latencies else 0
throughput = total_prompts / total_time if total_time > 0 else 0
print("\nBenchmark Summary:")
print(f" Total requests sent: {len(results)}")
print(f" Total prompts sent: {total_prompts}")
print(f" Successful requests: {successful_requests}")
print(f" Failed requests: {failed_requests}")
print(f" Total latency (all requests): {total_latency:.2f} ms")
print(f" Avg per request latency: {avg_request_latency:.2f} ms")
print(f" Avg per prompt latency: {avg_per_prompt_latency:.2f} ms")
print(f" Throughput: {throughput:.2f} prompts/second\n")
###############################################################################
# MAIN
###############################################################################
def main():
# Initialize endpoint
endpoint = RuntimeEndpoint(ENDPOINT_URL)
# Generate prompts
batched_prompts = prepare_all_prompts(
NUM_REQUESTS, BATCH_SIZE, NUM_TOKENS, TOKENIZER_DIR
)
# Flush cache before benchmark
# endpoint.flush_cache()
# Run benchmark
print(
f"Starting benchmark: NUM_TOKENS={NUM_TOKENS}, BATCH_SIZE={BATCH_SIZE}, NUM_REQUESTS={NUM_REQUESTS}\n"
)
results, total_latency = run_benchmark(
endpoint, batched_prompts, BATCH_SIZE, GEN_TOKENS
)
# Process and display results
process_results(results, total_latency, NUM_REQUESTS)
if __name__ == "__main__":
random.seed(0)
main()
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