112 lines
4.5 KiB
Python
112 lines
4.5 KiB
Python
import os
|
|
import time
|
|
from random import randint, seed
|
|
from nanovllm import LLM, SamplingParams
|
|
|
|
|
|
def bench_decode(llm, num_seqs, input_len, output_len):
|
|
"""Benchmark decode performance"""
|
|
seed(0)
|
|
prompt_token_ids = [[randint(0, 10000) for _ in range(input_len)] for _ in range(num_seqs)]
|
|
sampling_params = SamplingParams(temperature=0.6, ignore_eos=True, max_tokens=output_len)
|
|
|
|
t = time.time()
|
|
llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
|
|
t = time.time() - t
|
|
|
|
# Calculate metrics
|
|
prefill_tokens = num_seqs * input_len
|
|
decode_tokens = num_seqs * output_len
|
|
|
|
# Approximate: assume prefill takes ~input_len/prefill_speed, rest is decode
|
|
# For more accurate measurement, we'd need internal timing
|
|
decode_throughput = decode_tokens / t # This includes prefill time, so it's a lower bound
|
|
|
|
print(f"[Decode] Input: {num_seqs}x{input_len}tok, Output: {decode_tokens}tok, Time: {t:.2f}s")
|
|
print(f" Throughput: {decode_throughput:.2f} tok/s (includes prefill overhead)")
|
|
|
|
|
|
def bench_prefill(llm, num_seqs, input_len):
|
|
"""Benchmark prefill performance"""
|
|
seed(0)
|
|
# Fixed length input, minimal output to focus on prefill
|
|
prompt_token_ids = [[randint(0, 10000) for _ in range(input_len)] for _ in range(num_seqs)]
|
|
sampling_params = SamplingParams(temperature=0.6, ignore_eos=True, max_tokens=1)
|
|
|
|
t = time.time()
|
|
llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
|
|
t = time.time() - t
|
|
total_input_tokens = num_seqs * input_len
|
|
throughput = total_input_tokens / t
|
|
print(f"[Prefill] Input: {total_input_tokens}tok ({num_seqs}x{input_len}), Time: {t:.2f}s, Throughput: {throughput:.2f}tok/s")
|
|
|
|
|
|
def main():
|
|
import argparse
|
|
from nanovllm.config import SparsePolicyType
|
|
|
|
parser = argparse.ArgumentParser(description="Benchmark CPU offload performance")
|
|
parser.add_argument("--enable-quest", action="store_true", help="Enable Quest sparse attention for decode")
|
|
parser.add_argument("--topk", type=int, default=16, help="Top-K blocks for Quest (default: 16)")
|
|
parser.add_argument("--threshold", type=int, default=4, help="Apply sparse only when blocks > threshold (default: 4)")
|
|
parser.add_argument("--input-len", type=int, default=None, help="Input length in tokens")
|
|
parser.add_argument("--output-len", type=int, default=64, help="Output length for decode benchmark (default: 64)")
|
|
parser.add_argument("--num-gpu-blocks", type=int, default=6, help="Number of GPU blocks (default: 6)")
|
|
parser.add_argument("--max-len", type=int, default=32*1024, help="Max model length (default: 32K)")
|
|
parser.add_argument("--bench-decode", action="store_true", help="Run decode benchmark (default: prefill only)")
|
|
parser.add_argument("--bench-all", action="store_true", help="Run both prefill and decode benchmarks")
|
|
args = parser.parse_args()
|
|
|
|
path = os.path.expanduser("~/models/Qwen3-4B-Instruct-2507/")
|
|
max_len = args.max_len
|
|
|
|
# Setup policy configuration
|
|
if args.enable_quest:
|
|
sparse_policy = SparsePolicyType.QUEST
|
|
print(f"\n[Quest Sparse Attention] topk={args.topk}, threshold={args.threshold}")
|
|
else:
|
|
sparse_policy = SparsePolicyType.FULL
|
|
print("\n[Full Attention] baseline (no sparse)")
|
|
|
|
print(f"[Config] max_len={max_len}, num_gpu_blocks={args.num_gpu_blocks}")
|
|
|
|
llm = LLM(
|
|
path,
|
|
enforce_eager=False,
|
|
max_model_len=max_len,
|
|
max_num_batched_tokens=max_len,
|
|
enable_cpu_offload=True,
|
|
num_gpu_blocks=args.num_gpu_blocks,
|
|
sparse_policy=sparse_policy,
|
|
sparse_topk_blocks=args.topk,
|
|
sparse_threshold_blocks=args.threshold,
|
|
)
|
|
|
|
# Warmup
|
|
print("\nWarming up...")
|
|
llm.generate(["Benchmark warmup: "], SamplingParams(max_tokens=10))
|
|
|
|
# Default input lengths
|
|
prefill_input_len = args.input_len if args.input_len else max_len - 1
|
|
decode_input_len = args.input_len if args.input_len else max_len - args.output_len
|
|
|
|
# Determine which benchmarks to run
|
|
run_prefill = not args.bench_decode or args.bench_all
|
|
run_decode = args.bench_decode or args.bench_all
|
|
|
|
if run_prefill:
|
|
print("\n" + "=" * 60)
|
|
print("Prefill Benchmark (CPU Offload)")
|
|
print("=" * 60)
|
|
bench_prefill(llm, num_seqs=1, input_len=prefill_input_len)
|
|
|
|
if run_decode:
|
|
print("\n" + "=" * 60)
|
|
print("Decode Benchmark (CPU Offload)")
|
|
print("=" * 60)
|
|
bench_decode(llm, num_seqs=1, input_len=decode_input_len, output_len=args.output_len)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|