vllm_test.py

from vllm import LLM, SamplingParams
import torch 
from torch import distributed as dist 
import time
from tqdm import tqdm
import numpy as np

# # Create an LLM.
llm = LLM(
        model="/home/lclcq/share/llama-7b", 
        # model="/home/lclcq/share/models--bigscience--bloom-560m/snapshots/4f42c91d806a19ae1a46af6c3fb5f4990d884cd6", 
        #   model="facebook/opt-125m", 
        tensor_parallel_size=1,
            # max_num_seqs=1,
            # max_num_batched_tokens=2048,
            gpu_memory_utilization=0.95,
            trust_remote_code=True)

def run_to_completion(sampling_params, dummy_prompt_token_ids, profile: bool = False):
    if profile:
        torch.cuda.cudart().cudaProfilerStart()
        
    torch.cuda.synchronize()
    start_time = time.time()
    llm.generate(prompt_token_ids=dummy_prompt_token_ids,
                    sampling_params=sampling_params,
                    use_tqdm=False)
    torch.cuda.synchronize()
    end_time = time.time()
    latency = end_time - start_time
    if profile:
        torch.cuda.cudart().cudaProfilerStop()
    return latency

batch = 32
input_len = 1024
out_len =  128
sampling_params = SamplingParams(
    n=1,
    temperature=1.0,
    top_p=1.0,
    use_beam_search=False,
    ignore_eos=True,
    max_tokens=out_len,
)
dummy_prompt_token_ids = []
dummy_prompt_token_ids_s = torch.randint(1, 10240, (batch, input_len))
for t in range(batch):
    a = []
    for i in range(input_len):
        a.append(i)
    dummy_prompt_token_ids.append(a)        # print(dummy_prompt_token_ids)
# print("Warming up...")
for i in range(2):
    run_to_completion(sampling_params, dummy_prompt_token_ids, profile=False)

# Benchmark.
#latencies = []
#for _ in range(5): #tqdm(range(5), desc="Profiling iterations"):
#    latencies.append(run_to_completion(sampling_params, dummy_prompt_token_ids, profile=False))

#prefill_avg_latency = np.mean(latencies)
#print(f'prefill latency: {prefill_avg_latency*1000 / out_len} ms')
# print(f'Avg throughput: {out_len/avg_latency} tokens/seconds')

#out_len =  128
#sampling_params = SamplingParams(
 #   n=1,
 #   temperature=1.0,
 #   top_p=1.0,
#    use_beam_search=False,
#    ignore_eos=True,
#    max_tokens=out_len,
#)
dummy_prompt_token_ids = []
dummy_prompt_token_ids_s = torch.randint(1, 10240, (batch, input_len))
for t in range(batch):
    a = []
    for i in range(input_len):
        a.append(i)
    dummy_prompt_token_ids.append(a)

latencies = []
for _ in range(5): #tqdm(range(5), desc="Profiling iterations"):
    latencies.append(run_to_completion(sampling_params, dummy_prompt_token_ids, profile=False))

avg_latency = np.mean(latencies)
# print(f'Avg latency: {avg_latency*1000 / out_len} ms')
# print(f'Decode throughput: {batch*out_len/(avg_latency - prefill_avg_latency)} tokens/s')
print(f'total throughput: {batch*out_len/avg_latency} tokens/s')