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benchmark_inference_time.py
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benchmark_inference_time.py
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import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from torch.profiler import ProfilerActivity, profile, record_function
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
from torch import nn
import torch
torch.set_float32_matmul_precision('high')
import json
from argparse import ArgumentParser
def sample(outputs):
next_token_logits = outputs.logits[:, -1, :]
probs = nn.functional.softmax(next_token_logits, dim=-1)
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
return next_tokens
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--device",default='cuda')
parser.add_argument("--model",required=True)
parser.add_argument("--use_cache",action='store_true')
parser.add_argument("--max_new_tokens",type=int,default=16_000)
parser.add_argument("--output_path")
args = parser.parse_args()
prompt = 'hello' ## dummpy input
config = AutoConfig.from_pretrained(args.model)
config.max_position_embeddings = args.max_new_tokens+10
model = AutoModelForCausalLM.from_config(config)
model.eval()
model = model.to(args.device)
model = torch.compile(model)
model_size = sum(p.numel() for p in model.parameters())
tokenizer = AutoTokenizer.from_pretrained(args.model)
tokenized_prompt = tokenizer(prompt, return_tensors="pt")
tokenized_prompt = tokenized_prompt['input_ids'].to(args.device)
model_input = {
"input_ids":tokenized_prompt,
"use_cache":args.use_cache,
}
cache_name = "state" if args.model.startswith("RWKV") else "past_key_values"
model_input[cache_name]=None
os.makedirs(os.path.dirname(args.output_path),exist_ok=True)
writer = open(args.output_path,'w')
for tok_idx in range(args.max_new_tokens):
with torch.no_grad():
if args.use_cache and model_input[cache_name] is not None:model_input["input_ids"] = tokenized_prompt[:,-1:].to(args.device)
else:model_input["input_ids"] = tokenized_prompt.to(args.device)
with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], profile_memory=True, record_shapes=False) as prof:
with record_function("model_inference"):
output = model.forward(**model_input)
model_input[cache_name]=getattr(output,cache_name)
next_tokens = sample(output)
tokenized_prompt = torch.cat([tokenized_prompt.cpu(), next_tokens[:, None].cpu()], dim=-1)
full_profile = next(event for event in prof.key_averages() if event.key == 'model_inference')
writer.write(json.dumps({
"model_name": args.model,
"model_size": model_size,
"token_id": tok_idx,
"strategy": args.device,
"cpu_time": full_profile.cpu_time,
"cuda_time": full_profile.cuda_time,
"cpu_memory_usage": full_profile.cpu_memory_usage,
"cuda_memory_usage": full_profile.cuda_memory_usage,
"self_cpu_memory_usage": full_profile.self_cpu_memory_usage,
"self_cuda_memory_usage": full_profile.self_cuda_memory_usage,
"max_memory_allocated":torch.cuda.max_memory_allocated(),
})+'\n'
)
torch.cuda.empty_cache()
writer.close()
"""
python benchmark_inference_time.py --model RWKV/rwkv-4-3b-pile --use_cache --output_path data/inference_time/rwkv-3b.jsonl
python benchmark_inference_time.py --model RWKV/rwkv-4-7b-pile --use_cache --output_path data/inference_time/rwkv-7b.jsonl
python benchmark_inference_time.py --model RWKV/rwkv-4-14b-pile --use_cache --output_path data/inference_time/rwkv-14b.jsonl
python benchmark_inference_time.py --model facebook/opt-2.7b --use_cache --output_path data/inference_time/opt-2.7b.jsonl
python benchmark_inference_time.py --model facebook/opt-6.7b --use_cache --output_path data/inference_time/opt-6.7b.jsonl
python benchmark_inference_time.py --model EleutherAI/pythia-2.8b --use_cache --output_path data/inference_time/pythia-2.8b.jsonl
python benchmark_inference_time.py --model EleutherAI/pythia-6.9b --use_cache --output_path data/inference_time/pythia-6.9b.jsonl
python benchmark_inference_time.py --model EleutherAI/gpt-neo-2.7B --use_cache --output_path data/inference_time/gpt-neo-2.7B.jsonl
############# Poltting Code ##############
import numpy as np
import json
def get_jsonl(f): return [json.loads(x) for x in open(f).readlines()]
import matplotlib.pyplot as plt
fig, (ax1,ax2,ax3) = plt.subplots(1, 3,figsize=(18, 4))
for model_name in [
"rwkv-3b",
# "rwkv-7b",
# "rwkv-14b",
"opt-2.7b",
"gpt-neo-2.7B",
"pythia-2.8b"
]:
data = get_jsonl(f"data/inference_time/{model_name}.jsonl")
cuda_time = [x['cuda_time'] for x in data]
cumulative_time = np.cumsum(cuda_time)/(1000*1000)
memory_usage = [x['max_memory_allocated']/(2**10)/(2**10)/(2**10) for x in data]
ax1.plot([x/1000 for x in cuda_time][100:],label=model_name)
ax2.plot(cumulative_time,label=model_name)
ax3.plot(memory_usage,label=model_name)
ax1.set_xlabel("# Tokens")
ax1.set_ylabel("Time (ms) to generated the #-th token")
ax1.grid()
ax1.legend()
ax1.set_title("Single Token Generation Latency")
ax2.set_xlabel("# Tokens")
ax2.set_ylabel("Cumulative time (s) to generated the #-th token")
ax2.grid()
ax2.legend()
ax2.set_title("Cumulative Generation Latency")
ax3.set_xlabel("# Tokens")
ax3.set_ylabel("Memory usage (GB)")
ax3.grid()
ax3.legend()
ax3.set_title("Memory usage in Generation")
"""