Add throughput benchmark example
Browse files- hf_benchmark_example.py +212 -0
hf_benchmark_example.py
ADDED
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"""
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cmd example
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You need a file called "sample.txt" (default path) with text to take tokens for prompts or supply --text_file "path/to/text.txt" as an argument to a text file.
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You can use our attached "sample.txt" file with one of Deci's blogs as a prompt.
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# Run this and record tokens per second (652 tokens per second on A10 for DeciLM-6b)
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python time_hf.py --model Deci/DeciLM-6b-instruct
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# Run this and record tokens per second (136 tokens per second on A10 for meta-llama/Llama-2-7b-hf), CUDA OOM above batch size 8
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python time_hf.py --model meta-llama/Llama-2-7b-hf --batch_size 8
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"""
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import json
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import datasets
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import torch
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import transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
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from argparse import ArgumentParser
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def parse_args():
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parser = ArgumentParser()
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parser.add_argument(
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"--model",
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required=True,
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help="Model to evaluate, provide a repo name in Hugging Face hub or a local path",
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)
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parser.add_argument(
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"--temperature",
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default=0.2,
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type=float
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)
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parser.add_argument(
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"--top_p",
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default=0.95,
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type=float
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)
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parser.add_argument(
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"--top_k",
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default=0,
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type=float
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)
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parser.add_argument(
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"--revision",
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default=None,
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help="Model revision to use",
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)
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parser.add_argument(
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"--iterations",
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type=int,
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default=6,
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help="Model revision to use",
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)
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parser.add_argument(
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"--batch_size",
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type=int,
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default=64,
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help="Batch size for evaluation on each worker, can be larger for HumanEval",
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)
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parser.add_argument(
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"--prompt_length",
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type=int,
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default=512,
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)
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parser.add_argument(
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"--max_new_tokens",
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type=int,
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default=512,
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help="Maximum length of generated sequence (prompt+generation)",
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)
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parser.add_argument(
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"--precision",
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type=str,
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default="bf16",
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help="Model precision, from: fp32, fp16 or bf16",
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)
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parser.add_argument(
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"--text_file",
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type=str,
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default="sample.txt",
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help="text file that will be used to generate tokens for prompts",
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)
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parser.add_argument(
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"--load_in_8bit",
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action="store_true",
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help="Load model in 8bit",
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)
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parser.add_argument(
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"--load_in_4bit",
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action="store_true",
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help="Load model in 4bit",
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)
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return parser.parse_args()
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def main():
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args = parse_args()
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transformers.logging.set_verbosity_error()
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datasets.logging.set_verbosity_error()
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results = {}
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dict_precisions = {
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"fp32": torch.float32,
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"fp16": torch.float16,
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"bf16": torch.bfloat16,
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}
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if args.precision not in dict_precisions:
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raise ValueError(
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f"Non valid precision {args.precision}, choose from: fp16, fp32, bf16"
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)
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if args.load_in_8bit:
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print("Loading model in 8bit")
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# the model needs to fit in one GPU
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model = AutoModelForCausalLM.from_pretrained(
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args.model,
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revision=args.revision,
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load_in_8bit=args.load_in_8bit,
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trust_remote_code=args.trust_remote_code,
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use_auth_token=args.use_auth_token,
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device_map={"": 'cuda'},
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)
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elif args.load_in_4bit:
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print("Loading model in 4bit")
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# the model needs to fit in one GPU
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model = AutoModelForCausalLM.from_pretrained(
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args.model,
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revision=args.revision,
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load_in_4bit=args.load_in_4bit,
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trust_remote_code=args.trust_remote_code,
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use_auth_token=args.use_auth_token,
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device_map={"": 'cuda'},
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)
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else:
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print(f"Loading model in {args.precision}")
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model = AutoModelForCausalLM.from_pretrained(
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args.model,
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torch_dtype=torch.bfloat16,
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trust_remote_code=True,
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use_auth_token=True
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)
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tokenizer = AutoTokenizer.from_pretrained(
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args.model,
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revision=args.revision,
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trust_remote_code=True,
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use_auth_token=True,
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)
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starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
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model.cuda()
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model.eval()
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with open(args.text_file, "r") as f:
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prompt = f.read()
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prompt = torch.tensor(tokenizer.encode(prompt))[:args.prompt_length].cuda()
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results = {'prefill': [], 'gen': [], 'max_new_tokens': args.max_new_tokens, 'prompt_length': args.prompt_length, 'model': args.model, 'batch_size': args.batch_size}
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inputs = prompt.repeat(args.batch_size, 1)
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#warmup
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print('start warmup')
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for _ in range(10):
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with torch.no_grad():
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_ = model.generate(
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input_ids=inputs,
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max_new_tokens=1,
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do_sample=False,
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)
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print('finish warmup')
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torch.cuda.synchronize()
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for prefill_iter in range(args.iterations):
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starter.record()
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with torch.no_grad():
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_ = model.generate(
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input_ids=inputs,
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max_new_tokens=1,
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do_sample=False,
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)
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ender.record()
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torch.cuda.synchronize()
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t = starter.elapsed_time(ender) / 1000
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results['prefill'].append(t)
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print(f'{args.batch_size} prefill iter {prefill_iter} took: {t}')
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for gen_iter in range(args.iterations):
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starter.record()
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with torch.no_grad():
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_ = model.generate(
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input_ids=inputs,
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max_new_tokens=args.max_new_tokens,
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do_sample=False,
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)
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ender.record()
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torch.cuda.synchronize()
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t = starter.elapsed_time(ender) / 1000
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results['gen'].append(t)
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print(f'{args.batch_size} total generation iter {gen_iter} took: {t}')
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print(f'{args.batch_size * args.max_new_tokens / t} tokens per seconds')
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model_str = args.model.split('/')[-1]
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with open(f'timing_{model_str}_{args.batch_size}.json', 'w') as f:
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json.dump(results, f)
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if __name__ == "__main__":
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main()
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