| |
|
|
| import argparse |
| import time |
| import json |
|
|
| import torch |
| import torch.nn.functional as F |
|
|
| from einops import rearrange |
|
|
| from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
| from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel |
|
|
|
|
| parser = argparse.ArgumentParser(description="Generation benchmarking") |
| parser.add_argument("--model-name", type=str, default="state-spaces/mamba-130m") |
| parser.add_argument("--prompt", type=str, default=None) |
| parser.add_argument("--promptlen", type=int, default=100) |
| parser.add_argument("--genlen", type=int, default=100) |
| parser.add_argument("--temperature", type=float, default=1.0) |
| parser.add_argument("--topk", type=int, default=1) |
| parser.add_argument("--topp", type=float, default=1.0) |
| parser.add_argument("--batch", type=int, default=1) |
| args = parser.parse_args() |
|
|
| repeats = 3 |
| device = "cuda" |
| dtype = torch.float16 |
|
|
| print(f"Loading model {args.model_name}") |
| is_mamba = args.model_name.startswith("state-spaces/mamba-") or "mamba" in args.model_name |
|
|
| if is_mamba: |
| tokenizer = AutoTokenizer.from_pretrained("/home/zhulianghui/VisionProjects/mamba/ckpts/gpt-neox-20b-tokenizer") |
| model = MambaLMHeadModel.from_pretrained(args.model_name, device=device, dtype=dtype) |
| else: |
| tokenizer = AutoTokenizer.from_pretrained(args.model_name) |
| model = AutoModelForCausalLM.from_pretrained(args.model_name, device_map={"": device}, torch_dtype=dtype) |
| model.eval() |
| print(f"Number of parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}") |
|
|
| torch.random.manual_seed(0) |
| if args.prompt is None: |
| input_ids = torch.randint(1, 1000, (args.batch, args.promptlen), dtype=torch.long, device="cuda") |
| attn_mask = torch.ones_like(input_ids, dtype=torch.long, device="cuda") |
| else: |
| tokens = tokenizer(args.prompt, return_tensors="pt") |
| input_ids = tokens.input_ids.to(device=device) |
| attn_mask = tokens.attention_mask.to(device=device) |
| max_length = input_ids.shape[1] + args.genlen |
|
|
| if is_mamba: |
| fn = lambda: model.generate( |
| input_ids=input_ids, |
| max_length=max_length, |
| cg=True, |
| return_dict_in_generate=True, |
| output_scores=True, |
| enable_timing=False, |
| temperature=args.temperature, |
| top_k=args.topk, |
| top_p=args.topp, |
| ) |
| else: |
| fn = lambda: model.generate( |
| input_ids=input_ids, |
| attention_mask=attn_mask, |
| max_length=max_length, |
| return_dict_in_generate=True, |
| pad_token_id=tokenizer.eos_token_id, |
| do_sample=True, |
| temperature=args.temperature, |
| top_k=args.topk, |
| top_p=args.topp, |
| ) |
| out = fn() |
| if args.prompt is not None: |
| print(tokenizer.batch_decode(out.sequences.tolist())) |
|
|
| torch.cuda.synchronize() |
| start = time.time() |
| for _ in range(repeats): |
| fn() |
| torch.cuda.synchronize() |
| print(f"Prompt length: {len(input_ids[0])}, generation length: {len(out.sequences[0]) - len(input_ids[0])}") |
| print(f"{args.model_name} prompt processing + decoding time: {(time.time() - start) / repeats * 1000:.0f}ms") |
|
|
