93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
# Copyright (c) 2023, Tri Dao, Albert Gu.
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import argparse
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import time
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import json
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import torch
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import torch.nn.functional as F
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from einops import rearrange
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
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parser = argparse.ArgumentParser(description="Generation benchmarking")
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parser.add_argument("--model-name", type=str, default="state-spaces/mamba-130m")
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parser.add_argument("--prompt", type=str, default=None)
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parser.add_argument("--promptlen", type=int, default=100)
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parser.add_argument("--genlen", type=int, default=100)
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parser.add_argument("--temperature", type=float, default=1.0)
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parser.add_argument("--topk", type=int, default=1)
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parser.add_argument("--topp", type=float, default=1.0)
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parser.add_argument("--minp", type=float, default=0.0)
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parser.add_argument("--repetition-penalty", type=float, default=1.0)
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parser.add_argument("--batch", type=int, default=1)
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args = parser.parse_args()
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repeats = 3
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device = "cuda"
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dtype = torch.float16
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print(f"Loading model {args.model_name}")
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is_mamba = args.model_name.startswith("state-spaces/mamba") or args.model_name.startswith("state-spaces/transformerpp")
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if is_mamba:
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
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model = MambaLMHeadModel.from_pretrained(args.model_name, device=device, dtype=dtype)
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else:
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tokenizer = AutoTokenizer.from_pretrained(args.model_name)
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model = AutoModelForCausalLM.from_pretrained(args.model_name, device_map={"": device}, torch_dtype=dtype)
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model.eval()
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print(f"Number of parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
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torch.random.manual_seed(0)
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if args.prompt is None:
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input_ids = torch.randint(1, 1000, (args.batch, args.promptlen), dtype=torch.long, device="cuda")
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attn_mask = torch.ones_like(input_ids, dtype=torch.long, device="cuda")
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else:
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tokens = tokenizer(args.prompt, return_tensors="pt")
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input_ids = tokens.input_ids.to(device=device)
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attn_mask = tokens.attention_mask.to(device=device)
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max_length = input_ids.shape[1] + args.genlen
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if is_mamba:
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fn = lambda: model.generate(
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input_ids=input_ids,
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max_length=max_length,
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cg=True,
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return_dict_in_generate=True,
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output_scores=True,
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enable_timing=False,
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temperature=args.temperature,
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top_k=args.topk,
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top_p=args.topp,
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min_p=args.minp,
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repetition_penalty=args.repetition_penalty,
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)
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else:
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fn = lambda: model.generate(
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input_ids=input_ids,
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attention_mask=attn_mask,
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max_length=max_length,
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return_dict_in_generate=True,
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pad_token_id=tokenizer.eos_token_id,
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do_sample=True,
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temperature=args.temperature,
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top_k=args.topk,
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top_p=args.topp,
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repetition_penalty=args.repetition_penalty,
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)
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out = fn()
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if args.prompt is not None:
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print(tokenizer.batch_decode(out.sequences.tolist()))
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torch.cuda.synchronize()
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start = time.time()
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for _ in range(repeats):
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fn()
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torch.cuda.synchronize()
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print(f"Prompt length: {len(input_ids[0])}, generation length: {len(out.sequences[0]) - len(input_ids[0])}")
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print(f"{args.model_name} prompt processing + decoding time: {(time.time() - start) / repeats * 1000:.0f}ms")
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