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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, LlamaTokenizer |
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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("--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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tokenizer = LlamaTokenizer.from_pretrained(args.model_name) |
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model = MambaLMHeadModel.from_pretrained(args.model_name, device=device, 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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args.prompt = args.prompt.replace('\\n', '\n') |
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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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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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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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l = tokenizer.batch_decode(out.sequences.tolist()) |
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text = ''.join(l) |
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print(text) |
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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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