1094 The Largest Generation (25)

#include<bits/stdc++.h>
using namespace std;
const int MAXN=1001;
struct Node{
	int level;
	vector<int> child;
	Node(){
		level=0;
	}
}node[MAXN];
void levetra(int root,int _level){
	if(node[root].child.size()==0) return;
	queue<int> q;
	node[root].level=_level;
	q.push(root);
	while(!q.empty()){
		int now=q.front();
		q.pop();
		for(int i=0;i<node[now].child.size();i++){
			node[node[now].child[i]].level=node[now].level+1;
			q.push(node[now].child[i]);
		}
	}
}
int main()
{
	#ifndef ONLINE_JUDGE
	freopen("in.txt","r",stdin);
	#endif
	int n,k;
	cin>>n>>k;
	for(int i=0;i<k;i++){
		int a,b;
		cin>>a>>b;
		for(int j=0;j<b;j++){
			int temp;
			cin>>temp;
			node[a].child.push_back(temp);
		}
	}
	if(node[1].child.size()==0){
		cout<<1<<' '<<1<<endl;
		return 0;
	}
	levetra(1,1);
	int le[MAXN]={};
	for(int i=1;i<=n;i++){
		le[node[i].level]++;
	}
	int maxlevel=0;
	int cnt=0;
	for(int i=0;i<MAXN;i++){
		if(le[i]>cnt){
			cnt=le[i];
			maxlevel=i;
		}
	}
	cout<<cnt<<' '<<maxlevel<<endl;
	return 0;
}

 

import time import torch, torch_npu from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig # 替换成本地的模型权重路径 MODEL_PATH = "/models/z50051264/Qwen2.5-7B-Instruct" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, # Support torch.float16, torch.float32, torch.bfloat16 bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=False, bnb_4bit_quant_storage=torch.uint8 ) torch.npu.synchronize() start_time = time.time() model = AutoModelForCausalLM.from_pretrained( MODEL_PATH, device_map={"":0}, quantization_config=bnb_config, low_cpu_mem_usage=True, torch_dtype=torch.float16 # Support torch.float16, torch.float32, torch.bfloat16 ) torch.npu.synchronize() print(f"[+] load time: {time.time() - start_time:.6}s") tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) model.eval() prompt = "Once upon a time, " inputs = tokenizer([prompt], return_tensors="pt") input_ids = inputs.input_ids.npu() attention_mask = inputs.attention_mask.npu() torch.npu.synchronize() start_time = time.time() generated_ids = model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=32, do_sample=False, ) torch.npu.synchronize() print(f"[+] inference time: {time.time() - start_time:.6}s") print(tokenizer.batch_decode(generated_ids)) 我在使用npu版本的bitsandbytes,但是执行以上代码,出现错误: [root@190f3c453709 inference]# python nf4.py /usr/local/python3.10.17/lib/python3.10/site-packages/torch_npu/utils/storage.py:38: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() if self.device.type != 'cpu': Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 4/4 [00:13<00:00, 3.26s/it] [+] load time: 14.9728s The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details. [+] inference time: 3.78472s ['Once upon a time, 123456789 was the largest known prime number. If a new prime number, 123456789'] 请分析问题原因,并给出详细解决方法
最新发布
07-23
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