1094 The Largest Generation

本文介绍了一个使用深度优先搜索(DFS)遍历树形数据结构的C++程序实例。通过递归的方式,该程序能够计算出树的最大高度,并找出出现频率最高的节点高度及其对应的节点数量。涉及的技术包括C++标准库的使用、递归函数的设计以及树形数据结构的操作。
#include <iostream>
#include <vector>
#include <map>
using namespace std;
vector<int> vectors[100];
int maxHeight=0;
map<int,int> nums;
void dfs(int id,int height){
    if(height>maxHeight){
        maxHeight=height;
    }
    nums[height]++;
    for (auto x:vectors[id]){
        dfs(x,height+1);
    }
}
int main() {
    int N,M;
    cin>>N>>M;
    for (int i = 0; i < M; ++i) {
        int id,num;
        cin>>id>>num;
        for (int j = 0; j < num; ++j) {
            int val;
            cin>>val;
            vectors[id].push_back(val);
        }
    }
    dfs(1,1);
    int maxVal=0,index=1;
    for (int i = 1; i <= maxHeight; ++i) {
        if(nums[i]>maxVal){
            index=i;
            maxVal=nums[i];
        }
    }
    cout<<maxVal<<" "<<index<<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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