1094 The Largest Generation (25分)

本文介绍了一种使用层序遍历或深度优先搜索(DFS)的方法来找出二叉树中节点数最多的一层。通过邻接矩阵存储非叶子节点与其子节点的关系,并利用vector容器实现DFS遍历,最终找到并输出节点数最多的层级及其数量。

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思路:
找出节点个数最多的一层
层序遍历、DFS

通过代码:
不用queue用vector实现DFS

#include <iostream>
#include <cstring>
#include <vector>

using namespace std;

const int N = 110;

int n, m;
bool g[N][N];   //邻接矩阵存储
vector<int> level[N];  //每层存到一个vector里,最多有N层

int main()
{
    cin >> n >> m;  //节点总数,非叶子结点数

    while (m -- ) 
    {
        int id, k;
        cin >> id >> k;  //非叶子结点编号,子节点数
        while (k -- )
        {
            int son;
            cin >> son;
            g[id][son] = true;
        }
    }

    level[1].push_back(1);   //第一层只有一个根节点
    int l = 1;

    while (level[l].size())  //如果当前层有元素
    {
        for (auto ver : level[l])   //遍历当前层的每一个点,把他的儿子加到下一层
            for (int j = 1; j <= n; j ++ )
                if (g[ver][j])
                    level[l + 1].push_back(j);
        l ++ ;
    }

    int k = 1;   //寻找节点数最多的那一层
    for(int i = 1; i < l; i ++ )
        if (level[i].size() > level[k].size())
            k = i;

    cout << level[k].size() << ' ' << k << endl;   //输出数量和层级

    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'] 请析问题原因,并给出详细解决方法
07-23
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