实现一个 Deepseek 之类的大跃进有多容易

问： ChatGPT 能给我一段代码 用 pytorch 实现 简单的 GPT 神经网络吗？
Ask: Can you give me sample codes to set up  a simple GPT NN model with pytorch?

答：当然，下面就给
ChatGPT said:
Sure! Below is a simple implementation of a GPT (Generative Pre-trained Transformer) model using PyTorch. This includes:

包括 符号 嵌入，位置编码， （大语言变形）块， GPT 模型，训练Loop

• Token Embeddings & Positional Encoding
• Transformer Blocks
• GPT Model
• Training Loop (Basic Example)

代码：

import torch

import torch.nn as nn

import torch.nn.functional as F



class GPT(nn.Module):

    def __init__(self, vocab_size, max_seq_len, d_model, n_heads, num_layers, ff_dim, dropout=0.1):

        super(GPT, self).__init__()

        

        self.token_embedding = nn.Embedding(vocab_size, d_model)

        self.position_embedding = nn.Embedding(max_seq_len, d_model)

        

        transformer_layer = nn.TransformerEncoderLayer(

            d_model=d_model, 

            nhead=n_heads, 

            dim_feedforward=ff_dim, 

            dropout=dropout,

            activation='gelu',

            batch_first=True

        )

        

        self.transformer = nn.TransformerEncoder(transformer_layer, num_layers=num_layers)

        self.ln_f = nn.LayerNorm(d_model)  # Final Layer Norm

        self.fc_out = nn.Linear(d_model, vocab_size)  # Output layer



        self.max_seq_len = max_seq_len

        self.d_model = d_model



    def forward(self, x):

        batch_size, seq_length = x.shape

        positions = torch.arange(0, seq_length, device=x.device).unsqueeze(0)

        

        x = self.token_embedding(x) + self.position_embedding(positions)

        x = self.transformer(x)

        x = self.ln_f(x)

        logits = self.fc_out(x)



        return logits



# Hyperparameters

vocab_size = 50257  # Example: GPT-2 vocab size

max_seq_len = 128

d_model = 768

n_heads = 8

num_layers = 6

ff_dim = 2048

dropout = 0.1



# Model Initialization

model = GPT(vocab_size, max_seq_len, d_model, n_heads, num_layers, ff_dim, dropout)



# Sample input (batch_size=1, sequence_length=10)

sample_input = torch.randint(0, vocab_size, (1, 10))

output = model(sample_input)



print("Output Shape:", output.shape)  # Expected: (1, 10, vocab_size)

然后给了四句解释：
Explanation:
Token Embeddings: Maps input tokens to a high-dimensional space.
Positional Embeddings: Since transformers don't have recurrence, this encodes token positions.
Transformer Blocks: Multiple layers of self-attention + feedforward networks.
Output Projection: Transforms the final hidden states into logits for vocabulary prediction.
Would you like a training loop added as well? 🚀

好了，别啰索了， 开工，骗经费，，，不，，，是为国家的人工智能领域增加一群天才