Create model.py

model.py

@@ -0,0 +1,144 @@
+import json
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from transformers import PreTrainedModel, PretrainedConfig
+import os
+
+# 超参数
+batch_size = 64  # 一批包含的文本序列个数
+block_size = 256  # 一个文本序列包含的字符个数
+n_embed = 384  # embedding维度
+n_head = 6
+n_layer = 6
+dropout = 0.2
+
+# 准备词汇表
+current_dir = os.path.dirname(os.path.abspath(__file__))
+with open(os.path.join(current_dir, 'input.txt'), 'r', encoding='utf-8') as f:
+    text = f.read()
+
+chars = sorted(list(set(text)))
+vocab_size = len(chars)
+
+# decode、encode函数，在序号和字符间转换
+stoi = { ch:i for i,ch in enumerate(chars) }
+itos = { i:ch for i,ch in enumerate(chars) }
+encode = lambda s: [stoi[c] for c in s]
+decode = lambda l: ''.join([itos[i] for i in l])
+
+class NoobConfig(PretrainedConfig):
+    model_type = "Noob"
+    vocab_size = vocab_size
+    n_positions = block_size
+    n_embd = n_embed
+    n_layer = n_layer
+    n_head = n_head
+
+class Head(nn.Module):
+    """ one head of self-attention """
+    def __init__(self, head_size):
+        super().__init__()
+        self.key = nn.Linear(n_embed, head_size, bias=False)
+        self.query = nn.Linear(n_embed, head_size, bias=False)
+        self.value = nn.Linear(n_embed, head_size, bias=False)
+        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        B, T, C = x.shape
+        k = self.key(x)
+        q = self.query(x)
+        wei = q @ k.transpose(-2, -1) * C**-0.5
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
+        wei = F.softmax(wei, dim=-1)
+        wei = self.dropout(wei)
+        v = self.value(x)
+        out = wei @ v
+        return out
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, num_heads, head_size):
+        super().__init__()
+        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
+        self.proj = nn.Linear(n_embed, n_embed)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1)
+        out = self.proj(out)
+        out = self.dropout(out)
+        return out
+
+class FeedFoward(nn.Module):
+    def __init__(self, n_embed):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(n_embed, 4 * n_embed),
+            nn.ReLU(),
+            nn.Linear(4 * n_embed, n_embed),
+            nn.Dropout(dropout),
+        )
+    
+    def forward(self, x):
+        return self.net(x)
+
+class Block(nn.Module):
+    """ transformer block: communication followed by computation """
+    def __init__(self, n_embed, n_head):
+        super().__init__()
+        head_size = n_embed // n_head
+        self.sa = MultiHeadAttention(n_head, head_size)
+        self.ffwd = FeedFoward(n_embed)
+        self.ln1 = nn.LayerNorm(n_embed)
+        self.ln2 = nn.LayerNorm(n_embed)
+
+    def forward(self, x):
+        x = x + self.sa(self.ln1(x))
+        x = x + self.ffwd(self.ln2(x))
+        return x
+
+class Noob(PreTrainedModel):
+    config_class = NoobConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.token_embedding_table = nn.Embedding(config.vocab_size, config.n_embd)
+        self.position_embedding_table = nn.Embedding(config.n_positions, config.n_embd)
+        self.blocks = nn.Sequential(*[Block(config.n_embd, config.n_head) for _ in range(config.n_layer)])
+        self.ln_final = nn.LayerNorm(config.n_embd)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+        tok_emb = self.token_embedding_table(idx)
+        pos_emb = self.position_embedding_table(torch.arange(T, device=idx.device))
+        x = tok_emb + pos_emb
+        x = self.blocks(x)
+        x = self.ln_final(x)
+        logits = self.lm_head(x)
+
+        if targets is None:
+            loss = None
+        else:
+            B, T, C = logits.shape
+            logits = logits.view(B*T, C)
+            targets = targets.view(B*T)
+            loss = F.cross_entropy(logits, targets)
+
+        return logits, loss
+
+    def generate(self, idx, max_new_tokens):
+        for _ in range(max_new_tokens):
+            idx_cond = idx[:, -block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :]
+            probs = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx
+
+    def save_pretrained(self, save_directory, **kwargs):
+        super().save_pretrained(save_directory, **kwargs)
+        with open(f"{save_directory}/vocab.json", "w") as f:
+            json.dump(stoi, f)