Delete model.py

model.py

@@ -1,118 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import matplotlib.pyplot as plt
-
-# hyperparameters
-batch_size = 16 # how many independent sequences will we process in parallel?
-block_size = 64 # what is the maximum context length for predictions?
-max_iters = 5000
-eval_interval = 100
-learning_rate = 1e-3
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-eval_iters = 200
-n_embd = 128
-n_head = 8
-n_layer = 4
-dropout = 0.0
-vocab = 101
-# ------------
-
-
-class Head(nn.Module):
-    def __init__(self, head_size):
-        super(Head,self).__init__()
-        self.head_size = head_size
-        self.dropout = nn.Dropout(dropout)
-        self.key = nn.Linear(n_embd, head_size, bias=False)
-        self.query = nn.Linear(n_embd, head_size, bias=False)
-        self.value = nn.Linear(n_embd, head_size, bias=False)
-        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
-    def forward(self,x):
-        k = self.key(x)
-        q = self.query(x)
-        wei = q @ k.transpose(-2,-1) * (self.head_size ** -0.5)
-        wei = wei.masked_fill(self.tril == 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, n_head, head_size):
-        super(MultiHeadAttention,self).__init__()
-        self.head_size = head_size
-        self.n_head = n_head
-        self.heads = nn.ModuleList([Head(head_size) for _ in range(n_head)])
-        self.out = nn.Linear(n_embd, n_embd)
-        self.dropout = nn.Dropout(dropout)
-    
-    def forward(self,x):
-        out = torch.cat([h(x) for h in self.heads], dim=-1)
-        out = self.out(out)
-        out = self.dropout(out)
-        return out
-
-class FeedForwardLayer(nn.Module):
-    def __init__(self, n_embd):
-        super(FeedForwardLayer, self).__init__()
-        self.n_embd = n_embd
-        self.fc1 = nn.Linear(n_embd, 4*n_embd)
-        self.fc2 = nn.Linear(4*n_embd,n_embd)
-        self.dropout = nn.Dropout(dropout)
-    
-    def forward(self, x):
-        out = self.fc1(x)
-        out = F.gelu(out)
-        out = self.fc2(out)
-        out = self.dropout(out)
-        return out
-
-class Block(nn.Module):
-    def __init__(self):
-        super(Block, self).__init__()
-        self.attn = MultiHeadAttention(n_head, n_embd // n_head)
-        self.ff = FeedForwardLayer(n_embd)
-        self.ln1 = nn.LayerNorm(n_embd)
-        self.ln2 = nn.LayerNorm(n_embd)
-    def forward(self,x):
-        x = x + self.attn(self.ln1(x))
-        x = x + self.ff(self.ln2(x))
-        return x
-
-class Transformer(nn.Module):
-    def __init__(self, n_embd, n_layer):
-        super(Transformer, self).__init__()
-        self.n_embd = n_embd
-        self.n_layer = n_layer
-        self.token_embedding = nn.Embedding(vocab, n_embd)
-        self.position_embedding = nn.Embedding(block_size,n_embd)
-        self.blocks = nn.Sequential(*[Block() for _ in range(n_layer)])
-        self.ln_f = nn.LayerNorm(n_embd) # final layer norm
-        self.ffwd = nn.Linear(n_embd, vocab)
-    
-    def forward(self, idx, targets=None):
-        B,T = idx.shape
-        x = self.token_embedding(idx) + self.position_embedding(torch.arange(T, device=idx.device))
-        x = self.blocks(x)
-        x = self.ln_f(x)
-        logits = self.ffwd(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, ignore_index=0)
-        return logits,loss
-    
-    def generate(self, idx, max_tokens):
-        for _ in range(max_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