add app.py

app.py

@@ -0,0 +1,157 @@
+import streamlit as st
+import torch # we use PyTorch: https://pytorch.org
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# model hyperparameters
+batch_size = 32
+block_size = 128
+max_iters = 5000
+eval_interval = 500
+learning_rate = 3e-4
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+eval_iters = 200
+n_embed = 256
+n_heads = 8
+n_layers = 6
+dropout = 0.2
+# -------------------------------------------------
+
+# model architecture
+class AttentionHead(nn.Module):
+  """a single 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) # (B, T, C)
+    Q = self.query(x) # (B, T, C)
+    
+    wei = Q @ K.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, H, C) -> (B, T, T)
+    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) # (B, T, C)
+    out = wei @ V # (B, T, T) @ (B, T, C) -> (B, T, C)
+    return out
+  
+class MultiHeadAttention(nn.Module):
+  """a multi-head self attention layer"""
+  
+  def __init__(self, n_heads, head_size):
+    super().__init__()
+    self.heads = nn.ModuleList([AttentionHead(head_size) for _ in range(n_heads)])
+    self.fc = nn.Linear(head_size * n_heads, n_embed)
+    self.dropout = nn.Dropout(dropout)
+    
+  def forward(self, x):
+    out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, n_heads*C)    
+    out = self.fc(out) # (B, T, C)
+    out = self.dropout(out) 
+    return out
+  
+class FeedForward(nn.Module):
+  def __init__(self, n_hidden):
+    super().__init__()
+    self.net = nn.Sequential(
+      nn.Linear(n_embed, n_hidden),
+      nn.ReLU(),
+      nn.Linear(n_hidden, n_embed),
+      nn.Dropout(dropout)
+    )
+    
+  def forward(self, x):
+   return self.net(x)
+  
+class Block(nn.Module):
+  def __init__(self, n_embed, n_heads):
+    super().__init__()
+    self.sa_heads = MultiHeadAttention(n_heads, n_embed // n_heads)
+    self.ffwd = FeedForward(n_embed*4)
+    self.ln1 = nn.LayerNorm(n_embed)
+    self.ln2 = nn.LayerNorm(n_embed)
+    
+    
+  def forward(self, x):
+    x = x + self.sa_heads(self.ln1(x)) #  [batch_size, block_size, n_embed]
+    x = x + self.ffwd(self.ln2(x)) # [batch_size, block_size, n_embed]
+    return x
+
+class BigramModel(nn.Module):
+  def __init__(self):
+    super().__init__()
+    self.token_embedding_table = nn.Embedding(vocab_size, n_embed)
+    self.position_embedding_table = nn.Embedding(block_size, n_embed)
+    self.blocks = nn.Sequential(*[Block(n_embed, n_heads) for _ in range(n_layers)])
+    self.ln_f = nn.LayerNorm(n_embed)
+    self.lm_head = nn.Linear(n_embed, vocab_size)
+      
+  def forward(self, idx, targets=None):
+    # idx and target are both [batch_size, block_size]
+    B, T = idx.shape
+    
+    tok_emb = self.token_embedding_table(idx) # [batch_size, block_size, n_embed]
+    pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # [block_size, n_embed]
+    x = tok_emb + pos_emb # [batch_size, block_size, n_embed]
+    x = self.blocks(x)
+    x = self.ln_f(x)
+    logits = self.lm_head(x) # [batch_size, block_size, vocab_size]
+    
+    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=100):
+    # idx is (B, T)
+    for _ in range(max_new_tokens):
+      # get the last block_size tokens
+      idx_cond = idx[:, -block_size:] # (B, T)
+      # get the predictions
+      logits, _ = self(idx_cond)
+      # focus only on the last time step
+      logits = logits[:, -1, :] # becomes (B, C)
+      # apply softmax to get probabilities
+      probs = F.softmax(logits, dim=1) # (B, C)
+      # sample from the distribution
+      idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
+      # append sampled index to the running sequence
+      idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
+      
+    return idx
+# ----------------------------------------------------------------
+
+# helpers
+chars = list("!$&',-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz")
+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] # encoder: take a string, output a list of integers
+decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
+# ----------------------------------------------------------------
+
+# load model
+model = torch.load('model/complete-model.pt')
+
+# inference
+slider_value = st.slider('Amount of text to generate', min_value=100, max_value=2000, value=500, step=5)
+if st.button('Generat text')
+    context = torch.zeros((1, 1), dtype=torch.long, device='cuda')
+    text = model.generate(context, max_new_tokens=slider_value)[0].tolist()
+    st.json(decode(text))
+# 
+