make streamlit app

README.md

@@ -1,6 +1,6 @@
 ---
-title: Testing
-emoji: 🏆
+title: LLM From Scratch
+emoji: 🧠
 colorFrom: green
 colorTo: red
 sdk: streamlit

app.py

@@ -1,4 +1,188 @@
 import streamlit as st
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import pickle
+import os
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.title('LLM from scratch Demo')
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+block_size = 128
+batch_size = 32
+max_iters = 4000
+learning_rate = 3e-4
+eval_every = 500
+n_embd = 384
+n_head = 8
+n_layer = 8
+dropout = 0.2
+
+
+class Head(nn.Module):
+    """ one head of self-attention """
+
+    def __init__(self, head_size):
+        super().__init__()
+        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)))
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        # input of size (batch, time-step, channels)
+        # output of size (batch, time-step, head size)
+        B,T,C = x.shape
+        k = self.key(x)   # (B,T,hs)
+        q = self.query(x) # (B,T,hs)
+        # compute attention scores ("affinities")
+        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
+        wei = F.softmax(wei, dim=-1) # (B, T, T)
+        wei = self.dropout(wei)
+        # perform the weighted aggregation of the values
+        v = self.value(x) # (B,T,hs)
+        out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)
+        return out
+
+class MultiHeadAttention(nn.Module):
+    """ multiple heads of self-attention in parallel """
+
+    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(head_size * num_heads, n_embd)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3])
+        out = self.dropout(self.proj(out))
+        return out
+    
+class FeedFoward(nn.Module):
+    """ a simple linear layer followed by a non-linearity """
+
+    def __init__(self, n_embd):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(n_embd, 4 * n_embd),
+            nn.ReLU(),
+            nn.Linear(4 * n_embd, n_embd),
+            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_embd, n_head):
+        # n_embd: embedding dimension, n_head: the number of heads we'd like
+        super().__init__()
+        head_size = n_embd // n_head
+        self.sa = MultiHeadAttention(n_head, head_size)
+        self.ffwd = FeedFoward(n_embd)
+        self.ln1 = nn.LayerNorm(n_embd)
+        self.ln2 = nn.LayerNorm(n_embd)
+
+    def forward(self, x):
+        y = self.sa(x)
+        x = self.ln1(x + y)
+        y = self.ffwd(x)
+        x = self.ln2(x + y)
+        return x
+    
+class GPTLanguageModel(nn.Module):
+    def __init__(self, vocab_size):
+        super().__init__()
+        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
+        self.position_embedding_table = nn.Embedding(block_size, n_embd)
+        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
+        self.ln_f = nn.LayerNorm(n_embd) # final layer norm
+        self.lm_head = nn.Linear(n_embd, vocab_size)
+        
+        
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, index, targets=None):
+        B, T = index.shape
+        
+        
+        # idx and targets are both (B,T) tensor of integers
+        tok_emb = self.token_embedding_table(index) # (B,T,C)
+        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)
+        x = tok_emb + pos_emb # (B,T,C)
+        x = self.blocks(x) # (B,T,C)
+        x = self.ln_f(x) # (B,T,C)
+        logits = self.lm_head(x) # (B,T,vocab_size)
+        
+        if targets is None:
+            loss = None
+        else:
+            B, T, C = logits.shape
+            logits = logits.view(B*T, C) # reshape to what torch.cross_entropy expects
+            targets = targets.view(B*T)
+            loss = F.cross_entropy(logits, targets) 
+        return logits, loss
+    
+    def generate(self, index, max_new_tokens):
+        # index is (B, T) array of indices in the current context
+        for _ in range(max_new_tokens):
+            # crop idx to the last block_size tokens
+            index_cond = index[:, -block_size:]
+            # get the predictions
+            logits, loss = self.forward(index_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
+            index_next = torch.multinomial(probs, num_samples=1) # (B, 1)
+            # append sampled index to the running sequence
+            index = torch.cat((index, index_next), dim=1) # (B, T+1)
+        return index
+
+if not os.path.exists("./openwebtext/vocab.txt"):
+    raise Exception("Please run extract.py first")
+chars = ""
+with open("./openwebtext/vocab.txt", 'r', encoding='utf-8') as f:
+    text = f.read()
+    chars = sorted(list(set(text)))
+
+string_to_int = {ch: i for i, ch in enumerate(chars)}
+int_to_string = {i: ch for i, ch in enumerate(chars)}
+
+encode = lambda s: [string_to_int[ch] for ch in s]
+decode = lambda x: ''.join([int_to_string[i] for i in x])
+
+
+model_pickle_path = './model.pkl'
+
+try:
+    st.write('loading model parameters...')
+    with open(model_pickle_path, 'rb') as f:
+        model = pickle.load(f)
+    st.write('model loaded successfully!')
+except:
+    st.error('ERROR: model loading failed/model not found. Please run ./train_gpt_openwebtext.py first.')
+    exit()
+
+prompt = ''
+prompt = st.text_area('Prompt:', value=prompt, height=100, max_chars=block_size - 1, key='prompt')
+if len(prompt) != 0:
+    context = torch.tensor(encode(prompt), dtype=torch.long, device=device)
+    max_new_tokens = block_size - len(prompt)
+    generated_chars = decode(model.generate(context.unsqueeze(0), max_new_tokens=max_new_tokens)[0].tolist())
+    st.write('Generated text:')
+    st.write(generated_chars)