clean up and retrain

.gitignore

@@ -3,5 +3,6 @@
 
 venv/
 .ipynb_checkpoints/
+__pycache__/
 
 openwebtext/

GPTLanguageModelClass.py

@@ -0,0 +1,161 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+class hyperparams:
+    block_size = 128
+    batch_size = 32
+    max_iters = 12000
+    learning_rate = 3e-4
+    eval_every = 100
+    n_embd = 384
+    n_head = 8
+    n_layer = 8
+    dropout = 0.2
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+block_size = hyperparams.block_size
+batch_size = hyperparams.batch_size
+max_iters = hyperparams.max_iters
+learning_rate = hyperparams.learning_rate
+eval_every = hyperparams.eval_every
+n_embd = hyperparams.n_embd
+n_head = hyperparams.n_head
+n_layer = hyperparams.n_layer
+dropout = hyperparams.dropout
+device = hyperparams.device
+
+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
+

app.py

@@ -1,158 +1,22 @@
 import streamlit as st
 import torch
-import torch.nn as nn
-from torch.nn import functional as F
 import os
+from GPTLanguageModelClass import *
+
+block_size = hyperparams.block_size
+batch_size = hyperparams.batch_size
+max_iters = hyperparams.max_iters
+learning_rate = hyperparams.learning_rate
+eval_every = hyperparams.eval_every
+n_embd = hyperparams.n_embd
+n_head = hyperparams.n_head
+n_layer = hyperparams.n_layer
+dropout = hyperparams.dropout
+device = hyperparams.device
 
 st.title('LLM from scratch Demo')
-st.subheader('Maintenance mode: please come back later')
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 st.write(f"Using device: {device}")
-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("./vocab.txt"):
     raise Exception("Please run extract.py first")

model.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:04e95f8e46dd7b7b894d288f3c2b75bb0a535fb266960803587a9f552e6b5a73
-size 160274578
+oid sha256:c91a6742cac446d27f433efefdd50501c421e443a3927cf31c37454f9b23247c
+size 160301382

train_gpt_openwebtext.py

@@ -1,22 +1,21 @@
 import torch
-import torch.nn as nn
-from torch.nn import functional as F
 import mmap
 import random
 import os
+from GPTLanguageModelClass import *
+
+block_size = hyperparams.block_size
+batch_size = hyperparams.batch_size
+max_iters = hyperparams.max_iters
+learning_rate = hyperparams.learning_rate
+eval_every = hyperparams.eval_every
+n_embd = hyperparams.n_embd
+n_head = hyperparams.n_head
+n_layer = hyperparams.n_layer
+dropout = hyperparams.dropout
+device = hyperparams.device
 
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(device)
-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
 
 if not os.path.exists("./vocab.txt") or not os.path.exists("./openwebtext/train_split.txt") or not os.path.exists("./openwebtext/val_split.txt"):
     raise Exception("Please run extract.py first")
@@ -53,7 +52,6 @@ def get_random_chunk(split):
             
     return data
 
-
 def get_batch(split):
     data = get_random_chunk(split)
     ix = torch.randint(len(data) - block_size, (batch_size,))
@@ -76,145 +74,6 @@ def estimate_loss():
     model.train()
     return out
 
-
-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
-
-# [1, 0, 0]
-# [1, 0.6, 0]
-# [1, 0.6, 0.4]
-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
-
 model = GPTLanguageModel(vocab_size).to(device)
 
 model_pickle_path = './model.pt'