Upload gpt_dev.py

gpt_dev.py

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+# -*- coding: utf-8 -*-
+"""gpt_dev.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1zxxLfIi8_EDLqYODY8TyNLpr8RTxV-Ct
+
+## Building a GPT
+
+Companion notebook to the [Zero To Hero](https://karpathy.ai/zero-to-hero.html) video on GPT.
+"""
+
+# We always start with a dataset to train on. Let's download the tiny shakespeare dataset
+!wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
+
+# read it in to inspect it
+with open('input.txt', 'r', encoding='utf-8') as f:
+    text = f.read()
+
+print("length of dataset in characters: ", len(text))
+
+# let's look at the first 1000 characters
+print(text[:1000])
+
+# here are all the unique characters that occur in this text
+chars = sorted(list(set(text)))
+vocab_size = len(chars)
+print(''.join(chars))
+print(vocab_size)
+
+# create a mapping from characters to integers
+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
+
+print(encode("hii there"))
+print(decode(encode("hii there")))
+
+# let's now encode the entire text dataset and store it into a torch.Tensor
+import torch # we use PyTorch: https://pytorch.org
+data = torch.tensor(encode(text), dtype=torch.long)
+print(data.shape, data.dtype)
+print(data[:1000]) # the 1000 characters we looked at earier will to the GPT look like this
+
+# Let's now split up the data into train and validation sets
+n = int(0.9*len(data)) # first 90% will be train, rest val
+train_data = data[:n]
+val_data = data[n:]
+
+block_size = 8
+train_data[:block_size+1]
+
+x = train_data[:block_size]
+y = train_data[1:block_size+1]
+for t in range(block_size):
+    context = x[:t+1]
+    target = y[t]
+    print(f"when input is {context} the target: {target}")
+
+torch.manual_seed(1337)
+batch_size = 4 # how many independent sequences will we process in parallel?
+block_size = 8 # what is the maximum context length for predictions?
+
+def get_batch(split):
+    # generate a small batch of data of inputs x and targets y
+    data = train_data if split == 'train' else val_data
+    ix = torch.randint(len(data) - block_size, (batch_size,))
+    x = torch.stack([data[i:i+block_size] for i in ix])
+    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
+    return x, y
+
+xb, yb = get_batch('train')
+print('inputs:')
+print(xb.shape)
+print(xb)
+print('targets:')
+print(yb.shape)
+print(yb)
+
+print('----')
+
+for b in range(batch_size): # batch dimension
+    for t in range(block_size): # time dimension
+        context = xb[b, :t+1]
+        target = yb[b,t]
+        print(f"when input is {context.tolist()} the target: {target}")
+
+print(xb) # our input to the transformer
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+torch.manual_seed(1337)
+
+class BigramLanguageModel(nn.Module):
+
+    def __init__(self, vocab_size):
+        super().__init__()
+        # each token directly reads off the logits for the next token from a lookup table
+        self.token_embedding_table = nn.Embedding(vocab_size, vocab_size)
+
+    def forward(self, idx, targets=None):
+
+        # idx and targets are both (B,T) tensor of integers
+        logits = self.token_embedding_table(idx) # (B,T,C)
+
+        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):
+        # idx is (B, T) array of indices in the current context
+        for _ in range(max_new_tokens):
+            # get the predictions
+            logits, loss = self(idx)
+            # 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
+
+m = BigramLanguageModel(vocab_size)
+
+
+logits, loss = m(xb, yb)
+print(logits.shape)
+print(loss)
+
+print(decode(m.generate(idx = torch.zeros((1, 1), dtype=torch.long), max_new_tokens=100)[0].tolist()))
+
+
+
+# create a PyTorch optimizer
+optimizer = torch.optim.AdamW(m.parameters(), lr=1e-3)
+
+batch_size = 32
+for steps in range(100): # increase number of steps for good results...
+
+    # sample a batch of data
+    xb, yb = get_batch('train')
+
+    # evaluate the loss
+    logits, loss = m(xb, yb)
+    optimizer.zero_grad(set_to_none=True)
+    loss.backward()
+    optimizer.step()
+
+print(loss.item())
+
+print(decode(m.generate(idx = torch.zeros((1, 1), dtype=torch.long), max_new_tokens=500)[0].tolist()))
+
+"""## The mathematical trick in self-attention"""
+
+# toy example illustrating how matrix multiplication can be used for a "weighted aggregation"
+torch.manual_seed(42)
+a = torch.tril(torch.ones(3, 3))
+a = a / torch.sum(a, 1, keepdim=True)
+b = torch.randint(0,10,(3,2)).float()
+c = a @ b
+print('a=')
+print(a)
+print('--')
+print('b=')
+print(b)
+print('--')
+print('c=')
+print(c)
+
+# consider the following toy example:
+
+torch.manual_seed(1337)
+B,T,C = 4,8,2 # batch, time, channels
+x = torch.randn(B,T,C)
+x.shape
+
+# We want x[b,t] = mean_{i<=t} x[b,i]
+xbow = torch.zeros((B,T,C))
+for b in range(B):
+    for t in range(T):
+        xprev = x[b,:t+1] # (t,C)
+        xbow[b,t] = torch.mean(xprev, 0)
+
+# version 2: using matrix multiply for a weighted aggregation
+wei = torch.tril(torch.ones(T, T))
+wei = wei / wei.sum(1, keepdim=True)
+xbow2 = wei @ x # (B, T, T) @ (B, T, C) ----> (B, T, C)
+torch.allclose(xbow, xbow2)
+
+# version 3: use Softmax
+tril = torch.tril(torch.ones(T, T))
+wei = torch.zeros((T,T))
+wei = wei.masked_fill(tril == 0, float('-inf'))
+wei = F.softmax(wei, dim=-1)
+xbow3 = wei @ x
+torch.allclose(xbow, xbow3)
+
+# version 4: self-attention!
+torch.manual_seed(1337)
+B,T,C = 4,8,32 # batch, time, channels
+x = torch.randn(B,T,C)
+
+# let's see a single Head perform self-attention
+head_size = 16
+key = nn.Linear(C, head_size, bias=False)
+query = nn.Linear(C, head_size, bias=False)
+value = nn.Linear(C, head_size, bias=False)
+k = key(x)   # (B, T, 16)
+q = query(x) # (B, T, 16)
+wei =  q @ k.transpose(-2, -1) # (B, T, 16) @ (B, 16, T) ---> (B, T, T)
+
+tril = torch.tril(torch.ones(T, T))
+#wei = torch.zeros((T,T))
+wei = wei.masked_fill(tril == 0, float('-inf'))
+wei = F.softmax(wei, dim=-1)
+
+v = value(x)
+out = wei @ v
+#out = wei @ x
+
+out.shape
+
+wei[0]
+
+"""Notes:
+- Attention is a **communication mechanism**. Can be seen as nodes in a directed graph looking at each other and aggregating information with a weighted sum from all nodes that point to them, with data-dependent weights.
+- There is no notion of space. Attention simply acts over a set of vectors. This is why we need to positionally encode tokens.
+- Each example across batch dimension is of course processed completely independently and never "talk" to each other
+- In an "encoder" attention block just delete the single line that does masking with `tril`, allowing all tokens to communicate. This block here is called a "decoder" attention block because it has triangular masking, and is usually used in autoregressive settings, like language modeling.
+- "self-attention" just means that the keys and values are produced from the same source as queries. In "cross-attention", the queries still get produced from x, but the keys and values come from some other, external source (e.g. an encoder module)
+- "Scaled" attention additional divides `wei` by 1/sqrt(head_size). This makes it so when input Q,K are unit variance, wei will be unit variance too and Softmax will stay diffuse and not saturate too much. Illustration below
+"""
+
+k = torch.randn(B,T,head_size)
+q = torch.randn(B,T,head_size)
+wei = q @ k.transpose(-2, -1) * head_size**-0.5
+
+k.var()
+
+q.var()
+
+wei.var()
+
+torch.softmax(torch.tensor([0.1, -0.2, 0.3, -0.2, 0.5]), dim=-1)
+
+torch.softmax(torch.tensor([0.1, -0.2, 0.3, -0.2, 0.5])*8, dim=-1) # gets too peaky, converges to one-hot
+
+class LayerNorm1d: # (used to be BatchNorm1d)
+
+  def __init__(self, dim, eps=1e-5, momentum=0.1):
+    self.eps = eps
+    self.gamma = torch.ones(dim)
+    self.beta = torch.zeros(dim)
+
+  def __call__(self, x):
+    # calculate the forward pass
+    xmean = x.mean(1, keepdim=True) # batch mean
+    xvar = x.var(1, keepdim=True) # batch variance
+    xhat = (x - xmean) / torch.sqrt(xvar + self.eps) # normalize to unit variance
+    self.out = self.gamma * xhat + self.beta
+    return self.out
+
+  def parameters(self):
+    return [self.gamma, self.beta]
+
+torch.manual_seed(1337)
+module = LayerNorm1d(100)
+x = torch.randn(32, 100) # batch size 32 of 100-dimensional vectors
+x = module(x)
+x.shape
+
+x[:,0].mean(), x[:,0].std() # mean,std of one feature across all batch inputs
+
+x[0,:].mean(), x[0,:].std() # mean,std of a single input from the batch, of its features
+
+# French to English translation example:
+
+# <--------- ENCODE ------------------><--------------- DECODE ----------------->
+# les réseaux de neurones sont géniaux! <START> neural networks are awesome!<END>
+
+"""### Full finished code, for reference
+
+You may want to refer directly to the git repo instead though.
+"""
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+# hyperparameters
+batch_size = 16 # how many independent sequences will we process in parallel?
+block_size = 32 # what is the maximum context length for predictions?
+max_iters = 5000
+#00
+eval_interval = 100
+learning_rate = 1e-3
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+eval_iters = 200
+n_embd = 64
+n_head = 4
+n_layer = 4
+dropout = 0.0
+# ------------
+
+torch.manual_seed(1337)
+
+# wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
+with open('input.txt', 'r', encoding='utf-8') as f:
+    text = f.read()
+
+# here are all the unique characters that occur in this text
+chars = sorted(list(set(text)))
+vocab_size = len(chars)
+# create a mapping from characters to integers
+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
+
+# Train and test splits
+data = torch.tensor(encode(text), dtype=torch.long)
+n = int(0.9*len(data)) # first 90% will be train, rest val
+train_data = data[:n]
+val_data = data[n:]
+
+# data loading
+def get_batch(split):
+    # generate a small batch of data of inputs x and targets y
+    data = train_data if split == 'train' else val_data
+    ix = torch.randint(len(data) - block_size, (batch_size,))
+    x = torch.stack([data[i:i+block_size] for i in ix])
+    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
+    x, y = x.to(device), y.to(device)
+    return x, y
+
+@torch.no_grad()
+def estimate_loss():
+    out = {}
+    model.eval()
+    for split in ['train', 'val']:
+        losses = torch.zeros(eval_iters)
+        for k in range(eval_iters):
+            X, Y = get_batch(split)
+            logits, loss = model(X, Y)
+            losses[k] = loss.item()
+        out[split] = losses.mean()
+    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):
+        B,T,C = x.shape
+        k = self.key(x)   # (B,T,C)
+        q = self.query(x) # (B,T,C)
+        # compute attention scores ("affinities")
+        wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, 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,C)
+        out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)
+        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(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.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):
+        x = x + self.sa(self.ln1(x))
+        x = x + self.ffwd(self.ln2(x))
+        return x
+
+# super simple bigram model
+class BigramLanguageModel(nn.Module):
+
+    def __init__(self):
+        #super().__init__()
+        super(BigramLanguageModel, self).__init__()
+        # each token directly reads off the logits for the next token from a lookup table
+        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)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+
+        # idx and targets are both (B,T) tensor of integers
+        tok_emb = self.token_embedding_table(idx) # (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)
+            targets = targets.view(B*T)
+            loss = F.cross_entropy(logits, targets)
+
+        return logits, loss
+
+    def generate(self, idx, max_new_tokens):
+        # idx is (B, T) array of indices in the current context
+        for _ in range(max_new_tokens):
+            # crop idx to the last block_size tokens
+            idx_cond = idx[:, -block_size:]
+            # get the predictions
+            logits, loss = 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
+
+model = BigramLanguageModel()
+torch.save(model.state_dict(), 'transformer_weights.pth')
+m = model.to(device)
+# print the number of parameters in the model
+print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')
+torch.save(model, 'transformer_model.pth')
+
+# create a PyTorch optimizer
+optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
+
+for iter in range(max_iters):
+
+    # every once in a while evaluate the loss on train and val sets
+    if iter % eval_interval == 0 or iter == max_iters - 1:
+        losses = estimate_loss()
+        print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
+
+    # sample a batch of data
+    xb, yb = get_batch('train')
+
+    # evaluate the loss
+    logits, loss = model(xb, yb)
+    optimizer.zero_grad(set_to_none=True)
+    loss.backward()
+    optimizer.step()
+
+# generate from the model
+context = torch.zeros((1, 1), dtype=torch.long, device=device)
+print(decode(m.generate(context, max_new_tokens=2000)[0].tolist()))
+
+# Load the saved weights into the model
+model.load_state_dict(torch.load('transformer_weights.pth'))
+
+print("Model weights loaded successfully.")
+
+import torch
+
+# Load the entire model
+model = torch.load('transformer_model.pth')
+model.eval()  # Set the model to evaluation mode
+
+print("Entire model loaded successfully.")