app.py

import gradio as gr

import torch
import torch.nn as nn
import torch.nn.functional as F

batch_size = 64 # how many independent sequences will we process in parallel?
block_size = 256 # what is the maximum context length for predictions?
max_iters = 5000
eval_interval = 500
learning_rate = 3e-4
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"The code is running on {device} : GPU={torch.cuda.get_device_name(0)}")
eval_iters = 200
n_embd = 384
n_head = 6
n_layer = 6
dropout = 0.2


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

        
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))) # create lower triangular matrix
        
        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)
        tril = torch.tril(torch.ones(T, T)).to(device)
        wei = wei.masked_fill(tril == 0, float('-inf'))
        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
        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) # h(x) call forward function is Head class
        out = self.dropout(self.proj(out))
        return out

class FeedForward(nn.Module): # per token level, every token does this independently, its allowing tokens to think on data provided by self attention
    """ 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), # we multiply by 4 cause the paper says so 
            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_embed, 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 = FeedForward(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 .. is residual connection
        x = x + self.ffwd(self.ln2(x))
        return x
        

class BigramLanguageModel(nn.Module):

    def __init__(self):
        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, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd) # so each position from 0 to block_size - 1 will also get its own embedding vector
        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=n_embed)
        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T, C)
        # pos_emb tensor will be a (block_size, n_emb) tensor # block_size is max context length for predictions
        # each row represents the embedding vector for the corresponding position
        # so 0th row will represent the vector for 0th position
        x = tok_emb + pos_emb # (B, T, C)
        x = self.blocks(x) # (B, T, C)
        logits = self.lm_head(x) # (B, T, C=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.forward(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        


# Instantiate the model
model = BigramLanguageModel()

# Specify the path to the pre-trained model checkpoint
checkpoint_path = 'checkpoint.pth'

# Load the model checkpoint
checkpoint = torch.load(checkpoint_path)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
model.to(device)


# generate from the model
context = torch.zeros((1, 1), dtype=torch.long, device=device)

def greet(start_character, number_of_tokens):
    context[0][0] = encode(start_character)
    max_new_tokens = number_of_tokens
    return decode(model.generate(context, max_new_tokens=max_new_tokens)[0].tolist())

iface = gr.Interface(fn=greet, inputs=["text", "number"], outputs="text")
iface.launch()