import torch import torch.nn as nn from torch.nn import functional as F import numpy as np import random import re import gradio as gr # 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 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) # 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 # super simple bigram model class BigramLanguageModel(nn.Module): def __init__(self, dataset_text, n_embd): super().__init__() # Compute character-related parameters self.chars = sorted(list(set(dataset_text))) self.vocab_size = len(self.chars) self.stoi = {ch: i for i, ch in enumerate(self.chars)} self.itos = {i: ch for ch, i in self.stoi.items()} self.token_embedding_table = nn.Embedding(self.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) self.lm_head = nn.Linear(n_embd, self.vocab_size) self.encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers self.decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string 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 # Reading shakespeare data with open('input.txt', 'r', encoding='utf-8') as f: shakespeare_text = f.read() # Reading wikipedia data DATA_PATH = 'wikisent2.txt' # load wikipedia sentences with open(DATA_PATH, 'r') as f: lines = f.read().splitlines() # Selecting 250k lines from the dataset. random.seed(42) texts = random.choices(lines, k=250000) del lines def preprocess(text): text = re.sub('@.*?\s+', '', text) # Remove mentions text = re.sub('#.*?\s+', '', text) # Remove hashtags text = re.sub(r'https?:\/\/.*[\r\n]*', '', text) # Remove URLs text = re.sub(r'[^\w\s\'.]', '', text) # Remove special characters except for single quotes and periods text = re.sub('\s+', ' ', text) # Replace multiple spaces with a single space text = re.sub('^\d+\s*|^\d+\.\d+\s*|^\d+\.\d+\.\d+\s*', '', text) # Remove digits at the start of sentences text = text.strip() # Remove leading and trailing whitespace return text wiki_text = [preprocess(t) for t in texts] wiki_text = '\n'.join(wiki_text) # Load the shakespeaere model shakespeare_model = BigramLanguageModel(shakespeare_text, n_embd).to(device) # Initialize an instance of your model shakespeare_model.load_state_dict(torch.load('shakespeaere_language_model.pth', map_location=torch.device('cpu'))) shakespeare_model.eval() # Set the model to evaluation mode # Load the wikipedia model wikipedia_model = BigramLanguageModel(wiki_text, n_embd).to(device) # Initialize an instance of your model wikipedia_model.load_state_dict(torch.load('wikipedia_language_model.pth', map_location=torch.device('cpu'))) wikipedia_model.eval() # Set the model to evaluation mode def generate_shakespeare_outputs(prompt=None, max_new_tokens=2000): if prompt: context = torch.tensor(shakespeare_model.encode(prompt), dtype=torch.long, device=device).view(1, -1) else: context = torch.zeros((1, 1), dtype=torch.long, device=device) text_output = decode(shakespeare_model.generate(context, max_new_tokens=max_new_tokens)[0].tolist()) return text_output def generate_wikipedia_outputs(prompt=None, max_new_tokens=2000): if prompt: context = torch.tensor(wikipedia_model.encode(prompt), dtype=torch.long, device=device).view(1, -1) else: context = torch.zeros((1, 1), dtype=torch.long, device=device) text_output = decode(wikipedia_model.generate(context, max_new_tokens=max_new_tokens)[0].tolist()) return text_output title = "Nano GPT" description = "Nano GPT trained on Shakespeare and Wikipedia datasets. It is trained on a very small amount of data to understand how GPT's are trained and built. The implementation can be found here " shakespeare_interface = gr.Interface(generate_shakespeare_outputs, inputs=[gr.Textbox(label="Enter any prompt ", type="text", value="Once upon a time,"), gr.Slider(minimum=100, maximum=5000, step=100, value=2000, label="Max new tokens")], outputs=gr.Textbox(label="Output generated", type="text"), description=description) wiki_interface = gr.Interface(generate_wikipedia_outputs, inputs=[gr.Textbox(label="Enter any prompt ", type="text", value="James Bond"), gr.Slider(minimum=100, maximum=5000, step=100, value=2000, label="Max new tokens")], outputs=gr.Textbox(label="Output generated", type="text"), description=description) demo = gr.TabbedInterface([shakespeare_interface, wiki_interface], tab_names=["Shakespeare Data", "Wikipedia Data"], title=title) demo.launch()