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}") 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, map_location=torch.device(device)) 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(number_of_tokens, start_character): context[0][0] = encode(start_character)[0] max_new_tokens = number_of_tokens return decode(model.generate(context, max_new_tokens=int(max_new_tokens))[0].tolist()) iface = gr.Interface(fn=greet, inputs=["number", "text"], outputs="text") iface.launch()