My first attempt to build a model from scrach. Following model can be used for language translation only. # Model Card for Model gpt-dev-french-english-scrach-model-nn """ Full definition of a GPT Language Model, all of it in this single file. References: 1) the official GPT-2 TensorFlow implementation released by OpenAI: https://github.com/openai/gpt-2/blob/master/src/model.py 2) huggingface/transformers PyTorch implementation: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py """ - **Developed by:** [Jayesh Thakare] ## Training Details ```python 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 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__() # 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() m = model.to(device) # print the number of parameters in the model print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters') # 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())) ``` ] ### Training Data [ https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt ]