import torch import torch.nn as nn import collections from torch.nn import functional as F from torch.nn import RMSNorm from tokenizer import vocab_size, encode, decode, tiktoken_encoding # hyperparameters batch_size = 64 # how many independent sequences will we process in parallel? block_size = 128 # what is the maximum context length for predictions? max_iters = 45 * 1000 eval_interval = 500 learning_rate = 1e-3 device = 'cuda' if torch.cuda.is_available() else 'cpu' eval_iters = 500 n_embd = 128 n_head = 4 n_layer = 10 dropout = 0.02 TRAIN = True PRETRAIN_PERCENTAGE = 0.6 REP_PENALTY_DECAY = 0.95 # ------------ 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), SwiGLU(4 * n_embd, 4 * n_embd), nn.Linear(4 * n_embd, n_embd), nn.Dropout(dropout), ) def forward(self, x): return self.net(x) # NOTE: I AM TESTING CODE FROM https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/activations.py # be aware I do not know how this works entirely class SwiGLU(nn.Module): r""" A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function. It's similar to `GEGLU` but uses SiLU / Swish instead of GeLU. Parameters: dim_in (`int`): The number of channels in the input. dim_out (`int`): The number of channels in the output. bias (`bool`, defaults to True): Whether to use a bias in the linear layer. """ def __init__(self, dim_in: int, dim_out: int, bias: bool = True): super().__init__() self.proj = nn.Linear(dim_in, dim_out * 2, bias=bias) self.activation = nn.SiLU() def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states, gate = hidden_states.chunk(2, dim=-1) return hidden_states * self.activation(gate) 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.RMSNorm(n_embd) # orig a LayerNorm self.ln2 = nn.RMSNorm(n_embd) # orig a LayerNorm 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 TokenBasedLanguageModel(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.RMSNorm(n_embd) # final orig 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 @torch.no_grad def generate(self, idx, max_new_tokens, stream = False, stream_probs = False): # idx is (B, T) array of indices in the current context token_modifiers = collections.defaultdict(lambda x: 1) 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) # apply rep penalty #for token in token_modifiers: # token_modifiers[token] *= REP_PENALTY_DECAY # for batch in range(probs.shape[0]): # probs[batch][token] *= (1 - REP_PENALTY_DECAY) # print(probs.shape) # sample from the distribution idx_next = torch.multinomial(probs, num_samples=1) # (B, 1) if stream: if stream_probs: yield [idx_next, probs[0].tolist()] else: yield idx_next token_modifiers[idx_next] = REP_PENALTY_DECAY; # append sampled index to the running sequence idx = torch.cat((idx, idx_next), dim=1) # (B, T+1) if not stream: return idx