from transformers import PreTrainedModel import torch import torch.nn as nn from torch.nn import functional as F from .config import CustomAIConfig batch_size = 4 block_size = 128 max_iters = 10 learning_rate = 3e-4 eval_iters = 100 device = 'cuda' if torch.cuda.is_available() else 'cpu' print(device) # n_embd = 384 # n_head = 4 # n_layer = 4 dropout = 0.2 # vocab_size=1000 # model architecture class Head(nn.Module): """ one head of self-attention """ def __init__(self, n_embd, 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): # input of size (batch, time-step, channels) # output of size (batch, time-step, head size) B,T,C = x.shape k = self.key(x) # (B,T,hs) q = self.query(x) # (B,T,hs) # compute attention scores ("affinities") wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, 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,hs) out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs) return out class MultiHeadAttention(nn.Module): """ multiple heads of self-attention in parallel """ def __init__(self, num_heads, head_size, n_embd): super().__init__() self.heads = nn.ModuleList([Head(n_embd,head_size) for _ in range(num_heads)]) self.proj = nn.Linear(head_size * num_heads, n_embd) self.dropout = nn.Dropout(dropout) def forward(self, x): out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3]) 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,n_embd) self.ffwd = FeedFoward(n_embd) self.ln1 = nn.LayerNorm(n_embd) self.ln2 = nn.LayerNorm(n_embd) def forward(self, x): y = self.sa(x) x = self.ln1(x + y) y = self.ffwd(x) x = self.ln2(x + y) return x class CustomAI(PreTrainedModel): config_class = CustomAIConfig def __init__(self, config): super().__init__(config) self.token_embedding_table = nn.Embedding(config.vocab_size, config.n_embd) self.position_embedding_table = nn.Embedding(block_size, config.n_embd) self.blocks = nn.Sequential(*[Block(config.n_embd, n_head=config.n_head) for _ in range(config.n_layer)]) self.ln_f = nn.LayerNorm(config.n_embd) # final layer norm self.lm_head = nn.Linear(config.n_embd, config.vocab_size) self.apply(self._init_weights) def _init_weights(self, module): if isinstance(module, nn.Linear): torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) if module.bias is not None: torch.nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) def forward(self, index, targets=None): B, T = index.shape # idx and targets are both (B,T) tensor of integers tok_emb = self.token_embedding_table(index) # (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, index, max_new_tokens): # index is (B, T) array of indices in the current context for _ in range(max_new_tokens): # crop idx to the last block_size tokens index_cond = index[:, -block_size:] # get the predictions logits, loss = self.forward(index_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 index_next = torch.multinomial(probs, num_samples=1) # (B, 1) # append sampled index to the running sequence index = torch.cat((index, index_next), dim=1) # (B, T+1) return index config = CustomAIConfig( vocab_size=1000, n_embd=384, n_head=4, n_layer=4, dropout=0.2, # any other parameters you want to set ) model = CustomAI(config) # model.load_state_dict(torch.load('/content/BharatAI.pth'))