: Replicates the model across all GPUs; each processes a distinct slice of the batch.
class CausalSelfAttention(nn.Module): def __init__(self, config): super().__init__() self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd) self.c_proj = nn.Linear(config.n_embd, config.n_embd) def forward(self, x): B, T, C = x.size() # batch, time, channels qkv = self.c_attn(x) q, k, v = qkv.split(self.config.n_embd, dim=2) # Manual implementation of scaled dot-product attention with causal mask att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf')) att = F.softmax(att, dim=-1) y = att @ v return self.c_proj(y) build a large language model from scratch pdf
import torch from torch.utils.data import Dataset, DataLoader class SimpleTokenizer: def __init__(self, vocab): self.str_to_int = vocab self.int_to_str = v: k for k, v in vocab.items() def encode(self, text): return [self.str_to_int[token] for token in text.split()] def decode(self, ids): return " ".join([self.int_to_str[i] for i in ids]) class TextDataset(Dataset): def __init__(self, text, tokenizer, max_length, stride): self.input_ids = [] self.target_ids = [] # Tokenize the entire raw corpus token_ids = tokenizer.encode(text) # Slide a chunk window across the data stream for i in range(0, len(token_ids) - max_length, stride): input_chunk = token_ids[i:i + max_length] target_chunk = token_ids[i + 1:i + max_length + 1] self.input_ids.append(torch.tensor(input_chunk)) self.target_ids.append(torch.tensor(target_chunk)) def __len__(self): return len(self.input_ids) def __getitem__(self, idx): return self.input_ids[idx], self.target_ids[idx] Use code with caution. 3. Step 2: Implementing Causal Multi-Head Attention : Replicates the model across all GPUs; each
You need two matrices:
If you are looking for the definitive resource titled it is a highly-regarded book by Sebastian Raschka , published by Manning Publications . Step 2: Implementing Causal Multi-Head Attention You need
: For a more academic look, you can find research papers on ResearchGate that examine the complications of pre-training and transformer architecture.