| import json |
| import torch |
| import torch.nn as nn |
| from torch.nn import functional as F |
| from transformers import PreTrainedModel, PretrainedConfig |
|
|
| |
| batch_size = 64 |
| block_size = 256 |
| n_embed = 384 |
| n_head = 6 |
| n_layer = 6 |
| dropout = 0.2 |
|
|
| vocab_size = 65 |
|
|
| class NoobConfig(PretrainedConfig): |
| model_type = "Noob" |
| vocab_size = vocab_size |
| n_positions = block_size |
| n_embd = n_embed |
| n_layer = n_layer |
| n_head = n_head |
|
|
| class Head(nn.Module): |
| """ one head of self-attention """ |
| def __init__(self, head_size): |
| super().__init__() |
| self.key = nn.Linear(n_embed, head_size, bias=False) |
| self.query = nn.Linear(n_embed, head_size, bias=False) |
| self.value = nn.Linear(n_embed, 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) |
| q = self.query(x) |
| wei = q @ k.transpose(-2, -1) * C**-0.5 |
| wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) |
| wei = F.softmax(wei, dim=-1) |
| wei = self.dropout(wei) |
| v = self.value(x) |
| out = wei @ v |
| return out |
|
|
| class MultiHeadAttention(nn.Module): |
| 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_embed, n_embed) |
| self.dropout = nn.Dropout(dropout) |
|
|
| def forward(self, x): |
| out = torch.cat([h(x) for h in self.heads], dim=-1) |
| out = self.proj(out) |
| out = self.dropout(out) |
| return out |
|
|
| class FeedFoward(nn.Module): |
| def __init__(self, n_embed): |
| super().__init__() |
| self.net = nn.Sequential( |
| nn.Linear(n_embed, 4 * n_embed), |
| nn.ReLU(), |
| nn.Linear(4 * n_embed, n_embed), |
| 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): |
| super().__init__() |
| head_size = n_embed // n_head |
| self.sa = MultiHeadAttention(n_head, head_size) |
| self.ffwd = FeedFoward(n_embed) |
| self.ln1 = nn.LayerNorm(n_embed) |
| self.ln2 = nn.LayerNorm(n_embed) |
|
|
| def forward(self, x): |
| x = x + self.sa(self.ln1(x)) |
| x = x + self.ffwd(self.ln2(x)) |
| return x |
|
|
| class Noob(PreTrainedModel): |
| config_class = NoobConfig |
|
|
| 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(config.n_positions, config.n_embd) |
| self.blocks = nn.Sequential(*[Block(config.n_embd, config.n_head) for _ in range(config.n_layer)]) |
| self.ln_final = nn.LayerNorm(config.n_embd) |
| self.lm_head = nn.Linear(config.n_embd, config.vocab_size) |
|
|
| def forward(self, idx, targets=None): |
| B, T = idx.shape |
| tok_emb = self.token_embedding_table(idx) |
| pos_emb = self.position_embedding_table(torch.arange(T, device=idx.device)) |
| x = tok_emb + pos_emb |
| x = self.blocks(x) |
| x = self.ln_final(x) |
| logits = self.lm_head(x) |
|
|
| 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): |
| for _ in range(max_new_tokens): |
| idx_cond = idx[:, -block_size:] |
| logits, _ = self(idx_cond) |
| logits = logits[:, -1, :] |
| probs = F.softmax(logits, dim=-1) |
| idx_next = torch.multinomial(probs, num_samples=1) |
| idx = torch.cat((idx, idx_next), dim=1) |
| return idx |
