| from __future__ import annotations |
|
|
| import math |
| from dataclasses import asdict, dataclass |
|
|
| import torch |
| import torch.nn as nn |
| from torch.nn import functional as F |
|
|
|
|
| @dataclass |
| class GPTConfig: |
| block_size: int = 1024 |
| vocab_size: int = 8192 |
| n_layer: int = 12 |
| n_head: int = 12 |
| n_embd: int = 768 |
| dropout: float = 0.0 |
| bias: bool = False |
|
|
|
|
| class CausalSelfAttention(nn.Module): |
| def __init__(self, config: GPTConfig) -> None: |
| super().__init__() |
| if config.n_embd % config.n_head: |
| raise ValueError("embedding dimension must be divisible by number of heads") |
| self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias) |
| self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias) |
| self.attn_dropout = nn.Dropout(config.dropout) |
| self.resid_dropout = nn.Dropout(config.dropout) |
| self.n_head = config.n_head |
| self.n_embd = config.n_embd |
| self.dropout = config.dropout |
|
|
| def forward(self, value: torch.Tensor) -> torch.Tensor: |
| batch, time, channels = value.size() |
| query, key, val = self.c_attn(value).split(self.n_embd, dim=2) |
| head_size = channels // self.n_head |
| query = query.view(batch, time, self.n_head, head_size).transpose(1, 2) |
| key = key.view(batch, time, self.n_head, head_size).transpose(1, 2) |
| val = val.view(batch, time, self.n_head, head_size).transpose(1, 2) |
| attended = F.scaled_dot_product_attention( |
| query, |
| key, |
| val, |
| attn_mask=None, |
| dropout_p=self.dropout if self.training else 0, |
| is_causal=True, |
| ) |
| attended = attended.transpose(1, 2).contiguous().view(batch, time, channels) |
| return self.resid_dropout(self.c_proj(attended)) |
|
|
|
|
| class MLP(nn.Module): |
| def __init__(self, config: GPTConfig) -> None: |
| super().__init__() |
| self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias) |
| self.gelu = nn.GELU() |
| self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias) |
| self.dropout = nn.Dropout(config.dropout) |
|
|
| def forward(self, value: torch.Tensor) -> torch.Tensor: |
| return self.dropout(self.c_proj(self.gelu(self.c_fc(value)))) |
|
|
|
|
| class Block(nn.Module): |
| def __init__(self, config: GPTConfig) -> None: |
| super().__init__() |
| self.ln_1 = nn.LayerNorm(config.n_embd, bias=config.bias) |
| self.attn = CausalSelfAttention(config) |
| self.ln_2 = nn.LayerNorm(config.n_embd, bias=config.bias) |
| self.mlp = MLP(config) |
|
|
| def forward(self, value: torch.Tensor) -> torch.Tensor: |
| value = value + self.attn(self.ln_1(value)) |
| return value + self.mlp(self.ln_2(value)) |
|
|
|
|
| class GPT(nn.Module): |
| def __init__(self, config: GPTConfig) -> None: |
| super().__init__() |
| self.config = config |
| self.transformer = nn.ModuleDict( |
| { |
| "wte": nn.Embedding(config.vocab_size, config.n_embd), |
| "wpe": nn.Embedding(config.block_size, config.n_embd), |
| "drop": nn.Dropout(config.dropout), |
| "h": nn.ModuleList([Block(config) for _ in range(config.n_layer)]), |
| "ln_f": nn.LayerNorm(config.n_embd, bias=config.bias), |
| } |
| ) |
| self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) |
| self.transformer.wte.weight = self.lm_head.weight |
| self.apply(self._init_weights) |
| for name, parameter in self.named_parameters(): |
| if name.endswith("c_proj.weight"): |
| torch.nn.init.normal_( |
| parameter, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer) |
| ) |
|
|
| @staticmethod |
| def _init_weights(module: nn.Module) -> None: |
| 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: torch.Tensor, targets: torch.Tensor | None = None |
| ) -> tuple[torch.Tensor, torch.Tensor | None]: |
| _, time = index.shape |
| if time > self.config.block_size: |
| raise ValueError("sequence exceeds model block size") |
| positions = torch.arange(0, time, dtype=torch.long, device=index.device) |
| value = self.transformer.drop(self.transformer.wte(index) + self.transformer.wpe(positions)) |
| for block in self.transformer.h: |
| value = block(value) |
| value = self.transformer.ln_f(value) |
| logits = self.lm_head(value) |
| loss = ( |
| F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1) |
| if targets is not None |
| else None |
| ) |
| return logits, loss |
|
|
| @torch.no_grad() |
| def generate( |
| self, |
| index: torch.Tensor, |
| max_new_tokens: int, |
| temperature: float = 0.8, |
| top_k: int | None = 200, |
| ) -> torch.Tensor: |
| for _ in range(max_new_tokens): |
| cropped = index[:, -self.config.block_size :] |
| logits, _ = self(cropped) |
| logits = logits[:, -1, :] / temperature |
| if top_k is not None: |
| values, _ = torch.topk(logits, min(top_k, logits.size(-1))) |
| logits[logits < values[:, [-1]]] = -float("Inf") |
| probabilities = F.softmax(logits, dim=-1) |
| index = torch.cat((index, torch.multinomial(probabilities, num_samples=1)), dim=1) |
| return index |
|
|
| def parameter_count(self, non_embedding: bool = False) -> int: |
| count = sum(parameter.numel() for parameter in self.parameters()) |
| if non_embedding: |
| count -= self.transformer.wpe.weight.numel() |
| return count |
|
|
| def config_dict(self) -> dict[str, object]: |
| return asdict(self.config) |
|
|
|
|
