from __future__ import annotations import math import torch import torch.nn as nn import torch.nn.functional as F class RMSNorm(nn.Module): def __init__(self, dim, eps=1e-06): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def forward(self, x): dtype = x.dtype x = x.float() x = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) return x.to(dtype) * self.weight def rope_freqs(seq_len, head_dim, theta=100000.0, device=None): inv_freq = 1.0 / theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim) t = torch.arange(seq_len, device=device).float() freqs = torch.outer(t, inv_freq) return torch.cat([freqs, freqs], dim=-1) def rotate_half(x): x1, x2 = x.chunk(2, dim=-1) return torch.cat([-x2, x1], dim=-1) def apply_rope(x, cos, sin): return x * cos + rotate_half(x) * sin class Attention(nn.Module): def __init__(self, dim, n_heads, n_kv_heads, head_dim, qk_norm_eps=1e-06): super().__init__() if n_heads % n_kv_heads != 0: raise ValueError(f'n_heads ({n_heads}) must be divisible by n_kv_heads ({n_kv_heads}); valid choices for {n_heads} heads: {[k for k in range(1, n_heads + 1) if n_heads % k == 0]}') self.n_heads = n_heads self.n_kv_heads = n_kv_heads self.head_dim = head_dim self.n_rep = n_heads // n_kv_heads self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False) self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False) self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False) self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False) self.q_norm = RMSNorm(head_dim, eps=qk_norm_eps) self.k_norm = RMSNorm(head_dim, eps=qk_norm_eps) def forward(self, x, cos, sin): B, T, _ = x.shape q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim) k = self.k_proj(x).view(B, T, self.n_kv_heads, self.head_dim) v = self.v_proj(x).view(B, T, self.n_kv_heads, self.head_dim) q = self.q_norm(q) k = self.k_norm(k) q = apply_rope(q, cos, sin) k = apply_rope(k, cos, sin) q = q.transpose(1, 2) k = k.transpose(1, 2).repeat_interleave(self.n_rep, dim=1) v = v.transpose(1, 2).repeat_interleave(self.n_rep, dim=1) out = F.scaled_dot_product_attention(q, k, v, is_causal=True) out = out.transpose(1, 2).contiguous().view(B, T, -1) return self.o_proj(out) class SwiGLU(nn.Module): def __init__(self, dim, hidden): super().__init__() self.gate_proj = nn.Linear(dim, hidden, bias=False) self.up_proj = nn.Linear(dim, hidden, bias=False) self.down_proj = nn.Linear(hidden, dim, bias=False) def forward(self, x): return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) class DecoderLayer(nn.Module): def __init__(self, dim, n_heads, n_kv_heads, head_dim, mlp_hidden, norm_eps=1e-06): super().__init__() self.attn_norm = RMSNorm(dim, eps=norm_eps) self.attn = Attention(dim, n_heads, n_kv_heads, head_dim) self.mlp_norm = RMSNorm(dim, eps=norm_eps) self.mlp = SwiGLU(dim, mlp_hidden) def forward(self, x, cos, sin): x = x + self.attn(self.attn_norm(x), cos, sin) x = x + self.mlp(self.mlp_norm(x)) return x class LogosModel(nn.Module): def __init__(self, vocab_size=32768, dim=640, n_layers=30, n_heads=10, n_kv_heads=5, mlp_hidden=1728, max_seq_len=4096, rope_theta=100000.0, norm_eps=1e-06, tie_embeddings=True): super().__init__() self.dim = dim self.n_layers = n_layers self.head_dim = dim // n_heads self.max_seq_len = max_seq_len self.rope_theta = rope_theta self.embed_tokens = nn.Embedding(vocab_size, dim) self.layers = nn.ModuleList([DecoderLayer(dim, n_heads, n_kv_heads, self.head_dim, mlp_hidden, norm_eps) for _ in range(n_layers)]) self.norm_out = RMSNorm(dim, eps=norm_eps) self.lm_head = nn.Linear(dim, vocab_size, bias=False) if tie_embeddings: self.lm_head.weight = self.embed_tokens.weight self.apply(self._init_weights) for layer in self.layers: nn.init.normal_(layer.mlp.down_proj.weight, mean=0.0, std=0.02 / math.sqrt(2 * n_layers)) nn.init.normal_(layer.attn.o_proj.weight, mean=0.0, std=0.02 / math.sqrt(2 * n_layers)) cos, sin = self._build_rope_cache(max_seq_len) self.register_buffer('rope_cos', cos, persistent=False) self.register_buffer('rope_sin', sin, persistent=False) def _init_weights(self, m): if isinstance(m, nn.Linear): nn.init.normal_(m.weight, mean=0.0, std=0.02) if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, nn.Embedding): nn.init.normal_(m.weight, mean=0.0, std=0.02) def _build_rope_cache(self, seq_len): freqs = rope_freqs(seq_len, self.head_dim, self.rope_theta) return (freqs.cos()[None, :, None, :], freqs.sin()[None, :, None, :]) def forward(self, input_ids, labels=None, loss_chunk_size=2048): B, T = input_ids.shape x = self.embed_tokens(input_ids) cos = self.rope_cos[:, :T].to(x.dtype) sin = self.rope_sin[:, :T].to(x.dtype) for layer in self.layers: x = layer(x, cos, sin) x = self.norm_out(x) if labels is None: return (self.lm_head(x), None) shift_x = x[:, :-1].reshape(-1, x.size(-1)) shift_labels = labels[:, 1:].reshape(-1) n = shift_x.size(0) total_loss = x.new_zeros((), dtype=torch.float32) total_count = x.new_zeros((), dtype=torch.float32) for start in range(0, n, loss_chunk_size): end = min(start + loss_chunk_size, n) chunk_labels = shift_labels[start:end] valid = chunk_labels != -100 count = valid.sum() if count == 0: continue chunk_logits = self.lm_head(shift_x[start:end]).float() chunk_loss = F.cross_entropy(chunk_logits, chunk_labels, ignore_index=-100, reduction='sum') total_loss = total_loss + chunk_loss total_count = total_count + count loss = total_loss / total_count.clamp(min=1) return (None, loss) def num_params(self, exclude_embeddings=False): n = sum((p.numel() for p in self.parameters())) if exclude_embeddings: n -= self.embed_tokens.weight.numel() if self.lm_head.weight is not self.embed_tokens.weight: n -= self.lm_head.weight.numel() return n if __name__ == '__main__': m = LogosModel() print(f'total params: {m.num_params():,}') print(f'non-embedding params: {m.num_params(exclude_embeddings=True):,}') x = torch.randint(0, 32768, (2, 128)) logits, loss = m(x, labels=x) print('loss:', loss.item() if loss is not None else None) logits, _ = m(x) print('logits shape (no labels):', logits.shape)