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GuppyLM — a tiny fish brain.
Vanilla transformer: multi-head attention, ReLU FFN, LayerNorm, learned positional embeddings.
No GQA, no SwiGLU, no parallel residual, no RoPE. As simple as it gets.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from config import GuppyConfig
class Attention(nn.Module):
def __init__(self, config):
super().__init__()
self.n_heads = config.n_heads
self.head_dim = config.d_model // config.n_heads
self.qkv = nn.Linear(config.d_model, 3 * config.d_model)
self.out = nn.Linear(config.d_model, config.d_model)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x, mask=None):
B, T, C = x.shape
qkv = self.qkv(x).reshape(B, T, 3, self.n_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
if mask is not None:
attn = attn.masked_fill(mask == 0, float("-inf"))
attn = self.dropout(F.softmax(attn, dim=-1))
return self.out((attn @ v).transpose(1, 2).contiguous().view(B, T, C))
class FFN(nn.Module):
def __init__(self, config):
super().__init__()
self.up = nn.Linear(config.d_model, config.ffn_hidden)
self.down = nn.Linear(config.ffn_hidden, config.d_model)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
return self.dropout(self.down(F.relu(self.up(x))))
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.norm1 = nn.LayerNorm(config.d_model)
self.attn = Attention(config)
self.norm2 = nn.LayerNorm(config.d_model)
self.ffn = FFN(config)
def forward(self, x, mask=None):
x = x + self.attn(self.norm1(x), mask)
x = x + self.ffn(self.norm2(x))
return x
class GuppyLM(nn.Module):
def __init__(self, config: GuppyConfig):
super().__init__()
self.config = config
self.tok_emb = nn.Embedding(config.vocab_size, config.d_model)
self.pos_emb = nn.Embedding(config.max_seq_len, config.d_model)
self.drop = nn.Dropout(config.dropout)
self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layers)])
self.norm = nn.LayerNorm(config.d_model)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.lm_head.weight = self.tok_emb.weight # tie weights
self.apply(self._init_weights)
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 forward(self, idx, targets=None):
B, T = idx.shape
pos = torch.arange(T, device=idx.device)
x = self.drop(self.tok_emb(idx) + self.pos_emb(pos))
mask = torch.tril(torch.ones(T, T, device=idx.device)).unsqueeze(0).unsqueeze(0)
for block in self.blocks:
x = block(x, mask)
logits = self.lm_head(self.norm(x))
loss = None
if targets is not None:
loss = F.cross_entropy(
logits.view(-1, self.config.vocab_size),
targets.view(-1),
ignore_index=0,
)
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens=64, temperature=0.7, top_k=50, **kwargs):
self.eval()
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.config.max_seq_len:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / temperature
if top_k > 0:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = float("-inf")
probs = F.softmax(logits, dim=-1)
next_id = torch.multinomial(probs, num_samples=1)
idx = torch.cat([idx, next_id], dim=1)
if next_id.item() == self.config.eos_id:
break
return idx, []
def param_count(self):
total = sum(p.numel() for p in self.parameters())
return total, 0
def param_summary(self):
total, _ = self.param_count()
return f"GuppyLM: {total:,} params ({total/1e6:.1f}M)"
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