File size: 6,405 Bytes
5992a18 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | """
1B Parameter Decoder-Only Transformer — built from scratch.
Techniques:
- RoPE (Rotary Position Embeddings)
- Grouped Query Attention (GQA)
- SwiGLU Feed-Forward
- RMSNorm (pre-norm architecture)
- Flash Attention 2 (via PyTorch SDPA)
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import ModelConfig
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
norm = x.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
return (x.float() * norm).type_as(x) * self.weight
def precompute_rope_freqs(dim: int, max_seq_len: int, theta: float = 10000.0) -> torch.Tensor:
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
t = torch.arange(max_seq_len, dtype=torch.float32)
freqs = torch.outer(t, freqs)
return torch.polar(torch.ones_like(freqs), freqs) # complex64
def apply_rope(xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor):
B, S, H, D = xq.shape
xq_c = torch.view_as_complex(xq.float().reshape(B, S, H, D // 2, 2))
xk_c = torch.view_as_complex(xk.float().reshape(B, S, xk.shape[2], D // 2, 2))
freqs = freqs_cis[:S].clone().unsqueeze(0).unsqueeze(2)
xq_out = torch.view_as_real(xq_c * freqs).flatten(3)
xk_out = torch.view_as_real(xk_c * freqs).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
class GroupedQueryAttention(nn.Module):
def __init__(self, config: ModelConfig):
super().__init__()
self.num_heads = config.num_attention_heads
self.num_kv_heads = config.num_kv_heads
self.head_dim = config.head_dim
self.num_groups = self.num_heads // self.num_kv_heads
self.wq = nn.Linear(config.hidden_dim, self.num_heads * self.head_dim, bias=False)
self.wk = nn.Linear(config.hidden_dim, self.num_kv_heads * self.head_dim, bias=False)
self.wv = nn.Linear(config.hidden_dim, self.num_kv_heads * self.head_dim, bias=False)
self.wo = nn.Linear(self.num_heads * self.head_dim, config.hidden_dim, bias=False)
def forward(self, x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
B, S, _ = x.shape
q = self.wq(x).view(B, S, self.num_heads, self.head_dim)
k = self.wk(x).view(B, S, self.num_kv_heads, self.head_dim)
v = self.wv(x).view(B, S, self.num_kv_heads, self.head_dim)
q, k = apply_rope(q, k, freqs_cis)
# Expand KV heads for GQA
if self.num_groups > 1:
k = k.unsqueeze(3).expand(B, S, self.num_kv_heads, self.num_groups, self.head_dim)
k = k.reshape(B, S, self.num_heads, self.head_dim)
v = v.unsqueeze(3).expand(B, S, self.num_kv_heads, self.num_groups, self.head_dim)
v = v.reshape(B, S, self.num_heads, self.head_dim)
# (B, num_heads, S, head_dim) for SDPA
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
out = out.transpose(1, 2).contiguous().view(B, S, -1)
return self.wo(out)
class SwiGLUFFN(nn.Module):
def __init__(self, config: ModelConfig):
super().__init__()
self.w_gate = nn.Linear(config.hidden_dim, config.intermediate_dim, bias=False)
self.w_up = nn.Linear(config.hidden_dim, config.intermediate_dim, bias=False)
self.w_down = nn.Linear(config.intermediate_dim, config.hidden_dim, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.w_down(F.silu(self.w_gate(x)) * self.w_up(x))
class TransformerBlock(nn.Module):
def __init__(self, config: ModelConfig):
super().__init__()
self.attention_norm = RMSNorm(config.hidden_dim, eps=config.rms_norm_eps)
self.attention = GroupedQueryAttention(config)
self.ffn_norm = RMSNorm(config.hidden_dim, eps=config.rms_norm_eps)
self.ffn = SwiGLUFFN(config)
def forward(self, x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
x = x + self.attention(self.attention_norm(x), freqs_cis)
x = x + self.ffn(self.ffn_norm(x))
return x
class Transformer(nn.Module):
def __init__(self, config: ModelConfig):
super().__init__()
self.config = config
self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_dim)
self.layers = nn.ModuleList([TransformerBlock(config) for _ in range(config.num_layers)])
self.norm = RMSNorm(config.hidden_dim, eps=config.rms_norm_eps)
self.output = nn.Linear(config.hidden_dim, config.vocab_size, bias=False)
# Pre-compute RoPE frequencies
self.register_buffer(
"freqs_cis",
precompute_rope_freqs(config.head_dim, config.max_seq_len * 2, config.rope_theta),
persistent=False,
)
self._init_weights()
def _init_weights(self):
"""Initialize with scaled normal, following GPT-NeoX / LLaMA conventions."""
for module in self.modules():
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
# Scale residual projections by 1/sqrt(2*num_layers)
scale = (2 * self.config.num_layers) ** -0.5
for layer in self.layers:
nn.init.normal_(layer.attention.wo.weight, mean=0.0, std=0.02 * scale)
nn.init.normal_(layer.ffn.w_down.weight, mean=0.0, std=0.02 * scale)
def forward(self, tokens: torch.Tensor, targets: torch.Tensor = None):
B, S = tokens.shape
h = self.tok_embeddings(tokens)
freqs_cis = self.freqs_cis[:S]
for layer in self.layers:
h = layer(h, freqs_cis)
h = self.norm(h)
logits = self.output(h)
loss = None
if targets is not None:
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)),
targets.view(-1),
ignore_index=-100,
)
return logits, loss
|