File size: 7,554 Bytes
27d37fd | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 | """
Core Transformer layers.
Implemented:
- RMSNorm
- Rotary Positional Embedding
- Causal Self-Attention
- SwiGLU MLP
- Pre-Norm Transformer block
"""
import math
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.model.config import ModelConfig
class RMSNorm(nn.Module):
"""
RMSNorm from Zhang & Sennrich.
Difference from LayerNorm:
- Does not subtract mean.
- Normalizes by root mean square.
- Usually cheaper and stable for LLMs.
Input:
x: Tensor of shape [batch, seq, hidden]
Output:
Normalized tensor of same shape.
"""
def __init__(self, dim: int, eps: float):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Upcast to float32 for the variance computation: under AMP (bf16/fp16)
# the mean-of-squares can lose precision / overflow, which destabilizes
# normalization. LLaMA-style: normalize in fp32, cast back, then scale.
orig_dtype = x.dtype
x_fp32 = x.float()
rms = torch.rsqrt(x_fp32.pow(2).mean(dim=-1, keepdim=True) + self.eps)
return self.weight * (x_fp32 * rms).to(orig_dtype)
class RotaryEmbedding(nn.Module):
"""
Rotary Positional Embedding.
RoPE rotates query/key vectors based on token position.
It avoids learned absolute positional embeddings.
Input:
q/k tensor: [batch, heads, seq, head_dim]
Output:
position-rotated q/k tensor.
"""
def __init__(self, head_dim: int, theta: float = 10000.0):
super().__init__()
if head_dim % 2 != 0:
raise ValueError("RoPE requires even head_dim.")
inv_freq = 1.0 / (
theta ** (torch.arange(0, head_dim, 2).float() / head_dim)
)
self.register_buffer("inv_freq", inv_freq, persistent=False)
def get_cos_sin(
self,
seq_len: int,
device: torch.device,
dtype: torch.dtype,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Input:
seq_len: Sequence length.
device: CPU/GPU.
dtype: Target dtype.
Output:
cos, sin tensors shaped [1, 1, seq, head_dim/2]
"""
positions = torch.arange(seq_len, device=device, dtype=torch.float32)
freqs = torch.einsum("i,j->ij", positions, self.inv_freq.to(device))
cos = freqs.cos()[None, None, :, :].to(dtype)
sin = freqs.sin()[None, None, :, :].to(dtype)
return cos, sin
@staticmethod
def apply_rope(
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
) -> torch.Tensor:
"""
Input:
x: [batch, heads, seq, head_dim]
cos/sin: [1, 1, seq, head_dim/2]
Output:
RoPE-applied tensor with same shape.
"""
# Store original dtype
orig_dtype = x.dtype
# Upcast to float32 for precision stability during rotation
x = x.float()
cos = cos.float()
sin = sin.float()
x_even = x[..., 0::2]
x_odd = x[..., 1::2]
rotated_even = x_even * cos - x_odd * sin
rotated_odd = x_even * sin + x_odd * cos
out = torch.stack((rotated_even, rotated_odd), dim=-1).flatten(-2)
return out.to(orig_dtype)
def forward(
self,
q: torch.Tensor,
k: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
seq_len = q.size(2)
cos, sin = self.get_cos_sin(seq_len, q.device, q.dtype)
q = self.apply_rope(q, cos, sin)
k = self.apply_rope(k, cos, sin)
return q, k
class CausalSelfAttention(nn.Module):
"""
Multi-head causal self-attention.
Uses PyTorch scaled_dot_product_attention.
This still counts as your own model architecture;
the CUDA kernel is optimized internally by PyTorch.
Input:
x: [batch, seq, hidden]
Output:
[batch, seq, hidden]
"""
def __init__(self, config: ModelConfig):
super().__init__()
if config.n_embd % config.n_head != 0:
raise ValueError("n_embd must be divisible by n_head.")
self.n_head = config.n_head
self.head_dim = config.n_embd // config.n_head
self.dropout = config.dropout
self.q_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
self.k_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
self.v_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
self.out_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
self.rope = RotaryEmbedding(
head_dim=self.head_dim,
theta=config.rope_theta,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
batch, seq_len, hidden = x.shape
q = self.q_proj(x)
k = self.k_proj(x)
v = self.v_proj(x)
# [B, T, H] -> [B, heads, T, head_dim]
q = q.view(batch, seq_len, self.n_head, self.head_dim).transpose(1, 2)
k = k.view(batch, seq_len, self.n_head, self.head_dim).transpose(1, 2)
v = v.view(batch, seq_len, self.n_head, self.head_dim).transpose(1, 2)
q, k = self.rope(q, k)
attn_output = F.scaled_dot_product_attention(
q,
k,
v,
attn_mask=None,
dropout_p=self.dropout if self.training else 0.0,
is_causal=True,
)
# [B, heads, T, head_dim] -> [B, T, H]
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(batch, seq_len, hidden)
return self.out_proj(attn_output)
class SwiGLUMLP(nn.Module):
"""
SwiGLU feed-forward network.
Formula:
FFN(x) = W_down( SiLU(W_gate x) * W_up x )
Used in modern LLMs because it gives better quality than plain GELU MLP.
Input:
x: [batch, seq, hidden]
Output:
[batch, seq, hidden]
"""
def __init__(self, config: ModelConfig):
super().__init__()
hidden_dim = int(2 * (4 * config.n_embd) / 3)
if config.ffn_dim_multiplier is not None:
hidden_dim = int(config.ffn_dim_multiplier * hidden_dim)
# Round to hardware-friendly multiple.
hidden_dim = config.multiple_of * math.ceil(hidden_dim / config.multiple_of)
self.gate_proj = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
self.up_proj = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
self.down_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.down_proj(
F.silu(self.gate_proj(x)) * self.up_proj(x)
)
class TransformerBlock(nn.Module):
"""
Pre-Norm Transformer block.
Structure:
x = x + Attention(RMSNorm(x))
x = x + MLP(RMSNorm(x))
Pre-norm is more stable for deep LMs.
"""
def __init__(self, config: ModelConfig):
super().__init__()
self.attn_norm = RMSNorm(config.n_embd, config.norm_eps)
self.ffn_norm = RMSNorm(config.n_embd, config.norm_eps)
self.attn = CausalSelfAttention(config)
self.mlp = SwiGLUMLP(config)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.attn(self.attn_norm(x))
x = x + self.mlp(self.ffn_norm(x))
return x
|