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161d2fa | 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 | from dataclasses import dataclass
import torch
import torch.nn as nn
@dataclass
class TransformerConfig:
model_dim: int # Dimension of the model
num_heads: int
N_ctx: int # Number of context tokens
use_moe: bool # Whether to use MoE or MLP
num_experts : int
active_experts : int
shared_experts : int
expert_width : int
vocab_size : int
layers : int
class MHA(nn.Module):
def __init__(self, num_heads, model_dim):
super().__init__()
self.num_heads = num_heads
self.model_dim = model_dim
self.head_dim = model_dim // num_heads
self.q = nn.Linear(in_features=model_dim, out_features=model_dim)
self.k = nn.Linear(in_features=model_dim, out_features=model_dim)
self.v = nn.Linear(in_features=model_dim, out_features=model_dim)
self.out_proj = nn.Linear(in_features=model_dim, out_features=model_dim)
def forward(self, x, mask=None):
q = self.q(x)
k = self.k(x)
v = self.v(x)
q_heads = q.reshape(x.shape[0], x.shape[1], self.num_heads, self.head_dim).permute(0, 2, 1, 3)
k_heads = k.reshape(x.shape[0], x.shape[1], self.num_heads, self.head_dim).permute(0, 2, 1, 3)
v_heads = v.reshape(x.shape[0], x.shape[1], self.num_heads, self.head_dim).permute(0, 2, 1, 3)
attention_scores = (q_heads @ torch.transpose(k_heads, 3, 2)) / self.head_dim ** (1/2)
if mask is not None:
attention_scores = attention_scores.masked_fill(mask == 0, float('-inf'))
attention_scores = torch.softmax(attention_scores, dim=-1)
self_attention = attention_scores @ v_heads
mha_output = self_attention.permute(0, 2, 1, 3).reshape(x.shape[0], x.shape[1], self.model_dim)
output = self.out_proj(mha_output)
return output
class RMSNorm(nn.Module):
def __init__(self, model_dim):
super().__init__()
self.model_dim = model_dim
self.gamma = nn.Parameter(torch.ones(model_dim))
def forward(self, x):
eps = 1e-5
rms = (((torch.sum(torch.square(x), dim=-1, keepdim=True)) / self.model_dim) + eps) ** (1/2)
norm = self.gamma * (x / rms)
return norm
class SinusoidalPositionalEmbeddings(nn.Module):
def __init__(self, model_dim, N=10000):
super().__init__()
self.d = model_dim
self.N = N
self.register_buffer('k', torch.arange(0, model_dim, step=2))
def forward(self, x):
t = torch.arange(x.shape[1], device=x.device).unsqueeze(1)
sin = torch.sin(t/self.N ** (self.k/self.d))
cos = torch.cos(t/self.N ** (self.k/self.d))
total_angles = torch.cat([sin, cos], dim=-1).unsqueeze(0)
return x + total_angles
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.self_attn = MHA(config.num_heads, config.model_dim)
self.norms = nn.ModuleList([RMSNorm(config.model_dim) for _ in range(2)])
if config.use_moe:
self.ffn = MoE(self.config)
else:
self.ffn = MLP(self.config)
def forward(self, x, mask=None, pad_mask=None):
x = x + self.self_attn(self.norms[0](x), mask)
h, aux, counts = self.ffn(self.norms[1](x), pad_mask)
return x + h, aux, counts
class Expert(nn.Module):
def __init__(self, config):
super().__init__()
self.expert = nn.Sequential(
nn.Linear(config.model_dim, config.expert_width * config.model_dim),
nn.GELU(),
nn.Linear(config.expert_width * config.model_dim, config.model_dim)
)
def forward(self, x):
x = self.expert(x)
return x
class MoE(nn.Module):
def __init__(self, config: TransformerConfig):
super().__init__()
self.config = config
self.router = nn.Sequential(
nn.Linear(config.model_dim, config.num_experts - config.shared_experts),
nn.Softmax(-1)
)
self.experts = nn.ModuleList([Expert(config) for _ in range(config.num_experts)])
def forward(self, x, pad_mask=None):
B, N, D = x.shape
x_flat = x.reshape(-1, D) # (T, D) where T = B*N tokens
probs = self.router(x_flat) # (T, num_routed)
top_w, top_idx = torch.topk(probs, self.config.active_experts) # both (T, k)
out = torch.zeros_like(x_flat) # (T, D)
# shared routers
if self.config.shared_experts:
for i in range(self.config.shared_experts):
out += self.experts[i](x_flat)
real = pad_mask.reshape(-1).bool()
r_probs = probs[real]
r_idx = top_idx[real]
num_routed = self.config.num_experts - self.config.shared_experts
counts = torch.bincount(r_idx.reshape(-1), minlength=num_routed)
for e in range(self.config.num_experts - self.config.shared_experts):
e_mask = (top_idx == e).any(dim=-1)
if not e_mask.any():
continue
e_wt = (top_w * (top_idx == e)).sum(dim=-1)
out[e_mask] += e_wt[e_mask, None] * self.experts[self.config.shared_experts + e](x_flat[e_mask])
f = counts / r_idx.numel()
P = r_probs.mean(dim=0)
aux = num_routed * (f * P).sum()
return out.reshape(B, N, D), aux, counts
class MLP(nn.Module):
def __init__(self, config: TransformerConfig):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(config.model_dim, 4 * config.model_dim),
nn.GELU(),
nn.Linear(4 * config.model_dim, config.model_dim)
)
def forward(self, x, pad_mask=None):
x = self.mlp(x)
return x, 0, None
class Transformer(nn.Module):
def __init__(self, config: TransformerConfig):
super().__init__()
self.config = config
self.token_embedding = nn.Embedding(config.vocab_size, config.model_dim)
self.pos_embedding = SinusoidalPositionalEmbeddings(config.model_dim)
self.blocks = nn.ModuleList([Block(config) for _ in range(config.layers)])
self.norm = RMSNorm(config.model_dim)
self.lm_head = nn.Linear(config.model_dim, config.vocab_size, bias=False)
self.lm_head.weight = self.token_embedding.weight
nn.init.normal_(self.token_embedding.weight, std=0.02)
def forward(self, input_ids, attention_mask, device, last_positions=None):
N = input_ids.shape[-1]
causal_mask = torch.tril(torch.ones(N, N, device=device, dtype=torch.bool))
pad_mask = attention_mask
attn_mask = causal_mask & pad_mask[:, None, None, :].bool()
x = self.token_embedding(input_ids)
x = self.pos_embedding(x)
aux_total = 0
all_counts = []
for block in self.blocks:
x, aux, counts = block(x, attn_mask, pad_mask)
aux_total = aux_total + aux
all_counts.append(counts)
if last_positions is not None: # generation: only each row's next-token logits are needed -> (B, V)
x = x[torch.arange(x.shape[0], device=x.device), last_positions]
logits = self.lm_head(self.norm(x))
return logits, aux_total, all_counts
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